From d2cd43601b0c903ec03fe5dac908ea6a8d3a281a Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Tue, 15 Oct 2024 13:11:23 -0700
Subject: [PATCH 01/84] infra: add databricks api build (#27374)

---
 .github/workflows/api_doc_build.yml | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/api_doc_build.yml b/.github/workflows/api_doc_build.yml
index 9faaaa1c20ad3..6973e64f742e6 100644
--- a/.github/workflows/api_doc_build.yml
+++ b/.github/workflows/api_doc_build.yml
@@ -73,6 +73,10 @@ jobs:
         with:
           repository: langchain-ai/langchain-unstructured
           path: langchain-unstructured
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-databricks
+          path: langchain-databricks
 
 
       - name: Set Git config
@@ -97,7 +101,8 @@ jobs:
             langchain/libs/standard-tests \
             langchain/libs/experimental \
             langchain/libs/partners/milvus \
-            langchain/libs/partners/unstructured
+            langchain/libs/partners/unstructured \
+            langchain/libs/databricks
           mv langchain-google/libs/genai langchain/libs/partners/google-genai
           mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
           mv langchain-google/libs/community langchain/libs/partners/google-community
@@ -113,6 +118,7 @@ jobs:
           mv langchain-experimental/libs/experimental langchain/libs/experimental
           mv langchain-milvus/libs/milvus langchain/libs/partners/milvus
           mv langchain-unstructured/libs/unstructured langchain/libs/partners/unstructured
+          mv langchain-databricks/libs/databricks langchain/libs/partners/databricks
 
       - name: Rm old html
         run:

From edf6d0a0fb4887238f1c3d8063ddc4c0cb5189ca Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Tue, 15 Oct 2024 14:51:05 -0700
Subject: [PATCH 02/84] partners/couchbase: release 0.2.0 (attempt 2) (#27375)

---
 libs/partners/couchbase/poetry.lock    | 1758 +++++++++++++-----------
 libs/partners/couchbase/pyproject.toml |    4 +-
 2 files changed, 970 insertions(+), 792 deletions(-)

diff --git a/libs/partners/couchbase/poetry.lock b/libs/partners/couchbase/poetry.lock
index 14f0353635bb0..39ad5dce0b999 100644
--- a/libs/partners/couchbase/poetry.lock
+++ b/libs/partners/couchbase/poetry.lock
@@ -1,100 +1,127 @@
-# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.3"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
+    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
+]
 
 [[package]]
 name = "aiohttp"
-version = "3.9.5"
+version = "3.10.10"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
+aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
 async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-yarl = ">=1.0,<2.0"
+yarl = ">=1.12.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "brotlicffi"]
+speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
@@ -123,13 +150,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.4.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 [package.dependencies]
@@ -139,9 +166,9 @@ sniffio = ">=1.1"
 typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.23)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "async-timeout"
@@ -156,131 +183,146 @@ files = [
 
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 name = "attrs"
-version = "23.2.0"
+version = "24.2.0"
 description = "Classes Without Boilerplate"
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 python-versions = ">=3.7"
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-dev = ["attrs[tests]", "pre-commit"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
-tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
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+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
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+version = "2024.8.30"
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 [[package]]
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 ]
 
 [package.extras]
@@ -529,13 +592,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.5"
+version = "1.0.6"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -546,7 +609,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.26.0)"]
+trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httpx"
@@ -575,15 +638,18 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "idna"
-version = "3.7"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 files = [
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 ]
 
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -622,19 +688,19 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.3.0"
+version = "0.3.3"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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 [package.dependencies]
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 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
-langchain-core = ">=0.3.0,<0.4.0"
+langchain-core = ">=0.3.10,<0.4.0"
 langchain-text-splitters = ">=0.3.0,<0.4.0"
 langsmith = ">=0.1.17,<0.2.0"
 numpy = [
@@ -649,7 +715,7 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0"
+version = "0.3.10"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -658,14 +724,14 @@ develop = true
 
 [package.dependencies]
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-langsmith = "^0.1.117"
+langsmith = "^0.1.125"
 packaging = ">=23.2,<25"
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 PyYAML = ">=5.3"
-tenacity = "^8.1.0,!=8.4.0"
+tenacity = ">=8.1.0,!=8.4.0,<10.0.0"
 typing-extensions = ">=4.7"
 
 [package.source]
@@ -688,13 +754,13 @@ langchain-core = ">=0.3.0,<0.4.0"
 
 [[package]]
 name = "langsmith"
-version = "0.1.120"
+version = "0.1.135"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 [package.dependencies]
@@ -705,146 +771,157 @@ pydantic = [
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 requests = ">=2,<3"
+requests-toolbelt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "multidict"
-version = "6.0.5"
+version = "6.1.0"
 description = "multidict implementation"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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 testing = ["pytest", "pytest-benchmark"]
 
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 [package.extras]
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+timezone = ["tzdata"]
 
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 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
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 mssql-pymssql = ["pymssql"]
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@@ -1378,13 +1547,13 @@ sqlcipher = ["sqlcipher3_binary"]
 
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+    {file = "yarl-1.15.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:833547179c31f9bec39b49601d282d6f0ea1633620701288934c5f66d88c3e50"},
+    {file = "yarl-1.15.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2aa738e0282be54eede1e3f36b81f1e46aee7ec7602aa563e81e0e8d7b67963f"},
+    {file = "yarl-1.15.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9a13a07532e8e1c4a5a3afff0ca4553da23409fad65def1b71186fb867eeae8d"},
+    {file = "yarl-1.15.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c45817e3e6972109d1a2c65091504a537e257bc3c885b4e78a95baa96df6a3f8"},
+    {file = "yarl-1.15.2-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:670eb11325ed3a6209339974b276811867defe52f4188fe18dc49855774fa9cf"},
+    {file = "yarl-1.15.2-cp38-cp38-musllinux_1_2_armv7l.whl", hash = "sha256:d417a4f6943112fae3924bae2af7112562285848d9bcee737fc4ff7cbd450e6c"},
+    {file = "yarl-1.15.2-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:bc8936d06cd53fddd4892677d65e98af514c8d78c79864f418bbf78a4a2edde4"},
+    {file = "yarl-1.15.2-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:954dde77c404084c2544e572f342aef384240b3e434e06cecc71597e95fd1ce7"},
+    {file = "yarl-1.15.2-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:5bc0df728e4def5e15a754521e8882ba5a5121bd6b5a3a0ff7efda5d6558ab3d"},
+    {file = "yarl-1.15.2-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:b71862a652f50babab4a43a487f157d26b464b1dedbcc0afda02fd64f3809d04"},
+    {file = "yarl-1.15.2-cp38-cp38-win32.whl", hash = "sha256:63eab904f8630aed5a68f2d0aeab565dcfc595dc1bf0b91b71d9ddd43dea3aea"},
+    {file = "yarl-1.15.2-cp38-cp38-win_amd64.whl", hash = "sha256:2cf441c4b6e538ba0d2591574f95d3fdd33f1efafa864faa077d9636ecc0c4e9"},
+    {file = "yarl-1.15.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:a32d58f4b521bb98b2c0aa9da407f8bd57ca81f34362bcb090e4a79e9924fefc"},
+    {file = "yarl-1.15.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:766dcc00b943c089349d4060b935c76281f6be225e39994c2ccec3a2a36ad627"},
+    {file = "yarl-1.15.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:bed1b5dbf90bad3bfc19439258c97873eab453c71d8b6869c136346acfe497e7"},
+    {file = "yarl-1.15.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ed20a4bdc635f36cb19e630bfc644181dd075839b6fc84cac51c0f381ac472e2"},
+    {file = "yarl-1.15.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d538df442c0d9665664ab6dd5fccd0110fa3b364914f9c85b3ef9b7b2e157980"},
+    {file = "yarl-1.15.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c6cf1d92edf936ceedc7afa61b07e9d78a27b15244aa46bbcd534c7458ee1b"},
+    {file = "yarl-1.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ce44217ad99ffad8027d2fde0269ae368c86db66ea0571c62a000798d69401fb"},
+    {file = "yarl-1.15.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b47a6000a7e833ebfe5886b56a31cb2ff12120b1efd4578a6fcc38df16cc77bd"},
+    {file = "yarl-1.15.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:e52f77a0cd246086afde8815039f3e16f8d2be51786c0a39b57104c563c5cbb0"},
+    {file = "yarl-1.15.2-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:f9ca0e6ce7774dc7830dc0cc4bb6b3eec769db667f230e7c770a628c1aa5681b"},
+    {file = "yarl-1.15.2-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:136f9db0f53c0206db38b8cd0c985c78ded5fd596c9a86ce5c0b92afb91c3a19"},
+    {file = "yarl-1.15.2-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:173866d9f7409c0fb514cf6e78952e65816600cb888c68b37b41147349fe0057"},
+    {file = "yarl-1.15.2-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:6e840553c9c494a35e449a987ca2c4f8372668ee954a03a9a9685075228e5036"},
+    {file = "yarl-1.15.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:458c0c65802d816a6b955cf3603186de79e8fdb46d4f19abaec4ef0a906f50a7"},
+    {file = "yarl-1.15.2-cp39-cp39-win32.whl", hash = "sha256:5b48388ded01f6f2429a8c55012bdbd1c2a0c3735b3e73e221649e524c34a58d"},
+    {file = "yarl-1.15.2-cp39-cp39-win_amd64.whl", hash = "sha256:81dadafb3aa124f86dc267a2168f71bbd2bfb163663661ab0038f6e4b8edb810"},
+    {file = "yarl-1.15.2-py3-none-any.whl", hash = "sha256:0d3105efab7c5c091609abacad33afff33bdff0035bece164c98bcf5a85ef90a"},
+    {file = "yarl-1.15.2.tar.gz", hash = "sha256:a39c36f4218a5bb668b4f06874d676d35a035ee668e6e7e3538835c703634b84"},
 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
+propcache = ">=0.2.0"
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "0834872527b82aef5921c6b869a70c49ce36507d858e1371c05a079a77cfad86"
+content-hash = "16e5703f1da093250c2007b84ea3e087d151cb0b6785d493c2c803c3a7dfecf7"
diff --git a/libs/partners/couchbase/pyproject.toml b/libs/partners/couchbase/pyproject.toml
index ea172790ebc1f..f051b4336d468 100644
--- a/libs/partners/couchbase/pyproject.toml
+++ b/libs/partners/couchbase/pyproject.toml
@@ -21,8 +21,8 @@ ignore_missing_imports = "True"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.3.0.dev"
-couchbase = "^4.2.1"
+langchain-core = "^0.3.10"
+couchbase = "^4.3.2"
 
 [tool.ruff.lint]
 select = ["E", "F", "I", "T201"]

From 3f74dfc3d8efb7c58065a336b12d99853d275e15 Mon Sep 17 00:00:00 2001
From: Enes Bol <76845631+enesbol@users.noreply.github.com>
Date: Tue, 15 Oct 2024 23:57:50 +0200
Subject: [PATCH 03/84] community[patch]: Fix vLLM integration to filter
 SamplingParams (#27367)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

**Description:**
- This pull request addresses a bug in Langchain's VLLM integration,
where the use_beam_search parameter was erroneously passed to
SamplingParams. The SamplingParams class in vLLM does not support the
use_beam_search argument, which caused a TypeError.

- This PR introduces logic to filter out unsupported parameters,
ensuring that only valid parameters are passed to SamplingParams. As a
result, the integration now functions as expected without errors.

- The bug was reproduced by running the code sample from Langchain’s
documentation, which triggered the error due to the invalid parameter.
This fix resolves that error by implementing proper parameter filtering.

**VLLM Sampling Params Class:**
https://github.com/vllm-project/vllm/blob/main/vllm/sampling_params.py

**Issue:**
I could not found an Issue that belongs to this. Fixes "TypeError:
Unexpected keyword argument 'use_beam_search'" error when using VLLM
from Langchain.

**Dependencies:**
None.

**Tests and Documentation**:
Tests:
No new functionality was added, but I tested the changes by running
multiple prompts through the VLLM integration with various parameter
configurations. All tests passed successfully without breaking
compatibility.

Docs
No documentation changes were necessary as this is a bug fix.

**Reproducing the Error:**

https://python.langchain.com/docs/integrations/llms/vllm/

The code sample from the original documentation can be used to reproduce
the error I got.

from langchain_community.llms import VLLM
llm = VLLM(
    model="mosaicml/mpt-7b",
    trust_remote_code=True,  # mandatory for hf models
    max_new_tokens=128,
    top_k=10,
    top_p=0.95,
    temperature=0.8,
)
print(llm.invoke("What is the capital of France ?"))

![image](https://github.com/user-attachments/assets/3782d6ac-1f7b-4acc-bf2c-186216149de5)


This PR resolves the issue by ensuring that only valid parameters are
passed to SamplingParams.
---
 libs/community/langchain_community/llms/vllm.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/libs/community/langchain_community/llms/vllm.py b/libs/community/langchain_community/llms/vllm.py
index b887a8b0ab246..dc8a7a76d24ed 100644
--- a/libs/community/langchain_community/llms/vllm.py
+++ b/libs/community/langchain_community/llms/vllm.py
@@ -123,14 +123,19 @@ def _generate(
         **kwargs: Any,
     ) -> LLMResult:
         """Run the LLM on the given prompt and input."""
-
         from vllm import SamplingParams
 
         # build sampling parameters
         params = {**self._default_params, **kwargs, "stop": stop}
-        sampling_params = SamplingParams(**params)
+
+        # filter params for SamplingParams
+        known_keys = SamplingParams.__annotations__.keys()
+        sample_params = SamplingParams(
+            **{k: v for k, v in params.items() if k in known_keys}
+        )
+
         # call the model
-        outputs = self.client.generate(prompts, sampling_params)
+        outputs = self.client.generate(prompts, sample_params)
 
         generations = []
         for output in outputs:

From 8e66822100676096dd2b27070269667c27ec72d1 Mon Sep 17 00:00:00 2001
From: Leonid Ganeline <leo.gan.57@gmail.com>
Date: Tue, 15 Oct 2024 16:08:52 -0700
Subject: [PATCH 04/84] docs: `integrations google` update (#27218)

I've made several titles more compact hence a more compact menu.

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/docs/integrations/platforms/google.mdx | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/docs/docs/integrations/platforms/google.mdx b/docs/docs/integrations/platforms/google.mdx
index ea07b283634a7..f27587dc3e694 100644
--- a/docs/docs/integrations/platforms/google.mdx
+++ b/docs/docs/integrations/platforms/google.mdx
@@ -72,7 +72,7 @@ See a [usage example](/docs/integrations/chat/google_vertex_ai_palm).
 from langchain_google_vertexai import ChatVertexAI
 ```
 
-### Chat Anthropic on Vertex AI Model Garden
+### Anthropic on Vertex AI Model Garden
 
 See a [usage example](/docs/integrations/llms/google_vertex_ai_palm).
 
@@ -80,19 +80,19 @@ See a [usage example](/docs/integrations/llms/google_vertex_ai_palm).
 from langchain_google_vertexai.model_garden import ChatAnthropicVertex
 ```
 
-### Chat Llama on Vertex AI Model Garden
+### Llama on Vertex AI Model Garden
 
 ```python
 from langchain_google_vertexai.model_garden_maas.llama import VertexModelGardenLlama
 ```
 
-### Chat Mistral on Vertex AI Model Garden
+### Mistral on Vertex AI Model Garden
 
 ```python
 from langchain_google_vertexai.model_garden_maas.mistral import VertexModelGardenMistral
 ```
 
-### Chat Gemma local from Hugging Face
+### Gemma local from Hugging Face
 
 >Local `Gemma` model loaded from `HuggingFace`.
 
@@ -106,7 +106,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.gemma import GemmaChatLocalHF
 ```
 
-### Chat Gemma local from Kaggle
+### Gemma local from Kaggle
 
 >Local `Gemma` model loaded from `Kaggle`.
 
@@ -120,7 +120,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.gemma import GemmaChatLocalKaggle
 ```
 
-### Chat Gemma on Vertex AI Model Garden
+### Gemma on Vertex AI Model Garden
 
 We need to install `langchain-google-vertexai` python package.
 
@@ -132,7 +132,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.gemma import GemmaChatVertexAIModelGarden
 ```
 
-### Vertex AI image captioning chat
+### Vertex AI image captioning
 
 >Implementation of the `Image Captioning model` as a chat.
 
@@ -146,7 +146,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.vision_models import VertexAIImageCaptioningChat
 ```
 
-### Vertex AI image editor chat
+### Vertex AI image editor
 
 >Given an image and a prompt, edit the image. Currently only supports mask-free editing.
 
@@ -160,7 +160,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.vision_models import VertexAIImageEditorChat
 ```
 
-### Vertex AI image generator chat
+### Vertex AI image generator
 
 >Generates an image from a prompt.
 
@@ -174,7 +174,7 @@ pip install langchain-google-vertexai
 from langchain_google_vertexai.vision_models import VertexAIImageGeneratorChat
 ```
 
-### Vertex AI visual QnA chat
+### Vertex AI visual QnA
 
 >Chat implementation of a visual QnA model
 

From 15c1ddaf996fe305624f39e7b9e05adc6a4b2ffc Mon Sep 17 00:00:00 2001
From: xsai9101 <xiao.sai@oracle.com>
Date: Tue, 15 Oct 2024 19:19:20 -0700
Subject: [PATCH 05/84] community: Add support for clob datatype in oracle
 database (#27330)

**Description**:
This PR add support of clob/blob data type for oracle document loader,
clob/blob can only be read by oracledb package when connection is open,
so reformat code to process data before connection closes.

**Dependencies**:
oracledb package same as before. pip install oracledb

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../document_loaders/oracleadb_loader.py                  | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/libs/community/langchain_community/document_loaders/oracleadb_loader.py b/libs/community/langchain_community/document_loaders/oracleadb_loader.py
index 90a8d60885596..35da49c9affe9 100644
--- a/libs/community/langchain_community/document_loaders/oracleadb_loader.py
+++ b/libs/community/langchain_community/document_loaders/oracleadb_loader.py
@@ -100,7 +100,13 @@ def _run_query(self) -> List[Dict[str, Any]]:
             cursor.execute(self.query)
             columns = [col[0] for col in cursor.description]
             data = cursor.fetchall()
-            data = [dict(zip(columns, row)) for row in data]
+            data = [
+                {
+                    i: (j if not isinstance(j, oracledb.LOB) else j.read())
+                    for i, j in zip(columns, row)
+                }
+                for row in data
+            ]
         except oracledb.DatabaseError as e:
             print("Got error while connecting: " + str(e))  # noqa: T201
             data = []

From b8bfebd382caff3cebeb2d2ca158df4176816d8a Mon Sep 17 00:00:00 2001
From: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
Date: Wed, 16 Oct 2024 11:20:40 +0900
Subject: [PATCH 06/84] community: Add deprecation notice for Databricks
 integration in langchain-community (#27355)

We have released the
[langchain-databricks](https://github.com/langchain-ai/langchain-databricks)
package for Databricks integration. This PR deprecates the legacy
classes within `langchain-community`.

---------

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../langchain_community/chat_models/databricks.py          | 7 +++++++
 .../community/langchain_community/embeddings/databricks.py | 7 +++++++
 libs/community/langchain_community/llms/databricks.py      | 6 ++++++
 .../vectorstores/databricks_vector_search.py               | 7 ++++++-
 4 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/libs/community/langchain_community/chat_models/databricks.py b/libs/community/langchain_community/chat_models/databricks.py
index 992adf574cc29..5f17e3690aaf3 100644
--- a/libs/community/langchain_community/chat_models/databricks.py
+++ b/libs/community/langchain_community/chat_models/databricks.py
@@ -1,11 +1,18 @@
 import logging
 from urllib.parse import urlparse
 
+from langchain_core._api import deprecated
+
 from langchain_community.chat_models.mlflow import ChatMlflow
 
 logger = logging.getLogger(__name__)
 
 
+@deprecated(
+    since="0.3.3",
+    removal="1.0",
+    alternative_import="langchain_databricks.ChatDatabricks",
+)
 class ChatDatabricks(ChatMlflow):
     """`Databricks` chat models API.
 
diff --git a/libs/community/langchain_community/embeddings/databricks.py b/libs/community/langchain_community/embeddings/databricks.py
index 8ea3867fa4208..b83f15fa19823 100644
--- a/libs/community/langchain_community/embeddings/databricks.py
+++ b/libs/community/langchain_community/embeddings/databricks.py
@@ -3,6 +3,8 @@
 from typing import Iterator, List
 from urllib.parse import urlparse
 
+from langchain_core._api import deprecated
+
 from langchain_community.embeddings.mlflow import MlflowEmbeddings
 
 
@@ -11,6 +13,11 @@ def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
         yield texts[i : i + size]
 
 
+@deprecated(
+    since="0.3.3",
+    removal="1.0",
+    alternative_import="langchain_databricks.DatabricksEmbeddings",
+)
 class DatabricksEmbeddings(MlflowEmbeddings):
     """Databricks embeddings.
 
diff --git a/libs/community/langchain_community/llms/databricks.py b/libs/community/langchain_community/llms/databricks.py
index 9f535d7373d82..3f7aa42f2fda6 100644
--- a/libs/community/langchain_community/llms/databricks.py
+++ b/libs/community/langchain_community/llms/databricks.py
@@ -5,6 +5,7 @@
 from typing import Any, Callable, Dict, List, Mapping, Optional
 
 import requests
+from langchain_core._api import deprecated
 from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models import LLM
 from pydantic import (
@@ -262,6 +263,11 @@ def _pickle_fn_to_hex_string(fn: Callable) -> str:
         raise ValueError(f"Failed to pickle the function: {e}")
 
 
+@deprecated(
+    since="0.3.3",
+    removal="1.0",
+    alternative_import="langchain_databricks.ChatDatabricks",
+)
 class Databricks(LLM):
     """Databricks serving endpoint or a cluster driver proxy app for LLM.
 
diff --git a/libs/community/langchain_community/vectorstores/databricks_vector_search.py b/libs/community/langchain_community/vectorstores/databricks_vector_search.py
index 2c0727647a8a1..9d8e1228515bf 100644
--- a/libs/community/langchain_community/vectorstores/databricks_vector_search.py
+++ b/libs/community/langchain_community/vectorstores/databricks_vector_search.py
@@ -16,7 +16,7 @@
 )
 
 import numpy as np
-from langchain_core._api import warn_deprecated
+from langchain_core._api import deprecated, warn_deprecated
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.vectorstores import VST, VectorStore
@@ -29,6 +29,11 @@
 logger = logging.getLogger(__name__)
 
 
+@deprecated(
+    since="0.3.3",
+    removal="1.0",
+    alternative_import="langchain_databricks.DatabricksVectorSearch",
+)
 class DatabricksVectorSearch(VectorStore):
     """`Databricks Vector Search` vector store.
 

From 591a3db4fb74f03f1f0bb0b47c5a8d3fcf0cdb0f Mon Sep 17 00:00:00 2001
From: Mateusz Szewczyk <139469471+MateuszOssGit@users.noreply.github.com>
Date: Wed, 16 Oct 2024 04:27:56 +0200
Subject: [PATCH 07/84] docs: Update IBM `ChatWatsonx` documentation (#27358)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Thank you for contributing to LangChain!

We would like update IBM ChatWatsonx documentation in LangChain
documentation

Changes:
- Added support for `JSON mode`
- Added support for `Image Input`
- Added support for `Logprobs`

Chat Standard tests ->
https://github.com/langchain-ai/langchain-ibm/blob/main/libs/ibm/tests/integration_tests/test_chat_models_standard.py

Integration_tests job ✅ ->
https://github.com/langchain-ai/langchain-ibm/actions/runs/11327509188

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/docs/integrations/chat/ibm_watsonx.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/integrations/chat/ibm_watsonx.ipynb b/docs/docs/integrations/chat/ibm_watsonx.ipynb
index 6ec76de78d5a1..28a6b4072b04b 100644
--- a/docs/docs/integrations/chat/ibm_watsonx.ipynb
+++ b/docs/docs/integrations/chat/ibm_watsonx.ipynb
@@ -41,7 +41,7 @@
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
     "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | "
+    "| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | "
    ]
   },
   {

From 316541536940a0e063ed09f48d2bec05ded862c3 Mon Sep 17 00:00:00 2001
From: Leonid Ganeline <leo.gan.57@gmail.com>
Date: Tue, 15 Oct 2024 19:41:06 -0700
Subject: [PATCH 08/84] docs: `integrations` updates 21 (#27380)

Added missed provider pages. Added descriptions and links. Fixed
inconsistency in text formatting.

---------

Co-authored-by: Erick Friis <erickfriis@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../integrations/providers/byte_dance.mdx     | 16 +++++++++----
 docs/docs/integrations/providers/localai.mdx  | 24 +++++++++++++++++++
 docs/docs/integrations/providers/milvus.mdx   | 18 ++++++++++----
 docs/docs/integrations/providers/mongodb.mdx  | 24 +++++++++++++++++++
 .../integrations/providers/mongodb_atlas.mdx  | 22 ++++++++++++++++-
 5 files changed, 94 insertions(+), 10 deletions(-)
 create mode 100644 docs/docs/integrations/providers/localai.mdx
 create mode 100644 docs/docs/integrations/providers/mongodb.mdx

diff --git a/docs/docs/integrations/providers/byte_dance.mdx b/docs/docs/integrations/providers/byte_dance.mdx
index 8746bcf519fe4..25f6e2533ef62 100644
--- a/docs/docs/integrations/providers/byte_dance.mdx
+++ b/docs/docs/integrations/providers/byte_dance.mdx
@@ -8,15 +8,23 @@ Get the access token.
 You can find the access instructions [here](https://open.larksuite.com/document)
 
 
-## Document Loader
-
-### Lark Suite
+## Document Loaders
 
 >[Lark Suite](https://www.larksuite.com/) is an enterprise collaboration platform 
 > developed by `ByteDance`.
 
-See a [usage example](/docs/integrations/document_loaders/larksuite).
+### Lark Suite for Document
+
+See a [usage example](/docs/integrations/document_loaders/larksuite/#load-from-document).
 
 ```python
 from langchain_community.document_loaders.larksuite import LarkSuiteDocLoader
 ```
+
+### Lark Suite for Wiki
+
+See a [usage example](/docs/integrations/document_loaders/larksuite/#load-from-wiki).
+
+```python
+from langchain_community.document_loaders.larksuite import LarkSuiteWikiLoader
+```
diff --git a/docs/docs/integrations/providers/localai.mdx b/docs/docs/integrations/providers/localai.mdx
new file mode 100644
index 0000000000000..1424420b53338
--- /dev/null
+++ b/docs/docs/integrations/providers/localai.mdx
@@ -0,0 +1,24 @@
+# LocalAI
+
+>[LocalAI](https://localai.io/) is the free, Open Source OpenAI alternative. 
+> `LocalAI` act as a drop-in replacement REST API that’s compatible with OpenAI API 
+> specifications for local inferencing. It allows you to run LLMs, generate images, 
+> audio (and not only) locally or on-prem with consumer grade hardware, 
+> supporting multiple model families and architectures.
+
+## Installation and Setup
+
+We have to install several python packages: 
+
+```bash
+pip install tenacity openai
+```
+
+
+## Embedding models
+
+See a [usage example](/docs/integrations/text_embedding/localai).
+
+```python
+from langchain_community.embeddings import LocalAIEmbeddings
+```
diff --git a/docs/docs/integrations/providers/milvus.mdx b/docs/docs/integrations/providers/milvus.mdx
index ea11c08fd1b4c..d73590fdff0c0 100644
--- a/docs/docs/integrations/providers/milvus.mdx
+++ b/docs/docs/integrations/providers/milvus.mdx
@@ -9,17 +9,25 @@
 Install the Python SDK:
 
 ```bash
-pip install pymilvus
+pip install langchain-milvus
 ```
 
 ## Vector Store
 
-There exists a wrapper around `Milvus` indexes, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
+See a [usage example](/docs/integrations/vectorstores/milvus).
 
 To import this vectorstore:
 ```python
-from langchain_community.vectorstores import Milvus
+from langchain_milvus import Milvus
+```
+
+## Retrievers
+
+See a [usage example](/docs/integrations/retrievers/milvus_hybrid_search).
+
+To import this vectorstore:
+```python
+from langchain_milvus.retrievers import MilvusCollectionHybridSearchRetriever
+from langchain_milvus.utils.sparse import BM25SparseEmbedding
 ```
 
-For a more detailed walkthrough of the `Miluvs` wrapper, see [this notebook](/docs/integrations/vectorstores/milvus)
diff --git a/docs/docs/integrations/providers/mongodb.mdx b/docs/docs/integrations/providers/mongodb.mdx
new file mode 100644
index 0000000000000..f981cdc130271
--- /dev/null
+++ b/docs/docs/integrations/providers/mongodb.mdx
@@ -0,0 +1,24 @@
+# MongoDB
+
+>[MongoDB](https://www.mongodb.com/) is a NoSQL, document-oriented 
+> database that supports JSON-like documents with a dynamic schema.
+ 
+**NOTE:** 
+- See other `MongoDB` integrations on the [MongoDB Atlas page](/docs/integrations/providers/mongodb_atlas).
+
+## Installation and Setup
+
+Install the Python package:
+
+```bash
+pip install langchain-mongodb
+```
+
+## Message Histories
+
+See a [usage example](/docs/integrations/memory/mongodb_chat_message_history).
+
+To import this vectorstore:
+```python
+from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
+```
diff --git a/docs/docs/integrations/providers/mongodb_atlas.mdx b/docs/docs/integrations/providers/mongodb_atlas.mdx
index 67fd9b2395c3f..601b84f015cbd 100644
--- a/docs/docs/integrations/providers/mongodb_atlas.mdx
+++ b/docs/docs/integrations/providers/mongodb_atlas.mdx
@@ -22,10 +22,30 @@ See a [usage example](/docs/integrations/vectorstores/mongodb_atlas).
 from langchain_mongodb import MongoDBAtlasVectorSearch
 ```
 
+## Retrievers
 
-## LLM Caches
+### Full Text Search Retriever
+
+>`Hybrid Search Retriever` performs full-text searches using 
+> Lucene’s standard (`BM25`) analyzer.
+
+```python
+from langchain_mongodb.retrievers import MongoDBAtlasFullTextSearchRetriever
+```
+
+### Hybrid Search Retriever
+
+>`Hybrid Search Retriever` combines vector and full-text searches weighting 
+> them the via `Reciprocal Rank Fusion` (`RRF`) algorithm.
+ 
+```python
+from langchain_mongodb.retrievers import MongoDBAtlasHybridSearchRetriever
+```
+
+## Model Caches
 
 ### MongoDBCache
+
 An abstraction to store a simple cache in MongoDB. This does not use Semantic Caching, nor does it require an index to be made on the collection before generation.
 
 To import this cache:

From 31e7664afd3a9f31d469f643ed35b8ac43468288 Mon Sep 17 00:00:00 2001
From: sByteman <78813459+ccq1@users.noreply.github.com>
Date: Thu, 17 Oct 2024 00:29:59 +0800
Subject: [PATCH 09/84] community[minor]: add proxy support to
 RecursiveUrlLoader (#27364)

**Description**
This PR introduces the proxies parameter to the RecursiveUrlLoader
class, allowing the user to specify proxy servers for requests. This
update enables crawling through proxy servers, providing enhanced
flexibility for network configurations.
The key changes include:
  1.Added an optional proxies parameter to the constructor (__init__).
2.Updated the documentation to explain the proxies parameter usage with
an example.
3.Modified the _get_child_links_recursive method to pass the proxies
parameter to the requests.get function.



**Sample Usage**

```python
from bs4 import BeautifulSoup as Soup
from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader

proxies = {
    "http": "http://localhost:1080",
    "https": "http://localhost:1080",
}
url = "https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel"
loader = RecursiveUrlLoader(
    url=url, max_depth=1, extractor=lambda x: Soup(x, "html.parser").text,proxies=proxies
)
docs = loader.load()
```

---------

Co-authored-by: root <root@thb>
---
 docs/docs/security.md                            |  2 +-
 .../document_loaders/recursive_url_loader.py     | 16 +++++++++++++++-
 2 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/docs/docs/security.md b/docs/docs/security.md
index e583642c66a75..08e841c89a25d 100644
--- a/docs/docs/security.md
+++ b/docs/docs/security.md
@@ -6,7 +6,7 @@ LangChain has a large ecosystem of integrations with various external resources
 
 When building such applications developers should remember to follow good security practices:
 
-* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), etc. as appropriate for your application.
+* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), specifying proxy configurations to control external requests, etc. as appropriate for your application.
 * **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it’s safest to assume that any LLM able to use those credentials may in fact delete data.
 * [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It’s best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
 
diff --git a/libs/community/langchain_community/document_loaders/recursive_url_loader.py b/libs/community/langchain_community/document_loaders/recursive_url_loader.py
index d204344474736..ebf119bafc220 100644
--- a/libs/community/langchain_community/document_loaders/recursive_url_loader.py
+++ b/libs/community/langchain_community/document_loaders/recursive_url_loader.py
@@ -268,6 +268,7 @@ def __init__(
         base_url: Optional[str] = None,
         autoset_encoding: bool = True,
         encoding: Optional[str] = None,
+        proxies: Optional[dict] = None,
     ) -> None:
         """Initialize with URL to crawl and any subdirectories to exclude.
 
@@ -313,6 +314,16 @@ def simple_metadata_extractor(
                 encoding, unless the `encoding` argument has already been explicitly set.
             encoding: The encoding of the response. If manually set, the encoding will be
                 set to given value, regardless of the `autoset_encoding` argument.
+            proxies: A dictionary mapping protocol names to the proxy URLs to be used for requests.
+                This allows the crawler to route its requests through specified proxy servers.
+                If None, no proxies will be used and requests will go directly to the target URL.
+                Example usage:
+                ..code-block:: python
+
+                    proxies = {
+                        "http": "http://10.10.1.10:3128",
+                        "https": "https://10.10.1.10:1080",
+                    }
         """  # noqa: E501
 
         self.url = url
@@ -342,6 +353,7 @@ def simple_metadata_extractor(
         self.check_response_status = check_response_status
         self.continue_on_failure = continue_on_failure
         self.base_url = base_url if base_url is not None else url
+        self.proxies = proxies
 
     def _get_child_links_recursive(
         self, url: str, visited: Set[str], *, depth: int = 0
@@ -360,7 +372,9 @@ def _get_child_links_recursive(
         # Get all links that can be accessed from the current URL
         visited.add(url)
         try:
-            response = requests.get(url, timeout=self.timeout, headers=self.headers)
+            response = requests.get(
+                url, timeout=self.timeout, headers=self.headers, proxies=self.proxies
+            )
 
             if self.encoding is not None:
                 response.encoding = self.encoding

From a4392b070db5e46a23e08b9c7336d23510598a8d Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Thu, 17 Oct 2024 02:10:10 +0900
Subject: [PATCH 10/84] core[patch]: add convert_to_openai_messages util
 (#27263)

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 libs/core/langchain_core/messages/__init__.py |   2 +
 libs/core/langchain_core/messages/utils.py    | 380 +++++++++++++++++-
 .../tests/unit_tests/messages/test_imports.py |   1 +
 .../tests/unit_tests/messages/test_utils.py   | 307 +++++++++++++-
 libs/langchain/langchain/chat_models/base.py  |   4 +-
 .../langchain_anthropic/chat_models.py        |  12 +-
 6 files changed, 686 insertions(+), 20 deletions(-)

diff --git a/libs/core/langchain_core/messages/__init__.py b/libs/core/langchain_core/messages/__init__.py
index 4a28feedd8f02..f8fdf4d4a3bed 100644
--- a/libs/core/langchain_core/messages/__init__.py
+++ b/libs/core/langchain_core/messages/__init__.py
@@ -43,6 +43,7 @@
     MessageLikeRepresentation,
     _message_from_dict,
     convert_to_messages,
+    convert_to_openai_messages,
     filter_messages,
     get_buffer_string,
     merge_message_runs,
@@ -83,4 +84,5 @@
     "filter_messages",
     "merge_message_runs",
     "trim_messages",
+    "convert_to_openai_messages",
 ]
diff --git a/libs/core/langchain_core/messages/utils.py b/libs/core/langchain_core/messages/utils.py
index 8f88502c1df30..d3194226ea217 100644
--- a/libs/core/langchain_core/messages/utils.py
+++ b/libs/core/langchain_core/messages/utils.py
@@ -9,6 +9,7 @@
 
 from __future__ import annotations
 
+import base64
 import inspect
 import json
 from collections.abc import Iterable, Sequence
@@ -34,7 +35,7 @@
 from langchain_core.messages.human import HumanMessage, HumanMessageChunk
 from langchain_core.messages.modifier import RemoveMessage
 from langchain_core.messages.system import SystemMessage, SystemMessageChunk
-from langchain_core.messages.tool import ToolMessage, ToolMessageChunk
+from langchain_core.messages.tool import ToolCall, ToolMessage, ToolMessageChunk
 
 if TYPE_CHECKING:
     from langchain_text_splitters import TextSplitter
@@ -171,7 +172,7 @@ def messages_from_dict(messages: Sequence[dict]) -> list[BaseMessage]:
         messages: Sequence of messages (as dicts) to convert.
 
     Returns:
-        List of messages (BaseMessages).
+        list of messages (BaseMessages).
     """
     return [_message_from_dict(m) for m in messages]
 
@@ -217,9 +218,9 @@ def _create_message_from_message_type(
         content: (str) the content string.
         name: (str) the name of the message. Default is None.
         tool_call_id: (str) the tool call id. Default is None.
-        tool_calls: (List[Dict[str, Any]]) the tool calls. Default is None.
+        tool_calls: (list[dict[str, Any]]) the tool calls. Default is None.
         id: (str) the id of the message. Default is None.
-        **additional_kwargs: (Dict[str, Any]) additional keyword arguments.
+        **additional_kwargs: (dict[str, Any]) additional keyword arguments.
 
     Returns:
         a message of the appropriate type.
@@ -337,7 +338,7 @@ def convert_to_messages(
         messages: Sequence of messages to convert.
 
     Returns:
-        List of messages (BaseMessages).
+        list of messages (BaseMessages).
     """
     # Import here to avoid circular imports
     from langchain_core.prompt_values import PromptValue
@@ -359,7 +360,8 @@ def wrapped(
     ) -> list[BaseMessage]: ...
 
     def wrapped(
-        messages: Optional[Sequence[MessageLikeRepresentation]] = None, **kwargs: Any
+        messages: Union[Sequence[MessageLikeRepresentation], None] = None,
+        **kwargs: Any,
     ) -> Union[
         list[BaseMessage],
         Runnable[Sequence[MessageLikeRepresentation], list[BaseMessage]],
@@ -479,7 +481,7 @@ def merge_message_runs(
         Default is "\n".
 
     Returns:
-        List of BaseMessages with consecutive runs of message types merged into single
+        list of BaseMessages with consecutive runs of message types merged into single
         messages. By default, if two messages being merged both have string contents,
         the merged content is a concatenation of the two strings with a new-line separator.
         The separator inserted between message chunks can be controlled by specifying
@@ -655,7 +657,7 @@ def trim_messages(
             newlines.
 
     Returns:
-        List of trimmed BaseMessages.
+        list of trimmed BaseMessages.
 
     Raises:
         ValueError: if two incompatible arguments are specified or an unrecognized
@@ -668,7 +670,7 @@ def trim_messages(
 
         .. code-block:: python
 
-            from typing import List
+            from typing import list
 
             from langchain_core.messages import (
                 AIMessage,
@@ -768,7 +770,7 @@ def trim_messages(
                 AIMessage("This is a 4 token text. The full message is 10 tokens.", id="fourth"),
             ]
 
-            def dummy_token_counter(messages: List[BaseMessage]) -> int:
+            def dummy_token_counter(messages: list[BaseMessage]) -> int:
                 # treat each message like it adds 3 default tokens at the beginning
                 # of the message and at the end of the message. 3 + 4 + 3 = 10 tokens
                 # per message.
@@ -868,6 +870,328 @@ def list_token_counter(messages: Sequence[BaseMessage]) -> int:
         raise ValueError(msg)
 
 
+def convert_to_openai_messages(
+    messages: Union[MessageLikeRepresentation, Sequence[MessageLikeRepresentation]],
+    *,
+    text_format: Literal["string", "block"] = "string",
+) -> Union[dict, list[dict]]:
+    """Convert LangChain messages into OpenAI message dicts.
+
+    .. versionadded:: 0.3.11
+
+    Args:
+        messages: Message-like object or iterable of objects whose contents are
+            in OpenAI, Anthropic, Bedrock Converse, or VertexAI formats.
+        text_format: How to format string or text block contents:
+
+                - "string":
+                    If a message has a string content, this is left as a string. If
+                    a message has content blocks that are all of type 'text', these are
+                    joined with a newline to make a single string. If a message has
+                    content blocks and at least one isn't of type 'text', then
+                    all blocks are left as dicts.
+                - "block":
+                    If a message has a string content, this is turned into a list
+                    with a single content block of type 'text'. If a message has content
+                    blocks these are left as is.
+
+    Returns:
+        The return type depends on the input type:
+            - dict:
+                If a single message-like object is passed in, a single OpenAI message
+                dict is returned.
+            - list[dict]:
+                If a sequence of message-like objects are passed in, a list of OpenAI
+                message dicts is returned.
+
+    Example:
+
+        .. code-block:: python
+
+            from langchain_core.messages import (
+                convert_to_openai_messages,
+                AIMessage,
+                SystemMessage,
+                ToolMessage,
+            )
+
+            messages = [
+                SystemMessage([{"type": "text", "text": "foo"}]),
+                {"role": "user", "content": [{"type": "text", "text": "whats in this"}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,'/9j/4AAQSk'"}}]},
+                AIMessage("", tool_calls=[{"name": "analyze", "args": {"baz": "buz"}, "id": "1", "type": "tool_call"}]),
+                ToolMessage("foobar", tool_call_id="1", name="bar"),
+                {"role": "assistant", "content": "thats nice"},
+            ]
+            oai_messages = convert_to_openai_messages(messages)
+            # -> [
+            #   {'role': 'system', 'content': 'foo'},
+            #   {'role': 'user', 'content': [{'type': 'text', 'text': 'whats in this'}, {'type': 'image_url', 'image_url': {'url': "data:image/png;base64,'/9j/4AAQSk'"}}]},
+            #   {'role': 'assistant', 'tool_calls': [{'type': 'function', 'id': '1','function': {'name': 'analyze', 'arguments': '{"baz": "buz"}'}}], 'content': ''},
+            #   {'role': 'tool', 'name': 'bar', 'content': 'foobar'},
+            #   {'role': 'assistant', 'content': 'thats nice'}
+            # ]
+
+    """  # noqa: E501
+    if text_format not in ("string", "block"):
+        err = f"Unrecognized {text_format=}, expected one of 'string' or 'block'."
+        raise ValueError(err)
+
+    oai_messages: list = []
+
+    if is_single := isinstance(messages, (BaseMessage, dict)):
+        messages = [messages]
+    messages = convert_to_messages(messages)
+
+    for i, message in enumerate(messages):
+        oai_msg: dict = {"role": _get_message_openai_role(message)}
+        tool_messages: list = []
+        content: Union[str, list[dict]]
+
+        if message.name:
+            oai_msg["name"] = message.name
+        if isinstance(message, AIMessage) and message.tool_calls:
+            oai_msg["tool_calls"] = _convert_to_openai_tool_calls(message.tool_calls)
+        if message.additional_kwargs.get("refusal"):
+            oai_msg["refusal"] = message.additional_kwargs["refusal"]
+        if isinstance(message, ToolMessage):
+            oai_msg["tool_call_id"] = message.tool_call_id
+
+        if not message.content:
+            content = "" if text_format == "string" else []
+        elif isinstance(message.content, str):
+            if text_format == "string":
+                content = message.content
+            else:
+                content = [{"type": "text", "text": message.content}]
+        else:
+            if text_format == "string" and all(
+                isinstance(block, str) or block.get("type") == "text"
+                for block in message.content
+            ):
+                content = "\n".join(
+                    block if isinstance(block, str) else block["text"]
+                    for block in message.content
+                )
+            else:
+                content = []
+                for j, block in enumerate(message.content):
+                    # OpenAI format
+                    if isinstance(block, str):
+                        content.append({"type": "text", "text": block})
+                    elif block.get("type") == "text":
+                        if missing := [k for k in ("text",) if k not in block]:
+                            err = (
+                                f"Unrecognized content block at "
+                                f"messages[{i}].content[{j}] has 'type': 'text' "
+                                f"but is missing expected key(s) "
+                                f"{missing}. Full content block:\n\n{block}"
+                            )
+                            raise ValueError(err)
+                        content.append({"type": block["type"], "text": block["text"]})
+                    elif block.get("type") == "image_url":
+                        if missing := [k for k in ("image_url",) if k not in block]:
+                            err = (
+                                f"Unrecognized content block at "
+                                f"messages[{i}].content[{j}] has 'type': 'image_url' "
+                                f"but is missing expected key(s) "
+                                f"{missing}. Full content block:\n\n{block}"
+                            )
+                            raise ValueError(err)
+                        content.append(
+                            {"type": "image_url", "image_url": block["image_url"]}
+                        )
+                    # Anthropic and Bedrock converse format
+                    elif (block.get("type") == "image") or "image" in block:
+                        # Anthropic
+                        if source := block.get("source"):
+                            if missing := [
+                                k
+                                for k in ("media_type", "type", "data")
+                                if k not in source
+                            ]:
+                                err = (
+                                    f"Unrecognized content block at "
+                                    f"messages[{i}].content[{j}] has 'type': 'image' "
+                                    f"but 'source' is missing expected key(s) "
+                                    f"{missing}. Full content block:\n\n{block}"
+                                )
+                                raise ValueError(err)
+                            content.append(
+                                {
+                                    "type": "image_url",
+                                    "image_url": {
+                                        "url": (
+                                            f"data:{source['media_type']};"
+                                            f"{source['type']},{source['data']}"
+                                        )
+                                    },
+                                }
+                            )
+                        # Bedrock converse
+                        elif image := block.get("image"):
+                            if missing := [
+                                k for k in ("source", "format") if k not in image
+                            ]:
+                                err = (
+                                    f"Unrecognized content block at "
+                                    f"messages[{i}].content[{j}] has key 'image', "
+                                    f"but 'image' is missing expected key(s) "
+                                    f"{missing}. Full content block:\n\n{block}"
+                                )
+                                raise ValueError(err)
+                            b64_image = _bytes_to_b64_str(image["source"]["bytes"])
+                            content.append(
+                                {
+                                    "type": "image_url",
+                                    "image_url": {
+                                        "url": (
+                                            f"data:image/{image['format']};"
+                                            f"base64,{b64_image}"
+                                        )
+                                    },
+                                }
+                            )
+                        else:
+                            err = (
+                                f"Unrecognized content block at "
+                                f"messages[{i}].content[{j}] has 'type': 'image' "
+                                f"but does not have a 'source' or 'image' key. Full "
+                                f"content block:\n\n{block}"
+                            )
+                            raise ValueError(err)
+                    elif block.get("type") == "tool_use":
+                        if missing := [
+                            k for k in ("id", "name", "input") if k not in block
+                        ]:
+                            err = (
+                                f"Unrecognized content block at "
+                                f"messages[{i}].content[{j}] has 'type': "
+                                f"'tool_use', but is missing expected key(s) "
+                                f"{missing}. Full content block:\n\n{block}"
+                            )
+                            raise ValueError(err)
+                        if not any(
+                            tool_call["id"] == block["id"]
+                            for tool_call in cast(AIMessage, message).tool_calls
+                        ):
+                            oai_msg["tool_calls"] = oai_msg.get("tool_calls", [])
+                            oai_msg["tool_calls"].append(
+                                {
+                                    "type": "function",
+                                    "id": block["id"],
+                                    "function": {
+                                        "name": block["name"],
+                                        "arguments": json.dumps(block["input"]),
+                                    },
+                                }
+                            )
+                    elif block.get("type") == "tool_result":
+                        if missing := [
+                            k for k in ("content", "tool_use_id") if k not in block
+                        ]:
+                            msg = (
+                                f"Unrecognized content block at "
+                                f"messages[{i}].content[{j}] has 'type': "
+                                f"'tool_result', but is missing expected key(s) "
+                                f"{missing}. Full content block:\n\n{block}"
+                            )
+                            raise ValueError(msg)
+                        tool_message = ToolMessage(
+                            block["content"],
+                            tool_call_id=block["tool_use_id"],
+                            status="error" if block.get("is_error") else "success",
+                        )
+                        # Recurse to make sure tool message contents are OpenAI format.
+                        tool_messages.extend(
+                            convert_to_openai_messages(
+                                [tool_message], text_format=text_format
+                            )
+                        )
+                    elif (block.get("type") == "json") or "json" in block:
+                        if "json" not in block:
+                            msg = (
+                                f"Unrecognized content block at "
+                                f"messages[{i}].content[{j}] has 'type': 'json' "
+                                f"but does not have a 'json' key. Full "
+                                f"content block:\n\n{block}"
+                            )
+                            raise ValueError(msg)
+                        content.append(
+                            {"type": "text", "text": json.dumps(block["json"])}
+                        )
+                    elif (
+                        block.get("type") == "guard_content"
+                    ) or "guard_content" in block:
+                        if (
+                            "guard_content" not in block
+                            or "text" not in block["guard_content"]
+                        ):
+                            msg = (
+                                f"Unrecognized content block at "
+                                f"messages[{i}].content[{j}] has 'type': "
+                                f"'guard_content' but does not have a "
+                                f"messages[{i}].content[{j}]['guard_content']['text'] "
+                                f"key. Full content block:\n\n{block}"
+                            )
+                            raise ValueError(msg)
+                        text = block["guard_content"]["text"]
+                        if isinstance(text, dict):
+                            text = text["text"]
+                        content.append({"type": "text", "text": text})
+                    # VertexAI format
+                    elif block.get("type") == "media":
+                        if missing := [
+                            k for k in ("mime_type", "data") if k not in block
+                        ]:
+                            err = (
+                                f"Unrecognized content block at "
+                                f"messages[{i}].content[{j}] has 'type': "
+                                f"'media' but does not have key(s) {missing}. Full "
+                                f"content block:\n\n{block}"
+                            )
+                            raise ValueError(err)
+                        if "image" not in block["mime_type"]:
+                            err = (
+                                f"OpenAI messages can only support text and image data."
+                                f" Received content block with media of type:"
+                                f" {block['mime_type']}"
+                            )
+                            raise ValueError(err)
+                        b64_image = _bytes_to_b64_str(block["data"])
+                        content.append(
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": (
+                                        f"data:{block['mime_type']};base64,{b64_image}"
+                                    )
+                                },
+                            }
+                        )
+                    else:
+                        err = (
+                            f"Unrecognized content block at "
+                            f"messages[{i}].content[{j}] does not match OpenAI, "
+                            f"Anthropic, Bedrock Converse, or VertexAI format. Full "
+                            f"content block:\n\n{block}"
+                        )
+                        raise ValueError(err)
+                if text_format == "string" and not any(
+                    block["type"] != "text" for block in content
+                ):
+                    content = "\n".join(block["text"] for block in content)
+        oai_msg["content"] = content
+        if message.content and not oai_msg["content"] and tool_messages:
+            oai_messages.extend(tool_messages)
+        else:
+            oai_messages.extend([oai_msg, *tool_messages])
+
+    if is_single:
+        return oai_messages[0]
+    else:
+        return oai_messages
+
+
 def _first_max_tokens(
     messages: Sequence[BaseMessage],
     *,
@@ -1036,3 +1360,39 @@ def _is_message_type(
     types_types = tuple(t for t in types if isinstance(t, type))
 
     return message.type in types_str or isinstance(message, types_types)
+
+
+def _bytes_to_b64_str(bytes_: bytes) -> str:
+    return base64.b64encode(bytes_).decode("utf-8")
+
+
+def _get_message_openai_role(message: BaseMessage) -> str:
+    if isinstance(message, AIMessage):
+        return "assistant"
+    elif isinstance(message, HumanMessage):
+        return "user"
+    elif isinstance(message, ToolMessage):
+        return "tool"
+    elif isinstance(message, SystemMessage):
+        return "system"
+    elif isinstance(message, FunctionMessage):
+        return "function"
+    elif isinstance(message, ChatMessage):
+        return message.role
+    else:
+        msg = f"Unknown BaseMessage type {message.__class__}."
+        raise ValueError(msg)
+
+
+def _convert_to_openai_tool_calls(tool_calls: list[ToolCall]) -> list[dict]:
+    return [
+        {
+            "type": "function",
+            "id": tool_call["id"],
+            "function": {
+                "name": tool_call["name"],
+                "arguments": json.dumps(tool_call["args"]),
+            },
+        }
+        for tool_call in tool_calls
+    ]
diff --git a/libs/core/tests/unit_tests/messages/test_imports.py b/libs/core/tests/unit_tests/messages/test_imports.py
index 531409a42614d..18d8cd5243f40 100644
--- a/libs/core/tests/unit_tests/messages/test_imports.py
+++ b/libs/core/tests/unit_tests/messages/test_imports.py
@@ -32,6 +32,7 @@
     "filter_messages",
     "merge_message_runs",
     "trim_messages",
+    "convert_to_openai_messages",
 ]
 
 
diff --git a/libs/core/tests/unit_tests/messages/test_utils.py b/libs/core/tests/unit_tests/messages/test_utils.py
index f994ace071c62..9cd9543dc7c8e 100644
--- a/libs/core/tests/unit_tests/messages/test_utils.py
+++ b/libs/core/tests/unit_tests/messages/test_utils.py
@@ -1,3 +1,4 @@
+import base64
 import json
 
 import pytest
@@ -13,6 +14,7 @@
 )
 from langchain_core.messages.utils import (
     convert_to_messages,
+    convert_to_openai_messages,
     filter_messages,
     merge_message_runs,
     trim_messages,
@@ -565,6 +567,307 @@ def test_convert_to_messages() -> None:
     assert expected == actual
 
 
-@pytest.mark.xfail(reason="AI message does not support refusal key yet.")
 def test_convert_to_messages_openai_refusal() -> None:
-    convert_to_messages([{"role": "assistant", "refusal": "9.1"}])
+    actual = convert_to_messages(
+        [{"role": "assistant", "content": "", "refusal": "9.1"}]
+    )
+    expected = [AIMessage("", additional_kwargs={"refusal": "9.1"})]
+    assert actual == expected
+
+    # Raises error if content is missing.
+    with pytest.raises(ValueError):
+        convert_to_messages([{"role": "assistant", "refusal": "9.1"}])
+
+
+def create_image_data() -> str:
+    return "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"  # noqa: E501
+
+
+def create_base64_image(format: str = "jpeg") -> str:
+    data = create_image_data()
+    return f"data:image/{format};base64,{data}"  # noqa: E501
+
+
+def test_convert_to_openai_messages_single_message() -> None:
+    message = HumanMessage(content="Hello")
+    result = convert_to_openai_messages(message)
+    assert result == {"role": "user", "content": "Hello"}
+
+
+def test_convert_to_openai_messages_multiple_messages() -> None:
+    messages = [
+        SystemMessage(content="System message"),
+        HumanMessage(content="Human message"),
+        AIMessage(content="AI message"),
+    ]
+    result = convert_to_openai_messages(messages)
+    expected = [
+        {"role": "system", "content": "System message"},
+        {"role": "user", "content": "Human message"},
+        {"role": "assistant", "content": "AI message"},
+    ]
+    assert result == expected
+
+
+def test_convert_to_openai_messages_openai_string() -> None:
+    messages = [
+        HumanMessage(
+            content=[
+                {"type": "text", "text": "Hello"},
+                {"type": "text", "text": "World"},
+            ]
+        ),
+        AIMessage(
+            content=[{"type": "text", "text": "Hi"}, {"type": "text", "text": "there"}]
+        ),
+    ]
+    result = convert_to_openai_messages(messages)
+    expected = [
+        {"role": "user", "content": "Hello\nWorld"},
+        {"role": "assistant", "content": "Hi\nthere"},
+    ]
+    assert result == expected
+
+
+def test_convert_to_openai_messages_openai_block() -> None:
+    messages = [HumanMessage(content="Hello"), AIMessage(content="Hi there")]
+    result = convert_to_openai_messages(messages, text_format="block")
+    expected = [
+        {"role": "user", "content": [{"type": "text", "text": "Hello"}]},
+        {"role": "assistant", "content": [{"type": "text", "text": "Hi there"}]},
+    ]
+    assert result == expected
+
+
+def test_convert_to_openai_messages_invalid_format() -> None:
+    with pytest.raises(ValueError, match="Unrecognized text_format="):
+        convert_to_openai_messages(
+            [HumanMessage(content="Hello")],
+            text_format="invalid",  # type: ignore[arg-type]
+        )
+
+
+def test_convert_to_openai_messages_openai_image() -> None:
+    base64_image = create_base64_image()
+    messages = [
+        HumanMessage(
+            content=[
+                {"type": "text", "text": "Here's an image:"},
+                {"type": "image_url", "image_url": {"url": base64_image}},
+            ]
+        )
+    ]
+    result = convert_to_openai_messages(messages, text_format="block")
+    expected = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Here's an image:"},
+                {"type": "image_url", "image_url": {"url": base64_image}},
+            ],
+        }
+    ]
+    assert result == expected
+
+
+def test_convert_to_openai_messages_anthropic() -> None:
+    image_data = create_image_data()
+    messages = [
+        HumanMessage(
+            content=[
+                {
+                    "type": "text",
+                    "text": "Here's an image:",
+                    "cache_control": {"type": "ephemeral"},
+                },
+                {
+                    "type": "image",
+                    "source": {
+                        "type": "base64",
+                        "media_type": "image/jpeg",
+                        "data": image_data,
+                    },
+                },
+            ]
+        ),
+        AIMessage(
+            content=[
+                {"type": "tool_use", "name": "foo", "input": {"bar": "baz"}, "id": "1"}
+            ]
+        ),
+        HumanMessage(
+            content=[
+                {
+                    "type": "tool_result",
+                    "tool_use_id": "1",
+                    "is_error": False,
+                    "content": [
+                        {
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": "image/jpeg",
+                                "data": image_data,
+                            },
+                        },
+                    ],
+                }
+            ]
+        ),
+    ]
+    result = convert_to_openai_messages(messages)
+    expected = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Here's an image:"},
+                {"type": "image_url", "image_url": {"url": create_base64_image()}},
+            ],
+        },
+        {
+            "role": "assistant",
+            "content": "",
+            "tool_calls": [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "foo",
+                        "arguments": json.dumps({"bar": "baz"}),
+                    },
+                    "id": "1",
+                }
+            ],
+        },
+        {
+            "role": "tool",
+            "content": [
+                {"type": "image_url", "image_url": {"url": create_base64_image()}}
+            ],
+            "tool_call_id": "1",
+        },
+    ]
+    assert result == expected
+
+
+def test_convert_to_openai_messages_bedrock_converse_image() -> None:
+    image_data = create_image_data()
+    messages = [
+        HumanMessage(
+            content=[
+                {"type": "text", "text": "Here's an image:"},
+                {
+                    "image": {
+                        "format": "jpeg",
+                        "source": {"bytes": base64.b64decode(image_data)},
+                    }
+                },
+            ]
+        )
+    ]
+    result = convert_to_openai_messages(messages)
+    assert result[0]["content"][1]["type"] == "image_url"
+    assert result[0]["content"][1]["image_url"]["url"] == create_base64_image()
+
+
+def test_convert_to_openai_messages_vertexai_image() -> None:
+    image_data = create_image_data()
+    messages = [
+        HumanMessage(
+            content=[
+                {"type": "text", "text": "Here's an image:"},
+                {
+                    "type": "media",
+                    "mime_type": "image/jpeg",
+                    "data": base64.b64decode(image_data),
+                },
+            ]
+        )
+    ]
+    result = convert_to_openai_messages(messages)
+    assert result[0]["content"][1]["type"] == "image_url"
+    assert result[0]["content"][1]["image_url"]["url"] == create_base64_image()
+
+
+def test_convert_to_openai_messages_tool_message() -> None:
+    tool_message = ToolMessage(content="Tool result", tool_call_id="123")
+    result = convert_to_openai_messages([tool_message], text_format="block")
+    assert len(result) == 1
+    assert result[0]["content"] == [{"type": "text", "text": "Tool result"}]
+    assert result[0]["tool_call_id"] == "123"
+
+
+def test_convert_to_openai_messages_tool_use() -> None:
+    messages = [
+        AIMessage(
+            content=[
+                {
+                    "type": "tool_use",
+                    "id": "123",
+                    "name": "calculator",
+                    "input": {"a": "b"},
+                }
+            ]
+        )
+    ]
+    result = convert_to_openai_messages(messages, text_format="block")
+    assert result[0]["tool_calls"][0]["type"] == "function"
+    assert result[0]["tool_calls"][0]["id"] == "123"
+    assert result[0]["tool_calls"][0]["function"]["name"] == "calculator"
+    assert result[0]["tool_calls"][0]["function"]["arguments"] == json.dumps({"a": "b"})
+
+
+def test_convert_to_openai_messages_json() -> None:
+    json_data = {"key": "value"}
+    messages = [HumanMessage(content=[{"type": "json", "json": json_data}])]
+    result = convert_to_openai_messages(messages, text_format="block")
+    assert result[0]["content"][0]["type"] == "text"
+    assert json.loads(result[0]["content"][0]["text"]) == json_data
+
+
+def test_convert_to_openai_messages_guard_content() -> None:
+    messages = [
+        HumanMessage(
+            content=[
+                {
+                    "type": "guard_content",
+                    "guard_content": {"text": "Protected content"},
+                }
+            ]
+        )
+    ]
+    result = convert_to_openai_messages(messages, text_format="block")
+    assert result[0]["content"][0]["type"] == "text"
+    assert result[0]["content"][0]["text"] == "Protected content"
+
+
+def test_convert_to_openai_messages_invalid_block() -> None:
+    messages = [HumanMessage(content=[{"type": "invalid", "foo": "bar"}])]
+    with pytest.raises(ValueError, match="Unrecognized content block"):
+        convert_to_openai_messages(messages, text_format="block")
+
+
+def test_convert_to_openai_messages_empty_message() -> None:
+    result = convert_to_openai_messages(HumanMessage(content=""))
+    assert result == {"role": "user", "content": ""}
+
+
+def test_convert_to_openai_messages_empty_list() -> None:
+    result = convert_to_openai_messages([])
+    assert result == []
+
+
+def test_convert_to_openai_messages_mixed_content_types() -> None:
+    messages = [
+        HumanMessage(
+            content=[
+                "Text message",
+                {"type": "text", "text": "Structured text"},
+                {"type": "image_url", "image_url": {"url": create_base64_image()}},
+            ]
+        )
+    ]
+    result = convert_to_openai_messages(messages, text_format="block")
+    assert len(result[0]["content"]) == 3
+    assert isinstance(result[0]["content"][0], dict)
+    assert isinstance(result[0]["content"][1], dict)
+    assert isinstance(result[0]["content"][2], dict)
diff --git a/libs/langchain/langchain/chat_models/base.py b/libs/langchain/langchain/chat_models/base.py
index 6c66a6f3475f0..ca4978ec3d215 100644
--- a/libs/langchain/langchain/chat_models/base.py
+++ b/libs/langchain/langchain/chat_models/base.py
@@ -30,7 +30,7 @@
     generate_from_stream,
 )
 from langchain_core.messages import AnyMessage, BaseMessage
-from langchain_core.runnables import Runnable, RunnableConfig
+from langchain_core.runnables import Runnable, RunnableConfig, ensure_config
 from langchain_core.runnables.schema import StreamEvent
 from langchain_core.tools import BaseTool
 from langchain_core.tracers import RunLog, RunLogPatch
@@ -530,7 +530,7 @@ def _model(self, config: Optional[RunnableConfig] = None) -> Runnable:
         return model
 
     def _model_params(self, config: Optional[RunnableConfig]) -> dict:
-        config = config or {}
+        config = ensure_config(config)
         model_params = {
             _remove_prefix(k, self._config_prefix): v
             for k, v in config.get("configurable", {}).items()
diff --git a/libs/partners/anthropic/langchain_anthropic/chat_models.py b/libs/partners/anthropic/langchain_anthropic/chat_models.py
index 5f69b62228ab3..c4fb0b1775cef 100644
--- a/libs/partners/anthropic/langchain_anthropic/chat_models.py
+++ b/libs/partners/anthropic/langchain_anthropic/chat_models.py
@@ -1218,12 +1218,12 @@ def _make_message_chunk_from_anthropic_event(
             content_block = event.delta.model_dump()
             content_block["index"] = event.index
             content_block["type"] = "tool_use"
-            tool_call_chunk = {
-                "index": event.index,
-                "id": None,
-                "name": None,
-                "args": event.delta.partial_json,
-            }
+            tool_call_chunk = create_tool_call_chunk(
+                index=event.index,
+                id=None,
+                name=None,
+                args=event.delta.partial_json,
+            )
             message_chunk = AIMessageChunk(
                 content=[content_block],
                 tool_call_chunks=[tool_call_chunk],  # type: ignore

From 88d71f69861a5b784c2c7793dd9abb97470ecc10 Mon Sep 17 00:00:00 2001
From: Artur Barseghyan <artur.barseghyan@gmail.com>
Date: Wed, 16 Oct 2024 19:42:52 +0200
Subject: [PATCH 11/84] docs: Cosmetic documentation fix. Update
 `llm_chain.ipynb`. (#27394)

ATM [LLM chain
docs](https://python.langchain.com/docs/tutorials/llm_chain/#server)
say:

```
# 3. Create parser
parser = StrOutputParser()

# 4. Create chain
chain = prompt_template | model | parser


# 4. App definition
app = FastAPI(
  title="LangChain Server",
  version="1.0",
  description="A simple API server using LangChain's Runnable interfaces",
)

# 5. Adding chain route
add_routes(
    app,
    chain,
    path="/chain",
)
```

I corrected it to:


```
# 3. Create parser
parser = StrOutputParser()

# 4. Create chain
chain = prompt_template | model | parser

# 5. App definition
app = FastAPI(
  title="LangChain Server",
  version="1.0",
  description="A simple API server using LangChain's Runnable interfaces",
)

# 6. Adding chain route
add_routes(
    app,
    chain,
    path="/chain",
)
```
---
 docs/docs/tutorials/llm_chain.ipynb | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/docs/docs/tutorials/llm_chain.ipynb b/docs/docs/tutorials/llm_chain.ipynb
index 24f024173bd5b..c698c34473da2 100644
--- a/docs/docs/tutorials/llm_chain.ipynb
+++ b/docs/docs/tutorials/llm_chain.ipynb
@@ -497,15 +497,14 @@
     "# 4. Create chain\n",
     "chain = prompt_template | model | parser\n",
     "\n",
-    "\n",
-    "# 4. App definition\n",
+    "# 5. App definition\n",
     "app = FastAPI(\n",
     "  title=\"LangChain Server\",\n",
     "  version=\"1.0\",\n",
     "  description=\"A simple API server using LangChain's Runnable interfaces\",\n",
     ")\n",
     "\n",
-    "# 5. Adding chain route\n",
+    "# 6. Adding chain route\n",
     "add_routes(\n",
     "    app,\n",
     "    chain,\n",

From fdb7f951c8099c0e78020b7e5820d7614fd0a29e Mon Sep 17 00:00:00 2001
From: ccurme <chester.curme@gmail.com>
Date: Wed, 16 Oct 2024 13:46:49 -0400
Subject: [PATCH 12/84] monorepo: add script for updating notebook cassettes
 (#27399)

1. Move dependencies for running notebooks into monorepo poetry test
deps;
2. Add script to update cassettes for a single notebook;
3. Add cassettes for some how-to guides.

---

To update cassettes for a single notebook, run
`docs/scripts/update_cassettes.sh`. For example:
```
./docs/scripts/update_cassettes.sh docs/docs/how_to/binding.ipynb
```
Requires:
1. monorepo dev and test dependencies installed;
2. env vars required by notebook are set.

Note: How-to guides are not currently run in [scheduled
job](https://github.com/langchain-ai/langchain/actions/workflows/run_notebooks.yml).
Will add cassettes for more how-to guides in subsequent PRs before
adding them to scheduled job.
---
 .github/workflows/run_notebooks.yml           |   14 +-
 .../agent_executor_114ba50d.msgpack.zlib      |    1 +
 .../agent_executor_1f5932b6.msgpack.zlib      |    1 +
 .../agent_executor_3fa4780a.msgpack.zlib      |    1 +
 .../agent_executor_550e0c6e.msgpack.zlib      |    1 +
 .../agent_executor_688b465d.msgpack.zlib      |    1 +
 .../agent_executor_77c2f769.msgpack.zlib      |    1 +
 .../agent_executor_9c9ce713.msgpack.zlib      |    1 +
 .../agent_executor_ae627966.msgpack.zlib      |    1 +
 .../agent_executor_af83d3e3.msgpack.zlib      |    1 +
 .../agent_executor_b6a7e925.msgpack.zlib      |    1 +
 .../agent_executor_c4073e35.msgpack.zlib      |    1 +
 .../agent_executor_c96c960b.msgpack.zlib      |    1 +
 .../agent_executor_dae53ec6.msgpack.zlib      |    1 +
 .../agent_executor_e593bbf6.msgpack.zlib      |    1 +
 docs/cassettes/assign_5.msgpack.zlib          |    1 +
 ...b-48bb-46ff-a5a4-ef8ac95a513c.msgpack.zlib |    1 +
 ...a-78c5-4570-a00b-20d597245a96.msgpack.zlib |    1 +
 ...d-155f-4587-b7cd-52d363970c1d.msgpack.zlib |    1 +
 docs/scripts/prepare_notebooks_for_ci.py      |   23 +-
 docs/scripts/update_cassettes.sh              |   33 +
 poetry.lock                                   | 2793 ++++++++++++++++-
 pyproject.toml                                |   10 +
 23 files changed, 2827 insertions(+), 64 deletions(-)
 create mode 100644 docs/cassettes/agent_executor_114ba50d.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_1f5932b6.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_3fa4780a.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_550e0c6e.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_688b465d.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_77c2f769.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_9c9ce713.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_ae627966.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_af83d3e3.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_b6a7e925.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_c4073e35.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_c96c960b.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_dae53ec6.msgpack.zlib
 create mode 100644 docs/cassettes/agent_executor_e593bbf6.msgpack.zlib
 create mode 100644 docs/cassettes/assign_5.msgpack.zlib
 create mode 100644 docs/cassettes/binding_2b65beab-48bb-46ff-a5a4-ef8ac95a513c.msgpack.zlib
 create mode 100644 docs/cassettes/binding_32e0484a-78c5-4570-a00b-20d597245a96.msgpack.zlib
 create mode 100644 docs/cassettes/binding_f3fdf86d-155f-4587-b7cd-52d363970c1d.msgpack.zlib
 create mode 100755 docs/scripts/update_cassettes.sh

diff --git a/.github/workflows/run_notebooks.yml b/.github/workflows/run_notebooks.yml
index 14c70f779bf63..9d4091c107c26 100644
--- a/.github/workflows/run_notebooks.yml
+++ b/.github/workflows/run_notebooks.yml
@@ -35,23 +35,15 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install -e libs/core
-          pip install -e libs/langchain
-          pip install -e libs/community
-          pip install --upgrade langchain-experimental
-          pip install -e libs//partners/anthropic
-          pip install -e libs//partners/chroma
-          pip install -e libs//partners/openai
-          pip install -e libs//partners/mistralai
-          pip install jupyter langgraph click pypdf vcrpy
+          poetry install --with dev,test
 
       - name: Pre-download tiktoken files
         run: |
-          python docs/scripts/download_tiktoken.py
+          poetry run python docs/scripts/download_tiktoken.py
 
       - name: Prepare notebooks
         run: |
-          python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells
+          poetry run python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells --working-directory ${{ github.event.inputs.working-directory || 'all' }}
 
       - name: Run notebooks
         env:
diff --git a/docs/cassettes/agent_executor_114ba50d.msgpack.zlib b/docs/cassettes/agent_executor_114ba50d.msgpack.zlib
new file mode 100644
index 0000000000000..7d5627083ae1a
--- /dev/null
+++ b/docs/cassettes/agent_executor_114ba50d.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/agent_executor_1f5932b6.msgpack.zlib b/docs/cassettes/agent_executor_1f5932b6.msgpack.zlib
new file mode 100644
index 0000000000000..946d9680a4ee9
--- /dev/null
+++ b/docs/cassettes/agent_executor_1f5932b6.msgpack.zlib
@@ -0,0 +1 @@
+eNrlV01v48YZbtHb9tJL7rNEgQCFSJMSJZE2jECWnazi2Nq1HK+9gSGMyKHIiORwZ4b68MKHbnvshT+hXa8UGO4mwS7aNO323EP/gHPoj+gv6DsUFcvxAi2QUyEfaA7nnWfej+f90PPZkDAe0PinV0EsCMOOgAXPns8YeZoSLn47jYjwqXvxsN05fJGy4PpXvhAJX19bw0mg0YTEONAcGq0NjTXHx2IN3pOQ5DAXPepOvvvZ7JkSEc5xn3BlHX32THEo3BULWCgnNEWYEYSRT8LES0OEOQ+4wLBdQgqjIZFifMIFiZTzErp12g/uo9b7EerR3rJ0yglTzk/hS0RdEspP/USoppSJYWXAfy4YwREsBEsJrAE+AftFyiSErtXlN0rDQmUxSXJoL41zF0mo79/XQasYR7mAwMMgnHQ5wczxu4zwNBS8+zmfH3EJd1iQFKeUBprLIRL3g5ggCjtRcEZc5FGGpCcZ8UnMgyGoiB0nBQXlW+xKtcElLipu0NCnnEj3yYMjOIMm4NmYgISgcICPCEN5RGVgEO7RVCDAY+BJRIbwBIhWnMBX7tM0dFFPBqVQDw6yiSYNSDADQ4EpPLc6YcAAJgIyX+Zy+dsPDF3GkQqFlA5QmkjEhWchIEHcV87PZSCBfAEjrvR9AXq6JEp7nxNHgGjOh/85NCGO+zwKhF9E5x0B6cz1lE4MYnhGWO4U7voEznfkeQ19CAI4ntzx6Pcipdz/EcQIARuR8AOOJJ3u/wgn/tB7oCKEH9wG8WM/2pWn5zOfYBeU+tdPfnHhUy6yV7dT/EtgIIE8IrFDXbgg+2P/LEhKyCVeCLy8hMSMSe727HJASKLiEIg7nZ/KvsJJEgZO7tA1mQ9XRSKrUpe725cyi1UoGrHI3rRBiUZr7eEEalGMDM20NP2rsQp1IohDqC2qjG02TfL9vy5vJNgZAIha1LlsOj/8almG8uzlHnbanVuQkgnZS8yimvl6+TtLY8hSks2aD+9eV2zeXFfRDEOrf30LmE9iJ3vp4ZCTP986DMGcqA4FjOz3+tSBMAcku/53t+t43V60eTRotkTtkbv1NG0O3d0j4+Ou5SZEK+9ru4fdLXYwap3Zj72Tx3sD1aiXbd2o12uGami6ZmiGaj1m7kmn91Qzd467WnnncaVhHjUrW71ju9rYKZ919z+uxXvGhLHybjh+6h1tPymXe9anx+PxbqO+v18Vk0cfjR3LbOvacKdsPWmkA6uxgUC7dBi4m0652es+IgeTB+ZZu6r3E3PLqo2CCvvkias37cDvbw8ob+p9c2dJvWrZUvVCw5puWrr8e7XgRgiVUfjZC7tS/QJKXQKpRn4zBZeJlD+/AB6Sf/5jVnSXP7R3byj83sU2cDJ7eyiru1FFbUegsl42kWGtm5V1w0Af7R1eNYtrDiUFr5EgY7GWV0N13h82EPQ0xonYTIWnWl8fMiilHvByZ5EDM8dP4wFxL5vvZP9byX4IrbQH2pNKxgnlRC3UzK6O1YN5o1Vb26/nqaZS1sdxcJanQvY2T4PR2XjkOqnr+sNRpNtnZiXokdTx3hRHoITIa0AhNeLZi0pFf1XsLIh4CcbrqqGruvHtWJV9JIRWAw7On0W359lFFbz/zV0BQQfQh7KXeXj+vrzPSAT8lTffgJi2bf/t3UILoLpt18vf3pYBPy+BGOWIf3NXoAC4KBsRvxov5NXAza5/CYuua5sWqTvEs4jtVO1K3XZdXLUNy6qVDbvi/EVWRwdwZCgTyiDUBBphICbZdSnCY1lyNitGtVIDSzegxjph6pJO2tum0ga+gaAphxS7XzY/VJvY8YnayemYzbZP9ht7reafjtVlXqntZD5UzWLK48Dzph3CICzZpRPS1IXaycgUsA4aJ9kby60YnqnbGNc8yyN1davdmeEQlBw62Wu/sqmsm2ZF2UAR3rRqpq7nM9avp/OS/93Pf+digWXvCKDiK3Igc2AcUxutPrSzo11Bjf090TiIY7dZjmh7d3TYGSqlRRuYn9BuRjgtZzcIOJANQnaRReLaRmkxXc2HK1WvGRWQnI9qXQ/UISwBrQA2TsMQMHwaOLLJwTwVxC4ZK+t6CZpcKLCy/qyY3pSl+e9m1FNgwWC+4Tico52XlJD2gfY9voCHGwMuRy4sh6251On5vXv/vx65Mf8BCUOqrJjRaAvG+hWz+f7KBfkBHa2czQ6OV87m1spZPO9kK2c2/PBdOZsFdfFk1az+YCUM/u82KlzQRFmy8rPt9v7O6b17/wHa8ni8
\ No newline at end of file
diff --git a/docs/cassettes/agent_executor_3fa4780a.msgpack.zlib b/docs/cassettes/agent_executor_3fa4780a.msgpack.zlib
new file mode 100644
index 0000000000000..7bc4f0dd9d61d
--- /dev/null
+++ b/docs/cassettes/agent_executor_3fa4780a.msgpack.zlib
@@ -0,0 +1 @@
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diff --git a/docs/cassettes/agent_executor_550e0c6e.msgpack.zlib b/docs/cassettes/agent_executor_550e0c6e.msgpack.zlib
new file mode 100644
index 0000000000000..22c73160ce94c
--- /dev/null
+++ b/docs/cassettes/agent_executor_550e0c6e.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/agent_executor_688b465d.msgpack.zlib b/docs/cassettes/agent_executor_688b465d.msgpack.zlib
new file mode 100644
index 0000000000000..16074cab7b0e8
--- /dev/null
+++ b/docs/cassettes/agent_executor_688b465d.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/agent_executor_77c2f769.msgpack.zlib b/docs/cassettes/agent_executor_77c2f769.msgpack.zlib
new file mode 100644
index 0000000000000..e1ce3d30a8138
--- /dev/null
+++ b/docs/cassettes/agent_executor_77c2f769.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/agent_executor_9c9ce713.msgpack.zlib b/docs/cassettes/agent_executor_9c9ce713.msgpack.zlib
new file mode 100644
index 0000000000000..c14e358a5f6f8
--- /dev/null
+++ b/docs/cassettes/agent_executor_9c9ce713.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/agent_executor_ae627966.msgpack.zlib b/docs/cassettes/agent_executor_ae627966.msgpack.zlib
new file mode 100644
index 0000000000000..000b000e35cdc
--- /dev/null
+++ b/docs/cassettes/agent_executor_ae627966.msgpack.zlib
@@ -0,0 +1 @@
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diff --git a/docs/cassettes/agent_executor_af83d3e3.msgpack.zlib b/docs/cassettes/agent_executor_af83d3e3.msgpack.zlib
new file mode 100644
index 0000000000000..3ca4a89f06a90
--- /dev/null
+++ b/docs/cassettes/agent_executor_af83d3e3.msgpack.zlib
@@ -0,0 +1 @@
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diff --git a/docs/cassettes/agent_executor_b6a7e925.msgpack.zlib b/docs/cassettes/agent_executor_b6a7e925.msgpack.zlib
new file mode 100644
index 0000000000000..a90d03e125a5b
--- /dev/null
+++ b/docs/cassettes/agent_executor_b6a7e925.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/agent_executor_c4073e35.msgpack.zlib b/docs/cassettes/agent_executor_c4073e35.msgpack.zlib
new file mode 100644
index 0000000000000..20c6bc9c3e205
--- /dev/null
+++ b/docs/cassettes/agent_executor_c4073e35.msgpack.zlib
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\ No newline at end of file
diff --git a/docs/cassettes/agent_executor_c96c960b.msgpack.zlib b/docs/cassettes/agent_executor_c96c960b.msgpack.zlib
new file mode 100644
index 0000000000000..188d04c7020bc
--- /dev/null
+++ b/docs/cassettes/agent_executor_c96c960b.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/agent_executor_dae53ec6.msgpack.zlib b/docs/cassettes/agent_executor_dae53ec6.msgpack.zlib
new file mode 100644
index 0000000000000..14bb8754fc23c
--- /dev/null
+++ b/docs/cassettes/agent_executor_dae53ec6.msgpack.zlib
@@ -0,0 +1 @@
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+R+Mw6U2uUfY+zz4KzuOlV90DIF6KkiaDu7Sn/FIcl++2F5xl8UjuOQbXdsuRJzB4fE8rwDMaOYTJ8t825BD0kX5OkJyafeOi0YbrcvaPdvv2DIzCSXYs2/j3sDoph7UMokd14ydOxLwG+5I0BavI3R53ZeR2WrsaVEczsPuLqQjIu6GeaSrSvckskjv4WKlI3vbpzaWarYs/VEruojRscBNz/ZJSD9NJhk/DBFs0T0V3/bYdCvXkAm26NsKuRGM4kjcS/ZeCFRq8XDuOozKY65YVdn+S7sd8gdnUB3bNAVyyv93KptUQH6wpvWEK94hqnNBSBE+Tsl/6tO18Eh94zd0q+FQpoT47U4a5NurWMaNIfTBBEuxYcPC1mW/cd9xGSXVb0E+PIyVl9Ys9j3QBM10N3ehEcO3BGquyVopr9dnOVnLgyBWLEql7HiWBccecNb5ZdPWtSoc7T/d1cCxHb3io0uUrNbLXkQ4tPOF8LzXbZNqAPH47x5qkFqHcjyYosNNqJ68Cw3OC92nGnOVoy5U6q+LQdK9wt69wJV68dbT78JmrJKkzCp7clI8pwM2A/E83j7wrzZ/IueW9NmsH6r3ogl2DAW8gupep4LXqmhHkq+9071y7uGDGxlzEI27/Ok+eteXyIr1+/dv5ouo9brH4SDoK1N630O+ZsmKOSixsUhroTjc3xZ+JuSATTdOEvDTlJZlOTu4ymMOrHuZZewCzoWJl+czmYkUVX+1kChNH6MPpFuaHl15uOX45Paw9OGxlThFKCg4Htg+tvtzU0mUPKE/wiGV9k/BlaNHFGzxFMRjTnf7BVwfU6nf+nX92pvbWrCOgsji3Yz7vtYfm1wh/WYplGlJGUr5wdm4YLODJhyfeRZ3cyzV5tZ8KP2lGshUUIUYGhL9tz/RcTlalLF+IehppoN/mO2lr+vOifsP+Bth9er8gePt4ngiWso6P4Ao37Jbbyh9OuJ4W268nYx5793OsQ84KpZ8iryn1na6GtEGR6BPx4Is6N1xjY9OtIwdv5Nu994oQPXl65uq6emdcbLjX9kfe0T0Mj5Jgwzjd+tuPILvrC/QCso99djwIN3AY4aI5WJ3XXJMt/0sk6fiEz4eYKUpvo2+MgaMiRlf5TIS5z6Jo5fpRRxyto7DZY/QOGee0Ol9x60mRb8eFIIvKzb6VXcVK3hzYiisn51uHljsb4YOKcfI++2oW79VjpnzSrssCunl9gB49ZqAmC7+YQW0RvvprJIYEVhsYU2xkN21M6oKJfFOKZeyj0jSskWhO9PzWnW86t+rV6dBLkE9qIbo+mZPRzIXp6NCnb0a12wfpl1O+0cFNxkhpprt4hCvi5KZWXSzrjkTv18TBJLc6H5saO/nt1MJigRVul3WW3i3sEoZK+CNUWdoPyLB7cmLmAtRKkPt8aQMtnkX83BQsTG+JcA6mW7F9PndllWEhJ8Nbm82mJsyBEQVVRi72Zgos5tMT00aPerktlNyu/FCbGLTKieUXwB4FmaeeJJVUbdqHAr5Kfi79EuhZSBufy3aFrph6cd5NZ57HijVSflnzg5ZbggTGT1ubxm1e+afv1HTGOKoFM8yza48prypAshamf215aOUoTjavG/wdZr1P7TP8ecVfnzX32mvcbTk15dJM8ntKewMGBnegs1dKYXe+zLmVzfQyUzBqrYvM6Jv0zuLGvFPvsHcnnjq/ry5k9PTtiB/bK7wdlfVuKmkbcCv9NiX2+9R6eSfn8mb9mulzM/OyMRag1TTkjO1FDNh1YY6ZQ7gkzPHyuiz2akZmWh6N1kZXDCurDOcaagxz5q5U81YY/n0tXm1HQG40IErXh4ruq7qIf6qdALuIlGMz76KVWHEyj8uGItK0RIeUn3WGb/blpDvRm8Jlnb/b6c4tsRdToMAOw32tuG7scNx4cYhdjW7gGogrKZUoQ78D3BkOjveO66O0NRFI2OrrP86NQlJ1NLbW5jXLyUSWAWI2VxdZ8hoLVMPbkqXsjwboO7zF+2ywc6boT1eEWSJHCNY2Ouutqkn2Fd5ZDU8i66mGSXHX7K4CAic9PbQEJbfyYXpsNmMCvTNZ31i32n2o6aPhNKi4Ios72X2fFOTLmW63cMoW5WgzzVCHt3cq1hsWwwDHvsm5snaw7umMx32bdfnAo8xhtmxJckazGOvj1dKhksX1nJYjLxWzrWLCm6uOLMhvhkFEQcEKKmZyBhU/rk9XruM3MVosdx/sfI3WRn7Om+Tao9sBidhX1KvTdGKTIztKVBw3TQeepsMqETerTwL5BHO4/U40RlPGrZ9RyfYeelnolggSQFz4znlJ0S2RqVaHBxMjP7yW7u96aVzgeVKbuBp3s6DztbebRMpVAcvAaN0Toqgp34UwoPdS9ZJp6/5zmw8eXrIlDsMKha9HU73lWMtV81+1KLN5W1MoBUqs90Htiqd6ZLaOBtbelPqdWJSNmVo4VSLEwxmFlt5Rg0afegnSJUzl9kfs7Eaqv9nmugLKY8wEKnFN7STzbLolXmg7kBWnbEmZRxgkaroa90Tc2PvZcSVkoLJhTTtDjFrahvV5l4Zd0cubuDasb/DlsiBKWPxFzve0poYVXtL7OtqvJ19XC4i/3UvaeLRXWVQ1lrAFUr/HBzI2Reu9NEx8wHtbanViee+WQijvnfbV+tF+ZsO7apKqVRyeb1cOxOaX9KJP4F3xBysWlt+uBDGqR5QyZONW52EPpJqTnlyqy6NL9xJI+v7c9Ymyc+zHVbPMjL2cL9ZWy3qWQ2mzetL8RfGbQbtVL5nZvb5OdG87iDtRVlyzKRwpSxJZo4xfm6f3Oa4EaHMnhKsl0hY4RmfA5rISIoVU8BCRRehM/CXxw4lJ6BGVfPscQ+1MqDdwv0xZQ1mtc6ovW2Uvq3h15ai21Ve42ErpnnFJLs5diYW84xi6otQV8C69OoOjXAW2cvsjqXsiaFGDKAnCZVUEZsS9PPxANgc4aDtaHvjEdr36LSGEr4ZeaNZ098lluga3fG+PzfqTmbPeUglmMXTUxaXB3D79Bx/vH809X06xS3z/iOPjbbR4YmtKpYIJQGLws58+bI9t2/rE2ff97MQ6O4WwDTx5TvQgODCm4Djv/dDnjzz1bRfzeEacV5oR0Gfh1p3rY3+86xdYjP/O3HWfftevsrm6rz2UXVeqGuXR+25/k6Zm+dk0rfECaGt2MFSR7nqTPvYtwmmKdMX3IKb8BoLbT0WdFG19D53OMclxsoJ+vf8WfPA8VO4srCR/JbLqSo/HXDQOSaL6Rq1ha7n3MjJcoai/InJ8vNgV9syGo25uo2Lq893LqBoJUaaJRvtFv1sGnCtXVXbGD8QL710xjq6huanzbuNxDtPctscN8iPJxGXT+FC/cemDq+i16NKXTftc9oKaHSclfns8bIXCQb3fzGKjNbaAAM/13Gre9/3pq27vs61X2o4br90L40VbXRogoak0mCaIb+aoAg7m7lXW9Flteuy5XSEjKpHdPmJjA/rl2CdsVKVN1hHq4AW2gkUj1Qo1C6Ll4i3PltKVFwhvY/nbBxy6aMHvmNHvXEtcb1Us0Bg/1Sl/UcW8XhIUxlvOAOwz27w8pCqWDLAQVosk4IChy8KtE4IGXh99mG5BlplQb+/Ey9ujAgbC87D0JxsrK0+bYL338D31UbzZC+Jh71do5hdUngBS4XXf1L6TmHYwv18m70h6R7JoWNZd77zzACflj5xodGe20R62SM65GN33RoeHoeh5f46BP1cW307aAT0M6RyResC4c1MOylNjyCH0zbTmzeaSLYuav8gNKfdYRxMpKTK5S9WZEhadIONWKC818lsJp8P9EPsNRvkfPrhcXsweQpzGk7f+CsdQmiexVKgqlKfFgqjG9DHxjWQVDw+hcQq1GU1dsrP5irKpargLvSnonXzbHyKlVmlOZgxr6/Cae5fhWySPaiZNPaAeZaijo9PcvSn2ViGEFajyTGjHQuhAAg0cG3DdHsVkLVBXboduMXMccCrT9Kagu9vLwW2TToEP1EXw0608XxXleiLqVwJ98jee5dN/MgDcNH+wl9t+FsuGvUCVPpDSMNPFk0UMyfO/kpv8oVqLSfytN/Plcofa2FiZAn3XLPtys5r1OxAMjNESLt/r5GrVEu42RJpD9tgzZCDYb6dLJIF0Fy0ZJDc5vN6XV+7e686mohxEltPZ8BR/lKIoVtRsFBoTGFSiOwaP9KSZ8C1EeAt3EQV1RnTEdjsEiIp2A8/nOYAPjFoo6vZ85rp71sltTdgCGMJby8yzTLJnpW511a2orrsF9D4g5jFk4YZJoAbPLAC7rStejrEzlVJ2zItffkrs1Hhva778EFLZsW+QbPrpylQ5yXI6d29phI/KNQT9A4VBxx9naT8Nq5jIvrtsNLLQLc9uCm1jB5drmO+8wE73QUW0/K4xWGQ+kiaaCWUuTCZeX0Yfv+IIl2RVCSj4Umox0nZXVD91ruHg+2nro7FX3P4zpXwZhlvdyEkeYQAsmOrdfEW7UF6v5UTzV4rPXFXl+QF8As8eGh9g5gl7MG8zISvBI9AwhCW6rs1w/+q3z7AfIXf9cUUMK1kos11Bv+6n2C712edBNR8KhTlxbHk8CQ/mj0fkixpNNw82vhdrkIYntNSfSvhuJtIzeOSGnn4gyxj+7vvYSE7JhcMrTihfyez1ocDu8XhH8gus0tJYR3oeAT60dpfN0iRa2m/Qb/UpSY83ScfWLAY2wsrb1p2K87vRHc1bjzK6X0C4Pzv5k8H5Iksrgw8wOvj5vOLrugZ69/oo4lAwM2/nnM3D6mfmt+/UgNRIyz+W6y5DoA3ikdTpoKgVgWrAm9WhMuqtyhc3O0on6meme8MowsT6vAIuS1PHeGH2a2eo1CXHSJoU1PYynJVLs1/ZlNJ4BJ5U23fFG3ZKb+X4n50O1Jyd+M2xXNI/aZHqafSXYjk6O67JunBWJr12RqdwsH7zwuKax26H38k6bZCfr6PZyeHZ8MD6yvDJlsWZwWnuzeOegZOTrbOq47zF0NMji7OLv/4/XCQR3YdCxgAkJP8PeHYZ5g==
\ No newline at end of file
diff --git a/docs/cassettes/agent_executor_e593bbf6.msgpack.zlib b/docs/cassettes/agent_executor_e593bbf6.msgpack.zlib
new file mode 100644
index 0000000000000..86ae03de69e2d
--- /dev/null
+++ b/docs/cassettes/agent_executor_e593bbf6.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/assign_5.msgpack.zlib b/docs/cassettes/assign_5.msgpack.zlib
new file mode 100644
index 0000000000000..16688387baa1f
--- /dev/null
+++ b/docs/cassettes/assign_5.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/binding_2b65beab-48bb-46ff-a5a4-ef8ac95a513c.msgpack.zlib b/docs/cassettes/binding_2b65beab-48bb-46ff-a5a4-ef8ac95a513c.msgpack.zlib
new file mode 100644
index 0000000000000..1367489608972
--- /dev/null
+++ b/docs/cassettes/binding_2b65beab-48bb-46ff-a5a4-ef8ac95a513c.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/binding_32e0484a-78c5-4570-a00b-20d597245a96.msgpack.zlib b/docs/cassettes/binding_32e0484a-78c5-4570-a00b-20d597245a96.msgpack.zlib
new file mode 100644
index 0000000000000..190301e99772f
--- /dev/null
+++ b/docs/cassettes/binding_32e0484a-78c5-4570-a00b-20d597245a96.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/binding_f3fdf86d-155f-4587-b7cd-52d363970c1d.msgpack.zlib b/docs/cassettes/binding_f3fdf86d-155f-4587-b7cd-52d363970c1d.msgpack.zlib
new file mode 100644
index 0000000000000..a08ac1e5251e9
--- /dev/null
+++ b/docs/cassettes/binding_f3fdf86d-155f-4587-b7cd-52d363970c1d.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/scripts/prepare_notebooks_for_ci.py b/docs/scripts/prepare_notebooks_for_ci.py
index fe63daafe8b04..9286906f22be8 100644
--- a/docs/scripts/prepare_notebooks_for_ci.py
+++ b/docs/scripts/prepare_notebooks_for_ci.py
@@ -122,7 +122,10 @@ def add_vcr_to_notebook(
     return notebook
 
 
-def process_notebooks(should_comment_install_cells: bool) -> None:
+def process_notebooks(
+    should_comment_install_cells: bool,
+    working_directory: str,
+) -> None:
     for directory in NOTEBOOK_DIRS:
         for root, _, files in os.walk(directory):
             for file in files:
@@ -130,6 +133,12 @@ def process_notebooks(should_comment_install_cells: bool) -> None:
                     continue
 
                 notebook_path = os.path.join(root, file)
+                # Filter notebooks based on the working_directory input
+                if working_directory != "all" and not notebook_path.startswith(
+                    working_directory
+                ):
+                    continue
+
                 try:
                     notebook = nbformat.read(notebook_path, as_version=4)
 
@@ -172,8 +181,16 @@ def process_notebooks(should_comment_install_cells: bool) -> None:
     default=False,
     help="Whether to comment out install cells",
 )
-def main(comment_install_cells):
-    process_notebooks(should_comment_install_cells=comment_install_cells)
+@click.option(
+    "--working-directory",
+    default="all",
+    help="Working directory or specific notebook to process",
+)
+def main(comment_install_cells, working_directory):
+    process_notebooks(
+        should_comment_install_cells=comment_install_cells,
+        working_directory=working_directory,
+    )
     logger.info("All notebooks processed successfully.")
 
 
diff --git a/docs/scripts/update_cassettes.sh b/docs/scripts/update_cassettes.sh
new file mode 100755
index 0000000000000..df1a7981ae000
--- /dev/null
+++ b/docs/scripts/update_cassettes.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+# Get the working directory from the input argument, default to 'all' if not provided
+WORKING_DIRECTORY=${1:-all}
+
+# Function to delete cassettes
+delete_cassettes() {
+  local dir=$1
+  if [ "$dir" == "all" ]; then
+    echo "Deleting all cassettes..."
+    rm -f docs/cassettes/*.msgpack.zlib
+  else
+    # Extract the filename from the directory path
+    local filename=$(basename "$dir" .ipynb)
+    echo "Deleting cassettes for $filename..."
+    rm -f docs/cassettes/${filename}_*.msgpack.zlib
+  fi
+}
+
+# Delete existing cassettes
+delete_cassettes "$WORKING_DIRECTORY"
+
+# Pre-download tiktoken files
+echo "Pre-downloading tiktoken files..."
+poetry run python docs/scripts/download_tiktoken.py
+
+# Prepare notebooks
+echo "Preparing notebooks for CI..."
+poetry run python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells --working-directory "$WORKING_DIRECTORY"
+
+# Run notebooks
+echo "Running notebooks..."
+./docs/scripts/execute_notebooks.sh "$WORKING_DIRECTORY"
diff --git a/poetry.lock b/poetry.lock
index 5071452ed9abf..c23df188e38f2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "accessible-pygments"
@@ -177,6 +177,31 @@ files = [
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
 ]
 
+[[package]]
+name = "anthropic"
+version = "0.36.0"
+description = "The official Python library for the anthropic API"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "anthropic-0.36.0-py3-none-any.whl", hash = "sha256:9183b9eaa0f409f2047244d7ef02c9c3eb916959c0b2960f7605dcb6cabbf548"},
+    {file = "anthropic-0.36.0.tar.gz", hash = "sha256:7b0b1457096605572a29559d9a8ce224b9389d379b410e7d1bf5e0c1379f9ee2"},
+]
+
+[package.dependencies]
+anyio = ">=3.5.0,<5"
+distro = ">=1.7.0,<2"
+httpx = ">=0.23.0,<1"
+jiter = ">=0.4.0,<1"
+pydantic = ">=1.9.0,<3"
+sniffio = "*"
+tokenizers = ">=0.13.0"
+typing-extensions = ">=4.7,<5"
+
+[package.extras]
+bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"]
+vertex = ["google-auth (>=2,<3)"]
+
 [[package]]
 name = "anyio"
 version = "4.6.0"
@@ -210,6 +235,99 @@ files = [
     {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
+[[package]]
+name = "argon2-cffi"
+version = "23.1.0"
+description = "Argon2 for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"},
+    {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"},
+]
+
+[package.dependencies]
+argon2-cffi-bindings = "*"
+
+[package.extras]
+dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
+tests = ["hypothesis", "pytest"]
+typing = ["mypy"]
+
+[[package]]
+name = "argon2-cffi-bindings"
+version = "21.2.0"
+description = "Low-level CFFI bindings for Argon2"
+optional = false
+python-versions = ">=3.6"
+files = [
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+description = "Better dates & times for Python"
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+
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@@ -228,6 +346,20 @@ six = ">=1.12.0"
 astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
 test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
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+
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 name = "async-timeout"
 version = "4.0.3"
@@ -295,6 +427,57 @@ files = [
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 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
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+optional = false
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 [[package]]
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@@ -316,6 +499,60 @@ charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
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+
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+
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 [[package]]
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 version = "2024.8.30"
@@ -505,6 +742,88 @@ files = [
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+optional = false
+python-versions = "*"
+files = [
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+
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+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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+
 [[package]]
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 version = "1.9.0"
@@ -643,6 +1007,17 @@ files = [
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@@ -671,6 +1046,112 @@ files = [
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 tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
 
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+optional = false
+python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
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 [[package]]
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+python-versions = ">=3.8"
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+    {file = "httptools-0.6.2.tar.gz", hash = "sha256:ae694efefcb61317c79b2fa1caebc122060992408e389bb00889567e463a47f1"},
+]
+
+[package.extras]
+test = ["Cython (>=0.29.24)"]
+
 [[package]]
 name = "httpx"
 version = "0.27.2"
@@ -901,25 +1583,84 @@ socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
-name = "idna"
-version = "3.10"
-description = "Internationalized Domain Names in Applications (IDNA)"
+name = "httpx-sse"
+version = "0.4.0"
+description = "Consume Server-Sent Event (SSE) messages with HTTPX."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
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+    {file = "httpx-sse-0.4.0.tar.gz", hash = "sha256:1e81a3a3070ce322add1d3529ed42eb5f70817f45ed6ec915ab753f961139721"},
+    {file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
 ]
 
-[package.extras]
-all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
-
 [[package]]
-name = "imagesize"
-version = "1.4.1"
-description = "Getting image size from png/jpeg/jpeg2000/gif file"
+name = "huggingface-hub"
+version = "0.25.2"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.8.0"
+files = [
+    {file = "huggingface_hub-0.25.2-py3-none-any.whl", hash = "sha256:1897caf88ce7f97fe0110603d8f66ac264e3ba6accdf30cd66cc0fed5282ad25"},
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+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=2023.5.0"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp", "minijinja (>=1.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors[torch]", "torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+
+[[package]]
+name = "humanfriendly"
+version = "10.0"
+description = "Human friendly output for text interfaces using Python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "humanfriendly-10.0-py2.py3-none-any.whl", hash = "sha256:1697e1a8a8f550fd43c2865cd84542fc175a61dcb779b6fee18cf6b6ccba1477"},
+    {file = "humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc"},
+]
+
+[package.dependencies]
+pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_version >= \"3.8\""}
+
+[[package]]
+name = "idna"
+version = "3.10"
+description = "Internationalized Domain Names in Applications (IDNA)"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
+    {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
+]
+
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
+[[package]]
+name = "imagesize"
+version = "1.4.1"
+description = "Getting image size from png/jpeg/jpeg2000/gif file"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
     {file = "imagesize-1.4.1-py2.py3-none-any.whl", hash = "sha256:0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b"},
     {file = "imagesize-1.4.1.tar.gz", hash = "sha256:69150444affb9cb0d5cc5a92b3676f0b2fb7cd9ae39e947a5e11a36b4497cd4a"},
@@ -927,25 +1668,43 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "8.5.0"
+version = "8.4.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b"},
-    {file = "importlib_metadata-8.5.0.tar.gz", hash = "sha256:71522656f0abace1d072b9e5481a48f07c138e00f079c38c8f883823f9c26bd7"},
+    {file = "importlib_metadata-8.4.0-py3-none-any.whl", hash = "sha256:66f342cc6ac9818fc6ff340576acd24d65ba0b3efabb2b4ac08b598965a4a2f1"},
+    {file = "importlib_metadata-8.4.0.tar.gz", hash = "sha256:9a547d3bc3608b025f93d403fdd1aae741c24fbb8314df4b155675742ce303c5"},
 ]
 
 [package.dependencies]
-zipp = ">=3.20"
+zipp = ">=0.5"
+
+[package.extras]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+perf = ["ipython"]
+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+
+[[package]]
+name = "importlib-resources"
+version = "6.4.5"
+description = "Read resources from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717"},
+    {file = "importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065"},
+]
+
+[package.dependencies]
+zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
 check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 enabler = ["pytest-enabler (>=2.2)"]
-perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
 type = ["pytest-mypy"]
 
 [[package]]
@@ -1018,6 +1777,41 @@ qtconsole = ["qtconsole"]
 test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
 
+[[package]]
+name = "ipywidgets"
+version = "8.1.5"
+description = "Jupyter interactive widgets"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "ipywidgets-8.1.5-py3-none-any.whl", hash = "sha256:3290f526f87ae6e77655555baba4f36681c555b8bdbbff430b70e52c34c86245"},
+    {file = "ipywidgets-8.1.5.tar.gz", hash = "sha256:870e43b1a35656a80c18c9503bbf2d16802db1cb487eec6fab27d683381dde17"},
+]
+
+[package.dependencies]
+comm = ">=0.1.3"
+ipython = ">=6.1.0"
+jupyterlab-widgets = ">=3.0.12,<3.1.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=4.0.12,<4.1.0"
+
+[package.extras]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
+[[package]]
+name = "isoduration"
+version = "20.11.0"
+description = "Operations with ISO 8601 durations"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
+    {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
+]
+
+[package.dependencies]
+arrow = ">=0.15.0"
+
 [[package]]
 name = "jedi"
 version = "0.19.1"
@@ -1124,6 +1918,17 @@ files = [
     {file = "jiter-0.5.0.tar.gz", hash = "sha256:1d916ba875bcab5c5f7d927df998c4cb694d27dceddf3392e58beaf10563368a"},
 ]
 
+[[package]]
+name = "json5"
+version = "0.9.25"
+description = "A Python implementation of the JSON5 data format."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "json5-0.9.25-py3-none-any.whl", hash = "sha256:34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f"},
+    {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
+]
+
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1149,6 +1954,68 @@ files = [
     {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
 ]
 
+[[package]]
+name = "jsonschema"
+version = "4.23.0"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
+    {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rpds-py = ">=0.7.1"
+uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""}
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2024.10.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
+    {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
+]
+
+[package.dependencies]
+referencing = ">=0.31.0"
+
+[[package]]
+name = "jupyter"
+version = "1.1.1"
+description = "Jupyter metapackage. Install all the Jupyter components in one go."
+optional = false
+python-versions = "*"
+files = [
+    {file = "jupyter-1.1.1-py2.py3-none-any.whl", hash = "sha256:7a59533c22af65439b24bbe60373a4e95af8f16ac65a6c00820ad378e3f7cc83"},
+    {file = "jupyter-1.1.1.tar.gz", hash = "sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a"},
+]
+
+[package.dependencies]
+ipykernel = "*"
+ipywidgets = "*"
+jupyter-console = "*"
+jupyterlab = "*"
+nbconvert = "*"
+notebook = "*"
+
 [[package]]
 name = "jupyter-client"
 version = "8.6.3"
@@ -1172,6 +2039,30 @@ traitlets = ">=5.3"
 docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
 test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
+[[package]]
+name = "jupyter-console"
+version = "6.6.3"
+description = "Jupyter terminal console"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485"},
+    {file = "jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539"},
+]
+
+[package.dependencies]
+ipykernel = ">=6.14"
+ipython = "*"
+jupyter-client = ">=7.0.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+prompt-toolkit = ">=3.0.30"
+pygments = "*"
+pyzmq = ">=17"
+traitlets = ">=5.4"
+
+[package.extras]
+test = ["flaky", "pexpect", "pytest"]
+
 [[package]]
 name = "jupyter-core"
 version = "5.7.2"
@@ -1192,9 +2083,214 @@ traitlets = ">=5.3"
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
 test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "jupyter-events"
+version = "0.10.0"
+description = "Jupyter Event System library"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
+    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
+]
+
+[package.dependencies]
+jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]}
+python-json-logger = ">=2.0.4"
+pyyaml = ">=5.3"
+referencing = "*"
+rfc3339-validator = "*"
+rfc3986-validator = ">=0.1.1"
+traitlets = ">=5.3"
+
+[package.extras]
+cli = ["click", "rich"]
+docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
+test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
+
+[[package]]
+name = "jupyter-lsp"
+version = "2.2.5"
+description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001"},
+    {file = "jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-server = ">=1.1.2"
+
+[[package]]
+name = "jupyter-server"
+version = "2.14.2"
+description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_server-2.14.2-py3-none-any.whl", hash = "sha256:47ff506127c2f7851a17bf4713434208fc490955d0e8632e95014a9a9afbeefd"},
+    {file = "jupyter_server-2.14.2.tar.gz", hash = "sha256:66095021aa9638ced276c248b1d81862e4c50f292d575920bbe960de1c56b12b"},
+]
+
+[package.dependencies]
+anyio = ">=3.1.0"
+argon2-cffi = ">=21.1"
+jinja2 = ">=3.0.3"
+jupyter-client = ">=7.4.4"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+jupyter-events = ">=0.9.0"
+jupyter-server-terminals = ">=0.4.4"
+nbconvert = ">=6.4.4"
+nbformat = ">=5.3.0"
+overrides = ">=5.0"
+packaging = ">=22.0"
+prometheus-client = ">=0.9"
+pywinpty = {version = ">=2.0.1", markers = "os_name == \"nt\""}
+pyzmq = ">=24"
+send2trash = ">=1.8.2"
+terminado = ">=0.8.3"
+tornado = ">=6.2.0"
+traitlets = ">=5.6.0"
+websocket-client = ">=1.7"
+
+[package.extras]
+docs = ["ipykernel", "jinja2", "jupyter-client", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
+test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0,<9)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"]
+
+[[package]]
+name = "jupyter-server-terminals"
+version = "0.5.3"
+description = "A Jupyter Server Extension Providing Terminals."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"},
+    {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"},
+]
+
+[package.dependencies]
+pywinpty = {version = ">=2.0.3", markers = "os_name == \"nt\""}
+terminado = ">=0.8.3"
+
+[package.extras]
+docs = ["jinja2", "jupyter-server", "mistune (<4.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"]
+test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"]
+
+[[package]]
+name = "jupyterlab"
+version = "4.2.5"
+description = "JupyterLab computational environment"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyterlab-4.2.5-py3-none-any.whl", hash = "sha256:73b6e0775d41a9fee7ee756c80f58a6bed4040869ccc21411dc559818874d321"},
+    {file = "jupyterlab-4.2.5.tar.gz", hash = "sha256:ae7f3a1b8cb88b4f55009ce79fa7c06f99d70cd63601ee4aa91815d054f46f75"},
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+
+[package.dependencies]
+async-lru = ">=1.0.0"
+httpx = ">=0.25.0"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+ipykernel = ">=6.5.0"
+jinja2 = ">=3.0.3"
+jupyter-core = "*"
+jupyter-lsp = ">=2.0.0"
+jupyter-server = ">=2.4.0,<3"
+jupyterlab-server = ">=2.27.1,<3"
+notebook-shim = ">=0.2"
+packaging = "*"
+setuptools = ">=40.1.0"
+tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
+tornado = ">=6.2.0"
+traitlets = "*"
+
+[package.extras]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.3.5)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.3.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.2)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.1.post2)", "matplotlib (==3.8.3)", "nbconvert (>=7.0.0)", "pandas (==2.2.1)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
+test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
+upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"]
+
+[[package]]
+name = "jupyterlab-pygments"
+version = "0.3.0"
+description = "Pygments theme using JupyterLab CSS variables"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780"},
+    {file = "jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d"},
+]
+
+[[package]]
+name = "jupyterlab-server"
+version = "2.27.3"
+description = "A set of server components for JupyterLab and JupyterLab like applications."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyterlab_server-2.27.3-py3-none-any.whl", hash = "sha256:e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4"},
+    {file = "jupyterlab_server-2.27.3.tar.gz", hash = "sha256:eb36caca59e74471988f0ae25c77945610b887f777255aa21f8065def9e51ed4"},
+]
+
+[package.dependencies]
+babel = ">=2.10"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jinja2 = ">=3.0.3"
+json5 = ">=0.9.0"
+jsonschema = ">=4.18.0"
+jupyter-server = ">=1.21,<3"
+packaging = ">=21.3"
+requests = ">=2.31"
+
+[package.extras]
+docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
+openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0,<8)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+
+[[package]]
+name = "jupyterlab-widgets"
+version = "3.0.13"
+description = "Jupyter interactive widgets for JupyterLab"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jupyterlab_widgets-3.0.13-py3-none-any.whl", hash = "sha256:e3cda2c233ce144192f1e29914ad522b2f4c40e77214b0cc97377ca3d323db54"},
+    {file = "jupyterlab_widgets-3.0.13.tar.gz", hash = "sha256:a2966d385328c1942b683a8cd96b89b8dd82c8b8f81dda902bb2bc06d46f5bed"},
+]
+
+[[package]]
+name = "kubernetes"
+version = "31.0.0"
+description = "Kubernetes python client"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "kubernetes-31.0.0-py2.py3-none-any.whl", hash = "sha256:bf141e2d380c8520eada8b351f4e319ffee9636328c137aa432bc486ca1200e1"},
+    {file = "kubernetes-31.0.0.tar.gz", hash = "sha256:28945de906c8c259c1ebe62703b56a03b714049372196f854105afe4e6d014c0"},
+]
+
+[package.dependencies]
+certifi = ">=14.05.14"
+durationpy = ">=0.7"
+google-auth = ">=1.0.1"
+oauthlib = ">=3.2.2"
+python-dateutil = ">=2.5.3"
+pyyaml = ">=5.4.1"
+requests = "*"
+requests-oauthlib = "*"
+six = ">=1.9.0"
+urllib3 = ">=1.24.2"
+websocket-client = ">=0.32.0,<0.40.0 || >0.40.0,<0.41.dev0 || >=0.43.dev0"
+
+[package.extras]
+adal = ["adal (>=1.0.2)"]
+
 [[package]]
 name = "langchain"
-version = "0.3.1"
+version = "0.3.3"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1204,7 +2300,7 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-langchain-core = "^0.3.6"
+langchain-core = "^0.3.10"
 langchain-text-splitters = "^0.3.0"
 langsmith = "^0.1.17"
 numpy = [
@@ -1215,15 +2311,54 @@ pydantic = "^2.7.4"
 PyYAML = ">=5.3"
 requests = "^2"
 SQLAlchemy = ">=1.4,<3"
-tenacity = "^8.1.0,!=8.4.0"
+tenacity = ">=8.1.0,!=8.4.0,<10"
 
 [package.source]
 type = "directory"
 url = "libs/langchain"
 
+[[package]]
+name = "langchain-anthropic"
+version = "0.2.3"
+description = "An integration package connecting AnthropicMessages and LangChain"
+optional = false
+python-versions = ">=3.9,<4.0"
+files = []
+develop = true
+
+[package.dependencies]
+anthropic = ">=0.30.0,<1"
+langchain-core = "^0.3.9"
+pydantic = "^2.7.4"
+
+[package.source]
+type = "directory"
+url = "libs/partners/anthropic"
+
+[[package]]
+name = "langchain-chroma"
+version = "0.1.5"
+description = "An integration package connecting Chroma and LangChain"
+optional = false
+python-versions = ">=3.8.1,<4"
+files = []
+develop = true
+
+[package.dependencies]
+chromadb = ">=0.4.0,<0.6.0,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,!=0.5.10,!=0.5.11,!=0.5.12"
+langchain-core = {version = ">=0.1.40,<0.4", markers = "python_version >= \"3.9\""}
+numpy = [
+    {version = ">=1,<2", markers = "python_version < \"3.12\""},
+    {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
+]
+
+[package.source]
+type = "directory"
+url = "libs/partners/chroma"
+
 [[package]]
 name = "langchain-community"
-version = "0.3.1"
+version = "0.3.2"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1233,8 +2368,8 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 dataclasses-json = ">= 0.5.7, < 0.7"
-langchain = "^0.3.1"
-langchain-core = "^0.3.6"
+langchain = "^0.3.3"
+langchain-core = "^0.3.10"
 langsmith = "^0.1.125"
 numpy = [
     {version = ">=1,<2", markers = "python_version < \"3.12\""},
@@ -1244,7 +2379,7 @@ pydantic-settings = "^2.4.0"
 PyYAML = ">=5.3"
 requests = "^2"
 SQLAlchemy = ">=1.4,<3"
-tenacity = "^8.1.0,!=8.4.0"
+tenacity = ">=8.1.0,!=8.4.0,<10"
 
 [package.source]
 type = "directory"
@@ -1252,7 +2387,7 @@ url = "libs/community"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.6"
+version = "0.3.10"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1268,16 +2403,56 @@ pydantic = [
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 PyYAML = ">=5.3"
-tenacity = "^8.1.0,!=8.4.0"
+tenacity = ">=8.1.0,!=8.4.0,<10.0.0"
 typing-extensions = ">=4.7"
 
 [package.source]
 type = "directory"
 url = "libs/core"
 
+[[package]]
+name = "langchain-experimental"
+version = "0.3.2"
+description = "Building applications with LLMs through composability"
+optional = false
+python-versions = ">=3.9,<4.0"
+files = []
+develop = false
+
+[package.dependencies]
+langchain-community = "^0.3.0"
+langchain-core = "^0.3.6"
+
+[package.source]
+type = "git"
+url = "https://github.com/langchain-ai/langchain-experimental.git"
+reference = "HEAD"
+resolved_reference = "6fdfdbe2d66325a2e81555890715e69946e7e26b"
+subdirectory = "libs/experimental"
+
+[[package]]
+name = "langchain-mistralai"
+version = "0.2.0"
+description = "An integration package connecting Mistral and LangChain"
+optional = false
+python-versions = ">=3.9,<4.0"
+files = []
+develop = true
+
+[package.dependencies]
+httpx = ">=0.25.2,<1"
+httpx-sse = ">=0.3.1,<1"
+langchain-core = "^0.3.0"
+pydantic = ">=2,<3"
+tokenizers = ">=0.15.1,<1"
+
+[package.source]
+type = "directory"
+url = "libs/partners/mistralai"
+
 [[package]]
 name = "langchain-openai"
-version = "0.2.1"
+version = "0.2.2"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1285,7 +2460,7 @@ files = []
 develop = true
 
 [package.dependencies]
-langchain-core = "^0.3"
+langchain-core = "^0.3.9"
 openai = "^1.40.0"
 tiktoken = ">=0.7,<1"
 
@@ -1309,6 +2484,58 @@ langchain-core = "^0.3.0"
 type = "directory"
 url = "libs/text-splitters"
 
+[[package]]
+name = "langgraph"
+version = "0.2.36"
+description = "Building stateful, multi-actor applications with LLMs"
+optional = false
+python-versions = ">=3.9.0,<4.0"
+files = []
+develop = false
+
+[package.dependencies]
+langchain-core = ">=0.2.39,<0.4"
+langgraph-checkpoint = "^2.0.0"
+langgraph-sdk = "^0.1.32"
+
+[package.source]
+type = "git"
+url = "https://github.com/langchain-ai/langgraph.git"
+reference = "HEAD"
+resolved_reference = "f2dc537696fcf9b86fb7723a7939d6e891e64136"
+subdirectory = "libs/langgraph"
+
+[[package]]
+name = "langgraph-checkpoint"
+version = "2.0.1"
+description = "Library with base interfaces for LangGraph checkpoint savers."
+optional = false
+python-versions = "<4.0.0,>=3.9.0"
+files = [
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 [[package]]
 name = "markupsafe"
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 [package.dependencies]
 traitlets = "*"
 
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+]
+
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 name = "multidict"
 version = "6.1.0"
@@ -1546,6 +3033,87 @@ files = [
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+[[package]]
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+version = "0.10.0"
+description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+nbformat = ">=5.1"
+traitlets = ">=5.4"
+
+[package.extras]
+dev = ["pre-commit"]
+docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
+
+[[package]]
+name = "nbconvert"
+version = "7.16.4"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
+optional = false
+python-versions = ">=3.8"
+files = [
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+jinja2 = ">=3.0"
+jupyter-core = ">=4.7"
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+mistune = ">=2.0.3,<4"
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+packaging = "*"
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+tinycss2 = "*"
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+
+[package.extras]
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+qtpdf = ["pyqtwebengine (>=5.15)"]
+qtpng = ["pyqtwebengine (>=5.15)"]
+serve = ["tornado (>=6.1)"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
+webpdf = ["playwright"]
+
+[[package]]
+name = "nbformat"
+version = "5.10.4"
+description = "The Jupyter Notebook format"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
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+test = ["pep440", "pre-commit", "pytest", "testpath"]
+
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 version = "1.6.0"
@@ -1557,6 +3125,46 @@ files = [
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+description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
+optional = false
+python-versions = ">=3.8"
+files = [
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+notebook-shim = ">=0.2,<0.3"
+tornado = ">=6.2.0"
+
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+
+[[package]]
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+version = "0.2.4"
+description = "A shim layer for notebook traits and config"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.extras]
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+
 [[package]]
 name = "numpy"
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@@ -1602,6 +3210,64 @@ files = [
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+description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
+optional = false
+python-versions = ">=3.6"
+files = [
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+
+[package.extras]
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+signals = ["blinker (>=1.4.0)"]
+signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
+
+[[package]]
+name = "onnxruntime"
+version = "1.19.2"
+description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
+optional = false
+python-versions = "*"
+files = [
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+numpy = ">=1.21.6"
+packaging = "*"
+protobuf = "*"
+sympy = "*"
+
 [[package]]
 name = "openai"
 version = "1.48.0"
@@ -1626,6 +3292,169 @@ typing-extensions = ">=4.11,<5"
 [package.extras]
 datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
+[[package]]
+name = "opentelemetry-api"
+version = "1.27.0"
+description = "OpenTelemetry Python API"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
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+importlib-metadata = ">=6.0,<=8.4.0"
+
+[[package]]
+name = "opentelemetry-exporter-otlp-proto-common"
+version = "1.27.0"
+description = "OpenTelemetry Protobuf encoding"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
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+
+[[package]]
+name = "opentelemetry-exporter-otlp-proto-grpc"
+version = "1.27.0"
+description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.27.0-py3-none-any.whl", hash = "sha256:56b5bbd5d61aab05e300d9d62a6b3c134827bbd28d0b12f2649c2da368006c9e"},
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+opentelemetry-proto = "1.27.0"
+opentelemetry-sdk = ">=1.27.0,<1.28.0"
+
+[[package]]
+name = "opentelemetry-instrumentation"
+version = "0.48b0"
+description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[[package]]
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+description = "ASGI instrumentation for OpenTelemetry"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
+instruments = ["asgiref (>=3.0,<4.0)"]
+
+[[package]]
+name = "opentelemetry-instrumentation-fastapi"
+version = "0.48b0"
+description = "OpenTelemetry FastAPI Instrumentation"
+optional = false
+python-versions = ">=3.8"
+files = [
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+opentelemetry-semantic-conventions = "0.48b0"
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+
+[package.extras]
+instruments = ["fastapi (>=0.58,<1.0)"]
+
+[[package]]
+name = "opentelemetry-proto"
+version = "1.27.0"
+description = "OpenTelemetry Python Proto"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_proto-1.27.0-py3-none-any.whl", hash = "sha256:b133873de5581a50063e1e4b29cdcf0c5e253a8c2d8dc1229add20a4c3830ace"},
+    {file = "opentelemetry_proto-1.27.0.tar.gz", hash = "sha256:33c9345d91dafd8a74fc3d7576c5a38f18b7fdf8d02983ac67485386132aedd6"},
+]
+
+[package.dependencies]
+protobuf = ">=3.19,<5.0"
+
+[[package]]
+name = "opentelemetry-sdk"
+version = "1.27.0"
+description = "OpenTelemetry Python SDK"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_sdk-1.27.0-py3-none-any.whl", hash = "sha256:365f5e32f920faf0fd9e14fdfd92c086e317eaa5f860edba9cdc17a380d9197d"},
+    {file = "opentelemetry_sdk-1.27.0.tar.gz", hash = "sha256:d525017dea0ccce9ba4e0245100ec46ecdc043f2d7b8315d56b19aff0904fa6f"},
+]
+
+[package.dependencies]
+opentelemetry-api = "1.27.0"
+opentelemetry-semantic-conventions = "0.48b0"
+typing-extensions = ">=3.7.4"
+
+[[package]]
+name = "opentelemetry-semantic-conventions"
+version = "0.48b0"
+description = "OpenTelemetry Semantic Conventions"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_semantic_conventions-0.48b0-py3-none-any.whl", hash = "sha256:a0de9f45c413a8669788a38569c7e0a11ce6ce97861a628cca785deecdc32a1f"},
+    {file = "opentelemetry_semantic_conventions-0.48b0.tar.gz", hash = "sha256:12d74983783b6878162208be57c9effcb89dc88691c64992d70bb89dc00daa1a"},
+]
+
+[package.dependencies]
+deprecated = ">=1.2.6"
+opentelemetry-api = "1.27.0"
+
+[[package]]
+name = "opentelemetry-util-http"
+version = "0.48b0"
+description = "Web util for OpenTelemetry"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_util_http-0.48b0-py3-none-any.whl", hash = "sha256:76f598af93aab50328d2a69c786beaedc8b6a7770f7a818cc307eb353debfffb"},
+    {file = "opentelemetry_util_http-0.48b0.tar.gz", hash = "sha256:60312015153580cc20f322e5cdc3d3ecad80a71743235bdb77716e742814623c"},
+]
+
 [[package]]
 name = "orjson"
 version = "3.10.7"
@@ -1692,6 +3521,17 @@ files = [
     {file = "orjson-3.10.7.tar.gz", hash = "sha256:75ef0640403f945f3a1f9f6400686560dbfb0fb5b16589ad62cd477043c4eee3"},
 ]
 
+[[package]]
+name = "overrides"
+version = "7.7.0"
+description = "A decorator to automatically detect mismatch when overriding a method."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49"},
+    {file = "overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a"},
+]
+
 [[package]]
 name = "packaging"
 version = "24.1"
@@ -1703,6 +3543,17 @@ files = [
     {file = "packaging-24.1.tar.gz", hash = "sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002"},
 ]
 
+[[package]]
+name = "pandocfilters"
+version = "1.5.1"
+description = "Utilities for writing pandoc filters in python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc"},
+    {file = "pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e"},
+]
+
 [[package]]
 name = "parso"
 version = "0.8.4"
@@ -1748,6 +3599,43 @@ docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-a
 test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
 type = ["mypy (>=1.11.2)"]
 
+[[package]]
+name = "posthog"
+version = "3.7.0"
+description = "Integrate PostHog into any python application."
+optional = false
+python-versions = "*"
+files = [
+    {file = "posthog-3.7.0-py2.py3-none-any.whl", hash = "sha256:3555161c3a9557b5666f96d8e1f17f410ea0f07db56e399e336a1656d4e5c722"},
+    {file = "posthog-3.7.0.tar.gz", hash = "sha256:b095d4354ba23f8b346ab5daed8ecfc5108772f922006982dfe8b2d29ebc6e0e"},
+]
+
+[package.dependencies]
+backoff = ">=1.10.0"
+monotonic = ">=1.5"
+python-dateutil = ">2.1"
+requests = ">=2.7,<3.0"
+six = ">=1.5"
+
+[package.extras]
+dev = ["black", "flake8", "flake8-print", "isort", "pre-commit"]
+sentry = ["django", "sentry-sdk"]
+test = ["coverage", "django", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint", "pytest", "pytest-timeout"]
+
+[[package]]
+name = "prometheus-client"
+version = "0.21.0"
+description = "Python client for the Prometheus monitoring system."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "prometheus_client-0.21.0-py3-none-any.whl", hash = "sha256:4fa6b4dd0ac16d58bb587c04b1caae65b8c5043e85f778f42f5f632f6af2e166"},
+    {file = "prometheus_client-0.21.0.tar.gz", hash = "sha256:96c83c606b71ff2b0a433c98889d275f51ffec6c5e267de37c7a2b5c9aa9233e"},
+]
+
+[package.extras]
+twisted = ["twisted"]
+
 [[package]]
 name = "prompt-toolkit"
 version = "3.0.48"
@@ -1762,6 +3650,26 @@ files = [
 [package.dependencies]
 wcwidth = "*"
 
+[[package]]
+name = "protobuf"
+version = "4.25.5"
+description = ""
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "protobuf-4.25.5-cp310-abi3-win32.whl", hash = "sha256:5e61fd921603f58d2f5acb2806a929b4675f8874ff5f330b7d6f7e2e784bbcd8"},
+    {file = "protobuf-4.25.5-cp310-abi3-win_amd64.whl", hash = "sha256:4be0571adcbe712b282a330c6e89eae24281344429ae95c6d85e79e84780f5ea"},
+    {file = "protobuf-4.25.5-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:b2fde3d805354df675ea4c7c6338c1aecd254dfc9925e88c6d31a2bcb97eb173"},
+    {file = "protobuf-4.25.5-cp37-abi3-manylinux2014_aarch64.whl", hash = "sha256:919ad92d9b0310070f8356c24b855c98df2b8bd207ebc1c0c6fcc9ab1e007f3d"},
+    {file = "protobuf-4.25.5-cp37-abi3-manylinux2014_x86_64.whl", hash = "sha256:fe14e16c22be926d3abfcb500e60cab068baf10b542b8c858fa27e098123e331"},
+    {file = "protobuf-4.25.5-cp38-cp38-win32.whl", hash = "sha256:98d8d8aa50de6a2747efd9cceba361c9034050ecce3e09136f90de37ddba66e1"},
+    {file = "protobuf-4.25.5-cp38-cp38-win_amd64.whl", hash = "sha256:b0234dd5a03049e4ddd94b93400b67803c823cfc405689688f59b34e0742381a"},
+    {file = "protobuf-4.25.5-cp39-cp39-win32.whl", hash = "sha256:abe32aad8561aa7cc94fc7ba4fdef646e576983edb94a73381b03c53728a626f"},
+    {file = "protobuf-4.25.5-cp39-cp39-win_amd64.whl", hash = "sha256:7a183f592dc80aa7c8da7ad9e55091c4ffc9497b3054452d629bb85fa27c2a45"},
+    {file = "protobuf-4.25.5-py3-none-any.whl", hash = "sha256:0aebecb809cae990f8129ada5ca273d9d670b76d9bfc9b1809f0a9c02b7dbf41"},
+    {file = "protobuf-4.25.5.tar.gz", hash = "sha256:7f8249476b4a9473645db7f8ab42b02fe1488cbe5fb72fddd445e0665afd8584"},
+]
+
 [[package]]
 name = "psutil"
 version = "6.0.0"
@@ -1803,18 +3711,43 @@ files = [
 ]
 
 [[package]]
-name = "pure-eval"
-version = "0.2.3"
-description = "Safely evaluate AST nodes without side effects"
+name = "pure-eval"
+version = "0.2.3"
+description = "Safely evaluate AST nodes without side effects"
+optional = false
+python-versions = "*"
+files = [
+    {file = "pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0"},
+    {file = "pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42"},
+]
+
+[package.extras]
+tests = ["pytest"]
+
+[[package]]
+name = "pyasn1"
+version = "0.6.1"
+description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
+    {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
+]
+
+[[package]]
+name = "pyasn1-modules"
+version = "0.4.1"
+description = "A collection of ASN.1-based protocols modules"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0"},
-    {file = "pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42"},
+    {file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
+    {file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
 ]
 
-[package.extras]
-tests = ["pytest"]
+[package.dependencies]
+pyasn1 = ">=0.4.6,<0.7.0"
 
 [[package]]
 name = "pycparser"
@@ -2013,6 +3946,62 @@ files = [
 [package.extras]
 windows-terminal = ["colorama (>=0.4.6)"]
 
+[[package]]
+name = "pypdf"
+version = "5.0.1"
+description = "A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pypdf-5.0.1-py3-none-any.whl", hash = "sha256:ff8a32da6c7a63fea9c32fa4dd837cdd0db7966adf6c14f043e3f12592e992db"},
+    {file = "pypdf-5.0.1.tar.gz", hash = "sha256:a361c3c372b4a659f9c8dd438d5ce29a753c79c620dc6e1fd66977651f5547ea"},
+]
+
+[package.dependencies]
+typing_extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+crypto = ["PyCryptodome", "cryptography"]
+dev = ["black", "flit", "pip-tools", "pre-commit (<2.18.0)", "pytest-cov", "pytest-socket", "pytest-timeout", "pytest-xdist", "wheel"]
+docs = ["myst_parser", "sphinx", "sphinx_rtd_theme"]
+full = ["Pillow (>=8.0.0)", "PyCryptodome", "cryptography"]
+image = ["Pillow (>=8.0.0)"]
+
+[[package]]
+name = "pypika"
+version = "0.48.9"
+description = "A SQL query builder API for Python"
+optional = false
+python-versions = "*"
+files = [
+    {file = "PyPika-0.48.9.tar.gz", hash = "sha256:838836a61747e7c8380cd1b7ff638694b7a7335345d0f559b04b2cd832ad5378"},
+]
+
+[[package]]
+name = "pyproject-hooks"
+version = "1.2.0"
+description = "Wrappers to call pyproject.toml-based build backend hooks."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pyproject_hooks-1.2.0-py3-none-any.whl", hash = "sha256:9e5c6bfa8dcc30091c74b0cf803c81fdd29d94f01992a7707bc97babb1141913"},
+    {file = "pyproject_hooks-1.2.0.tar.gz", hash = "sha256:1e859bd5c40fae9448642dd871adf459e5e2084186e8d2c2a79a824c970da1f8"},
+]
+
+[[package]]
+name = "pyreadline3"
+version = "3.5.4"
+description = "A python implementation of GNU readline."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyreadline3-3.5.4-py3-none-any.whl", hash = "sha256:eaf8e6cc3c49bcccf145fc6067ba8643d1df34d604a1ec0eccbf7a18e6d3fae6"},
+    {file = "pyreadline3-3.5.4.tar.gz", hash = "sha256:8d57d53039a1c75adba8e50dd3d992b28143480816187ea5efbd5c78e6c885b7"},
+]
+
+[package.extras]
+dev = ["build", "flake8", "mypy", "pytest", "twine"]
+
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"
@@ -2041,6 +4030,17 @@ files = [
 [package.extras]
 cli = ["click (>=5.0)"]
 
+[[package]]
+name = "python-json-logger"
+version = "2.0.7"
+description = "A python library adding a json log formatter"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "python-json-logger-2.0.7.tar.gz", hash = "sha256:23e7ec02d34237c5aa1e29a070193a4ea87583bb4e7f8fd06d3de8264c4b2e1c"},
+    {file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"},
+]
+
 [[package]]
 name = "pywin32"
 version = "306"
@@ -2064,6 +4064,21 @@ files = [
     {file = "pywin32-306-cp39-cp39-win_amd64.whl", hash = "sha256:39b61c15272833b5c329a2989999dcae836b1eed650252ab1b7bfbe1d59f30f4"},
 ]
 
+[[package]]
+name = "pywinpty"
+version = "2.0.13"
+description = "Pseudo terminal support for Windows from Python."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pywinpty-2.0.13-cp310-none-win_amd64.whl", hash = "sha256:697bff211fb5a6508fee2dc6ff174ce03f34a9a233df9d8b5fe9c8ce4d5eaf56"},
+    {file = "pywinpty-2.0.13-cp311-none-win_amd64.whl", hash = "sha256:b96fb14698db1284db84ca38c79f15b4cfdc3172065b5137383910567591fa99"},
+    {file = "pywinpty-2.0.13-cp312-none-win_amd64.whl", hash = "sha256:2fd876b82ca750bb1333236ce98488c1be96b08f4f7647cfdf4129dfad83c2d4"},
+    {file = "pywinpty-2.0.13-cp38-none-win_amd64.whl", hash = "sha256:61d420c2116c0212808d31625611b51caf621fe67f8a6377e2e8b617ea1c1f7d"},
+    {file = "pywinpty-2.0.13-cp39-none-win_amd64.whl", hash = "sha256:71cb613a9ee24174730ac7ae439fd179ca34ccb8c5349e8d7b72ab5dea2c6f4b"},
+    {file = "pywinpty-2.0.13.tar.gz", hash = "sha256:c34e32351a3313ddd0d7da23d27f835c860d32fe4ac814d372a3ea9594f41dde"},
+]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.2"
@@ -2247,6 +4262,21 @@ files = [
 [package.dependencies]
 cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
+[[package]]
+name = "referencing"
+version = "0.35.1"
+description = "JSON Referencing + Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "referencing-0.35.1-py3-none-any.whl", hash = "sha256:eda6d3234d62814d1c64e305c1331c9a3a6132da475ab6382eaa997b21ee75de"},
+    {file = "referencing-0.35.1.tar.gz", hash = "sha256:25b42124a6c8b632a425174f24087783efb348a6f1e0008e63cd4466fedf703c"},
+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+rpds-py = ">=0.7.0"
+
 [[package]]
 name = "regex"
 version = "2024.9.11"
@@ -2371,6 +4401,194 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "requests-oauthlib"
+version = "2.0.0"
+description = "OAuthlib authentication support for Requests."
+optional = false
+python-versions = ">=3.4"
+files = [
+    {file = "requests-oauthlib-2.0.0.tar.gz", hash = "sha256:b3dffaebd884d8cd778494369603a9e7b58d29111bf6b41bdc2dcd87203af4e9"},
+    {file = "requests_oauthlib-2.0.0-py2.py3-none-any.whl", hash = "sha256:7dd8a5c40426b779b0868c404bdef9768deccf22749cde15852df527e6269b36"},
+]
+
+[package.dependencies]
+oauthlib = ">=3.0.0"
+requests = ">=2.0.0"
+
+[package.extras]
+rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
+
+[[package]]
+name = "rfc3339-validator"
+version = "0.1.4"
+description = "A pure python RFC3339 validator"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa"},
+    {file = "rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b"},
+]
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+name = "rfc3986-validator"
+version = "0.1.1"
+description = "Pure python rfc3986 validator"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9"},
+    {file = "rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055"},
+]
+
+[[package]]
+name = "rich"
+version = "13.9.2"
+description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "rich-13.9.2-py3-none-any.whl", hash = "sha256:8c82a3d3f8dcfe9e734771313e606b39d8247bb6b826e196f4914b333b743cf1"},
+    {file = "rich-13.9.2.tar.gz", hash = "sha256:51a2c62057461aaf7152b4d611168f93a9fc73068f8ded2790f29fe2b5366d0c"},
+]
+
+[package.dependencies]
+markdown-it-py = ">=2.2.0"
+pygments = ">=2.13.0,<3.0.0"
+typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+jupyter = ["ipywidgets (>=7.5.1,<9)"]
+
+[[package]]
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+
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+[package.dependencies]
+huggingface-hub = ">=0.16.4,<1.0"
+
+[package.extras]
+dev = ["tokenizers[testing]"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
+
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 version = "0.10.2"
@@ -2862,6 +5291,34 @@ files = [
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 test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
+[[package]]
+name = "typer"
+version = "0.12.5"
+description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
+optional = false
+python-versions = ">=3.7"
+files = [
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+shellingham = ">=1.3.0"
+typing-extensions = ">=3.7.4.3"
+
+[[package]]
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+version = "2.9.0.20241003"
+description = "Typing stubs for python-dateutil"
+optional = false
+python-versions = ">=3.8"
+files = [
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@@ -2888,6 +5345,36 @@ files = [
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 typing-extensions = ">=3.7.4"
 
+[[package]]
+name = "uri-template"
+version = "1.3.0"
+description = "RFC 6570 URI Template Processor"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[[package]]
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+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+files = [
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+socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
+
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 name = "urllib3"
 version = "2.2.3"
@@ -2918,12 +5405,86 @@ files = [
 
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+python-dotenv = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
+pyyaml = {version = ">=5.1", optional = true, markers = "extra == \"standard\""}
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+watchfiles = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
+websockets = {version = ">=10.4", optional = true, markers = "extra == \"standard\""}
 
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+[[package]]
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+
+[[package]]
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+version = "6.0.2"
+description = "Automatically mock your HTTP interactions to simplify and speed up testing"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
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+    {version = "<2", markers = "platform_python_implementation == \"PyPy\" or python_version < \"3.10\""},
+]
+wrapt = "*"
+yarl = "*"
+
+[package.extras]
+tests = ["Werkzeug (==2.0.3)", "aiohttp", "boto3", "httplib2", "httpx", "pytest", "pytest-aiohttp", "pytest-asyncio", "pytest-cov", "pytest-httpbin", "requests (>=2.22.0)", "tornado", "urllib3"]
+
 [[package]]
 name = "watchfiles"
 version = "0.24.0"
@@ -3030,6 +5591,48 @@ files = [
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+[[package]]
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+optional = false
+python-versions = ">=3.8"
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+[package.extras]
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+tests = ["coverage[toml]"]
+
+[[package]]
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+description = "Character encoding aliases for legacy web content"
+optional = false
+python-versions = "*"
+files = [
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+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.extras]
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+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
 [[package]]
 name = "websockets"
 version = "13.1"
@@ -3125,6 +5728,96 @@ files = [
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+description = "Jupyter interactive widgets for Jupyter Notebook"
+optional = false
+python-versions = ">=3.7"
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+optional = false
+python-versions = ">=3.6"
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+    {file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"},
+]
+
 [[package]]
 name = "yarl"
 version = "1.12.1"
@@ -3252,4 +5945,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "4017dc40f5e58304aff484656ff103fc1fa0072af0c915f2aa2e598be3c7e983"
+content-hash = "18d3fafdb2b5509b9f476aa2ed52de9aeac9ef1990ab7e85c91240c1caec0d9f"
diff --git a/pyproject.toml b/pyproject.toml
index 1afa0437c5010..e63de41890dee 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,6 +46,16 @@ numpy = [
 ]
 
 [tool.poetry.group.test.dependencies]
+langchain-experimental = { git = "https://github.com/langchain-ai/langchain-experimental.git", subdirectory = "libs/experimental" }
+langchain-anthropic = { path = "libs/partners/anthropic", develop = true }
+langchain-chroma = { path = "libs/partners/chroma", develop = true }
+langchain-mistralai = { path = "libs/partners/mistralai", develop = true }
+langgraph = { git = "https://github.com/langchain-ai/langgraph.git", subdirectory = "libs/langgraph" }
+jupyter = "^1.1.1"
+click = "^8.1.7"
+faiss-cpu = "^1.7.4"
+pypdf = "^5.0.0"
+vcrpy = "^6.0.1"
 
 [tool.poetry.group.test_integration.dependencies]
 

From a38e9033605ddada70f03b321f94c8e01202bc27 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Wed, 16 Oct 2024 11:27:07 -0700
Subject: [PATCH 13/84] docs: platforms -> providers (#27285)

---
 README.md                                     |  2 +-
 docs/Makefile                                 |  2 +-
 docs/api_reference/guide_imports.json         |  2 +-
 .../how_to/convert_runnable_to_tool.ipynb     |  2 +-
 docs/docs/how_to/installation.mdx             |  2 +-
 .../{platforms => providers}/anthropic.mdx    |  0
 .../{platforms => providers}/aws.mdx          |  0
 .../docs/integrations/providers/databricks.md |  2 +-
 .../{platforms => providers}/google.mdx       |  0
 .../{platforms => providers}/huggingface.mdx  |  0
 .../{platforms => providers}/microsoft.mdx    |  0
 .../{platforms => providers}/openai.mdx       |  0
 .../google_vertex_ai_palm.ipynb               |  2 +-
 docs/docs/introduction.mdx                    |  2 +-
 docs/docs/versions/v0_2/overview.mdx          |  2 +-
 docs/scripts/partner_pkg_table.py             | 18 ++++-----
 docs/sidebars.js                              | 31 +++++++++++---
 docs/src/theme/NotFound/Content/index.js      | 40 +++++++++----------
 docs/vercel.json                              |  4 ++
 19 files changed, 68 insertions(+), 43 deletions(-)
 rename docs/docs/integrations/{platforms => providers}/anthropic.mdx (100%)
 rename docs/docs/integrations/{platforms => providers}/aws.mdx (100%)
 rename docs/docs/integrations/{platforms => providers}/google.mdx (100%)
 rename docs/docs/integrations/{platforms => providers}/huggingface.mdx (100%)
 rename docs/docs/integrations/{platforms => providers}/microsoft.mdx (100%)
 rename docs/docs/integrations/{platforms => providers}/openai.mdx (100%)

diff --git a/README.md b/README.md
index 4b724e9b5ecef..5f85d427e23d2 100644
--- a/README.md
+++ b/README.md
@@ -38,7 +38,7 @@ conda install langchain -c conda-forge
 
 For these applications, LangChain simplifies the entire application lifecycle:
 
-- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel), [components](https://python.langchain.com/docs/concepts/), and [third-party integrations](https://python.langchain.com/docs/integrations/platforms/).
+- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel), [components](https://python.langchain.com/docs/concepts/), and [third-party integrations](https://python.langchain.com/docs/integrations/providers/).
   Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
 - **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
 - **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
diff --git a/docs/Makefile b/docs/Makefile
index 532df0c457923..fabed9f041ac5 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -18,7 +18,7 @@ for dir; do \
     if find "$$dir" -maxdepth 1 -type f \( -name "pyproject.toml" -o -name "setup.py" \) | grep -q .; then \
         echo "$$dir"; \
     fi \
-done' sh {} + | grep -vE "airbyte|ibm|couchbase|databricks" | tr '\n' ' ')
+done' sh {} + | grep -vE "airbyte|ibm|databricks" | tr '\n' ' ')
 
 PORT ?= 3001
 
diff --git a/docs/api_reference/guide_imports.json b/docs/api_reference/guide_imports.json
index e0fcde404bfb4..4f372db195a11 100644
--- a/docs/api_reference/guide_imports.json
+++ b/docs/api_reference/guide_imports.json
@@ -1 +1 @@
-{"ChatPromptTemplate": {"\ud83e\udd9c\ufe0f\ud83c\udfd3 LangServe": "https://python.langchain.com/docs/langserve/", "Conceptual guide": "https://python.langchain.com/docs/concepts/", "# Example": "https://python.langchain.com/docs/versions/migrating_chains/map_rerank_docs_chain/", "# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_router_chain/", "Load docs": "https://python.langchain.com/docs/versions/migrating_chains/conversation_retrieval_chain/", "# Basic example (short documents)": "https://python.langchain.com/docs/versions/migrating_chains/map_reduce_chain/", "How to add a semantic layer over graph database": "https://python.langchain.com/docs/how_to/graph_semantic/", "How to handle long text when doing extraction": "https://python.langchain.com/docs/how_to/extraction_long_text/", "How to add values to a chain's state": "https://python.langchain.com/docs/how_to/assign/", "How to do per-user retrieval": "https://python.langchain.com/docs/how_to/qa_per_user/", "How to track token usage in ChatModels": "https://python.langchain.com/docs/how_to/chat_token_usage_tracking/", "How to create a custom LLM class": "https://python.langchain.com/docs/how_to/custom_llm/", "How to inspect runnables": "https://python.langchain.com/docs/how_to/inspect/", "How to handle cases where no queries are generated": "https://python.langchain.com/docs/how_to/query_no_queries/", "How to use few shot examples in chat models": "https://python.langchain.com/docs/how_to/few_shot_examples_chat/", "How to summarize text through iterative refinement": "https://python.langchain.com/docs/how_to/summarize_refine/", "How to do tool/function calling": "https://python.langchain.com/docs/how_to/function_calling/", "How to create tools": "https://python.langchain.com/docs/how_to/custom_tools/", "How to use prompting alone (no tool calling) to do extraction": "https://python.langchain.com/docs/how_to/extraction_parse/", "How to deal with large databases when doing SQL question-answering": "https://python.langchain.com/docs/how_to/sql_large_db/", "How to use reference examples when doing extraction": "https://python.langchain.com/docs/how_to/extraction_examples/", "How to handle multiple queries when doing query analysis": "https://python.langchain.com/docs/how_to/query_multiple_queries/", "How to add fallbacks to a runnable": "https://python.langchain.com/docs/how_to/fallbacks/", "How to propagate callbacks  constructor": "https://python.langchain.com/docs/how_to/callbacks_constructor/", "How to map values to a graph database": "https://python.langchain.com/docs/how_to/graph_mapping/", "How to save and load LangChain objects": "https://python.langchain.com/docs/how_to/serialization/", "How to do question answering over CSVs": "https://python.langchain.com/docs/how_to/sql_csv/", "How to stream results from your RAG application": "https://python.langchain.com/docs/how_to/qa_streaming/", "How to get your RAG 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"https://python.langchain.com/docs/how_to/tool_results_pass_to_model/", "How to add ad-hoc tool calling capability to LLMs and Chat Models": "https://python.langchain.com/docs/how_to/tools_prompting/", "How to return artifacts from a tool": "https://python.langchain.com/docs/how_to/tool_artifacts/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "How to stream tool calls": "https://python.langchain.com/docs/how_to/tool_streaming/", "How to pass runtime secrets to runnables": "https://python.langchain.com/docs/how_to/runnable_runtime_secrets/", "How to use few-shot prompting with tool calling": "https://python.langchain.com/docs/how_to/tools_few_shot/", "FinancialDatasets Toolkit": "https://python.langchain.com/docs/integrations/tools/financial_datasets/", "Exa Search": "https://python.langchain.com/docs/integrations/tools/exa_search/", "DeepInfra": "https://python.langchain.com/docs/integrations/chat/deepinfra/", "ChatOllama": "https://python.langchain.com/docs/integrations/chat/ollama/", "Llama.cpp": "https://python.langchain.com/docs/integrations/chat/llamacpp/", "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere/", "Eden AI": "https://python.langchain.com/docs/integrations/chat/edenai/", "ChatTongyi": "https://python.langchain.com/docs/integrations/chat/tongyi/", "ChatPremAI": "https://python.langchain.com/docs/integrations/chat/premai/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor/", "PremAI": "https://python.langchain.com/docs/integrations/providers/premai/", "Log, Trace, and Monitor": "https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey/", "Portkey": "https://python.langchain.com/docs/integrations/providers/portkey/index/", "JSONFormer": "https://python.langchain.com/docs/integrations/llms/jsonformer_experimental/"}, "MultiPromptChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/multi_prompt_chain/"}, "ConversationChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/"}, "ConversationBufferMemory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Memorize": "https://python.langchain.com/docs/integrations/tools/memorize/", "Gradio": "https://python.langchain.com/docs/integrations/tools/gradio_tools/", "SceneXplain": "https://python.langchain.com/docs/integrations/tools/sceneXplain/", "Reddit Search ": "https://python.langchain.com/docs/integrations/tools/reddit_search/", "Xata": "https://python.langchain.com/docs/integrations/memory/xata_chat_message_history/", "Llama2Chat": "https://python.langchain.com/docs/integrations/chat/llama2_chat/", "SAP HANA Cloud Vector Engine": "https://python.langchain.com/docs/integrations/vectorstores/sap_hanavector/", "Bittensor": "https://python.langchain.com/docs/integrations/llms/bittensor/"}, "InMemoryChatMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/"}, "RunnableWithMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add message history": "https://python.langchain.com/docs/how_to/message_history/", "How to add memory to chatbots": "https://python.langchain.com/docs/how_to/chatbots_memory/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "Redis": "https://python.langchain.com/docs/integrations/memory/redis_chat_message_history/", "Google SQL for MySQL": "https://python.langchain.com/docs/integrations/memory/google_sql_mysql/", "Google AlloyDB for PostgreSQL": "https://python.langchain.com/docs/integrations/memory/google_alloydb/", "ZepCloudChatMessageHistory": "https://python.langchain.com/docs/integrations/memory/zep_cloud_chat_message_history/", "AWS DynamoDB": "https://python.langchain.com/docs/integrations/memory/aws_dynamodb/", "Couchbase": "https://python.langchain.com/docs/integrations/memory/couchbase_chat_message_history/", "MongoDB": "https://python.langchain.com/docs/integrations/memory/mongodb_chat_message_history/", "SQL (SQLAlchemy)": "https://python.langchain.com/docs/integrations/memory/sql_chat_message_history/", "Streamlit": "https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history/", "Google El Carro Oracle": "https://python.langchain.com/docs/integrations/memory/google_el_carro/", "SQLite": "https://python.langchain.com/docs/integrations/memory/sqlite/", "Google SQL for PostgreSQL": "https://python.langchain.com/docs/integrations/memory/google_sql_pg/", "Google SQL for SQL Server": "https://python.langchain.com/docs/integrations/memory/google_sql_mssql/", "TiDB": "https://python.langchain.com/docs/integrations/memory/tidb_chat_message_history/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/"}, "BaseChatMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/"}, "ConstitutionalChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "ConstitutionalPrinciple": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "OpenAI": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/", "# Example": "https://python.langchain.com/docs/versions/migrating_chains/map_rerank_docs_chain/", "How to use output parsers to parse an LLM response into structured format": "https://python.langchain.com/docs/how_to/output_parser_structured/", "How to reorder retrieved results to mitigate the \"lost in the middle\" effect": "https://python.langchain.com/docs/how_to/long_context_reorder/", "How to add fallbacks to a runnable": "https://python.langchain.com/docs/how_to/fallbacks/", "How to do retrieval with contextual compression": "https://python.langchain.com/docs/how_to/contextual_compression/", "How to retry when a parsing error occurs": "https://python.langchain.com/docs/how_to/output_parser_retry/", "How to stream responses from an LLM": "https://python.langchain.com/docs/how_to/streaming_llm/", "How to cache LLM responses": "https://python.langchain.com/docs/how_to/llm_caching/", "How to track token usage for LLMs": "https://python.langchain.com/docs/how_to/llm_token_usage_tracking/", "Model caches": "https://python.langchain.com/docs/integrations/llm_caching/", "Milvus": "https://python.langchain.com/docs/integrations/retrievers/self_query/milvus_self_query/", "PGVector (Postgres)": "https://python.langchain.com/docs/integrations/retrievers/self_query/pgvector_self_query/", "Weaviate": "https://python.langchain.com/docs/integrations/vectorstores/weaviate/", "Databricks Vector Search": "https://python.langchain.com/docs/integrations/retrievers/self_query/databricks_vector_search/", "DingoDB": "https://python.langchain.com/docs/integrations/retrievers/self_query/dingo/", "OpenSearch": "https://python.langchain.com/docs/integrations/retrievers/self_query/opensearch_self_query/", "Elasticsearch": "https://python.langchain.com/docs/integrations/retrievers/self_query/elasticsearch_self_query/", "Chroma": "https://python.langchain.com/docs/integrations/retrievers/self_query/chroma_self_query/", "Timescale Vector (Postgres) ": "https://python.langchain.com/docs/integrations/retrievers/self_query/timescalevector_self_query/", "Astra DB (Cassandra)": "https://python.langchain.com/docs/integrations/retrievers/self_query/astradb/", "Pinecone": "https://python.langchain.com/docs/integrations/retrievers/self_query/pinecone/", "Supabase (Postgres)": "https://python.langchain.com/docs/integrations/retrievers/self_query/supabase_self_query/", "Redis": "https://python.langchain.com/docs/integrations/retrievers/self_query/redis_self_query/", "Deep Lake": "https://python.langchain.com/docs/integrations/retrievers/self_query/activeloop_deeplake_self_query/", "MongoDB Atlas": "https://python.langchain.com/docs/integrations/retrievers/self_query/mongodb_atlas/", "Qdrant": "https://python.langchain.com/docs/integrations/retrievers/self_query/qdrant_self_query/", "OpenAI": "https://python.langchain.com/docs/integrations/llms/openai/", "Jira Toolkit": "https://python.langchain.com/docs/integrations/tools/jira/", "Google Jobs": "https://python.langchain.com/docs/integrations/tools/google_jobs/", "Google Serper": "https://python.langchain.com/docs/integrations/tools/google_serper/", "Azure Cognitive Services Toolkit": "https://python.langchain.com/docs/integrations/tools/azure_cognitive_services/", "Human as a tool": "https://python.langchain.com/docs/integrations/tools/human_tools/", "Natural Language API Toolkits": "https://python.langchain.com/docs/integrations/tools/openapi_nla/", "Steam Toolkit": "https://python.langchain.com/docs/integrations/tools/steam/", "JSON Toolkit": "https://python.langchain.com/docs/integrations/tools/json/", "Google Finance": "https://python.langchain.com/docs/integrations/tools/google_finance/", "ClickUp Toolkit": "https://python.langchain.com/docs/integrations/tools/clickup/", "AWS Lambda": "https://python.langchain.com/docs/integrations/tools/awslambda/", "Google Drive": "https://python.langchain.com/docs/integrations/tools/google_drive/", "OpenWeatherMap": "https://python.langchain.com/docs/integrations/tools/openweathermap/", "Eleven Labs Text2Speech": "https://python.langchain.com/docs/integrations/tools/eleven_labs_tts/", "Office365 Toolkit": "https://python.langchain.com/docs/integrations/tools/office365/", "Pandas Dataframe": "https://python.langchain.com/docs/integrations/tools/pandas/", "Zapier Natural Language Actions": "https://python.langchain.com/docs/integrations/tools/zapier/", "Lemon Agent": "https://python.langchain.com/docs/integrations/tools/lemonai/", "NASA Toolkit": "https://python.langchain.com/docs/integrations/tools/nasa/", "GraphQL": "https://python.langchain.com/docs/integrations/tools/graphql/", "SearchApi": "https://python.langchain.com/docs/integrations/providers/searchapi/", "Gradio": "https://python.langchain.com/docs/integrations/tools/gradio_tools/", "SceneXplain": "https://python.langchain.com/docs/integrations/tools/sceneXplain/", "Azure AI Services Toolkit": "https://python.langchain.com/docs/integrations/tools/azure_ai_services/", "OpenAPI Toolkit": "https://python.langchain.com/docs/integrations/tools/openapi/", "Dall-E Image Generator": "https://python.langchain.com/docs/integrations/tools/dalle_image_generator/", "Gitlab Toolkit": "https://python.langchain.com/docs/integrations/tools/gitlab/", "Ionic Shopping Tool": "https://python.langchain.com/docs/integrations/tools/ionic_shopping/", "Zep Open Source Memory": "https://python.langchain.com/docs/integrations/memory/zep_memory/", "Mot\u00f6rhead": "https://python.langchain.com/docs/integrations/memory/motorhead_memory/", "Zep Cloud Memory": "https://python.langchain.com/docs/integrations/memory/zep_memory_cloud/", "Confident": "https://python.langchain.com/docs/integrations/callbacks/confident/", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor/", "Fiddler": "https://python.langchain.com/docs/integrations/callbacks/fiddler/", "SageMaker Tracking": "https://python.langchain.com/docs/integrations/callbacks/sagemaker_tracking/", "Label Studio": "https://python.langchain.com/docs/integrations/callbacks/labelstudio/", "Comet Tracing": "https://python.langchain.com/docs/integrations/callbacks/comet_tracing/", "Argilla": "https://python.langchain.com/docs/integrations/callbacks/argilla/", "PromptLayer": "https://python.langchain.com/docs/integrations/callbacks/promptlayer/", "Streamlit": "https://python.langchain.com/docs/integrations/callbacks/streamlit/", "Trubrics": "https://python.langchain.com/docs/integrations/callbacks/trubrics/", "Infino": "https://python.langchain.com/docs/integrations/callbacks/infino/", "DSPy": "https://python.langchain.com/docs/integrations/providers/dspy/", "Comet": "https://python.langchain.com/docs/integrations/providers/comet_tracking/", "Aim": "https://python.langchain.com/docs/integrations/providers/aim_tracking/", "Weights & Biases": "https://python.langchain.com/docs/integrations/providers/wandb_tracking/", "Log10": "https://python.langchain.com/docs/integrations/providers/log10/", "LangChain Decorators \u2728": "https://python.langchain.com/docs/integrations/providers/langchain_decorators/", "Rebuff": "https://python.langchain.com/docs/integrations/providers/rebuff/", "Serper - Google Search API": "https://python.langchain.com/docs/integrations/providers/google_serper/", "Helicone": "https://python.langchain.com/docs/integrations/providers/helicone/", "Shale Protocol": "https://python.langchain.com/docs/integrations/providers/shaleprotocol/", "WhyLabs": "https://python.langchain.com/docs/integrations/providers/whylabs_profiling/", "WandB Tracing": "https://python.langchain.com/docs/integrations/providers/wandb_tracing/", "ClearML": "https://python.langchain.com/docs/integrations/providers/clearml_tracking/", "Ray Serve": "https://python.langchain.com/docs/integrations/providers/ray_serve/", "Identity-enabled RAG using PebbloRetrievalQA": "https://python.langchain.com/docs/integrations/providers/pebblo/pebblo_retrieval_qa/", "Apache Doris": "https://python.langchain.com/docs/integrations/vectorstores/apache_doris/", "Jaguar Vector Database": "https://python.langchain.com/docs/integrations/vectorstores/jaguar/", "StarRocks": "https://python.langchain.com/docs/integrations/vectorstores/starrocks/", "Marqo": "https://python.langchain.com/docs/integrations/vectorstores/marqo/", "Timescale Vector (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/timescalevector/", "Amazon Document DB": "https://python.langchain.com/docs/integrations/vectorstores/documentdb/", "VoyageAI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/voyageai-reranker/", "Apify Dataset": "https://python.langchain.com/docs/integrations/document_loaders/apify_dataset/", "Psychic": "https://python.langchain.com/docs/integrations/document_loaders/psychic/", "Docugami": "https://python.langchain.com/docs/integrations/document_loaders/docugami/", "Amazon Textract ": "https://python.langchain.com/docs/integrations/document_loaders/amazon_textract/", "NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/", "OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/", "Layerup Security": "https://python.langchain.com/docs/integrations/llms/layerup_security/", "Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "CRITIQUE_PROMPT": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "REVISION_PROMPT": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "WebBaseLoader": {"Load docs": "https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/", "# Basic example (short documents)": "https://python.langchain.com/docs/versions/migrating_chains/map_reduce_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to stream results from your RAG application": "https://python.langchain.com/docs/how_to/qa_streaming/", "How to get your RAG application to return sources": "https://python.langchain.com/docs/how_to/qa_sources/", "How to summarize text through parallelization": "https://python.langchain.com/docs/how_to/summarize_map_reduce/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add retrieval to chatbots": "https://python.langchain.com/docs/how_to/chatbots_retrieval/", "How to use the MultiQueryRetriever": "https://python.langchain.com/docs/how_to/MultiQueryRetriever/", "RePhraseQuery": "https://python.langchain.com/docs/integrations/retrievers/re_phrase/", "Infino": "https://python.langchain.com/docs/integrations/callbacks/infino/", "ApertureDB": "https://python.langchain.com/docs/integrations/vectorstores/aperturedb/", "Zep Cloud": "https://python.langchain.com/docs/integrations/vectorstores/zep_cloud/", "Zep": "https://python.langchain.com/docs/integrations/vectorstores/zep/", "Google Cloud Vertex AI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/google_cloud_vertexai_rerank/", "Merge Documents Loader": "https://python.langchain.com/docs/integrations/document_loaders/merge_doc/", "WebBaseLoader": "https://python.langchain.com/docs/integrations/document_loaders/web_base/", "Build a Retrieval Augmented Generation (RAG) App": "https://python.langchain.com/docs/tutorials/rag/", "Build a Local RAG Application": "https://python.langchain.com/docs/tutorials/local_rag/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/"}, "FAISS": {"Load docs": "https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to handle long text when doing extraction": "https://python.langchain.com/docs/how_to/extraction_long_text/", "How to load PDFs": "https://python.langchain.com/docs/how_to/document_loader_pdf/", "How to better prompt when doing SQL question-answering": "https://python.langchain.com/docs/how_to/sql_prompting/", "How to add values to a chain's state": "https://python.langchain.com/docs/how_to/assign/", "How to inspect runnables": "https://python.langchain.com/docs/how_to/inspect/", "How to deal with large databases when doing SQL question-answering": "https://python.langchain.com/docs/how_to/sql_large_db/", "How to stream runnables": "https://python.langchain.com/docs/how_to/streaming/", "How to invoke runnables in parallel": "https://python.langchain.com/docs/how_to/parallel/", "How to pass through arguments from one step to the next": "https://python.langchain.com/docs/how_to/passthrough/", "How to do retrieval with contextual compression": "https://python.langchain.com/docs/how_to/contextual_compression/", "How to use a time-weighted vector store retriever": "https://python.langchain.com/docs/how_to/time_weighted_vectorstore/", "How to create and query vector stores": "https://python.langchain.com/docs/how_to/vectorstores/", "How to use a vectorstore as a retriever": "https://python.langchain.com/docs/how_to/vectorstore_retriever/", "Caching": "https://python.langchain.com/docs/how_to/caching_embeddings/", "How to combine results from multiple retrievers": "https://python.langchain.com/docs/how_to/ensemble_retriever/", "How to select examples by maximal marginal relevance (MMR)": "https://python.langchain.com/docs/how_to/example_selectors_mmr/", "NVIDIA NIMs ": "https://python.langchain.com/docs/integrations/text_embedding/nvidia_ai_endpoints/", "Fleet AI Context": "https://python.langchain.com/docs/integrations/retrievers/fleet_context/", "LLMLingua Document Compressor": "https://python.langchain.com/docs/integrations/retrievers/llmlingua/", "Cohere reranker": "https://python.langchain.com/docs/integrations/retrievers/cohere-reranker/", "FlashRank reranker": "https://python.langchain.com/docs/integrations/retrievers/flashrank-reranker/", "UpTrain": "https://python.langchain.com/docs/integrations/callbacks/uptrain/", "RAGatouille": "https://python.langchain.com/docs/integrations/providers/ragatouille/", "Facebook - Meta": "https://python.langchain.com/docs/integrations/providers/facebook/", "Faiss (Async)": "https://python.langchain.com/docs/integrations/vectorstores/faiss_async/", "Faiss": "https://python.langchain.com/docs/integrations/vectorstores/faiss/", "VoyageAI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/voyageai-reranker/", "RankLLM Reranker": "https://python.langchain.com/docs/integrations/document_transformers/rankllm-reranker/", "Volcengine Reranker": 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"https://python.langchain.com/docs/integrations/vectorstores/weaviate/", "Jaguar Vector Database": "https://python.langchain.com/docs/integrations/vectorstores/jaguar/", "SAP HANA Cloud Vector Engine": "https://python.langchain.com/docs/integrations/vectorstores/sap_hanavector/", "DashVector": "https://python.langchain.com/docs/integrations/vectorstores/dashvector/", "Databricks Vector Search": "https://python.langchain.com/docs/integrations/vectorstores/databricks_vector_search/", "ScaNN": "https://python.langchain.com/docs/integrations/vectorstores/scann/", "Xata": "https://python.langchain.com/docs/integrations/vectorstores/xata/", "Hippo": "https://python.langchain.com/docs/integrations/vectorstores/hippo/", "Vespa": "https://python.langchain.com/docs/integrations/vectorstores/vespa/", "Rockset": "https://python.langchain.com/docs/integrations/vectorstores/rockset/", "DingoDB": "https://python.langchain.com/docs/integrations/vectorstores/dingo/", "Zilliz": "https://python.langchain.com/docs/integrations/vectorstores/zilliz/", "Azure Cosmos DB Mongo vCore": "https://python.langchain.com/docs/integrations/vectorstores/azure_cosmos_db/", "Annoy": "https://python.langchain.com/docs/integrations/vectorstores/annoy/", "Couchbase ": "https://python.langchain.com/docs/integrations/vectorstores/couchbase/", "Typesense": "https://python.langchain.com/docs/integrations/vectorstores/typesense/", "Momento Vector Index (MVI)": "https://python.langchain.com/docs/integrations/vectorstores/momento_vector_index/", "TiDB Vector": "https://python.langchain.com/docs/integrations/vectorstores/tidb_vector/", "Relyt": "https://python.langchain.com/docs/integrations/vectorstores/relyt/", "Activeloop Deep Lake": "https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake/", "vlite": "https://python.langchain.com/docs/integrations/vectorstores/vlite/", "Neo4j Vector Index": "https://python.langchain.com/docs/integrations/vectorstores/neo4jvector/", "Lantern": "https://python.langchain.com/docs/integrations/vectorstores/lantern/", "Tair": "https://python.langchain.com/docs/integrations/vectorstores/tair/", "DuckDB": "https://python.langchain.com/docs/integrations/vectorstores/duckdb/", "Alibaba Cloud OpenSearch": "https://python.langchain.com/docs/integrations/vectorstores/alibabacloud_opensearch/", "Clarifai": "https://python.langchain.com/docs/integrations/vectorstores/clarifai/", "scikit-learn": "https://python.langchain.com/docs/integrations/vectorstores/sklearn/", "Tencent Cloud VectorDB": "https://python.langchain.com/docs/integrations/vectorstores/tencentvectordb/", "DocArray HnswSearch": "https://python.langchain.com/docs/integrations/vectorstores/docarray_hnsw/", "MyScale": "https://python.langchain.com/docs/integrations/vectorstores/myscale/", "TileDB": "https://python.langchain.com/docs/integrations/vectorstores/tiledb/", "Google Memorystore for Redis": "https://python.langchain.com/docs/integrations/vectorstores/google_memorystore_redis/", "Tigris": "https://python.langchain.com/docs/integrations/vectorstores/tigris/", "China Mobile ECloud ElasticSearch VectorSearch": "https://python.langchain.com/docs/integrations/vectorstores/ecloud_vector_search/", "Bagel": "https://python.langchain.com/docs/integrations/vectorstores/bagel/", "Baidu Cloud ElasticSearch VectorSearch": "https://python.langchain.com/docs/integrations/vectorstores/baiducloud_vector_search/", "AwaDB": "https://python.langchain.com/docs/integrations/vectorstores/awadb/", "Supabase (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/supabase/", "SurrealDB": "https://python.langchain.com/docs/integrations/vectorstores/surrealdb/", "OpenSearch": "https://python.langchain.com/docs/integrations/vectorstores/opensearch/", "Faiss (Async)": "https://python.langchain.com/docs/integrations/vectorstores/faiss_async/", "BagelDB": "https://python.langchain.com/docs/integrations/vectorstores/bageldb/", "ManticoreSearch VectorStore": "https://python.langchain.com/docs/integrations/vectorstores/manticore_search/", "Azure AI Search": "https://python.langchain.com/docs/integrations/vectorstores/azuresearch/", "USearch": "https://python.langchain.com/docs/integrations/vectorstores/usearch/", "PGVecto.rs": "https://python.langchain.com/docs/integrations/vectorstores/pgvecto_rs/", "Marqo": "https://python.langchain.com/docs/integrations/vectorstores/marqo/", "DocArray InMemorySearch": "https://python.langchain.com/docs/integrations/vectorstores/docarray_in_memory/", "Postgres Embedding": "https://python.langchain.com/docs/integrations/vectorstores/pgembedding/", "Intel's Visual Data Management System (VDMS)": "https://python.langchain.com/docs/integrations/vectorstores/vdms/", "Timescale Vector (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/timescalevector/", "Epsilla": "https://python.langchain.com/docs/integrations/vectorstores/epsilla/", "Amazon Document DB": "https://python.langchain.com/docs/integrations/vectorstores/documentdb/", "SemaDB": "https://python.langchain.com/docs/integrations/vectorstores/semadb/", "AnalyticDB": "https://python.langchain.com/docs/integrations/vectorstores/analyticdb/", "Hologres": "https://python.langchain.com/docs/integrations/vectorstores/hologres/", "Baidu VectorDB": "https://python.langchain.com/docs/integrations/vectorstores/baiduvectordb/", "Meilisearch": "https://python.langchain.com/docs/integrations/vectorstores/meilisearch/", "Psychic": "https://python.langchain.com/docs/integrations/document_loaders/psychic/", "Manifest": "https://python.langchain.com/docs/integrations/llms/manifest/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/"}, "acollapse_docs": {"# Basic example (short documents)": "https://python.langchain.com/docs/versions/migrating_chains/map_reduce_chain/", "How to summarize text through parallelization": "https://python.langchain.com/docs/how_to/summarize_map_reduce/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/"}, "split_list_of_docs": {"# Basic example (short documents)": "https://python.langchain.com/docs/versions/migrating_chains/map_reduce_chain/", "How to summarize text through parallelization": "https://python.langchain.com/docs/how_to/summarize_map_reduce/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/"}, "RefineDocumentsChain": {"# Example": "https://python.langchain.com/docs/versions/migrating_chains/refine_docs_chain/"}, "RetrievalQA": {"Load docs": "https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/", "LLMLingua Document Compressor": "https://python.langchain.com/docs/integrations/retrievers/llmlingua/", "Bedrock (Knowledge Bases) Retriever": "https://python.langchain.com/docs/integrations/retrievers/bedrock/", "Cohere reranker": "https://python.langchain.com/docs/integrations/retrievers/cohere-reranker/", "Activeloop Deep Memory": "https://python.langchain.com/docs/integrations/retrievers/activeloop/", "FlashRank reranker": "https://python.langchain.com/docs/integrations/retrievers/flashrank-reranker/", "Confident": "https://python.langchain.com/docs/integrations/callbacks/confident/", "UpTrain": "https://python.langchain.com/docs/integrations/callbacks/uptrain/", "Apache Doris": "https://python.langchain.com/docs/integrations/vectorstores/apache_doris/", "ScaNN": "https://python.langchain.com/docs/integrations/vectorstores/scann/", "Google Vertex AI Vector Search": "https://python.langchain.com/docs/integrations/vectorstores/google_vertex_ai_vector_search/", "Momento Vector Index (MVI)": "https://python.langchain.com/docs/integrations/vectorstores/momento_vector_index/", "Activeloop Deep Lake": "https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake/", "StarRocks": "https://python.langchain.com/docs/integrations/vectorstores/starrocks/", "KDB.AI": "https://python.langchain.com/docs/integrations/vectorstores/kdbai/", "Timescale Vector (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/timescalevector/", "Amazon Document DB": "https://python.langchain.com/docs/integrations/vectorstores/documentdb/", "VoyageAI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/voyageai-reranker/", "RankLLM Reranker": "https://python.langchain.com/docs/integrations/document_transformers/rankllm-reranker/", "YouTube audio": "https://python.langchain.com/docs/integrations/document_loaders/youtube_audio/", "Docugami": "https://python.langchain.com/docs/integrations/document_loaders/docugami/"}, "RunnablePassthrough": {"Load docs": "https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/", "# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_router_chain/", "How to add values to a chain's state": "https://python.langchain.com/docs/how_to/assign/", "How to route between sub-chains": "https://python.langchain.com/docs/how_to/routing/", "How to do per-user retrieval": "https://python.langchain.com/docs/how_to/qa_per_user/", "How to inspect runnables": "https://python.langchain.com/docs/how_to/inspect/", "How to handle cases where no queries are generated": "https://python.langchain.com/docs/how_to/query_no_queries/", "How to do tool/function calling": "https://python.langchain.com/docs/how_to/function_calling/", "How to add a human-in-the-loop for tools": "https://python.langchain.com/docs/how_to/tools_human/", "How to deal with large databases when doing SQL question-answering": "https://python.langchain.com/docs/how_to/sql_large_db/", "How to handle multiple queries when doing query analysis": "https://python.langchain.com/docs/how_to/query_multiple_queries/", "How to map values to a graph database": "https://python.langchain.com/docs/how_to/graph_mapping/", "How to do question answering over CSVs": "https://python.langchain.com/docs/how_to/sql_csv/", "How to get your RAG application to return sources": "https://python.langchain.com/docs/how_to/qa_sources/", "How to add default invocation args to a Runnable": "https://python.langchain.com/docs/how_to/binding/", "How to convert Runnables as Tools": "https://python.langchain.com/docs/how_to/convert_runnable_to_tool/", "How to create a dynamic (self-constructing) chain": "https://python.langchain.com/docs/how_to/dynamic_chain/", "How to stream runnables": "https://python.langchain.com/docs/how_to/streaming/", "How to invoke runnables in parallel": "https://python.langchain.com/docs/how_to/parallel/", "How to pass through arguments from one step to the next": "https://python.langchain.com/docs/how_to/passthrough/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add retrieval to chatbots": "https://python.langchain.com/docs/how_to/chatbots_retrieval/", "How to handle multiple retrievers when doing query analysis": "https://python.langchain.com/docs/how_to/query_multiple_retrievers/", "How to get a RAG application to add citations": "https://python.langchain.com/docs/how_to/qa_citations/", "How to add memory to chatbots": "https://python.langchain.com/docs/how_to/chatbots_memory/", "How deal with high cardinality categoricals when doing query analysis": "https://python.langchain.com/docs/how_to/query_high_cardinality/", "How to add ad-hoc tool calling capability to LLMs and Chat Models": "https://python.langchain.com/docs/how_to/tools_prompting/", "LangChain Expression Language Cheatsheet": "https://python.langchain.com/docs/how_to/lcel_cheatsheet/", "Hybrid Search": "https://python.langchain.com/docs/how_to/hybrid/", "How to use few-shot prompting with tool calling": "https://python.langchain.com/docs/how_to/tools_few_shot/", "How to add examples to the prompt for query analysis": "https://python.langchain.com/docs/how_to/query_few_shot/", "NVIDIA NIMs ": "https://python.langchain.com/docs/integrations/text_embedding/nvidia_ai_endpoints/", "AzureAISearchRetriever": "https://python.langchain.com/docs/integrations/retrievers/azure_ai_search/", "You.com": "https://python.langchain.com/docs/integrations/retrievers/you-retriever/", "Fleet AI Context": "https://python.langchain.com/docs/integrations/retrievers/fleet_context/", "AskNews": "https://python.langchain.com/docs/integrations/retrievers/asknews/", "WikipediaRetriever": "https://python.langchain.com/docs/integrations/retrievers/wikipedia/", "TavilySearchAPIRetriever": "https://python.langchain.com/docs/integrations/retrievers/tavily/", "ArxivRetriever": "https://python.langchain.com/docs/integrations/retrievers/arxiv/", "ElasticsearchRetriever": "https://python.langchain.com/docs/integrations/retrievers/elasticsearch_retriever/", "Milvus Hybrid Search Retriever": "https://python.langchain.com/docs/integrations/retrievers/milvus_hybrid_search/", "Google Vertex AI Search": "https://python.langchain.com/docs/integrations/retrievers/google_vertex_ai_search/", "UpTrain": "https://python.langchain.com/docs/integrations/callbacks/uptrain/", "DSPy": "https://python.langchain.com/docs/integrations/providers/dspy/", "Weaviate": "https://python.langchain.com/docs/integrations/vectorstores/weaviate/", "Jaguar Vector Database": "https://python.langchain.com/docs/integrations/vectorstores/jaguar/", "Apache Cassandra": "https://python.langchain.com/docs/integrations/vectorstores/cassandra/", "Google Cloud Vertex AI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/google_cloud_vertexai_rerank/", "OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/", "Build a Retrieval Augmented Generation (RAG) App": "https://python.langchain.com/docs/tutorials/rag/", "Build a Local RAG Application": "https://python.langchain.com/docs/tutorials/local_rag/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/", "Build a Query Analysis System": "https://python.langchain.com/docs/tutorials/query_analysis/", "Build a Question/Answering system over SQL data": "https://python.langchain.com/docs/tutorials/sql_qa/", "Vector stores and retrievers": "https://python.langchain.com/docs/tutorials/retrievers/"}, "LLMRouterChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_router_chain/"}, "RouterOutputParser": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_router_chain/"}, "MapRerankDocumentsChain": {"# Example": "https://python.langchain.com/docs/versions/migrating_chains/map_rerank_docs_chain/"}, "RegexParser": {"# Example": "https://python.langchain.com/docs/versions/migrating_chains/map_rerank_docs_chain/"}, "TavilySearchResults": {"Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to debug your LLM apps": "https://python.langchain.com/docs/how_to/debugging/", "Tavily Search": "https://python.langchain.com/docs/integrations/tools/tavily_search/", "ZHIPU AI": "https://python.langchain.com/docs/integrations/chat/zhipuai/", "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere/", "Build an Agent": "https://python.langchain.com/docs/tutorials/agents/"}, "create_retriever_tool": {"Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "Xata": "https://python.langchain.com/docs/integrations/memory/xata_chat_message_history/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/", "Build a Question/Answering system over SQL data": "https://python.langchain.com/docs/tutorials/sql_qa/"}, "create_tool_calling_agent": {"Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to use tools in a chain": "https://python.langchain.com/docs/how_to/tools_chain/", "How to track token usage in ChatModels": "https://python.langchain.com/docs/how_to/chat_token_usage_tracking/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to debug your LLM apps": "https://python.langchain.com/docs/how_to/debugging/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "Azure Container Apps dynamic sessions": "https://python.langchain.com/docs/integrations/tools/azure_dynamic_sessions/", "FinancialDatasets Toolkit": "https://python.langchain.com/docs/integrations/tools/financial_datasets/", "Databricks Unity Catalog (UC)": "https://python.langchain.com/docs/integrations/tools/databricks/", "Riza Code Interpreter": "https://python.langchain.com/docs/integrations/tools/riza/", "Bing Search": "https://python.langchain.com/docs/integrations/tools/bing_search/"}, "AgentExecutor": {"Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add a semantic layer over graph database": "https://python.langchain.com/docs/how_to/graph_semantic/", "How to use tools in a chain": "https://python.langchain.com/docs/how_to/tools_chain/", "How to track token usage in ChatModels": "https://python.langchain.com/docs/how_to/chat_token_usage_tracking/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to debug your LLM apps": "https://python.langchain.com/docs/how_to/debugging/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "Infobip": "https://python.langchain.com/docs/integrations/tools/infobip/", "AskNews": "https://python.langchain.com/docs/integrations/tools/asknews/", "Azure Container Apps dynamic sessions": "https://python.langchain.com/docs/integrations/tools/azure_dynamic_sessions/", "FinancialDatasets Toolkit": "https://python.langchain.com/docs/integrations/tools/financial_datasets/", "Cassandra Database Toolkit": "https://python.langchain.com/docs/integrations/tools/cassandra_database/", "Polygon IO Toolkit": "https://python.langchain.com/docs/integrations/tools/polygon_toolkit/", "Semantic Scholar API Tool": "https://python.langchain.com/docs/integrations/tools/semanticscholar/", "Databricks Unity Catalog (UC)": "https://python.langchain.com/docs/integrations/tools/databricks/", "Passio NutritionAI": "https://python.langchain.com/docs/integrations/tools/passio_nutrition_ai/", "Memorize": "https://python.langchain.com/docs/integrations/tools/memorize/", "Riza Code Interpreter": "https://python.langchain.com/docs/integrations/tools/riza/", "ArXiv": "https://python.langchain.com/docs/integrations/tools/arxiv/", "Robocorp Toolkit": "https://python.langchain.com/docs/integrations/tools/robocorp/", "MultiOn Toolkit": "https://python.langchain.com/docs/integrations/tools/multion/", "Exa Search": "https://python.langchain.com/docs/integrations/tools/exa_search/", "Amadeus Toolkit": "https://python.langchain.com/docs/integrations/tools/amadeus/", "Reddit Search ": "https://python.langchain.com/docs/integrations/tools/reddit_search/", "You.com Search": "https://python.langchain.com/docs/integrations/tools/you/", "Bing Search": "https://python.langchain.com/docs/integrations/tools/bing_search/", "Azure AI Services Toolkit": "https://python.langchain.com/docs/integrations/tools/azure_ai_services/", "Polygon IO Toolkit and Tools": "https://python.langchain.com/docs/integrations/tools/polygon/", "Ionic Shopping Tool": "https://python.langchain.com/docs/integrations/tools/ionic_shopping/", "MLX": "https://python.langchain.com/docs/integrations/chat/mlx/", "ZHIPU AI": "https://python.langchain.com/docs/integrations/chat/zhipuai/", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor/", "Streamlit": "https://python.langchain.com/docs/integrations/callbacks/streamlit/", "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere/", "Dataherald": "https://python.langchain.com/docs/integrations/providers/dataherald/", "Log, Trace, and Monitor": "https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey/", "Portkey": "https://python.langchain.com/docs/integrations/providers/portkey/index/", "Bittensor": "https://python.langchain.com/docs/integrations/llms/bittensor/"}, "AIMessage": {"Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add a semantic layer over graph 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"https://python.langchain.com/docs/integrations/llms/mosaicml/", "CerebriumAI": "https://python.langchain.com/docs/integrations/llms/cerebriumai/", "Predibase": "https://python.langchain.com/docs/integrations/llms/predibase/", "GigaChat": "https://python.langchain.com/docs/integrations/llms/gigachat/", "OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/", "Aphrodite Engine": "https://python.langchain.com/docs/integrations/llms/aphrodite/", "Eden AI": "https://python.langchain.com/docs/integrations/llms/edenai/", "Javelin AI Gateway Tutorial": "https://python.langchain.com/docs/integrations/llms/javelin/", "C Transformers": "https://python.langchain.com/docs/integrations/llms/ctransformers/", "vLLM": "https://python.langchain.com/docs/integrations/llms/vllm/", "Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/", "Minimax": "https://python.langchain.com/docs/integrations/llms/minimax/", "Yuan2.0": "https://python.langchain.com/docs/integrations/llms/yuan2/", "Huggingface Endpoints": "https://python.langchain.com/docs/integrations/llms/huggingface_endpoint/", "Runhouse": "https://python.langchain.com/docs/integrations/llms/runhouse/", "Anyscale": "https://python.langchain.com/docs/integrations/llms/anyscale/", "YandexGPT": "https://python.langchain.com/docs/integrations/llms/yandex/", "GooseAI": "https://python.langchain.com/docs/integrations/llms/gooseai/", "OpenLM": "https://python.langchain.com/docs/integrations/llms/openlm/", "Cloudflare Workers AI": "https://python.langchain.com/docs/integrations/llms/cloudflare_workersai/", "CTranslate2": "https://python.langchain.com/docs/integrations/llms/ctranslate2/", "ChatGLM": "https://python.langchain.com/docs/integrations/llms/chatglm/", "Gradient": "https://python.langchain.com/docs/integrations/llms/gradient/", "Petals": "https://python.langchain.com/docs/integrations/llms/petals/", "Replicate": "https://python.langchain.com/docs/integrations/llms/replicate/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/"}, "StuffDocumentsChain": {"# Example": "https://python.langchain.com/docs/versions/migrating_chains/stuff_docs_chain/", "# Basic example (short documents)": "https://python.langchain.com/docs/versions/migrating_chains/map_reduce_chain/"}, "create_stuff_documents_chain": {"# Example": "https://python.langchain.com/docs/versions/migrating_chains/stuff_docs_chain/", "Load docs": "https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/", "How to reorder retrieved results to mitigate the \"lost in the middle\" effect": "https://python.langchain.com/docs/how_to/long_context_reorder/", "How to stream results from your RAG application": "https://python.langchain.com/docs/how_to/qa_streaming/", "How to get your RAG application to return sources": "https://python.langchain.com/docs/how_to/qa_sources/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add retrieval to chatbots": "https://python.langchain.com/docs/how_to/chatbots_retrieval/", "How to summarize text in a single LLM call": "https://python.langchain.com/docs/how_to/summarize_stuff/", "RAGatouille": "https://python.langchain.com/docs/integrations/retrievers/ragatouille/", "ApertureDB": "https://python.langchain.com/docs/integrations/vectorstores/aperturedb/", "Jina Reranker": "https://python.langchain.com/docs/integrations/document_transformers/jina_rerank/", "Image captions": "https://python.langchain.com/docs/integrations/document_loaders/image_captions/", "Build a Retrieval Augmented Generation (RAG) App": "https://python.langchain.com/docs/tutorials/rag/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/", "Build a PDF ingestion and Question/Answering system": "https://python.langchain.com/docs/tutorials/pdf_qa/"}, "LLMMathChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_math_chain/"}, "BaseMessage": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_math_chain/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to use reference examples when doing extraction": "https://python.langchain.com/docs/how_to/extraction_examples/", "How to propagate callbacks  constructor": "https://python.langchain.com/docs/how_to/callbacks_constructor/", "How to attach callbacks to a runnable": "https://python.langchain.com/docs/how_to/callbacks_attach/", "How to create a custom chat model class": "https://python.langchain.com/docs/how_to/custom_chat_model/", "How to pass callbacks in at runtime": "https://python.langchain.com/docs/how_to/callbacks_runtime/", "How to add examples to the prompt for query analysis": "https://python.langchain.com/docs/how_to/query_few_shot/", "WeChat": "https://python.langchain.com/docs/integrations/chat_loaders/wechat/", "Discord": "https://python.langchain.com/docs/integrations/chat_loaders/discord/", "Chat Bot Feedback Template": "https://python.langchain.com/docs/templates/chat-bot-feedback/"}, "RunnableConfig": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/multi_prompt_chain/", "# Example": "https://python.langchain.com/docs/versions/migrating_chains/refine_docs_chain/", "How to access the RunnableConfig from a tool": "https://python.langchain.com/docs/how_to/tool_configure/", "How to summarize text through iterative refinement": "https://python.langchain.com/docs/how_to/summarize_refine/", "How to handle tool errors": "https://python.langchain.com/docs/how_to/tools_error/", "How to stream events from a tool": "https://python.langchain.com/docs/how_to/tool_stream_events/", "How to run custom functions": "https://python.langchain.com/docs/how_to/functions/", "How to add ad-hoc tool calling capability to LLMs and Chat Models": "https://python.langchain.com/docs/how_to/tools_prompting/", "LangChain Expression Language Cheatsheet": "https://python.langchain.com/docs/how_to/lcel_cheatsheet/", "How to dispatch custom callback events": "https://python.langchain.com/docs/how_to/callbacks_custom_events/", "How to pass runtime secrets to runnables": "https://python.langchain.com/docs/how_to/runnable_runtime_secrets/", "Tavily Search": "https://python.langchain.com/docs/integrations/tools/tavily_search/"}, "tool": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_math_chain/", "How to disable parallel tool calling": "https://python.langchain.com/docs/how_to/tool_calling_parallel/", "How to use tools in a chain": "https://python.langchain.com/docs/how_to/tools_chain/", "How to access the RunnableConfig from a tool": "https://python.langchain.com/docs/how_to/tool_configure/", "How to do tool/function calling": "https://python.langchain.com/docs/how_to/function_calling/", "How to pass run time values to tools": "https://python.langchain.com/docs/how_to/tool_runtime/", "How to add a human-in-the-loop for tools": "https://python.langchain.com/docs/how_to/tools_human/", "How to create tools": "https://python.langchain.com/docs/how_to/custom_tools/", "How to pass multimodal data directly to models": "https://python.langchain.com/docs/how_to/multimodal_inputs/", "How to force models to call a tool": "https://python.langchain.com/docs/how_to/tool_choice/", "How to handle tool errors": "https://python.langchain.com/docs/how_to/tools_error/", "How to stream events from a tool": "https://python.langchain.com/docs/how_to/tool_stream_events/", "How to stream runnables": "https://python.langchain.com/docs/how_to/streaming/", "How to pass tool outputs to chat models": "https://python.langchain.com/docs/how_to/tool_results_pass_to_model/", "How to add ad-hoc tool calling capability to LLMs and Chat Models": "https://python.langchain.com/docs/how_to/tools_prompting/", "How to return artifacts from a tool": "https://python.langchain.com/docs/how_to/tool_artifacts/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "How to stream tool calls": "https://python.langchain.com/docs/how_to/tool_streaming/", "How to pass runtime secrets to runnables": "https://python.langchain.com/docs/how_to/runnable_runtime_secrets/", "How to use few-shot prompting with tool calling": "https://python.langchain.com/docs/how_to/tools_few_shot/", "FinancialDatasets Toolkit": "https://python.langchain.com/docs/integrations/tools/financial_datasets/", "Exa Search": "https://python.langchain.com/docs/integrations/tools/exa_search/", "DeepInfra": "https://python.langchain.com/docs/integrations/chat/deepinfra/", "ChatOllama": "https://python.langchain.com/docs/integrations/chat/ollama/", "Llama.cpp": "https://python.langchain.com/docs/integrations/chat/llamacpp/", "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere/", "Eden AI": "https://python.langchain.com/docs/integrations/chat/edenai/", "ChatTongyi": "https://python.langchain.com/docs/integrations/chat/tongyi/", "ChatPremAI": "https://python.langchain.com/docs/integrations/chat/premai/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor/", "PremAI": "https://python.langchain.com/docs/integrations/providers/premai/", "Log, Trace, and Monitor": "https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey/", "Portkey": "https://python.langchain.com/docs/integrations/providers/portkey/index/", "JSONFormer": "https://python.langchain.com/docs/integrations/llms/jsonformer_experimental/"}, "MultiPromptChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/multi_prompt_chain/"}, "ConversationChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/"}, "ConversationBufferMemory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Memorize": "https://python.langchain.com/docs/integrations/tools/memorize/", "Gradio": "https://python.langchain.com/docs/integrations/tools/gradio_tools/", "SceneXplain": "https://python.langchain.com/docs/integrations/tools/sceneXplain/", "Reddit Search ": "https://python.langchain.com/docs/integrations/tools/reddit_search/", "Xata": "https://python.langchain.com/docs/integrations/memory/xata_chat_message_history/", "Llama2Chat": "https://python.langchain.com/docs/integrations/chat/llama2_chat/", "SAP HANA Cloud Vector Engine": "https://python.langchain.com/docs/integrations/vectorstores/sap_hanavector/", "Bittensor": "https://python.langchain.com/docs/integrations/llms/bittensor/"}, "InMemoryChatMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/"}, "RunnableWithMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add message history": "https://python.langchain.com/docs/how_to/message_history/", "How to add memory to chatbots": "https://python.langchain.com/docs/how_to/chatbots_memory/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "Redis": "https://python.langchain.com/docs/integrations/memory/redis_chat_message_history/", "Google SQL for MySQL": "https://python.langchain.com/docs/integrations/memory/google_sql_mysql/", "Google AlloyDB for PostgreSQL": "https://python.langchain.com/docs/integrations/memory/google_alloydb/", "ZepCloudChatMessageHistory": "https://python.langchain.com/docs/integrations/memory/zep_cloud_chat_message_history/", "AWS DynamoDB": "https://python.langchain.com/docs/integrations/memory/aws_dynamodb/", "Couchbase": "https://python.langchain.com/docs/integrations/memory/couchbase_chat_message_history/", "MongoDB": "https://python.langchain.com/docs/integrations/memory/mongodb_chat_message_history/", "SQL (SQLAlchemy)": "https://python.langchain.com/docs/integrations/memory/sql_chat_message_history/", "Streamlit": "https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history/", "Google El Carro Oracle": "https://python.langchain.com/docs/integrations/memory/google_el_carro/", "SQLite": "https://python.langchain.com/docs/integrations/memory/sqlite/", "Google SQL for PostgreSQL": "https://python.langchain.com/docs/integrations/memory/google_sql_pg/", "Google SQL for SQL Server": "https://python.langchain.com/docs/integrations/memory/google_sql_mssql/", "TiDB": "https://python.langchain.com/docs/integrations/memory/tidb_chat_message_history/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/"}, "BaseChatMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/"}, "ConstitutionalChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "ConstitutionalPrinciple": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "OpenAI": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/", "# Example": 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"https://python.langchain.com/docs/integrations/retrievers/self_query/timescalevector_self_query/", "Astra DB (Cassandra)": "https://python.langchain.com/docs/integrations/retrievers/self_query/astradb/", "Pinecone": "https://python.langchain.com/docs/integrations/retrievers/self_query/pinecone/", "Supabase (Postgres)": "https://python.langchain.com/docs/integrations/retrievers/self_query/supabase_self_query/", "Redis": "https://python.langchain.com/docs/integrations/retrievers/self_query/redis_self_query/", "Deep Lake": "https://python.langchain.com/docs/integrations/retrievers/self_query/activeloop_deeplake_self_query/", "MongoDB Atlas": "https://python.langchain.com/docs/integrations/retrievers/self_query/mongodb_atlas/", "Qdrant": "https://python.langchain.com/docs/integrations/retrievers/self_query/qdrant_self_query/", "OpenAI": "https://python.langchain.com/docs/integrations/llms/openai/", "Jira Toolkit": "https://python.langchain.com/docs/integrations/tools/jira/", "Google 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"https://python.langchain.com/docs/integrations/document_loaders/youtube_transcript/"}, "ScrapingAntLoader": {"ScrapingAnt": "https://python.langchain.com/docs/integrations/document_loaders/scrapingant/"}, "AirbyteSalesforceLoader": {"Airbyte Salesforce (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_salesforce/"}, "AirbyteCDKLoader": {"Airbyte CDK (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_cdk/"}, "Docx2txtLoader": {"Microsoft Word": "https://python.langchain.com/docs/integrations/document_loaders/microsoft_word/"}, "RSpaceLoader": {"# replace these ids with some from your own research notes.": "https://python.langchain.com/docs/integrations/document_loaders/rspace/"}, "SeleniumURLLoader": {"URL": "https://python.langchain.com/docs/integrations/document_loaders/url/"}, "PlaywrightURLLoader": {"URL": "https://python.langchain.com/docs/integrations/document_loaders/url/"}, "AirbyteJSONLoader": {"Airbyte JSON (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_json/"}, "AirbyteStripeLoader": {"Airbyte Stripe (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_stripe/"}, "GeoDataFrameLoader": {"Geopandas": "https://python.langchain.com/docs/integrations/document_loaders/geopandas/"}, "VectorstoreIndexCreator": {"HuggingFace dataset": "https://python.langchain.com/docs/integrations/document_loaders/hugging_face_dataset/", "Spreedly": "https://python.langchain.com/docs/integrations/document_loaders/spreedly/", "Figma": "https://python.langchain.com/docs/integrations/document_loaders/figma/", "Apify Dataset": "https://python.langchain.com/docs/integrations/document_loaders/apify_dataset/", "Iugu": "https://python.langchain.com/docs/integrations/document_loaders/iugu/", "Stripe": "https://python.langchain.com/docs/integrations/document_loaders/stripe/", "Modern Treasury": "https://python.langchain.com/docs/integrations/document_loaders/modern_treasury/"}, "AirbyteTypeformLoader": {"Airbyte Typeform (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_typeform/"}, "MHTMLLoader": {"mhtml": "https://python.langchain.com/docs/integrations/document_loaders/mhtml/"}, "SpiderLoader": {"Spider": "https://python.langchain.com/docs/integrations/document_loaders/spider/"}, "NewsURLLoader": {"News URL": "https://python.langchain.com/docs/integrations/document_loaders/news/"}, "ImageCaptionLoader": {"Image captions": "https://python.langchain.com/docs/integrations/document_loaders/image_captions/"}, "LLMSherpaFileLoader": {"LLM Sherpa": "https://python.langchain.com/docs/integrations/document_loaders/llmsherpa/"}, "PyMuPDFLoader": {"PyMuPDF": "https://python.langchain.com/docs/integrations/document_loaders/pymupdf/"}, "ScrapflyLoader": {"# ScrapFly": "https://python.langchain.com/docs/integrations/document_loaders/scrapfly/"}, "TomlLoader": {"TOML": "https://python.langchain.com/docs/integrations/document_loaders/toml/"}, "PsychicLoader": {"Psychic": "https://python.langchain.com/docs/integrations/document_loaders/psychic/"}, "FireCrawlLoader": {"FireCrawl": "https://python.langchain.com/docs/integrations/document_loaders/firecrawl/"}, "LarkSuiteWikiLoader": {"LarkSuite (FeiShu)": "https://python.langchain.com/docs/integrations/document_loaders/larksuite/"}, "FakeListLLM": {"LarkSuite (FeiShu)": "https://python.langchain.com/docs/integrations/document_loaders/larksuite/"}, "MergedDataLoader": {"Merge Documents Loader": "https://python.langchain.com/docs/integrations/document_loaders/merge_doc/"}, "RecursiveUrlLoader": {"Recursive URL": "https://python.langchain.com/docs/integrations/document_loaders/recursive_url/"}, "PDFPlumberLoader": {"PDFPlumber": "https://python.langchain.com/docs/integrations/document_loaders/pdfplumber/"}, "PyPDFium2Loader": {"PyPDFium2Loader": "https://python.langchain.com/docs/integrations/document_loaders/pypdfium2/"}, "AirbyteHubspotLoader": {"Airbyte Hubspot (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_hubspot/"}, "AirbyteGongLoader": {"Airbyte Gong (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_gong/"}, "AstraDBLoader": {"AstraDB": "https://python.langchain.com/docs/integrations/document_loaders/astradb/"}, "ReadTheDocsLoader": {"ReadTheDocs Documentation": "https://python.langchain.com/docs/integrations/document_loaders/readthedocs_documentation/"}, "MathpixPDFLoader": {"MathPixPDFLoader": "https://python.langchain.com/docs/integrations/document_loaders/mathpix/"}, "PolarsDataFrameLoader": {"Polars DataFrame": "https://python.langchain.com/docs/integrations/document_loaders/polars_dataframe/"}, "DataFrameLoader": {"Pandas DataFrame": "https://python.langchain.com/docs/integrations/document_loaders/pandas_dataframe/"}, "SurrealDBLoader": {"SurrealDB": "https://python.langchain.com/docs/integrations/document_loaders/surrealdb/"}, "DedocFileLoader": {"Dedoc": "https://python.langchain.com/docs/integrations/document_loaders/dedoc/"}, "DedocPDFLoader": {"Dedoc": "https://python.langchain.com/docs/integrations/document_loaders/dedoc/"}, "DedocAPIFileLoader": {"Dedoc": "https://python.langchain.com/docs/integrations/document_loaders/dedoc/"}, "GoogleApiClient": {"YouTube transcripts": "https://python.langchain.com/docs/integrations/document_loaders/youtube_transcript/"}, "ConcurrentLoader": {"Concurrent Loader": "https://python.langchain.com/docs/integrations/document_loaders/concurrent/"}, "RSSFeedLoader": {"RSS Feeds": "https://python.langchain.com/docs/integrations/document_loaders/rss/"}, "PebbloSafeLoader": {"Pebblo Safe DocumentLoader": "https://python.langchain.com/docs/integrations/document_loaders/pebblo/"}, "VsdxLoader": {"Vsdx": "https://python.langchain.com/docs/integrations/document_loaders/vsdx/"}, "NotebookLoader": {"Jupyter Notebook": "https://python.langchain.com/docs/integrations/document_loaders/jupyter_notebook/"}, "OracleAutonomousDatabaseLoader": {"Oracle Autonomous Database": "https://python.langchain.com/docs/integrations/document_loaders/oracleadb_loader/"}, "LanguageParser": {"Source Code": "https://python.langchain.com/docs/integrations/document_loaders/source_code/"}, "SRTLoader": {"Subtitle": "https://python.langchain.com/docs/integrations/document_loaders/subtitle/"}, "MastodonTootsLoader": {"Mastodon": "https://python.langchain.com/docs/integrations/document_loaders/mastodon/"}, "AirbyteShopifyLoader": {"Airbyte Shopify (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_shopify/"}, "PyPDFDirectoryLoader": {"PyPDFDirectoryLoader": "https://python.langchain.com/docs/integrations/document_loaders/pypdfdirectory/"}, "PySparkDataFrameLoader": {"PySpark": "https://python.langchain.com/docs/integrations/document_loaders/pyspark_dataframe/"}, "AirbyteZendeskSupportLoader": {"Airbyte Zendesk Support (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_zendesk_support/"}, "CoNLLULoader": {"CoNLL-U": "https://python.langchain.com/docs/integrations/document_loaders/conll-u/"}, "MongodbLoader": {"MongoDB": "https://python.langchain.com/docs/integrations/document_loaders/mongodb/"}, "SitemapLoader": {"Sitemap": "https://python.langchain.com/docs/integrations/document_loaders/sitemap/"}, "YuqueLoader": {"Yuque": "https://python.langchain.com/docs/integrations/document_loaders/yuque/"}, "PDFMinerLoader": {"PDFMiner": "https://python.langchain.com/docs/integrations/document_loaders/pdfminer/"}, "PDFMinerPDFasHTMLLoader": {"PDFMiner": "https://python.langchain.com/docs/integrations/document_loaders/pdfminer/"}, "QuipLoader": {"Quip": "https://python.langchain.com/docs/integrations/document_loaders/quip/"}, "LangSmithLoader": {"LangSmithLoader": "https://python.langchain.com/docs/integrations/document_loaders/langsmith/"}, "MemgraphGraph": {"Memgraph": "https://python.langchain.com/docs/integrations/graphs/memgraph/"}, "GraphSparqlQAChain": {"RDFLib": "https://python.langchain.com/docs/integrations/graphs/rdflib_sparql/"}, "RdfGraph": {"RDFLib": "https://python.langchain.com/docs/integrations/graphs/rdflib_sparql/"}, "NebulaGraphQAChain": {"NebulaGraph": "https://python.langchain.com/docs/integrations/graphs/nebula_graph/"}, "NebulaGraph": {"NebulaGraph": "https://python.langchain.com/docs/integrations/graphs/nebula_graph/"}, "GremlinQAChain": {"Azure Cosmos DB for Apache Gremlin": "https://python.langchain.com/docs/integrations/graphs/azure_cosmosdb_gremlin/"}, "GraphIndexCreator": {"NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/"}, "GraphQAChain": {"NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/"}, "NetworkxEntityGraph": {"NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/"}, "HugeGraphQAChain": {"HugeGraph": "https://python.langchain.com/docs/integrations/graphs/hugegraph/"}, "HugeGraph": {"HugeGraph": "https://python.langchain.com/docs/integrations/graphs/hugegraph/"}, "AGEGraph": {"Apache AGE": "https://python.langchain.com/docs/integrations/graphs/apache_age/"}, "KuzuQAChain": {"Kuzu": "https://python.langchain.com/docs/integrations/graphs/kuzu_db/"}, "KuzuGraph": {"Kuzu": "https://python.langchain.com/docs/integrations/graphs/kuzu_db/"}, "FalkorDBQAChain": {"FalkorDB": "https://python.langchain.com/docs/integrations/graphs/falkordb/"}, "FalkorDBGraph": {"FalkorDB": "https://python.langchain.com/docs/integrations/graphs/falkordb/"}, "ConversationBufferWindowMemory": {"Baseten": "https://python.langchain.com/docs/integrations/llms/baseten/", "OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/"}, "Solar": {"Solar": "https://python.langchain.com/docs/integrations/llms/solar/"}, "GoogleSearchAPIWrapper": {"Bittensor": "https://python.langchain.com/docs/integrations/llms/bittensor/"}, "IpexLLM": {"IPEX-LLM": "https://python.langchain.com/docs/integrations/llms/ipex_llm/"}, "LLMContentHandler": {"SageMakerEndpoint": "https://python.langchain.com/docs/integrations/llms/sagemaker/"}, "TextGen": {"TextGen": "https://python.langchain.com/docs/integrations/llms/textgen/"}, "MosaicML": {"MosaicML": "https://python.langchain.com/docs/integrations/llms/mosaicml/"}, "VolcEngineMaasLLM": {"Volc Engine Maas": "https://python.langchain.com/docs/integrations/llms/volcengine_maas/"}, "KoboldApiLLM": {"KoboldAI API": "https://python.langchain.com/docs/integrations/llms/koboldai/"}, "Konko": {"Konko": "https://python.langchain.com/docs/integrations/llms/konko/"}, "OpaquePrompts": {"OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/"}, "TitanTakeoff": {"Titan Takeoff": "https://python.langchain.com/docs/integrations/llms/titan_takeoff/"}, "Friendli": {"Friendli": "https://python.langchain.com/docs/integrations/llms/friendli/"}, "Databricks": {"Databricks": "https://python.langchain.com/docs/integrations/llms/databricks/"}, "LMFormatEnforcer": {"LM Format Enforcer": "https://python.langchain.com/docs/integrations/llms/lmformatenforcer_experimental/"}, "VLLM": {"vLLM": "https://python.langchain.com/docs/integrations/llms/vllm/"}, "VLLMOpenAI": {"vLLM": "https://python.langchain.com/docs/integrations/llms/vllm/"}, "CustomOpenAIContentFormatter": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "ContentFormatterBase": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "DollyContentFormatter": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "load_llm": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "MapReduceChain": {"Manifest": "https://python.langchain.com/docs/integrations/llms/manifest/"}, "ModelLaboratory": {"Manifest": "https://python.langchain.com/docs/integrations/llms/manifest/"}, "ExLlamaV2": {"ExLlamaV2": "https://python.langchain.com/docs/integrations/llms/exllamav2/"}, "RELLM": {"RELLM": "https://python.langchain.com/docs/integrations/llms/rellm_experimental/"}, "Moonshot": {"MoonshotChat": "https://python.langchain.com/docs/integrations/llms/moonshot/"}, "OpenLM": {"OpenLM": "https://python.langchain.com/docs/integrations/llms/openlm/"}, "CloudflareWorkersAI": {"Cloudflare Workers AI": "https://python.langchain.com/docs/integrations/llms/cloudflare_workersai/"}, "ChatGLM3": {"ChatGLM": "https://python.langchain.com/docs/integrations/llms/chatglm/"}, "ChatGLM": {"ChatGLM": "https://python.langchain.com/docs/integrations/llms/chatglm/"}, "Sambaverse": {"SambaNova": "https://python.langchain.com/docs/integrations/llms/sambanova/"}, "SambaStudio": {"SambaNova": "https://python.langchain.com/docs/integrations/llms/sambanova/"}, "LayerupSecurity": {"Layerup Security": "https://python.langchain.com/docs/integrations/llms/layerup_security/"}, "JsonFormer": {"JSONFormer": "https://python.langchain.com/docs/integrations/llms/jsonformer_experimental/"}, "WeightOnlyQuantPipeline": {"Intel Weight-Only Quantization": "https://python.langchain.com/docs/integrations/llms/weight_only_quantization/"}, "Replicate": {"Replicate": "https://python.langchain.com/docs/integrations/llms/replicate/"}, "tracing_v2_enabled": {"Chat Bot Feedback Template": "https://python.langchain.com/docs/templates/chat-bot-feedback/"}, "QuerySQLDataBaseTool": {"Build a Question/Answering system over SQL data": "https://python.langchain.com/docs/tutorials/sql_qa/"}, "OPENAI_TEMPLATE": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "create_openai_data_generator": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "DatasetGenerator": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "create_data_generation_chain": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "create_extraction_chain_pydantic": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}}
\ No newline at end of file
diff --git a/docs/docs/how_to/convert_runnable_to_tool.ipynb b/docs/docs/how_to/convert_runnable_to_tool.ipynb
index 0497582920f43..bee037d6acd6f 100644
--- a/docs/docs/how_to/convert_runnable_to_tool.ipynb
+++ b/docs/docs/how_to/convert_runnable_to_tool.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "## Dependencies\n",
     "\n",
-    "**Note**: this guide requires `langchain-core` >= 0.2.13. We will also use [OpenAI](/docs/integrations/platforms/openai/) for embeddings, but any LangChain embeddings should suffice. We will use a simple [LangGraph](https://langchain-ai.github.io/langgraph/) agent for demonstration purposes."
+    "**Note**: this guide requires `langchain-core` >= 0.2.13. We will also use [OpenAI](/docs/integrations/providers/openai/) for embeddings, but any LangChain embeddings should suffice. We will use a simple [LangGraph](https://langchain-ai.github.io/langgraph/) agent for demonstration purposes."
    ]
   },
   {
diff --git a/docs/docs/how_to/installation.mdx b/docs/docs/how_to/installation.mdx
index ea3de2d26aa7a..f56946f921af2 100644
--- a/docs/docs/how_to/installation.mdx
+++ b/docs/docs/how_to/installation.mdx
@@ -50,7 +50,7 @@ pip install langchain-core
 ### Integration packages
 
 Certain integrations like OpenAI and Anthropic have their own packages.
-Any integrations that require their own package will be documented as such in the [Integration docs](/docs/integrations/platforms/).
+Any integrations that require their own package will be documented as such in the [Integration docs](/docs/integrations/providers/).
 You can see a list of all integration packages in the [API reference](https://api.python.langchain.com) under the "Partner libs" dropdown.
 To install one of these run:
 
diff --git a/docs/docs/integrations/platforms/anthropic.mdx b/docs/docs/integrations/providers/anthropic.mdx
similarity index 100%
rename from docs/docs/integrations/platforms/anthropic.mdx
rename to docs/docs/integrations/providers/anthropic.mdx
diff --git a/docs/docs/integrations/platforms/aws.mdx b/docs/docs/integrations/providers/aws.mdx
similarity index 100%
rename from docs/docs/integrations/platforms/aws.mdx
rename to docs/docs/integrations/providers/aws.mdx
diff --git a/docs/docs/integrations/providers/databricks.md b/docs/docs/integrations/providers/databricks.md
index 899c9bc1e2383..7cd8273874f9f 100644
--- a/docs/docs/integrations/providers/databricks.md
+++ b/docs/docs/integrations/providers/databricks.md
@@ -113,7 +113,7 @@ See [Databricks SQL Agent](https://docs.databricks.com/en/large-language-models/
 Open Models
 -----------
 
-To directly integrate Databricks's open models hosted on HuggingFace, you can use the [HuggingFace Integration](/docs/integrations/platforms/huggingface) of LangChain.
+To directly integrate Databricks's open models hosted on HuggingFace, you can use the [HuggingFace Integration](/docs/integrations/providers/huggingface) of LangChain.
 
 ```
 from langchain_huggingface import HuggingFaceEndpoint
diff --git a/docs/docs/integrations/platforms/google.mdx b/docs/docs/integrations/providers/google.mdx
similarity index 100%
rename from docs/docs/integrations/platforms/google.mdx
rename to docs/docs/integrations/providers/google.mdx
diff --git a/docs/docs/integrations/platforms/huggingface.mdx b/docs/docs/integrations/providers/huggingface.mdx
similarity index 100%
rename from docs/docs/integrations/platforms/huggingface.mdx
rename to docs/docs/integrations/providers/huggingface.mdx
diff --git a/docs/docs/integrations/platforms/microsoft.mdx b/docs/docs/integrations/providers/microsoft.mdx
similarity index 100%
rename from docs/docs/integrations/platforms/microsoft.mdx
rename to docs/docs/integrations/providers/microsoft.mdx
diff --git a/docs/docs/integrations/platforms/openai.mdx b/docs/docs/integrations/providers/openai.mdx
similarity index 100%
rename from docs/docs/integrations/platforms/openai.mdx
rename to docs/docs/integrations/providers/openai.mdx
diff --git a/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb b/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
index 4e08f468ecbfc..384fcaa894a20 100644
--- a/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
+++ b/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
@@ -25,7 +25,7 @@
     "\n",
     "| Provider | Package |\n",
     "|:--------:|:-------:|\n",
-    "| [Google](https://python.langchain.com/docs/integrations/platforms/google/) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html) |\n",
+    "| [Google](https://python.langchain.com/docs/integrations/providers/google/) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html) |\n",
     "\n",
     "## Setup\n",
     "\n",
diff --git a/docs/docs/introduction.mdx b/docs/docs/introduction.mdx
index 9853579323ee6..039cbca49db82 100644
--- a/docs/docs/introduction.mdx
+++ b/docs/docs/introduction.mdx
@@ -8,7 +8,7 @@ sidebar_class_name: hidden
 **LangChain** is a framework for developing applications powered by large language models (LLMs).
 
 LangChain simplifies every stage of the LLM application lifecycle:
-- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language-lcel), [components](/docs/concepts), and [third-party integrations](/docs/integrations/platforms/).
+- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language-lcel), [components](/docs/concepts), and [third-party integrations](/docs/integrations/providers/).
 Use [LangGraph](/docs/concepts/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
 - **Productionization**: Use [LangSmith](https://docs.smith.langchain.com/) to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
 - **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
diff --git a/docs/docs/versions/v0_2/overview.mdx b/docs/docs/versions/v0_2/overview.mdx
index 147c03886a1f7..7fdb3fa78a740 100644
--- a/docs/docs/versions/v0_2/overview.mdx
+++ b/docs/docs/versions/v0_2/overview.mdx
@@ -17,7 +17,7 @@ The following features have been added during the development of 0.1.x:
 - Include response metadata in `AIMessage` to make it easy to access raw output from the underlying models
 - Tooling to visualize [your runnables](https://python.langchain.com/docs/expression_language/how_to/inspect/) or [your langgraph app](https://github.com/langchain-ai/langgraph/blob/main/examples/visualization.ipynb)
 - Interoperability of chat message histories across most providers
-- [Over 20+ partner packages in python](https://python.langchain.com/docs/integrations/platforms/) for popular integrations
+- [Over 20+ partner packages in python](https://python.langchain.com/docs/integrations/providers/) for popular integrations
 
 
 ## What’s coming to LangChain?
diff --git a/docs/scripts/partner_pkg_table.py b/docs/scripts/partner_pkg_table.py
index 4b733e59fcf7d..00219fe23f9a2 100644
--- a/docs/scripts/partner_pkg_table.py
+++ b/docs/scripts/partner_pkg_table.py
@@ -8,7 +8,7 @@
 PLATFORMS = {
     path.split("/")[-1][:-4]
     for path in glob.glob(
-        str(DOCS_DIR) + "/docs/integrations/platforms/*.mdx", recursive=True
+        str(DOCS_DIR) + "/docs/integrations/providers/*.mdx", recursive=True
     )
 }
 EXTERNAL_PACKAGES = {
@@ -71,15 +71,15 @@
     "airbyte": "Airbyte",
 }
 CUSTOM_PROVIDER_PAGES = {
-    "azure-dynamic-sessions": "/docs/integrations/platforms/microsoft/",
-    "google-community": "/docs/integrations/platforms/google/",
-    "google-genai": "/docs/integrations/platforms/google/",
-    "google-vertexai": "/docs/integrations/platforms/google/",
+    "azure-dynamic-sessions": "/docs/integrations/providers/microsoft/",
+    "google-community": "/docs/integrations/providers/google/",
+    "google-genai": "/docs/integrations/providers/google/",
+    "google-vertexai": "/docs/integrations/providers/google/",
     "nvidia-ai-endpoints": "/docs/integrations/providers/nvidia/",
     "exa": "/docs/integrations/providers/exa_search/",
     "mongodb": "/docs/integrations/providers/mongodb_atlas/",
 }
-PLATFORM_PAGES = {name: f"/docs/integrations/platforms/{name}/" for name in PLATFORMS}
+PLATFORM_PAGES = {name: f"/docs/integrations/providers/{name}/" for name in PLATFORMS}
 PROVIDER_PAGES = {
     name: f"/docs/integrations/providers/{name}/"
     for name in ALL_PACKAGES
@@ -104,7 +104,7 @@ def package_row(name: str) -> str:
 
 
 def table() -> str:
-    header = """| Provider | Package | Downloads | Latest | [JS](https://js.langchain.com/docs/integrations/platforms/) |
+    header = """| Provider | Package | Downloads | Latest | [JS](https://js.langchain.com/docs/integrations/providers/) |
 | :--- | :---: | :---: | :---: | :---: |
 """
     return header + "\n".join(package_row(name) for name in sorted(ALL_PACKAGES))
@@ -136,12 +136,12 @@ def doc() -> str:
 
 ## All Providers
 
-Click [here](/docs/integrations/providers/) to see all providers.
+Click [here](/docs/integrations/providers/all) to see all providers.
 
 """
 
 
 if __name__ == "__main__":
-    output_dir = Path(sys.argv[1]) / "integrations" / "platforms"
+    output_dir = Path(sys.argv[1]) / "integrations" / "providers"
     with open(output_dir / "index.mdx", "w") as f:
         f.write(doc())
diff --git a/docs/sidebars.js b/docs/sidebars.js
index af0dae43d9e18..d3539a2d8e90f 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -126,28 +126,49 @@ module.exports = {
       collapsible: false,
       items: [
         {
-          type: "autogenerated",
-          dirName: "integrations/platforms",
+          type: "doc",
+          id: "integrations/providers/anthropic",
+        },
+        {
+          type: "doc",
+          id: "integrations/providers/aws",
+        },
+        {
+          type: "doc",
+          id: "integrations/providers/google",
+        },
+        {
+          type: "doc",
+          id: "integrations/providers/huggingface",
+        },
+        {
+          type: "doc",
+          id: "integrations/providers/microsoft",
+        },
+        {
+          type: "doc",
+          id: "integrations/providers/openai",
         },
         {
           type: "category",
           label: "More",
-          collapsed: true,
+          collapsible: false,
           items: [
             {
               type: "autogenerated",
               dirName: "integrations/providers",
+              className: "hidden",
             },
           ],
           link: {
             type: "generated-index",
-            slug: "integrations/providers",
+            slug: "integrations/providers/all",
           },
         },
       ],
       link: {
         type: "doc",
-        id: "integrations/platforms/index",
+        id: "integrations/providers/index",
       },
     },
     {
diff --git a/docs/src/theme/NotFound/Content/index.js b/docs/src/theme/NotFound/Content/index.js
index dc56f0702753a..aad591a8d477c 100644
--- a/docs/src/theme/NotFound/Content/index.js
+++ b/docs/src/theme/NotFound/Content/index.js
@@ -2769,8 +2769,8 @@ const suggestedLinks = {
   "/docs/integrations/chat/hunyuan/": {"canonical": "/docs/integrations/chat/tencent_hunyuan/"},
   "/docs/integrations/document_loaders/excel/": {"canonical": "/docs/integrations/document_loaders/microsoft_excel/"},
   "/docs/integrations/document_loaders/onenote/": {"canonical": "/docs/integrations/document_loaders/microsoft_onenote/"},
-  "/docs/integrations/providers/aws_dynamodb/": {"canonical": "/docs/integrations/platforms/aws/"},
-  "/docs/integrations/providers/scann/": {"canonical": "/docs/integrations/platforms/google/"},
+  "/docs/integrations/providers/aws_dynamodb/": {"canonical": "/docs/integrations/providers/aws/"},
+  "/docs/integrations/providers/scann/": {"canonical": "/docs/integrations/providers/google/"},
   "/docs/integrations/toolkits/google_drive/": {"canonical": "/docs/integrations/tools/google_drive/"},
   "/docs/use_cases/question_answering/chat_vector_db/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]},
   "/docs/use_cases/question_answering/in_memory_question_answering/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]},
@@ -2791,24 +2791,24 @@ const suggestedLinks = {
   "/docs/integrations/": {"canonical": "/docs/integrations/providers/"},
   "/docs/expression_language/cookbook/routing/": {"canonical": "/docs/how_to/routing/", "alternative": ["/v0.1/docs/expression_language/how_to/routing/"]},
   "/docs/guides/expression_language/": {"canonical": "/docs/how_to/#langchain-expression-language-lcel", "alternative": ["/v0.1/docs/expression_language/"]},
-  "/docs/integrations/providers/amazon_api_gateway/": {"canonical": "/docs/integrations/platforms/aws/"},
-  "/docs/integrations/providers/huggingface/": {"canonical": "/docs/integrations/platforms/huggingface/"},
-  "/docs/integrations/providers/azure_blob_storage/": {"canonical": "/docs/integrations/platforms/microsoft/"},
-  "/docs/integrations/providers/google_vertexai_matchingengine/": {"canonical": "/docs/integrations/platforms/google/"},
-  "/docs/integrations/providers/aws_s3/": {"canonical": "/docs/integrations/platforms/aws/"},
-  "/docs/integrations/providers/azure_openai/": {"canonical": "/docs/integrations/platforms/microsoft/"},
-  "/docs/integrations/providers/azure_cognitive_search_/": {"canonical": "/docs/integrations/platforms/microsoft/"},
-  "/docs/integrations/providers/bedrock/": {"canonical": "/docs/integrations/platforms/aws/"},
-  "/docs/integrations/providers/google_bigquery/": {"canonical": "/docs/integrations/platforms/google/"},
-  "/docs/integrations/providers/google_cloud_storage/": {"canonical": "/docs/integrations/platforms/google/"},
-  "/docs/integrations/providers/google_drive/": {"canonical": "/docs/integrations/platforms/google/"},
-  "/docs/integrations/providers/google_search/": {"canonical": "/docs/integrations/platforms/google/"},
-  "/docs/integrations/providers/microsoft_onedrive/": {"canonical": "/docs/integrations/platforms/microsoft/"},
-  "/docs/integrations/providers/microsoft_powerpoint/": {"canonical": "/docs/integrations/platforms/microsoft/"},
-  "/docs/integrations/providers/microsoft_word/": {"canonical": "/docs/integrations/platforms/microsoft/"},
-  "/docs/integrations/providers/sagemaker_endpoint/": {"canonical": "/docs/integrations/platforms/aws/"},
+  "/docs/integrations/providers/amazon_api_gateway/": {"canonical": "/docs/integrations/providers/aws/"},
+  "/docs/integrations/providers/huggingface/": {"canonical": "/docs/integrations/providers/huggingface/"},
+  "/docs/integrations/providers/azure_blob_storage/": {"canonical": "/docs/integrations/providers/microsoft/"},
+  "/docs/integrations/providers/google_vertexai_matchingengine/": {"canonical": "/docs/integrations/providers/google/"},
+  "/docs/integrations/providers/aws_s3/": {"canonical": "/docs/integrations/providers/aws/"},
+  "/docs/integrations/providers/azure_openai/": {"canonical": "/docs/integrations/providers/microsoft/"},
+  "/docs/integrations/providers/azure_cognitive_search_/": {"canonical": "/docs/integrations/providers/microsoft/"},
+  "/docs/integrations/providers/bedrock/": {"canonical": "/docs/integrations/providers/aws/"},
+  "/docs/integrations/providers/google_bigquery/": {"canonical": "/docs/integrations/providers/google/"},
+  "/docs/integrations/providers/google_cloud_storage/": {"canonical": "/docs/integrations/providers/google/"},
+  "/docs/integrations/providers/google_drive/": {"canonical": "/docs/integrations/providers/google/"},
+  "/docs/integrations/providers/google_search/": {"canonical": "/docs/integrations/providers/google/"},
+  "/docs/integrations/providers/microsoft_onedrive/": {"canonical": "/docs/integrations/providers/microsoft/"},
+  "/docs/integrations/providers/microsoft_powerpoint/": {"canonical": "/docs/integrations/providers/microsoft/"},
+  "/docs/integrations/providers/microsoft_word/": {"canonical": "/docs/integrations/providers/microsoft/"},
+  "/docs/integrations/providers/sagemaker_endpoint/": {"canonical": "/docs/integrations/providers/aws/"},
   "/docs/integrations/providers/sagemaker_tracking/": {"canonical": "/docs/integrations/callbacks/sagemaker_tracking/"},
-  "/docs/integrations/providers/openai/": {"canonical": "/docs/integrations/platforms/openai/"},
+  "/docs/integrations/providers/openai/": {"canonical": "/docs/integrations/providers/openai/"},
   "/docs/integrations/cassandra/": {"canonical": "/docs/integrations/providers/cassandra/"},
   "/docs/integrations/providers/providers/semadb/": {"canonical": "/docs/integrations/providers/semadb/"},
   "/docs/integrations/vectorstores/vectorstores/semadb/": {"canonical": "/docs/integrations/vectorstores/semadb/"},
@@ -2819,7 +2819,7 @@ const suggestedLinks = {
   "/docs/integrations/document_loaders/Etherscan/": {"canonical": "/docs/integrations/document_loaders/etherscan/"},
   "/docs/integrations/document_loaders/merge_doc_loader/": {"canonical": "/docs/integrations/document_loaders/merge_doc/"},
   "/docs/integrations/document_loaders/recursive_url_loader/": {"canonical": "/docs/integrations/document_loaders/recursive_url/"},
-  "/docs/integrations/providers/google_document_ai/": {"canonical": "/docs/integrations/platforms/google/"},
+  "/docs/integrations/providers/google_document_ai/": {"canonical": "/docs/integrations/providers/google/"},
   "/docs/integrations/memory/motorhead_memory_managed/": {"canonical": "/docs/integrations/memory/motorhead_memory/"},
   "/docs/integrations/memory/dynamodb_chat_message_history/": {"canonical": "/docs/integrations/memory/aws_dynamodb/"},
   "/docs/integrations/memory/entity_memory_with_sqlite/": {"canonical": "/docs/integrations/memory/sqlite/"},
diff --git a/docs/vercel.json b/docs/vercel.json
index 04be0e01562c0..09ede0a6583ce 100644
--- a/docs/vercel.json
+++ b/docs/vercel.json
@@ -81,6 +81,10 @@
     {
       "source": "/docs/integrations/providers/mlflow_ai_gateway(/?)",
       "destination": "/docs/integrations/providers/mlflow/"
+    },
+    {
+      "source": "/docs/integrations/platforms/:path((?:anthropic|aws|google|huggingface|microsoft|openai)?/?)*",
+      "destination": "/docs/integrations/providers/:path*"
     }
   ]
 }

From 202d7f6c4a2ca8c7e5949d935bcf0ba9b0c23fb0 Mon Sep 17 00:00:00 2001
From: Eugene Yurtsev <eyurtsev@gmail.com>
Date: Wed, 16 Oct 2024 15:39:37 -0400
Subject: [PATCH 14/84] core[patch]: 0.3.11 release (#27403)

Core bump to 0.3.11
---
 libs/core/poetry.lock    | 515 ++++++++++++++++++++-------------------
 libs/core/pyproject.toml |   2 +-
 2 files changed, 268 insertions(+), 249 deletions(-)

diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock
index 6946b0b969ffd..15fb395e20cb6 100644
--- a/libs/core/poetry.lock
+++ b/libs/core/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -13,13 +13,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.6.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
-    {file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
+    {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
+    {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
 ]
 
 [package.dependencies]
@@ -30,7 +30,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -316,101 +316,116 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.2"
+version = "3.4.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
-    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
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 [[package]]
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 [[package]]
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 [[package]]
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+    {file = "simsimd-5.7.1.tar.gz", hash = "sha256:1b54e9808dbfa2afbc9c5f0f5548dd572eb542ca260a57c32034acaf170919da"},
 ]
 
 [[package]]
@@ -2897,13 +2916,13 @@ files = [
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240914"
+version = "2.32.0.20241016"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.32.0.20240914.tar.gz", hash = "sha256:2850e178db3919d9bf809e434eef65ba49d0e7e33ac92d588f4a5e295fffd405"},
-    {file = "types_requests-2.32.0.20240914-py3-none-any.whl", hash = "sha256:59c2f673eb55f32a99b2894faf6020e1a9f4a402ad0f192bfee0b64469054310"},
+    {file = "types-requests-2.32.0.20241016.tar.gz", hash = "sha256:0d9cad2f27515d0e3e3da7134a1b6f28fb97129d86b867f24d9c726452634d95"},
+    {file = "types_requests-2.32.0.20241016-py3-none-any.whl", hash = "sha256:4195d62d6d3e043a4eaaf08ff8a62184584d2e8684e9d2aa178c7915a7da3747"},
 ]
 
 [package.dependencies]
diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml
index 55682bff998a2..0f48099afb131 100644
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-core"
-version = "0.3.10"
+version = "0.3.11"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"

From 0ebddabf7ddafab30fefcbfae2148bf8e0fca42f Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Wed, 16 Oct 2024 20:39:36 -0700
Subject: [PATCH 15/84] docs, core: error messaging [wip] (#27397)

---
 .../errors/INVALID_PROMPT_INPUT.mdx           |  14 +
 .../errors/INVALID_TOOL_RESULTS.ipynb         | 272 ++++++++++++++++++
 .../errors/MESSAGE_COERCION_FAILURE.ipynb     | 122 ++++++++
 .../errors/MODEL_AUTHENTICATION.mdx           |  18 ++
 .../errors/MODEL_NOT_FOUND.mdx                |  10 +
 .../errors/MODEL_RATE_LIMIT.mdx               |  13 +
 .../errors/OUTPUT_PARSING_FAILURE.ipynb       | 126 ++++++++
 docs/docs/troubleshooting/errors/index.mdx    |  12 +
 docs/docusaurus.config.js                     |   5 +
 libs/core/langchain_core/exceptions.py        |  23 ++
 libs/core/langchain_core/messages/utils.py    |   8 +
 libs/core/langchain_core/prompts/base.py      |  27 +-
 12 files changed, 644 insertions(+), 6 deletions(-)
 create mode 100644 docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx
 create mode 100644 docs/docs/troubleshooting/errors/INVALID_TOOL_RESULTS.ipynb
 create mode 100644 docs/docs/troubleshooting/errors/MESSAGE_COERCION_FAILURE.ipynb
 create mode 100644 docs/docs/troubleshooting/errors/MODEL_AUTHENTICATION.mdx
 create mode 100644 docs/docs/troubleshooting/errors/MODEL_NOT_FOUND.mdx
 create mode 100644 docs/docs/troubleshooting/errors/MODEL_RATE_LIMIT.mdx
 create mode 100644 docs/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE.ipynb
 create mode 100644 docs/docs/troubleshooting/errors/index.mdx

diff --git a/docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx b/docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx
new file mode 100644
index 0000000000000..ee2662bc66cd8
--- /dev/null
+++ b/docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx
@@ -0,0 +1,14 @@
+# INVALID_PROMPT_INPUT
+
+A [prompt template](/docs/concepts#prompt-templates) received missing or invalid input variables.
+
+## Troubleshooting
+
+The following may help resolve this error:
+
+- Double-check your prompt template to ensure that it is correct.
+  - If you are using the default f-string format and you are using curly braces `{` anywhere in your template, they should be double escaped like this: `{{` (and if you want to render a double curly brace, you should use four curly braces: `{{{{`).
+- If you are using a [`MessagesPlaceholder`](/docs/concepts/#messagesplaceholder), make sure that you are passing in an array of messages or message-like objects.
+  - If you are using shorthand tuples to declare your prompt template, make sure that the variable name is wrapped in curly braces (`["placeholder", "{messages}"]`).
+- Try viewing the inputs into your prompt template using [LangSmith](https://docs.smith.langchain.com/) or log statements to confirm they appear as expected.
+- If you are pulling a prompt from the [LangChain Prompt Hub](https://smith.langchain.com/prompts), try pulling and logging it or running it in isolation with a sample input to confirm that it is what you expect.
diff --git a/docs/docs/troubleshooting/errors/INVALID_TOOL_RESULTS.ipynb b/docs/docs/troubleshooting/errors/INVALID_TOOL_RESULTS.ipynb
new file mode 100644
index 0000000000000..e2e036521b058
--- /dev/null
+++ b/docs/docs/troubleshooting/errors/INVALID_TOOL_RESULTS.ipynb
@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# INVALID_TOOL_RESULTS\n",
+    "\n",
+    "You are passing too many, too few, or mismatched [`ToolMessages`](https://api.js.langchain.com/classes/_langchain_core.messages_tool.ToolMessage.html) to a model.\n",
+    "\n",
+    "When [using a model to call tools](/docs/concepts#functiontool-calling), the [`AIMessage`](https://api.js.langchain.com/classes/_langchain_core.messages.AIMessage.html)\n",
+    "the model responds with will contain a `tool_calls` array. To continue the flow, the next messages you pass back to the model must\n",
+    "be exactly one `ToolMessage` for each item in that array containing the result of that tool call. Each `ToolMessage` must have a `tool_call_id` field\n",
+    "that matches one of the `tool_calls` on the `AIMessage`.\n",
+    "\n",
+    "For example, given the following response from a model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'name': 'foo_tool', 'args': {}, 'id': 'call_dq9O0eGHrryBwDRCnk0deHK4', 'type': 'tool_call'}, {'name': 'foo_tool', 'args': {}, 'id': 'call_mjLuNyXNHoUIXHiBtXhaWdxN', 'type': 'tool_call'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from langchain_core.messages import BaseMessage, HumanMessage\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def foo_tool() -> str:\n",
+    "    \"\"\"\n",
+    "    A dummy tool that returns 'action complete!'\n",
+    "    \"\"\"\n",
+    "    return \"action complete!\"\n",
+    "\n",
+    "\n",
+    "model_with_tools = model.bind_tools([foo_tool])\n",
+    "\n",
+    "chat_history: List[BaseMessage] = [\n",
+    "    HumanMessage(content='Call tool \"foo\" twice with no arguments')\n",
+    "]\n",
+    "\n",
+    "response_message = model_with_tools.invoke(chat_history)\n",
+    "\n",
+    "print(response_message.tool_calls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calling the model with only one tool response would result in an error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "BadRequestError",
+     "evalue": "Error code: 400 - {'error': {'message': \"An assistant message with 'tool_calls' must be followed by tool messages responding to each 'tool_call_id'. The following tool_call_ids did not have response messages: call_mjLuNyXNHoUIXHiBtXhaWdxN\", 'type': 'invalid_request_error', 'param': 'messages', 'code': None}}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m chat_history\u001b[38;5;241m.\u001b[39mappend(AIMessage(content\u001b[38;5;241m=\u001b[39mresponse_message\u001b[38;5;241m.\u001b[39mcontent, additional_kwargs\u001b[38;5;241m=\u001b[39mresponse_message\u001b[38;5;241m.\u001b[39madditional_kwargs))\n\u001b[1;32m      7\u001b[0m chat_history\u001b[38;5;241m.\u001b[39mappend(ToolMessage(content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(tool_response), tool_call_id\u001b[38;5;241m=\u001b[39mtool_call\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m)))\n\u001b[0;32m----> 9\u001b[0m final_response \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_with_tools\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchat_history\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(final_response)\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:5354\u001b[0m, in \u001b[0;36mRunnableBindingBase.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   5348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m   5349\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   5350\u001b[0m     \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m   5351\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   5352\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m   5353\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Output:\n\u001b[0;32m-> 5354\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbound\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5355\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5356\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5357\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5358\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:286\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    277\u001b[0m     \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    282\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[1;32m    283\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[1;32m    284\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[1;32m    285\u001b[0m         ChatGeneration,\n\u001b[0;32m--> 286\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    287\u001b[0m \u001b[43m            \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    289\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    290\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    291\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    292\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    293\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    296\u001b[0m     )\u001b[38;5;241m.\u001b[39mmessage\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:786\u001b[0m, in \u001b[0;36mBaseChatModel.generate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    778\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[1;32m    779\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    780\u001b[0m     prompts: \u001b[38;5;28mlist\u001b[39m[PromptValue],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    783\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    784\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[1;32m    785\u001b[0m     prompt_messages \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[0;32m--> 786\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:643\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n\u001b[1;32m    642\u001b[0m             run_managers[i]\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[0;32m--> 643\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    644\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    645\u001b[0m     LLMResult(generations\u001b[38;5;241m=\u001b[39m[res\u001b[38;5;241m.\u001b[39mgenerations], llm_output\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39mllm_output)  \u001b[38;5;66;03m# type: ignore[list-item]\u001b[39;00m\n\u001b[1;32m    646\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results\n\u001b[1;32m    647\u001b[0m ]\n\u001b[1;32m    648\u001b[0m llm_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_combine_llm_outputs([res\u001b[38;5;241m.\u001b[39mllm_output \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results])\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:633\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    630\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(messages):\n\u001b[1;32m    631\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    632\u001b[0m         results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m--> 633\u001b[0m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[43m                \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    635\u001b[0m \u001b[43m                \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    636\u001b[0m \u001b[43m                \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    638\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    639\u001b[0m         )\n\u001b[1;32m    640\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    641\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:851\u001b[0m, in \u001b[0;36mBaseChatModel._generate_with_cache\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    849\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    850\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 851\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    852\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    853\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    854\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    855\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:686\u001b[0m, in \u001b[0;36mBaseChatOpenAI._generate\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    684\u001b[0m     generation_info \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheaders\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mdict\u001b[39m(raw_response\u001b[38;5;241m.\u001b[39mheaders)}\n\u001b[1;32m    685\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 686\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpayload\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    687\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_chat_result(response, generation_info)\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_utils/_utils.py:274\u001b[0m, in \u001b[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    272\u001b[0m             msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    273\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 274\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/resources/chat/completions.py:742\u001b[0m, in \u001b[0;36mCompletions.create\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    705\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    706\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    739\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[1;32m    740\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[1;32m    741\u001b[0m     validate_response_format(response_format)\n\u001b[0;32m--> 742\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    743\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/chat/completions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    744\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    745\u001b[0m \u001b[43m            \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m    746\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmessages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    747\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    748\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfrequency_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    749\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunction_call\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    750\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunctions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogit_bias\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    752\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    753\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_completion_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_completion_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    754\u001b[0m \u001b[43m                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\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    781\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1270\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1256\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m   1257\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1258\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1265\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1266\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m   1267\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m   1268\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m   1269\u001b[0m     )\n\u001b[0;32m-> 1270\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:947\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    944\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    945\u001b[0m     retries_taken \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 947\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    948\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    949\u001b[0m \u001b[43m    \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    950\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    951\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    952\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries_taken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    953\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1051\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1048\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m   1050\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1051\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1053\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[1;32m   1054\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[1;32m   1055\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1059\u001b[0m     retries_taken\u001b[38;5;241m=\u001b[39mretries_taken,\n\u001b[1;32m   1060\u001b[0m )\n",
+      "\u001b[0;31mBadRequestError\u001b[0m: Error code: 400 - {'error': {'message': \"An assistant message with 'tool_calls' must be followed by tool messages responding to each 'tool_call_id'. The following tool_call_ids did not have response messages: call_mjLuNyXNHoUIXHiBtXhaWdxN\", 'type': 'invalid_request_error', 'param': 'messages', 'code': None}}"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import AIMessage, ToolMessage\n",
+    "\n",
+    "tool_call = response_message.tool_calls[0]\n",
+    "tool_response = foo_tool.invoke(tool_call)\n",
+    "\n",
+    "chat_history.append(\n",
+    "    AIMessage(\n",
+    "        content=response_message.content,\n",
+    "        additional_kwargs=response_message.additional_kwargs,\n",
+    "    )\n",
+    ")\n",
+    "chat_history.append(\n",
+    "    ToolMessage(content=str(tool_response), tool_call_id=tool_call.get(\"id\"))\n",
+    ")\n",
+    "\n",
+    "final_response = model_with_tools.invoke(chat_history)\n",
+    "print(final_response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we add a second response, the call will succeed as expected because we now have one tool response per tool call:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Both calls to the tool \"foo\" have been completed successfully. The output for each call is \"action complete!\".', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 24, 'prompt_tokens': 137, 'total_tokens': 161, 'completion_tokens_details': {'audio_tokens': None, 'reasoning_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_e2bde53e6e', 'finish_reason': 'stop', 'logprobs': None}, id='run-b5ac3c54-4e26-4da4-853a-d0ab1cba90e0-0', usage_metadata={'input_tokens': 137, 'output_tokens': 24, 'total_tokens': 161, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 0}})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool_response_2 = foo_tool.invoke(response_message.tool_calls[1])\n",
+    "\n",
+    "chat_history.append(tool_response_2)\n",
+    "\n",
+    "model_with_tools.invoke(chat_history)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if we add a duplicate, extra tool response, the call will fail again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "BadRequestError",
+     "evalue": "Error code: 400 - {'error': {'message': \"Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.\", 'type': 'invalid_request_error', 'param': 'messages.[4].role', 'code': None}}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m duplicate_tool_response_2 \u001b[38;5;241m=\u001b[39m foo_tool\u001b[38;5;241m.\u001b[39minvoke(response_message\u001b[38;5;241m.\u001b[39mtool_calls[\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m      3\u001b[0m chat_history\u001b[38;5;241m.\u001b[39mappend(duplicate_tool_response_2)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[43mmodel_with_tools\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchat_history\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:5354\u001b[0m, in \u001b[0;36mRunnableBindingBase.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   5348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m   5349\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   5350\u001b[0m     \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m   5351\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   5352\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m   5353\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Output:\n\u001b[0;32m-> 5354\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbound\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5355\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5356\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5357\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5358\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:286\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    277\u001b[0m     \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    282\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[1;32m    283\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[1;32m    284\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[1;32m    285\u001b[0m         ChatGeneration,\n\u001b[0;32m--> 286\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    287\u001b[0m \u001b[43m            \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    289\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    290\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    291\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    292\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    293\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    296\u001b[0m     )\u001b[38;5;241m.\u001b[39mmessage\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:786\u001b[0m, in \u001b[0;36mBaseChatModel.generate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    778\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[1;32m    779\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    780\u001b[0m     prompts: \u001b[38;5;28mlist\u001b[39m[PromptValue],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    783\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    784\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[1;32m    785\u001b[0m     prompt_messages \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[0;32m--> 786\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:643\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n\u001b[1;32m    642\u001b[0m             run_managers[i]\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[0;32m--> 643\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    644\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    645\u001b[0m     LLMResult(generations\u001b[38;5;241m=\u001b[39m[res\u001b[38;5;241m.\u001b[39mgenerations], llm_output\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39mllm_output)  \u001b[38;5;66;03m# type: ignore[list-item]\u001b[39;00m\n\u001b[1;32m    646\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results\n\u001b[1;32m    647\u001b[0m ]\n\u001b[1;32m    648\u001b[0m llm_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_combine_llm_outputs([res\u001b[38;5;241m.\u001b[39mllm_output \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results])\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:633\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    630\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(messages):\n\u001b[1;32m    631\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    632\u001b[0m         results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m--> 633\u001b[0m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[43m                \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    635\u001b[0m \u001b[43m                \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    636\u001b[0m \u001b[43m                \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    638\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    639\u001b[0m         )\n\u001b[1;32m    640\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    641\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:851\u001b[0m, in \u001b[0;36mBaseChatModel._generate_with_cache\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    849\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    850\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 851\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    852\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    853\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    854\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    855\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:686\u001b[0m, in \u001b[0;36mBaseChatOpenAI._generate\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    684\u001b[0m     generation_info \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheaders\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mdict\u001b[39m(raw_response\u001b[38;5;241m.\u001b[39mheaders)}\n\u001b[1;32m    685\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 686\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpayload\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    687\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_chat_result(response, generation_info)\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_utils/_utils.py:274\u001b[0m, in \u001b[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    272\u001b[0m             msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    273\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 274\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/resources/chat/completions.py:742\u001b[0m, in \u001b[0;36mCompletions.create\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    705\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    706\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    739\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[1;32m    740\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[1;32m    741\u001b[0m     validate_response_format(response_format)\n\u001b[0;32m--> 742\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    743\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/chat/completions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    744\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    745\u001b[0m \u001b[43m            \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m    746\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmessages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    747\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    748\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfrequency_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    749\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunction_call\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    750\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunctions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogit_bias\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    752\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    753\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_completion_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_completion_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    754\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    755\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    756\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    757\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparallel_tool_calls\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mparallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    758\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpresence_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpresence_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    759\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresponse_format\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    760\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseed\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    761\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mservice_tier\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    762\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstop\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    763\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstore\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    764\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m 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\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    777\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    778\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    779\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    780\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    781\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1270\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1256\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m   1257\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1258\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1265\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1266\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m   1267\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m   1268\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m   1269\u001b[0m     )\n\u001b[0;32m-> 1270\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:947\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    944\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    945\u001b[0m     retries_taken \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 947\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    948\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    949\u001b[0m \u001b[43m    \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    950\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    951\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    952\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries_taken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    953\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1051\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1048\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m   1050\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1051\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1053\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[1;32m   1054\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[1;32m   1055\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1059\u001b[0m     retries_taken\u001b[38;5;241m=\u001b[39mretries_taken,\n\u001b[1;32m   1060\u001b[0m )\n",
+      "\u001b[0;31mBadRequestError\u001b[0m: Error code: 400 - {'error': {'message': \"Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.\", 'type': 'invalid_request_error', 'param': 'messages.[4].role', 'code': None}}"
+     ]
+    }
+   ],
+   "source": [
+    "duplicate_tool_response_2 = foo_tool.invoke(response_message.tool_calls[1])\n",
+    "\n",
+    "chat_history.append(duplicate_tool_response_2)\n",
+    "\n",
+    "await model_with_tools.invoke(chat_history)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should additionally not pass `ToolMessages` back to to a model if they are not preceded by an `AIMessage` with tool calls. For example, this will fail:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "BadRequestError",
+     "evalue": "Error code: 400 - {'error': {'message': \"Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.\", 'type': 'invalid_request_error', 'param': 'messages.[0].role', 'code': None}}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel_with_tools\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mToolMessage\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43maction completed!\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_call_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdummy\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:5354\u001b[0m, in \u001b[0;36mRunnableBindingBase.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   5348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m   5349\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   5350\u001b[0m     \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m   5351\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   5352\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m   5353\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Output:\n\u001b[0;32m-> 5354\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbound\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5355\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5356\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5357\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5358\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:286\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    277\u001b[0m     \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    282\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[1;32m    283\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[1;32m    284\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[1;32m    285\u001b[0m         ChatGeneration,\n\u001b[0;32m--> 286\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    287\u001b[0m \u001b[43m            \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    289\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    290\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    291\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    292\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    293\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    296\u001b[0m     )\u001b[38;5;241m.\u001b[39mmessage\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:786\u001b[0m, in \u001b[0;36mBaseChatModel.generate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    778\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[1;32m    779\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    780\u001b[0m     prompts: \u001b[38;5;28mlist\u001b[39m[PromptValue],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    783\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    784\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[1;32m    785\u001b[0m     prompt_messages \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[0;32m--> 786\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:643\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n\u001b[1;32m    642\u001b[0m             run_managers[i]\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[0;32m--> 643\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    644\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    645\u001b[0m     LLMResult(generations\u001b[38;5;241m=\u001b[39m[res\u001b[38;5;241m.\u001b[39mgenerations], llm_output\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39mllm_output)  \u001b[38;5;66;03m# type: ignore[list-item]\u001b[39;00m\n\u001b[1;32m    646\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results\n\u001b[1;32m    647\u001b[0m ]\n\u001b[1;32m    648\u001b[0m llm_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_combine_llm_outputs([res\u001b[38;5;241m.\u001b[39mllm_output \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results])\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:633\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    630\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(messages):\n\u001b[1;32m    631\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    632\u001b[0m         results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m--> 633\u001b[0m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[43m                \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    635\u001b[0m \u001b[43m                \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    636\u001b[0m \u001b[43m                \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    638\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    639\u001b[0m         )\n\u001b[1;32m    640\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    641\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:851\u001b[0m, in \u001b[0;36mBaseChatModel._generate_with_cache\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    849\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    850\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 851\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    852\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    853\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    854\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    855\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:686\u001b[0m, in \u001b[0;36mBaseChatOpenAI._generate\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    684\u001b[0m     generation_info \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheaders\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mdict\u001b[39m(raw_response\u001b[38;5;241m.\u001b[39mheaders)}\n\u001b[1;32m    685\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 686\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpayload\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    687\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_chat_result(response, generation_info)\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_utils/_utils.py:274\u001b[0m, in \u001b[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    272\u001b[0m             msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    273\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 274\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/resources/chat/completions.py:742\u001b[0m, in \u001b[0;36mCompletions.create\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    705\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    706\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    739\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[1;32m    740\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[1;32m    741\u001b[0m     validate_response_format(response_format)\n\u001b[0;32m--> 742\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    743\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/chat/completions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    744\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    745\u001b[0m \u001b[43m            \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m    746\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmessages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    747\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    748\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfrequency_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    749\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunction_call\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    750\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunctions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogit_bias\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    752\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    753\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_completion_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_completion_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    754\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    755\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    756\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    757\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparallel_tool_calls\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mparallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    758\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpresence_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpresence_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    759\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresponse_format\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    760\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseed\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    761\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mservice_tier\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    762\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstop\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    763\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstore\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    764\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    765\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    766\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtemperature\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    767\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtool_choice\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    768\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtools\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    769\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop_logprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_logprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    770\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop_p\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    771\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43muser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    772\u001b[0m \u001b[43m            \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    773\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCompletionCreateParams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    774\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    775\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    776\u001b[0m \u001b[43m            \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    777\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    778\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    779\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    780\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    781\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1270\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1256\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m   1257\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1258\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1265\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1266\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m   1267\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m   1268\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m   1269\u001b[0m     )\n\u001b[0;32m-> 1270\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:947\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    944\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    945\u001b[0m     retries_taken \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 947\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    948\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    949\u001b[0m \u001b[43m    \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    950\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    951\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    952\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries_taken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    953\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1051\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1048\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m   1050\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1051\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1053\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[1;32m   1054\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[1;32m   1055\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1059\u001b[0m     retries_taken\u001b[38;5;241m=\u001b[39mretries_taken,\n\u001b[1;32m   1060\u001b[0m )\n",
+      "\u001b[0;31mBadRequestError\u001b[0m: Error code: 400 - {'error': {'message': \"Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.\", 'type': 'invalid_request_error', 'param': 'messages.[0].role', 'code': None}}"
+     ]
+    }
+   ],
+   "source": [
+    "model_with_tools.invoke(\n",
+    "    [ToolMessage(content=\"action completed!\", tool_call_id=\"dummy\")]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See [this guide](/docs/how_to/tool_results_pass_to_model/) for more details on tool calling.\n",
+    "\n",
+    "## Troubleshooting\n",
+    "\n",
+    "The following may help resolve this error:\n",
+    "\n",
+    "- If you are using a custom executor rather than a prebuilt one like LangGraph's [`ToolNode`](https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph_prebuilt.ToolNode.html)\n",
+    "  or the legacy LangChain [AgentExecutor](/docs/how_to/agent_executor), verify that you are invoking and returning the result for one tool per tool call.\n",
+    "- If you are using [few-shot tool call examples](/docs/how_to/tools_few_shot) with messages that you manually create, and you want to simulate a failure,\n",
+    "  you still need to pass back a `ToolMessage` whose content indicates that failure.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/docs/troubleshooting/errors/MESSAGE_COERCION_FAILURE.ipynb b/docs/docs/troubleshooting/errors/MESSAGE_COERCION_FAILURE.ipynb
new file mode 100644
index 0000000000000..904448ae1b3d2
--- /dev/null
+++ b/docs/docs/troubleshooting/errors/MESSAGE_COERCION_FAILURE.ipynb
@@ -0,0 +1,122 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MESSAGE_COERCION_FAILURE\n",
+    "\n",
+    "Instead of always requiring instances of `BaseMessage`, several modules in LangChain take `MessageLikeRepresentation`, which is defined as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Union\n",
+    "\n",
+    "from langchain_core.prompts.chat import (\n",
+    "    BaseChatPromptTemplate,\n",
+    "    BaseMessage,\n",
+    "    BaseMessagePromptTemplate,\n",
+    ")\n",
+    "\n",
+    "MessageLikeRepresentation = Union[\n",
+    "    Union[BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate],\n",
+    "    tuple[\n",
+    "        Union[str, type],\n",
+    "        Union[str, list[dict], list[object]],\n",
+    "    ],\n",
+    "    str,\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These include OpenAI style message objects (`{ role: \"user\", content: \"Hello world!\" }`),\n",
+    "tuples, and plain strings (which are converted to [`HumanMessages`](/docs/concepts#humanmessage)).\n",
+    "\n",
+    "If one of these modules receives a value outside of one of these formats, you will receive an error like the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Message dict must contain 'role' and 'content' keys, got {'role': 'HumanMessage', 'random_field': 'random value'}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/messages/utils.py:318\u001b[0m, in \u001b[0;36m_convert_to_message\u001b[0;34m(message)\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[38;5;66;03m# None msg content is not allowed\u001b[39;00m\n\u001b[0;32m--> 318\u001b[0m     msg_content \u001b[38;5;241m=\u001b[39m \u001b[43mmsg_kwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    319\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'content'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 10\u001b[0m\n\u001b[1;32m      3\u001b[0m uncoercible_message \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHumanMessage\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom_field\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom value\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      6\u001b[0m }\n\u001b[1;32m      8\u001b[0m model \u001b[38;5;241m=\u001b[39m ChatAnthropic(model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclaude-3-5-sonnet-20240620\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 10\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43muncoercible_message\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:287\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    277\u001b[0m     \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    282\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[1;32m    283\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[1;32m    284\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[1;32m    285\u001b[0m         ChatGeneration,\n\u001b[1;32m    286\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate_prompt(\n\u001b[0;32m--> 287\u001b[0m             [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m],\n\u001b[1;32m    288\u001b[0m             stop\u001b[38;5;241m=\u001b[39mstop,\n\u001b[1;32m    289\u001b[0m             callbacks\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    290\u001b[0m             tags\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtags\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    291\u001b[0m             metadata\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    292\u001b[0m             run_name\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    293\u001b[0m             run_id\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_id\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m    294\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    295\u001b[0m         )\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    296\u001b[0m     )\u001b[38;5;241m.\u001b[39mmessage\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:267\u001b[0m, in \u001b[0;36mBaseChatModel._convert_input\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m StringPromptValue(text\u001b[38;5;241m=\u001b[39m\u001b[38;5;28minput\u001b[39m)\n\u001b[1;32m    266\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, Sequence):\n\u001b[0;32m--> 267\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ChatPromptValue(messages\u001b[38;5;241m=\u001b[39m\u001b[43mconvert_to_messages\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    269\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    270\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid input type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28minput\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    271\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMust be a PromptValue, str, or list of BaseMessages.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    272\u001b[0m     )\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/messages/utils.py:348\u001b[0m, in \u001b[0;36mconvert_to_messages\u001b[0;34m(messages)\u001b[0m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(messages, PromptValue):\n\u001b[1;32m    347\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m messages\u001b[38;5;241m.\u001b[39mto_messages()\n\u001b[0;32m--> 348\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m[\u001b[49m\u001b[43m_convert_to_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m]\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/messages/utils.py:348\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(messages, PromptValue):\n\u001b[1;32m    347\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m messages\u001b[38;5;241m.\u001b[39mto_messages()\n\u001b[0;32m--> 348\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43m_convert_to_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m messages]\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/messages/utils.py:321\u001b[0m, in \u001b[0;36m_convert_to_message\u001b[0;34m(message)\u001b[0m\n\u001b[1;32m    319\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    320\u001b[0m         msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMessage dict must contain \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m keys, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmessage\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 321\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    322\u001b[0m     _message \u001b[38;5;241m=\u001b[39m _create_message_from_message_type(\n\u001b[1;32m    323\u001b[0m         msg_type, msg_content, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmsg_kwargs\n\u001b[1;32m    324\u001b[0m     )\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "\u001b[0;31mValueError\u001b[0m: Message dict must contain 'role' and 'content' keys, got {'role': 'HumanMessage', 'random_field': 'random value'}"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "uncoercible_message = {\"role\": \"HumanMessage\", \"random_field\": \"random value\"}\n",
+    "\n",
+    "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
+    "\n",
+    "model.invoke([uncoercible_message])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Troubleshooting\n",
+    "\n",
+    "The following may help resolve this error:\n",
+    "\n",
+    "- Ensure that all inputs to chat models are an array of LangChain message classes or a supported message-like.\n",
+    "  - Check that there is no stringification or other unexpected transformation occuring.\n",
+    "- Check the error's stack trace and add log or debugger statements."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/docs/troubleshooting/errors/MODEL_AUTHENTICATION.mdx b/docs/docs/troubleshooting/errors/MODEL_AUTHENTICATION.mdx
new file mode 100644
index 0000000000000..e3d58b5e6e79b
--- /dev/null
+++ b/docs/docs/troubleshooting/errors/MODEL_AUTHENTICATION.mdx
@@ -0,0 +1,18 @@
+# MODEL_AUTHENTICATION
+
+Your model provider is denying you access to their service.
+
+## Troubleshooting
+
+The following may help resolve this error:
+
+- Confirm that your API key or other credentials are correct.
+- If you are relying on an environment variable to authenticate, confirm that the variable name is correct and that it has a value set.
+  - Note that environment variables can also be set by packages like `dotenv`.
+  - For models, you can try explicitly passing an `api_key` parameter to rule out any environment variable issues like this:
+
+```python
+model = ChatOpenAI(api_key="YOUR_KEY_HERE")
+```
+
+- If you are using a proxy or other custom endpoint, make sure that your custom provider does not expect an alternative authentication scheme.
diff --git a/docs/docs/troubleshooting/errors/MODEL_NOT_FOUND.mdx b/docs/docs/troubleshooting/errors/MODEL_NOT_FOUND.mdx
new file mode 100644
index 0000000000000..32b8d6896a6a2
--- /dev/null
+++ b/docs/docs/troubleshooting/errors/MODEL_NOT_FOUND.mdx
@@ -0,0 +1,10 @@
+# MODEL_NOT_FOUND
+
+The model name you have specified is not acknowledged by your provider.
+
+## Troubleshooting
+
+The following may help resolve this error:
+
+- Double check the model string you are passing in.
+- If you are using a proxy or other alternative host with a model wrapper, confirm that the permitted model names are not restricted or altered.
diff --git a/docs/docs/troubleshooting/errors/MODEL_RATE_LIMIT.mdx b/docs/docs/troubleshooting/errors/MODEL_RATE_LIMIT.mdx
new file mode 100644
index 0000000000000..d589913479e1f
--- /dev/null
+++ b/docs/docs/troubleshooting/errors/MODEL_RATE_LIMIT.mdx
@@ -0,0 +1,13 @@
+# MODEL_RATE_LIMIT
+
+You have hit the maximum number of requests that a model provider allows over a given time period and are being temporarily blocked.
+Generally, this error is temporary and your limit will reset after a certain amount of time.
+
+## Troubleshooting
+
+The following may help resolve this error:
+
+- Contact your model provider and ask for a rate limit increase.
+- If many of your incoming requests are the same, utilize [model response caching](/docs/how_to/chat_model_caching/).
+- Spread requests across different providers if your application allows it.
+- Use a [`rate_limiter`](/docs/how_to/chat_model_rate_limiting/) to control the rate of requests to the model.
diff --git a/docs/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE.ipynb b/docs/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE.ipynb
new file mode 100644
index 0000000000000..75d2eb7978d09
--- /dev/null
+++ b/docs/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE.ipynb
@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# OUTPUT_PARSING_FAILURE\n",
+    "\n",
+    "An [output parser](/docs/concepts#output-parsers) was unable to handle model output as expected.\n",
+    "\n",
+    "To illustrate this, let's say you have an output parser that expects a chat model to output JSON surrounded by a markdown code tag (triple backticks). Here would be an example of good input:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'foo': 'bar'}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import AIMessage\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "message = AIMessage(content='```\\n{\"foo\": \"bar\"}\\n```')\n",
+    "output_parser = JsonOutputParser()\n",
+    "output_parser.invoke(message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Internally, our JSON parser stripped out the markdown fence and newlines and then ran `json.loads`.\n",
+    "\n",
+    "If instead the chat model generated an output with malformed JSON, we will get an error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "OutputParserException",
+     "evalue": "Invalid json output: ```\n{{\"foo\":\n```",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/output_parsers/json.py:83\u001b[0m, in \u001b[0;36mJsonOutputParser.parse_result\u001b[0;34m(self, result, partial)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 83\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparse_json_markdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m JSONDecodeError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/utils/json.py:144\u001b[0m, in \u001b[0;36mparse_json_markdown\u001b[0;34m(json_string, parser)\u001b[0m\n\u001b[1;32m    143\u001b[0m     json_str \u001b[38;5;241m=\u001b[39m json_string \u001b[38;5;28;01mif\u001b[39;00m match \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m match\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_parse_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_str\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparser\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparser\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/utils/json.py:160\u001b[0m, in \u001b[0;36m_parse_json\u001b[0;34m(json_str, parser)\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;66;03m# Parse the JSON string into a Python dictionary\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparser\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_str\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/utils/json.py:118\u001b[0m, in \u001b[0;36mparse_partial_json\u001b[0;34m(s, strict)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;66;03m# If we got here, we ran out of characters to remove\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;66;03m# and still couldn't parse the string as JSON, so return the parse error\u001b[39;00m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;66;03m# for the original string.\u001b[39;00m\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloads\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.11.4/lib/python3.11/json/__init__.py:359\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    358\u001b[0m     kw[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparse_constant\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m parse_constant\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.11.4/lib/python3.11/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;124;03mcontaining a JSON document).\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \n\u001b[1;32m    336\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_w\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    338\u001b[0m end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.11.4/lib/python3.11/json/decoder.py:353\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    352\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 353\u001b[0m     obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscan_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    354\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mOutputParserException\u001b[0m                     Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m message \u001b[38;5;241m=\u001b[39m AIMessage(content\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m```\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m{{\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfoo\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m```\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m output_parser \u001b[38;5;241m=\u001b[39m JsonOutputParser()\n\u001b[0;32m----> 3\u001b[0m \u001b[43moutput_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/output_parsers/base.py:193\u001b[0m, in \u001b[0;36mBaseOutputParser.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    187\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    188\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, BaseMessage],\n\u001b[1;32m    189\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    191\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m    192\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, BaseMessage):\n\u001b[0;32m--> 193\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    194\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43minner_input\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_result\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    195\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatGeneration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minner_input\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    196\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    198\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    202\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_with_config(\n\u001b[1;32m    203\u001b[0m             \u001b[38;5;28;01mlambda\u001b[39;00m inner_input: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparse_result([Generation(text\u001b[38;5;241m=\u001b[39minner_input)]),\n\u001b[1;32m    204\u001b[0m             \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m    205\u001b[0m             config,\n\u001b[1;32m    206\u001b[0m             run_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparser\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    207\u001b[0m         )\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:1927\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[0;34m(self, func, input, config, run_type, serialized, **kwargs)\u001b[0m\n\u001b[1;32m   1923\u001b[0m     context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m   1924\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(_set_config_context, child_config)\n\u001b[1;32m   1925\u001b[0m     output \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m   1926\u001b[0m         Output,\n\u001b[0;32m-> 1927\u001b[0m         \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1928\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1929\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1930\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1931\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1932\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1933\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1934\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m   1935\u001b[0m     )\n\u001b[1;32m   1936\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   1937\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/runnables/config.py:396\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    394\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    395\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 396\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/output_parsers/base.py:194\u001b[0m, in \u001b[0;36mBaseOutputParser.invoke.<locals>.<lambda>\u001b[0;34m(inner_input)\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    187\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    188\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, BaseMessage],\n\u001b[1;32m    189\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    191\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m    192\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, BaseMessage):\n\u001b[1;32m    193\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_with_config(\n\u001b[0;32m--> 194\u001b[0m             \u001b[38;5;28;01mlambda\u001b[39;00m inner_input: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_result\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    195\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatGeneration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minner_input\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    196\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    197\u001b[0m             \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m    198\u001b[0m             config,\n\u001b[1;32m    199\u001b[0m             run_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparser\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    200\u001b[0m         )\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    202\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_with_config(\n\u001b[1;32m    203\u001b[0m             \u001b[38;5;28;01mlambda\u001b[39;00m inner_input: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparse_result([Generation(text\u001b[38;5;241m=\u001b[39minner_input)]),\n\u001b[1;32m    204\u001b[0m             \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m    205\u001b[0m             config,\n\u001b[1;32m    206\u001b[0m             run_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparser\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    207\u001b[0m         )\n",
+      "File \u001b[0;32m~/langchain/oss-py/libs/core/langchain_core/output_parsers/json.py:86\u001b[0m, in \u001b[0;36mJsonOutputParser.parse_result\u001b[0;34m(self, result, partial)\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m JSONDecodeError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     85\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid json output: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtext\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 86\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m OutputParserException(msg, llm_output\u001b[38;5;241m=\u001b[39mtext) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
+      "\u001b[0;31mOutputParserException\u001b[0m: Invalid json output: ```\n{{\"foo\":\n```"
+     ]
+    }
+   ],
+   "source": [
+    "message = AIMessage(content='```\\n{{\"foo\":\\n```')\n",
+    "output_parser = JsonOutputParser()\n",
+    "output_parser.invoke(message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that some prebuilt constructs like [legacy LangChain agents](/docs/how_to/agent_executor) and chains may use output parsers internally,\n",
+    "so you may see this error even if you're not visibly instantiating and using an output parser.\n",
+    "\n",
+    "## Troubleshooting\n",
+    "\n",
+    "The following may help resolve this error:\n",
+    "\n",
+    "- Consider using [tool calling or other structured output techniques](/docs/how_to/structured_output/) if possible without an output parser to reliably output parseable values.\n",
+    "  - If you are using a prebuilt chain or agent, use [LangGraph](https://langchain-ai.github.io/langgraph/) to compose your logic explicitly instead.\n",
+    "- Add more precise formatting instructions to your prompt. In the above example, adding `\"You must always return valid JSON fenced by a markdown code block. Do not return any additional text.\"` to your input may help steer the model to returning the expected format.\n",
+    "- If you are using a smaller or less capable model, try using a more capable one.\n",
+    "- Add [LLM-powered retries](/docs/how_to/output_parser_fixing/)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/docs/troubleshooting/errors/index.mdx b/docs/docs/troubleshooting/errors/index.mdx
new file mode 100644
index 0000000000000..2258714de1e1f
--- /dev/null
+++ b/docs/docs/troubleshooting/errors/index.mdx
@@ -0,0 +1,12 @@
+# Error codes
+
+This page contains guides around resolving common errors you may find while building with LangChain.
+Errors referenced below will have an `lc_error_code` property corresponding to one of the below codes when they are thrown in code.
+
+- [INVALID_PROMPT_INPUT](/docs/troubleshooting/errors/INVALID_PROMPT_INPUT)
+- [INVALID_TOOL_RESULTS](/docs/troubleshooting/errors/INVALID_TOOL_RESULTS)
+- [MESSAGE_COERCION_FAILURE](/docs/troubleshooting/errors/MESSAGE_COERCION_FAILURE)
+- [MODEL_AUTHENTICATION](/docs/troubleshooting/errors/MODEL_AUTHENTICATION)
+- [MODEL_NOT_FOUND](/docs/troubleshooting/errors/MODEL_NOT_FOUND)
+- [MODEL_RATE_LIMIT](/docs/troubleshooting/errors/MODEL_RATE_LIMIT)
+- [OUTPUT_PARSING_FAILURE](/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE)
diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js
index b1b7487c27b33..305b14df135ac 100644
--- a/docs/docusaurus.config.js
+++ b/docs/docusaurus.config.js
@@ -181,6 +181,11 @@ const config = {
                 docId: "people",
                 label: "People",
               },
+              {
+                type: "doc",
+                docId: "troubleshooting/errors/index",
+                label: "Error reference",
+              },
               {
                 type: 'html',
                 value: '<hr class="dropdown-separator" style="margin-top: 0.5rem; margin-bottom: 0.5rem">',
diff --git a/libs/core/langchain_core/exceptions.py b/libs/core/langchain_core/exceptions.py
index f10197b04662a..38158f37bac12 100644
--- a/libs/core/langchain_core/exceptions.py
+++ b/libs/core/langchain_core/exceptions.py
@@ -1,5 +1,6 @@
 """Custom **exceptions** for LangChain."""
 
+from enum import Enum
 from typing import Any, Optional
 
 
@@ -39,6 +40,10 @@ def __init__(
         llm_output: Optional[str] = None,
         send_to_llm: bool = False,
     ):
+        if isinstance(error, str):
+            error = create_message(
+                message=error, error_code=ErrorCode.OUTPUT_PARSING_FAILURE
+            )
         super().__init__(error)
         if send_to_llm and (observation is None or llm_output is None):
             msg = (
@@ -49,3 +54,21 @@ def __init__(
         self.observation = observation
         self.llm_output = llm_output
         self.send_to_llm = send_to_llm
+
+
+class ErrorCode(Enum):
+    INVALID_PROMPT_INPUT = "INVALID_PROMPT_INPUT"
+    INVALID_TOOL_RESULTS = "INVALID_TOOL_RESULTS"
+    MESSAGE_COERCION_FAILURE = "MESSAGE_COERCION_FAILURE"
+    MODEL_AUTHENTICATION = "MODEL_AUTHENTICATION"
+    MODEL_NOT_FOUND = "MODEL_NOT_FOUND"
+    MODEL_RATE_LIMIT = "MODEL_RATE_LIMIT"
+    OUTPUT_PARSING_FAILURE = "OUTPUT_PARSING_FAILURE"
+
+
+def create_message(*, message: str, error_code: ErrorCode) -> str:
+    return (
+        f"{message}\n"
+        "For troubleshooting, visit: https://python.langchain.com/docs/"
+        f"troubleshooting/errors/{error_code.value}"
+    )
diff --git a/libs/core/langchain_core/messages/utils.py b/libs/core/langchain_core/messages/utils.py
index d3194226ea217..2399639341202 100644
--- a/libs/core/langchain_core/messages/utils.py
+++ b/libs/core/langchain_core/messages/utils.py
@@ -28,6 +28,7 @@
 
 from pydantic import Discriminator, Field, Tag
 
+from langchain_core.exceptions import ErrorCode, create_message
 from langchain_core.messages.ai import AIMessage, AIMessageChunk
 from langchain_core.messages.base import BaseMessage, BaseMessageChunk
 from langchain_core.messages.chat import ChatMessage, ChatMessageChunk
@@ -274,6 +275,7 @@ def _create_message_from_message_type(
             f"Unexpected message type: '{message_type}'. Use one of 'human',"
             f" 'user', 'ai', 'assistant', 'function', 'tool', or 'system'."
         )
+        msg = create_message(message=msg, error_code=ErrorCode.MESSAGE_COERCION_FAILURE)
         raise ValueError(msg)
     return message
 
@@ -318,12 +320,16 @@ def _convert_to_message(message: MessageLikeRepresentation) -> BaseMessage:
             msg_content = msg_kwargs.pop("content") or ""
         except KeyError as e:
             msg = f"Message dict must contain 'role' and 'content' keys, got {message}"
+            msg = create_message(
+                message=msg, error_code=ErrorCode.MESSAGE_COERCION_FAILURE
+            )
             raise ValueError(msg) from e
         _message = _create_message_from_message_type(
             msg_type, msg_content, **msg_kwargs
         )
     else:
         msg = f"Unsupported message type: {type(message)}"
+        msg = create_message(message=msg, error_code=ErrorCode.MESSAGE_COERCION_FAILURE)
         raise NotImplementedError(msg)
 
     return _message
@@ -1327,6 +1333,7 @@ def _msg_to_chunk(message: BaseMessage) -> BaseMessageChunk:
         f"Unrecognized message class {message.__class__}. Supported classes are "
         f"{list(_MSG_CHUNK_MAP.keys())}"
     )
+    msg = create_message(message=msg, error_code=ErrorCode.MESSAGE_COERCION_FAILURE)
     raise ValueError(msg)
 
 
@@ -1343,6 +1350,7 @@ def _chunk_to_msg(chunk: BaseMessageChunk) -> BaseMessage:
         f"Unrecognized message chunk class {chunk.__class__}. Supported classes are "
         f"{list(_CHUNK_MSG_MAP.keys())}"
     )
+    msg = create_message(message=msg, error_code=ErrorCode.MESSAGE_COERCION_FAILURE)
     raise ValueError(msg)
 
 
diff --git a/libs/core/langchain_core/prompts/base.py b/libs/core/langchain_core/prompts/base.py
index e399974a5b0ef..042b2d77ea80f 100644
--- a/libs/core/langchain_core/prompts/base.py
+++ b/libs/core/langchain_core/prompts/base.py
@@ -21,6 +21,7 @@
 from pydantic import BaseModel, ConfigDict, Field, model_validator
 from typing_extensions import Self, override
 
+from langchain_core.exceptions import ErrorCode, create_message
 from langchain_core.load import dumpd
 from langchain_core.output_parsers.base import BaseOutputParser
 from langchain_core.prompt_values import (
@@ -74,18 +75,24 @@ def validate_variable_names(self) -> Self:
                 "Cannot have an input variable named 'stop', as it is used internally,"
                 " please rename."
             )
-            raise ValueError(msg)
+            raise ValueError(
+                create_message(message=msg, error_code=ErrorCode.INVALID_PROMPT_INPUT)
+            )
         if "stop" in self.partial_variables:
             msg = (
                 "Cannot have an partial variable named 'stop', as it is used "
                 "internally, please rename."
             )
-            raise ValueError(msg)
+            raise ValueError(
+                create_message(message=msg, error_code=ErrorCode.INVALID_PROMPT_INPUT)
+            )
 
         overall = set(self.input_variables).intersection(self.partial_variables)
         if overall:
             msg = f"Found overlapping input and partial variables: {overall}"
-            raise ValueError(msg)
+            raise ValueError(
+                create_message(message=msg, error_code=ErrorCode.INVALID_PROMPT_INPUT)
+            )
         return self
 
     @classmethod
@@ -148,7 +155,11 @@ def _validate_input(self, inner_input: Any) -> dict:
                     f"Expected mapping type as input to {self.__class__.__name__}. "
                     f"Received {type(inner_input)}."
                 )
-                raise TypeError(msg)
+                raise TypeError(
+                    create_message(
+                        message=msg, error_code=ErrorCode.INVALID_PROMPT_INPUT
+                    )
+                )
         missing = set(self.input_variables).difference(inner_input)
         if missing:
             msg = (
@@ -162,7 +173,9 @@ def _validate_input(self, inner_input: Any) -> dict:
                 " and not a variable, please escape it with double curly braces like: "
                 f"'{{{{{example_key}}}}}'."
             )
-            raise KeyError(msg)
+            raise KeyError(
+                create_message(message=msg, error_code=ErrorCode.INVALID_PROMPT_INPUT)
+            )
         return inner_input
 
     def _format_prompt_with_error_handling(self, inner_input: dict) -> PromptValue:
@@ -381,7 +394,9 @@ def _get_document_info(doc: Document, prompt: BasePromptTemplate[str]) -> dict:
             f"{required_metadata}. Received document with missing metadata: "
             f"{list(missing_metadata)}."
         )
-        raise ValueError(msg)
+        raise ValueError(
+            create_message(message=msg, error_code=ErrorCode.INVALID_PROMPT_INPUT)
+        )
     return {k: base_info[k] for k in prompt.input_variables}
 
 

From f9cc9bdcf31b2683127ac089fc47ab4780d28dcd Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Thu, 17 Oct 2024 06:32:40 -0700
Subject: [PATCH 16/84] core: release 0.3.12 (#27410)

---
 libs/core/poetry.lock    | 202 +++++++++++++++++++--------------------
 libs/core/pyproject.toml |   8 +-
 2 files changed, 108 insertions(+), 102 deletions(-)

diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock
index 15fb395e20cb6..6b960860db2cd 100644
--- a/libs/core/poetry.lock
+++ b/libs/core/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -2595,110 +2595,110 @@ testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (
 
 [[package]]
 name = "simsimd"
-version = "5.7.1"
+version = "5.7.2"
 description = "Fastest SIMD-Accelerated Vector Similarity Functions for x86 and Arm"
 optional = false
 python-versions = "*"
 files = [
-    {file = "simsimd-5.7.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d9af57be5c2f327b2c343fcbcdee36b99358beb649b925698defc7719a4904c0"},
-    {file = "simsimd-5.7.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1eaf9cb6a57ef0f3bfe9108a70c6c178daf023d6e2b586ff4126db2927278114"},
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+    {file = "simsimd-5.7.2-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:34e29a7db85d632cf78d7bdd60906d07d9a9c8c93ef9f5dd6d591a55a0dbb37c"},
+    {file = "simsimd-5.7.2-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:03ad6c19c5642909e1e582b09a4c8700c1d36094667cae294d6d08d0aafdc879"},
+    {file = "simsimd-5.7.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:f565f7f4685985c00d734d87d92dfede6d50760da354642f3464273b49caecaf"},
+    {file = "simsimd-5.7.2-cp39-cp39-win32.whl", hash = "sha256:b70fed46f59e6da24932bc19b6af092e94764030918572f11eb2a0eed9d6f73c"},
+    {file = "simsimd-5.7.2-cp39-cp39-win_amd64.whl", hash = "sha256:4b0b36634a9a3d6b2d7dfaed4233c802028882c13c36f49d613b76c0269581af"},
+    {file = "simsimd-5.7.2-cp39-cp39-win_arm64.whl", hash = "sha256:de69350e4491cb25321b22575404d6ab2eb47548264147213827fcdcbb344ec1"},
+    {file = "simsimd-5.7.2.tar.gz", hash = "sha256:a1a0f2b025484044864b62ca6ad64afc1f9e5a8b020963d1db1d4a5a8f105616"},
 ]
 
 [[package]]
diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml
index 0f48099afb131..4a26b7f35a16f 100644
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-core"
-version = "0.3.11"
+version = "0.3.12"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -133,6 +133,7 @@ classmethod-decorators = [
 ruff = "^0.5"
 
 
+
 [tool.poetry.group.typing.dependencies]
 mypy = ">=1.10,<1.11"
 types-pyyaml = "^6.0.12.2"
@@ -141,12 +142,14 @@ types-jinja2 = "^2.11.9"
 simsimd = "^5.0.0"
 
 
+
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"
 setuptools = "^67.6.1"
 grandalf = "^0.8"
 
 
+
 [tool.poetry.group.test.dependencies]
 pytest = "^7.3.0"
 freezegun = "^1.2.2"
@@ -166,14 +169,17 @@ version = "^1.26.0"
 python = ">=3.12"
 
 
+
 [tool.poetry.group.test_integration.dependencies]
 
 
+
 [tool.poetry.group.typing.dependencies.langchain-text-splitters]
 path = "../text-splitters"
 develop = true
 
 
+
 [tool.poetry.group.test.dependencies.langchain-standard-tests]
 path = "../standard-tests"
 develop = true

From 4d11211c8998e9b8e3243417c275290cade0c71f Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Thu, 17 Oct 2024 07:31:14 -0700
Subject: [PATCH 17/84] infra: schedule triggers monorepo only by default
 (#27428)

fixes https://github.com/langchain-ai/langchain/issues/27426
---
 .github/workflows/api_doc_build.yml      | 1 +
 .github/workflows/check-broken-links.yml | 2 +-
 .github/workflows/people.yml             | 2 +-
 .github/workflows/run_notebooks.yml      | 5 +++--
 .github/workflows/scheduled_test.yml     | 2 +-
 5 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/api_doc_build.yml b/.github/workflows/api_doc_build.yml
index 6973e64f742e6..361dfd775c1fa 100644
--- a/.github/workflows/api_doc_build.yml
+++ b/.github/workflows/api_doc_build.yml
@@ -10,6 +10,7 @@ env:
 
 jobs:
   build:
+    if: github.repository == 'langchain-ai/langchain' || github.event_name != 'schedule'
     runs-on: ubuntu-latest
     permissions: write-all
     steps:
diff --git a/.github/workflows/check-broken-links.yml b/.github/workflows/check-broken-links.yml
index 37c94910fa141..626d345051a73 100644
--- a/.github/workflows/check-broken-links.yml
+++ b/.github/workflows/check-broken-links.yml
@@ -7,7 +7,7 @@ on:
 
 jobs:
   check-links:
-    if: github.repository_owner == 'langchain-ai'
+    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
diff --git a/.github/workflows/people.yml b/.github/workflows/people.yml
index adc3a789f6a7d..8321a3197923b 100644
--- a/.github/workflows/people.yml
+++ b/.github/workflows/people.yml
@@ -14,7 +14,7 @@ on:
 
 jobs:
   langchain-people:
-    if: github.repository_owner == 'langchain-ai'
+    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     runs-on: ubuntu-latest
     permissions: write-all
     steps:
diff --git a/.github/workflows/run_notebooks.yml b/.github/workflows/run_notebooks.yml
index 9d4091c107c26..06c9a74d9051f 100644
--- a/.github/workflows/run_notebooks.yml
+++ b/.github/workflows/run_notebooks.yml
@@ -20,7 +20,7 @@ env:
 jobs:
   build:
     runs-on: ubuntu-latest
-
+    if: github.repository == 'langchain-ai/langchain' || github.event_name != 'schedule'
     name: "Test docs"
     steps:
       - uses: actions/checkout@v4
@@ -51,5 +51,6 @@ jobs:
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
+          WORKING_DIRECTORY: ${{ github.event.inputs.working-directory || 'all' }}
         run: |
-          ./docs/scripts/execute_notebooks.sh ${{ github.event.inputs.working-directory || 'all' }}
+          ./docs/scripts/execute_notebooks.sh $WORKING_DIRECTORY
diff --git a/.github/workflows/scheduled_test.yml b/.github/workflows/scheduled_test.yml
index 0523fbab8fa8f..6a4767988bf2a 100644
--- a/.github/workflows/scheduled_test.yml
+++ b/.github/workflows/scheduled_test.yml
@@ -10,7 +10,7 @@ env:
 
 jobs:
   build:
-    if: github.repository_owner == 'langchain-ai'
+    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     name: Python ${{ matrix.python-version }} - ${{ matrix.working-directory }}
     runs-on: ubuntu-latest
     strategy:

From 322ca8481245d3e50350236de55c8aa5c9235e31 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Thu, 17 Oct 2024 07:47:29 -0700
Subject: [PATCH 18/84] infra: add ibm api build (#27425)

test build:
https://github.com/langchain-ai/langchain/actions/runs/11386155179
---
 .github/workflows/api_doc_build.yml | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/api_doc_build.yml b/.github/workflows/api_doc_build.yml
index 361dfd775c1fa..460699f6de377 100644
--- a/.github/workflows/api_doc_build.yml
+++ b/.github/workflows/api_doc_build.yml
@@ -78,6 +78,11 @@ jobs:
         with:
           repository: langchain-ai/langchain-databricks
           path: langchain-databricks
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-ibm
+          path: langchain-ibm
+
 
 
       - name: Set Git config
@@ -103,7 +108,8 @@ jobs:
             langchain/libs/experimental \
             langchain/libs/partners/milvus \
             langchain/libs/partners/unstructured \
-            langchain/libs/databricks
+            langchain/libs/partners/databricks \
+            langchain/libs/partners/ibm
           mv langchain-google/libs/genai langchain/libs/partners/google-genai
           mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
           mv langchain-google/libs/community langchain/libs/partners/google-community
@@ -120,6 +126,7 @@ jobs:
           mv langchain-milvus/libs/milvus langchain/libs/partners/milvus
           mv langchain-unstructured/libs/unstructured langchain/libs/partners/unstructured
           mv langchain-databricks/libs/databricks langchain/libs/partners/databricks
+          mv langchain-ibm/libs/ibm langchain/libs/partners/ibm
 
       - name: Rm old html
         run:
@@ -138,8 +145,8 @@ jobs:
         run: |
           python -m pip install -U uv
           python -m uv pip install --upgrade --no-cache-dir pip setuptools
-          # skip airbyte and ibm due to pandas dependency issue
-          python -m uv pip install $(ls ./libs/partners | grep -vE "airbyte|ibm" | xargs -I {} echo "./libs/partners/{}")
+          # skip airbyte due to pandas dependency issue
+          python -m uv pip install $(ls ./libs/partners | grep -vE "airbyte" | xargs -I {} echo "./libs/partners/{}")
           python -m uv pip install libs/core libs/langchain libs/text-splitters libs/community libs/experimental
           python -m uv pip install -r docs/api_reference/requirements.txt
 

From 2a27234a772da182ece41db4fda888729fa80448 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Thu, 17 Oct 2024 08:51:33 -0700
Subject: [PATCH 19/84] docs: fix error reference header (#27431)

---
 docs/docs/troubleshooting/errors/index.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/troubleshooting/errors/index.mdx b/docs/docs/troubleshooting/errors/index.mdx
index 2258714de1e1f..aad0308b9c494 100644
--- a/docs/docs/troubleshooting/errors/index.mdx
+++ b/docs/docs/troubleshooting/errors/index.mdx
@@ -1,4 +1,4 @@
-# Error codes
+# Error reference
 
 This page contains guides around resolving common errors you may find while building with LangChain.
 Errors referenced below will have an `lc_error_code` property corresponding to one of the below codes when they are thrown in code.

From 6e228c84a895d1889a5378eea1763244dfeba8ea Mon Sep 17 00:00:00 2001
From: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Date: Thu, 17 Oct 2024 11:29:29 -0700
Subject: [PATCH 20/84] docs: update mongo table (#27434)

---
 docs/src/theme/FeatureTables.js | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/src/theme/FeatureTables.js b/docs/src/theme/FeatureTables.js
index 4a46e08cb1781..2016c7c5454b2 100644
--- a/docs/src/theme/FeatureTables.js
+++ b/docs/src/theme/FeatureTables.js
@@ -1026,8 +1026,8 @@ const FEATURE_TABLES = {
                 link: "mongodb_atlas",
                 deleteById: true,
                 filtering: true,
-                searchByVector: false,
-                searchWithScore: false,
+                searchByVector: true,
+                searchWithScore: true,
                 async: true,
                 passesStandardTests: false,
                 multiTenancy: false,

From 97dc578d473d684e495a3c5e6bf9bf5c74ab6134 Mon Sep 17 00:00:00 2001
From: Mateusz Szewczyk <139469471+MateuszOssGit@users.noreply.github.com>
Date: Thu, 17 Oct 2024 21:41:24 +0200
Subject: [PATCH 21/84] docs: Update custom name for IBM (#27437)

Thank you for contributing to LangChain!

PR: Update custom name for IBM in api_reference docs

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/api_reference/create_api_rst.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/api_reference/create_api_rst.py b/docs/api_reference/create_api_rst.py
index bb67e0bcd007a..d907eec4c360e 100644
--- a/docs/api_reference/create_api_rst.py
+++ b/docs/api_reference/create_api_rst.py
@@ -533,6 +533,7 @@ def _build_index(dirs: List[str]) -> None:
         "airbyte": "Airbyte",
         "aws": "AWS",
         "ai21": "AI21",
+        "ibm": "IBM",
     }
     ordered = ["core", "langchain", "text-splitters", "community", "experimental"]
     main_ = [dir_ for dir_ in ordered if dir_ in dirs]

From 7d65a32ee0859bf1cf1f3cf86e41791f3be8ba3c Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 18 Oct 2024 08:02:09 -0700
Subject: [PATCH 22/84] openai: audio modality, remove sockets from unit tests
 (#27436)

---
 libs/partners/openai/.gitignore               |   1 +
 libs/partners/openai/Makefile                 |  14 +-
 .../langchain_openai/chat_models/base.py      |  13 +
 libs/partners/openai/poetry.lock              | 586 ++++++++++--------
 libs/partners/openai/pyproject.toml           |   3 +-
 .../chat_models/audio_input.wav               | Bin 0 -> 223278 bytes
 .../chat_models/test_base.py                  |  69 +++
 .../tests/unit_tests/chat_models/test_base.py |   7 +-
 .../openai/tests/unit_tests/llms/test_base.py |   2 +-
 .../tests/unit_tests/test_token_counts.py     |   1 +
 10 files changed, 417 insertions(+), 279 deletions(-)
 create mode 100644 libs/partners/openai/tests/integration_tests/chat_models/audio_input.wav

diff --git a/libs/partners/openai/.gitignore b/libs/partners/openai/.gitignore
index bee8a64b79a99..fb181ca6a53df 100644
--- a/libs/partners/openai/.gitignore
+++ b/libs/partners/openai/.gitignore
@@ -1 +1,2 @@
 __pycache__
+tiktoken_cache
\ No newline at end of file
diff --git a/libs/partners/openai/Makefile b/libs/partners/openai/Makefile
index 28b03e388ad25..210bdb4479007 100644
--- a/libs/partners/openai/Makefile
+++ b/libs/partners/openai/Makefile
@@ -8,7 +8,19 @@ TEST_FILE ?= tests/unit_tests/
 
 integration_test integration_tests: TEST_FILE=tests/integration_tests/
 
-test tests integration_test integration_tests:
+# unit tests are run with the --disable-socket flag to prevent network calls
+# use tiktoken cache to enable token counting without socket (internet) access
+test tests:
+	mkdir -p tiktoken_cache
+	@if [ ! -f tiktoken_cache/9b5ad71b2ce5302211f9c61530b329a4922fc6a4 ]; then \
+		curl -o tiktoken_cache/9b5ad71b2ce5302211f9c61530b329a4922fc6a4 https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken; \
+	fi
+	@if [ ! -f tiktoken_cache/fb374d419588a4632f3f557e76b4b70aebbca790 ]; then \
+		curl -o tiktoken_cache/fb374d419588a4632f3f557e76b4b70aebbca790 https://openaipublic.blob.core.windows.net/encodings/o200k_base.tiktoken; \
+	fi
+	TIKTOKEN_CACHE_DIR=tiktoken_cache poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+integration_test integration_tests:
 	poetry run pytest $(TEST_FILE)
 
 test_watch:
diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py
index 233fa8700a436..a1b7e989e7181 100644
--- a/libs/partners/openai/langchain_openai/chat_models/base.py
+++ b/libs/partners/openai/langchain_openai/chat_models/base.py
@@ -129,6 +129,8 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
                     invalid_tool_calls.append(
                         make_invalid_tool_call(raw_tool_call, str(e))
                     )
+        if audio := _dict.get("audio"):
+            additional_kwargs["audio"] = audio
         return AIMessage(
             content=content,
             additional_kwargs=additional_kwargs,
@@ -219,6 +221,17 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
         # If tool calls present, content null value should be None not empty string.
         if "function_call" in message_dict or "tool_calls" in message_dict:
             message_dict["content"] = message_dict["content"] or None
+
+        if "audio" in message.additional_kwargs:
+            # openai doesn't support passing the data back - only the id
+            # https://platform.openai.com/docs/guides/audio/multi-turn-conversations
+            raw_audio = message.additional_kwargs["audio"]
+            audio = (
+                {"id": message.additional_kwargs["audio"]["id"]}
+                if "id" in raw_audio
+                else raw_audio
+            )
+            message_dict["audio"] = audio
     elif isinstance(message, SystemMessage):
         message_dict["role"] = "system"
     elif isinstance(message, FunctionMessage):
diff --git a/libs/partners/openai/poetry.lock b/libs/partners/openai/poetry.lock
index 9d9e88d6a471f..e253f4abf7e18 100644
--- a/libs/partners/openai/poetry.lock
+++ b/libs/partners/openai/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -13,13 +13,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.6.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
-    {file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
+    {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
+    {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
 ]
 
 [package.dependencies]
@@ -30,7 +30,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -46,101 +46,116 @@ files = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.2"
+version = "3.4.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
-    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12"},
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@@ -476,7 +493,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.9"
+version = "0.3.12"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -492,7 +509,7 @@ pydantic = [
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 PyYAML = ">=5.3"
-tenacity = "^8.1.0,!=8.4.0"
+tenacity = ">=8.1.0,!=8.4.0,<10.0.0"
 typing-extensions = ">=4.7"
 
 [package.source]
@@ -520,13 +537,13 @@ url = "../../standard-tests"
 
 [[package]]
 name = "langsmith"
-version = "0.1.131"
+version = "0.1.136"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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-    {file = "langsmith-0.1.131.tar.gz", hash = "sha256:626101a3bf3ca481e5110d5155ace8aa066e4e9cc2fa7d96c8290ade0fbff797"},
+    {file = "langsmith-0.1.136-py3-none-any.whl", hash = "sha256:cad2215eb7a754ee259878e19c558f4f8d3795aa1b699f087d4500e640f80d0a"},
+    {file = "langsmith-0.1.136.tar.gz", hash = "sha256:5c0de01a313db70dd9a85845c0f416a69b5b653b3e98ba413d7d41e8851315b1"},
 ]
 
 [package.dependencies]
@@ -541,38 +558,43 @@ requests-toolbelt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "mypy"
-version = "1.11.2"
+version = "1.12.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
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+    {file = "mypy-1.12.0-cp39-cp39-win_amd64.whl", hash = "sha256:06de0498798527451ffb60f68db0d368bd2bae2bbfb5237eae616d4330cc87aa"},
+    {file = "mypy-1.12.0-py3-none-any.whl", hash = "sha256:fd313226af375d52e1e36c383f39bf3836e1f192801116b31b090dfcd3ec5266"},
+    {file = "mypy-1.12.0.tar.gz", hash = "sha256:65a22d87e757ccd95cbbf6f7e181e6caa87128255eb2b6be901bb71b26d8a99d"},
 ]
 
 [package.dependencies]
@@ -644,13 +666,13 @@ files = [
 
 [[package]]
 name = "openai"
-version = "1.51.0"
+version = "1.52.0"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.51.0-py3-none-any.whl", hash = "sha256:d9affafb7e51e5a27dce78589d4964ce4d6f6d560307265933a94b2e3f3c5d2c"},
-    {file = "openai-1.51.0.tar.gz", hash = "sha256:8dc4f9d75ccdd5466fc8c99a952186eddceb9fd6ba694044773f3736a847149d"},
+    {file = "openai-1.52.0-py3-none-any.whl", hash = "sha256:0c249f20920183b0a2ca4f7dba7b0452df3ecd0fa7985eb1d91ad884bc3ced9c"},
+    {file = "openai-1.52.0.tar.gz", hash = "sha256:95c65a5f77559641ab8f3e4c3a050804f7b51d278870e2ec1f7444080bfe565a"},
 ]
 
 [package.dependencies]
@@ -1054,6 +1076,20 @@ pytest = ">=6.2.5"
 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]
 
+[[package]]
+name = "pytest-socket"
+version = "0.6.0"
+description = "Pytest Plugin to disable socket calls during tests"
+optional = false
+python-versions = ">=3.7,<4.0"
+files = [
+    {file = "pytest_socket-0.6.0-py3-none-any.whl", hash = "sha256:cca72f134ff01e0023c402e78d31b32e68da3efdf3493bf7788f8eba86a6824c"},
+    {file = "pytest_socket-0.6.0.tar.gz", hash = "sha256:363c1d67228315d4fc7912f1aabfd570de29d0e3db6217d61db5728adacd7138"},
+]
+
+[package.dependencies]
+pytest = ">=3.6.3"
+
 [[package]]
 name = "pytest-watcher"
 version = "0.3.5"
@@ -1334,13 +1370,13 @@ files = [
 
 [[package]]
 name = "syrupy"
-version = "4.7.1"
+version = "4.7.2"
 description = "Pytest Snapshot Test Utility"
 optional = false
 python-versions = ">=3.8.1"
 files = [
-    {file = "syrupy-4.7.1-py3-none-any.whl", hash = "sha256:be002267a512a4bedddfae2e026c93df1ea928ae10baadc09640516923376d41"},
-    {file = "syrupy-4.7.1.tar.gz", hash = "sha256:f9d4485f3f27d0e5df6ed299cac6fa32eb40a441915d988e82be5a4bdda335c8"},
+    {file = "syrupy-4.7.2-py3-none-any.whl", hash = "sha256:eae7ba6be5aed190237caa93be288e97ca1eec5ca58760e4818972a10c4acc64"},
+    {file = "syrupy-4.7.2.tar.gz", hash = "sha256:ea45e099f242de1bb53018c238f408a5bb6c82007bc687aefcbeaa0e1c2e935a"},
 ]
 
 [package.dependencies]
@@ -1348,13 +1384,13 @@ pytest = ">=7.0.0,<9.0.0"
 
 [[package]]
 name = "tenacity"
-version = "8.5.0"
+version = "9.0.0"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"},
-    {file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"},
+    {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
+    {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
 ]
 
 [package.extras]
@@ -1523,4 +1559,4 @@ watchmedo = ["PyYAML (>=3.10)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "fec30c155fdf4518310435f840b374ff86183936452fed3f59ad8ee0fcfefac5"
+content-hash = "82984f2d0f65cf0d18789eda536b66448839e19edf0466583e06ca3f0c709159"
diff --git a/libs/partners/openai/pyproject.toml b/libs/partners/openai/pyproject.toml
index c33100e5ecbd4..e5f2f3f9b717e 100644
--- a/libs/partners/openai/pyproject.toml
+++ b/libs/partners/openai/pyproject.toml
@@ -24,7 +24,7 @@ ignore_missing_imports = true
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
 langchain-core = "^0.3.9"
-openai = "^1.40.0"
+openai = "^1.52.0"
 tiktoken = ">=0.7,<1"
 
 [tool.ruff.lint]
@@ -72,6 +72,7 @@ syrupy = "^4.0.2"
 pytest-watcher = "^0.3.4"
 pytest-asyncio = "^0.21.1"
 pytest-cov = "^4.1.0"
+pytest-socket = "^0.6.0"
 [[tool.poetry.group.test.dependencies.numpy]]
 version = "^1"
 python = "<3.12"
diff --git a/libs/partners/openai/tests/integration_tests/chat_models/audio_input.wav b/libs/partners/openai/tests/integration_tests/chat_models/audio_input.wav
new file mode 100644
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HcmV?d00001

diff --git a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
index 950a52ce3ee93..ccce5722c3c8e 100644
--- a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
+++ b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
@@ -2,6 +2,7 @@
 
 import base64
 import json
+from pathlib import Path
 from typing import Any, AsyncIterator, List, Literal, Optional, cast
 
 import httpx
@@ -949,3 +950,71 @@ async def test_json_mode_async() -> None:
     assert isinstance(full, AIMessageChunk)
     assert isinstance(full.content, str)
     assert json.loads(full.content) == {"a": 1}
+
+
+def test_audio_output_modality() -> None:
+    llm = ChatOpenAI(
+        model="gpt-4o-audio-preview",
+        temperature=0,
+        model_kwargs={
+            "modalities": ["text", "audio"],
+            "audio": {"voice": "alloy", "format": "wav"},
+        },
+    )
+
+    history: List[BaseMessage] = [
+        HumanMessage("Make me a short audio clip of you yelling")
+    ]
+
+    output = llm.invoke(history)
+
+    assert isinstance(output, AIMessage)
+    assert "audio" in output.additional_kwargs
+
+    history.append(output)
+    history.append(HumanMessage("Make me a short audio clip of you whispering"))
+
+    output = llm.invoke(history)
+
+    assert isinstance(output, AIMessage)
+    assert "audio" in output.additional_kwargs
+
+
+def test_audio_input_modality() -> None:
+    llm = ChatOpenAI(
+        model="gpt-4o-audio-preview",
+        temperature=0,
+        model_kwargs={
+            "modalities": ["text", "audio"],
+            "audio": {"voice": "alloy", "format": "wav"},
+        },
+    )
+    filepath = Path(__file__).parent / "audio_input.wav"
+
+    audio_data = filepath.read_bytes()
+    b64_audio_data = base64.b64encode(audio_data).decode("utf-8")
+
+    history: list[BaseMessage] = [
+        HumanMessage(
+            [
+                {"type": "text", "text": "What is happening in this audio clip"},
+                {
+                    "type": "input_audio",
+                    "input_audio": {"data": b64_audio_data, "format": "wav"},
+                },
+            ]
+        )
+    ]
+
+    output = llm.invoke(history)
+
+    assert isinstance(output, AIMessage)
+    assert "audio" in output.additional_kwargs
+
+    history.append(output)
+    history.append(HumanMessage("Why?"))
+
+    output = llm.invoke(history)
+
+    assert isinstance(output, AIMessage)
+    assert "audio" in output.additional_kwargs
diff --git a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
index c6065a137e5c7..c6118a414a489 100644
--- a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
+++ b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
@@ -162,7 +162,12 @@ def test__convert_dict_to_message_tool_call() -> None:
                 name="GenerateUsername",
                 args="oops",
                 id="call_wm0JY6CdwOMZ4eTxHWUThDNz",
-                error="Function GenerateUsername arguments:\n\noops\n\nare not valid JSON. Received JSONDecodeError Expecting value: line 1 column 1 (char 0)",  # noqa: E501
+                error=(
+                    "Function GenerateUsername arguments:\n\noops\n\nare not "
+                    "valid JSON. Received JSONDecodeError Expecting value: line 1 "
+                    "column 1 (char 0)\nFor troubleshooting, visit: https://python"
+                    ".langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE"
+                ),
                 type="invalid_tool_call",
             )
         ],
diff --git a/libs/partners/openai/tests/unit_tests/llms/test_base.py b/libs/partners/openai/tests/unit_tests/llms/test_base.py
index e9b43190cd6d4..e6b281324f970 100644
--- a/libs/partners/openai/tests/unit_tests/llms/test_base.py
+++ b/libs/partners/openai/tests/unit_tests/llms/test_base.py
@@ -58,7 +58,7 @@ def mock_completion() -> dict:
     }
 
 
-@pytest.mark.parametrize("model", ["gpt-3.5-turbo-instruct", "text-davinci-003"])
+@pytest.mark.parametrize("model", ["gpt-3.5-turbo-instruct"])
 def test_get_token_ids(model: str) -> None:
     OpenAI(model=model).get_token_ids("foo")
     return
diff --git a/libs/partners/openai/tests/unit_tests/test_token_counts.py b/libs/partners/openai/tests/unit_tests/test_token_counts.py
index 66abb3869abab..6ffcda94c8611 100644
--- a/libs/partners/openai/tests/unit_tests/test_token_counts.py
+++ b/libs/partners/openai/tests/unit_tests/test_token_counts.py
@@ -16,6 +16,7 @@
 _CHAT_MODELS = ["gpt-4", "gpt-4-32k", "gpt-3.5-turbo"]
 
 
+@pytest.mark.xfail(reason="Old models require different tiktoken cached file")
 @pytest.mark.parametrize("model", _MODELS)
 def test_openai_get_num_tokens(model: str) -> None:
     """Test get_tokens."""

From 2cf2cefe3909f55dfe1e9171bb15752f6a4ee2df Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 18 Oct 2024 08:16:01 -0700
Subject: [PATCH 23/84] partners/openai: release 0.2.3 (#27457)

---
 libs/partners/openai/poetry.lock    | 2 +-
 libs/partners/openai/pyproject.toml | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/libs/partners/openai/poetry.lock b/libs/partners/openai/poetry.lock
index e253f4abf7e18..00d9445245c76 100644
--- a/libs/partners/openai/poetry.lock
+++ b/libs/partners/openai/poetry.lock
@@ -1559,4 +1559,4 @@ watchmedo = ["PyYAML (>=3.10)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "82984f2d0f65cf0d18789eda536b66448839e19edf0466583e06ca3f0c709159"
+content-hash = "8a60367ec11be17e65f368842c2da805aa6d450d4330b23eb6828964be2ad16c"
diff --git a/libs/partners/openai/pyproject.toml b/libs/partners/openai/pyproject.toml
index e5f2f3f9b717e..b889892039f00 100644
--- a/libs/partners/openai/pyproject.toml
+++ b/libs/partners/openai/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-openai"
-version = "0.2.2"
+version = "0.2.3"
 description = "An integration package connecting OpenAI and LangChain"
 authors = []
 readme = "README.md"
@@ -23,7 +23,7 @@ ignore_missing_imports = true
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.3.9"
+langchain-core = "^0.3.12"
 openai = "^1.52.0"
 tiktoken = ">=0.7,<1"
 

From 82242dfbb1d302958527d851be65341b3d532839 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 18 Oct 2024 10:06:55 -0700
Subject: [PATCH 24/84] docs: openai audio docs (#27459)

---
 docs/docs/integrations/chat/openai.ipynb | 156 ++++++++++++++++++++++-
 1 file changed, 155 insertions(+), 1 deletion(-)

diff --git a/docs/docs/integrations/chat/openai.ipynb b/docs/docs/integrations/chat/openai.ipynb
index 1aa43bcd08fd5..5d7f5c9424350 100644
--- a/docs/docs/integrations/chat/openai.ipynb
+++ b/docs/docs/integrations/chat/openai.ipynb
@@ -434,6 +434,160 @@
     "fine_tuned_model.invoke(messages)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5d5d9793",
+   "metadata": {},
+   "source": [
+    "## Multimodal Inputs\n",
+    "\n",
+    "OpenAI has models that support multimodal inputs. You can pass in images or audio to these models. For more information on how to do this in LangChain, head to the [multimodal inputs](/docs/how_to/multimodal_inputs) docs.\n",
+    "\n",
+    "You can see the list of models that support different modalities in [OpenAI's documentation](https://platform.openai.com/docs/models).\n",
+    "\n",
+    "At the time of this doc's writing, the main OpenAI models you would use would be:\n",
+    "\n",
+    "- Image inputs: `gpt-4o`, `gpt-4o-mini`\n",
+    "- Audio inputs: `gpt-4o-audio-preview`\n",
+    "\n",
+    "For an example of passing in image inputs, see the [multimodal inputs how-to guide](/docs/how_to/multimodal_inputs).\n",
+    "\n",
+    "Below is an example of passing audio inputs to `gpt-4o-audio-preview`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "39d08780",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"I'm sorry, but I can't create audio content that involves yelling. Is there anything else I can help you with?\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import base64\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(\n",
+    "    model=\"gpt-4o-audio-preview\",\n",
+    "    temperature=0,\n",
+    ")\n",
+    "\n",
+    "with open(\n",
+    "    \"../../../../libs/partners/openai/tests/integration_tests/chat_models/audio_input.wav\",\n",
+    "    \"rb\",\n",
+    ") as f:\n",
+    "    # b64 encode it\n",
+    "    audio = f.read()\n",
+    "    audio_b64 = base64.b64encode(audio).decode()\n",
+    "\n",
+    "\n",
+    "output_message = llm.invoke(\n",
+    "    [\n",
+    "        (\n",
+    "            \"human\",\n",
+    "            [\n",
+    "                {\"type\": \"text\", \"text\": \"Transcribe the following:\"},\n",
+    "                # the audio clip says \"I'm sorry, but I can't create...\"\n",
+    "                {\n",
+    "                    \"type\": \"input_audio\",\n",
+    "                    \"input_audio\": {\"data\": audio_b64, \"format\": \"wav\"},\n",
+    "                },\n",
+    "            ],\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "output_message.content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "feb4a499",
+   "metadata": {},
+   "source": [
+    "## Audio Generation (Preview)\n",
+    "\n",
+    ":::info\n",
+    "Requires `langchain-openai>=0.2.3`\n",
+    ":::\n",
+    "\n",
+    "OpenAI has a new [audio generation feature](https://platform.openai.com/docs/guides/audio?audio-generation-quickstart-example=audio-out) that allows you to use audio inputs and outputs with the `gpt-4o-audio-preview` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f67a2cac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(\n",
+    "    model=\"gpt-4o-audio-preview\",\n",
+    "    temperature=0,\n",
+    "    model_kwargs={\n",
+    "        \"modalities\": [\"text\", \"audio\"],\n",
+    "        \"audio\": {\"voice\": \"alloy\", \"format\": \"wav\"},\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "output_message = llm.invoke(\n",
+    "    [\n",
+    "        (\"human\", \"Are you made by OpenAI? Just answer yes or no\"),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7dd4e8b",
+   "metadata": {},
+   "source": [
+    "`output_message.additional_kwargs['audio']` will contain a dictionary like\n",
+    "```python\n",
+    "{\n",
+    "    'data': '<audio data b64-encoded',\n",
+    "    'expires_at': 1729268602,\n",
+    "    'id': 'audio_67127d6a44348190af62c1530ef0955a',\n",
+    "    'transcript': 'Yes.'\n",
+    "}\n",
+    "```\n",
+    "and the format will be what was passed in `model_kwargs['audio']['format']`.\n",
+    "\n",
+    "We can also pass this message with audio data back to the model as part of a message history before openai `expires_at` is reached.\n",
+    "\n",
+    ":::note\n",
+    "Output audio is stored under the `audio` key in `AIMessage.additional_kwargs`, but input content blocks are typed with an `input_audio` type and key in `HumanMessage.content` lists. \n",
+    "\n",
+    "For more information, see OpenAI's [audio docs](https://platform.openai.com/docs/guides/audio).\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f5ae473d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "history = [\n",
+    "    (\"human\", \"Are you made by OpenAI? Just answer yes or no\"),\n",
+    "    output_message,\n",
+    "    (\"human\", \"And what is your name? Just give your name.\"),\n",
+    "]\n",
+    "second_output_message = llm.invoke(history)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a796d728-971b-408b-88d5-440015bbb941",
@@ -447,7 +601,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },

From b468552859d4f4fca085138483628c619ba3db86 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 18 Oct 2024 10:39:32 -0700
Subject: [PATCH 25/84] docs: langgraph error code redirects (#27465)

---
 docs/vercel.json | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/vercel.json b/docs/vercel.json
index 09ede0a6583ce..d8a94fd108318 100644
--- a/docs/vercel.json
+++ b/docs/vercel.json
@@ -85,6 +85,10 @@
     {
       "source": "/docs/integrations/platforms/:path((?:anthropic|aws|google|huggingface|microsoft|openai)?/?)*",
       "destination": "/docs/integrations/providers/:path*"
+    },
+    {
+      "source": "/docs/troubleshooting/errors/:path((?:GRAPH_RECURSION_LIMIT|INVALID_CONCURRENT_GRAPH_UPDATE|INVALID_GRAPH_NODE_RETURN_VALUE|MULTIPLE_SUBGRAPHS)/?)*",
+      "destination": "https://langchain-ai.github.io/langgraph/troubleshooting/errors/:path*"
     }
   ]
 }

From 30660786b397d94e8101753f1f61949777bbc788 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 18 Oct 2024 11:59:54 -0700
Subject: [PATCH 26/84] langchain: release 0.3.4 (#27458)

---
 libs/langchain/poetry.lock    | 1110 ++++++++++++++++-----------------
 libs/langchain/pyproject.toml |    4 +-
 2 files changed, 540 insertions(+), 574 deletions(-)

diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock
index d9f1062304400..26982ada7beae 100644
--- a/libs/langchain/poetry.lock
+++ b/libs/langchain/poetry.lock
@@ -13,102 +13,102 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.10.9"
+version = "3.10.10"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -150,13 +150,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.6.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -167,7 +167,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
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 trio = ["trio (>=0.26.1)"]
 
 [[package]]
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 python-versions = ">=3.7.0"
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 ]
 
 [[package]]
@@ -1950,7 +1959,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.10"
+version = "0.3.12"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1975,7 +1984,7 @@ url = "../core"
 
 [[package]]
 name = "langchain-openai"
-version = "0.2.2"
+version = "0.2.3"
 description = "An integration package connecting OpenAI and LangChain"
 optional = true
 python-versions = ">=3.9,<4.0"
@@ -1983,8 +1992,8 @@ files = []
 develop = true
 
 [package.dependencies]
-langchain-core = "^0.3.9"
-openai = "^1.40.0"
+langchain-core = "^0.3.12"
+openai = "^1.52.0"
 tiktoken = ">=0.7,<1"
 
 [package.source]
@@ -2044,13 +2053,13 @@ types-requests = ">=2.31.0.2,<3.0.0.0"
 
 [[package]]
 name = "langsmith"
-version = "0.1.132"
+version = "0.1.136"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
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 ]
 
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@@ -4088,7 +4064,7 @@ aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
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 mssql = ["pyodbc"]
 mssql-pymssql = ["pymssql"]
 mssql-pyodbc = ["pyodbc"]
@@ -4433,13 +4409,13 @@ types-urllib3 = "*"
 
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+    {file = "yarl-1.15.4.tar.gz", hash = "sha256:a0c5e271058d148d730219ca4f33c5d841c6bd46e05b0da60fea7b516906ccd3"},
 ]
 
 [package.dependencies]
@@ -4883,4 +4849,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "8a6125974be50208f3262d31ee8863621e53c3f06048f7aba2d6fa823b19dea3"
+content-hash = "ed5b8b9206f8b9af193bdfac048d7084d7c5a8aeaef4fbdf6d90176c5f183985"
diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml
index 04419c29cf38b..051099d208166 100644
--- a/libs/langchain/pyproject.toml
+++ b/libs/langchain/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain"
-version = "0.3.3"
+version = "0.3.4"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -33,7 +33,7 @@ langchain-server = "langchain.server:main"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.3.10"
+langchain-core = "^0.3.12"
 langchain-text-splitters = "^0.3.0"
 langsmith = "^0.1.17"
 pydantic = "^2.7.4"

From a562c54f7d7d8396db2bb84fff02804f27d73f1e Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 18 Oct 2024 12:30:48 -0700
Subject: [PATCH 27/84] azure-dynamic-sessions: migrate to repo (#27468)

---
 .../azure-dynamic-sessions/.gitignore         |    1 -
 libs/partners/azure-dynamic-sessions/LICENSE  |   21 -
 libs/partners/azure-dynamic-sessions/Makefile |   61 -
 .../partners/azure-dynamic-sessions/README.md |   37 +-
 .../__init__.py                               |    7 -
 .../langchain_azure_dynamic_sessions/py.typed |    0
 .../tools/__init__.py                         |    7 -
 .../tools/sessions.py                         |  314 ---
 .../azure-dynamic-sessions/poetry.lock        | 2232 -----------------
 .../azure-dynamic-sessions/pyproject.toml     |  115 -
 .../scripts/check_imports.py                  |   19 -
 .../scripts/lint_imports.sh                   |   17 -
 .../azure-dynamic-sessions/tests/__init__.py  |    0
 .../tests/integration_tests/__init__.py       |    0
 .../tests/integration_tests/data/testdata.txt |    1 -
 .../tests/integration_tests/test_compile.py   |    7 -
 .../integration_tests/test_end_to_end.py      |   68 -
 .../tests/unit_tests/__init__.py              |    0
 .../tests/unit_tests/test_imports.py          |    9 -
 .../test_sessions_python_repl_tool.py         |  208 --
 20 files changed, 2 insertions(+), 3122 deletions(-)
 delete mode 100644 libs/partners/azure-dynamic-sessions/.gitignore
 delete mode 100644 libs/partners/azure-dynamic-sessions/LICENSE
 delete mode 100644 libs/partners/azure-dynamic-sessions/Makefile
 delete mode 100644 libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/__init__.py
 delete mode 100644 libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/py.typed
 delete mode 100644 libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/tools/__init__.py
 delete mode 100644 libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/tools/sessions.py
 delete mode 100644 libs/partners/azure-dynamic-sessions/poetry.lock
 delete mode 100644 libs/partners/azure-dynamic-sessions/pyproject.toml
 delete mode 100644 libs/partners/azure-dynamic-sessions/scripts/check_imports.py
 delete mode 100755 libs/partners/azure-dynamic-sessions/scripts/lint_imports.sh
 delete mode 100644 libs/partners/azure-dynamic-sessions/tests/__init__.py
 delete mode 100644 libs/partners/azure-dynamic-sessions/tests/integration_tests/__init__.py
 delete mode 100644 libs/partners/azure-dynamic-sessions/tests/integration_tests/data/testdata.txt
 delete mode 100644 libs/partners/azure-dynamic-sessions/tests/integration_tests/test_compile.py
 delete mode 100644 libs/partners/azure-dynamic-sessions/tests/integration_tests/test_end_to_end.py
 delete mode 100644 libs/partners/azure-dynamic-sessions/tests/unit_tests/__init__.py
 delete mode 100644 libs/partners/azure-dynamic-sessions/tests/unit_tests/test_imports.py
 delete mode 100644 libs/partners/azure-dynamic-sessions/tests/unit_tests/test_sessions_python_repl_tool.py

diff --git a/libs/partners/azure-dynamic-sessions/.gitignore b/libs/partners/azure-dynamic-sessions/.gitignore
deleted file mode 100644
index bee8a64b79a99..0000000000000
--- a/libs/partners/azure-dynamic-sessions/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-__pycache__
diff --git a/libs/partners/azure-dynamic-sessions/LICENSE b/libs/partners/azure-dynamic-sessions/LICENSE
deleted file mode 100644
index 426b65090341f..0000000000000
--- a/libs/partners/azure-dynamic-sessions/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2023 LangChain, Inc.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/libs/partners/azure-dynamic-sessions/Makefile b/libs/partners/azure-dynamic-sessions/Makefile
deleted file mode 100644
index ee56a8b930eaa..0000000000000
--- a/libs/partners/azure-dynamic-sessions/Makefile
+++ /dev/null
@@ -1,61 +0,0 @@
-.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
-
-# Default target executed when no arguments are given to make.
-all: help
-
-# Define a variable for the test file path.
-TEST_FILE ?= tests/unit_tests/
-
-test:
-	poetry run pytest $(TEST_FILE)
-
-tests:
-	poetry run pytest $(TEST_FILE)
-
-test_watch:
-	poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
-
-
-######################
-# LINTING AND FORMATTING
-######################
-
-# Define a variable for Python and notebook files.
-PYTHON_FILES=.
-MYPY_CACHE=.mypy_cache
-lint format: PYTHON_FILES=.
-lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/azure --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
-lint_package: PYTHON_FILES=langchain_azure_dynamic_sessions
-lint_tests: PYTHON_FILES=tests
-lint_tests: MYPY_CACHE=.mypy_cache_test
-
-lint lint_diff lint_package lint_tests:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
-	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
-
-format format_diff:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
-
-spell_check:
-	poetry run codespell --toml pyproject.toml
-
-spell_fix:
-	poetry run codespell --toml pyproject.toml -w
-
-check_imports: $(shell find langchain_azure_dynamic_sessions -name '*.py')
-	poetry run python ./scripts/check_imports.py $^
-
-######################
-# HELP
-######################
-
-help:
-	@echo '----'
-	@echo 'check_imports				- check imports'
-	@echo 'format                       - run code formatters'
-	@echo 'lint                         - run linters'
-	@echo 'test                         - run unit tests'
-	@echo 'tests                        - run unit tests'
-	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
diff --git a/libs/partners/azure-dynamic-sessions/README.md b/libs/partners/azure-dynamic-sessions/README.md
index 32b6af3e4b0f1..2e46ff1e5a62b 100644
--- a/libs/partners/azure-dynamic-sessions/README.md
+++ b/libs/partners/azure-dynamic-sessions/README.md
@@ -1,36 +1,3 @@
-# langchain-azure-dynamic-sessions
-
-This package contains the LangChain integration for Azure Container Apps dynamic sessions. You can use it to add a secure and scalable code interpreter to your agents.
-
-## Installation
-
-```bash
-pip install -U langchain-azure-dynamic-sessions
-```
-
-## Usage
-
-You first need to create an Azure Container Apps session pool and obtain its management endpoint. Then you can use the `SessionsPythonREPLTool` tool to give your agent the ability to execute Python code.
-
-```python
-from langchain_azure_dynamic_sessions import SessionsPythonREPLTool
-
-
-# get the management endpoint from the session pool in the Azure portal
-tool = SessionsPythonREPLTool(pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT)
-
-prompt = hub.pull("hwchase17/react")
-tools=[tool]
-react_agent = create_react_agent(
-    llm=llm,
-    tools=tools,
-    prompt=prompt,
-)
-
-react_agent_executor = AgentExecutor(agent=react_agent, tools=tools, verbose=True, handle_parsing_errors=True)
-
-react_agent_executor.invoke({"input": "What is the current time in Vancouver, Canada?"})
-```
-
-By default, the tool uses `DefaultAzureCredential` to authenticate with Azure. If you're using a user-assigned managed identity, you must set the `AZURE_CLIENT_ID` environment variable to the ID of the managed identity.
+This package has moved!
 
+https://github.com/langchain-ai/langchain-azure/tree/main/libs/azure-dynamic-sessions
diff --git a/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/__init__.py b/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/__init__.py
deleted file mode 100644
index 7fcbdf207f36a..0000000000000
--- a/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""This package provides tools for managing dynamic sessions in Azure."""
-
-from langchain_azure_dynamic_sessions.tools.sessions import SessionsPythonREPLTool
-
-__all__ = [
-    "SessionsPythonREPLTool",
-]
diff --git a/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/py.typed b/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/py.typed
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/tools/__init__.py b/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/tools/__init__.py
deleted file mode 100644
index 7fcbdf207f36a..0000000000000
--- a/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/tools/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""This package provides tools for managing dynamic sessions in Azure."""
-
-from langchain_azure_dynamic_sessions.tools.sessions import SessionsPythonREPLTool
-
-__all__ = [
-    "SessionsPythonREPLTool",
-]
diff --git a/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/tools/sessions.py b/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/tools/sessions.py
deleted file mode 100644
index f71464488cfea..0000000000000
--- a/libs/partners/azure-dynamic-sessions/langchain_azure_dynamic_sessions/tools/sessions.py
+++ /dev/null
@@ -1,314 +0,0 @@
-"""This is the Azure Dynamic Sessions module.
-
-This module provides the SessionsPythonREPLTool class for
-managing dynamic sessions in Azure.
-"""
-
-import importlib.metadata
-import json
-import os
-import re
-import urllib
-from copy import deepcopy
-from dataclasses import dataclass
-from datetime import datetime, timedelta, timezone
-from io import BytesIO
-from typing import Any, BinaryIO, Callable, List, Literal, Optional, Tuple
-from uuid import uuid4
-
-import requests
-from azure.core.credentials import AccessToken
-from azure.identity import DefaultAzureCredential
-from langchain_core.tools import BaseTool
-
-try:
-    _package_version = importlib.metadata.version("langchain-azure-dynamic-sessions")
-except importlib.metadata.PackageNotFoundError:
-    _package_version = "0.0.0"
-USER_AGENT = f"langchain-azure-dynamic-sessions/{_package_version} (Language=Python)"
-
-
-def _access_token_provider_factory() -> Callable[[], Optional[str]]:
-    """Factory function for creating an access token provider function.
-
-    Returns:
-        Callable[[], Optional[str]]: The access token provider function
-    """
-    access_token: Optional[AccessToken] = None
-
-    def access_token_provider() -> Optional[str]:
-        nonlocal access_token
-        if access_token is None or datetime.fromtimestamp(
-            access_token.expires_on, timezone.utc
-        ) < datetime.now(timezone.utc) + timedelta(minutes=5):
-            credential = DefaultAzureCredential()
-            access_token = credential.get_token("https://dynamicsessions.io/.default")
-        return access_token.token
-
-    return access_token_provider
-
-
-def _sanitize_input(query: str) -> str:
-    """Sanitize input to the python REPL.
-
-    Remove whitespace, backtick & python (if llm mistakes python console as terminal)
-
-    Args:
-        query: The query to sanitize
-
-    Returns:
-        str: The sanitized query
-    """
-    # Removes `, whitespace & python from start
-    query = re.sub(r"^(\s|`)*(?i:python)?\s*", "", query)
-    # Removes whitespace & ` from end
-    query = re.sub(r"(\s|`)*$", "", query)
-    return query
-
-
-@dataclass
-class RemoteFileMetadata:
-    """Metadata for a file in the session."""
-
-    filename: str
-    """The filename relative to `/mnt/data`."""
-
-    size_in_bytes: int
-    """The size of the file in bytes."""
-
-    @property
-    def full_path(self) -> str:
-        """Get the full path of the file."""
-        return f"/mnt/data/{self.filename}"
-
-    @staticmethod
-    def from_dict(data: dict) -> "RemoteFileMetadata":
-        """Create a RemoteFileMetadata object from a dictionary."""
-        properties = data.get("properties", {})
-        return RemoteFileMetadata(
-            filename=properties.get("filename"),
-            size_in_bytes=properties.get("size"),
-        )
-
-
-class SessionsPythonREPLTool(BaseTool):
-    r"""Azure Dynamic Sessions tool.
-
-    Setup:
-        Install ``langchain-azure-dynamic-sessions`` and create a session pool, which you can do by following the instructions [here](https://learn.microsoft.com/en-us/azure/container-apps/sessions-code-interpreter?tabs=azure-cli#create-a-session-pool-with-azure-cli).
-
-        .. code-block:: bash
-
-            pip install -U langchain-azure-dynamic-sessions
-
-        .. code-block:: python
-
-            import getpass
-
-            POOL_MANAGEMENT_ENDPOINT = getpass.getpass("Enter the management endpoint of the session pool: ")
-
-    Instantiation:
-        .. code-block:: python
-
-            from langchain_azure_dynamic_sessions import SessionsPythonREPLTool
-
-            tool = SessionsPythonREPLTool(
-                pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT
-            )
-
-
-    Invocation with args:
-        .. code-block:: python
-
-            tool.invoke("6 * 7")
-
-        .. code-block:: python
-
-            '{\\n  "result": 42,\\n  "stdout": "",\\n  "stderr": ""\\n}'
-
-    Invocation with ToolCall:
-
-        .. code-block:: python
-
-            tool.invoke({"args": {"input":"6 * 7"}, "id": "1", "name": tool.name, "type": "tool_call"})
-
-        .. code-block:: python
-
-            '{\\n  "result": 42,\\n  "stdout": "",\\n  "stderr": ""\\n}'
-    """  # noqa: E501
-
-    name: str = "Python_REPL"
-    description: str = (
-        "A Python shell. Use this to execute python commands "
-        "when you need to perform calculations or computations. "
-        "Input should be a valid python command. "
-        "Returns a JSON object with the result, stdout, and stderr. "
-    )
-
-    sanitize_input: bool = True
-    """Whether to sanitize input to the python REPL."""
-
-    pool_management_endpoint: str
-    """The management endpoint of the session pool. Should end with a '/'."""
-
-    access_token_provider: Callable[[], Optional[str]] = (
-        _access_token_provider_factory()
-    )
-    """A function that returns the access token to use for the session pool."""
-
-    session_id: str = str(uuid4())
-    """The session ID to use for the code interpreter. Defaults to a random UUID."""
-
-    response_format: Literal["content_and_artifact"] = "content_and_artifact"
-
-    def _build_url(self, path: str) -> str:
-        pool_management_endpoint = self.pool_management_endpoint
-        if not pool_management_endpoint:
-            raise ValueError("pool_management_endpoint is not set")
-        if not pool_management_endpoint.endswith("/"):
-            pool_management_endpoint += "/"
-        encoded_session_id = urllib.parse.quote(self.session_id)
-        query = f"identifier={encoded_session_id}&api-version=2024-02-02-preview"
-        query_separator = "&" if "?" in pool_management_endpoint else "?"
-        full_url = pool_management_endpoint + path + query_separator + query
-        return full_url
-
-    def execute(self, python_code: str) -> Any:
-        """Execute Python code in the session."""
-        if self.sanitize_input:
-            python_code = _sanitize_input(python_code)
-
-        access_token = self.access_token_provider()
-        api_url = self._build_url("code/execute")
-        headers = {
-            "Authorization": f"Bearer {access_token}",
-            "Content-Type": "application/json",
-            "User-Agent": USER_AGENT,
-        }
-        body = {
-            "properties": {
-                "codeInputType": "inline",
-                "executionType": "synchronous",
-                "code": python_code,
-            }
-        }
-
-        response = requests.post(api_url, headers=headers, json=body)
-        response.raise_for_status()
-        response_json = response.json()
-        properties = response_json.get("properties", {})
-        return properties
-
-    def _run(self, python_code: str, **kwargs: Any) -> Tuple[str, dict]:
-        response = self.execute(python_code)
-
-        # if the result is an image, remove the base64 data
-        result = deepcopy(response.get("result"))
-        if isinstance(result, dict):
-            if result.get("type") == "image" and "base64_data" in result:
-                result.pop("base64_data")
-
-        content = json.dumps(
-            {
-                "result": result,
-                "stdout": response.get("stdout"),
-                "stderr": response.get("stderr"),
-            },
-            indent=2,
-        )
-        return content, response
-
-    def upload_file(
-        self,
-        *,
-        data: Optional[BinaryIO] = None,
-        remote_file_path: Optional[str] = None,
-        local_file_path: Optional[str] = None,
-    ) -> RemoteFileMetadata:
-        """Upload a file to the session.
-
-        Args:
-            data: The data to upload.
-            remote_file_path: The path to upload the file to, relative to
-                `/mnt/data`. If local_file_path is provided, this is defaulted
-                to its filename.
-            local_file_path: The path to the local file to upload.
-
-        Returns:
-            RemoteFileMetadata: The metadata for the uploaded file
-        """
-        if data and local_file_path:
-            raise ValueError("data and local_file_path cannot be provided together")
-
-        if data:
-            file_data = data
-        elif local_file_path:
-            if not remote_file_path:
-                remote_file_path = os.path.basename(local_file_path)
-            file_data = open(local_file_path, "rb")
-
-        access_token = self.access_token_provider()
-        api_url = self._build_url("files/upload")
-        headers = {
-            "Authorization": f"Bearer {access_token}",
-            "User-Agent": USER_AGENT,
-        }
-        files = [("file", (remote_file_path, file_data, "application/octet-stream"))]
-
-        response = requests.request(
-            "POST", api_url, headers=headers, data={}, files=files
-        )
-        response.raise_for_status()
-
-        response_json = response.json()
-        return RemoteFileMetadata.from_dict(response_json["value"][0])
-
-    def download_file(
-        self, *, remote_file_path: str, local_file_path: Optional[str] = None
-    ) -> BinaryIO:
-        """Download a file from the session.
-
-        Args:
-            remote_file_path: The path to download the file from,
-                relative to `/mnt/data`.
-            local_file_path: The path to save the downloaded file to.
-                If not provided, the file is returned as a BufferedReader.
-
-        Returns:
-            BinaryIO: The data of the downloaded file.
-        """
-        access_token = self.access_token_provider()
-        encoded_remote_file_path = urllib.parse.quote(remote_file_path)
-        api_url = self._build_url(f"files/content/{encoded_remote_file_path}")
-        headers = {
-            "Authorization": f"Bearer {access_token}",
-            "User-Agent": USER_AGENT,
-        }
-
-        response = requests.get(api_url, headers=headers)
-        response.raise_for_status()
-
-        if local_file_path:
-            with open(local_file_path, "wb") as f:
-                f.write(response.content)
-
-        return BytesIO(response.content)
-
-    def list_files(self) -> List[RemoteFileMetadata]:
-        """List the files in the session.
-
-        Returns:
-            list[RemoteFileMetadata]: The metadata for the files in the session
-        """
-        access_token = self.access_token_provider()
-        api_url = self._build_url("files")
-        headers = {
-            "Authorization": f"Bearer {access_token}",
-            "User-Agent": USER_AGENT,
-        }
-
-        response = requests.get(api_url, headers=headers)
-        response.raise_for_status()
-
-        response_json = response.json()
-        return [RemoteFileMetadata.from_dict(entry) for entry in response_json["value"]]
diff --git a/libs/partners/azure-dynamic-sessions/poetry.lock b/libs/partners/azure-dynamic-sessions/poetry.lock
deleted file mode 100644
index f98adbfe21014..0000000000000
--- a/libs/partners/azure-dynamic-sessions/poetry.lock
+++ /dev/null
@@ -1,2232 +0,0 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
-
-[[package]]
-name = "annotated-types"
-version = "0.7.0"
-description = "Reusable constraint types to use with typing.Annotated"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
-    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
-]
-
-[[package]]
-name = "anyio"
-version = "4.4.0"
-description = "High level compatibility layer for multiple asynchronous event loop implementations"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
-    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
-]
-
-[package.dependencies]
-exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
-idna = ">=2.8"
-sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
-
-[package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.23)"]
-
-[[package]]
-name = "appnope"
-version = "0.1.4"
-description = "Disable App Nap on macOS >= 10.9"
-optional = false
-python-versions = ">=3.6"
-files = [
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-    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
-]
-
-[[package]]
-name = "asttokens"
-version = "2.4.1"
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-optional = false
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-    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
-]
-
-[package.dependencies]
-six = ">=1.12.0"
-
-[package.extras]
-astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
-test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
-
-[[package]]
-name = "azure-core"
-version = "1.31.0"
-description = "Microsoft Azure Core Library for Python"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "azure_core-1.31.0-py3-none-any.whl", hash = "sha256:22954de3777e0250029360ef31d80448ef1be13b80a459bff80ba7073379e2cd"},
-    {file = "azure_core-1.31.0.tar.gz", hash = "sha256:656a0dd61e1869b1506b7c6a3b31d62f15984b1a573d6326f6aa2f3e4123284b"},
-]
-
-[package.dependencies]
-requests = ">=2.21.0"
-six = ">=1.11.0"
-typing-extensions = ">=4.6.0"
-
-[package.extras]
-aio = ["aiohttp (>=3.0)"]
-
-[[package]]
-name = "azure-identity"
-version = "1.17.1"
-description = "Microsoft Azure Identity Library for Python"
-optional = false
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-    {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
-]
-
-[[package]]
-name = "zipp"
-version = "3.20.2"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
-    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.9,<4.0"
-content-hash = "d11e5891e0d87b594d20f287cff793c80f836a0faae3648141ab62669baf7c0b"
diff --git a/libs/partners/azure-dynamic-sessions/pyproject.toml b/libs/partners/azure-dynamic-sessions/pyproject.toml
deleted file mode 100644
index 4bd203f06b3bd..0000000000000
--- a/libs/partners/azure-dynamic-sessions/pyproject.toml
+++ /dev/null
@@ -1,115 +0,0 @@
-[build-system]
-requires = ["poetry-core>=1.0.0"]
-build-backend = "poetry.core.masonry.api"
-
-[tool.poetry]
-name = "langchain-azure-dynamic-sessions"
-version = "0.2.0"
-description = "An integration package connecting Azure Container Apps dynamic sessions and LangChain"
-authors = []
-readme = "README.md"
-repository = "https://github.com/langchain-ai/langchain"
-license = "MIT"
-
-[tool.mypy]
-disallow_untyped_defs = "True"
-
-[tool.poetry.urls]
-"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/azure-dynamic-sessions"
-"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-azure-dynamic-sessions%3D%3D0%22&expanded=true"
-
-[tool.poetry.dependencies]
-python = ">=3.9,<4.0"
-langchain-core = "^0.3.0"
-azure-identity = "^1.16.0"
-requests = "^2.31.0"
-
-[tool.ruff.lint]
-select = ["E", "F", "I", "D"]
-
-[tool.coverage.run]
-omit = ["tests/*"]
-
-[tool.pytest.ini_options]
-addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
-markers = [
-    "requires: mark tests as requiring a specific library",
-    "compile: mark placeholder test used to compile integration tests without running them",
-]
-asyncio_mode = "auto"
-
-[tool.poetry.group.test]
-optional = true
-
-[tool.poetry.group.test_integration]
-optional = true
-
-[tool.poetry.group.codespell]
-optional = true
-
-[tool.poetry.group.lint]
-optional = true
-
-[tool.poetry.group.dev]
-optional = true
-
-[tool.ruff.lint.pydocstyle]
-convention = "google"
-
-[tool.ruff.lint.per-file-ignores]
-"tests/**" = ["D"]
-
-[tool.poetry.group.test.dependencies]
-pytest = "^7.3.0"
-freezegun = "^1.2.2"
-pytest-mock = "^3.10.0"
-syrupy = "^4.0.2"
-pytest-watcher = "^0.3.4"
-pytest-asyncio = "^0.21.1"
-python-dotenv = "^1.0.1"
-# TODO: hack to fix 3.9 builds
-cffi = [
-    { version = "<1.17.1", python = "<3.10" },
-    { version = "*", python = ">=3.10" },
-]
-
-[tool.poetry.group.test_integration.dependencies]
-pytest = "^7.3.0"
-python-dotenv = "^1.0.1"
-
-[tool.poetry.group.codespell.dependencies]
-codespell = "^2.2.0"
-
-[tool.poetry.group.lint.dependencies]
-ruff = "^0.5"
-python-dotenv = "^1.0.1"
-pytest = "^7.3.0"
-# TODO: hack to fix 3.9 builds
-cffi = [
-    { version = "<1.17.1", python = "<3.10" },
-    { version = "*", python = ">=3.10" },
-]
-
-[tool.poetry.group.dev.dependencies]
-ipykernel = "^6.29.4"
-langchainhub = "^0.1.15"
-
-[tool.poetry.group.typing.dependencies]
-mypy = "^1.10"
-types-requests = "^2.31.0.20240406"
-
-[tool.poetry.group.test.dependencies.langchain-core]
-path = "../../core"
-develop = true
-
-[tool.poetry.group.dev.dependencies.langchain-core]
-path = "../../core"
-develop = true
-
-[tool.poetry.group.dev.dependencies.langchain-openai]
-path = "../openai"
-develop = true
-
-[tool.poetry.group.typing.dependencies.langchain-core]
-path = "../../core"
-develop = true
diff --git a/libs/partners/azure-dynamic-sessions/scripts/check_imports.py b/libs/partners/azure-dynamic-sessions/scripts/check_imports.py
deleted file mode 100644
index dc7f5ea8b049f..0000000000000
--- a/libs/partners/azure-dynamic-sessions/scripts/check_imports.py
+++ /dev/null
@@ -1,19 +0,0 @@
-"""This module checks for specific import statements in the codebase."""
-
-import sys
-import traceback
-from importlib.machinery import SourceFileLoader
-
-if __name__ == "__main__":
-    files = sys.argv[1:]
-    has_failure = False
-    for file in files:
-        try:
-            SourceFileLoader("x", file).load_module()
-        except Exception:
-            has_failure = True
-            print(file)
-            traceback.print_exc()
-            print()
-
-    sys.exit(1 if has_failure else 0)
diff --git a/libs/partners/azure-dynamic-sessions/scripts/lint_imports.sh b/libs/partners/azure-dynamic-sessions/scripts/lint_imports.sh
deleted file mode 100755
index 695613c7ba8fd..0000000000000
--- a/libs/partners/azure-dynamic-sessions/scripts/lint_imports.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/bin/bash
-
-set -eu
-
-# Initialize a variable to keep track of errors
-errors=0
-
-# make sure not importing from langchain or langchain_experimental
-git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
-git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
-
-# Decide on an exit status based on the errors
-if [ "$errors" -gt 0 ]; then
-    exit 1
-else
-    exit 0
-fi
diff --git a/libs/partners/azure-dynamic-sessions/tests/__init__.py b/libs/partners/azure-dynamic-sessions/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/libs/partners/azure-dynamic-sessions/tests/integration_tests/__init__.py b/libs/partners/azure-dynamic-sessions/tests/integration_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/libs/partners/azure-dynamic-sessions/tests/integration_tests/data/testdata.txt b/libs/partners/azure-dynamic-sessions/tests/integration_tests/data/testdata.txt
deleted file mode 100644
index 2211df3faee13..0000000000000
--- a/libs/partners/azure-dynamic-sessions/tests/integration_tests/data/testdata.txt
+++ /dev/null
@@ -1 +0,0 @@
-test file content
\ No newline at end of file
diff --git a/libs/partners/azure-dynamic-sessions/tests/integration_tests/test_compile.py b/libs/partners/azure-dynamic-sessions/tests/integration_tests/test_compile.py
deleted file mode 100644
index 5196f4c0f8c51..0000000000000
--- a/libs/partners/azure-dynamic-sessions/tests/integration_tests/test_compile.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import pytest  # type: ignore[import-not-found]
-
-
-@pytest.mark.compile
-def test_placeholder() -> None:
-    """Used for compiling integration tests without running any real tests."""
-    pass
diff --git a/libs/partners/azure-dynamic-sessions/tests/integration_tests/test_end_to_end.py b/libs/partners/azure-dynamic-sessions/tests/integration_tests/test_end_to_end.py
deleted file mode 100644
index aa2a32df69be0..0000000000000
--- a/libs/partners/azure-dynamic-sessions/tests/integration_tests/test_end_to_end.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import json
-import os
-from io import BytesIO
-
-import dotenv  # type: ignore[import-not-found]
-
-from langchain_azure_dynamic_sessions import SessionsPythonREPLTool
-
-dotenv.load_dotenv()
-
-POOL_MANAGEMENT_ENDPOINT = os.getenv("AZURE_DYNAMIC_SESSIONS_POOL_MANAGEMENT_ENDPOINT")
-TEST_DATA_PATH = os.path.join(os.path.dirname(__file__), "data", "testdata.txt")
-TEST_DATA_CONTENT = open(TEST_DATA_PATH, "rb").read()
-
-
-def test_end_to_end() -> None:
-    tool = SessionsPythonREPLTool(pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT)  # type: ignore[arg-type]
-    result = tool.run("print('hello world')\n1 + 1")
-    assert json.loads(result) == {
-        "result": 2,
-        "stdout": "hello world\n",
-        "stderr": "",
-    }
-
-    # upload file content
-    uploaded_file1_metadata = tool.upload_file(
-        remote_file_path="test1.txt", data=BytesIO(b"hello world!!!!!")
-    )
-    assert uploaded_file1_metadata.filename == "test1.txt"
-    assert uploaded_file1_metadata.size_in_bytes == 16
-    assert uploaded_file1_metadata.full_path == "/mnt/data/test1.txt"
-    downloaded_file1 = tool.download_file(remote_file_path="test1.txt")
-    assert downloaded_file1.read() == b"hello world!!!!!"
-
-    # upload file from buffer
-    with open(TEST_DATA_PATH, "rb") as f:
-        uploaded_file2_metadata = tool.upload_file(remote_file_path="test2.txt", data=f)
-        assert uploaded_file2_metadata.filename == "test2.txt"
-        downloaded_file2 = tool.download_file(remote_file_path="test2.txt")
-        assert downloaded_file2.read() == TEST_DATA_CONTENT
-
-    # upload file from disk, specifying remote file path
-    uploaded_file3_metadata = tool.upload_file(
-        remote_file_path="test3.txt", local_file_path=TEST_DATA_PATH
-    )
-    assert uploaded_file3_metadata.filename == "test3.txt"
-    downloaded_file3 = tool.download_file(remote_file_path="test3.txt")
-    assert downloaded_file3.read() == TEST_DATA_CONTENT
-
-    # upload file from disk, without specifying remote file path
-    uploaded_file4_metadata = tool.upload_file(local_file_path=TEST_DATA_PATH)
-    assert uploaded_file4_metadata.filename == os.path.basename(TEST_DATA_PATH)
-    downloaded_file4 = tool.download_file(
-        remote_file_path=uploaded_file4_metadata.filename
-    )
-    assert downloaded_file4.read() == TEST_DATA_CONTENT
-
-    # list files
-    remote_files_metadata = tool.list_files()
-    assert len(remote_files_metadata) == 4
-    remote_file_paths = [metadata.filename for metadata in remote_files_metadata]
-    expected_filenames = [
-        "test1.txt",
-        "test2.txt",
-        "test3.txt",
-        os.path.basename(TEST_DATA_PATH),
-    ]
-    assert set(remote_file_paths) == set(expected_filenames)
diff --git a/libs/partners/azure-dynamic-sessions/tests/unit_tests/__init__.py b/libs/partners/azure-dynamic-sessions/tests/unit_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/libs/partners/azure-dynamic-sessions/tests/unit_tests/test_imports.py b/libs/partners/azure-dynamic-sessions/tests/unit_tests/test_imports.py
deleted file mode 100644
index b39c459182e28..0000000000000
--- a/libs/partners/azure-dynamic-sessions/tests/unit_tests/test_imports.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from langchain_azure_dynamic_sessions import __all__
-
-EXPECTED_ALL = [
-    "SessionsPythonREPLTool",
-]
-
-
-def test_all_imports() -> None:
-    assert sorted(EXPECTED_ALL) == sorted(__all__)
diff --git a/libs/partners/azure-dynamic-sessions/tests/unit_tests/test_sessions_python_repl_tool.py b/libs/partners/azure-dynamic-sessions/tests/unit_tests/test_sessions_python_repl_tool.py
deleted file mode 100644
index 7d11a8255d6fa..0000000000000
--- a/libs/partners/azure-dynamic-sessions/tests/unit_tests/test_sessions_python_repl_tool.py
+++ /dev/null
@@ -1,208 +0,0 @@
-import json
-import re
-import time
-from unittest import mock
-from urllib.parse import parse_qs, urlparse
-
-from azure.core.credentials import AccessToken
-
-from langchain_azure_dynamic_sessions import SessionsPythonREPLTool
-from langchain_azure_dynamic_sessions.tools.sessions import (
-    _access_token_provider_factory,
-)
-
-POOL_MANAGEMENT_ENDPOINT = "https://westus2.dynamicsessions.io/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/sessions-rg/sessionPools/my-pool"
-
-
-def test_default_access_token_provider_returns_token() -> None:
-    access_token_provider = _access_token_provider_factory()
-    with mock.patch(
-        "azure.identity.DefaultAzureCredential.get_token"
-    ) as mock_get_token:
-        mock_get_token.return_value = AccessToken("token_value", 0)
-        access_token = access_token_provider()
-        assert access_token == "token_value"
-
-
-def test_default_access_token_provider_returns_cached_token() -> None:
-    access_token_provider = _access_token_provider_factory()
-    with mock.patch(
-        "azure.identity.DefaultAzureCredential.get_token"
-    ) as mock_get_token:
-        mock_get_token.return_value = AccessToken(
-            "token_value", int(time.time() + 1000)
-        )
-        access_token = access_token_provider()
-        assert access_token == "token_value"
-        assert mock_get_token.call_count == 1
-
-        mock_get_token.return_value = AccessToken(
-            "new_token_value", int(time.time() + 1000)
-        )
-        access_token = access_token_provider()
-        assert access_token == "token_value"
-        assert mock_get_token.call_count == 1
-
-
-def test_default_access_token_provider_refreshes_expiring_token() -> None:
-    access_token_provider = _access_token_provider_factory()
-    with mock.patch(
-        "azure.identity.DefaultAzureCredential.get_token"
-    ) as mock_get_token:
-        mock_get_token.return_value = AccessToken("token_value", int(time.time() - 1))
-        access_token = access_token_provider()
-        assert access_token == "token_value"
-        assert mock_get_token.call_count == 1
-
-        mock_get_token.return_value = AccessToken(
-            "new_token_value", int(time.time() + 1000)
-        )
-        access_token = access_token_provider()
-        assert access_token == "new_token_value"
-        assert mock_get_token.call_count == 2
-
-
-@mock.patch("requests.post")
-@mock.patch("azure.identity.DefaultAzureCredential.get_token")
-def test_code_execution_calls_api(
-    mock_get_token: mock.MagicMock, mock_post: mock.MagicMock
-) -> None:
-    tool = SessionsPythonREPLTool(pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT)
-    mock_post.return_value.json.return_value = {
-        "$id": "1",
-        "properties": {
-            "$id": "2",
-            "status": "Success",
-            "stdout": "hello world\n",
-            "stderr": "",
-            "result": "",
-            "executionTimeInMilliseconds": 33,
-        },
-    }
-    mock_get_token.return_value = AccessToken("token_value", int(time.time() + 1000))
-
-    result = tool.run("print('hello world')")
-
-    assert json.loads(result) == {
-        "result": "",
-        "stdout": "hello world\n",
-        "stderr": "",
-    }
-
-    api_url = f"{POOL_MANAGEMENT_ENDPOINT}/code/execute"
-    headers = {
-        "Authorization": "Bearer token_value",
-        "Content-Type": "application/json",
-        "User-Agent": mock.ANY,
-    }
-    body = {
-        "properties": {
-            "codeInputType": "inline",
-            "executionType": "synchronous",
-            "code": "print('hello world')",
-        }
-    }
-    mock_post.assert_called_once_with(mock.ANY, headers=headers, json=body)
-
-    called_headers = mock_post.call_args.kwargs["headers"]
-    assert re.match(
-        r"^langchain-azure-dynamic-sessions/\d+\.\d+\.\d+.* \(Language=Python\)",
-        called_headers["User-Agent"],
-    )
-
-    called_api_url = mock_post.call_args.args[0]
-    assert called_api_url.startswith(api_url)
-
-
-@mock.patch("requests.post")
-@mock.patch("azure.identity.DefaultAzureCredential.get_token")
-def test_uses_specified_session_id(
-    mock_get_token: mock.MagicMock, mock_post: mock.MagicMock
-) -> None:
-    tool = SessionsPythonREPLTool(
-        pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT,
-        session_id="00000000-0000-0000-0000-000000000003",
-    )
-    mock_post.return_value.json.return_value = {
-        "$id": "1",
-        "properties": {
-            "$id": "2",
-            "status": "Success",
-            "stdout": "",
-            "stderr": "",
-            "result": "2",
-            "executionTimeInMilliseconds": 33,
-        },
-    }
-    mock_get_token.return_value = AccessToken("token_value", int(time.time() + 1000))
-    tool.run("1 + 1")
-    call_url = mock_post.call_args.args[0]
-    parsed_url = urlparse(call_url)
-    call_identifier = parse_qs(parsed_url.query)["identifier"][0]
-    assert call_identifier == "00000000-0000-0000-0000-000000000003"
-
-
-def test_sanitizes_input() -> None:
-    tool = SessionsPythonREPLTool(pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT)
-    with mock.patch("requests.post") as mock_post:
-        mock_post.return_value.json.return_value = {
-            "$id": "1",
-            "properties": {
-                "$id": "2",
-                "status": "Success",
-                "stdout": "",
-                "stderr": "",
-                "result": "",
-                "executionTimeInMilliseconds": 33,
-            },
-        }
-        tool.run("```python\nprint('hello world')\n```")
-        body = mock_post.call_args.kwargs["json"]
-        assert body["properties"]["code"] == "print('hello world')"
-
-
-def test_does_not_sanitize_input() -> None:
-    tool = SessionsPythonREPLTool(
-        pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT, sanitize_input=False
-    )
-    with mock.patch("requests.post") as mock_post:
-        mock_post.return_value.json.return_value = {
-            "$id": "1",
-            "properties": {
-                "$id": "2",
-                "status": "Success",
-                "stdout": "",
-                "stderr": "",
-                "result": "",
-                "executionTimeInMilliseconds": 33,
-            },
-        }
-        tool.run("```python\nprint('hello world')\n```")
-        body = mock_post.call_args.kwargs["json"]
-        assert body["properties"]["code"] == "```python\nprint('hello world')\n```"
-
-
-def test_uses_custom_access_token_provider() -> None:
-    def custom_access_token_provider() -> str:
-        return "custom_token"
-
-    tool = SessionsPythonREPLTool(
-        pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT,
-        access_token_provider=custom_access_token_provider,
-    )
-
-    with mock.patch("requests.post") as mock_post:
-        mock_post.return_value.json.return_value = {
-            "$id": "1",
-            "properties": {
-                "$id": "2",
-                "status": "Success",
-                "stdout": "",
-                "stderr": "",
-                "result": "",
-                "executionTimeInMilliseconds": 33,
-            },
-        }
-        tool.run("print('hello world')")
-        headers = mock_post.call_args.kwargs["headers"]
-        assert headers["Authorization"] == "Bearer custom_token"

From 4ceb28009af0621234cf93c61539f6c93088c3b8 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 18 Oct 2024 12:35:12 -0700
Subject: [PATCH 28/84] mongodb: migrate to repo (#27467)

---
 libs/partners/mongodb/.gitignore              |    1 -
 libs/partners/mongodb/LICENSE                 |   21 -
 libs/partners/mongodb/Makefile                |   59 -
 libs/partners/mongodb/README.md               |   40 +-
 .../mongodb/langchain_mongodb/__init__.py     |   20 -
 .../mongodb/langchain_mongodb/cache.py        |  308 ---
 .../chat_message_histories.py                 |  162 --
 .../mongodb/langchain_mongodb/index.py        |  270 ---
 .../mongodb/langchain_mongodb/pipelines.py    |  160 --
 .../mongodb/langchain_mongodb/py.typed        |    0
 .../langchain_mongodb/retrievers/__init__.py  |   15 -
 .../retrievers/full_text_search.py            |   59 -
 .../retrievers/hybrid_search.py               |  126 -
 .../mongodb/langchain_mongodb/utils.py        |  183 --
 .../mongodb/langchain_mongodb/vectorstores.py |  796 ------
 libs/partners/mongodb/poetry.lock             | 2158 -----------------
 libs/partners/mongodb/pyproject.toml          |  103 -
 .../partners/mongodb/scripts/check_imports.py |   17 -
 libs/partners/mongodb/scripts/lint_imports.sh |   17 -
 libs/partners/mongodb/tests/__init__.py       |    0
 .../tests/integration_tests/__init__.py       |    0
 .../tests/integration_tests/test_cache.py     |  161 --
 .../integration_tests/test_chain_example.py   |  144 --
 .../test_chat_message_histories.py            |   43 -
 .../tests/integration_tests/test_compile.py   |    7 -
 .../tests/integration_tests/test_index.py     |   83 -
 .../integration_tests/test_retrievers.py      |  176 --
 .../integration_tests/test_vectorstores.py    |  473 ----
 .../mongodb/tests/unit_tests/__init__.py      |    0
 .../mongodb/tests/unit_tests/test_cache.py    |  215 --
 .../unit_tests/test_chat_message_histories.py |   44 -
 .../mongodb/tests/unit_tests/test_imports.py  |   12 -
 .../mongodb/tests/unit_tests/test_index.py    |   72 -
 .../tests/unit_tests/test_vectorstores.py     |  191 --
 libs/partners/mongodb/tests/utils.py          |  273 ---
 35 files changed, 2 insertions(+), 6407 deletions(-)
 delete mode 100644 libs/partners/mongodb/.gitignore
 delete mode 100644 libs/partners/mongodb/LICENSE
 delete mode 100644 libs/partners/mongodb/Makefile
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/__init__.py
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/cache.py
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/chat_message_histories.py
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/index.py
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/pipelines.py
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/py.typed
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/retrievers/__init__.py
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/retrievers/full_text_search.py
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/retrievers/hybrid_search.py
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/utils.py
 delete mode 100644 libs/partners/mongodb/langchain_mongodb/vectorstores.py
 delete mode 100644 libs/partners/mongodb/poetry.lock
 delete mode 100644 libs/partners/mongodb/pyproject.toml
 delete mode 100644 libs/partners/mongodb/scripts/check_imports.py
 delete mode 100755 libs/partners/mongodb/scripts/lint_imports.sh
 delete mode 100644 libs/partners/mongodb/tests/__init__.py
 delete mode 100644 libs/partners/mongodb/tests/integration_tests/__init__.py
 delete mode 100644 libs/partners/mongodb/tests/integration_tests/test_cache.py
 delete mode 100644 libs/partners/mongodb/tests/integration_tests/test_chain_example.py
 delete mode 100644 libs/partners/mongodb/tests/integration_tests/test_chat_message_histories.py
 delete mode 100644 libs/partners/mongodb/tests/integration_tests/test_compile.py
 delete mode 100644 libs/partners/mongodb/tests/integration_tests/test_index.py
 delete mode 100644 libs/partners/mongodb/tests/integration_tests/test_retrievers.py
 delete mode 100644 libs/partners/mongodb/tests/integration_tests/test_vectorstores.py
 delete mode 100644 libs/partners/mongodb/tests/unit_tests/__init__.py
 delete mode 100644 libs/partners/mongodb/tests/unit_tests/test_cache.py
 delete mode 100644 libs/partners/mongodb/tests/unit_tests/test_chat_message_histories.py
 delete mode 100644 libs/partners/mongodb/tests/unit_tests/test_imports.py
 delete mode 100644 libs/partners/mongodb/tests/unit_tests/test_index.py
 delete mode 100644 libs/partners/mongodb/tests/unit_tests/test_vectorstores.py
 delete mode 100644 libs/partners/mongodb/tests/utils.py

diff --git a/libs/partners/mongodb/.gitignore b/libs/partners/mongodb/.gitignore
deleted file mode 100644
index bee8a64b79a99..0000000000000
--- a/libs/partners/mongodb/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-__pycache__
diff --git a/libs/partners/mongodb/LICENSE b/libs/partners/mongodb/LICENSE
deleted file mode 100644
index fc0602feecdd6..0000000000000
--- a/libs/partners/mongodb/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2024 LangChain, Inc.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/libs/partners/mongodb/Makefile b/libs/partners/mongodb/Makefile
deleted file mode 100644
index 63fc9d46f0ead..0000000000000
--- a/libs/partners/mongodb/Makefile
+++ /dev/null
@@ -1,59 +0,0 @@
-.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
-
-# Default target executed when no arguments are given to make.
-all: help
-
-# Define a variable for the test file path.
-TEST_FILE ?= tests/unit_tests/
-integration_test integration_tests: TEST_FILE=tests/integration_tests/
-
-test tests integration_test integration_tests:
-	poetry run pytest $(TEST_FILE)
-
-test_watch:
-	poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
-
-
-######################
-# LINTING AND FORMATTING
-######################
-
-# Define a variable for Python and notebook files.
-PYTHON_FILES=.
-MYPY_CACHE=.mypy_cache
-lint format: PYTHON_FILES=.
-lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/mongodb --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
-lint_package: PYTHON_FILES=langchain_mongodb
-lint_tests: PYTHON_FILES=tests
-lint_tests: MYPY_CACHE=.mypy_cache_test
-
-lint lint_diff lint_package lint_tests:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
-	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
-
-format format_diff:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
-
-spell_check:
-	poetry run codespell --toml pyproject.toml
-
-spell_fix:
-	poetry run codespell --toml pyproject.toml -w
-
-check_imports: $(shell find langchain_mongodb -name '*.py')
-	poetry run python ./scripts/check_imports.py $^
-
-######################
-# HELP
-######################
-
-help:
-	@echo '----'
-	@echo 'check_imports				- check imports'
-	@echo 'format                       - run code formatters'
-	@echo 'lint                         - run linters'
-	@echo 'test                         - run unit tests'
-	@echo 'tests                        - run unit tests'
-	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
diff --git a/libs/partners/mongodb/README.md b/libs/partners/mongodb/README.md
index a8b08a2a71bca..d99bbccd53264 100644
--- a/libs/partners/mongodb/README.md
+++ b/libs/partners/mongodb/README.md
@@ -1,39 +1,3 @@
-# langchain-mongodb
+This package has moved!
 
-# Installation
-```
-pip install -U langchain-mongodb
-```
-
-# Usage
-- See [Getting Started with the LangChain Integration](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/#get-started-with-the-langchain-integration) for a walkthrough on using your first LangChain implementation with MongoDB Atlas.
-
-## Using MongoDBAtlasVectorSearch
-```python
-from langchain_mongodb import MongoDBAtlasVectorSearch
-
-# Pull MongoDB Atlas URI from environment variables
-MONGODB_ATLAS_CLUSTER_URI = os.environ.get("MONGODB_ATLAS_CLUSTER_URI")
-
-DB_NAME = "langchain_db"
-COLLECTION_NAME = "test"
-ATLAS_VECTOR_SEARCH_INDEX_NAME = "index_name"
-MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]
-
-# Create the vector search via `from_connection_string`
-vector_search = MongoDBAtlasVectorSearch.from_connection_string(
-    MONGODB_ATLAS_CLUSTER_URI,
-    DB_NAME + "." + COLLECTION_NAME,
-    OpenAIEmbeddings(disallowed_special=()),
-    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
-)
-
-# Initialize MongoDB python client
-client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
-# Create the vector search via instantiation
-vector_search_2 = MongoDBAtlasVectorSearch(
-    collection=MONGODB_COLLECTION,
-    embeddings=OpenAIEmbeddings(disallowed_special=()),
-    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
-)
-```
+https://github.com/langchain-ai/langchain-mongodb/tree/main/libs/mongodb
diff --git a/libs/partners/mongodb/langchain_mongodb/__init__.py b/libs/partners/mongodb/langchain_mongodb/__init__.py
deleted file mode 100644
index d7aab3e785f87..0000000000000
--- a/libs/partners/mongodb/langchain_mongodb/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-"""
-Integrate your operational database and vector search in a single, unified,
-fully managed platform with full vector database capabilities on MongoDB Atlas.
-
-
-Store your operational data, metadata, and vector embeddings in oue VectorStore,
-MongoDBAtlasVectorSearch.
-Insert into a Chain via a Vector, FullText, or Hybrid Retriever.
-"""
-
-from langchain_mongodb.cache import MongoDBAtlasSemanticCache, MongoDBCache
-from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
-from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
-
-__all__ = [
-    "MongoDBAtlasVectorSearch",
-    "MongoDBChatMessageHistory",
-    "MongoDBCache",
-    "MongoDBAtlasSemanticCache",
-]
diff --git a/libs/partners/mongodb/langchain_mongodb/cache.py b/libs/partners/mongodb/langchain_mongodb/cache.py
deleted file mode 100644
index a6ce680c5168f..0000000000000
--- a/libs/partners/mongodb/langchain_mongodb/cache.py
+++ /dev/null
@@ -1,308 +0,0 @@
-"""LangChain MongoDB Caches."""
-
-import json
-import logging
-import time
-from importlib.metadata import version
-from typing import Any, Callable, Dict, Optional, Union
-
-from langchain_core.caches import RETURN_VAL_TYPE, BaseCache
-from langchain_core.embeddings import Embeddings
-from langchain_core.load.dump import dumps
-from langchain_core.load.load import loads
-from langchain_core.outputs import Generation
-from pymongo import MongoClient
-from pymongo.collection import Collection
-from pymongo.database import Database
-from pymongo.driver_info import DriverInfo
-
-from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
-
-logger = logging.getLogger(__file__)
-
-
-class MongoDBCache(BaseCache):
-    """MongoDB Atlas cache
-
-    A cache that uses MongoDB Atlas as a backend
-    """
-
-    PROMPT = "prompt"
-    LLM = "llm"
-    RETURN_VAL = "return_val"
-
-    def __init__(
-        self,
-        connection_string: str,
-        collection_name: str = "default",
-        database_name: str = "default",
-        **kwargs: Dict[str, Any],
-    ) -> None:
-        """
-        Initialize Atlas Cache. Creates collection on instantiation
-
-        Args:
-            collection_name (str): Name of collection for cache to live.
-                Defaults to "default".
-            connection_string (str): Connection URI to MongoDB Atlas.
-                Defaults to "default".
-            database_name (str): Name of database for cache to live.
-                Defaults to "default".
-        """
-        self.client = _generate_mongo_client(connection_string)
-        self.__database_name = database_name
-        self.__collection_name = collection_name
-
-        if self.__collection_name not in self.database.list_collection_names():
-            self.database.create_collection(self.__collection_name)
-            # Create an index on key and llm_string
-            self.collection.create_index([self.PROMPT, self.LLM])
-
-    @property
-    def database(self) -> Database:
-        """Returns the database used to store cache values."""
-        return self.client[self.__database_name]
-
-    @property
-    def collection(self) -> Collection:
-        """Returns the collection used to store cache values."""
-        return self.database[self.__collection_name]
-
-    def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
-        """Look up based on prompt and llm_string."""
-        return_doc = (
-            self.collection.find_one(self._generate_keys(prompt, llm_string)) or {}
-        )
-        return_val = return_doc.get(self.RETURN_VAL)
-        return _loads_generations(return_val) if return_val else None  # type: ignore
-
-    def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
-        """Update cache based on prompt and llm_string."""
-        self.collection.update_one(
-            {**self._generate_keys(prompt, llm_string)},
-            {"$set": {self.RETURN_VAL: _dumps_generations(return_val)}},
-            upsert=True,
-        )
-
-    def _generate_keys(self, prompt: str, llm_string: str) -> Dict[str, str]:
-        """Create keyed fields for caching layer"""
-        return {self.PROMPT: prompt, self.LLM: llm_string}
-
-    def clear(self, **kwargs: Any) -> None:
-        """Clear cache that can take additional keyword arguments.
-        Any additional arguments will propagate as filtration criteria for
-        what gets deleted.
-
-        E.g.
-            # Delete only entries that have llm_string as "fake-model"
-            self.clear(llm_string="fake-model")
-        """
-        self.collection.delete_many({**kwargs})
-
-
-class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
-    """MongoDB Atlas Semantic cache.
-
-    A Cache backed by a MongoDB Atlas server with vector-store support
-    """
-
-    LLM = "llm_string"
-    RETURN_VAL = "return_val"
-
-    def __init__(
-        self,
-        connection_string: str,
-        embedding: Embeddings,
-        collection_name: str = "default",
-        database_name: str = "default",
-        index_name: str = "default",
-        wait_until_ready: Optional[float] = None,
-        score_threshold: Optional[float] = None,
-        **kwargs: Dict[str, Any],
-    ):
-        """
-        Initialize Atlas VectorSearch Cache.
-        Assumes collection exists before instantiation
-
-        Args:
-            connection_string (str): MongoDB URI to connect to MongoDB Atlas cluster.
-            embedding (Embeddings): Text embedding model to use.
-            collection_name (str): MongoDB Collection to add the texts to.
-                Defaults to "default".
-            database_name (str): MongoDB Database where to store texts.
-                Defaults to "default".
-            index_name: Name of the Atlas Search index.
-                defaults to 'default'
-            wait_until_ready (float): Wait this time for Atlas to finish indexing
-                the stored text. Defaults to None.
-        """
-        client = _generate_mongo_client(connection_string)
-        self.collection = client[database_name][collection_name]
-        self.score_threshold = score_threshold
-        self._wait_until_ready = wait_until_ready
-        super().__init__(
-            collection=self.collection,
-            embedding=embedding,
-            index_name=index_name,
-            **kwargs,  # type: ignore
-        )
-
-    def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
-        """Look up based on prompt and llm_string."""
-        post_filter_pipeline = (
-            [{"$match": {"score": {"$gte": self.score_threshold}}}]
-            if self.score_threshold
-            else None
-        )
-
-        search_response = self.similarity_search_with_score(
-            prompt,
-            1,
-            pre_filter={self.LLM: {"$eq": llm_string}},
-            post_filter_pipeline=post_filter_pipeline,
-        )
-        if search_response:
-            return_val = search_response[0][0].metadata.get(self.RETURN_VAL)
-            response = _loads_generations(return_val) or return_val  # type: ignore
-            return response
-        return None
-
-    def update(
-        self,
-        prompt: str,
-        llm_string: str,
-        return_val: RETURN_VAL_TYPE,
-        wait_until_ready: Optional[float] = None,
-    ) -> None:
-        """Update cache based on prompt and llm_string."""
-        self.add_texts(
-            [prompt],
-            [
-                {
-                    self.LLM: llm_string,
-                    self.RETURN_VAL: _dumps_generations(return_val),
-                }
-            ],
-        )
-        wait = self._wait_until_ready if wait_until_ready is None else wait_until_ready
-
-        def is_indexed() -> bool:
-            return self.lookup(prompt, llm_string) == return_val
-
-        if wait:
-            _wait_until(is_indexed, return_val, timeout=wait)
-
-    def clear(self, **kwargs: Any) -> None:
-        """Clear cache that can take additional keyword arguments.
-        Any additional arguments will propagate as filtration criteria for
-        what gets deleted. It will delete any locally cached content regardless
-
-        E.g.
-            # Delete only entries that have llm_string as "fake-model"
-            self.clear(llm_string="fake-model")
-        """
-        self.collection.delete_many({**kwargs})
-
-
-def _generate_mongo_client(connection_string: str) -> MongoClient:
-    return MongoClient(
-        connection_string,
-        driver=DriverInfo(name="Langchain", version=version("langchain-mongodb")),
-    )
-
-
-def _dumps_generations(generations: RETURN_VAL_TYPE) -> str:
-    """
-    Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation`
-
-    Args:
-        generations (RETURN_VAL_TYPE): A list of language model generations.
-
-    Returns:
-        str: a single string representing a list of generations.
-
-    This, and "_dumps_generations" are duplicated in this utility
-    from modules: "libs/community/langchain_community/cache.py"
-
-    This function and its counterpart rely on
-    the dumps/loads pair with Reviver, so are able to deal
-    with all subclasses of Generation.
-
-    Each item in the list can be `dumps`ed to a string,
-    then we make the whole list of strings into a json-dumped.
-    """
-    return json.dumps([dumps(_item) for _item in generations])
-
-
-def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]:
-    """
-    Deserialization of a string into a generic RETURN_VAL_TYPE
-    (i.e. a sequence of `Generation`).
-
-    Args:
-        generations_str (str): A string representing a list of generations.
-
-    Returns:
-        RETURN_VAL_TYPE: A list of generations.
-
-
-    This function and its counterpart rely on
-    the dumps/loads pair with Reviver, so are able to deal
-    with all subclasses of Generation.
-
-    See `_dumps_generations`, the inverse of this function.
-
-    Compatible with the legacy cache-blob format
-    Does not raise exceptions for malformed entries, just logs a warning
-    and returns none: the caller should be prepared for such a cache miss.
-
-
-    """
-    try:
-        generations = [loads(_item_str) for _item_str in json.loads(generations_str)]
-        return generations
-    except (json.JSONDecodeError, TypeError):
-        # deferring the (soft) handling to after the legacy-format attempt
-        pass
-
-    try:
-        gen_dicts = json.loads(generations_str)
-        # not relying on `_load_generations_from_json` (which could disappear):
-        generations = [Generation(**generation_dict) for generation_dict in gen_dicts]
-        logger.warning(
-            f"Legacy 'Generation' cached blob encountered: '{generations_str}'"
-        )
-        return generations
-    except (json.JSONDecodeError, TypeError):
-        logger.warning(
-            f"Malformed/unparsable cached blob encountered: '{generations_str}'"
-        )
-        return None
-
-
-def _wait_until(
-    predicate: Callable, success_description: Any, timeout: float = 10.0
-) -> None:
-    """Wait up to 10 seconds (by default) for predicate to be true.
-
-    E.g.:
-
-        wait_until(lambda: client.primary == ('a', 1),
-                'connect to the primary')
-
-    If the lambda-expression isn't true after 10 seconds, we raise
-    AssertionError("Didn't ever connect to the primary").
-
-    Returns the predicate's first true value.
-    """
-    start = time.time()
-    interval = min(float(timeout) / 100, 0.1)
-    while True:
-        retval = predicate()
-        if retval:
-            return retval
-
-        if time.time() - start > timeout:
-            raise TimeoutError("Didn't ever %s" % success_description)
-
-        time.sleep(interval)
diff --git a/libs/partners/mongodb/langchain_mongodb/chat_message_histories.py b/libs/partners/mongodb/langchain_mongodb/chat_message_histories.py
deleted file mode 100644
index 7604f43e03c76..0000000000000
--- a/libs/partners/mongodb/langchain_mongodb/chat_message_histories.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import json
-import logging
-from typing import Dict, List, Optional
-
-from langchain_core.chat_history import BaseChatMessageHistory
-from langchain_core.messages import (
-    BaseMessage,
-    message_to_dict,
-    messages_from_dict,
-)
-from pymongo import MongoClient, errors
-
-logger = logging.getLogger(__name__)
-
-DEFAULT_DBNAME = "chat_history"
-DEFAULT_COLLECTION_NAME = "message_store"
-DEFAULT_SESSION_ID_KEY = "SessionId"
-DEFAULT_HISTORY_KEY = "History"
-
-
-class MongoDBChatMessageHistory(BaseChatMessageHistory):
-    """Chat message history that stores history in MongoDB.
-
-    Setup:
-        Install ``langchain-mongodb`` python package.
-
-        .. code-block:: bash
-
-            pip install langchain-mongodb
-
-    Instantiate:
-        .. code-block:: python
-
-            from langchain_mongodb import MongoDBChatMessageHistory
-
-
-            history = MongoDBChatMessageHistory(
-                connection_string="mongodb://your-host:your-port/",  # mongodb://localhost:27017/
-                session_id = "your-session-id",
-            )
-
-    Add and retrieve messages:
-        .. code-block:: python
-
-            # Add single message
-            history.add_message(message)
-
-            # Add batch messages
-            history.add_messages([message1, message2, message3, ...])
-
-            # Add human message
-            history.add_user_message(human_message)
-
-            # Add ai message
-            history.add_ai_message(ai_message)
-
-            # Retrieve messages
-            messages = history.messages
-    """  # noqa: E501
-
-    def __init__(
-        self,
-        connection_string: str,
-        session_id: str,
-        database_name: str = DEFAULT_DBNAME,
-        collection_name: str = DEFAULT_COLLECTION_NAME,
-        *,
-        session_id_key: str = DEFAULT_SESSION_ID_KEY,
-        history_key: str = DEFAULT_HISTORY_KEY,
-        create_index: bool = True,
-        history_size: Optional[int] = None,
-        index_kwargs: Optional[Dict] = None,
-    ):
-        """Initialize with a MongoDBChatMessageHistory instance.
-
-        Args:
-            connection_string: str
-                connection string to connect to MongoDB.
-            session_id: str
-                arbitrary key that is used to store the messages of
-                 a single chat session.
-            database_name: Optional[str]
-                name of the database to use.
-            collection_name: Optional[str]
-                name of the collection to use.
-            session_id_key: Optional[str]
-                name of the field that stores the session id.
-            history_key: Optional[str]
-                name of the field that stores the chat history.
-            create_index: Optional[bool]
-                whether to create an index on the session id field.
-            history_size: Optional[int]
-                count of (most recent) messages to fetch from MongoDB.
-            index_kwargs: Optional[Dict]
-                additional keyword arguments to pass to the index creation.
-        """
-        self.connection_string = connection_string
-        self.session_id = session_id
-        self.database_name = database_name
-        self.collection_name = collection_name
-        self.session_id_key = session_id_key
-        self.history_key = history_key
-        self.history_size = history_size
-
-        try:
-            self.client: MongoClient = MongoClient(connection_string)
-        except errors.ConnectionFailure as error:
-            logger.error(error)
-
-        self.db = self.client[database_name]
-        self.collection = self.db[collection_name]
-
-        if create_index:
-            index_kwargs = index_kwargs or {}
-            self.collection.create_index(self.session_id_key, **index_kwargs)
-
-    @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
-        """Retrieve the messages from MongoDB"""
-        try:
-            if self.history_size is None:
-                cursor = self.collection.find({self.session_id_key: self.session_id})
-            else:
-                skip_count = max(
-                    0,
-                    self.collection.count_documents(
-                        {self.session_id_key: self.session_id}
-                    )
-                    - self.history_size,
-                )
-                cursor = self.collection.find(
-                    {self.session_id_key: self.session_id}, skip=skip_count
-                )
-        except errors.OperationFailure as error:
-            logger.error(error)
-
-        if cursor:
-            items = [json.loads(document[self.history_key]) for document in cursor]
-        else:
-            items = []
-
-        messages = messages_from_dict(items)
-        return messages
-
-    def add_message(self, message: BaseMessage) -> None:
-        """Append the message to the record in MongoDB"""
-        try:
-            self.collection.insert_one(
-                {
-                    self.session_id_key: self.session_id,
-                    self.history_key: json.dumps(message_to_dict(message)),
-                }
-            )
-        except errors.WriteError as err:
-            logger.error(err)
-
-    def clear(self) -> None:
-        """Clear session memory from MongoDB"""
-        try:
-            self.collection.delete_many({self.session_id_key: self.session_id})
-        except errors.WriteError as err:
-            logger.error(err)
diff --git a/libs/partners/mongodb/langchain_mongodb/index.py b/libs/partners/mongodb/langchain_mongodb/index.py
deleted file mode 100644
index 59cb6d32eaadd..0000000000000
--- a/libs/partners/mongodb/langchain_mongodb/index.py
+++ /dev/null
@@ -1,270 +0,0 @@
-"""Search Index Commands"""
-
-import logging
-from time import monotonic, sleep
-from typing import Any, Callable, Dict, List, Optional
-
-from pymongo.collection import Collection
-from pymongo.errors import OperationFailure
-from pymongo.operations import SearchIndexModel
-
-logger = logging.getLogger(__file__)
-
-
-def _search_index_error_message() -> str:
-    return (
-        "Search index operations are not currently available on shared clusters, "
-        "such as MO. They require dedicated clusters >= M10. "
-        "You may still perform vector search. "
-        "You simply must set up indexes manually. Follow the instructions here: "
-        "https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/"
-    )
-
-
-def _vector_search_index_definition(
-    dimensions: int,
-    path: str,
-    similarity: str,
-    filters: Optional[List[str]] = None,
-    **kwargs: Any,
-) -> Dict[str, Any]:
-    # https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/
-    fields = [
-        {
-            "numDimensions": dimensions,
-            "path": path,
-            "similarity": similarity,
-            "type": "vector",
-        },
-    ]
-    if filters:
-        for field in filters:
-            fields.append({"type": "filter", "path": field})
-    definition = {"fields": fields}
-    definition.update(kwargs)
-    return definition
-
-
-def create_vector_search_index(
-    collection: Collection,
-    index_name: str,
-    dimensions: int,
-    path: str,
-    similarity: str,
-    filters: Optional[List[str]] = None,
-    *,
-    wait_until_complete: Optional[float] = None,
-    **kwargs: Any,
-) -> None:
-    """Experimental Utility function to create a vector search index
-
-    Args:
-        collection (Collection): MongoDB Collection
-        index_name (str): Name of Index
-        dimensions (int): Number of dimensions in embedding
-        path (str): field with vector embedding
-        similarity (str): The similarity score used for the index
-        filters (List[str]): Fields/paths to index to allow filtering in $vectorSearch
-        wait_until_complete (Optional[float]): If provided, number of seconds to wait
-            until search index is ready.
-        kwargs: Keyword arguments supplying any additional options to SearchIndexModel.
-    """
-    logger.info("Creating Search Index %s on %s", index_name, collection.name)
-
-    try:
-        result = collection.create_search_index(
-            SearchIndexModel(
-                definition=_vector_search_index_definition(
-                    dimensions=dimensions,
-                    path=path,
-                    similarity=similarity,
-                    filters=filters,
-                    **kwargs,
-                ),
-                name=index_name,
-                type="vectorSearch",
-            )
-        )
-    except OperationFailure as e:
-        raise OperationFailure(_search_index_error_message()) from e
-
-    if wait_until_complete:
-        _wait_for_predicate(
-            predicate=lambda: _is_index_ready(collection, index_name),
-            err=f"{index_name=} did not complete in {wait_until_complete}!",
-            timeout=wait_until_complete,
-        )
-    logger.info(result)
-
-
-def drop_vector_search_index(
-    collection: Collection,
-    index_name: str,
-    *,
-    wait_until_complete: Optional[float] = None,
-) -> None:
-    """Drop a created vector search index
-
-    Args:
-        collection (Collection): MongoDB Collection with index to be dropped
-        index_name (str): Name of the MongoDB index
-        wait_until_complete (Optional[float]): If provided, number of seconds to wait
-            until search index is ready.
-    """
-    logger.info(
-        "Dropping Search Index %s from Collection: %s", index_name, collection.name
-    )
-    try:
-        collection.drop_search_index(index_name)
-    except OperationFailure as e:
-        if "CommandNotSupported" in str(e):
-            raise OperationFailure(_search_index_error_message()) from e
-        # else this most likely means an ongoing drop request was made so skip
-    if wait_until_complete:
-        _wait_for_predicate(
-            predicate=lambda: len(list(collection.list_search_indexes())) == 0,
-            err=f"Index {index_name} did not drop in {wait_until_complete}!",
-            timeout=wait_until_complete,
-        )
-    logger.info("Vector Search index %s.%s dropped", collection.name, index_name)
-
-
-def update_vector_search_index(
-    collection: Collection,
-    index_name: str,
-    dimensions: int,
-    path: str,
-    similarity: str,
-    filters: Optional[List[str]] = None,
-    *,
-    wait_until_complete: Optional[float] = None,
-    **kwargs: Any,
-) -> None:
-    """Update a search index.
-
-    Replace the existing index definition with the provided definition.
-
-    Args:
-        collection (Collection): MongoDB Collection
-        index_name (str): Name of Index
-        dimensions (int): Number of dimensions in embedding
-        path (str): field with vector embedding
-        similarity (str): The similarity score used for the index.
-        filters (List[str]): Fields/paths to index to allow filtering in $vectorSearch
-        wait_until_complete (Optional[float]): If provided, number of seconds to wait
-            until search index is ready.
-        kwargs: Keyword arguments supplying any additional options to SearchIndexModel.
-    """
-
-    logger.info(
-        "Updating Search Index %s from Collection: %s", index_name, collection.name
-    )
-    try:
-        collection.update_search_index(
-            name=index_name,
-            definition=_vector_search_index_definition(
-                dimensions=dimensions,
-                path=path,
-                similarity=similarity,
-                filters=filters,
-                **kwargs,
-            ),
-        )
-    except OperationFailure as e:
-        raise OperationFailure(_search_index_error_message()) from e
-
-    if wait_until_complete:
-        _wait_for_predicate(
-            predicate=lambda: _is_index_ready(collection, index_name),
-            err=f"Index {index_name} update did not complete in {wait_until_complete}!",
-            timeout=wait_until_complete,
-        )
-    logger.info("Update succeeded")
-
-
-def _is_index_ready(collection: Collection, index_name: str) -> bool:
-    """Check for the index name in the list of available search indexes to see if the
-    specified index is of status READY
-
-    Args:
-        collection (Collection): MongoDB Collection to for the search indexes
-        index_name (str): Vector Search Index name
-
-    Returns:
-        bool : True if the index is present and READY false otherwise
-    """
-    try:
-        search_indexes = collection.list_search_indexes(index_name)
-    except OperationFailure as e:
-        raise OperationFailure(_search_index_error_message()) from e
-
-    for index in search_indexes:
-        if index["type"] == "vectorSearch" and index["status"] == "READY":
-            return True
-    return False
-
-
-def _wait_for_predicate(
-    predicate: Callable, err: str, timeout: float = 120, interval: float = 0.5
-) -> None:
-    """Generic to block until the predicate returns true
-
-    Args:
-        predicate (Callable[, bool]): A function that returns a boolean value
-        err (str): Error message to raise if nothing occurs
-        timeout (float, optional): Wait time for predicate. Defaults to TIMEOUT.
-        interval (float, optional): Interval to check predicate. Defaults to DELAY.
-
-    Raises:
-        TimeoutError: _description_
-    """
-    start = monotonic()
-    while not predicate():
-        if monotonic() - start > timeout:
-            raise TimeoutError(err)
-        sleep(interval)
-
-
-def create_fulltext_search_index(
-    collection: Collection,
-    index_name: str,
-    field: str,
-    *,
-    wait_until_complete: Optional[float] = None,
-    **kwargs: Any,
-) -> None:
-    """Experimental Utility function to create an Atlas Search index
-
-    Args:
-        collection (Collection): MongoDB Collection
-        index_name (str): Name of Index
-        field (str): Field to index
-        wait_until_complete (Optional[float]): If provided, number of seconds to wait
-            until search index is ready
-        kwargs: Keyword arguments supplying any additional options to SearchIndexModel.
-    """
-    logger.info("Creating Search Index %s on %s", index_name, collection.name)
-
-    definition = {
-        "mappings": {"dynamic": False, "fields": {field: [{"type": "string"}]}}
-    }
-
-    try:
-        result = collection.create_search_index(
-            SearchIndexModel(
-                definition=definition,
-                name=index_name,
-                type="search",
-                **kwargs,
-            )
-        )
-    except OperationFailure as e:
-        raise OperationFailure(_search_index_error_message()) from e
-
-    if wait_until_complete:
-        _wait_for_predicate(
-            predicate=lambda: _is_index_ready(collection, index_name),
-            err=f"{index_name=} did not complete in {wait_until_complete}!",
-            timeout=wait_until_complete,
-        )
-    logger.info(result)
diff --git a/libs/partners/mongodb/langchain_mongodb/pipelines.py b/libs/partners/mongodb/langchain_mongodb/pipelines.py
deleted file mode 100644
index 5820ce388f3d6..0000000000000
--- a/libs/partners/mongodb/langchain_mongodb/pipelines.py
+++ /dev/null
@@ -1,160 +0,0 @@
-"""Aggregation pipeline components used in Atlas Full-Text, Vector, and Hybrid Search
-
-See the following for more:
-    - `Full-Text Search <https://www.mongodb.com/docs/atlas/atlas-search/aggregation-stages/search/#mongodb-pipeline-pipe.-search>`_
-    - `MongoDB Operators <https://www.mongodb.com/docs/atlas/atlas-search/operators-and-collectors/#std-label-operators-ref>`_
-    - `Vector Search <https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/>`_
-    - `Filter Example <https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/#atlas-vector-search-pre-filter>`_
-"""
-
-from typing import Any, Dict, List, Optional
-
-
-def text_search_stage(
-    query: str,
-    search_field: str,
-    index_name: str,
-    limit: Optional[int] = None,
-    filter: Optional[Dict[str, Any]] = None,
-    include_scores: Optional[bool] = True,
-    **kwargs: Any,
-) -> List[Dict[str, Any]]:  # noqa: E501
-    """Full-Text search using Lucene's standard (BM25) analyzer
-
-    Args:
-        query: Input text to search for
-        search_field: Field in Collection that will be searched
-        index_name: Atlas Search Index name
-        limit: Maximum number of documents to return. Default of no limit
-        filter: Any MQL match expression comparing an indexed field
-        include_scores: Scores provide measure of relative relevance
-
-    Returns:
-        Dictionary defining the $search stage
-    """
-    pipeline = [
-        {
-            "$search": {
-                "index": index_name,
-                "text": {"query": query, "path": search_field},
-            }
-        }
-    ]
-    if filter:
-        pipeline.append({"$match": filter})  # type: ignore
-    if include_scores:
-        pipeline.append({"$set": {"score": {"$meta": "searchScore"}}})
-    if limit:
-        pipeline.append({"$limit": limit})  # type: ignore
-
-    return pipeline  # type: ignore
-
-
-def vector_search_stage(
-    query_vector: List[float],
-    search_field: str,
-    index_name: str,
-    top_k: int = 4,
-    filter: Optional[Dict[str, Any]] = None,
-    oversampling_factor: int = 10,
-    **kwargs: Any,
-) -> Dict[str, Any]:  # noqa: E501
-    """Vector Search Stage without Scores.
-
-    Scoring is applied later depending on strategy.
-    vector search includes a vectorSearchScore that is typically used.
-    hybrid uses Reciprocal Rank Fusion.
-
-    Args:
-        query_vector: List of embedding vector
-        search_field: Field in Collection containing embedding vectors
-        index_name: Name of Atlas Vector Search Index tied to Collection
-        top_k: Number of documents to return
-        oversampling_factor: this times limit is the number of candidates
-        filter: MQL match expression comparing an indexed field.
-            Some operators are not supported.
-            See `vectorSearch filter docs <https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/#atlas-vector-search-pre-filter>`_
-
-
-    Returns:
-        Dictionary defining the $vectorSearch
-    """
-    stage = {
-        "index": index_name,
-        "path": search_field,
-        "queryVector": query_vector,
-        "numCandidates": top_k * oversampling_factor,
-        "limit": top_k,
-    }
-    if filter:
-        stage["filter"] = filter
-    return {"$vectorSearch": stage}
-
-
-def combine_pipelines(
-    pipeline: List[Any], stage: List[Dict[str, Any]], collection_name: str
-) -> None:
-    """Combines two aggregations into a single result set in-place."""
-    if pipeline:
-        pipeline.append({"$unionWith": {"coll": collection_name, "pipeline": stage}})
-    else:
-        pipeline.extend(stage)
-
-
-def reciprocal_rank_stage(
-    score_field: str, penalty: float = 0, **kwargs: Any
-) -> List[Dict[str, Any]]:
-    """Stage adds Reciprocal Rank Fusion weighting.
-
-        First, it pushes documents retrieved from previous stage
-        into a temporary sub-document. It then unwinds to establish
-        the rank to each and applies the penalty.
-
-    Args:
-        score_field: A unique string to identify the search being ranked
-        penalty: A non-negative float.
-        extra_fields: Any fields other than text_field that one wishes to keep.
-
-    Returns:
-        RRF score := \frac{1}{rank + penalty} with rank in [1,2,..,n]
-    """
-
-    rrf_pipeline = [
-        {"$group": {"_id": None, "docs": {"$push": "$$ROOT"}}},
-        {"$unwind": {"path": "$docs", "includeArrayIndex": "rank"}},
-        {
-            "$addFields": {
-                f"docs.{score_field}": {
-                    "$divide": [1.0, {"$add": ["$rank", penalty, 1]}]
-                },
-                "docs.rank": "$rank",
-                "_id": "$docs._id",
-            }
-        },
-        {"$replaceRoot": {"newRoot": "$docs"}},
-    ]
-
-    return rrf_pipeline  # type: ignore
-
-
-def final_hybrid_stage(
-    scores_fields: List[str], limit: int, **kwargs: Any
-) -> List[Dict[str, Any]]:
-    """Sum weighted scores, sort, and apply limit.
-
-    Args:
-        scores_fields: List of fields given to scores of vector and text searches
-        limit: Number of documents to return
-
-    Returns:
-        Final aggregation stages
-    """
-
-    return [
-        {"$group": {"_id": "$_id", "docs": {"$mergeObjects": "$$ROOT"}}},
-        {"$replaceRoot": {"newRoot": "$docs"}},
-        {"$set": {score: {"$ifNull": [f"${score}", 0]} for score in scores_fields}},
-        {"$addFields": {"score": {"$add": [f"${score}" for score in scores_fields]}}},
-        {"$sort": {"score": -1}},
-        {"$limit": limit},
-    ]
diff --git a/libs/partners/mongodb/langchain_mongodb/py.typed b/libs/partners/mongodb/langchain_mongodb/py.typed
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/libs/partners/mongodb/langchain_mongodb/retrievers/__init__.py b/libs/partners/mongodb/langchain_mongodb/retrievers/__init__.py
deleted file mode 100644
index c78e3fba44aef..0000000000000
--- a/libs/partners/mongodb/langchain_mongodb/retrievers/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-"""Search Retrievers of various types.
-
-Use ``MongoDBAtlasVectorSearch.as_retriever(**)``
-to create MongoDB's core Vector Search Retriever.
-"""
-
-from langchain_mongodb.retrievers.full_text_search import (
-    MongoDBAtlasFullTextSearchRetriever,
-)
-from langchain_mongodb.retrievers.hybrid_search import MongoDBAtlasHybridSearchRetriever
-
-__all__ = [
-    "MongoDBAtlasHybridSearchRetriever",
-    "MongoDBAtlasFullTextSearchRetriever",
-]
diff --git a/libs/partners/mongodb/langchain_mongodb/retrievers/full_text_search.py b/libs/partners/mongodb/langchain_mongodb/retrievers/full_text_search.py
deleted file mode 100644
index 52f12c4780a2c..0000000000000
--- a/libs/partners/mongodb/langchain_mongodb/retrievers/full_text_search.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from typing import Any, Dict, List, Optional
-
-from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun
-from langchain_core.documents import Document
-from langchain_core.retrievers import BaseRetriever
-from pymongo.collection import Collection
-
-from langchain_mongodb.pipelines import text_search_stage
-from langchain_mongodb.utils import make_serializable
-
-
-class MongoDBAtlasFullTextSearchRetriever(BaseRetriever):
-    """Hybrid Search Retriever performs full-text searches
-    using Lucene's standard (BM25) analyzer.
-    """
-
-    collection: Collection
-    """MongoDB Collection on an Atlas cluster"""
-    search_index_name: str
-    """Atlas Search Index name"""
-    search_field: str
-    """Collection field that contains the text to be searched. It must be indexed"""
-    top_k: Optional[int] = None
-    """Number of documents to return. Default is no limit"""
-    filter: Optional[Dict[str, Any]] = None
-    """(Optional) List of MQL match expression comparing an indexed field"""
-    show_embeddings: float = False
-    """If true, returned Document metadata will include vectors"""
-
-    def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-    ) -> List[Document]:
-        """Retrieve documents that are highest scoring / most similar  to query.
-
-        Args:
-            query: String to find relevant documents for
-            run_manager: The callback handler to use
-        Returns:
-            List of relevant documents
-        """
-
-        pipeline = text_search_stage(  # type: ignore
-            query=query,
-            search_field=self.search_field,
-            index_name=self.search_index_name,
-            limit=self.top_k,
-            filter=self.filter,
-        )
-
-        # Execution
-        cursor = self.collection.aggregate(pipeline)  # type: ignore[arg-type]
-
-        # Formatting
-        docs = []
-        for res in cursor:
-            text = res.pop(self.search_field)
-            make_serializable(res)
-            docs.append(Document(page_content=text, metadata=res))
-        return docs
diff --git a/libs/partners/mongodb/langchain_mongodb/retrievers/hybrid_search.py b/libs/partners/mongodb/langchain_mongodb/retrievers/hybrid_search.py
deleted file mode 100644
index 9e876e9ff81e1..0000000000000
--- a/libs/partners/mongodb/langchain_mongodb/retrievers/hybrid_search.py
+++ /dev/null
@@ -1,126 +0,0 @@
-from typing import Any, Dict, List, Optional
-
-from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun
-from langchain_core.documents import Document
-from langchain_core.retrievers import BaseRetriever
-from pymongo.collection import Collection
-
-from langchain_mongodb import MongoDBAtlasVectorSearch
-from langchain_mongodb.pipelines import (
-    combine_pipelines,
-    final_hybrid_stage,
-    reciprocal_rank_stage,
-    text_search_stage,
-    vector_search_stage,
-)
-from langchain_mongodb.utils import make_serializable
-
-
-class MongoDBAtlasHybridSearchRetriever(BaseRetriever):
-    """Hybrid Search Retriever combines vector and full-text searches
-    weighting them the via Reciprocal Rank Fusion (RRF) algorithm.
-
-    Increasing the vector_penalty will reduce the importance on the vector search.
-    Increasing the fulltext_penalty will correspondingly reduce the fulltext score.
-    For more on the algorithm,see
-    https://learn.microsoft.com/en-us/azure/search/hybrid-search-ranking
-    """
-
-    vectorstore: MongoDBAtlasVectorSearch
-    """MongoDBAtlas VectorStore"""
-    search_index_name: str
-    """Atlas Search Index (full-text) name"""
-    top_k: int = 4
-    """Number of documents to return."""
-    oversampling_factor: int = 10
-    """This times top_k is the number of candidates chosen at each step"""
-    pre_filter: Optional[Dict[str, Any]] = None
-    """(Optional) Any MQL match expression comparing an indexed field"""
-    post_filter: Optional[List[Dict[str, Any]]] = None
-    """(Optional) Pipeline of MongoDB aggregation stages for postprocessing."""
-    vector_penalty: float = 60.0
-    """Penalty applied to vector search results in RRF: scores=1/(rank + penalty)"""
-    fulltext_penalty: float = 60.0
-    """Penalty applied to full-text search results in RRF: scores=1/(rank + penalty)"""
-    show_embeddings: float = False
-    """If true, returned Document metadata will include vectors."""
-
-    @property
-    def collection(self) -> Collection:
-        return self.vectorstore._collection
-
-    def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-    ) -> List[Document]:
-        """Retrieve documents that are highest scoring / most similar  to query.
-
-        Note that the same query is used in both searches,
-        embedded for vector search, and as-is for full-text search.
-
-        Args:
-            query: String to find relevant documents for
-            run_manager: The callback handler to use
-        Returns:
-            List of relevant documents
-        """
-
-        query_vector = self.vectorstore._embedding.embed_query(query)
-
-        scores_fields = ["vector_score", "fulltext_score"]
-        pipeline: List[Any] = []
-
-        # First we build up the aggregation pipeline,
-        # then it is passed to the server to execute
-        # Vector Search stage
-        vector_pipeline = [
-            vector_search_stage(
-                query_vector=query_vector,
-                search_field=self.vectorstore._embedding_key,
-                index_name=self.vectorstore._index_name,
-                top_k=self.top_k,
-                filter=self.pre_filter,
-                oversampling_factor=self.oversampling_factor,
-            )
-        ]
-        vector_pipeline += reciprocal_rank_stage("vector_score", self.vector_penalty)
-
-        combine_pipelines(pipeline, vector_pipeline, self.collection.name)
-
-        # Full-Text Search stage
-        text_pipeline = text_search_stage(
-            query=query,
-            search_field=self.vectorstore._text_key,
-            index_name=self.search_index_name,
-            limit=self.top_k,
-            filter=self.pre_filter,
-        )
-
-        text_pipeline.extend(
-            reciprocal_rank_stage("fulltext_score", self.fulltext_penalty)
-        )
-
-        combine_pipelines(pipeline, text_pipeline, self.collection.name)
-
-        # Sum and sort stage
-        pipeline.extend(
-            final_hybrid_stage(scores_fields=scores_fields, limit=self.top_k)
-        )
-
-        # Removal of embeddings unless requested.
-        if not self.show_embeddings:
-            pipeline.append({"$project": {self.vectorstore._embedding_key: 0}})
-        # Post filtering
-        if self.post_filter is not None:
-            pipeline.extend(self.post_filter)
-
-        # Execution
-        cursor = self.collection.aggregate(pipeline)  # type: ignore[arg-type]
-
-        # Formatting
-        docs = []
-        for res in cursor:
-            text = res.pop(self.vectorstore._text_key)
-            # score = res.pop("score")  # The score remains buried!
-            make_serializable(res)
-            docs.append(Document(page_content=text, metadata=res))
-        return docs
diff --git a/libs/partners/mongodb/langchain_mongodb/utils.py b/libs/partners/mongodb/langchain_mongodb/utils.py
deleted file mode 100644
index f228c56bcebb1..0000000000000
--- a/libs/partners/mongodb/langchain_mongodb/utils.py
+++ /dev/null
@@ -1,183 +0,0 @@
-"""Various Utility Functions
-
-- Tools for handling bson.ObjectId
-
-The help IDs live as ObjectId in MongoDB and str in Langchain and JSON.
-
-
-- Tools for the Maximal Marginal Relevance (MMR) reranking
-
-These are duplicated from langchain_community to avoid cross-dependencies.
-
-Functions "maximal_marginal_relevance" and "cosine_similarity"
-are duplicated in this utility respectively from modules:
-    - "libs/community/langchain_community/vectorstores/utils.py"
-    - "libs/community/langchain_community/utils/math.py"
-"""
-
-from __future__ import annotations
-
-import logging
-from datetime import date, datetime
-from typing import Any, Dict, List, Union
-
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
-Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
-
-
-def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
-    """Row-wise cosine similarity between two equal-width matrices."""
-    if len(X) == 0 or len(Y) == 0:
-        return np.array([])
-
-    X = np.array(X)
-    Y = np.array(Y)
-    if X.shape[1] != Y.shape[1]:
-        raise ValueError(
-            f"Number of columns in X and Y must be the same. X has shape {X.shape} "
-            f"and Y has shape {Y.shape}."
-        )
-    try:
-        import simsimd as simd
-
-        X = np.array(X, dtype=np.float32)
-        Y = np.array(Y, dtype=np.float32)
-        Z = 1 - np.array(simd.cdist(X, Y, metric="cosine"))
-        return Z
-    except ImportError:
-        logger.debug(
-            "Unable to import simsimd, defaulting to NumPy implementation. If you want "
-            "to use simsimd please install with `pip install simsimd`."
-        )
-        X_norm = np.linalg.norm(X, axis=1)
-        Y_norm = np.linalg.norm(Y, axis=1)
-        # Ignore divide by zero errors run time warnings as those are handled below.
-        with np.errstate(divide="ignore", invalid="ignore"):
-            similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
-        similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
-        return similarity
-
-
-def maximal_marginal_relevance(
-    query_embedding: np.ndarray,
-    embedding_list: list,
-    lambda_mult: float = 0.5,
-    k: int = 4,
-) -> List[int]:
-    """Compute Maximal Marginal Relevance (MMR).
-
-    MMR is a technique used to select documents that are both relevant to the query
-    and diverse among themselves. This function returns the indices
-    of the top-k embeddings that maximize the marginal relevance.
-
-    Args:
-        query_embedding (np.ndarray): The embedding vector of the query.
-        embedding_list (list of np.ndarray): A list containing the embedding vectors
-            of the candidate documents.
-        lambda_mult (float, optional): The trade-off parameter between
-            relevance and diversity. Defaults to 0.5.
-        k (int, optional): The number of embeddings to select. Defaults to 4.
-
-    Returns:
-        list of int: The indices of the embeddings that maximize the marginal relevance.
-
-    Notes:
-        The Maximal Marginal Relevance (MMR) is computed using the following formula:
-
-    MMR = argmax_{D_i ∈ R \ S} [λ * Sim(D_i, Q) - (1 - λ) * max_{D_j ∈ S} Sim(D_i, D_j)]
-
-        where:
-        - R is the set of candidate documents,
-        - S is the set of selected documents,
-        - Q is the query embedding,
-        - Sim(D_i, Q) is the similarity between document D_i and the query,
-        - Sim(D_i, D_j) is the similarity between documents D_i and D_j,
-        - λ is the trade-off parameter.
-    """
-
-    if min(k, len(embedding_list)) <= 0:
-        return []
-    if query_embedding.ndim == 1:
-        query_embedding = np.expand_dims(query_embedding, axis=0)
-    similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
-    most_similar = int(np.argmax(similarity_to_query))
-    idxs = [most_similar]
-    selected = np.array([embedding_list[most_similar]])
-    while len(idxs) < min(k, len(embedding_list)):
-        best_score = -np.inf
-        idx_to_add = -1
-        similarity_to_selected = cosine_similarity(embedding_list, selected)
-        for i, query_score in enumerate(similarity_to_query):
-            if i in idxs:
-                continue
-            redundant_score = max(similarity_to_selected[i])
-            equation_score = (
-                lambda_mult * query_score - (1 - lambda_mult) * redundant_score
-            )
-            if equation_score > best_score:
-                best_score = equation_score
-                idx_to_add = i
-        idxs.append(idx_to_add)
-        selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
-    return idxs
-
-
-def str_to_oid(str_repr: str) -> Any | str:
-    """Attempt to cast string representation of id to MongoDB's internal BSON ObjectId.
-
-    To be consistent with ObjectId, input must be a 24 character hex string.
-    If it is not, MongoDB will happily use the string in the main _id index.
-    Importantly, the str representation that comes out of MongoDB will have this form.
-
-    Args:
-        str_repr: id as string.
-
-    Returns:
-        ObjectID
-    """
-    from bson import ObjectId
-    from bson.errors import InvalidId
-
-    try:
-        return ObjectId(str_repr)
-    except InvalidId:
-        logger.debug(
-            "ObjectIds must be 12-character byte or 24-character hex strings. "
-            "Examples: b'heres12bytes', '6f6e6568656c6c6f68656768'"
-        )
-        return str_repr
-
-
-def oid_to_str(oid: Any) -> str:
-    """Convert MongoDB's internal BSON ObjectId into a simple str for compatibility.
-
-    Instructive helper to show where data is coming out of MongoDB.
-
-    Args:
-        oid: bson.ObjectId
-
-    Returns:
-        24 character hex string.
-    """
-    return str(oid)
-
-
-def make_serializable(
-    obj: Dict[str, Any],
-) -> None:
-    """Recursively cast values in a dict to a form able to json.dump"""
-
-    from bson import ObjectId
-
-    for k, v in obj.items():
-        if isinstance(v, dict):
-            make_serializable(v)
-        elif isinstance(v, list) and v and isinstance(v[0], (ObjectId, date, datetime)):
-            obj[k] = [oid_to_str(item) for item in v]
-        elif isinstance(v, ObjectId):
-            obj[k] = oid_to_str(v)
-        elif isinstance(v, (datetime, date)):
-            obj[k] = v.isoformat()
diff --git a/libs/partners/mongodb/langchain_mongodb/vectorstores.py b/libs/partners/mongodb/langchain_mongodb/vectorstores.py
deleted file mode 100644
index 7860281eda038..0000000000000
--- a/libs/partners/mongodb/langchain_mongodb/vectorstores.py
+++ /dev/null
@@ -1,796 +0,0 @@
-from __future__ import annotations
-
-import logging
-from importlib.metadata import version
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Generator,
-    Iterable,
-    List,
-    Optional,
-    Tuple,
-    TypeVar,
-    Union,
-)
-
-import numpy as np
-from langchain_core.documents import Document
-from langchain_core.embeddings import Embeddings
-from langchain_core.runnables.config import run_in_executor
-from langchain_core.vectorstores import VectorStore
-from pymongo import MongoClient
-from pymongo.collection import Collection
-from pymongo.driver_info import DriverInfo
-from pymongo.errors import CollectionInvalid
-
-from langchain_mongodb.index import (
-    create_vector_search_index,
-    update_vector_search_index,
-)
-from langchain_mongodb.pipelines import vector_search_stage
-from langchain_mongodb.utils import (
-    make_serializable,
-    maximal_marginal_relevance,
-    oid_to_str,
-    str_to_oid,
-)
-
-VST = TypeVar("VST", bound=VectorStore)
-
-logger = logging.getLogger(__name__)
-
-DEFAULT_INSERT_BATCH_SIZE = 100_000
-
-
-class MongoDBAtlasVectorSearch(VectorStore):
-    """MongoDB Atlas vector store integration.
-
-    MongoDBAtlasVectorSearch performs data operations on
-    text, embeddings and arbitrary data. In addition to CRUD operations,
-    the VectorStore provides Vector Search
-    based on similarity of embedding vectors following the
-    Hierarchical Navigable Small Worlds (HNSW) algorithm.
-
-    This supports a number of models to ascertain scores,
-    "similarity" (default), "MMR", and "similarity_score_threshold".
-    These are described in the search_type argument to as_retriever,
-    which provides the Runnable.invoke(query) API, allowing
-    MongoDBAtlasVectorSearch to be used within a chain.
-
-    Setup:
-        * Set up a MongoDB Atlas cluster. The free tier M0 will allow you to start.
-        Search Indexes are only available on Atlas, the fully managed cloud service,
-        not the self-managed MongoDB.
-        Follow [this guide](https://www.mongodb.com/basics/mongodb-atlas-tutorial)
-
-        * Create a Collection and a Vector Search Index.The procedure is described
-        [here](https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#procedure).
-
-        * Install ``langchain-mongodb``
-
-
-        .. code-block:: bash
-
-            pip install -qU langchain-mongodb pymongo
-
-
-        .. code-block:: python
-
-            import getpass
-            MONGODB_ATLAS_CLUSTER_URI = getpass.getpass("MongoDB Atlas Cluster URI:")
-
-    Key init args — indexing params:
-        embedding: Embeddings
-            Embedding function to use.
-
-    Key init args — client params:
-        collection: Collection
-            MongoDB collection to use.
-        index_name: str
-            Name of the Atlas Search index.
-
-    Instantiate:
-        .. code-block:: python
-
-            from pymongo import MongoClient
-            from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
-            from pymongo import MongoClient
-            from langchain_openai import OpenAIEmbeddings
-
-            # initialize MongoDB python client
-            client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
-
-            DB_NAME = "langchain_test_db"
-            COLLECTION_NAME = "langchain_test_vectorstores"
-            ATLAS_VECTOR_SEARCH_INDEX_NAME = "langchain-test-index-vectorstores"
-
-            MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]
-
-            vector_store = MongoDBAtlasVectorSearch(
-                collection=MONGODB_COLLECTION,
-                embedding=OpenAIEmbeddings(),
-                index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
-                relevance_score_fn="cosine",
-            )
-
-    Add Documents:
-        .. code-block:: python
-
-            from langchain_core.documents import Document
-
-            document_1 = Document(page_content="foo", metadata={"baz": "bar"})
-            document_2 = Document(page_content="thud", metadata={"bar": "baz"})
-            document_3 = Document(page_content="i will be deleted :(")
-
-            documents = [document_1, document_2, document_3]
-            ids = ["1", "2", "3"]
-            vector_store.add_documents(documents=documents, ids=ids)
-
-    Delete Documents:
-        .. code-block:: python
-
-            vector_store.delete(ids=["3"])
-
-    Search:
-        .. code-block:: python
-
-            results = vector_store.similarity_search(query="thud",k=1)
-            for doc in results:
-                print(f"* {doc.page_content} [{doc.metadata}]")
-
-        .. code-block:: python
-
-            * thud [{'_id': '2', 'baz': 'baz'}]
-
-
-    Search with filter:
-        .. code-block:: python
-
-            results = vector_store.similarity_search(query="thud",k=1,post_filter=[{"bar": "baz"]})
-            for doc in results:
-                print(f"* {doc.page_content} [{doc.metadata}]")
-
-        .. code-block:: python
-
-            * thud [{'_id': '2', 'baz': 'baz'}]
-
-    Search with score:
-        .. code-block:: python
-
-            results = vector_store.similarity_search_with_score(query="qux",k=1)
-            for doc, score in results:
-                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
-
-        .. code-block:: python
-
-            * [SIM=0.916096] foo [{'_id': '1', 'baz': 'bar'}]
-
-    Async:
-        .. code-block:: python
-
-            # add documents
-            # await vector_store.aadd_documents(documents=documents, ids=ids)
-
-            # delete documents
-            # await vector_store.adelete(ids=["3"])
-
-            # search
-            # results = vector_store.asimilarity_search(query="thud",k=1)
-
-            # search with score
-            results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
-            for doc,score in results:
-                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
-
-        .. code-block:: python
-
-            * [SIM=0.916096] foo [{'_id': '1', 'baz': 'bar'}]
-
-    Use as Retriever:
-        .. code-block:: python
-
-            retriever = vector_store.as_retriever(
-                search_type="mmr",
-                search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
-            )
-            retriever.invoke("thud")
-
-        .. code-block:: python
-
-            [Document(metadata={'_id': '2', 'embedding': [-0.01850726455450058, -0.0014740974875167012, -0.009762819856405258, ...], 'baz': 'baz'}, page_content='thud')]
-
-    """  # noqa: E501
-
-    def __init__(
-        self,
-        collection: Collection[Dict[str, Any]],
-        embedding: Embeddings,
-        index_name: str = "vector_index",
-        text_key: str = "text",
-        embedding_key: str = "embedding",
-        relevance_score_fn: str = "cosine",
-        **kwargs: Any,
-    ):
-        """
-        Args:
-            collection: MongoDB collection to add the texts to
-            embedding: Text embedding model to use
-            text_key: MongoDB field that will contain the text for each document
-            index_name: Existing Atlas Vector Search Index
-            embedding_key: Field that will contain the embedding for each document
-            vector_index_name: Name of the Atlas Vector Search index
-            relevance_score_fn: The similarity score used for the index
-                Currently supported: 'euclidean', 'cosine', and 'dotProduct'
-        """
-        self._collection = collection
-        self._embedding = embedding
-        self._index_name = index_name
-        self._text_key = text_key
-        self._embedding_key = embedding_key
-        self._relevance_score_fn = relevance_score_fn
-
-    @property
-    def embeddings(self) -> Embeddings:
-        return self._embedding
-
-    def _select_relevance_score_fn(self) -> Callable[[float], float]:
-        scoring: dict[str, Callable] = {
-            "euclidean": self._euclidean_relevance_score_fn,
-            "dotProduct": self._max_inner_product_relevance_score_fn,
-            "cosine": self._cosine_relevance_score_fn,
-        }
-        if self._relevance_score_fn in scoring:
-            return scoring[self._relevance_score_fn]
-        else:
-            raise NotImplementedError(
-                f"No relevance score function for ${self._relevance_score_fn}"
-            )
-
-    @classmethod
-    def from_connection_string(
-        cls,
-        connection_string: str,
-        namespace: str,
-        embedding: Embeddings,
-        **kwargs: Any,
-    ) -> MongoDBAtlasVectorSearch:
-        """Construct a `MongoDB Atlas Vector Search` vector store
-        from a MongoDB connection URI.
-
-        Args:
-            connection_string: A valid MongoDB connection URI.
-            namespace: A valid MongoDB namespace (database and collection).
-            embedding: The text embedding model to use for the vector store.
-
-        Returns:
-            A new MongoDBAtlasVectorSearch instance.
-
-        """
-        client: MongoClient = MongoClient(
-            connection_string,
-            driver=DriverInfo(name="Langchain", version=version("langchain")),
-        )
-        db_name, collection_name = namespace.split(".")
-        collection = client[db_name][collection_name]
-        return cls(collection, embedding, **kwargs)
-
-    def add_texts(
-        self,
-        texts: Iterable[str],
-        metadatas: Optional[List[Dict[str, Any]]] = None,
-        ids: Optional[List[str]] = None,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Add texts, create embeddings, and add to the Collection and index.
-
-        Important notes on ids:
-            - If _id or id is a key in the metadatas dicts, one must
-                pop them and provide as separate list.
-            - They must be unique.
-            - If they are not provided, the VectorStore will create unique ones,
-                stored as bson.ObjectIds internally, and strings in Langchain.
-                These will appear in Document.metadata with key, '_id'.
-
-        Args:
-            texts: Iterable of strings to add to the vectorstore.
-            metadatas: Optional list of metadatas associated with the texts.
-            ids: Optional list of unique ids that will be used as index in VectorStore.
-                See note on ids.
-
-        Returns:
-            List of ids added to the vectorstore.
-        """
-
-        # Check to see if metadata includes ids
-        if metadatas is not None and (
-            metadatas[0].get("_id") or metadatas[0].get("id")
-        ):
-            logger.warning(
-                "_id or id key found in metadata. "
-                "Please pop from each dict and input as separate list."
-                "Retrieving methods will include the same id as '_id' in metadata."
-            )
-
-        texts_batch = texts
-        _metadatas: Union[List, Generator] = metadatas or ({} for _ in texts)
-        metadatas_batch = _metadatas
-
-        result_ids = []
-        batch_size = kwargs.get("batch_size", DEFAULT_INSERT_BATCH_SIZE)
-        if batch_size:
-            texts_batch = []
-            metadatas_batch = []
-            size = 0
-            i = 0
-            for j, (text, metadata) in enumerate(zip(texts, _metadatas)):
-                size += len(text) + len(metadata)
-                texts_batch.append(text)
-                metadatas_batch.append(metadata)
-                if (j + 1) % batch_size == 0 or size >= 47_000_000:
-                    if ids:
-                        batch_res = self.bulk_embed_and_insert_texts(
-                            texts_batch, metadatas_batch, ids[i : j + 1]
-                        )
-                    else:
-                        batch_res = self.bulk_embed_and_insert_texts(
-                            texts_batch, metadatas_batch
-                        )
-                    result_ids.extend(batch_res)
-                    texts_batch = []
-                    metadatas_batch = []
-                    size = 0
-                    i = j + 1
-        if texts_batch:
-            if ids:
-                batch_res = self.bulk_embed_and_insert_texts(
-                    texts_batch, metadatas_batch, ids[i : j + 1]
-                )
-            else:
-                batch_res = self.bulk_embed_and_insert_texts(
-                    texts_batch, metadatas_batch
-                )
-            result_ids.extend(batch_res)
-        return result_ids
-
-    def bulk_embed_and_insert_texts(
-        self,
-        texts: Union[List[str], Iterable[str]],
-        metadatas: Union[List[dict], Generator[dict, Any, Any]],
-        ids: Optional[List[str]] = None,
-    ) -> List[str]:
-        """Bulk insert single batch of texts, embeddings, and optionally ids.
-
-        See add_texts for additional details.
-        """
-        if not texts:
-            return []
-        # Compute embedding vectors
-        embeddings = self._embedding.embed_documents(texts)  # type: ignore
-        if ids:
-            to_insert = [
-                {
-                    "_id": str_to_oid(i),
-                    self._text_key: t,
-                    self._embedding_key: embedding,
-                    **m,
-                }
-                for i, t, m, embedding in zip(ids, texts, metadatas, embeddings)
-            ]
-        else:
-            to_insert = [
-                {self._text_key: t, self._embedding_key: embedding, **m}
-                for t, m, embedding in zip(texts, metadatas, embeddings)
-            ]
-        # insert the documents in MongoDB Atlas
-        insert_result = self._collection.insert_many(to_insert)  # type: ignore
-        return [oid_to_str(_id) for _id in insert_result.inserted_ids]
-
-    def add_documents(
-        self,
-        documents: List[Document],
-        ids: Optional[List[str]] = None,
-        batch_size: int = DEFAULT_INSERT_BATCH_SIZE,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Add documents to the vectorstore.
-
-        Args:
-            documents: Documents to add to the vectorstore.
-            ids: Optional list of unique ids that will be used as index in VectorStore.
-                See note on ids in add_texts.
-            batch_size: Number of documents to insert at a time.
-                Tuning this may help with performance and sidestep MongoDB limits.
-
-        Returns:
-            List of IDs of the added texts.
-        """
-        n_docs = len(documents)
-        if ids:
-            assert len(ids) == n_docs, "Number of ids must equal number of documents."
-        result_ids = []
-        start = 0
-        for end in range(batch_size, n_docs + batch_size, batch_size):
-            texts, metadatas = zip(
-                *[(doc.page_content, doc.metadata) for doc in documents[start:end]]
-            )
-            if ids:
-                result_ids.extend(
-                    self.bulk_embed_and_insert_texts(
-                        texts=texts, metadatas=metadatas, ids=ids[start:end]
-                    )
-                )
-            else:
-                result_ids.extend(
-                    self.bulk_embed_and_insert_texts(texts=texts, metadatas=metadatas)
-                )
-            start = end
-        return result_ids
-
-    def similarity_search_with_score(
-        self,
-        query: str,
-        k: int = 4,
-        pre_filter: Optional[Dict[str, Any]] = None,
-        post_filter_pipeline: Optional[List[Dict]] = None,
-        oversampling_factor: int = 10,
-        include_embeddings: bool = False,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, float]]:  # noqa: E501
-        """Return MongoDB documents most similar to the given query and their scores.
-
-        Atlas Vector Search eliminates the need to run a separate
-        search system alongside your database.
-
-         Args:
-            query: Input text of semantic query
-            k: Number of documents to return. Also known as top_k.
-            pre_filter: List of MQL match expressions comparing an indexed field
-            post_filter_pipeline: (Optional) Arbitrary pipeline of MongoDB
-                aggregation stages applied after the search is complete.
-            oversampling_factor: This times k is the number of candidates chosen
-                at each step in the in HNSW Vector Search
-            include_embeddings: If True, the embedding vector of each result
-                will be included in metadata.
-            kwargs: Additional arguments are specific to the search_type
-
-        Returns:
-            List of documents most similar to the query and their scores.
-        """
-        embedding = self._embedding.embed_query(query)
-        docs = self._similarity_search_with_score(
-            embedding,
-            k=k,
-            pre_filter=pre_filter,
-            post_filter_pipeline=post_filter_pipeline,
-            oversampling_factor=oversampling_factor,
-            include_embeddings=include_embeddings,
-            **kwargs,
-        )
-        return docs
-
-    def similarity_search(
-        self,
-        query: str,
-        k: int = 4,
-        pre_filter: Optional[Dict[str, Any]] = None,
-        post_filter_pipeline: Optional[List[Dict]] = None,
-        oversampling_factor: int = 10,
-        include_scores: bool = False,
-        include_embeddings: bool = False,
-        **kwargs: Any,
-    ) -> List[Document]:  # noqa: E501
-        """Return MongoDB documents most similar to the given query.
-
-        Atlas Vector Search eliminates the need to run a separate
-        search system alongside your database.
-
-         Args:
-            query: Input text of semantic query
-            k: (Optional) number of documents to return. Defaults to 4.
-            pre_filter: List of MQL match expressions comparing an indexed field
-            post_filter_pipeline: (Optional) Pipeline of MongoDB aggregation stages
-                to filter/process results after $vectorSearch.
-            oversampling_factor: Multiple of k used when generating number of candidates
-                at each step in the HNSW Vector Search,
-            include_scores: If True, the query score of each result
-                will be included in metadata.
-            include_embeddings: If True, the embedding vector of each result
-                will be included in metadata.
-            kwargs: Additional arguments are specific to the search_type
-
-        Returns:
-            List of documents most similar to the query and their scores.
-        """
-        docs_and_scores = self.similarity_search_with_score(
-            query,
-            k=k,
-            pre_filter=pre_filter,
-            post_filter_pipeline=post_filter_pipeline,
-            oversampling_factor=oversampling_factor,
-            include_embeddings=include_embeddings,
-            **kwargs,
-        )
-
-        if include_scores:
-            for doc, score in docs_and_scores:
-                doc.metadata["score"] = score
-        return [doc for doc, _ in docs_and_scores]
-
-    def max_marginal_relevance_search(
-        self,
-        query: str,
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        pre_filter: Optional[Dict[str, Any]] = None,
-        post_filter_pipeline: Optional[List[Dict]] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return documents selected using the maximal marginal relevance.
-
-        Maximal marginal relevance optimizes for similarity to query AND diversity
-        among selected documents.
-
-        Args:
-            query: Text to look up documents similar to.
-            k: (Optional) number of documents to return. Defaults to 4.
-            fetch_k: (Optional) number of documents to fetch before passing to MMR
-                algorithm. Defaults to 20.
-            lambda_mult: Number between 0 and 1 that determines the degree
-                of diversity among the results with 0 corresponding
-                to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
-            pre_filter: List of MQL match expressions comparing an indexed field
-            post_filter_pipeline: (Optional) pipeline of MongoDB aggregation stages
-                following the $vectorSearch stage.
-        Returns:
-            List of documents selected by maximal marginal relevance.
-        """
-        return self.max_marginal_relevance_search_by_vector(
-            embedding=self._embedding.embed_query(query),
-            k=k,
-            fetch_k=fetch_k,
-            lambda_mult=lambda_mult,
-            pre_filter=pre_filter,
-            post_filter_pipeline=post_filter_pipeline,
-            **kwargs,
-        )
-
-    @classmethod
-    def from_texts(
-        cls,
-        texts: List[str],
-        embedding: Embeddings,
-        metadatas: Optional[List[Dict]] = None,
-        collection: Optional[Collection] = None,
-        ids: Optional[List[str]] = None,
-        **kwargs: Any,
-    ) -> MongoDBAtlasVectorSearch:
-        """Construct a `MongoDB Atlas Vector Search` vector store from raw documents.
-
-        This is a user-friendly interface that:
-            1. Embeds documents.
-            2. Adds the documents to a provided MongoDB Atlas Vector Search index
-                (Lucene)
-
-        This is intended to be a quick way to get started.
-
-        See `MongoDBAtlasVectorSearch` for kwargs and further description.
-
-
-        Example:
-            .. code-block:: python
-                from pymongo import MongoClient
-
-                from langchain_mongodb import MongoDBAtlasVectorSearch
-                from langchain_openai import OpenAIEmbeddings
-
-                mongo_client = MongoClient("<YOUR-CONNECTION-STRING>")
-                collection = mongo_client["<db_name>"]["<collection_name>"]
-                embeddings = OpenAIEmbeddings()
-                vectorstore = MongoDBAtlasVectorSearch.from_texts(
-                    texts,
-                    embeddings,
-                    metadatas=metadatas,
-                    collection=collection
-                )
-        """
-        if collection is None:
-            raise ValueError("Must provide 'collection' named parameter.")
-        vectorstore = cls(collection, embedding, **kwargs)
-        vectorstore.add_texts(texts=texts, metadatas=metadatas, ids=ids, **kwargs)
-        return vectorstore
-
-    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
-        """Delete documents from VectorStore by ids.
-
-        Args:
-            ids: List of ids to delete.
-            **kwargs: Other keyword arguments passed to Collection.delete_many()
-
-        Returns:
-            Optional[bool]: True if deletion is successful,
-            False otherwise, None if not implemented.
-        """
-        filter = {}
-        if ids:
-            oids = [str_to_oid(i) for i in ids]
-            filter = {"_id": {"$in": oids}}
-        return self._collection.delete_many(filter=filter, **kwargs).acknowledged
-
-    async def adelete(
-        self, ids: Optional[List[str]] = None, **kwargs: Any
-    ) -> Optional[bool]:
-        """Delete by vector ID or other criteria.
-
-        Args:
-            ids: List of ids to delete.
-            **kwargs: Other keyword arguments that subclasses might use.
-
-        Returns:
-            Optional[bool]: True if deletion is successful,
-            False otherwise, None if not implemented.
-        """
-        return await run_in_executor(None, self.delete, ids=ids, **kwargs)
-
-    def max_marginal_relevance_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        pre_filter: Optional[Dict[str, Any]] = None,
-        post_filter_pipeline: Optional[List[Dict]] = None,
-        oversampling_factor: int = 10,
-        **kwargs: Any,
-    ) -> List[Document]:  # type: ignore
-        """Return docs selected using the maximal marginal relevance.
-
-        Maximal marginal relevance optimizes for similarity to query AND diversity
-        among selected documents.
-
-        Args:
-            embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-            lambda_mult: Number between 0 and 1 that determines the degree
-                        of diversity among the results with 0 corresponding
-                        to maximum diversity and 1 to minimum diversity.
-                        Defaults to 0.5.
-            pre_filter: (Optional) dictionary of arguments to filter document fields on.
-            post_filter_pipeline: (Optional) pipeline of MongoDB aggregation stages
-                following the vectorSearch stage.
-            oversampling_factor: Multiple of k used when generating number
-                of candidates in HNSW Vector Search,
-            kwargs: Additional arguments are specific to the search_type
-
-        Returns:
-            List of Documents selected by maximal marginal relevance.
-        """
-        docs = self._similarity_search_with_score(
-            embedding,
-            k=fetch_k,
-            pre_filter=pre_filter,
-            post_filter_pipeline=post_filter_pipeline,
-            include_embeddings=True,
-            oversampling_factor=oversampling_factor,
-            **kwargs,
-        )
-        mmr_doc_indexes = maximal_marginal_relevance(
-            np.array(embedding),
-            [doc.metadata[self._embedding_key] for doc, _ in docs],
-            k=k,
-            lambda_mult=lambda_mult,
-        )
-        mmr_docs = [docs[i][0] for i in mmr_doc_indexes]
-        return mmr_docs
-
-    async def amax_marginal_relevance_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        pre_filter: Optional[Dict[str, Any]] = None,
-        post_filter_pipeline: Optional[List[Dict]] = None,
-        oversampling_factor: int = 10,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance."""
-        return await run_in_executor(
-            None,
-            self.max_marginal_relevance_search_by_vector,  # type: ignore[arg-type]
-            embedding,
-            k=k,
-            fetch_k=fetch_k,
-            lambda_mult=lambda_mult,
-            pre_filter=pre_filter,
-            post_filter_pipeline=post_filter_pipeline,
-            oversampling_factor=oversampling_factor,
-            **kwargs,
-        )
-
-    def _similarity_search_with_score(
-        self,
-        query_vector: List[float],
-        k: int = 4,
-        pre_filter: Optional[Dict[str, Any]] = None,
-        post_filter_pipeline: Optional[List[Dict]] = None,
-        oversampling_factor: int = 10,
-        include_embeddings: bool = False,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, float]]:
-        """Core search routine. See external methods for details."""
-
-        # Atlas Vector Search, potentially with filter
-        pipeline = [
-            vector_search_stage(
-                query_vector,
-                self._embedding_key,
-                self._index_name,
-                k,
-                pre_filter,
-                oversampling_factor,
-                **kwargs,
-            ),
-            {"$set": {"score": {"$meta": "vectorSearchScore"}}},
-        ]
-
-        # Remove embeddings unless requested.
-        if not include_embeddings:
-            pipeline.append({"$project": {self._embedding_key: 0}})
-        # Post-processing
-        if post_filter_pipeline is not None:
-            pipeline.extend(post_filter_pipeline)
-
-        # Execution
-        cursor = self._collection.aggregate(pipeline)  # type: ignore[arg-type]
-        docs = []
-
-        # Format
-        for res in cursor:
-            text = res.pop(self._text_key)
-            score = res.pop("score")
-            make_serializable(res)
-            docs.append((Document(page_content=text, metadata=res), score))
-        return docs
-
-    def create_vector_search_index(
-        self,
-        dimensions: int,
-        filters: Optional[List[str]] = None,
-        update: bool = False,
-    ) -> None:
-        """Creates a MongoDB Atlas vectorSearch index for the VectorStore
-
-        Note**: This method may fail as it requires a MongoDB Atlas with these
-        `pre-requisites <https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#prerequisites>`.
-        Currently, vector and full-text search index operations need to be
-        performed manually on the Atlas UI for shared M0 clusters.
-
-        Args:
-            dimensions (int): Number of dimensions in embedding
-            filters (Optional[List[Dict[str, str]]], optional): additional filters
-            for index definition.
-                Defaults to None.
-            update (bool, optional): Updates existing vectorSearch index.
-                Defaults to False.
-        """
-        try:
-            self._collection.database.create_collection(self._collection.name)
-        except CollectionInvalid:
-            pass
-
-        index_operation = (
-            update_vector_search_index if update else create_vector_search_index
-        )
-
-        index_operation(
-            collection=self._collection,
-            index_name=self._index_name,
-            dimensions=dimensions,
-            path=self._embedding_key,
-            similarity=self._relevance_score_fn,
-            filters=filters or [],
-        )  # type: ignore [operator]
diff --git a/libs/partners/mongodb/poetry.lock b/libs/partners/mongodb/poetry.lock
deleted file mode 100644
index 193ccae0fe1b4..0000000000000
--- a/libs/partners/mongodb/poetry.lock
+++ /dev/null
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-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.9,<4.0"
-content-hash = "681e0e1234215ead5420ff8c770d424c7f4d65079fde4709deafd183f786f29a"
diff --git a/libs/partners/mongodb/pyproject.toml b/libs/partners/mongodb/pyproject.toml
deleted file mode 100644
index a6faef2fd9c2a..0000000000000
--- a/libs/partners/mongodb/pyproject.toml
+++ /dev/null
@@ -1,103 +0,0 @@
-[build-system]
-requires = ["poetry-core>=1.0.0"]
-build-backend = "poetry.core.masonry.api"
-
-[tool.poetry]
-name = "langchain-mongodb"
-version = "0.2.0"
-description = "An integration package connecting MongoDB and LangChain"
-authors = []
-readme = "README.md"
-repository = "https://github.com/langchain-ai/langchain"
-license = "MIT"
-
-[tool.mypy]
-disallow_untyped_defs = "True"
-
-[tool.poetry.urls]
-"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/mongodb"
-"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-mongodb%3D%3D0%22&expanded=true"
-
-[tool.poetry.dependencies]
-python = ">=3.9,<4.0"
-pymongo = ">=4.6.1,<5.0"
-langchain-core = "^0.3"
-
-[[tool.poetry.dependencies.numpy]]
-version = "^1"
-python = "<3.12"
-
-[[tool.poetry.dependencies.numpy]]
-version = "^1.26.0"
-python = ">=3.12"
-
-[tool.ruff.lint]
-select = ["E", "F", "I"]
-
-[tool.coverage.run]
-omit = ["tests/*"]
-
-[tool.pytest.ini_options]
-addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
-markers = [
-    "requires: mark tests as requiring a specific library",
-    "compile: mark placeholder test used to compile integration tests without running them",
-]
-asyncio_mode = "auto"
-
-[tool.poetry.group.test]
-optional = true
-
-[tool.poetry.group.codespell]
-optional = true
-
-[tool.poetry.group.test_integration]
-optional = true
-
-[tool.poetry.group.lint]
-optional = true
-
-[tool.poetry.group.dev]
-optional = true
-
-[tool.poetry.group.test.dependencies]
-pytest = "^7.3.0"
-freezegun = "^1.2.2"
-pytest-mock = "^3.10.0"
-syrupy = "^4.0.2"
-pytest-watcher = "^0.3.4"
-pytest-asyncio = "^0.21.1"
-
-[tool.poetry.group.codespell.dependencies]
-codespell = "^2.2.0"
-
-[tool.poetry.group.test_integration.dependencies.langchain-openai]
-path = "../openai"
-develop = true
-
-[tool.poetry.group.lint.dependencies]
-ruff = "^0.5"
-
-[tool.poetry.group.typing.dependencies]
-mypy = "^1.10"
-simsimd = "^5.0.0"
-
-[tool.poetry.group.test.dependencies.langchain]
-path = "../../langchain"
-develop = true
-
-[tool.poetry.group.test.dependencies.langchain-core]
-path = "../../core"
-develop = true
-
-[tool.poetry.group.test.dependencies.langchain-text-splitters]
-path = "../../text-splitters"
-develop = true
-
-[tool.poetry.group.dev.dependencies.langchain-core]
-path = "../../core"
-develop = true
-
-[tool.poetry.group.typing.dependencies.langchain-core]
-path = "../../core"
-develop = true
diff --git a/libs/partners/mongodb/scripts/check_imports.py b/libs/partners/mongodb/scripts/check_imports.py
deleted file mode 100644
index ba8de50118c4e..0000000000000
--- a/libs/partners/mongodb/scripts/check_imports.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import sys
-import traceback
-from importlib.machinery import SourceFileLoader
-
-if __name__ == "__main__":
-    files = sys.argv[1:]
-    has_failure = False
-    for file in files:
-        try:
-            SourceFileLoader("x", file).load_module()
-        except Exception:
-            has_failure = True
-            print(file)
-            traceback.print_exc()
-            print()
-
-    sys.exit(1 if has_failure else 0)
diff --git a/libs/partners/mongodb/scripts/lint_imports.sh b/libs/partners/mongodb/scripts/lint_imports.sh
deleted file mode 100755
index 695613c7ba8fd..0000000000000
--- a/libs/partners/mongodb/scripts/lint_imports.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/bin/bash
-
-set -eu
-
-# Initialize a variable to keep track of errors
-errors=0
-
-# make sure not importing from langchain or langchain_experimental
-git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
-git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
-
-# Decide on an exit status based on the errors
-if [ "$errors" -gt 0 ]; then
-    exit 1
-else
-    exit 0
-fi
diff --git a/libs/partners/mongodb/tests/__init__.py b/libs/partners/mongodb/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/libs/partners/mongodb/tests/integration_tests/__init__.py b/libs/partners/mongodb/tests/integration_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/libs/partners/mongodb/tests/integration_tests/test_cache.py b/libs/partners/mongodb/tests/integration_tests/test_cache.py
deleted file mode 100644
index c0fe736700410..0000000000000
--- a/libs/partners/mongodb/tests/integration_tests/test_cache.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import os
-import uuid
-from typing import Any, List, Union
-
-import pytest  # type: ignore[import-not-found]
-from langchain_core.caches import BaseCache
-from langchain_core.globals import get_llm_cache, set_llm_cache
-from langchain_core.load.dump import dumps
-from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
-from langchain_core.outputs import ChatGeneration, Generation, LLMResult
-
-from langchain_mongodb.cache import MongoDBAtlasSemanticCache, MongoDBCache
-
-from ..utils import ConsistentFakeEmbeddings, FakeChatModel, FakeLLM
-
-CONN_STRING = os.environ.get("MONGODB_ATLAS_URI")
-INDEX_NAME = "langchain-test-index-semantic-cache"
-DATABASE = "langchain_test_db"
-COLLECTION = "langchain_test_cache"
-
-
-def random_string() -> str:
-    return str(uuid.uuid4())
-
-
-def llm_cache(cls: Any) -> BaseCache:
-    set_llm_cache(
-        cls(
-            embedding=ConsistentFakeEmbeddings(dimensionality=1536),
-            connection_string=CONN_STRING,
-            collection_name=COLLECTION,
-            database_name=DATABASE,
-            index_name=INDEX_NAME,
-            score_threshold=0.5,
-            wait_until_ready=15.0,
-        )
-    )
-    assert get_llm_cache()
-    return get_llm_cache()
-
-
-def _execute_test(
-    prompt: Union[str, List[BaseMessage]],
-    llm: Union[str, FakeLLM, FakeChatModel],
-    response: List[Generation],
-) -> None:
-    # Fabricate an LLM String
-
-    if not isinstance(llm, str):
-        params = llm.dict()
-        params["stop"] = None
-        llm_string = str(sorted([(k, v) for k, v in params.items()]))
-    else:
-        llm_string = llm
-
-    # If the prompt is a str then we should pass just the string
-    dumped_prompt: str = prompt if isinstance(prompt, str) else dumps(prompt)
-
-    # Update the cache
-    get_llm_cache().update(dumped_prompt, llm_string, response)
-
-    # Retrieve the cached result through 'generate' call
-    output: Union[List[Generation], LLMResult, None]
-    expected_output: Union[List[Generation], LLMResult]
-
-    if isinstance(llm, str):
-        output = get_llm_cache().lookup(dumped_prompt, llm)  # type: ignore
-        expected_output = response
-    else:
-        output = llm.generate([prompt])  # type: ignore
-        expected_output = LLMResult(
-            generations=[response],
-            llm_output={},
-        )
-
-    assert output == expected_output  # type: ignore
-
-
-@pytest.mark.parametrize(
-    "prompt, llm, response",
-    [
-        ("foo", "bar", [Generation(text="fizz")]),
-        ("foo", FakeLLM(), [Generation(text="fizz")]),
-        (
-            [HumanMessage(content="foo")],
-            FakeChatModel(),
-            [ChatGeneration(message=AIMessage(content="foo"))],
-        ),
-    ],
-    ids=[
-        "plain_cache",
-        "cache_with_llm",
-        "cache_with_chat",
-    ],
-)
-@pytest.mark.parametrize("cacher", [MongoDBCache, MongoDBAtlasSemanticCache])
-@pytest.mark.parametrize("remove_score", [True, False])
-def test_mongodb_cache(
-    remove_score: bool,
-    cacher: Union[MongoDBCache, MongoDBAtlasSemanticCache],
-    prompt: Union[str, List[BaseMessage]],
-    llm: Union[str, FakeLLM, FakeChatModel],
-    response: List[Generation],
-) -> None:
-    llm_cache(cacher)
-    if remove_score:
-        get_llm_cache().score_threshold = None  # type: ignore
-    try:
-        _execute_test(prompt, llm, response)
-    finally:
-        get_llm_cache().clear()
-
-
-@pytest.mark.parametrize(
-    "prompts, generations",
-    [
-        # Single prompt, single generation
-        ([random_string()], [[random_string()]]),
-        # Single prompt, multiple generations
-        ([random_string()], [[random_string(), random_string()]]),
-        # Single prompt, multiple generations
-        ([random_string()], [[random_string(), random_string(), random_string()]]),
-        # Multiple prompts, multiple generations
-        (
-            [random_string(), random_string()],
-            [[random_string()], [random_string(), random_string()]],
-        ),
-    ],
-    ids=[
-        "single_prompt_single_generation",
-        "single_prompt_two_generations",
-        "single_prompt_three_generations",
-        "multiple_prompts_multiple_generations",
-    ],
-)
-def test_mongodb_atlas_cache_matrix(
-    prompts: List[str],
-    generations: List[List[str]],
-) -> None:
-    llm_cache(MongoDBAtlasSemanticCache)
-    llm = FakeLLM()
-
-    # Fabricate an LLM String
-    params = llm.dict()
-    params["stop"] = None
-    llm_string = str(sorted([(k, v) for k, v in params.items()]))
-
-    llm_generations = [
-        [
-            Generation(text=generation, generation_info=params)
-            for generation in prompt_i_generations
-        ]
-        for prompt_i_generations in generations
-    ]
-
-    for prompt_i, llm_generations_i in zip(prompts, llm_generations):
-        _execute_test(prompt_i, llm_string, llm_generations_i)
-    assert llm.generate(prompts) == LLMResult(
-        generations=llm_generations, llm_output={}
-    )
-    get_llm_cache().clear()
diff --git a/libs/partners/mongodb/tests/integration_tests/test_chain_example.py b/libs/partners/mongodb/tests/integration_tests/test_chain_example.py
deleted file mode 100644
index d54fa6cb01cc3..0000000000000
--- a/libs/partners/mongodb/tests/integration_tests/test_chain_example.py
+++ /dev/null
@@ -1,144 +0,0 @@
-"Demonstrates MongoDBAtlasVectorSearch.as_retriever() invoked in a chain" ""
-
-from __future__ import annotations
-
-import os
-from time import sleep
-
-import pytest  # type: ignore[import-not-found]
-from langchain_core.documents import Document
-from langchain_core.output_parsers.string import StrOutputParser
-from langchain_core.prompts.chat import ChatPromptTemplate
-from langchain_core.runnables import RunnablePassthrough
-from pymongo import MongoClient
-from pymongo.collection import Collection
-
-from langchain_mongodb import index
-
-from ..utils import PatchedMongoDBAtlasVectorSearch
-
-CONNECTION_STRING = os.environ.get("MONGODB_ATLAS_URI")
-DB_NAME = "langchain_test_db"
-COLLECTION_NAME = "langchain_test_chain_example"
-INDEX_NAME = "vector_index"
-DIMENSIONS = 1536
-TIMEOUT = 60.0
-INTERVAL = 0.5
-
-
-@pytest.fixture
-def collection() -> Collection:
-    """A Collection with both a Vector and a Full-text Search Index"""
-    client: MongoClient = MongoClient(CONNECTION_STRING)
-    if COLLECTION_NAME not in client[DB_NAME].list_collection_names():
-        clxn = client[DB_NAME].create_collection(COLLECTION_NAME)
-    else:
-        clxn = client[DB_NAME][COLLECTION_NAME]
-
-    clxn.delete_many({})
-
-    if all([INDEX_NAME != ix["name"] for ix in clxn.list_search_indexes()]):
-        index.create_vector_search_index(
-            collection=clxn,
-            index_name=INDEX_NAME,
-            dimensions=DIMENSIONS,
-            path="embedding",
-            similarity="cosine",
-            filters=None,
-            wait_until_complete=TIMEOUT,
-        )
-
-    return clxn
-
-
-@pytest.mark.skipif(
-    "OPENAI_API_KEY" not in os.environ, reason="Requires OpenAI for chat responses."
-)
-def test_chain(
-    collection: Collection,
-) -> None:
-    """Demonstrate usage of MongoDBAtlasVectorSearch in a realistic chain
-
-    Follows example in the docs: https://python.langchain.com/docs/how_to/hybrid/
-
-    Requires OpenAI_API_KEY for embedding and chat model.
-    Requires INDEX_NAME to have been set up on MONGODB_ATLAS_URI
-    """
-
-    from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-
-    embedding_openai = OpenAIEmbeddings(
-        openai_api_key=os.environ["OPENAI_API_KEY"],  # type: ignore # noqa
-        model="text-embedding-3-small",
-    )
-
-    vectorstore = PatchedMongoDBAtlasVectorSearch(
-        collection=collection,
-        embedding=embedding_openai,
-        index_name=INDEX_NAME,
-        text_key="page_content",
-    )
-
-    texts = [
-        "In 2023, I visited Paris",
-        "In 2022, I visited New York",
-        "In 2021, I visited New Orleans",
-        "In 2019, I visited San Francisco",
-        "In 2020, I visited Vancouver",
-    ]
-    vectorstore.add_texts(texts)
-
-    # Give the index time to build (For CI)
-    sleep(TIMEOUT)
-
-    query = "In the United States, what city did I visit last?"
-    # One can do vector search on the vector store, using its various search types.
-    k = len(texts)
-
-    store_output = list(vectorstore.similarity_search(query=query, k=k))
-    assert len(store_output) == k
-    assert isinstance(store_output[0], Document)
-
-    # Unfortunately, the VectorStore output cannot be given to a Chat Model
-    # If we wish Chat Model to answer based on our own data,
-    # we have to give it the right things to work with.
-    # The way that Langchain does this is by piping results along in
-    # a Chain: https://python.langchain.com/v0.1/docs/modules/chains/
-
-    # Now, we can turn our VectorStore into something Runnable in a Chain
-    # by turning it into a Retriever.
-    # For the simple VectorSearch Retriever, we can do this like so.
-
-    retriever = vectorstore.as_retriever(search_kwargs=dict(k=k))
-
-    # This does not do much other than expose our search function
-    # as an invoke() method with a a certain API, a Runnable.
-    retriever_output = retriever.invoke(query)
-    assert len(retriever_output) == len(texts)
-    assert retriever_output[0].page_content == store_output[0].page_content
-
-    # To get a natural language response to our question,
-    # we need ChatOpenAI, a template to better frame the question as a prompt,
-    # and a parser to send the output to a string.
-    # Together, these become our Chain!
-    # Here goes:
-
-    template = """Answer the question based only on the following context.
-     Answer in as few words as possible.
-     {context}
-     Question: {question}
-     """
-    prompt = ChatPromptTemplate.from_template(template)
-
-    model = ChatOpenAI()
-
-    chain = (
-        {"context": retriever, "question": RunnablePassthrough()}  # type: ignore
-        | prompt
-        | model
-        | StrOutputParser()
-    )
-
-    answer = chain.invoke("What city did I visit last?")
-
-    assert "Paris" in answer
diff --git a/libs/partners/mongodb/tests/integration_tests/test_chat_message_histories.py b/libs/partners/mongodb/tests/integration_tests/test_chat_message_histories.py
deleted file mode 100644
index 5b0a7b493e875..0000000000000
--- a/libs/partners/mongodb/tests/integration_tests/test_chat_message_histories.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import json
-import os
-
-from langchain.memory import ConversationBufferMemory  # type: ignore[import-not-found]
-from langchain_core.messages import message_to_dict
-
-from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
-
-DATABASE = "langchain_test_db"
-COLLECTION = "langchain_test_chat"
-
-# Replace these with your mongodb connection string
-connection_string = os.environ.get("MONGODB_ATLAS_URI", "")
-
-
-def test_memory_with_message_store() -> None:
-    """Test the memory with a message store."""
-    # setup MongoDB as a message store
-    message_history = MongoDBChatMessageHistory(
-        connection_string=connection_string,
-        session_id="test-session",
-        database_name=DATABASE,
-        collection_name=COLLECTION,
-    )
-    memory = ConversationBufferMemory(
-        memory_key="baz", chat_memory=message_history, return_messages=True
-    )
-
-    # add some messages
-    memory.chat_memory.add_ai_message("This is me, the AI")
-    memory.chat_memory.add_user_message("This is me, the human")
-
-    # get the message history from the memory store and turn it into a json
-    messages = memory.chat_memory.messages
-    messages_json = json.dumps([message_to_dict(msg) for msg in messages])
-
-    assert "This is me, the AI" in messages_json
-    assert "This is me, the human" in messages_json
-
-    # remove the record from MongoDB, so the next test run won't pick it up
-    memory.chat_memory.clear()
-
-    assert memory.chat_memory.messages == []
diff --git a/libs/partners/mongodb/tests/integration_tests/test_compile.py b/libs/partners/mongodb/tests/integration_tests/test_compile.py
deleted file mode 100644
index 5196f4c0f8c51..0000000000000
--- a/libs/partners/mongodb/tests/integration_tests/test_compile.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import pytest  # type: ignore[import-not-found]
-
-
-@pytest.mark.compile
-def test_placeholder() -> None:
-    """Used for compiling integration tests without running any real tests."""
-    pass
diff --git a/libs/partners/mongodb/tests/integration_tests/test_index.py b/libs/partners/mongodb/tests/integration_tests/test_index.py
deleted file mode 100644
index 000cbed484d89..0000000000000
--- a/libs/partners/mongodb/tests/integration_tests/test_index.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""Search index commands are only supported on Atlas Clusters >=M10"""
-
-import os
-from typing import Generator, List, Optional
-
-import pytest
-from pymongo import MongoClient
-from pymongo.collection import Collection
-
-from langchain_mongodb import index
-
-DB_NAME = "langchain_test_index_db"
-COLLECTION_NAME = "test_index"
-VECTOR_INDEX_NAME = "vector_index"
-
-TIMEOUT = 120
-DIMENSIONS = 10
-
-
-@pytest.fixture
-def collection() -> Generator:
-    """Depending on uri, this could point to any type of cluster."""
-    uri = os.environ.get("MONGODB_ATLAS_URI")
-    client: MongoClient = MongoClient(uri)
-    clxn = client[DB_NAME][COLLECTION_NAME]
-    clxn.insert_one({"foo": "bar"})
-    yield clxn
-    clxn.drop()
-
-
-def test_search_index_commands(collection: Collection) -> None:
-    index_name = VECTOR_INDEX_NAME
-    dimensions = DIMENSIONS
-    path = "embedding"
-    similarity = "cosine"
-    filters: Optional[List[str]] = None
-    wait_until_complete = TIMEOUT
-
-    for index_info in collection.list_search_indexes():
-        index.drop_vector_search_index(
-            collection, index_info["name"], wait_until_complete=wait_until_complete
-        )
-
-    assert len(list(collection.list_search_indexes())) == 0
-
-    index.create_vector_search_index(
-        collection=collection,
-        index_name=index_name,
-        dimensions=dimensions,
-        path=path,
-        similarity=similarity,
-        filters=filters,
-        wait_until_complete=wait_until_complete,
-    )
-
-    assert index._is_index_ready(collection, index_name)
-    indexes = list(collection.list_search_indexes())
-    assert len(indexes) == 1
-    assert indexes[0]["name"] == index_name
-
-    new_similarity = "euclidean"
-    index.update_vector_search_index(
-        collection,
-        index_name,
-        DIMENSIONS,
-        "embedding",
-        new_similarity,
-        filters=[],
-        wait_until_complete=wait_until_complete,
-    )
-
-    assert index._is_index_ready(collection, index_name)
-    indexes = list(collection.list_search_indexes())
-    assert len(indexes) == 1
-    assert indexes[0]["name"] == index_name
-    assert indexes[0]["latestDefinition"]["fields"][0]["similarity"] == new_similarity
-
-    index.drop_vector_search_index(
-        collection, index_name, wait_until_complete=wait_until_complete
-    )
-
-    indexes = list(collection.list_search_indexes())
-    assert len(indexes) == 0
diff --git a/libs/partners/mongodb/tests/integration_tests/test_retrievers.py b/libs/partners/mongodb/tests/integration_tests/test_retrievers.py
deleted file mode 100644
index 8a0cf34c85c1d..0000000000000
--- a/libs/partners/mongodb/tests/integration_tests/test_retrievers.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import os
-from time import sleep
-from typing import List
-
-import pytest
-from langchain_core.documents import Document
-from langchain_core.embeddings import Embeddings
-from pymongo import MongoClient
-from pymongo.collection import Collection
-
-from langchain_mongodb import index
-from langchain_mongodb.retrievers import (
-    MongoDBAtlasFullTextSearchRetriever,
-    MongoDBAtlasHybridSearchRetriever,
-)
-
-from ..utils import ConsistentFakeEmbeddings, PatchedMongoDBAtlasVectorSearch
-
-CONNECTION_STRING = os.environ.get("MONGODB_ATLAS_URI")
-DB_NAME = "langchain_test_db"
-COLLECTION_NAME = "langchain_test_retrievers"
-VECTOR_INDEX_NAME = "vector_index"
-EMBEDDING_FIELD = "embedding"
-PAGE_CONTENT_FIELD = "text"
-SEARCH_INDEX_NAME = "text_index"
-
-DIMENSIONS = 1536
-TIMEOUT = 60.0
-INTERVAL = 0.5
-
-
-@pytest.fixture
-def example_documents() -> List[Document]:
-    return [
-        Document(page_content="In 2023, I visited Paris"),
-        Document(page_content="In 2022, I visited New York"),
-        Document(page_content="In 2021, I visited New Orleans"),
-        Document(page_content="Sandwiches are beautiful. Sandwiches are fine."),
-    ]
-
-
-@pytest.fixture
-def embedding_openai() -> Embeddings:
-    from langchain_openai import OpenAIEmbeddings
-
-    try:
-        return OpenAIEmbeddings(
-            openai_api_key=os.environ["OPENAI_API_KEY"],  # type: ignore # noqa
-            model="text-embedding-3-small",
-        )
-    except Exception:
-        return ConsistentFakeEmbeddings(DIMENSIONS)
-
-
-@pytest.fixture
-def collection() -> Collection:
-    """A Collection with both a Vector and a Full-text Search Index"""
-    client: MongoClient = MongoClient(CONNECTION_STRING)
-    if COLLECTION_NAME not in client[DB_NAME].list_collection_names():
-        clxn = client[DB_NAME].create_collection(COLLECTION_NAME)
-    else:
-        clxn = client[DB_NAME][COLLECTION_NAME]
-
-    clxn.delete_many({})
-
-    if not any([VECTOR_INDEX_NAME == ix["name"] for ix in clxn.list_search_indexes()]):
-        index.create_vector_search_index(
-            collection=clxn,
-            index_name=VECTOR_INDEX_NAME,
-            dimensions=DIMENSIONS,
-            path="embedding",
-            similarity="cosine",
-            wait_until_complete=TIMEOUT,
-        )
-
-    if not any([SEARCH_INDEX_NAME == ix["name"] for ix in clxn.list_search_indexes()]):
-        index.create_fulltext_search_index(
-            collection=clxn,
-            index_name=SEARCH_INDEX_NAME,
-            field=PAGE_CONTENT_FIELD,
-            wait_until_complete=TIMEOUT,
-        )
-
-    return clxn
-
-
-def test_hybrid_retriever(
-    embedding_openai: Embeddings,
-    collection: Collection,
-    example_documents: List[Document],
-) -> None:
-    """Test basic usage of MongoDBAtlasHybridSearchRetriever"""
-
-    vectorstore = PatchedMongoDBAtlasVectorSearch(
-        collection=collection,
-        embedding=embedding_openai,
-        index_name=VECTOR_INDEX_NAME,
-        text_key=PAGE_CONTENT_FIELD,
-    )
-
-    vectorstore.add_documents(example_documents)
-
-    sleep(TIMEOUT)  # Wait for documents to be sync'd
-
-    retriever = MongoDBAtlasHybridSearchRetriever(
-        vectorstore=vectorstore,
-        search_index_name=SEARCH_INDEX_NAME,
-        top_k=3,
-    )
-
-    query1 = "What was the latest city that I visited?"
-    results = retriever.invoke(query1)
-    assert len(results) == 3
-    assert "Paris" in results[0].page_content
-
-    query2 = "When was the last time I visited new orleans?"
-    results = retriever.invoke(query2)
-    assert "New Orleans" in results[0].page_content
-
-
-def test_fulltext_retriever(
-    collection: Collection,
-    example_documents: List[Document],
-) -> None:
-    """Test result of performing fulltext search
-
-    Independent of the VectorStore, one adds documents
-    via MongoDB's Collection API
-    """
-    #
-
-    collection.insert_many(
-        [{PAGE_CONTENT_FIELD: doc.page_content} for doc in example_documents]
-    )
-    sleep(TIMEOUT)  # Wait for documents to be sync'd
-
-    retriever = MongoDBAtlasFullTextSearchRetriever(
-        collection=collection,
-        search_index_name=SEARCH_INDEX_NAME,
-        search_field=PAGE_CONTENT_FIELD,
-    )
-
-    query = "When was the last time I visited new orleans?"
-    results = retriever.invoke(query)
-    assert "New Orleans" in results[0].page_content
-    assert "score" in results[0].metadata
-
-
-def test_vector_retriever(
-    embedding_openai: Embeddings,
-    collection: Collection,
-    example_documents: List[Document],
-) -> None:
-    """Test VectorStoreRetriever"""
-
-    vectorstore = PatchedMongoDBAtlasVectorSearch(
-        collection=collection,
-        embedding=embedding_openai,
-        index_name=VECTOR_INDEX_NAME,
-        text_key=PAGE_CONTENT_FIELD,
-    )
-
-    vectorstore.add_documents(example_documents)
-
-    sleep(TIMEOUT)  # Wait for documents to be sync'd
-
-    retriever = vectorstore.as_retriever()
-
-    query1 = "What was the latest city that I visited?"
-    results = retriever.invoke(query1)
-    assert len(results) == 4
-    assert "Paris" in results[0].page_content
-
-    query2 = "When was the last time I visited new orleans?"
-    results = retriever.invoke(query2)
-    assert "New Orleans" in results[0].page_content
diff --git a/libs/partners/mongodb/tests/integration_tests/test_vectorstores.py b/libs/partners/mongodb/tests/integration_tests/test_vectorstores.py
deleted file mode 100644
index 3033b20f4909c..0000000000000
--- a/libs/partners/mongodb/tests/integration_tests/test_vectorstores.py
+++ /dev/null
@@ -1,473 +0,0 @@
-"""Test MongoDB Atlas Vector Search functionality."""
-
-from __future__ import annotations
-
-import os
-from time import monotonic, sleep
-from typing import Any, Dict, List
-
-import pytest  # type: ignore[import-not-found]
-from bson import ObjectId
-from langchain_core.documents import Document
-from langchain_core.embeddings import Embeddings
-from pymongo import MongoClient
-from pymongo.collection import Collection
-from pymongo.errors import OperationFailure
-
-from langchain_mongodb.index import drop_vector_search_index
-from langchain_mongodb.utils import oid_to_str
-
-from ..utils import ConsistentFakeEmbeddings, PatchedMongoDBAtlasVectorSearch
-
-INDEX_NAME = "langchain-test-index-vectorstores"
-INDEX_CREATION_NAME = "langchain-test-index-vectorstores-create-test"
-NAMESPACE = "langchain_test_db.langchain_test_vectorstores"
-CONNECTION_STRING = os.environ.get("MONGODB_ATLAS_URI")
-DB_NAME, COLLECTION_NAME = NAMESPACE.split(".")
-INDEX_COLLECTION_NAME = "langchain_test_vectorstores_index"
-INDEX_DB_NAME = "langchain_test_index_db"
-DIMENSIONS = 1536
-TIMEOUT = 120.0
-INTERVAL = 0.5
-
-
-@pytest.fixture
-def example_documents() -> List[Document]:
-    return [
-        Document(page_content="Dogs are tough.", metadata={"a": 1}),
-        Document(page_content="Cats have fluff.", metadata={"b": 1}),
-        Document(page_content="What is a sandwich?", metadata={"c": 1}),
-        Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
-    ]
-
-
-def _await_index_deletion(coll: Collection, index_name: str) -> None:
-    start = monotonic()
-    try:
-        drop_vector_search_index(coll, index_name)
-    except OperationFailure:
-        # This most likely means an ongoing drop request was made so skip
-        pass
-
-    while list(coll.list_search_indexes(name=index_name)):
-        if monotonic() - start > TIMEOUT:
-            raise TimeoutError(f"Index Name: {index_name} never dropped")
-        sleep(INTERVAL)
-
-
-def get_collection(
-    database_name: str = DB_NAME, collection_name: str = COLLECTION_NAME
-) -> Collection:
-    test_client: MongoClient = MongoClient(CONNECTION_STRING)
-    return test_client[database_name][collection_name]
-
-
-@pytest.fixture()
-def collection() -> Collection:
-    return get_collection()
-
-
-@pytest.fixture
-def texts() -> List[str]:
-    return [
-        "Dogs are tough.",
-        "Cats have fluff.",
-        "What is a sandwich?",
-        "That fence is purple.",
-    ]
-
-
-@pytest.fixture()
-def index_collection() -> Collection:
-    return get_collection(INDEX_DB_NAME, INDEX_COLLECTION_NAME)
-
-
-class TestMongoDBAtlasVectorSearch:
-    @classmethod
-    def setup_class(cls) -> None:
-        # insure the test collection is empty
-        collection = get_collection()
-        if collection.count_documents({}):
-            collection.delete_many({})  # type: ignore[index]
-
-    @classmethod
-    def teardown_class(cls) -> None:
-        collection = get_collection()
-        # delete all the documents in the collection
-        collection.delete_many({})  # type: ignore[index]
-
-    @pytest.fixture(autouse=True)
-    def setup(self) -> None:
-        collection = get_collection()
-        # delete all the documents in the collection
-        collection.delete_many({})  # type: ignore[index]
-
-        # delete all indexes on index collection name
-        _await_index_deletion(
-            get_collection(INDEX_DB_NAME, INDEX_COLLECTION_NAME), INDEX_CREATION_NAME
-        )
-
-    @pytest.fixture
-    def embeddings(self) -> Embeddings:
-        try:
-            from langchain_openai import OpenAIEmbeddings
-
-            return OpenAIEmbeddings(
-                openai_api_key=os.environ["OPENAI_API_KEY"],  # type: ignore # noqa
-                model="text-embedding-3-small",
-            )
-        except Exception:
-            return ConsistentFakeEmbeddings(DIMENSIONS)
-
-    def test_from_documents(
-        self,
-        embeddings: Embeddings,
-        collection: Any,
-        example_documents: List[Document],
-    ) -> None:
-        """Test end to end construction and search."""
-        vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
-            example_documents,
-            embedding=embeddings,
-            collection=collection,
-            index_name=INDEX_NAME,
-        )
-        output = vectorstore.similarity_search("Sandwich", k=1)
-        assert len(output) == 1
-        # Check for the presence of the metadata key
-        assert any(
-            [key.page_content == output[0].page_content for key in example_documents]
-        )
-
-    def test_from_documents_no_embedding_return(
-        self,
-        embeddings: Embeddings,
-        collection: Any,
-        example_documents: List[Document],
-    ) -> None:
-        """Test end to end construction and search."""
-        vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
-            example_documents,
-            embedding=embeddings,
-            collection=collection,
-            index_name=INDEX_NAME,
-        )
-        output = vectorstore.similarity_search("Sandwich", k=1)
-        assert len(output) == 1
-        # Check for presence of embedding in each document
-        assert all(["embedding" not in key.metadata for key in output])
-        # Check for the presence of the metadata key
-        assert any(
-            [key.page_content == output[0].page_content for key in example_documents]
-        )
-
-    def test_from_documents_embedding_return(
-        self,
-        embeddings: Embeddings,
-        collection: Any,
-        example_documents: List[Document],
-    ) -> None:
-        """Test end to end construction and search."""
-        vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
-            example_documents,
-            embedding=embeddings,
-            collection=collection,
-            index_name=INDEX_NAME,
-        )
-        output = vectorstore.similarity_search("Sandwich", k=1, include_embeddings=True)
-        assert len(output) == 1
-        # Check for presence of embedding in each document
-        assert all([key.metadata.get("embedding") for key in output])
-        # Check for the presence of the metadata key
-        assert any(
-            [key.page_content == output[0].page_content for key in example_documents]
-        )
-
-    def test_from_texts(
-        self, embeddings: Embeddings, collection: Collection, texts: List[str]
-    ) -> None:
-        vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
-            texts,
-            embedding=embeddings,
-            collection=collection,
-            index_name=INDEX_NAME,
-        )
-        output = vectorstore.similarity_search("Sandwich", k=1)
-        assert len(output) == 1
-
-    def test_from_texts_with_metadatas(
-        self,
-        embeddings: Embeddings,
-        collection: Collection,
-        texts: List[str],
-    ) -> None:
-        metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
-        metakeys = ["a", "b", "c", "d", "e"]
-        vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
-            texts,
-            embedding=embeddings,
-            metadatas=metadatas,
-            collection=collection,
-            index_name=INDEX_NAME,
-        )
-        output = vectorstore.similarity_search("Sandwich", k=1)
-        assert len(output) == 1
-        # Check for the presence of the metadata key
-        assert any([key in output[0].metadata for key in metakeys])
-
-    def test_from_texts_with_metadatas_and_pre_filter(
-        self, embeddings: Embeddings, collection: Any, texts: List[str]
-    ) -> None:
-        metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
-        vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
-            texts,
-            embedding=embeddings,
-            metadatas=metadatas,
-            collection=collection,
-            index_name=INDEX_NAME,
-        )
-        does_not_match_filter = vectorstore.similarity_search(
-            "Sandwich", k=1, pre_filter={"c": {"$lte": 0}}
-        )
-        assert does_not_match_filter == []
-
-        matches_filter = vectorstore.similarity_search(
-            "Sandwich", k=3, pre_filter={"c": {"$gt": 0}}
-        )
-        assert len(matches_filter) == 1
-
-    def test_mmr(self, embeddings: Embeddings, collection: Any) -> None:
-        texts = ["foo", "foo", "fou", "foy"]
-        vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
-            texts,
-            embedding=embeddings,
-            collection=collection,
-            index_name=INDEX_NAME,
-        )
-        query = "foo"
-        output = vectorstore.max_marginal_relevance_search(query, k=10, lambda_mult=0.1)
-        assert len(output) == len(texts)
-        assert output[0].page_content == "foo"
-        assert output[1].page_content != "foo"
-
-    def test_retriever(
-        self,
-        embeddings: Embeddings,
-        collection: Any,
-        example_documents: List[Document],
-    ) -> None:
-        """Demonstrate usage and parity of VectorStore similarity_search
-        with Retriever.invoke."""
-        vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
-            example_documents,
-            embedding=embeddings,
-            collection=collection,
-            index_name=INDEX_NAME,
-        )
-        query = "sandwich"
-
-        retriever_default_kwargs = vectorstore.as_retriever()
-        result_retriever = retriever_default_kwargs.invoke(query)
-        result_vectorstore = vectorstore.similarity_search(query)
-        assert all(
-            [
-                result_retriever[i].page_content == result_vectorstore[i].page_content
-                for i in range(len(result_retriever))
-            ]
-        )
-
-    def test_include_embeddings(
-        self,
-        embeddings: Embeddings,
-        collection: Any,
-        example_documents: List[Document],
-    ) -> None:
-        """Test explicitly passing vector kwarg matches default."""
-        vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
-            documents=example_documents,
-            embedding=embeddings,
-            collection=collection,
-            index_name=INDEX_NAME,
-        )
-
-        output_with = vectorstore.similarity_search(
-            "Sandwich", include_embeddings=True, k=1
-        )
-        assert vectorstore._embedding_key in output_with[0].metadata
-        output_without = vectorstore.similarity_search("Sandwich", k=1)
-        assert vectorstore._embedding_key not in output_without[0].metadata
-
-    def test_delete(
-        self, embeddings: Embeddings, collection: Any, texts: List[str]
-    ) -> None:
-        vectorstore = PatchedMongoDBAtlasVectorSearch(
-            collection=collection,
-            embedding=embeddings,
-            index_name=INDEX_NAME,
-        )
-        clxn: Collection = vectorstore._collection
-        assert clxn.count_documents({}) == 0
-        ids = vectorstore.add_texts(texts)
-        assert clxn.count_documents({}) == len(texts)
-
-        deleted = vectorstore.delete(ids[-2:])
-        assert deleted
-        assert clxn.count_documents({}) == len(texts) - 2
-
-        new_ids = vectorstore.add_texts(["Pigs eat stuff", "Pigs eat sandwiches"])
-        assert set(new_ids).intersection(set(ids)) == set()  # new ids will be unique.
-        assert isinstance(new_ids, list)
-        assert all(isinstance(i, str) for i in new_ids)
-        assert len(new_ids) == 2
-        assert clxn.count_documents({}) == 4
-
-    def test_add_texts(
-        self,
-        embeddings: Embeddings,
-        collection: Collection,
-        texts: List[str],
-    ) -> None:
-        """Tests API of add_texts, focussing on id treatment
-
-        Warning: This is slow because of the number of cases
-        """
-        metadatas: List[Dict[str, Any]] = [
-            {"a": 1},
-            {"b": 1},
-            {"c": 1},
-            {"d": 1, "e": 2},
-        ]
-
-        vectorstore = PatchedMongoDBAtlasVectorSearch(
-            collection=collection, embedding=embeddings, index_name=INDEX_NAME
-        )
-
-        # Case 1. Add texts without ids
-        provided_ids = vectorstore.add_texts(texts=texts, metadatas=metadatas)
-        all_docs = list(vectorstore._collection.find({}))
-        assert all("_id" in doc for doc in all_docs)
-        docids = set(doc["_id"] for doc in all_docs)
-        assert all(isinstance(_id, ObjectId) for _id in docids)  #
-        assert set(provided_ids) == set(oid_to_str(oid) for oid in docids)
-
-        # Case 2: Test Document.metadata looks right. i.e. contains _id
-        search_res = vectorstore.similarity_search_with_score("sandwich", k=1)
-        doc, score = search_res[0]
-        assert "_id" in doc.metadata
-
-        # Case 3: Add new ids that are 24-char hex strings
-        hex_ids = [oid_to_str(ObjectId()) for _ in range(2)]
-        hex_texts = ["Text for hex_id"] * len(hex_ids)
-        out_ids = vectorstore.add_texts(texts=hex_texts, ids=hex_ids)
-        assert set(out_ids) == set(hex_ids)
-        assert collection.count_documents({}) == len(texts) + len(hex_texts)
-        assert all(
-            isinstance(doc["_id"], ObjectId) for doc in vectorstore._collection.find({})
-        )
-
-        # Case 4: Add new ids that cannot be cast to ObjectId
-        #   - We can still index and search on them
-        str_ids = ["Sandwiches are beautiful,", "..sandwiches are fine."]
-        str_texts = str_ids  # No reason for them to differ
-        out_ids = vectorstore.add_texts(texts=str_texts, ids=str_ids)
-        assert set(out_ids) == set(str_ids)
-        assert collection.count_documents({}) == 8
-        res = vectorstore.similarity_search("sandwich", k=8)
-        assert any(str_ids[0] in doc.metadata["_id"] for doc in res)
-
-        # Case 5: Test adding in multiple batches
-        batch_size = 2
-        batch_ids = [oid_to_str(ObjectId()) for _ in range(2 * batch_size)]
-        batch_texts = [f"Text for batch text {i}" for i in range(2 * batch_size)]
-        out_ids = vectorstore.add_texts(
-            texts=batch_texts, ids=batch_ids, batch_size=batch_size
-        )
-        assert set(out_ids) == set(batch_ids)
-        assert collection.count_documents({}) == 12
-
-        # Case 6: _ids in metadata
-        collection.delete_many({})
-        # 6a. Unique _id in metadata, but ids=None
-        # Will be added as if ids kwarg provided
-        i = 0
-        n = len(texts)
-        assert len(metadatas) == n
-        _ids = [str(i) for i in range(n)]
-        for md in metadatas:
-            md["_id"] = _ids[i]
-            i += 1
-        returned_ids = vectorstore.add_texts(texts=texts, metadatas=metadatas)
-        assert returned_ids == ["0", "1", "2", "3"]
-        assert set(d["_id"] for d in vectorstore._collection.find({})) == set(_ids)
-
-        # 6b. Unique "id", not "_id", but ids=None
-        # New ids will be assigned
-        i = 1
-        for md in metadatas:
-            md.pop("_id")
-            md["id"] = f"{1}"
-            i += 1
-        returned_ids = vectorstore.add_texts(texts=texts, metadatas=metadatas)
-        assert len(set(returned_ids).intersection(set(_ids))) == 0
-
-    def test_add_documents(
-        self,
-        embeddings: Embeddings,
-        collection: Collection,
-    ) -> None:
-        """Tests add_documents."""
-        vectorstore = PatchedMongoDBAtlasVectorSearch(
-            collection=collection, embedding=embeddings, index_name=INDEX_NAME
-        )
-
-        # Case 1: No ids
-        n_docs = 10
-        batch_size = 3
-        docs = [
-            Document(page_content=f"document {i}", metadata={"i": i})
-            for i in range(n_docs)
-        ]
-        result_ids = vectorstore.add_documents(docs, batch_size=batch_size)
-        assert len(result_ids) == n_docs
-        assert collection.count_documents({}) == n_docs
-
-        # Case 2: ids
-        collection.delete_many({})
-        n_docs = 10
-        batch_size = 3
-        docs = [
-            Document(page_content=f"document {i}", metadata={"i": i})
-            for i in range(n_docs)
-        ]
-        ids = [str(i) for i in range(n_docs)]
-        result_ids = vectorstore.add_documents(docs, ids, batch_size=batch_size)
-        assert len(result_ids) == n_docs
-        assert set(ids) == set(collection.distinct("_id"))
-
-        # Case 3: Single batch
-        collection.delete_many({})
-        n_docs = 3
-        batch_size = 10
-        docs = [
-            Document(page_content=f"document {i}", metadata={"i": i})
-            for i in range(n_docs)
-        ]
-        ids = [str(i) for i in range(n_docs)]
-        result_ids = vectorstore.add_documents(docs, ids, batch_size=batch_size)
-        assert len(result_ids) == n_docs
-        assert set(ids) == set(collection.distinct("_id"))
-
-    def test_index_creation(
-        self, embeddings: Embeddings, index_collection: Any
-    ) -> None:
-        vectorstore = PatchedMongoDBAtlasVectorSearch(
-            index_collection, embedding=embeddings, index_name=INDEX_CREATION_NAME
-        )
-        vectorstore.create_vector_search_index(dimensions=1536)
-
-    def test_index_update(self, embeddings: Embeddings, index_collection: Any) -> None:
-        vectorstore = PatchedMongoDBAtlasVectorSearch(
-            index_collection, embedding=embeddings, index_name=INDEX_CREATION_NAME
-        )
-        vectorstore.create_vector_search_index(dimensions=1536)
-        vectorstore.create_vector_search_index(dimensions=1536, update=True)
diff --git a/libs/partners/mongodb/tests/unit_tests/__init__.py b/libs/partners/mongodb/tests/unit_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/libs/partners/mongodb/tests/unit_tests/test_cache.py b/libs/partners/mongodb/tests/unit_tests/test_cache.py
deleted file mode 100644
index 56f5b85714029..0000000000000
--- a/libs/partners/mongodb/tests/unit_tests/test_cache.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import uuid
-from typing import Any, Dict, List, Union
-
-import pytest  # type: ignore[import-not-found]
-from langchain_core.caches import BaseCache
-from langchain_core.embeddings import Embeddings
-from langchain_core.globals import get_llm_cache, set_llm_cache
-from langchain_core.load.dump import dumps
-from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
-from langchain_core.outputs import ChatGeneration, Generation, LLMResult
-from pymongo.collection import Collection
-
-from langchain_mongodb.cache import MongoDBAtlasSemanticCache, MongoDBCache
-from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
-
-from ..utils import ConsistentFakeEmbeddings, FakeChatModel, FakeLLM, MockCollection
-
-CONN_STRING = "MockString"
-COLLECTION = "default"
-DATABASE = "default"
-
-
-class PatchedMongoDBCache(MongoDBCache):
-    def __init__(
-        self,
-        connection_string: str,
-        collection_name: str = "default",
-        database_name: str = "default",
-        **kwargs: Dict[str, Any],
-    ) -> None:
-        self.__database_name = database_name
-        self.__collection_name = collection_name
-        self.client = {self.__database_name: {self.__collection_name: MockCollection()}}  # type: ignore
-
-    @property
-    def database(self) -> Any:  # type: ignore
-        """Returns the database used to store cache values."""
-        return self.client[self.__database_name]
-
-    @property
-    def collection(self) -> Collection:
-        """Returns the collection used to store cache values."""
-        return self.database[self.__collection_name]
-
-
-class PatchedMongoDBAtlasSemanticCache(MongoDBAtlasSemanticCache):
-    def __init__(
-        self,
-        connection_string: str,
-        embedding: Embeddings,
-        collection_name: str = "default",
-        database_name: str = "default",
-        wait_until_ready: bool = False,
-        **kwargs: Dict[str, Any],
-    ):
-        self.collection = MockCollection()
-        self._wait_until_ready = False
-        self.score_threshold = None
-        MongoDBAtlasVectorSearch.__init__(
-            self,
-            self.collection,
-            embedding=embedding,
-            **kwargs,  # type: ignore
-        )
-
-
-def random_string() -> str:
-    return str(uuid.uuid4())
-
-
-def llm_cache(cls: Any) -> BaseCache:
-    set_llm_cache(
-        cls(
-            embedding=ConsistentFakeEmbeddings(dimensionality=1536),
-            connection_string=CONN_STRING,
-            collection_name=COLLECTION,
-            database_name=DATABASE,
-            wait_until_ready=15.0,
-        )
-    )
-    assert get_llm_cache()
-    return get_llm_cache()
-
-
-def _execute_test(
-    prompt: Union[str, List[BaseMessage]],
-    llm: Union[str, FakeLLM, FakeChatModel],
-    response: List[Generation],
-) -> None:
-    # Fabricate an LLM String
-
-    if not isinstance(llm, str):
-        params = llm.dict()
-        params["stop"] = None
-        llm_string = str(sorted([(k, v) for k, v in params.items()]))
-    else:
-        llm_string = llm
-
-    # If the prompt is a str then we should pass just the string
-    dumped_prompt: str = prompt if isinstance(prompt, str) else dumps(prompt)
-
-    # Update the cache
-    llm_cache = get_llm_cache()
-    llm_cache.update(dumped_prompt, llm_string, response)
-
-    # Retrieve the cached result through 'generate' call
-    output: Union[List[Generation], LLMResult, None]
-    expected_output: Union[List[Generation], LLMResult]
-    if isinstance(llm_cache, PatchedMongoDBAtlasSemanticCache):
-        llm_cache._collection._aggregate_result = [  # type: ignore
-            data
-            for data in llm_cache._collection._data  # type: ignore
-            if data.get("text") == dumped_prompt
-            and data.get("llm_string") == llm_string
-        ]  # type: ignore
-    if isinstance(llm, str):
-        output = get_llm_cache().lookup(dumped_prompt, llm)  # type: ignore
-        expected_output = response
-    else:
-        output = llm.generate([prompt])  # type: ignore
-        expected_output = LLMResult(
-            generations=[response],
-            llm_output={},
-        )
-
-    assert output == expected_output  # type: ignore
-
-
-@pytest.mark.parametrize(
-    "prompt, llm, response",
-    [
-        ("foo", "bar", [Generation(text="fizz")]),
-        ("foo", FakeLLM(), [Generation(text="fizz")]),
-        (
-            [HumanMessage(content="foo")],
-            FakeChatModel(),
-            [ChatGeneration(message=AIMessage(content="foo"))],
-        ),
-    ],
-    ids=[
-        "plain_cache",
-        "cache_with_llm",
-        "cache_with_chat",
-    ],
-)
-@pytest.mark.parametrize(
-    "cacher", [PatchedMongoDBCache, PatchedMongoDBAtlasSemanticCache]
-)
-@pytest.mark.parametrize("remove_score", [True, False])
-def test_mongodb_cache(
-    remove_score: bool,
-    cacher: Union[MongoDBCache, MongoDBAtlasSemanticCache],
-    prompt: Union[str, List[BaseMessage]],
-    llm: Union[str, FakeLLM, FakeChatModel],
-    response: List[Generation],
-) -> None:
-    llm_cache(cacher)
-    if remove_score:
-        get_llm_cache().score_threshold = None  # type: ignore
-    try:
-        _execute_test(prompt, llm, response)
-    finally:
-        get_llm_cache().clear()
-
-
-@pytest.mark.parametrize(
-    "prompts, generations",
-    [
-        # Single prompt, single generation
-        ([random_string()], [[random_string()]]),
-        # Single prompt, multiple generations
-        ([random_string()], [[random_string(), random_string()]]),
-        # Single prompt, multiple generations
-        ([random_string()], [[random_string(), random_string(), random_string()]]),
-        # Multiple prompts, multiple generations
-        (
-            [random_string(), random_string()],
-            [[random_string()], [random_string(), random_string()]],
-        ),
-    ],
-    ids=[
-        "single_prompt_single_generation",
-        "single_prompt_two_generations",
-        "single_prompt_three_generations",
-        "multiple_prompts_multiple_generations",
-    ],
-)
-def test_mongodb_atlas_cache_matrix(
-    prompts: List[str],
-    generations: List[List[str]],
-) -> None:
-    llm_cache(PatchedMongoDBAtlasSemanticCache)
-    llm = FakeLLM()
-
-    # Fabricate an LLM String
-    params = llm.dict()
-    params["stop"] = None
-    llm_string = str(sorted([(k, v) for k, v in params.items()]))
-
-    llm_generations = [
-        [
-            Generation(text=generation, generation_info=params)
-            for generation in prompt_i_generations
-        ]
-        for prompt_i_generations in generations
-    ]
-
-    for prompt_i, llm_generations_i in zip(prompts, llm_generations):
-        _execute_test(prompt_i, llm_string, llm_generations_i)
-
-    get_llm_cache()._collection._simulate_cache_aggregation_query = True  # type: ignore
-    assert llm.generate(prompts) == LLMResult(
-        generations=llm_generations, llm_output={}
-    )
-    get_llm_cache().clear()
diff --git a/libs/partners/mongodb/tests/unit_tests/test_chat_message_histories.py b/libs/partners/mongodb/tests/unit_tests/test_chat_message_histories.py
deleted file mode 100644
index 2031602c2b281..0000000000000
--- a/libs/partners/mongodb/tests/unit_tests/test_chat_message_histories.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import json
-
-from langchain.memory import ConversationBufferMemory  # type: ignore[import-not-found]
-from langchain_core.messages import message_to_dict
-
-from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
-
-from ..utils import MockCollection
-
-
-class PatchedMongoDBChatMessageHistory(MongoDBChatMessageHistory):
-    def __init__(self) -> None:
-        self.session_id = "test-session"
-        self.database_name = "test-database"
-        self.collection_name = "test-collection"
-        self.collection = MockCollection()
-        self.session_id_key = "SessionId"
-        self.history_key = "History"
-        self.history_size = None
-
-
-def test_memory_with_message_store() -> None:
-    """Test the memory with a message store."""
-    # setup MongoDB as a message store
-    message_history = PatchedMongoDBChatMessageHistory()
-    memory = ConversationBufferMemory(
-        memory_key="baz", chat_memory=message_history, return_messages=True
-    )
-
-    # add some messages
-    memory.chat_memory.add_ai_message("This is me, the AI")
-    memory.chat_memory.add_user_message("This is me, the human")
-
-    # get the message history from the memory store and turn it into a json
-    messages = memory.chat_memory.messages
-    messages_json = json.dumps([message_to_dict(msg) for msg in messages])
-
-    assert "This is me, the AI" in messages_json
-    assert "This is me, the human" in messages_json
-
-    # remove the record from MongoDB, so the next test run won't pick it up
-    memory.chat_memory.clear()
-
-    assert memory.chat_memory.messages == []
diff --git a/libs/partners/mongodb/tests/unit_tests/test_imports.py b/libs/partners/mongodb/tests/unit_tests/test_imports.py
deleted file mode 100644
index e0bb498a7dfd7..0000000000000
--- a/libs/partners/mongodb/tests/unit_tests/test_imports.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from langchain_mongodb import __all__
-
-EXPECTED_ALL = [
-    "MongoDBAtlasVectorSearch",
-    "MongoDBChatMessageHistory",
-    "MongoDBCache",
-    "MongoDBAtlasSemanticCache",
-]
-
-
-def test_all_imports() -> None:
-    assert sorted(EXPECTED_ALL) == sorted(__all__)
diff --git a/libs/partners/mongodb/tests/unit_tests/test_index.py b/libs/partners/mongodb/tests/unit_tests/test_index.py
deleted file mode 100644
index acb6cb5b18926..0000000000000
--- a/libs/partners/mongodb/tests/unit_tests/test_index.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""Search index commands are only supported on Atlas Clusters >=M10"""
-
-import os
-from time import sleep
-
-import pytest
-from pymongo import MongoClient
-from pymongo.collection import Collection
-from pymongo.errors import OperationFailure, ServerSelectionTimeoutError
-
-from langchain_mongodb import index
-
-DIMENSION = 10
-TIMEOUT = 10
-
-
-@pytest.fixture
-def collection() -> Collection:
-    """Depending on uri, this could point to any type of cluster.
-
-    For unit tests, MONGODB_URI should be localhost, None, or Atlas cluster <M10.
-    """
-    uri = os.environ.get("MONGODB_URI")
-    client: MongoClient = MongoClient(uri)
-    return client["db"]["collection"]
-
-
-def test_create_vector_search_index(collection: Collection) -> None:
-    with pytest.raises((OperationFailure, ServerSelectionTimeoutError)):
-        index.create_vector_search_index(
-            collection,
-            "index_name",
-            DIMENSION,
-            "embedding",
-            "cosine",
-            [],
-            wait_until_complete=TIMEOUT,
-        )
-
-
-def test_drop_vector_search_index(collection: Collection) -> None:
-    with pytest.raises((OperationFailure, ServerSelectionTimeoutError)):
-        index.drop_vector_search_index(
-            collection, "index_name", wait_until_complete=TIMEOUT
-        )
-
-
-def test_update_vector_search_index(collection: Collection) -> None:
-    with pytest.raises((OperationFailure, ServerSelectionTimeoutError)):
-        index.update_vector_search_index(
-            collection,
-            "index_name",
-            DIMENSION,
-            "embedding",
-            "cosine",
-            [],
-            wait_until_complete=TIMEOUT,
-        )
-
-
-def test___is_index_ready(collection: Collection) -> None:
-    with pytest.raises((OperationFailure, ServerSelectionTimeoutError)):
-        index._is_index_ready(collection, "index_name")
-
-
-def test__wait_for_predicate() -> None:
-    err = "error string"
-    with pytest.raises(TimeoutError) as e:
-        index._wait_for_predicate(lambda: sleep(5), err=err, timeout=0.5, interval=0.1)
-        assert err in str(e)
-
-    index._wait_for_predicate(lambda: True, err=err, timeout=1.0, interval=0.5)
diff --git a/libs/partners/mongodb/tests/unit_tests/test_vectorstores.py b/libs/partners/mongodb/tests/unit_tests/test_vectorstores.py
deleted file mode 100644
index 5ed936ab7e487..0000000000000
--- a/libs/partners/mongodb/tests/unit_tests/test_vectorstores.py
+++ /dev/null
@@ -1,191 +0,0 @@
-from json import dumps, loads
-from typing import Any, Optional
-
-import pytest  # type: ignore[import-not-found]
-from langchain_core.documents import Document
-from langchain_core.embeddings import Embeddings
-from pymongo.collection import Collection
-
-from langchain_mongodb import MongoDBAtlasVectorSearch
-
-from ..utils import ConsistentFakeEmbeddings, MockCollection
-
-INDEX_NAME = "langchain-test-index"
-NAMESPACE = "langchain_test_db.langchain_test_collection"
-DB_NAME, COLLECTION_NAME = NAMESPACE.split(".")
-
-
-def get_collection() -> MockCollection:
-    return MockCollection()
-
-
-@pytest.fixture()
-def collection() -> MockCollection:
-    return get_collection()
-
-
-@pytest.fixture(scope="module")
-def embedding_openai() -> Embeddings:
-    return ConsistentFakeEmbeddings()
-
-
-def test_initialization(collection: Collection, embedding_openai: Embeddings) -> None:
-    """Test initialization of vector store class"""
-    assert MongoDBAtlasVectorSearch(collection, embedding_openai)
-
-
-def test_init_from_texts(collection: Collection, embedding_openai: Embeddings) -> None:
-    """Test from_texts operation on an empty list"""
-    assert MongoDBAtlasVectorSearch.from_texts(
-        [], embedding_openai, collection=collection
-    )
-
-
-class TestMongoDBAtlasVectorSearch:
-    @classmethod
-    def setup_class(cls) -> None:
-        # ensure the test collection is empty
-        collection = get_collection()
-        assert collection.count_documents({}) == 0  # type: ignore[index]
-
-    @classmethod
-    def teardown_class(cls) -> None:
-        collection = get_collection()
-        # delete all the documents in the collection
-        collection.delete_many({})  # type: ignore[index]
-
-    @pytest.fixture(autouse=True)
-    def setup(self) -> None:
-        collection = get_collection()
-        # delete all the documents in the collection
-        collection.delete_many({})  # type: ignore[index]
-
-    def _validate_search(
-        self,
-        vectorstore: MongoDBAtlasVectorSearch,
-        collection: MockCollection,
-        search_term: str = "sandwich",
-        page_content: str = "What is a sandwich?",
-        metadata: Optional[Any] = 1,
-    ) -> None:
-        collection._aggregate_result = list(
-            filter(
-                lambda x: search_term.lower() in x[vectorstore._text_key].lower(),
-                collection._data,
-            )
-        )
-        output = vectorstore.similarity_search("", k=1)
-        assert output[0].page_content == page_content
-        assert output[0].metadata.get("c") == metadata
-        # Validate the ObjectId provided is json serializable
-        assert loads(dumps(output[0].page_content)) == output[0].page_content
-        assert loads(dumps(output[0].metadata)) == output[0].metadata
-        assert isinstance(output[0].metadata["_id"], str)
-
-    def test_from_documents(
-        self, embedding_openai: Embeddings, collection: MockCollection
-    ) -> None:
-        """Test end to end construction and search."""
-        documents = [
-            Document(page_content="Dogs are tough.", metadata={"a": 1}),
-            Document(page_content="Cats have fluff.", metadata={"b": 1}),
-            Document(page_content="What is a sandwich?", metadata={"c": 1}),
-            Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
-        ]
-        vectorstore = MongoDBAtlasVectorSearch.from_documents(
-            documents,
-            embedding_openai,
-            collection=collection,
-            vector_index_name=INDEX_NAME,
-        )
-        self._validate_search(
-            vectorstore, collection, metadata=documents[2].metadata["c"]
-        )
-
-    def test_from_texts(
-        self, embedding_openai: Embeddings, collection: MockCollection
-    ) -> None:
-        texts = [
-            "Dogs are tough.",
-            "Cats have fluff.",
-            "What is a sandwich?",
-            "That fence is purple.",
-        ]
-        vectorstore = MongoDBAtlasVectorSearch.from_texts(
-            texts,
-            embedding_openai,
-            collection=collection,
-            vector_index_name=INDEX_NAME,
-        )
-        self._validate_search(vectorstore, collection, metadata=None)
-
-    def test_from_texts_with_metadatas(
-        self, embedding_openai: Embeddings, collection: MockCollection
-    ) -> None:
-        texts = [
-            "Dogs are tough.",
-            "Cats have fluff.",
-            "What is a sandwich?",
-            "The fence is purple.",
-        ]
-        metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
-        vectorstore = MongoDBAtlasVectorSearch.from_texts(
-            texts,
-            embedding_openai,
-            metadatas=metadatas,
-            collection=collection,
-            vector_index_name=INDEX_NAME,
-        )
-        self._validate_search(vectorstore, collection, metadata=metadatas[2]["c"])
-
-    def test_from_texts_with_metadatas_and_pre_filter(
-        self, embedding_openai: Embeddings, collection: MockCollection
-    ) -> None:
-        texts = [
-            "Dogs are tough.",
-            "Cats have fluff.",
-            "What is a sandwich?",
-            "The fence is purple.",
-        ]
-        metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
-        vectorstore = MongoDBAtlasVectorSearch.from_texts(
-            texts,
-            embedding_openai,
-            metadatas=metadatas,
-            collection=collection,
-            vector_index_name=INDEX_NAME,
-        )
-        collection._aggregate_result = list(
-            filter(
-                lambda x: "sandwich" in x[vectorstore._text_key].lower()
-                and x.get("c") < 0,
-                collection._data,
-            )
-        )
-        output = vectorstore.similarity_search(
-            "Sandwich", k=1, pre_filter={"range": {"lte": 0, "path": "c"}}
-        )
-        assert output == []
-
-    def test_mmr(
-        self, embedding_openai: Embeddings, collection: MockCollection
-    ) -> None:
-        texts = ["foo", "foo", "fou", "foy"]
-        vectorstore = MongoDBAtlasVectorSearch.from_texts(
-            texts,
-            embedding=embedding_openai,
-            collection=collection,
-            vector_index_name=INDEX_NAME,
-        )
-        query = "foo"
-        self._validate_search(
-            vectorstore,
-            collection,
-            search_term=query[0:2],
-            page_content=query,
-            metadata=None,
-        )
-        output = vectorstore.max_marginal_relevance_search(query, k=10, lambda_mult=0.1)
-        assert len(output) == len(texts)
-        assert output[0].page_content == "foo"
-        assert output[1].page_content != "foo"
diff --git a/libs/partners/mongodb/tests/utils.py b/libs/partners/mongodb/tests/utils.py
deleted file mode 100644
index d62b2025f48ea..0000000000000
--- a/libs/partners/mongodb/tests/utils.py
+++ /dev/null
@@ -1,273 +0,0 @@
-from __future__ import annotations
-
-from copy import deepcopy
-from time import monotonic, sleep
-from typing import Any, Dict, Generator, Iterable, List, Mapping, Optional, Union, cast
-
-from bson import ObjectId
-from langchain_core.callbacks.manager import (
-    AsyncCallbackManagerForLLMRun,
-    CallbackManagerForLLMRun,
-)
-from langchain_core.embeddings import Embeddings
-from langchain_core.language_models.chat_models import SimpleChatModel
-from langchain_core.language_models.llms import LLM
-from langchain_core.messages import (
-    AIMessage,
-    BaseMessage,
-)
-from langchain_core.outputs import ChatGeneration, ChatResult
-from pydantic import model_validator
-from pymongo.collection import Collection
-from pymongo.results import DeleteResult, InsertManyResult
-
-from langchain_mongodb import MongoDBAtlasVectorSearch
-from langchain_mongodb.cache import MongoDBAtlasSemanticCache
-
-TIMEOUT = 120
-INTERVAL = 0.5
-
-
-class PatchedMongoDBAtlasVectorSearch(MongoDBAtlasVectorSearch):
-    def bulk_embed_and_insert_texts(
-        self,
-        texts: Union[List[str], Iterable[str]],
-        metadatas: Union[List[dict], Generator[dict, Any, Any]],
-        ids: Optional[List[str]] = None,
-    ) -> List:
-        """Patched insert_texts that waits for data to be indexed before returning"""
-        ids_inserted = super().bulk_embed_and_insert_texts(texts, metadatas, ids)
-        start = monotonic()
-        while len(ids_inserted) != len(self.similarity_search("sandwich")) and (
-            monotonic() - start <= TIMEOUT
-        ):
-            sleep(INTERVAL)
-        return ids_inserted
-
-    def create_vector_search_index(
-        self,
-        dimensions: int,
-        filters: Optional[List[str]] = None,
-        update: bool = False,
-    ) -> None:
-        result = super().create_vector_search_index(
-            dimensions=dimensions, filters=filters, update=update
-        )
-        start = monotonic()
-        while monotonic() - start <= TIMEOUT:
-            if indexes := list(
-                self._collection.list_search_indexes(name=self._index_name)
-            ):
-                if indexes[0].get("status") == "READY":
-                    return result
-            sleep(INTERVAL)
-
-
-class ConsistentFakeEmbeddings(Embeddings):
-    """Fake embeddings functionality for testing."""
-
-    def __init__(self, dimensionality: int = 10) -> None:
-        self.known_texts: List[str] = []
-        self.dimensionality = dimensionality
-
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Return consistent embeddings for each text seen so far."""
-        out_vectors = []
-        for text in texts:
-            if text not in self.known_texts:
-                self.known_texts.append(text)
-            vector = [float(1.0)] * (self.dimensionality - 1) + [
-                float(self.known_texts.index(text))
-            ]
-            out_vectors.append(vector)
-        return out_vectors
-
-    def embed_query(self, text: str) -> List[float]:
-        """Return consistent embeddings for the text, if seen before, or a constant
-        one if the text is unknown."""
-        return self.embed_documents([text])[0]
-
-    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
-        return self.embed_documents(texts)
-
-    async def aembed_query(self, text: str) -> List[float]:
-        return self.embed_query(text)
-
-
-class FakeChatModel(SimpleChatModel):
-    """Fake Chat Model wrapper for testing purposes."""
-
-    def _call(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> str:
-        return "fake response"
-
-    async def _agenerate(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> ChatResult:
-        output_str = "fake response"
-        message = AIMessage(content=output_str)
-        generation = ChatGeneration(message=message)
-        return ChatResult(generations=[generation])
-
-    @property
-    def _llm_type(self) -> str:
-        return "fake-chat-model"
-
-    @property
-    def _identifying_params(self) -> Dict[str, Any]:
-        return {"key": "fake"}
-
-
-class FakeLLM(LLM):
-    """Fake LLM wrapper for testing purposes."""
-
-    queries: Optional[Mapping] = None
-    sequential_responses: Optional[bool] = False
-    response_index: int = 0
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_queries_required(cls, values: dict) -> dict:
-        if values.get("sequential_response") and not values.get("queries"):
-            raise ValueError(
-                "queries is required when sequential_response is set to True"
-            )
-        return values
-
-    def get_num_tokens(self, text: str) -> int:
-        """Return number of tokens."""
-        return len(text.split())
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of llm."""
-        return "fake"
-
-    def _call(
-        self,
-        prompt: str,
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> str:
-        if self.sequential_responses:
-            return self._get_next_response_in_sequence
-        if self.queries is not None:
-            return self.queries[prompt]
-        if stop is None:
-            return "foo"
-        else:
-            return "bar"
-
-    @property
-    def _identifying_params(self) -> Dict[str, Any]:
-        return {}
-
-    @property
-    def _get_next_response_in_sequence(self) -> str:
-        queries = cast(Mapping, self.queries)
-        response = queries[list(queries.keys())[self.response_index]]
-        self.response_index = self.response_index + 1
-        return response
-
-
-class MockCollection(Collection):
-    """Mocked Mongo Collection"""
-
-    _aggregate_result: List[Any]
-    _insert_result: Optional[InsertManyResult]
-    _data: List[Any]
-    _simulate_cache_aggregation_query: bool
-
-    def __init__(self) -> None:
-        self._data = []
-        self._aggregate_result = []
-        self._insert_result = None
-        self._simulate_cache_aggregation_query = False
-
-    def delete_many(self, *args, **kwargs) -> DeleteResult:  # type: ignore
-        old_len = len(self._data)
-        self._data = []
-        return DeleteResult({"n": old_len}, acknowledged=True)
-
-    def insert_many(self, to_insert: List[Any], *args, **kwargs) -> InsertManyResult:  # type: ignore
-        mongodb_inserts = [
-            {"_id": ObjectId(), "score": 1, **insert} for insert in to_insert
-        ]
-        self._data.extend(mongodb_inserts)
-        return self._insert_result or InsertManyResult(
-            [k["_id"] for k in mongodb_inserts], acknowledged=True
-        )
-
-    def insert_one(self, to_insert: Any, *args, **kwargs) -> Any:  # type: ignore
-        return self.insert_many([to_insert])
-
-    def find_one(self, find_query: Dict[str, Any]) -> Optional[Dict[str, Any]]:  # type: ignore
-        find = self.find(find_query) or [None]  # type: ignore
-        return find[0]
-
-    def find(self, find_query: Dict[str, Any]) -> Optional[List[Dict[str, Any]]]:  # type: ignore
-        def _is_match(item: Dict[str, Any]) -> bool:
-            for key, match_val in find_query.items():
-                if item.get(key) != match_val:
-                    return False
-            return True
-
-        return [document for document in self._data if _is_match(document)]
-
-    def update_one(  # type: ignore
-        self,
-        find_query: Dict[str, Any],
-        options: Dict[str, Any],
-        *args: Any,
-        upsert=True,
-        **kwargs: Any,
-    ) -> None:  # type: ignore
-        result = self.find_one(find_query)
-        set_options = options.get("$set", {})
-
-        if result:
-            result.update(set_options)
-        elif upsert:
-            self._data.append({**find_query, **set_options})
-
-    def _execute_cache_aggregation_query(self, *args, **kwargs) -> List[Dict[str, Any]]:  # type: ignore
-        """Helper function only to be used for MongoDBAtlasSemanticCache Testing
-
-        Returns:
-            List[Dict[str, Any]]: Aggregation query result
-        """
-        pipeline: List[Dict[str, Any]] = args[0]
-        params = pipeline[0]["$vectorSearch"]
-        embedding = params["queryVector"]
-        # Assumes MongoDBAtlasSemanticCache.LLM == "llm_string"
-        llm_string = params["filter"][MongoDBAtlasSemanticCache.LLM]["$eq"]
-
-        acc = []
-        for document in self._data:
-            if (
-                document.get("embedding") == embedding
-                and document.get(MongoDBAtlasSemanticCache.LLM) == llm_string
-            ):
-                acc.append(document)
-        return acc
-
-    def aggregate(self, *args, **kwargs) -> List[Any]:  # type: ignore
-        if self._simulate_cache_aggregation_query:
-            return deepcopy(self._execute_cache_aggregation_query(*args, **kwargs))
-        return deepcopy(self._aggregate_result)
-
-    def count_documents(self, *args, **kwargs) -> int:  # type: ignore
-        return len(self._data)
-
-    def __repr__(self) -> str:
-        return "MockCollection"

From c397baa85f632c86e09c119cb6c8c55300b4600a Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 18 Oct 2024 12:52:15 -0700
Subject: [PATCH 29/84] community: release 0.3.3 (#27472)

---
 libs/community/poetry.lock    | 10 +++++-----
 libs/community/pyproject.toml |  6 +++---
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index 706e8cfc4d89b..4ce7095867a17 100644
--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -1801,7 +1801,7 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.3.3"
+version = "0.3.4"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1811,7 +1811,7 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-langchain-core = "^0.3.10"
+langchain-core = "^0.3.12"
 langchain-text-splitters = "^0.3.0"
 langsmith = "^0.1.17"
 numpy = [
@@ -1830,7 +1830,7 @@ url = "../langchain"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.10"
+version = "0.3.12"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -4552,4 +4552,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "ceb97d5b463eacb11b6c7d9c572101e7f25c3f6b008178c6e9a273ed7e02920b"
+content-hash = "f7e01a390820c17e2703dfcdd195375a09f4b6c055e26a5aa435e35ca0baad52"
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index 6406350abf2bd..3b501221716c1 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-community"
-version = "0.3.2"
+version = "0.3.3"
 description = "Community contributed LangChain integrations."
 authors = []
 license = "MIT"
@@ -33,8 +33,8 @@ ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogy
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.3.10"
-langchain = "^0.3.3"
+langchain-core = "^0.3.12"
+langchain = "^0.3.4"
 SQLAlchemy = ">=1.4,<3"
 requests = "^2"
 PyYAML = ">=5.3"

From 97a819d5786a3f69d4fb2d75b6e73d7760d47388 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 18 Oct 2024 13:08:03 -0700
Subject: [PATCH 30/84] community: fix lint from new mypy (#27474)

---
 .../langchain_community/llms/gradient_ai.py   | 40 ++++++------
 .../utilities/google_trends.py                |  2 +-
 libs/community/poetry.lock                    | 63 ++++++++++---------
 libs/community/pyproject.toml                 |  2 +-
 4 files changed, 58 insertions(+), 49 deletions(-)

diff --git a/libs/community/langchain_community/llms/gradient_ai.py b/libs/community/langchain_community/llms/gradient_ai.py
index b603fd498d9a2..ee088808e9e5d 100644
--- a/libs/community/langchain_community/llms/gradient_ai.py
+++ b/libs/community/langchain_community/llms/gradient_ai.py
@@ -173,21 +173,23 @@ def _kwargs_post_fine_tune_request(
                 "content-type": "application/json",
             },
             json=dict(
-                samples=tuple(
-                    {
-                        "inputs": input,
-                    }
-                    for input in inputs
-                )
-                if multipliers is None
-                else tuple(
-                    {
-                        "inputs": input,
-                        "fineTuningParameters": {
-                            "multiplier": multiplier,
-                        },
-                    }
-                    for input, multiplier in zip(inputs, multipliers)
+                samples=(
+                    tuple(
+                        {
+                            "inputs": input,
+                        }
+                        for input in inputs
+                    )
+                    if multipliers is None
+                    else tuple(
+                        {
+                            "inputs": input,
+                            "fineTuningParameters": {
+                                "multiplier": multiplier,
+                            },
+                        }
+                        for input, multiplier in zip(inputs, multipliers)
+                    )
                 ),
             ),
         )
@@ -338,9 +340,11 @@ async def _agenerate(
     ) -> LLMResult:
         """Run the LLM on the given prompt and input."""
         generations = []
-        for generation in asyncio.gather(
-            [self._acall(prompt, stop=stop, run_manager=run_manager, **kwargs)]
-            for prompt in prompts
+        for generation in await asyncio.gather(
+            *[
+                self._acall(prompt, stop=stop, run_manager=run_manager, **kwargs)
+                for prompt in prompts
+            ]
         ):
             generations.append([Generation(text=generation)])
         return LLMResult(generations=generations)
diff --git a/libs/community/langchain_community/utilities/google_trends.py b/libs/community/langchain_community/utilities/google_trends.py
index 00f790d91c88c..94d019fef575e 100644
--- a/libs/community/langchain_community/utilities/google_trends.py
+++ b/libs/community/langchain_community/utilities/google_trends.py
@@ -64,7 +64,7 @@ def run(self, query: str) -> str:
             "q": query,
         }
 
-        total_results = []
+        total_results: Any = []
         client = self.serp_search_engine(params)
         client_dict = client.get_dict()
         total_results = (
diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index 4ce7095867a17..18c816b646533 100644
--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -2146,38 +2146,43 @@ typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "mypy"
-version = "1.11.2"
+version = "1.12.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mypy-1.11.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d42a6dd818ffce7be66cce644f1dff482f1d97c53ca70908dff0b9ddc120b77a"},
-    {file = "mypy-1.11.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:801780c56d1cdb896eacd5619a83e427ce436d86a3bdf9112527f24a66618fef"},
-    {file = "mypy-1.11.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:41ea707d036a5307ac674ea172875f40c9d55c5394f888b168033177fce47383"},
-    {file = "mypy-1.11.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:6e658bd2d20565ea86da7d91331b0eed6d2eee22dc031579e6297f3e12c758c8"},
-    {file = "mypy-1.11.2-cp310-cp310-win_amd64.whl", hash = "sha256:478db5f5036817fe45adb7332d927daa62417159d49783041338921dcf646fc7"},
-    {file = "mypy-1.11.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:75746e06d5fa1e91bfd5432448d00d34593b52e7e91a187d981d08d1f33d4385"},
-    {file = "mypy-1.11.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a976775ab2256aadc6add633d44f100a2517d2388906ec4f13231fafbb0eccca"},
-    {file = "mypy-1.11.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cd953f221ac1379050a8a646585a29574488974f79d8082cedef62744f0a0104"},
-    {file = "mypy-1.11.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:57555a7715c0a34421013144a33d280e73c08df70f3a18a552938587ce9274f4"},
-    {file = "mypy-1.11.2-cp311-cp311-win_amd64.whl", hash = "sha256:36383a4fcbad95f2657642a07ba22ff797de26277158f1cc7bd234821468b1b6"},
-    {file = "mypy-1.11.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e8960dbbbf36906c5c0b7f4fbf2f0c7ffb20f4898e6a879fcf56a41a08b0d318"},
-    {file = "mypy-1.11.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:06d26c277962f3fb50e13044674aa10553981ae514288cb7d0a738f495550b36"},
-    {file = "mypy-1.11.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6e7184632d89d677973a14d00ae4d03214c8bc301ceefcdaf5c474866814c987"},
-    {file = "mypy-1.11.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:3a66169b92452f72117e2da3a576087025449018afc2d8e9bfe5ffab865709ca"},
-    {file = "mypy-1.11.2-cp312-cp312-win_amd64.whl", hash = "sha256:969ea3ef09617aff826885a22ece0ddef69d95852cdad2f60c8bb06bf1f71f70"},
-    {file = "mypy-1.11.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:37c7fa6121c1cdfcaac97ce3d3b5588e847aa79b580c1e922bb5d5d2902df19b"},
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+    {file = "mypy-1.12.0-py3-none-any.whl", hash = "sha256:fd313226af375d52e1e36c383f39bf3836e1f192801116b31b090dfcd3ec5266"},
+    {file = "mypy-1.12.0.tar.gz", hash = "sha256:65a22d87e757ccd95cbbf6f7e181e6caa87128255eb2b6be901bb71b26d8a99d"},
 ]
 
 [package.dependencies]
@@ -4552,4 +4557,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "f7e01a390820c17e2703dfcdd195375a09f4b6c055e26a5aa435e35ca0baad52"
+content-hash = "5c436a9ba9a1695c5c456c1ad8a81c9772a2ba0248624278c6cb606dd019b338"
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index 3b501221716c1..41a3cb4d9cc99 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -130,7 +130,7 @@ jupyter = "^1.0.0"
 setuptools = "^67.6.1"
 
 [tool.poetry.group.typing.dependencies]
-mypy = "^1.10"
+mypy = "^1.12"
 types-pyyaml = "^6.0.12.2"
 types-requests = "^2.28.11.5"
 types-toml = "^0.10.8.1"

From e62e390ca01dff3e9e20e9fcc9a12520b5ec7931 Mon Sep 17 00:00:00 2001
From: Connor Park <connorpark024@gmail.com>
Date: Fri, 18 Oct 2024 20:44:58 -0400
Subject: [PATCH 31/84] docs: update API Reference Link in
 /docs/how_to/vectorstore_retriever/ (#27477)

Description: updated docs
[here](https://python.langchain.com/docs/how_to/vectorstore_retriever/#:~:text=VectorStoreRetriever)
for creating VectorStoreRetrievers. The URL was missing a `.base`, and
now works as expected.

This was a fix for Issue #27196
---
 docs/docs/how_to/vectorstore_retriever.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/how_to/vectorstore_retriever.ipynb b/docs/docs/how_to/vectorstore_retriever.ipynb
index 55408b911162e..760bcb2f90e4a 100644
--- a/docs/docs/how_to/vectorstore_retriever.ipynb
+++ b/docs/docs/how_to/vectorstore_retriever.ipynb
@@ -77,7 +77,7 @@
    "id": "08f8b820-5912-49c1-9d76-40be0571dffb",
    "metadata": {},
    "source": [
-    "This creates a retriever (specifically a [VectorStoreRetriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStoreRetriever.html)), which we can use in the usual way:"
+    "This creates a retriever (specifically a [VectorStoreRetriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStoreRetriever.html)), which we can use in the usual way:"
    ]
   },
   {

From edfe35c2a8444c146c247bc6eda3ca86dc04dcb1 Mon Sep 17 00:00:00 2001
From: Seungha Jeon <seungha@friendli.ai>
Date: Mon, 21 Oct 2024 23:58:49 +0900
Subject: [PATCH 32/84] docs: fix typo on friendli.ipynb (#27412)

This PR fixes typos in `chat/friendli.ipynb` and `llms/friendli.ipynb`
docs.
---
 docs/docs/integrations/chat/friendli.ipynb | 2 +-
 docs/docs/integrations/llms/friendli.ipynb | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/docs/integrations/chat/friendli.ipynb b/docs/docs/integrations/chat/friendli.ipynb
index bdce836562e52..dab9e0bdd1f74 100644
--- a/docs/docs/integrations/chat/friendli.ipynb
+++ b/docs/docs/integrations/chat/friendli.ipynb
@@ -29,7 +29,7 @@
     "Ensure the `langchain_community` and `friendli-client` are installed.\n",
     "\n",
     "```sh\n",
-    "pip install -U langchain-comminity friendli-client.\n",
+    "pip install -U langchain-community friendli-client.\n",
     "```\n",
     "\n",
     "Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, and set it as the `FRIENDLI_TOKEN` environment."
diff --git a/docs/docs/integrations/llms/friendli.ipynb b/docs/docs/integrations/llms/friendli.ipynb
index 3a457e0cc433f..529b6aec7a0ba 100644
--- a/docs/docs/integrations/llms/friendli.ipynb
+++ b/docs/docs/integrations/llms/friendli.ipynb
@@ -29,7 +29,7 @@
     "Ensure the `langchain_community` and `friendli-client` are installed.\n",
     "\n",
     "```sh\n",
-    "pip install -U langchain-comminity friendli-client.\n",
+    "pip install -U langchain-community friendli-client.\n",
     "```\n",
     "\n",
     "Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, and set it as the `FRIENDLI_TOKEN` environment."

From cfcf783cb5415790cdc4e3b1a59f5a081709e89a Mon Sep 17 00:00:00 2001
From: nodfans <axuplus@gmail.com>
Date: Mon, 21 Oct 2024 22:59:33 +0800
Subject: [PATCH 33/84] community: fix a typo in planner_prompt.py (#27489)

Description: Fix typo in planner_prompt.py.
---
 .../agent_toolkits/openapi/planner_prompt.py                    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libs/community/langchain_community/agent_toolkits/openapi/planner_prompt.py b/libs/community/langchain_community/agent_toolkits/openapi/planner_prompt.py
index ec99e823e06c1..666260f5040a6 100644
--- a/libs/community/langchain_community/agent_toolkits/openapi/planner_prompt.py
+++ b/libs/community/langchain_community/agent_toolkits/openapi/planner_prompt.py
@@ -6,7 +6,7 @@
 API_PLANNER_PROMPT = """You are a planner that plans a sequence of API calls to assist with user queries against an API.
 
 You should:
-1) evaluate whether the user query can be solved by the API documentated below. If no, say why.
+1) evaluate whether the user query can be solved by the API documented below. If no, say why.
 2) if yes, generate a plan of API calls and say what they are doing step by step.
 3) If the plan includes a DELETE call, you should always return an ask from the User for authorization first unless the User has specifically asked to delete something.
 

From 8f80dd28d998aefc2080b7528cde684a2df8806f Mon Sep 17 00:00:00 2001
From: Erez Zohar <innovaterz@gmail.com>
Date: Mon, 21 Oct 2024 18:01:13 +0300
Subject: [PATCH 34/84] docs: typo fix athena.ipynb and glue_catalog.ipynb
 (#27435)

**Description:** This PR fixes typos in
```
docs/docs/integrations/document_loaders/athena.ipynb
docs/docs/integrations/document_loaders/glue_catalog.ipynb
```
---
 docs/docs/integrations/document_loaders/athena.ipynb       | 2 +-
 docs/docs/integrations/document_loaders/glue_catalog.ipynb | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/docs/integrations/document_loaders/athena.ipynb b/docs/docs/integrations/document_loaders/athena.ipynb
index fada38b984118..73b3f72fef298 100644
--- a/docs/docs/integrations/document_loaders/athena.ipynb
+++ b/docs/docs/integrations/document_loaders/athena.ipynb
@@ -24,7 +24,7 @@
    "source": [
     "## Setting up\n",
     "\n",
-    "Follow [instructions to set up an AWS accoung](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html).\n",
+    "Follow [instructions to set up an AWS account](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html).\n",
     "\n",
     "Install a python library:"
    ]
diff --git a/docs/docs/integrations/document_loaders/glue_catalog.ipynb b/docs/docs/integrations/document_loaders/glue_catalog.ipynb
index a1e14e5836c0e..212df921145bb 100644
--- a/docs/docs/integrations/document_loaders/glue_catalog.ipynb
+++ b/docs/docs/integrations/document_loaders/glue_catalog.ipynb
@@ -22,7 +22,7 @@
    "source": [
     "## Setting up\n",
     "\n",
-    "- Follow [instructions to set up an AWS accoung](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html).\n",
+    "- Follow [instructions to set up an AWS account](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html).\n",
     "- Install the boto3 library: `pip install boto3`\n"
    ]
   },

From 2678cda83bf31e2a75007bdc95620e70d8f926fe Mon Sep 17 00:00:00 2001
From: venkatram-dev <45727389+venkatram-dev@users.noreply.github.com>
Date: Mon, 21 Oct 2024 08:01:23 -0700
Subject: [PATCH 35/84] docs:tutorials:sql_qa.ipynb: fix typo (#27405)

Thank you for contributing to LangChain!

- [x] **PR title**: "package: description"

docs:docs:tutorials:sql_qa.ipynb: fix typo

- [x] **PR message**: ***Delete this entire checklist*** and replace
with
Fix typo in docs:docs:tutorials:sql_qa.ipynb


- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/docs/tutorials/sql_qa.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/docs/tutorials/sql_qa.ipynb b/docs/docs/tutorials/sql_qa.ipynb
index 0bab8a066938a..529bfaac49b02 100644
--- a/docs/docs/tutorials/sql_qa.ipynb
+++ b/docs/docs/tutorials/sql_qa.ipynb
@@ -87,7 +87,7 @@
     "* Run `.read Chinook.sql`\n",
     "* Test `SELECT * FROM Artist LIMIT 10;`\n",
     "\n",
-    "Now, `Chinhook.db` is in our directory and we can interface with it using the SQLAlchemy-driven `SQLDatabase` class:"
+    "Now, `Chinook.db` is in our directory and we can interface with it using the SQLAlchemy-driven `SQLDatabase` class:"
    ]
   },
   {
@@ -377,7 +377,7 @@
     "\n",
     "* [Prompting strategies](/docs/how_to/sql_prompting): Advanced prompt engineering techniques.\n",
     "* [Query checking](/docs/how_to/sql_query_checking): Add query validation and error handling.\n",
-    "* [Large databses](/docs/how_to/sql_large_db): Techniques for working with large databases."
+    "* [Large databases](/docs/how_to/sql_large_db): Techniques for working with large databases."
    ]
   },
   {

From 689e8b7e666d0803cb27259eb517a0365c55bc7f Mon Sep 17 00:00:00 2001
From: Radi <evaradi18@gmail.com>
Date: Mon, 21 Oct 2024 16:06:28 +0100
Subject: [PATCH 36/84] docs: Update chatbot.ipynb (#27422)

- [ ] **PR title**: "docs: Typo fix"

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/docs/tutorials/chatbot.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/tutorials/chatbot.ipynb b/docs/docs/tutorials/chatbot.ipynb
index 9a64507f5464c..dd6ab2544d037 100644
--- a/docs/docs/tutorials/chatbot.ipynb
+++ b/docs/docs/tutorials/chatbot.ipynb
@@ -449,7 +449,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Right now, all we've done is add a simple persistence layer around the model. We can start to make the more complicated and personalized by adding in a prompt template.\n",
+    "Right now, all we've done is add a simple persistence layer around the model. We can start to make the chatbot more complicated and personalized by adding in a prompt template.\n",
     "\n",
     "## Prompt templates\n",
     "\n",

From 6ed92f13d0d74c32aaa8b2fb007594e8f576eb3f Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Mon, 21 Oct 2024 08:27:46 -0700
Subject: [PATCH 37/84] infra: azure/mongo api docs build (#27512)

---
 .github/workflows/api_doc_build.yml | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/api_doc_build.yml b/.github/workflows/api_doc_build.yml
index 460699f6de377..6c412d3855b27 100644
--- a/.github/workflows/api_doc_build.yml
+++ b/.github/workflows/api_doc_build.yml
@@ -82,6 +82,14 @@ jobs:
         with:
           repository: langchain-ai/langchain-ibm
           path: langchain-ibm
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-azure
+          path: langchain-azure
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-mongodb
+          path: langchain-mongodb
 
 
 
@@ -109,7 +117,9 @@ jobs:
             langchain/libs/partners/milvus \
             langchain/libs/partners/unstructured \
             langchain/libs/partners/databricks \
-            langchain/libs/partners/ibm
+            langchain/libs/partners/ibm \
+            langchain/libs/partners/azure-dynamic-sessions \
+            langchain/libs/partners/mongodb
           mv langchain-google/libs/genai langchain/libs/partners/google-genai
           mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
           mv langchain-google/libs/community langchain/libs/partners/google-community
@@ -127,6 +137,8 @@ jobs:
           mv langchain-unstructured/libs/unstructured langchain/libs/partners/unstructured
           mv langchain-databricks/libs/databricks langchain/libs/partners/databricks
           mv langchain-ibm/libs/ibm langchain/libs/partners/ibm
+          mv langchain-azure/libs/azure-dynamic-sessions langchain/libs/partners/azure-dynamic-sessions
+          mv langchain-mongodb/libs/mongodb langchain/libs/partners/mongodb
 
       - name: Rm old html
         run:

From c6d088bc15c256aa1b72fca673c65fbe911895d3 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Mon, 21 Oct 2024 11:36:51 -0700
Subject: [PATCH 38/84] docs: giscus component strict (#27515)

---
 docs/src/theme/DocItem/Paginator/index.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/src/theme/DocItem/Paginator/index.js b/docs/src/theme/DocItem/Paginator/index.js
index d9be074997f72..7beab4a7f8f1d 100644
--- a/docs/src/theme/DocItem/Paginator/index.js
+++ b/docs/src/theme/DocItem/Paginator/index.js
@@ -13,7 +13,7 @@ export default function PaginatorWrapper(props) {
         category="Docs Discussions"
         categoryId="DIC_kwDOIPDwls4CjJYb"
         mapping="pathname"
-        strict="0"
+        strict="1"
         reactionsEnabled="0"
         emitMetadata="0"
         inputPosition="bottom"

From 403c0ea801e96464e1389970f5c90a73e33a237a Mon Sep 17 00:00:00 2001
From: bbaltagi-dtsl <85242845+bbaltagi-dtsl@users.noreply.github.com>
Date: Mon, 21 Oct 2024 21:46:56 +0200
Subject: [PATCH 39/84] community: fix DallE hidden open_api_key (#26996)

Thank you for contributing to LangChain!

- [ X] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
  - Example: "community: add foobar LLM"


- [ X]
    - **Issue:** issue #26941


Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../langchain_community/utilities/dalle_image_generator.py    | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/libs/community/langchain_community/utilities/dalle_image_generator.py b/libs/community/langchain_community/utilities/dalle_image_generator.py
index ba7970effa6c4..fea18c3958da5 100644
--- a/libs/community/langchain_community/utilities/dalle_image_generator.py
+++ b/libs/community/langchain_community/utilities/dalle_image_generator.py
@@ -116,7 +116,9 @@ def validate_environment(self) -> Self:
 
         if is_openai_v1():
             client_params = {
-                "api_key": self.openai_api_key,
+                "api_key": self.openai_api_key.get_secret_value()
+                if self.openai_api_key
+                else None,
                 "organization": self.openai_organization,
                 "base_url": self.openai_api_base,
                 "timeout": self.request_timeout,

From 380449a7a924422c252c901943e924ae741b8d8a Mon Sep 17 00:00:00 2001
From: Chun Kang Lu <chunkanglu@gmail.com>
Date: Mon, 21 Oct 2024 17:31:40 -0400
Subject: [PATCH 40/84] core: fix Image prompt template hardcoded template
 format (#27495)

Fixes #27411

**Description:** Adds `template_format` to the `ImagePromptTemplate`
class and updates passing in the `template_format` parameter from
ChatPromptTemplate instead of the hardcoded "f-string".
Also updated docs and typing related to `template_format` to be more
up-to-date and specific.

**Dependencies:** None

**Add tests and docs**: Added unit tests to validate fix. Needed to
update `test_chat` snapshot due to adding new attribute
`template_format` in `ImagePromptTemplate`.

---------

Co-authored-by: Vadym Barda <vadym@langchain.dev>
---
 libs/core/extended_testing_deps.txt           |  1 +
 libs/core/langchain_core/prompts/chat.py      | 34 +++++----
 .../prompts/few_shot_with_templates.py        |  6 +-
 libs/core/langchain_core/prompts/image.py     | 11 ++-
 libs/core/langchain_core/prompts/prompt.py    | 12 ++--
 libs/core/langchain_core/prompts/string.py    |  4 +-
 .../core/langchain_core/prompts/structured.py |  4 +-
 .../prompts/__snapshots__/test_chat.ambr      |  7 ++
 .../tests/unit_tests/prompts/test_chat.py     | 72 +++++++++++++++++++
 .../tests/unit_tests/prompts/test_prompt.py   |  5 +-
 10 files changed, 131 insertions(+), 25 deletions(-)

diff --git a/libs/core/extended_testing_deps.txt b/libs/core/extended_testing_deps.txt
index 5ad9c8930daf9..0e216a61157d8 100644
--- a/libs/core/extended_testing_deps.txt
+++ b/libs/core/extended_testing_deps.txt
@@ -1 +1,2 @@
 jinja2>=3,<4
+mustache>=0.1.4,<1
diff --git a/libs/core/langchain_core/prompts/chat.py b/libs/core/langchain_core/prompts/chat.py
index 111aed89b3c97..1629962ba1333 100644
--- a/libs/core/langchain_core/prompts/chat.py
+++ b/libs/core/langchain_core/prompts/chat.py
@@ -8,7 +8,6 @@
 from typing import (
     Annotated,
     Any,
-    Literal,
     Optional,
     TypedDict,
     TypeVar,
@@ -40,7 +39,11 @@
 from langchain_core.prompts.base import BasePromptTemplate
 from langchain_core.prompts.image import ImagePromptTemplate
 from langchain_core.prompts.prompt import PromptTemplate
-from langchain_core.prompts.string import StringPromptTemplate, get_template_variables
+from langchain_core.prompts.string import (
+    PromptTemplateFormat,
+    StringPromptTemplate,
+    get_template_variables,
+)
 from langchain_core.utils import get_colored_text
 from langchain_core.utils.interactive_env import is_interactive_env
 
@@ -296,7 +299,7 @@ def get_lc_namespace(cls) -> list[str]:
     def from_template(
         cls: type[MessagePromptTemplateT],
         template: str,
-        template_format: str = "f-string",
+        template_format: PromptTemplateFormat = "f-string",
         partial_variables: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> MessagePromptTemplateT:
@@ -486,7 +489,7 @@ def get_lc_namespace(cls) -> list[str]:
     def from_template(
         cls: type[_StringImageMessagePromptTemplateT],
         template: Union[str, list[Union[str, _TextTemplateParam, _ImageTemplateParam]]],
-        template_format: str = "f-string",
+        template_format: PromptTemplateFormat = "f-string",
         *,
         partial_variables: Optional[dict[str, Any]] = None,
         **kwargs: Any,
@@ -495,7 +498,8 @@ def from_template(
 
         Args:
             template: a template.
-            template_format: format of the template. Defaults to "f-string".
+            template_format: format of the template.
+                Options are: 'f-string', 'mustache', 'jinja2'. Defaults to "f-string".
             partial_variables: A dictionary of variables that can be used too partially.
                 Defaults to None.
             **kwargs: keyword arguments to pass to the constructor.
@@ -533,7 +537,7 @@ def from_template(
                     img_template = cast(_ImageTemplateParam, tmpl)["image_url"]
                     input_variables = []
                     if isinstance(img_template, str):
-                        vars = get_template_variables(img_template, "f-string")
+                        vars = get_template_variables(img_template, template_format)
                         if vars:
                             if len(vars) > 1:
                                 msg = (
@@ -545,7 +549,9 @@ def from_template(
                             input_variables = [vars[0]]
                         img_template = {"url": img_template}
                         img_template_obj = ImagePromptTemplate(
-                            input_variables=input_variables, template=img_template
+                            input_variables=input_variables,
+                            template=img_template,
+                            template_format=template_format,
                         )
                     elif isinstance(img_template, dict):
                         img_template = dict(img_template)
@@ -553,11 +559,13 @@ def from_template(
                             if key in img_template:
                                 input_variables.extend(
                                     get_template_variables(
-                                        img_template[key], "f-string"
+                                        img_template[key], template_format
                                     )
                                 )
                         img_template_obj = ImagePromptTemplate(
-                            input_variables=input_variables, template=img_template
+                            input_variables=input_variables,
+                            template=img_template,
+                            template_format=template_format,
                         )
                     else:
                         msg = f"Invalid image template: {tmpl}"
@@ -943,7 +951,7 @@ def __init__(
         self,
         messages: Sequence[MessageLikeRepresentation],
         *,
-        template_format: Literal["f-string", "mustache", "jinja2"] = "f-string",
+        template_format: PromptTemplateFormat = "f-string",
         **kwargs: Any,
     ) -> None:
         """Create a chat prompt template from a variety of message formats.
@@ -1160,7 +1168,7 @@ def from_strings(
     def from_messages(
         cls,
         messages: Sequence[MessageLikeRepresentation],
-        template_format: Literal["f-string", "mustache", "jinja2"] = "f-string",
+        template_format: PromptTemplateFormat = "f-string",
     ) -> ChatPromptTemplate:
         """Create a chat prompt template from a variety of message formats.
 
@@ -1354,7 +1362,7 @@ def pretty_repr(self, html: bool = False) -> str:
 def _create_template_from_message_type(
     message_type: str,
     template: Union[str, list],
-    template_format: Literal["f-string", "mustache", "jinja2"] = "f-string",
+    template_format: PromptTemplateFormat = "f-string",
 ) -> BaseMessagePromptTemplate:
     """Create a message prompt template from a message type and template string.
 
@@ -1426,7 +1434,7 @@ def _create_template_from_message_type(
 
 def _convert_to_message(
     message: MessageLikeRepresentation,
-    template_format: Literal["f-string", "mustache", "jinja2"] = "f-string",
+    template_format: PromptTemplateFormat = "f-string",
 ) -> Union[BaseMessage, BaseMessagePromptTemplate, BaseChatPromptTemplate]:
     """Instantiate a message from a variety of message formats.
 
diff --git a/libs/core/langchain_core/prompts/few_shot_with_templates.py b/libs/core/langchain_core/prompts/few_shot_with_templates.py
index f293aae1c80f8..da53d5b8c59d1 100644
--- a/libs/core/langchain_core/prompts/few_shot_with_templates.py
+++ b/libs/core/langchain_core/prompts/few_shot_with_templates.py
@@ -9,6 +9,7 @@
 from langchain_core.prompts.prompt import PromptTemplate
 from langchain_core.prompts.string import (
     DEFAULT_FORMATTER_MAPPING,
+    PromptTemplateFormat,
     StringPromptTemplate,
 )
 
@@ -36,8 +37,9 @@ class FewShotPromptWithTemplates(StringPromptTemplate):
     prefix: Optional[StringPromptTemplate] = None
     """A PromptTemplate to put before the examples."""
 
-    template_format: str = "f-string"
-    """The format of the prompt template. Options are: 'f-string', 'jinja2'."""
+    template_format: PromptTemplateFormat = "f-string"
+    """The format of the prompt template.
+    Options are: 'f-string', 'jinja2', 'mustache'."""
 
     validate_template: bool = False
     """Whether or not to try validating the template."""
diff --git a/libs/core/langchain_core/prompts/image.py b/libs/core/langchain_core/prompts/image.py
index a75a5eece0f91..9336e20f60ac5 100644
--- a/libs/core/langchain_core/prompts/image.py
+++ b/libs/core/langchain_core/prompts/image.py
@@ -4,6 +4,10 @@
 
 from langchain_core.prompt_values import ImagePromptValue, ImageURL, PromptValue
 from langchain_core.prompts.base import BasePromptTemplate
+from langchain_core.prompts.string import (
+    DEFAULT_FORMATTER_MAPPING,
+    PromptTemplateFormat,
+)
 from langchain_core.runnables import run_in_executor
 from langchain_core.utils import image as image_utils
 
@@ -13,6 +17,9 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
 
     template: dict = Field(default_factory=dict)
     """Template for the prompt."""
+    template_format: PromptTemplateFormat = "f-string"
+    """The format of the prompt template.
+    Options are: 'f-string', 'mustache', 'jinja2'."""
 
     def __init__(self, **kwargs: Any) -> None:
         if "input_variables" not in kwargs:
@@ -85,7 +92,9 @@ def format(
         formatted = {}
         for k, v in self.template.items():
             if isinstance(v, str):
-                formatted[k] = v.format(**kwargs)
+                formatted[k] = DEFAULT_FORMATTER_MAPPING[self.template_format](
+                    v, **kwargs
+                )
             else:
                 formatted[k] = v
         url = kwargs.get("url") or formatted.get("url")
diff --git a/libs/core/langchain_core/prompts/prompt.py b/libs/core/langchain_core/prompts/prompt.py
index 5c52ef36d076c..325ee067ecaf9 100644
--- a/libs/core/langchain_core/prompts/prompt.py
+++ b/libs/core/langchain_core/prompts/prompt.py
@@ -4,12 +4,13 @@
 
 import warnings
 from pathlib import Path
-from typing import Any, Literal, Optional, Union
+from typing import Any, Optional, Union
 
 from pydantic import BaseModel, model_validator
 
 from langchain_core.prompts.string import (
     DEFAULT_FORMATTER_MAPPING,
+    PromptTemplateFormat,
     StringPromptTemplate,
     check_valid_template,
     get_template_variables,
@@ -24,7 +25,8 @@ class PromptTemplate(StringPromptTemplate):
     A prompt template consists of a string template. It accepts a set of parameters
     from the user that can be used to generate a prompt for a language model.
 
-    The template can be formatted using either f-strings (default) or jinja2 syntax.
+    The template can be formatted using either f-strings (default), jinja2,
+    or mustache syntax.
 
     *Security warning*:
         Prefer using `template_format="f-string"` instead of
@@ -67,7 +69,7 @@ def get_lc_namespace(cls) -> list[str]:
     template: str
     """The prompt template."""
 
-    template_format: Literal["f-string", "mustache", "jinja2"] = "f-string"
+    template_format: PromptTemplateFormat = "f-string"
     """The format of the prompt template.
     Options are: 'f-string', 'mustache', 'jinja2'."""
 
@@ -248,7 +250,7 @@ def from_template(
         cls,
         template: str,
         *,
-        template_format: str = "f-string",
+        template_format: PromptTemplateFormat = "f-string",
         partial_variables: Optional[dict[str, Any]] = None,
         **kwargs: Any,
     ) -> PromptTemplate:
@@ -270,7 +272,7 @@ def from_template(
         Args:
             template: The template to load.
             template_format: The format of the template. Use `jinja2` for jinja2,
-                             and `f-string` or None for f-strings.
+                             `mustache` for mustache, and `f-string` for f-strings.
                              Defaults to `f-string`.
             partial_variables: A dictionary of variables that can be used to partially
                                fill in the template. For example, if the template is
diff --git a/libs/core/langchain_core/prompts/string.py b/libs/core/langchain_core/prompts/string.py
index 01c19486b6760..0040f4b712279 100644
--- a/libs/core/langchain_core/prompts/string.py
+++ b/libs/core/langchain_core/prompts/string.py
@@ -5,7 +5,7 @@
 import warnings
 from abc import ABC
 from string import Formatter
-from typing import Any, Callable
+from typing import Any, Callable, Literal
 
 from pydantic import BaseModel, create_model
 
@@ -16,6 +16,8 @@
 from langchain_core.utils.formatting import formatter
 from langchain_core.utils.interactive_env import is_interactive_env
 
+PromptTemplateFormat = Literal["f-string", "mustache", "jinja2"]
+
 
 def jinja2_formatter(template: str, /, **kwargs: Any) -> str:
     """Format a template using jinja2.
diff --git a/libs/core/langchain_core/prompts/structured.py b/libs/core/langchain_core/prompts/structured.py
index 360f14ccf2fde..543dbb57e5d90 100644
--- a/libs/core/langchain_core/prompts/structured.py
+++ b/libs/core/langchain_core/prompts/structured.py
@@ -2,7 +2,6 @@
 from typing import (
     Any,
     Callable,
-    Literal,
     Optional,
     Union,
 )
@@ -15,6 +14,7 @@
     ChatPromptTemplate,
     MessageLikeRepresentation,
 )
+from langchain_core.prompts.string import PromptTemplateFormat
 from langchain_core.runnables.base import (
     Other,
     Runnable,
@@ -38,7 +38,7 @@ def __init__(
         schema_: Optional[Union[dict, type[BaseModel]]] = None,
         *,
         structured_output_kwargs: Optional[dict[str, Any]] = None,
-        template_format: Literal["f-string", "mustache", "jinja2"] = "f-string",
+        template_format: PromptTemplateFormat = "f-string",
         **kwargs: Any,
     ) -> None:
         schema_ = schema_ or kwargs.pop("schema")
diff --git a/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr b/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr
index 7e35bd6c46548..8e5e5c61ef48c 100644
--- a/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr
+++ b/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr
@@ -3119,6 +3119,7 @@
                   'template': dict({
                     'url': 'data:image/jpeg;base64,{my_image}',
                   }),
+                  'template_format': 'f-string',
                 }),
                 'lc': 1,
                 'name': 'ImagePromptTemplate',
@@ -3138,6 +3139,7 @@
                   'template': dict({
                     'url': 'data:image/jpeg;base64,{my_image}',
                   }),
+                  'template_format': 'f-string',
                 }),
                 'lc': 1,
                 'name': 'ImagePromptTemplate',
@@ -3157,6 +3159,7 @@
                   'template': dict({
                     'url': '{my_other_image}',
                   }),
+                  'template_format': 'f-string',
                 }),
                 'lc': 1,
                 'name': 'ImagePromptTemplate',
@@ -3177,6 +3180,7 @@
                     'detail': 'medium',
                     'url': '{my_other_image}',
                   }),
+                  'template_format': 'f-string',
                 }),
                 'lc': 1,
                 'name': 'ImagePromptTemplate',
@@ -3195,6 +3199,7 @@
                   'template': dict({
                     'url': 'https://www.langchain.com/image.png',
                   }),
+                  'template_format': 'f-string',
                 }),
                 'lc': 1,
                 'name': 'ImagePromptTemplate',
@@ -3213,6 +3218,7 @@
                   'template': dict({
                     'url': 'data:image/jpeg;base64,foobar',
                   }),
+                  'template_format': 'f-string',
                 }),
                 'lc': 1,
                 'name': 'ImagePromptTemplate',
@@ -3231,6 +3237,7 @@
                   'template': dict({
                     'url': 'data:image/jpeg;base64,foobar',
                   }),
+                  'template_format': 'f-string',
                 }),
                 'lc': 1,
                 'name': 'ImagePromptTemplate',
diff --git a/libs/core/tests/unit_tests/prompts/test_chat.py b/libs/core/tests/unit_tests/prompts/test_chat.py
index 72056f6c5a22c..95cbdc5a30c68 100644
--- a/libs/core/tests/unit_tests/prompts/test_chat.py
+++ b/libs/core/tests/unit_tests/prompts/test_chat.py
@@ -31,6 +31,7 @@
     SystemMessagePromptTemplate,
     _convert_to_message,
 )
+from langchain_core.prompts.string import PromptTemplateFormat
 from tests.unit_tests.pydantic_utils import _normalize_schema
 
 
@@ -298,6 +299,77 @@ def test_chat_prompt_template_from_messages_mustache() -> None:
     ]
 
 
+@pytest.mark.requires("jinja2")
+def test_chat_prompt_template_from_messages_jinja2() -> None:
+    template = ChatPromptTemplate.from_messages(
+        [
+            ("system", "You are a helpful AI bot. Your name is {{ name }}."),
+            ("human", "Hello, how are you doing?"),
+            ("ai", "I'm doing well, thanks!"),
+            ("human", "{{ user_input }}"),
+        ],
+        "jinja2",
+    )
+
+    messages = template.format_messages(name="Bob", user_input="What is your name?")
+
+    assert messages == [
+        SystemMessage(
+            content="You are a helpful AI bot. Your name is Bob.", additional_kwargs={}
+        ),
+        HumanMessage(
+            content="Hello, how are you doing?", additional_kwargs={}, example=False
+        ),
+        AIMessage(
+            content="I'm doing well, thanks!", additional_kwargs={}, example=False
+        ),
+        HumanMessage(content="What is your name?", additional_kwargs={}, example=False),
+    ]
+
+
+@pytest.mark.requires("jinja2")
+@pytest.mark.requires("mustache")
+@pytest.mark.parametrize(
+    "template_format,image_type_placeholder,image_data_placeholder",
+    [
+        ("f-string", "{image_type}", "{image_data}"),
+        ("mustache", "{{image_type}}", "{{image_data}}"),
+        ("jinja2", "{{ image_type }}", "{{ image_data }}"),
+    ],
+)
+def test_chat_prompt_template_image_prompt_from_message(
+    template_format: PromptTemplateFormat,
+    image_type_placeholder: str,
+    image_data_placeholder: str,
+) -> None:
+    prompt = {
+        "type": "image_url",
+        "image_url": {
+            "url": f"data:{image_type_placeholder};base64, {image_data_placeholder}",
+            "detail": "low",
+        },
+    }
+
+    template = ChatPromptTemplate.from_messages(
+        [("human", [prompt])], template_format=template_format
+    )
+    assert template.format_messages(
+        image_type="image/png", image_data="base64data"
+    ) == [
+        HumanMessage(
+            content=[
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": "data:image/png;base64, base64data",
+                        "detail": "low",
+                    },
+                }
+            ]
+        )
+    ]
+
+
 def test_chat_prompt_template_with_messages(
     messages: list[BaseMessagePromptTemplate],
 ) -> None:
diff --git a/libs/core/tests/unit_tests/prompts/test_prompt.py b/libs/core/tests/unit_tests/prompts/test_prompt.py
index cf256a8ae1d30..d56654d874d5b 100644
--- a/libs/core/tests/unit_tests/prompts/test_prompt.py
+++ b/libs/core/tests/unit_tests/prompts/test_prompt.py
@@ -8,6 +8,7 @@
 from syrupy import SnapshotAssertion
 
 from langchain_core.prompts.prompt import PromptTemplate
+from langchain_core.prompts.string import PromptTemplateFormat
 from langchain_core.tracers.run_collector import RunCollectorCallbackHandler
 from tests.unit_tests.pydantic_utils import _normalize_schema
 
@@ -610,7 +611,9 @@ async def test_prompt_ainvoke_with_metadata() -> None:
 )
 @pytest.mark.parametrize("template_format", ["f-string", "mustache"])
 def test_prompt_falsy_vars(
-    template_format: str, value: Any, expected: Union[str, dict[str, str]]
+    template_format: PromptTemplateFormat,
+    value: Any,
+    expected: Union[str, dict[str, str]],
 ) -> None:
     # each line is value, f-string, mustache
     if template_format == "f-string":

From 0640cbf2f126f773b7ae78b0f94c1ba0caabb2c1 Mon Sep 17 00:00:00 2001
From: Vadym Barda <vadym@langchain.dev>
Date: Mon, 21 Oct 2024 17:37:07 -0400
Subject: [PATCH 41/84] huggingface[patch]: hide client field in
 HuggingFaceEmbeddings (#27522)

---
 .../embeddings/huggingface.py                 | 21 ++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py b/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py
index a53e0e18b6e36..180a9ed3b5e79 100644
--- a/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py
+++ b/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional  # type: ignore[import-not-found]
+from typing import Any, Dict, List, Optional
 
 from langchain_core.embeddings import Embeddings
 from pydantic import BaseModel, ConfigDict, Field
@@ -26,7 +26,6 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
             )
     """
 
-    client: Any = None  #: :meta private:
     model_name: str = DEFAULT_MODEL_NAME
     """Model name to use."""
     cache_folder: Optional[str] = None
@@ -57,7 +56,7 @@ def __init__(self, **kwargs: Any):
                 "Please install it with `pip install sentence-transformers`."
             ) from exc
 
-        self.client = sentence_transformers.SentenceTransformer(
+        self._client = sentence_transformers.SentenceTransformer(
             self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
         )
 
@@ -79,12 +78,20 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]:
 
         texts = list(map(lambda x: x.replace("\n", " "), texts))
         if self.multi_process:
-            pool = self.client.start_multi_process_pool()
-            embeddings = self.client.encode_multi_process(texts, pool)
+            pool = self._client.start_multi_process_pool()
+            embeddings = self._client.encode_multi_process(texts, pool)
             sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
         else:
-            embeddings = self.client.encode(
-                texts, show_progress_bar=self.show_progress, **self.encode_kwargs
+            embeddings = self._client.encode(
+                texts,
+                show_progress_bar=self.show_progress,
+                **self.encode_kwargs,  # type: ignore
+            )
+
+        if isinstance(embeddings, list):
+            raise TypeError(
+                "Expected embeddings to be a Tensor or a numpy array, "
+                "got a list instead."
             )
 
         return embeddings.tolist()

From f636c83321031e2fb15f8f53165d88572ce4f59c Mon Sep 17 00:00:00 2001
From: Eric Pinzur <2641606+epinzur@users.noreply.github.com>
Date: Tue, 22 Oct 2024 20:11:11 +0200
Subject: [PATCH 42/84] community: Cassandra Vector Store: modernize
 implementation (#27253)

**Description:**

This PR updates `CassandraGraphVectorStore` to be based off
`CassandraVectorStore`, instead of using a custom CQL implementation.
This allows users using a `CassandraVectorStore` to upgrade to a
`GraphVectorStore` without having to change their database schema or
re-embed documents.

This PR also updates the documentation of the `GraphVectorStore` base
class and contains native async implementations for the standard graph
methods: `traversal_search` and `mmr_traversal_search` in
`CassandraVectorStore`.

**Issue:** No issue number.

**Dependencies:** https://github.com/langchain-ai/langchain/pull/27078
(already-merged)

**Lint and test**:
- Lint and tests all pass, including existing
`CassandraGraphVectorStore` tests.
- Also added numerous additional tests based of the tests in
`langchain-astradb` which cover many more scenarios than the existing
tests for `Cassandra` and `CassandraGraphVectorStore`

** BREAKING CHANGE**

Note that this is a breaking change for existing users of
`CassandraGraphVectorStore`. They will need to wipe their database table
and restart.

However:
- The interfaces have not changed. Just the underlying storage
mechanism.
- Any one using `langchain_community.vectorstores.Cassandra` can instead
use `langchain_community.graph_vectorstores.CassandraGraphVectorStore`
and they will gain Graph capabilities without having to re-embed their
existing documents. This is the primary goal of this PR.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../graph_vectorstores/__init__.py            |    2 +
 .../graph_vectorstores/base.py                |  108 +-
 .../graph_vectorstores/cassandra.py           | 1294 +++++++++++--
 .../graph_vectorstores/mmr_helper.py          |  272 +++
 .../vectorstores/cassandra.py                 |  205 +-
 .../graph_vectorstores/test_cassandra.py      |  738 +++++--
 .../test_upgrade_to_cassandra.py              |  269 +++
 .../vectorstores/test_cassandra.py            | 1694 ++++++++++++++---
 .../graph_vectorstores/test_mmr_helper.py     |  143 ++
 9 files changed, 4058 insertions(+), 667 deletions(-)
 create mode 100644 libs/community/langchain_community/graph_vectorstores/mmr_helper.py
 create mode 100644 libs/community/tests/integration_tests/graph_vectorstores/test_upgrade_to_cassandra.py
 create mode 100644 libs/community/tests/unit_tests/graph_vectorstores/test_mmr_helper.py

diff --git a/libs/community/langchain_community/graph_vectorstores/__init__.py b/libs/community/langchain_community/graph_vectorstores/__init__.py
index 5773b224dc565..ad044624b1b4f 100644
--- a/libs/community/langchain_community/graph_vectorstores/__init__.py
+++ b/libs/community/langchain_community/graph_vectorstores/__init__.py
@@ -144,6 +144,7 @@
 from langchain_community.graph_vectorstores.links import (
     Link,
 )
+from langchain_community.graph_vectorstores.mmr_helper import MmrHelper
 
 __all__ = [
     "GraphVectorStore",
@@ -151,4 +152,5 @@
     "Node",
     "Link",
     "CassandraGraphVectorStore",
+    "MmrHelper",
 ]
diff --git a/libs/community/langchain_community/graph_vectorstores/base.py b/libs/community/langchain_community/graph_vectorstores/base.py
index 97a4747131182..0a320d98f9eca 100644
--- a/libs/community/langchain_community/graph_vectorstores/base.py
+++ b/libs/community/langchain_community/graph_vectorstores/base.py
@@ -1,11 +1,13 @@
 from __future__ import annotations
 
+import logging
 from abc import abstractmethod
 from collections.abc import AsyncIterable, Collection, Iterable, Iterator
 from typing import (
     Any,
     ClassVar,
     Optional,
+    Sequence,
 )
 
 from langchain_core._api import beta
@@ -21,6 +23,8 @@
 
 from langchain_community.graph_vectorstores.links import METADATA_LINKS_KEY, Link
 
+logger = logging.getLogger(__name__)
+
 
 def _has_next(iterator: Iterator) -> bool:
     """Checks if the iterator has more elements.
@@ -158,6 +162,7 @@ def add_nodes(
 
         Args:
             nodes: the nodes to add.
+            **kwargs: Additional keyword arguments.
         """
 
     async def aadd_nodes(
@@ -169,6 +174,7 @@ async def aadd_nodes(
 
         Args:
             nodes: the nodes to add.
+            **kwargs: Additional keyword arguments.
         """
         iterator = iter(await run_in_executor(None, self.add_nodes, nodes, **kwargs))
         done = object()
@@ -186,7 +192,7 @@ def add_texts(
         ids: Optional[Iterable[str]] = None,
         **kwargs: Any,
     ) -> list[str]:
-        """Run more texts through the embeddings and add to the vectorstore.
+        """Run more texts through the embeddings and add to the vector store.
 
         The Links present in the metadata field `links` will be extracted to create
         the `Node` links.
@@ -214,15 +220,15 @@ def add_texts(
             )
 
         Args:
-            texts: Iterable of strings to add to the vectorstore.
+            texts: Iterable of strings to add to the vector store.
             metadatas: Optional list of metadatas associated with the texts.
                 The metadata key `links` shall be an iterable of
                 :py:class:`~langchain_community.graph_vectorstores.links.Link`.
             ids: Optional list of IDs associated with the texts.
-            **kwargs: vectorstore specific parameters.
+            **kwargs: vector store specific parameters.
 
         Returns:
-            List of ids from adding the texts into the vectorstore.
+            List of ids from adding the texts into the vector store.
         """
         nodes = _texts_to_nodes(texts, metadatas, ids)
         return list(self.add_nodes(nodes, **kwargs))
@@ -235,7 +241,7 @@ async def aadd_texts(
         ids: Optional[Iterable[str]] = None,
         **kwargs: Any,
     ) -> list[str]:
-        """Run more texts through the embeddings and add to the vectorstore.
+        """Run more texts through the embeddings and add to the vector store.
 
         The Links present in the metadata field `links` will be extracted to create
         the `Node` links.
@@ -263,15 +269,15 @@ async def aadd_texts(
             )
 
         Args:
-            texts: Iterable of strings to add to the vectorstore.
+            texts: Iterable of strings to add to the vector store.
             metadatas: Optional list of metadatas associated with the texts.
                 The metadata key `links` shall be an iterable of
                 :py:class:`~langchain_community.graph_vectorstores.links.Link`.
             ids: Optional list of IDs associated with the texts.
-            **kwargs: vectorstore specific parameters.
+            **kwargs: vector store specific parameters.
 
         Returns:
-            List of ids from adding the texts into the vectorstore.
+            List of ids from adding the texts into the vector store.
         """
         nodes = _texts_to_nodes(texts, metadatas, ids)
         return [_id async for _id in self.aadd_nodes(nodes, **kwargs)]
@@ -281,7 +287,7 @@ def add_documents(
         documents: Iterable[Document],
         **kwargs: Any,
     ) -> list[str]:
-        """Run more documents through the embeddings and add to the vectorstore.
+        """Run more documents through the embeddings and add to the vector store.
 
         The Links present in the document metadata field `links` will be extracted to
         create the `Node` links.
@@ -316,7 +322,7 @@ def add_documents(
             )
 
         Args:
-            documents: Documents to add to the vectorstore.
+            documents: Documents to add to the vector store.
                 The document's metadata key `links` shall be an iterable of
                 :py:class:`~langchain_community.graph_vectorstores.links.Link`.
 
@@ -331,7 +337,7 @@ async def aadd_documents(
         documents: Iterable[Document],
         **kwargs: Any,
     ) -> list[str]:
-        """Run more documents through the embeddings and add to the vectorstore.
+        """Run more documents through the embeddings and add to the vector store.
 
         The Links present in the document metadata field `links` will be extracted to
         create the `Node` links.
@@ -366,7 +372,7 @@ async def aadd_documents(
             )
 
         Args:
-            documents: Documents to add to the vectorstore.
+            documents: Documents to add to the vector store.
                 The document's metadata key `links` shall be an iterable of
                 :py:class:`~langchain_community.graph_vectorstores.links.Link`.
 
@@ -383,6 +389,7 @@ def traversal_search(
         *,
         k: int = 4,
         depth: int = 1,
+        filter: dict[str, Any] | None = None,  # noqa: A002
         **kwargs: Any,
     ) -> Iterable[Document]:
         """Retrieve documents from traversing this graph store.
@@ -396,8 +403,10 @@ def traversal_search(
             k: The number of Documents to return from the initial search.
                 Defaults to 4. Applies to each of the query strings.
             depth: The maximum depth of edges to traverse. Defaults to 1.
+            filter: Optional metadata to filter the results.
+            **kwargs: Additional keyword arguments.
         Returns:
-            Retrieved documents.
+            Collection of retrieved documents.
         """
 
     async def atraversal_search(
@@ -406,6 +415,7 @@ async def atraversal_search(
         *,
         k: int = 4,
         depth: int = 1,
+        filter: dict[str, Any] | None = None,  # noqa: A002
         **kwargs: Any,
     ) -> AsyncIterable[Document]:
         """Retrieve documents from traversing this graph store.
@@ -419,12 +429,20 @@ async def atraversal_search(
             k: The number of Documents to return from the initial search.
                 Defaults to 4. Applies to each of the query strings.
             depth: The maximum depth of edges to traverse. Defaults to 1.
+            filter: Optional metadata to filter the results.
+            **kwargs: Additional keyword arguments.
         Returns:
-            Retrieved documents.
+            Collection of retrieved documents.
         """
         iterator = iter(
             await run_in_executor(
-                None, self.traversal_search, query, k=k, depth=depth, **kwargs
+                None,
+                self.traversal_search,
+                query,
+                k=k,
+                depth=depth,
+                filter=filter,
+                **kwargs,
             )
         )
         done = object()
@@ -439,12 +457,14 @@ def mmr_traversal_search(
         self,
         query: str,
         *,
+        initial_roots: Sequence[str] = (),
         k: int = 4,
         depth: int = 2,
         fetch_k: int = 100,
         adjacent_k: int = 10,
         lambda_mult: float = 0.5,
         score_threshold: float = float("-inf"),
+        filter: dict[str, Any] | None = None,  # noqa: A002
         **kwargs: Any,
     ) -> Iterable[Document]:
         """Retrieve documents from this graph store using MMR-traversal.
@@ -459,6 +479,10 @@ def mmr_traversal_search(
 
         Args:
             query: The query string to search for.
+            initial_roots: Optional list of document IDs to use for initializing search.
+                The top `adjacent_k` nodes adjacent to each initial root will be
+                included in the set of initial candidates. To fetch only in the
+                neighborhood of these nodes, set `fetch_k = 0`.
             k: Number of Documents to return. Defaults to 4.
             fetch_k: Number of Documents to fetch via similarity.
                 Defaults to 100.
@@ -471,18 +495,22 @@ def mmr_traversal_search(
                 diversity and 1 to minimum diversity. Defaults to 0.5.
             score_threshold: Only documents with a score greater than or equal
                 this threshold will be chosen. Defaults to negative infinity.
+            filter: Optional metadata to filter the results.
+            **kwargs: Additional keyword arguments.
         """
 
     async def ammr_traversal_search(
         self,
         query: str,
         *,
+        initial_roots: Sequence[str] = (),
         k: int = 4,
         depth: int = 2,
         fetch_k: int = 100,
         adjacent_k: int = 10,
         lambda_mult: float = 0.5,
         score_threshold: float = float("-inf"),
+        filter: dict[str, Any] | None = None,  # noqa: A002
         **kwargs: Any,
     ) -> AsyncIterable[Document]:
         """Retrieve documents from this graph store using MMR-traversal.
@@ -497,6 +525,10 @@ async def ammr_traversal_search(
 
         Args:
             query: The query string to search for.
+            initial_roots: Optional list of document IDs to use for initializing search.
+                The top `adjacent_k` nodes adjacent to each initial root will be
+                included in the set of initial candidates. To fetch only in the
+                neighborhood of these nodes, set `fetch_k = 0`.
             k: Number of Documents to return. Defaults to 4.
             fetch_k: Number of Documents to fetch via similarity.
                 Defaults to 100.
@@ -509,18 +541,22 @@ async def ammr_traversal_search(
                 diversity and 1 to minimum diversity. Defaults to 0.5.
             score_threshold: Only documents with a score greater than or equal
                 this threshold will be chosen. Defaults to negative infinity.
+            filter: Optional metadata to filter the results.
+            **kwargs: Additional keyword arguments.
         """
         iterator = iter(
             await run_in_executor(
                 None,
                 self.mmr_traversal_search,
                 query,
+                initial_roots=initial_roots,
                 k=k,
                 fetch_k=fetch_k,
                 adjacent_k=adjacent_k,
                 depth=depth,
                 lambda_mult=lambda_mult,
                 score_threshold=score_threshold,
+                filter=filter,
                 **kwargs,
             )
         )
@@ -544,6 +580,11 @@ def max_marginal_relevance_search(
         lambda_mult: float = 0.5,
         **kwargs: Any,
     ) -> list[Document]:
+        if kwargs.get("depth", 0) > 0:
+            logger.warning(
+                "'mmr' search started with depth > 0. "
+                "Maybe you meant to do a 'mmr_traversal' search?"
+            )
         return list(
             self.mmr_traversal_search(
                 query, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, depth=0
@@ -573,7 +614,7 @@ def search(self, query: str, search_type: str, **kwargs: Any) -> list[Document]:
             raise ValueError(
                 f"search_type of {search_type} not allowed. Expected "
                 "search_type to be 'similarity', 'similarity_score_threshold', "
-                "'mmr' or 'traversal'."
+                "'mmr', 'traversal', or 'mmr_traversal'."
             )
 
     async def asearch(
@@ -590,11 +631,13 @@ async def asearch(
             return await self.amax_marginal_relevance_search(query, **kwargs)
         elif search_type == "traversal":
             return [doc async for doc in self.atraversal_search(query, **kwargs)]
+        elif search_type == "mmr_traversal":
+            return [doc async for doc in self.ammr_traversal_search(query, **kwargs)]
         else:
             raise ValueError(
                 f"search_type of {search_type} not allowed. Expected "
                 "search_type to be 'similarity', 'similarity_score_threshold', "
-                "'mmr' or 'traversal'."
+                "'mmr', 'traversal', or 'mmr_traversal'."
             )
 
     def as_retriever(self, **kwargs: Any) -> GraphVectorStoreRetriever:
@@ -606,13 +649,14 @@ def as_retriever(self, **kwargs: Any) -> GraphVectorStoreRetriever:
 
                 - search_type (Optional[str]): Defines the type of search that
                   the Retriever should perform.
-                  Can be ``traversal`` (default), ``similarity``, ``mmr``, or
-                  ``similarity_score_threshold``.
+                  Can be ``traversal`` (default), ``similarity``, ``mmr``,
+                   ``mmr_traversal``, or ``similarity_score_threshold``.
                 - search_kwargs (Optional[Dict]): Keyword arguments to pass to the
                   search function. Can include things like:
 
                   - k(int): Amount of documents to return (Default: 4).
                   - depth(int): The maximum depth of edges to traverse (Default: 1).
+                    Only applies to search_type: ``traversal`` and ``mmr_traversal``.
                   - score_threshold(float): Minimum relevance threshold
                     for similarity_score_threshold.
                   - fetch_k(int): Amount of documents to pass to MMR algorithm
@@ -629,21 +673,21 @@ def as_retriever(self, **kwargs: Any) -> GraphVectorStoreRetriever:
             # Retrieve documents traversing edges
             docsearch.as_retriever(
                 search_type="traversal",
-                search_kwargs={'k': 6, 'depth': 3}
+                search_kwargs={'k': 6, 'depth': 2}
             )
 
-            # Retrieve more documents with higher diversity
+            # Retrieve documents with higher diversity
             # Useful if your dataset has many similar documents
             docsearch.as_retriever(
-                search_type="mmr",
-                search_kwargs={'k': 6, 'lambda_mult': 0.25}
+                search_type="mmr_traversal",
+                search_kwargs={'k': 6, 'lambda_mult': 0.25, 'depth': 2}
             )
 
             # Fetch more documents for the MMR algorithm to consider
             # But only return the top 5
             docsearch.as_retriever(
-                search_type="mmr",
-                search_kwargs={'k': 5, 'fetch_k': 50}
+                search_type="mmr_traversal",
+                search_kwargs={'k': 5, 'fetch_k': 50, 'depth': 2}
             )
 
             # Only retrieve documents that have a relevance score
@@ -657,7 +701,7 @@ def as_retriever(self, **kwargs: Any) -> GraphVectorStoreRetriever:
             docsearch.as_retriever(search_kwargs={'k': 1})
 
         """
-        return GraphVectorStoreRetriever(vectorstore=self, **kwargs)
+        return GraphVectorStoreRetriever(vector_store=self, **kwargs)
 
 
 @beta(message="Added in version 0.3.1 of langchain_community. API subject to change.")
@@ -744,7 +788,7 @@ class GraphVectorStoreRetriever(VectorStoreRetriever):
     Passing search parameters
     -------------------------
 
-    We can pass parameters to the underlying graph vectorstore's search methods using
+    We can pass parameters to the underlying graph vector store's search methods using
     ``search_kwargs``.
 
     Specifying graph traversal depth
@@ -793,7 +837,7 @@ class GraphVectorStoreRetriever(VectorStoreRetriever):
         retriever = graph_vectorstore.as_retriever(search_kwargs={"score_threshold": 0.5})
     """  # noqa: E501
 
-    vectorstore: GraphVectorStore
+    vector_store: GraphVectorStore
     """GraphVectorStore to use for retrieval."""
     search_type: str = "traversal"
     """Type of search to perform. Defaults to "traversal"."""
@@ -809,10 +853,10 @@ def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> list[Document]:
         if self.search_type == "traversal":
-            return list(self.vectorstore.traversal_search(query, **self.search_kwargs))
+            return list(self.vector_store.traversal_search(query, **self.search_kwargs))
         elif self.search_type == "mmr_traversal":
             return list(
-                self.vectorstore.mmr_traversal_search(query, **self.search_kwargs)
+                self.vector_store.mmr_traversal_search(query, **self.search_kwargs)
             )
         else:
             return super()._get_relevant_documents(query, run_manager=run_manager)
@@ -823,14 +867,14 @@ async def _aget_relevant_documents(
         if self.search_type == "traversal":
             return [
                 doc
-                async for doc in self.vectorstore.atraversal_search(
+                async for doc in self.vector_store.atraversal_search(
                     query, **self.search_kwargs
                 )
             ]
         elif self.search_type == "mmr_traversal":
             return [
                 doc
-                async for doc in self.vectorstore.ammr_traversal_search(
+                async for doc in self.vector_store.ammr_traversal_search(
                     query, **self.search_kwargs
                 )
             ]
diff --git a/libs/community/langchain_community/graph_vectorstores/cassandra.py b/libs/community/langchain_community/graph_vectorstores/cassandra.py
index 42525cf03dded..5b377d61f5112 100644
--- a/libs/community/langchain_community/graph_vectorstores/cassandra.py
+++ b/libs/community/langchain_community/graph_vectorstores/cassandra.py
@@ -1,27 +1,107 @@
+"""Apache Cassandra DB graph vector store integration."""
+
 from __future__ import annotations
 
+import asyncio
+import json
+import logging
+import secrets
+from dataclasses import asdict, is_dataclass
 from typing import (
     TYPE_CHECKING,
     Any,
+    AsyncIterable,
     Iterable,
     List,
     Optional,
+    Sequence,
+    Tuple,
     Type,
+    TypeVar,
+    cast,
 )
 
 from langchain_core._api import beta
 from langchain_core.documents import Document
-from langchain_core.embeddings import Embeddings
+from typing_extensions import override
 
-from langchain_community.graph_vectorstores.base import (
-    GraphVectorStore,
-    Node,
-    nodes_to_documents,
-)
+from langchain_community.graph_vectorstores.base import GraphVectorStore, Node
+from langchain_community.graph_vectorstores.links import METADATA_LINKS_KEY, Link
+from langchain_community.graph_vectorstores.mmr_helper import MmrHelper
 from langchain_community.utilities.cassandra import SetupMode
+from langchain_community.vectorstores.cassandra import Cassandra as CassandraVectorStore
+
+CGVST = TypeVar("CGVST", bound="CassandraGraphVectorStore")
 
 if TYPE_CHECKING:
     from cassandra.cluster import Session
+    from langchain_core.embeddings import Embeddings
+
+
+logger = logging.getLogger(__name__)
+
+
+class AdjacentNode:
+    id: str
+    links: list[Link]
+    embedding: list[float]
+
+    def __init__(self, node: Node, embedding: list[float]) -> None:
+        """Create an Adjacent Node."""
+        self.id = node.id or ""
+        self.links = node.links
+        self.embedding = embedding
+
+
+def _serialize_links(links: list[Link]) -> str:
+    class SetAndLinkEncoder(json.JSONEncoder):
+        def default(self, obj: Any) -> Any:  # noqa: ANN401
+            if not isinstance(obj, type) and is_dataclass(obj):
+                return asdict(obj)
+
+            if isinstance(obj, Iterable):
+                return list(obj)
+
+            # Let the base class default method raise the TypeError
+            return super().default(obj)
+
+    return json.dumps(links, cls=SetAndLinkEncoder)
+
+
+def _deserialize_links(json_blob: str | None) -> set[Link]:
+    return {
+        Link(kind=link["kind"], direction=link["direction"], tag=link["tag"])
+        for link in cast(list[dict[str, Any]], json.loads(json_blob or "[]"))
+    }
+
+
+def _metadata_link_key(link: Link) -> str:
+    return f"link:{link.kind}:{link.tag}"
+
+
+def _metadata_link_value() -> str:
+    return "link"
+
+
+def _doc_to_node(doc: Document) -> Node:
+    metadata = doc.metadata.copy()
+    links = _deserialize_links(metadata.get(METADATA_LINKS_KEY))
+    metadata[METADATA_LINKS_KEY] = links
+
+    return Node(
+        id=doc.id,
+        text=doc.page_content,
+        metadata=metadata,
+        links=list(links),
+    )
+
+
+def _incoming_links(node: Node | AdjacentNode) -> set[Link]:
+    return {link for link in node.links if link.direction in ["in", "bidir"]}
+
+
+def _outgoing_links(node: Node | AdjacentNode) -> set[Link]:
+    return {link for link in node.links if link.direction in ["out", "bidir"]}
 
 
 @beta()
@@ -29,162 +109,1160 @@ class CassandraGraphVectorStore(GraphVectorStore):
     def __init__(
         self,
         embedding: Embeddings,
+        session: Session | None = None,
+        keyspace: str | None = None,
+        table_name: str = "",
+        ttl_seconds: int | None = None,
         *,
-        node_table: str = "graph_nodes",
-        session: Optional[Session] = None,
-        keyspace: Optional[str] = None,
+        body_index_options: list[tuple[str, Any]] | None = None,
         setup_mode: SetupMode = SetupMode.SYNC,
-        **kwargs: Any,
-    ):
-        """
-        Create the hybrid graph store.
+        metadata_deny_list: Optional[list[str]] = None,
+    ) -> None:
+        """Apache Cassandra(R) for graph-vector-store workloads.
 
-        Args:
-            embedding: The embeddings to use for the document content.
-            setup_mode: Mode used to create the Cassandra table (SYNC,
-                ASYNC or OFF).
-        """
-        try:
-            from ragstack_knowledge_store import EmbeddingModel, graph_store
-        except (ImportError, ModuleNotFoundError):
-            raise ImportError(
-                "Could not import ragstack_knowledge_store python package. "
-                "Please install it with `pip install ragstack-ai-knowledge-store`."
-            )
-
-        self._embedding = embedding
-        _setup_mode = getattr(graph_store.SetupMode, setup_mode.name)
+        To use it, you need a recent installation of the `cassio` library
+        and a Cassandra cluster / Astra DB instance supporting vector capabilities.
 
-        class _EmbeddingModelAdapter(EmbeddingModel):
-            def __init__(self, embeddings: Embeddings):
-                self.embeddings = embeddings
+        Example:
+            .. code-block:: python
 
-            def embed_texts(self, texts: List[str]) -> List[List[float]]:
-                return self.embeddings.embed_documents(texts)
+                    from langchain_community.graph_vectorstores import
+                        CassandraGraphVectorStore
+                    from langchain_openai import OpenAIEmbeddings
 
-            def embed_query(self, text: str) -> List[float]:
-                return self.embeddings.embed_query(text)
+                    embeddings = OpenAIEmbeddings()
+                    session = ...             # create your Cassandra session object
+                    keyspace = 'my_keyspace'  # the keyspace should exist already
+                    table_name = 'my_graph_vector_store'
+                    vectorstore = CassandraGraphVectorStore(
+                        embeddings,
+                        session,
+                        keyspace,
+                        table_name,
+                    )
 
-            async def aembed_texts(self, texts: List[str]) -> List[List[float]]:
-                return await self.embeddings.aembed_documents(texts)
+        Args:
+            embedding: Embedding function to use.
+            session: Cassandra driver session. If not provided, it is resolved from
+                cassio.
+            keyspace: Cassandra keyspace. If not provided, it is resolved from cassio.
+            table_name: Cassandra table (required).
+            ttl_seconds: Optional time-to-live for the added texts.
+            body_index_options: Optional options used to create the body index.
+                Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
+            setup_mode: mode used to create the Cassandra table (SYNC,
+                ASYNC or OFF).
+            metadata_deny_list: Optional list of metadata keys to not index.
+                i.e. to fine-tune which of the metadata fields are indexed.
+                Note: if you plan to have massive unique text metadata entries,
+                consider not indexing them for performance
+                (and to overcome max-length limitations).
+                Note: the `metadata_indexing` parameter from
+                langchain_community.utilities.cassandra.Cassandra is not
+                exposed since CassandraGraphVectorStore only supports the
+                deny_list option.
+        """
+        self.embedding = embedding
 
-            async def aembed_query(self, text: str) -> List[float]:
-                return await self.embeddings.aembed_query(text)
+        if metadata_deny_list is None:
+            metadata_deny_list = []
+        metadata_deny_list.append(METADATA_LINKS_KEY)
 
-        self.store = graph_store.GraphStore(
-            embedding=_EmbeddingModelAdapter(embedding),
-            node_table=node_table,
+        self.vector_store = CassandraVectorStore(
+            embedding=embedding,
             session=session,
             keyspace=keyspace,
-            setup_mode=_setup_mode,
-            **kwargs,
+            table_name=table_name,
+            ttl_seconds=ttl_seconds,
+            body_index_options=body_index_options,
+            setup_mode=setup_mode,
+            metadata_indexing=("deny_list", metadata_deny_list),
+        )
+
+        store_session: Session = self.vector_store.session
+
+        self._insert_node = store_session.prepare(
+            f"""
+            INSERT INTO {keyspace}.{table_name} (
+                row_id, body_blob, vector, attributes_blob, metadata_s
+            ) VALUES (?, ?, ?, ?, ?)
+            """  # noqa: S608
         )
 
     @property
-    def embeddings(self) -> Optional[Embeddings]:
-        return self._embedding
+    @override
+    def embeddings(self) -> Embeddings | None:
+        return self.embedding
 
+    def _get_metadata_filter(
+        self,
+        metadata: dict[str, Any] | None = None,
+        outgoing_link: Link | None = None,
+    ) -> dict[str, Any]:
+        if outgoing_link is None:
+            return metadata or {}
+
+        metadata_filter = {} if metadata is None else metadata.copy()
+        metadata_filter[_metadata_link_key(link=outgoing_link)] = _metadata_link_value()
+        return metadata_filter
+
+    def _restore_links(self, doc: Document) -> Document:
+        """Restores the links in the document by deserializing them from metadata.
+
+        Args:
+            doc: A single Document
+
+        Returns:
+            The same Document with restored links.
+        """
+        links = _deserialize_links(doc.metadata.get(METADATA_LINKS_KEY))
+        doc.metadata[METADATA_LINKS_KEY] = links
+        # TODO: Could this be skipped if we put these metadata entries
+        # only in the searchable `metadata_s` column?
+        for incoming_link_key in [
+            _metadata_link_key(link=link)
+            for link in links
+            if link.direction in ["in", "bidir"]
+        ]:
+            if incoming_link_key in doc.metadata:
+                del doc.metadata[incoming_link_key]
+
+        return doc
+
+    def _get_node_metadata_for_insertion(self, node: Node) -> dict[str, Any]:
+        metadata = node.metadata.copy()
+        metadata[METADATA_LINKS_KEY] = _serialize_links(node.links)
+        # TODO: Could we could put these metadata entries
+        # only in the searchable `metadata_s` column?
+        for incoming_link in _incoming_links(node=node):
+            metadata[_metadata_link_key(link=incoming_link)] = _metadata_link_value()
+        return metadata
+
+    def _get_docs_for_insertion(
+        self, nodes: Iterable[Node]
+    ) -> tuple[list[Document], list[str]]:
+        docs = []
+        ids = []
+        for node in nodes:
+            node_id = secrets.token_hex(8) if not node.id else node.id
+
+            doc = Document(
+                page_content=node.text,
+                metadata=self._get_node_metadata_for_insertion(node=node),
+                id=node_id,
+            )
+            docs.append(doc)
+            ids.append(node_id)
+        return (docs, ids)
+
+    @override
     def add_nodes(
         self,
         nodes: Iterable[Node],
         **kwargs: Any,
     ) -> Iterable[str]:
-        return self.store.add_nodes(nodes)
+        """Add nodes to the graph store.
 
-    @classmethod
-    def from_texts(
-        cls: Type["CassandraGraphVectorStore"],
-        texts: Iterable[str],
-        embedding: Embeddings,
-        metadatas: Optional[List[dict]] = None,
-        ids: Optional[Iterable[str]] = None,
-        **kwargs: Any,
-    ) -> "CassandraGraphVectorStore":
-        """Return CassandraGraphVectorStore initialized from texts and embeddings."""
-        store = cls(embedding, **kwargs)
-        store.add_texts(texts, metadatas, ids=ids)
-        return store
+        Args:
+            nodes: the nodes to add.
+            **kwargs: Additional keyword arguments.
+        """
+        (docs, ids) = self._get_docs_for_insertion(nodes=nodes)
+        return self.vector_store.add_documents(docs, ids=ids)
 
-    @classmethod
-    def from_documents(
-        cls: Type["CassandraGraphVectorStore"],
-        documents: Iterable[Document],
-        embedding: Embeddings,
-        ids: Optional[Iterable[str]] = None,
+    @override
+    async def aadd_nodes(
+        self,
+        nodes: Iterable[Node],
         **kwargs: Any,
-    ) -> "CassandraGraphVectorStore":
-        """Return CassandraGraphVectorStore initialized from documents and
-        embeddings."""
-        store = cls(embedding, **kwargs)
-        store.add_documents(documents, ids=ids)
-        return store
+    ) -> AsyncIterable[str]:
+        """Add nodes to the graph store.
 
+        Args:
+            nodes: the nodes to add.
+            **kwargs: Additional keyword arguments.
+        """
+        (docs, ids) = self._get_docs_for_insertion(nodes=nodes)
+        for inserted_id in await self.vector_store.aadd_documents(docs, ids=ids):
+            yield inserted_id
+
+    @override
     def similarity_search(
         self,
         query: str,
         k: int = 4,
-        metadata_filter: dict[str, Any] = {},
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
-    ) -> List[Document]:
-        embedding_vector = self._embedding.embed_query(query)
-        return self.similarity_search_by_vector(
-            embedding_vector,
-            k=k,
-            metadata_filter=metadata_filter,
-        )
+    ) -> list[Document]:
+        """Retrieve documents from this graph store.
+
+        Args:
+            query: The query string.
+            k: The number of Documents to return. Defaults to 4.
+            filter: Optional metadata to filter the results.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            Collection of retrieved documents.
+        """
+        return [
+            self._restore_links(doc)
+            for doc in self.vector_store.similarity_search(
+                query=query,
+                k=k,
+                filter=filter,
+                **kwargs,
+            )
+        ]
 
+    @override
+    async def asimilarity_search(
+        self,
+        query: str,
+        k: int = 4,
+        filter: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> list[Document]:
+        """Retrieve documents from this graph store.
+
+        Args:
+            query: The query string.
+            k: The number of Documents to return. Defaults to 4.
+            filter: Optional metadata to filter the results.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            Collection of retrieved documents.
+        """
+        return [
+            self._restore_links(doc)
+            for doc in await self.vector_store.asimilarity_search(
+                query=query,
+                k=k,
+                filter=filter,
+                **kwargs,
+            )
+        ]
+
+    @override
     def similarity_search_by_vector(
         self,
-        embedding: List[float],
+        embedding: list[float],
         k: int = 4,
-        metadata_filter: dict[str, Any] = {},
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
-    ) -> List[Document]:
-        nodes = self.store.similarity_search(
-            embedding,
-            k=k,
-            metadata_filter=metadata_filter,
-        )
-        return list(nodes_to_documents(nodes))
+    ) -> list[Document]:
+        """Return docs most similar to embedding vector.
 
-    def traversal_search(
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            filter: Filter on the metadata to apply.
+            **kwargs: Additional arguments are ignored.
+
+        Returns:
+            The list of Documents most similar to the query vector.
+        """
+        return [
+            self._restore_links(doc)
+            for doc in self.vector_store.similarity_search_by_vector(
+                embedding,
+                k=k,
+                filter=filter,
+                **kwargs,
+            )
+        ]
+
+    @override
+    async def asimilarity_search_by_vector(
+        self,
+        embedding: list[float],
+        k: int = 4,
+        filter: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> list[Document]:
+        """Return docs most similar to embedding vector.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            filter: Filter on the metadata to apply.
+            **kwargs: Additional arguments are ignored.
+
+        Returns:
+            The list of Documents most similar to the query vector.
+        """
+        return [
+            self._restore_links(doc)
+            for doc in await self.vector_store.asimilarity_search_by_vector(
+                embedding,
+                k=k,
+                filter=filter,
+                **kwargs,
+            )
+        ]
+
+    def metadata_search(
+        self,
+        filter: dict[str, Any] | None = None,  # noqa: A002
+        n: int = 5,
+    ) -> Iterable[Document]:
+        """Get documents via a metadata search.
+
+        Args:
+            filter: the metadata to query for.
+            n: the maximum number of documents to return.
+        """
+        return [
+            self._restore_links(doc)
+            for doc in self.vector_store.metadata_search(
+                filter=filter or {},
+                n=n,
+            )
+        ]
+
+    async def ametadata_search(
+        self,
+        filter: dict[str, Any] | None = None,  # noqa: A002
+        n: int = 5,
+    ) -> Iterable[Document]:
+        """Get documents via a metadata search.
+
+        Args:
+            filter: the metadata to query for.
+            n: the maximum number of documents to return.
+        """
+        return [
+            self._restore_links(doc)
+            for doc in await self.vector_store.ametadata_search(
+                filter=filter or {},
+                n=n,
+            )
+        ]
+
+    def get_by_document_id(self, document_id: str) -> Document | None:
+        """Retrieve a single document from the store, given its document ID.
+
+        Args:
+            document_id: The document ID
+
+        Returns:
+            The the document if it exists. Otherwise None.
+        """
+        doc = self.vector_store.get_by_document_id(document_id=document_id)
+        return self._restore_links(doc) if doc is not None else None
+
+    async def aget_by_document_id(self, document_id: str) -> Document | None:
+        """Retrieve a single document from the store, given its document ID.
+
+        Args:
+            document_id: The document ID
+
+        Returns:
+            The the document if it exists. Otherwise None.
+        """
+        doc = await self.vector_store.aget_by_document_id(document_id=document_id)
+        return self._restore_links(doc) if doc is not None else None
+
+    def get_node(self, node_id: str) -> Node | None:
+        """Retrieve a single node from the store, given its ID.
+
+        Args:
+            node_id: The node ID
+
+        Returns:
+            The the node if it exists. Otherwise None.
+        """
+        doc = self.vector_store.get_by_document_id(document_id=node_id)
+        if doc is None:
+            return None
+        return _doc_to_node(doc=doc)
+
+    @override
+    async def ammr_traversal_search(  # noqa: C901
         self,
         query: str,
         *,
+        initial_roots: Sequence[str] = (),
         k: int = 4,
-        depth: int = 1,
-        metadata_filter: dict[str, Any] = {},
+        depth: int = 2,
+        fetch_k: int = 100,
+        adjacent_k: int = 10,
+        lambda_mult: float = 0.5,
+        score_threshold: float = float("-inf"),
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
-    ) -> Iterable[Document]:
-        nodes = self.store.traversal_search(
-            query,
+    ) -> AsyncIterable[Document]:
+        """Retrieve documents from this graph store using MMR-traversal.
+
+        This strategy first retrieves the top `fetch_k` results by similarity to
+        the question. It then selects the top `k` results based on
+        maximum-marginal relevance using the given `lambda_mult`.
+
+        At each step, it considers the (remaining) documents from `fetch_k` as
+        well as any documents connected by edges to a selected document
+        retrieved based on similarity (a "root").
+
+        Args:
+            query: The query string to search for.
+            initial_roots: Optional list of document IDs to use for initializing search.
+                The top `adjacent_k` nodes adjacent to each initial root will be
+                included in the set of initial candidates. To fetch only in the
+                neighborhood of these nodes, set `fetch_k = 0`.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of initial Documents to fetch via similarity.
+                Will be added to the nodes adjacent to `initial_roots`.
+                Defaults to 100.
+            adjacent_k: Number of adjacent Documents to fetch.
+                Defaults to 10.
+            depth: Maximum depth of a node (number of edges) from a node
+                retrieved via similarity. Defaults to 2.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                of diversity among the results with 0 corresponding to maximum
+                diversity and 1 to minimum diversity. Defaults to 0.5.
+            score_threshold: Only documents with a score greater than or equal
+                this threshold will be chosen. Defaults to -infinity.
+            filter: Optional metadata to filter the results.
+            **kwargs: Additional keyword arguments.
+        """
+        query_embedding = self.embedding.embed_query(query)
+        helper = MmrHelper(
             k=k,
-            depth=depth,
-            metadata_filter=metadata_filter,
+            query_embedding=query_embedding,
+            lambda_mult=lambda_mult,
+            score_threshold=score_threshold,
         )
-        return nodes_to_documents(nodes)
 
+        # For each unselected node, stores the outgoing links.
+        outgoing_links_map: dict[str, set[Link]] = {}
+        visited_links: set[Link] = set()
+        # Map from id to Document
+        retrieved_docs: dict[str, Document] = {}
+
+        async def fetch_neighborhood(neighborhood: Sequence[str]) -> None:
+            nonlocal outgoing_links_map, visited_links, retrieved_docs
+
+            # Put the neighborhood into the outgoing links, to avoid adding it
+            # to the candidate set in the future.
+            outgoing_links_map.update(
+                {content_id: set() for content_id in neighborhood}
+            )
+
+            # Initialize the visited_links with the set of outgoing links from the
+            # neighborhood. This prevents re-visiting them.
+            visited_links = await self._get_outgoing_links(neighborhood)
+
+            # Call `self._get_adjacent` to fetch the candidates.
+            adjacent_nodes = await self._get_adjacent(
+                links=visited_links,
+                query_embedding=query_embedding,
+                k_per_link=adjacent_k,
+                filter=filter,
+                retrieved_docs=retrieved_docs,
+            )
+
+            new_candidates: dict[str, list[float]] = {}
+            for adjacent_node in adjacent_nodes:
+                if adjacent_node.id not in outgoing_links_map:
+                    outgoing_links_map[adjacent_node.id] = _outgoing_links(
+                        node=adjacent_node
+                    )
+                    new_candidates[adjacent_node.id] = adjacent_node.embedding
+            helper.add_candidates(new_candidates)
+
+        async def fetch_initial_candidates() -> None:
+            nonlocal outgoing_links_map, visited_links, retrieved_docs
+
+            results = (
+                await self.vector_store.asimilarity_search_with_embedding_id_by_vector(
+                    embedding=query_embedding,
+                    k=fetch_k,
+                    filter=filter,
+                )
+            )
+
+            candidates: dict[str, list[float]] = {}
+            for doc, embedding, doc_id in results:
+                if doc_id not in retrieved_docs:
+                    retrieved_docs[doc_id] = doc
+
+                if doc_id not in outgoing_links_map:
+                    node = _doc_to_node(doc)
+                    outgoing_links_map[doc_id] = _outgoing_links(node=node)
+                    candidates[doc_id] = embedding
+            helper.add_candidates(candidates)
+
+        if initial_roots:
+            await fetch_neighborhood(initial_roots)
+        if fetch_k > 0:
+            await fetch_initial_candidates()
+
+        # Tracks the depth of each candidate.
+        depths = {candidate_id: 0 for candidate_id in helper.candidate_ids()}
+
+        # Select the best item, K times.
+        for _ in range(k):
+            selected_id = helper.pop_best()
+
+            if selected_id is None:
+                break
+
+            next_depth = depths[selected_id] + 1
+            if next_depth < depth:
+                # If the next nodes would not exceed the depth limit, find the
+                # adjacent nodes.
+
+                # Find the links linked to from the selected ID.
+                selected_outgoing_links = outgoing_links_map.pop(selected_id)
+
+                # Don't re-visit already visited links.
+                selected_outgoing_links.difference_update(visited_links)
+
+                # Find the nodes with incoming links from those links.
+                adjacent_nodes = await self._get_adjacent(
+                    links=selected_outgoing_links,
+                    query_embedding=query_embedding,
+                    k_per_link=adjacent_k,
+                    filter=filter,
+                    retrieved_docs=retrieved_docs,
+                )
+
+                # Record the selected_outgoing_links as visited.
+                visited_links.update(selected_outgoing_links)
+
+                new_candidates = {}
+                for adjacent_node in adjacent_nodes:
+                    if adjacent_node.id not in outgoing_links_map:
+                        outgoing_links_map[adjacent_node.id] = _outgoing_links(
+                            node=adjacent_node
+                        )
+                        new_candidates[adjacent_node.id] = adjacent_node.embedding
+                        if next_depth < depths.get(adjacent_node.id, depth + 1):
+                            # If this is a new shortest depth, or there was no
+                            # previous depth, update the depths. This ensures that
+                            # when we discover a node we will have the shortest
+                            # depth available.
+                            #
+                            # NOTE: No effort is made to traverse from nodes that
+                            # were previously selected if they become reachable via
+                            # a shorter path via nodes selected later. This is
+                            # currently "intended", but may be worth experimenting
+                            # with.
+                            depths[adjacent_node.id] = next_depth
+                helper.add_candidates(new_candidates)
+
+        for doc_id, similarity_score, mmr_score in zip(
+            helper.selected_ids,
+            helper.selected_similarity_scores,
+            helper.selected_mmr_scores,
+        ):
+            if doc_id in retrieved_docs:
+                doc = self._restore_links(retrieved_docs[doc_id])
+                doc.metadata["similarity_score"] = similarity_score
+                doc.metadata["mmr_score"] = mmr_score
+                yield doc
+            else:
+                msg = f"retrieved_docs should contain id: {doc_id}"
+                raise RuntimeError(msg)
+
+    @override
     def mmr_traversal_search(
         self,
         query: str,
         *,
+        initial_roots: Sequence[str] = (),
         k: int = 4,
         depth: int = 2,
         fetch_k: int = 100,
         adjacent_k: int = 10,
         lambda_mult: float = 0.5,
         score_threshold: float = float("-inf"),
-        metadata_filter: dict[str, Any] = {},
+        filter: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> Iterable[Document]:
-        nodes = self.store.mmr_traversal_search(
-            query,
+        """Retrieve documents from this graph store using MMR-traversal.
+
+        This strategy first retrieves the top `fetch_k` results by similarity to
+        the question. It then selects the top `k` results based on
+        maximum-marginal relevance using the given `lambda_mult`.
+
+        At each step, it considers the (remaining) documents from `fetch_k` as
+        well as any documents connected by edges to a selected document
+        retrieved based on similarity (a "root").
+
+        Args:
+            query: The query string to search for.
+            initial_roots: Optional list of document IDs to use for initializing search.
+                The top `adjacent_k` nodes adjacent to each initial root will be
+                included in the set of initial candidates. To fetch only in the
+                neighborhood of these nodes, set `fetch_k = 0`.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of initial Documents to fetch via similarity.
+                Will be added to the nodes adjacent to `initial_roots`.
+                Defaults to 100.
+            adjacent_k: Number of adjacent Documents to fetch.
+                Defaults to 10.
+            depth: Maximum depth of a node (number of edges) from a node
+                retrieved via similarity. Defaults to 2.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                of diversity among the results with 0 corresponding to maximum
+                diversity and 1 to minimum diversity. Defaults to 0.5.
+            score_threshold: Only documents with a score greater than or equal
+                this threshold will be chosen. Defaults to -infinity.
+            filter: Optional metadata to filter the results.
+            **kwargs: Additional keyword arguments.
+        """
+
+        async def collect_docs() -> Iterable[Document]:
+            async_iter = self.ammr_traversal_search(
+                query=query,
+                initial_roots=initial_roots,
+                k=k,
+                depth=depth,
+                fetch_k=fetch_k,
+                adjacent_k=adjacent_k,
+                lambda_mult=lambda_mult,
+                score_threshold=score_threshold,
+                filter=filter,
+                **kwargs,
+            )
+            return [doc async for doc in async_iter]
+
+        return asyncio.run(collect_docs())
+
+    @override
+    async def atraversal_search(  # noqa: C901
+        self,
+        query: str,
+        *,
+        k: int = 4,
+        depth: int = 1,
+        filter: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterable[Document]:
+        """Retrieve documents from this knowledge store.
+
+        First, `k` nodes are retrieved using a vector search for the `query` string.
+        Then, additional nodes are discovered up to the given `depth` from those
+        starting nodes.
+
+        Args:
+            query: The query string.
+            k: The number of Documents to return from the initial vector search.
+                Defaults to 4.
+            depth: The maximum depth of edges to traverse. Defaults to 1.
+            filter: Optional metadata to filter the results.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            Collection of retrieved documents.
+        """
+        # Depth 0:
+        #   Query for `k` nodes similar to the question.
+        #   Retrieve `content_id` and `outgoing_links()`.
+        #
+        # Depth 1:
+        #   Query for nodes that have an incoming link in the `outgoing_links()` set.
+        #   Combine node IDs.
+        #   Query for `outgoing_links()` of those "new" node IDs.
+        #
+        # ...
+
+        # Map from visited ID to depth
+        visited_ids: dict[str, int] = {}
+
+        # Map from visited link to depth
+        visited_links: dict[Link, int] = {}
+
+        # Map from id to Document
+        retrieved_docs: dict[str, Document] = {}
+
+        async def visit_nodes(d: int, docs: Iterable[Document]) -> None:
+            """Recursively visit nodes and their outgoing links."""
+            nonlocal visited_ids, visited_links, retrieved_docs
+
+            # Iterate over nodes, tracking the *new* outgoing links for this
+            # depth. These are links that are either new, or newly discovered at a
+            # lower depth.
+            outgoing_links: set[Link] = set()
+            for doc in docs:
+                if doc.id is not None:
+                    if doc.id not in retrieved_docs:
+                        retrieved_docs[doc.id] = doc
+
+                    # If this node is at a closer depth, update visited_ids
+                    if d <= visited_ids.get(doc.id, depth):
+                        visited_ids[doc.id] = d
+
+                        # If we can continue traversing from this node,
+                        if d < depth:
+                            node = _doc_to_node(doc=doc)
+                            # Record any new (or newly discovered at a lower depth)
+                            # links to the set to traverse.
+                            for link in _outgoing_links(node=node):
+                                if d <= visited_links.get(link, depth):
+                                    # Record that we'll query this link at the
+                                    # given depth, so we don't fetch it again
+                                    # (unless we find it an earlier depth)
+                                    visited_links[link] = d
+                                    outgoing_links.add(link)
+
+            if outgoing_links:
+                metadata_search_tasks = []
+                for outgoing_link in outgoing_links:
+                    metadata_filter = self._get_metadata_filter(
+                        metadata=filter,
+                        outgoing_link=outgoing_link,
+                    )
+                    metadata_search_tasks.append(
+                        asyncio.create_task(
+                            self.vector_store.ametadata_search(
+                                filter=metadata_filter, n=1000
+                            )
+                        )
+                    )
+                results = await asyncio.gather(*metadata_search_tasks)
+
+                # Visit targets concurrently
+                visit_target_tasks = [
+                    visit_targets(d=d + 1, docs=docs) for docs in results
+                ]
+                await asyncio.gather(*visit_target_tasks)
+
+        async def visit_targets(d: int, docs: Iterable[Document]) -> None:
+            """Visit target nodes retrieved from outgoing links."""
+            nonlocal visited_ids, retrieved_docs
+
+            new_ids_at_next_depth = set()
+            for doc in docs:
+                if doc.id is not None:
+                    if doc.id not in retrieved_docs:
+                        retrieved_docs[doc.id] = doc
+
+                    if d <= visited_ids.get(doc.id, depth):
+                        new_ids_at_next_depth.add(doc.id)
+
+            if new_ids_at_next_depth:
+                visit_node_tasks = [
+                    visit_nodes(d=d, docs=[retrieved_docs[doc_id]])
+                    for doc_id in new_ids_at_next_depth
+                    if doc_id in retrieved_docs
+                ]
+
+                fetch_tasks = [
+                    asyncio.create_task(
+                        self.vector_store.aget_by_document_id(document_id=doc_id)
+                    )
+                    for doc_id in new_ids_at_next_depth
+                    if doc_id not in retrieved_docs
+                ]
+
+                new_docs: list[Document | None] = await asyncio.gather(*fetch_tasks)
+
+                visit_node_tasks.extend(
+                    visit_nodes(d=d, docs=[new_doc])
+                    for new_doc in new_docs
+                    if new_doc is not None
+                )
+
+                await asyncio.gather(*visit_node_tasks)
+
+        # Start the traversal
+        initial_docs = self.vector_store.similarity_search(
+            query=query,
             k=k,
-            depth=depth,
-            fetch_k=fetch_k,
-            adjacent_k=adjacent_k,
-            lambda_mult=lambda_mult,
-            score_threshold=score_threshold,
-            metadata_filter=metadata_filter,
+            filter=filter,
+        )
+        await visit_nodes(d=0, docs=initial_docs)
+
+        for doc_id in visited_ids:
+            if doc_id in retrieved_docs:
+                yield self._restore_links(retrieved_docs[doc_id])
+            else:
+                msg = f"retrieved_docs should contain id: {doc_id}"
+                raise RuntimeError(msg)
+
+    @override
+    def traversal_search(
+        self,
+        query: str,
+        *,
+        k: int = 4,
+        depth: int = 1,
+        filter: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> Iterable[Document]:
+        """Retrieve documents from this knowledge store.
+
+        First, `k` nodes are retrieved using a vector search for the `query` string.
+        Then, additional nodes are discovered up to the given `depth` from those
+        starting nodes.
+
+        Args:
+            query: The query string.
+            k: The number of Documents to return from the initial vector search.
+                Defaults to 4.
+            depth: The maximum depth of edges to traverse. Defaults to 1.
+            filter: Optional metadata to filter the results.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            Collection of retrieved documents.
+        """
+
+        async def collect_docs() -> Iterable[Document]:
+            async_iter = self.atraversal_search(
+                query=query,
+                k=k,
+                depth=depth,
+                filter=filter,
+                **kwargs,
+            )
+            return [doc async for doc in async_iter]
+
+        return asyncio.run(collect_docs())
+
+    async def _get_outgoing_links(self, source_ids: Iterable[str]) -> set[Link]:
+        """Return the set of outgoing links for the given source IDs asynchronously.
+
+        Args:
+            source_ids: The IDs of the source nodes to retrieve outgoing links for.
+
+        Returns:
+            A set of `Link` objects representing the outgoing links from the source
+            nodes.
+        """
+        links = set()
+
+        # Create coroutine objects without scheduling them yet
+        coroutines = [
+            self.vector_store.aget_by_document_id(document_id=source_id)
+            for source_id in source_ids
+        ]
+
+        # Schedule and await all coroutines
+        docs = await asyncio.gather(*coroutines)
+
+        for doc in docs:
+            if doc is not None:
+                node = _doc_to_node(doc=doc)
+                links.update(_outgoing_links(node=node))
+
+        return links
+
+    async def _get_adjacent(
+        self,
+        links: set[Link],
+        query_embedding: list[float],
+        retrieved_docs: dict[str, Document],
+        k_per_link: int | None = None,
+        filter: dict[str, Any] | None = None,  # noqa: A002
+    ) -> Iterable[AdjacentNode]:
+        """Return the target nodes with incoming links from any of the given links.
+
+        Args:
+            links: The links to look for.
+            query_embedding: The query embedding. Used to rank target nodes.
+            retrieved_docs: A cache of retrieved docs. This will be added to.
+            k_per_link: The number of target nodes to fetch for each link.
+            filter: Optional metadata to filter the results.
+
+        Returns:
+            Iterable of adjacent edges.
+        """
+        targets: dict[str, AdjacentNode] = {}
+
+        tasks = []
+        for link in links:
+            metadata_filter = self._get_metadata_filter(
+                metadata=filter,
+                outgoing_link=link,
+            )
+
+            tasks.append(
+                self.vector_store.asimilarity_search_with_embedding_id_by_vector(
+                    embedding=query_embedding,
+                    k=k_per_link or 10,
+                    filter=metadata_filter,
+                )
+            )
+
+        results = await asyncio.gather(*tasks)
+
+        for result in results:
+            for doc, embedding, doc_id in result:
+                if doc_id not in retrieved_docs:
+                    retrieved_docs[doc_id] = doc
+                if doc_id not in targets:
+                    node = _doc_to_node(doc=doc)
+                    targets[doc_id] = AdjacentNode(node=node, embedding=embedding)
+
+        # TODO: Consider a combined limit based on the similarity and/or
+        # predicated MMR score?
+        return targets.values()
+
+    @staticmethod
+    def _build_docs_from_texts(
+        texts: List[str],
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+    ) -> List[Document]:
+        docs: List[Document] = []
+        for i, text in enumerate(texts):
+            doc = Document(
+                page_content=text,
+            )
+            if metadatas is not None:
+                doc.metadata = metadatas[i]
+            if ids is not None:
+                doc.id = ids[i]
+            docs.append(doc)
+        return docs
+
+    @classmethod
+    def from_texts(
+        cls: Type[CGVST],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        *,
+        session: Optional[Session] = None,
+        keyspace: Optional[str] = None,
+        table_name: str = "",
+        ids: Optional[List[str]] = None,
+        ttl_seconds: Optional[int] = None,
+        body_index_options: Optional[List[Tuple[str, Any]]] = None,
+        metadata_deny_list: Optional[list[str]] = None,
+        **kwargs: Any,
+    ) -> CGVST:
+        """Create a CassandraGraphVectorStore from raw texts.
+
+        Args:
+            texts: Texts to add to the vectorstore.
+            embedding: Embedding function to use.
+            metadatas: Optional list of metadatas associated with the texts.
+            session: Cassandra driver session.
+                If not provided, it is resolved from cassio.
+            keyspace: Cassandra key space.
+                If not provided, it is resolved from cassio.
+            table_name: Cassandra table (required).
+            ids: Optional list of IDs associated with the texts.
+            ttl_seconds: Optional time-to-live for the added texts.
+            body_index_options: Optional options used to create the body index.
+                Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
+            metadata_deny_list: Optional list of metadata keys to not index.
+                i.e. to fine-tune which of the metadata fields are indexed.
+                Note: if you plan to have massive unique text metadata entries,
+                consider not indexing them for performance
+                (and to overcome max-length limitations).
+                Note: the `metadata_indexing` parameter from
+                langchain_community.utilities.cassandra.Cassandra is not
+                exposed since CassandraGraphVectorStore only supports the
+                deny_list option.
+
+        Returns:
+            a CassandraGraphVectorStore.
+        """
+        docs = cls._build_docs_from_texts(
+            texts=texts,
+            metadatas=metadatas,
+            ids=ids,
         )
-        return nodes_to_documents(nodes)
+
+        return cls.from_documents(
+            documents=docs,
+            embedding=embedding,
+            session=session,
+            keyspace=keyspace,
+            table_name=table_name,
+            ttl_seconds=ttl_seconds,
+            body_index_options=body_index_options,
+            metadata_deny_list=metadata_deny_list,
+            **kwargs,
+        )
+
+    @classmethod
+    async def afrom_texts(
+        cls: Type[CGVST],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        *,
+        session: Optional[Session] = None,
+        keyspace: Optional[str] = None,
+        table_name: str = "",
+        ids: Optional[List[str]] = None,
+        ttl_seconds: Optional[int] = None,
+        body_index_options: Optional[List[Tuple[str, Any]]] = None,
+        metadata_deny_list: Optional[list[str]] = None,
+        **kwargs: Any,
+    ) -> CGVST:
+        """Create a CassandraGraphVectorStore from raw texts.
+
+        Args:
+            texts: Texts to add to the vectorstore.
+            embedding: Embedding function to use.
+            metadatas: Optional list of metadatas associated with the texts.
+            session: Cassandra driver session.
+                If not provided, it is resolved from cassio.
+            keyspace: Cassandra key space.
+                If not provided, it is resolved from cassio.
+            table_name: Cassandra table (required).
+            ids: Optional list of IDs associated with the texts.
+            ttl_seconds: Optional time-to-live for the added texts.
+            body_index_options: Optional options used to create the body index.
+                Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
+            metadata_deny_list: Optional list of metadata keys to not index.
+                i.e. to fine-tune which of the metadata fields are indexed.
+                Note: if you plan to have massive unique text metadata entries,
+                consider not indexing them for performance
+                (and to overcome max-length limitations).
+                Note: the `metadata_indexing` parameter from
+                langchain_community.utilities.cassandra.Cassandra is not
+                exposed since CassandraGraphVectorStore only supports the
+                deny_list option.
+
+        Returns:
+            a CassandraGraphVectorStore.
+        """
+        docs = cls._build_docs_from_texts(
+            texts=texts,
+            metadatas=metadatas,
+            ids=ids,
+        )
+
+        return await cls.afrom_documents(
+            documents=docs,
+            embedding=embedding,
+            session=session,
+            keyspace=keyspace,
+            table_name=table_name,
+            ttl_seconds=ttl_seconds,
+            body_index_options=body_index_options,
+            metadata_deny_list=metadata_deny_list,
+            **kwargs,
+        )
+
+    @staticmethod
+    def _add_ids_to_docs(
+        docs: List[Document],
+        ids: Optional[List[str]] = None,
+    ) -> List[Document]:
+        if ids is not None:
+            for doc, doc_id in zip(docs, ids):
+                doc.id = doc_id
+        return docs
+
+    @classmethod
+    def from_documents(
+        cls: Type[CGVST],
+        documents: List[Document],
+        embedding: Embeddings,
+        *,
+        session: Optional[Session] = None,
+        keyspace: Optional[str] = None,
+        table_name: str = "",
+        ids: Optional[List[str]] = None,
+        ttl_seconds: Optional[int] = None,
+        body_index_options: Optional[List[Tuple[str, Any]]] = None,
+        metadata_deny_list: Optional[list[str]] = None,
+        **kwargs: Any,
+    ) -> CGVST:
+        """Create a CassandraGraphVectorStore from a document list.
+
+        Args:
+            documents: Documents to add to the vectorstore.
+            embedding: Embedding function to use.
+            session: Cassandra driver session.
+                If not provided, it is resolved from cassio.
+            keyspace: Cassandra key space.
+                If not provided, it is resolved from cassio.
+            table_name: Cassandra table (required).
+            ids: Optional list of IDs associated with the documents.
+            ttl_seconds: Optional time-to-live for the added documents.
+            body_index_options: Optional options used to create the body index.
+                Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
+            metadata_deny_list: Optional list of metadata keys to not index.
+                i.e. to fine-tune which of the metadata fields are indexed.
+                Note: if you plan to have massive unique text metadata entries,
+                consider not indexing them for performance
+                (and to overcome max-length limitations).
+                Note: the `metadata_indexing` parameter from
+                langchain_community.utilities.cassandra.Cassandra is not
+                exposed since CassandraGraphVectorStore only supports the
+                deny_list option.
+
+        Returns:
+            a CassandraGraphVectorStore.
+        """
+        store = cls(
+            embedding=embedding,
+            session=session,
+            keyspace=keyspace,
+            table_name=table_name,
+            ttl_seconds=ttl_seconds,
+            body_index_options=body_index_options,
+            metadata_deny_list=metadata_deny_list,
+            **kwargs,
+        )
+        store.add_documents(documents=cls._add_ids_to_docs(docs=documents, ids=ids))
+        return store
+
+    @classmethod
+    async def afrom_documents(
+        cls: Type[CGVST],
+        documents: List[Document],
+        embedding: Embeddings,
+        *,
+        session: Optional[Session] = None,
+        keyspace: Optional[str] = None,
+        table_name: str = "",
+        ids: Optional[List[str]] = None,
+        ttl_seconds: Optional[int] = None,
+        body_index_options: Optional[List[Tuple[str, Any]]] = None,
+        metadata_deny_list: Optional[list[str]] = None,
+        **kwargs: Any,
+    ) -> CGVST:
+        """Create a CassandraGraphVectorStore from a document list.
+
+        Args:
+            documents: Documents to add to the vectorstore.
+            embedding: Embedding function to use.
+            session: Cassandra driver session.
+                If not provided, it is resolved from cassio.
+            keyspace: Cassandra key space.
+                If not provided, it is resolved from cassio.
+            table_name: Cassandra table (required).
+            ids: Optional list of IDs associated with the documents.
+            ttl_seconds: Optional time-to-live for the added documents.
+            body_index_options: Optional options used to create the body index.
+                Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
+            metadata_deny_list: Optional list of metadata keys to not index.
+                i.e. to fine-tune which of the metadata fields are indexed.
+                Note: if you plan to have massive unique text metadata entries,
+                consider not indexing them for performance
+                (and to overcome max-length limitations).
+                Note: the `metadata_indexing` parameter from
+                langchain_community.utilities.cassandra.Cassandra is not
+                exposed since CassandraGraphVectorStore only supports the
+                deny_list option.
+
+
+        Returns:
+            a CassandraGraphVectorStore.
+        """
+        store = cls(
+            embedding=embedding,
+            session=session,
+            keyspace=keyspace,
+            table_name=table_name,
+            ttl_seconds=ttl_seconds,
+            setup_mode=SetupMode.ASYNC,
+            body_index_options=body_index_options,
+            metadata_deny_list=metadata_deny_list,
+            **kwargs,
+        )
+        await store.aadd_documents(
+            documents=cls._add_ids_to_docs(docs=documents, ids=ids)
+        )
+        return store
diff --git a/libs/community/langchain_community/graph_vectorstores/mmr_helper.py b/libs/community/langchain_community/graph_vectorstores/mmr_helper.py
new file mode 100644
index 0000000000000..43aa8c0949fc4
--- /dev/null
+++ b/libs/community/langchain_community/graph_vectorstores/mmr_helper.py
@@ -0,0 +1,272 @@
+"""Tools for the Graph Traversal Maximal Marginal Relevance (MMR) reranking."""
+
+from __future__ import annotations
+
+import dataclasses
+from typing import TYPE_CHECKING, Iterable
+
+import numpy as np
+
+from langchain_community.utils.math import cosine_similarity
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
+
+def _emb_to_ndarray(embedding: list[float]) -> NDArray[np.float32]:
+    emb_array = np.array(embedding, dtype=np.float32)
+    if emb_array.ndim == 1:
+        emb_array = np.expand_dims(emb_array, axis=0)
+    return emb_array
+
+
+NEG_INF = float("-inf")
+
+
+@dataclasses.dataclass
+class _Candidate:
+    id: str
+    similarity: float
+    weighted_similarity: float
+    weighted_redundancy: float
+    score: float = dataclasses.field(init=False)
+
+    def __post_init__(self) -> None:
+        self.score = self.weighted_similarity - self.weighted_redundancy
+
+    def update_redundancy(self, new_weighted_redundancy: float) -> None:
+        if new_weighted_redundancy > self.weighted_redundancy:
+            self.weighted_redundancy = new_weighted_redundancy
+            self.score = self.weighted_similarity - self.weighted_redundancy
+
+
+class MmrHelper:
+    """Helper for executing an MMR traversal query.
+
+    Args:
+        query_embedding: The embedding of the query to use for scoring.
+        lambda_mult: Number between 0 and 1 that determines the degree
+            of diversity among the results with 0 corresponding to maximum
+            diversity and 1 to minimum diversity. Defaults to 0.5.
+        score_threshold: Only documents with a score greater than or equal
+            this threshold will be chosen. Defaults to -infinity.
+    """
+
+    dimensions: int
+    """Dimensions of the embedding."""
+
+    query_embedding: NDArray[np.float32]
+    """Embedding of the query as a (1,dim) ndarray."""
+
+    lambda_mult: float
+    """Number between 0 and 1.
+
+    Determines the degree of diversity among the results with 0 corresponding to
+    maximum diversity and 1 to minimum diversity."""
+
+    lambda_mult_complement: float
+    """1 - lambda_mult."""
+
+    score_threshold: float
+    """Only documents with a score greater than or equal to this will be chosen."""
+
+    selected_ids: list[str]
+    """List of selected IDs (in selection order)."""
+
+    selected_mmr_scores: list[float]
+    """List of MMR score at the time each document is selected."""
+
+    selected_similarity_scores: list[float]
+    """List of similarity score for each selected document."""
+
+    selected_embeddings: NDArray[np.float32]
+    """(N, dim) ndarray with a row for each selected node."""
+
+    candidate_id_to_index: dict[str, int]
+    """Dictionary of candidate IDs to indices in candidates and candidate_embeddings."""
+    candidates: list[_Candidate]
+    """List containing information about candidates.
+
+    Same order as rows in `candidate_embeddings`.
+    """
+    candidate_embeddings: NDArray[np.float32]
+    """(N, dim) ndarray with a row for each candidate."""
+
+    best_score: float
+    best_id: str | None
+
+    def __init__(
+        self,
+        k: int,
+        query_embedding: list[float],
+        lambda_mult: float = 0.5,
+        score_threshold: float = NEG_INF,
+    ) -> None:
+        """Create a new Traversal MMR helper."""
+        self.query_embedding = _emb_to_ndarray(query_embedding)
+        self.dimensions = self.query_embedding.shape[1]
+
+        self.lambda_mult = lambda_mult
+        self.lambda_mult_complement = 1 - lambda_mult
+        self.score_threshold = score_threshold
+
+        self.selected_ids = []
+        self.selected_similarity_scores = []
+        self.selected_mmr_scores = []
+
+        # List of selected embeddings (in selection order).
+        self.selected_embeddings = np.ndarray((k, self.dimensions), dtype=np.float32)
+
+        self.candidate_id_to_index = {}
+
+        # List of the candidates.
+        self.candidates = []
+        # numpy n-dimensional array of the candidate embeddings.
+        self.candidate_embeddings = np.ndarray((0, self.dimensions), dtype=np.float32)
+
+        self.best_score = NEG_INF
+        self.best_id = None
+
+    def candidate_ids(self) -> Iterable[str]:
+        """Return the IDs of the candidates."""
+        return self.candidate_id_to_index.keys()
+
+    def _already_selected_embeddings(self) -> NDArray[np.float32]:
+        """Return the selected embeddings sliced to the already assigned values."""
+        selected = len(self.selected_ids)
+        return np.vsplit(self.selected_embeddings, [selected])[0]
+
+    def _pop_candidate(self, candidate_id: str) -> tuple[float, NDArray[np.float32]]:
+        """Pop the candidate with the given ID.
+
+        Returns:
+            The similarity score and embedding of the candidate.
+        """
+        # Get the embedding for the id.
+        index = self.candidate_id_to_index.pop(candidate_id)
+        if self.candidates[index].id != candidate_id:
+            msg = (
+                "ID in self.candidate_id_to_index doesn't match the ID of the "
+                "corresponding index in self.candidates"
+            )
+            raise ValueError(msg)
+        embedding: NDArray[np.float32] = self.candidate_embeddings[index].copy()
+
+        # Swap that index with the last index in the candidates and
+        # candidate_embeddings.
+        last_index = self.candidate_embeddings.shape[0] - 1
+
+        similarity = 0.0
+        if index == last_index:
+            # Already the last item. We don't need to swap.
+            similarity = self.candidates.pop().similarity
+        else:
+            self.candidate_embeddings[index] = self.candidate_embeddings[last_index]
+
+            similarity = self.candidates[index].similarity
+
+            old_last = self.candidates.pop()
+            self.candidates[index] = old_last
+            self.candidate_id_to_index[old_last.id] = index
+
+        self.candidate_embeddings = np.vsplit(self.candidate_embeddings, [last_index])[
+            0
+        ]
+
+        return similarity, embedding
+
+    def pop_best(self) -> str | None:
+        """Select and pop the best item being considered.
+
+        Updates the consideration set based on it.
+
+        Returns:
+            A tuple containing the ID of the best item.
+        """
+        if self.best_id is None or self.best_score < self.score_threshold:
+            return None
+
+        # Get the selection and remove from candidates.
+        selected_id = self.best_id
+        selected_similarity, selected_embedding = self._pop_candidate(selected_id)
+
+        # Add the ID and embedding to the selected information.
+        selection_index = len(self.selected_ids)
+        self.selected_ids.append(selected_id)
+        self.selected_mmr_scores.append(self.best_score)
+        self.selected_similarity_scores.append(selected_similarity)
+        self.selected_embeddings[selection_index] = selected_embedding
+
+        # Reset the best score / best ID.
+        self.best_score = NEG_INF
+        self.best_id = None
+
+        # Update the candidates redundancy, tracking the best node.
+        if self.candidate_embeddings.shape[0] > 0:
+            similarity = cosine_similarity(
+                self.candidate_embeddings, np.expand_dims(selected_embedding, axis=0)
+            )
+            for index, candidate in enumerate(self.candidates):
+                candidate.update_redundancy(similarity[index][0])
+                if candidate.score > self.best_score:
+                    self.best_score = candidate.score
+                    self.best_id = candidate.id
+
+        return selected_id
+
+    def add_candidates(self, candidates: dict[str, list[float]]) -> None:
+        """Add candidates to the consideration set."""
+        # Determine the keys to actually include.
+        # These are the candidates that aren't already selected
+        # or under consideration.
+        include_ids_set = set(candidates.keys())
+        include_ids_set.difference_update(self.selected_ids)
+        include_ids_set.difference_update(self.candidate_id_to_index.keys())
+        include_ids = list(include_ids_set)
+
+        # Now, build up a matrix of the remaining candidate embeddings.
+        # And add them to the
+        new_embeddings: NDArray[np.float32] = np.ndarray(
+            (
+                len(include_ids),
+                self.dimensions,
+            )
+        )
+        offset = self.candidate_embeddings.shape[0]
+        for index, candidate_id in enumerate(include_ids):
+            if candidate_id in include_ids:
+                self.candidate_id_to_index[candidate_id] = offset + index
+                embedding = candidates[candidate_id]
+                new_embeddings[index] = embedding
+
+        # Compute the similarity to the query.
+        similarity = cosine_similarity(new_embeddings, self.query_embedding)
+
+        # Compute the distance metrics of all of pairs in the selected set with
+        # the new candidates.
+        redundancy = cosine_similarity(
+            new_embeddings, self._already_selected_embeddings()
+        )
+        for index, candidate_id in enumerate(include_ids):
+            max_redundancy = 0.0
+            if redundancy.shape[0] > 0:
+                max_redundancy = redundancy[index].max()
+            candidate = _Candidate(
+                id=candidate_id,
+                similarity=similarity[index][0],
+                weighted_similarity=self.lambda_mult * similarity[index][0],
+                weighted_redundancy=self.lambda_mult_complement * max_redundancy,
+            )
+            self.candidates.append(candidate)
+
+            if candidate.score >= self.best_score:
+                self.best_score = candidate.score
+                self.best_id = candidate.id
+
+        # Add the new embeddings to the candidate set.
+        self.candidate_embeddings = np.vstack(
+            (
+                self.candidate_embeddings,
+                new_embeddings,
+            )
+        )
diff --git a/libs/community/langchain_community/vectorstores/cassandra.py b/libs/community/langchain_community/vectorstores/cassandra.py
index 3e9ea17cbb0ae..b5e63d3e3f2f7 100644
--- a/libs/community/langchain_community/vectorstores/cassandra.py
+++ b/libs/community/langchain_community/vectorstores/cassandra.py
@@ -4,6 +4,7 @@
 import importlib.metadata
 import typing
 import uuid
+import warnings
 from typing import (
     Any,
     Awaitable,
@@ -501,10 +502,13 @@ def _row_to_document(row: Dict[str, Any]) -> Document:
         )
 
     def get_by_document_id(self, document_id: str) -> Document | None:
-        """Get by document ID.
+        """Retrieve a single document from the store, given its document ID.
 
         Args:
-            document_id: the document ID to get.
+            document_id: The document ID
+
+        Returns:
+            The the document if it exists. Otherwise None.
         """
         row = self.table.get(row_id=document_id)
         if row is None:
@@ -512,10 +516,13 @@ def get_by_document_id(self, document_id: str) -> Document | None:
         return self._row_to_document(row=row)
 
     async def aget_by_document_id(self, document_id: str) -> Document | None:
-        """Get by document ID.
+        """Retrieve a single document from the store, given its document ID.
 
         Args:
-            document_id: the document ID to get.
+            document_id: The document ID
+
+        Returns:
+            The the document if it exists. Otherwise None.
         """
         row = await self.table.aget(row_id=document_id)
         if row is None:
@@ -524,28 +531,30 @@ async def aget_by_document_id(self, document_id: str) -> Document | None:
 
     def metadata_search(
         self,
-        metadata: dict[str, Any] = {},  # noqa: B006
+        filter: dict[str, Any] = {},  # noqa: B006
         n: int = 5,
     ) -> Iterable[Document]:
         """Get documents via a metadata search.
 
         Args:
-            metadata: the metadata to query for.
+            filter: the metadata to query for.
+            n: the maximum number of documents to return.
         """
-        rows = self.table.find_entries(metadata=metadata, n=n)
+        rows = self.table.find_entries(metadata=filter, n=n)
         return [self._row_to_document(row=row) for row in rows if row]
 
     async def ametadata_search(
         self,
-        metadata: dict[str, Any] = {},  # noqa: B006
+        filter: dict[str, Any] = {},  # noqa: B006
         n: int = 5,
     ) -> Iterable[Document]:
         """Get documents via a metadata search.
 
         Args:
-            metadata: the metadata to query for.
+            filter: the metadata to query for.
+            n: the maximum number of documents to return.
         """
-        rows = await self.table.afind_entries(metadata=metadata, n=n)
+        rows = await self.table.afind_entries(metadata=filter, n=n)
         return [self._row_to_document(row=row) for row in rows]
 
     async def asimilarity_search_with_embedding_id_by_vector(
@@ -1126,6 +1135,24 @@ async def amax_marginal_relevance_search(
             body_search=body_search,
         )
 
+    @staticmethod
+    def _build_docs_from_texts(
+        texts: List[str],
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+    ) -> List[Document]:
+        docs: List[Document] = []
+        for i, text in enumerate(texts):
+            doc = Document(
+                page_content=text,
+            )
+            if metadatas is not None:
+                doc.metadata = metadatas[i]
+            if ids is not None:
+                doc.id = ids[i]
+            docs.append(doc)
+        return docs
+
     @classmethod
     def from_texts(
         cls: Type[CVST],
@@ -1137,13 +1164,12 @@ def from_texts(
         keyspace: Optional[str] = None,
         table_name: str = "",
         ids: Optional[List[str]] = None,
-        batch_size: int = 16,
         ttl_seconds: Optional[int] = None,
         body_index_options: Optional[List[Tuple[str, Any]]] = None,
         metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
         **kwargs: Any,
     ) -> CVST:
-        """Create a Cassandra vectorstore from raw texts.
+        """Create a Cassandra vector store from raw texts.
 
         Args:
             texts: Texts to add to the vectorstore.
@@ -1155,16 +1181,32 @@ def from_texts(
                 If not provided, it is resolved from cassio.
             table_name: Cassandra table (required).
             ids: Optional list of IDs associated with the texts.
-            batch_size: Number of concurrent requests to send to the server.
-                Defaults to 16.
             ttl_seconds: Optional time-to-live for the added texts.
             body_index_options: Optional options used to create the body index.
                 Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
+            metadata_indexing: Optional specification of a metadata indexing policy,
+                i.e. to fine-tune which of the metadata fields are indexed.
+                It can be a string ("all" or "none"), or a 2-tuple. The following
+                means that all fields except 'f1', 'f2' ... are NOT indexed:
+                    metadata_indexing=("allowlist", ["f1", "f2", ...])
+                The following means all fields EXCEPT 'g1', 'g2', ... are indexed:
+                    metadata_indexing("denylist", ["g1", "g2", ...])
+                The default is to index every metadata field.
+                Note: if you plan to have massive unique text metadata entries,
+                consider not indexing them for performance
+                (and to overcome max-length limitations).
 
         Returns:
-            a Cassandra vectorstore.
+            a Cassandra vector store.
         """
-        store = cls(
+        docs = cls._build_docs_from_texts(
+            texts=texts,
+            metadatas=metadatas,
+            ids=ids,
+        )
+
+        return cls.from_documents(
+            documents=docs,
             embedding=embedding,
             session=session,
             keyspace=keyspace,
@@ -1172,11 +1214,8 @@ def from_texts(
             ttl_seconds=ttl_seconds,
             body_index_options=body_index_options,
             metadata_indexing=metadata_indexing,
+            **kwargs,
         )
-        store.add_texts(
-            texts=texts, metadatas=metadatas, ids=ids, batch_size=batch_size
-        )
-        return store
 
     @classmethod
     async def afrom_texts(
@@ -1189,13 +1228,12 @@ async def afrom_texts(
         keyspace: Optional[str] = None,
         table_name: str = "",
         ids: Optional[List[str]] = None,
-        concurrency: int = 16,
         ttl_seconds: Optional[int] = None,
         body_index_options: Optional[List[Tuple[str, Any]]] = None,
         metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
         **kwargs: Any,
     ) -> CVST:
-        """Create a Cassandra vectorstore from raw texts.
+        """Create a Cassandra vector store from raw texts.
 
         Args:
             texts: Texts to add to the vectorstore.
@@ -1207,29 +1245,51 @@ async def afrom_texts(
                 If not provided, it is resolved from cassio.
             table_name: Cassandra table (required).
             ids: Optional list of IDs associated with the texts.
-            concurrency: Number of concurrent queries to send to the database.
-                Defaults to 16.
             ttl_seconds: Optional time-to-live for the added texts.
             body_index_options: Optional options used to create the body index.
                 Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
+            metadata_indexing: Optional specification of a metadata indexing policy,
+                i.e. to fine-tune which of the metadata fields are indexed.
+                It can be a string ("all" or "none"), or a 2-tuple. The following
+                means that all fields except 'f1', 'f2' ... are NOT indexed:
+                    metadata_indexing=("allowlist", ["f1", "f2", ...])
+                The following means all fields EXCEPT 'g1', 'g2', ... are indexed:
+                    metadata_indexing("denylist", ["g1", "g2", ...])
+                The default is to index every metadata field.
+                Note: if you plan to have massive unique text metadata entries,
+                consider not indexing them for performance
+                (and to overcome max-length limitations).
 
         Returns:
-            a Cassandra vectorstore.
+            a Cassandra vector store.
         """
-        store = cls(
+        docs = cls._build_docs_from_texts(
+            texts=texts,
+            metadatas=metadatas,
+            ids=ids,
+        )
+
+        return await cls.afrom_documents(
+            documents=docs,
             embedding=embedding,
             session=session,
             keyspace=keyspace,
             table_name=table_name,
             ttl_seconds=ttl_seconds,
-            setup_mode=SetupMode.ASYNC,
             body_index_options=body_index_options,
             metadata_indexing=metadata_indexing,
+            **kwargs,
         )
-        await store.aadd_texts(
-            texts=texts, metadatas=metadatas, ids=ids, concurrency=concurrency
-        )
-        return store
+
+    @staticmethod
+    def _add_ids_to_docs(
+        docs: List[Document],
+        ids: Optional[List[str]] = None,
+    ) -> List[Document]:
+        if ids is not None:
+            for doc, doc_id in zip(docs, ids):
+                doc.id = doc_id
+        return docs
 
     @classmethod
     def from_documents(
@@ -1241,13 +1301,12 @@ def from_documents(
         keyspace: Optional[str] = None,
         table_name: str = "",
         ids: Optional[List[str]] = None,
-        batch_size: int = 16,
         ttl_seconds: Optional[int] = None,
         body_index_options: Optional[List[Tuple[str, Any]]] = None,
         metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
         **kwargs: Any,
     ) -> CVST:
-        """Create a Cassandra vectorstore from a document list.
+        """Create a Cassandra vector store from a document list.
 
         Args:
             documents: Documents to add to the vectorstore.
@@ -1258,31 +1317,48 @@ def from_documents(
                 If not provided, it is resolved from cassio.
             table_name: Cassandra table (required).
             ids: Optional list of IDs associated with the documents.
-            batch_size: Number of concurrent requests to send to the server.
-                Defaults to 16.
             ttl_seconds: Optional time-to-live for the added documents.
             body_index_options: Optional options used to create the body index.
                 Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
+            metadata_indexing: Optional specification of a metadata indexing policy,
+                i.e. to fine-tune which of the metadata fields are indexed.
+                It can be a string ("all" or "none"), or a 2-tuple. The following
+                means that all fields except 'f1', 'f2' ... are NOT indexed:
+                    metadata_indexing=("allowlist", ["f1", "f2", ...])
+                The following means all fields EXCEPT 'g1', 'g2', ... are indexed:
+                    metadata_indexing("denylist", ["g1", "g2", ...])
+                The default is to index every metadata field.
+                Note: if you plan to have massive unique text metadata entries,
+                consider not indexing them for performance
+                (and to overcome max-length limitations).
 
         Returns:
-            a Cassandra vectorstore.
+            a Cassandra vector store.
         """
-        texts = [doc.page_content for doc in documents]
-        metadatas = [doc.metadata for doc in documents]
-        return cls.from_texts(
-            texts=texts,
+        if ids is not None:
+            warnings.warn(
+                (
+                    "Parameter `ids` to Cassandra's `from_documents` "
+                    "method is deprecated. Please set the supplied documents' "
+                    "`.id` attribute instead. The id attribute of Document "
+                    "is ignored as long as the `ids` parameter is passed."
+                ),
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
+        store = cls(
             embedding=embedding,
-            metadatas=metadatas,
             session=session,
             keyspace=keyspace,
             table_name=table_name,
-            ids=ids,
-            batch_size=batch_size,
             ttl_seconds=ttl_seconds,
             body_index_options=body_index_options,
             metadata_indexing=metadata_indexing,
             **kwargs,
         )
+        store.add_documents(documents=cls._add_ids_to_docs(docs=documents, ids=ids))
+        return store
 
     @classmethod
     async def afrom_documents(
@@ -1294,13 +1370,12 @@ async def afrom_documents(
         keyspace: Optional[str] = None,
         table_name: str = "",
         ids: Optional[List[str]] = None,
-        concurrency: int = 16,
         ttl_seconds: Optional[int] = None,
         body_index_options: Optional[List[Tuple[str, Any]]] = None,
         metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
         **kwargs: Any,
     ) -> CVST:
-        """Create a Cassandra vectorstore from a document list.
+        """Create a Cassandra vector store from a document list.
 
         Args:
             documents: Documents to add to the vectorstore.
@@ -1311,31 +1386,51 @@ async def afrom_documents(
                 If not provided, it is resolved from cassio.
             table_name: Cassandra table (required).
             ids: Optional list of IDs associated with the documents.
-            concurrency: Number of concurrent queries to send to the database.
-                Defaults to 16.
             ttl_seconds: Optional time-to-live for the added documents.
             body_index_options: Optional options used to create the body index.
                 Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
+            metadata_indexing: Optional specification of a metadata indexing policy,
+                i.e. to fine-tune which of the metadata fields are indexed.
+                It can be a string ("all" or "none"), or a 2-tuple. The following
+                means that all fields except 'f1', 'f2' ... are NOT indexed:
+                    metadata_indexing=("allowlist", ["f1", "f2", ...])
+                The following means all fields EXCEPT 'g1', 'g2', ... are indexed:
+                    metadata_indexing("denylist", ["g1", "g2", ...])
+                The default is to index every metadata field.
+                Note: if you plan to have massive unique text metadata entries,
+                consider not indexing them for performance
+                (and to overcome max-length limitations).
 
         Returns:
-            a Cassandra vectorstore.
+            a Cassandra vector store.
         """
-        texts = [doc.page_content for doc in documents]
-        metadatas = [doc.metadata for doc in documents]
-        return await cls.afrom_texts(
-            texts=texts,
+        if ids is not None:
+            warnings.warn(
+                (
+                    "Parameter `ids` to Cassandra's `afrom_documents` "
+                    "method is deprecated. Please set the supplied documents' "
+                    "`.id` attribute instead. The id attribute of Document "
+                    "is ignored as long as the `ids` parameter is passed."
+                ),
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
+        store = cls(
             embedding=embedding,
-            metadatas=metadatas,
             session=session,
             keyspace=keyspace,
             table_name=table_name,
-            ids=ids,
-            concurrency=concurrency,
             ttl_seconds=ttl_seconds,
+            setup_mode=SetupMode.ASYNC,
             body_index_options=body_index_options,
             metadata_indexing=metadata_indexing,
             **kwargs,
         )
+        await store.aadd_documents(
+            documents=cls._add_ids_to_docs(docs=documents, ids=ids)
+        )
+        return store
 
     def as_retriever(
         self,
diff --git a/libs/community/tests/integration_tests/graph_vectorstores/test_cassandra.py b/libs/community/tests/integration_tests/graph_vectorstores/test_cassandra.py
index 870c5141f8486..d55f5469e546a 100644
--- a/libs/community/tests/integration_tests/graph_vectorstores/test_cassandra.py
+++ b/libs/community/tests/integration_tests/graph_vectorstores/test_cassandra.py
@@ -1,116 +1,255 @@
-import math
+"""Test of Apache Cassandra graph vector g_store class `CassandraGraphVectorStore`"""
+
+import json
 import os
-from typing import Iterable, List, Optional, Type
+import random
+from contextlib import contextmanager
+from typing import Any, Generator, Iterable, List, Optional
 
+import pytest
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 
 from langchain_community.graph_vectorstores import CassandraGraphVectorStore
-from langchain_community.graph_vectorstores.links import METADATA_LINKS_KEY, Link
+from langchain_community.graph_vectorstores.base import Node
+from langchain_community.graph_vectorstores.links import (
+    METADATA_LINKS_KEY,
+    Link,
+    add_links,
+)
+from tests.integration_tests.cache.fake_embeddings import (
+    AngularTwoDimensionalEmbeddings,
+    FakeEmbeddings,
+)
+
+TEST_KEYSPACE = "graph_test_keyspace"
+
+
+class ParserEmbeddings(Embeddings):
+    """Parse input texts: if they are json for a List[float], fine.
+    Otherwise, return all zeros and call it a day.
+    """
 
-CASSANDRA_DEFAULT_KEYSPACE = "graph_test_keyspace"
+    def __init__(self, dimension: int) -> None:
+        self.dimension = dimension
 
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        return [self.embed_query(txt) for txt in texts]
 
-def _get_graph_store(
-    embedding_class: Type[Embeddings], documents: Iterable[Document] = ()
-) -> CassandraGraphVectorStore:
-    import cassio
-    from cassandra.cluster import Cluster
-    from cassio.config import check_resolve_session, resolve_keyspace
+    def embed_query(self, text: str) -> list[float]:
+        try:
+            vals = json.loads(text)
+        except json.JSONDecodeError:
+            return [0.0] * self.dimension
+        else:
+            assert len(vals) == self.dimension
+            return vals
 
-    node_table = "graph_test_node_table"
-    edge_table = "graph_test_edge_table"
 
-    if any(
-        env_var in os.environ
-        for env_var in [
-            "CASSANDRA_CONTACT_POINTS",
-            "ASTRA_DB_APPLICATION_TOKEN",
-            "ASTRA_DB_INIT_STRING",
-        ]
-    ):
-        cassio.init(auto=True)
-        session = check_resolve_session()
-    else:
-        cluster = Cluster()
-        session = cluster.connect()
-    keyspace = resolve_keyspace() or CASSANDRA_DEFAULT_KEYSPACE
-    cassio.init(session=session, keyspace=keyspace)
-    # ensure keyspace exists
-    session.execute(
-        (
-            f"CREATE KEYSPACE IF NOT EXISTS {keyspace} "
-            f"WITH replication = {{'class': 'SimpleStrategy', 'replication_factor': 1}}"
-        )
-    )
-    session.execute(f"DROP TABLE IF EXISTS {keyspace}.{node_table}")
-    session.execute(f"DROP TABLE IF EXISTS {keyspace}.{edge_table}")
-    store = CassandraGraphVectorStore.from_documents(
-        documents,
-        embedding=embedding_class(),
-        session=session,
-        keyspace=keyspace,
-        node_table=node_table,
-        targets_table=edge_table,
-    )
-    return store
+@pytest.fixture
+def embedding_d2() -> Embeddings:
+    return ParserEmbeddings(dimension=2)
 
 
-class FakeEmbeddings(Embeddings):
-    """Fake embeddings functionality for testing."""
+class EarthEmbeddings(Embeddings):
+    def get_vector_near(self, value: float) -> List[float]:
+        base_point = [value, (1 - value**2) ** 0.5]
+        fluctuation = random.random() / 100.0
+        return [base_point[0] + fluctuation, base_point[1] - fluctuation]
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Return simple embeddings.
-        Embeddings encode each text as its index."""
-        return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        return [self.embed_query(txt) for txt in texts]
 
-    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
-        return self.embed_documents(texts)
+    def embed_query(self, text: str) -> list[float]:
+        words = set(text.lower().split())
+        if "earth" in words:
+            vector = self.get_vector_near(0.9)
+        elif {"planet", "world", "globe", "sphere"}.intersection(words):
+            vector = self.get_vector_near(0.8)
+        else:
+            vector = self.get_vector_near(0.1)
+        return vector
 
-    def embed_query(self, text: str) -> List[float]:
-        """Return constant query embeddings.
-        Embeddings are identical to embed_documents(texts)[0].
-        Distance to each text will be that text's index,
-        as it was passed to embed_documents."""
-        return [float(1.0)] * 9 + [float(0.0)]
 
-    async def aembed_query(self, text: str) -> List[float]:
-        return self.embed_query(text)
+def _result_ids(docs: Iterable[Document]) -> List[Optional[str]]:
+    return [doc.id for doc in docs]
 
 
-class AngularTwoDimensionalEmbeddings(Embeddings):
+@pytest.fixture
+def graph_vector_store_docs() -> list[Document]:
     """
-    From angles (as strings in units of pi) to unit embedding vectors on a circle.
+    This is a set of Documents to pre-populate a graph vector store,
+    with entries placed in a certain way.
+
+    Space of the entries (under Euclidean similarity):
+
+                      A0    (*)
+        ....        AL   AR       <....
+        :              |              :
+        :              |  ^           :
+        v              |  .           v
+                       |   :
+       TR              |   :          BL
+    T0   --------------x--------------   B0
+       TL              |   :          BR
+                       |   :
+                       |  .
+                       | .
+                       |
+                    FL   FR
+                      F0
+
+    the query point is meant to be at (*).
+    the A are bidirectionally with B
+    the A are outgoing to T
+    the A are incoming from F
+    The links are like: L with L, 0 with 0 and R with R.
     """
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """
-        Make a list of texts into a list of embedding vectors.
-        """
-        return [self.embed_query(text) for text in texts]
-
-    def embed_query(self, text: str) -> List[float]:
-        """
-        Convert input text to a 'vector' (list of floats).
-        If the text is a number, use it as the angle for the
-        unit vector in units of pi.
-        Any other input text becomes the singular result [0, 0] !
-        """
-        try:
-            angle = float(text)
-            return [math.cos(angle * math.pi), math.sin(angle * math.pi)]
-        except ValueError:
-            # Assume: just test string, no attention is paid to values.
-            return [0.0, 0.0]
+    docs_a = [
+        Document(id="AL", page_content="[-1, 9]", metadata={"label": "AL"}),
+        Document(id="A0", page_content="[0, 10]", metadata={"label": "A0"}),
+        Document(id="AR", page_content="[1, 9]", metadata={"label": "AR"}),
+    ]
+    docs_b = [
+        Document(id="BL", page_content="[9, 1]", metadata={"label": "BL"}),
+        Document(id="B0", page_content="[10, 0]", metadata={"label": "B0"}),
+        Document(id="BL", page_content="[9, -1]", metadata={"label": "BR"}),
+    ]
+    docs_f = [
+        Document(id="FL", page_content="[1, -9]", metadata={"label": "FL"}),
+        Document(id="F0", page_content="[0, -10]", metadata={"label": "F0"}),
+        Document(id="FR", page_content="[-1, -9]", metadata={"label": "FR"}),
+    ]
+    docs_t = [
+        Document(id="TL", page_content="[-9, -1]", metadata={"label": "TL"}),
+        Document(id="T0", page_content="[-10, 0]", metadata={"label": "T0"}),
+        Document(id="TR", page_content="[-9, 1]", metadata={"label": "TR"}),
+    ]
+    for doc_a, suffix in zip(docs_a, ["l", "0", "r"]):
+        add_links(doc_a, Link.bidir(kind="ab_example", tag=f"tag_{suffix}"))
+        add_links(doc_a, Link.outgoing(kind="at_example", tag=f"tag_{suffix}"))
+        add_links(doc_a, Link.incoming(kind="af_example", tag=f"tag_{suffix}"))
+    for doc_b, suffix in zip(docs_b, ["l", "0", "r"]):
+        add_links(doc_b, Link.bidir(kind="ab_example", tag=f"tag_{suffix}"))
+    for doc_t, suffix in zip(docs_t, ["l", "0", "r"]):
+        add_links(doc_t, Link.incoming(kind="at_example", tag=f"tag_{suffix}"))
+    for doc_f, suffix in zip(docs_f, ["l", "0", "r"]):
+        add_links(doc_f, Link.outgoing(kind="af_example", tag=f"tag_{suffix}"))
+    return docs_a + docs_b + docs_f + docs_t
+
+
+class CassandraSession:
+    table_name: str
+    session: Any
+
+    def __init__(self, table_name: str, session: Any):
+        self.table_name = table_name
+        self.session = session
+
+
+@contextmanager
+def get_cassandra_session(
+    table_name: str, drop: bool = True
+) -> Generator[CassandraSession, None, None]:
+    """Initialize the Cassandra cluster and session"""
+    from cassandra.cluster import Cluster
 
+    if "CASSANDRA_CONTACT_POINTS" in os.environ:
+        contact_points = [
+            cp.strip()
+            for cp in os.environ["CASSANDRA_CONTACT_POINTS"].split(",")
+            if cp.strip()
+        ]
+    else:
+        contact_points = None
 
-def _result_ids(docs: Iterable[Document]) -> List[Optional[str]]:
-    return [doc.id for doc in docs]
+    cluster = Cluster(contact_points)
+    session = cluster.connect()
 
+    try:
+        session.execute(
+            (
+                f"CREATE KEYSPACE IF NOT EXISTS {TEST_KEYSPACE}"
+                " WITH replication = "
+                "{'class': 'SimpleStrategy', 'replication_factor': 1}"
+            )
+        )
+        if drop:
+            session.execute(f"DROP TABLE IF EXISTS {TEST_KEYSPACE}.{table_name}")
+
+        # Yield the session for usage
+        yield CassandraSession(table_name=table_name, session=session)
+    finally:
+        # Ensure proper shutdown/cleanup of resources
+        session.shutdown()
+        cluster.shutdown()
+
+
+@pytest.fixture(scope="function")
+def graph_vector_store_angular(
+    table_name: str = "graph_test_table",
+) -> Generator[CassandraGraphVectorStore, None, None]:
+    with get_cassandra_session(table_name=table_name) as session:
+        yield CassandraGraphVectorStore(
+            embedding=AngularTwoDimensionalEmbeddings(),
+            session=session.session,
+            keyspace=TEST_KEYSPACE,
+            table_name=session.table_name,
+        )
 
-def test_mmr_traversal() -> None:
-    """
-    Test end to end construction and MMR search.
+
+@pytest.fixture(scope="function")
+def graph_vector_store_earth(
+    table_name: str = "graph_test_table",
+) -> Generator[CassandraGraphVectorStore, None, None]:
+    with get_cassandra_session(table_name=table_name) as session:
+        yield CassandraGraphVectorStore(
+            embedding=EarthEmbeddings(),
+            session=session.session,
+            keyspace=TEST_KEYSPACE,
+            table_name=session.table_name,
+        )
+
+
+@pytest.fixture(scope="function")
+def graph_vector_store_fake(
+    table_name: str = "graph_test_table",
+) -> Generator[CassandraGraphVectorStore, None, None]:
+    with get_cassandra_session(table_name=table_name) as session:
+        yield CassandraGraphVectorStore(
+            embedding=FakeEmbeddings(),
+            session=session.session,
+            keyspace=TEST_KEYSPACE,
+            table_name=session.table_name,
+        )
+
+
+@pytest.fixture(scope="function")
+def graph_vector_store_d2(
+    embedding_d2: Embeddings,
+    table_name: str = "graph_test_table",
+) -> Generator[CassandraGraphVectorStore, None, None]:
+    with get_cassandra_session(table_name=table_name) as session:
+        yield CassandraGraphVectorStore(
+            embedding=embedding_d2,
+            session=session.session,
+            keyspace=TEST_KEYSPACE,
+            table_name=session.table_name,
+        )
+
+
+@pytest.fixture(scope="function")
+def populated_graph_vector_store_d2(
+    graph_vector_store_d2: CassandraGraphVectorStore,
+    graph_vector_store_docs: list[Document],
+) -> Generator[CassandraGraphVectorStore, None, None]:
+    graph_vector_store_d2.add_documents(graph_vector_store_docs)
+    yield graph_vector_store_d2
+
+
+def test_mmr_traversal(graph_vector_store_angular: CassandraGraphVectorStore) -> None:
+    """ Test end to end construction and MMR search.
     The embedding function used here ensures `texts` become
     the following vectors on a circle (numbered v0 through v3):
 
@@ -128,140 +267,128 @@ def test_mmr_traversal() -> None:
     Both v2 and v3 are reachable via edges from v0, so once it is
     selected, those are both considered.
     """
-    store = _get_graph_store(AngularTwoDimensionalEmbeddings)
-
-    v0 = Document(
+    v0 = Node(
         id="v0",
-        page_content="-0.124",
-        metadata={
-            METADATA_LINKS_KEY: [
-                Link.outgoing(kind="explicit", tag="link"),
-            ],
-        },
+        text="-0.124",
+        links=[
+            Link.outgoing(kind="explicit", tag="link"),
+        ],
     )
-    v1 = Document(
+    v1 = Node(
         id="v1",
-        page_content="+0.127",
+        text="+0.127",
     )
-    v2 = Document(
+    v2 = Node(
         id="v2",
-        page_content="+0.25",
-        metadata={
-            METADATA_LINKS_KEY: [
-                Link.incoming(kind="explicit", tag="link"),
-            ],
-        },
+        text="+0.25",
+        links=[
+            Link.incoming(kind="explicit", tag="link"),
+        ],
     )
-    v3 = Document(
+    v3 = Node(
         id="v3",
-        page_content="+1.0",
-        metadata={
-            METADATA_LINKS_KEY: [
-                Link.incoming(kind="explicit", tag="link"),
-            ],
-        },
+        text="+1.0",
+        links=[
+            Link.incoming(kind="explicit", tag="link"),
+        ],
     )
-    store.add_documents([v0, v1, v2, v3])
 
-    results = store.mmr_traversal_search("0.0", k=2, fetch_k=2)
+    g_store = graph_vector_store_angular
+    g_store.add_nodes([v0, v1, v2, v3])
+
+    results = g_store.mmr_traversal_search("0.0", k=2, fetch_k=2)
     assert _result_ids(results) == ["v0", "v2"]
 
     # With max depth 0, no edges are traversed, so this doesn't reach v2 or v3.
     # So it ends up picking "v1" even though it's similar to "v0".
-    results = store.mmr_traversal_search("0.0", k=2, fetch_k=2, depth=0)
+    results = g_store.mmr_traversal_search("0.0", k=2, fetch_k=2, depth=0)
     assert _result_ids(results) == ["v0", "v1"]
 
     # With max depth 0 but higher `fetch_k`, we encounter v2
-    results = store.mmr_traversal_search("0.0", k=2, fetch_k=3, depth=0)
+    results = g_store.mmr_traversal_search("0.0", k=2, fetch_k=3, depth=0)
     assert _result_ids(results) == ["v0", "v2"]
 
     # v0 score is .46, v2 score is 0.16 so it won't be chosen.
-    results = store.mmr_traversal_search("0.0", k=2, score_threshold=0.2)
+    results = g_store.mmr_traversal_search("0.0", k=2, score_threshold=0.2)
     assert _result_ids(results) == ["v0"]
 
     # with k=4 we should get all of the documents.
-    results = store.mmr_traversal_search("0.0", k=4)
+    results = g_store.mmr_traversal_search("0.0", k=4)
     assert _result_ids(results) == ["v0", "v2", "v1", "v3"]
 
 
-def test_write_retrieve_keywords() -> None:
-    from langchain_openai import OpenAIEmbeddings
-
-    greetings = Document(
+def test_write_retrieve_keywords(
+    graph_vector_store_earth: CassandraGraphVectorStore,
+) -> None:
+    greetings = Node(
         id="greetings",
-        page_content="Typical Greetings",
-        metadata={
-            METADATA_LINKS_KEY: [
-                Link.incoming(kind="parent", tag="parent"),
-            ],
-        },
+        text="Typical Greetings",
+        links=[
+            Link.incoming(kind="parent", tag="parent"),
+        ],
     )
-    doc1 = Document(
+
+    node1 = Node(
         id="doc1",
-        page_content="Hello World",
-        metadata={
-            METADATA_LINKS_KEY: [
-                Link.outgoing(kind="parent", tag="parent"),
-                Link.bidir(kind="kw", tag="greeting"),
-                Link.bidir(kind="kw", tag="world"),
-            ],
-        },
+        text="Hello World",
+        links=[
+            Link.outgoing(kind="parent", tag="parent"),
+            Link.bidir(kind="kw", tag="greeting"),
+            Link.bidir(kind="kw", tag="world"),
+        ],
     )
-    doc2 = Document(
+
+    node2 = Node(
         id="doc2",
-        page_content="Hello Earth",
-        metadata={
-            METADATA_LINKS_KEY: [
-                Link.outgoing(kind="parent", tag="parent"),
-                Link.bidir(kind="kw", tag="greeting"),
-                Link.bidir(kind="kw", tag="earth"),
-            ],
-        },
+        text="Hello Earth",
+        links=[
+            Link.outgoing(kind="parent", tag="parent"),
+            Link.bidir(kind="kw", tag="greeting"),
+            Link.bidir(kind="kw", tag="earth"),
+        ],
     )
-    store = _get_graph_store(OpenAIEmbeddings, [greetings, doc1, doc2])
+
+    g_store = graph_vector_store_earth
+    g_store.add_nodes(nodes=[greetings, node1, node2])
 
     # Doc2 is more similar, but World and Earth are similar enough that doc1 also
     # shows up.
-    results: Iterable[Document] = store.similarity_search("Earth", k=2)
+    results: Iterable[Document] = g_store.similarity_search("Earth", k=2)
     assert _result_ids(results) == ["doc2", "doc1"]
 
-    results = store.similarity_search("Earth", k=1)
+    results = g_store.similarity_search("Earth", k=1)
     assert _result_ids(results) == ["doc2"]
 
-    results = store.traversal_search("Earth", k=2, depth=0)
+    results = g_store.traversal_search("Earth", k=2, depth=0)
     assert _result_ids(results) == ["doc2", "doc1"]
 
-    results = store.traversal_search("Earth", k=2, depth=1)
+    results = g_store.traversal_search("Earth", k=2, depth=1)
     assert _result_ids(results) == ["doc2", "doc1", "greetings"]
 
     # K=1 only pulls in doc2 (Hello Earth)
-    results = store.traversal_search("Earth", k=1, depth=0)
+    results = g_store.traversal_search("Earth", k=1, depth=0)
     assert _result_ids(results) == ["doc2"]
 
     # K=1 only pulls in doc2 (Hello Earth). Depth=1 traverses to parent and via
     # keyword edge.
-    results = store.traversal_search("Earth", k=1, depth=1)
+    results = g_store.traversal_search("Earth", k=1, depth=1)
     assert set(_result_ids(results)) == {"doc2", "doc1", "greetings"}
 
 
-def test_metadata() -> None:
-    store = _get_graph_store(FakeEmbeddings)
-    store.add_documents(
-        [
-            Document(
-                id="a",
-                page_content="A",
-                metadata={
-                    METADATA_LINKS_KEY: [
-                        Link.incoming(kind="hyperlink", tag="http://a"),
-                        Link.bidir(kind="other", tag="foo"),
-                    ],
-                    "other": "some other field",
-                },
-            )
-        ]
+def test_metadata(graph_vector_store_fake: CassandraGraphVectorStore) -> None:
+    doc_a = Node(
+        id="a",
+        text="A",
+        metadata={"other": "some other field"},
+        links=[
+            Link.incoming(kind="hyperlink", tag="http://a"),
+            Link.bidir(kind="other", tag="foo"),
+        ],
     )
-    results = store.similarity_search("A")
+
+    g_store = graph_vector_store_fake
+    g_store.add_nodes([doc_a])
+    results = g_store.similarity_search("A")
     assert len(results) == 1
     assert results[0].id == "a"
     metadata = results[0].metadata
@@ -270,3 +397,274 @@ def test_metadata() -> None:
         Link.incoming(kind="hyperlink", tag="http://a"),
         Link.bidir(kind="other", tag="foo"),
     }
+
+
+class TestCassandraGraphVectorStore:
+    def test_gvs_similarity_search_sync(
+        self,
+        populated_graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        """Simple (non-graph) similarity search on a graph vector g_store."""
+        g_store = populated_graph_vector_store_d2
+        ss_response = g_store.similarity_search(query="[2, 10]", k=2)
+        ss_labels = [doc.metadata["label"] for doc in ss_response]
+        assert ss_labels == ["AR", "A0"]
+        ss_by_v_response = g_store.similarity_search_by_vector(embedding=[2, 10], k=2)
+        ss_by_v_labels = [doc.metadata["label"] for doc in ss_by_v_response]
+        assert ss_by_v_labels == ["AR", "A0"]
+
+    async def test_gvs_similarity_search_async(
+        self,
+        populated_graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        """Simple (non-graph) similarity search on a graph vector store."""
+        g_store = populated_graph_vector_store_d2
+        ss_response = await g_store.asimilarity_search(query="[2, 10]", k=2)
+        ss_labels = [doc.metadata["label"] for doc in ss_response]
+        assert ss_labels == ["AR", "A0"]
+        ss_by_v_response = await g_store.asimilarity_search_by_vector(
+            embedding=[2, 10], k=2
+        )
+        ss_by_v_labels = [doc.metadata["label"] for doc in ss_by_v_response]
+        assert ss_by_v_labels == ["AR", "A0"]
+
+    def test_gvs_traversal_search_sync(
+        self,
+        populated_graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        """Graph traversal search on a graph vector store."""
+        g_store = populated_graph_vector_store_d2
+        ts_response = g_store.traversal_search(query="[2, 10]", k=2, depth=2)
+        # this is a set, as some of the internals of trav.search are set-driven
+        # so ordering is not deterministic:
+        ts_labels = {doc.metadata["label"] for doc in ts_response}
+        assert ts_labels == {"AR", "A0", "BR", "B0", "TR", "T0"}
+
+    async def test_gvs_traversal_search_async(
+        self,
+        populated_graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        """Graph traversal search on a graph vector store."""
+        g_store = populated_graph_vector_store_d2
+        ts_labels = set()
+        async for doc in g_store.atraversal_search(query="[2, 10]", k=2, depth=2):
+            ts_labels.add(doc.metadata["label"])
+        # this is a set, as some of the internals of trav.search are set-driven
+        # so ordering is not deterministic:
+        assert ts_labels == {"AR", "A0", "BR", "B0", "TR", "T0"}
+
+    def test_gvs_mmr_traversal_search_sync(
+        self,
+        populated_graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        """MMR Graph traversal search on a graph vector store."""
+        g_store = populated_graph_vector_store_d2
+        mt_response = g_store.mmr_traversal_search(
+            query="[2, 10]",
+            k=2,
+            depth=2,
+            fetch_k=1,
+            adjacent_k=2,
+            lambda_mult=0.1,
+        )
+        # TODO: can this rightfully be a list (or must it be a set)?
+        mt_labels = {doc.metadata["label"] for doc in mt_response}
+        assert mt_labels == {"AR", "BR"}
+
+    async def test_gvs_mmr_traversal_search_async(
+        self,
+        populated_graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        """MMR Graph traversal search on a graph vector store."""
+        g_store = populated_graph_vector_store_d2
+        mt_labels = set()
+        async for doc in g_store.ammr_traversal_search(
+            query="[2, 10]",
+            k=2,
+            depth=2,
+            fetch_k=1,
+            adjacent_k=2,
+            lambda_mult=0.1,
+        ):
+            mt_labels.add(doc.metadata["label"])
+        # TODO: can this rightfully be a list (or must it be a set)?
+        assert mt_labels == {"AR", "BR"}
+
+    def test_gvs_metadata_search_sync(
+        self,
+        populated_graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        """Metadata search on a graph vector store."""
+        g_store = populated_graph_vector_store_d2
+        mt_response = g_store.metadata_search(
+            filter={"label": "T0"},
+            n=2,
+        )
+        doc: Document = next(iter(mt_response))
+        assert doc.page_content == "[-10, 0]"
+        links = doc.metadata["links"]
+        assert len(links) == 1
+        link: Link = links.pop()
+        assert isinstance(link, Link)
+        assert link.direction == "in"
+        assert link.kind == "at_example"
+        assert link.tag == "tag_0"
+
+    async def test_gvs_metadata_search_async(
+        self,
+        populated_graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        """Metadata search on a graph vector store."""
+        g_store = populated_graph_vector_store_d2
+        mt_response = await g_store.ametadata_search(
+            filter={"label": "T0"},
+            n=2,
+        )
+        doc: Document = next(iter(mt_response))
+        assert doc.page_content == "[-10, 0]"
+        links: set[Link] = doc.metadata["links"]
+        assert len(links) == 1
+        link: Link = links.pop()
+        assert isinstance(link, Link)
+        assert link.direction == "in"
+        assert link.kind == "at_example"
+        assert link.tag == "tag_0"
+
+    def test_gvs_get_by_document_id_sync(
+        self,
+        populated_graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        """Get by document_id on a graph vector store."""
+        g_store = populated_graph_vector_store_d2
+        doc = g_store.get_by_document_id(document_id="FL")
+        assert doc is not None
+        assert doc.page_content == "[1, -9]"
+        links = doc.metadata["links"]
+        assert len(links) == 1
+        link: Link = links.pop()
+        assert isinstance(link, Link)
+        assert link.direction == "out"
+        assert link.kind == "af_example"
+        assert link.tag == "tag_l"
+
+        invalid_doc = g_store.get_by_document_id(document_id="invalid")
+        assert invalid_doc is None
+
+    async def test_gvs_get_by_document_id_async(
+        self,
+        populated_graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        """Get by document_id on a graph vector store."""
+        g_store = populated_graph_vector_store_d2
+        doc = await g_store.aget_by_document_id(document_id="FL")
+        assert doc is not None
+        assert doc.page_content == "[1, -9]"
+        links = doc.metadata["links"]
+        assert len(links) == 1
+        link: Link = links.pop()
+        assert isinstance(link, Link)
+        assert link.direction == "out"
+        assert link.kind == "af_example"
+        assert link.tag == "tag_l"
+
+        invalid_doc = await g_store.aget_by_document_id(document_id="invalid")
+        assert invalid_doc is None
+
+    def test_gvs_from_texts(
+        self,
+        graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        g_store = graph_vector_store_d2
+        g_store.add_texts(
+            texts=["[1, 2]"],
+            metadatas=[{"md": 1}],
+            ids=["x_id"],
+        )
+
+        hits = g_store.similarity_search("[2, 1]", k=2)
+        assert len(hits) == 1
+        assert hits[0].page_content == "[1, 2]"
+        assert hits[0].id == "x_id"
+        # there may be more re:graph structure.
+        assert hits[0].metadata["md"] == "1.0"
+
+    def test_gvs_from_documents_containing_ids(
+        self,
+        graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        the_document = Document(
+            page_content="[1, 2]",
+            metadata={"md": 1},
+            id="x_id",
+        )
+        g_store = graph_vector_store_d2
+        g_store.add_documents([the_document])
+        hits = g_store.similarity_search("[2, 1]", k=2)
+        assert len(hits) == 1
+        assert hits[0].page_content == "[1, 2]"
+        assert hits[0].id == "x_id"
+        # there may be more re:graph structure.
+        assert hits[0].metadata["md"] == "1.0"
+
+    def test_gvs_add_nodes_sync(
+        self,
+        *,
+        graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        links0 = [
+            Link(kind="kA", direction="out", tag="tA"),
+            Link(kind="kB", direction="bidir", tag="tB"),
+        ]
+        links1 = [
+            Link(kind="kC", direction="in", tag="tC"),
+        ]
+        nodes = [
+            Node(id="id0", text="[1, 0]", metadata={"m": 0}, links=links0),
+            Node(text="[-1, 0]", metadata={"m": 1}, links=links1),
+        ]
+        graph_vector_store_d2.add_nodes(nodes)
+        hits = graph_vector_store_d2.similarity_search_by_vector([0.9, 0.1])
+        assert len(hits) == 2
+        assert hits[0].id == "id0"
+        assert hits[0].page_content == "[1, 0]"
+        md0 = hits[0].metadata
+        assert md0["m"] == "0.0"
+        assert any(isinstance(v, set) for k, v in md0.items() if k != "m")
+
+        assert hits[1].id != "id0"
+        assert hits[1].page_content == "[-1, 0]"
+        md1 = hits[1].metadata
+        assert md1["m"] == "1.0"
+        assert any(isinstance(v, set) for k, v in md1.items() if k != "m")
+
+    async def test_gvs_add_nodes_async(
+        self,
+        *,
+        graph_vector_store_d2: CassandraGraphVectorStore,
+    ) -> None:
+        links0 = [
+            Link(kind="kA", direction="out", tag="tA"),
+            Link(kind="kB", direction="bidir", tag="tB"),
+        ]
+        links1 = [
+            Link(kind="kC", direction="in", tag="tC"),
+        ]
+        nodes = [
+            Node(id="id0", text="[1, 0]", metadata={"m": 0}, links=links0),
+            Node(text="[-1, 0]", metadata={"m": 1}, links=links1),
+        ]
+        async for _ in graph_vector_store_d2.aadd_nodes(nodes):
+            pass
+
+        hits = await graph_vector_store_d2.asimilarity_search_by_vector([0.9, 0.1])
+        assert len(hits) == 2
+        assert hits[0].id == "id0"
+        assert hits[0].page_content == "[1, 0]"
+        md0 = hits[0].metadata
+        assert md0["m"] == "0.0"
+        assert any(isinstance(v, set) for k, v in md0.items() if k != "m")
+        assert hits[1].id != "id0"
+        assert hits[1].page_content == "[-1, 0]"
+        md1 = hits[1].metadata
+        assert md1["m"] == "1.0"
+        assert any(isinstance(v, set) for k, v in md1.items() if k != "m")
diff --git a/libs/community/tests/integration_tests/graph_vectorstores/test_upgrade_to_cassandra.py b/libs/community/tests/integration_tests/graph_vectorstores/test_upgrade_to_cassandra.py
new file mode 100644
index 0000000000000..6a09a2c5cb04b
--- /dev/null
+++ b/libs/community/tests/integration_tests/graph_vectorstores/test_upgrade_to_cassandra.py
@@ -0,0 +1,269 @@
+"""Test of Upgrading to Apache Cassandra graph vector store class:
+`CassandraGraphVectorStore` from an existing table used
+by the Cassandra vector store class: `Cassandra`
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from contextlib import contextmanager
+from typing import Any, Generator, Iterable, Optional, Tuple, Union
+
+import pytest
+from langchain_core.documents import Document
+from langchain_core.embeddings import Embeddings
+
+from langchain_community.graph_vectorstores import CassandraGraphVectorStore
+from langchain_community.utilities.cassandra import SetupMode
+from langchain_community.vectorstores import Cassandra
+
+TEST_KEYSPACE = "graph_test_keyspace"
+
+TABLE_NAME_ALLOW_INDEXING = "allow_graph_table"
+TABLE_NAME_DEFAULT = "default_graph_table"
+TABLE_NAME_DENY_INDEXING = "deny_graph_table"
+
+
+class ParserEmbeddings(Embeddings):
+    """Parse input texts: if they are json for a List[float], fine.
+    Otherwise, return all zeros and call it a day.
+    """
+
+    def __init__(self, dimension: int) -> None:
+        self.dimension = dimension
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        return [self.embed_query(txt) for txt in texts]
+
+    async def aembed_documents(self, texts: list[str]) -> list[list[float]]:
+        return self.embed_documents(texts)
+
+    def embed_query(self, text: str) -> list[float]:
+        try:
+            vals = json.loads(text)
+        except json.JSONDecodeError:
+            return [0.0] * self.dimension
+        else:
+            assert len(vals) == self.dimension
+            return vals
+
+    async def aembed_query(self, text: str) -> list[float]:
+        return self.embed_query(text)
+
+
+@pytest.fixture
+def embedding_d2() -> Embeddings:
+    return ParserEmbeddings(dimension=2)
+
+
+class CassandraSession:
+    table_name: str
+    session: Any
+
+    def __init__(self, table_name: str, session: Any):
+        self.table_name = table_name
+        self.session = session
+
+
+@contextmanager
+def get_cassandra_session(
+    table_name: str, drop: bool = True
+) -> Generator[CassandraSession, None, None]:
+    """Initialize the Cassandra cluster and session"""
+    from cassandra.cluster import Cluster
+
+    if "CASSANDRA_CONTACT_POINTS" in os.environ:
+        contact_points = [
+            cp.strip()
+            for cp in os.environ["CASSANDRA_CONTACT_POINTS"].split(",")
+            if cp.strip()
+        ]
+    else:
+        contact_points = None
+
+    cluster = Cluster(contact_points)
+    session = cluster.connect()
+
+    try:
+        session.execute(
+            (
+                f"CREATE KEYSPACE IF NOT EXISTS {TEST_KEYSPACE}"
+                " WITH replication = "
+                "{'class': 'SimpleStrategy', 'replication_factor': 1}"
+            )
+        )
+        if drop:
+            session.execute(f"DROP TABLE IF EXISTS {TEST_KEYSPACE}.{table_name}")
+
+        # Yield the session for usage
+        yield CassandraSession(table_name=table_name, session=session)
+    finally:
+        # Ensure proper shutdown/cleanup of resources
+        session.shutdown()
+        cluster.shutdown()
+
+
+@contextmanager
+def vector_store(
+    embedding: Embeddings,
+    table_name: str,
+    setup_mode: SetupMode,
+    metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
+    drop: bool = True,
+) -> Generator[Cassandra, None, None]:
+    with get_cassandra_session(table_name=table_name, drop=drop) as session:
+        yield Cassandra(
+            table_name=session.table_name,
+            keyspace=TEST_KEYSPACE,
+            session=session.session,
+            embedding=embedding,
+            setup_mode=setup_mode,
+            metadata_indexing=metadata_indexing,
+        )
+
+
+@contextmanager
+def graph_vector_store(
+    embedding: Embeddings,
+    table_name: str,
+    setup_mode: SetupMode,
+    metadata_deny_list: Optional[list[str]] = None,
+    drop: bool = True,
+) -> Generator[CassandraGraphVectorStore, None, None]:
+    with get_cassandra_session(table_name=table_name, drop=drop) as session:
+        yield CassandraGraphVectorStore(
+            table_name=session.table_name,
+            keyspace=TEST_KEYSPACE,
+            session=session.session,
+            embedding=embedding,
+            setup_mode=setup_mode,
+            metadata_deny_list=metadata_deny_list,
+        )
+
+
+def _vs_indexing_policy(table_name: str) -> Union[Tuple[str, Iterable[str]], str]:
+    if table_name == TABLE_NAME_ALLOW_INDEXING:
+        return ("allowlist", ["test"])
+    if table_name == TABLE_NAME_DEFAULT:
+        return "all"
+    if table_name == TABLE_NAME_DENY_INDEXING:
+        return ("denylist", ["test"])
+    msg = f"Unknown table_name: {table_name} in _vs_indexing_policy()"
+    raise ValueError(msg)
+
+
+class TestUpgradeToGraphVectorStore:
+    @pytest.mark.parametrize(
+        ("table_name", "gvs_setup_mode", "gvs_metadata_deny_list"),
+        [
+            (TABLE_NAME_DEFAULT, SetupMode.SYNC, None),
+            (TABLE_NAME_DENY_INDEXING, SetupMode.SYNC, ["test"]),
+            (TABLE_NAME_DEFAULT, SetupMode.OFF, None),
+            (TABLE_NAME_DENY_INDEXING, SetupMode.OFF, ["test"]),
+            # for this one, even though the passed policy doesn't
+            # match the policy used to create the collection,
+            # there is no error since the SetupMode is OFF and
+            # and no attempt is made to re-create the collection.
+            (TABLE_NAME_DENY_INDEXING, SetupMode.OFF, None),
+        ],
+        ids=[
+            "default_upgrade_no_policy_sync",
+            "deny_list_upgrade_same_policy_sync",
+            "default_upgrade_no_policy_off",
+            "deny_list_upgrade_same_policy_off",
+            "deny_list_upgrade_change_policy_off",
+        ],
+    )
+    def test_upgrade_to_gvs_success_sync(
+        self,
+        *,
+        embedding_d2: Embeddings,
+        gvs_setup_mode: SetupMode,
+        table_name: str,
+        gvs_metadata_deny_list: list[str],
+    ) -> None:
+        doc_id = "AL"
+        doc_al = Document(id=doc_id, page_content="[-1, 9]", metadata={"label": "AL"})
+
+        # Create vector store using SetupMode.SYNC
+        with vector_store(
+            embedding=embedding_d2,
+            table_name=table_name,
+            setup_mode=SetupMode.SYNC,
+            metadata_indexing=_vs_indexing_policy(table_name=table_name),
+            drop=True,
+        ) as v_store:
+            # load a document to the vector store
+            v_store.add_documents([doc_al])
+
+            # get the document from the vector store
+            v_doc = v_store.get_by_document_id(document_id=doc_id)
+            assert v_doc is not None
+            assert v_doc.page_content == doc_al.page_content
+
+        # Create a GRAPH Vector Store using the existing collection from above
+        # with setup_mode=gvs_setup_mode and indexing_policy=gvs_indexing_policy
+        with graph_vector_store(
+            embedding=embedding_d2,
+            table_name=table_name,
+            setup_mode=gvs_setup_mode,
+            metadata_deny_list=gvs_metadata_deny_list,
+            drop=False,
+        ) as gv_store:
+            # get the document from the GRAPH vector store
+            gv_doc = gv_store.get_by_document_id(document_id=doc_id)
+            assert gv_doc is not None
+            assert gv_doc.page_content == doc_al.page_content
+
+    @pytest.mark.parametrize(
+        ("table_name", "gvs_setup_mode", "gvs_metadata_deny_list"),
+        [
+            (TABLE_NAME_DEFAULT, SetupMode.ASYNC, None),
+            (TABLE_NAME_DENY_INDEXING, SetupMode.ASYNC, ["test"]),
+        ],
+        ids=[
+            "default_upgrade_no_policy_async",
+            "deny_list_upgrade_same_policy_async",
+        ],
+    )
+    async def test_upgrade_to_gvs_success_async(
+        self,
+        *,
+        embedding_d2: Embeddings,
+        gvs_setup_mode: SetupMode,
+        table_name: str,
+        gvs_metadata_deny_list: list[str],
+    ) -> None:
+        doc_id = "AL"
+        doc_al = Document(id=doc_id, page_content="[-1, 9]", metadata={"label": "AL"})
+
+        # Create vector store using SetupMode.ASYNC
+        with vector_store(
+            embedding=embedding_d2,
+            table_name=table_name,
+            setup_mode=SetupMode.ASYNC,
+            metadata_indexing=_vs_indexing_policy(table_name=table_name),
+            drop=True,
+        ) as v_store:
+            # load a document to the vector store
+            await v_store.aadd_documents([doc_al])
+
+            # get the document from the vector store
+            v_doc = await v_store.aget_by_document_id(document_id=doc_id)
+            assert v_doc is not None
+            assert v_doc.page_content == doc_al.page_content
+
+        # Create a GRAPH Vector Store using the existing collection from above
+        # with setup_mode=gvs_setup_mode and indexing_policy=gvs_indexing_policy
+        with graph_vector_store(
+            embedding=embedding_d2,
+            table_name=table_name,
+            setup_mode=gvs_setup_mode,
+            metadata_deny_list=gvs_metadata_deny_list,
+            drop=False,
+        ) as gv_store:
+            # get the document from the GRAPH vector store
+            gv_doc = await gv_store.aget_by_document_id(document_id=doc_id)
+            assert gv_doc is not None
+            assert gv_doc.page_content == doc_al.page_content
diff --git a/libs/community/tests/integration_tests/vectorstores/test_cassandra.py b/libs/community/tests/integration_tests/vectorstores/test_cassandra.py
index fd55bab2d3163..70158f1fc5e8f 100644
--- a/libs/community/tests/integration_tests/vectorstores/test_cassandra.py
+++ b/libs/community/tests/integration_tests/vectorstores/test_cassandra.py
@@ -1,21 +1,38 @@
 """Test Cassandra functionality."""
 
 import asyncio
+import json
+import math
 import os
 import time
-from typing import Iterable, List, Optional, Tuple, Type, Union
+from contextlib import asynccontextmanager, contextmanager
+from typing import (
+    Any,
+    AsyncGenerator,
+    Generator,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+    Union,
+)
 
 import pytest
 from langchain_core.documents import Document
 
 from langchain_community.vectorstores import Cassandra
-from langchain_community.vectorstores.cassandra import SetupMode
 from tests.integration_tests.vectorstores.fake_embeddings import (
     AngularTwoDimensionalEmbeddings,
     ConsistentFakeEmbeddings,
     Embeddings,
 )
 
+TEST_KEYSPACE = "vector_test_keyspace"
+
+# similarity threshold definitions
+EUCLIDEAN_MIN_SIM_UNIT_VECTORS = 0.2
+MATCH_EPSILON = 0.0001
+
 
 def _strip_docs(documents: List[Document]) -> List[Document]:
     return [_strip_doc(doc) for doc in documents]
@@ -28,18 +45,85 @@ def _strip_doc(document: Document) -> Document:
     )
 
 
-def _vectorstore_from_texts(
-    texts: List[str],
-    metadatas: Optional[List[dict]] = None,
-    embedding_class: Type[Embeddings] = ConsistentFakeEmbeddings,
-    drop: bool = True,
-    metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
-    table_name: str = "vector_test_table",
-) -> Cassandra:
+class ParserEmbeddings(Embeddings):
+    """Parse input texts: if they are json for a List[float], fine.
+    Otherwise, return all zeros and call it a day.
+    """
+
+    def __init__(self, dimension: int) -> None:
+        self.dimension = dimension
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        return [self.embed_query(txt) for txt in texts]
+
+    def embed_query(self, text: str) -> list[float]:
+        try:
+            vals = json.loads(text)
+        except json.JSONDecodeError:
+            return [0.0] * self.dimension
+        else:
+            assert len(vals) == self.dimension
+            return vals
+
+
+@pytest.fixture
+def embedding_d2() -> Embeddings:
+    return ParserEmbeddings(dimension=2)
+
+
+@pytest.fixture
+def metadata_documents() -> list[Document]:
+    """Documents for metadata and id tests"""
+    return [
+        Document(
+            id="q",
+            page_content="[1,2]",
+            metadata={"ord": str(ord("q")), "group": "consonant", "letter": "q"},
+        ),
+        Document(
+            id="w",
+            page_content="[3,4]",
+            metadata={"ord": str(ord("w")), "group": "consonant", "letter": "w"},
+        ),
+        Document(
+            id="r",
+            page_content="[5,6]",
+            metadata={"ord": str(ord("r")), "group": "consonant", "letter": "r"},
+        ),
+        Document(
+            id="e",
+            page_content="[-1,2]",
+            metadata={"ord": str(ord("e")), "group": "vowel", "letter": "e"},
+        ),
+        Document(
+            id="i",
+            page_content="[-3,4]",
+            metadata={"ord": str(ord("i")), "group": "vowel", "letter": "i"},
+        ),
+        Document(
+            id="o",
+            page_content="[-5,6]",
+            metadata={"ord": str(ord("o")), "group": "vowel", "letter": "o"},
+        ),
+    ]
+
+
+class CassandraSession:
+    table_name: str
+    session: Any
+
+    def __init__(self, table_name: str, session: Any):
+        self.table_name = table_name
+        self.session = session
+
+
+@contextmanager
+def get_cassandra_session(
+    table_name: str, drop: bool = True
+) -> Generator[CassandraSession, None, None]:
+    """Initialize the Cassandra cluster and session"""
     from cassandra.cluster import Cluster
 
-    keyspace = "vector_test_keyspace"
-    # get db connection
     if "CASSANDRA_CONTACT_POINTS" in os.environ:
         contact_points = [
             cp.strip()
@@ -48,107 +132,133 @@ def _vectorstore_from_texts(
         ]
     else:
         contact_points = None
+
     cluster = Cluster(contact_points)
     session = cluster.connect()
-    # ensure keyspace exists
-    session.execute(
-        (
-            f"CREATE KEYSPACE IF NOT EXISTS {keyspace} "
-            f"WITH replication = {{'class': 'SimpleStrategy', 'replication_factor': 1}}"
+
+    try:
+        session.execute(
+            (
+                f"CREATE KEYSPACE IF NOT EXISTS {TEST_KEYSPACE}"
+                " WITH replication = "
+                "{'class': 'SimpleStrategy', 'replication_factor': 1}"
+            )
         )
-    )
-    # drop table if required
-    if drop:
-        session.execute(f"DROP TABLE IF EXISTS {keyspace}.{table_name}")
-    #
-    return Cassandra.from_texts(
-        texts,
-        embedding_class(),
-        metadatas=metadatas,
-        session=session,
-        keyspace=keyspace,
-        table_name=table_name,
-        metadata_indexing=metadata_indexing,
-    )
+        if drop:
+            session.execute(f"DROP TABLE IF EXISTS {TEST_KEYSPACE}.{table_name}")
 
+        # Yield the session for usage
+        yield CassandraSession(table_name=table_name, session=session)
+    finally:
+        # Ensure proper shutdown/cleanup of resources
+        session.shutdown()
+        cluster.shutdown()
 
-async def _vectorstore_from_texts_async(
+
+@pytest.fixture
+def cassandra_session(
+    request: pytest.FixtureRequest,
+) -> Generator[CassandraSession, None, None]:
+    request_param = getattr(request, "param", {})
+    table_name = request_param.get("table_name", "vector_test_table")
+    drop = request_param.get("drop", True)
+
+    with get_cassandra_session(table_name, drop) as session:
+        yield session
+
+
+@contextmanager
+def vector_store_from_texts(
     texts: List[str],
     metadatas: Optional[List[dict]] = None,
-    embedding_class: Type[Embeddings] = ConsistentFakeEmbeddings,
+    embedding: Optional[Embeddings] = None,
     drop: bool = True,
     metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
     table_name: str = "vector_test_table",
-) -> Cassandra:
-    from cassandra.cluster import Cluster
+) -> Generator[Cassandra, None, None]:
+    if embedding is None:
+        embedding = ConsistentFakeEmbeddings()
+    with get_cassandra_session(table_name=table_name, drop=drop) as session:
+        yield Cassandra.from_texts(
+            texts,
+            embedding=embedding,
+            metadatas=metadatas,
+            session=session.session,
+            keyspace=TEST_KEYSPACE,
+            table_name=session.table_name,
+            metadata_indexing=metadata_indexing,
+        )
 
-    keyspace = "vector_test_keyspace"
-    # get db connection
-    if "CASSANDRA_CONTACT_POINTS" in os.environ:
-        contact_points = [
-            cp.strip()
-            for cp in os.environ["CASSANDRA_CONTACT_POINTS"].split(",")
-            if cp.strip()
-        ]
-    else:
-        contact_points = None
-    cluster = Cluster(contact_points)
-    session = cluster.connect()
-    # ensure keyspace exists
-    session.execute(
-        (
-            f"CREATE KEYSPACE IF NOT EXISTS {keyspace} "
-            f"WITH replication = {{'class': 'SimpleStrategy', 'replication_factor': 1}}"
+
+@asynccontextmanager
+async def vector_store_from_texts_async(
+    texts: List[str],
+    metadatas: Optional[List[dict]] = None,
+    embedding: Optional[Embeddings] = None,
+    drop: bool = True,
+    metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
+    table_name: str = "vector_test_table",
+) -> AsyncGenerator[Cassandra, None]:
+    if embedding is None:
+        embedding = ConsistentFakeEmbeddings()
+    with get_cassandra_session(table_name=table_name, drop=drop) as session:
+        yield await Cassandra.afrom_texts(
+            texts,
+            embedding=embedding,
+            metadatas=metadatas,
+            session=session.session,
+            keyspace=TEST_KEYSPACE,
+            table_name=session.table_name,
+            metadata_indexing=metadata_indexing,
+        )
+
+
+@pytest.fixture(scope="function")
+def vector_store_d2(
+    embedding_d2: Embeddings,
+    table_name: str = "vector_test_table_d2",
+) -> Generator[Cassandra, None, None]:
+    with get_cassandra_session(table_name=table_name) as session:
+        yield Cassandra(
+            embedding=embedding_d2,
+            session=session.session,
+            keyspace=TEST_KEYSPACE,
+            table_name=session.table_name,
         )
-    )
-    # drop table if required
-    if drop:
-        session.execute(f"DROP TABLE IF EXISTS {keyspace}.{table_name}")
-    #
-    return await Cassandra.afrom_texts(
-        texts,
-        embedding_class(),
-        metadatas=metadatas,
-        session=session,
-        keyspace=keyspace,
-        table_name=table_name,
-        setup_mode=SetupMode.ASYNC,
-    )
 
 
 async def test_cassandra() -> None:
     """Test end to end construction and search."""
     texts = ["foo", "bar", "baz"]
-    docsearch = _vectorstore_from_texts(texts)
-    output = docsearch.similarity_search("foo", k=1)
-    assert _strip_docs(output) == _strip_docs([Document(page_content="foo")])
-    output = await docsearch.asimilarity_search("foo", k=1)
-    assert _strip_docs(output) == _strip_docs([Document(page_content="foo")])
+    with vector_store_from_texts(texts) as vstore:
+        output = vstore.similarity_search("foo", k=1)
+        assert _strip_docs(output) == _strip_docs([Document(page_content="foo")])
+        output = await vstore.asimilarity_search("foo", k=1)
+        assert _strip_docs(output) == _strip_docs([Document(page_content="foo")])
 
 
 async def test_cassandra_with_score() -> None:
     """Test end to end construction and search with scores and IDs."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
-    docsearch = _vectorstore_from_texts(texts, metadatas=metadatas)
-
-    expected_docs = [
-        Document(page_content="foo", metadata={"page": "0.0"}),
-        Document(page_content="bar", metadata={"page": "1.0"}),
-        Document(page_content="baz", metadata={"page": "2.0"}),
-    ]
+    with vector_store_from_texts(texts, metadatas=metadatas) as vstore:
+        expected_docs = [
+            Document(page_content="foo", metadata={"page": "0.0"}),
+            Document(page_content="bar", metadata={"page": "1.0"}),
+            Document(page_content="baz", metadata={"page": "2.0"}),
+        ]
 
-    output = docsearch.similarity_search_with_score("foo", k=3)
-    docs = [o[0] for o in output]
-    scores = [o[1] for o in output]
-    assert _strip_docs(docs) == _strip_docs(expected_docs)
-    assert scores[0] > scores[1] > scores[2]
+        output = vstore.similarity_search_with_score("foo", k=3)
+        docs = [o[0] for o in output]
+        scores = [o[1] for o in output]
+        assert _strip_docs(docs) == _strip_docs(expected_docs)
+        assert scores[0] > scores[1] > scores[2]
 
-    output = await docsearch.asimilarity_search_with_score("foo", k=3)
-    docs = [o[0] for o in output]
-    scores = [o[1] for o in output]
-    assert _strip_docs(docs) == _strip_docs(expected_docs)
-    assert scores[0] > scores[1] > scores[2]
+        output = await vstore.asimilarity_search_with_score("foo", k=3)
+        docs = [o[0] for o in output]
+        scores = [o[1] for o in output]
+        assert _strip_docs(docs) == _strip_docs(expected_docs)
+        assert scores[0] > scores[1] > scores[2]
 
 
 async def test_cassandra_max_marginal_relevance_search() -> None:
@@ -169,285 +279,1265 @@ async def test_cassandra_max_marginal_relevance_search() -> None:
     """
     texts = ["-0.124", "+0.127", "+0.25", "+1.0"]
     metadatas = [{"page": i} for i in range(len(texts))]
-    docsearch = _vectorstore_from_texts(
-        texts, metadatas=metadatas, embedding_class=AngularTwoDimensionalEmbeddings
-    )
-
-    expected_set = {
-        ("+0.25", "2.0"),
-        ("-0.124", "0.0"),
-    }
-
-    output = docsearch.max_marginal_relevance_search("0.0", k=2, fetch_k=3)
-    output_set = {
-        (mmr_doc.page_content, mmr_doc.metadata["page"]) for mmr_doc in output
-    }
-    assert output_set == expected_set
-
-    output = await docsearch.amax_marginal_relevance_search("0.0", k=2, fetch_k=3)
-    output_set = {
-        (mmr_doc.page_content, mmr_doc.metadata["page"]) for mmr_doc in output
-    }
-    assert output_set == expected_set
+    with vector_store_from_texts(
+        texts,
+        metadatas=metadatas,
+        embedding=AngularTwoDimensionalEmbeddings(),
+    ) as vstore:
+        expected_set = {
+            ("+0.25", "2.0"),
+            ("-0.124", "0.0"),
+        }
+
+        output = vstore.max_marginal_relevance_search("0.0", k=2, fetch_k=3)
+        output_set = {
+            (mmr_doc.page_content, mmr_doc.metadata["page"]) for mmr_doc in output
+        }
+        assert output_set == expected_set
+
+        output = await vstore.amax_marginal_relevance_search("0.0", k=2, fetch_k=3)
+        output_set = {
+            (mmr_doc.page_content, mmr_doc.metadata["page"]) for mmr_doc in output
+        }
+        assert output_set == expected_set
 
 
 def test_cassandra_add_texts() -> None:
     """Test end to end construction with further insertions."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
-    docsearch = _vectorstore_from_texts(texts, metadatas=metadatas)
-
-    texts2 = ["foo2", "bar2", "baz2"]
-    metadatas2 = [{"page": i + 3} for i in range(len(texts))]
-    docsearch.add_texts(texts2, metadatas2)
+    with vector_store_from_texts(texts, metadatas=metadatas) as vstore:
+        texts2 = ["foo2", "bar2", "baz2"]
+        metadatas2 = [{"page": i + 3} for i in range(len(texts))]
+        vstore.add_texts(texts2, metadatas2)
 
-    output = docsearch.similarity_search("foo", k=10)
-    assert len(output) == 6
+        output = vstore.similarity_search("foo", k=10)
+        assert len(output) == 6
 
 
-async def test_cassandra_aadd_texts() -> None:
+async def test_cassandra_add_texts_async() -> None:
     """Test end to end construction with further insertions."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
-    docsearch = _vectorstore_from_texts(texts, metadatas=metadatas)
+    async with vector_store_from_texts_async(texts, metadatas=metadatas) as vstore:
+        texts2 = ["foo2", "bar2", "baz2"]
+        metadatas2 = [{"page": i + 3} for i in range(len(texts))]
+        await vstore.aadd_texts(texts2, metadatas2)
 
-    texts2 = ["foo2", "bar2", "baz2"]
-    metadatas2 = [{"page": i + 3} for i in range(len(texts))]
-    await docsearch.aadd_texts(texts2, metadatas2)
-
-    output = await docsearch.asimilarity_search("foo", k=10)
-    assert len(output) == 6
+        output = await vstore.asimilarity_search("foo", k=10)
+        assert len(output) == 6
 
 
 def test_cassandra_no_drop() -> None:
     """Test end to end construction and re-opening the same index."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
-    _vectorstore_from_texts(texts, metadatas=metadatas)
+    with vector_store_from_texts(texts, metadatas=metadatas) as vstore:
+        output = vstore.similarity_search("foo", k=10)
+        assert len(output) == 3
 
     texts2 = ["foo2", "bar2", "baz2"]
-    docsearch = _vectorstore_from_texts(texts2, metadatas=metadatas, drop=False)
-
-    output = docsearch.similarity_search("foo", k=10)
-    assert len(output) == 6
+    with vector_store_from_texts(texts2, metadatas=metadatas, drop=False) as vstore:
+        output = vstore.similarity_search("foo", k=10)
+        assert len(output) == 6
 
 
 async def test_cassandra_no_drop_async() -> None:
     """Test end to end construction and re-opening the same index."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
-    await _vectorstore_from_texts_async(texts, metadatas=metadatas)
+    async with vector_store_from_texts_async(texts, metadatas=metadatas) as vstore:
+        output = await vstore.asimilarity_search("foo", k=10)
+        assert len(output) == 3
 
     texts2 = ["foo2", "bar2", "baz2"]
-    docsearch = await _vectorstore_from_texts_async(
+    async with vector_store_from_texts_async(
         texts2, metadatas=metadatas, drop=False
-    )
-
-    output = await docsearch.asimilarity_search("foo", k=10)
-    assert len(output) == 6
+    ) as vstore:
+        output = await vstore.asimilarity_search("foo", k=10)
+        assert len(output) == 6
 
 
 def test_cassandra_delete() -> None:
     """Test delete methods from vector store."""
     texts = ["foo", "bar", "baz", "gni"]
     metadatas = [{"page": i, "mod2": i % 2} for i in range(len(texts))]
-    docsearch = _vectorstore_from_texts([], metadatas=metadatas)
+    with vector_store_from_texts([], metadatas=metadatas) as vstore:
+        ids = vstore.add_texts(texts, metadatas)
+        output = vstore.similarity_search("foo", k=10)
+        assert len(output) == 4
 
-    ids = docsearch.add_texts(texts, metadatas)
-    output = docsearch.similarity_search("foo", k=10)
-    assert len(output) == 4
+        vstore.delete_by_document_id(ids[0])
+        output = vstore.similarity_search("foo", k=10)
+        assert len(output) == 3
 
-    docsearch.delete_by_document_id(ids[0])
-    output = docsearch.similarity_search("foo", k=10)
-    assert len(output) == 3
+        vstore.delete(ids[1:3])
+        output = vstore.similarity_search("foo", k=10)
+        assert len(output) == 1
 
-    docsearch.delete(ids[1:3])
-    output = docsearch.similarity_search("foo", k=10)
-    assert len(output) == 1
+        vstore.delete(["not-existing"])
+        output = vstore.similarity_search("foo", k=10)
+        assert len(output) == 1
 
-    docsearch.delete(["not-existing"])
-    output = docsearch.similarity_search("foo", k=10)
-    assert len(output) == 1
+        vstore.clear()
+        time.sleep(0.3)
+        output = vstore.similarity_search("foo", k=10)
+        assert len(output) == 0
 
-    docsearch.clear()
-    time.sleep(0.3)
-    output = docsearch.similarity_search("foo", k=10)
-    assert len(output) == 0
+        vstore.add_texts(texts, metadatas)
+        num_deleted = vstore.delete_by_metadata_filter({"mod2": 0}, batch_size=1)
+        assert num_deleted == 2
+        output = vstore.similarity_search("foo", k=10)
+        assert len(output) == 2
+        vstore.clear()
 
-    docsearch.add_texts(texts, metadatas)
-    num_deleted = docsearch.delete_by_metadata_filter({"mod2": 0}, batch_size=1)
-    assert num_deleted == 2
-    output = docsearch.similarity_search("foo", k=10)
-    assert len(output) == 2
-    docsearch.clear()
+        with pytest.raises(ValueError):
+            vstore.delete_by_metadata_filter({})
 
-    with pytest.raises(ValueError):
-        docsearch.delete_by_metadata_filter({})
 
-
-async def test_cassandra_adelete() -> None:
+async def test_cassandra_delete_async() -> None:
     """Test delete methods from vector store."""
     texts = ["foo", "bar", "baz", "gni"]
     metadatas = [{"page": i, "mod2": i % 2} for i in range(len(texts))]
-    docsearch = await _vectorstore_from_texts_async([], metadatas=metadatas)
-
-    ids = await docsearch.aadd_texts(texts, metadatas)
-    output = await docsearch.asimilarity_search("foo", k=10)
-    assert len(output) == 4
+    async with vector_store_from_texts_async([], metadatas=metadatas) as vstore:
+        ids = await vstore.aadd_texts(texts, metadatas)
+        output = await vstore.asimilarity_search("foo", k=10)
+        assert len(output) == 4
 
-    await docsearch.adelete_by_document_id(ids[0])
-    output = await docsearch.asimilarity_search("foo", k=10)
-    assert len(output) == 3
+        await vstore.adelete_by_document_id(ids[0])
+        output = await vstore.asimilarity_search("foo", k=10)
+        assert len(output) == 3
 
-    await docsearch.adelete(ids[1:3])
-    output = await docsearch.asimilarity_search("foo", k=10)
-    assert len(output) == 1
+        await vstore.adelete(ids[1:3])
+        output = await vstore.asimilarity_search("foo", k=10)
+        assert len(output) == 1
 
-    await docsearch.adelete(["not-existing"])
-    output = await docsearch.asimilarity_search("foo", k=10)
-    assert len(output) == 1
+        await vstore.adelete(["not-existing"])
+        output = await vstore.asimilarity_search("foo", k=10)
+        assert len(output) == 1
 
-    await docsearch.aclear()
-    await asyncio.sleep(0.3)
-    output = docsearch.similarity_search("foo", k=10)
-    assert len(output) == 0
+        await vstore.aclear()
+        await asyncio.sleep(0.3)
+        output = vstore.similarity_search("foo", k=10)
+        assert len(output) == 0
 
-    await docsearch.aadd_texts(texts, metadatas)
-    num_deleted = await docsearch.adelete_by_metadata_filter({"mod2": 0}, batch_size=1)
-    assert num_deleted == 2
-    output = await docsearch.asimilarity_search("foo", k=10)
-    assert len(output) == 2
-    await docsearch.aclear()
+        await vstore.aadd_texts(texts, metadatas)
+        num_deleted = await vstore.adelete_by_metadata_filter({"mod2": 0}, batch_size=1)
+        assert num_deleted == 2
+        output = await vstore.asimilarity_search("foo", k=10)
+        assert len(output) == 2
+        await vstore.aclear()
 
-    with pytest.raises(ValueError):
-        await docsearch.adelete_by_metadata_filter({})
+        with pytest.raises(ValueError):
+            await vstore.adelete_by_metadata_filter({})
 
 
 def test_cassandra_metadata_indexing() -> None:
     """Test comparing metadata indexing policies."""
     texts = ["foo"]
     metadatas = [{"field1": "a", "field2": "b"}]
-    vstore_all = _vectorstore_from_texts(texts, metadatas=metadatas)
-    vstore_f1 = _vectorstore_from_texts(
-        texts,
-        metadatas=metadatas,
-        metadata_indexing=("allowlist", ["field1"]),
-        table_name="vector_test_table_indexing",
+    with vector_store_from_texts(texts, metadatas=metadatas) as vstore_all:
+        with vector_store_from_texts(
+            texts,
+            metadatas=metadatas,
+            metadata_indexing=("allowlist", ["field1"]),
+            table_name="vector_test_table_indexing",
+            embedding=ConsistentFakeEmbeddings(),
+        ) as vstore_f1:
+            output_all = vstore_all.similarity_search("bar", k=2)
+            output_f1 = vstore_f1.similarity_search("bar", filter={"field1": "a"}, k=2)
+            output_f1_no = vstore_f1.similarity_search(
+                "bar", filter={"field1": "Z"}, k=2
+            )
+            assert len(output_all) == 1
+            assert output_all[0].metadata == metadatas[0]
+            assert len(output_f1) == 1
+            assert output_f1[0].metadata == metadatas[0]
+            assert len(output_f1_no) == 0
+
+            with pytest.raises(ValueError):
+                # "Non-indexed metadata fields cannot be used in queries."
+                vstore_f1.similarity_search("bar", filter={"field2": "b"}, k=2)
+
+
+class TestCassandraVectorStore:
+    @pytest.mark.parametrize(
+        "page_contents",
+        [
+            [
+                "[1,2]",
+                "[3,4]",
+                "[5,6]",
+                "[7,8]",
+                "[9,10]",
+                "[11,12]",
+            ],
+        ],
     )
+    def test_cassandra_vectorstore_from_texts_sync(
+        self,
+        *,
+        cassandra_session: CassandraSession,
+        embedding_d2: Embeddings,
+        page_contents: list[str],
+    ) -> None:
+        """from_texts methods and the associated warnings."""
+        v_store = Cassandra.from_texts(
+            texts=page_contents[0:2],
+            metadatas=[{"m": 1}, {"m": 3}],
+            ids=["ft1", "ft3"],
+            table_name=cassandra_session.table_name,
+            session=cassandra_session.session,
+            keyspace=TEST_KEYSPACE,
+            embedding=embedding_d2,
+        )
+        search_results_triples_0 = v_store.similarity_search_with_score_id(
+            page_contents[1],
+            k=1,
+        )
+        assert len(search_results_triples_0) == 1
+        res_doc_0, _, res_id_0 = search_results_triples_0[0]
+        assert res_doc_0.page_content == page_contents[1]
+        assert res_doc_0.metadata == {"m": "3.0"}
+        assert res_id_0 == "ft3"
+
+        Cassandra.from_texts(
+            texts=page_contents[2:4],
+            metadatas=[{"m": 5}, {"m": 7}],
+            ids=["ft5", "ft7"],
+            table_name=cassandra_session.table_name,
+            session=cassandra_session.session,
+            keyspace=TEST_KEYSPACE,
+            embedding=embedding_d2,
+        )
 
-    output_all = vstore_all.similarity_search("bar", k=2)
-    output_f1 = vstore_f1.similarity_search("bar", filter={"field1": "a"}, k=2)
-    output_f1_no = vstore_f1.similarity_search("bar", filter={"field1": "Z"}, k=2)
-    assert len(output_all) == 1
-    assert output_all[0].metadata == metadatas[0]
-    assert len(output_f1) == 1
-    assert output_f1[0].metadata == metadatas[0]
-    assert len(output_f1_no) == 0
-
-    with pytest.raises(ValueError):
-        # "Non-indexed metadata fields cannot be used in queries."
-        vstore_f1.similarity_search("bar", filter={"field2": "b"}, k=2)
-
-
-def test_cassandra_replace_metadata() -> None:
-    """Test of replacing metadata."""
-    N_DOCS = 100
-    REPLACE_RATIO = 2  # one in ... will have replaced metadata
-    BATCH_SIZE = 3
-
-    vstore_f1 = _vectorstore_from_texts(
-        texts=[],
-        metadata_indexing=("allowlist", ["field1", "field2"]),
-        table_name="vector_test_table_indexing",
+        search_results_triples_1 = v_store.similarity_search_with_score_id(
+            page_contents[3],
+            k=1,
+        )
+        assert len(search_results_triples_1) == 1
+        res_doc_1, _, res_id_1 = search_results_triples_1[0]
+        assert res_doc_1.page_content == page_contents[3]
+        assert res_doc_1.metadata == {"m": "7.0"}
+        assert res_id_1 == "ft7"
+        v_store_2 = Cassandra.from_texts(
+            texts=page_contents[4:6],
+            metadatas=[{"m": 9}, {"m": 11}],
+            ids=["ft9", "ft11"],
+            table_name=cassandra_session.table_name,
+            session=cassandra_session.session,
+            keyspace=TEST_KEYSPACE,
+            embedding=embedding_d2,
+        )
+        search_results_triples_2 = v_store_2.similarity_search_with_score_id(
+            page_contents[5],
+            k=1,
+        )
+        assert len(search_results_triples_2) == 1
+        res_doc_2, _, res_id_2 = search_results_triples_2[0]
+        assert res_doc_2.page_content == page_contents[5]
+        assert res_doc_2.metadata == {"m": "11.0"}
+        assert res_id_2 == "ft11"
+        v_store_2.clear()
+
+    @pytest.mark.parametrize(
+        "page_contents",
+        [
+            ["[1,2]", "[3,4]"],
+        ],
     )
-    orig_documents = [
-        Document(
-            page_content=f"doc_{doc_i}",
-            id=f"doc_id_{doc_i}",
-            metadata={"field1": f"f1_{doc_i}", "otherf": "pre"},
+    def test_cassandra_vectorstore_from_documents_sync(
+        self,
+        *,
+        cassandra_session: CassandraSession,
+        embedding_d2: Embeddings,
+        page_contents: list[str],
+    ) -> None:
+        """from_documents, esp. the various handling of ID-in-doc vs external."""
+        pc1, pc2 = page_contents
+        # no IDs.
+        v_store = Cassandra.from_documents(
+            [
+                Document(page_content=pc1, metadata={"m": 1}),
+                Document(page_content=pc2, metadata={"m": 3}),
+            ],
+            table_name=cassandra_session.table_name,
+            session=cassandra_session.session,
+            keyspace=TEST_KEYSPACE,
+            embedding=embedding_d2,
         )
-        for doc_i in range(N_DOCS)
-    ]
-    vstore_f1.add_documents(orig_documents)
+        hits = v_store.similarity_search(pc2, k=1)
+        assert len(hits) == 1
+        assert hits[0].page_content == pc2
+        assert hits[0].metadata == {"m": "3.0"}
+        v_store.clear()
+
+        # IDs passed separately.
+        with pytest.warns(DeprecationWarning) as rec_warnings:
+            v_store_2 = Cassandra.from_documents(
+                [
+                    Document(page_content=pc1, metadata={"m": 1}),
+                    Document(page_content=pc2, metadata={"m": 3}),
+                ],
+                ids=["idx1", "idx3"],
+                table_name=cassandra_session.table_name,
+                session=cassandra_session.session,
+                keyspace=TEST_KEYSPACE,
+                embedding=embedding_d2,
+            )
+        f_rec_warnings = [
+            wrn for wrn in rec_warnings if issubclass(wrn.category, DeprecationWarning)
+        ]
+        assert len(f_rec_warnings) == 1
+        hits = v_store_2.similarity_search(pc2, k=1)
+        assert len(hits) == 1
+        assert hits[0].page_content == pc2
+        assert hits[0].metadata == {"m": "3.0"}
+        assert hits[0].id == "idx3"
+        v_store_2.clear()
+
+        # IDs in documents.
+        v_store_3 = Cassandra.from_documents(
+            [
+                Document(page_content=pc1, metadata={"m": 1}, id="idx1"),
+                Document(page_content=pc2, metadata={"m": 3}, id="idx3"),
+            ],
+            table_name=cassandra_session.table_name,
+            session=cassandra_session.session,
+            keyspace=TEST_KEYSPACE,
+            embedding=embedding_d2,
+        )
+        hits = v_store_3.similarity_search(pc2, k=1)
+        assert len(hits) == 1
+        assert hits[0].page_content == pc2
+        assert hits[0].metadata == {"m": "3.0"}
+        assert hits[0].id == "idx3"
+        v_store_3.clear()
+
+        # IDs both in documents and aside.
+        with pytest.warns(DeprecationWarning) as rec_warnings:
+            v_store_4 = Cassandra.from_documents(
+                [
+                    Document(page_content=pc1, metadata={"m": 1}),
+                    Document(page_content=pc2, metadata={"m": 3}, id="idy3"),
+                ],
+                ids=["idx1", "idx3"],
+                table_name=cassandra_session.table_name,
+                session=cassandra_session.session,
+                keyspace=TEST_KEYSPACE,
+                embedding=embedding_d2,
+            )
+        f_rec_warnings = [
+            wrn for wrn in rec_warnings if issubclass(wrn.category, DeprecationWarning)
+        ]
+        hits = v_store_4.similarity_search(pc2, k=1)
+        assert len(hits) == 1
+        assert hits[0].page_content == pc2
+        assert hits[0].metadata == {"m": "3.0"}
+        assert hits[0].id == "idx3"
+        v_store_4.clear()
+
+    @pytest.mark.parametrize(
+        "page_contents",
+        [
+            [
+                "[1,2]",
+                "[3,4]",
+                "[5,6]",
+                "[7,8]",
+                "[9,10]",
+                "[11,12]",
+            ],
+        ],
+    )
+    async def test_cassandra_vectorstore_from_texts_async(
+        self,
+        *,
+        cassandra_session: CassandraSession,
+        embedding_d2: Embeddings,
+        page_contents: list[str],
+    ) -> None:
+        """from_texts methods and the associated warnings, async version."""
+        v_store = await Cassandra.afrom_texts(
+            texts=page_contents[0:2],
+            metadatas=[{"m": 1}, {"m": 3}],
+            ids=["ft1", "ft3"],
+            table_name=cassandra_session.table_name,
+            session=cassandra_session.session,
+            keyspace=TEST_KEYSPACE,
+            embedding=embedding_d2,
+        )
+        search_results_triples_0 = await v_store.asimilarity_search_with_score_id(
+            page_contents[1],
+            k=1,
+        )
+        assert len(search_results_triples_0) == 1
+        res_doc_0, _, res_id_0 = search_results_triples_0[0]
+        assert res_doc_0.page_content == page_contents[1]
+        assert res_doc_0.metadata == {"m": "3.0"}
+        assert res_id_0 == "ft3"
+
+        await Cassandra.afrom_texts(
+            texts=page_contents[2:4],
+            metadatas=[{"m": 5}, {"m": 7}],
+            ids=["ft5", "ft7"],
+            table_name=cassandra_session.table_name,
+            session=cassandra_session.session,
+            keyspace=TEST_KEYSPACE,
+            embedding=embedding_d2,
+        )
+        search_results_triples_1 = await v_store.asimilarity_search_with_score_id(
+            page_contents[3],
+            k=1,
+        )
+        assert len(search_results_triples_1) == 1
+        res_doc_1, _, res_id_1 = search_results_triples_1[0]
+        assert res_doc_1.page_content == page_contents[3]
+        assert res_doc_1.metadata == {"m": "7.0"}
+        assert res_id_1 == "ft7"
+
+        v_store_2 = await Cassandra.afrom_texts(
+            texts=page_contents[4:6],
+            metadatas=[{"m": 9}, {"m": 11}],
+            ids=["ft9", "ft11"],
+            table_name=cassandra_session.table_name,
+            session=cassandra_session.session,
+            keyspace=TEST_KEYSPACE,
+            embedding=embedding_d2,
+        )
+        search_results_triples_2 = await v_store_2.asimilarity_search_with_score_id(
+            page_contents[5],
+            k=1,
+        )
+        assert len(search_results_triples_2) == 1
+        res_doc_2, _, res_id_2 = search_results_triples_2[0]
+        assert res_doc_2.page_content == page_contents[5]
+        assert res_doc_2.metadata == {"m": "11.0"}
+        assert res_id_2 == "ft11"
+        await v_store_2.aclear()
+
+    @pytest.mark.parametrize(
+        "page_contents",
+        [
+            ["[1,2]", "[3,4]"],
+        ],
+    )
+    async def test_cassandra_vectorstore_from_documents_async(
+        self,
+        *,
+        cassandra_session: CassandraSession,
+        embedding_d2: Embeddings,
+        page_contents: list[str],
+    ) -> None:
+        """
+        from_documents, esp. the various handling of ID-in-doc vs external.
+        Async version.
+        """
+        pc1, pc2 = page_contents
+
+        # no IDs.
+        v_store = await Cassandra.afrom_documents(
+            [
+                Document(page_content=pc1, metadata={"m": 1}),
+                Document(page_content=pc2, metadata={"m": 3}),
+            ],
+            table_name=cassandra_session.table_name,
+            session=cassandra_session.session,
+            keyspace=TEST_KEYSPACE,
+            embedding=embedding_d2,
+        )
+        hits = await v_store.asimilarity_search(pc2, k=1)
+        assert len(hits) == 1
+        assert hits[0].page_content == pc2
+        assert hits[0].metadata == {"m": "3.0"}
+        await v_store.aclear()
+
+        # IDs passed separately.
+        with pytest.warns(DeprecationWarning) as rec_warnings:
+            v_store_2 = await Cassandra.afrom_documents(
+                [
+                    Document(page_content=pc1, metadata={"m": 1}),
+                    Document(page_content=pc2, metadata={"m": 3}),
+                ],
+                ids=["idx1", "idx3"],
+                table_name=cassandra_session.table_name,
+                session=cassandra_session.session,
+                keyspace=TEST_KEYSPACE,
+                embedding=embedding_d2,
+            )
+        f_rec_warnings = [
+            wrn for wrn in rec_warnings if issubclass(wrn.category, DeprecationWarning)
+        ]
+        assert len(f_rec_warnings) == 1
+        hits = await v_store_2.asimilarity_search(pc2, k=1)
+        assert len(hits) == 1
+        assert hits[0].page_content == pc2
+        assert hits[0].metadata == {"m": "3.0"}
+        assert hits[0].id == "idx3"
+        await v_store_2.aclear()
+
+        # IDs in documents.
+
+        v_store_3 = await Cassandra.afrom_documents(
+            [
+                Document(page_content=pc1, metadata={"m": 1}, id="idx1"),
+                Document(page_content=pc2, metadata={"m": 3}, id="idx3"),
+            ],
+            table_name=cassandra_session.table_name,
+            session=cassandra_session.session,
+            keyspace=TEST_KEYSPACE,
+            embedding=embedding_d2,
+        )
+        hits = await v_store_3.asimilarity_search(pc2, k=1)
+        assert len(hits) == 1
+        assert hits[0].page_content == pc2
+        assert hits[0].metadata == {"m": "3.0"}
+        assert hits[0].id == "idx3"
+        await v_store_3.aclear()
+
+        # IDs both in documents and aside.
+        with pytest.warns(DeprecationWarning) as rec_warnings:
+            v_store_4 = await Cassandra.afrom_documents(
+                [
+                    Document(page_content=pc1, metadata={"m": 1}),
+                    Document(page_content=pc2, metadata={"m": 3}, id="idy3"),
+                ],
+                ids=["idx1", "idx3"],
+                table_name=cassandra_session.table_name,
+                session=cassandra_session.session,
+                keyspace=TEST_KEYSPACE,
+                embedding=embedding_d2,
+            )
+        f_rec_warnings = [
+            wrn for wrn in rec_warnings if issubclass(wrn.category, DeprecationWarning)
+        ]
+        assert len(f_rec_warnings) == 1
+        hits = await v_store_4.asimilarity_search(pc2, k=1)
+        assert len(hits) == 1
+        assert hits[0].page_content == pc2
+        assert hits[0].metadata == {"m": "3.0"}
+        assert hits[0].id == "idx3"
+        await v_store_4.aclear()
+
+    def test_cassandra_vectorstore_crud_sync(
+        self,
+        vector_store_d2: Cassandra,
+    ) -> None:
+        """Add/delete/update behaviour."""
+        vstore = vector_store_d2
+
+        res0 = vstore.similarity_search("[-1,-1]", k=2)
+        assert res0 == []
+        # write and check again
+        added_ids = vstore.add_texts(
+            texts=["[1,2]", "[3,4]", "[5,6]"],
+            metadatas=[
+                {"k": "a", "ord": 0},
+                {"k": "b", "ord": 1},
+                {"k": "c", "ord": 2},
+            ],
+            ids=["a", "b", "c"],
+        )
+        # not requiring ordered match (elsewhere it may be overwriting some)
+        assert set(added_ids) == {"a", "b", "c"}
+        res1 = vstore.similarity_search("[-1,-1]", k=5)
+        assert {doc.page_content for doc in res1} == {"[1,2]", "[3,4]", "[5,6]"}
+        res2 = vstore.similarity_search("[3,4]", k=1)
+        assert len(res2) == 1
+        assert res2[0].page_content == "[3,4]"
+        assert res2[0].metadata == {"k": "b", "ord": "1.0"}
+        assert res2[0].id == "b"
+        # partial overwrite and count total entries
+        added_ids_1 = vstore.add_texts(
+            texts=["[5,6]", "[7,8]"],
+            metadatas=[
+                {"k": "c_new", "ord": 102},
+                {"k": "d_new", "ord": 103},
+            ],
+            ids=["c", "d"],
+        )
+        # not requiring ordered match (elsewhere it may be overwriting some)
+        assert set(added_ids_1) == {"c", "d"}
+        res2 = vstore.similarity_search("[-1,-1]", k=10)
+        assert len(res2) == 4
+        # pick one that was just updated and check its metadata
+        res3 = vstore.similarity_search_with_score_id(
+            query="[5,6]", k=1, filter={"k": "c_new"}
+        )
+        doc3, _, id3 = res3[0]
+        assert doc3.page_content == "[5,6]"
+        assert doc3.metadata == {"k": "c_new", "ord": "102.0"}
+        assert id3 == "c"
+        # delete and count again
+        del1_res = vstore.delete(["b"])
+        assert del1_res is True
+        del2_res = vstore.delete(["a", "c", "Z!"])
+        assert del2_res is True  # a non-existing ID was supplied
+        assert len(vstore.similarity_search("[-1,-1]", k=10)) == 1
+        # clear store
+        vstore.clear()
+        assert vstore.similarity_search("[-1,-1]", k=2) == []
+        # add_documents with "ids" arg passthrough
+        vstore.add_documents(
+            [
+                Document(page_content="[9,10]", metadata={"k": "v", "ord": 204}),
+                Document(page_content="[11,12]", metadata={"k": "w", "ord": 205}),
+            ],
+            ids=["v", "w"],
+        )
+        assert len(vstore.similarity_search("[-1,-1]", k=10)) == 2
+        res4 = vstore.similarity_search("[11,12]", k=1, filter={"k": "w"})
+        assert res4[0].metadata["ord"] == "205.0"
+        assert res4[0].id == "w"
+        # add_texts with "ids" arg passthrough
+        vstore.add_texts(
+            texts=["[13,14]", "[15,16]"],
+            metadatas=[{"k": "r", "ord": 306}, {"k": "s", "ord": 307}],
+            ids=["r", "s"],
+        )
+        assert len(vstore.similarity_search("[-1,-1]", k=10)) == 4
+        res4 = vstore.similarity_search("[-1,-1]", k=1, filter={"k": "s"})
+        assert res4[0].metadata["ord"] == "307.0"
+        assert res4[0].id == "s"
+        # delete_by_document_id
+        vstore.delete_by_document_id("s")
+        assert len(vstore.similarity_search("[-1,-1]", k=10)) == 3
+
+    async def test_cassandra_vectorstore_crud_async(
+        self,
+        vector_store_d2: Cassandra,
+    ) -> None:
+        """Add/delete/update behaviour, async version."""
+        vstore = vector_store_d2
+
+        res0 = await vstore.asimilarity_search("[-1,-1]", k=2)
+        assert res0 == []
+        # write and check again
+        added_ids = await vstore.aadd_texts(
+            texts=["[1,2]", "[3,4]", "[5,6]"],
+            metadatas=[
+                {"k": "a", "ord": 0},
+                {"k": "b", "ord": 1},
+                {"k": "c", "ord": 2},
+            ],
+            ids=["a", "b", "c"],
+        )
+        # not requiring ordered match (elsewhere it may be overwriting some)
+        assert set(added_ids) == {"a", "b", "c"}
+        res1 = await vstore.asimilarity_search("[-1,-1]", k=5)
+        assert {doc.page_content for doc in res1} == {"[1,2]", "[3,4]", "[5,6]"}
+        res2 = await vstore.asimilarity_search("[3,4]", k=1)
+        assert len(res2) == 1
+        assert res2[0].page_content == "[3,4]"
+        assert res2[0].metadata == {"k": "b", "ord": "1.0"}
+        assert res2[0].id == "b"
+        # partial overwrite and count total entries
+        added_ids_1 = await vstore.aadd_texts(
+            texts=["[5,6]", "[7,8]"],
+            metadatas=[
+                {"k": "c_new", "ord": 102},
+                {"k": "d_new", "ord": 103},
+            ],
+            ids=["c", "d"],
+        )
+        # not requiring ordered match (elsewhere it may be overwriting some)
+        assert set(added_ids_1) == {"c", "d"}
+        res2 = await vstore.asimilarity_search("[-1,-1]", k=10)
+        assert len(res2) == 4
+        # pick one that was just updated and check its metadata
+        res3 = await vstore.asimilarity_search_with_score_id(
+            query="[5,6]", k=1, filter={"k": "c_new"}
+        )
+        doc3, _, id3 = res3[0]
+        assert doc3.page_content == "[5,6]"
+        assert doc3.metadata == {"k": "c_new", "ord": "102.0"}
+        assert id3 == "c"
+        # delete and count again
+        del1_res = await vstore.adelete(["b"])
+        assert del1_res is True
+        del2_res = await vstore.adelete(["a", "c", "Z!"])
+        assert del2_res is True  # a non-existing ID was supplied
+        assert len(await vstore.asimilarity_search("[-1,-1]", k=10)) == 1
+        # clear store
+        await vstore.aclear()
+        assert await vstore.asimilarity_search("[-1,-1]", k=2) == []
+        # add_documents with "ids" arg passthrough
+        await vstore.aadd_documents(
+            [
+                Document(page_content="[9,10]", metadata={"k": "v", "ord": 204}),
+                Document(page_content="[11,12]", metadata={"k": "w", "ord": 205}),
+            ],
+            ids=["v", "w"],
+        )
+        assert len(await vstore.asimilarity_search("[-1,-1]", k=10)) == 2
+        res4 = await vstore.asimilarity_search("[11,12]", k=1, filter={"k": "w"})
+        assert res4[0].metadata["ord"] == "205.0"
+        assert res4[0].id == "w"
+        # add_texts with "ids" arg passthrough
+        await vstore.aadd_texts(
+            texts=["[13,14]", "[15,16]"],
+            metadatas=[{"k": "r", "ord": 306}, {"k": "s", "ord": 307}],
+            ids=["r", "s"],
+        )
+        assert len(await vstore.asimilarity_search("[-1,-1]", k=10)) == 4
+        res4 = await vstore.asimilarity_search("[-1,-1]", k=1, filter={"k": "s"})
+        assert res4[0].metadata["ord"] == "307.0"
+        assert res4[0].id == "s"
+        # delete_by_document_id
+        await vstore.adelete_by_document_id("s")
+        assert len(await vstore.asimilarity_search("[-1,-1]", k=10)) == 3
+
+    def test_cassandra_vectorstore_massive_insert_replace_sync(
+        self,
+        vector_store_d2: Cassandra,
+    ) -> None:
+        """Testing the insert-many-and-replace-some patterns thoroughly."""
+        full_size = 300
+        first_group_size = 150
+        second_group_slicer = [30, 100, 2]
+
+        all_ids = [f"doc_{idx}" for idx in range(full_size)]
+        all_texts = [f"[0,{idx + 1}]" for idx in range(full_size)]
+
+        # massive insertion on empty
+        group0_ids = all_ids[0:first_group_size]
+        group0_texts = all_texts[0:first_group_size]
+        inserted_ids0 = vector_store_d2.add_texts(
+            texts=group0_texts,
+            ids=group0_ids,
+        )
+        assert set(inserted_ids0) == set(group0_ids)
+        # massive insertion with many overwrites scattered through
+        # (we change the text to later check on DB for successful update)
+        _s, _e, _st = second_group_slicer
+        group1_ids = all_ids[_s:_e:_st] + all_ids[first_group_size:full_size]
+        group1_texts = [
+            txt.upper()
+            for txt in (all_texts[_s:_e:_st] + all_texts[first_group_size:full_size])
+        ]
+        inserted_ids1 = vector_store_d2.add_texts(
+            texts=group1_texts,
+            ids=group1_ids,
+        )
+        assert set(inserted_ids1) == set(group1_ids)
+        # final read (we want the IDs to do a full check)
+        expected_text_by_id = {
+            **dict(zip(group0_ids, group0_texts)),
+            **dict(zip(group1_ids, group1_texts)),
+        }
+        full_results = vector_store_d2.similarity_search_with_score_id_by_vector(
+            embedding=[1.0, 1.0],
+            k=full_size,
+        )
+        for doc, _, doc_id in full_results:
+            assert doc.page_content == expected_text_by_id[doc_id]
+
+    async def test_cassandra_vectorstore_massive_insert_replace_async(
+        self,
+        vector_store_d2: Cassandra,
+    ) -> None:
+        """
+        Testing the insert-many-and-replace-some patterns thoroughly.
+        Async version.
+        """
+        full_size = 300
+        first_group_size = 150
+        second_group_slicer = [30, 100, 2]
+
+        all_ids = [f"doc_{idx}" for idx in range(full_size)]
+        all_texts = [f"[0,{idx + 1}]" for idx in range(full_size)]
+        all_embeddings = [[0, idx + 1] for idx in range(full_size)]
+
+        # massive insertion on empty
+        group0_ids = all_ids[0:first_group_size]
+        group0_texts = all_texts[0:first_group_size]
+
+        inserted_ids0 = await vector_store_d2.aadd_texts(
+            texts=group0_texts,
+            ids=group0_ids,
+        )
+        assert set(inserted_ids0) == set(group0_ids)
+        # massive insertion with many overwrites scattered through
+        # (we change the text to later check on DB for successful update)
+        _s, _e, _st = second_group_slicer
+        group1_ids = all_ids[_s:_e:_st] + all_ids[first_group_size:full_size]
+        group1_texts = [
+            txt.upper()
+            for txt in (all_texts[_s:_e:_st] + all_texts[first_group_size:full_size])
+        ]
+        inserted_ids1 = await vector_store_d2.aadd_texts(
+            texts=group1_texts,
+            ids=group1_ids,
+        )
+        assert set(inserted_ids1) == set(group1_ids)
+        # final read (we want the IDs to do a full check)
+        expected_text_by_id = dict(zip(all_ids, all_texts))
+        full_results = await vector_store_d2.asimilarity_search_with_score_id_by_vector(
+            embedding=[1.0, 1.0],
+            k=full_size,
+        )
+        for doc, _, doc_id in full_results:
+            assert doc.page_content == expected_text_by_id[doc_id]
+        expected_embedding_by_id = dict(zip(all_ids, all_embeddings))
+        full_results_with_embeddings = (
+            await vector_store_d2.asimilarity_search_with_embedding_id_by_vector(
+                embedding=[1.0, 1.0],
+                k=full_size,
+            )
+        )
+        for doc, embedding, doc_id in full_results_with_embeddings:
+            assert doc.page_content == expected_text_by_id[doc_id]
+            assert embedding == expected_embedding_by_id[doc_id]
+
+    def test_cassandra_vectorstore_delete_by_metadata_sync(
+        self,
+        vector_store_d2: Cassandra,
+    ) -> None:
+        """Testing delete_by_metadata_filter."""
+        full_size = 400
+        # one in ... will be deleted
+        deletee_ratio = 3
+
+        documents = [
+            Document(
+                page_content="[1,1]", metadata={"deletee": doc_i % deletee_ratio == 0}
+            )
+            for doc_i in range(full_size)
+        ]
+        num_deletees = len([doc for doc in documents if doc.metadata["deletee"]])
 
-    ids_to_replace = [
-        f"doc_id_{doc_i}" for doc_i in range(N_DOCS) if doc_i % REPLACE_RATIO == 0
-    ]
+        inserted_ids0 = vector_store_d2.add_documents(documents)
+        assert len(inserted_ids0) == len(documents)
 
-    # various kinds of replacement at play here:
-    def _make_new_md(mode: int, doc_id: str) -> dict[str, str]:
-        if mode == 0:
-            return {}
-        elif mode == 1:
-            return {"field2": f"NEW_{doc_id}"}
-        elif mode == 2:
-            return {"field2": f"NEW_{doc_id}", "ofherf2": "post"}
-        else:
-            return {"ofherf2": "post"}
-
-    ids_to_new_md = {
-        doc_id: _make_new_md(rep_i % 4, doc_id)
-        for rep_i, doc_id in enumerate(ids_to_replace)
-    }
-
-    vstore_f1.replace_metadata(ids_to_new_md, batch_size=BATCH_SIZE)
-    # thorough check
-    expected_id_to_metadata: dict[str, dict] = {
-        **{(document.id or ""): document.metadata for document in orig_documents},
-        **ids_to_new_md,
-    }
-    for hit in vstore_f1.similarity_search("doc", k=N_DOCS + 1):
-        assert hit.id is not None
-        assert hit.metadata == expected_id_to_metadata[hit.id]
-
-
-async def test_cassandra_areplace_metadata() -> None:
-    """Test of replacing metadata."""
-    N_DOCS = 100
-    REPLACE_RATIO = 2  # one in ... will have replaced metadata
-    BATCH_SIZE = 3
-
-    vstore_f1 = _vectorstore_from_texts(
-        texts=[],
-        metadata_indexing=("allowlist", ["field1", "field2"]),
-        table_name="vector_test_table_indexing",
-    )
-    orig_documents = [
-        Document(
-            page_content=f"doc_{doc_i}",
-            id=f"doc_id_{doc_i}",
-            metadata={"field1": f"f1_{doc_i}", "otherf": "pre"},
+        d_result0 = vector_store_d2.delete_by_metadata_filter({"deletee": True})
+        assert d_result0 == num_deletees
+        count_on_store0 = len(
+            vector_store_d2.similarity_search("[1,1]", k=full_size + 1)
         )
-        for doc_i in range(N_DOCS)
-    ]
-    await vstore_f1.aadd_documents(orig_documents)
+        assert count_on_store0 == full_size - num_deletees
 
-    ids_to_replace = [
-        f"doc_id_{doc_i}" for doc_i in range(N_DOCS) if doc_i % REPLACE_RATIO == 0
-    ]
+        with pytest.raises(ValueError, match="does not accept an empty"):
+            vector_store_d2.delete_by_metadata_filter({})
+        count_on_store1 = len(
+            vector_store_d2.similarity_search("[1,1]", k=full_size + 1)
+        )
+        assert count_on_store1 == full_size - num_deletees
+
+    async def test_cassandra_vectorstore_delete_by_metadata_async(
+        self,
+        vector_store_d2: Cassandra,
+    ) -> None:
+        """Testing delete_by_metadata_filter, async version."""
+        full_size = 400
+        # one in ... will be deleted
+        deletee_ratio = 3
+
+        documents = [
+            Document(
+                page_content="[1,1]", metadata={"deletee": doc_i % deletee_ratio == 0}
+            )
+            for doc_i in range(full_size)
+        ]
+        num_deletees = len([doc for doc in documents if doc.metadata["deletee"]])
 
-    # various kinds of replacement at play here:
-    def _make_new_md(mode: int, doc_id: str) -> dict[str, str]:
-        if mode == 0:
-            return {}
-        elif mode == 1:
-            return {"field2": f"NEW_{doc_id}"}
-        elif mode == 2:
-            return {"field2": f"NEW_{doc_id}", "ofherf2": "post"}
-        else:
-            return {"ofherf2": "post"}
-
-    ids_to_new_md = {
-        doc_id: _make_new_md(rep_i % 4, doc_id)
-        for rep_i, doc_id in enumerate(ids_to_replace)
-    }
-
-    await vstore_f1.areplace_metadata(ids_to_new_md, concurrency=BATCH_SIZE)
-    # thorough check
-    expected_id_to_metadata: dict[str, dict] = {
-        **{(document.id or ""): document.metadata for document in orig_documents},
-        **ids_to_new_md,
-    }
-    for hit in await vstore_f1.asimilarity_search("doc", k=N_DOCS + 1):
-        assert hit.id is not None
-        assert hit.metadata == expected_id_to_metadata[hit.id]
+        inserted_ids0 = await vector_store_d2.aadd_documents(documents)
+        assert len(inserted_ids0) == len(documents)
+
+        d_result0 = await vector_store_d2.adelete_by_metadata_filter({"deletee": True})
+        assert d_result0 == num_deletees
+        count_on_store0 = len(
+            await vector_store_d2.asimilarity_search("[1,1]", k=full_size + 1)
+        )
+        assert count_on_store0 == full_size - num_deletees
+
+        with pytest.raises(ValueError, match="does not accept an empty"):
+            await vector_store_d2.adelete_by_metadata_filter({})
+        count_on_store1 = len(
+            await vector_store_d2.asimilarity_search("[1,1]", k=full_size + 1)
+        )
+        assert count_on_store1 == full_size - num_deletees
+
+    def test_cassandra_replace_metadata(self) -> None:
+        """Test of replacing metadata."""
+        N_DOCS = 100
+        REPLACE_RATIO = 2  # one in ... will have replaced metadata
+        BATCH_SIZE = 3
+
+        with vector_store_from_texts(
+            texts=[],
+            metadata_indexing=("allowlist", ["field1", "field2"]),
+            table_name="vector_test_table_indexing",
+        ) as vstore_f1:
+            orig_documents = [
+                Document(
+                    page_content=f"doc_{doc_i}",
+                    id=f"doc_id_{doc_i}",
+                    metadata={"field1": f"f1_{doc_i}", "otherf": "pre"},
+                )
+                for doc_i in range(N_DOCS)
+            ]
+            vstore_f1.add_documents(orig_documents)
+
+            ids_to_replace = [
+                f"doc_id_{doc_i}"
+                for doc_i in range(N_DOCS)
+                if doc_i % REPLACE_RATIO == 0
+            ]
+
+            # various kinds of replacement at play here:
+            def _make_new_md(mode: int, doc_id: str) -> dict[str, str]:
+                if mode == 0:
+                    return {}
+                elif mode == 1:
+                    return {"field2": f"NEW_{doc_id}"}
+                elif mode == 2:
+                    return {"field2": f"NEW_{doc_id}", "ofherf2": "post"}
+                else:
+                    return {"ofherf2": "post"}
+
+            ids_to_new_md = {
+                doc_id: _make_new_md(rep_i % 4, doc_id)
+                for rep_i, doc_id in enumerate(ids_to_replace)
+            }
+
+            vstore_f1.replace_metadata(ids_to_new_md, batch_size=BATCH_SIZE)
+            # thorough check
+            expected_id_to_metadata: dict[str, dict] = {
+                **{
+                    (document.id or ""): document.metadata
+                    for document in orig_documents
+                },
+                **ids_to_new_md,
+            }
+            for hit in vstore_f1.similarity_search("doc", k=N_DOCS + 1):
+                assert hit.id is not None
+                assert hit.metadata == expected_id_to_metadata[hit.id]
+
+    async def test_cassandra_replace_metadata_async(self) -> None:
+        """Test of replacing metadata."""
+        N_DOCS = 100
+        REPLACE_RATIO = 2  # one in ... will have replaced metadata
+        BATCH_SIZE = 3
+
+        async with vector_store_from_texts_async(
+            texts=[],
+            metadata_indexing=("allowlist", ["field1", "field2"]),
+            table_name="vector_test_table_indexing",
+            embedding=ConsistentFakeEmbeddings(),
+        ) as vstore_f1:
+            orig_documents = [
+                Document(
+                    page_content=f"doc_{doc_i}",
+                    id=f"doc_id_{doc_i}",
+                    metadata={"field1": f"f1_{doc_i}", "otherf": "pre"},
+                )
+                for doc_i in range(N_DOCS)
+            ]
+            await vstore_f1.aadd_documents(orig_documents)
+
+            ids_to_replace = [
+                f"doc_id_{doc_i}"
+                for doc_i in range(N_DOCS)
+                if doc_i % REPLACE_RATIO == 0
+            ]
+
+            # various kinds of replacement at play here:
+            def _make_new_md(mode: int, doc_id: str) -> dict[str, str]:
+                if mode == 0:
+                    return {}
+                elif mode == 1:
+                    return {"field2": f"NEW_{doc_id}"}
+                elif mode == 2:
+                    return {"field2": f"NEW_{doc_id}", "ofherf2": "post"}
+                else:
+                    return {"ofherf2": "post"}
+
+            ids_to_new_md = {
+                doc_id: _make_new_md(rep_i % 4, doc_id)
+                for rep_i, doc_id in enumerate(ids_to_replace)
+            }
+
+            await vstore_f1.areplace_metadata(ids_to_new_md, concurrency=BATCH_SIZE)
+            # thorough check
+            expected_id_to_metadata: dict[str, dict] = {
+                **{
+                    (document.id or ""): document.metadata
+                    for document in orig_documents
+                },
+                **ids_to_new_md,
+            }
+            for hit in await vstore_f1.asimilarity_search("doc", k=N_DOCS + 1):
+                assert hit.id is not None
+                assert hit.metadata == expected_id_to_metadata[hit.id]
+
+    def test_cassandra_vectorstore_mmr_sync(
+        self,
+        vector_store_d2: Cassandra,
+    ) -> None:
+        """MMR testing. We work on the unit circle with angle multiples
+        of 2*pi/20 and prepare a store with known vectors for a controlled
+        MMR outcome.
+        """
+
+        def _v_from_i(i: int, n: int) -> str:
+            angle = 2 * math.pi * i / n
+            vector = [math.cos(angle), math.sin(angle)]
+            return json.dumps(vector)
+
+        i_vals = [0, 4, 5, 13]
+        n_val = 20
+        vector_store_d2.add_texts(
+            [_v_from_i(i, n_val) for i in i_vals], metadatas=[{"i": i} for i in i_vals]
+        )
+        res1 = vector_store_d2.max_marginal_relevance_search(
+            _v_from_i(3, n_val),
+            k=2,
+            fetch_k=3,
+        )
+        res_i_vals = {doc.metadata["i"] for doc in res1}
+        assert res_i_vals == {"0.0", "4.0"}
+
+    async def test_cassandra_vectorstore_mmr_async(
+        self,
+        vector_store_d2: Cassandra,
+    ) -> None:
+        """MMR testing. We work on the unit circle with angle multiples
+        of 2*pi/20 and prepare a store with known vectors for a controlled
+        MMR outcome.
+        Async version.
+        """
+
+        def _v_from_i(i: int, n: int) -> str:
+            angle = 2 * math.pi * i / n
+            vector = [math.cos(angle), math.sin(angle)]
+            return json.dumps(vector)
+
+        i_vals = [0, 4, 5, 13]
+        n_val = 20
+        await vector_store_d2.aadd_texts(
+            [_v_from_i(i, n_val) for i in i_vals],
+            metadatas=[{"i": i} for i in i_vals],
+        )
+        res1 = await vector_store_d2.amax_marginal_relevance_search(
+            _v_from_i(3, n_val),
+            k=2,
+            fetch_k=3,
+        )
+        res_i_vals = {doc.metadata["i"] for doc in res1}
+        assert res_i_vals == {"0.0", "4.0"}
+
+    def test_cassandra_vectorstore_metadata_filter(
+        self,
+        vector_store_d2: Cassandra,
+        metadata_documents: list[Document],
+    ) -> None:
+        """Metadata filtering."""
+        vstore = vector_store_d2
+        vstore.add_documents(metadata_documents)
+        # no filters
+        res0 = vstore.similarity_search("[-1,-1]", k=10)
+        assert {doc.metadata["letter"] for doc in res0} == set("qwreio")
+        # single filter
+        res1 = vstore.similarity_search(
+            "[-1,-1]",
+            k=10,
+            filter={"group": "vowel"},
+        )
+        assert {doc.metadata["letter"] for doc in res1} == set("eio")
+        # multiple filters
+        res2 = vstore.similarity_search(
+            "[-1,-1]",
+            k=10,
+            filter={"group": "consonant", "ord": str(ord("q"))},
+        )
+        assert {doc.metadata["letter"] for doc in res2} == set("q")
+        # excessive filters
+        res3 = vstore.similarity_search(
+            "[-1,-1]",
+            k=10,
+            filter={"group": "consonant", "ord": str(ord("q")), "case": "upper"},
+        )
+        assert res3 == []
+
+    def test_cassandra_vectorstore_metadata_search_sync(
+        self,
+        vector_store_d2: Cassandra,
+        metadata_documents: list[Document],
+    ) -> None:
+        """Metadata Search"""
+        vstore = vector_store_d2
+        vstore.add_documents(metadata_documents)
+        # no filters
+        res0 = vstore.metadata_search(filter={}, n=10)
+        assert {doc.metadata["letter"] for doc in res0} == set("qwreio")
+        # single filter
+        res1 = vstore.metadata_search(
+            n=10,
+            filter={"group": "vowel"},
+        )
+        assert {doc.metadata["letter"] for doc in res1} == set("eio")
+        # multiple filters
+        res2 = vstore.metadata_search(
+            n=10,
+            filter={"group": "consonant", "ord": str(ord("q"))},
+        )
+        assert {doc.metadata["letter"] for doc in res2} == set("q")
+        # excessive filters
+        res3 = vstore.metadata_search(
+            n=10,
+            filter={"group": "consonant", "ord": str(ord("q")), "case": "upper"},
+        )
+        assert res3 == []
+
+    async def test_cassandra_vectorstore_metadata_search_async(
+        self,
+        vector_store_d2: Cassandra,
+        metadata_documents: list[Document],
+    ) -> None:
+        """Metadata Search"""
+        vstore = vector_store_d2
+        await vstore.aadd_documents(metadata_documents)
+        # no filters
+        res0 = await vstore.ametadata_search(filter={}, n=10)
+        assert {doc.metadata["letter"] for doc in res0} == set("qwreio")
+        # single filter
+        res1 = vstore.metadata_search(
+            n=10,
+            filter={"group": "vowel"},
+        )
+        assert {doc.metadata["letter"] for doc in res1} == set("eio")
+        # multiple filters
+        res2 = await vstore.ametadata_search(
+            n=10,
+            filter={"group": "consonant", "ord": str(ord("q"))},
+        )
+        assert {doc.metadata["letter"] for doc in res2} == set("q")
+        # excessive filters
+        res3 = await vstore.ametadata_search(
+            n=10,
+            filter={"group": "consonant", "ord": str(ord("q")), "case": "upper"},
+        )
+        assert res3 == []
+
+    def test_cassandra_vectorstore_get_by_document_id_sync(
+        self,
+        vector_store_d2: Cassandra,
+        metadata_documents: list[Document],
+    ) -> None:
+        """Get by document_id"""
+        vstore = vector_store_d2
+        vstore.add_documents(metadata_documents)
+        # invalid id
+        invalid = vstore.get_by_document_id(document_id="z")
+        assert invalid is None
+        # valid id
+        valid = vstore.get_by_document_id(document_id="q")
+        assert isinstance(valid, Document)
+        assert valid.id == "q"
+        assert valid.page_content == "[1,2]"
+        assert valid.metadata["group"] == "consonant"
+        assert valid.metadata["letter"] == "q"
+
+    async def test_cassandra_vectorstore_get_by_document_id_async(
+        self,
+        vector_store_d2: Cassandra,
+        metadata_documents: list[Document],
+    ) -> None:
+        """Get by document_id"""
+        vstore = vector_store_d2
+        await vstore.aadd_documents(metadata_documents)
+        # invalid id
+        invalid = await vstore.aget_by_document_id(document_id="z")
+        assert invalid is None
+        # valid id
+        valid = await vstore.aget_by_document_id(document_id="q")
+        assert isinstance(valid, Document)
+        assert valid.id == "q"
+        assert valid.page_content == "[1,2]"
+        assert valid.metadata["group"] == "consonant"
+        assert valid.metadata["letter"] == "q"
+
+    @pytest.mark.parametrize(
+        ("texts", "query"),
+        [
+            (
+                ["[1,1]", "[-1,-1]"],
+                "[0.99999,1.00001]",
+            ),
+        ],
+    )
+    def test_cassandra_vectorstore_similarity_scale_sync(
+        self,
+        *,
+        vector_store_d2: Cassandra,
+        texts: list[str],
+        query: str,
+    ) -> None:
+        """Scale of the similarity scores."""
+        vstore = vector_store_d2
+        vstore.add_texts(
+            texts=texts,
+            ids=["near", "far"],
+        )
+        res1 = vstore.similarity_search_with_score(
+            query,
+            k=2,
+        )
+        scores = [sco for _, sco in res1]
+        sco_near, sco_far = scores
+        assert sco_far >= 0
+        assert abs(1 - sco_near) < MATCH_EPSILON
+        assert sco_far < EUCLIDEAN_MIN_SIM_UNIT_VECTORS + MATCH_EPSILON
+
+    @pytest.mark.parametrize(
+        ("texts", "query"),
+        [
+            (
+                ["[1,1]", "[-1,-1]"],
+                "[0.99999,1.00001]",
+            ),
+        ],
+    )
+    async def test_cassandra_vectorstore_similarity_scale_async(
+        self,
+        *,
+        vector_store_d2: Cassandra,
+        texts: list[str],
+        query: str,
+    ) -> None:
+        """Scale of the similarity scores, async version."""
+        vstore = vector_store_d2
+        await vstore.aadd_texts(
+            texts=texts,
+            ids=["near", "far"],
+        )
+        res1 = await vstore.asimilarity_search_with_score(
+            query,
+            k=2,
+        )
+        scores = [sco for _, sco in res1]
+        sco_near, sco_far = scores
+        assert sco_far >= 0
+        assert abs(1 - sco_near) < MATCH_EPSILON
+        assert sco_far < EUCLIDEAN_MIN_SIM_UNIT_VECTORS + MATCH_EPSILON
+
+    def test_cassandra_vectorstore_massive_delete(
+        self,
+        vector_store_d2: Cassandra,
+    ) -> None:
+        """Larger-scale bulk deletes."""
+        vstore = vector_store_d2
+        m = 150
+        texts = [f"[0,{i + 1 / 7.0}]" for i in range(2 * m)]
+        ids0 = [f"doc_{i}" for i in range(m)]
+        ids1 = [f"doc_{i + m}" for i in range(m)]
+        ids = ids0 + ids1
+        vstore.add_texts(texts=texts, ids=ids)
+        # deleting a bunch of these
+        del_res0 = vstore.delete(ids0)
+        assert del_res0 is True
+        # deleting the rest plus a fake one
+        del_res1 = vstore.delete([*ids1, "ghost!"])
+        assert del_res1 is True  # ensure no error
+        # nothing left
+        assert vstore.similarity_search("[-1,-1]", k=2 * m) == []
diff --git a/libs/community/tests/unit_tests/graph_vectorstores/test_mmr_helper.py b/libs/community/tests/unit_tests/graph_vectorstores/test_mmr_helper.py
new file mode 100644
index 0000000000000..0f5fef97ce95b
--- /dev/null
+++ b/libs/community/tests/unit_tests/graph_vectorstores/test_mmr_helper.py
@@ -0,0 +1,143 @@
+from __future__ import annotations
+
+import math
+
+from langchain_community.graph_vectorstores.mmr_helper import MmrHelper
+
+IDS = {
+    "-1",
+    "-2",
+    "-3",
+    "-4",
+    "-5",
+    "+1",
+    "+2",
+    "+3",
+    "+4",
+    "+5",
+}
+
+
+class TestMmrHelper:
+    def test_mmr_helper_functional(self) -> None:
+        helper = MmrHelper(k=3, query_embedding=[6, 5], lambda_mult=0.5)
+
+        assert len(list(helper.candidate_ids())) == 0
+
+        helper.add_candidates({"-1": [3, 5]})
+        helper.add_candidates({"-2": [3, 5]})
+        helper.add_candidates({"-3": [2, 6]})
+        helper.add_candidates({"-4": [1, 6]})
+        helper.add_candidates({"-5": [0, 6]})
+
+        assert len(list(helper.candidate_ids())) == 5
+
+        helper.add_candidates({"+1": [5, 3]})
+        helper.add_candidates({"+2": [5, 3]})
+        helper.add_candidates({"+3": [6, 2]})
+        helper.add_candidates({"+4": [6, 1]})
+        helper.add_candidates({"+5": [6, 0]})
+
+        assert len(list(helper.candidate_ids())) == 10
+
+        for idx in range(3):
+            best_id = helper.pop_best()
+            assert best_id in IDS
+            assert len(list(helper.candidate_ids())) == 9 - idx
+            assert best_id not in helper.candidate_ids()
+
+    def test_mmr_helper_max_diversity(self) -> None:
+        helper = MmrHelper(k=2, query_embedding=[6, 5], lambda_mult=0)
+        helper.add_candidates({"-1": [3, 5]})
+        helper.add_candidates({"-2": [3, 5]})
+        helper.add_candidates({"-3": [2, 6]})
+        helper.add_candidates({"-4": [1, 6]})
+        helper.add_candidates({"-5": [0, 6]})
+
+        best = {helper.pop_best(), helper.pop_best()}
+        assert best == {"-1", "-5"}
+
+    def test_mmr_helper_max_similarity(self) -> None:
+        helper = MmrHelper(k=2, query_embedding=[6, 5], lambda_mult=1)
+        helper.add_candidates({"-1": [3, 5]})
+        helper.add_candidates({"-2": [3, 5]})
+        helper.add_candidates({"-3": [2, 6]})
+        helper.add_candidates({"-4": [1, 6]})
+        helper.add_candidates({"-5": [0, 6]})
+
+        best = {helper.pop_best(), helper.pop_best()}
+        assert best == {"-1", "-2"}
+
+    def test_mmr_helper_add_candidate(self) -> None:
+        helper = MmrHelper(5, [0.0, 1.0])
+        helper.add_candidates(
+            {
+                "a": [0.0, 1.0],
+                "b": [1.0, 0.0],
+            }
+        )
+        assert helper.best_id == "a"
+
+    def test_mmr_helper_pop_best(self) -> None:
+        helper = MmrHelper(5, [0.0, 1.0])
+        helper.add_candidates(
+            {
+                "a": [0.0, 1.0],
+                "b": [1.0, 0.0],
+            }
+        )
+        assert helper.pop_best() == "a"
+        assert helper.pop_best() == "b"
+        assert helper.pop_best() is None
+
+    def angular_embedding(self, angle: float) -> list[float]:
+        return [math.cos(angle * math.pi), math.sin(angle * math.pi)]
+
+    def test_mmr_helper_added_documents(self) -> None:
+        """Test end to end construction and MMR search.
+        The embedding function used here ensures `texts` become
+        the following vectors on a circle (numbered v0 through v3):
+
+               ______ v2
+              /      \
+             /        |  v1
+        v3  |     .    | query
+             |        /  v0
+              |______/                 (N.B. very crude drawing)
+
+
+        With fetch_k==2 and k==2, when query is at 0.0, (1, ),
+        one expects that v2 and v0 are returned (in some order)
+        because v1 is "too close" to v0 (and v0 is closer than v1)).
+
+        Both v2 and v3 are discovered after v0.
+        """
+        helper = MmrHelper(5, self.angular_embedding(0.0))
+
+        # Fetching the 2 nearest neighbors to 0.0
+        helper.add_candidates(
+            {
+                "v0": self.angular_embedding(-0.124),
+                "v1": self.angular_embedding(+0.127),
+            }
+        )
+        assert helper.pop_best() == "v0"
+
+        # After v0 is selected, new nodes are discovered.
+        # v2 is closer than v3. v1 is "too similar" to "v0" so it's not included.
+        helper.add_candidates(
+            {
+                "v2": self.angular_embedding(+0.25),
+                "v3": self.angular_embedding(+1.0),
+            }
+        )
+        assert helper.pop_best() == "v2"
+
+        assert math.isclose(
+            helper.selected_similarity_scores[0], 0.9251, abs_tol=0.0001
+        )
+        assert math.isclose(
+            helper.selected_similarity_scores[1], 0.7071, abs_tol=0.0001
+        )
+        assert math.isclose(helper.selected_mmr_scores[0], 0.4625, abs_tol=0.0001)
+        assert math.isclose(helper.selected_mmr_scores[1], 0.1608, abs_tol=0.0001)

From 9a277cbe007706b48fa98787ab85a11c59ccba2e Mon Sep 17 00:00:00 2001
From: orkhank <118054233+orkhank@users.noreply.github.com>
Date: Tue, 22 Oct 2024 18:18:36 +0000
Subject: [PATCH 43/84] community: Update `file_path` type in
 `JSONLoader.__init__()` signature (#27535)

- **Description:** Change the type of the `file_path` argument from `str
| pathlib.Path` to `str | os.PathLike`, since the latter is more widely
used: https://stackoverflow.com/a/58541858

This is a very minor fix. I was just annoyed to see the red underline
displayed by Pylance in VS Code: `reportArgumentType`.

![image](https://github.com/user-attachments/assets/719a7f8e-acca-4dfa-89df-925e1d938c71)

  The changes do not affect the behavior of the code.
---
 .../langchain_community/document_loaders/json_loader.py      | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/libs/community/langchain_community/document_loaders/json_loader.py b/libs/community/langchain_community/document_loaders/json_loader.py
index 839fe482f62de..d255523e7f2b5 100644
--- a/libs/community/langchain_community/document_loaders/json_loader.py
+++ b/libs/community/langchain_community/document_loaders/json_loader.py
@@ -1,4 +1,5 @@
 import json
+from os import PathLike
 from pathlib import Path
 from typing import Any, Callable, Dict, Iterator, Optional, Union
 
@@ -82,7 +83,7 @@ class JSONLoader(BaseLoader):
 
     def __init__(
         self,
-        file_path: Union[str, Path],
+        file_path: Union[str, PathLike],
         jq_schema: str,
         content_key: Optional[str] = None,
         is_content_key_jq_parsable: Optional[bool] = False,
@@ -93,7 +94,7 @@ def __init__(
         """Initialize the JSONLoader.
 
         Args:
-            file_path (Union[str, Path]): The path to the JSON or JSON Lines file.
+            file_path (Union[str, PathLike]): The path to the JSON or JSON Lines file.
             jq_schema (str): The jq schema to use to extract the data or text from
                 the JSON.
             content_key (str): The key to use to extract the content from

From 0cae37b0a9facfc915477e2ab8d6a8e3cab2cff6 Mon Sep 17 00:00:00 2001
From: Savar Bhasin <51899822+savarbhasin@users.noreply.github.com>
Date: Wed, 23 Oct 2024 01:12:51 +0530
Subject: [PATCH 44/84] docs: fix docker command for RedisChatMessageHistory
 (#27484)

docs: "fix docker command"

- **Description**: The Redis chat message history component requires the
Redis Stack to create indexes. When using only Redis, the following
error occurs: "Unknown command 'FT.INFO', with args beginning with:
'chat_history'".
- **Twitter handle**: savar_bhasin

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/docs/integrations/memory/redis_chat_message_history.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/integrations/memory/redis_chat_message_history.ipynb b/docs/docs/integrations/memory/redis_chat_message_history.ipynb
index 3b23833b0fba2..ba76b9e456bc5 100644
--- a/docs/docs/integrations/memory/redis_chat_message_history.ipynb
+++ b/docs/docs/integrations/memory/redis_chat_message_history.ipynb
@@ -36,7 +36,7 @@
     "Make sure you have a Redis server running. You can start one using Docker with the following command:\n",
     "\n",
     "```\n",
-    "docker run -d -p 6379:6379 redis:latest\n",
+    "docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest\n",
     "```\n",
     "\n",
     "Or install and run Redis locally according to the instructions for your operating system."

From 34684423bf0896cf0f5c4e56c2c1a99b34e0c1cd Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Tue, 22 Oct 2024 14:12:38 -0700
Subject: [PATCH 45/84] docs: rm Legacy API ref link (#27559)

---
 docs/api_reference/conf.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/docs/api_reference/conf.py b/docs/api_reference/conf.py
index f8c8895a2d1de..3f742b051571a 100644
--- a/docs/api_reference/conf.py
+++ b/docs/api_reference/conf.py
@@ -222,9 +222,7 @@ def setup(app):
         },
     ],
     "icon_links_label": "Quick Links",
-    "external_links": [
-        {"name": "Legacy reference", "url": "https://api.python.langchain.com/"},
-    ],
+    "external_links": [],
 }
 
 

From ab205e738982fe9b4f756476c57ca1c1f0edd134 Mon Sep 17 00:00:00 2001
From: Fernando de Oliveira <5161098+fedeoliv@users.noreply.github.com>
Date: Tue, 22 Oct 2024 17:43:06 -0400
Subject: [PATCH 46/84] partners/openai + community: Async Azure AD token
 provider support for Azure OpenAI (#27488)

This PR introduces a new `azure_ad_async_token_provider` attribute to
the `AzureOpenAI` and `AzureChatOpenAI` classes in `partners/openai` and
`community` packages, given it's currently supported on `openai` package
as
[AsyncAzureADTokenProvider](https://github.com/openai/openai-python/blob/main/src/openai/lib/azure.py#L33)
type.

The reason for creating a new attribute is to avoid breaking changes.
Let's say you have an existing code that uses a `AzureOpenAI` or
`AzureChatOpenAI` instance to perform both sync and async operations.
The `azure_ad_token_provider` will work exactly as it is today, while
`azure_ad_async_token_provider` will override it for async requests.


If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---
 .../chat_models/azure_openai.py               | 16 +++++++++--
 .../embeddings/azure_openai.py                | 16 +++++++++--
 .../langchain_community/llms/openai.py        | 15 +++++++++-
 .../langchain_openai/chat_models/azure.py     | 28 +++++++++++++++++--
 .../langchain_openai/embeddings/azure.py      | 16 +++++++++--
 .../openai/langchain_openai/llms/azure.py     | 16 +++++++++--
 6 files changed, 96 insertions(+), 11 deletions(-)

diff --git a/libs/community/langchain_community/chat_models/azure_openai.py b/libs/community/langchain_community/chat_models/azure_openai.py
index 83bc551d86a1b..182e10f6f3b55 100644
--- a/libs/community/langchain_community/chat_models/azure_openai.py
+++ b/libs/community/langchain_community/chat_models/azure_openai.py
@@ -5,7 +5,7 @@
 import logging
 import os
 import warnings
-from typing import Any, Callable, Dict, List, Union
+from typing import Any, Awaitable, Callable, Dict, List, Union
 
 from langchain_core._api.deprecation import deprecated
 from langchain_core.outputs import ChatResult
@@ -90,7 +90,13 @@ class AzureChatOpenAI(ChatOpenAI):
     azure_ad_token_provider: Union[Callable[[], str], None] = None
     """A function that returns an Azure Active Directory token.
         
-        Will be invoked on every request.
+        Will be invoked on every sync request. For async requests,
+        will be invoked if `azure_ad_async_token_provider` is not provided.
+    """
+    azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
+    """A function that returns an Azure Active Directory token.
+
+        Will be invoked on every async request.
     """
     model_version: str = ""
     """Legacy, for openai<1.0.0 support."""
@@ -208,6 +214,12 @@ def validate_environment(cls, values: Dict) -> Dict:
                 "http_client": values["http_client"],
             }
             values["client"] = openai.AzureOpenAI(**client_params).chat.completions
+
+            azure_ad_async_token_provider = values["azure_ad_async_token_provider"]
+
+            if azure_ad_async_token_provider:
+                client_params["azure_ad_token_provider"] = azure_ad_async_token_provider
+
             values["async_client"] = openai.AsyncAzureOpenAI(
                 **client_params
             ).chat.completions
diff --git a/libs/community/langchain_community/embeddings/azure_openai.py b/libs/community/langchain_community/embeddings/azure_openai.py
index f760d796feb93..b65422320a505 100644
--- a/libs/community/langchain_community/embeddings/azure_openai.py
+++ b/libs/community/langchain_community/embeddings/azure_openai.py
@@ -4,7 +4,7 @@
 
 import os
 import warnings
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Awaitable, Callable, Dict, Optional, Union
 
 from langchain_core._api.deprecation import deprecated
 from langchain_core.utils import get_from_dict_or_env
@@ -49,7 +49,13 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):
     azure_ad_token_provider: Union[Callable[[], str], None] = None
     """A function that returns an Azure Active Directory token.
 
-        Will be invoked on every request.
+        Will be invoked on every sync request. For async requests,
+        will be invoked if `azure_ad_async_token_provider` is not provided.
+    """
+    azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
+    """A function that returns an Azure Active Directory token.
+
+        Will be invoked on every async request.
     """
     openai_api_version: Optional[str] = Field(default=None, alias="api_version")
     """Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
@@ -162,6 +168,12 @@ def post_init_validator(self) -> Self:
                 "http_client": self.http_client,
             }
             self.client = openai.AzureOpenAI(**client_params).embeddings
+
+            if self.azure_ad_async_token_provider:
+                client_params["azure_ad_token_provider"] = (
+                    self.azure_ad_async_token_provider
+                )
+
             self.async_client = openai.AsyncAzureOpenAI(**client_params).embeddings
         else:
             self.client = openai.Embedding
diff --git a/libs/community/langchain_community/llms/openai.py b/libs/community/langchain_community/llms/openai.py
index cc33d07ac4e70..8a2b5d2e0a457 100644
--- a/libs/community/langchain_community/llms/openai.py
+++ b/libs/community/langchain_community/llms/openai.py
@@ -8,6 +8,7 @@
     AbstractSet,
     Any,
     AsyncIterator,
+    Awaitable,
     Callable,
     Collection,
     Dict,
@@ -804,7 +805,13 @@ class AzureOpenAI(BaseOpenAI):
     azure_ad_token_provider: Union[Callable[[], str], None] = None
     """A function that returns an Azure Active Directory token.
 
-        Will be invoked on every request.
+        Will be invoked on every sync request. For async requests,
+        will be invoked if `azure_ad_async_token_provider` is not provided.
+    """
+    azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
+    """A function that returns an Azure Active Directory token.
+
+        Will be invoked on every async request.
     """
     openai_api_type: str = ""
     """Legacy, for openai<1.0.0 support."""
@@ -922,6 +929,12 @@ def validate_environment(cls, values: Dict) -> Dict:
                 "http_client": values["http_client"],
             }
             values["client"] = openai.AzureOpenAI(**client_params).completions
+
+            azure_ad_async_token_provider = values["azure_ad_async_token_provider"]
+
+            if azure_ad_async_token_provider:
+                client_params["azure_ad_token_provider"] = azure_ad_async_token_provider
+
             values["async_client"] = openai.AsyncAzureOpenAI(
                 **client_params
             ).completions
diff --git a/libs/partners/openai/langchain_openai/chat_models/azure.py b/libs/partners/openai/langchain_openai/chat_models/azure.py
index 0bfd220888eff..2e1e5f8abfe03 100644
--- a/libs/partners/openai/langchain_openai/chat_models/azure.py
+++ b/libs/partners/openai/langchain_openai/chat_models/azure.py
@@ -4,7 +4,18 @@
 
 import logging
 import os
-from typing import Any, Callable, Dict, List, Optional, Type, TypedDict, TypeVar, Union
+from typing import (
+    Any,
+    Awaitable,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Type,
+    TypedDict,
+    TypeVar,
+    Union,
+)
 
 import openai
 from langchain_core.language_models.chat_models import LangSmithParams
@@ -494,7 +505,14 @@ class Joke(BaseModel):
     azure_ad_token_provider: Union[Callable[[], str], None] = None
     """A function that returns an Azure Active Directory token.
         
-        Will be invoked on every request.
+        Will be invoked on every sync request. For async requests,
+        will be invoked if `azure_ad_async_token_provider` is not provided.
+    """
+
+    azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
+    """A function that returns an Azure Active Directory token.
+        
+        Will be invoked on every async request.
     """
 
     model_version: str = ""
@@ -633,6 +651,12 @@ def validate_environment(self) -> Self:
             self.client = self.root_client.chat.completions
         if not self.async_client:
             async_specific = {"http_client": self.http_async_client}
+
+            if self.azure_ad_async_token_provider:
+                client_params["azure_ad_token_provider"] = (
+                    self.azure_ad_async_token_provider
+                )
+
             self.root_async_client = openai.AsyncAzureOpenAI(
                 **client_params,
                 **async_specific,  # type: ignore[arg-type]
diff --git a/libs/partners/openai/langchain_openai/embeddings/azure.py b/libs/partners/openai/langchain_openai/embeddings/azure.py
index 06349e36a5195..0341c908fcd2f 100644
--- a/libs/partners/openai/langchain_openai/embeddings/azure.py
+++ b/libs/partners/openai/langchain_openai/embeddings/azure.py
@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import Callable, Optional, Union
+from typing import Awaitable, Callable, Optional, Union
 
 import openai
 from langchain_core.utils import from_env, secret_from_env
@@ -146,7 +146,13 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):
     azure_ad_token_provider: Union[Callable[[], str], None] = None
     """A function that returns an Azure Active Directory token.
 
-        Will be invoked on every request.
+        Will be invoked on every sync request. For async requests,
+        will be invoked if `azure_ad_async_token_provider` is not provided.
+    """
+    azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
+    """A function that returns an Azure Active Directory token.
+
+        Will be invoked on every async request.
     """
     openai_api_type: Optional[str] = Field(
         default_factory=from_env("OPENAI_API_TYPE", default="azure")
@@ -203,6 +209,12 @@ def validate_environment(self) -> Self:
             ).embeddings
         if not self.async_client:
             async_specific: dict = {"http_client": self.http_async_client}
+
+            if self.azure_ad_async_token_provider:
+                client_params["azure_ad_token_provider"] = (
+                    self.azure_ad_async_token_provider
+                )
+
             self.async_client = openai.AsyncAzureOpenAI(
                 **client_params,  # type: ignore[arg-type]
                 **async_specific,
diff --git a/libs/partners/openai/langchain_openai/llms/azure.py b/libs/partners/openai/langchain_openai/llms/azure.py
index 90c20c9d4d7d0..731b3e567f07f 100644
--- a/libs/partners/openai/langchain_openai/llms/azure.py
+++ b/libs/partners/openai/langchain_openai/llms/azure.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Callable, Dict, List, Mapping, Optional, Union
+from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional, Union
 
 import openai
 from langchain_core.language_models import LangSmithParams
@@ -73,7 +73,13 @@ class AzureOpenAI(BaseOpenAI):
     azure_ad_token_provider: Union[Callable[[], str], None] = None
     """A function that returns an Azure Active Directory token.
 
-        Will be invoked on every request.
+        Will be invoked on every sync request. For async requests,
+        will be invoked if `azure_ad_async_token_provider` is not provided.
+    """
+    azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
+    """A function that returns an Azure Active Directory token.
+
+        Will be invoked on every async request.
     """
     openai_api_type: Optional[str] = Field(
         default_factory=from_env("OPENAI_API_TYPE", default="azure")
@@ -158,6 +164,12 @@ def validate_environment(self) -> Self:
             ).completions
         if not self.async_client:
             async_specific = {"http_client": self.http_async_client}
+
+            if self.azure_ad_async_token_provider:
+                client_params["azure_ad_token_provider"] = (
+                    self.azure_ad_async_token_provider
+                )
+
             self.async_client = openai.AsyncAzureOpenAI(
                 **client_params,
                 **async_specific,  # type: ignore[arg-type]

From 031d0e4725b025c461c43ea34b43a4777f78a683 Mon Sep 17 00:00:00 2001
From: Prakul <discover.prakul@gmail.com>
Date: Tue, 22 Oct 2024 17:21:37 -0700
Subject: [PATCH 47/84] docs:update to MongoDB Docs (#27531)

**Description:** Update to MongoDB docs

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../vectorstores/mongodb_atlas.ipynb          | 66 ++++++++++++++-----
 1 file changed, 48 insertions(+), 18 deletions(-)

diff --git a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
index 004da71dac252..dd29cf56dc15e 100644
--- a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
+++ b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "This notebook covers how to MongoDB Atlas vector search in LangChain, using the `langchain-mongodb` package.\n",
     "\n",
-    ">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS, Azure, and GCP.  It supports native Vector Search and full text search (BM25) on your MongoDB document data.\n",
+    ">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS, Azure, and GCP.  It supports native Vector Search, full text search (BM25), and hybrid search on your MongoDB document data.\n",
     "\n",
     ">[MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) allows to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm (`Hierarchical Navigable Small Worlds`). It uses the [$vectorSearch MQL Stage](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). "
    ]
@@ -23,7 +23,7 @@
     "\n",
     ">*An Atlas cluster running MongoDB version 6.0.11, 7.0.2, or later (including RCs).\n",
     "\n",
-    "To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available. To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/).\n",
+    "To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of cluster on a cloud of your choice available. To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/).\n",
     "\n",
     "You'll need to install `langchain-mongodb` and `pymongo` to use this integration."
    ]
@@ -104,7 +104,7 @@
     "# | echo: false\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
-    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")"
+    "embeddings = OpenAIEmbeddings()"
    ]
   },
   {
@@ -114,7 +114,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch\n",
+    "from langchain_mongodb import MongoDBAtlasVectorSearch\n",
     "from pymongo import MongoClient\n",
     "\n",
     "# initialize MongoDB python client\n",
@@ -131,7 +131,31 @@
     "    embedding=embeddings,\n",
     "    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,\n",
     "    relevance_score_fn=\"cosine\",\n",
-    ")"
+    ")\n",
+    "\n",
+    "# Create vector search index on the collection\n",
+    "# Since we are using the default OpenAI embedding model (ada-v2) we need to specify the dimensions as 1536\n",
+    "vector_store.create_vector_search_index(dimensions=1536)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aec0bb58",
+   "metadata": {},
+   "source": [
+    "[OPTIONAL] Alternative to the `vector_store.create_vector_search_index` command above, you can also create the vector search index using the Atlas UI with the following index definition:\n",
+    "```json\n",
+    "{\n",
+    "  \"fields\":[\n",
+    "    {\n",
+    "      \"type\": \"vector\",\n",
+    "      \"path\": \"embedding\",\n",
+    "      \"numDimensions\": 1536,\n",
+    "      \"similarity\": \"cosine\"\n",
+    "    }\n",
+    "  ]\n",
+    "}\n",
+    "```"
    ]
   },
   {
@@ -367,6 +391,21 @@
    "id": "dacac7b8",
    "metadata": {},
    "source": [
+    "\n",
+    "\n",
+    "To enable pre-filtering you need to update the index definition to include a filter field. In this example, we will use the `source` field as the filter field.\n",
+    "\n",
+    "This can be done programmatically using the `MongoDBAtlasVectorSearch.create_vector_search_index` method.\n",
+    "\n",
+    "```python\n",
+    "vectorstore.create_vector_search_index(\n",
+    "  dimensions=1536,\n",
+    "  filters=[{\"type\":\"filter\", \"path\":\"source\"}],\n",
+    "  update=True\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "Alternatively, you can also update the index using the Atlas UI with the following index definition:\n",
     "```json\n",
     "{\n",
     "  \"fields\":[\n",
@@ -384,20 +423,10 @@
     "}\n",
     "```\n",
     "\n",
-    "You can also update the index programmatically using the `MongoDBAtlasVectorSearch.create_index` method.\n",
-    "\n",
-    "```python\n",
-    "vectorstore.create_index(\n",
-    "  dimensions=1536,\n",
-    "  filters=[{\"type\":\"filter\", \"path\":\"source\"}],\n",
-    "  update=True\n",
-    ")\n",
-    "```\n",
-    "\n",
     "And then you can run a query with filter as follows:\n",
     "\n",
     "```python\n",
-    "results = vector_store.similarity_search(query=\"foo\",k=1,pre_filter={\"source\": {\"$eq\": \"https://example.com\"}})\n",
+    "results = vector_store.similarity_search(query=\"foo\", k=1, pre_filter={\"source\": {\"$eq\": \"https://example.com\"}})\n",
     "for doc in results:\n",
     "    print(f\"* {doc.page_content} [{doc.metadata}]\")\n",
     "```"
@@ -410,7 +439,7 @@
    "source": [
     "#### Other search methods\n",
     "\n",
-    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html)."
+    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `MongoDBAtlasVectorStore` check out the [API reference](https://api.python.langchain.com/en/latest/vectorstores/langchain_mongodb.vectorstores.MongoDBAtlasVectorSearch.html)."
    ]
   },
   {
@@ -470,7 +499,7 @@
    "metadata": {},
    "source": [
     "# Other Notes\n",
-    ">* More documentation can be found at [LangChain-MongoDB](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/) site\n",
+    ">* More documentation can be found at [MongoDB's LangChain Docs](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/) site\n",
     ">* This feature is Generally Available and ready for production deployments.\n",
     ">* The langchain version 0.0.305 ([release notes](https://github.com/langchain-ai/langchain/releases/tag/v0.0.305)) introduces the support for $vectorSearch MQL stage, which is available with MongoDB Atlas 6.0.11 and 7.0.2. Users utilizing earlier versions of MongoDB Atlas need to pin their LangChain version to &lt;=0.0.304\n",
     "> "
@@ -509,3 +538,4 @@
  "nbformat": 4,
  "nbformat_minor": 5
 }
+

From 6d2a76ac05e1003ce4cd1406956853065db36eeb Mon Sep 17 00:00:00 2001
From: Kwan Kin Chan <55791584+kenchanLOL@users.noreply.github.com>
Date: Tue, 22 Oct 2024 20:41:47 -0500
Subject: [PATCH 48/84] langchain_huggingface: Fix multiple GPU usage bug in
 from_model_id function (#23628)

- [ ]  **Description:**
   - pass the device_map into model_kwargs
- removing the unused device_map variable in the hf_pipeline function
call
- [ ] **Issue:** issue #13128
When using the from_model_id function to load a Hugging Face model for
text generation across multiple GPUs, the model defaults to loading on
the CPU despite multiple GPUs being available using the expected format
``` python
llm = HuggingFacePipeline.from_model_id(
    model_id="model-id",
    task="text-generation",
    device_map="auto",
)
```
Currently, to enable multiple GPU , we have to pass in variable in this
format instead
``` python
llm = HuggingFacePipeline.from_model_id(
    model_id="model-id",
    task="text-generation",
    device=None,
    model_kwargs={
        "device_map": "auto",
    }
)
```
This issue arises due to improper handling of the device and device_map
parameters.

- [ ] **Explanation:**
1. In from_model_id, the model is created using model_kwargs and passed
as the model variable of the pipeline function. So at this moment, to
load the model with multiple GPUs, "device_map" needs to be set to
"auto" within model_kwargs. Otherwise, the model defaults to loading on
the CPU.
2. The device_map variable in from_model_id is not utilized correctly.
In the pipeline function's source code of tnansformer:
- The device_map variable is stored in the model_kwargs dictionary
(lines 867-878 of transformers/src/transformers/pipelines/\__init__.py).
```python
    if device_map is not None:
        ......
        model_kwargs["device_map"] = device_map
```
- The model is constructed with model_kwargs containing the device_map
value ONLY IF it is a string (lines 893-903 of
transformers/src/transformers/pipelines/\__init__.py).
```python
    if isinstance(model, str) or framework is None:
        model_classes = {"tf": targeted_task["tf"], "pt": targeted_task["pt"]}
        framework, model = infer_framework_load_model( ... , **model_kwargs, )
```
- Consequently, since a model object is already passed to the pipeline
function, the device_map variable from from_model_id is never used.

3. The device_map variable in from_model_id not only appears unused but
also causes errors. Without explicitly setting device=None, attempting
to load the model on multiple GPUs may result in the following error:
 ```
Device has 2 GPUs available. Provide device={deviceId} to
`from_model_id` to use available GPUs for execution. deviceId is -1
(default) for CPU and can be a positive integer associated with CUDA
device id.
  Traceback (most recent call last):
    File "foo.py", line 15, in <module>
      llm = HuggingFacePipeline.from_model_id(
File
"foo\site-packages\langchain_huggingface\llms\huggingface_pipeline.py",
line 217, in from_model_id
      pipeline = hf_pipeline(
File "foo\lib\site-packages\transformers\pipelines\__init__.py", line
1108, in pipeline
return pipeline_class(model=model, framework=framework, task=task,
**kwargs)
File "foo\lib\site-packages\transformers\pipelines\text_generation.py",
line 96, in __init__
      super().__init__(*args, **kwargs)
File "foo\lib\site-packages\transformers\pipelines\base.py", line 835,
in __init__
      raise ValueError(
ValueError: The model has been loaded with `accelerate` and therefore
cannot be moved to a specific device. Please discard the `device`
argument when creating your pipeline object.
```
This error occurs because, in from_model_id, the default values in from_model_id for device and device_map are -1 and None, respectively. It would passes the statement (`device_map is not None and device < 0`) and keep the device as -1 so the pipeline function later raises an error when trying to move a GPU-loaded model back to the CPU.
https://github.com/langchain-ai/langchain/blob/19eb82e68bbba12f8c7cde01aba1ee92dfae2ec2/libs/community/langchain_community/llms/huggingface_pipeline.py#L204-L213




If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: vbarda <vadym@langchain.dev>
---
 .../llms/huggingface_pipeline.py              | 19 ++++++++++++++++---
 1 file changed, 16 insertions(+), 3 deletions(-)

diff --git a/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py b/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py
index 42754d4698ad1..9c8d5f7145a21 100644
--- a/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py
+++ b/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py
@@ -74,7 +74,7 @@ def from_model_id(
         model_id: str,
         task: str,
         backend: str = "default",
-        device: Optional[int] = -1,
+        device: Optional[int] = None,
         device_map: Optional[str] = None,
         model_kwargs: Optional[dict] = None,
         pipeline_kwargs: Optional[dict] = None,
@@ -96,7 +96,21 @@ def from_model_id(
                 "Please install it with `pip install transformers`."
             )
 
-        _model_kwargs = model_kwargs or {}
+        _model_kwargs = model_kwargs.copy() if model_kwargs else {}
+        if device_map is not None:
+            if device is not None:
+                raise ValueError(
+                    "Both `device` and `device_map` are specified. "
+                    "`device` will override `device_map`. "
+                    "You will most likely encounter unexpected behavior."
+                    "Please remove `device` and keep "
+                    "`device_map`."
+                )
+
+            if "device_map" in _model_kwargs:
+                raise ValueError("`device_map` is already specified in `model_kwargs`.")
+
+            _model_kwargs["device_map"] = device_map
         tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
 
         try:
@@ -218,7 +232,6 @@ def from_model_id(
             model=model,
             tokenizer=tokenizer,
             device=device,
-            device_map=device_map,
             batch_size=batch_size,
             model_kwargs=_model_kwargs,
             **_pipeline_kwargs,

From f2dbf01d4a4aaedf8a67b8e3378304475b39c2a6 Mon Sep 17 00:00:00 2001
From: Eugene Yurtsev <eyurtsev@gmail.com>
Date: Tue, 22 Oct 2024 22:08:20 -0400
Subject: [PATCH 49/84] Docs: Re-organize conceptual docs (#27047)

Reorganization of conceptual documentation

---------

Co-authored-by: Lance Martin <122662504+rlancemartin@users.noreply.github.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
---
 docs/docs/concepts.mdx                      | 1391 -------------------
 docs/docs/concepts/agents.mdx               |   25 +
 docs/docs/concepts/architecture.mdx         |   78 ++
 docs/docs/concepts/async.mdx                |   81 ++
 docs/docs/concepts/callbacks.mdx            |   73 +
 docs/docs/concepts/chat_history.mdx         |   46 +
 docs/docs/concepts/chat_models.mdx          |  168 +++
 docs/docs/concepts/document_loaders.mdx     |   45 +
 docs/docs/concepts/embedding_models.mdx     |  130 ++
 docs/docs/concepts/evaluation.mdx           |   17 +
 docs/docs/concepts/example_selectors.mdx    |   20 +
 docs/docs/concepts/few_shot_prompting.mdx   |   85 ++
 docs/docs/concepts/index.mdx                |   89 ++
 docs/docs/concepts/key_value_stores.mdx     |   38 +
 docs/docs/concepts/lcel.mdx                 |  221 +++
 docs/docs/concepts/llms.mdx                 |    3 +
 docs/docs/concepts/messages.mdx             |  244 ++++
 docs/docs/concepts/multimodality.mdx        |   88 ++
 docs/docs/concepts/output_parsers.mdx       |   41 +
 docs/docs/concepts/prompt_templates.mdx     |   79 ++
 docs/docs/concepts/rag.mdx                  |   98 ++
 docs/docs/concepts/retrieval.mdx            |  240 ++++
 docs/docs/concepts/retrievers.mdx           |  145 ++
 docs/docs/concepts/runnables.mdx            |  352 +++++
 docs/docs/concepts/streaming.mdx            |  191 +++
 docs/docs/concepts/structured_outputs.mdx   |  148 ++
 docs/docs/concepts/text_splitters.mdx       |  135 ++
 docs/docs/concepts/tokens.mdx               |   58 +
 docs/docs/concepts/tool_calling.mdx         |  149 ++
 docs/docs/concepts/tools.mdx                |  211 +++
 docs/docs/concepts/tracing.mdx              |   10 +
 docs/docs/concepts/vectorstores.mdx         |  191 +++
 docs/docs/concepts/why_langchain.mdx        |  109 ++
 docs/docs/integrations/chat/groq.ipynb      |    2 +-
 docs/docs/integrations/chat/together.ipynb  |    2 +-
 docs/sidebars.js                            |   12 +-
 docs/static/img/agent_types.png             |  Bin 0 -> 72952 bytes
 docs/static/img/conversation_patterns.png   |  Bin 0 -> 106974 bytes
 docs/static/img/embeddings_concept.png      |  Bin 0 -> 101510 bytes
 docs/static/img/rag_concepts.png            |  Bin 0 -> 72552 bytes
 docs/static/img/retrieval_concept.png       |  Bin 0 -> 122171 bytes
 docs/static/img/retrieval_high_level.png    |  Bin 0 -> 25839 bytes
 docs/static/img/retriever_concept.png       |  Bin 0 -> 19936 bytes
 docs/static/img/retriever_full_docs.png     |  Bin 0 -> 127464 bytes
 docs/static/img/structured_output.png       |  Bin 0 -> 89292 bytes
 docs/static/img/text_splitters.png          |  Bin 0 -> 27792 bytes
 docs/static/img/tool_call_example.png       |  Bin 0 -> 86016 bytes
 docs/static/img/tool_calling_agent.png      |  Bin 0 -> 66868 bytes
 docs/static/img/tool_calling_components.png |  Bin 0 -> 179296 bytes
 docs/static/img/tool_calling_concept.png    |  Bin 0 -> 123308 bytes
 docs/static/img/vectorstores.png            |  Bin 0 -> 118309 bytes
 docs/static/img/with_structured_output.png  |  Bin 0 -> 87132 bytes
 52 files changed, 3621 insertions(+), 1394 deletions(-)
 delete mode 100644 docs/docs/concepts.mdx
 create mode 100644 docs/docs/concepts/agents.mdx
 create mode 100644 docs/docs/concepts/architecture.mdx
 create mode 100644 docs/docs/concepts/async.mdx
 create mode 100644 docs/docs/concepts/callbacks.mdx
 create mode 100644 docs/docs/concepts/chat_history.mdx
 create mode 100644 docs/docs/concepts/chat_models.mdx
 create mode 100644 docs/docs/concepts/document_loaders.mdx
 create mode 100644 docs/docs/concepts/embedding_models.mdx
 create mode 100644 docs/docs/concepts/evaluation.mdx
 create mode 100644 docs/docs/concepts/example_selectors.mdx
 create mode 100644 docs/docs/concepts/few_shot_prompting.mdx
 create mode 100644 docs/docs/concepts/index.mdx
 create mode 100644 docs/docs/concepts/key_value_stores.mdx
 create mode 100644 docs/docs/concepts/lcel.mdx
 create mode 100644 docs/docs/concepts/llms.mdx
 create mode 100644 docs/docs/concepts/messages.mdx
 create mode 100644 docs/docs/concepts/multimodality.mdx
 create mode 100644 docs/docs/concepts/output_parsers.mdx
 create mode 100644 docs/docs/concepts/prompt_templates.mdx
 create mode 100644 docs/docs/concepts/rag.mdx
 create mode 100644 docs/docs/concepts/retrieval.mdx
 create mode 100644 docs/docs/concepts/retrievers.mdx
 create mode 100644 docs/docs/concepts/runnables.mdx
 create mode 100644 docs/docs/concepts/streaming.mdx
 create mode 100644 docs/docs/concepts/structured_outputs.mdx
 create mode 100644 docs/docs/concepts/text_splitters.mdx
 create mode 100644 docs/docs/concepts/tokens.mdx
 create mode 100644 docs/docs/concepts/tool_calling.mdx
 create mode 100644 docs/docs/concepts/tools.mdx
 create mode 100644 docs/docs/concepts/tracing.mdx
 create mode 100644 docs/docs/concepts/vectorstores.mdx
 create mode 100644 docs/docs/concepts/why_langchain.mdx
 create mode 100644 docs/static/img/agent_types.png
 create mode 100644 docs/static/img/conversation_patterns.png
 create mode 100644 docs/static/img/embeddings_concept.png
 create mode 100644 docs/static/img/rag_concepts.png
 create mode 100644 docs/static/img/retrieval_concept.png
 create mode 100644 docs/static/img/retrieval_high_level.png
 create mode 100644 docs/static/img/retriever_concept.png
 create mode 100644 docs/static/img/retriever_full_docs.png
 create mode 100644 docs/static/img/structured_output.png
 create mode 100644 docs/static/img/text_splitters.png
 create mode 100644 docs/static/img/tool_call_example.png
 create mode 100644 docs/static/img/tool_calling_agent.png
 create mode 100644 docs/static/img/tool_calling_components.png
 create mode 100644 docs/static/img/tool_calling_concept.png
 create mode 100644 docs/static/img/vectorstores.png
 create mode 100644 docs/static/img/with_structured_output.png

diff --git a/docs/docs/concepts.mdx b/docs/docs/concepts.mdx
deleted file mode 100644
index 136433240b364..0000000000000
--- a/docs/docs/concepts.mdx
+++ /dev/null
@@ -1,1391 +0,0 @@
-# Conceptual guide
-
-import ThemedImage from '@theme/ThemedImage';
-import useBaseUrl from '@docusaurus/useBaseUrl';
-
-This section contains introductions to key parts of LangChain.
-
-## Architecture
-
-LangChain as a framework consists of a number of packages.
-
-### `langchain-core`
-This package contains base abstractions of different components and ways to compose them together.
-The interfaces for core components like LLMs, vector stores, retrievers and more are defined here.
-No third party integrations are defined here.
-The dependencies are kept purposefully very lightweight.
-
-### `langchain`
-
-The main `langchain` package contains chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-These are NOT third party integrations.
-All chains, agents, and retrieval strategies here are NOT specific to any one integration, but rather generic across all integrations.
-
-### `langchain-community`
-
-This package contains third party integrations that are maintained by the LangChain community.
-Key partner packages are separated out (see below).
-This contains all integrations for various components (LLMs, vector stores, retrievers).
-All dependencies in this package are optional to keep the package as lightweight as possible.
-
-### Partner packages
-
-While the long tail of integrations is in `langchain-community`, we split popular integrations into their own packages (e.g. `langchain-openai`, `langchain-anthropic`, etc).
-This was done in order to improve support for these important integrations.
-
-### [`langgraph`](https://langchain-ai.github.io/langgraph)
-
-`langgraph` is an extension of `langchain` aimed at
-building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
-
-LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows.
-
-### [`langserve`](/docs/langserve)
-
-A package to deploy LangChain chains as REST APIs. Makes it easy to get a production ready API up and running.
-
-### [LangSmith](https://docs.smith.langchain.com)
-
-A developer platform that lets you debug, test, evaluate, and monitor LLM applications.
-
-<ThemedImage
-  alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
-  sources={{
-    light: useBaseUrl('/svg/langchain_stack_062024.svg'),
-    dark: useBaseUrl('/svg/langchain_stack_062024_dark.svg'),
-  }}
-  title="LangChain Framework Overview"
-  style={{ width: "100%" }}
-/>
-
-## LangChain Expression Language (LCEL)
-<span data-heading-keywords="lcel"></span>
-
-`LangChain Expression Language`, or `LCEL`, is a declarative way to chain LangChain components.
-LCEL was designed from day 1 to **support putting prototypes in production, with no code changes**, from the simplest “prompt + LLM” chain to the most complex chains (we’ve seen folks successfully run LCEL chains with 100s of steps in production). To highlight a few of the reasons you might want to use LCEL:
-
-- **First-class streaming support:**
-When you build your chains with LCEL you get the best possible time-to-first-token (time elapsed until the first chunk of output comes out). For some chains this means eg. we stream tokens straight from an LLM to a streaming output parser, and you get back parsed, incremental chunks of output at the same rate as the LLM provider outputs the raw tokens.
-
-- **Async support:**
-Any chain built with LCEL can be called both with the synchronous API (eg. in your Jupyter notebook while prototyping) as well as with the asynchronous API (eg. in a [LangServe](/docs/langserve/) server). This enables using the same code for prototypes and in production, with great performance, and the ability to handle many concurrent requests in the same server.
-
-- **Optimized parallel execution:**
-Whenever your LCEL chains have steps that can be executed in parallel (eg if you fetch documents from multiple retrievers) we automatically do it, both in the sync and the async interfaces, for the smallest possible latency.
-
-- **Retries and fallbacks:**
-Configure retries and fallbacks for any part of your LCEL chain. This is a great way to make your chains more reliable at scale. We’re currently working on adding streaming support for retries/fallbacks, so you can get the added reliability without any latency cost.
-
-- **Access intermediate results:**
-For more complex chains it’s often very useful to access the results of intermediate steps even before the final output is produced. This can be used to let end-users know something is happening, or even just to debug your chain. You can stream intermediate results, and it’s available on every [LangServe](/docs/langserve) server.
-
-- **Input and output schemas**
-Input and output schemas give every LCEL chain Pydantic and JSONSchema schemas inferred from the structure of your chain. This can be used for validation of inputs and outputs, and is an integral part of LangServe.
-
-- [**Seamless LangSmith tracing**](https://docs.smith.langchain.com)
-As your chains get more and more complex, it becomes increasingly important to understand what exactly is happening at every step.
-With LCEL, **all** steps are automatically logged to [LangSmith](https://docs.smith.langchain.com/) for maximum observability and debuggability.
-
-LCEL aims to provide consistency around behavior and customization over legacy subclassed chains such as `LLMChain` and
-`ConversationalRetrievalChain`. Many of these legacy chains hide important details like prompts, and as a wider variety
-of viable models emerge, customization has become more and more important.
-
-If you are currently using one of these legacy chains, please see [this guide for guidance on how to migrate](/docs/versions/migrating_chains).
-
-For guides on how to do specific tasks with LCEL, check out [the relevant how-to guides](/docs/how_to/#langchain-expression-language-lcel).
-
-### Runnable interface
-<span data-heading-keywords="invoke,runnable"></span>
-
-To make it as easy as possible to create custom chains, we've implemented a ["Runnable"](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) protocol. Many LangChain components implement the `Runnable` protocol, including chat models, LLMs, output parsers, retrievers, prompt templates, and more. There are also several useful primitives for working with runnables, which you can read about below.
-
-This is a standard interface, which makes it easy to define custom chains as well as invoke them in a standard way.
-The standard interface includes:
-
-- `stream`: stream back chunks of the response
-- `invoke`: call the chain on an input
-- `batch`: call the chain on a list of inputs
-
-These also have corresponding async methods that should be used with [asyncio](https://docs.python.org/3/library/asyncio.html) `await` syntax for concurrency:
-
-- `astream`: stream back chunks of the response async
-- `ainvoke`: call the chain on an input async
-- `abatch`: call the chain on a list of inputs async
-- `astream_log`: stream back intermediate steps as they happen, in addition to the final response
-- `astream_events`: **beta** stream events as they happen in the chain (introduced in `langchain-core` 0.1.14)
-
-The **input type** and **output type** varies by component:
-
-| Component    | Input Type                                            | Output Type           |
-|--------------|-------------------------------------------------------|-----------------------|
-| Prompt       | Dictionary                                            | PromptValue           |
-| ChatModel    | Single string, list of chat messages or a PromptValue | ChatMessage           |
-| LLM          | Single string, list of chat messages or a PromptValue | String                |
-| OutputParser | The output of an LLM or ChatModel                     | Depends on the parser |
-| Retriever    | Single string                                         | List of Documents     |
-| Tool         | Single string or dictionary, depending on the tool    | Depends on the tool   |
-
-
-All runnables expose input and output **schemas** to inspect the inputs and outputs:
-- `input_schema`: an input Pydantic model auto-generated from the structure of the Runnable
-- `output_schema`: an output Pydantic model auto-generated from the structure of the Runnable
-
-## Components
-
-LangChain provides standard, extendable interfaces and external integrations for various components useful for building with LLMs.
-Some components LangChain implements, some components we rely on third-party integrations for, and others are a mix.
-
-### Chat models
-<span data-heading-keywords="chat model,chat models"></span>
-
-Language models that use a sequence of messages as inputs and return chat messages as outputs (as opposed to using plain text).
-These are traditionally newer models (older models are generally `LLMs`, see below).
-Chat models support the assignment of distinct roles to conversation messages, helping to distinguish messages from the AI, users, and instructions such as system messages.
-
-Although the underlying models are messages in, message out, the LangChain wrappers also allow these models to take a string as input. This means you can easily use chat models in place of LLMs.
-
-When a string is passed in as input, it is converted to a `HumanMessage` and then passed to the underlying model.
-
-LangChain does not host any Chat Models, rather we rely on third party integrations.
-
-We have some standardized parameters when constructing ChatModels:
-- `model`: the name of the model
-- `temperature`: the sampling temperature
-- `timeout`: request timeout
-- `max_tokens`: max tokens to generate
-- `stop`: default stop sequences
-- `max_retries`: max number of times to retry requests
-- `api_key`: API key for the model provider
-- `base_url`: endpoint to send requests to
-
-Some important things to note:
-- standard params only apply to model providers that expose parameters with the intended functionality. For example, some providers do not expose a configuration for maximum output tokens, so max_tokens can't be supported on these.
-- standard params are currently only enforced on integrations that have their own integration packages (e.g. `langchain-openai`, `langchain-anthropic`, etc.), they're not enforced on models in ``langchain-community``.
-
-ChatModels also accept other parameters that are specific to that integration. To find all the parameters supported by a ChatModel head to the API reference for that model.
-
-:::important
-Some chat models have been fine-tuned for **tool calling** and provide a dedicated API for it.
-Generally, such models are better at tool calling than non-fine-tuned models, and are recommended for use cases that require tool calling.
-Please see the [tool calling section](/docs/concepts/#functiontool-calling) for more information.
-:::
-
-For specifics on how to use chat models, see the [relevant how-to guides here](/docs/how_to/#chat-models).
-
-#### Multimodality
-
-Some chat models are multimodal, accepting images, audio and even video as inputs. These are still less common, meaning model providers haven't standardized on the "best" way to define the API. Multimodal **outputs** are even less common. As such, we've kept our multimodal abstractions fairly light weight and plan to further solidify the multimodal APIs and interaction patterns as the field matures.
-
-In LangChain, most chat models that support multimodal inputs also accept those values in OpenAI's content blocks format. So far this is restricted to image inputs. For models like Gemini which support video and other bytes input, the APIs also support the native, model-specific representations.
-
-For specifics on how to use multimodal models, see the [relevant how-to guides here](/docs/how_to/#multimodal).
-
-For a full list of LangChain model providers with multimodal models, [check out this table](/docs/integrations/chat/#advanced-features).
-
-### LLMs
-<span data-heading-keywords="llm,llms"></span>
-
-:::caution
-Pure text-in/text-out LLMs tend to be older or lower-level. Many new popular models are best used as [chat completion models](/docs/concepts/#chat-models),
-even for non-chat use cases.
-
-You are probably looking for [the section above instead](/docs/concepts/#chat-models).
-:::
-
-Language models that takes a string as input and returns a string.
-These are traditionally older models (newer models generally are [Chat Models](/docs/concepts/#chat-models), see above).
-
-Although the underlying models are string in, string out, the LangChain wrappers also allow these models to take messages as input.
-This gives them the same interface as [Chat Models](/docs/concepts/#chat-models).
-When messages are passed in as input, they will be formatted into a string under the hood before being passed to the underlying model.
-
-LangChain does not host any LLMs, rather we rely on third party integrations.
-
-For specifics on how to use LLMs, see the [how-to guides](/docs/how_to/#llms).
-
-### Messages
-
-Some language models take a list of messages as input and return a message.
-There are a few different types of messages.
-All messages have a `role`, `content`, and `response_metadata` property.
-
-The `role` describes WHO is saying the message. The standard roles are "user", "assistant", "system", and "tool".
-LangChain has different message classes for different roles.
-
-The `content` property describes the content of the message.
-This can be a few different things:
-
-- A string (most models deal with this type of content)
-- A List of dictionaries (this is used for multimodal input, where the dictionary contains information about that input type and that input location)
-
-Optionally, messages can have a `name` property which allows for differentiating between multiple speakers with the same role.
-For example, if there are two users in the chat history it can be useful to differentiate between them. Not all models support this.
-
-#### HumanMessage
-
-This represents a message with role "user".
-
-#### AIMessage
-
-This represents a message with role "assistant". In addition to the `content` property, these messages also have:
-
-**`response_metadata`**
-
-The `response_metadata` property contains additional metadata about the response. The data here is often specific to each model provider.
-This is where information like log-probs and token usage may be stored.
-
-**`tool_calls`**
-
-These represent a decision from a language model to call a tool. They are included as part of an `AIMessage` output.
-They can be accessed from there with the `.tool_calls` property.
-
-This property returns a list of `ToolCall`s. A `ToolCall` is a dictionary with the following arguments:
-
-- `name`: The name of the tool that should be called.
-- `args`: The arguments to that tool.
-- `id`: The id of that tool call.
-
-#### SystemMessage
-
-This represents a message with role "system", which tells the model how to behave. Not every model provider supports this.
-
-#### ToolMessage
-
-This represents a message with role "tool", which contains the result of calling a tool. In addition to `role` and `content`, this message has:
-
-- a `tool_call_id` field which conveys the id of the call to the tool that was called to produce this result.
-- an `artifact` field which can be used to pass along arbitrary artifacts of the tool execution which are useful to track but which should not be sent to the model.
-
-With most chat models, a `ToolMessage` can only appear in the chat history after an `AIMessage` that has a populated `tool_calls` field.
-
-#### (Legacy) FunctionMessage
-
-This is a legacy message type, corresponding to OpenAI's legacy function-calling API. `ToolMessage` should be used instead to correspond to the updated tool-calling API.
-
-This represents the result of a function call. In addition to `role` and `content`, this message has a `name` parameter which conveys the name of the function that was called to produce this result.
-
-
-### Prompt templates
-<span data-heading-keywords="prompt,prompttemplate,chatprompttemplate"></span>
-
-Prompt templates help to translate user input and parameters into instructions for a language model.
-This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output.
-
-Prompt Templates take as input a dictionary, where each key represents a variable in the prompt template to fill in.
-
-Prompt Templates output a PromptValue. This PromptValue can be passed to an LLM or a ChatModel, and can also be cast to a string or a list of messages.
-The reason this PromptValue exists is to make it easy to switch between strings and messages.
-
-There are a few different types of prompt templates:
-
-#### String PromptTemplates
-
-These prompt templates are used to format a single string, and generally are used for simpler inputs.
-For example, a common way to construct and use a PromptTemplate is as follows:
-
-```python
-from langchain_core.prompts import PromptTemplate
-
-prompt_template = PromptTemplate.from_template("Tell me a joke about {topic}")
-
-prompt_template.invoke({"topic": "cats"})
-```
-
-#### ChatPromptTemplates
-
-These prompt templates are used to format a list of messages. These "templates" consist of a list of templates themselves.
-For example, a common way to construct and use a ChatPromptTemplate is as follows:
-
-```python
-from langchain_core.prompts import ChatPromptTemplate
-
-prompt_template = ChatPromptTemplate.from_messages([
-    ("system", "You are a helpful assistant"),
-    ("user", "Tell me a joke about {topic}")
-])
-
-prompt_template.invoke({"topic": "cats"})
-```
-
-In the above example, this ChatPromptTemplate will construct two messages when called.
-The first is a system message, that has no variables to format.
-The second is a HumanMessage, and will be formatted by the `topic` variable the user passes in.
-
-#### MessagesPlaceholder
-<span data-heading-keywords="messagesplaceholder"></span>
-
-This prompt template is responsible for adding a list of messages in a particular place.
-In the above ChatPromptTemplate, we saw how we could format two messages, each one a string.
-But what if we wanted the user to pass in a list of messages that we would slot into a particular spot?
-This is how you use MessagesPlaceholder.
-
-```python
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_core.messages import HumanMessage
-
-prompt_template = ChatPromptTemplate.from_messages([
-    ("system", "You are a helpful assistant"),
-    MessagesPlaceholder("msgs")
-])
-
-prompt_template.invoke({"msgs": [HumanMessage(content="hi!")]})
-```
-
-This will produce a list of two messages, the first one being a system message, and the second one being the HumanMessage we passed in.
-If we had passed in 5 messages, then it would have produced 6 messages in total (the system message plus the 5 passed in).
-This is useful for letting a list of messages be slotted into a particular spot.
-
-An alternative way to accomplish the same thing without using the `MessagesPlaceholder` class explicitly is:
-
-```python
-prompt_template = ChatPromptTemplate.from_messages([
-    ("system", "You are a helpful assistant"),
-    ("placeholder", "{msgs}") # <-- This is the changed part
-])
-```
-
-For specifics on how to use prompt templates, see the [relevant how-to guides here](/docs/how_to/#prompt-templates).
-
-### Example selectors
-One common prompting technique for achieving better performance is to include examples as part of the prompt.
-This is known as [few-shot prompting](/docs/concepts/#few-shot-prompting).
-This gives the language model concrete examples of how it should behave.
-Sometimes these examples are hardcoded into the prompt, but for more advanced situations it may be nice to dynamically select them.
-Example Selectors are classes responsible for selecting and then formatting examples into prompts.
-
-For specifics on how to use example selectors, see the [relevant how-to guides here](/docs/how_to/#example-selectors).
-
-### Output parsers
-<span data-heading-keywords="output parser"></span>
-
-:::note
-
-The information here refers to parsers that take a text output from a model try to parse it into a more structured representation.
-More and more models are supporting function (or tool) calling, which handles this automatically.
-It is recommended to use function/tool calling rather than output parsing.
-See documentation for that [here](/docs/concepts/#function-tool-calling).
-
-:::
-
-`Output parser` is responsible for taking the output of a model and transforming it to a more suitable format for downstream tasks.
-Useful when you are using LLMs to generate structured data, or to normalize output from chat models and LLMs.
-
-LangChain has lots of different types of output parsers. This is a list of output parsers LangChain supports. The table below has various pieces of information:
-
-- **Name**: The name of the output parser
-- **Supports Streaming**: Whether the output parser supports streaming.
-- **Has Format Instructions**: Whether the output parser has format instructions. This is generally available except when (a) the desired schema is not specified in the prompt but rather in other parameters (like OpenAI function calling), or (b) when the OutputParser wraps another OutputParser.
-- **Calls LLM**: Whether this output parser itself calls an LLM. This is usually only done by output parsers that attempt to correct misformatted output.
-- **Input Type**: Expected input type. Most output parsers work on both strings and messages, but some (like OpenAI Functions) need a message with specific kwargs.
-- **Output Type**: The output type of the object returned by the parser.
-- **Description**: Our commentary on this output parser and when to use it.
-
-| Name            | Supports Streaming | Has Format Instructions       | Calls LLM | Input Type                       | Output Type          | Description                                                                                                                                                                                                                                              |
-|-----------------|--------------------|-------------------------------|-----------|----------------------------------|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [JSON](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html#langchain_core.output_parsers.json.JsonOutputParser)            | ✅                  | ✅                             |           | `str` \| `Message`               | JSON object          | Returns a JSON object as specified. You can specify a Pydantic model and it will return JSON for that model. Probably the most reliable output parser for getting structured data that does NOT use function calling.                                    |
-| [XML](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html#langchain_core.output_parsers.xml.XMLOutputParser)            | ✅                  | ✅                             |           | `str` \| `Message`                 | `dict`               | Returns a dictionary of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's).                                                                                                                            |
-| [CSV](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.list.CommaSeparatedListOutputParser.html#langchain_core.output_parsers.list.CommaSeparatedListOutputParser)           | ✅                  | ✅                             |           | `str` \| `Message`                 | `List[str]`          | Returns a list of comma separated values.                                                                                                                                                                                                                |
-| [OutputFixing](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html#langchain.output_parsers.fix.OutputFixingParser)    |                    |                               | ✅         | `str` \| `Message`                 |                      | Wraps another output parser. If that output parser errors, then this will pass the error message and the bad output to an LLM and ask it to fix the output.                                                                                              |
-| [RetryWithError](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.retry.RetryWithErrorOutputParser.html#langchain.output_parsers.retry.RetryWithErrorOutputParser)  |                    |                               | ✅         | `str` \| `Message`                 |                      | Wraps another output parser. If that output parser errors, then this will pass the original inputs, the bad output, and the error message to an LLM and ask it to fix it. Compared to OutputFixingParser, this one also sends the original instructions. |
-| [Pydantic](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html#langchain_core.output_parsers.pydantic.PydanticOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format.                                                                                                                                                                                     |
-| [YAML](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.yaml.YamlOutputParser.html#langchain.output_parsers.yaml.YamlOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format. Uses YAML to encode it.                                                                                                                                                                                    |
-| [PandasDataFrame](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser.html#langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser) |                    | ✅                             |           | `str` \| `Message`                 | `dict`               | Useful for doing operations with pandas DataFrames.                                                                                                                                                                                                      |
-| [Enum](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.enum.EnumOutputParser.html#langchain.output_parsers.enum.EnumOutputParser)            |                    | ✅                             |           | `str` \| `Message`                 | `Enum`               | Parses response into one of the provided enum values.                                                                                                                                                                                                    |
-| [Datetime](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html#langchain.output_parsers.datetime.DatetimeOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `datetime.datetime`  | Parses response into a datetime string.                                                                                                                                                                                                                  |
-| [Structured](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.structured.StructuredOutputParser.html#langchain.output_parsers.structured.StructuredOutputParser)      |                    | ✅                             |           | `str` \| `Message`                 | `Dict[str, str]`     | An output parser that returns structured information. It is less powerful than other output parsers since it only allows for fields to be strings. This can be useful when you are working with smaller LLMs.                                            |
-
-For specifics on how to use output parsers, see the [relevant how-to guides here](/docs/how_to/#output-parsers).
-
-### Chat history
-Most LLM applications have a conversational interface.
-An essential component of a conversation is being able to refer to information introduced earlier in the conversation.
-At bare minimum, a conversational system should be able to access some window of past messages directly.
-
-The concept of `ChatHistory` refers to a class in LangChain which can be used to wrap an arbitrary chain.
-This `ChatHistory` will keep track of inputs and outputs of the underlying chain, and append them as messages to a message database.
-Future interactions will then load those messages and pass them into the chain as part of the input.
-
-### Documents
-<span data-heading-keywords="document,documents"></span>
-
-A Document object in LangChain contains information about some data. It has two attributes:
-
-- `page_content: str`: The content of this document. Currently is only a string.
-- `metadata: dict`: Arbitrary metadata associated with this document. Can track the document id, file name, etc.
-
-### Document loaders
-<span data-heading-keywords="document loader,document loaders"></span>
-
-These classes load Document objects. LangChain has hundreds of integrations with various data sources to load data from: Slack, Notion, Google Drive, etc.
-
-Each DocumentLoader has its own specific parameters, but they can all be invoked in the same way with the `.load` method.
-An example use case is as follows:
-
-```python
-from langchain_community.document_loaders.csv_loader import CSVLoader
-
-loader = CSVLoader(
-    ...  # <-- Integration specific parameters here
-)
-data = loader.load()
-```
-
-For specifics on how to use document loaders, see the [relevant how-to guides here](/docs/how_to/#document-loaders).
-
-### Text splitters
-
-Once you've loaded documents, you'll often want to transform them to better suit your application. The simplest example is you may want to split a long document into smaller chunks that can fit into your model's context window. LangChain has a number of built-in document transformers that make it easy to split, combine, filter, and otherwise manipulate documents.
-
-When you want to deal with long pieces of text, it is necessary to split up that text into chunks. As simple as this sounds, there is a lot of potential complexity here. Ideally, you want to keep the semantically related pieces of text together. What "semantically related" means could depend on the type of text. This notebook showcases several ways to do that.
-
-At a high level, text splitters work as following:
-
-1. Split the text up into small, semantically meaningful chunks (often sentences).
-2. Start combining these small chunks into a larger chunk until you reach a certain size (as measured by some function).
-3. Once you reach that size, make that chunk its own piece of text and then start creating a new chunk of text with some overlap (to keep context between chunks).
-
-That means there are two different axes along which you can customize your text splitter:
-
-1. How the text is split
-2. How the chunk size is measured
-
-For specifics on how to use text splitters, see the [relevant how-to guides here](/docs/how_to/#text-splitters).
-
-### Embedding models
-<span data-heading-keywords="embedding,embeddings"></span>
-
-Embedding models create a vector representation of a piece of text. You can think of a vector as an array of numbers that captures the semantic meaning of the text.
-By representing the text in this way, you can perform mathematical operations that allow you to do things like search for other pieces of text that are most similar in meaning.
-These natural language search capabilities underpin many types of [context retrieval](/docs/concepts/#retrieval),
-where we provide an LLM with the relevant data it needs to effectively respond to a query.
-
-![](/img/embeddings.png)
-
-The `Embeddings` class is a class designed for interfacing with text embedding models. There are many different embedding model providers (OpenAI, Cohere, Hugging Face, etc) and local models, and this class is designed to provide a standard interface for all of them.
-
-The base Embeddings class in LangChain provides two methods: one for embedding documents and one for embedding a query. The former takes as input multiple texts, while the latter takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).
-
-For specifics on how to use embedding models, see the [relevant how-to guides here](/docs/how_to/#embedding-models).
-
-### Vector stores
-<span data-heading-keywords="vector,vectorstore,vectorstores,vector store,vector stores"></span>
-
-One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors,
-and then at query time to embed the unstructured query and retrieve the embedding vectors that are 'most similar' to the embedded query.
-A vector store takes care of storing embedded data and performing vector search for you.
-
-Most vector stores can also store metadata about embedded vectors and support filtering on that metadata before
-similarity search, allowing you more control over returned documents.
-
-Vector stores can be converted to the retriever interface by doing:
-
-```python
-vectorstore = MyVectorStore()
-retriever = vectorstore.as_retriever()
-```
-
-For specifics on how to use vector stores, see the [relevant how-to guides here](/docs/how_to/#vector-stores).
-
-### Retrievers
-<span data-heading-keywords="retriever,retrievers"></span>
-
-A retriever is an interface that returns documents given an unstructured query.
-It is more general than a vector store.
-A retriever does not need to be able to store documents, only to return (or retrieve) them.
-Retrievers can be created from vector stores, but are also broad enough to include [Wikipedia search](/docs/integrations/retrievers/wikipedia/) and [Amazon Kendra](/docs/integrations/retrievers/amazon_kendra_retriever/).
-
-Retrievers accept a string query as input and return a list of Document's as output.
-
-For specifics on how to use retrievers, see the [relevant how-to guides here](/docs/how_to/#retrievers).
-
-### Key-value stores
-
-For some techniques, such as [indexing and retrieval with multiple vectors per document](/docs/how_to/multi_vector/) or
-[caching embeddings](/docs/how_to/caching_embeddings/), having a form of key-value (KV) storage is helpful.
-
-LangChain includes a [`BaseStore`](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.BaseStore.html) interface,
-which allows for storage of arbitrary data. However, LangChain components that require KV-storage accept a
-more specific `BaseStore[str, bytes]` instance that stores binary data (referred to as a `ByteStore`), and internally take care of
-encoding and decoding data for their specific needs.
-
-This means that as a user, you only need to think about one type of store rather than different ones for different types of data.
-
-#### Interface
-
-All [`BaseStores`](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.BaseStore.html) support the following interface. Note that the interface allows
-for modifying **multiple** key-value pairs at once:
-
-- `mget(key: Sequence[str]) -> List[Optional[bytes]]`: get the contents of multiple keys, returning `None` if the key does not exist
-- `mset(key_value_pairs: Sequence[Tuple[str, bytes]]) -> None`: set the contents of multiple keys
-- `mdelete(key: Sequence[str]) -> None`: delete multiple keys
-- `yield_keys(prefix: Optional[str] = None) -> Iterator[str]`: yield all keys in the store, optionally filtering by a prefix
-
-For key-value store implementations, see [this section](/docs/integrations/stores/).
-
-### Tools
-<span data-heading-keywords="tool,tools"></span>
-
-Tools are utilities designed to be called by a model: their inputs are designed to be generated by models, and their outputs are designed to be passed back to models.
-Tools are needed whenever you want a model to control parts of your code or call out to external APIs.
-
-A tool consists of:
-
-1. The `name` of the tool.
-2. A `description` of what the tool does.
-3. A `JSON schema` defining the inputs to the tool.
-4. A `function` (and, optionally, an async variant of the function).
-
-When a tool is bound to a model, the name, description and JSON schema are provided as context to the model.
-Given a list of tools and a set of instructions, a model can request to call one or more tools with specific inputs.
-Typical usage may look like the following:
-
-```python
-tools = [...] # Define a list of tools
-llm_with_tools = llm.bind_tools(tools)
-ai_msg = llm_with_tools.invoke("do xyz...")
-# -> AIMessage(tool_calls=[ToolCall(...), ...], ...)
-```
-
-The `AIMessage` returned from the model MAY have `tool_calls` associated with it.
-Read [this guide](/docs/concepts/#aimessage) for more information on what the response type may look like.
-
-Once the chosen tools are invoked, the results can be passed back to the model so that it can complete whatever task
-it's performing.
-There are generally two different ways to invoke the tool and pass back the response:
-
-#### Invoke with just the arguments
-
-When you invoke a tool with just the arguments, you will get back the raw tool output (usually a string).
-This generally looks like:
-
-```python
-# You will want to previously check that the LLM returned tool calls
-tool_call = ai_msg.tool_calls[0]
-# ToolCall(args={...}, id=..., ...)
-tool_output = tool.invoke(tool_call["args"])
-tool_message = ToolMessage(
-    content=tool_output,
-    tool_call_id=tool_call["id"],
-    name=tool_call["name"]
-)
-```
-
-Note that the `content` field will generally be passed back to the model.
-If you do not want the raw tool response to be passed to the model, but you still want to keep it around,
-you can transform the tool output but also pass it as an artifact (read more about [`ToolMessage.artifact` here](/docs/concepts/#toolmessage))
-
-```python
-... # Same code as above
-response_for_llm = transform(response)
-tool_message = ToolMessage(
-    content=response_for_llm,
-    tool_call_id=tool_call["id"],
-    name=tool_call["name"],
-    artifact=tool_output
-)
-```
-
-#### Invoke with `ToolCall`
-
-The other way to invoke a tool is to call it with the full `ToolCall` that was generated by the model.
-When you do this, the tool will return a ToolMessage.
-The benefits of this are that you don't have to write the logic yourself to transform the tool output into a ToolMessage.
-This generally looks like:
-
-```python
-tool_call = ai_msg.tool_calls[0]
-# -> ToolCall(args={...}, id=..., ...)
-tool_message = tool.invoke(tool_call)
-# -> ToolMessage(
-#      content="tool result foobar...",
-#      tool_call_id=...,
-#      name="tool_name"
-#    )
-```
-
-If you are invoking the tool this way and want to include an [artifact](/docs/concepts/#toolmessage) for the ToolMessage, you will need to have the tool return two things.
-Read more about [defining tools that return artifacts here](/docs/how_to/tool_artifacts/).
-
-#### Best practices
-
-When designing tools to be used by a model, it is important to keep in mind that:
-
-- Chat models that have explicit [tool-calling APIs](/docs/concepts/#functiontool-calling) will be better at tool calling than non-fine-tuned models.
-- Models will perform better if the tools have well-chosen names, descriptions, and JSON schemas. This is another form of prompt engineering.
-- Simple, narrowly scoped tools are easier for models to use than complex tools.
-
-#### Related
-
-For specifics on how to use tools, see the [tools how-to guides](/docs/how_to/#tools).
-
-To use a pre-built tool, see the [tool integration docs](/docs/integrations/tools/).
-
-### Toolkits
-<span data-heading-keywords="toolkit,toolkits"></span>
-
-Toolkits are collections of tools that are designed to be used together for specific tasks. They have convenient loading methods.
-
-All Toolkits expose a `get_tools` method which returns a list of tools.
-You can therefore do:
-
-```python
-# Initialize a toolkit
-toolkit = ExampleTookit(...)
-
-# Get list of tools
-tools = toolkit.get_tools()
-```
-
-### Agents
-
-By themselves, language models can't take actions - they just output text.
-A big use case for LangChain is creating **agents**.
-Agents are systems that use an LLM as a reasoning engine to determine which actions to take and what the inputs to those actions should be.
-The results of those actions can then be fed back into the agent and it determine whether more actions are needed, or whether it is okay to finish.
-
-[LangGraph](https://github.com/langchain-ai/langgraph) is an extension of LangChain specifically aimed at creating highly controllable and customizable agents.
-Please check out that documentation for a more in depth overview of agent concepts.
-
-There is a legacy `agent` concept in LangChain that we are moving towards deprecating: `AgentExecutor`.
-AgentExecutor was essentially a runtime for agents.
-It was a great place to get started, however, it was not flexible enough as you started to have more customized agents.
-In order to solve that we built LangGraph to be this flexible, highly-controllable runtime.
-
-If you are still using AgentExecutor, do not fear: we still have a guide on [how to use AgentExecutor](/docs/how_to/agent_executor).
-It is recommended, however, that you start to transition to LangGraph.
-In order to assist in this, we have put together a [transition guide on how to do so](/docs/how_to/migrate_agent).
-
-#### ReAct agents
-<span data-heading-keywords="react,react agent"></span>
-
-One popular architecture for building agents is [**ReAct**](https://arxiv.org/abs/2210.03629).
-ReAct combines reasoning and acting in an iterative process - in fact the name "ReAct" stands for "Reason" and "Act".
-
-The general flow looks like this:
-
-- The model will "think" about what step to take in response to an input and any previous observations.
-- The model will then choose an action from available tools (or choose to respond to the user).
-- The model will generate arguments to that tool.
-- The agent runtime (executor) will parse out the chosen tool and call it with the generated arguments.
-- The executor will return the results of the tool call back to the model as an observation.
-- This process repeats until the agent chooses to respond.
-
-There are general prompting based implementations that do not require any model-specific features, but the most
-reliable implementations use features like [tool calling](/docs/how_to/tool_calling/) to reliably format outputs
-and reduce variance.
-
-Please see the [LangGraph documentation](https://langchain-ai.github.io/langgraph/) for more information,
-or [this how-to guide](/docs/how_to/migrate_agent/) for specific information on migrating to LangGraph.
-
-### Callbacks
-
-LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
-
-You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.
-
-#### Callback Events
-
-| Event            | Event Trigger                               | Associated Method     |
-|------------------|---------------------------------------------|-----------------------|
-| Chat model start | When a chat model starts                    | `on_chat_model_start` |
-| LLM start        | When a llm starts                           | `on_llm_start`        |
-| LLM new token    | When an llm OR chat model emits a new token | `on_llm_new_token`    |
-| LLM ends         | When an llm OR chat model ends              | `on_llm_end`          |
-| LLM errors       | When an llm OR chat model errors            | `on_llm_error`        |
-| Chain start      | When a chain starts running                 | `on_chain_start`      |
-| Chain end        | When a chain ends                           | `on_chain_end`        |
-| Chain error      | When a chain errors                         | `on_chain_error`      |
-| Tool start       | When a tool starts running                  | `on_tool_start`       |
-| Tool end         | When a tool ends                            | `on_tool_end`         |
-| Tool error       | When a tool errors                          | `on_tool_error`       |
-| Agent action     | When an agent takes an action               | `on_agent_action`     |
-| Agent finish     | When an agent ends                          | `on_agent_finish`     |
-| Retriever start  | When a retriever starts                     | `on_retriever_start`  |
-| Retriever end    | When a retriever ends                       | `on_retriever_end`    |
-| Retriever error  | When a retriever errors                     | `on_retriever_error`  |
-| Text             | When arbitrary text is run                  | `on_text`             |
-| Retry            | When a retry event is run                   | `on_retry`            |
-
-#### Callback handlers
-
-Callback handlers can either be `sync` or `async`:
-
-* Sync callback handlers implement the [BaseCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html) interface.
-* Async callback handlers implement the [AsyncCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) interface.
-
-During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html)) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.
-
-#### Passing callbacks
-
-The `callbacks` property is available on most objects throughout the API (Models, Tools, Agents, etc.) in two different places:
-
-- **Request time callbacks**: Passed at the time of the request in addition to the input data.
-    Available on all standard `Runnable` objects. These callbacks are INHERITED by all children
-    of the object they are defined on. For example, `chain.invoke({"number": 25}, {"callbacks": [handler]})`.
-- **Constructor callbacks**: `chain = TheNameOfSomeChain(callbacks=[handler])`. These callbacks
-    are passed as arguments to the constructor of the object. The callbacks are scoped
-    only to the object they are defined on, and are **not** inherited by any children of the object.
-
-:::warning
-Constructor callbacks are scoped only to the object they are defined on. They are **not** inherited by children
-of the object.
-:::
-
-If you're creating a custom chain or runnable, you need to remember to propagate request time
-callbacks to any child objects.
-
-:::important Async in Python&lt;=3.10
-
-Any `RunnableLambda`, a `RunnableGenerator`, or `Tool` that invokes other runnables
-and is running `async` in python&lt;=3.10, will have to propagate callbacks to child
-objects manually. This is because LangChain cannot automatically propagate
-callbacks to child objects in this case.
-
-This is a common reason why you may fail to see events being emitted from custom
-runnables or tools.
-:::
-
-For specifics on how to use callbacks, see the [relevant how-to guides here](/docs/how_to/#callbacks).
-
-## Techniques
-
-### Streaming
-<span data-heading-keywords="stream,streaming"></span>
-
-Individual LLM calls often run for much longer than traditional resource requests.
-This compounds when you build more complex chains or agents that require multiple reasoning steps.
-
-Fortunately, LLMs generate output iteratively, which means it's possible to show sensible intermediate results
-before the final response is ready. Consuming output as soon as it becomes available has therefore become a vital part of the UX
-around building apps with LLMs to help alleviate latency issues, and LangChain aims to have first-class support for streaming.
-
-Below, we'll discuss some concepts and considerations around streaming in LangChain.
-
-#### `.stream()` and `.astream()`
-
-Most modules in LangChain include the `.stream()` method (and the equivalent `.astream()` method for [async](https://docs.python.org/3/library/asyncio.html) environments) as an ergonomic streaming interface.
-`.stream()` returns an iterator, which you can consume with a simple `for` loop. Here's an example with a chat model:
-
-```python
-from langchain_anthropic import ChatAnthropic
-
-model = ChatAnthropic(model="claude-3-sonnet-20240229")
-
-for chunk in model.stream("what color is the sky?"):
-    print(chunk.content, end="|", flush=True)
-```
-
-For models (or other components) that don't support streaming natively, this iterator would just yield a single chunk, but
-you could still use the same general pattern when calling them. Using `.stream()` will also automatically call the model in streaming mode
-without the need to provide additional config.
-
-The type of each outputted chunk depends on the type of component - for example, chat models yield [`AIMessageChunks`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html).
-Because this method is part of [LangChain Expression Language](/docs/concepts/#langchain-expression-language-lcel),
-you can handle formatting differences from different outputs using an [output parser](/docs/concepts/#output-parsers) to transform
-each yielded chunk.
-
-You can check out [this guide](/docs/how_to/streaming/#using-stream) for more detail on how to use `.stream()`.
-
-#### `.astream_events()`
-<span data-heading-keywords="astream_events,stream_events,stream events"></span>
-
-While the `.stream()` method is intuitive, it can only return the final generated value of your chain. This is fine for single LLM calls,
-but as you build more complex chains of several LLM calls together, you may want to use the intermediate values of
-the chain alongside the final output - for example, returning sources alongside the final generation when building a chat
-over documents app.
-
-There are ways to do this [using callbacks](/docs/concepts/#callbacks-1), or by constructing your chain in such a way that it passes intermediate
-values to the end with something like chained [`.assign()`](/docs/how_to/passthrough/) calls, but LangChain also includes an
-`.astream_events()` method that combines the flexibility of callbacks with the ergonomics of `.stream()`. When called, it returns an iterator
-which yields [various types of events](/docs/how_to/streaming/#event-reference) that you can filter and process according
-to the needs of your project.
-
-Here's one small example that prints just events containing streamed chat model output:
-
-```python
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_anthropic import ChatAnthropic
-
-model = ChatAnthropic(model="claude-3-sonnet-20240229")
-
-prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
-parser = StrOutputParser()
-chain = prompt | model | parser
-
-async for event in chain.astream_events({"topic": "parrot"}, version="v2"):
-    kind = event["event"]
-    if kind == "on_chat_model_stream":
-        print(event, end="|", flush=True)
-```
-
-You can roughly think of it as an iterator over callback events (though the format differs) - and you can use it on almost all LangChain components!
-
-See [this guide](/docs/how_to/streaming/#using-stream-events) for more detailed information on how to use `.astream_events()`,
-including a table listing available events.
-
-#### Callbacks
-
-The lowest level way to stream outputs from LLMs in LangChain is via the [callbacks](/docs/concepts/#callbacks) system. You can pass a
-callback handler that handles the [`on_llm_new_token`](https://python.langchain.com/api_reference/langchain/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html#langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.on_llm_new_token) event into LangChain components. When that component is invoked, any
-[LLM](/docs/concepts/#llms) or [chat model](/docs/concepts/#chat-models) contained in the component calls
-the callback with the generated token. Within the callback, you could pipe the tokens into some other destination, e.g. a HTTP response.
-You can also handle the [`on_llm_end`](https://python.langchain.com/api_reference/langchain/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html#langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.on_llm_end) event to perform any necessary cleanup.
-
-You can see [this how-to section](/docs/how_to/#callbacks) for more specifics on using callbacks.
-
-Callbacks were the first technique for streaming introduced in LangChain. While powerful and generalizable,
-they can be unwieldy for developers. For example:
-
-- You need to explicitly initialize and manage some aggregator or other stream to collect results.
-- The execution order isn't explicitly guaranteed, and you could theoretically have a callback run after the `.invoke()` method finishes.
-- Providers would often make you pass an additional parameter to stream outputs instead of returning them all at once.
-- You would often ignore the result of the actual model call in favor of callback results.
-
-#### Tokens
-
-The unit that most model providers use to measure input and output is via a unit called a **token**.
-Tokens are the basic units that language models read and generate when processing or producing text.
-The exact definition of a token can vary depending on the specific way the model was trained -
-for instance, in English, a token could be a single word like "apple", or a part of a word like "app".
-
-When you send a model a prompt, the words and characters in the prompt are encoded into tokens using a **tokenizer**.
-The model then streams back generated output tokens, which the tokenizer decodes into human-readable text.
-The below example shows how OpenAI models tokenize `LangChain is cool!`:
-
-![](/img/tokenization.png)
-
-You can see that it gets split into 5 different tokens, and that the boundaries between tokens are not exactly the same as word boundaries.
-
-The reason language models use tokens rather than something more immediately intuitive like "characters"
-has to do with how they process and understand text. At a high-level, language models iteratively predict their next generated output based on
-the initial input and their previous generations. Training the model using tokens language models to handle linguistic
-units (like words or subwords) that carry meaning, rather than individual characters, which makes it easier for the model
-to learn and understand the structure of the language, including grammar and context.
-Furthermore, using tokens can also improve efficiency, since the model processes fewer units of text compared to character-level processing.
-
-### Function/tool calling
-
-:::info
-We use the term `tool calling` interchangeably with `function calling`. Although
-function calling is sometimes meant to refer to invocations of a single function,
-we treat all models as though they can return multiple tool or function calls in
-each message.
-:::
-
-Tool calling allows a [chat model](/docs/concepts/#chat-models) to respond to a given prompt by generating output that
-matches a user-defined schema.
-
-While the name implies that the model is performing
-some action, this is actually not the case! The model only generates the arguments to a tool, and actually running the tool (or not) is up to the user.
-One common example where you **wouldn't** want to call a function with the generated arguments
-is if you want to [extract structured output matching some schema](/docs/concepts/#structured-output)
-from unstructured text. You would give the model an "extraction" tool that takes
-parameters matching the desired schema, then treat the generated output as your final
-result.
-
-![Diagram of a tool call by a chat model](/img/tool_call.png)
-
-Tool calling is not universal, but is supported by many popular LLM providers, including [Anthropic](/docs/integrations/chat/anthropic/), 
-[Cohere](/docs/integrations/chat/cohere/), [Google](/docs/integrations/chat/google_vertex_ai_palm/), 
-[Mistral](/docs/integrations/chat/mistralai/), [OpenAI](/docs/integrations/chat/openai/), and even for locally-running models via [Ollama](/docs/integrations/chat/ollama/).
-
-LangChain provides a standardized interface for tool calling that is consistent across different models.
-
-The standard interface consists of:
-
-* `ChatModel.bind_tools()`: a method for specifying which tools are available for a model to call. This method accepts [LangChain tools](/docs/concepts/#tools) as well as [Pydantic](https://pydantic.dev/) objects.
-* `AIMessage.tool_calls`: an attribute on the `AIMessage` returned from the model for accessing the tool calls requested by the model.
-
-#### Tool usage
-
-After the model calls tools, you can use the tool by invoking it, then passing the arguments back to the model.
-LangChain provides the [`Tool`](/docs/concepts/#tools) abstraction to help you handle this.
-
-The general flow is this:
-
-1. Generate tool calls with a chat model in response to a query.
-2. Invoke the appropriate tools using the generated tool call as arguments.
-3. Format the result of the tool invocations as [`ToolMessages`](/docs/concepts/#toolmessage).
-4. Pass the entire list of messages back to the model so that it can generate a final answer (or call more tools).
-
-![Diagram of a complete tool calling flow](/img/tool_calling_flow.png)
-
-This is how tool calling [agents](/docs/concepts/#agents) perform tasks and answer queries.
-
-Check out some more focused guides below:
-
-- [How to use chat models to call tools](/docs/how_to/tool_calling/)
-- [How to pass tool outputs to chat models](/docs/how_to/tool_results_pass_to_model/)
-- [Building an agent with LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/)
-
-### Structured output
-
-LLMs are capable of generating arbitrary text. This enables the model to respond appropriately to a wide
-range of inputs, but for some use-cases, it can be useful to constrain the LLM's output
-to a specific format or structure. This is referred to as **structured output**.
-
-For example, if the output is to be stored in a relational database,
-it is much easier if the model generates output that adheres to a defined schema or format.
-[Extracting specific information](/docs/tutorials/extraction/) from unstructured text is another
-case where this is particularly useful. Most commonly, the output format will be JSON,
-though other formats such as [YAML](/docs/how_to/output_parser_yaml/) can be useful too. Below, we'll discuss
-a few ways to get structured output from models in LangChain.
-
-#### `.with_structured_output()`
-
-For convenience, some LangChain chat models support a [`.with_structured_output()`](/docs/how_to/structured_output/#the-with_structured_output-method)
-method. This method only requires a schema as input, and returns a dict or Pydantic object.
-Generally, this method is only present on models that support one of the more advanced methods described below,
-and will use one of them under the hood. It takes care of importing a suitable output parser and
-formatting the schema in the right format for the model.
-
-Here's an example:
-
-```python
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
-
-class Joke(BaseModel):
-    """Joke to tell user."""
-
-    setup: str = Field(description="The setup of the joke")
-    punchline: str = Field(description="The punchline to the joke")
-    rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
-
-structured_llm = llm.with_structured_output(Joke)
-
-structured_llm.invoke("Tell me a joke about cats")
-```
-
-```
-Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=None)
-```
-
-We recommend this method as a starting point when working with structured output:
-
-- It uses other model-specific features under the hood, without the need to import an output parser.
-- For the models that use tool calling, no special prompting is needed.
-- If multiple underlying techniques are supported, you can supply a `method` parameter to
-[toggle which one is used](/docs/how_to/structured_output/#advanced-specifying-the-method-for-structuring-outputs).
-
-You may want or need to use other techniques if:
-
-- The chat model you are using does not support tool calling.
-- You are working with very complex schemas and the model is having trouble generating outputs that conform.
-
-For more information, check out this [how-to guide](/docs/how_to/structured_output/#the-with_structured_output-method).
-
-You can also check out [this table](/docs/integrations/chat/#advanced-features) for a list of models that support
-`with_structured_output()`.
-
-#### Raw prompting
-
-The most intuitive way to get a model to structure output is to ask nicely.
-In addition to your query, you can give instructions describing what kind of output you'd like, then
-parse the output using an [output parser](/docs/concepts/#output-parsers) to convert the raw
-model message or string output into something more easily manipulated.
-
-The biggest benefit to raw prompting is its flexibility:
-
-- Raw prompting does not require any special model features, only sufficient reasoning capability to understand
-the passed schema.
-- You can prompt for any format you'd like, not just JSON. This can be useful if the model you
-are using is more heavily trained on a certain type of data, such as XML or YAML.
-
-However, there are some drawbacks too:
-
-- LLMs are non-deterministic, and prompting a LLM to consistently output data in the exactly correct format
-for smooth parsing can be surprisingly difficult and model-specific.
-- Individual models have quirks depending on the data they were trained on, and optimizing prompts can be quite difficult.
-Some may be better at interpreting [JSON schema](https://json-schema.org/), others may be best with TypeScript definitions,
-and still others may prefer XML.
-
-While features offered by model providers may increase reliability, prompting techniques remain important for tuning your
-results no matter which method you choose.
-
-#### JSON mode
-<span data-heading-keywords="json mode"></span>
-
-Some models, such as [Mistral](/docs/integrations/chat/mistralai/), [OpenAI](/docs/integrations/chat/openai/),
-[Together AI](/docs/integrations/chat/together/) and [Ollama](/docs/integrations/chat/ollama/),
-support a feature called **JSON mode**, usually enabled via config.
-
-When enabled, JSON mode will constrain the model's output to always be some sort of valid JSON.
-Often they require some custom prompting, but it's usually much less burdensome than completely raw prompting and
-more along the lines of, `"you must always return JSON"`. The [output also generally easier to parse](/docs/how_to/output_parser_json/).
-
-It's also generally simpler to use directly and more commonly available than tool calling, and can give
-more flexibility around prompting and shaping results than tool calling.
-
-Here's an example:
-
-```python
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_openai import ChatOpenAI
-from langchain.output_parsers.json import SimpleJsonOutputParser
-
-model = ChatOpenAI(
-    model="gpt-4o",
-    model_kwargs={ "response_format": { "type": "json_object" } },
-)
-
-prompt = ChatPromptTemplate.from_template(
-    "Answer the user's question to the best of your ability."
-    'You must always output a JSON object with an "answer" key and a "followup_question" key.'
-    "{question}"
-)
-
-chain = prompt | model | SimpleJsonOutputParser()
-
-chain.invoke({ "question": "What is the powerhouse of the cell?" })
-```
-
-```
-{'answer': 'The powerhouse of the cell is the mitochondrion. It is responsible for producing energy in the form of ATP through cellular respiration.',
- 'followup_question': 'Would you like to know more about how mitochondria produce energy?'}
-```
-
-For a full list of model providers that support JSON mode, see [this table](/docs/integrations/chat/#advanced-features).
-
-#### Tool calling {#structured-output-tool-calling}
-
-For models that support it, [tool calling](/docs/concepts/#functiontool-calling) can be very convenient for structured output. It removes the
-guesswork around how best to prompt schemas in favor of a built-in model feature.
-
-It works by first binding the desired schema either directly or via a [LangChain tool](/docs/concepts/#tools) to a
-[chat model](/docs/concepts/#chat-models) using the `.bind_tools()` method. The model will then generate an `AIMessage` containing
-a `tool_calls` field containing `args` that match the desired shape.
-
-There are several acceptable formats you can use to bind tools to a model in LangChain. Here's one example:
-
-```python
-from pydantic import BaseModel, Field
-from langchain_openai import ChatOpenAI
-
-class ResponseFormatter(BaseModel):
-    """Always use this tool to structure your response to the user."""
-
-    answer: str = Field(description="The answer to the user's question")
-    followup_question: str = Field(description="A followup question the user could ask")
-
-model = ChatOpenAI(
-    model="gpt-4o",
-    temperature=0,
-)
-
-model_with_tools = model.bind_tools([ResponseFormatter])
-
-ai_msg = model_with_tools.invoke("What is the powerhouse of the cell?")
-
-ai_msg.tool_calls[0]["args"]
-```
-
-```
-{'answer': "The powerhouse of the cell is the mitochondrion. It generates most of the cell's supply of adenosine triphosphate (ATP), which is used as a source of chemical energy.",
- 'followup_question': 'How do mitochondria generate ATP?'}
-```
-
-Tool calling is a generally consistent way to get a model to generate structured output, and is the default technique
-used for the [`.with_structured_output()`](/docs/concepts/#with_structured_output) method when a model supports it.
-
-The following how-to guides are good practical resources for using function/tool calling for structured output:
-
-- [How to return structured data from an LLM](/docs/how_to/structured_output/)
-- [How to use a model to call tools](/docs/how_to/tool_calling)
-
-For a full list of model providers that support tool calling, [see this table](/docs/integrations/chat/#advanced-features).
-
-### Few-shot prompting
-
-One of the most effective ways to improve model performance is to give a model examples of 
-what you want it to do. The technique of adding example inputs and expected outputs 
-to a model prompt is known as "few-shot prompting". The technique is based on the
-[Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) paper.
-There are a few things to think about when doing few-shot prompting:
-
-1. How are examples generated?
-2. How many examples are in each prompt?
-3. How are examples selected at runtime?
-4. How are examples formatted in the prompt?
-
-Here are the considerations for each.
-
-#### 1. Generating examples
-
-The first and most important step of few-shot prompting is coming up with a good dataset of examples. Good examples should be relevant at runtime, clear, informative, and provide information that was not already known to the model.
-
-At a high-level, the basic ways to generate examples are:
-- Manual: a person/people generates examples they think are useful.
-- Better model: a better (presumably more expensive/slower) model's responses are used as examples for a worse (presumably cheaper/faster) model.
-- User feedback: users (or labelers) leave feedback on interactions with the application and examples are generated based on that feedback (for example, all interactions with positive feedback could be turned into examples).
-- LLM feedback: same as user feedback but the process is automated by having models evaluate themselves.
-
-Which approach is best depends on your task. For tasks where a small number core principles need to be understood really well, it can be valuable hand-craft a few really good examples.
-For tasks where the space of correct behaviors is broader and more nuanced, it can be useful to generate many examples in a more automated fashion so that there's a higher likelihood of there being some highly relevant examples for any runtime input.
-
-**Single-turn v.s. multi-turn examples**
-
-Another dimension to think about when generating examples is what the example is actually showing.
-
-The simplest types of examples just have a user input and an expected model output. These are single-turn examples.
-
-One more complex type if example is where the example is an entire conversation, usually in which a model initially responds incorrectly and a user then tells the model how to correct its answer.
-This is called a multi-turn example. Multi-turn examples can be useful for more nuanced tasks where its useful to show common errors and spell out exactly why they're wrong and what should be done instead.
-
-#### 2. Number of examples
-
-Once we have a dataset of examples, we need to think about how many examples should be in each prompt.
-The key tradeoff is that more examples generally improve performance, but larger prompts increase costs and latency.
-And beyond some threshold having too many examples can start to confuse the model.
-Finding the right number of examples is highly dependent on the model, the task, the quality of the examples, and your cost and latency constraints.
-Anecdotally, the better the model is the fewer examples it needs to perform well and the more quickly you hit steeply diminishing returns on adding more examples.
-But, the best/only way to reliably answer this question is to run some experiments with different numbers of examples.
-
-#### 3. Selecting examples
-
-Assuming we are not adding our entire example dataset into each prompt, we need to have a way of selecting examples from our dataset based on a given input. We can do this:
-- Randomly
-- By (semantic or keyword-based) similarity of the inputs
-- Based on some other constraints, like token size
-
-LangChain has a number of [`ExampleSelectors`](/docs/concepts/#example-selectors) which make it easy to use any of these techniques.
-
-Generally, selecting by semantic similarity leads to the best model performance. But how important this is is again model and task specific, and is something worth experimenting with.
-
-#### 4. Formatting examples
-
-Most state-of-the-art models these days are chat models, so we'll focus on formatting examples for those. Our basic options are to insert the examples:
-- In the system prompt as a string
-- As their own messages
-
-If we insert our examples into the system prompt as a string, we'll need to make sure it's clear to the model where each example begins and which parts are the input versus output. Different models respond better to different syntaxes, like [ChatML](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chat-markup-language), XML, TypeScript, etc.
-
-If we insert our examples as messages, where each example is represented as a sequence of Human, AI messages, we might want to also assign [names](/docs/concepts/#messages) to our messages like `"example_user"` and `"example_assistant"` to make it clear that these messages correspond to different actors than the latest input message.
-
-**Formatting tool call examples**
-
-One area where formatting examples as messages can be tricky is when our example outputs have tool calls. This is because different models have different constraints on what types of message sequences are allowed when any tool calls are generated.
-- Some models require that any AIMessage with tool calls be immediately followed by ToolMessages for every tool call,
-- Some models additionally require that any ToolMessages be immediately followed by an AIMessage before the next HumanMessage,
-- Some models require that tools are passed in to the model if there are any tool calls / ToolMessages in the chat history.
-
-These requirements are model-specific and should be checked for the model you are using. If your model requires ToolMessages after tool calls and/or AIMessages after ToolMessages and your examples only include expected tool calls and not the actual tool outputs, you can try adding dummy ToolMessages / AIMessages to the end of each example with generic contents to satisfy the API constraints.
-In these cases it's especially worth experimenting with inserting your examples as strings versus messages, as having dummy messages can adversely affect certain models.
-
-You can see a case study of how Anthropic and OpenAI respond to different few-shot prompting techniques on two different tool calling benchmarks [here](https://blog.langchain.dev/few-shot-prompting-to-improve-tool-calling-performance/).
-
-### Retrieval
-
-LLMs are trained on a large but fixed dataset, limiting their ability to reason over private or recent information. 
-Fine-tuning an LLM with specific facts is one way to mitigate this, but is often [poorly suited for factual recall](https://www.anyscale.com/blog/fine-tuning-is-for-form-not-facts) and [can be costly](https://www.glean.com/blog/how-to-build-an-ai-assistant-for-the-enterprise). 
-`Retrieval` is the process of providing relevant information to an LLM to improve its response for a given input. 
-`Retrieval augmented generation` (`RAG`) [paper](https://arxiv.org/abs/2005.11401) is the process of grounding the LLM generation (output) using the retrieved information.
-
-:::tip
-
-* See our RAG from Scratch [code](https://github.com/langchain-ai/rag-from-scratch) and [video series](https://youtube.com/playlist?list=PLfaIDFEXuae2LXbO1_PKyVJiQ23ZztA0x&feature=shared).
-* For a high-level guide on retrieval, see this [tutorial on RAG](/docs/tutorials/rag/).
-
-:::
-
-RAG is only as good as the retrieved documents’ relevance and quality. Fortunately, an emerging set of techniques can be employed to design and improve RAG systems. We've focused on taxonomizing and summarizing many of these techniques (see below figure) and will share some high-level strategic guidance in the following sections.
-You can and should experiment with using different pieces together. You might also find [this LangSmith guide](https://docs.smith.langchain.com/how_to_guides/evaluation/evaluate_llm_application) useful for showing how to evaluate different iterations of your app.
-
-![](/img/rag_landscape.png)
-
-#### Query Translation
-
-First, consider the user input(s) to your RAG system. Ideally, a RAG system can handle a wide range of inputs, from poorly worded questions to complex multi-part queries.
-**Using an LLM to review and optionally modify the input is the central idea behind query translation.** This serves as a general buffer, optimizing raw user inputs for your retrieval system. 
-For example, this can be as simple as extracting keywords or as complex as generating multiple sub-questions for a complex query.
-
-| Name          | When to use | Description                                                                                                                                                                                                                                                                            |
-|---------------|-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Multi-query](/docs/how_to/MultiQueryRetriever/)   | When you need to cover multiple perspectives of a question. | Rewrite the user question from multiple perspectives, retrieve documents for each rewritten question, return the unique documents for all queries. |
-| [Decomposition](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb) | When a question can be broken down into smaller subproblems. | Decompose a question into a set of subproblems / questions, which can either be solved sequentially (use the answer from first + retrieval to answer the second) or in parallel (consolidate each answer into final answer).                                                           |
-| [Step-back](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb)     | When a higher-level conceptual understanding is required. | First prompt the LLM to ask a generic step-back question about higher-level concepts or principles, and retrieve relevant facts about them. Use this grounding to help answer the user question. [Paper](https://arxiv.org/pdf/2310.06117).                                            |
-| [HyDE](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb)          | If you have challenges retrieving relevant documents using the raw user inputs. | Use an LLM to convert questions into hypothetical documents that answer the question. Use the embedded hypothetical documents to retrieve real documents with the premise that doc-doc similarity search can produce more relevant matches. [Paper](https://arxiv.org/abs/2212.10496). |
-
-:::tip
-
-See our RAG from Scratch videos for a few different specific approaches:
-- [Multi-query](https://youtu.be/JChPi0CRnDY?feature=shared)
-- [Decomposition](https://youtu.be/h0OPWlEOank?feature=shared)
-- [Step-back](https://youtu.be/xn1jEjRyJ2U?feature=shared)
-- [HyDE](https://youtu.be/SaDzIVkYqyY?feature=shared)
-
-:::
-
-#### Routing
-
-Second, consider the data sources available to your RAG system. You want to query across more than one database or across structured and unstructured data sources. **Using an LLM to review the input and route it to the appropriate data source is a simple and effective approach for querying across sources.**
-
-| Name             | When to use                                | Description |
-|------------------|--------------------------------------------|-------------|
-| [Logical routing](/docs/how_to/routing/)  | When you can prompt an LLM with rules to decide where to route the input. | Logical routing can use an LLM to reason about the query and choose which datastore is most appropriate. |
-| [Semantic routing](/docs/how_to/routing/#routing-by-semantic-similarity) | When semantic similarity is an effective way to determine where to route the input. | Semantic routing embeds both query and, typically a set of prompts. It then chooses the appropriate prompt based upon similarity. |
-
-:::tip
-
-See our RAG from Scratch video on [routing](https://youtu.be/pfpIndq7Fi8?feature=shared).  
-
-:::
-
-#### Query Construction
-
-Third, consider whether any of your data sources require specific query formats. Many structured databases use SQL. Vector stores often have specific syntax for applying keyword filters to document metadata. **Using an LLM to convert a natural language query into a query syntax is a popular and powerful approach.**
-In particular, [text-to-SQL](/docs/tutorials/sql_qa/), [text-to-Cypher](/docs/tutorials/graph/), and [query analysis for metadata filters](/docs/tutorials/query_analysis/#query-analysis) are useful ways to interact with structured, graph, and vector databases respectively. 
-
-| Name                                        | When to Use                                                                                                                                   | Description                                                                                                                                                                                                                                                                                      |
-|---------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Text to SQL](/docs/tutorials/sql_qa/)      | If users are asking questions that require information housed in a relational database, accessible via SQL.                                   | This uses an LLM to transform user input into a SQL query.                                             |
-| [Text-to-Cypher](/docs/tutorials/graph/)    | If users are asking questions that require information housed in a graph database, accessible via Cypher.                                     | This uses an LLM to transform user input into a Cypher query.                                              |
-| [Self Query](/docs/how_to/self_query/)      | If users are asking questions that are better answered by fetching documents based on metadata rather than similarity with the text.          | This uses an LLM to transform user input into two things: (1) a string to look up semantically, (2) a metadata filter to go along with it. This is useful because oftentimes questions are about the METADATA of documents (not the content itself).                                              |
-
-:::tip
-
-See our [blog post overview](https://blog.langchain.dev/query-construction/) and RAG from Scratch video on [query construction](https://youtu.be/kl6NwWYxvbM?feature=shared), the process of text-to-DSL where DSL is a domain specific language required to interact with a given database. This converts user questions into structured queries. 
-
-:::
-
-#### Indexing
-
-Fourth, consider the design of your document index. A simple and powerful idea is to **decouple the documents that you index for retrieval from the documents that you pass to the LLM for generation.** Indexing frequently uses embedding models with vector stores, which [compress the semantic information in documents to fixed-size vectors](/docs/concepts/#embedding-models).
-
-Many RAG approaches focus on splitting documents into chunks and retrieving some number based on similarity to an input question for the LLM. But chunk size and chunk number can be difficult to set and affect results if they do not provide full context for the LLM to answer a question. Furthermore, LLMs are increasingly capable of processing millions of tokens. 
-
-Two approaches can address this tension: (1) [Multi Vector](/docs/how_to/multi_vector/) retriever using an LLM to translate documents into any form (e.g., often into a summary) that is well-suited for indexing, but returns full documents to the LLM for generation. (2) [ParentDocument](/docs/how_to/parent_document_retriever/) retriever embeds document chunks, but also returns full documents. The idea is to get the best of both worlds: use concise representations (summaries or chunks) for retrieval, but use the full documents for answer generation.
-
-| Name                      | Index Type                   | Uses an LLM               | When to Use                                                                                                                                   | Description                                                                                                                                                                                                                                                                                      |
-|---------------------------|------------------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Vector store](/docs/how_to/vectorstore_retriever/)               | Vector store                  | No                        | If you are just getting started and looking for something quick and easy.                                                                     | This is the simplest method and the one that is easiest to get started with. It involves creating embeddings for each piece of text.                                                                                                                                                             |
-| [ParentDocument](/docs/how_to/parent_document_retriever/)            | Vector store + Document Store | No                        | If your pages have lots of smaller pieces of distinct information that are best indexed by themselves, but best retrieved all together.       | This involves indexing multiple chunks for each document. Then you find the chunks that are most similar in embedding space, but you retrieve the whole parent document and return that (rather than individual chunks).                                                                         |
-| [Multi Vector](/docs/how_to/multi_vector/)              | Vector store + Document Store | Sometimes during indexing | If you are able to extract information from documents that you think is more relevant to index than the text itself.                          | This involves creating multiple vectors for each document. Each vector could be created in a myriad of ways - examples include summaries of the text and hypothetical questions.                                                                                                                 |
-| [Time-Weighted Vector store](/docs/how_to/time_weighted_vectorstore/) | Vector store                  | No                        | If you have timestamps associated with your documents, and you want to retrieve the most recent ones                                          | This fetches documents based on a combination of semantic similarity (as in normal vector retrieval) and recency (looking at timestamps of indexed documents)                                                                                                                                    |
-
-:::tip
-
-- See our RAG from Scratch video on [indexing fundamentals](https://youtu.be/bjb_EMsTDKI?feature=shared)
-- See our RAG from Scratch video on [multi vector retriever](https://youtu.be/gTCU9I6QqCE?feature=shared)
-
-:::
-
-Fifth, consider ways to improve the quality of your similarity search itself. Embedding models compress text into fixed-length (vector) representations that capture the semantic content of the document. This compression is useful for search / retrieval, but puts a heavy burden on that single vector representation to capture the semantic nuance / detail of the document. In some cases, irrelevant or redundant content can dilute the semantic usefulness of the embedding.
-
-[ColBERT](https://docs.google.com/presentation/d/1IRhAdGjIevrrotdplHNcc4aXgIYyKamUKTWtB3m3aMU/edit?usp=sharing) is an interesting approach to address this with a higher granularity embeddings: (1) produce a contextually influenced embedding for each token in the document and query, (2) score similarity between each query token and all document tokens, (3) take the max, (4) do this for all query tokens, and (5) take the sum of the max scores (in step 3) for all query tokens to get a query-document similarity score; this token-wise scoring can yield strong results. 
-
-![](/img/colbert.png)
-
-There are some additional tricks to improve the quality of your retrieval. Embeddings excel at capturing semantic information, but may struggle with keyword-based queries. Many [vector stores](/docs/integrations/retrievers/pinecone_hybrid_search/) offer built-in [hybrid-search](https://docs.pinecone.io/guides/data/understanding-hybrid-search) to combine keyword and semantic similarity, which marries the benefits of both approaches. Furthermore, many vector stores have [maximal marginal relevance](https://python.langchain.com/v0.1/docs/modules/model_io/prompts/example_selectors/mmr/), which attempts to diversify the results of a search to avoid returning similar and redundant documents. 
-
-| Name              | When to use                                              | Description                                                                                                                                                                            |
-|-------------------|----------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [ColBERT](/docs/integrations/providers/ragatouille/#using-colbert-as-a-reranker)           | When higher granularity embeddings are needed.           | ColBERT uses contextually influenced embeddings for each token in the document and query to get a granular query-document similarity score. [Paper](https://arxiv.org/abs/2112.01488). |
-| [Hybrid search](/docs/integrations/retrievers/pinecone_hybrid_search/)     | When combining keyword-based and semantic similarity.    | Hybrid search combines keyword and semantic similarity, marrying the benefits of both approaches. [Paper](https://arxiv.org/abs/2210.11934).                                                                               |
-| [Maximal Marginal Relevance (MMR)](/docs/integrations/vectorstores/pinecone/#maximal-marginal-relevance-searches) | When needing to diversify search results. | MMR attempts to diversify the results of a search to avoid returning similar and redundant documents.                                                                                  |
-
-:::tip
-
-See our RAG from Scratch video on [ColBERT](https://youtu.be/cN6S0Ehm7_8?feature=shared>).
-
-:::
-
-#### Post-processing
-
-Sixth, consider ways to filter or rank retrieved documents. This is very useful if you are [combining documents returned from multiple sources](/docs/integrations/retrievers/cohere-reranker/#doing-reranking-with-coherererank), since it can can down-rank less relevant documents and / or [compress similar documents](/docs/how_to/contextual_compression/#more-built-in-compressors-filters). 
-
-| Name                      | Index Type                   | Uses an LLM               | When to Use                                                                                                                                   | Description                                                                                                                                                                                                                                                                                      |
-|---------------------------|------------------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Contextual Compression](/docs/how_to/contextual_compression/)    | Any                          | Sometimes                 | If you are finding that your retrieved documents contain too much irrelevant information and are distracting the LLM.                         | This puts a post-processing step on top of another retriever and extracts only the most relevant information from retrieved documents. This can be done with embeddings or an LLM.                                                                                                               |
-| [Ensemble](/docs/how_to/ensemble_retriever/)                  | Any                          | No                        | If you have multiple retrieval methods and want to try combining them.                                                                        | This fetches documents from multiple retrievers and then combines them.                                                                                                                                                                                                                          |
-| [Re-ranking](/docs/integrations/retrievers/cohere-reranker/)                  | Any                          | Yes                        |  If you want to rank retrieved documents based upon relevance, especially if you want to combine results from multiple retrieval methods .                                                                         | Given a query and a list of documents, Rerank indexes the documents from most to least semantically relevant to the query.                                                                                                                                                                                                                         |
-
-:::tip
-
-See our RAG from Scratch video on [RAG-Fusion](https://youtu.be/77qELPbNgxA?feature=shared) ([paper](https://arxiv.org/abs/2402.03367)), on approach for post-processing across multiple queries:  Rewrite the user question from multiple perspectives, retrieve documents for each rewritten question, and combine the ranks of multiple search result lists to produce a single, unified ranking with [Reciprocal Rank Fusion (RRF)](https://towardsdatascience.com/forget-rag-the-future-is-rag-fusion-1147298d8ad1).
-
-:::
-
-#### Generation
-
-**Finally, consider ways to build self-correction into your RAG system.** RAG systems can suffer from low quality retrieval (e.g., if a user question is out of the domain for the index) and / or hallucinations in generation. A naive retrieve-generate pipeline has no ability to detect or self-correct from these kinds of errors. The concept of ["flow engineering"](https://x.com/karpathy/status/1748043513156272416) has been introduced [in the context of code generation](https://arxiv.org/abs/2401.08500): iteratively build an answer to a code question with unit tests to check and self-correct errors. Several works have applied this RAG, such as Self-RAG and Corrective-RAG. In both cases, checks for document relevance, hallucinations, and / or answer quality are performed in the RAG answer generation flow.
-
-We've found that graphs are a great way to reliably express logical flows and have implemented ideas from several of these papers [using LangGraph](https://github.com/langchain-ai/langgraph/tree/main/examples/rag), as shown in the figure below (red - routing, blue - fallback, green - self-correction):
-- **Routing:** Adaptive RAG ([paper](https://arxiv.org/abs/2403.14403)). Route questions to different retrieval approaches, as discussed above 
-- **Fallback:** Corrective RAG ([paper](https://arxiv.org/pdf/2401.15884.pdf)). Fallback to web search if docs are not relevant to query
-- **Self-correction:** Self-RAG ([paper](https://arxiv.org/abs/2310.11511)). Fix answers w/ hallucinations or don’t address question
-
-![](/img/langgraph_rag.png)
-
-| Name              | When to use                                               | Description |
-|-------------------|-----------------------------------------------------------|-------------|
-| Self-RAG          | When needing to fix answers with hallucinations or irrelevant content. | Self-RAG performs checks for document relevance, hallucinations, and answer quality during the RAG answer generation flow, iteratively building an answer and self-correcting errors. |
-| Corrective-RAG    | When needing a fallback mechanism for low relevance docs. | Corrective-RAG includes a fallback (e.g., to web search) if the retrieved documents are not relevant to the query, ensuring higher quality and more relevant retrieval. |
-
-:::tip
-
-See several videos and cookbooks showcasing RAG with LangGraph: 
-- [LangGraph Corrective RAG](https://www.youtube.com/watch?v=E2shqsYwxck)
-- [LangGraph combining Adaptive, Self-RAG, and Corrective RAG](https://www.youtube.com/watch?v=-ROS6gfYIts) 
-- [Cookbooks for RAG using LangGraph](https://github.com/langchain-ai/langgraph/tree/main/examples/rag)
-
-See our LangGraph RAG recipes with partners:
-- [Meta](https://github.com/meta-llama/llama-recipes/tree/main/recipes/3p_integrations/langchain)
-- [Mistral](https://github.com/mistralai/cookbook/tree/main/third_party/langchain)
-
-:::
-
-### Text splitting
-
-LangChain offers many different types of `text splitters`.
-These all live in the `langchain-text-splitters` package.
-
-Table columns:
-
-- **Name**: Name of the text splitter
-- **Classes**: Classes that implement this text splitter
-- **Splits On**: How this text splitter splits text
-- **Adds Metadata**: Whether or not this text splitter adds metadata about where each chunk came from.
-- **Description**: Description of the splitter, including recommendation on when to use it.
-
-
-| Name     | Classes                                                                                                                                                                                                             | Splits On                                                   | Adds Metadata | Description                                                                                                                                                                                                                                                                  |
-|----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Recursive | [RecursiveCharacterTextSplitter](/docs/how_to/recursive_text_splitter/), [RecursiveJsonSplitter](/docs/how_to/recursive_json_splitter/) | A list of user defined characters     |               | Recursively splits text. This splitting is trying to keep related pieces of text next to each other. This is the `recommended way` to start splitting text.                                                                                                                    |
-| HTML      | [HTMLHeaderTextSplitter](/docs/how_to/HTML_header_metadata_splitter/), [HTMLSectionSplitter](/docs/how_to/HTML_section_aware_splitter/)          | HTML specific characters                                                                                                 | ✅             | Splits text based on HTML-specific characters. Notably, this adds in relevant information about where that chunk came from (based on the HTML)                                                                                                                               |
-| Markdown  | [MarkdownHeaderTextSplitter](/docs/how_to/markdown_header_metadata_splitter/),                                                                                                           | Markdown specific characters                                                                                    | ✅             | Splits text based on Markdown-specific characters. Notably, this adds in relevant information about where that chunk came from (based on the Markdown)                                                                                                                       |
-| Code      | [many languages](/docs/how_to/code_splitter/)                                                                                                                                 | Code (Python, JS) specific characters                                                                           |               | Splits text based on characters specific to coding languages. 15 different languages are available to choose from.                                                                                                                                                           |
-| Token    | [many classes](/docs/how_to/split_by_token/)                                                                                                                                  | Tokens                                                                                                          |               | Splits text on tokens. There exist a few different ways to measure tokens.                                                                                                                                                                                                   |
-| Character  | [CharacterTextSplitter](/docs/how_to/character_text_splitter/)                                                                                                                | A user defined character                                                                                        |               | Splits text based on a user defined character. One of the simpler methods.                                                                                                                                                                                                   |
-| Semantic Chunker (Experimental) | [SemanticChunker](/docs/how_to/semantic-chunker/)                                                                                                                             | Sentences                                                                                                       |               | First splits on sentences. Then combines ones next to each other if they are semantically similar enough. Taken from [Greg Kamradt](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/tutorials/LevelsOfTextSplitting/5_Levels_Of_Text_Splitting.ipynb) |
-| Integration: AI21 Semantic | [AI21SemanticTextSplitter](/docs/integrations/document_transformers/ai21_semantic_text_splitter/)                                                                                                 |                  |    ✅           | Identifies distinct topics that form coherent pieces of text and splits along those.                                                                                                                                                                                         |
-
-### Evaluation
-<span data-heading-keywords="evaluation,evaluate"></span>
-
-Evaluation is the process of assessing the performance and effectiveness of your LLM-powered applications.
-It involves testing the model's responses against a set of predefined criteria or benchmarks to ensure it meets the desired quality standards and fulfills the intended purpose.
-This process is vital for building reliable applications.
-
-![](/img/langsmith_evaluate.png)
-
-[LangSmith](https://docs.smith.langchain.com/) helps with this process in a few ways:
-
-- It makes it easier to create and curate datasets via its tracing and annotation features
-- It provides an evaluation framework that helps you define metrics and run your app against your dataset
-- It allows you to track results over time and automatically run your evaluators on a schedule or as part of CI/Code
-
-To learn more, check out [this LangSmith guide](https://docs.smith.langchain.com/concepts/evaluation).
-
-### Tracing
-<span data-heading-keywords="trace,tracing"></span>
-
-A trace is essentially a series of steps that your application takes to go from input to output.
-Traces contain individual steps called `runs`. These can be individual calls from a model, retriever,
-tool, or sub-chains.
-Tracing gives you observability inside your chains and agents, and is vital in diagnosing issues.
-
-For a deeper dive, check out [this LangSmith conceptual guide](https://docs.smith.langchain.com/concepts/tracing).
diff --git a/docs/docs/concepts/agents.mdx b/docs/docs/concepts/agents.mdx
new file mode 100644
index 0000000000000..960eb2a975d1e
--- /dev/null
+++ b/docs/docs/concepts/agents.mdx
@@ -0,0 +1,25 @@
+# Agents
+
+By themselves, language models can't take actions - they just output text. Agents are systems that take a high-level task and use an LLM as a reasoning engine to decide what actions to take and execute those actions.
+
+[LangGraph](/docs/concepts/architecture#langgraph) is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. We recommend that you use LangGraph for building agents.
+
+Please see the following resources for more information:
+
+* LangGraph docs on [common agent architectures](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/)
+* [Pre-built agents in LangGraph](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent)
+
+## Legacy agent concept: AgentExecutor
+
+LangChain previously introduced the `AgentExecutor` as a runtime for agents. 
+While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents. 
+As a result, we're gradually phasing out `AgentExecutor` in favor of more flexible solutions in LangGraph.
+
+### Transitioning from AgentExecutor to langgraph
+
+If you're currently using `AgentExecutor`, don't worry! We've prepared resources to help you:
+
+1. For those who still need to use `AgentExecutor`, we offer a comprehensive guide on [how to use AgentExecutor](/docs/how_to/agent_executor).
+
+2. However, we strongly recommend transitioning to LangGraph for improved flexibility and control. To facilitate this transition, we've created a detailed [migration guide](/docs/how_to/migrate_agent) to help you move from `AgentExecutor` to LangGraph seamlessly.
+
diff --git a/docs/docs/concepts/architecture.mdx b/docs/docs/concepts/architecture.mdx
new file mode 100644
index 0000000000000..6a76b58fb297f
--- /dev/null
+++ b/docs/docs/concepts/architecture.mdx
@@ -0,0 +1,78 @@
+import ThemedImage from '@theme/ThemedImage';
+import useBaseUrl from '@docusaurus/useBaseUrl';
+
+# Architecture
+
+LangChain as a framework consists of a number of packages.
+
+<ThemedImage
+    alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
+    sources={{
+        light: useBaseUrl('/svg/langchain_stack_062024.svg'),
+        dark: useBaseUrl('/svg/langchain_stack_062024_dark.svg'),
+    }}
+    title="LangChain Framework Overview"
+    style={{ width: "100%" }}
+/>
+
+
+## langchain-core
+
+This package contains base abstractions of different components and ways to compose them together.
+The interfaces for core components like LLMs, vector stores, retrievers and more are defined here.
+No third party integrations are defined here.
+The dependencies are kept purposefully very lightweight.
+
+## langchain
+
+The main `langchain` package contains chains, agents, and retrieval strategies that make up an application's cognitive architecture.
+These are NOT third party integrations.
+All chains, agents, and retrieval strategies here are NOT specific to any one integration, but rather generic across all integrations.
+
+## langchain-community
+
+This package contains third party integrations that are maintained by the LangChain community.
+Key partner packages are separated out (see below).
+This contains all integrations for various components (LLMs, vector stores, retrievers).
+All dependencies in this package are optional to keep the package as lightweight as possible.
+
+## Partner packages
+
+While the long tail of integrations is in `langchain-community`, we split popular integrations into their own packages (e.g. `langchain-openai`, `langchain-anthropic`, etc). This was done in order to improve support for these important integrations.
+
+For more information see:
+
+* A list [LangChain integrations](/docs/integrations/providers/)
+* The [LangChain API Reference](https://python.langchain.com/api_reference/) where you can find detailed information about the API reference of each partner package.
+
+## LangGraph
+
+`langgraph` is an extension of `langchain` aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
+
+LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows.
+
+:::info[Further reading]
+
+* See our LangGraph overview [here](https://langchain-ai.github.io/langgraph/concepts/high_level/#core-principles).
+* See our LangGraph Academy Course [here](https://academy.langchain.com/courses/intro-to-langgraph).
+
+:::
+
+## LangServe
+
+A package to deploy LangChain chains as REST APIs. Makes it easy to get a production ready API up and running.
+
+:::important
+LangServe is designed to primarily deploy simple Runnables and work with well-known primitives in langchain-core.
+
+If you need a deployment option for LangGraph, you should instead be looking at LangGraph Cloud (beta) which will be better suited for deploying LangGraph applications.
+:::
+
+For more information, see the [LangServe documentation](/docs/langserve).
+
+
+## LangSmith
+
+A developer platform that lets you debug, test, evaluate, and monitor LLM applications.
+
+For more information, see the [LangSmith documentation](https://docs.smith.langchain.com)
diff --git a/docs/docs/concepts/async.mdx b/docs/docs/concepts/async.mdx
new file mode 100644
index 0000000000000..2a1d5acf57845
--- /dev/null
+++ b/docs/docs/concepts/async.mdx
@@ -0,0 +1,81 @@
+# Async programming with langchain
+
+:::info Prerequisites
+* [Runnable interface](/docs/concepts/runnables)
+* [asyncio](https://docs.python.org/3/library/asyncio.html)
+:::
+
+LLM based applications often involve a lot of I/O-bound operations, such as making API calls to language models, databases, or other services. Asynchronous programming (or async programming) is a paradigm that allows a program to perform multiple tasks concurrently without blocking the execution of other tasks, improving efficiency and responsiveness, particularly in I/O-bound operations.
+
+:::note
+You are expected to be familiar with asynchronous programming in Python before reading this guide. If you are not, please find appropriate resources online to learn how to program asynchronously in Python.
+This guide specifically focuses on what you need to know to work with LangChain in an asynchronous context, assuming that you are already familiar with asynch
+:::
+
+## Langchain asynchronous apis
+
+Many LangChain APIs are designed to be asynchronous, allowing you to build efficient and responsive applications.
+
+Typically, any method that may perform I/O operations (e.g., making API calls, reading files) will have an asynchronous counterpart.
+
+In LangChain, async implementations are located in the same classes as their synchronous counterparts, with the asynchronous methods having an "a" prefix. For example, the synchronous `invoke` method has an asynchronous counterpart called `ainvoke`.
+
+Many components of LangChain implement the [Runnable Interface](/docs/concepts/runnables), which includes support for asynchronous execution. This means that you can run Runnables asynchronously using the `await` keyword in Python.
+
+```python
+await some_runnable.ainvoke(some_input)
+```
+
+Other components like [Embedding Models](/docs/concepts/embedding_models) and [VectorStore](/docs/concepts/vectorstores) that do not implement the [Runnable Interface](/docs/concepts/runnables) usually still follow the same rule and include the asynchronous version of method in the same class with an "a" prefix.
+
+For example,
+
+```python
+await some_vectorstore.aadd_documents(documents)
+```
+
+Runnables created using the [LangChain Expression Language (LCEL)](/docs/concepts/lcel) can also be run asynchronously as they implement
+the full [Runnable Interface](/docs/concepts/runnables).
+
+Fore more information, please review the [API reference](https://python.langchain.com/api_reference/) for the specific component you are using.
+
+## Delegation to sync methods
+
+Most popular LangChain integrations implement asynchronous support of their APIs. For example, the `ainvoke` method of many ChatModel implementations uses the `httpx.AsyncClient` to make asynchronous HTTP requests to the model provider's API.
+
+When an asynchronous implementation is not available, LangChain tries to provide a default implementation, even if it incurs
+a **slight** overhead.
+
+By default, LangChain will delegate the execution of a unimplemented asynchronous methods to the synchronous counterparts. LangChain almost always assumes that the synchronous method should be treated as a blocking operation and should be run in a separate thread.
+This is done using [asyncio.loop.run_in_executor](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor) functionality provided by the `asyncio` library. LangChain uses the default executor provided by the `asyncio` library, which lazily initializes a thread pool executor with a default number of threads that is reused in the given event loop. While this strategy incurs a slight overhead due to context switching between threads, it guarantees that every asynchronous method has a default implementation that works out of the box.
+
+## Performance
+
+Async code in LangChain should generally perform relatively well with minimal overhead out of the box, and is unlikely
+to be a bottleneck in most applications.
+
+The two main sources of overhead are:
+
+1. Cost of context switching between threads when [delegating to synchronous methods](#delegation-to-sync-methods). This can be addressed by providing a native asynchronous implementation.
+2. In [LCEL](/docs/concepts/lcel) any "cheap functions" that appear as part of the chain will be either scheduled as tasks on the event loop (if they are async) or run in a separate thread (if they are sync), rather than just be run inline.
+
+The latency overhead you should expect from these is between tens of microseconds to a few milliseconds.
+
+A more common source of performance issues arises from users accidentally blocking the event loop by calling synchronous code in an async context (e.g., calling `invoke` rather than `ainvoke`).
+
+## Compatibility
+
+LangChain is only compatible with the `asyncio` library, which is distributed as part of the Python standard library. It will not work with other async libraries like `trio` or `curio`.
+
+In Python 3.9 and 3.10, [asyncio's tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task) did not
+accept a `context` parameter. Due to this limitation, LangChain cannot automatically propagate the `RunnableConfig` down the call chain
+in certain scenarios.
+
+If you are experiencing issues with streaming, callbacks or tracing in async code and are using Python 3.9 or 3.10, this is a likely cause.
+
+Please read [Propagation RunnableConfig](/docs/concepts/runnables#propagation-RunnableConfig) for more details to learn how to propagate the `RunnableConfig` down the call chain manually (or upgrade to Python 3.11 where this is no longer an issue).
+
+## How to use in ipython and jupyter notebooks
+
+As of IPython 7.0, IPython supports asynchronous REPLs. This means that you can use the `await` keyword in the IPython REPL and Jupyter Notebooks without any additional setup. For more information, see the [IPython blog post](https://blog.jupyter.org/ipython-7-0-async-repl-a35ce050f7f7).
+
diff --git a/docs/docs/concepts/callbacks.mdx b/docs/docs/concepts/callbacks.mdx
new file mode 100644
index 0000000000000..6e3975271d8eb
--- /dev/null
+++ b/docs/docs/concepts/callbacks.mdx
@@ -0,0 +1,73 @@
+# Callbacks
+
+:::note Prerequisites
+- [Runnable interface](/docs/concepts/#runnable-interface)
+:::
+
+LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
+
+You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.
+
+## Callback events
+
+| Event            | Event Trigger                               | Associated Method     |
+|------------------|---------------------------------------------|-----------------------|
+| Chat model start | When a chat model starts                    | `on_chat_model_start` |
+| LLM start        | When a llm starts                           | `on_llm_start`        |
+| LLM new token    | When an llm OR chat model emits a new token | `on_llm_new_token`    |
+| LLM ends         | When an llm OR chat model ends              | `on_llm_end`          |
+| LLM errors       | When an llm OR chat model errors            | `on_llm_error`        |
+| Chain start      | When a chain starts running                 | `on_chain_start`      |
+| Chain end        | When a chain ends                           | `on_chain_end`        |
+| Chain error      | When a chain errors                         | `on_chain_error`      |
+| Tool start       | When a tool starts running                  | `on_tool_start`       |
+| Tool end         | When a tool ends                            | `on_tool_end`         |
+| Tool error       | When a tool errors                          | `on_tool_error`       |
+| Agent action     | When an agent takes an action               | `on_agent_action`     |
+| Agent finish     | When an agent ends                          | `on_agent_finish`     |
+| Retriever start  | When a retriever starts                     | `on_retriever_start`  |
+| Retriever end    | When a retriever ends                       | `on_retriever_end`    |
+| Retriever error  | When a retriever errors                     | `on_retriever_error`  |
+| Text             | When arbitrary text is run                  | `on_text`             |
+| Retry            | When a retry event is run                   | `on_retry`            |
+
+## Callback handlers
+
+Callback handlers can either be `sync` or `async`:
+
+* Sync callback handlers implement the [BaseCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html) interface.
+* Async callback handlers implement the [AsyncCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) interface.
+
+During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.
+
+## Passing callbacks
+
+The `callbacks` property is available on most objects throughout the API (Models, Tools, Agents, etc.) in two different places:
+
+- **Request time callbacks**: Passed at the time of the request in addition to the input data.
+Available on all standard `Runnable` objects. These callbacks are INHERITED by all children
+of the object they are defined on. For example, `chain.invoke({"number": 25}, {"callbacks": [handler]})`.
+- **Constructor callbacks**: `chain = TheNameOfSomeChain(callbacks=[handler])`. These callbacks
+are passed as arguments to the constructor of the object. The callbacks are scoped
+only to the object they are defined on, and are **not** inherited by any children of the object.
+
+:::warning
+Constructor callbacks are scoped only to the object they are defined on. They are **not** inherited by children
+of the object.
+:::
+
+If you're creating a custom chain or runnable, you need to remember to propagate request time
+callbacks to any child objects.
+
+:::important Async in Python&lt;=3.10
+
+Any `RunnableLambda`, a `RunnableGenerator`, or `Tool` that invokes other runnables
+and is running `async` in python&lt;=3.10, will have to propagate callbacks to child
+objects manually. This is because LangChain cannot automatically propagate
+callbacks to child objects in this case.
+
+This is a common reason why you may fail to see events being emitted from custom
+runnables or tools.
+:::
+
+For specifics on how to use callbacks, see the [relevant how-to guides here](/docs/how_to/#callbacks).
\ No newline at end of file
diff --git a/docs/docs/concepts/chat_history.mdx b/docs/docs/concepts/chat_history.mdx
new file mode 100644
index 0000000000000..967f93af968e0
--- /dev/null
+++ b/docs/docs/concepts/chat_history.mdx
@@ -0,0 +1,46 @@
+# Chat history
+
+:::info Prerequisites
+
+- [Messages](/docs/concepts/messages)
+- [Chat models](/docs/concepts/chat_models)
+- [Tool calling](/docs/concepts/tool_calling)
+:::
+
+Chat history is a record of the conversation between the user and the chat model. It is used to maintain context and state throughout the conversation. The chat history is sequence of [messages](/docs/concepts/messages), each of which is associated with a specific [role](/docs/concepts/messages#role), such as "user", "assistant", "system", or "tool".
+
+## Conversation patterns
+
+![Conversation patterns](/img/conversation_patterns.png)
+
+Most conversations start with a **system message** that sets the context for the conversation. This is followed by a **user message** containing the user's input, and then an **assistant message** containing the model's response.
+
+The **assistant** may respond directly to the user or if configured with tools request that a [tool](/docs/concepts/tool_calling) be invoked to perform a specific task.
+
+So a full conversation often involves a combination of two patterns of alternating messages:
+
+1. The **user** and the **assistant** representing a back-and-forth conversation.
+2. The **assistant** and **tool messages** representing an ["agentic" workflow](/docs/concepts/agents) where the assistant is invoking tools to perform specific tasks.
+
+## Managing chat history
+
+Since chat models have a maximum limit on input size, it's important to manage chat history and trim it as needed to avoid exceeding the [context window](/docs/concepts/chat_models#context_window).
+
+While processing chat history, it's essential to preserve a correct conversation structure. 
+
+Key guidelines for managing chat history:
+
+- The conversation should follow one of these structures:
+    - The first message is either a "user" message or a "system" message, followed by a "user" and then an "assistant" message.
+    - The last message should be either a "user" message or a "tool" message containing the result of a tool call.
+- When using [tool calling](/docs/concepts/tool_calling), a "tool" message should only follow an "assistant" message that requested the tool invocation.
+
+:::tip
+Understanding correct conversation structure is essential for being able to properly implement
+[memory](https://langchain-ai.github.io/langgraph/concepts/memory/) in chat models.
+:::
+
+## Related resources
+
+- [How to trim messages](https://python.langchain.com/docs/how_to/trim_messages/)
+- [Memory guide](https://langchain-ai.github.io/langgraph/concepts/memory/) for information on implementing short-term and long-term memory in chat models using [LangGraph](https://langchain-ai.github.io/langgraph/).
diff --git a/docs/docs/concepts/chat_models.mdx b/docs/docs/concepts/chat_models.mdx
new file mode 100644
index 0000000000000..e924168e2cd71
--- /dev/null
+++ b/docs/docs/concepts/chat_models.mdx
@@ -0,0 +1,168 @@
+# Chat models
+
+## Overview
+
+Large Language Models (LLMs) are advanced machine learning models that excel in a wide range of language-related tasks such as text generation, translation, summarization, question answering, and more, without needing task-specific tuning for every scenario.
+
+Modern LLMs are typically accessed through a chat model interface that takes a list of [messages](/docs/concepts/messages) as input and returns a [message](/docs/concepts/messages) as output.
+
+The newest generation of chat models offer additional capabilities:
+
+* [Tool calling](/docs/concepts#tool-calling): Many popular chat models offer a native [tool calling](/docs/concepts#tool-calling) API. This API allows developers to build rich applications that enable AI to interact with external services, APIs, and databases. Tool calling can also be used to extract structured information from unstructured data and perform various other tasks.
+* [Structured output](/docs/concepts/structured_outputs): A technique to make a chat model respond in a structured format, such as JSON that matches a given schema.
+* [Multimodality](/docs/concepts/multimodality): The ability to work with data other than text; for example, images, audio, and video.
+
+## Features
+
+LangChain provides a consistent interface for working with chat models from different providers while offering additional features for monitoring, debugging, and optimizing the performance of applications that use LLMs.
+
+* Integrations with many chat model providers (e.g., Anthropic, OpenAI, Ollama, Microsoft Azure, Google Vertex, Amazon Bedrock, Hugging Face, Cohere, Groq). Please see [chat model integrations](/docs/integrations/chat/) for an up-to-date list of supported models.
+* Use either LangChain's [messages](/docs/concepts/messages) format or OpenAI format.
+* Standard [tool calling API](/docs/concepts#tool-calling): standard interface for binding tools to models, accessing tool call requests made by models, and sending tool results back to the model.
+* Standard API for structuring outputs (/docs/concepts/structured_outputs) via the `with_structured_output` method.
+* Provides support for [async programming](/docs/concepts/async), [efficient batching](/docs/concepts/runnables#batch), [a rich streaming API](/docs/concepts/streaming).
+* Integration with [LangSmith](https://docs.smith.langchain.com) for monitoring and debugging production-grade applications based on LLMs.
+* Additional features like standardized [token usage](/docs/concepts/messages#token_usage), [rate limiting](#rate-limiting), [caching](#cache) and more.
+
+## Integrations
+
+LangChain has many chat model integrations that allow you to use a wide variety of models from different providers.
+
+These integrations are one of two types:
+
+1. **Official models**: These are models that are officially supported by LangChain and/or model provider. You can find these models in the `langchain-<provider>` packages.
+2. **Community models**: There are models that are mostly contributed and supported by the community. You can find these models in the `langchain-community` package.
+
+LangChain chat models are named with a convention that prefixes "Chat" to their class names (e.g., `ChatOllama`, `ChatAnthropic`, `ChatOpenAI`, etc.).
+
+Please review the [chat model integrations](/docs/integrations/chat/) for a list of supported models.
+
+:::note
+Models that do **not** include the prefix "Chat" in their name or include "LLM" as a suffix in their name typically refer to older models that do not follow the chat model interface and instead use an interface that takes a string as input and returns a string as output.
+:::
+
+
+## Interface
+
+LangChain chat models implement the [BaseChatModel](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) interface. Because [BaseChatModel] also implements the [Runnable Interface](/docs/concepts/runnables), chat models support a [standard streaming interface](/docs/concepts/streaming), [async programming](/docs/concepts/async), optimized [batching](/docs/concepts/runnables#batch), and more. Please see the [Runnable Interface](/docs/concepts/runnables) for more details.
+
+Many of the key methods of chat models operate on [messages](/docs/concepts/messages) as input and return messages as output.
+
+Chat models offer a standard set of parameters that can be used to configure the model. These parameters are typically used to control the behavior of the model, such as the temperature of the output, the maximum number of tokens in the response, and the maximum time to wait for a response. Please see the [standard parameters](#standard-parameters) section for more details.
+
+:::note
+In documentation, we will often use the terms "LLM" and "Chat Model" interchangeably. This is because most modern LLMs are exposed to users via a chat model interface.
+
+However, LangChain also has implementations of older LLMs that do not follow the chat model interface and instead use an interface that takes a string as input and returns a string as output. These models are typically named without the "Chat" prefix (e.g., `Ollama`, `Anthropic`, `OpenAI`, etc.).
+These models implement the [BaseLLM](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.llms.BaseLLM.html#langchain_core.language_models.llms.BaseLLM) interface and may be named with the "LLM" suffix (e.g., `OllamaLLM`, `AnthropicLLM`, `OpenAILLM`, etc.). Generally, users should not use these models.
+:::
+
+### Key methods
+
+The key methods of a chat model are:
+
+1. **invoke**: The primary method for interacting with a chat model. It takes a list of [messages](/docs/concepts/messages) as input and returns a list of messages as output.
+2. **stream**: A method that allows you to stream the output of a chat model as it is generated.
+3. **batch**: A method that allows you to batch multiple requests to a chat model together for more efficient processing.
+4. **bind_tools**: A method that allows you to bind a tool to a chat model for use in the model's execution context.
+5. **with_structured_output**: A wrapper around the `invoke` method for models that natively support [structured output](/docs/concepts#structured_output).
+
+Other important methods can be found in the [BaseChatModel API Reference](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html).
+
+### Inputs and outputs
+
+Modern LLMs are typically accessed through a chat model interface that takes [messages](/docs/concepts/messages) as input and returns [messages](/docs/concepts/messages) as output. Messages are typically associated with a role (e.g., "system", "human", "assistant") and one or more content blocks that contain text or potentially multimodal data (e.g., images, audio, video).
+
+LangChain supports two message formats to interact with chat models:
+
+1. **LangChain Message Format**: LangChain's own message format, which is used by default and is used internally by LangChain.
+2. **OpenAI's Message Format**: OpenAI's message format.
+
+### Standard parameters
+
+Many chat models have standardized parameters that can be used to configure the model:
+
+| Parameter      | Description                                                                                                                                                                                                                                                                                                    |
+|----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `model`        | The name or identifier of the specific AI model you want to use (e.g., `"gpt-3.5-turbo"` or `"gpt-4"`).                                                                                                                                                                                                        |
+| `temperature`  | Controls the randomness of the model's output. A higher value (e.g., 1.0) makes responses more creative, while a lower value (e.g., 0.1) makes them more deterministic and focused.                                                                                                                            |
+| `timeout`      | The maximum time (in seconds) to wait for a response from the model before canceling the request. Ensures the request doesn’t hang indefinitely.                                                                                                                                                               |
+| `max_tokens`   | Limits the total number of tokens (words and punctuation) in the response. This controls how long the output can be.                                                                                                                                                                                           |
+| `stop`         | Specifies stop sequences that indicate when the model should stop generating tokens. For example, you might use specific strings to signal the end of a response.                                                                                                                                              |
+| `max_retries`  | The maximum number of attempts the system will make to resend a request if it fails due to issues like network timeouts or rate limits.                                                                                                                                                                        |
+| `api_key`      | The API key required for authenticating with the model provider. This is usually issued when you sign up for access to the model.                                                                                                                                                                              |
+| `base_url`     | The URL of the API endpoint where requests are sent. This is typically provided by the model's provider and is necessary for directing your requests.                                                                                                                                                          |
+| `rate_limiter` | An optional [BaseRateLimiter](https://python.langchain.com/api_reference/core/rate_limiters/langchain_core.rate_limiters.BaseRateLimiter.html#langchain_core.rate_limiters.BaseRateLimiter) to space out requests to avoid exceeding rate limits.  See [rate-limiting](#rate-limiting) below for more details. |
+
+Some important things to note:
+
+- Standard parameters only apply to model providers that expose parameters with the intended functionality. For example, some providers do not expose a configuration for maximum output tokens, so max_tokens can't be supported on these.
+- Standard params are currently only enforced on integrations that have their own integration packages (e.g. `langchain-openai`, `langchain-anthropic`, etc.), they're not enforced on models in ``langchain-community``.
+
+ChatModels also accept other parameters that are specific to that integration. To find all the parameters supported by a ChatModel head to the [API reference](https://python.langchain.com/api_reference/) for that model.
+
+## Tool calling
+
+Chat models can call [tools](/docs/concepts/tools) to perform tasks such as fetching data from a database, making API requests, or running custom code. Please
+see the [tool calling](/docs/concepts#tool-calling) guide for more information.
+
+## Structured outputs
+
+Chat models can be requested to respond in a particular format (e.g., JSON or matching a particular schema). This feature is extremely
+useful for information extraction tasks. Please read more about
+the technique in the [structured outputs](/docs/concepts#structured_output) guide.
+
+## Multimodality
+
+Large Language Models (LLMs) are not limited to processing text. They can also be used to process other types of data, such as images, audio, and video. This is known as [multimodality](/docs/concepts/multimodality).
+
+Currently, only some LLMs support multimodal inputs, and almost none support multimodal outputs. Please consult the specific model documentation for details.
+
+## Context window
+
+A chat model's context window refers to the maximum size of the input sequence the model can process at one time. While the context windows of modern LLMs are quite large, they still present a limitation that developers must keep in mind when working with chat models.
+
+If the input exceeds the context window, the model may not be able to process the entire input and could raise an error. In conversational applications, this is especially important because the context window determines how much information the model can "remember" throughout a conversation. Developers often need to manage the input within the context window to maintain a coherent dialogue without exceeding the limit. For more details on handling memory in conversations, refer to the [memory](https://langchain-ai.github.io/langgraph/concepts/memory/).
+
+The size of the input is measured in [tokens](/docs/concepts/tokens) which are the unit of processing that the model uses.
+
+## Advanced topics
+ 
+### Rate-limiting
+
+Many chat model providers impose a limit on the number of requests that can be made in a given time period.
+
+If you hit a rate limit, you will typically receive a rate limit error response from the provider, and will need to wait before making more requests.
+
+You have a few options to deal with rate limits:
+
+1. Try to avoid hitting rate limits by spacing out requests: Chat models accept a `rate_limiter` parameter that can be provided during initialization. This parameter is used to control the rate at which requests are made to the model provider. Spacing out the requests to a given model is a particularly useful strategy when benchmarking models to evaluate their performance. Please see the [how to handle rate limits](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/) for more information on how to use this feature.
+2. Try to recover from rate limit errors: If you receive a rate limit error, you can wait a certain amount of time before retrying the request. The amount of time to wait can be increased with each subsequent rate limit error. Chat models have a `max_retries` parameter that can be used to control the number of retries. See the [standard parameters](#standard-parameters) section for more information.
+3. Fallback to another chat model: If you hit a rate limit with one chat model, you can switch to another chat model that is not rate-limited.
+
+### Caching
+
+Chat model APIs can be slow, so a natural question is whether to cache the results of previous conversations. Theoretically, caching can help improve performance by reducing the number of requests made to the model provider. In practice, caching chat model responses is a complex problem and should be approached with caution.
+
+The reason is that getting a cache hit is unlikely after the first or second interaction in a conversation if relying on caching the **exact** inputs into the model. For example, how likely do you think that multiple conversations start with the exact same message? What about the exact same three messages?
+
+An alternative approach is to use semantic caching, where you cache responses based on the meaning of the input rather than the exact input itself. This can be effective in some situations, but not in others.
+
+A semantic cache introduces a dependency on another model on the critical path of your application (e.g., the semantic cache may rely on an [embedding model](/docs/concepts/embedding_models) to convert text to a vector representation), and it's not guaranteed to capture the meaning of the input accurately.
+
+However, there might be situations where caching chat model responses is beneficial. For example, if you have a chat model that is used to answer frequently asked questions, caching responses can help reduce the load on the model provider and improve response times.
+
+Please see the [how to cache chat model responses](/docs/how_to/#chat-model-caching) guide for more details.
+
+## Related resources
+
+* How-to guides on using chat models: [how-to guides](/docs/how_to/#chat-models).
+* List of supported chat models: [chat model integrations](/docs/integrations/chat/).
+
+### Conceptual guides
+
+* [Messages](/docs/concepts/messages)
+* [Tool calling](/docs/concepts#tool-calling)
+* [Multimodality](/docs/concepts/multimodality)
+* [Structured outputs](/docs/concepts#structured_output)
+* [Tokens](/docs/concepts/tokens)
\ No newline at end of file
diff --git a/docs/docs/concepts/document_loaders.mdx b/docs/docs/concepts/document_loaders.mdx
new file mode 100644
index 0000000000000..a6a11ddfe7104
--- /dev/null
+++ b/docs/docs/concepts/document_loaders.mdx
@@ -0,0 +1,45 @@
+# Document loaders
+<span data-heading-keywords="document loader,document loaders"></span>
+
+:::info[Prerequisites]
+
+* [Document loaders API reference](https://python.langchain.com/docs/how_to/#document-loaders)
+:::
+
+Document loaders are designed to load document objects. LangChain has hundreds of integrations with various data sources to load data from: Slack, Notion, Google Drive, etc.
+
+## Integrations
+
+You can find available integrations on the [Document Loaders Integrations page](https://python.langchain.com/docs/integrations/document_loaders/).
+
+## Interface
+
+Documents loaders implement the [BaseLoader interface](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.base.BaseLoader.html).
+
+Each DocumentLoader has its own specific parameters, but they can all be invoked in the same way with the `.load` method or `.lazy_load`.
+
+Here's a simple example:
+
+```python
+from langchain_community.document_loaders.csv_loader import CSVLoader
+
+loader = CSVLoader(
+    ...  # <-- Integration specific parameters here
+)
+data = loader.load()
+```
+
+or if working with large datasets, you can use the `.lazy_load` method:
+
+```python
+for document in loader.lazy_load():
+    print(document)
+```
+
+## Related resources
+
+Please see the following resources for more information:
+
+* [How-to guides for document loaders](https://python.langchain.com/docs/how_to/#document-loaders)
+* [Document API reference](https://python.langchain.com/docs/how_to/#document-loaders)
+* [Document loaders integrations](https://python.langchain.com/docs/integrations/document_loaders/)
diff --git a/docs/docs/concepts/embedding_models.mdx b/docs/docs/concepts/embedding_models.mdx
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+# Embedding models
+<span data-heading-keywords="embedding,embeddings"></span>
+
+:::info[Prerequisites]
+
+* [Documents](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html)
+
+:::
+
+:::info[Note]
+This conceptual overview focuses on text-based embedding models.
+
+Embedding models can also be [multimodal](/docs/concepts/multimodality) though such models are not currently supported by LangChain.
+:::
+
+Imagine being able to capture the essence of any text - a tweet, document, or book - in a single, compact representation.
+This is the power of embedding models, which lie at the heart of many retrieval systems.
+Embedding models transform human language into a format that machines can understand and compare with speed and accuracy. 
+These models take text as input and produce a fixed-length array of numbers, a numerical fingerprint of the text's semantic meaning.
+Embeddings allow search system to find relevant documents not just based on keyword matches, but on semantic understanding. 
+
+## Key concepts
+
+![Conceptual Overview](/img/embeddings_concept.png)
+
+(1) **Embed text as a vector**: Embeddings transform text into a numerical vector representation.
+
+(2) **Measure similarity**: Embedding vectors can be comparing using simple mathematical operations.
+
+## Embedding 
+
+### Historical context 
+
+The landscape of embedding models has evolved significantly over the years. 
+A pivotal moment came in 2018 when Google introduced [BERT (Bidirectional Encoder Representations from Transformers)](https://www.nvidia.com/en-us/glossary/bert/). 
+BERT applied transformer models to embed text as a simple vector representation, which lead to unprecedented performance across various NLP tasks.
+However, BERT wasn't optimized for generating sentence embeddings efficiently. 
+This limitation spurred the creation of [SBERT (Sentence-BERT)](https://www.sbert.net/examples/training/sts/README.html), which adapted the BERT architecture to generate semantically rich sentence embeddings, easily comparable via similarity metrics like cosine similarity, dramatically reduced the computational overhead for tasks like finding similar sentences.
+Today, the embedding model ecosystem is diverse, with numerous providers offering their own implementations. 
+To navigate this variety, researchers and practitioners often turn to benchmarks like the Massive Text Embedding Benchmark (MTEB) [here](https://huggingface.co/blog/mteb) for objective comparisons.
+
+:::info[Further reading]
+
+* See the [seminal BERT paper](https://arxiv.org/abs/1810.04805).
+* See Cameron Wolfe's [excellent review](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2) of embedding models.
+* See the [Massive Text Embedding Benchmark (MTEB)](https://huggingface.co/blog/mteb) leaderboard for a comprehensive overview of embedding models.
+
+:::
+
+### Interface
+
+LangChain provides a universal interface for working with them, providing standard methods for common operations.
+This common interface simplifies interaction with various embedding providers through two central methods:
+
+- `embed_documents`: For embedding multiple texts (documents)
+- `embed_query`: For embedding a single text (query)
+
+This distinction is important, as some providers employ different embedding strategies for documents (which are to be searched) versus queries (the search input itself).
+To illustrate, here's a practical example using LangChain's `.embed_documents` method to embed a list of strings:
+
+```python
+from langchain_openai import OpenAIEmbeddings
+embeddings_model = OpenAIEmbeddings()
+embeddings = embeddings_model.embed_documents(
+    [
+        "Hi there!",
+        "Oh, hello!",
+        "What's your name?",
+        "My friends call me World",
+        "Hello World!"
+    ]
+)
+len(embeddings), len(embeddings[0])
+(5, 1536)
+```
+
+For convenience, you can also use the `embed_query` method to embed a single text:
+
+```python
+query_embedding = embeddings_model.embed_query("What is the meaning of life?")
+```
+
+:::info[Further reading]
+
+* See the full list of [LangChain embedding model integrations](/docs/integrations/text_embedding/).
+* See these [how-to guides](/docs/how_to/embed_text) for working with embedding models.
+
+:::
+
+### Integrations
+
+LangChain offers many embedding model integrations which you can find [on the embedding models](/docs/integrations/text_embedding/) integrations page.
+
+## Measure similarity
+
+Each embedding is essentially a set of coordinates, often in a high-dimensional space. 
+In this space, the position of each point (embedding) reflects the meaning of its corresponding text.
+Just as similar words might be close to each other in a thesaurus, similar concepts end up close to each other in this embedding space. 
+This allows for intuitive comparisons between different pieces of text.
+By reducing text to these numerical representations, we can use simple mathematical operations to quickly measure how alike two pieces of text are, regardless of their original length or structure.
+Some common similarity metrics include:
+
+- **Cosine Similarity**: Measures the cosine of the angle between two vectors.
+- **Euclidean Distance**: Measures the straight-line distance between two points.
+- **Dot Product**: Measures the projection of one vector onto another.
+
+The choice of similarity metric should be chosen based on the model.
+As an example, [OpenAI suggests cosine similarity for their embeddings](https://platform.openai.com/docs/guides/embeddings/which-distance-function-should-i-use), which can be easily implemented:
+
+```python
+import numpy as np
+
+def cosine_similarity(vec1, vec2):
+    dot_product = np.dot(vec1, vec2)
+    norm_vec1 = np.linalg.norm(vec1)
+    norm_vec2 = np.linalg.norm(vec2)
+    return dot_product / (norm_vec1 * norm_vec2)
+
+similarity = cosine_similarity(query_result, document_result)
+print("Cosine Similarity:", similarity)
+```  
+
+:::info[Further reading]
+
+* See Simon Willison’s [nice blog post and video](https://simonwillison.net/2023/Oct/23/embeddings/) on embeddings and similarity metrics.
+* See [this documentation](https://developers.google.com/machine-learning/clustering/dnn-clustering/supervised-similarity) from Google on similarity metrics to consider with embeddings.
+* See Pinecone's [blog post](https://www.pinecone.io/learn/vector-similarity/) on similarity metrics.
+* See OpenAI's [FAQ](https://platform.openai.com/docs/guides/embeddings/faq) on what similarity metric to use with OpenAI embeddings.
+
+::: 
diff --git a/docs/docs/concepts/evaluation.mdx b/docs/docs/concepts/evaluation.mdx
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+# Evaluation
+<span data-heading-keywords="evaluation,evaluate"></span>
+
+Evaluation is the process of assessing the performance and effectiveness of your LLM-powered applications.
+It involves testing the model's responses against a set of predefined criteria or benchmarks to ensure it meets the desired quality standards and fulfills the intended purpose.
+This process is vital for building reliable applications.
+
+![](/img/langsmith_evaluate.png)
+
+[LangSmith](https://docs.smith.langchain.com/) helps with this process in a few ways:
+
+- It makes it easier to create and curate datasets via its tracing and annotation features
+- It provides an evaluation framework that helps you define metrics and run your app against your dataset
+- It allows you to track results over time and automatically run your evaluators on a schedule or as part of CI/Code
+
+To learn more, check out [this LangSmith guide](https://docs.smith.langchain.com/concepts/evaluation).
+
diff --git a/docs/docs/concepts/example_selectors.mdx b/docs/docs/concepts/example_selectors.mdx
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+# Example selectors
+
+:::note Prerequisites
+
+- [Chat models](/docs/concepts/chat_models/)
+- [Few-shot prompting](/docs/concepts/few_shot_prompting/)
+:::
+
+## Overview
+
+One common prompting technique for achieving better performance is to include examples as part of the prompt. This is known as [few-shot prompting](/docs/concepts/few_shot_prompting).
+
+This gives the [language model](/docs/concepts/chat_models/) concrete examples of how it should behave.
+Sometimes these examples are hardcoded into the prompt, but for more advanced situations it may be nice to dynamically select them.
+
+**Example Selectors** are classes responsible for selecting and then formatting examples into prompts.
+
+## Related resources
+
+* [Example selector how-to guides](/docs/how_to/#example-selectors)
\ No newline at end of file
diff --git a/docs/docs/concepts/few_shot_prompting.mdx b/docs/docs/concepts/few_shot_prompting.mdx
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+# Few-shot prompting
+
+:::note Prerequisites
+
+- [Chat models](/docs/concepts/chat_models/)
+:::
+
+## Overview
+
+One of the most effective ways to improve model performance is to give a model examples of
+what you want it to do. The technique of adding example inputs and expected outputs
+to a model prompt is known as "few-shot prompting". The technique is based on the
+[Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) paper.
+There are a few things to think about when doing few-shot prompting:
+
+1. How are examples generated?
+2. How many examples are in each prompt?
+3. How are examples selected at runtime?
+4. How are examples formatted in the prompt?
+
+Here are the considerations for each.
+
+## 1. Generating examples
+
+The first and most important step of few-shot prompting is coming up with a good dataset of examples. Good examples should be relevant at runtime, clear, informative, and provide information that was not already known to the model.
+
+At a high-level, the basic ways to generate examples are:
+- Manual: a person/people generates examples they think are useful.
+- Better model: a better (presumably more expensive/slower) model's responses are used as examples for a worse (presumably cheaper/faster) model.
+- User feedback: users (or labelers) leave feedback on interactions with the application and examples are generated based on that feedback (for example, all interactions with positive feedback could be turned into examples).
+- LLM feedback: same as user feedback but the process is automated by having models evaluate themselves.
+
+Which approach is best depends on your task. For tasks where a small number core principles need to be understood really well, it can be valuable hand-craft a few really good examples.
+For tasks where the space of correct behaviors is broader and more nuanced, it can be useful to generate many examples in a more automated fashion so that there's a higher likelihood of there being some highly relevant examples for any runtime input.
+
+**Single-turn v.s. multi-turn examples**
+
+Another dimension to think about when generating examples is what the example is actually showing.
+
+The simplest types of examples just have a user input and an expected model output. These are single-turn examples.
+
+One more complex type if example is where the example is an entire conversation, usually in which a model initially responds incorrectly and a user then tells the model how to correct its answer.
+This is called a multi-turn example. Multi-turn examples can be useful for more nuanced tasks where its useful to show common errors and spell out exactly why they're wrong and what should be done instead.
+
+## 2. Number of examples
+
+Once we have a dataset of examples, we need to think about how many examples should be in each prompt.
+The key tradeoff is that more examples generally improve performance, but larger prompts increase costs and latency.
+And beyond some threshold having too many examples can start to confuse the model.
+Finding the right number of examples is highly dependent on the model, the task, the quality of the examples, and your cost and latency constraints.
+Anecdotally, the better the model is the fewer examples it needs to perform well and the more quickly you hit steeply diminishing returns on adding more examples.
+But, the best/only way to reliably answer this question is to run some experiments with different numbers of examples.
+
+## 3. Selecting examples
+
+Assuming we are not adding our entire example dataset into each prompt, we need to have a way of selecting examples from our dataset based on a given input. We can do this:
+- Randomly
+- By (semantic or keyword-based) similarity of the inputs
+- Based on some other constraints, like token size
+
+LangChain has a number of [`ExampleSelectors`](/docs/concepts/example_selectors) which make it easy to use any of these techniques.
+
+Generally, selecting by semantic similarity leads to the best model performance. But how important this is is again model and task specific, and is something worth experimenting with.
+
+## 4. Formatting examples
+
+Most state-of-the-art models these days are chat models, so we'll focus on formatting examples for those. Our basic options are to insert the examples:
+- In the system prompt as a string
+- As their own messages
+
+If we insert our examples into the system prompt as a string, we'll need to make sure it's clear to the model where each example begins and which parts are the input versus output. Different models respond better to different syntaxes, like [ChatML](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chat-markup-language), XML, TypeScript, etc.
+
+If we insert our examples as messages, where each example is represented as a sequence of Human, AI messages, we might want to also assign [names](/docs/concepts/#messages) to our messages like `"example_user"` and `"example_assistant"` to make it clear that these messages correspond to different actors than the latest input message.
+
+**Formatting tool call examples**
+
+One area where formatting examples as messages can be tricky is when our example outputs have tool calls. This is because different models have different constraints on what types of message sequences are allowed when any tool calls are generated.
+- Some models require that any AIMessage with tool calls be immediately followed by ToolMessages for every tool call,
+- Some models additionally require that any ToolMessages be immediately followed by an AIMessage before the next HumanMessage,
+- Some models require that tools are passed in to the model if there are any tool calls / ToolMessages in the chat history.
+
+These requirements are model-specific and should be checked for the model you are using. If your model requires ToolMessages after tool calls and/or AIMessages after ToolMessages and your examples only include expected tool calls and not the actual tool outputs, you can try adding dummy ToolMessages / AIMessages to the end of each example with generic contents to satisfy the API constraints.
+In these cases it's especially worth experimenting with inserting your examples as strings versus messages, as having dummy messages can adversely affect certain models.
+
+You can see a case study of how Anthropic and OpenAI respond to different few-shot prompting techniques on two different tool calling benchmarks [here](https://blog.langchain.dev/few-shot-prompting-to-improve-tool-calling-performance/).
diff --git a/docs/docs/concepts/index.mdx b/docs/docs/concepts/index.mdx
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+++ b/docs/docs/concepts/index.mdx
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+# Conceptual guide
+
+This guide provides explanations of the key concepts behind the LangChain framework and AI applications more broadly.
+
+We recommend that you go through at least one of the [Tutorials](/docs/tutorials) before diving into the conceptual guide. This will provide practical context that will make it easier to understand the concepts discussed here.
+
+The conceptual guide does not cover step-by-step instructions or specific implementation examples — those are found in the [How-to guides](/docs/how_to/) and [Tutorials](/docs/tutorials). For detailed reference material, please see the [API reference](https://python.langchain.com/api_reference/).
+
+## High level
+
+- **[Why LangChain?](/docs/concepts/why_langchain)**: Overview of the value that LangChain provides.
+- **[Architecture](/docs/concepts/architecture)**: How packages are organized in the LangChain ecosystem.
+
+## Concepts
+
+- **[Chat models](/docs/concepts/chat_models)**: LLMs exposed via a chat API that process sequences of messages as input and output a message.
+- **[Messages](/docs/concepts/messages)**: The unit of communication in chat models, used to represent model input and output.
+- **[Chat history](/docs/concepts/chat_history)**: A conversation represented as a sequence of messages, alternating between user messages and model responses.
+- **[Tools](/docs/concepts/tools)**: A function with an associated schema defining the function's name, description, and the arguments it accepts.
+- **[Tool calling](/docs/concepts/tool_calling)**: A type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
+- **[Structured output](/docs/concepts/structured_outputs)**: A technique to make a chat model respond in a structured format, such as JSON that matches a given schema.
+- **[Memory](https://langchain-ai.github.io/langgraph/concepts/memory/)**: Information about a conversation that is persisted so that it can be used in future conversations.
+- **[Multimodality](/docs/concepts/multimodality)**: The ability to work with data that comes in different forms, such as text, audio, images, and video.
+- **[Runnable interface](/docs/concepts/runnables)**: The base abstraction that many LangChain components and the LangChain Expression Language are built on.
+- **[LangChain Expression Language (LCEL)](/docs/concepts/lcel)**: A syntax for orchestrating LangChain components. Most useful for simpler applications.
+- **[Document loaders](/docs/concepts/document_loaders)**: Load a source as a list of documents.
+- **[Retrieval](/docs/concepts/retrieval)**: Information retrieval systems can retrieve structured or unstructured data from a datasource in response to a query.
+- **[Text splitters](/docs/concepts/text_splitters)**: Split long text into smaller chunks that can be individually indexed to enable granular retrieval.
+- **[Embedding models](/docs/concepts/embedding_models)**: Models that represent data such as text or images in a vector space.
+- **[Vector stores](/docs/concepts/vectorstores)**: Storage of and efficient search over vectors and associated metadata.
+- **[Retriever](/docs/concepts/retrievers)**: A component that returns relevant documents from a knowledge base in response to a query.
+- **[Retrieval Augmented Generation (RAG)](/docs/concepts/rag)**: A technique that enhances language models by combining them with external knowledge bases.
+- **[Agents](/docs/concepts/agents)**: Use a [language model](/docs/concepts/chat_models) to choose a sequence of actions to take. Agents can interact with external resources via [tool](/docs/concepts/tools).
+- **[Prompt templates](/docs/concepts/prompt_templates)**: Component for factoring out the static parts of a model "prompt" (usually a sequence of messages). Useful for serializing, versioning, and reusing these static parts.
+- **[Output parsers](/docs/concepts/output_parsers)**: Responsible for taking the output of a model and transforming it into a more suitable format for downstream tasks. Output parsers were primarily useful prior to the general availability of [tool calling](/docs/concepts/tool_calling) and [structured outputs](/docs/concepts/structured_outputs).
+- **[Few-shot prompting](/docs/concepts/few_shot_prompting)**: A technique for improving model performance by providing a few examples of the task to perform in the prompt.
+- **[Example selectors](/docs/concepts/example_selectors)**: Used to select the most relevant examples from a dataset based on a given input. Example selectors are used in few-shot prompting to select examples for a prompt.
+- **[Async programming](/docs/concepts/async)**: The basics that one should know to use LangChain in an asynchronous context.
+- **[Callbacks](/docs/concepts/callbacks)**: Callbacks enable the execution of custom auxiliary code in built-in components. Callbacks are used to stream outputs from LLMs in LangChain, trace the intermediate steps of an application, and more.
+- **[Tracing](/docs/concepts/tracing)**: The process of recording the steps that an application takes to go from input to output. Tracing is essential for debugging and diagnosing issues in complex applications.
+- **[Evaluation](/docs/concepts/evaluation)**: The process of assessing the performance and effectiveness of AI applications. This involves testing the model's responses against a set of predefined criteria or benchmarks to ensure it meets the desired quality standards and fulfills the intended purpose. This process is vital for building reliable applications.
+
+## Glossary
+
+- **[AIMessageChunk](/docs/concepts/messages#aimessagechunk)**: A partial response from an AI message. Used when streaming responses from a chat model.
+- **[AIMessage](/docs/concepts/messages#aimessage)**: Represents a complete response from an AI model.
+- **[astream_events](/docs/concepts/chat_models#key-methods)**: Stream granular information from [LCEL](/docs/concepts/lcel) chains.
+- **[BaseTool](/docs/concepts/tools#basetool)**: The base class for all tools in LangChain.
+- **[batch](/docs/concepts/runnables)**: Use to execute a runnable with batch inputs a Runnable.
+- **[bind_tools](/docs/concepts/chat_models#bind-tools)**: Allows models to interact with tools.
+- **[Caching](/docs/concepts/chat_models#caching)**: Storing results to avoid redundant calls to a chat model.
+- **[Chat models](/docs/concepts/multimodality#chat-models)**: Chat models that handle multiple data modalities.
+- **[Configurable runnables](/docs/concepts/runnables#configurable-Runnables)**: Creating configurable Runnables.
+- **[Context window](/docs/concepts/chat_models#context-window)**: The maximum size of input a chat model can process.
+- **[Conversation patterns](/docs/concepts/chat_history#conversation-patterns)**: Common patterns in chat interactions.
+- **[Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html)**: LangChain's representation of a document.
+- **[Embedding models](/docs/concepts/multimodality#embedding-models)**: Models that generate vector embeddings for various data types.
+- **[HumanMessage](/docs/concepts/messages#humanmessage)**: Represents a message from a human user.
+- **[InjectedState](/docs/concepts/tools#injectedstate)**: A state injected into a tool function.
+- **[InjectedStore](/docs/concepts/tools#injectedstore)**: A store that can be injected into a tool for data persistence.
+- **[InjectedToolArg](/docs/concepts/tools#injectedtoolarg)**: Mechanism to inject arguments into tool functions.
+- **[input and output types](/docs/concepts/runnables#input-and-output-types)**: Types used for input and output in Runnables.
+- **[Integration packages](/docs/concepts/architecture#partner-packages)**: Third-party packages that integrate with LangChain.
+- **[invoke](/docs/concepts/runnables)**: A standard method to invoke a Runnable.
+- **[JSON mode](/docs/concepts/structured_outputs#json-mode)**: Returning responses in JSON format.
+- **[langchain-community](/docs/concepts/architecture#langchain-community)**: Community-driven components for LangChain.
+- **[langchain-core](/docs/concepts/architecture#langchain-core)**: Core langchain package. Includes base interfaces and in-memory implementations.
+- **[langchain](/docs/concepts/architecture#langchain)**: A package for higher level components (e.g., some pre-built chains).
+- **[langgraph](/docs/concepts/architecture#langgraph)**: Powerful orchestration layer for LangChain. Use to build complex pipelines and workflows.
+- **[langserve](/docs/concepts/architecture#langserve)**: Use to deploy LangChain Runnables as REST endpoints. Uses FastAPI. Works primarily for LangChain Runnables, does not currently integrate with LangGraph.
+- **[Managing chat history](/docs/concepts/chat_history#managing-chat-history)**: Techniques to maintain and manage the chat history.
+- **[OpenAI format](/docs/concepts/messages#openai-format)**: OpenAI's message format for chat models.
+- **[Propagation of RunnableConfig](/docs/concepts/runnables#propagation-RunnableConfig)**: Propagating configuration through Runnables. Read if working with python 3.9, 3.10 and async.
+- **[rate-limiting](/docs/concepts/chat_models#rate-limiting)**: Client side rate limiting for chat models.
+- **[RemoveMessage](/docs/concepts/messages#remove-message)**: An abstraction used to remove a message from chat history, used primarily in LangGraph.
+- **[role](/docs/concepts/messages#role)**: Represents the role (e.g., user, assistant) of a chat message.
+- **[RunnableConfig](/docs/concepts/runnables#RunnableConfig)**: Use to pass run time information to Runnables (e.g., `run_name`, `run_id`, `tags`, `metadata`, `max_concurrency`, `recursion_limit`, `configurable`).
+- **[Standard parameters for chat models](/docs/concepts/chat_models#standard-parameters)**: Parameters such as API key, `temperature`, and `max_tokens`,
+- **[stream](/docs/concepts/streaming)**: Use to stream output from a Runnable or a graph.
+- **[Tokenization](/docs/concepts/tokens)**: The process of converting data into tokens and vice versa.
+- **[Tokens](/docs/concepts/tokens)**: The basic unit that a language model reads, processes, and generates under the hood.
+- **[Tool artifacts](/docs/concepts/tools#tool-artifacts)**: Add artifacts to the output of a tool that will not be sent to the model, but will be available for downstream processing.
+- **[Tool binding](/docs/concepts/tool_calling#tool-binding)**: Binding tools to models.
+- **[@tool](/docs/concepts/tools#@tool)**: Decorator for creating tools in LangChain.
+- **[Toolkits](/docs/concepts/tools#toolkits)**: A collection of tools that can be used together.
+- **[ToolMessage](/docs/concepts/messages#toolmessage)**: Represents a message that contains the results of a tool execution.
+- **[Vector stores](/docs/concepts/vectorstores)**: Datastores specialized for storing and efficiently searching vector embeddings.
+- **[with_structured_output](/docs/concepts/chat_models#with-structured-output)**: A helper method for chat models that natively support [tool calling](/docs/concepts/tool_calling) to get structured output matching a given schema specified via Pydantic, JSON schema or a function.
+- **[with_types](/docs/concepts/runnables#with_types)**: Method to overwrite the input and output types of a runnable. Useful when working with complex LCEL chains and deploying with LangServe.
diff --git a/docs/docs/concepts/key_value_stores.mdx b/docs/docs/concepts/key_value_stores.mdx
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+# Key-value stores
+
+## Overview
+
+LangChain provides a key-value store interface for storing and retrieving data.
+
+LangChain includes a [`BaseStore`](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.BaseStore.html) interface,
+which allows for storage of arbitrary data. However, LangChain components that require KV-storage accept a
+more specific `BaseStore[str, bytes]` instance that stores binary data (referred to as a `ByteStore`), and internally take care of
+encoding and decoding data for their specific needs.
+
+This means that as a user, you only need to think about one type of store rather than different ones for different types of data.
+
+## Usage
+
+The key-value store interface in LangChain is used primarily for:
+
+1. Caching [embeddings](/docs/concepts/embedding_models) via [CachedBackedEmbeddings](https://python.langchain.com/api_reference/langchain/embeddings/langchain.embeddings.cache.CacheBackedEmbeddings.html#langchain.embeddings.cache.CacheBackedEmbeddings) to avoid recomputing embeddings for repeated queries or when re-indexing content.
+
+2. As a simple [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) persistence layer in some retrievers.
+
+Please see these how-to guides for more information:
+
+* [How to cache embeddings guide](https://python.langchain.com/docs/how_to/caching_embeddings/).
+* [How to retriever using multiple vectors per document](https://python.langchain.com/docs/how_to/custom_retriever/).
+
+## Interface
+
+All [`BaseStores`](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.BaseStore.html) support the following interface. Note that the interface allows for modifying **multiple** key-value pairs at once:
+
+- `mget(key: Sequence[str]) -> List[Optional[bytes]]`: get the contents of multiple keys, returning `None` if the key does not exist
+- `mset(key_value_pairs: Sequence[Tuple[str, bytes]]) -> None`: set the contents of multiple keys
+- `mdelete(key: Sequence[str]) -> None`: delete multiple keys
+- `yield_keys(prefix: Optional[str] = None) -> Iterator[str]`: yield all keys in the store, optionally filtering by a prefix
+
+## Integrations
+
+Please reference the [stores integration page](/docs/integrations/stores/) for a list of available key-value store integrations.
diff --git a/docs/docs/concepts/lcel.mdx b/docs/docs/concepts/lcel.mdx
new file mode 100644
index 0000000000000..9378ec8e92852
--- /dev/null
+++ b/docs/docs/concepts/lcel.mdx
@@ -0,0 +1,221 @@
+# LangChain Expression Language (LCEL)
+
+:::info Prerequisites
+* [Runnable Interface](/docs/concepts/runnables)
+:::
+
+The **L**ang**C**hain **E**xpression **L**anguage (LCEL) takes a [declarative](https://en.wikipedia.org/wiki/Declarative_programming) approach to building new [Runnables](/docs/concepts/runnables) from existing Runnables.
+
+This means that you describe what you want to happen, rather than how you want it to happen, allowing LangChain to optimize the run-time execution of the chains.
+
+We often refer to a `Runnable` created using LCEL as a "chain". It's important to remember that a "chain" is `Runnable` and it implements the full [Runnable Interface](/docs/concepts/runnables).
+
+:::note
+* The [LCEL cheatsheet](https://python.langchain.com/docs/how_to/lcel_cheatsheet/) shows common patterns that involve the Runnable interface and LCEL expressions.
+* Please see the following list of [how-to guides](/docs/how_to/#langchain-expression-language-lcel) that cover common tasks with LCEL.
+* A list of built-in `Runnables` can be found in the [LangChain Core API Reference](https://python.langchain.com/api_reference/core/runnables.html). Many of these Runnables are useful when composing custom "chains" in LangChain using LCEL.
+:::
+
+## Benefits of LCEL
+
+LangChain optimizes the run-time execution of chains built with LCEL in a number of ways:
+
+- **Optimize parallel execution**: Run Runnables in parallel using [RunnableParallel](#RunnableParallel) or run multiple inputs through a given chain in parallel using the [Runnable Batch API](/docs/concepts/runnables#batch). Parallel execution can significantly reduce the latency as processing can be done in parallel instead of sequentially.
+- **Guarantee Async support**: Any chain built with LCEL can be run asynchronously using the [Runnable Async API](/docs/concepts/runnables#async-api). This can be useful when running chains in a server environment where you want to handle large number of requests concurrently.
+- **Simplify streaming**: LCEL chains can be streamed, allowing for incremental output as the chain is executed. LangChain can optimize the streaming of the output to minimize the time-to-first-token(time elapsed until the first chunk of output from a [chat model](/docs/concepts/chat_models) or [llm](/docs/concepts/llms) comes out).
+
+Other benefits include:
+
+- [**Seamless LangSmith tracing**](https://docs.smith.langchain.com)
+As your chains get more and more complex, it becomes increasingly important to understand what exactly is happening at every step.
+With LCEL, **all** steps are automatically logged to [LangSmith](https://docs.smith.langchain.com/) for maximum observability and debuggability.
+- **Standard API**: Because all chains are built using the Runnable interface, they can be used in the same way as any other Runnable.
+- [**Deployable with LangServe**](/docs/concepts/architecture#langserve): Chains built with LCEL can be deployed using for production use.
+
+## Should I use LCEL?
+
+LCEL is an [orchestration solution](https://en.wikipedia.org/wiki/Orchestration_(computing)) -- it allows LangChain to handle run-time execution of chains in an optimized way.
+
+While we have seen users run chains with hundreds of steps in production, we generally recommend using LCEL for simpler orchestration tasks. When the application requires complex state management, branching, cycles or multiple agents, we recommend that users take advantage of [LangGraph](/docs/concepts/architecture#langgraph).
+
+In LangGraph, users define graphs that specify the flow of the application. This allows users to keep using LCEL within individual nodes when LCEL is needed, while making it easy to define complex orchestration logic that is more readable and maintainable.
+
+Here are some guidelines:
+
+* If you are making a single LLM call, you don't need LCEL; instead call the underlying [chat model](/docs/concepts/chat_models) directly.
+* If you have a simple chain (e.g., prompt + llm + parser, simple retrieval set up etc.), LCEL is a reasonable fit, if you're taking advantage of the LCEL benefits.
+* If you're building a complex chain (e.g., with branching, cycles, multiple agents, etc.) use [LangGraph](/docs/concepts/architecture#langgraph) instead. Remember that you can always use LCEL within individual nodes in LangGraph.
+
+## Composition Primitives
+
+`LCEL` chains are built by composing existing `Runnables` together. The two main composition primitives are [RunnableSequence](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableSequence.html#langchain_core.runnables.base.RunnableSequence) and [RunnableParallel](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableParallel.html#langchain_core.runnables.base.RunnableParallel).
+
+Many other composition primitives (e.g., [RunnableAssign](
+https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnableAssign.html#langchain_core.runnables.passthrough.RunnableAssign
+)) can be thought of as variations of these two primitives.
+
+:::note
+You can find a list of all composition primitives in the [LangChain Core API Reference](https://python.langchain.com/api_reference/core/runnables.html).
+:::
+
+### RunnableSequence
+
+`RunnableSequence` is a composition primitive that allows you "chain" multiple runnables sequentially, with the output of one runnable serving as the input to the next.
+
+```python
+from langchain_core.runnables import RunnableSequence
+chain = RunnableSequence([runnable1, runnable2])
+```
+
+Invoking the `chain` with some input:
+
+```python
+final_output = chain.invoke(some_input)
+```
+
+corresponds to the following:
+
+```python
+output1 = runnable1.invoke(some_input)
+final_output = runnable2.invoke(output1)
+```
+
+:::note
+`runnable1` and `runnable2` are placeholders for any `Runnable` that you want to chain together.
+:::
+
+### RunnableParallel
+
+`RunnableParallel` is a composition primitive that allows you to run multiple runnables concurrently, with the same input provided to each.
+
+```python
+from langchain_core.runnables import RunnableParallel
+chain = RunnableParallel({
+    "key1": runnable1,
+    "key2": runnable2,
+})
+```
+
+Invoking the `chain` with some input:
+
+```python
+final_output = chain.invoke(some_input)
+```
+
+Will yield a `final_output` dictionary with the same keys as the input dictionary, but with the values replaced by the output of the corresponding runnable.
+
+```python
+{
+    "key1": runnable1.invoke(some_input),
+    "key2": runnable2.invoke(some_input),
+}
+```
+
+Recall, that the runnables are executed in parallel, so while the result is the same as
+dictionary comprehension shown above, the execution time is much faster.
+
+:::note
+`RunnableParallel`supports both synchronous and asynchronous execution (as all `Runnables` do).
+
+* For synchronous execution, `RunnableParallel` uses a [ThreadPoolExecutor](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor) to run the runnables concurrently.
+* For asynchronous execution, `RunnableParallel` uses [asyncio.gather](https://docs.python.org/3/library/asyncio.html#asyncio.gather) to run the runnables concurrently.
+:::
+
+## Composition Syntax
+
+The usage of `RunnableSequence` and `RunnableParallel` is so common that we created a shorthand syntax for using them. This helps
+to make the code more readable and concise.
+
+### The `|` operator
+
+We have [overloaded](https://docs.python.org/3/reference/datamodel.html#special-method-names) the `|` operator to create a `RunnableSequence` from two `Runnables`.
+
+```python
+chain = runnable1 | runnable2
+```
+
+is Equivalent to:
+
+```python
+chain = RunnableSequence([runnable1, runnable2])
+```
+
+### The `.pipe` method`
+
+If you have moral qualms with operator overloading, you can use the `.pipe` method instead. This is equivalent to the `|` operator.
+
+```python
+chain = runnable1.pipe(runnable2)
+```
+
+### Coercion
+
+LCEL applies automatic type coercion to make it easier to compose chains.
+
+If you do not understand the type coercion, you can always use the `RunnableSequence` and `RunnableParallel` classes directly.
+
+This will make the code more verbose, but it will also make it more explicit.
+
+#### Dictionary to RunnableParallel
+
+Inside an LCEL expression, a dictionary is automatically converted to a `RunnableParallel`.
+
+For example, the following code:
+
+```python
+mapping = {
+    "key1": runnable1,
+    "key2": runnable2,
+}
+
+chain = mapping | runnable3
+```
+
+It gets automatically converted to the following:
+
+```python
+chain = RunnableSequence([RunnableParallel(mapping), runnable3])
+```
+
+:::caution
+You have to be careful because the `mapping` dictionary is not a `RunnableParallel` object, it is just a dictionary. This means that the following code will raise an `AttributeError`:
+
+```python
+mapping.invoke(some_input)
+```
+:::
+
+#### Function to RunnableLambda
+
+Inside an LCEL expression, a function is automatically converted to a `RunnableLambda`.
+
+```
+def some_func(x):
+    return x
+
+chain = some_func | runnable1
+```
+
+It gets automatically converted to the following:
+
+```python
+chain = RunnableSequence([RunnableLambda(some_func), runnable1])
+```
+
+:::caution
+You have to be careful because the lambda function is not a `RunnableLambda` object, it is just a function. This means that the following code will raise an `AttributeError`:
+
+```python
+lambda x: x + 1.invoke(some_input)
+```
+:::
+
+## Legacy Chains
+
+LCEL aims to provide consistency around behavior and customization over legacy subclassed chains such as `LLMChain` and
+`ConversationalRetrievalChain`. Many of these legacy chains hide important details like prompts, and as a wider variety
+of viable models emerge, customization has become more and more important.
+
+If you are currently using one of these legacy chains, please see [this guide for guidance on how to migrate](/docs/versions/migrating_chains).
+
+For guides on how to do specific tasks with LCEL, check out [the relevant how-to guides](/docs/how_to/#langchain-expression-language-lcel).
diff --git a/docs/docs/concepts/llms.mdx b/docs/docs/concepts/llms.mdx
new file mode 100644
index 0000000000000..5e2f7d98c7256
--- /dev/null
+++ b/docs/docs/concepts/llms.mdx
@@ -0,0 +1,3 @@
+# Large language models (llms)
+
+Please see the [Chat Model Concept Guide](/docs/concepts/chat_models) page for more information.
\ No newline at end of file
diff --git a/docs/docs/concepts/messages.mdx b/docs/docs/concepts/messages.mdx
new file mode 100644
index 0000000000000..811396883af06
--- /dev/null
+++ b/docs/docs/concepts/messages.mdx
@@ -0,0 +1,244 @@
+# Messages
+
+:::info Prerequisites
+- [Chat Models](/docs/concepts/chat_models)
+:::
+
+## Overview
+
+Messages are the unit of communication in [chat models](/docs/concepts/chat_models). They are used to represent the input and output of a chat model, as well as any additional context or metadata that may be associated with a conversation.
+
+Each message has a **role** (e.g., "user", "assistant"), **content** (e.g., text, multimodal data), and additional metadata that can vary depending on the chat model provider.
+
+LangChain provides a unified message format that can be used across chat models, allowing users to work with different chat models without worrying about the specific details of the message format used by each model provider.
+
+## What inside a message?
+
+A message typically consists of the following pieces of information:
+
+- **Role**: The role of the message (e.g., "user", "assistant").
+- **Content**: The content of the message (e.g., text, multimodal data).
+- Additional metadata: id, name, [token usage](/docs/concepts/tokens) and other model-specific metadata.
+
+### Role
+
+Roles are used to distinguish between different types of messages in a conversation and help the chat model understand how to respond to a given sequence of messages.
+
+| **Role**              | **Description**                                                                                                                                                                                                 |
+|-----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| **system**            | Used to tell the chat model how to behave and provide additional context. Not supported by all chat model providers.                                                                                            |
+| **user**              | Represents input from a user interacting with the model, usually in the form of text or other interactive input.                                                                                                |
+| **assistant**         | Represents a response from the model, which can include text or a request to invoke tools.                                                                                                                      |
+| **tool**              | A message used to pass the results of a tool invocation back to the model after external data or processing has been retrieved. Used with chat models that support [tool calling](/docs/concepts/tool_calling). |
+| **function (legacy)** | This is a legacy role, corresponding to OpenAI's legacy function-calling API. **tool** role should be used instead.                                                                                             |
+
+### Content
+
+The content of a message text or a list of dictionaries representing [multimodal data](/docs/concepts/multimodality) (e.g., images, audio, video). The exact format of the content can vary between different chat model providers.
+
+Currently, most chat models support text as the primary content type, with some models also supporting multimodal data. However, support for multimodal data is still limited across most chat model providers.
+
+For more information see:
+* [HumanMessage](#humanmessage) -- for content in the input from the user.
+* [AIMessage](#aimessage) -- for content in the response from the model.
+* [Multimodality](/docs/concepts/multimodality) -- for more information on multimodal content.
+
+### Other Message Data
+
+Depending on the chat model provider, messages can include other data such as:
+
+- **ID**: An optional unique identifier for the message.
+- **Name**: An optional `name` property which allows differentiate between different entities/speakers with the same role. Not all models support this!
+- **Metadata**: Additional information about the message, such as timestamps, token usage, etc.
+- **Tool Calls**: A request made by the model to call one or more tools> See [tool calling](/docs/concepts/tool_calling) for more information.
+
+## Conversation Structure
+
+The sequence of messages into a chat model should follow a specific structure to ensure that the chat model can generate a valid response.
+
+For example, a typical conversation structure might look like this:
+
+1. **User Message**: "Hello, how are you?"
+2. **Assistant Message**: "I'm doing well, thank you for asking."
+3. **User Message**: "Can you tell me a joke?"
+4. **Assistant Message**: "Sure! Why did the scarecrow win an award? Because he was outstanding in his field!"
+
+Please read the [chat history](/docs/concepts/chat_history) guide for more information on managing chat history and ensuring that the conversation structure is correct.
+
+## LangChain Messages
+
+LangChain provides a unified message format that can be used across all chat models, allowing users to work with different chat models without worrying about the specific details of the message format used by each model provider.
+
+LangChain messages are Python objects that subclass from a [BaseMessage](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.base.BaseMessage.html).
+
+The five main message types are:
+
+- [SystemMessage](#systemmessage): corresponds to **system** role
+- [HumanMessage](#humanmessage): corresponds to **user** role
+- [AIMessage](#aimessage): corresponds to **assistant** role
+- [AIMessageChunk](#aimessagechunk): corresponds to **assistant** role, used for [streaming](/docs/concepts/streaming) responses
+- [ToolMessage](#toolmessage): corresponds to **tool** role
+
+Other important messages include:
+
+- [RemoveMessage](#removemessage) -- does not correspond to any role. This is an abstraction, mostly used in [LangGraph](/docs/concepts/architecture#langgraph) to manage chat history.
+- **Legacy** [FunctionMessage](#legacy-functionmessage): corresponds to the **function** role in OpenAI's **legacy** function-calling API.
+
+You can find more information about **messages** in the [API Reference](https://python.langchain.com/api_reference/core/messages.html).
+
+### SystemMessage
+
+A `SystemMessage` is used to prime the behavior of the AI model and provide additional context, such as instructing the model to adopt a specific persona or setting the tone of the conversation (e.g., "This is a conversation about cooking").
+
+Different chat providers may support system message in one of the following ways:
+
+* **Through a "system" message role**: In this case, a system message is included as part of the message sequence with the role explicitly set as "system."
+* **Through a separate API parameter for system instructions**: Instead of being included as a message, system instructions are passed via a dedicated API parameter.
+* **No support for system messages**: Some models do not support system messages at all.
+
+Most major chat model providers support system instructions via either a chat message or a separate API parameter. LangChain will automatically adapt based on the provider’s capabilities. If the provider supports a separate API parameter for system instructions, LangChain will extract the content of a system message and pass it through that parameter.
+
+If no system message is supported by the provider, in most cases LangChain will attempt to incorporate the system message's content into a HumanMessage or raise an exception if that is not possible. However, this behavior is not yet consistently enforced across all implementations, and if using a less popular implementation of a chat model (e.g., an implementation from the `langchain-community` package) it is recommended to check the specific documentation for that model.
+
+### HumanMessage
+
+The `HumanMessage` corresponds to the **"user"** role. A human message represents input from a user interacting with the model.
+
+#### Text Content
+
+Most chat models expect the user input to be in the form of text.
+
+```python
+from langchain_core.messages import HumanMessage
+
+model.invoke([HumanMessage(content="Hello, how are you?")])
+```
+
+:::tip
+When invoking a chat model with a string as input, LangChain will automatically convert the string into a `HumanMessage` object. This is mostly useful for quick testing.
+
+```python
+model.invoke("Hello, how are you?")
+```
+:::
+
+#### Multi-modal Content
+
+Some chat models accept multimodal inputs, such as images, audio, video, or files like PDFs.
+
+Please see the [multimodality](/docs/concepts/multimodality) guide for more information.
+
+### AIMessage
+
+`AIMessage` is used to represent a message with the role **"assistant"**. This is the response from the model, which can include text or a request to invoke tools. It could also include other media types like images, audio, or video -- though this is still uncommon at the moment.
+
+```python
+from langchain_core.messages import HumanMessage
+ai_message = model.invoke([HumanMessage("Tell me a joke")])
+ai_message # <-- AIMessage
+```
+
+An `AIMessage` has the following attributes. The attributes which are **standardized** are the ones that LangChain attempts to standardize across different chat model providers. **raw** fields are specific to the model provider and may vary.
+
+| Attribute            | Standardized/Raw | Description                                                                                                                                                                                                             |
+|----------------------|:-----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `content`            | Raw              | Usually a string, but can be a list of content blocks. See [content](#content) for details.                                                                                                                             |
+| `tool_calls`         | Standardized     | Tool calls associated with the message. See [tool calling](/docs/concepts/tool_calling) for details.                                                                                                                    |
+| `invalid_tool_calls` | Standardized     | Tool calls with parsing errors associated with the message. See [tool calling](/docs/concepts/tool_calling) for details.                                                                                                |
+| `usage_metadata`     | Standardized     | Usage metadata for a message, such as [token counts](/docs/concepts/tokens). See [Usage Metadata API Reference](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html) |
+| `id`                 | Standardized     | An optional unique identifier for the message, ideally provided by the provider/model that created the message.                                                                                                         |
+| `response_metadata`  | Raw              | Response metadata, e.g., response headers, logprobs, token counts.                                                                                                                                                      |
+
+#### content
+
+The **content** property of an `AIMessage` represents the response generated by the chat model.
+
+The content is either:
+
+- **text** -- the norm for virtually all chat models.
+- A **list of dictionaries** -- Each dictionary represents a content block and is associated with a `type`.
+    * Used by Anthropic for surfacing agent thought process when doing [tool calling](/docs/concepts/tool_calling).
+    * Used by OpenAI for audio outputs. Please see [multi-modal content](/docs/concepts/multimodality) for more information.
+
+:::important
+The **content** property is **not** standardized across different chat model providers, mostly because there are
+still few examples to generalize from.
+:::
+
+### AIMessageChunk
+
+It is common to [stream](/docs/concepts/streaming) responses for the chat model as they are being generated, so the user can see the response in real-time instead of waiting for the entire response to be generated before displaying it.
+
+It is returned from the `stream`, `astream` and `astream_events` methods of the chat model.
+
+For example,
+
+```python
+for chunk in model.stream([HumanMessage("what color is the sky?")]):
+    print(chunk)
+```
+
+`AIMessageChunk` follows nearly the same structure as `AIMessage`, but uses a different [ToolCallChunk](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.ToolCallChunk.html#langchain_core.messages.tool.ToolCallChunk)
+to be able to stream tool calling in a standardized manner.
+
+
+#### Aggregating
+
+`AIMessageChunks` support the `+` operator to merge them into a single `AIMessage`. This is useful when you want to display the final response to the user.
+
+```python
+ai_message = chunk1 + chunk2 + chunk3 + ...
+```
+
+### ToolMessage
+
+This represents a message with role "tool", which contains the result of [calling a tool](/docs/concepts/tool_calling). In addition to `role` and `content`, this message has:
+
+- a `tool_call_id` field which conveys the id of the call to the tool that was called to produce this result.
+- an `artifact` field which can be used to pass along arbitrary artifacts of the tool execution which are useful to track but which should not be sent to the model.
+
+Please see [tool calling](/docs/concepts/tool_calling) for more information.
+
+### RemoveMessage
+
+This is a special message type that does not correspond to any roles. It is used
+for managing chat history in [LangGraph](/docs/concepts/architecture#langgraph).
+
+Please see the following for more information on how to use the `RemoveMessage`:
+
+* [Memory conceptual guide](https://langchain-ai.github.io/langgraph/concepts/memory/)
+* [How to delete messages](https://langchain-ai.github.io/langgraph/how-tos/memory/delete-messages/)
+
+### (Legacy) FunctionMessage
+
+This is a legacy message type, corresponding to OpenAI's legacy function-calling API. `ToolMessage` should be used instead to correspond to the updated tool-calling API.
+
+## OpenAI Format
+
+### Inputs
+
+Chat models also accept OpenAI's format as **inputs** to chat models:
+
+```python
+chat_model.invoke([
+    {
+        "role": "user",
+        "content": "Hello, how are you?",
+    },
+    {
+        "role": "assistant",
+        "content": "I'm doing well, thank you for asking.",
+    },
+    {
+        "role": "user",
+        "content": "Can you tell me a joke?",
+    }
+])
+```
+
+### Outputs
+
+At the moment, the output of the model will be in terms of LangChain messages, so you will need to convert the output to the OpenAI format if you
+need OpenAI format for the output as well.
+
+The [convert_to_openai_messages](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.convert_to_openai_messages.html) utility function can be used to convert from LangChain messages to OpenAI format.
\ No newline at end of file
diff --git a/docs/docs/concepts/multimodality.mdx b/docs/docs/concepts/multimodality.mdx
new file mode 100644
index 0000000000000..3692e4e1ef1ef
--- /dev/null
+++ b/docs/docs/concepts/multimodality.mdx
@@ -0,0 +1,88 @@
+# Multimodality
+
+## Overview
+
+**Multimodality** refers to the ability to work with data that comes in different forms, such as text, audio, images, and video. Multimodality can appear in various components, allowing models and systems to handle and process a mix of these data types seamlessly.
+
+- **Chat Models**: These could, in theory, accept and generate multimodal inputs and outputs, handling a variety of data types like text, images, audio, and video.
+- **Embedding Models**: Embedding Models can represent multimodal content, embedding various forms of data—such as text, images, and audio—into vector spaces.
+- **Vector Stores**: Vector stores could search over embeddings that represent multimodal data, enabling retrieval across different types of information.
+
+## Multimodality in chat models
+
+:::info Pre-requisites
+* [Chat models](/docs/concepts/chat_models)
+* [Messages](/docs/concepts/messages)
+:::
+ 
+Multimodal support is still relatively new and less common, model providers have not yet standardized on the "best" way to define the API. As such, LangChain's multimodal abstractions are lightweight and flexible, designed to accommodate different model providers' APIs and interaction patterns, but are **not** standardized across models.
+
+### How to use multimodal models
+
+* Use the [chat model integration table](/docs/integrations/chat/) to identify which models support multimodality.
+* Reference the [relevant how-to guides](/docs/how_to/#multimodal) for specific examples of how to use multimodal models.
+
+### What kind of multimodality is supported?
+
+#### Inputs
+
+Some models can accept multimodal inputs, such as images, audio, video, or files. The types of multimodal inputs supported depend on the model provider. For instance, [Google's Gemini](https://python.langchain.com/docs/integrations/chat/google_generative_ai/) supports documents like PDFs as inputs.
+
+Most chat models that support **multimodal inputs** also accept those values in OpenAI's content blocks format. So far this is restricted to image inputs. For models like Gemini which support video and other bytes input, the APIs also support the native, model-specific representations.
+
+The gist of passing multimodal inputs to a chat model is to use content blocks that specify a type and corresponding data. For example, to pass an image to a chat model:
+
+```python
+from langchain_core.messages import HumanMessage
+
+message = HumanMessage(
+    content=[
+        {"type": "text", "text": "describe the weather in this image"},
+        {"type": "image_url", "image_url": {"url": image_url}},
+    ],
+)
+response = model.invoke([message])
+```
+
+:::caution
+The exact format of the content blocks may vary depending on the model provider. Please refer to the chat model's
+integration documentation for the correct format. Find the integration in the [chat model integration table](/docs/integrations/chat/).
+:::
+
+#### Outputs
+
+Virtually no popular chat models support multimodal outputs at the time of writing (October 2024). 
+
+The only exception is OpenAI's chat model ([gpt-4o-audio-preview](https://python.langchain.com/docs/integrations/chat/openai/)), which can generate audio outputs.
+
+Multimodal outputs will appear as part of the [AIMessage](/docs/concepts/messages/#aimessage) response object.
+
+Please see the [ChatOpenAI](/docs/integrations/chat/openai/) for more information on how to use multimodal outputs.
+
+#### Tools
+
+Currently, no chat model is designed to work **directly** with multimodal data in a [tool call request](/docs/concepts/tool_calling) or [ToolMessage](/docs/concepts/tool_calling) result.
+
+However, a chat model can easily interact with multimodal data by invoking tools with references (e.g., a URL) to the multimodal data, rather than the data itself. For example, any model capable of [tool calling](/docs/concepts/tool_calling) can be equipped with tools to download and process images, audio, or video.
+
+## Multimodality in embedding models
+
+:::info Prerequisites
+* [Embedding Models](/docs/concepts/embedding_models)
+:::
+
+**Embeddings** are vector representations of data used for tasks like similarity search and retrieval.
+
+The current [embedding interface](https://python.langchain.com/api_reference/core/embeddings/langchain_core.embeddings.embeddings.Embeddings.html#langchain_core.embeddings.embeddings.Embeddings) used in LangChain is optimized entirely for text-based data, and will **not** work with multimodal data.
+
+As use cases involving multimodal search and retrieval tasks become more common, we expect to expand the embedding interface to accommodate other data types like images, audio, and video.
+
+## Multimodality in vector stores
+
+:::info Prerequisites
+* [Vectorstores](/docs/concepts/vectorstores)
+:::
+
+Vector stores are databases for storing and retrieving embeddings, which are typically used in search and retrieval tasks. Similar to embeddings, vector stores are currently optimized for text-based data.
+
+As use cases involving multimodal search and retrieval tasks become more common, we expect to expand the vector store interface to accommodate other data types like images, audio, and video.
diff --git a/docs/docs/concepts/output_parsers.mdx b/docs/docs/concepts/output_parsers.mdx
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+++ b/docs/docs/concepts/output_parsers.mdx
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+# Output parsers
+
+<span data-heading-keywords="output parser"></span>
+
+:::note
+
+The information here refers to parsers that take a text output from a model try to parse it into a more structured representation.
+More and more models are supporting function (or tool) calling, which handles this automatically.
+It is recommended to use function/tool calling rather than output parsing.
+See documentation for that [here](/docs/concepts/#function-tool-calling).
+
+:::
+
+`Output parser` is responsible for taking the output of a model and transforming it to a more suitable format for downstream tasks.
+Useful when you are using LLMs to generate structured data, or to normalize output from chat models and LLMs.
+
+LangChain has lots of different types of output parsers. This is a list of output parsers LangChain supports. The table below has various pieces of information:
+
+- **Name**: The name of the output parser
+- **Supports Streaming**: Whether the output parser supports streaming.
+- **Has Format Instructions**: Whether the output parser has format instructions. This is generally available except when (a) the desired schema is not specified in the prompt but rather in other parameters (like OpenAI function calling), or (b) when the OutputParser wraps another OutputParser.
+- **Calls LLM**: Whether this output parser itself calls an LLM. This is usually only done by output parsers that attempt to correct misformatted output.
+- **Input Type**: Expected input type. Most output parsers work on both strings and messages, but some (like OpenAI Functions) need a message with specific kwargs.
+- **Output Type**: The output type of the object returned by the parser.
+- **Description**: Our commentary on this output parser and when to use it.
+
+| Name                                                                                                                                                                                                                                    | Supports Streaming | Has Format Instructions | Calls LLM | Input Type         | Output Type          | Description                                                                                                                                                                                                                                              |
+|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------|-------------------------|-----------|--------------------|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [JSON](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.json.JSONOutputParser.html#langchain_core.output_parsers.json.JSONOutputParser)                                                     | ✅                  | ✅                       |           | `str` \| `Message` | JSON object          | Returns a JSON object as specified. You can specify a Pydantic model and it will return JSON for that model. Probably the most reliable output parser for getting structured data that does NOT use function calling.                                    |
+| [XML](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html#langchain_core.output_parsers.xml.XMLOutputParser)                                                          | ✅                  | ✅                       |           | `str` \| `Message` | `dict`               | Returns a dictionary of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's).                                                                                                                            |
+| [CSV](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.list.CommaSeparatedListOutputParser.html#langchain_core.output_parsers.list.CommaSeparatedListOutputParser)                          | ✅                  | ✅                       |           | `str` \| `Message` | `List[str]`          | Returns a list of comma separated values.                                                                                                                                                                                                                |
+| [OutputFixing](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html#langchain.output_parsers.fix.OutputFixingParser)                                                |                    |                         | ✅         | `str` \| `Message` |                      | Wraps another output parser. If that output parser errors, then this will pass the error message and the bad output to an LLM and ask it to fix the output.                                                                                              |
+| [RetryWithError](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.retry.RetryWithErrorOutputParser.html#langchain.output_parsers.retry.RetryWithErrorOutputParser)                          |                    |                         | ✅         | `str` \| `Message` |                      | Wraps another output parser. If that output parser errors, then this will pass the original inputs, the bad output, and the error message to an LLM and ask it to fix it. Compared to OutputFixingParser, this one also sends the original instructions. |
+| [Pydantic](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html#langchain_core.output_parsers.pydantic.PydanticOutputParser)                                 |                    | ✅                       |           | `str` \| `Message` | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format.                                                                                                                                                                                     |
+| [YAML](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.yaml.YamlOutputParser.html#langchain.output_parsers.yaml.YamlOutputParser)                                                          |                    | ✅                       |           | `str` \| `Message` | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format. Uses YAML to encode it.                                                                                                                                                             |
+| [PandasDataFrame](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser.html#langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser) |                    | ✅                       |           | `str` \| `Message` | `dict`               | Useful for doing operations with pandas DataFrames.                                                                                                                                                                                                      |
+| [Enum](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.enum.EnumOutputParser.html#langchain.output_parsers.enum.EnumOutputParser)                                                          |                    | ✅                       |           | `str` \| `Message` | `Enum`               | Parses response into one of the provided enum values.                                                                                                                                                                                                    |
+| [Datetime](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html#langchain.output_parsers.datetime.DatetimeOutputParser)                                      |                    | ✅                       |           | `str` \| `Message` | `datetime.datetime`  | Parses response into a datetime string.                                                                                                                                                                                                                  |
+| [Structured](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.structured.StructuredOutputParser.html#langchain.output_parsers.structured.StructuredOutputParser)                            |                    | ✅                       |           | `str` \| `Message` | `Dict[str, str]`     | An output parser that returns structured information. It is less powerful than other output parsers since it only allows for fields to be strings. This can be useful when you are working with smaller LLMs.                                            |
+
+For specifics on how to use output parsers, see the [relevant how-to guides here](/docs/how_to/#output-parsers).
diff --git a/docs/docs/concepts/prompt_templates.mdx b/docs/docs/concepts/prompt_templates.mdx
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+# Prompt Templates
+
+Prompt templates help to translate user input and parameters into instructions for a language model.
+This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output.
+
+Prompt Templates take as input a dictionary, where each key represents a variable in the prompt template to fill in.
+
+Prompt Templates output a PromptValue. This PromptValue can be passed to an LLM or a ChatModel, and can also be cast to a string or a list of messages.
+The reason this PromptValue exists is to make it easy to switch between strings and messages.
+
+There are a few different types of prompt templates:
+
+## String PromptTemplates
+
+These prompt templates are used to format a single string, and generally are used for simpler inputs.
+For example, a common way to construct and use a PromptTemplate is as follows:
+
+```python
+from langchain_core.prompts import PromptTemplate
+
+prompt_template = PromptTemplate.from_template("Tell me a joke about {topic}")
+
+prompt_template.invoke({"topic": "cats"})
+```
+
+## ChatPromptTemplates
+
+These prompt templates are used to format a list of messages. These "templates" consist of a list of templates themselves.
+For example, a common way to construct and use a ChatPromptTemplate is as follows:
+
+```python
+from langchain_core.prompts import ChatPromptTemplate
+
+prompt_template = ChatPromptTemplate([
+    ("system", "You are a helpful assistant"),
+    ("user", "Tell me a joke about {topic}")
+])
+
+prompt_template.invoke({"topic": "cats"})
+```
+
+In the above example, this ChatPromptTemplate will construct two messages when called.
+The first is a system message, that has no variables to format.
+The second is a HumanMessage, and will be formatted by the `topic` variable the user passes in.
+
+## MessagesPlaceholder
+<span data-heading-keywords="messagesplaceholder"></span>
+
+This prompt template is responsible for adding a list of messages in a particular place.
+In the above ChatPromptTemplate, we saw how we could format two messages, each one a string.
+But what if we wanted the user to pass in a list of messages that we would slot into a particular spot?
+This is how you use MessagesPlaceholder.
+
+```python
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import HumanMessage
+
+prompt_template = ChatPromptTemplate([
+    ("system", "You are a helpful assistant"),
+    MessagesPlaceholder("msgs")
+])
+
+prompt_template.invoke({"msgs": [HumanMessage(content="hi!")]})
+```
+
+This will produce a list of two messages, the first one being a system message, and the second one being the HumanMessage we passed in.
+If we had passed in 5 messages, then it would have produced 6 messages in total (the system message plus the 5 passed in).
+This is useful for letting a list of messages be slotted into a particular spot.
+
+An alternative way to accomplish the same thing without using the `MessagesPlaceholder` class explicitly is:
+
+```python
+prompt_template = ChatPromptTemplate([
+    ("system", "You are a helpful assistant"),
+    ("placeholder", "{msgs}") # <-- This is the changed part
+])
+```
+
+For specifics on how to use prompt templates, see the [relevant how-to guides here](/docs/how_to/#prompt-templates).
diff --git a/docs/docs/concepts/rag.mdx b/docs/docs/concepts/rag.mdx
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+# Retrieval augmented generation (rag)
+
+:::info[Prerequisites]
+
+* [Retrieval](/docs/concepts/retrieval/)
+
+:::
+
+## Overview
+
+Retrieval Augmented Generation (RAG) is a powerful technique that enhances [language models](/docs/concepts/chat_models/) by combining them with external knowledge bases. 
+RAG addresses [a key limitation of models](https://www.glean.com/blog/how-to-build-an-ai-assistant-for-the-enterprise): models rely on fixed training datasets, which can lead to outdated or incomplete information.
+When given a query, RAG systems first search a knowledge base for relevant information.
+The system then incorporates this retrieved information into the model's prompt.
+The model uses the provided context to generate a response to the query.
+By bridging the gap between vast language models and dynamic, targeted information retrieval, RAG is a powerful technique for building more capable and reliable AI systems.
+
+## Key concepts
+
+![Conceptual Overview](/img/rag_concepts.png)
+
+(1) **Retrieval system**: Retrieve relevant information from a knowledge base.
+
+(2) **Adding external knowledge**: Pass retrieved information to a model.
+
+## Retrieval system
+
+Model's have internal knowledge that is often fixed, or at least not updated frequently due to the high cost of training.
+This limits their ability to answer questions about current events, or to provide specific domain knowledge.
+To address this, there are various knowledge injection techniques like [fine-tuning](https://hamel.dev/blog/posts/fine_tuning_valuable.html) or continued pre-training.
+Both are [costly](https://www.glean.com/blog/how-to-build-an-ai-assistant-for-the-enterprise) and often [poorly suited](https://www.anyscale.com/blog/fine-tuning-is-for-form-not-facts) for factual retrieval.
+Using a retrieval system offers several advantages:
+
+- **Up-to-date information**: RAG can access and utilize the latest data, keeping responses current.
+- **Domain-specific expertise**: With domain-specific knowledge bases, RAG can provide answers in specific domains.
+- **Reduced hallucination**: Grounding responses in retrieved facts helps minimize false or invented information.
+- **Cost-effective knowledge integration**: RAG offers a more efficient alternative to expensive model fine-tuning.
+
+:::info[Further reading]
+
+See our conceptual guide on [retrieval](/docs/concepts/retrieval/).
+
+:::
+
+## Adding external knowledge
+
+With a retrieval system in place, we need to pass knowledge from this system to the model. 
+A RAG pipeline typically achieves this following these steps:
+
+- Receive an input query.
+- Use the retrieval system to search for relevant information based on the query.
+- Incorporate the retrieved information into the prompt sent to the LLM.
+- Generate a response that leverages the retrieved context.
+
+As an example, here's a simple RAG workflow that passes information from a [retriever](/docs/concepts/retrievers/) to a [chat model](/docs/concepts/chat_models/):
+
+```python
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import SystemMessage, HumanMessage
+
+# Define a system prompt that tells the model how to use the retrieved context
+system_prompt = """You are an assistant for question-answering tasks. 
+Use the following pieces of retrieved context to answer the question. 
+If you don't know the answer, just say that you don't know. 
+Use three sentences maximum and keep the answer concise.
+Context: {context}:"""
+    
+# Define a question
+question = """What are the main components of an LLM-powered autonomous agent system?"""
+
+# Retrieve relevant documents
+docs = retriever.invoke(question)
+
+# Combine the documents into a single string
+docs_text = "".join(d.page_content for d in docs)
+
+# Populate the system prompt with the retrieved context
+system_prompt_fmt = system_prompt.format(context=docs_text)
+
+# Create a model
+model = ChatOpenAI(model="gpt-4o", temperature=0) 
+
+# Generate a response
+questions = model.invoke([SystemMessage(content=system_prompt_fmt),
+                          HumanMessage(content=question)])
+```
+
+:::info[Further reading]
+
+RAG a deep area with many possible optimization and design choices:
+
+* See [this excellent blog](https://cameronrwolfe.substack.com/p/a-practitioners-guide-to-retrieval?utm_source=profile&utm_medium=reader2) from Cameron Wolfe for a comprehensive overview and history of RAG.
+* See our [RAG how-to guides](/docs/how_to/#qa-with-rag).
+* See our RAG [tutorials](/docs/tutorials/#working-with-external-knowledge).
+* See our RAG from Scratch course, with [code](https://github.com/langchain-ai/rag-from-scratch) and [video playlist](https://www.youtube.com/playlist?list=PLfaIDFEXuae2LXbO1_PKyVJiQ23ZztA0x).
+* Also, see our RAG from Scratch course [on Freecodecamp](https://youtu.be/sVcwVQRHIc8?feature=shared).
+
+:::
diff --git a/docs/docs/concepts/retrieval.mdx b/docs/docs/concepts/retrieval.mdx
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+# Retrieval
+
+:::info[Prerequisites]
+
+* [Retrievers](/docs/concepts/retrievers/)
+* [Vectorstores](/docs/concepts/vectorstores/)
+* [Embeddings](/docs/concepts/embedding_models/)
+* [Text splitters](/docs/concepts/text_splitters/)
+
+:::
+
+:::danger[Security]
+ 
+Some of the concepts reviewed here utilize models to generate queries (e.g., for SQL or graph databases).
+There are inherent risks in doing this. 
+Make sure that your database connection permissions are scoped as narrowly as possible for your application's needs. 
+This will mitigate, though not eliminate, the risks of building a model-driven system capable of querying databases. 
+For more on general security best practices, see our [security guide](/docs/security/).
+
+:::
+
+## Overview 
+
+Retrieval systems are fundamental to many AI applications, efficiently identifying relevant information from large datasets. 
+These systems accommodate various data formats:
+
+- Unstructured text (e.g., documents) is often stored in vector stores or lexical search indexes.
+- Structured data is typically housed in relational or graph databases with defined schemas.
+
+Despite this diversity in data formats, modern AI applications increasingly aim to make all types of data accessible through natural language interfaces. 
+Models play a crucial role in this process by translating natural language queries into formats compatible with the underlying search index or database. 
+This translation enables more intuitive and flexible interactions with complex data structures.
+
+## Key concepts 
+
+![Retrieval](/img/retrieval_concept.png)
+
+(1) **Query analysis**: A process where models transform or construct search queries to optimize retrieval.
+
+(2) **Information retrieval**: Search queries are used to fetch information from various retrieval systems.
+
+## Query analysis 
+
+While users typically prefer to interact with retrieval systems using natural language, retrieval systems can specific query syntax or benefit from particular keywords. 
+Query analysis serves as a bridge between raw user input and optimized search queries. Some common applications of query analysis include:
+
+1. **Query Re-writing**: Queries can be re-written or expanded to improve semantic or lexical searches.
+2. **Query Construction**: Search indexes may require structured queries (e.g., SQL for databases).
+
+Query analysis employs models to transform or construct optimized search queries from raw user input. 
+
+### Query re-writing
+
+Retrieval systems should ideally handle a wide spectrum of user inputs, from simple and poorly worded queries to complex, multi-faceted questions. 
+To achieve this versatility, a popular approach is to use models to transform raw user queries into more effective search queries. 
+This transformation can range from simple keyword extraction to sophisticated query expansion and reformulation.
+Here are some key benefits of using models for query analysis in unstructured data retrieval:
+
+1. **Query Clarification**: Models can rephrase ambiguous or poorly worded queries for clarity.
+2. **Semantic Understanding**: They can capture the intent behind a query, going beyond literal keyword matching.
+3. **Query Expansion**: Models can generate related terms or concepts to broaden the search scope.
+4. **Complex Query Handling**: They can break down multi-part questions into simpler sub-queries.
+
+Various techniques have been developed to leverage models for query re-writing, including:
+
+| Name                                                                                                      | When to use                                                                                     | Description                                                                                                                                                                                                                                                                            |
+|-----------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [Multi-query](/docs/how_to/MultiQueryRetriever/)                                                          | When you want to ensure high recall in retrieval by providing multiple pharsings of a question. | Rewrite the user question with multiple pharsings, retrieve documents for each rewritten question, return the unique documents for all queries.                                                                                                                                        |
+| [Decomposition](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb) | When a question can be broken down into smaller subproblems.                                    | Decompose a question into a set of subproblems / questions, which can either be solved sequentially (use the answer from first + retrieval to answer the second) or in parallel (consolidate each answer into final answer).                                                           |
+| [Step-back](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb)     | When a higher-level conceptual understanding is required.                                       | First prompt the LLM to ask a generic step-back question about higher-level concepts or principles, and retrieve relevant facts about them. Use this grounding to help answer the user question. [Paper](https://arxiv.org/pdf/2310.06117).                                            |
+| [HyDE](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb)          | If you have challenges retrieving relevant documents using the raw user inputs.                 | Use an LLM to convert questions into hypothetical documents that answer the question. Use the embedded hypothetical documents to retrieve real documents with the premise that doc-doc similarity search can produce more relevant matches. [Paper](https://arxiv.org/abs/2212.10496). |
+
+As an example, query decomposition can simply be accomplished using prompting and a structured output that enforces a list of sub-questions.
+These can then be run sequentially or in parallel on a downstream retrieval system.
+
+```python
+from pydantic import BaseModel, Field
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import SystemMessage, HumanMessage
+
+# Define a pydantic model to enforce the output structure
+class Questions(BaseModel):
+    questions: List[str] = Field(
+        description="A list of sub-questions related to the input query."
+    )
+
+# Create an instance of the model and enforce the output structure
+model = ChatOpenAI(model="gpt-4o", temperature=0) 
+structured_model = model.with_structured_output(Questions)
+
+# Define the system prompt
+system = """You are a helpful assistant that generates multiple sub-questions related to an input question. \n
+The goal is to break down the input into a set of sub-problems / sub-questions that can be answers in isolation. \n"""
+
+# Pass the question to the model
+question = """What are the main components of an LLM-powered autonomous agent system?"""
+questions = structured_model.invoke([SystemMessage(content=system)]+[HumanMessage(content=question)])
+```
+
+:::tip
+
+See our RAG from Scratch videos for a few different specific approaches:
+- [Multi-query](https://youtu.be/JChPi0CRnDY?feature=shared)
+- [Decomposition](https://youtu.be/h0OPWlEOank?feature=shared)
+- [Step-back](https://youtu.be/xn1jEjRyJ2U?feature=shared)
+- [HyDE](https://youtu.be/SaDzIVkYqyY?feature=shared)
+
+:::
+
+### Query construction
+
+Query analysis also can focus on translating natural language queries into specialized query languages or filters. 
+This translation is crucial for effectively interacting with various types of databases that house structured or semi-structured data.
+
+1. **Structured Data examples**: For relational and graph databases, Domain-Specific Languages (DSLs) are used to query data.
+   - **Text-to-SQL**: [Converts natural language to SQL](https://paperswithcode.com/task/text-to-sql) for relational databases.
+   - **Text-to-Cypher**: [Converts natural language to Cypher](https://neo4j.com/labs/neodash/2.4/user-guide/extensions/natural-language-queries/) for graph databases.
+
+2. **Semi-structured Data examples**: For vectorstores, queries can combine semantic search with metadata filtering.
+   - **Natural Language to Metadata Filters**: Converts user queries into [appropriate metadata filters](https://docs.pinecone.io/guides/data/filter-with-metadata).
+
+These approaches leverage models to bridge the gap between user intent and the specific query requirements of different data storage systems. Here are some popular techniques:
+
+| Name                                     | When to Use                                                                                                                          | Description                                                                                                                                                                                                                                          |
+|------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [Self Query](/docs/how_to/self_query/)   | If users are asking questions that are better answered by fetching documents based on metadata rather than similarity with the text. | This uses an LLM to transform user input into two things: (1) a string to look up semantically, (2) a metadata filter to go along with it. This is useful because oftentimes questions are about the METADATA of documents (not the content itself). |
+| [Text to SQL](/docs/tutorials/sql_qa/)   | If users are asking questions that require information housed in a relational database, accessible via SQL.                          | This uses an LLM to transform user input into a SQL query.                                                                                                                                                                                           |
+| [Text-to-Cypher](/docs/tutorials/graph/) | If users are asking questions that require information housed in a graph database, accessible via Cypher.                            | This uses an LLM to transform user input into a Cypher query.                                                                                                                                                                                        |
+
+As an example, here is how to use the `SelfQueryRetriever` to convert natural language queries into metadata filters.  
+
+```python
+metadata_field_info = schema_for_metadata 
+document_content_description = "Brief summary of a movie"
+llm = ChatOpenAI(temperature=0)
+retriever = SelfQueryRetriever.from_llm(
+    llm,
+    vectorstore,
+    document_content_description,
+    metadata_field_info,
+)
+```
+
+:::info[Further reading]
+
+* See our tutorials on [text-to-SQL](/docs/tutorials/sql_qa/), [text-to-Cypher](/docs/tutorials/graph/), and [query analysis for metadata filters](/docs/tutorials/query_analysis/).
+* See our [blog post overview](https://blog.langchain.dev/query-construction/).
+* See our RAG from Scratch video on [query construction](https://youtu.be/kl6NwWYxvbM?feature=shared).
+
+::: 
+
+## Information retrieval 
+
+### Common retrieval systems
+
+#### Lexical search indexes
+
+Many search engines are based upon matching words in a query to the words in each document. 
+This approach is called lexical retrieval, using search [algorithms that are typically based upon word frequencies](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2).
+The intution is simple: a word appears frequently both in the user’s query and a particular document, then this document might be a good match.
+
+The particular data structure used to implement this is often an [*inverted index*](https://www.geeksforgeeks.org/inverted-index/).
+This types of index contains a list of words and a mapping of each word to a list of locations at which it occurs in various documents. 
+Using this data structure, it is possible to efficiently match the words in search queries to the documents in which they appear.
+[BM25](https://en.wikipedia.org/wiki/Okapi_BM25#:~:text=BM25%20is%20a%20bag%2Dof,slightly%20different%20components%20and%20parameters.) and [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) are [two popular lexical search algorithms](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2).
+
+:::info[Further reading]
+
+* See the [BM25](/docs/integrations/retrievers/bm25/) retriever integration.
+* See the [Elasticsearch](/docs/integrations/retrievers/elasticsearch_retriever/) retriever integration.
+
+::: 
+
+#### Vector indexes
+
+Vector indexes are an alternative way to index and store unstructured data.
+See our conceptual guide on [vectorstores](/docs/concepts/vectorstores/) for a detailed overview.  
+In short, rather than using word frequencies, vectorstores use an [embedding model](/docs/concepts/embedding_models/) to compress documents into high-dimensional vector representation. 
+This allows for efficient similarity search over embedding vectors using simple mathematical operations like cosine similarity.
+
+:::info[Further reading]
+
+* See our [how-to guide](/docs/how_to/vectorstore_retriever/) for more details on working with vectorstores.
+* See our [list of vectorstore integrations](/docs/integrations/vectorstores/).
+* See Cameron Wolfe's [blog post](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2) on the basics of vector search.
+
+:::
+
+#### Relational databases
+
+Relational databases are a fundamental type of structured data storage used in many applications. 
+They organize data into tables with predefined schemas, where each table represents an entity or relationship. 
+Data is stored in rows (records) and columns (attributes), allowing for efficient querying and manipulation through SQL (Structured Query Language). 
+Relational databases excel at maintaining data integrity, supporting complex queries, and handling relationships between different data entities.
+
+:::info[Further reading]
+
+* See our [tutorial](/docs/tutorials/sql_qa/) for working with SQL databases.
+* See our [SQL database toolkit](/docs/integrations/tools/sql_database/).
+
+:::
+
+#### Graph databases
+
+Graph databases are a specialized type of database designed to store and manage highly interconnected data. 
+Unlike traditional relational databases, graph databases use a flexible structure consisting of nodes (entities), edges (relationships), and properties. 
+This structure allows for efficient representation and querying of complex, interconnected data.
+Graph databases store data in a graph structure, with nodes, edges, and properties.
+They are particularly useful for storing and querying complex relationships between data points, such as social networks, supply-chain management, fraud detection, and recommendation services
+
+:::info[Further reading]
+
+* See our [tutorial](/docs/tutorials/graph/) for working with graph databases.
+* See our [list of graph database integrations](/docs/integrations/graphs/). 
+* See Neo4j's [starter kit for LangChain](https://neo4j.com/developer-blog/langchain-neo4j-starter-kit/).
+
+:::
+
+### Retriever  
+
+LangChain provides a unified interface for interacting with various retrieval systems through the [retriever](/docs/concepts/retrievers/) concept. The interface is straightforward:
+
+1. Input: A query (string)
+2. Output: A list of documents (standardized LangChain [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) objects)
+
+You can create a retriever using any of the retrieval systems mentioned earlier. The query analysis techniques we discussed are particularly useful here, as they enable natural language interfaces for databases that typically require structured query languages.
+For example, you can build a retriever for a SQL database using text-to-SQL conversion. This allows a natural language query (string) to be transformed into a SQL query behind the scenes.
+Regardless of the underlying retrieval system, all retrievers in LangChain share a common interface. You can use them with the simple `invoke` method:
+
+
+```python
+docs = retriever.invoke(query)
+```
+
+:::info[Further reading]
+
+* See our [conceptual guide on retrievers](/docs/concepts/retrievers/).
+* See our [how-to guide](/docs/how_to/#retrievers) on working with retrievers.
+
+:::
diff --git a/docs/docs/concepts/retrievers.mdx b/docs/docs/concepts/retrievers.mdx
new file mode 100644
index 0000000000000..5aaa893b7fde1
--- /dev/null
+++ b/docs/docs/concepts/retrievers.mdx
@@ -0,0 +1,145 @@
+# Retrievers
+
+<span data-heading-keywords="retriever,retrievers"></span>
+
+:::info[Prerequisites]
+
+* [Vectorstores](/docs/concepts/vectorstores/)
+* [Embeddings](/docs/concepts/embedding_models/)
+* [Text splitters](/docs/concepts/text_splitters/)
+
+:::
+
+## Overview
+
+Many different types of retrieval systems exist, including vectorstores, graph databases, and relational databases.
+With the rise on popularity of large language models, retrieval systems have become an important component in AI application (e.g., [RAG](/docs/concepts/rag/)).
+Because of their importance and variability, LangChain provides a uniform interface for interacting with different types of retrieval systems.
+The LangChain [retriever](/docs/concepts/retrievers/) interface is straightforward:
+
+1. Input: A query (string)
+2. Output: A list of documents (standardized LangChain [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) objects)
+
+## Key concept
+
+![Retriever](/img/retriever_concept.png)
+ 
+All retrievers implement a simple interface for retrieving documents using natural language queries.
+
+## Interface 
+
+The only requirement for a retriever is the ability to accepts a query and return documents. 
+In particular, [LangChain's retriever class](https://api.python.langchain.com/en/latest/retrievers/langchain_core.retrievers.BaseRetriever.html) only requires that the `_get_relevant_documents` method is implemented, which takes a `query: str` and returns a list of [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) objects that are most relevant to the query.
+The underlying logic used to get relevant documents is specified by the retriever and can be whatever is most useful for the application.
+
+A LangChain retriever is a [runnable](/docs/how_to/lcel_cheatsheet/), which is a standard interface is for LangChain components. 
+This means that it has a few common methods, including `invoke`, that are used to interact with it. A retriever can be invoked with a query:
+
+```python
+docs = retriever.invoke(query)
+```
+
+Retrievers return a list of [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) objects, which have two attributes:
+
+* `page_content`: The content of this document. Currently is a string.
+* `metadata`: Arbitrary metadata associated with this document (e.g., document id, file name, source, etc). 
+
+:::info[Further reading]
+
+* See our [how-to guide](/docs/how_to/custom_retriever/) on building your own custom retriever.
+
+:::
+ 
+## Common types
+
+Despite the flexibility of the retriever interface, a few common types of retrieval systems are frequently used.
+
+### Search apis
+
+It's important to note that retrievers don't need to actually *store* documents. 
+For example, we can be built retrievers on top of search APIs that simply return search results! 
+See our retriever integrations with [Amazon Kendra](https://python.langchain.com/docs/integrations/retrievers/amazon_kendra_retriever/) or [Wikipedia Search](https://python.langchain.com/docs/integrations/retrievers/wikipedia/). 
+
+### Relational or graph database
+
+Retrievers can be built on top of relational or graph databases.
+In these cases, [query analysis](/docs/concepts/retrieval/) techniques to construct a structured query from natural language is critical.
+For example, you can build a retriever for a SQL database using text-to-SQL conversion. This allows a natural language query (string) retriever to be transformed into a SQL query behind the scenes.
+
+:::info[Further reading]
+
+* See our [tutorial](/docs/tutorials/sql_qa/) for context on how to build a retreiver using a SQL database and text-to-SQL.
+* See our [tutorial](/docs/tutorials/graph/) for context on how to build a retreiver using a graph database and text-to-Cypher.
+
+:::
+
+### Lexical search
+
+As discussed in our conceptual review of [retrieval](/docs/concepts/retrieval/), many search engines are based upon matching words in a query to the words in each document. 
+[BM25](https://en.wikipedia.org/wiki/Okapi_BM25#:~:text=BM25%20is%20a%20bag%2Dof,slightly%20different%20components%20and%20parameters.) and [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) are [two popular lexical search algorithms](https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search?utm_source=profile&utm_medium=reader2).
+LangChain has retrievers for many popular lexical search algorithms / engines.
+
+:::info[Further reading]
+
+* See the [BM25](/docs/integrations/retrievers/bm25/) retriever integration.
+* See the [TF-IDF](/docs/integrations/retrievers/tf_idf/) retriever integration.
+* See the [Elasticsearch](/docs/integrations/retrievers/elasticsearch_retriever/) retriever integration.
+
+::: 
+
+### Vectorstore 
+
+[Vectorstores](/docs/concepts/vectorstores/) are a powerful and efficient way to index and retrieve unstructured data. 
+An vectorstore can be used as a retriever by calling the `as_retriever()` method.
+
+```python
+vectorstore = MyVectorStore()
+retriever = vectorstore.as_retriever()
+```
+
+## Advanced retrieval patterns
+
+### Ensemble 
+
+Because the retriever interface is so simple, returning a list of `Document` objects given a search query, it is possible to combine multiple retrievers using ensembling.
+This is particularly useful when you have multiple retrievers that are good at finding different types of relevant documents.
+It is easy to create an [ensemble retriever](/docs/how_to/ensemble_retriever/) that combines multiple retrievers with linear weighted scores:
+
+```python
+# Initialize the ensemble retriever
+ensemble_retriever = EnsembleRetriever(
+    retrievers=[bm25_retriever, vector_store_retriever], weights=[0.5, 0.5]
+)
+```
+
+When ensembling, how do we combine search results from many retrievers? 
+This motivates the concept of re-ranking, which takes the output of multiple retrievers and combines them using a more sophisticated algorithm such as [Reciprocal Rank Fusion (RRF)](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf).
+
+### Source document retention 
+
+Many retrievers utilize some kind of index to make documents easily searchable.
+The process of indexing can include a transformation step (e.g., vectorstores often use document splitting). 
+Whatever transformation is used, can be very useful to retain a link between the *transformed document* and the original, giving the retriever the ability to return the *original* document.
+
+![Retrieval with full docs](/img/retriever_full_docs.png)
+
+This is particularly useful in AI applications, because it ensures no loss in document context for the model.
+For example, you may use small chunk size for indexing documents in a vectorstore. 
+If you return *only* the chunks as the retrieval result, then the model will have lost the original document context for the chunks. 
+
+LangChain has two different retrievers that can be used to address this challenge. 
+The [Multi-Vector](/docs/how_to/multi_vector/) retriever allows the user to use any document transformation (e.g., use an LLM to write a summary of the document) for indexing while retaining linkage to the source document. 
+The [ParentDocument](/docs/how_to/parent_document_retriever/) retriever links document chunks from a text-splitter transformation for indexing while retaining linkage to the source document. 
+
+| Name                                                      | Index Type                    | Uses an LLM               | When to Use                                                                                                                             | Description                                                                                                                                                                                                              |
+|-----------------------------------------------------------|-------------------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [ParentDocument](/docs/how_to/parent_document_retriever/) | Vector store + Document Store | No                        | If your pages have lots of smaller pieces of distinct information that are best indexed by themselves, but best retrieved all together. | This involves indexing multiple chunks for each document. Then you find the chunks that are most similar in embedding space, but you retrieve the whole parent document and return that (rather than individual chunks). |
+| [Multi Vector](/docs/how_to/multi_vector/)                | Vector store + Document Store | Sometimes during indexing | If you are able to extract information from documents that you think is more relevant to index than the text itself.                    | This involves creating multiple vectors for each document. Each vector could be created in a myriad of ways - examples include summaries of the text and hypothetical questions.                                         |
+
+:::info[Further reading]
+
+* See our [how-to guide](/docs/how_to/parent_document_retriever/) on using the ParentDocument retriever.
+* See our [how-to guide](/docs/how_to/multi_vector/) on using the MultiVector retriever.
+* See our RAG from Scratch video on the [multi vector retriever](https://youtu.be/gTCU9I6QqCE?feature=shared).
+
+:::
diff --git a/docs/docs/concepts/runnables.mdx b/docs/docs/concepts/runnables.mdx
new file mode 100644
index 0000000000000..678d38bddf7b6
--- /dev/null
+++ b/docs/docs/concepts/runnables.mdx
@@ -0,0 +1,352 @@
+# Runnable interface
+
+The Runnable interface is foundational for working with LangChain components, and it's implemented across many of them, such as [language models](/docs/concepts/chat_models), [output parsers](/docs/concepts/output_parsers), [retrievers](/docs/concepts/retrievers), [compiled LangGraph graphs](
+https://langchain-ai.github.io/langgraph/concepts/low_level/#compiling-your-graph) and more.
+
+This guide covers the main concepts and methods of the Runnable interface, which allows developers to interact with various LangChain components in a consistent and predictable manner.
+
+:::info Related Resources
+* The ["Runnable" Interface API Reference](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) provides a detailed overview of the Runnable interface and its methods.
+* A list of built-in `Runnables` can be found in the [LangChain Core API Reference](https://python.langchain.com/api_reference/core/runnables.html). Many of these Runnables are useful when composing custom "chains" in LangChain using the [LangChain Expression Language (LCEL)](/docs/concepts/lcel).
+:::
+
+## Overview of runnable interface
+
+The Runnable way defines a standard interface that allows a Runnable component to be:
+
+* [Invoked](/docs/how_to/lcel_cheatsheet/#invoke-a-runnable): A single input is transformed into an output.
+* [Batched](/docs/how_to/lcel_cheatsheet/#batch-a-runnable/): Multiple inputs are efficiently transformed into outputs.
+* [Streamed](/docs/how_to/lcel_cheatsheet/#stream-a-runnable): Outputs are streamed as they are produced.
+* Inspected: Schematic information about Runnable's input, output, and configuration can be accessed.
+* Composed: Multiple Runnables can be composed to work together using [the LangChain Expression Language (LCEL)](/docs/concepts/lcel) to create complex pipelines.
+
+Please review the [LCEL Cheatsheet](/docs/how_to/lcel_cheatsheet) for some common patterns that involve the Runnable interface and LCEL expressions.
+
+<a id="batch"></a>
+### Optimized parallel execution (batch)
+<span data-heading-keywords="batch"></span>
+
+LangChain Runnables offer a built-in `batch` (and `batch_as_completed`) API that allow you to process multiple inputs in parallel.
+
+Using these methods can significantly improve performance when needing to process multiple independent inputs, as the
+processing can be done in parallel instead of sequentially.
+
+The two batching options are:
+
+* `batch`: Process multiple inputs in parallel, returning results in the same order as the inputs.
+* `batch_as_completed`: Process multiple inputs in parallel, returning results as they complete. Results may arrive out of order, but each includes the input index for matching.
+
+The default implementation of `batch` and `batch_as_completed` use a thread pool executor to run the `invoke` method in parallel. This allows for efficient parallel execution without the need for users to manage threads, and speeds up code that is I/O-bound (e.g., making API requests, reading files, etc.). It will not be as effective for CPU-bound operations, as the GIL (Global Interpreter Lock) in Python will prevent true parallel execution.
+
+Some Runnables may provide their own implementations of `batch` and `batch_as_completed` that are optimized for their specific use case (e.g.,
+rely on a `batch` API provided by a model provider).
+
+:::note
+The async versions of `abatch` and `abatch_as_completed` these rely on asyncio's [gather](https://docs.python.org/3/library/asyncio-task.html#asyncio.gather) and [as_completed](https://docs.python.org/3/library/asyncio-task.html#asyncio.as_completed) functions to run the `ainvoke` method in parallel.
+:::
+
+:::tip
+When processing a large number of inputs using `batch` or `batch_as_completed`, users may want to control the maximum number of parallel calls. This can be done by setting the `max_concurrency` attribute in the `RunnableConfig` dictionary. See the [RunnableConfig](/docs/concepts/runnables#RunnableConfig) for more information.
+
+Chat Models also have a built-in [rate limiter](/docs/concepts/chat_models#rate-limiting) that can be used to control the rate at which requests are made.
+:::
+
+### Asynchronous support
+<span data-heading-keywords="async-api"></span>
+
+Runnables expose an asynchronous API, allowing them to be called using the `await` syntax in Python. Asynchronous methods can be identified by the "a" prefix (e.g., `ainvoke`, `abatch`, `astream`, `abatch_as_completed`).
+
+Please refer to the [Async Programming with LangChain](/docs/concepts/async) guide for more details.
+
+## Streaming apis
+<span data-heading-keywords="streaming-api"></span>
+
+Streaming is critical in making applications based on LLMs feel responsive to end-users.
+
+Runnables expose the following three streaming APIs:
+
+1. sync [stream](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) and async [astream](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream): yields the output a Runnable as it is generated.
+2. The async `astream_events`: a more advanced streaming API that allows streaming intermediate steps and final output
+3. The **legacy** async `astream_log`: a legacy streaming API that streams intermediate steps and final output
+
+Please refer to the [Streaming Conceptual Guide](/docs/concepts/streaming) for more details on how to stream in LangChain.
+
+## Input and output types
+
+Every `Runnable` is characterized by an input and output type. These input and output types can be any Python object, and are defined by the Runnable itself.
+
+Runnable methods that result in the execution of the Runnable (e.g., `invoke`, `batch`, `stream`, `astream_events`) work with these input and output types.
+
+* invoke: Accepts an input and returns an output.
+* batch: Accepts a list of inputs and returns a list of outputs.
+* stream: Accepts an input and returns a generator that yields outputs.
+
+The **input type** and **output type** vary by component:
+
+| Component    | Input Type                                       | Output Type           |
+|--------------|--------------------------------------------------|-----------------------|
+| Prompt       | dictionary                                       | PromptValue           |
+| ChatModel    | a string, list of chat messages or a PromptValue | ChatMessage           |
+| LLM          | a string, list of chat messages or a PromptValue | String                |
+| OutputParser | the output of an LLM or ChatModel                | Depends on the parser |
+| Retriever    | a string                                         | List of Documents     |
+| Tool         | a string or dictionary, depending on the tool    | Depends on the tool   |
+
+Please refer to the individual component documentation for more information on the input and output types and how to use them.
+
+### Inspecting schemas
+
+:::note
+This is an advanced feature that is unnecessary for most users. You should probably
+skip this section unless you have a specific need to inspect the schema of a Runnable.
+:::
+
+In some advanced uses, you may want to programmatically **inspect** the Runnable and determine what input and output types the Runnable expects and produces.
+
+The Runnable interface provides methods to get the [JSON Schema](https://json-schema.org/) of the input and output types of a Runnable, as well as [Pydantic schemas](https://docs.pydantic.dev/latest/) for the input and output types.
+
+These APIs are mostly used internally for unit-testing and by [LangServe](/docs/concepts/architecture#langserve) which uses the APIs for input validation and generation of [OpenAPI documentation](https://www.openapis.org/).
+
+In addition, to the input and output types, some Runnables have been set up with additional run time configuration options. 
+There are corresponding APIs to get the Pydantic Schema and JSON Schema of the configuration options for the Runnable.
+Please see the [Configurable Runnables](#configurable-runnables) section for more information.
+
+| Method                  | Description                                                      |
+|-------------------------|------------------------------------------------------------------|
+| `get_input_schema`      | Gives the Pydantic Schema of the input schema for the Runnable.  |
+| `get_output_chema`      | Gives the Pydantic Schema of the output schema for the Runnable. |
+| `config_schema`         | Gives the Pydantic Schema of the config schema for the Runnable. |
+| `get_input_jsonschema`  | Gives the JSONSchema of the input schema for the Runnable.       |
+| `get_output_jsonschema` | Gives the JSONSchema of the output schema for the Runnable.      |
+| `get_config_jsonschema` | Gives the JSONSchema of the config schema for the Runnable.      |
+
+
+#### With_types
+
+LangChain will automatically try to infer the input and output types of a Runnable based on available information.
+
+Currently, this inference does not work well for more complex Runnables that are built using [LCEL](/docs/concepts/lcel) composition, and the inferred input and / or output types may be incorrect. In these cases, we recommend that users override the inferred input and output types using the `with_types` method ([API Reference](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_types
+).
+
+## RunnableConfig
+
+Any of the methods that are used to execute the runnable (e.g., `invoke`, `batch`, `stream`, `astream_events`) accept a second argument called
+`RunnableConfig` ([API Reference](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.config.RunnableConfig.html#RunnableConfig)). This argument is a dictionary that contains configuration for the Runnable that will be used
+at run time during the execution of the runnable.
+
+A `RunnableConfig` can have any of the following properties defined:
+
+| Attribute       | Description                                                                                |
+|-----------------|--------------------------------------------------------------------------------------------|
+| run_name        | Name used for the given Runnable (not inherited).                                          |
+| run_id          | Unique identifier for this call. sub-calls will get their own unique run ids.              |
+| tags            | Tags for this call and any sub-calls.                                                      |
+| metadata        | Metadata for this call and any sub-calls.                                                  |
+| callbacks       | Callbacks for this call and any sub-calls.                                                 |
+| max_concurrency | Maximum number of parallel calls to make (e.g., used by batch).                            |
+| recursion_limit | Maximum number of times a call can recurse (e.g., used by Runnables that return Runnables) |
+| configurable    | Runtime values for configurable attributes of the Runnable.                                |
+
+Passing `config` to the `invoke` method is done like so:
+
+```python
+some_runnable.invoke(
+   some_input, 
+   config={
+      'run_name': 'my_run', 
+      'tags': ['tag1', 'tag2'], 
+      'metadata': {'key': 'value'}
+      
+   }
+)
+```
+
+### Propagation of RunnableConfig
+
+Many `Runnables` are composed of other Runnables, and it is important that the `RunnableConfig` is propagated to all sub-calls made by the Runnable. This allows providing run time configuration values to the parent Runnable that are inherited by all sub-calls.
+
+If this were not the case, it would be impossible to set and propagate [callbacks](/docs/concepts/callbacks) or other configuration values like `tags` and `metadata` which
+are expected to be inherited by all sub-calls.
+
+There are two main patterns by which new `Runnables` are created:
+
+1. Declaratively using [LangChain Expression Language (LCEL)](/docs/concepts/lcel):
+
+    ```python
+    chain = prompt | chat_model | output_parser
+    ```
+
+2. Using a [custom Runnable](#custom-runnables)  (e.g., `RunnableLambda`) or using the `@tool` decorator:
+
+    ```python
+    def foo(input):
+        # Note that .invoke() is used directly here
+        return bar_runnable.invoke(input)
+    foo_runnable = RunnableLambda(foo)
+    ```
+
+LangChain will try to propagate `RunnableConfig` automatically for both of the patterns. 
+
+For handling the second pattern, LangChain relies on Python's [contextvars](https://docs.python.org/3/library/contextvars.html).
+
+In Python 3.11 and above, this works out of the box, and you do not need to do anything special to propagate the `RunnableConfig` to the sub-calls.
+
+In Python 3.9 and 3.10, if you are using **async code**, you need to manually pass the `RunnableConfig` through to the `Runnable` when invoking it. 
+
+This is due to a limitation in [asyncio's tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)  in Python 3.9 and 3.10 which did
+not accept a `context` argument).
+
+Propagating the `RunnableConfig` manually is done like so:
+
+```python
+async def foo(input, config): # <-- Note the config argument
+    return await bar_runnable.ainvoke(input, config=config)
+    
+foo_runnable = RunnableLambda(foo)
+```
+
+:::caution
+When using Python 3.10 or lower and writing async code, `RunnableConfig` cannot be propagated
+automatically, and you will need to do it manually! This is a common pitfall when
+attempting to stream data using `astream_events` and `astream_log` as these methods
+rely on proper propagation of [callbacks](/docs/concepts/callbacks) defined inside of `RunnableConfig`.
+:::
+
+### Setting custom run name, tags, and metadata
+
+The `run_name`, `tags`, and `metadata` attributes of the `RunnableConfig` dictionary can be used to set custom values for the run name, tags, and metadata for a given Runnable.
+
+The `run_name` is a string that can be used to set a custom name for the run. This name will be used in logs and other places to identify the run. It is not inherited by sub-calls.
+
+The `tags` and `metadata` attributes are lists and dictionaries, respectively, that can be used to set custom tags and metadata for the run. These values are inherited by sub-calls.
+
+Using these attributes can be useful for tracking and debugging runs, as they will be surfaced in [LangSmith](https://docs.smith.langchain.com/) as trace attributes that you can
+filter and search on.
+
+The attributes will also be propagated to [callbacks](/docs/concepts/callbacks), and will appear in streaming APIs like [astream_events](/docs/concepts/streaming) as part of each event in the stream.
+
+:::note Related
+* [How-to trace with LangChain](https://docs.smith.langchain.com/how_to_guides/tracing/trace_with_langchain)
+:::
+
+### Setting run id
+
+:::note
+This is an advanced feature that is unnecessary for most users.
+:::
+
+You may need to set a custom `run_id` for a given run, in case you want 
+to reference it later or correlate it with other systems.
+
+The `run_id` MUST be a valid UUID string and **unique** for each run. It is used to identify
+the parent run, sub-class will get their own unique run ids automatically.
+
+To set a custom `run_id`, you can pass it as a key-value pair in the `config` dictionary when invoking the Runnable:
+
+```python
+import uuid
+
+run_id = uuid.uuid4()
+
+some_runnable.invoke(
+   some_input, 
+   config={
+      'run_id': run_id
+   }
+)
+
+# Do something with the run_id
+```
+
+### Setting recursion limit
+
+:::note
+This is an advanced feature that is unnecessary for most users.
+:::
+
+Some Runnables may return other Runnables, which can lead to infinite recursion if not handled properly. To prevent this, you can set a `recursion_limit` in the `RunnableConfig` dictionary. This will limit the number of times a Runnable can recurse.
+
+### Setting max concurrency
+
+If using the `batch` or `batch_as_completed` methods, you can set the `max_concurrency` attribute in the `RunnableConfig` dictionary to control the maximum number of parallel calls to make. This can be useful when you want to limit the number of parallel calls to prevent overloading a server or API.
+
+
+:::tip
+If you're trying to rate limit the number of requests made by a **Chat Model**, you can use the built-in [rate limiter](/docs/concepts/chat_models#rate-limiting) instead of setting `max_concurrency`, which will be more effective.
+
+See the [How to handle rate limits](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/) guide for more information.
+:::
+
+### Setting configurable
+
+The `configurable` field is used to pass runtime values for configurable attributes of the Runnable.
+
+It is used frequently in [LangGraph](/docs/concepts/architecture#langgraph) with
+[LangGraph Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/)
+and [memory](https://langchain-ai.github.io/langgraph/concepts/memory/).
+
+It is used for a similar purpose in [RunnableWithMessageHistory](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html#langchain_core.runnables.history.RunnableWithMessageHistory) to specify either
+a `session_id` / `conversation_id` to keep track of conversation history.
+
+In addition, you can use it to specify any custom configuration options to pass to any [Configurable Runnable](#configurable-runnables) that they create.
+
+### Setting callbacks
+
+Use this option to configure [callbacks](/docs/concepts/callbacks) for the runnable at 
+runtime. The callbacks will be passed to all sub-calls made by the runnable.
+
+```python
+some_runnable.invoke(
+   some_input,
+   {
+      "callbacks": [
+         SomeCallbackHandler(),
+         AnotherCallbackHandler(),
+      ]
+   }
+)
+```
+
+Please read the [Callbacks Conceptual Guide](/docs/concepts/callbacks) for more information on how to use callbacks in LangChain.
+
+:::important
+If you're using Python 3.9 or 3.10 in an async environment, you must propagate
+the `RunnableConfig` manually to sub-calls in some cases. Please see the
+[Propagating RunnableConfig](#propagation-of-RunnableConfig) section for more information.
+:::
+
+## Creating a runnable from a function
+
+You may need to create a custom Runnable that runs arbitrary logic. This is especially
+useful if using [LangChain Expression Language (LCEL)](/docs/concepts/lcel) to compose
+multiple Runnables and you need to add custom processing logic in one of the steps.
+
+There are two ways to create a custom Runnable from a function:
+
+* `RunnableLambda`: Use this simple transformations where streaming is not required.
+* `RunnableGenerator`: use this for more complex transformations when streaming is needed.
+
+See the [How to run custom functions](/docs/how_to/functions) guide for more information on how to use `RunnableLambda` and `RunnableGenerator`.
+
+:::important
+Users should not try to subclass Runnables to create a new custom Runnable. It is
+much more complex and error-prone than simply using `RunnableLambda` or `RunnableGenerator`.
+:::
+
+## Configurable runnables
+
+:::note
+This is an advanced feature that is unnecessary for most users.
+
+It helps with configuration of large "chains" created using the [LangChain Expression Language (LCEL)](/docs/concepts/lcel)
+and is leveraged by [LangServe](/docs/concepts/architecture#langserve) for deployed Runnables.
+:::
+
+Sometimes you may want to experiment with, or even expose to the end user, multiple different ways of doing things with your Runnable. This could involve adjusting parameters like the temperature in a chat model or even switching between different chat models.
+
+To simplify this process, the Runnable interface provides two methods for creating configurable Runnables at runtime:
+
+* `configurable_fields`: This method allows you to configure specific **attributes** in a Runnable. For example, the `temperature` attribute of a chat model.
+* `configurable_alternatives`: This method enables you to specify **alternative** Runnables that can be run during run time. For example, you could specify a list of different chat models that can be used.
+
+See the [How to configure runtime chain internals](/docs/how_to/configure) guide for more information on how to configure runtime chain internals.
diff --git a/docs/docs/concepts/streaming.mdx b/docs/docs/concepts/streaming.mdx
new file mode 100644
index 0000000000000..7ab681b533ebb
--- /dev/null
+++ b/docs/docs/concepts/streaming.mdx
@@ -0,0 +1,191 @@
+# Streaming
+
+:::info Prerequisites
+* [Runnable Interface](/docs/concepts/runnables)
+* [Chat Models](/docs/concepts/chat_models)
+:::
+
+**Streaming** is crucial for enhancing the responsiveness of applications built on [LLMs](/docs/concepts/chat_models). By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
+
+## Overview
+
+Generating full responses from [LLMs](/docs/concepts/chat_models) often incurs a delay of several seconds, which becomes more noticeable in complex applications with multiple model calls. Fortunately, LLMs generate responses iteratively, allowing for intermediate results to be displayed as they are produced. By streaming these intermediate outputs, LangChain enables smoother UX in LLM-powered apps and offers built-in support for streaming at the core of its design.
+
+In this guide, we'll discuss streaming in LLM applications and explore how LangChain's streaming APIs facilitate real-time output from various components in your application.
+
+## What to stream in LLM applications
+
+In applications involving LLMs, several types of data can be streamed to improve user experience by reducing perceived latency and increasing transparency. These include:
+
+### 1. Streaming LLM outputs
+
+The most common and critical data to stream is the output generated by the LLM itself. LLMs often take time to generate full responses, and by streaming the output in real-time, users can see partial results as they are produced. This provides immediate feedback and helps reduce the wait time for users.
+
+### 2. Streaming pipeline or workflow progress
+
+Beyond just streaming LLM output, it’s useful to stream progress through more complex workflows or pipelines, giving users a sense of how the application is progressing overall. This could include:
+
+- **In LangGraph Workflows:**
+With [LangGraph](/docs/concepts/architecture#langgraph), workflows are composed of nodes and edges that represent various steps. Streaming here involves tracking changes to the **graph state** as individual **nodes** request updates. This allows for more granular monitoring of which node in the workflow is currently active, giving real-time updates about the status of the workflow as it progresses through different stages.
+
+- **In LCEL Pipelines:**
+Streaming updates from an [LCEL](/docs/concepts/lcel) pipeline involves capturing progress from individual **sub-runnables**. For example, as different steps or components of the pipeline execute, you can stream which sub-runnable is currently running, providing real-time insight into the overall pipeline's progress.
+
+Streaming pipeline or workflow progress is essential in providing users with a clear picture of where the application is in the execution process.
+
+### 3. Streaming custom data
+
+In some cases, you may need to stream **custom data** that goes beyond the information provided by the pipeline or workflow structure. This custom information is injected within a specific step in the workflow, whether that step is a tool or a LangGraph node. For example, you could stream updates about what a tool is doing in real-time or the progress through a LangGraph node. This granular data, which is emitted directly from within the step, provides more detailed insights into the execution of the workflow and is especially useful in complex processes where more visibility is needed.
+
+## Streaming APIs
+
+LangChain two main APIs for streaming output in real-time. These APIs are supported by any component that implements the [Runnable Interface](/docs/concepts/runnables), including [LLMs](/docs/concepts/chat_models), [compiled LangGraph graphs](https://langchain-ai.github.io/langgraph/concepts/low_level/), and any Runnable generated with [LCEL](/docs/concepts/lcel).
+
+1. sync [stream](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) and async [astream](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream): Use to stream outputs from individual Runnables (e.g., a chat model) as they are generated or stream any workflow created with LangGraph.
+2. The async only [astream_events](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events): Use this API to get access to custom events and intermediate outputs from LLM  applications built entirely with [LCEL](/docs/concepts/lcel). Note that this API is available, but not needed when working with LangGraph.
+
+:::note
+In addition, there is a **legacy** async [astream_log](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_log) API. This API is not recommended for new projects it is more complex and less feature-rich than the other streaming APIs.
+:::
+
+### `stream()` and `astream()`
+
+The `stream()` method returns an iterator that yields chunks of output synchronously as they are produced. You can use a `for` loop to process each chunk in real-time. For example, when using an LLM, this allows the output to be streamed incrementally as it is generated, reducing the wait time for users.
+
+The type of chunk yielded by the `stream()` and `astream()` methods depends on the component being streamed. For example, when streaming from an [LLM](/docs/concepts/chat_models) each component will be an [AIMessageChunk](/docs/concepts/messages#aimessagechunk); however, for other components, the chunk may be different. 
+
+The `stream()` method returns an iterator that yields these chunks as they are produced. For example,
+
+```python
+for chunk in component.stream(some_input):
+    # IMPORTANT: Keep the processing of each chunk as efficient as possible.
+    # While you're processing the current chunk, the upstream component is
+    # waiting to produce the next one. For example, if working with LangGraph,
+    # graph execution is paused while the current chunk is being processed.
+    # In extreme cases, this could even result in timeouts (e.g., when llm outputs are
+    # streamed from an API that has a timeout).
+    print(chunk)
+```
+
+The [asynchronous version](/docs/concepts/async), `astream()`, works similarly but is designed for non-blocking workflows. You can use it in asynchronous code to achieve the same real-time streaming behavior.
+
+#### Usage with chat models
+
+When using `stream()` or `astream()` with chat models, the output is streamed as [AIMessageChunks](/docs/concepts/messages#aimessagechunk) as it is generated by the LLM. This allows you to present or process the LLM's output incrementally as it's being produced, which is particularly useful in interactive applications or interfaces.
+
+#### Usage with LangGraph
+
+[LangGraph](/docs/concepts/architecture#langgraph) compiled graphs are [Runnables](/docs/concepts/runnables) and support the standard streaming APIs.
+
+When using the *stream* and *astream* methods with LangGraph, you can **one or more** [streaming mode](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamMode) which allow you to control the type of output that is streamed. The available streaming modes are:
+
+- **"values"**: Emit all values of the [state](https://langchain-ai.github.io/langgraph/concepts/low_level/) for each step.
+- **"updates"**: Emit only the node name(s) and updates that were returned by the node(s) after each step.
+- **"debug"**: Emit debug events for each step.
+- **"messages"**: Emit LLM [messages](/docs/concepts/messages) [token-by-token](/docs/concepts/tokens).
+- **"custom"**: Emit custom output witten using [LangGraph's StreamWriter](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamWriter).
+
+For more information, please see:
+* [LangGraph streaming conceptual guide](https://langchain-ai.github.io/langgraph/concepts/streaming/) for more information on how to stream when working with LangGraph.
+* [LangGraph streaming how-to guides](https://langchain-ai.github.io/langgraph/how-tos/#streaming) for specific examples of streaming in LangGraph.
+
+#### Usage with LCEL
+
+If you compose multiple Runnables using [LangChain’s Expression Language (LCEL)](/docs/concepts/lcel), the `stream()` and `astream()` methods will, by convention, stream the output of the last step in the chain. This allows the final processed result to be streamed incrementally. **LCEL** tries to optimize streaming latency in pipelines such that the streaming results from the last step are available as soon as possible.
+
+
+
+### `astream_events`
+<span data-heading-keywords="astream_events,stream_events,stream events"></span>
+
+:::tip
+Use the `astream_events` API to access custom data and intermediate outputs from LLM applications built entirely with [LCEL](/docs/concepts/lcel). 
+
+While this API is available for use with [LangGraph](/docs/concepts/architecture#langgraph) as well, it is usually not necessary when working with LangGraph, as the `stream` and `astream` methods provide comprehensive streaming capabilities for LangGraph graphs.
+:::
+
+For chains constructed using **LCEL**, the `.stream()` method only streams the output of the final step from te chain. This might be sufficient for some applications, but as you build more complex chains of several LLM calls together, you may want to use the intermediate values of the chain alongside the final output. For example, you may want to return sources alongside the final generation when building a chat-over-documents app.
+
+There are ways to do this [using callbacks](/docs/concepts/#callbacks-1), or by constructing your chain in such a way that it passes intermediate
+values to the end with something like chained [`.assign()`](/docs/how_to/passthrough/) calls, but LangChain also includes an
+`.astream_events()` method that combines the flexibility of callbacks with the ergonomics of `.stream()`. When called, it returns an iterator
+which yields [various types of events](/docs/how_to/streaming/#event-reference) that you can filter and process according
+to the needs of your project.
+
+Here's one small example that prints just events containing streamed chat model output:
+
+```python
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_anthropic import ChatAnthropic
+
+model = ChatAnthropic(model="claude-3-sonnet-20240229")
+
+prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
+parser = StrOutputParser()
+chain = prompt | model | parser
+
+async for event in chain.astream_events({"topic": "parrot"}, version="v2"):
+    kind = event["event"]
+    if kind == "on_chat_model_stream":
+        print(event, end="|", flush=True)
+```
+
+You can roughly think of it as an iterator over callback events (though the format differs) - and you can use it on almost all LangChain components!
+
+See [this guide](/docs/how_to/streaming/#using-stream-events) for more detailed information on how to use `.astream_events()`, including a table listing available events.
+
+## Writing custom data to the stream
+
+To write custom data to the stream, you will need to choose one of the following methods based on the component you are working with:
+
+1. LangGraph's [StreamWriter](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamWriter) can be used to write custom data that will surface through **stream** and **astream** APIs when working with LangGraph. **Important** this is a LangGraph feature, so it is not available when working with pure LCEL. See [how to streaming custom data](https://langchain-ai.github.io/langgraph/how-tos/streaming-content/) for more information.
+2. [dispatch_events](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.dispatch_custom_event.html#) / [adispatch_events](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.adispatch_custom_event.html) can be used to write custom data that will be surfaced through the **astream_events** API. See [how to dispatch custom callback events](https://python.langchain.com/docs/how_to/callbacks_custom_events/#astream-events-api) for more information.
+
+## "Auto-Streaming" Chat Models
+
+LangChain simplifies streaming from [chat models](/docs/concepts/chat_models) by automatically enabling streaming mode in certain cases, even when you’re not explicitly calling the streaming methods. This is particularly useful when you use the non-streaming `invoke` method but still want to stream the entire application, including intermediate results from the chat model.
+
+### How It Works
+
+When you call the `invoke` (or `ainvoke`) method on a chat model, LangChain will automatically switch to streaming mode if it detects that you are trying to stream the overall application. 
+
+Under the hood, it'll have `invoke` (or `ainvoke`) use the `stream` (or `astream`) method to generate its output. The result of the invocation will be the same as far as the code that was using `invoke` is concerned; however, while the chat model is being streamed, LangChain will take care of invoking `on_llm_new_token` events in LangChain's [callback system](/docs/concepts/callbacks). These callback events
+allow LangGraph `stream`/`astream` and `astream_events` to surface the chat model's output in real-time.
+
+Example:
+
+```python
+def node(state):
+    ...
+    # The code below uses the invoke method, but LangChain will 
+    # automatically switch to streaming mode
+    # when it detects that the overall 
+    # application is being streamed.
+    ai_message = model.invoke(state["messages"])
+    ...
+
+for chunk in compiled_graph.stream(..., mode="messages"): 
+    ...
+```
+## Async Programming
+
+LangChain offers both synchronous (sync) and asynchronous (async) versions of many of its methods. The async methods are typically prefixed with an "a" (e.g., `ainvoke`, `astream`). When writing async code, it's crucial to consistently use these asynchronous methods to ensure non-blocking behavior and optimal performance.
+
+If streaming data fails to appear in real-time, please ensure that you are using the correct async methods for your workflow.
+
+Please review the [async programming in LangChain guide](/docs/concepts/async) for more information on writing async code with LangChain.
+
+## Related Resources
+
+Please see the following how-to guides for specific examples of streaming in LangChain:
+* [LangGraph conceptual guide on streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/)
+* [LangGraph streaming how-to guides](https://langchain-ai.github.io/langgraph/how-tos/#streaming)
+* [How to stream runnables](/docs/how_to/streaming/): This how-to guide goes over common streaming patterns with LangChain components (e.g., chat models) and with [LCEL](/docs/concepts/lcel).
+* [How to stream chat models](/docs/how_to/chat_streaming/)
+* [How to stream tool calls](/docs/how_to/tool_streaming/)
+
+For writing custom data to the stream, please see the following resources:
+
+* If using LangGraph, see [how to stream custom data](https://langchain-ai.github.io/langgraph/how-tos/streaming-content/).
+* If using LCEL, see [how to dispatch custom callback events](https://python.langchain.com/docs/how_to/callbacks_custom_events/#astream-events-api).
\ No newline at end of file
diff --git a/docs/docs/concepts/structured_outputs.mdx b/docs/docs/concepts/structured_outputs.mdx
new file mode 100644
index 0000000000000..f58150d5c609d
--- /dev/null
+++ b/docs/docs/concepts/structured_outputs.mdx
@@ -0,0 +1,148 @@
+# Structured outputs
+
+## Overview 
+
+For many applications, such as chatbots, models need to respond to users directly in natural language. 
+However, there are scenarios where we need models to output in a *structured format*. 
+For example, we might want to store the model output in a database and ensure that the output conforms to the database schema.
+This need motivates the concept of structured output, where models can be instructed to respond with a particular output structure.
+
+![Structured output](/img/structured_output.png)
+
+## Key concepts 
+
+**(1) Schema definition:** The output structure is represented as a schema, which can be defined in several ways. 
+**(2) Returning structured output:** The model is given this schema, and is instructed to return output that conforms to it.
+
+## Recommended usage
+
+This pseudo-code illustrates the recommended workflow when using structured output.
+LangChain provides a method, [`with_structured_output()`](/docs/how_to/structured_output/#the-with_structured_output-method), that automates the process of binding the schema to the [model](/docs/concepts/chat_models/) and parsing the output.
+This helper function is available for all model providers that support structured output. 
+
+```python
+# Define schema
+schema = {"foo": "bar"}
+# Bind schema to model
+model_with_structure = model.with_structured_output(schema)
+# Invoke the model to produce structured output that matches the schema
+structured_output = model_with_structure.invoke(user_input)
+```
+
+## Schema definition
+
+The central concept is that the output structure of model responses needs to be represented in some way. 
+While types of objects you can use depend on the model you're working with, there are common types of objects that are typically allowed or recommended for structured output in Python.
+
+The simplest and most common format for structured output is a JSON-like structure, which in Python can be represented as a dictionary (dict) or list (list).
+JSON objects (or dicts in Python) are often used directly when the tool requires raw, flexible, and minimal-overhead structured data.
+
+```json
+{
+  "answer": "The answer to the user's question",
+  "followup_question": "A followup question the user could ask"
+}
+```
+
+As a second example, [Pydantic](https://docs.pydantic.dev/latest/) is particularly useful for defining structured output schemas because it offers type hints and validation.
+Here's an example of a Pydantic schema: 
+
+```python
+from pydantic import BaseModel, Field
+class ResponseFormatter(BaseModel):
+    """Always use this tool to structure your response to the user."""
+    answer: str = Field(description="The answer to the user's question")
+    followup_question: str = Field(description="A followup question the user could ask")
+
+```
+
+## Returning structured output
+
+With a schema defined, we need a way to instruct the model to use it.
+While one approach is to include this schema in the prompt and *ask nicely* for the model to use it, this is not recommended. 
+Several more powerful methods that utilizes native features in the model provider's API are available.
+
+### Using tool calling
+
+Many [model providers support](/docs/integrations/chat/) tool calling, a concept discussed in more detail in our [tool calling guide](/docs/concepts/tool_calling/).
+In short, tool calling involves binding a tool to a model and, when appropriate, the model can *decide* to call this tool and ensure its response conforms to the tool's schema.
+With this in mind, the central concept is strightforward: *simply bind our schema to a model as a tool!*
+Here is an example using the `ResponseFormatter` schema defined above:
+
+```python
+from langchain_openai import ChatOpenAI
+model = ChatOpenAI(model="gpt-4o", temperature=0)
+# Bind responseformatter schema as a tool to the model
+model_with_tools = model.bind_tools([ResponseFormatter])
+# Invoke the model
+ai_msg = model_with_tools.invoke("What is the powerhouse of the cell?")
+```
+
+The arguments of the tool call are already extracted as a dictionary. 
+This dictionary can be optionally parsed into a Pydantic object, matching our original `ResponseFormatter` schema.
+
+```python
+# Get the tool call arguments
+ai_msg.tool_calls[0]["args"]
+{'answer': "The powerhouse of the cell is the mitochondrion. Mitochondria are organelles that generate most of the cell's supply of adenosine triphosphate (ATP), which is used as a source of chemical energy.",
+ 'followup_question': 'What is the function of ATP in the cell?'}
+# Parse the dictionary into a pydantic object
+pydantic_object = ResponseFormatter.model_validate(ai_msg.tool_calls[0]["args"])
+```
+
+### JSON mode
+
+In addition to tool calling, some model providers support a feature called `JSON mode`. 
+This supports JSON schema definition as input and enforces the model to produce a conforming JSON output.
+You can find a table of model providers that support JSON mode [here](/docs/integrations/chat/).
+Here is an example of how to use JSON mode with OpenAI:
+
+```python
+from langchain_openai import ChatOpenAI
+model = ChatOpenAI(model="gpt-4o", model_kwargs={ "response_format": { "type": "json_object" } })
+ai_msg = model.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
+ai_msg.content
+'\n{\n  "random_ints": [23, 47, 89, 15, 34, 76, 58, 3, 62, 91]\n}'
+```
+
+One important point to flag: the model *still* returns a string, which needs to be parsed into a JSON object.
+This can, of course, simply use the `json` library or a JSON output parser if you need more adavanced functionality.
+See this [how-to guide on the JSON output parser](/docs/how_to/output_parser_json) for more details.
+
+```python
+import json
+json_object = json.loads(ai_msg.content)
+{'random_ints': [23, 47, 89, 15, 34, 76, 58, 3, 62, 91]}
+```
+
+## Structured output method 
+
+There a few challenges when producing structured output with the above methods: 
+
+(1) If using tool calling, tool call arguments needs to be parsed from a dictionary back to the original schema.  
+
+(2) In addition, the model needs to be instructed to *always* use the tool when we want to enforce structured output, which is a provider specific setting. 
+
+(3) If using JSON mode, the output needs to be parsed into a JSON object. 
+
+With these challenges in mind, LangChain provides a helper function (`with_structured_output()`) to streamline the process.
+
+![Diagram of with structured output](/img/with_structured_output.png)
+
+This both binds the schema to the model as a tool and parses the output to the specified output schema. 
+
+```python
+# Bind the schema to the model
+model_with_structure = model.with_structured_output(ResponseFormatter)
+# Invoke the model
+structured_output = model_with_structure.invoke("What is the powerhouse of the cell?")
+# Get back the pydantic object
+structured_output
+ResponseFormatter(answer="The powerhouse of the cell is the mitochondrion. Mitochondria are organelles that generate most of the cell's supply of adenosine triphosphate (ATP), which is used as a source of chemical energy.", followup_question='What is the function of ATP in the cell?')
+```
+
+:::info[Further reading]
+
+For more details on usage, see our [how-to guide](/docs/how_to/structured_output/#the-with_structured_output-method).
+
+:::
\ No newline at end of file
diff --git a/docs/docs/concepts/text_splitters.mdx b/docs/docs/concepts/text_splitters.mdx
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+# Text splitters
+<span data-heading-keywords="text splitter,text splitting"></span>
+
+:::info[Prerequisites]
+
+* [Documents](/docs/concepts/retrievers/#interface)
+* Tokenization(/docs/concepts/tokens)
+:::
+
+## Overview
+
+Document splitting is often a crucial preprocessing step for many applications.
+It involves breaking down large texts into smaller, manageable chunks.
+This process offers several benefits, such as ensuring consistent processing of varying document lengths, overcoming input size limitations of models, and improving the quality of text representations used in retrieval systems.
+There are several strategies for splitting documents, each with its own advantages.
+
+## Key concepts
+
+![Conceptual Overview](/img/text_splitters.png)
+
+Text splitters split documents into smaller chunks for use in downstream applications.
+
+## Why split documents?
+
+There are several reasons to split documents:
+
+- **Handling non-uniform document lengths**: Real-world document collections often contain texts of varying sizes. Splitting ensures consistent processing across all documents.
+- **Overcoming model limitations**: Many embedding models and language models have maximum input size constraints. Splitting allows us to process documents that would otherwise exceed these limits.
+- **Improving representation quality**: For longer documents, the quality of embeddings or other representations may degrade as they try to capture too much information. Splitting can lead to more focused and accurate representations of each section.
+- **Enhancing retrieval precision**: In information retrieval systems, splitting can improve the granularity of search results, allowing for more precise matching of queries to relevant document sections.
+- **Optimizing computational resources**: Working with smaller chunks of text can be more memory-efficient and allow for better parallelization of processing tasks.
+
+Now, the next question is *how* to split the documents into chunks! There are several strategies, each with its own advantages.
+
+:::info[Further reading]
+* See Greg Kamradt's [chunkviz](https://chunkviz.up.railway.app/) to visualize different splitting strategies discussed below.
+:::
+
+## Approaches
+
+### Length-based
+
+The most intuitive strategy is to split documents based on their length. This simple yet effective approach ensures that each chunk doesn't exceed a specified size limit.
+Key benefits of length-based splitting:
+- Straightforward implementation
+- Consistent chunk sizes
+- Easily adaptable to different model requirements
+
+Types of length-based splitting:
+- **Token-based**: Splits text based on the number of tokens, which is useful when working with language models.
+- **Character-based**: Splits text based on the number of characters, which can be more consistent across different types of text.
+
+Example implementation using LangChain's `CharacterTextSplitter` with token-based splitting:
+
+```python
+from langchain_text_splitters import CharacterTextSplitter
+text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
+    encoding_name="cl100k_base", chunk_size=100, chunk_overlap=0
+)
+texts = text_splitter.split_text(document)
+```
+
+:::info[Further reading]
+
+* See the how-to guide for [token-based](/docs/how_to/split_by_token/) splitting.
+* See the how-to guide for [character-based](/docs/how_to/character_text_splitter/) splitting.
+
+:::
+
+### Text-structured based
+
+Text is naturally organized into hierarchical units such as paragraphs, sentences, and words. 
+We can leverage this inherent structure to inform our splitting strategy, creating split that maintain natural language flow, maintain semantic coherence within split, and adapts to varying levels of text granularity.
+LangChain's [`RecursiveCharacterTextSplitter`](/docs/how_to/recursive_text_splitter/) implements this concept:
+- The `RecursiveCharacterTextSplitter` attempts to keep larger units (e.g., paragraphs) intact.
+- If a unit exceeds the chunk size, it moves to the next level (e.g., sentences).
+- This process continues down to the word level if necessary.
+
+Here is example usage:
+
+```python
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=0)
+texts = text_splitter.split_text(document)
+```
+
+:::info[Further reading]
+
+* See the how-to guide for [recursive text splitting](/docs/how_to/recursive_text_splitter/).
+
+:::
+
+### Document-structured based
+
+Some documents have an inherent structure, such as HTML, Markdown, or JSON files. 
+In these cases, it's beneficial to split the document based on its structure, as it often naturally groups semantically related text.
+Key benefits of structure-based splitting:
+- Preserves the logical organization of the document
+- Maintains context within each chunk
+- Can be more effective for downstream tasks like retrieval or summarization
+
+Examples of structure-based splitting:
+- **Markdown**: Split based on headers (e.g., #, ##, ###)
+- **HTML**: Split using tags
+- **JSON**: Split by object or array elements
+- **Code**: Split by functions, classes, or logical blocks
+
+:::info[Further reading]
+
+* See the how-to guide for [Markdown splitting](/docs/how_to/markdown_header_metadata_splitter/).
+* See the how-to guide for [Recursive JSON splitting](/docs/how_to/recursive_json_splitter/).
+* See the how-to guide for [Code splitting](/docs/how_to/code_splitter/).
+* See the how-to guide for [HTML splitting](/docs/how_to/HTML_header_metadata_splitter/).
+
+:::
+
+### Semantic meaning based
+
+Unlike the previous methods, semantic-based splitting actually considers the *content* of the text. 
+While other approaches use document or text structure as proxies for semantic meaning, this method directly analyzes the text's semantics.
+There are several ways to implement this, but conceptually the approach is split text when there are significant changes in text *meaning*.
+As an example, we can use a sliding window approach to generate embeddings, and compare the embeddings to find significant differences:
+
+- Start with the first few sentences and generate an embedding.
+- Move to the next group of sentences and generate another embedding (e.g., using a sliding window approach).
+- Compare the embeddings to find significant differences, which indicate potential "break points" between semantic sections.
+
+This technique helps create chunks that are more semantically coherent, potentially improving the quality of downstream tasks like retrieval or summarization.
+
+:::info[Further reading]
+
+* See the how-to guide for [splitting text based on semantic meaning](/docs/how_to/semantic-chunker/).
+* See Greg Kamradt's [notebook](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/tutorials/LevelsOfTextSplitting/5_Levels_Of_Text_Splitting.ipynb) showcasing semantic splitting.
+
+:::
diff --git a/docs/docs/concepts/tokens.mdx b/docs/docs/concepts/tokens.mdx
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--- /dev/null
+++ b/docs/docs/concepts/tokens.mdx
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+# Tokens
+
+Modern large language models (LLMs) are typically based on a transformer architecture that processes a sequence of units known as tokens. Tokens are the fundamental elements that models use to break down input and generate output. In this section, we'll discuss what tokens are and how they are used by language models.
+
+## What is a token?
+
+A **token** is the basic unit that a language model reads, processes, and generates. These units can vary based on how the model provider defines them, but in general, they could represent:
+
+* A whole word (e.g., "apple"),
+* A part of a word (e.g., "app"),
+* Or other linguistic components such as punctuation or spaces.
+
+The way the model tokenizes the input depends on its **tokenizer algorithm**, which converts the input into tokens. Similarly, the model’s output comes as a stream of tokens, which is then decoded back into human-readable text.
+
+## How tokens work in language models
+
+The reason language models use tokens is tied to how they understand and predict language. Rather than processing characters or entire sentences directly, language models focus on **tokens**, which represent meaningful linguistic units. Here's how the process works:
+
+1. **Input Tokenization**: When you provide a model with a prompt (e.g., "LangChain is cool!"), the tokenizer algorithm splits the text into tokens. For example, the sentence could be tokenized into parts like `["Lang", "Chain", " is", " cool", "!"]`. Note that token boundaries don’t always align with word boundaries.
+    ![](/img/tokenization.png)
+
+2. **Processing**: The transformer architecture behind these models processes tokens sequentially to predict the next token in a sentence. It does this by analyzing the relationships between tokens, capturing context and meaning from the input.
+3. **Output Generation**: The model generates new tokens one by one. These output tokens are then decoded back into human-readable text.
+
+Using tokens instead of raw characters allows the model to focus on linguistically meaningful units, which helps it capture grammar, structure, and context more effectively.
+
+## Tokens don’t have to be text
+
+Although tokens are most commonly used to represent text, they don’t have to be limited to textual data. Tokens can also serve as abstract representations of **multi-modal data**, such as:
+
+- **Images**,
+- **Audio**,
+- **Video**,
+- And other types of data.
+
+At the time of writing, virtually no models support **multi-modal output**, and only a few models can handle **multi-modal inputs** (e.g., text combined with images or audio). However, as advancements in AI continue, we expect **multi-modality** to become much more common. This would allow models to process and generate a broader range of media, significantly expanding the scope of what tokens can represent and how models can interact with diverse types of data.
+
+:::note
+In principle, **anything that can be represented as a sequence of tokens** could be modeled in a similar way. For example, **DNA sequences**—which are composed of a series of nucleotides (A, T, C, G)—can be tokenized and modeled to capture patterns, make predictions, or generate sequences. This flexibility allows transformer-based models to handle diverse types of sequential data, further broadening their potential applications across various domains, including bioinformatics, signal processing, and other fields that involve structured or unstructured sequences.
+:::
+
+Please see the [multimodality](/docs/concepts/multimodality) section for more information on multi-modal inputs and outputs.
+
+## Why not use characters?
+
+Using tokens instead of individual characters makes models both more efficient and better at understanding context and grammar. Tokens represent meaningful units, like whole words or parts of words, allowing models to capture language structure more effectively than by processing raw characters. Token-level processing also reduces the number of units the model has to handle, leading to faster computation.
+
+In contrast, character-level processing would require handling a much larger sequence of input, making it harder for the model to learn relationships and context. Tokens enable models to focus on linguistic meaning, making them more accurate and efficient in generating responses.
+
+## How tokens correspond to text
+
+Please see this post from [OpenAI](https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them) for more details on how tokens are counted and how they correspond to text.
+
+According to the OpenAI post, the approximate token counts for English text are as follows:
+
+* 1 token ~= 4 chars in English
+* 1 token ~= ¾ words
+* 100 tokens ~= 75 words
\ No newline at end of file
diff --git a/docs/docs/concepts/tool_calling.mdx b/docs/docs/concepts/tool_calling.mdx
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+# Tool calling
+
+:::info[Prerequisites]
+* [Tools](/docs/concepts/tools)
+* [Chat Models](/docs/concepts/chat_models)
+:::
+
+
+## Overview 
+
+Many AI applications interact directly with humans. In these cases, it is appropriate for models to respond in natural language.
+But what about cases where we want a model to also interact *directly* with systems, such as databases or an API?
+These systems often have a particular input schema; for example, APIs frequently have a required payload structure.
+This need motivates the concept of *tool calling*. You can use [tool calling](https://platform.openai.com/docs/guides/function-calling/example-use-cases) to request model responses that match a particular schema.
+
+:::info
+You will sometimes hear the term `function calling`. We use this term interchangeably with `tool calling`. 
+:::
+
+![Conceptual overview of tool calling](/img/tool_calling_concept.png)
+
+## Key concepts 
+
+**(1) Tool Creation:** Use the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator to create a [tool](/docs/concepts/tools). A tool is an association between a function and its schema.
+**(2) Tool Binding:** The tool needs to be connected to a model that supports tool calling. This gives the model awareness of the tool and the associated input schema required by the tool.
+**(3) Tool Calling:** When appropriate, the model can decide to call a tool and ensure its response conforms to the tool's input schema.
+**(4) Tool Execution:** The tool can be executed using the arguments provided by the model.
+
+![Conceptual parts of tool calling](/img/tool_calling_components.png)
+
+## Recommended usage
+
+This pseudo-code illustrates the recommended workflow for using tool calling. 
+Created tools are passed to `.bind_tools()` method as a list.
+This model can be called, as usual. If a tool call is made, model's response will contain the tool call arguments.
+The tool call arguments can be passed directly to the tool.
+
+```python
+# Tool creation
+tools = [my_tool]
+# Tool binding
+model_with_tools = model.bind_tools(tools)
+# Tool calling 
+response = model_with_tools.invoke(user_input)
+```
+
+## Tool creation
+
+The recommended way to create a tool is using the `@tool` decorator.
+
+```python
+from langchain_core.tools import tool
+
+@tool
+def multiply(a: int, b: int) -> int:
+    """Multiply a and b."""
+    return a * b
+```
+
+:::info[Further reading]
+
+* See our conceptual guide on [tools](/docs/concepts/tools/) for more details.
+* See our [model integrations](/docs/integrations/chat/) that support tool calling.
+* See our [how-to guide](/docs/how_to/tool_calling/) on tool calling.
+
+:::
+
+## Tool binding 
+
+[Many](https://platform.openai.com/docs/guides/function-calling) [model providers](https://platform.openai.com/docs/guides/function-calling) support tool calling. 
+
+:::tip
+See our [model integration page](/docs/integrations/chat/) for a list of providers that support tool calling.
+:::
+
+The central concept to understand is that LangChain provides a standardized interface for connecting tools to models. 
+The `.bind_tools()` method can be used to specify which tools are available for a model to call. 
+
+```python
+model_with_tools = model.bind_tools([tools_list])
+```
+
+As a specific example, let's take a function `multiply` and bind it as a tool to a model that supports tool calling.
+
+```python
+def multiply(a: int, b: int) -> int:
+    """Multiply a and b.
+
+    Args:
+        a: first int
+        b: second int
+    """
+    return a * b
+
+llm_with_tools = tool_calling_model.bind_tools([multiply])
+```
+
+## Tool calling
+
+![Diagram of a tool call by a model](/img/tool_call_example.png)
+
+A key principle of tool calling is that the model decides when to use a tool based on the input's relevance. The model doesn't always need to call a tool.
+For example, given an unrelated input, the model would not call the tool:
+
+```python
+result = llm_with_tools.invoke("Hello world!")
+```
+
+The result would be an `AIMessage` containing the model's response in natural language (e.g., "Hello!").
+However, if we pass an input *relevant to the tool*, the model should choose to call it:
+
+```python
+result = llm_with_tools.invoke("What is 2 multiplied by 3?")
+```
+
+As before, the output `result` will be an `AIMessage`. 
+But, if the tool was called, `result` will have a `tool_calls` attribute.
+This attribute includes everything needed to execute the tool, including the tool name and input arguments:
+
+```
+result.tool_calls
+{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'xxx', 'type': 'tool_call'}
+```
+
+For more details on usage, see our [how-to guides](/docs/how_to/#tools)!
+
+## Tool execution
+
+[Tools](/docs/concepts/tools/) implement the [Runnable](/docs/concepts/runnables/) interface, which means that they can be invoked (e.g., `tool.invoke(args)`) directly.
+
+[LangGraph](https://langchain-ai.github.io/langgraph/) offers pre-built components (e.g., [`ToolNode`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)) that will often invoke the tool in behalf of the user.
+
+:::info[Further reading]
+
+* See our [how-to guide](/docs/how_to/tool_calling/) on tool calling.
+* See the [LangGraph documentation on using ToolNode](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/).
+
+:::
+
+## Best practices
+
+When designing [tools](/docs/concepts/tools/) to be used by a model, it is important to keep in mind that:
+
+* Models that have explicit [tool-calling APIs](/docs/concepts/#functiontool-calling) will be better at tool calling than non-fine-tuned models.
+* Models will perform better if the tools have well-chosen names and descriptions.
+* Simple, narrowly scoped tools are easier for models to use than complex tools.
+* Asking the model to select from a large list of tools poses challenges for the model.
+
+
diff --git a/docs/docs/concepts/tools.mdx b/docs/docs/concepts/tools.mdx
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+# Tools
+
+:::info Prerequisites
+- [Chat models](/docs/concepts/chat_models/)
+:::
+
+## Overview
+
+The **tool** abstraction in LangChain associates a python **function** with a **schema** that defines the function's **name**, **description** and **input**. 
+
+**Tools** can be passed to [chat models](/docs/concepts/chat_models) that support [tool calling](/docs/concepts/tool_calling) allowing the model to request the execution of a specific function with specific inputs.
+
+## Key concepts
+
+- Tools are a way to encapsulate a function and its schema in a way that can be passed to a chat model.
+- Create tools using the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator, which simplifies the process of tool creation, supporting the following:
+   - Automatically infer the tool's **name**, **description** and **inputs**, while also supporting customization.
+   - Defining tools that return **artifacts** (e.g. images, dataframes, etc.)
+   - Hiding input arguments from the schema (and hence from the model) using **injected tool arguments**.
+
+## Tool interface
+
+The tool interface is defined in the [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html#langchain_core.tools.base.BaseTool) class which is a subclass of the [Runnable Interface](/docs/concepts/runnables).
+
+The key attributes that correspond to the tool's **schema**:
+
+- **name**: The name of the tool.
+- **description**: A description of what the tool does.
+- **args**: Property that returns the JSON schema for the tool's arguments.
+
+The key methods to execute the function associated with the **tool**:
+
+- **invoke**: Invokes the tool with the given arguments.
+- **ainvoke**: Invokes the tool with the given arguments, asynchronously. Used for [async programming with Langchain](/docs/concepts/async).
+
+## Create tools using the `@tool` decorator
+
+The recommended way to create tools is using the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator. This decorator is designed to simplify the process of tool creation and should be used in most cases. After defining a function, you can decorate it with [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) to create a tool that implements the [Tool Interface](#tool-interface).
+
+```python
+from langchain_core.tools import tool
+
+@tool
+def multiply(a: int, b: int) -> int:
+   """Multiply two numbers."""
+   return a * b
+```
+
+For more details on how to create tools, see the [how to create custom tools](/docs/how_to/custom_tools/) guide.
+
+:::note
+LangChain has a few other ways to create tools; e.g., by sub-classing the [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html#langchain_core.tools.base.BaseTool) class or by using `StructuredTool`. These methods are shown in the [how to create custom tools guide](/docs/how_to/custom_tools/), but
+we generally recommend using the `@tool` decorator for most cases.
+:::
+
+## Use the tool directly
+
+Once you have defined a tool, you can use it directly by calling the function. For example, to use the `multiply` tool defined above:
+
+```python
+multiply.invoke({"a": 2, "b": 3})
+```
+
+### Inspect
+
+You can also inspect the tool's schema and other properties:
+
+```python
+print(multiply.name) # multiply
+print(multiply.description) # Multiply two numbers.
+print(multiply.args) 
+# {
+# 'type': 'object', 
+# 'properties': {'a': {'type': 'integer'}, 'b': {'type': 'integer'}}, 
+# 'required': ['a', 'b']
+# }
+```
+
+:::note
+If you're using pre-built LangChain or LangGraph components like [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent),you might not need to interact with tools directly. However, understanding how to use them can be valuable for debugging and testing. Additionally, when building custom LangGraph workflows, you may find it necessary to work with tools directly.
+:::
+
+## Configuring the schema
+
+The `@tool` decorator offers additional options to configure the schema of the tool (e.g., modify name, description
+or parse the function's doc-string to infer the schema).
+
+Please see the [API reference for @tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) for more details and review the [how to create custom tools](/docs/how_to/custom_tools/) guide for examples.
+
+## Tool artifacts
+
+**Tools** are utilities that can be called by a model, and whose outputs are designed to be fed back to a model. Sometimes, however, there are artifacts of a tool's execution that we want to make accessible to downstream components in our chain or agent, but that we don't want to expose to the model itself. For example if a tool returns a custom object, a dataframe or an image, we may want to pass some metadata about this output to the model without passing the actual output to the model. At the same time, we may want to be able to access this full output elsewhere, for example in downstream tools.
+
+```python
+@tool(response_format="content_and_artifact")
+def some_tool(...) -> Tuple[str, Any]:
+    """Tool that does something."""
+    ...
+    return 'Message for chat model', some_artifact 
+```
+
+See [how to return artifacts from tools](/docs/how_to/tool_artifacts/) for more details.
+
+## Special type annotations
+
+There are a number of special type annotations that can be used in the tool's function signature to configure the run time behavior of the tool.
+
+The following type annotations will end up **removing** the argument from the tool's schema. This can be useful for arguments that should not be exposed to the model and that the model should not be able to control.
+
+- **InjectedToolArg**: Value should be injected manually at runtime using `.invoke` or `.ainvoke`.
+- **RunnableConfig**: Pass in the RunnableConfig object to the tool.
+- **InjectedState**: Pass in the overall state of the LangGraph graph to the tool.
+- **InjectedStore**: Pass in the LangGraph store object to the tool.
+
+You can also use the `Annotated` type with a string literal to provide a **description** for the corresponding argument that **WILL** be exposed in the tool's schema.
+
+- **Annotated[..., "string literal"]** -- Adds a description to the argument that will be exposed in the tool's schema.
+
+### InjectedToolArg
+
+There are cases where certain arguments need to be passed to a tool at runtime but should not be generated by the model itself. For this, we use the `InjectedToolArg` annotation, which allows certain parameters to be hidden from the tool's schema.
+
+For example, if a tool requires a `user_id` to be injected dynamically at runtime, it can be structured in this way:
+
+```python
+from langchain_core.tools import tool, InjectedToolArg
+
+@tool
+def user_specific_tool(input_data: str, user_id: InjectedToolArg) -> str:
+    """Tool that processes input data."""
+    return f"User {user_id} processed {input_data}"
+```
+
+Annotating the `user_id` argument with `InjectedToolArg` tells LangChain that this argument should not be exposed as part of the
+tool's schema.
+
+See [how to pass run time values to tools](https://python.langchain.com/docs/how_to/tool_runtime/) for more details on how to use `InjectedToolArg`.  
+
+
+### RunnableConfig
+
+You can use the `RunnableConfig` object to pass custom run time values to tools.
+
+If you need to access the [RunnableConfig](/docs/concepts/runnables/#RunnableConfig) object from within a tool. This can be done by using the `RunnableConfig` annotation in the tool's function signature.
+
+```python
+from langchain_core.runnables import RunnableConfig
+
+@tool
+async def some_func(..., config: RunnableConfig) -> ...:
+    """Tool that does something."""
+    # do something with config
+    ...
+
+await some_func.ainvoke(..., config={"configurable": {"value": "some_value"}})
+```
+
+The `config` will not be part of the tool's schema and will be injected at runtime with appropriate values.
+
+:::note
+You may need to access the `config` object to manually propagate it to subclass. This happens if you're working with python 3.9 / 3.10 in an [async](/docs/concepts/async) environment and need to manually propagate the `config` object to sub-calls.
+
+Please read [Propagation RunnableConfig](/docs/concepts/runnables#propagation-RunnableConfig) for more details to learn how to propagate the `RunnableConfig` down the call chain manually (or upgrade to Python 3.11 where this is no longer an issue).
+:::
+
+### InjectedState
+
+Please see the [InjectedState](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.InjectedState) documentation for more details.
+
+### InjectedStore
+
+Please see the [InjectedStore](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.InjectedStore) documentation for more details.
+
+## Best practices
+
+When designing tools to be used by models, keep the following in mind:
+
+- Tools that are well-named, correctly-documented and properly type-hinted are easier for models to use.
+- Design simple and narrowly scoped tools, as they are easier for models to use correctly.
+- Use chat models that support [tool-calling](/docs/concepts/tool_calling) APIs to take advantage of tools.
+
+
+## Toolkits
+<span data-heading-keywords="toolkit,toolkits"></span>
+
+LangChain has a concept of **toolkits**. This a very thin abstraction that groups tools together that
+are designed to be used together for specific tasks.
+
+### Interface
+
+All Toolkits expose a `get_tools` method which returns a list of tools. You can therefore do:
+
+```python
+# Initialize a toolkit
+toolkit = ExampleTookit(...)
+
+# Get list of tools
+tools = toolkit.get_tools()
+```
+
+## Related resources
+
+See the following resources for more information:
+
+- [API Reference for @tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html)
+- [How to create custom tools](https://python.langchain.com/docs/how_to/custom_tools/)
+- [How to pass run time values to tools](https://python.langchain.com/docs/how_to/tool_runtime/)
+- [All LangChain tool how-to guides](https://docs.langchain.com/docs/how_to/#tools)
+- [Additional how-to guides that show usage with LangGraph](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/)
+- Tool integrations, see the [tool integration docs](https://docs.langchain.com/docs/integrations/tools/).
+
diff --git a/docs/docs/concepts/tracing.mdx b/docs/docs/concepts/tracing.mdx
new file mode 100644
index 0000000000000..659992eeb9573
--- /dev/null
+++ b/docs/docs/concepts/tracing.mdx
@@ -0,0 +1,10 @@
+# Tracing
+
+<span data-heading-keywords="trace,tracing"></span>
+
+A trace is essentially a series of steps that your application takes to go from input to output.
+Traces contain individual steps called `runs`. These can be individual calls from a model, retriever,
+tool, or sub-chains.
+Tracing gives you observability inside your chains and agents, and is vital in diagnosing issues.
+
+For a deeper dive, check out [this LangSmith conceptual guide](https://docs.smith.langchain.com/concepts/tracing).
diff --git a/docs/docs/concepts/vectorstores.mdx b/docs/docs/concepts/vectorstores.mdx
new file mode 100644
index 0000000000000..44cefe54dee13
--- /dev/null
+++ b/docs/docs/concepts/vectorstores.mdx
@@ -0,0 +1,191 @@
+# Vector stores
+<span data-heading-keywords="vector,vectorstore,vectorstores,vector store,vector stores"></span>
+
+:::info[Prerequisites]
+
+* [Embeddings](/docs/concepts/embedding_models/)
+* [Text splitters](/docs/concepts/text_splitters/)
+
+:::
+:::info[Note]
+
+This conceptual overview focuses on text-based indexing and retrieval for simplicity. 
+However, embedding models can be [multi-modal](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings)
+and vector stores can be used to store and retrieve a variety of data types beyond text.
+:::
+
+## Overview
+
+Vector stores are specialized data stores that enable indexing and retrieving information based on vector representations.
+
+These vectors, called [embeddings](/docs/concepts/embedding_models/), capture the semantic meaning of data that has been embedded.
+
+Vector stores are frequently used to search over unstructured data, such as text, images, and audio, to retrieve relevant information based on semantic similarity rather than exact keyword matches.
+
+![Vectorstores](/img/vectorstores.png)
+
+## Integrations
+
+LangChain has a large number of vectorstore integrations, allowing users to easily switch between different vectorstore implementations.
+
+Please see the [full list of LangChain vectorstore integrations](/docs/integrations/vectorstores/).
+
+## Interface
+
+LangChain provides a standard interface for working with vector stores, allowing users to easily switch between different vectorstore implementations.
+
+The interface consists of basic methods for writing, deleting and searching for documents in the vector store.
+
+The key methods are:
+
+- `add_documents`: Add a list of texts to the vector store.
+- `delete_documents`: Delete a list of documents from the vector store.
+- `similarity_search`: Search for similar documents to a given query.
+
+
+## Initialization
+
+Most vectors in LangChain accept an embedding model as an argument when initializing the vector store.
+
+We will use LangChain's [InMemoryVectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.in_memory.InMemoryVectorStore.html) implementation to illustrate the API.
+
+```python
+from langchain_core.vectorstores import InMemoryVectorStore
+# Initialize with an embedding model
+vector_store = InMemoryVectorStore(embedding=SomeEmbeddingModel())
+```
+
+## Adding documents
+
+To add documents, use the `add_documents` method.
+
+This API works with a list of [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) objects.
+`Document` objects all have `page_content` and `metadata` attributes, making them a universal way to store unstructured text and associated metadata.
+
+```python
+from langchain_core.documents import Document
+
+document_1 = Document(
+    page_content="I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
+    metadata={"source": "tweet"},
+)
+
+document_2 = Document(
+    page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
+    metadata={"source": "news"},
+)
+
+documents = [document_1, document_2]
+
+vector_store.add_documents(documents=documents)
+```
+
+You should usually provide IDs for the documents you add to the vector store, so
+that instead of adding the same document multiple times, you can update the existing document.
+
+```python
+vector_store.add_documents(documents=documents, ids=["doc1", "doc2"])
+```
+
+## Delete
+
+To delete documents, use the `delete_documents` method which takes a list of document IDs to delete.
+
+```python
+vector_store.delete_documents(ids=["doc1"])
+```
+
+## Search
+
+Vectorstores embed and store the documents that added.
+If we pass in a query, the vectorstore will embed the query, perform a similarity search over the embedded documents, and return the most similar ones.
+This captures two important concepts: first, there needs to be a way to measure the similarity between the query and *any* [embedded](/docs/concepts/embedding_models/) document.
+Second, there needs to be an algorithm to efficiently perform this similarity search across *all* embedded documents.
+
+### Similarity metrics
+
+A critical advantage of embeddings vectors is they can be compared using many simple mathematical operations:
+
+- **Cosine Similarity**: Measures the cosine of the angle between two vectors.
+- **Euclidean Distance**: Measures the straight-line distance between two points.
+- **Dot Product**: Measures the projection of one vector onto another.
+
+The choice of similarity metric can sometimes be selected when initializing the vectorstore. Please refer
+to the documentation of the specific vectorstore you are using to see what similarity metrics are supported.
+
+:::info[Further reading]
+
+* See [this documentation](https://developers.google.com/machine-learning/clustering/dnn-clustering/supervised-similarity) from Google on similarity metrics to consider with embeddings.
+* See Pinecone's [blog post](https://www.pinecone.io/learn/vector-similarity/) on similarity metrics.
+* See OpenAI's [FAQ](https://platform.openai.com/docs/guides/embeddings/faq) on what similarity metric to use with OpenAI embeddings.
+
+:::
+
+### Similarity search
+
+Given a similarity metric to measure the distance between the embedded query and any embedded document, we need an algorithm to efficiently search over *all* the embedded documents to find the most similar ones.
+There are various ways to do this. As an example, many vectorstores implement [HNSW (Hierarchical Navigable Small World)](https://www.pinecone.io/learn/series/faiss/hnsw/), a graph-based index structure that allows for efficient similarity search.
+Regardless of the search algorithm used under the hood, the LangChain vectorstore interface has a `similarity_search` method for all integrations. 
+This will take the search query, create an embedding, find similar documents, and return them as a list of [Documents](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html).
+
+```python
+query = "my query"
+docs = vectorstore.similarity_search(query)
+```
+
+Many vectorstores support search parameters to be passed with the `similarity_search` method. See the documentation for the specific vectorstore you are using to see what parameters are supported.
+As an example [Pinecone](https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html#langchain_pinecone.vectorstores.PineconeVectorStore.similarity_search) several parameters that are important general concepts:
+Many vectorstores support [the `k`](/docs/integrations/vectorstores/pinecone/#query-directly), which controls the number of Documents to return, and `filter`, which allows for filtering documents by metadata.
+
+- `query (str) – Text to look up documents similar to.`
+- `k (int) – Number of Documents to return. Defaults to 4.`
+- `filter (dict | None) – Dictionary of argument(s) to filter on metadata`
+
+:::info[Further reading]
+
+* See the [how-to guide](/docs/how_to/vectorstores/) for more details on how to use the `similarity_search` method.
+* See the [integrations page](/docs/integrations/vectorstores/) for more details on arguments that can be passed in to the `similarity_search` method for specific vectorstores.
+
+:::
+
+### Metadata filtering
+
+While vectorstore implement a search algorithm to efficiently search over *all* the embedded documents to find the most similar ones, many also support filtering on metadata.
+This allows structured filters to reduce the size of the similarity search space. These two concepts work well together:
+
+1. **Semantic search**: Query the unstructured data directly, often using via embedding or keyword similarity.
+2. **Metadata search**: Apply structured query to the metadata, filering specific documents.
+
+Vectorstore support for metadata filtering is typically dependent on the underlying vector store implementation.
+
+Here is example usage with [Pinecone](/docs/integrations/vectorstores/pinecone/#query-directly), showing that we filter for all documents that have the metadata key `source` with value `tweet`.
+
+```python
+vectorstore.similarity_search(
+    "LangChain provides abstractions to make working with LLMs easy",
+    k=2,
+    filter={"source": "tweet"},
+)
+```  
+
+:::info[Further reading]
+
+* See Pinecone's [documentation](https://docs.pinecone.io/guides/data/filter-with-metadata) on filtering with metadata.
+* See the [list of LangChain vectorstore integrations](/docs/integrations/retrievers/self_query/) that support metadata filtering.
+
+:::
+
+## Advanced search and retrieval techniques
+
+While algorithms like HNSW provide the foundation for efficient similarity search in many cases, additional techniques can be employed to improve search quality and diversity.
+For example, [maximal marginal relevance](https://python.langchain.com/v0.1/docs/modules/model_io/prompts/example_selectors/mmr/) is a re-ranking algorithm used to diversify search results, which is applied after the initial similarity search to ensure a more diverse set of results.
+As a second example, some [vector stores](/docs/integrations/retrievers/pinecone_hybrid_search/) offer built-in [hybrid-search](https://docs.pinecone.io/guides/data/understanding-hybrid-search) to combine keyword and semantic similarity search, which marries the benefits of both approaches. 
+At the moment, there is no unified way to perform hybrid search using LangChain vectorstores, but it is generally exposed as a keyword argument that is passed in with `similarity_search`.
+See this [how-to guide on hybrid search](/docs/how_to/hybrid/) for more details.
+
+| Name                                                                                                              | When to use                                           | Description                                                                                                                                  |
+|-------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------|
+| [Hybrid search](/docs/integrations/retrievers/pinecone_hybrid_search/)                                            | When combining keyword-based and semantic similarity. | Hybrid search combines keyword and semantic similarity, marrying the benefits of both approaches. [Paper](https://arxiv.org/abs/2210.11934). |
+| [Maximal Marginal Relevance (MMR)](/docs/integrations/vectorstores/pinecone/#maximal-marginal-relevance-searches) | When needing to diversify search results.             | MMR attempts to diversify the results of a search to avoid returning similar and redundant documents.                                        |
+
+ 
diff --git a/docs/docs/concepts/why_langchain.mdx b/docs/docs/concepts/why_langchain.mdx
new file mode 100644
index 0000000000000..1eae06eea3705
--- /dev/null
+++ b/docs/docs/concepts/why_langchain.mdx
@@ -0,0 +1,109 @@
+# Why langchain?
+
+The goal of `langchain` the Python package and LangChain the company is to make it as easy possible for developers to build applications that reason.
+While LangChain originally started as a single open source package, it has evolved into a company and a whole ecosystem.
+This page will talk about the LangChain ecosystem as a whole.
+Most of the components within in the LangChain ecosystem can be used by themselves - so if you feel particularly drawn to certain components but not others, that is totally fine! Pick and choose whichever components you like best.
+
+## Features
+
+There are several primary needs that LangChain aims to address:
+
+1. **Standardized component interfaces:** The growing number of [models](/docs/integrations/chat/) and [related components](/docs/integrations/vectorstores/) for AI applications has resulted in a wide variety of different APIs that developers need to learn and use.
+This diversity can make it challenging for developers to switch between providers or combine components when building applications.
+LangChain exposes a standard interface for key components, making it easy to switch between providers.
+
+2. **Orchestration:** As applications become more complex, combining multiple components and models, there's [a growing need to efficiently connect these elements into control flows](https://lilianweng.github.io/posts/2023-06-23-agent/) that can [accomplish diverse tasks](https://www.sequoiacap.com/article/generative-ais-act-o1/).
+[Orchestration](https://en.wikipedia.org/wiki/Orchestration_(computing)) is crucial for building such applications.
+
+3. **Observability and evaluation:** As applications become more complex, it becomes increasingly difficult to understand what is happening within them.
+Furthermore, the pace of development can become rate-limited by the [paradox of choice](https://en.wikipedia.org/wiki/Paradox_of_choice):
+for example, developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost. 
+[Observability](https://en.wikipedia.org/wiki/Observability) and evaluations can help developers monitor their applications and rapidly answer these types of questions with confidence.
+
+
+## Standardized component interfaces
+
+LangChain provides common interfaces for components that are central to many AI applications.
+As an example, all [chat models](/docs/concepts/chat_models/) implement the [BaseChatModel](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) interface.
+This provides a standard way to interact with chat models, supporting important but often provider-specific features like [tool calling](/docs/concepts/tool_calling/) and [structured outputs](/docs/concepts/structured_outputs/).
+
+
+### Example: chat models 
+
+Many [model providers](/docs/concepts/chat_models/) support [tool calling](/docs/concepts/tool_calling/), a critical features for many applications (e.g., [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/)), that allows a developer to request model responses that match a particular schema.
+The APIs for each provider differ. 
+LangChain's [chat model](/docs/concepts/chat_models/) interface provides a common way to bind [tools](/docs/concepts/tools) to a model in order to support [tool calling](/docs/concepts/tool_calling/):
+
+```python
+# Tool creation
+tools = [my_tool]
+# Tool binding
+model_with_tools = model.bind_tools(tools)
+```
+
+Similarly, getting models to produce [structured outputs](/docs/concepts/structured_outputs/) is an extremely common use case. 
+Providers support different approaches for this, including [JSON mode or tool calling](https://platform.openai.com/docs/guides/structured-outputs), with different APIs.
+LangChain's [chat model](/docs/concepts/chat_models/) interface provides a common way to produce structured outputs using the `with_structured_output()` method:
+
+```python
+# Define schema
+schema = ...
+# Bind schema to model
+model_with_structure = model.with_structured_output(schema)
+```
+
+### Example: retrievers
+
+In the context of [RAG](/docs/concepts/rag/) and LLM application components, LangChain's [retriever](/docs/concepts/retrievers/) interface provides a standard way to connect to many different types of data services or databases (e.g., [vector stores](/docs/concepts/vectorstores) or databases).
+The underlying implementation of the retriever depends on the type of data store or database you are connecting to, but all retrievers implement the [runnable interface](/docs/concepts/runnables/), meaning they can be invoked in a common manner.
+
+```python
+documents = my_retriever.invoke("What is the meaning of life?")
+```
+
+## Orchestration 
+
+While standardization for individual components is useful, we've increasingly seen that developers want to *combine* components into more complex applications. 
+This motivates the need for [orchestration](https://en.wikipedia.org/wiki/Orchestration_(computing)).
+There are several common characteristics of LLM applications that this orchestration layer should support:
+
+* **Complex control flow:** The application requires complex patterns such as cycles (e.g., a loop that reiterates until a condition is met).
+* **[Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/):** The application needs to maintain [short-term and / or long-term memory](https://langchain-ai.github.io/langgraph/concepts/memory/).
+* **[Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/):** The application needs human interaction, e.g., pausing, reviewing, editing, approving certain steps.
+
+The recommended way to do orchestration for these complex applications is [LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/).
+LangGraph is a library that gives developers a high degree of control by expressing the flow of the application as a set of nodes and edges.
+LangGraph comes with built-in support for [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/), [human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/), [memory](https://langchain-ai.github.io/langgraph/concepts/memory/), and other features.
+It's particularly  well suited for building [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/) or [multi-agent](https://langchain-ai.github.io/langgraph/concepts/multi_agent/) applications. 
+Importantly, individual LangChain components can be used within LangGraph nodes, but you can also use LangGraph **without** using LangChain components.
+
+:::info[Further reading]
+
+Have a look at our free course, [Introduction to LangGraph](https://academy.langchain.com/courses/intro-to-langgraph), to learn more about how to use LangGraph to build complex applications.
+
+:::
+
+## Observability and evaluation
+
+The pace of AI application development is often rate-limited by high-quality evaluations because there is a paradox of choice. 
+Developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost. 
+High quality tracing and evaluations can help you rapidly answer these types of questions with confidence.
+[LangSmith](https://docs.smith.langchain.com/) is our platform that supports observability and evaluation for AI applications.
+See our conceptual guides on [evaluations](https://docs.smith.langchain.com/concepts/evaluation) and [tracing](https://docs.smith.langchain.com/concepts/tracing) for more details.
+
+:::info[Further reading]
+
+See our video playlist on [LangSmith tracing and evaluations](https://youtube.com/playlist?list=PLfaIDFEXuae0um8Fj0V4dHG37fGFU8Q5S&feature=shared) for more details.
+
+:::
+
+## Conclusion
+
+LangChain offers standard interfaces for components that are central to many AI applications, which offers a few specific advantages:
+- **Ease of swapping providers:** It allows you to swap out different component providers without having to change the underlying code.
+- **Advanced features:** It provides common methods for more advanced features, such as [streaming](/docs/concepts/runnables/#streaming) and [tool calling](/docs/concepts/tool_calling/).
+
+[LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/) makes it possible to orchestrate complex applications (e.g., [agents](/docs/concepts/agents/)) and provide features like including [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/), [human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/), or [memory](https://langchain-ai.github.io/langgraph/concepts/memory/).
+
+[LangSmith](https://docs.smith.langchain.com/) makes it possible to iterate with confidence on your applications, by providing LLM-specific observability and framework for testing and evaluating your application.
diff --git a/docs/docs/integrations/chat/groq.ipynb b/docs/docs/integrations/chat/groq.ipynb
index 59898319b5474..e4a4ad24aa4ac 100644
--- a/docs/docs/integrations/chat/groq.ipynb
+++ b/docs/docs/integrations/chat/groq.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatGroq\n",
     "\n",
-    "This will help you getting started with Groq [chat models](../../concepts.mdx#chat-models). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models).\n",
+    "This will help you getting started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
diff --git a/docs/docs/integrations/chat/together.ipynb b/docs/docs/integrations/chat/together.ipynb
index 9cbdbfe47beff..cd47bc390f403 100644
--- a/docs/docs/integrations/chat/together.ipynb
+++ b/docs/docs/integrations/chat/together.ipynb
@@ -18,7 +18,7 @@
     "# ChatTogether\n",
     "\n",
     "\n",
-    "This page will help you get started with Together AI [chat models](../../concepts.mdx#chat-models). For detailed documentation of all ChatTogether features and configurations head to the [API reference](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html).\n",
+    "This page will help you get started with Together AI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatTogether features and configurations head to the [API reference](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html).\n",
     "\n",
     "[Together AI](https://www.together.ai/) offers an API to query [50+ leading open-source models](https://docs.together.ai/docs/chat-models)\n",
     "\n",
diff --git a/docs/sidebars.js b/docs/sidebars.js
index d3539a2d8e90f..6f02bf20fdcfb 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -47,7 +47,17 @@ module.exports = {
         className: 'hidden',
       }],
     },
-    "concepts",
+    {
+      type: "category",
+      link: {type: 'doc', id: 'concepts/index'},
+      label: "Conceptual Guide",
+      collapsible: false,
+      items: [{
+        type: 'autogenerated',
+        dirName: 'concepts',
+        className: 'hidden',
+      }],
+    },
     {
       type: "category",
       label: "Ecosystem",
diff --git a/docs/static/img/agent_types.png b/docs/static/img/agent_types.png
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From 4466caadba44e199cbfb2eeb0a939cc46a9fbd2e Mon Sep 17 00:00:00 2001
From: Eugene Yurtsev <eyurtsev@gmail.com>
Date: Tue, 22 Oct 2024 23:03:36 -0400
Subject: [PATCH 50/84] concepts: update llm stub page and re-link (#27567)

Update text llm stub page and re-link content
---
 docs/docs/concepts/callbacks.mdx              |  2 +-
 docs/docs/concepts/few_shot_prompting.mdx     |  2 +-
 docs/docs/concepts/lcel.mdx                   |  2 +-
 docs/docs/concepts/llms.mdx                   |  3 ---
 docs/docs/concepts/output_parsers.mdx         |  2 +-
 docs/docs/concepts/streaming.mdx              |  4 +--
 docs/docs/concepts/text_llms.mdx              | 10 +++++++
 docs/docs/concepts/tool_calling.mdx           |  2 +-
 .../documentation/style_guide.mdx             |  4 +--
 docs/docs/how_to/add_scores_retriever.ipynb   |  2 +-
 docs/docs/how_to/agent_executor.ipynb         | 12 ++++-----
 docs/docs/how_to/assign.ipynb                 |  4 +--
 docs/docs/how_to/binding.ipynb                |  2 +-
 docs/docs/how_to/callbacks_async.ipynb        |  2 +-
 docs/docs/how_to/callbacks_attach.ipynb       |  2 +-
 docs/docs/how_to/callbacks_constructor.ipynb  |  2 +-
 .../docs/how_to/callbacks_custom_events.ipynb | 10 +++----
 docs/docs/how_to/callbacks_runtime.ipynb      |  2 +-
 docs/docs/how_to/chat_model_caching.ipynb     |  4 +--
 .../how_to/chat_model_rate_limiting.ipynb     |  4 +--
 .../how_to/chat_token_usage_tracking.ipynb    |  2 +-
 docs/docs/how_to/chatbots_tools.ipynb         |  4 +--
 docs/docs/how_to/configure.ipynb              |  2 +-
 .../how_to/convert_runnable_to_tool.ipynb     |  6 ++---
 docs/docs/how_to/custom_callbacks.ipynb       |  2 +-
 docs/docs/how_to/custom_chat_model.ipynb      |  4 +--
 docs/docs/how_to/document_loader_pdf.ipynb    |  6 ++---
 docs/docs/how_to/document_loader_web.ipynb    |  2 +-
 docs/docs/how_to/dynamic_chain.ipynb          |  2 +-
 .../how_to/example_selectors_langsmith.ipynb  |  4 +--
 docs/docs/how_to/few_shot_examples.ipynb      |  6 ++---
 docs/docs/how_to/few_shot_examples_chat.ipynb |  8 +++---
 docs/docs/how_to/functions.ipynb              |  2 +-
 docs/docs/how_to/index.mdx                    | 26 +++++++++----------
 docs/docs/how_to/inspect.ipynb                |  4 +--
 .../how_to/llm_token_usage_tracking.ipynb     |  2 +-
 docs/docs/how_to/local_llms.ipynb             |  2 +-
 docs/docs/how_to/logprobs.ipynb               |  2 +-
 docs/docs/how_to/long_context_reorder.ipynb   |  2 +-
 docs/docs/how_to/message_history.ipynb        |  6 ++---
 docs/docs/how_to/migrate_agent.ipynb          |  4 +--
 docs/docs/how_to/multimodal_inputs.ipynb      |  2 +-
 docs/docs/how_to/output_parser_json.ipynb     |  6 ++---
 docs/docs/how_to/output_parser_xml.ipynb      |  6 ++---
 docs/docs/how_to/output_parser_yaml.ipynb     |  6 ++---
 docs/docs/how_to/parallel.ipynb               |  2 +-
 docs/docs/how_to/passthrough.ipynb            |  2 +-
 docs/docs/how_to/prompts_composition.ipynb    |  2 +-
 docs/docs/how_to/prompts_partial.ipynb        |  2 +-
 docs/docs/how_to/qa_chat_history_how_to.ipynb |  2 +-
 docs/docs/how_to/qa_sources.ipynb             |  2 +-
 docs/docs/how_to/routing.ipynb                |  6 ++---
 docs/docs/how_to/sequence.ipynb               | 10 +++----
 docs/docs/how_to/serialization.ipynb          |  2 +-
 docs/docs/how_to/streaming.ipynb              | 12 ++++-----
 docs/docs/how_to/structured_output.ipynb      |  6 ++---
 docs/docs/how_to/summarize_refine.ipynb       |  2 +-
 docs/docs/how_to/summarize_stuff.ipynb        |  2 +-
 docs/docs/how_to/tool_artifacts.ipynb         |  6 ++---
 docs/docs/how_to/tool_calling.ipynb           | 10 +++----
 docs/docs/how_to/tool_choice.ipynb            |  4 +--
 docs/docs/how_to/tool_configure.ipynb         |  4 +--
 .../how_to/tool_results_pass_to_model.ipynb   |  6 ++---
 docs/docs/how_to/tool_runtime.ipynb           |  4 +--
 docs/docs/how_to/tool_stream_events.ipynb     |  2 +-
 docs/docs/how_to/tools_builtin.ipynb          |  4 +--
 docs/docs/how_to/tools_error.ipynb            |  4 +--
 docs/docs/how_to/tools_prompting.ipynb        |  8 +++---
 docs/docs/how_to/trim_messages.ipynb          |  8 +++---
 docs/docs/integrations/chat/anthropic.ipynb   |  2 +-
 .../integrations/chat/azure_chat_openai.ipynb |  2 +-
 docs/docs/integrations/chat/bedrock.ipynb     |  2 +-
 docs/docs/integrations/chat/cerebras.ipynb    |  2 +-
 docs/docs/integrations/chat/databricks.ipynb  |  2 +-
 docs/docs/integrations/chat/fireworks.ipynb   |  2 +-
 .../chat/google_generative_ai.ipynb           |  2 +-
 .../chat/google_vertex_ai_palm.ipynb          |  2 +-
 docs/docs/integrations/chat/huggingface.ipynb |  2 +-
 docs/docs/integrations/chat/index.mdx         |  2 +-
 docs/docs/integrations/chat/mistralai.ipynb   |  2 +-
 .../chat/nvidia_ai_endpoints.ipynb            |  2 +-
 .../integrations/chat/oci_data_science.ipynb  |  2 +-
 .../integrations/chat/oci_generative_ai.ipynb |  2 +-
 docs/docs/integrations/chat/openai.ipynb      |  2 +-
 docs/docs/integrations/chat/sambanova.ipynb   |  2 +-
 docs/docs/integrations/chat/sambastudio.ipynb |  2 +-
 docs/docs/integrations/chat/vllm.ipynb        |  2 +-
 docs/docs/integrations/chat/yi.ipynb          |  2 +-
 .../document_loaders/bshtml.ipynb             |  2 +-
 .../integrations/document_loaders/json.ipynb  |  2 +-
 .../document_loaders/langsmith.ipynb          |  2 +-
 .../document_loaders/pypdfium2.ipynb          |  2 +-
 .../document_loaders/pypdfloader.ipynb        |  2 +-
 .../document_loaders/unstructured_file.ipynb  |  2 +-
 .../unstructured_markdown.ipynb               |  2 +-
 .../integrations/document_loaders/xml.ipynb   |  2 +-
 docs/docs/integrations/llms/anthropic.ipynb   |  2 +-
 .../docs/integrations/llms/azure_openai.ipynb |  2 +-
 docs/docs/integrations/llms/bedrock.ipynb     |  2 +-
 docs/docs/integrations/llms/cohere.ipynb      |  2 +-
 docs/docs/integrations/llms/databricks.ipynb  |  2 +-
 docs/docs/integrations/llms/fireworks.ipynb   |  2 +-
 docs/docs/integrations/llms/google_ai.ipynb   |  2 +-
 .../llms/google_vertex_ai_palm.ipynb          |  2 +-
 docs/docs/integrations/llms/index.mdx         |  4 +--
 .../llms/nvidia_ai_endpoints.ipynb            |  2 +-
 docs/docs/integrations/llms/ollama.ipynb      |  2 +-
 docs/docs/integrations/llms/openai.ipynb      |  2 +-
 docs/docs/integrations/llms/together.ipynb    |  2 +-
 .../docs/integrations/providers/databricks.md |  2 +-
 .../providers/mlflow_tracking.ipynb           |  2 +-
 .../retrievers/azure_ai_search.ipynb          |  2 +-
 .../integrations/retrievers/bedrock.ipynb     |  2 +-
 docs/docs/integrations/retrievers/box.ipynb   |  2 +-
 .../retrievers/elasticsearch_retriever.ipynb  |  6 ++---
 .../retrievers/google_vertex_ai_search.ipynb  |  4 +--
 docs/docs/integrations/retrievers/index.mdx   |  2 +-
 .../retrievers/milvus_hybrid_search.ipynb     |  2 +-
 docs/docs/integrations/stores/astradb.ipynb   |  2 +-
 docs/docs/integrations/stores/cassandra.ipynb |  2 +-
 .../integrations/stores/elasticsearch.ipynb   |  2 +-
 .../integrations/stores/file_system.ipynb     |  2 +-
 docs/docs/integrations/stores/in_memory.ipynb |  2 +-
 docs/docs/integrations/stores/redis.ipynb     |  2 +-
 .../integrations/stores/upstash_redis.ipynb   |  2 +-
 .../text_embedding/databricks.ipynb           |  2 +-
 docs/docs/integrations/tools/gmail.ipynb      |  2 +-
 .../docs/integrations/tools/jina_search.ipynb |  4 +--
 docs/docs/integrations/tools/requests.ipynb   |  2 +-
 docs/docs/integrations/tools/slack.ipynb      |  2 +-
 .../integrations/tools/sql_database.ipynb     |  2 +-
 .../integrations/tools/tavily_search.ipynb    |  4 +--
 .../integrations/vectorstores/astradb.ipynb   |  2 +-
 .../integrations/vectorstores/chroma.ipynb    |  2 +-
 .../vectorstores/clickhouse.ipynb             |  2 +-
 .../integrations/vectorstores/couchbase.ipynb |  2 +-
 .../databricks_vector_search.ipynb            |  2 +-
 .../vectorstores/elasticsearch.ipynb          |  2 +-
 .../integrations/vectorstores/faiss.ipynb     |  2 +-
 docs/docs/integrations/vectorstores/index.mdx |  2 +-
 .../integrations/vectorstores/milvus.ipynb    |  2 +-
 .../vectorstores/mongodb_atlas.ipynb          |  2 +-
 .../integrations/vectorstores/pgvector.ipynb  |  2 +-
 .../integrations/vectorstores/pinecone.ipynb  |  2 +-
 .../integrations/vectorstores/qdrant.ipynb    |  2 +-
 .../integrations/vectorstores/redis.ipynb     |  2 +-
 .../errors/INVALID_PROMPT_INPUT.mdx           |  2 +-
 docs/docs/tutorials/agents.ipynb              |  6 ++---
 docs/docs/tutorials/chatbot.ipynb             |  6 ++---
 docs/docs/tutorials/extraction.ipynb          |  6 ++---
 docs/docs/tutorials/llm_chain.ipynb           |  8 +++---
 docs/docs/tutorials/local_rag.ipynb           |  8 +++---
 docs/docs/tutorials/pdf_qa.ipynb              | 14 +++++-----
 docs/docs/tutorials/qa_chat_history.ipynb     | 10 +++----
 docs/docs/tutorials/query_analysis.ipynb      |  8 +++---
 docs/docs/tutorials/rag.ipynb                 | 14 +++++-----
 docs/docs/tutorials/retrievers.ipynb          |  2 +-
 docs/docs/tutorials/sql_qa.ipynb              |  6 ++---
 docs/docs/tutorials/summarization.ipynb       |  4 +--
 .../constitutional_chain.ipynb                |  2 +-
 .../migrating_chains/conversation_chain.ipynb |  2 +-
 .../conversation_retrieval_chain.ipynb        |  2 +-
 .../versions/migrating_chains/index.ipynb     |  4 +--
 .../versions/migrating_chains/llm_chain.ipynb |  2 +-
 .../migrating_chains/llm_math_chain.ipynb     |  4 +--
 .../migrating_chains/llm_router_chain.ipynb   |  4 +--
 .../map_rerank_docs_chain.ipynb               |  2 +-
 .../migrating_chains/multi_prompt_chain.ipynb |  2 +-
 .../migrating_chains/refine_docs_chain.ipynb  |  2 +-
 .../migrating_chains/retrieval_qa.ipynb       |  2 +-
 .../migrating_chains/stuff_docs_chain.ipynb   |  4 +--
 .../migrating_memory/chat_history.ipynb       |  4 +--
 docs/docs/versions/migrating_memory/index.mdx |  2 +-
 173 files changed, 306 insertions(+), 299 deletions(-)
 delete mode 100644 docs/docs/concepts/llms.mdx
 create mode 100644 docs/docs/concepts/text_llms.mdx

diff --git a/docs/docs/concepts/callbacks.mdx b/docs/docs/concepts/callbacks.mdx
index 6e3975271d8eb..62fd28341bb47 100644
--- a/docs/docs/concepts/callbacks.mdx
+++ b/docs/docs/concepts/callbacks.mdx
@@ -1,7 +1,7 @@
 # Callbacks
 
 :::note Prerequisites
-- [Runnable interface](/docs/concepts/#runnable-interface)
+- [Runnable interface](/docs/concepts/runnables)
 :::
 
 LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
diff --git a/docs/docs/concepts/few_shot_prompting.mdx b/docs/docs/concepts/few_shot_prompting.mdx
index b7147addea25c..e48d76905dc41 100644
--- a/docs/docs/concepts/few_shot_prompting.mdx
+++ b/docs/docs/concepts/few_shot_prompting.mdx
@@ -70,7 +70,7 @@ Most state-of-the-art models these days are chat models, so we'll focus on forma
 
 If we insert our examples into the system prompt as a string, we'll need to make sure it's clear to the model where each example begins and which parts are the input versus output. Different models respond better to different syntaxes, like [ChatML](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chat-markup-language), XML, TypeScript, etc.
 
-If we insert our examples as messages, where each example is represented as a sequence of Human, AI messages, we might want to also assign [names](/docs/concepts/#messages) to our messages like `"example_user"` and `"example_assistant"` to make it clear that these messages correspond to different actors than the latest input message.
+If we insert our examples as messages, where each example is represented as a sequence of Human, AI messages, we might want to also assign [names](/docs/concepts/messages) to our messages like `"example_user"` and `"example_assistant"` to make it clear that these messages correspond to different actors than the latest input message.
 
 **Formatting tool call examples**
 
diff --git a/docs/docs/concepts/lcel.mdx b/docs/docs/concepts/lcel.mdx
index 9378ec8e92852..63cced2dd4764 100644
--- a/docs/docs/concepts/lcel.mdx
+++ b/docs/docs/concepts/lcel.mdx
@@ -22,7 +22,7 @@ LangChain optimizes the run-time execution of chains built with LCEL in a number
 
 - **Optimize parallel execution**: Run Runnables in parallel using [RunnableParallel](#RunnableParallel) or run multiple inputs through a given chain in parallel using the [Runnable Batch API](/docs/concepts/runnables#batch). Parallel execution can significantly reduce the latency as processing can be done in parallel instead of sequentially.
 - **Guarantee Async support**: Any chain built with LCEL can be run asynchronously using the [Runnable Async API](/docs/concepts/runnables#async-api). This can be useful when running chains in a server environment where you want to handle large number of requests concurrently.
-- **Simplify streaming**: LCEL chains can be streamed, allowing for incremental output as the chain is executed. LangChain can optimize the streaming of the output to minimize the time-to-first-token(time elapsed until the first chunk of output from a [chat model](/docs/concepts/chat_models) or [llm](/docs/concepts/llms) comes out).
+- **Simplify streaming**: LCEL chains can be streamed, allowing for incremental output as the chain is executed. LangChain can optimize the streaming of the output to minimize the time-to-first-token(time elapsed until the first chunk of output from a [chat model](/docs/concepts/chat_models) or [llm](/docs/concepts/text_llms) comes out).
 
 Other benefits include:
 
diff --git a/docs/docs/concepts/llms.mdx b/docs/docs/concepts/llms.mdx
deleted file mode 100644
index 5e2f7d98c7256..0000000000000
--- a/docs/docs/concepts/llms.mdx
+++ /dev/null
@@ -1,3 +0,0 @@
-# Large language models (llms)
-
-Please see the [Chat Model Concept Guide](/docs/concepts/chat_models) page for more information.
\ No newline at end of file
diff --git a/docs/docs/concepts/output_parsers.mdx b/docs/docs/concepts/output_parsers.mdx
index a03daea8737a5..d15bc6fdb6894 100644
--- a/docs/docs/concepts/output_parsers.mdx
+++ b/docs/docs/concepts/output_parsers.mdx
@@ -7,7 +7,7 @@
 The information here refers to parsers that take a text output from a model try to parse it into a more structured representation.
 More and more models are supporting function (or tool) calling, which handles this automatically.
 It is recommended to use function/tool calling rather than output parsing.
-See documentation for that [here](/docs/concepts/#function-tool-calling).
+See documentation for that [here](/docs/concepts/tool_calling).
 
 :::
 
diff --git a/docs/docs/concepts/streaming.mdx b/docs/docs/concepts/streaming.mdx
index 7ab681b533ebb..ba2c89d58ca59 100644
--- a/docs/docs/concepts/streaming.mdx
+++ b/docs/docs/concepts/streaming.mdx
@@ -106,7 +106,7 @@ While this API is available for use with [LangGraph](/docs/concepts/architecture
 
 For chains constructed using **LCEL**, the `.stream()` method only streams the output of the final step from te chain. This might be sufficient for some applications, but as you build more complex chains of several LLM calls together, you may want to use the intermediate values of the chain alongside the final output. For example, you may want to return sources alongside the final generation when building a chat-over-documents app.
 
-There are ways to do this [using callbacks](/docs/concepts/#callbacks-1), or by constructing your chain in such a way that it passes intermediate
+There are ways to do this [using callbacks](/docs/concepts/callbacks), or by constructing your chain in such a way that it passes intermediate
 values to the end with something like chained [`.assign()`](/docs/how_to/passthrough/) calls, but LangChain also includes an
 `.astream_events()` method that combines the flexibility of callbacks with the ergonomics of `.stream()`. When called, it returns an iterator
 which yields [various types of events](/docs/how_to/streaming/#event-reference) that you can filter and process according
@@ -188,4 +188,4 @@ Please see the following how-to guides for specific examples of streaming in Lan
 For writing custom data to the stream, please see the following resources:
 
 * If using LangGraph, see [how to stream custom data](https://langchain-ai.github.io/langgraph/how-tos/streaming-content/).
-* If using LCEL, see [how to dispatch custom callback events](https://python.langchain.com/docs/how_to/callbacks_custom_events/#astream-events-api).
\ No newline at end of file
+* If using LCEL, see [how to dispatch custom callback events](https://python.langchain.com/docs/how_to/callbacks_custom_events/#astream-events-api).
diff --git a/docs/docs/concepts/text_llms.mdx b/docs/docs/concepts/text_llms.mdx
new file mode 100644
index 0000000000000..d35a72476af3e
--- /dev/null
+++ b/docs/docs/concepts/text_llms.mdx
@@ -0,0 +1,10 @@
+# String-in, string-out llms
+
+:::tip
+You are probably looking for the [Chat Model Concept Guide](/docs/concepts/chat_models) page for more information.
+:::
+
+LangChain has implementations for older language models that take a string as input and return a string as output. These models are typically named without the "Chat" prefix (e.g., `Ollama`, `Anthropic`, `OpenAI`, etc.), and may include the "LLM" suffix (e.g., `OllamaLLM`, `AnthropicLLM`, `OpenAILLM`, etc.). These models implement the [BaseLLM](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.llms.BaseLLM.html#langchain_core.language_models.llms.BaseLLM) interface.
+
+Users should be using almost exclusively the newer [Chat Models](/docs/concepts/chat_models) as most
+model providers have adopted a chat like interface for interacting with language models.
\ No newline at end of file
diff --git a/docs/docs/concepts/tool_calling.mdx b/docs/docs/concepts/tool_calling.mdx
index e377688334640..c3c753ee52570 100644
--- a/docs/docs/concepts/tool_calling.mdx
+++ b/docs/docs/concepts/tool_calling.mdx
@@ -141,7 +141,7 @@ For more details on usage, see our [how-to guides](/docs/how_to/#tools)!
 
 When designing [tools](/docs/concepts/tools/) to be used by a model, it is important to keep in mind that:
 
-* Models that have explicit [tool-calling APIs](/docs/concepts/#functiontool-calling) will be better at tool calling than non-fine-tuned models.
+* Models that have explicit [tool-calling APIs](/docs/concepts/tool_calling) will be better at tool calling than non-fine-tuned models.
 * Models will perform better if the tools have well-chosen names and descriptions.
 * Simple, narrowly scoped tools are easier for models to use than complex tools.
 * Asking the model to select from a large list of tools poses challenges for the model.
diff --git a/docs/docs/contributing/documentation/style_guide.mdx b/docs/docs/contributing/documentation/style_guide.mdx
index 83a3ae3c803c7..76a6ab837b361 100644
--- a/docs/docs/contributing/documentation/style_guide.mdx
+++ b/docs/docs/contributing/documentation/style_guide.mdx
@@ -92,8 +92,8 @@ To quote the Diataxis website:
 
 Some examples include:
 
-- [Retrieval conceptual docs](/docs/concepts/#retrieval)
-- [Chat model conceptual docs](/docs/concepts/#chat-models)
+- [Retrieval conceptual docs](/docs/concepts/retrieval)
+- [Chat model conceptual docs](/docs/concepts/chat_models)
 
 Here are some high-level tips on writing a good conceptual guide:
 
diff --git a/docs/docs/how_to/add_scores_retriever.ipynb b/docs/docs/how_to/add_scores_retriever.ipynb
index 989b23594e19d..65d56cbcf83a3 100644
--- a/docs/docs/how_to/add_scores_retriever.ipynb
+++ b/docs/docs/how_to/add_scores_retriever.ipynb
@@ -75,7 +75,7 @@
     "\n",
     "To obtain scores from a vector store retriever, we wrap the underlying vector store's `.similarity_search_with_score` method in a short function that packages scores into the associated document's metadata.\n",
     "\n",
-    "We add a `@chain` decorator to the function to create a [Runnable](/docs/concepts/#langchain-expression-language) that can be used similarly to a typical retriever."
+    "We add a `@chain` decorator to the function to create a [Runnable](/docs/concepts/lcel) that can be used similarly to a typical retriever."
    ]
   },
   {
diff --git a/docs/docs/how_to/agent_executor.ipynb b/docs/docs/how_to/agent_executor.ipynb
index 657a6e5b37ca3..995b631f17bb6 100644
--- a/docs/docs/how_to/agent_executor.ipynb
+++ b/docs/docs/how_to/agent_executor.ipynb
@@ -31,10 +31,10 @@
     "## Concepts\n",
     "\n",
     "Concepts we will cover are:\n",
-    "- Using [language models](/docs/concepts/#chat-models), in particular their tool calling ability\n",
-    "- Creating a [Retriever](/docs/concepts/#retrievers) to expose specific information to our agent\n",
-    "- Using a Search [Tool](/docs/concepts/#tools) to look up things online\n",
-    "- [`Chat History`](/docs/concepts/#chat-history), which allows a chatbot to \"remember\" past interactions and take them into account when responding to follow-up questions. \n",
+    "- Using [language models](/docs/concepts/chat_models), in particular their tool calling ability\n",
+    "- Creating a [Retriever](/docs/concepts/retrievers) to expose specific information to our agent\n",
+    "- Using a Search [Tool](/docs/concepts/tools) to look up things online\n",
+    "- [`Chat History`](/docs/concepts/chat_history), which allows a chatbot to \"remember\" past interactions and take them into account when responding to follow-up questions. \n",
     "- Debugging and tracing your application using [LangSmith](/docs/concepts/#langsmith)\n",
     "\n",
     "## Setup\n",
@@ -415,7 +415,7 @@
    "source": [
     "## Create the agent\n",
     "\n",
-    "Now that we have defined the tools and the LLM, we can create the agent. We will be using a tool calling agent - for more information on this type of agent, as well as other options, see [this guide](/docs/concepts/#agent_types/).\n",
+    "Now that we have defined the tools and the LLM, we can create the agent. We will be using a tool calling agent - for more information on this type of agent, as well as other options, see [this guide](/docs/concepts/agents/).\n",
     "\n",
     "We can first choose the prompt we want to use to guide the agent.\n",
     "\n",
@@ -457,7 +457,7 @@
    "id": "f8014c9d",
    "metadata": {},
    "source": [
-    "Now, we can initialize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/#agents).\n",
+    "Now, we can initialize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/agents).\n",
     "\n",
     "Note that we are passing in the `model`, not `model_with_tools`. That is because `create_tool_calling_agent` will call `.bind_tools` for us under the hood."
    ]
diff --git a/docs/docs/how_to/assign.ipynb b/docs/docs/how_to/assign.ipynb
index 59e4a0ed3ec98..b231b0fb781f3 100644
--- a/docs/docs/how_to/assign.ipynb
+++ b/docs/docs/how_to/assign.ipynb
@@ -19,7 +19,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "- [Calling runnables in parallel](/docs/how_to/parallel/)\n",
     "- [Custom functions](/docs/how_to/functions/)\n",
@@ -29,7 +29,7 @@
     "\n",
     "An alternate way of [passing data through](/docs/how_to/passthrough) steps of a chain is to leave the current values of the chain state unchanged while assigning a new value under a given key. The [`RunnablePassthrough.assign()`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html#langchain_core.runnables.passthrough.RunnablePassthrough.assign) static method takes an input value and adds the extra arguments passed to the assign function.\n",
     "\n",
-    "This is useful in the common [LangChain Expression Language](/docs/concepts/#langchain-expression-language) pattern of additively creating a dictionary to use as input to a later step.\n",
+    "This is useful in the common [LangChain Expression Language](/docs/concepts/lcel) pattern of additively creating a dictionary to use as input to a later step.\n",
     "\n",
     "Here's an example:"
    ]
diff --git a/docs/docs/how_to/binding.ipynb b/docs/docs/how_to/binding.ipynb
index c25f038e2a377..0d41fd89402fc 100644
--- a/docs/docs/how_to/binding.ipynb
+++ b/docs/docs/how_to/binding.ipynb
@@ -21,7 +21,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "- [Tool calling](/docs/how_to/tool_calling)\n",
     "\n",
diff --git a/docs/docs/how_to/callbacks_async.ipynb b/docs/docs/how_to/callbacks_async.ipynb
index 4e977852eb79b..f42f7f0a045cd 100644
--- a/docs/docs/how_to/callbacks_async.ipynb
+++ b/docs/docs/how_to/callbacks_async.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
+    "- [Callbacks](/docs/concepts/callbacks)\n",
     "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/callbacks_attach.ipynb b/docs/docs/how_to/callbacks_attach.ipynb
index 8115da5a4a472..7d06f3b764346 100644
--- a/docs/docs/how_to/callbacks_attach.ipynb
+++ b/docs/docs/how_to/callbacks_attach.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
+    "- [Callbacks](/docs/concepts/callbacks)\n",
     "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
     "- [Chaining runnables](/docs/how_to/sequence)\n",
     "- [Attach runtime arguments to a Runnable](/docs/how_to/binding)\n",
diff --git a/docs/docs/how_to/callbacks_constructor.ipynb b/docs/docs/how_to/callbacks_constructor.ipynb
index 20cc043d63ff0..bae51a02f29f0 100644
--- a/docs/docs/how_to/callbacks_constructor.ipynb
+++ b/docs/docs/how_to/callbacks_constructor.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
+    "- [Callbacks](/docs/concepts/callbacks)\n",
     "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/how_to/callbacks_custom_events.ipynb b/docs/docs/how_to/callbacks_custom_events.ipynb
index 429824c34e82a..7282cd8de7695 100644
--- a/docs/docs/how_to/callbacks_custom_events.ipynb
+++ b/docs/docs/how_to/callbacks_custom_events.ipynb
@@ -10,13 +10,13 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
+    "- [Callbacks](/docs/concepts/callbacks)\n",
     "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
-    "- [Astream Events API](/docs/concepts/#astream_events) the `astream_events` method will surface custom callback events.\n",
+    "- [Astream Events API](/docs/concepts/streaming/#astream_events) the `astream_events` method will surface custom callback events.\n",
     ":::\n",
     "\n",
-    "In some situations, you may want to dipsatch a custom callback event from within a [Runnable](/docs/concepts/#runnable-interface) so it can be surfaced\n",
-    "in a custom callback handler or via the [Astream Events API](/docs/concepts/#astream_events).\n",
+    "In some situations, you may want to dipsatch a custom callback event from within a [Runnable](/docs/concepts/runnables) so it can be surfaced\n",
+    "in a custom callback handler or via the [Astream Events API](/docs/concepts/streaming/#astream_events).\n",
     "\n",
     "For example, if you have a long running tool with multiple steps, you can dispatch custom events between the steps and use these custom events to monitor progress.\n",
     "You could also surface these custom events to an end user of your application to show them how the current task is progressing.\n",
@@ -64,7 +64,7 @@
    "source": [
     "## Astream Events API\n",
     "\n",
-    "The most useful way to consume custom events is via the [Astream Events API](/docs/concepts/#astream_events).\n",
+    "The most useful way to consume custom events is via the [Astream Events API](/docs/concepts/streaming/#astream_events).\n",
     "\n",
     "We can use the `async` `adispatch_custom_event` API to emit custom events in an async setting. \n",
     "\n",
diff --git a/docs/docs/how_to/callbacks_runtime.ipynb b/docs/docs/how_to/callbacks_runtime.ipynb
index a4b15416dfc55..ff04fcde58894 100644
--- a/docs/docs/how_to/callbacks_runtime.ipynb
+++ b/docs/docs/how_to/callbacks_runtime.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
+    "- [Callbacks](/docs/concepts/callbacks)\n",
     "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/how_to/chat_model_caching.ipynb b/docs/docs/how_to/chat_model_caching.ipynb
index b305223904cac..d9a7f38445816 100644
--- a/docs/docs/how_to/chat_model_caching.ipynb
+++ b/docs/docs/how_to/chat_model_caching.ipynb
@@ -10,8 +10,8 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [LLMs](/docs/concepts/#llms)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [LLMs](/docs/concepts/text_llms)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/chat_model_rate_limiting.ipynb b/docs/docs/how_to/chat_model_rate_limiting.ipynb
index d34084becbefe..32bbaf3779cea 100644
--- a/docs/docs/how_to/chat_model_rate_limiting.ipynb
+++ b/docs/docs/how_to/chat_model_rate_limiting.ipynb
@@ -10,8 +10,8 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [LLMs](/docs/concepts/#llms)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [LLMs](/docs/concepts/text_llms)\n",
     ":::\n",
     "\n",
     "\n",
diff --git a/docs/docs/how_to/chat_token_usage_tracking.ipynb b/docs/docs/how_to/chat_token_usage_tracking.ipynb
index 84948920e8c2d..29290b2d7d13f 100644
--- a/docs/docs/how_to/chat_token_usage_tracking.ipynb
+++ b/docs/docs/how_to/chat_token_usage_tracking.ipynb
@@ -10,7 +10,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/chatbots_tools.ipynb b/docs/docs/how_to/chatbots_tools.ipynb
index 4bbd425579609..0c9bbf5259e4d 100644
--- a/docs/docs/how_to/chatbots_tools.ipynb
+++ b/docs/docs/how_to/chatbots_tools.ipynb
@@ -10,9 +10,9 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Chatbots](/docs/concepts/#messages)\n",
+    "- [Chatbots](/docs/concepts/messages)\n",
     "- [Agents](/docs/tutorials/agents)\n",
-    "- [Chat history](/docs/concepts/#chat-history)\n",
+    "- [Chat history](/docs/concepts/chat_history)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/configure.ipynb b/docs/docs/how_to/configure.ipynb
index 3864a9ea80d63..8b67a4a3f8b31 100644
--- a/docs/docs/how_to/configure.ipynb
+++ b/docs/docs/how_to/configure.ipynb
@@ -21,7 +21,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "- [Binding runtime arguments](/docs/how_to/binding/)\n",
     "\n",
diff --git a/docs/docs/how_to/convert_runnable_to_tool.ipynb b/docs/docs/how_to/convert_runnable_to_tool.ipynb
index bee037d6acd6f..d9967023a6024 100644
--- a/docs/docs/how_to/convert_runnable_to_tool.ipynb
+++ b/docs/docs/how_to/convert_runnable_to_tool.ipynb
@@ -42,7 +42,7 @@
    "source": [
     "LangChain [tools](/docs/concepts#tools) are interfaces that an agent, chain, or chat model can use to interact with the world. See [here](/docs/how_to/#tools) for how-to guides covering tool-calling, built-in tools, custom tools, and more information.\n",
     "\n",
-    "LangChain tools-- instances of [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html)-- are [Runnables](/docs/concepts/#runnable-interface) with additional constraints that enable them to be invoked effectively by language models:\n",
+    "LangChain tools-- instances of [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html)-- are [Runnables](/docs/concepts/runnables) with additional constraints that enable them to be invoked effectively by language models:\n",
     "\n",
     "- Their inputs are constrained to be serializable, specifically strings and Python `dict` objects;\n",
     "- They contain names and descriptions indicating how and when they should be used;\n",
@@ -259,9 +259,9 @@
    "source": [
     "## In agents\n",
     "\n",
-    "Below we will incorporate LangChain Runnables as tools in an [agent](/docs/concepts/#agents) application. We will demonstrate with:\n",
+    "Below we will incorporate LangChain Runnables as tools in an [agent](/docs/concepts/agents) application. We will demonstrate with:\n",
     "\n",
-    "- a document [retriever](/docs/concepts/#retrievers);\n",
+    "- a document [retriever](/docs/concepts/retrievers);\n",
     "- a simple [RAG](/docs/tutorials/rag/) chain, allowing an agent to delegate relevant queries to it.\n",
     "\n",
     "We first instantiate a chat model that supports [tool calling](/docs/how_to/tool_calling/):\n",
diff --git a/docs/docs/how_to/custom_callbacks.ipynb b/docs/docs/how_to/custom_callbacks.ipynb
index 2579f8b8ae8de..211f23ae22220 100644
--- a/docs/docs/how_to/custom_callbacks.ipynb
+++ b/docs/docs/how_to/custom_callbacks.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Callbacks](/docs/concepts/#callbacks)\n",
+    "- [Callbacks](/docs/concepts/callbacks)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/custom_chat_model.ipynb b/docs/docs/how_to/custom_chat_model.ipynb
index 09a34b521a2aa..708a0942c9ec5 100644
--- a/docs/docs/how_to/custom_chat_model.ipynb
+++ b/docs/docs/how_to/custom_chat_model.ipynb
@@ -10,7 +10,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
     "\n",
     ":::\n",
     "\n",
@@ -28,7 +28,7 @@
     "\n",
     "Chat models take messages as inputs and return a message as output. \n",
     "\n",
-    "LangChain has a few [built-in message types](/docs/concepts/#message-types):\n",
+    "LangChain has a few [built-in message types](/docs/concepts/messages):\n",
     "\n",
     "| Message Type          | Description                                                                                     |\n",
     "|-----------------------|-------------------------------------------------------------------------------------------------|\n",
diff --git a/docs/docs/how_to/document_loader_pdf.ipynb b/docs/docs/how_to/document_loader_pdf.ipynb
index 4daac5a7f56c6..83ccefdb47c7f 100644
--- a/docs/docs/how_to/document_loader_pdf.ipynb
+++ b/docs/docs/how_to/document_loader_pdf.ipynb
@@ -50,7 +50,7 @@
     "\n",
     "If you are looking for a simple string representation of text that is embedded in a PDF, the method below is appropriate. It will return a list of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects-- one per page-- containing a single string of the page's text in the Document's `page_content` attribute. It will not parse text in images or scanned PDF pages. Under the hood it uses the [pypydf](https://pypdf.readthedocs.io/en/stable/) Python library.\n",
     "\n",
-    "LangChain [document loaders](/docs/concepts/#document-loaders) implement `lazy_load` and its async variant, `alazy_load`, which return iterators of `Document` objects. We will use these below."
+    "LangChain [document loaders](/docs/concepts/document_loaders) implement `lazy_load` and its async variant, `alazy_load`, which return iterators of `Document` objects. We will use these below."
    ]
   },
   {
@@ -147,7 +147,7 @@
     "\n",
     "### Vector search over PDFs\n",
     "\n",
-    "Once we have loaded PDFs into LangChain `Document` objects, we can index them (e.g., a RAG application) in the usual way. Below we use OpenAI embeddings, although any LangChain [embeddings](https://python.langchain.com/docs/concepts/#embedding-models) model will suffice."
+    "Once we have loaded PDFs into LangChain `Document` objects, we can index them (e.g., a RAG application) in the usual way. Below we use OpenAI embeddings, although any LangChain [embeddings](https://python.langchain.com/docs/concepts/embedding_models) model will suffice."
    ]
   },
   {
@@ -804,7 +804,7 @@
     "\n",
     "Many modern LLMs support inference over multimodal inputs (e.g., images). In some applications-- such as question-answering over PDFs with complex layouts, diagrams, or scans-- it may be advantageous to skip the PDF parsing, instead casting a PDF page to an image and passing it to a model directly. This allows a model to reason over the two dimensional content on the page, instead of a \"one-dimensional\" string representation.\n",
     "\n",
-    "In principle we can use any LangChain [chat model](/docs/concepts/#chat-models) that supports multimodal inputs. A list of these models is documented [here](/docs/integrations/chat/). Below we use OpenAI's `gpt-4o-mini`.\n",
+    "In principle we can use any LangChain [chat model](/docs/concepts/chat_models) that supports multimodal inputs. A list of these models is documented [here](/docs/integrations/chat/). Below we use OpenAI's `gpt-4o-mini`.\n",
     "\n",
     "First we define a short utility function to convert a PDF page to a base64-encoded image:"
    ]
diff --git a/docs/docs/how_to/document_loader_web.ipynb b/docs/docs/how_to/document_loader_web.ipynb
index 9eaa321822e9e..04c4a3b7c68fb 100644
--- a/docs/docs/how_to/document_loader_web.ipynb
+++ b/docs/docs/how_to/document_loader_web.ipynb
@@ -389,7 +389,7 @@
    "source": [
     "### Vector search over page content\n",
     "\n",
-    "Once we have loaded the page contents into LangChain `Document` objects, we can index them (e.g., for a RAG application) in the usual way. Below we use OpenAI [embeddings](/docs/concepts/#embedding-models), although any LangChain embeddings model will suffice."
+    "Once we have loaded the page contents into LangChain `Document` objects, we can index them (e.g., for a RAG application) in the usual way. Below we use OpenAI [embeddings](/docs/concepts/embedding_models), although any LangChain embeddings model will suffice."
    ]
   },
   {
diff --git a/docs/docs/how_to/dynamic_chain.ipynb b/docs/docs/how_to/dynamic_chain.ipynb
index ce2b98ef2cd73..f292e6a1f4ae2 100644
--- a/docs/docs/how_to/dynamic_chain.ipynb
+++ b/docs/docs/how_to/dynamic_chain.ipynb
@@ -10,7 +10,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
     "- [How to turn any function into a runnable](/docs/how_to/functions)\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/how_to/example_selectors_langsmith.ipynb b/docs/docs/how_to/example_selectors_langsmith.ipynb
index 0db835427c0b8..2c59ed313cdb1 100644
--- a/docs/docs/how_to/example_selectors_langsmith.ipynb
+++ b/docs/docs/how_to/example_selectors_langsmith.ipynb
@@ -11,8 +11,8 @@
     "import Compatibility from \"@theme/Compatibility\";\n",
     "\n",
     "<Prerequisites titlesAndLinks={[\n",
-    "  [\"Chat models\", \"/docs/concepts/#chat-models\"],\n",
-    "  [\"Few-shot-prompting\", \"/docs/concepts/#few-shot-prompting\"],\n",
+    "  [\"Chat models\", \"/docs/concepts/chat_models\"],\n",
+    "  [\"Few-shot-prompting\", \"/docs/concepts/few-shot-prompting\"],\n",
     "  [\"LangSmith\", \"/docs/concepts/#langsmith\"],\n",
     "]} />\n",
     "\n",
diff --git a/docs/docs/how_to/few_shot_examples.ipynb b/docs/docs/how_to/few_shot_examples.ipynb
index 69ffd691bc024..d583be400ab7b 100644
--- a/docs/docs/how_to/few_shot_examples.ipynb
+++ b/docs/docs/how_to/few_shot_examples.ipynb
@@ -20,9 +20,9 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
-    "- [Example selectors](/docs/concepts/#example-selectors)\n",
-    "- [LLMs](/docs/concepts/#llms)\n",
+    "- [Prompt templates](/docs/concepts/prompt_templates)\n",
+    "- [Example selectors](/docs/concepts/example_selectors)\n",
+    "- [LLMs](/docs/concepts/text_llms)\n",
     "- [Vectorstores](/docs/concepts/#vector-stores)\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/how_to/few_shot_examples_chat.ipynb b/docs/docs/how_to/few_shot_examples_chat.ipynb
index 5ccc06d9fcf2c..1192a211f1152 100644
--- a/docs/docs/how_to/few_shot_examples_chat.ipynb
+++ b/docs/docs/how_to/few_shot_examples_chat.ipynb
@@ -20,9 +20,9 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
-    "- [Example selectors](/docs/concepts/#example-selectors)\n",
-    "- [Chat models](/docs/concepts/#chat-model)\n",
+    "- [Prompt templates](/docs/concepts/prompt_templates)\n",
+    "- [Example selectors](/docs/concepts/example_selectors)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
     "- [Vectorstores](/docs/concepts/#vector-stores)\n",
     "\n",
     ":::\n",
@@ -33,7 +33,7 @@
     "\n",
     "The goal of few-shot prompt templates are to dynamically select examples based on an input, and then format the examples in a final prompt to provide for the model.\n",
     "\n",
-    "**Note:** The following code examples are for chat models only, since `FewShotChatMessagePromptTemplates` are designed to output formatted [chat messages](/docs/concepts/#message-types) rather than pure strings. For similar few-shot prompt examples for pure string templates compatible with completion models (LLMs), see the [few-shot prompt templates](/docs/how_to/few_shot_examples/) guide."
+    "**Note:** The following code examples are for chat models only, since `FewShotChatMessagePromptTemplates` are designed to output formatted [chat messages](/docs/concepts/messages) rather than pure strings. For similar few-shot prompt examples for pure string templates compatible with completion models (LLMs), see the [few-shot prompt templates](/docs/how_to/few_shot_examples/) guide."
    ]
   },
   {
diff --git a/docs/docs/how_to/functions.ipynb b/docs/docs/how_to/functions.ipynb
index be228013a988a..c5b781f2edbca 100644
--- a/docs/docs/how_to/functions.ipynb
+++ b/docs/docs/how_to/functions.ipynb
@@ -21,7 +21,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/how_to/index.mdx b/docs/docs/how_to/index.mdx
index f0be664384651..3ca54fe42a52e 100644
--- a/docs/docs/how_to/index.mdx
+++ b/docs/docs/how_to/index.mdx
@@ -27,7 +27,7 @@ This highlights functionality that is core to using LangChain.
 
 ## LangChain Expression Language (LCEL)
 
-[LangChain Expression Language](/docs/concepts/#langchain-expression-language-lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) protocol.
+[LangChain Expression Language](/docs/concepts/lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) protocol.
 
 [**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives.
 
@@ -53,7 +53,7 @@ These are the core building blocks you can use when building applications.
 
 ### Prompt templates
 
-[Prompt Templates](/docs/concepts/#prompt-templates) are responsible for formatting user input into a format that can be passed to a language model.
+[Prompt Templates](/docs/concepts/prompt_templates) are responsible for formatting user input into a format that can be passed to a language model.
 
 - [How to: use few shot examples](/docs/how_to/few_shot_examples)
 - [How to: use few shot examples in chat models](/docs/how_to/few_shot_examples_chat/)
@@ -62,7 +62,7 @@ These are the core building blocks you can use when building applications.
 
 ### Example selectors
 
-[Example Selectors](/docs/concepts/#example-selectors) are responsible for selecting the correct few shot examples to pass to the prompt.
+[Example Selectors](/docs/concepts/example_selectors) are responsible for selecting the correct few shot examples to pass to the prompt.
 
 - [How to: use example selectors](/docs/how_to/example_selectors)
 - [How to: select examples by length](/docs/how_to/example_selectors_length_based)
@@ -73,7 +73,7 @@ These are the core building blocks you can use when building applications.
 
 ### Chat models
 
-[Chat Models](/docs/concepts/#chat-models) are newer forms of language models that take messages in and output a message.
+[Chat Models](/docs/concepts/chat_models) are newer forms of language models that take messages in and output a message.
 
 - [How to: do function/tool calling](/docs/how_to/tool_calling)
 - [How to: get models to return structured output](/docs/how_to/structured_output)
@@ -94,7 +94,7 @@ These are the core building blocks you can use when building applications.
 
 ### Messages
 
-[Messages](/docs/concepts/#messages) are the input and output of chat models. They have some `content` and a `role`, which describes the source of the message.
+[Messages](/docs/concepts/messages) are the input and output of chat models. They have some `content` and a `role`, which describes the source of the message.
 
 - [How to: trim messages](/docs/how_to/trim_messages/)
 - [How to: filter messages](/docs/how_to/filter_messages/)
@@ -102,7 +102,7 @@ These are the core building blocks you can use when building applications.
 
 ### LLMs
 
-What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language models that take a string in and output a string.
+What LangChain calls [LLMs](/docs/concepts/text_llms) are older forms of language models that take a string in and output a string.
 
 - [How to: cache model responses](/docs/how_to/llm_caching)
 - [How to: create a custom LLM class](/docs/how_to/custom_llm)
@@ -112,7 +112,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
 
 ### Output parsers
 
-[Output Parsers](/docs/concepts/#output-parsers) are responsible for taking the output of an LLM and parsing into more structured format.
+[Output Parsers](/docs/concepts/output_parsers) are responsible for taking the output of an LLM and parsing into more structured format.
 
 - [How to: use output parsers to parse an LLM response into structured format](/docs/how_to/output_parser_structured)
 - [How to: parse JSON output](/docs/how_to/output_parser_json)
@@ -124,7 +124,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
 
 ### Document loaders
 
-[Document Loaders](/docs/concepts/#document-loaders) are responsible for loading documents from a variety of sources.
+[Document Loaders](/docs/concepts/document_loaders) are responsible for loading documents from a variety of sources.
 
 - [How to: load PDF files](/docs/how_to/document_loader_pdf)
 - [How to: load web pages](/docs/how_to/document_loader_web)
@@ -138,7 +138,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
 
 ### Text splitters
 
-[Text Splitters](/docs/concepts/#text-splitters) take a document and split into chunks that can be used for retrieval.
+[Text Splitters](/docs/concepts/text_splitters) take a document and split into chunks that can be used for retrieval.
 
 - [How to: recursively split text](/docs/how_to/recursive_text_splitter)
 - [How to: split by HTML headers](/docs/how_to/HTML_header_metadata_splitter)
@@ -152,7 +152,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
 
 ### Embedding models
 
-[Embedding Models](/docs/concepts/#embedding-models) take a piece of text and create a numerical representation of it.
+[Embedding Models](/docs/concepts/embedding_models) take a piece of text and create a numerical representation of it.
 
 - [How to: embed text data](/docs/how_to/embed_text)
 - [How to: cache embedding results](/docs/how_to/caching_embeddings)
@@ -165,7 +165,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
 
 ### Retrievers
 
-[Retrievers](/docs/concepts/#retrievers) are responsible for taking a query and returning relevant documents.
+[Retrievers](/docs/concepts/retrievers) are responsible for taking a query and returning relevant documents.
 
 - [How to: use a vector store to retrieve data](/docs/how_to/vectorstore_retriever)
 - [How to: generate multiple queries to retrieve data for](/docs/how_to/MultiQueryRetriever)
@@ -188,7 +188,7 @@ Indexing is the process of keeping your vectorstore in-sync with the underlying
 
 ### Tools
 
-LangChain [Tools](/docs/concepts/#tools) contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. Refer [here](/docs/integrations/tools/) for a list of pre-buit tools. 
+LangChain [Tools](/docs/concepts/tools) contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. Refer [here](/docs/integrations/tools/) for a list of pre-buit tools. 
 
 - [How to: create tools](/docs/how_to/custom_tools)
 - [How to: use built-in tools and toolkits](/docs/how_to/tools_builtin)
@@ -225,7 +225,7 @@ For in depth how-to guides for agents, please check out [LangGraph](https://lang
 
 ### Callbacks
 
-[Callbacks](/docs/concepts/#callbacks) allow you to hook into the various stages of your LLM application's execution.
+[Callbacks](/docs/concepts/callbacks) allow you to hook into the various stages of your LLM application's execution.
 
 - [How to: pass in callbacks at runtime](/docs/how_to/callbacks_runtime)
 - [How to: attach callbacks to a module](/docs/how_to/callbacks_attach)
diff --git a/docs/docs/how_to/inspect.ipynb b/docs/docs/how_to/inspect.ipynb
index e3f82cbcc99cf..a77f214a7571e 100644
--- a/docs/docs/how_to/inspect.ipynb
+++ b/docs/docs/how_to/inspect.ipynb
@@ -10,12 +10,12 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "\n",
     ":::\n",
     "\n",
-    "Once you create a runnable with [LangChain Expression Language](/docs/concepts/#langchain-expression-language), you may often want to inspect it to get a better sense for what is going on. This notebook covers some methods for doing so.\n",
+    "Once you create a runnable with [LangChain Expression Language](/docs/concepts/lcel), you may often want to inspect it to get a better sense for what is going on. This notebook covers some methods for doing so.\n",
     "\n",
     "This guide shows some ways you can programmatically introspect the internal steps of chains. If you are instead interested in debugging issues in your chain, see [this section](/docs/how_to/debugging) instead.\n",
     "\n",
diff --git a/docs/docs/how_to/llm_token_usage_tracking.ipynb b/docs/docs/how_to/llm_token_usage_tracking.ipynb
index e32c98a32ae79..2f1a8a92c1e13 100644
--- a/docs/docs/how_to/llm_token_usage_tracking.ipynb
+++ b/docs/docs/how_to/llm_token_usage_tracking.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [LLMs](/docs/concepts/#llms)\n",
+    "- [LLMs](/docs/concepts/text_llms)\n",
     ":::\n",
     "\n",
     "## Using LangSmith\n",
diff --git a/docs/docs/how_to/local_llms.ipynb b/docs/docs/how_to/local_llms.ipynb
index 2f8359a99185b..2bd4c44556251 100644
--- a/docs/docs/how_to/local_llms.ipynb
+++ b/docs/docs/how_to/local_llms.ipynb
@@ -68,7 +68,7 @@
     "\n",
     "### Formatting prompts\n",
     "\n",
-    "Some providers have [chat model](/docs/concepts/#chat-models) wrappers that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models with a [text-in/text-out LLM](/docs/concepts/#llms) wrapper, you may need to use a prompt tailed for your specific model.\n",
+    "Some providers have [chat model](/docs/concepts/chat_models) wrappers that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models with a [text-in/text-out LLM](/docs/concepts/text_llms) wrapper, you may need to use a prompt tailed for your specific model.\n",
     "\n",
     "This can [require the inclusion of special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). [Here's an example for LLaMA 2](https://smith.langchain.com/hub/rlm/rag-prompt-llama).\n",
     "\n",
diff --git a/docs/docs/how_to/logprobs.ipynb b/docs/docs/how_to/logprobs.ipynb
index c15565e0b955f..33e56c83db758 100644
--- a/docs/docs/how_to/logprobs.ipynb
+++ b/docs/docs/how_to/logprobs.ipynb
@@ -10,7 +10,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/long_context_reorder.ipynb b/docs/docs/how_to/long_context_reorder.ipynb
index 29bed05bbe072..fe54330e0eb9e 100644
--- a/docs/docs/how_to/long_context_reorder.ipynb
+++ b/docs/docs/how_to/long_context_reorder.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "Substantial performance degradations in [RAG](/docs/tutorials/rag) applications have been [documented](https://arxiv.org/abs/2307.03172) as the number of retrieved documents grows (e.g., beyond ten). In brief: models are liable to miss relevant information in the middle of long contexts.\n",
     "\n",
-    "By contrast, queries against vector stores will typically return documents in descending order of relevance (e.g., as measured by cosine similarity of [embeddings](/docs/concepts/#embedding-models)).\n",
+    "By contrast, queries against vector stores will typically return documents in descending order of relevance (e.g., as measured by cosine similarity of [embeddings](/docs/concepts/embedding_models)).\n",
     "\n",
     "To mitigate the [\"lost in the middle\"](https://arxiv.org/abs/2307.03172) effect, you can re-order documents after retrieval such that the most relevant documents are positioned at extrema (e.g., the first and last pieces of context), and the least relevant documents are positioned in the middle. In some cases this can help surface the most relevant information to LLMs.\n",
     "\n",
diff --git a/docs/docs/how_to/message_history.ipynb b/docs/docs/how_to/message_history.ipynb
index 9d0d8f44b60d7..ec4b981c6eea5 100644
--- a/docs/docs/how_to/message_history.ipynb
+++ b/docs/docs/how_to/message_history.ipynb
@@ -25,8 +25,8 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
-    "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
-    "- [Chat Messages](/docs/concepts/#message-types)\n",
+    "- [Prompt templates](/docs/concepts/prompt_templates)\n",
+    "- [Chat Messages](/docs/concepts/messages)\n",
     "- [LangGraph persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence/)\n",
     "\n",
     ":::\n",
@@ -85,7 +85,7 @@
    "source": [
     "## Example: message inputs\n",
     "\n",
-    "Adding memory to a [chat model](/docs/concepts/#chat-models) provides a simple example. Chat models accept a list of messages as input and output a message. LangGraph includes a built-in `MessagesState` that we can use for this purpose.\n",
+    "Adding memory to a [chat model](/docs/concepts/chat_models) provides a simple example. Chat models accept a list of messages as input and output a message. LangGraph includes a built-in `MessagesState` that we can use for this purpose.\n",
     "\n",
     "Below, we:\n",
     "1. Define the graph state to be a list of messages;\n",
diff --git a/docs/docs/how_to/migrate_agent.ipynb b/docs/docs/how_to/migrate_agent.ipynb
index 49d374266b8bf..975bbd8e76651 100644
--- a/docs/docs/how_to/migrate_agent.ipynb
+++ b/docs/docs/how_to/migrate_agent.ipynb
@@ -24,7 +24,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Agents](/docs/concepts/#agents)\n",
+    "- [Agents](/docs/concepts/agents)\n",
     "- [LangGraph](https://langchain-ai.github.io/langgraph/)\n",
     "- [Tool calling](/docs/how_to/tool_calling/)\n",
     "\n",
@@ -298,7 +298,7 @@
     "- A `SystemMessage`, which is added to the beginning of the list of messages.\n",
     "- A `string`, which is converted to a `SystemMessage` and added to the beginning of the list of messages.\n",
     "- A `Callable`, which should take in full graph state. The output is then passed to the language model.\n",
-    "- Or a [`Runnable`](/docs/concepts/#langchain-expression-language-lcel), which should take in full graph state. The output is then passed to the language model.\n",
+    "- Or a [`Runnable`](/docs/concepts/lcel), which should take in full graph state. The output is then passed to the language model.\n",
     "\n",
     "Here's how it looks in action:"
    ]
diff --git a/docs/docs/how_to/multimodal_inputs.ipynb b/docs/docs/how_to/multimodal_inputs.ipynb
index 3dd5c9c06c484..6d0b0b736a471 100644
--- a/docs/docs/how_to/multimodal_inputs.ipynb
+++ b/docs/docs/how_to/multimodal_inputs.ipynb
@@ -162,7 +162,7 @@
    "source": [
     "## Tool calls\n",
     "\n",
-    "Some multimodal models support [tool calling](/docs/concepts/#functiontool-calling) features as well. To call tools using such models, simply bind tools to them in the [usual way](/docs/how_to/tool_calling), and invoke the model using content blocks of the desired type (e.g., containing image data)."
+    "Some multimodal models support [tool calling](/docs/concepts/tool_calling) features as well. To call tools using such models, simply bind tools to them in the [usual way](/docs/how_to/tool_calling), and invoke the model using content blocks of the desired type (e.g., containing image data)."
    ]
   },
   {
diff --git a/docs/docs/how_to/output_parser_json.ipynb b/docs/docs/how_to/output_parser_json.ipynb
index 479a48d11f474..0baadb17f67b4 100644
--- a/docs/docs/how_to/output_parser_json.ipynb
+++ b/docs/docs/how_to/output_parser_json.ipynb
@@ -10,9 +10,9 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [Output parsers](/docs/concepts/#output-parsers)\n",
-    "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [Output parsers](/docs/concepts/output_parsers)\n",
+    "- [Prompt templates](/docs/concepts/prompt_templates)\n",
     "- [Structured output](/docs/how_to/structured_output)\n",
     "- [Chaining runnables together](/docs/how_to/sequence/)\n",
     "\n",
diff --git a/docs/docs/how_to/output_parser_xml.ipynb b/docs/docs/how_to/output_parser_xml.ipynb
index 4acfc968304df..0ae3d1ed0d41b 100644
--- a/docs/docs/how_to/output_parser_xml.ipynb
+++ b/docs/docs/how_to/output_parser_xml.ipynb
@@ -10,9 +10,9 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [Output parsers](/docs/concepts/#output-parsers)\n",
-    "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [Output parsers](/docs/concepts/output_parsers)\n",
+    "- [Prompt templates](/docs/concepts/prompt_templates)\n",
     "- [Structured output](/docs/how_to/structured_output)\n",
     "- [Chaining runnables together](/docs/how_to/sequence/)\n",
     "\n",
diff --git a/docs/docs/how_to/output_parser_yaml.ipynb b/docs/docs/how_to/output_parser_yaml.ipynb
index 36a3e095c0267..fedc1f88fd803 100644
--- a/docs/docs/how_to/output_parser_yaml.ipynb
+++ b/docs/docs/how_to/output_parser_yaml.ipynb
@@ -10,9 +10,9 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [Output parsers](/docs/concepts/#output-parsers)\n",
-    "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [Output parsers](/docs/concepts/output_parsers)\n",
+    "- [Prompt templates](/docs/concepts/prompt_templates)\n",
     "- [Structured output](/docs/how_to/structured_output)\n",
     "- [Chaining runnables together](/docs/how_to/sequence/)\n",
     "\n",
diff --git a/docs/docs/how_to/parallel.ipynb b/docs/docs/how_to/parallel.ipynb
index 8d58b3b1ceb82..56c3c88b95266 100644
--- a/docs/docs/how_to/parallel.ipynb
+++ b/docs/docs/how_to/parallel.ipynb
@@ -21,7 +21,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
     "- [Chaining runnables](/docs/how_to/sequence)\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/how_to/passthrough.ipynb b/docs/docs/how_to/passthrough.ipynb
index efe185a779380..6a1aa359187fd 100644
--- a/docs/docs/how_to/passthrough.ipynb
+++ b/docs/docs/how_to/passthrough.ipynb
@@ -21,7 +21,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "- [Calling runnables in parallel](/docs/how_to/parallel/)\n",
     "- [Custom functions](/docs/how_to/functions/)\n",
diff --git a/docs/docs/how_to/prompts_composition.ipynb b/docs/docs/how_to/prompts_composition.ipynb
index 899ea104524a1..bf8d0f5fb232d 100644
--- a/docs/docs/how_to/prompts_composition.ipynb
+++ b/docs/docs/how_to/prompts_composition.ipynb
@@ -20,7 +20,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
+    "- [Prompt templates](/docs/concepts/prompt_templates)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/prompts_partial.ipynb b/docs/docs/how_to/prompts_partial.ipynb
index 5823b40bc0119..b32e2586c17e9 100644
--- a/docs/docs/how_to/prompts_partial.ipynb
+++ b/docs/docs/how_to/prompts_partial.ipynb
@@ -20,7 +20,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
+    "- [Prompt templates](/docs/concepts/prompt_templates)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/qa_chat_history_how_to.ipynb b/docs/docs/how_to/qa_chat_history_how_to.ipynb
index 743f60c6349d6..d3dfc8d772f2d 100644
--- a/docs/docs/how_to/qa_chat_history_how_to.ipynb
+++ b/docs/docs/how_to/qa_chat_history_how_to.ipynb
@@ -161,7 +161,7 @@
    "id": "15f8ad59-19de-42e3-85a8-3ba95ee0bd43",
    "metadata": {},
    "source": [
-    "For the retriever, we will use [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load the content of a web page. Here we instantiate a `InMemoryVectorStore` vectorstore and then use its [.as_retriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method to build a retriever that can be incorporated into [LCEL](/docs/concepts/#langchain-expression-language) chains."
+    "For the retriever, we will use [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load the content of a web page. Here we instantiate a `InMemoryVectorStore` vectorstore and then use its [.as_retriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method to build a retriever that can be incorporated into [LCEL](/docs/concepts/lcel) chains."
    ]
   },
   {
diff --git a/docs/docs/how_to/qa_sources.ipynb b/docs/docs/how_to/qa_sources.ipynb
index 756a428def02b..c6a939821ba99 100644
--- a/docs/docs/how_to/qa_sources.ipynb
+++ b/docs/docs/how_to/qa_sources.ipynb
@@ -326,7 +326,7 @@
     "\n",
     "Up to this point, we've simply propagated the documents returned from the retrieval step through to the final response. But this may not illustrate what subset of information the model relied on when generating its answer. Below, we show how to structure sources into the model response, allowing the model to report what specific context it relied on for its answer.\n",
     "\n",
-    "Because the above LCEL implementation is composed of [Runnable](/docs/concepts/#runnable-interface) primitives, it is straightforward to extend. Below, we make a simple change:\n",
+    "Because the above LCEL implementation is composed of [Runnable](/docs/concepts/runnables) primitives, it is straightforward to extend. Below, we make a simple change:\n",
     "\n",
     "- We use the model's tool-calling features to generate [structured output](/docs/how_to/structured_output/), consisting of an answer and list of sources. The schema for the response is represented in the `AnswerWithSources` TypedDict, below.\n",
     "- We remove the `StrOutputParser()`, as we expect `dict` output in this scenario."
diff --git a/docs/docs/how_to/routing.ipynb b/docs/docs/how_to/routing.ipynb
index 50da40f6ddcba..81ec752e99d17 100644
--- a/docs/docs/how_to/routing.ipynb
+++ b/docs/docs/how_to/routing.ipynb
@@ -21,11 +21,11 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "- [Configuring chain parameters at runtime](/docs/how_to/configure)\n",
-    "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
-    "- [Chat Messages](/docs/concepts/#message-types)\n",
+    "- [Prompt templates](/docs/concepts/prompt_templates)\n",
+    "- [Chat Messages](/docs/concepts/messages)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/sequence.ipynb b/docs/docs/how_to/sequence.ipynb
index 8fc7be8d8f66d..4f20f4b1d32c1 100644
--- a/docs/docs/how_to/sequence.ipynb
+++ b/docs/docs/how_to/sequence.ipynb
@@ -22,14 +22,14 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
-    "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [Output parser](/docs/concepts/#output-parsers)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
+    "- [Prompt templates](/docs/concepts/prompt_templates)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [Output parser](/docs/concepts/output_parsers)\n",
     "\n",
     ":::\n",
     "\n",
-    "One point about [LangChain Expression Language](/docs/concepts/#langchain-expression-language) is that any two runnables can be \"chained\" together into sequences. The output of the previous runnable's `.invoke()` call is passed as input to the next runnable. This can be done using the pipe operator (`|`), or the more explicit `.pipe()` method, which does the same thing.\n",
+    "One point about [LangChain Expression Language](/docs/concepts/lcel) is that any two runnables can be \"chained\" together into sequences. The output of the previous runnable's `.invoke()` call is passed as input to the next runnable. This can be done using the pipe operator (`|`), or the more explicit `.pipe()` method, which does the same thing.\n",
     "\n",
     "The resulting [`RunnableSequence`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableSequence.html) is itself a runnable, which means it can be invoked, streamed, or further chained just like any other runnable. Advantages of chaining runnables in this way are efficient streaming (the sequence will stream output as soon as it is available), and debugging and tracing with tools like [LangSmith](/docs/how_to/debugging).\n",
     "\n",
diff --git a/docs/docs/how_to/serialization.ipynb b/docs/docs/how_to/serialization.ipynb
index e0355baf93d07..f572f6b369be6 100644
--- a/docs/docs/how_to/serialization.ipynb
+++ b/docs/docs/how_to/serialization.ipynb
@@ -14,7 +14,7 @@
     "\n",
     "To save and load LangChain objects using this system, use the `dumpd`, `dumps`, `load`, and `loads` functions in the [load module](https://python.langchain.com/api_reference/core/load.html) of `langchain-core`. These functions support JSON and JSON-serializable objects.\n",
     "\n",
-    "All LangChain objects that inherit from [Serializable](https://python.langchain.com/api_reference/core/load/langchain_core.load.serializable.Serializable.html) are JSON-serializable. Examples include [messages](https://python.langchain.com/api_reference//python/core_api_reference.html#module-langchain_core.messages), [document objects](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) (e.g., as returned from [retrievers](/docs/concepts/#retrievers)), and most [Runnables](/docs/concepts/#langchain-expression-language-lcel), such as chat models, retrievers, and [chains](/docs/how_to/sequence) implemented with the LangChain Expression Language.\n",
+    "All LangChain objects that inherit from [Serializable](https://python.langchain.com/api_reference/core/load/langchain_core.load.serializable.Serializable.html) are JSON-serializable. Examples include [messages](https://python.langchain.com/api_reference//python/core_api_reference.html#module-langchain_core.messages), [document objects](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) (e.g., as returned from [retrievers](/docs/concepts/retrievers)), and most [Runnables](/docs/concepts/lcel), such as chat models, retrievers, and [chains](/docs/how_to/sequence) implemented with the LangChain Expression Language.\n",
     "\n",
     "Below we walk through an example with a simple [LLM chain](/docs/tutorials/llm_chain).\n",
     "\n",
diff --git a/docs/docs/how_to/streaming.ipynb b/docs/docs/how_to/streaming.ipynb
index 02b6e63466fc3..25118b153aeac 100644
--- a/docs/docs/how_to/streaming.ipynb
+++ b/docs/docs/how_to/streaming.ipynb
@@ -24,15 +24,15 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [LangChain Expression Language](/docs/concepts/#langchain-expression-language)\n",
-    "- [Output parsers](/docs/concepts/#output-parsers)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [LangChain Expression Language](/docs/concepts/lcel)\n",
+    "- [Output parsers](/docs/concepts/output_parsers)\n",
     "\n",
     ":::\n",
     "\n",
     "Streaming is critical in making applications based on LLMs feel responsive to end-users.\n",
     "\n",
-    "Important LangChain primitives like [chat models](/docs/concepts/#chat-models), [output parsers](/docs/concepts/#output-parsers), [prompts](/docs/concepts/#prompt-templates), [retrievers](/docs/concepts/#retrievers), and [agents](/docs/concepts/#agents) implement the LangChain [Runnable Interface](/docs/concepts#interface).\n",
+    "Important LangChain primitives like [chat models](/docs/concepts/chat_models), [output parsers](/docs/concepts/output_parsers), [prompts](/docs/concepts/prompt_templates), [retrievers](/docs/concepts/retrievers), and [agents](/docs/concepts/agents) implement the LangChain [Runnable Interface](/docs/concepts#interface).\n",
     "\n",
     "This interface provides two general approaches to stream content:\n",
     "\n",
@@ -42,7 +42,7 @@
     "Let's take a look at both approaches, and try to understand how to use them.\n",
     "\n",
     ":::info\n",
-    "For a higher-level overview of streaming techniques in LangChain, see [this section of the conceptual guide](/docs/concepts/#streaming).\n",
+    "For a higher-level overview of streaming techniques in LangChain, see [this section of the conceptual guide](/docs/concepts/streaming).\n",
     ":::\n",
     "\n",
     "## Using Stream\n",
@@ -1510,7 +1510,7 @@
     "\n",
     "Now you've learned some ways to stream both final outputs and internal steps with LangChain.\n",
     "\n",
-    "To learn more, check out the other how-to guides in this section, or the [conceptual guide on Langchain Expression Language](/docs/concepts/#langchain-expression-language/)."
+    "To learn more, check out the other how-to guides in this section, or the [conceptual guide on Langchain Expression Language](/docs/concepts/lcel/)."
    ]
   }
  ],
diff --git a/docs/docs/how_to/structured_output.ipynb b/docs/docs/how_to/structured_output.ipynb
index 8a691a6e17a53..3e333e340b1a7 100644
--- a/docs/docs/how_to/structured_output.ipynb
+++ b/docs/docs/how_to/structured_output.ipynb
@@ -25,8 +25,8 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [Function/tool calling](/docs/concepts/tool_calling)\n",
     ":::\n",
     "\n",
     "It is often useful to have a model return output that matches a specific schema. One common use-case is extracting data from text to insert into a database or use with some other downstream system. This guide covers a few strategies for getting structured outputs from a model.\n",
@@ -776,7 +776,7 @@
     "\n",
     "### Custom Parsing\n",
     "\n",
-    "You can also create a custom prompt and parser with [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language), using a plain function to parse the output from the model:"
+    "You can also create a custom prompt and parser with [LangChain Expression Language (LCEL)](/docs/concepts/lcel), using a plain function to parse the output from the model:"
    ]
   },
   {
diff --git a/docs/docs/how_to/summarize_refine.ipynb b/docs/docs/how_to/summarize_refine.ipynb
index 269be50eb83cd..10540606cf1a1 100644
--- a/docs/docs/how_to/summarize_refine.ipynb
+++ b/docs/docs/how_to/summarize_refine.ipynb
@@ -33,7 +33,7 @@
     "\n",
     "- LangGraph allows for individual steps (such as successive summarizations) to be streamed, allowing for greater control of execution;\n",
     "- LangGraph's [checkpointing](https://langchain-ai.github.io/langgraph/how-tos/persistence/) supports error recovery, extending with human-in-the-loop workflows, and easier incorporation into conversational applications.\n",
-    "- Because it is assembled from modular components, it is also simple to extend or modify (e.g., to incorporate [tool calling](/docs/concepts/#functiontool-calling) or other behavior).\n",
+    "- Because it is assembled from modular components, it is also simple to extend or modify (e.g., to incorporate [tool calling](/docs/concepts/tool_calling) or other behavior).\n",
     "\n",
     "Below, we demonstrate how to summarize text via iterative refinement."
    ]
diff --git a/docs/docs/how_to/summarize_stuff.ipynb b/docs/docs/how_to/summarize_stuff.ipynb
index 0e3f069b8bbe0..86fbb86e10811 100644
--- a/docs/docs/how_to/summarize_stuff.ipynb
+++ b/docs/docs/how_to/summarize_stuff.ipynb
@@ -117,7 +117,7 @@
    "source": [
     "## Invoke chain\n",
     "\n",
-    "Because the chain is a [Runnable](/docs/concepts/#runnable-interface), it implements the usual methods for invocation:"
+    "Because the chain is a [Runnable](/docs/concepts/runnables), it implements the usual methods for invocation:"
    ]
   },
   {
diff --git a/docs/docs/how_to/tool_artifacts.ipynb b/docs/docs/how_to/tool_artifacts.ipynb
index 9687834cf7076..a6fa076c0c14c 100644
--- a/docs/docs/how_to/tool_artifacts.ipynb
+++ b/docs/docs/how_to/tool_artifacts.ipynb
@@ -10,9 +10,9 @@
     ":::info Prerequisites\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [ToolMessage](/docs/concepts/#toolmessage)\n",
-    "- [Tools](/docs/concepts/#tools)\n",
-    "- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
+    "- [ToolMessage](/docs/concepts/messages/#toolmessage)\n",
+    "- [Tools](/docs/concepts/tools)\n",
+    "- [Function/tool calling](/docs/concepts/tool_calling)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/tool_calling.ipynb b/docs/docs/how_to/tool_calling.ipynb
index c812617d19421..295e718a673df 100644
--- a/docs/docs/how_to/tool_calling.ipynb
+++ b/docs/docs/how_to/tool_calling.ipynb
@@ -23,13 +23,13 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [Tool calling](/docs/concepts/#functiontool-calling)\n",
-    "- [Tools](/docs/concepts/#tools)\n",
-    "- [Output parsers](/docs/concepts/#output-parsers)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [Tool calling](/docs/concepts/tool_calling)\n",
+    "- [Tools](/docs/concepts/tools)\n",
+    "- [Output parsers](/docs/concepts/output_parsers)\n",
     ":::\n",
     "\n",
-    "[Tool calling](/docs/concepts/#functiontool-calling) allows a chat model to respond to a given prompt by \"calling a tool\".\n",
+    "[Tool calling](/docs/concepts/tool_calling) allows a chat model to respond to a given prompt by \"calling a tool\".\n",
     "\n",
     "Remember, while the name \"tool calling\" implies that the model is directly performing some action, this is actually not the case! The model only generates the arguments to a tool, and actually running the tool (or not) is up to the user.\n",
     "\n",
diff --git a/docs/docs/how_to/tool_choice.ipynb b/docs/docs/how_to/tool_choice.ipynb
index 075d9a0a62933..1f59546303edb 100644
--- a/docs/docs/how_to/tool_choice.ipynb
+++ b/docs/docs/how_to/tool_choice.ipynb
@@ -9,8 +9,8 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [LangChain Tools](/docs/concepts/tools)\n",
     "- [How to use a model to call tools](/docs/how_to/tool_calling)\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/tool_configure.ipynb b/docs/docs/how_to/tool_configure.ipynb
index a97446e261a1f..474d2f3c5fea9 100644
--- a/docs/docs/how_to/tool_configure.ipynb
+++ b/docs/docs/how_to/tool_configure.ipynb
@@ -10,9 +10,9 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
+    "- [LangChain Tools](/docs/concepts/tools)\n",
     "- [Custom tools](/docs/how_to/custom_tools)\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language-lcel)\n",
+    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
     "- [Configuring runnable behavior](/docs/how_to/configure/)\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/how_to/tool_results_pass_to_model.ipynb b/docs/docs/how_to/tool_results_pass_to_model.ipynb
index 78e391e435918..6ace9a02c5de1 100644
--- a/docs/docs/how_to/tool_results_pass_to_model.ipynb
+++ b/docs/docs/how_to/tool_results_pass_to_model.ipynb
@@ -9,14 +9,14 @@
     ":::info Prerequisites\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
-    "- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
+    "- [LangChain Tools](/docs/concepts/tools)\n",
+    "- [Function/tool calling](/docs/concepts/tool_calling)\n",
     "- [Using chat models to call tools](/docs/how_to/tool_calling)\n",
     "- [Defining custom tools](/docs/how_to/custom_tools/)\n",
     "\n",
     ":::\n",
     "\n",
-    "Some models are capable of [**tool calling**](/docs/concepts/#functiontool-calling) - generating arguments that conform to a specific user-provided schema. This guide will demonstrate how to use those tool cals to actually call a function and properly pass the results back to the model.\n",
+    "Some models are capable of [**tool calling**](/docs/concepts/tool_calling) - generating arguments that conform to a specific user-provided schema. This guide will demonstrate how to use those tool cals to actually call a function and properly pass the results back to the model.\n",
     "\n",
     "![Diagram of a tool call invocation](/img/tool_invocation.png)\n",
     "\n",
diff --git a/docs/docs/how_to/tool_runtime.ipynb b/docs/docs/how_to/tool_runtime.ipynb
index fbbd2beba90d1..d0e9df846e82b 100644
--- a/docs/docs/how_to/tool_runtime.ipynb
+++ b/docs/docs/how_to/tool_runtime.ipynb
@@ -10,8 +10,8 @@
     "import Compatibility from \"@theme/Compatibility\";\n",
     "\n",
     "<Prerequisites titlesAndLinks={[\n",
-    "  [\"Chat models\", \"/docs/concepts/#chat-models\"],\n",
-    "  [\"LangChain Tools\", \"/docs/concepts/#tools\"],\n",
+    "  [\"Chat models\", \"/docs/concepts/chat_models\"],\n",
+    "  [\"LangChain Tools\", \"/docs/concepts/tools\"],\n",
     "  [\"How to create tools\", \"/docs/how_to/custom_tools\"],\n",
     "  [\"How to use a model to call tools\", \"/docs/how_to/tool_calling\"],\n",
     "]} />\n",
diff --git a/docs/docs/how_to/tool_stream_events.ipynb b/docs/docs/how_to/tool_stream_events.ipynb
index 6167e158d6c8a..43d53c40d6f77 100644
--- a/docs/docs/how_to/tool_stream_events.ipynb
+++ b/docs/docs/how_to/tool_stream_events.ipynb
@@ -9,7 +9,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
+    "- [LangChain Tools](/docs/concepts/tools)\n",
     "- [Custom tools](/docs/how_to/custom_tools)\n",
     "- [Using stream events](/docs/how_to/streaming/#using-stream-events)\n",
     "- [Accessing RunnableConfig within a custom tool](/docs/how_to/tool_configure/)\n",
diff --git a/docs/docs/how_to/tools_builtin.ipynb b/docs/docs/how_to/tools_builtin.ipynb
index ea137060c41e5..9e3b0ed56d85b 100644
--- a/docs/docs/how_to/tools_builtin.ipynb
+++ b/docs/docs/how_to/tools_builtin.ipynb
@@ -23,8 +23,8 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
-    "- [LangChain Toolkits](/docs/concepts/#tools)\n",
+    "- [LangChain Tools](/docs/concepts/tools)\n",
+    "- [LangChain Toolkits](/docs/concepts/tools)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/tools_error.ipynb b/docs/docs/how_to/tools_error.ipynb
index 989236b5efc3b..7a881b89fbf3a 100644
--- a/docs/docs/how_to/tools_error.ipynb
+++ b/docs/docs/how_to/tools_error.ipynb
@@ -10,8 +10,8 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [LangChain Tools](/docs/concepts/tools)\n",
     "- [How to use a model to call tools](/docs/how_to/tool_calling)\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/how_to/tools_prompting.ipynb b/docs/docs/how_to/tools_prompting.ipynb
index 03cc039e60f8b..29f82a914f0fa 100644
--- a/docs/docs/how_to/tools_prompting.ipynb
+++ b/docs/docs/how_to/tools_prompting.ipynb
@@ -27,10 +27,10 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
-    "- [Function/tool calling](https://python.langchain.com/docs/concepts/#functiontool-calling)\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [LLMs](/docs/concepts/#llms)\n",
+    "- [LangChain Tools](/docs/concepts/tools)\n",
+    "- [Function/tool calling](https://python.langchain.com/docs/concepts/tool_calling)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [LLMs](/docs/concepts/text_llms)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/trim_messages.ipynb b/docs/docs/how_to/trim_messages.ipynb
index 6a882345e19f2..97b725dd72cff 100644
--- a/docs/docs/how_to/trim_messages.ipynb
+++ b/docs/docs/how_to/trim_messages.ipynb
@@ -11,10 +11,10 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Messages](/docs/concepts/#messages)\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
+    "- [Messages](/docs/concepts/messages)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
     "- [Chaining](/docs/how_to/sequence/)\n",
-    "- [Chat history](/docs/concepts/#chat-history)\n",
+    "- [Chat history](/docs/concepts/chat_history)\n",
     "\n",
     "The methods in this guide also require `langchain-core>=0.2.9`.\n",
     "\n",
@@ -28,7 +28,7 @@
     "If passing the trimmed chat history back into a chat model directly, the trimmed chat history should satisfy the following properties:\n",
     "\n",
     "1. The resulting chat history should be **valid**. Usually this means that the following properties should be satisfied:\n",
-    "   - The chat history **starts** with either (1) a `HumanMessage` or (2) a [SystemMessage](/docs/concepts/#systemmessage) followed by a `HumanMessage`.\n",
+    "   - The chat history **starts** with either (1) a `HumanMessage` or (2) a [SystemMessage](/docs/concepts/messages/#systemmessage) followed by a `HumanMessage`.\n",
     "   - The chat history **ends** with either a `HumanMessage` or a `ToolMessage`.\n",
     "   - A `ToolMessage` can only appear after an `AIMessage` that involved a tool call. \n",
     "   This can be achieved by setting `start_on=\"human\"` and `ends_on=(\"human\", \"tool\")`.\n",
diff --git a/docs/docs/integrations/chat/anthropic.ipynb b/docs/docs/integrations/chat/anthropic.ipynb
index b813f6a9ee9e6..3b0fb9ee8f727 100644
--- a/docs/docs/integrations/chat/anthropic.ipynb
+++ b/docs/docs/integrations/chat/anthropic.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatAnthropic\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Anthropic [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatAnthropic features and configurations head to the [API reference](https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html).\n",
+    "This notebook provides a quick overview for getting started with Anthropic [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatAnthropic features and configurations head to the [API reference](https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html).\n",
     "\n",
     "Anthropic has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Anthropic docs](https://docs.anthropic.com/en/docs/models-overview).\n",
     "\n",
diff --git a/docs/docs/integrations/chat/azure_chat_openai.ipynb b/docs/docs/integrations/chat/azure_chat_openai.ipynb
index a0883d34b71d2..4552a5bb59b7c 100644
--- a/docs/docs/integrations/chat/azure_chat_openai.ipynb
+++ b/docs/docs/integrations/chat/azure_chat_openai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# AzureChatOpenAI\n",
     "\n",
-    "This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
+    "This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
     "\n",
     "Azure OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).\n",
     "\n",
diff --git a/docs/docs/integrations/chat/bedrock.ipynb b/docs/docs/integrations/chat/bedrock.ipynb
index 750f391822767..a24d23f7d7d1d 100644
--- a/docs/docs/integrations/chat/bedrock.ipynb
+++ b/docs/docs/integrations/chat/bedrock.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatBedrock\n",
     "\n",
-    "This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/#chat-models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
+    "This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/chat_models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
     "\n",
     "For more information on which models are accessible via Bedrock, head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html).\n",
     "\n",
diff --git a/docs/docs/integrations/chat/cerebras.ipynb b/docs/docs/integrations/chat/cerebras.ipynb
index 41183d1ec337e..76c0fb8e1720f 100644
--- a/docs/docs/integrations/chat/cerebras.ipynb
+++ b/docs/docs/integrations/chat/cerebras.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatCerebras\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Cerebras [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatCerebras features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_cerebras.chat_models.ChatCerebras.html).\n",
+    "This notebook provides a quick overview for getting started with Cerebras [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCerebras features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_cerebras.chat_models.ChatCerebras.html).\n",
     "\n",
     "At Cerebras, we've developed the world's largest and fastest AI processor, the Wafer-Scale Engine-3 (WSE-3). The Cerebras CS-3 system, powered by the WSE-3, represents a new class of AI supercomputer that sets the standard for generative AI training and inference with unparalleled performance and scalability.\n",
     "\n",
diff --git a/docs/docs/integrations/chat/databricks.ipynb b/docs/docs/integrations/chat/databricks.ipynb
index 74c80b86cf52d..071a9f168813e 100644
--- a/docs/docs/integrations/chat/databricks.ipynb
+++ b/docs/docs/integrations/chat/databricks.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "> [Databricks](https://www.databricks.com/) Lakehouse Platform unifies data, analytics, and AI on one platform. \n",
     "\n",
-    "This notebook provides a quick overview for getting started with Databricks [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatDatabricks features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html).\n",
+    "This notebook provides a quick overview for getting started with Databricks [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDatabricks features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html).\n",
     "\n",
     "## Overview\n",
     "\n",
diff --git a/docs/docs/integrations/chat/fireworks.ipynb b/docs/docs/integrations/chat/fireworks.ipynb
index b43c5f56f8b4e..0172b0a533c49 100644
--- a/docs/docs/integrations/chat/fireworks.ipynb
+++ b/docs/docs/integrations/chat/fireworks.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatFireworks\n",
     "\n",
-    "This doc help you get started with Fireworks AI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
+    "This doc help you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
     "\n",
     "Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the [Fireworks docs](https://fireworks.ai/models).\n",
     "\n",
diff --git a/docs/docs/integrations/chat/google_generative_ai.ipynb b/docs/docs/integrations/chat/google_generative_ai.ipynb
index 08aad6fefb489..7492e641d4c3d 100644
--- a/docs/docs/integrations/chat/google_generative_ai.ipynb
+++ b/docs/docs/integrations/chat/google_generative_ai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatGoogleGenerativeAI\n",
     "\n",
-    "This docs will help you get started with Google AI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html).\n",
+    "This docs will help you get started with Google AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html).\n",
     "\n",
     "Google AI offers a number of different chat models. For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini).\n",
     "\n",
diff --git a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
index 7261adba1600d..4709faa8564e6 100644
--- a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
+++ b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatVertexAI\n",
     "\n",
-    "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
+    "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
     "\n",
     "ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
     "\n",
diff --git a/docs/docs/integrations/chat/huggingface.ipynb b/docs/docs/integrations/chat/huggingface.ipynb
index b1ab4e16dfe2c..3970b5556e4bd 100644
--- a/docs/docs/integrations/chat/huggingface.ipynb
+++ b/docs/docs/integrations/chat/huggingface.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# ChatHuggingFace\n",
     "\n",
-    "This will help you getting started with `langchain_huggingface` [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
+    "This will help you getting started with `langchain_huggingface` [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
diff --git a/docs/docs/integrations/chat/index.mdx b/docs/docs/integrations/chat/index.mdx
index 5ccb0fa13ff68..557c11c34ae24 100644
--- a/docs/docs/integrations/chat/index.mdx
+++ b/docs/docs/integrations/chat/index.mdx
@@ -6,7 +6,7 @@ keywords: [compatibility]
 
 # Chat models
 
-[Chat models](/docs/concepts/#chat-models) are language models that use a sequence of [messages](/docs/concepts/#messages) as inputs and return messages as outputs (as opposed to using plain text). These are generally newer models.
+[Chat models](/docs/concepts/chat_models) are language models that use a sequence of [messages](/docs/concepts/messages) as inputs and return messages as outputs (as opposed to using plain text). These are generally newer models.
 
 :::info
 
diff --git a/docs/docs/integrations/chat/mistralai.ipynb b/docs/docs/integrations/chat/mistralai.ipynb
index 94f4717108be3..d399f416da830 100644
--- a/docs/docs/integrations/chat/mistralai.ipynb
+++ b/docs/docs/integrations/chat/mistralai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatMistralAI\n",
     "\n",
-    "This will help you getting started with Mistral [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
+    "This will help you getting started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
diff --git a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
index 58f5a01f91277..322c2955f89d4 100644
--- a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
+++ b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatNVIDIA\n",
     "\n",
-    "This will help you getting started with NVIDIA [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
+    "This will help you getting started with NVIDIA [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
     "\n",
     "## Overview\n",
     "The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on \n",
diff --git a/docs/docs/integrations/chat/oci_data_science.ipynb b/docs/docs/integrations/chat/oci_data_science.ipynb
index fdc1d8cda4463..b5a8f040d901b 100644
--- a/docs/docs/integrations/chat/oci_data_science.ipynb
+++ b/docs/docs/integrations/chat/oci_data_science.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# ChatOCIModelDeployment\n",
     "\n",
-    "This will help you getting started with OCIModelDeployment [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOCIModelDeployment features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ChatOCIModelDeployment.html).\n",
+    "This will help you getting started with OCIModelDeployment [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatOCIModelDeployment features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ChatOCIModelDeployment.html).\n",
     "\n",
     "[OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a fully managed and serverless platform for data science teams to build, train, and manage machine learning models in the Oracle Cloud Infrastructure. You can use [AI Quick Actions](https://blogs.oracle.com/ai-and-datascience/post/ai-quick-actions-in-oci-data-science) to easily deploy LLMs on [OCI Data Science Model Deployment Service](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). You may choose to deploy the model with popular inference frameworks such as vLLM or TGI. By default, the model deployment endpoint mimics the OpenAI API protocol.\n",
     "\n",
diff --git a/docs/docs/integrations/chat/oci_generative_ai.ipynb b/docs/docs/integrations/chat/oci_generative_ai.ipynb
index 563a9dc0c6fd8..d5dc26cf25eba 100644
--- a/docs/docs/integrations/chat/oci_generative_ai.ipynb
+++ b/docs/docs/integrations/chat/oci_generative_ai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatOCIGenAI\n",
     "\n",
-    "This notebook provides a quick overview for getting started with OCIGenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOCIGenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html).\n",
+    "This notebook provides a quick overview for getting started with OCIGenAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatOCIGenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html).\n",
     "\n",
     "Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases, and which is available through a single API.\n",
     "Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters. Detailed documentation of the service and API is available __[here](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)__ and __[here](https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai/20231130/)__.\n",
diff --git a/docs/docs/integrations/chat/openai.ipynb b/docs/docs/integrations/chat/openai.ipynb
index 5d7f5c9424350..687f6ddc30bc1 100644
--- a/docs/docs/integrations/chat/openai.ipynb
+++ b/docs/docs/integrations/chat/openai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatOpenAI\n",
     "\n",
-    "This notebook provides a quick overview for getting started with OpenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
+    "This notebook provides a quick overview for getting started with OpenAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
     "\n",
     "OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [OpenAI docs](https://platform.openai.com/docs/models).\n",
     "\n",
diff --git a/docs/docs/integrations/chat/sambanova.ipynb b/docs/docs/integrations/chat/sambanova.ipynb
index 2375cd6e1114f..7f00e09467979 100644
--- a/docs/docs/integrations/chat/sambanova.ipynb
+++ b/docs/docs/integrations/chat/sambanova.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# ChatSambaNovaCloud\n",
     "\n",
-    "This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaNovaCloud.html).\n",
+    "This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaNovaCloud.html).\n",
     "\n",
     "**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
     "\n",
diff --git a/docs/docs/integrations/chat/sambastudio.ipynb b/docs/docs/integrations/chat/sambastudio.ipynb
index 3c7d029ab80ce..64dd05fd96b8c 100644
--- a/docs/docs/integrations/chat/sambastudio.ipynb
+++ b/docs/docs/integrations/chat/sambastudio.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# ChatSambaStudio\n",
     "\n",
-    "This will help you getting started with SambaStudio [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n",
+    "This will help you getting started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n",
     "\n",
     "**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n",
     "\n",
diff --git a/docs/docs/integrations/chat/vllm.ipynb b/docs/docs/integrations/chat/vllm.ipynb
index 3fa481fe4d4c8..9fc97a650df22 100644
--- a/docs/docs/integrations/chat/vllm.ipynb
+++ b/docs/docs/integrations/chat/vllm.ipynb
@@ -20,7 +20,7 @@
     "vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.\n",
     "\n",
     "## Overview\n",
-    "This will help you getting started with vLLM [chat models](/docs/concepts/#chat-models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
+    "This will help you getting started with vLLM [chat models](/docs/concepts/chat_models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
     "\n",
     "### Integration details\n",
     "\n",
diff --git a/docs/docs/integrations/chat/yi.ipynb b/docs/docs/integrations/chat/yi.ipynb
index 27f1915e5740a..11392981b1cfa 100644
--- a/docs/docs/integrations/chat/yi.ipynb
+++ b/docs/docs/integrations/chat/yi.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# ChatYI\n",
     "\n",
-    "This will help you getting started with Yi [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
+    "This will help you getting started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
     "\n",
     "[01.AI](https://www.lingyiwanwu.com/en), founded by Dr. Kai-Fu Lee, is a global company at the forefront of AI 2.0. They offer cutting-edge large language models, including the Yi series, which range from 6B to hundreds of billions of parameters. 01.AI also provides multimodal models, an open API platform, and open-source options like Yi-34B/9B/6B and Yi-VL.\n",
     "\n",
diff --git a/docs/docs/integrations/document_loaders/bshtml.ipynb b/docs/docs/integrations/document_loaders/bshtml.ipynb
index b23ba8dca4bb4..29e9e8b1d5906 100644
--- a/docs/docs/integrations/document_loaders/bshtml.ipynb
+++ b/docs/docs/integrations/document_loaders/bshtml.ipynb
@@ -7,7 +7,7 @@
     "# BSHTMLLoader\n",
     "\n",
     "\n",
-    "This notebook provides a quick overview for getting started with BeautifulSoup4 [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html).\n",
+    "This notebook provides a quick overview for getting started with BeautifulSoup4 [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html).\n",
     "\n",
     "\n",
     "## Overview\n",
diff --git a/docs/docs/integrations/document_loaders/json.ipynb b/docs/docs/integrations/document_loaders/json.ipynb
index 299bc1aa75f3d..e3a11d0ace079 100644
--- a/docs/docs/integrations/document_loaders/json.ipynb
+++ b/docs/docs/integrations/document_loaders/json.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# JSONLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
+    "This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
     "\n",
     "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
     "\n",
diff --git a/docs/docs/integrations/document_loaders/langsmith.ipynb b/docs/docs/integrations/document_loaders/langsmith.ipynb
index 9a8dc2aee3dc6..54f791602db00 100644
--- a/docs/docs/integrations/document_loaders/langsmith.ipynb
+++ b/docs/docs/integrations/document_loaders/langsmith.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "# LangSmithLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with the LangSmith [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all LangSmithLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html).\n",
+    "This notebook provides a quick overview for getting started with the LangSmith [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all LangSmithLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
diff --git a/docs/docs/integrations/document_loaders/pypdfium2.ipynb b/docs/docs/integrations/document_loaders/pypdfium2.ipynb
index 48ca5ec0232c3..24740de99ac11 100644
--- a/docs/docs/integrations/document_loaders/pypdfium2.ipynb
+++ b/docs/docs/integrations/document_loaders/pypdfium2.ipynb
@@ -7,7 +7,7 @@
     "# PyPDFium2Loader\n",
     "\n",
     "\n",
-    "This notebook provides a quick overview for getting started with PyPDFium2 [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html).\n",
+    "This notebook provides a quick overview for getting started with PyPDFium2 [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
diff --git a/docs/docs/integrations/document_loaders/pypdfloader.ipynb b/docs/docs/integrations/document_loaders/pypdfloader.ipynb
index 5085aa1c6c575..b0cc79d92de87 100644
--- a/docs/docs/integrations/document_loaders/pypdfloader.ipynb
+++ b/docs/docs/integrations/document_loaders/pypdfloader.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# PyPDFLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with `PyPDF` [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all DocumentLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html).\n",
+    "This notebook provides a quick overview for getting started with `PyPDF` [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all DocumentLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html).\n",
     "\n",
     "\n",
     "## Overview\n",
diff --git a/docs/docs/integrations/document_loaders/unstructured_file.ipynb b/docs/docs/integrations/document_loaders/unstructured_file.ipynb
index 02ff6a7c799d2..89c79eedbe272 100644
--- a/docs/docs/integrations/document_loaders/unstructured_file.ipynb
+++ b/docs/docs/integrations/document_loaders/unstructured_file.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Unstructured\n",
     "\n",
-    "This notebook covers how to use `Unstructured` [document loader](https://python.langchain.com/docs/concepts/#document-loaders) to load files of many types. `Unstructured` currently supports loading of text files, powerpoints, html, pdfs, images, and more.\n",
+    "This notebook covers how to use `Unstructured` [document loader](https://python.langchain.com/docs/concepts/document_loaders) to load files of many types. `Unstructured` currently supports loading of text files, powerpoints, html, pdfs, images, and more.\n",
     "\n",
     "Please see [this guide](../../integrations/providers/unstructured.mdx) for more instructions on setting up Unstructured locally, including setting up required system dependencies.\n",
     "\n",
diff --git a/docs/docs/integrations/document_loaders/unstructured_markdown.ipynb b/docs/docs/integrations/document_loaders/unstructured_markdown.ipynb
index 0645950990d1c..3bd98c0bed3ea 100644
--- a/docs/docs/integrations/document_loaders/unstructured_markdown.ipynb
+++ b/docs/docs/integrations/document_loaders/unstructured_markdown.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# UnstructuredMarkdownLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with UnstructuredMarkdown [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html).\n",
+    "This notebook provides a quick overview for getting started with UnstructuredMarkdown [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
diff --git a/docs/docs/integrations/document_loaders/xml.ipynb b/docs/docs/integrations/document_loaders/xml.ipynb
index 0e936b07baed4..39fb2fb507c37 100644
--- a/docs/docs/integrations/document_loaders/xml.ipynb
+++ b/docs/docs/integrations/document_loaders/xml.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# UnstructuredXMLLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with UnstructuredXMLLoader [document loader](https://python.langchain.com/docs/concepts/#document-loaders). The `UnstructuredXMLLoader` is used to load `XML` files. The loader works with `.xml` files. The page content will be the text extracted from the XML tags.\n",
+    "This notebook provides a quick overview for getting started with UnstructuredXMLLoader [document loader](https://python.langchain.com/docs/concepts/document_loaders). The `UnstructuredXMLLoader` is used to load `XML` files. The loader works with `.xml` files. The page content will be the text extracted from the XML tags.\n",
     "\n",
     "\n",
     "## Overview\n",
diff --git a/docs/docs/integrations/llms/anthropic.ipynb b/docs/docs/integrations/llms/anthropic.ipynb
index 0f9c74c225adf..bdab46365c148 100644
--- a/docs/docs/integrations/llms/anthropic.ipynb
+++ b/docs/docs/integrations/llms/anthropic.ipynb
@@ -23,7 +23,7 @@
     "# AnthropicLLM\n",
     "\n",
     ":::caution\n",
-    "You are currently on a page documenting the use of Anthropic legacy Claude 2 models as [text completion models](/docs/concepts/#llms). The latest and most popular Anthropic models are [chat completion models](/docs/concepts/#chat-models), and the text completion models have been deprecated.\n",
+    "You are currently on a page documenting the use of Anthropic legacy Claude 2 models as [text completion models](/docs/concepts/text_llms). The latest and most popular Anthropic models are [chat completion models](/docs/concepts/chat_models), and the text completion models have been deprecated.\n",
     "\n",
     "You are probably looking for [this page instead](/docs/integrations/chat/anthropic/).\n",
     ":::\n",
diff --git a/docs/docs/integrations/llms/azure_openai.ipynb b/docs/docs/integrations/llms/azure_openai.ipynb
index 44af56a2f276c..17a6d0605ee50 100644
--- a/docs/docs/integrations/llms/azure_openai.ipynb
+++ b/docs/docs/integrations/llms/azure_openai.ipynb
@@ -8,7 +8,7 @@
     "# Azure OpenAI\n",
     "\n",
     ":::caution\n",
-    "You are currently on a page documenting the use of Azure OpenAI [text completion models](/docs/concepts/#llms). The latest and most popular Azure OpenAI models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of Azure OpenAI [text completion models](/docs/concepts/text_llms). The latest and most popular Azure OpenAI models are [chat completion models](/docs/concepts/chat_models).\n",
     "\n",
     "Unless you are specifically using `gpt-3.5-turbo-instruct`, you are probably looking for [this page instead](/docs/integrations/chat/azure_chat_openai/).\n",
     ":::\n",
diff --git a/docs/docs/integrations/llms/bedrock.ipynb b/docs/docs/integrations/llms/bedrock.ipynb
index 70ecf569dce1b..86be8dca20363 100644
--- a/docs/docs/integrations/llms/bedrock.ipynb
+++ b/docs/docs/integrations/llms/bedrock.ipynb
@@ -12,7 +12,7 @@
    "metadata": {},
    "source": [
     ":::caution\n",
-    "You are currently on a page documenting the use of Amazon Bedrock models as [text completion models](/docs/concepts/#llms). Many popular models available on Bedrock are [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of Amazon Bedrock models as [text completion models](/docs/concepts/text_llms). Many popular models available on Bedrock are [chat completion models](/docs/concepts/chat_models).\n",
     "\n",
     "You may be looking for [this page instead](/docs/integrations/chat/bedrock/).\n",
     ":::\n",
diff --git a/docs/docs/integrations/llms/cohere.ipynb b/docs/docs/integrations/llms/cohere.ipynb
index 566667cca4689..9e294f275cf8e 100644
--- a/docs/docs/integrations/llms/cohere.ipynb
+++ b/docs/docs/integrations/llms/cohere.ipynb
@@ -8,7 +8,7 @@
     "# Cohere\n",
     "\n",
     ":::caution\n",
-    "You are currently on a page documenting the use of Cohere models as [text completion models](/docs/concepts/#llms). Many popular Cohere models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of Cohere models as [text completion models](/docs/concepts/text_llms). Many popular Cohere models are [chat completion models](/docs/concepts/chat_models).\n",
     "\n",
     "You may be looking for [this page instead](/docs/integrations/chat/cohere/).\n",
     ":::\n",
diff --git a/docs/docs/integrations/llms/databricks.ipynb b/docs/docs/integrations/llms/databricks.ipynb
index a08ae1516f0f6..7cc805bde28d1 100644
--- a/docs/docs/integrations/llms/databricks.ipynb
+++ b/docs/docs/integrations/llms/databricks.ipynb
@@ -10,7 +10,7 @@
     "> [Databricks](https://www.databricks.com/) Lakehouse Platform unifies data, analytics, and AI on one platform.\n",
     "\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Databricks [LLM models](https://python.langchain.com/docs/concepts/#llms). For detailed documentation of all features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.databricks.Databricks.html).\n",
+    "This notebook provides a quick overview for getting started with Databricks [LLM models](https://python.langchain.com/docs/concepts/text_llms). For detailed documentation of all features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.databricks.Databricks.html).\n",
     "\n",
     "## Overview\n",
     "\n",
diff --git a/docs/docs/integrations/llms/fireworks.ipynb b/docs/docs/integrations/llms/fireworks.ipynb
index 16e4ca4b37b13..3dd7fc4483c7f 100644
--- a/docs/docs/integrations/llms/fireworks.ipynb
+++ b/docs/docs/integrations/llms/fireworks.ipynb
@@ -8,7 +8,7 @@
     "# Fireworks\n",
     "\n",
     ":::caution\n",
-    "You are currently on a page documenting the use of Fireworks models as [text completion models](/docs/concepts/#llms). Many popular Fireworks models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of Fireworks models as [text completion models](/docs/concepts/text_llms). Many popular Fireworks models are [chat completion models](/docs/concepts/chat_models).\n",
     "\n",
     "You may be looking for [this page instead](/docs/integrations/chat/fireworks/).\n",
     ":::\n",
diff --git a/docs/docs/integrations/llms/google_ai.ipynb b/docs/docs/integrations/llms/google_ai.ipynb
index a3ebe9dfec3e7..0ba3e56e0ac9c 100644
--- a/docs/docs/integrations/llms/google_ai.ipynb
+++ b/docs/docs/integrations/llms/google_ai.ipynb
@@ -26,7 +26,7 @@
    "metadata": {},
    "source": [
     ":::caution\n",
-    "You are currently on a page documenting the use of Google models as [text completion models](/docs/concepts/#llms). Many popular Google models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of Google models as [text completion models](/docs/concepts/text_llms). Many popular Google models are [chat completion models](/docs/concepts/chat_models).\n",
     "\n",
     "You may be looking for [this page instead](/docs/integrations/chat/google_generative_ai/).\n",
     ":::\n",
diff --git a/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb b/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb
index 83911014e44c0..8322e6d780cec 100644
--- a/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb
+++ b/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb
@@ -16,7 +16,7 @@
     "# Google Cloud Vertex AI\n",
     "\n",
     ":::caution\n",
-    "You are currently on a page documenting the use of Google Vertex [text completion models](/docs/concepts/#llms). Many Google models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of Google Vertex [text completion models](/docs/concepts/text_llms). Many Google models are [chat completion models](/docs/concepts/chat_models).\n",
     "\n",
     "You may be looking for [this page instead](/docs/integrations/chat/google_vertex_ai_palm/).\n",
     ":::\n",
diff --git a/docs/docs/integrations/llms/index.mdx b/docs/docs/integrations/llms/index.mdx
index 8af9679143640..b902bf7ea7f74 100644
--- a/docs/docs/integrations/llms/index.mdx
+++ b/docs/docs/integrations/llms/index.mdx
@@ -7,12 +7,12 @@ keywords: [compatibility]
 # LLMs
 
 :::caution
-You are currently on a page documenting the use of [text completion models](/docs/concepts/#llms). Many of the latest and most popular models are [chat completion models](/docs/concepts/#chat-models).
+You are currently on a page documenting the use of [text completion models](/docs/concepts/text_llms). Many of the latest and most popular models are [chat completion models](/docs/concepts/chat_models).
 
 Unless you are specifically using more advanced prompting techniques, you are probably looking for [this page instead](/docs/integrations/chat/).
 :::
 
-[LLMs](/docs/concepts/#llms) are language models that take a string as input and return a string as output.
+[LLMs](/docs/concepts/text_llms) are language models that take a string as input and return a string as output.
 
 :::info
 
diff --git a/docs/docs/integrations/llms/nvidia_ai_endpoints.ipynb b/docs/docs/integrations/llms/nvidia_ai_endpoints.ipynb
index 190cf8db74d57..7c3df140d317b 100644
--- a/docs/docs/integrations/llms/nvidia_ai_endpoints.ipynb
+++ b/docs/docs/integrations/llms/nvidia_ai_endpoints.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# NVIDIA\n",
     "\n",
-    "This will help you getting started with NVIDIA [models](/docs/concepts/#llms). For detailed documentation of all `NVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.chat_models.NVIDIA.html).\n",
+    "This will help you getting started with NVIDIA [models](/docs/concepts/text_llms). For detailed documentation of all `NVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.chat_models.NVIDIA.html).\n",
     "\n",
     "## Overview\n",
     "The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on \n",
diff --git a/docs/docs/integrations/llms/ollama.ipynb b/docs/docs/integrations/llms/ollama.ipynb
index 702bd912db55e..f7d18643da580 100644
--- a/docs/docs/integrations/llms/ollama.ipynb
+++ b/docs/docs/integrations/llms/ollama.ipynb
@@ -18,7 +18,7 @@
     "# OllamaLLM\n",
     "\n",
     ":::caution\n",
-    "You are currently on a page documenting the use of Ollama models as [text completion models](/docs/concepts/#llms). Many popular Ollama models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of Ollama models as [text completion models](/docs/concepts/text_llms). Many popular Ollama models are [chat completion models](/docs/concepts/chat_models).\n",
     "\n",
     "You may be looking for [this page instead](/docs/integrations/chat/ollama/).\n",
     ":::\n",
diff --git a/docs/docs/integrations/llms/openai.ipynb b/docs/docs/integrations/llms/openai.ipynb
index 58de2dec493da..e24d5a5fa832a 100644
--- a/docs/docs/integrations/llms/openai.ipynb
+++ b/docs/docs/integrations/llms/openai.ipynb
@@ -8,7 +8,7 @@
     "# OpenAI\n",
     "\n",
     ":::caution\n",
-    "You are currently on a page documenting the use of OpenAI [text completion models](/docs/concepts/#llms). The latest and most popular OpenAI models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of OpenAI [text completion models](/docs/concepts/text_llms). The latest and most popular OpenAI models are [chat completion models](/docs/concepts/chat_models).\n",
     "\n",
     "Unless you are specifically using `gpt-3.5-turbo-instruct`, you are probably looking for [this page instead](/docs/integrations/chat/openai/).\n",
     ":::\n",
diff --git a/docs/docs/integrations/llms/together.ipynb b/docs/docs/integrations/llms/together.ipynb
index 19c306baa853a..b98289f70c0f8 100644
--- a/docs/docs/integrations/llms/together.ipynb
+++ b/docs/docs/integrations/llms/together.ipynb
@@ -8,7 +8,7 @@
     "# Together AI\n",
     "\n",
     ":::caution\n",
-    "You are currently on a page documenting the use of Together AI models as [text completion models](/docs/concepts/#llms). Many popular Together AI models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of Together AI models as [text completion models](/docs/concepts/text_llms). Many popular Together AI models are [chat completion models](/docs/concepts/chat_models).\n",
     "\n",
     "You may be looking for [this page instead](/docs/integrations/chat/together/).\n",
     ":::\n",
diff --git a/docs/docs/integrations/providers/databricks.md b/docs/docs/integrations/providers/databricks.md
index 7cd8273874f9f..0acb3287b88bd 100644
--- a/docs/docs/integrations/providers/databricks.md
+++ b/docs/docs/integrations/providers/databricks.md
@@ -39,7 +39,7 @@ LLM
 `Databricks` is an LLM class to access completion endpoints hosted on Databricks.
 
 :::caution
-Text completion models have been deprecated and the latest and most popular models are [chat completion models](/docs/concepts/#chat-models). Use `ChatDatabricks` chat model instead to use those models and advanced features such as tool calling.
+Text completion models have been deprecated and the latest and most popular models are [chat completion models](/docs/concepts/chat_models). Use `ChatDatabricks` chat model instead to use those models and advanced features such as tool calling.
 :::
 
 ```
diff --git a/docs/docs/integrations/providers/mlflow_tracking.ipynb b/docs/docs/integrations/providers/mlflow_tracking.ipynb
index b30f2850649cf..c21161aaf6a85 100644
--- a/docs/docs/integrations/providers/mlflow_tracking.ipynb
+++ b/docs/docs/integrations/providers/mlflow_tracking.ipynb
@@ -523,7 +523,7 @@
    "metadata": {},
    "source": [
     "#### Where to Pass the Callback\n",
-    " LangChain supports two ways of passing callback instances: (1) Request time callbacks - pass them to the `invoke` method or bind with `with_config()` (2) Constructor callbacks - set them in the chain constructor. When using the `MlflowLangchainTracer` as a callback, you **must use request time callbacks**. Setting it in the constructor instead will only apply the callback to the top-level object, preventing it from being propagated to child components, resulting in incomplete traces. For more information on this behavior, please refer to [Callbacks Documentation](https://python.langchain.com/docs/concepts/#callbacks) for more details.\n",
+    " LangChain supports two ways of passing callback instances: (1) Request time callbacks - pass them to the `invoke` method or bind with `with_config()` (2) Constructor callbacks - set them in the chain constructor. When using the `MlflowLangchainTracer` as a callback, you **must use request time callbacks**. Setting it in the constructor instead will only apply the callback to the top-level object, preventing it from being propagated to child components, resulting in incomplete traces. For more information on this behavior, please refer to [Callbacks Documentation](https://python.langchain.com/docs/concepts/callbacks) for more details.\n",
     "\n",
     "```python\n",
     "# OK\n",
diff --git a/docs/docs/integrations/retrievers/azure_ai_search.ipynb b/docs/docs/integrations/retrievers/azure_ai_search.ipynb
index 6b5ce1f360044..e5a9bcec2a1fc 100644
--- a/docs/docs/integrations/retrievers/azure_ai_search.ipynb
+++ b/docs/docs/integrations/retrievers/azure_ai_search.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "`AzureAISearchRetriever` is an integration module that returns documents from an unstructured query. It's based on the BaseRetriever class and it targets the 2023-11-01 stable REST API version of Azure AI Search, which means it supports vector indexing and queries.\n",
     "\n",
-    "This guide will help you getting started with the Azure AI Search [retriever](/docs/concepts/#retrievers). For detailed documentation of all `AzureAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html).\n",
+    "This guide will help you getting started with the Azure AI Search [retriever](/docs/concepts/retrievers). For detailed documentation of all `AzureAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html).\n",
     "\n",
     "`AzureAISearchRetriever` replaces `AzureCognitiveSearchRetriever`, which will soon be deprecated. We recommend switching to the newer version that's based on the most recent stable version of the search APIs.\n",
     "\n",
diff --git a/docs/docs/integrations/retrievers/bedrock.ipynb b/docs/docs/integrations/retrievers/bedrock.ipynb
index b674b1175b7d0..42e0702de8b28 100644
--- a/docs/docs/integrations/retrievers/bedrock.ipynb
+++ b/docs/docs/integrations/retrievers/bedrock.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# Bedrock (Knowledge Bases) Retriever\n",
     "\n",
-    "This guide will help you getting started with the AWS Knowledge Bases [retriever](/docs/concepts/#retrievers).\n",
+    "This guide will help you getting started with the AWS Knowledge Bases [retriever](/docs/concepts/retrievers).\n",
     "\n",
     "[Knowledge Bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.\n",
     "\n",
diff --git a/docs/docs/integrations/retrievers/box.ipynb b/docs/docs/integrations/retrievers/box.ipynb
index a4abf132ac588..af2dd8bdd1813 100644
--- a/docs/docs/integrations/retrievers/box.ipynb
+++ b/docs/docs/integrations/retrievers/box.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# BoxRetriever\n",
     "\n",
-    "This will help you getting started with the Box [retriever](/docs/concepts/#retrievers). For detailed documentation of all BoxRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html).\n",
+    "This will help you getting started with the Box [retriever](/docs/concepts/retrievers). For detailed documentation of all BoxRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html).\n",
     "\n",
     "# Overview\n",
     "\n",
diff --git a/docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb b/docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb
index dafc757e2386f..2bf4dae5bacf5 100644
--- a/docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb
+++ b/docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "The `ElasticsearchRetriever` is a generic wrapper to enable flexible access to all `Elasticsearch` features through the [Query DSL](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html).  For most use cases the other classes (`ElasticsearchStore`, `ElasticsearchEmbeddings`, etc.) should suffice, but if they don't you can use `ElasticsearchRetriever`.\n",
     "\n",
-    "This guide will help you getting started with the Elasticsearch [retriever](/docs/concepts/#retrievers). For detailed documentation of all `ElasticsearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/elasticsearch/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html).\n",
+    "This guide will help you getting started with the Elasticsearch [retriever](/docs/concepts/retrievers). For detailed documentation of all `ElasticsearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/elasticsearch/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html).\n",
     "\n",
     "### Integration details\n",
     "\n",
@@ -66,7 +66,7 @@
    "source": [
     "### Installation\n",
     "\n",
-    "This retriever lives in the `langchain-elasticsearch` package. For demonstration purposes, we will also install `langchain-community` to generate text [embeddings](/docs/concepts/#embedding-models)."
+    "This retriever lives in the `langchain-elasticsearch` package. For demonstration purposes, we will also install `langchain-community` to generate text [embeddings](/docs/concepts/embedding_models)."
    ]
   },
   {
@@ -613,7 +613,7 @@
    "source": [
     "## Usage\n",
     "\n",
-    "Following the above examples, we use `.invoke` to issue a single query. Because retrievers are Runnables, we can use any method in the [Runnable interface](/docs/concepts/#runnable-interface), such as `.batch`, as well."
+    "Following the above examples, we use `.invoke` to issue a single query. Because retrievers are Runnables, we can use any method in the [Runnable interface](/docs/concepts/runnables), such as `.batch`, as well."
    ]
   },
   {
diff --git a/docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb b/docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb
index 60352ca64d830..dc19c2054087f 100644
--- a/docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb
+++ b/docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb
@@ -21,7 +21,7 @@
     "\n",
     ">`Vertex AI Search` is available in the `Google Cloud Console` and via an API for enterprise workflow integration.\n",
     "\n",
-    "This notebook demonstrates how to configure `Vertex AI Search` and use the Vertex AI Search [retriever](/docs/concepts/#retrievers). The Vertex AI Search retriever encapsulates the [Python client library](https://cloud.google.com/generative-ai-app-builder/docs/libraries#client-libraries-install-python) and uses it to access the [Search Service API](https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1beta.services.search_service).\n",
+    "This notebook demonstrates how to configure `Vertex AI Search` and use the Vertex AI Search [retriever](/docs/concepts/retrievers). The Vertex AI Search retriever encapsulates the [Python client library](https://cloud.google.com/generative-ai-app-builder/docs/libraries#client-libraries-install-python) and uses it to access the [Search Service API](https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1beta.services.search_service).\n",
     "\n",
     "For detailed documentation of all `VertexAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_community/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html).\n",
     "\n",
@@ -374,7 +374,7 @@
    "source": [
     "## Usage\n",
     "\n",
-    "Following the above examples, we use `.invoke` to issue a single query. Because retrievers are Runnables, we can use any method in the [Runnable interface](/docs/concepts/#runnable-interface), such as `.batch`, as well."
+    "Following the above examples, we use `.invoke` to issue a single query. Because retrievers are Runnables, we can use any method in the [Runnable interface](/docs/concepts/runnables), such as `.batch`, as well."
    ]
   },
   {
diff --git a/docs/docs/integrations/retrievers/index.mdx b/docs/docs/integrations/retrievers/index.mdx
index 6d749441e6f03..dee97cbb13e0c 100644
--- a/docs/docs/integrations/retrievers/index.mdx
+++ b/docs/docs/integrations/retrievers/index.mdx
@@ -7,7 +7,7 @@ import {CategoryTable, IndexTable} from '@theme/FeatureTables'
 
 # Retrievers
 
-A [retriever](/docs/concepts/#retrievers) is an interface that returns documents given an unstructured query.
+A [retriever](/docs/concepts/retrievers) is an interface that returns documents given an unstructured query.
 It is more general than a vector store.
 A retriever does not need to be able to store documents, only to return (or retrieve) them.
 Retrievers can be created from vector stores, but are also broad enough to include [Wikipedia search](/docs/integrations/retrievers/wikipedia/) and [Amazon Kendra](/docs/integrations/retrievers/amazon_kendra_retriever/).
diff --git a/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb b/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb
index fa498e2abe05e..11e91c78fdbd3 100644
--- a/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb
+++ b/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "> [Milvus](https://milvus.io/docs) is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.\n",
     "\n",
-    "This will help you getting started with the Milvus Hybrid Search [retriever](/docs/concepts/#retrievers), which combines the strengths of both dense and sparse vector search. For detailed documentation of all `MilvusCollectionHybridSearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/milvus/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html).\n",
+    "This will help you getting started with the Milvus Hybrid Search [retriever](/docs/concepts/retrievers), which combines the strengths of both dense and sparse vector search. For detailed documentation of all `MilvusCollectionHybridSearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/milvus/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html).\n",
     "\n",
     "See also the Milvus Multi-Vector Search [docs](https://milvus.io/docs/multi-vector-search.md).\n",
     "\n",
diff --git a/docs/docs/integrations/stores/astradb.ipynb b/docs/docs/integrations/stores/astradb.ipynb
index c38cf5b7a0db2..ce78e19bc9dea 100644
--- a/docs/docs/integrations/stores/astradb.ipynb
+++ b/docs/docs/integrations/stores/astradb.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# AstraDBByteStore\n",
     "\n",
-    "This will help you get started with Astra DB [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `AstraDBByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html).\n",
+    "This will help you get started with Astra DB [key-value stores](/docs/concepts/key_value_stores). For detailed documentation of all `AstraDBByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
diff --git a/docs/docs/integrations/stores/cassandra.ipynb b/docs/docs/integrations/stores/cassandra.ipynb
index 9c31d12864f83..309f6ea61edba 100644
--- a/docs/docs/integrations/stores/cassandra.ipynb
+++ b/docs/docs/integrations/stores/cassandra.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# CassandraByteStore\n",
     "\n",
-    "This will help you get started with Cassandra [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `CassandraByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.cassandra.CassandraByteStore.html).\n",
+    "This will help you get started with Cassandra [key-value stores](/docs/concepts/key_value_stores). For detailed documentation of all `CassandraByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.cassandra.CassandraByteStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
diff --git a/docs/docs/integrations/stores/elasticsearch.ipynb b/docs/docs/integrations/stores/elasticsearch.ipynb
index 370886867ed95..e7578ad362d96 100644
--- a/docs/docs/integrations/stores/elasticsearch.ipynb
+++ b/docs/docs/integrations/stores/elasticsearch.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# ElasticsearchEmbeddingsCache\n",
     "\n",
-    "This will help you get started with Elasticsearch [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `ElasticsearchEmbeddingsCache` features and configurations head to the [API reference](https://python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html).\n",
+    "This will help you get started with Elasticsearch [key-value stores](/docs/concepts/key_value_stores). For detailed documentation of all `ElasticsearchEmbeddingsCache` features and configurations head to the [API reference](https://python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html).\n",
     "\n",
     "## Overview\n",
     "\n",
diff --git a/docs/docs/integrations/stores/file_system.ipynb b/docs/docs/integrations/stores/file_system.ipynb
index 1a0baa82dd4a7..4e9c211ea3db2 100644
--- a/docs/docs/integrations/stores/file_system.ipynb
+++ b/docs/docs/integrations/stores/file_system.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# LocalFileStore\n",
     "\n",
-    "This will help you get started with local filesystem [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all LocalFileStore features and configurations head to the [API reference](https://python.langchain.com/api_reference/langchain/storage/langchain.storage.file_system.LocalFileStore.html).\n",
+    "This will help you get started with local filesystem [key-value stores](/docs/concepts/key_value_stores). For detailed documentation of all LocalFileStore features and configurations head to the [API reference](https://python.langchain.com/api_reference/langchain/storage/langchain.storage.file_system.LocalFileStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
diff --git a/docs/docs/integrations/stores/in_memory.ipynb b/docs/docs/integrations/stores/in_memory.ipynb
index 7f81222dab684..b253249e97cd5 100644
--- a/docs/docs/integrations/stores/in_memory.ipynb
+++ b/docs/docs/integrations/stores/in_memory.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# InMemoryByteStore\n",
     "\n",
-    "This guide will help you get started with in-memory [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `InMemoryByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.InMemoryByteStore.html).\n",
+    "This guide will help you get started with in-memory [key-value stores](/docs/concepts/key_value_stores). For detailed documentation of all `InMemoryByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.InMemoryByteStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
diff --git a/docs/docs/integrations/stores/redis.ipynb b/docs/docs/integrations/stores/redis.ipynb
index 1a90576acf9c9..8eaaf5a3bb242 100644
--- a/docs/docs/integrations/stores/redis.ipynb
+++ b/docs/docs/integrations/stores/redis.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# RedisStore\n",
     "\n",
-    "This will help you get started with Redis [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `RedisStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.redis.RedisStore.html).\n",
+    "This will help you get started with Redis [key-value stores](/docs/concepts/key_value_stores). For detailed documentation of all `RedisStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.redis.RedisStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
diff --git a/docs/docs/integrations/stores/upstash_redis.ipynb b/docs/docs/integrations/stores/upstash_redis.ipynb
index c512e9b376cff..54d9c668b0438 100644
--- a/docs/docs/integrations/stores/upstash_redis.ipynb
+++ b/docs/docs/integrations/stores/upstash_redis.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# UpstashRedisByteStore\n",
     "\n",
-    "This will help you get started with Upstash redis [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `UpstashRedisByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.upstash_redis.UpstashRedisByteStore.html).\n",
+    "This will help you get started with Upstash redis [key-value stores](/docs/concepts/key_value_stores). For detailed documentation of all `UpstashRedisByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.upstash_redis.UpstashRedisByteStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
diff --git a/docs/docs/integrations/text_embedding/databricks.ipynb b/docs/docs/integrations/text_embedding/databricks.ipynb
index ae6b6ab8c031b..4de876b37cc8d 100644
--- a/docs/docs/integrations/text_embedding/databricks.ipynb
+++ b/docs/docs/integrations/text_embedding/databricks.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "> [Databricks](https://www.databricks.com/) Lakehouse Platform unifies data, analytics, and AI on one platform.\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Databricks [embedding models](/docs/concepts/#embedding-models). For detailed documentation of all `DatabricksEmbeddings` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html).\n",
+    "This notebook provides a quick overview for getting started with Databricks [embedding models](/docs/concepts/embedding_models). For detailed documentation of all `DatabricksEmbeddings` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html).\n",
     "\n",
     "\n",
     "\n",
diff --git a/docs/docs/integrations/tools/gmail.ipynb b/docs/docs/integrations/tools/gmail.ipynb
index b105e0ae57528..2f91852c0559a 100644
--- a/docs/docs/integrations/tools/gmail.ipynb
+++ b/docs/docs/integrations/tools/gmail.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Gmail Toolkit\n",
     "\n",
-    "This will help you getting started with the GMail [toolkit](/docs/concepts/#toolkits). This toolkit interacts with the GMail API to read messages, draft and send messages, and more. For detailed documentation of all GmailToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.toolkit.GmailToolkit.html).\n",
+    "This will help you getting started with the GMail [toolkit](/docs/concepts/tools/#toolkits). This toolkit interacts with the GMail API to read messages, draft and send messages, and more. For detailed documentation of all GmailToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.toolkit.GmailToolkit.html).\n",
     "\n",
     "## Setup\n",
     "\n",
diff --git a/docs/docs/integrations/tools/jina_search.ipynb b/docs/docs/integrations/tools/jina_search.ipynb
index 511085a579a51..9003064566af2 100644
--- a/docs/docs/integrations/tools/jina_search.ipynb
+++ b/docs/docs/integrations/tools/jina_search.ipynb
@@ -117,7 +117,7 @@
    "source": [
     "## Invocation\n",
     "\n",
-    "### [Invoke directly with args](/docs/concepts/#invoke-with-just-the-arguments)"
+    "### [Invoke directly with args](/docs/concepts/tools)"
    ]
   },
   {
@@ -143,7 +143,7 @@
    "id": "d6e73897",
    "metadata": {},
    "source": [
-    "### [Invoke with ToolCall](/docs/concepts/#invoke-with-toolcall)\n",
+    "### [Invoke with ToolCall](/docs/concepts/tools)\n",
     "\n",
     "We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:"
    ]
diff --git a/docs/docs/integrations/tools/requests.ipynb b/docs/docs/integrations/tools/requests.ipynb
index 0db60b0aa9847..08fd4e9003631 100644
--- a/docs/docs/integrations/tools/requests.ipynb
+++ b/docs/docs/integrations/tools/requests.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Requests Toolkit\n",
     "\n",
-    "We can use the Requests [toolkit](/docs/concepts/#toolkits) to construct agents that generate HTTP requests.\n",
+    "We can use the Requests [toolkit](/docs/concepts/tools/#toolkits) to construct agents that generate HTTP requests.\n",
     "\n",
     "For detailed documentation of all API toolkit features and configurations head to the API reference for [RequestsToolkit](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit.html).\n",
     "\n",
diff --git a/docs/docs/integrations/tools/slack.ipynb b/docs/docs/integrations/tools/slack.ipynb
index 3a516eeb0d7c0..8420188168247 100644
--- a/docs/docs/integrations/tools/slack.ipynb
+++ b/docs/docs/integrations/tools/slack.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Slack Toolkit\n",
     "\n",
-    "This will help you getting started with the Slack [toolkit](/docs/concepts/#toolkits). For detailed documentation of all SlackToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html).\n",
+    "This will help you getting started with the Slack [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all SlackToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html).\n",
     "\n",
     "## Setup\n",
     "\n",
diff --git a/docs/docs/integrations/tools/sql_database.ipynb b/docs/docs/integrations/tools/sql_database.ipynb
index f5be93f2c5e48..0582040343746 100644
--- a/docs/docs/integrations/tools/sql_database.ipynb
+++ b/docs/docs/integrations/tools/sql_database.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# SQLDatabase Toolkit\n",
     "\n",
-    "This will help you getting started with the SQL Database [toolkit](/docs/concepts/#toolkits). For detailed documentation of all `SQLDatabaseToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html).\n",
+    "This will help you getting started with the SQL Database [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all `SQLDatabaseToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html).\n",
     "\n",
     "Tools within the `SQLDatabaseToolkit` are designed to interact with a `SQL` database. \n",
     "\n",
diff --git a/docs/docs/integrations/tools/tavily_search.ipynb b/docs/docs/integrations/tools/tavily_search.ipynb
index 7a89acb94a363..d4f6afdfff64d 100644
--- a/docs/docs/integrations/tools/tavily_search.ipynb
+++ b/docs/docs/integrations/tools/tavily_search.ipynb
@@ -126,7 +126,7 @@
    "source": [
     "## Invocation\n",
     "\n",
-    "### [Invoke directly with args](/docs/concepts/#invoke-with-just-the-arguments)\n",
+    "### [Invoke directly with args](/docs/concepts/tools)\n",
     "\n",
     "The `TavilySearchResults` tool takes a single \"query\" argument, which should be a natural language query:"
    ]
@@ -166,7 +166,7 @@
    "id": "d6e73897",
    "metadata": {},
    "source": [
-    "### [Invoke with ToolCall](/docs/concepts/#invoke-with-toolcall)\n",
+    "### [Invoke with ToolCall](/docs/concepts/tools)\n",
     "\n",
     "We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:"
    ]
diff --git a/docs/docs/integrations/vectorstores/astradb.ipynb b/docs/docs/integrations/vectorstores/astradb.ipynb
index 36c9e072741b9..b2598ac96420c 100644
--- a/docs/docs/integrations/vectorstores/astradb.ipynb
+++ b/docs/docs/integrations/vectorstores/astradb.ipynb
@@ -459,7 +459,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/chroma.ipynb b/docs/docs/integrations/vectorstores/chroma.ipynb
index d5c2133b7c660..90ea438f25849 100644
--- a/docs/docs/integrations/vectorstores/chroma.ipynb
+++ b/docs/docs/integrations/vectorstores/chroma.ipynb
@@ -463,7 +463,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/clickhouse.ipynb b/docs/docs/integrations/vectorstores/clickhouse.ipynb
index 2631ab40a919f..fecd3998e5ff5 100644
--- a/docs/docs/integrations/vectorstores/clickhouse.ipynb
+++ b/docs/docs/integrations/vectorstores/clickhouse.ipynb
@@ -360,7 +360,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/couchbase.ipynb b/docs/docs/integrations/vectorstores/couchbase.ipynb
index c80fbbd891464..c5aa85e218002 100644
--- a/docs/docs/integrations/vectorstores/couchbase.ipynb
+++ b/docs/docs/integrations/vectorstores/couchbase.ipynb
@@ -678,7 +678,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb b/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb
index adfe23173b4a6..db97f9fb4cb66 100644
--- a/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb
+++ b/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb
@@ -496,7 +496,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/elasticsearch.ipynb b/docs/docs/integrations/vectorstores/elasticsearch.ipynb
index f2be67d8d266e..339197a0fdae9 100644
--- a/docs/docs/integrations/vectorstores/elasticsearch.ipynb
+++ b/docs/docs/integrations/vectorstores/elasticsearch.ipynb
@@ -473,7 +473,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/faiss.ipynb b/docs/docs/integrations/vectorstores/faiss.ipynb
index 854e5a25dcb47..2837abd3602a6 100644
--- a/docs/docs/integrations/vectorstores/faiss.ipynb
+++ b/docs/docs/integrations/vectorstores/faiss.ipynb
@@ -366,7 +366,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/index.mdx b/docs/docs/integrations/vectorstores/index.mdx
index cc4d33418ded4..d6631ce945743 100644
--- a/docs/docs/integrations/vectorstores/index.mdx
+++ b/docs/docs/integrations/vectorstores/index.mdx
@@ -7,7 +7,7 @@ sidebar_class_name: hidden
 
 import { CategoryTable, IndexTable } from "@theme/FeatureTables";
 
-A [vector store](/docs/concepts/#vector-stores) stores [embedded](/docs/concepts/#embedding-models) data and performs similarity search.
+A [vector store](/docs/concepts/#vector-stores) stores [embedded](/docs/concepts/embedding_models) data and performs similarity search.
 
 <CategoryTable category="vectorstores" />
 
diff --git a/docs/docs/integrations/vectorstores/milvus.ipynb b/docs/docs/integrations/vectorstores/milvus.ipynb
index 2dfbbbb2b6e1b..34bda120d89aa 100644
--- a/docs/docs/integrations/vectorstores/milvus.ipynb
+++ b/docs/docs/integrations/vectorstores/milvus.ipynb
@@ -397,7 +397,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
index dd29cf56dc15e..812b2f12f29a5 100644
--- a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
+++ b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
@@ -490,7 +490,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/pgvector.ipynb b/docs/docs/integrations/vectorstores/pgvector.ipynb
index afbd082aa1a86..8a532b627b919 100644
--- a/docs/docs/integrations/vectorstores/pgvector.ipynb
+++ b/docs/docs/integrations/vectorstores/pgvector.ipynb
@@ -438,7 +438,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/pinecone.ipynb b/docs/docs/integrations/vectorstores/pinecone.ipynb
index ecdf557f3d58b..a54e4edbeec02 100644
--- a/docs/docs/integrations/vectorstores/pinecone.ipynb
+++ b/docs/docs/integrations/vectorstores/pinecone.ipynb
@@ -412,7 +412,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/qdrant.ipynb b/docs/docs/integrations/vectorstores/qdrant.ipynb
index 0ab58c72850c7..b4f2b5a428ad1 100644
--- a/docs/docs/integrations/vectorstores/qdrant.ipynb
+++ b/docs/docs/integrations/vectorstores/qdrant.ipynb
@@ -717,7 +717,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/redis.ipynb b/docs/docs/integrations/vectorstores/redis.ipynb
index 7cd3704b5d7c5..d43ec71cf4e3b 100644
--- a/docs/docs/integrations/vectorstores/redis.ipynb
+++ b/docs/docs/integrations/vectorstores/redis.ipynb
@@ -764,7 +764,7 @@
     "\n",
     "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
    ]
   },
   {
diff --git a/docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx b/docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx
index ee2662bc66cd8..74647e4b06b2d 100644
--- a/docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx
+++ b/docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx
@@ -8,7 +8,7 @@ The following may help resolve this error:
 
 - Double-check your prompt template to ensure that it is correct.
   - If you are using the default f-string format and you are using curly braces `{` anywhere in your template, they should be double escaped like this: `{{` (and if you want to render a double curly brace, you should use four curly braces: `{{{{`).
-- If you are using a [`MessagesPlaceholder`](/docs/concepts/#messagesplaceholder), make sure that you are passing in an array of messages or message-like objects.
+- If you are using a [`MessagesPlaceholder`](/docs/concepts/messages/#messagesplaceholder), make sure that you are passing in an array of messages or message-like objects.
   - If you are using shorthand tuples to declare your prompt template, make sure that the variable name is wrapped in curly braces (`["placeholder", "{messages}"]`).
 - Try viewing the inputs into your prompt template using [LangSmith](https://docs.smith.langchain.com/) or log statements to confirm they appear as expected.
 - If you are pulling a prompt from the [LangChain Prompt Hub](https://smith.langchain.com/prompts), try pulling and logging it or running it in isolation with a sample input to confirm that it is what you expect.
diff --git a/docs/docs/tutorials/agents.ipynb b/docs/docs/tutorials/agents.ipynb
index 6f688255e5670..fc920248943c4 100644
--- a/docs/docs/tutorials/agents.ipynb
+++ b/docs/docs/tutorials/agents.ipynb
@@ -25,9 +25,9 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Chat Models](/docs/concepts/#chat-models)\n",
-    "- [Tools](/docs/concepts/#tools)\n",
-    "- [Agents](/docs/concepts/#agents)\n",
+    "- [Chat Models](/docs/concepts/chat_models)\n",
+    "- [Tools](/docs/concepts/tools)\n",
+    "- [Agents](/docs/concepts/agents)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/tutorials/chatbot.ipynb b/docs/docs/tutorials/chatbot.ipynb
index dd6ab2544d037..afc8142c26400 100644
--- a/docs/docs/tutorials/chatbot.ipynb
+++ b/docs/docs/tutorials/chatbot.ipynb
@@ -29,9 +29,9 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Chat Models](/docs/concepts/#chat-models)\n",
-    "- [Prompt Templates](/docs/concepts/#prompt-templates)\n",
-    "- [Chat History](/docs/concepts/#chat-history)\n",
+    "- [Chat Models](/docs/concepts/chat_models)\n",
+    "- [Prompt Templates](/docs/concepts/prompt_templates)\n",
+    "- [Chat History](/docs/concepts/chat_history)\n",
     "\n",
     "This guide requires `langgraph >= 0.2.28`.\n",
     ":::\n",
diff --git a/docs/docs/tutorials/extraction.ipynb b/docs/docs/tutorials/extraction.ipynb
index 3fa1f7cabfa04..4470d47770cac 100644
--- a/docs/docs/tutorials/extraction.ipynb
+++ b/docs/docs/tutorials/extraction.ipynb
@@ -21,9 +21,9 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Chat Models](/docs/concepts/#chat-models)\n",
-    "- [Tools](/docs/concepts/#tools)\n",
-    "- [Tool calling](/docs/concepts/#function-tool-calling)\n",
+    "- [Chat Models](/docs/concepts/chat_models)\n",
+    "- [Tools](/docs/concepts/tools)\n",
+    "- [Tool calling](/docs/concepts/tool_calling)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/tutorials/llm_chain.ipynb b/docs/docs/tutorials/llm_chain.ipynb
index c698c34473da2..d9bd9500bce4c 100644
--- a/docs/docs/tutorials/llm_chain.ipynb
+++ b/docs/docs/tutorials/llm_chain.ipynb
@@ -21,11 +21,11 @@
     "\n",
     "After reading this tutorial, you'll have a high level overview of:\n",
     "\n",
-    "- Using [language models](/docs/concepts/#chat-models)\n",
+    "- Using [language models](/docs/concepts/chat_models)\n",
     "\n",
-    "- Using [PromptTemplates](/docs/concepts/#prompt-templates) and [OutputParsers](/docs/concepts/#output-parsers)\n",
+    "- Using [PromptTemplates](/docs/concepts/prompt_templates) and [OutputParsers](/docs/concepts/output_parsers)\n",
     "\n",
-    "- Using [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language-lcel) to chain components together\n",
+    "- Using [LangChain Expression Language (LCEL)](/docs/concepts/lcel) to chain components together\n",
     "\n",
     "- Debugging and tracing your application using [LangSmith](/docs/concepts/#langsmith)\n",
     "\n",
@@ -443,7 +443,7 @@
    "id": "0b19cecb",
    "metadata": {},
    "source": [
-    "This is a simple example of using [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language-lcel) to chain together LangChain modules. There are several benefits to this approach, including optimized streaming and tracing support.\n",
+    "This is a simple example of using [LangChain Expression Language (LCEL)](/docs/concepts/lcel) to chain together LangChain modules. There are several benefits to this approach, including optimized streaming and tracing support.\n",
     "\n",
     "If we take a look at the LangSmith trace, we can see all three components show up in the [LangSmith trace](https://smith.langchain.com/public/bc49bec0-6b13-4726-967f-dbd3448b786d/r)."
    ]
diff --git a/docs/docs/tutorials/local_rag.ipynb b/docs/docs/tutorials/local_rag.ipynb
index 01712fa94a5e4..d98b07732a513 100644
--- a/docs/docs/tutorials/local_rag.ipynb
+++ b/docs/docs/tutorials/local_rag.ipynb
@@ -11,9 +11,9 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Chat Models](/docs/concepts/#chat-models)\n",
+    "- [Chat Models](/docs/concepts/chat_models)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
-    "- [Embeddings](/docs/concepts/#embedding-models)\n",
+    "- [Embeddings](/docs/concepts/embedding_models)\n",
     "- [Vector stores](/docs/concepts/#vector-stores)\n",
     "- [Retrieval-augmented generation](/docs/tutorials/rag/)\n",
     "\n",
@@ -25,7 +25,7 @@
     "\n",
     "This guide will show how to run `LLaMA 3.1` via one provider, [Ollama](/docs/integrations/providers/ollama/) locally (e.g., on your laptop) using local embeddings and a local LLM. However, you can set up and swap in other local providers, such as [LlamaCPP](/docs/integrations/chat/llamacpp/) if you prefer.\n",
     "\n",
-    "**Note:** This guide uses a [chat model](/docs/concepts/#chat-models) wrapper that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models directly with a [text-in/text-out LLM](/docs/concepts/#llms) wrapper, you may need to use a prompt tailed for your specific model. This will often [require the inclusion of special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). [Here's an example for LLaMA 2](https://smith.langchain.com/hub/rlm/rag-prompt-llama).\n",
+    "**Note:** This guide uses a [chat model](/docs/concepts/chat_models) wrapper that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models directly with a [text-in/text-out LLM](/docs/concepts/text_llms) wrapper, you may need to use a prompt tailed for your specific model. This will often [require the inclusion of special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). [Here's an example for LLaMA 2](https://smith.langchain.com/hub/rlm/rag-prompt-llama).\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -445,7 +445,7 @@
     "\n",
     "- [Video: Reliable, fully local RAG agents with LLaMA 3](https://www.youtube.com/watch?v=-ROS6gfYIts) for an agentic approach to RAG with local models\n",
     "- [Video: Building Corrective RAG from scratch with open-source, local LLMs](https://www.youtube.com/watch?v=E2shqsYwxck)\n",
-    "- [Conceptual guide on retrieval](/docs/concepts/#retrieval) for an overview of various retrieval techniques you can apply to improve performance\n",
+    "- [Conceptual guide on retrieval](/docs/concepts/retrieval) for an overview of various retrieval techniques you can apply to improve performance\n",
     "- [How to guides on RAG](/docs/how_to/#qa-with-rag) for a deeper dive into different specifics around of RAG\n",
     "- [How to run models locally](/docs/how_to/local_llms/) for different approaches to setting up different providers"
    ]
diff --git a/docs/docs/tutorials/pdf_qa.ipynb b/docs/docs/tutorials/pdf_qa.ipynb
index 32dd23c0919e3..d5af436708abf 100644
--- a/docs/docs/tutorials/pdf_qa.ipynb
+++ b/docs/docs/tutorials/pdf_qa.ipynb
@@ -23,9 +23,9 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Document loaders](/docs/concepts/#document-loaders)\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [Embeddings](/docs/concepts/#embedding-models)\n",
+    "- [Document loaders](/docs/concepts/document_loaders)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [Embeddings](/docs/concepts/embedding_models)\n",
     "- [Vector stores](/docs/concepts/#vector-stores)\n",
     "- [Retrieval-augmented generation](/docs/tutorials/rag/)\n",
     "\n",
@@ -33,7 +33,7 @@
     "\n",
     "PDF files often hold crucial unstructured data unavailable from other sources. They can be quite lengthy, and unlike plain text files, cannot generally be fed directly into the prompt of a language model.\n",
     "\n",
-    "In this tutorial, you'll create a system that can answer questions about PDF files. More specifically, you'll use a [Document Loader](/docs/concepts/#document-loaders) to load text in a format usable by an LLM, then build a retrieval-augmented generation (RAG) pipeline to answer questions, including citations from the source material.\n",
+    "In this tutorial, you'll create a system that can answer questions about PDF files. More specifically, you'll use a [Document Loader](/docs/concepts/document_loaders) to load text in a format usable by an LLM, then build a retrieval-augmented generation (RAG) pipeline to answer questions, including citations from the source material.\n",
     "\n",
     "This tutorial will gloss over some concepts more deeply covered in our [RAG](/docs/tutorials/rag/) tutorial, so you may want to go through those first if you haven't already.\n",
     "\n",
@@ -111,13 +111,13 @@
     "\n",
     "- The loader reads the PDF at the specified path into memory.\n",
     "- It then extracts text data using the `pypdf` package.\n",
-    "- Finally, it creates a LangChain [Document](/docs/concepts/#documents) for each page of the PDF with the page's content and some metadata about where in the document the text came from.\n",
+    "- Finally, it creates a LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) for each page of the PDF with the page's content and some metadata about where in the document the text came from.\n",
     "\n",
     "LangChain has [many other document loaders](/docs/integrations/document_loaders/) for other data sources, or you can create a [custom document loader](/docs/how_to/document_loader_custom/).\n",
     "\n",
     "## Question answering with RAG\n",
     "\n",
-    "Next, you'll prepare the loaded documents for later retrieval. Using a [text splitter](/docs/concepts/#text-splitters), you'll split your loaded documents into smaller documents that can more easily fit into an LLM's context window, then load them into a [vector store](/docs/concepts/#vector-stores). You can then create a [retriever](/docs/concepts/#retrievers) from the vector store for use in our RAG chain:\n",
+    "Next, you'll prepare the loaded documents for later retrieval. Using a [text splitter](/docs/concepts/text_splitters), you'll split your loaded documents into smaller documents that can more easily fit into an LLM's context window, then load them into a [vector store](/docs/concepts/#vector-stores). You can then create a [retriever](/docs/concepts/retrievers) from the vector store for use in our RAG chain:\n",
     "\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
@@ -315,7 +315,7 @@
     "\n",
     "For more on document loaders, you can check out:\n",
     "\n",
-    "- [The entry in the conceptual guide](/docs/concepts/#document-loaders)\n",
+    "- [The entry in the conceptual guide](/docs/concepts/document_loaders)\n",
     "- [Related how-to guides](/docs/how_to/#document-loaders)\n",
     "- [Available integrations](/docs/integrations/document_loaders/)\n",
     "- [How to create a custom document loader](/docs/how_to/document_loader_custom/)\n",
diff --git a/docs/docs/tutorials/qa_chat_history.ipynb b/docs/docs/tutorials/qa_chat_history.ipynb
index 72cde13832a64..a74959d05b35d 100644
--- a/docs/docs/tutorials/qa_chat_history.ipynb
+++ b/docs/docs/tutorials/qa_chat_history.ipynb
@@ -21,13 +21,13 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Chat history](/docs/concepts/#chat-history)\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [Embeddings](/docs/concepts/#embedding-models)\n",
+    "- [Chat history](/docs/concepts/chat_history)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [Embeddings](/docs/concepts/embedding_models)\n",
     "- [Vector stores](/docs/concepts/#vector-stores)\n",
     "- [Retrieval-augmented generation](/docs/tutorials/rag/)\n",
-    "- [Tools](/docs/concepts/#tools)\n",
-    "- [Agents](/docs/concepts/#agents)\n",
+    "- [Tools](/docs/concepts/tools)\n",
+    "- [Agents](/docs/concepts/agents)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/tutorials/query_analysis.ipynb b/docs/docs/tutorials/query_analysis.ipynb
index 29c70c26f335f..102febcf1aa4d 100644
--- a/docs/docs/tutorials/query_analysis.ipynb
+++ b/docs/docs/tutorials/query_analysis.ipynb
@@ -21,11 +21,11 @@
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [Document loaders](/docs/concepts/#document-loaders)\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [Embeddings](/docs/concepts/#embedding-models)\n",
+    "- [Document loaders](/docs/concepts/document_loaders)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [Embeddings](/docs/concepts/embedding_models)\n",
     "- [Vector stores](/docs/concepts/#vector-stores)\n",
-    "- [Retrieval](/docs/concepts/#retrieval)\n",
+    "- [Retrieval](/docs/concepts/retrieval)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/tutorials/rag.ipynb b/docs/docs/tutorials/rag.ipynb
index dcd60fbc55223..dd2299346df13 100644
--- a/docs/docs/tutorials/rag.ipynb
+++ b/docs/docs/tutorials/rag.ipynb
@@ -17,7 +17,7 @@
     "complexity.\n",
     "\n",
     "If you're already familiar with basic retrieval, you might also be interested in\n",
-    "this [high-level overview of different retrieval techinques](/docs/concepts/#retrieval).\n",
+    "this [high-level overview of different retrieval techinques](/docs/concepts/retrieval).\n",
     "\n",
     "## What is RAG?\n",
     "\n",
@@ -39,15 +39,15 @@
     "The most common full sequence from raw data to answer looks like:\n",
     "\n",
     "### Indexing\n",
-    "1. **Load**: First we need to load our data. This is done with [Document Loaders](/docs/concepts/#document-loaders).\n",
-    "2. **Split**: [Text splitters](/docs/concepts/#text-splitters) break large `Documents` into smaller chunks. This is useful both for indexing data and for passing it in to a model, since large chunks are harder to search over and won't fit in a model's finite context window.\n",
-    "3. **Store**: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a [VectorStore](/docs/concepts/#vector-stores) and [Embeddings](/docs/concepts/#embedding-models) model.\n",
+    "1. **Load**: First we need to load our data. This is done with [Document Loaders](/docs/concepts/document_loaders).\n",
+    "2. **Split**: [Text splitters](/docs/concepts/text_splitters) break large `Documents` into smaller chunks. This is useful both for indexing data and for passing it in to a model, since large chunks are harder to search over and won't fit in a model's finite context window.\n",
+    "3. **Store**: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a [VectorStore](/docs/concepts/#vector-stores) and [Embeddings](/docs/concepts/embedding_models) model.\n",
     "\n",
     "![index_diagram](../../static/img/rag_indexing.png)\n",
     "\n",
     "### Retrieval and generation\n",
-    "4. **Retrieve**: Given a user input, relevant splits are retrieved from storage using a [Retriever](/docs/concepts/#retrievers).\n",
-    "5. **Generate**: A [ChatModel](/docs/concepts/#chat-models) / [LLM](/docs/concepts/#llms) produces an answer using a prompt that includes the question and the retrieved data\n",
+    "4. **Retrieve**: Given a user input, relevant splits are retrieved from storage using a [Retriever](/docs/concepts/retrievers).\n",
+    "5. **Generate**: A [ChatModel](/docs/concepts/chat_models) / [LLM](/docs/concepts/text_llms) produces an answer using a prompt that includes the question and the retrieved data\n",
     "\n",
     "![retrieval_diagram](../../static/img/rag_retrieval_generation.png)\n",
     "\n",
@@ -946,7 +946,7 @@
     "- [Return sources](/docs/how_to/qa_sources): Learn how to return source documents\n",
     "- [Streaming](/docs/how_to/streaming): Learn how to stream outputs and intermediate steps\n",
     "- [Add chat history](/docs/how_to/message_history): Learn how to add chat history to your app\n",
-    "- [Retrieval conceptual guide](/docs/concepts/#retrieval): A high-level overview of specific retrieval techniques\n",
+    "- [Retrieval conceptual guide](/docs/concepts/retrieval): A high-level overview of specific retrieval techniques\n",
     "- [Build a local RAG application](/docs/tutorials/local_rag): Create an app similar to the one above using all local components"
    ]
   }
diff --git a/docs/docs/tutorials/retrievers.ipynb b/docs/docs/tutorials/retrievers.ipynb
index acec0cc303f17..2eefbdd7499e1 100644
--- a/docs/docs/tutorials/retrievers.ipynb
+++ b/docs/docs/tutorials/retrievers.ipynb
@@ -309,7 +309,7 @@
     "\n",
     "## Retrievers\n",
     "\n",
-    "LangChain `VectorStore` objects do not subclass [Runnable](https://python.langchain.com/api_reference/core/index.html#module-langchain_core.runnables), and so cannot immediately be integrated into LangChain Expression Language [chains](/docs/concepts/#langchain-expression-language-lcel).\n",
+    "LangChain `VectorStore` objects do not subclass [Runnable](https://python.langchain.com/api_reference/core/index.html#module-langchain_core.runnables), and so cannot immediately be integrated into LangChain Expression Language [chains](/docs/concepts/lcel).\n",
     "\n",
     "LangChain [Retrievers](https://python.langchain.com/api_reference/core/index.html#module-langchain_core.retrievers) are Runnables, so they implement a standard set of methods (e.g., synchronous and asynchronous `invoke` and `batch` operations) and are designed to be incorporated in LCEL chains.\n",
     "\n",
diff --git a/docs/docs/tutorials/sql_qa.ipynb b/docs/docs/tutorials/sql_qa.ipynb
index 529bfaac49b02..2ba4c6acbb2ab 100644
--- a/docs/docs/tutorials/sql_qa.ipynb
+++ b/docs/docs/tutorials/sql_qa.ipynb
@@ -11,9 +11,9 @@
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [Tools](/docs/concepts/#tools)\n",
-    "- [Agents](/docs/concepts/#agents)\n",
+    "- [Chat models](/docs/concepts/chat_models)\n",
+    "- [Tools](/docs/concepts/tools)\n",
+    "- [Agents](/docs/concepts/agents)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/tutorials/summarization.ipynb b/docs/docs/tutorials/summarization.ipynb
index 7e178db1b110e..c63f4c6479140 100644
--- a/docs/docs/tutorials/summarization.ipynb
+++ b/docs/docs/tutorials/summarization.ipynb
@@ -52,9 +52,9 @@
     "\n",
     "Concepts we will cover are:\n",
     "\n",
-    "- Using [language models](/docs/concepts/#chat-models).\n",
+    "- Using [language models](/docs/concepts/chat_models).\n",
     "\n",
-    "- Using [document loaders](/docs/concepts/#document-loaders), specifically the [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load content from an HTML webpage.\n",
+    "- Using [document loaders](/docs/concepts/document_loaders), specifically the [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load content from an HTML webpage.\n",
     "\n",
     "- Two ways to summarize or otherwise combine documents.\n",
     "  1. [Stuff](/docs/tutorials/summarization#stuff), which simply concatenates documents into a prompt;\n",
diff --git a/docs/docs/versions/migrating_chains/constitutional_chain.ipynb b/docs/docs/versions/migrating_chains/constitutional_chain.ipynb
index 06adc77c9b95e..66ac36f687cb3 100644
--- a/docs/docs/versions/migrating_chains/constitutional_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/constitutional_chain.ipynb
@@ -15,7 +15,7 @@
     "\n",
     "- Leverage tool-calling capabilities of chat models that have been fine-tuned for this purpose;\n",
     "- Reduce parsing errors from extracting expression from a string LLM response;\n",
-    "- Delegation of instructions to [message roles](/docs/concepts/#messages) (e.g., chat models can understand what a `ToolMessage` represents without the need for additional prompting);\n",
+    "- Delegation of instructions to [message roles](/docs/concepts/messages) (e.g., chat models can understand what a `ToolMessage` represents without the need for additional prompting);\n",
     "- Support for streaming, both of individual tokens and chain steps."
    ]
   },
diff --git a/docs/docs/versions/migrating_chains/conversation_chain.ipynb b/docs/docs/versions/migrating_chains/conversation_chain.ipynb
index 87af17a655871..781505daca103 100644
--- a/docs/docs/versions/migrating_chains/conversation_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/conversation_chain.ipynb
@@ -243,7 +243,7 @@
     "\n",
     "See [this tutorial](/docs/tutorials/chatbot) for a more end-to-end guide on building with [`RunnableWithMessageHistory`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html).\n",
     "\n",
-    "Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information."
+    "Check out the [LCEL conceptual docs](/docs/concepts/lcel) for more background information."
    ]
   }
  ],
diff --git a/docs/docs/versions/migrating_chains/conversation_retrieval_chain.ipynb b/docs/docs/versions/migrating_chains/conversation_retrieval_chain.ipynb
index 31930082875d9..2e1db7be5df28 100644
--- a/docs/docs/versions/migrating_chains/conversation_retrieval_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/conversation_retrieval_chain.ipynb
@@ -254,7 +254,7 @@
     "\n",
     "You've now seen how to migrate existing usage of some legacy chains to LCEL.\n",
     "\n",
-    "Next, check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information."
+    "Next, check out the [LCEL conceptual docs](/docs/concepts/lcel) for more background information."
    ]
   },
   {
diff --git a/docs/docs/versions/migrating_chains/index.ipynb b/docs/docs/versions/migrating_chains/index.ipynb
index 9d9ddbba0cf0f..7a46b74d67d28 100644
--- a/docs/docs/versions/migrating_chains/index.ipynb
+++ b/docs/docs/versions/migrating_chains/index.ipynb
@@ -41,7 +41,7 @@
     "LangChain maintains a number of legacy abstractions. Many of these can be reimplemented via short combinations of LCEL and LangGraph primitives.\n",
     "\n",
     "### LCEL\n",
-    "[LCEL](/docs/concepts/#langchain-expression-language-lcel) is designed to streamline the process of building useful apps with LLMs and combining related components. It does this by providing:\n",
+    "[LCEL](/docs/concepts/lcel) is designed to streamline the process of building useful apps with LLMs and combining related components. It does this by providing:\n",
     "\n",
     "1. **A unified interface**: Every LCEL object implements the `Runnable` interface, which defines a common set of invocation methods (`invoke`, `batch`, `stream`, `ainvoke`, ...). This makes it possible to also automatically and consistently support useful operations like streaming of intermediate steps and batching, since every chain composed of LCEL objects is itself an LCEL object.\n",
     "2. **Composition primitives**: LCEL provides a number of primitives that make it easy to compose chains, parallelize components, add fallbacks, dynamically configure chain internals, and more.\n",
@@ -75,7 +75,7 @@
     "- [LLMMathChain](./llm_math_chain.ipynb)\n",
     "- [ConstitutionalChain](./constitutional_chain.ipynb)\n",
     "\n",
-    "Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) and [LangGraph docs](https://langchain-ai.github.io/langgraph/) for more background information."
+    "Check out the [LCEL conceptual docs](/docs/concepts/lcel) and [LangGraph docs](https://langchain-ai.github.io/langgraph/) for more background information."
    ]
   }
  ],
diff --git a/docs/docs/versions/migrating_chains/llm_chain.ipynb b/docs/docs/versions/migrating_chains/llm_chain.ipynb
index a4addea4a4cdc..6069a7ccfbf61 100644
--- a/docs/docs/versions/migrating_chains/llm_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/llm_chain.ipynb
@@ -200,7 +200,7 @@
     "\n",
     "See [this tutorial](/docs/tutorials/llm_chain) for more detail on building with prompt templates, LLMs, and output parsers.\n",
     "\n",
-    "Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information."
+    "Check out the [LCEL conceptual docs](/docs/concepts/lcel) for more background information."
    ]
   }
  ],
diff --git a/docs/docs/versions/migrating_chains/llm_math_chain.ipynb b/docs/docs/versions/migrating_chains/llm_math_chain.ipynb
index 2697d02fd175c..36bbd884cfea6 100644
--- a/docs/docs/versions/migrating_chains/llm_math_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/llm_math_chain.ipynb
@@ -9,11 +9,11 @@
     "\n",
     "[`LLMMathChain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.llm_math.base.LLMMathChain.html) enabled the evaluation of mathematical expressions generated by a LLM. Instructions for generating the expressions were formatted into the prompt, and the expressions were parsed out of the string response before evaluation using the [numexpr](https://numexpr.readthedocs.io/en/latest/user_guide.html) library.\n",
     "\n",
-    "This is more naturally achieved via [tool calling](/docs/concepts/#functiontool-calling). We can equip a chat model with a simple calculator tool leveraging `numexpr` and construct a simple chain around it using [LangGraph](https://langchain-ai.github.io/langgraph/). Some advantages of this approach include:\n",
+    "This is more naturally achieved via [tool calling](/docs/concepts/tool_calling). We can equip a chat model with a simple calculator tool leveraging `numexpr` and construct a simple chain around it using [LangGraph](https://langchain-ai.github.io/langgraph/). Some advantages of this approach include:\n",
     "\n",
     "- Leverage tool-calling capabilities of chat models that have been fine-tuned for this purpose;\n",
     "- Reduce parsing errors from extracting expression from a string LLM response;\n",
-    "- Delegation of instructions to [message roles](/docs/concepts/#messages) (e.g., chat models can understand what a `ToolMessage` represents without the need for additional prompting);\n",
+    "- Delegation of instructions to [message roles](/docs/concepts/messages) (e.g., chat models can understand what a `ToolMessage` represents without the need for additional prompting);\n",
     "- Support for streaming, both of individual tokens and chain steps."
    ]
   },
diff --git a/docs/docs/versions/migrating_chains/llm_router_chain.ipynb b/docs/docs/versions/migrating_chains/llm_router_chain.ipynb
index 5e37f2874c8d8..24391baaf290f 100644
--- a/docs/docs/versions/migrating_chains/llm_router_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/llm_router_chain.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "The [`LLMRouterChain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.router.llm_router.LLMRouterChain.html) routed an input query to one of multiple destinations-- that is, given an input query, it used a LLM to select from a list of destination chains, and passed its inputs to the selected chain.\n",
     "\n",
-    "`LLMRouterChain` does not support common [chat model](/docs/concepts/#chat-models) features, such as message roles and [tool calling](/docs/concepts/#functiontool-calling). Under the hood, `LLMRouterChain` routes a query by instructing the LLM to generate JSON-formatted text, and parsing out the intended destination.\n",
+    "`LLMRouterChain` does not support common [chat model](/docs/concepts/chat_models) features, such as message roles and [tool calling](/docs/concepts/tool_calling). Under the hood, `LLMRouterChain` routes a query by instructing the LLM to generate JSON-formatted text, and parsing out the intended destination.\n",
     "\n",
     "Consider an example from a [MultiPromptChain](/docs/versions/migrating_chains/multi_prompt_chain), which uses `LLMRouterChain`. Below is an (example) default prompt:"
    ]
@@ -240,7 +240,7 @@
     "\n",
     "See [this tutorial](/docs/tutorials/llm_chain) for more detail on building with prompt templates, LLMs, and output parsers.\n",
     "\n",
-    "Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information."
+    "Check out the [LCEL conceptual docs](/docs/concepts/lcel) for more background information."
    ]
   },
   {
diff --git a/docs/docs/versions/migrating_chains/map_rerank_docs_chain.ipynb b/docs/docs/versions/migrating_chains/map_rerank_docs_chain.ipynb
index 6b979eeaed937..f52e68e2bd9b5 100644
--- a/docs/docs/versions/migrating_chains/map_rerank_docs_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/map_rerank_docs_chain.ipynb
@@ -15,7 +15,7 @@
     "\n",
     "A common process in this scenario is question-answering using pieces of context from a document. Forcing the model to generate score along with its answer helps to select for answers generated only by relevant context.\n",
     "\n",
-    "An [LangGraph](https://langchain-ai.github.io/langgraph/) implementation allows for the incorporation of [tool calling](/docs/concepts/#functiontool-calling) and other features for this problem. Below we will go through both `MapRerankDocumentsChain` and a corresponding LangGraph implementation on a simple example for illustrative purposes."
+    "An [LangGraph](https://langchain-ai.github.io/langgraph/) implementation allows for the incorporation of [tool calling](/docs/concepts/tool_calling) and other features for this problem. Below we will go through both `MapRerankDocumentsChain` and a corresponding LangGraph implementation on a simple example for illustrative purposes."
    ]
   },
   {
diff --git a/docs/docs/versions/migrating_chains/multi_prompt_chain.ipynb b/docs/docs/versions/migrating_chains/multi_prompt_chain.ipynb
index 6c987880357c6..0efaee8b91f66 100644
--- a/docs/docs/versions/migrating_chains/multi_prompt_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/multi_prompt_chain.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "The [`MultiPromptChain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.router.multi_prompt.MultiPromptChain.html) routed an input query to one of multiple LLMChains-- that is, given an input query, it used a LLM to select from a list of prompts, formatted the query into the prompt, and generated a response.\n",
     "\n",
-    "`MultiPromptChain` does not support common [chat model](/docs/concepts/#chat-models) features, such as message roles and [tool calling](/docs/concepts/#functiontool-calling).\n",
+    "`MultiPromptChain` does not support common [chat model](/docs/concepts/chat_models) features, such as message roles and [tool calling](/docs/concepts/tool_calling).\n",
     "\n",
     "A [LangGraph](https://langchain-ai.github.io/langgraph/) implementation confers a number of advantages for this problem:\n",
     "\n",
diff --git a/docs/docs/versions/migrating_chains/refine_docs_chain.ipynb b/docs/docs/versions/migrating_chains/refine_docs_chain.ipynb
index 80ee5afbac056..6b8b3549a1251 100644
--- a/docs/docs/versions/migrating_chains/refine_docs_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/refine_docs_chain.ipynb
@@ -20,7 +20,7 @@
     "\n",
     "- Where `RefineDocumentsChain` refines the summary via a `for` loop inside the class, a LangGraph implementation lets you step through the execution to monitor or otherwise steer it if needed.\n",
     "- The LangGraph implementation supports streaming of both execution steps and individual tokens.\n",
-    "- Because it is assembled from modular components, it is also simple to extend or modify (e.g., to incorporate [tool calling](/docs/concepts/#functiontool-calling) or other behavior).\n",
+    "- Because it is assembled from modular components, it is also simple to extend or modify (e.g., to incorporate [tool calling](/docs/concepts/tool_calling) or other behavior).\n",
     "\n",
     "Below we will go through both `RefineDocumentsChain` and a corresponding LangGraph implementation on a simple example for illustrative purposes.\n",
     "\n",
diff --git a/docs/docs/versions/migrating_chains/retrieval_qa.ipynb b/docs/docs/versions/migrating_chains/retrieval_qa.ipynb
index fec2a75f1b435..64ec58b3eb039 100644
--- a/docs/docs/versions/migrating_chains/retrieval_qa.ipynb
+++ b/docs/docs/versions/migrating_chains/retrieval_qa.ipynb
@@ -226,7 +226,7 @@
     "\n",
     "## Next steps\n",
     "\n",
-    "Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information on the LangChain expression language."
+    "Check out the [LCEL conceptual docs](/docs/concepts/lcel) for more background information on the LangChain expression language."
    ]
   }
  ],
diff --git a/docs/docs/versions/migrating_chains/stuff_docs_chain.ipynb b/docs/docs/versions/migrating_chains/stuff_docs_chain.ipynb
index c09b0279b65fa..3d2a97b3a9700 100644
--- a/docs/docs/versions/migrating_chains/stuff_docs_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/stuff_docs_chain.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "[StuffDocumentsChain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.StuffDocumentsChain.html) combines documents by concatenating them into a single context window. It is a straightforward and effective strategy for combining documents for question-answering, summarization, and other purposes.\n",
     "\n",
-    "[create_stuff_documents_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) is the recommended alternative. It functions the same as `StuffDocumentsChain`, with better support for streaming and batch functionality. Because it is a simple combination of [LCEL primitives](/docs/concepts/#langchain-expression-language-lcel), it is also easier to extend and incorporate into other LangChain applications.\n",
+    "[create_stuff_documents_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) is the recommended alternative. It functions the same as `StuffDocumentsChain`, with better support for streaming and batch functionality. Because it is a simple combination of [LCEL primitives](/docs/concepts/lcel), it is also easier to extend and incorporate into other LangChain applications.\n",
     "\n",
     "Below we will go through both `StuffDocumentsChain` and `create_stuff_documents_chain` on a simple example for illustrative purposes.\n",
     "\n",
@@ -245,7 +245,7 @@
     "\n",
     "## Next steps\n",
     "\n",
-    "Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information.\n",
+    "Check out the [LCEL conceptual docs](/docs/concepts/lcel) for more background information.\n",
     "\n",
     "See these [how-to guides](/docs/how_to/#qa-with-rag) for more on question-answering tasks with RAG.\n",
     "\n",
diff --git a/docs/docs/versions/migrating_memory/chat_history.ipynb b/docs/docs/versions/migrating_memory/chat_history.ipynb
index fc164ee13580e..164e897369ffd 100644
--- a/docs/docs/versions/migrating_memory/chat_history.ipynb
+++ b/docs/docs/versions/migrating_memory/chat_history.ipynb
@@ -10,7 +10,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "* [Chat History](/docs/concepts/#chat-history)\n",
+    "* [Chat History](/docs/concepts/chat_history)\n",
     "* [RunnableWithMessageHistory](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html)\n",
     "* [LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/)\n",
     "* [Memory](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#memory)\n",
@@ -236,7 +236,7 @@
     "\n",
     "This how-to guide used the `messages` and `add_messages` interface of `BaseChatMessageHistory` directly. \n",
     "\n",
-    "Alternatively, you can use [RunnableWithMessageHistory](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html), as [LCEL](/docs/concepts/#langchain-expression-language-lcel/) can be used inside any [LangGraph node](https://langchain-ai.github.io/langgraph/concepts/low_level/#nodes).\n",
+    "Alternatively, you can use [RunnableWithMessageHistory](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html), as [LCEL](/docs/concepts/lcel/) can be used inside any [LangGraph node](https://langchain-ai.github.io/langgraph/concepts/low_level/#nodes).\n",
     "\n",
     "To do that replace the following code:\n",
     "\n",
diff --git a/docs/docs/versions/migrating_memory/index.mdx b/docs/docs/versions/migrating_memory/index.mdx
index fbd7cd6d3e54b..8b8e6681338da 100644
--- a/docs/docs/versions/migrating_memory/index.mdx
+++ b/docs/docs/versions/migrating_memory/index.mdx
@@ -18,7 +18,7 @@ The main advantages of persistence in LangGraph are:
   - Error recovery
   - Allowing human intervention in AI workflows
   - Exploring different conversation paths ("time travel")
-- Full compatibility with both traditional [language models](/docs/concepts/#llms) and modern [chat models](/docs/concepts/#chat-models). Early memory implementations in LangChain weren't designed for newer chat model APIs, causing issues with features like tool-calling. LangGraph memory can persist any custom state.
+- Full compatibility with both traditional [language models](/docs/concepts/text_llms) and modern [chat models](/docs/concepts/chat_models). Early memory implementations in LangChain weren't designed for newer chat model APIs, causing issues with features like tool-calling. LangGraph memory can persist any custom state.
 - Highly customizable, allowing you to fully control how memory works and use different storage backends.
 
 ## Evolution of memory in LangChain

From 217de4e6a6f26431e17e64269a1457d4a6f32be8 Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Wed, 23 Oct 2024 08:35:15 -0700
Subject: [PATCH 51/84] langchain[patch]: de-beta init_chat_model (#27558)

---
 libs/langchain/langchain/chat_models/base.py | 55 +++++++++++---------
 1 file changed, 29 insertions(+), 26 deletions(-)

diff --git a/libs/langchain/langchain/chat_models/base.py b/libs/langchain/langchain/chat_models/base.py
index ca4978ec3d215..95720a92032c8 100644
--- a/libs/langchain/langchain/chat_models/base.py
+++ b/libs/langchain/langchain/chat_models/base.py
@@ -19,7 +19,6 @@
     overload,
 )
 
-from langchain_core._api import beta
 from langchain_core.language_models import (
     BaseChatModel,
     LanguageModelInput,
@@ -83,7 +82,6 @@ def init_chat_model(
 # FOR CONTRIBUTORS: If adding support for a new provider, please append the provider
 # name to the supported list in the docstring below. Do *not* change the order of the
 # existing providers.
-@beta()
 def init_chat_model(
     model: Optional[str] = None,
     *,
@@ -96,38 +94,39 @@ def init_chat_model(
 ) -> Union[BaseChatModel, _ConfigurableModel]:
     """Initialize a ChatModel from the model name and provider.
 
-    Must have the integration package corresponding to the model provider installed.
+    **Note:** Must have the integration package corresponding to the model provider
+    installed.
 
     Args:
         model: The name of the model, e.g. "gpt-4o", "claude-3-opus-20240229".
         model_provider: The model provider. Supported model_provider values and the
-            corresponding integration package:
-
-            - openai (langchain-openai)
-            - anthropic (langchain-anthropic)
-            - azure_openai (langchain-openai)
-            - google_vertexai (langchain-google-vertexai)
-            - google_genai (langchain-google-genai)
-            - bedrock (langchain-aws)
-            - bedrock_converse (langchain-aws)
-            - cohere (langchain-cohere)
-            - fireworks (langchain-fireworks)
-            - together (langchain-together)
-            - mistralai (langchain-mistralai)
-            - huggingface (langchain-huggingface)
-            - groq (langchain-groq)
-            - ollama (langchain-ollama)  [support added in langchain==0.2.12]
+            corresponding integration package are:
+
+            - 'openai'              -> langchain-openai
+            - 'anthropic'           -> langchain-anthropic
+            - 'azure_openai'        -> langchain-openai
+            - 'google_vertexai'     -> langchain-google-vertexai
+            - 'google_genai'        -> langchain-google-genai
+            - 'bedrock'             -> langchain-aws
+            - 'bedrock_converse'    -> langchain-aws
+            - 'cohere'              -> langchain-cohere
+            - 'fireworks'           -> langchain-fireworks
+            - 'together'            -> langchain-together
+            - 'mistralai'           -> langchain-mistralai
+            - 'huggingface'         -> langchain-huggingface
+            - 'groq'                -> langchain-groq
+            - 'ollama'              -> langchain-ollama
 
             Will attempt to infer model_provider from model if not specified. The
             following providers will be inferred based on these model prefixes:
 
-            - gpt-3..., gpt-4..., or o1... -> openai
-            - claude... -> anthropic
-            - amazon.... -> bedrock
-            - gemini... -> google_vertexai
-            - command... -> cohere
-            - accounts/fireworks... -> fireworks
-            - mistral... -> mistralai
+            - 'gpt-3...' | 'gpt-4...' | 'o1...' -> 'openai'
+            - 'claude...'                       -> 'anthropic'
+            - 'amazon....'                      -> 'bedrock'
+            - 'gemini...'                       -> 'google_vertexai'
+            - 'command...'                      -> 'cohere'
+            - 'accounts/fireworks...'           -> 'fireworks'
+            - 'mistral...'                      -> 'mistralai'
         configurable_fields: Which model parameters are
             configurable:
 
@@ -286,6 +285,10 @@ class GetPopulation(BaseModel):
         Support for langchain_aws.ChatBedrockConverse added
         (model_provider="bedrock_converse").
 
+    .. versionchanged:: 0.3.5
+
+        Out of beta.
+
     """  # noqa: E501
     if not model and not configurable_fields:
         configurable_fields = ("model", "model_provider")

From 968dccee0458d78db2a60c9c30d3dfb041399af4 Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Wed, 23 Oct 2024 12:27:06 -0700
Subject: [PATCH 52/84] core[patch]: convert_to_openai_tool Anthropic support
 (#27591)

---
 libs/core/langchain_core/messages/utils.py    |  4 +-
 .../langchain_core/utils/function_calling.py  | 52 ++++++++++++-------
 .../unit_tests/utils/test_function_calling.py | 22 ++++++++
 3 files changed, 56 insertions(+), 22 deletions(-)

diff --git a/libs/core/langchain_core/messages/utils.py b/libs/core/langchain_core/messages/utils.py
index 2399639341202..9bf617af17d48 100644
--- a/libs/core/langchain_core/messages/utils.py
+++ b/libs/core/langchain_core/messages/utils.py
@@ -883,8 +883,6 @@ def convert_to_openai_messages(
 ) -> Union[dict, list[dict]]:
     """Convert LangChain messages into OpenAI message dicts.
 
-    .. versionadded:: 0.3.11
-
     Args:
         messages: Message-like object or iterable of objects whose contents are
             in OpenAI, Anthropic, Bedrock Converse, or VertexAI formats.
@@ -937,6 +935,8 @@ def convert_to_openai_messages(
             #   {'role': 'assistant', 'content': 'thats nice'}
             # ]
 
+    .. versionadded:: 0.3.11
+
     """  # noqa: E501
     if text_format not in ("string", "block"):
         err = f"Unrecognized {text_format=}, expected one of 'string' or 'block'."
diff --git a/libs/core/langchain_core/utils/function_calling.py b/libs/core/langchain_core/utils/function_calling.py
index 9bb62c92b9e20..e06aa04266d3d 100644
--- a/libs/core/langchain_core/utils/function_calling.py
+++ b/libs/core/langchain_core/utils/function_calling.py
@@ -336,30 +336,32 @@ def convert_to_openai_function(
     strict: Optional[bool] = None,
 ) -> dict[str, Any]:
     """Convert a raw function/class to an OpenAI function.
-
-    .. versionchanged:: 0.2.29
-
-        ``strict`` arg added.
-
     Args:
         function:
             A dictionary, Pydantic BaseModel class, TypedDict class, a LangChain
             Tool object, or a Python function. If a dictionary is passed in, it is
-            assumed to already be a valid OpenAI function or a JSON schema with
-            top-level 'title' and 'description' keys specified.
+            assumed to already be a valid OpenAI function, a JSON schema with
+            top-level 'title' and 'description' keys specified, or an Anthropic format
+            tool.
         strict:
             If True, model output is guaranteed to exactly match the JSON Schema
             provided in the function definition. If None, ``strict`` argument will not
             be included in function definition.
 
-            .. versionadded:: 0.2.29
-
     Returns:
         A dict version of the passed in function which is compatible with the OpenAI
         function-calling API.
 
     Raises:
         ValueError: If function is not in a supported format.
+
+    .. versionchanged:: 0.2.29
+
+        ``strict`` arg added.
+
+    .. versionchanged:: 0.3.13
+
+        Support for Anthropic format tools added.
     """
     from langchain_core.tools import BaseTool
 
@@ -378,6 +380,15 @@ def convert_to_openai_function(
             "description": function.pop("description"),
             "parameters": function,
         }
+    # an Anthropic format tool
+    elif isinstance(function, dict) and all(
+        k in function for k in ("name", "description", "input_schema")
+    ):
+        oai_function = {
+            "name": function["name"],
+            "description": function["description"],
+            "parameters": function["input_schema"],
+        }
     elif isinstance(function, type) and is_basemodel_subclass(function):
         oai_function = cast(dict, convert_pydantic_to_openai_function(function))
     elif is_typeddict(function):
@@ -414,34 +425,35 @@ def convert_to_openai_tool(
     *,
     strict: Optional[bool] = None,
 ) -> dict[str, Any]:
-    """Convert a raw function/class to an OpenAI tool.
-
-    .. versionchanged:: 0.2.29
-
-        ``strict`` arg added.
+    """Convert a tool-like object to an OpenAI tool schema.
 
     Args:
         tool:
             Either a dictionary, a pydantic.BaseModel class, Python function, or
             BaseTool. If a dictionary is passed in, it is assumed to already be a valid
-            OpenAI tool, OpenAI function, or a JSON schema with top-level 'title' and
-            'description' keys specified.
+            OpenAI tool, OpenAI function, a JSON schema with top-level 'title' and
+            'description' keys specified, or an Anthropic format tool.
         strict:
             If True, model output is guaranteed to exactly match the JSON Schema
             provided in the function definition. If None, ``strict`` argument will not
             be included in tool definition.
 
-            .. versionadded:: 0.2.29
-
     Returns:
         A dict version of the passed in tool which is compatible with the
         OpenAI tool-calling API.
+
+    .. versionchanged:: 0.2.29
+
+        ``strict`` arg added.
+
+    .. versionchanged:: 0.3.13
+
+        Support for Anthropic format tools added.
     """
     if isinstance(tool, dict) and tool.get("type") == "function" and "function" in tool:
         return tool
     oai_function = convert_to_openai_function(tool, strict=strict)
-    oai_tool: dict[str, Any] = {"type": "function", "function": oai_function}
-    return oai_tool
+    return {"type": "function", "function": oai_function}
 
 
 def tool_example_to_messages(
diff --git a/libs/core/tests/unit_tests/utils/test_function_calling.py b/libs/core/tests/unit_tests/utils/test_function_calling.py
index bd4694e0d52b7..a74ddf7ec15b3 100644
--- a/libs/core/tests/unit_tests/utils/test_function_calling.py
+++ b/libs/core/tests/unit_tests/utils/test_function_calling.py
@@ -210,6 +210,26 @@ def json_schema() -> dict:
     }
 
 
+@pytest.fixture()
+def anthropic_tool() -> dict:
+    return {
+        "name": "dummy_function",
+        "description": "dummy function",
+        "input_schema": {
+            "type": "object",
+            "properties": {
+                "arg1": {"description": "foo", "type": "integer"},
+                "arg2": {
+                    "description": "one of 'bar', 'baz'",
+                    "enum": ["bar", "baz"],
+                    "type": "string",
+                },
+            },
+            "required": ["arg1", "arg2"],
+        },
+    }
+
+
 class Dummy:
     def dummy_function(self, arg1: int, arg2: Literal["bar", "baz"]) -> None:
         """dummy function
@@ -237,6 +257,7 @@ def test_convert_to_openai_function(
     dummy_structured_tool: StructuredTool,
     dummy_tool: BaseTool,
     json_schema: dict,
+    anthropic_tool: dict,
     annotated_function: Callable,
     dummy_pydantic: type[BaseModel],
     runnable: Runnable,
@@ -268,6 +289,7 @@ def test_convert_to_openai_function(
         dummy_structured_tool,
         dummy_tool,
         json_schema,
+        anthropic_tool,
         expected,
         Dummy.dummy_function,
         DummyWithClassMethod.dummy_function,

From 71e0f4cd622bf648707eacb4b5f9508cefb6fbbf Mon Sep 17 00:00:00 2001
From: Ankur Singh <ankursingh91002@gmail.com>
Date: Thu, 24 Oct 2024 01:40:21 +0530
Subject: [PATCH 53/84] docs: Fix spelling mistake in concepts (#27589)

`Fore` => `For`

Documentation Correction:

*
[`docs/docs/concepts/async.mdx`](diffhunk://#diff-4959e81c20607c20c7a9c38db4405a687c5d94f24fc8220377701afeee7562b0L40-R40):
Corrected a typo from "Fore" to "For" in the sentence directing users to
the API reference.
---
 docs/docs/concepts/async.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/concepts/async.mdx b/docs/docs/concepts/async.mdx
index 2a1d5acf57845..bfe9f44486087 100644
--- a/docs/docs/concepts/async.mdx
+++ b/docs/docs/concepts/async.mdx
@@ -37,7 +37,7 @@ await some_vectorstore.aadd_documents(documents)
 Runnables created using the [LangChain Expression Language (LCEL)](/docs/concepts/lcel) can also be run asynchronously as they implement
 the full [Runnable Interface](/docs/concepts/runnables).
 
-Fore more information, please review the [API reference](https://python.langchain.com/api_reference/) for the specific component you are using.
+For more information, please review the [API reference](https://python.langchain.com/api_reference/) for the specific component you are using.
 
 ## Delegation to sync methods
 

From 562cf416c2297545caead767531da135d7d8cac3 Mon Sep 17 00:00:00 2001
From: Eugene Yurtsev <eyurtsev@gmail.com>
Date: Wed, 23 Oct 2024 16:18:27 -0400
Subject: [PATCH 54/84] docs: Update messages.mdx (#27592)

Add missing `.`
---
 docs/docs/concepts/messages.mdx | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/docs/concepts/messages.mdx b/docs/docs/concepts/messages.mdx
index 811396883af06..e2f459d244653 100644
--- a/docs/docs/concepts/messages.mdx
+++ b/docs/docs/concepts/messages.mdx
@@ -145,7 +145,7 @@ An `AIMessage` has the following attributes. The attributes which are **standard
 | `content`            | Raw              | Usually a string, but can be a list of content blocks. See [content](#content) for details.                                                                                                                             |
 | `tool_calls`         | Standardized     | Tool calls associated with the message. See [tool calling](/docs/concepts/tool_calling) for details.                                                                                                                    |
 | `invalid_tool_calls` | Standardized     | Tool calls with parsing errors associated with the message. See [tool calling](/docs/concepts/tool_calling) for details.                                                                                                |
-| `usage_metadata`     | Standardized     | Usage metadata for a message, such as [token counts](/docs/concepts/tokens). See [Usage Metadata API Reference](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html) |
+| `usage_metadata`     | Standardized     | Usage metadata for a message, such as [token counts](/docs/concepts/tokens). See [Usage Metadata API Reference](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html). |
 | `id`                 | Standardized     | An optional unique identifier for the message, ideally provided by the provider/model that created the message.                                                                                                         |
 | `response_metadata`  | Raw              | Response metadata, e.g., response headers, logprobs, token counts.                                                                                                                                                      |
 
@@ -241,4 +241,4 @@ chat_model.invoke([
 At the moment, the output of the model will be in terms of LangChain messages, so you will need to convert the output to the OpenAI format if you
 need OpenAI format for the output as well.
 
-The [convert_to_openai_messages](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.convert_to_openai_messages.html) utility function can be used to convert from LangChain messages to OpenAI format.
\ No newline at end of file
+The [convert_to_openai_messages](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.convert_to_openai_messages.html) utility function can be used to convert from LangChain messages to OpenAI format.

From 948e2e632243574addd23012ec5645eceff5660b Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Wed, 23 Oct 2024 14:52:44 -0700
Subject: [PATCH 55/84] docs: concept nits (#27586)

---
 docs/docs/concepts/architecture.mdx     | 38 ++++++++++++-------------
 docs/docs/concepts/async.mdx            |  2 +-
 docs/docs/concepts/callbacks.mdx        |  2 +-
 docs/docs/concepts/chat_history.mdx     |  2 +-
 docs/docs/concepts/chat_models.mdx      |  2 +-
 docs/docs/concepts/document_loaders.mdx | 10 +++----
 docs/docs/concepts/key_value_stores.mdx |  4 +--
 docs/docs/concepts/lcel.mdx             |  4 +--
 docs/docs/concepts/messages.mdx         |  2 +-
 docs/docs/concepts/multimodality.mdx    |  6 ++--
 docs/docs/concepts/retrieval.mdx        |  2 +-
 docs/docs/concepts/retrievers.mdx       |  8 +++---
 docs/docs/concepts/runnables.mdx        |  2 +-
 docs/docs/concepts/streaming.mdx        |  4 +--
 docs/docs/concepts/tools.mdx            |  6 ++--
 docs/docs/concepts/vectorstores.mdx     |  6 ++--
 16 files changed, 50 insertions(+), 50 deletions(-)

diff --git a/docs/docs/concepts/architecture.mdx b/docs/docs/concepts/architecture.mdx
index 6a76b58fb297f..58872f1c724eb 100644
--- a/docs/docs/concepts/architecture.mdx
+++ b/docs/docs/concepts/architecture.mdx
@@ -3,7 +3,7 @@ import useBaseUrl from '@docusaurus/useBaseUrl';
 
 # Architecture
 
-LangChain as a framework consists of a number of packages.
+LangChain is a framework that consists of a number of packages.
 
 <ThemedImage
     alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
@@ -18,34 +18,34 @@ LangChain as a framework consists of a number of packages.
 
 ## langchain-core
 
-This package contains base abstractions of different components and ways to compose them together.
-The interfaces for core components like LLMs, vector stores, retrievers and more are defined here.
-No third party integrations are defined here.
-The dependencies are kept purposefully very lightweight.
+This package contains base abstractions for different components and ways to compose them together.
+The interfaces for core components like chat models, vector stores, tools and more are defined here.
+No third-party integrations are defined here.
+The dependencies are very lightweight.
 
 ## langchain
 
-The main `langchain` package contains chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-These are NOT third party integrations.
+The main `langchain` package contains chains and retrieval strategies that make up an application's cognitive architecture.
+These are NOT third-party integrations.
 All chains, agents, and retrieval strategies here are NOT specific to any one integration, but rather generic across all integrations.
 
-## langchain-community
+## Integration packages
 
-This package contains third party integrations that are maintained by the LangChain community.
-Key partner packages are separated out (see below).
-This contains all integrations for various components (LLMs, vector stores, retrievers).
-All dependencies in this package are optional to keep the package as lightweight as possible.
+Popular integrations have their own packages (e.g. `langchain-openai`, `langchain-anthropic`, etc) so that they can be properly versioned and appropriately lightweight.
 
-## Partner packages
+For more information see:
 
-While the long tail of integrations is in `langchain-community`, we split popular integrations into their own packages (e.g. `langchain-openai`, `langchain-anthropic`, etc). This was done in order to improve support for these important integrations.
+* A list [integrations packages](/docs/integrations/providers/)
+* The [API Reference](https://python.langchain.com/api_reference/) where you can find detailed information about each of the integration package.
 
-For more information see:
+## langchain-community
 
-* A list [LangChain integrations](/docs/integrations/providers/)
-* The [LangChain API Reference](https://python.langchain.com/api_reference/) where you can find detailed information about the API reference of each partner package.
+This package contains third-party integrations that are maintained by the LangChain community.
+Key integration packages are separated out (see above).
+This contains integrations for various components (chat models, vector stores, tools, etc).
+All dependencies in this package are optional to keep the package as lightweight as possible.
 
-## LangGraph
+## langgraph
 
 `langgraph` is an extension of `langchain` aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
 
@@ -58,7 +58,7 @@ LangGraph exposes high level interfaces for creating common types of agents, as
 
 :::
 
-## LangServe
+## langserve
 
 A package to deploy LangChain chains as REST APIs. Makes it easy to get a production ready API up and running.
 
diff --git a/docs/docs/concepts/async.mdx b/docs/docs/concepts/async.mdx
index bfe9f44486087..644c49d1d7bcd 100644
--- a/docs/docs/concepts/async.mdx
+++ b/docs/docs/concepts/async.mdx
@@ -12,7 +12,7 @@ You are expected to be familiar with asynchronous programming in Python before r
 This guide specifically focuses on what you need to know to work with LangChain in an asynchronous context, assuming that you are already familiar with asynch
 :::
 
-## Langchain asynchronous apis
+## Langchain asynchronous APIs
 
 Many LangChain APIs are designed to be asynchronous, allowing you to build efficient and responsive applications.
 
diff --git a/docs/docs/concepts/callbacks.mdx b/docs/docs/concepts/callbacks.mdx
index 62fd28341bb47..5a6691baa5a34 100644
--- a/docs/docs/concepts/callbacks.mdx
+++ b/docs/docs/concepts/callbacks.mdx
@@ -4,7 +4,7 @@
 - [Runnable interface](/docs/concepts/runnables)
 :::
 
-LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
+LangChain provides a callback system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
 
 You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.
 
diff --git a/docs/docs/concepts/chat_history.mdx b/docs/docs/concepts/chat_history.mdx
index 967f93af968e0..b22862512a873 100644
--- a/docs/docs/concepts/chat_history.mdx
+++ b/docs/docs/concepts/chat_history.mdx
@@ -42,5 +42,5 @@ Understanding correct conversation structure is essential for being able to prop
 
 ## Related resources
 
-- [How to trim messages](https://python.langchain.com/docs/how_to/trim_messages/)
+- [How to trim messages](/docs/how_to/trim_messages/)
 - [Memory guide](https://langchain-ai.github.io/langgraph/concepts/memory/) for information on implementing short-term and long-term memory in chat models using [LangGraph](https://langchain-ai.github.io/langgraph/).
diff --git a/docs/docs/concepts/chat_models.mdx b/docs/docs/concepts/chat_models.mdx
index e924168e2cd71..1c264fcac1654 100644
--- a/docs/docs/concepts/chat_models.mdx
+++ b/docs/docs/concepts/chat_models.mdx
@@ -136,7 +136,7 @@ If you hit a rate limit, you will typically receive a rate limit error response
 
 You have a few options to deal with rate limits:
 
-1. Try to avoid hitting rate limits by spacing out requests: Chat models accept a `rate_limiter` parameter that can be provided during initialization. This parameter is used to control the rate at which requests are made to the model provider. Spacing out the requests to a given model is a particularly useful strategy when benchmarking models to evaluate their performance. Please see the [how to handle rate limits](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/) for more information on how to use this feature.
+1. Try to avoid hitting rate limits by spacing out requests: Chat models accept a `rate_limiter` parameter that can be provided during initialization. This parameter is used to control the rate at which requests are made to the model provider. Spacing out the requests to a given model is a particularly useful strategy when benchmarking models to evaluate their performance. Please see the [how to handle rate limits](/docs/how_to/chat_model_rate_limiting/) for more information on how to use this feature.
 2. Try to recover from rate limit errors: If you receive a rate limit error, you can wait a certain amount of time before retrying the request. The amount of time to wait can be increased with each subsequent rate limit error. Chat models have a `max_retries` parameter that can be used to control the number of retries. See the [standard parameters](#standard-parameters) section for more information.
 3. Fallback to another chat model: If you hit a rate limit with one chat model, you can switch to another chat model that is not rate-limited.
 
diff --git a/docs/docs/concepts/document_loaders.mdx b/docs/docs/concepts/document_loaders.mdx
index a6a11ddfe7104..d9b1f13babd00 100644
--- a/docs/docs/concepts/document_loaders.mdx
+++ b/docs/docs/concepts/document_loaders.mdx
@@ -3,14 +3,14 @@
 
 :::info[Prerequisites]
 
-* [Document loaders API reference](https://python.langchain.com/docs/how_to/#document-loaders)
+* [Document loaders API reference](/docs/how_to/#document-loaders)
 :::
 
 Document loaders are designed to load document objects. LangChain has hundreds of integrations with various data sources to load data from: Slack, Notion, Google Drive, etc.
 
 ## Integrations
 
-You can find available integrations on the [Document Loaders Integrations page](https://python.langchain.com/docs/integrations/document_loaders/).
+You can find available integrations on the [Document loaders integrations page](/docs/integrations/document_loaders/).
 
 ## Interface
 
@@ -40,6 +40,6 @@ for document in loader.lazy_load():
 
 Please see the following resources for more information:
 
-* [How-to guides for document loaders](https://python.langchain.com/docs/how_to/#document-loaders)
-* [Document API reference](https://python.langchain.com/docs/how_to/#document-loaders)
-* [Document loaders integrations](https://python.langchain.com/docs/integrations/document_loaders/)
+* [How-to guides for document loaders](/docs/how_to/#document-loaders)
+* [Document API reference](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html)
+* [Document loaders integrations](/docs/integrations/document_loaders/)
diff --git a/docs/docs/concepts/key_value_stores.mdx b/docs/docs/concepts/key_value_stores.mdx
index d8503dbc09360..2f5b7e640e7d5 100644
--- a/docs/docs/concepts/key_value_stores.mdx
+++ b/docs/docs/concepts/key_value_stores.mdx
@@ -21,8 +21,8 @@ The key-value store interface in LangChain is used primarily for:
 
 Please see these how-to guides for more information:
 
-* [How to cache embeddings guide](https://python.langchain.com/docs/how_to/caching_embeddings/).
-* [How to retriever using multiple vectors per document](https://python.langchain.com/docs/how_to/custom_retriever/).
+* [How to cache embeddings guide](/docs/how_to/caching_embeddings/).
+* [How to retriever using multiple vectors per document](/docs/how_to/custom_retriever/).
 
 ## Interface
 
diff --git a/docs/docs/concepts/lcel.mdx b/docs/docs/concepts/lcel.mdx
index 63cced2dd4764..32da281ec1c2e 100644
--- a/docs/docs/concepts/lcel.mdx
+++ b/docs/docs/concepts/lcel.mdx
@@ -11,7 +11,7 @@ This means that you describe what you want to happen, rather than how you want i
 We often refer to a `Runnable` created using LCEL as a "chain". It's important to remember that a "chain" is `Runnable` and it implements the full [Runnable Interface](/docs/concepts/runnables).
 
 :::note
-* The [LCEL cheatsheet](https://python.langchain.com/docs/how_to/lcel_cheatsheet/) shows common patterns that involve the Runnable interface and LCEL expressions.
+* The [LCEL cheatsheet](/docs/how_to/lcel_cheatsheet/) shows common patterns that involve the Runnable interface and LCEL expressions.
 * Please see the following list of [how-to guides](/docs/how_to/#langchain-expression-language-lcel) that cover common tasks with LCEL.
 * A list of built-in `Runnables` can be found in the [LangChain Core API Reference](https://python.langchain.com/api_reference/core/runnables.html). Many of these Runnables are useful when composing custom "chains" in LangChain using LCEL.
 :::
@@ -210,7 +210,7 @@ lambda x: x + 1.invoke(some_input)
 ```
 :::
 
-## Legacy Chains
+## Legacy chains
 
 LCEL aims to provide consistency around behavior and customization over legacy subclassed chains such as `LLMChain` and
 `ConversationalRetrievalChain`. Many of these legacy chains hide important details like prompts, and as a wider variety
diff --git a/docs/docs/concepts/messages.mdx b/docs/docs/concepts/messages.mdx
index e2f459d244653..a12b428719591 100644
--- a/docs/docs/concepts/messages.mdx
+++ b/docs/docs/concepts/messages.mdx
@@ -30,7 +30,7 @@ Roles are used to distinguish between different types of messages in a conversat
 | **user**              | Represents input from a user interacting with the model, usually in the form of text or other interactive input.                                                                                                |
 | **assistant**         | Represents a response from the model, which can include text or a request to invoke tools.                                                                                                                      |
 | **tool**              | A message used to pass the results of a tool invocation back to the model after external data or processing has been retrieved. Used with chat models that support [tool calling](/docs/concepts/tool_calling). |
-| **function (legacy)** | This is a legacy role, corresponding to OpenAI's legacy function-calling API. **tool** role should be used instead.                                                                                             |
+| **function** (legacy) | This is a legacy role, corresponding to OpenAI's legacy function-calling API. **tool** role should be used instead.                                                                                             |
 
 ### Content
 
diff --git a/docs/docs/concepts/multimodality.mdx b/docs/docs/concepts/multimodality.mdx
index 3692e4e1ef1ef..c74c697e8bc8d 100644
--- a/docs/docs/concepts/multimodality.mdx
+++ b/docs/docs/concepts/multimodality.mdx
@@ -26,7 +26,7 @@ Multimodal support is still relatively new and less common, model providers have
 
 #### Inputs
 
-Some models can accept multimodal inputs, such as images, audio, video, or files. The types of multimodal inputs supported depend on the model provider. For instance, [Google's Gemini](https://python.langchain.com/docs/integrations/chat/google_generative_ai/) supports documents like PDFs as inputs.
+Some models can accept multimodal inputs, such as images, audio, video, or files. The types of multimodal inputs supported depend on the model provider. For instance, [Google's Gemini](/docs/integrations/chat/google_generative_ai/) supports documents like PDFs as inputs.
 
 Most chat models that support **multimodal inputs** also accept those values in OpenAI's content blocks format. So far this is restricted to image inputs. For models like Gemini which support video and other bytes input, the APIs also support the native, model-specific representations.
 
@@ -53,7 +53,7 @@ integration documentation for the correct format. Find the integration in the [c
 
 Virtually no popular chat models support multimodal outputs at the time of writing (October 2024). 
 
-The only exception is OpenAI's chat model ([gpt-4o-audio-preview](https://python.langchain.com/docs/integrations/chat/openai/)), which can generate audio outputs.
+The only exception is OpenAI's chat model ([gpt-4o-audio-preview](/docs/integrations/chat/openai/)), which can generate audio outputs.
 
 Multimodal outputs will appear as part of the [AIMessage](/docs/concepts/messages/#aimessage) response object.
 
@@ -80,7 +80,7 @@ As use cases involving multimodal search and retrieval tasks become more common,
 ## Multimodality in vector stores
 
 :::info Prerequisites
-* [Vectorstores](/docs/concepts/vectorstores)
+* [Vector stores](/docs/concepts/vectorstores)
 :::
 
 Vector stores are databases for storing and retrieving embeddings, which are typically used in search and retrieval tasks. Similar to embeddings, vector stores are currently optimized for text-based data.
diff --git a/docs/docs/concepts/retrieval.mdx b/docs/docs/concepts/retrieval.mdx
index 37bb1eb506d41..1735f3565746b 100644
--- a/docs/docs/concepts/retrieval.mdx
+++ b/docs/docs/concepts/retrieval.mdx
@@ -3,7 +3,7 @@
 :::info[Prerequisites]
 
 * [Retrievers](/docs/concepts/retrievers/)
-* [Vectorstores](/docs/concepts/vectorstores/)
+* [Vector stores](/docs/concepts/vectorstores/)
 * [Embeddings](/docs/concepts/embedding_models/)
 * [Text splitters](/docs/concepts/text_splitters/)
 
diff --git a/docs/docs/concepts/retrievers.mdx b/docs/docs/concepts/retrievers.mdx
index 5aaa893b7fde1..3af42544004ee 100644
--- a/docs/docs/concepts/retrievers.mdx
+++ b/docs/docs/concepts/retrievers.mdx
@@ -4,7 +4,7 @@
 
 :::info[Prerequisites]
 
-* [Vectorstores](/docs/concepts/vectorstores/)
+* [Vector stores](/docs/concepts/vectorstores/)
 * [Embeddings](/docs/concepts/embedding_models/)
 * [Text splitters](/docs/concepts/text_splitters/)
 
@@ -58,7 +58,7 @@ Despite the flexibility of the retriever interface, a few common types of retrie
 
 It's important to note that retrievers don't need to actually *store* documents. 
 For example, we can be built retrievers on top of search APIs that simply return search results! 
-See our retriever integrations with [Amazon Kendra](https://python.langchain.com/docs/integrations/retrievers/amazon_kendra_retriever/) or [Wikipedia Search](https://python.langchain.com/docs/integrations/retrievers/wikipedia/). 
+See our retriever integrations with [Amazon Kendra](/docs/integrations/retrievers/amazon_kendra_retriever/) or [Wikipedia Search](/docs/integrations/retrievers/wikipedia/). 
 
 ### Relational or graph database
 
@@ -87,9 +87,9 @@ LangChain has retrievers for many popular lexical search algorithms / engines.
 
 ::: 
 
-### Vectorstore 
+### Vector store 
 
-[Vectorstores](/docs/concepts/vectorstores/) are a powerful and efficient way to index and retrieve unstructured data. 
+[Vector stores](/docs/concepts/vectorstores/) are a powerful and efficient way to index and retrieve unstructured data. 
 An vectorstore can be used as a retriever by calling the `as_retriever()` method.
 
 ```python
diff --git a/docs/docs/concepts/runnables.mdx b/docs/docs/concepts/runnables.mdx
index 678d38bddf7b6..4a383e623a3e5 100644
--- a/docs/docs/concepts/runnables.mdx
+++ b/docs/docs/concepts/runnables.mdx
@@ -274,7 +274,7 @@ If using the `batch` or `batch_as_completed` methods, you can set the `max_concu
 :::tip
 If you're trying to rate limit the number of requests made by a **Chat Model**, you can use the built-in [rate limiter](/docs/concepts/chat_models#rate-limiting) instead of setting `max_concurrency`, which will be more effective.
 
-See the [How to handle rate limits](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/) guide for more information.
+See the [How to handle rate limits](/docs/how_to/chat_model_rate_limiting/) guide for more information.
 :::
 
 ### Setting configurable
diff --git a/docs/docs/concepts/streaming.mdx b/docs/docs/concepts/streaming.mdx
index ba2c89d58ca59..b0dc9934f3404 100644
--- a/docs/docs/concepts/streaming.mdx
+++ b/docs/docs/concepts/streaming.mdx
@@ -140,7 +140,7 @@ See [this guide](/docs/how_to/streaming/#using-stream-events) for more detailed
 To write custom data to the stream, you will need to choose one of the following methods based on the component you are working with:
 
 1. LangGraph's [StreamWriter](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamWriter) can be used to write custom data that will surface through **stream** and **astream** APIs when working with LangGraph. **Important** this is a LangGraph feature, so it is not available when working with pure LCEL. See [how to streaming custom data](https://langchain-ai.github.io/langgraph/how-tos/streaming-content/) for more information.
-2. [dispatch_events](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.dispatch_custom_event.html#) / [adispatch_events](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.adispatch_custom_event.html) can be used to write custom data that will be surfaced through the **astream_events** API. See [how to dispatch custom callback events](https://python.langchain.com/docs/how_to/callbacks_custom_events/#astream-events-api) for more information.
+2. [dispatch_events](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.dispatch_custom_event.html#) / [adispatch_events](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.adispatch_custom_event.html) can be used to write custom data that will be surfaced through the **astream_events** API. See [how to dispatch custom callback events](/docs/how_to/callbacks_custom_events/#astream-events-api) for more information.
 
 ## "Auto-Streaming" Chat Models
 
@@ -188,4 +188,4 @@ Please see the following how-to guides for specific examples of streaming in Lan
 For writing custom data to the stream, please see the following resources:
 
 * If using LangGraph, see [how to stream custom data](https://langchain-ai.github.io/langgraph/how-tos/streaming-content/).
-* If using LCEL, see [how to dispatch custom callback events](https://python.langchain.com/docs/how_to/callbacks_custom_events/#astream-events-api).
+* If using LCEL, see [how to dispatch custom callback events](/docs/how_to/callbacks_custom_events/#astream-events-api).
diff --git a/docs/docs/concepts/tools.mdx b/docs/docs/concepts/tools.mdx
index fe5910cdb2237..5c079808bd65f 100644
--- a/docs/docs/concepts/tools.mdx
+++ b/docs/docs/concepts/tools.mdx
@@ -134,7 +134,7 @@ def user_specific_tool(input_data: str, user_id: InjectedToolArg) -> str:
 Annotating the `user_id` argument with `InjectedToolArg` tells LangChain that this argument should not be exposed as part of the
 tool's schema.
 
-See [how to pass run time values to tools](https://python.langchain.com/docs/how_to/tool_runtime/) for more details on how to use `InjectedToolArg`.  
+See [how to pass run time values to tools](/docs/how_to/tool_runtime/) for more details on how to use `InjectedToolArg`.  
 
 
 ### RunnableConfig
@@ -203,8 +203,8 @@ tools = toolkit.get_tools()
 See the following resources for more information:
 
 - [API Reference for @tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html)
-- [How to create custom tools](https://python.langchain.com/docs/how_to/custom_tools/)
-- [How to pass run time values to tools](https://python.langchain.com/docs/how_to/tool_runtime/)
+- [How to create custom tools](/docs/how_to/custom_tools/)
+- [How to pass run time values to tools](/docs/how_to/tool_runtime/)
 - [All LangChain tool how-to guides](https://docs.langchain.com/docs/how_to/#tools)
 - [Additional how-to guides that show usage with LangGraph](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/)
 - Tool integrations, see the [tool integration docs](https://docs.langchain.com/docs/integrations/tools/).
diff --git a/docs/docs/concepts/vectorstores.mdx b/docs/docs/concepts/vectorstores.mdx
index 44cefe54dee13..a42ccf45a4118 100644
--- a/docs/docs/concepts/vectorstores.mdx
+++ b/docs/docs/concepts/vectorstores.mdx
@@ -22,7 +22,7 @@ These vectors, called [embeddings](/docs/concepts/embedding_models/), capture th
 
 Vector stores are frequently used to search over unstructured data, such as text, images, and audio, to retrieve relevant information based on semantic similarity rather than exact keyword matches.
 
-![Vectorstores](/img/vectorstores.png)
+![Vector stores](/img/vectorstores.png)
 
 ## Integrations
 
@@ -97,7 +97,7 @@ vector_store.delete_documents(ids=["doc1"])
 
 ## Search
 
-Vectorstores embed and store the documents that added.
+Vector stores embed and store the documents that added.
 If we pass in a query, the vectorstore will embed the query, perform a similarity search over the embedded documents, and return the most similar ones.
 This captures two important concepts: first, there needs to be a way to measure the similarity between the query and *any* [embedded](/docs/concepts/embedding_models/) document.
 Second, there needs to be an algorithm to efficiently perform this similarity search across *all* embedded documents.
@@ -156,7 +156,7 @@ This allows structured filters to reduce the size of the similarity search space
 1. **Semantic search**: Query the unstructured data directly, often using via embedding or keyword similarity.
 2. **Metadata search**: Apply structured query to the metadata, filering specific documents.
 
-Vectorstore support for metadata filtering is typically dependent on the underlying vector store implementation.
+Vector store support for metadata filtering is typically dependent on the underlying vector store implementation.
 
 Here is example usage with [Pinecone](/docs/integrations/vectorstores/pinecone/#query-directly), showing that we filter for all documents that have the metadata key `source` with value `tweet`.
 

From 5e5647b5dd03f53eb9563375eb1789b3f7399bc4 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Wed, 23 Oct 2024 16:18:21 -0700
Subject: [PATCH 56/84] docs: render api ref urls in search (#27594)

---
 docs/src/theme/SearchBar/index.js           | 202 +++++++++
 docs/src/theme/SearchBar/styles.css         |  14 +
 docs/src/theme/SearchPage/index.js          | 464 ++++++++++++++++++++
 docs/src/theme/SearchPage/styles.module.css | 112 +++++
 4 files changed, 792 insertions(+)
 create mode 100644 docs/src/theme/SearchBar/index.js
 create mode 100644 docs/src/theme/SearchBar/styles.css
 create mode 100644 docs/src/theme/SearchPage/index.js
 create mode 100644 docs/src/theme/SearchPage/styles.module.css

diff --git a/docs/src/theme/SearchBar/index.js b/docs/src/theme/SearchBar/index.js
new file mode 100644
index 0000000000000..923a9cfac86f7
--- /dev/null
+++ b/docs/src/theme/SearchBar/index.js
@@ -0,0 +1,202 @@
+import React, {useCallback, useMemo, useRef, useState} from 'react';
+import {createPortal} from 'react-dom';
+import {DocSearchButton, useDocSearchKeyboardEvents} from '@docsearch/react';
+import Head from '@docusaurus/Head';
+import Link from '@docusaurus/Link';
+import {useHistory} from '@docusaurus/router';
+import {
+  isRegexpStringMatch,
+  useSearchLinkCreator,
+} from '@docusaurus/theme-common';
+import {
+  useAlgoliaContextualFacetFilters,
+  useSearchResultUrlProcessor,
+} from '@docusaurus/theme-search-algolia/client';
+import Translate from '@docusaurus/Translate';
+import useDocusaurusContext from '@docusaurus/useDocusaurusContext';
+import translations from '@theme/SearchTranslations';
+let DocSearchModal = null;
+function Hit({hit, children}) {
+  if (hit.url.includes('/api_reference/')) {
+    return (
+      <a href={
+        hit.url
+      }>
+        {children}
+      </a>
+    )
+  }
+  return <Link to={hit.url}>{children}</Link>;
+}
+function ResultsFooter({state, onClose}) {
+  const createSearchLink = useSearchLinkCreator();
+  return (
+    <Link to={createSearchLink(state.query)} onClick={onClose}>
+      <Translate
+        id="theme.SearchBar.seeAll"
+        values={{count: state.context.nbHits}}>
+        {'See all {count} results'}
+      </Translate>
+    </Link>
+  );
+}
+function mergeFacetFilters(f1, f2) {
+  const normalize = (f) => (typeof f === 'string' ? [f] : f);
+  return [...normalize(f1), ...normalize(f2)];
+}
+function DocSearch({contextualSearch, externalUrlRegex, ...props}) {
+  const {siteMetadata} = useDocusaurusContext();
+  const processSearchResultUrl = useSearchResultUrlProcessor();
+  const contextualSearchFacetFilters = useAlgoliaContextualFacetFilters();
+  const configFacetFilters = props.searchParameters?.facetFilters ?? [];
+  const facetFilters = contextualSearch
+    ? // Merge contextual search filters with config filters
+      mergeFacetFilters(contextualSearchFacetFilters, configFacetFilters)
+    : // ... or use config facetFilters
+      configFacetFilters;
+  // We let user override default searchParameters if she wants to
+  const searchParameters = {
+    ...props.searchParameters,
+    facetFilters,
+  };
+  const history = useHistory();
+  const searchContainer = useRef(null);
+  const searchButtonRef = useRef(null);
+  const [isOpen, setIsOpen] = useState(false);
+  const [initialQuery, setInitialQuery] = useState(undefined);
+  const importDocSearchModalIfNeeded = useCallback(() => {
+    if (DocSearchModal) {
+      return Promise.resolve();
+    }
+    return Promise.all([
+      import('@docsearch/react/modal'),
+      import('@docsearch/react/style'),
+      import('./styles.css'),
+    ]).then(([{DocSearchModal: Modal}]) => {
+      DocSearchModal = Modal;
+    });
+  }, []);
+  const prepareSearchContainer = useCallback(() => {
+    if (!searchContainer.current) {
+      const divElement = document.createElement('div');
+      searchContainer.current = divElement;
+      document.body.insertBefore(divElement, document.body.firstChild);
+    }
+  }, []);
+  const openModal = useCallback(() => {
+    prepareSearchContainer();
+    importDocSearchModalIfNeeded().then(() => setIsOpen(true));
+  }, [importDocSearchModalIfNeeded, prepareSearchContainer]);
+  const closeModal = useCallback(() => {
+    setIsOpen(false);
+    searchButtonRef.current?.focus();
+  }, []);
+  const handleInput = useCallback(
+    (event) => {
+      if (event.key === 'f' && (event.metaKey || event.ctrlKey)) {
+        // ignore browser's ctrl+f
+        return;
+      }
+      // prevents duplicate key insertion in the modal input
+      event.preventDefault();
+      setInitialQuery(event.key);
+      openModal();
+    },
+    [openModal],
+  );
+  const navigator = useRef({
+    navigate({itemUrl}) {
+      // Algolia results could contain URL's from other domains which cannot
+      // be served through history and should navigate with window.location
+      if (isRegexpStringMatch(externalUrlRegex, itemUrl) || itemUrl.includes('/api_reference/')) {
+        window.location.href = itemUrl;
+      } else {
+        history.push(itemUrl);
+      }
+    },
+  }).current;
+  const transformItems = useRef((items) =>
+    props.transformItems
+      ? // Custom transformItems
+        props.transformItems(items)
+      : // Default transformItems
+        items.map((item) => ({
+          ...item,
+          url: processSearchResultUrl(item.url),
+        })),
+  ).current;
+  const resultsFooterComponent = useMemo(
+    () =>
+      // eslint-disable-next-line react/no-unstable-nested-components
+      (footerProps) =>
+        <ResultsFooter {...footerProps} onClose={closeModal} />,
+    [closeModal],
+  );
+  const transformSearchClient = useCallback(
+    (searchClient) => {
+      searchClient.addAlgoliaAgent(
+        'docusaurus',
+        siteMetadata.docusaurusVersion,
+      );
+      return searchClient;
+    },
+    [siteMetadata.docusaurusVersion],
+  );
+  useDocSearchKeyboardEvents({
+    isOpen,
+    onOpen: openModal,
+    onClose: closeModal,
+    onInput: handleInput,
+    searchButtonRef,
+  });
+  return (
+    <>
+      <Head>
+        {/* This hints the browser that the website will load data from Algolia,
+        and allows it to preconnect to the DocSearch cluster. It makes the first
+        query faster, especially on mobile. */}
+        <link
+          rel="preconnect"
+          href={`https://${props.appId}-dsn.algolia.net`}
+          crossOrigin="anonymous"
+        />
+      </Head>
+
+      <DocSearchButton
+        onTouchStart={importDocSearchModalIfNeeded}
+        onFocus={importDocSearchModalIfNeeded}
+        onMouseOver={importDocSearchModalIfNeeded}
+        onClick={openModal}
+        ref={searchButtonRef}
+        translations={translations.button}
+      />
+
+      {isOpen &&
+        DocSearchModal &&
+        searchContainer.current &&
+        createPortal(
+          <DocSearchModal
+            onClose={closeModal}
+            initialScrollY={window.scrollY}
+            initialQuery={initialQuery}
+            navigator={navigator}
+            transformItems={transformItems}
+            hitComponent={Hit}
+            transformSearchClient={transformSearchClient}
+            {...(props.searchPagePath && {
+              resultsFooterComponent,
+            })}
+            {...props}
+            searchParameters={searchParameters}
+            placeholder={translations.placeholder}
+            translations={translations.modal}
+          />,
+          searchContainer.current,
+        )}
+    </>
+  );
+}
+export default function SearchBar() {
+  const {siteConfig} = useDocusaurusContext();
+  return <DocSearch {...siteConfig.themeConfig.algolia} />;
+}
diff --git a/docs/src/theme/SearchBar/styles.css b/docs/src/theme/SearchBar/styles.css
new file mode 100644
index 0000000000000..fdf8dff9a4a17
--- /dev/null
+++ b/docs/src/theme/SearchBar/styles.css
@@ -0,0 +1,14 @@
+:root {
+  --docsearch-primary-color: var(--ifm-color-primary);
+  --docsearch-text-color: var(--ifm-font-color-base);
+}
+
+.DocSearch-Button {
+  margin: 0;
+  transition: all var(--ifm-transition-fast)
+    var(--ifm-transition-timing-default);
+}
+
+.DocSearch-Container {
+  z-index: calc(var(--ifm-z-index-fixed) + 1);
+}
diff --git a/docs/src/theme/SearchPage/index.js b/docs/src/theme/SearchPage/index.js
new file mode 100644
index 0000000000000..7a5da716737b1
--- /dev/null
+++ b/docs/src/theme/SearchPage/index.js
@@ -0,0 +1,464 @@
+/* eslint-disable jsx-a11y/no-autofocus */
+import React, {useEffect, useReducer, useRef, useState} from 'react';
+import clsx from 'clsx';
+import algoliaSearchHelper from 'algoliasearch-helper';
+import algoliaSearch from 'algoliasearch/lite';
+import ExecutionEnvironment from '@docusaurus/ExecutionEnvironment';
+import Head from '@docusaurus/Head';
+import Link from '@docusaurus/Link';
+import {useAllDocsData} from '@docusaurus/plugin-content-docs/client';
+import {
+  HtmlClassNameProvider,
+  useEvent,
+  usePluralForm,
+  useSearchQueryString,
+} from '@docusaurus/theme-common';
+import {useTitleFormatter} from '@docusaurus/theme-common/internal';
+import Translate, {translate} from '@docusaurus/Translate';
+import useDocusaurusContext from '@docusaurus/useDocusaurusContext';
+import {
+  useAlgoliaThemeConfig,
+  useSearchResultUrlProcessor,
+} from '@docusaurus/theme-search-algolia/client';
+import Layout from '@theme/Layout';
+import Heading from '@theme/Heading';
+import styles from './styles.module.css';
+// Very simple pluralization: probably good enough for now
+function useDocumentsFoundPlural() {
+  const {selectMessage} = usePluralForm();
+  return (count) =>
+    selectMessage(
+      count,
+      translate(
+        {
+          id: 'theme.SearchPage.documentsFound.plurals',
+          description:
+            'Pluralized label for "{count} documents found". Use as much plural forms (separated by "|") as your language support (see https://www.unicode.org/cldr/cldr-aux/charts/34/supplemental/language_plural_rules.html)',
+          message: 'One document found|{count} documents found',
+        },
+        {count},
+      ),
+    );
+}
+function useDocsSearchVersionsHelpers() {
+  const allDocsData = useAllDocsData();
+  // State of the version select menus / algolia facet filters
+  // docsPluginId -> versionName map
+  const [searchVersions, setSearchVersions] = useState(() =>
+    Object.entries(allDocsData).reduce(
+      (acc, [pluginId, pluginData]) => ({
+        ...acc,
+        [pluginId]: pluginData.versions[0].name,
+      }),
+      {},
+    ),
+  );
+  // Set the value of a single select menu
+  const setSearchVersion = (pluginId, searchVersion) =>
+    setSearchVersions((s) => ({...s, [pluginId]: searchVersion}));
+  const versioningEnabled = Object.values(allDocsData).some(
+    (docsData) => docsData.versions.length > 1,
+  );
+  return {
+    allDocsData,
+    versioningEnabled,
+    searchVersions,
+    setSearchVersion,
+  };
+}
+// We want to display one select per versioned docs plugin instance
+function SearchVersionSelectList({docsSearchVersionsHelpers}) {
+  const versionedPluginEntries = Object.entries(
+    docsSearchVersionsHelpers.allDocsData,
+  )
+    // Do not show a version select for unversioned docs plugin instances
+    .filter(([, docsData]) => docsData.versions.length > 1);
+  return (
+    <div
+      className={clsx(
+        'col',
+        'col--3',
+        'padding-left--none',
+        styles.searchVersionColumn,
+      )}>
+      {versionedPluginEntries.map(([pluginId, docsData]) => {
+        const labelPrefix =
+          versionedPluginEntries.length > 1 ? `${pluginId}: ` : '';
+        return (
+          <select
+            key={pluginId}
+            onChange={(e) =>
+              docsSearchVersionsHelpers.setSearchVersion(
+                pluginId,
+                e.target.value,
+              )
+            }
+            defaultValue={docsSearchVersionsHelpers.searchVersions[pluginId]}
+            className={styles.searchVersionInput}>
+            {docsData.versions.map((version, i) => (
+              <option
+                key={i}
+                label={`${labelPrefix}${version.label}`}
+                value={version.name}
+              />
+            ))}
+          </select>
+        );
+      })}
+    </div>
+  );
+}
+function SearchPageContent() {
+  const {
+    i18n: {currentLocale},
+  } = useDocusaurusContext();
+  const {
+    algolia: {appId, apiKey, indexName, contextualSearch},
+  } = useAlgoliaThemeConfig();
+  const processSearchResultUrl = useSearchResultUrlProcessor();
+  const documentsFoundPlural = useDocumentsFoundPlural();
+  const docsSearchVersionsHelpers = useDocsSearchVersionsHelpers();
+  const [searchQuery, setSearchQuery] = useSearchQueryString();
+  const initialSearchResultState = {
+    items: [],
+    query: null,
+    totalResults: null,
+    totalPages: null,
+    lastPage: null,
+    hasMore: null,
+    loading: null,
+  };
+  const [searchResultState, searchResultStateDispatcher] = useReducer(
+    (prevState, data) => {
+      switch (data.type) {
+        case 'reset': {
+          return initialSearchResultState;
+        }
+        case 'loading': {
+          return {...prevState, loading: true};
+        }
+        case 'update': {
+          if (searchQuery !== data.value.query) {
+            return prevState;
+          }
+          return {
+            ...data.value,
+            items:
+              data.value.lastPage === 0
+                ? data.value.items
+                : prevState.items.concat(data.value.items),
+          };
+        }
+        case 'advance': {
+          const hasMore = prevState.totalPages > prevState.lastPage + 1;
+          return {
+            ...prevState,
+            lastPage: hasMore ? prevState.lastPage + 1 : prevState.lastPage,
+            hasMore,
+          };
+        }
+        default:
+          return prevState;
+      }
+    },
+    initialSearchResultState,
+  );
+  // respect settings from the theme config for facets
+  const disjunctiveFacets = contextualSearch
+    ? ['language', 'docusaurus_tag']
+    : [];
+  const algoliaClient = algoliaSearch(appId, apiKey);
+  const algoliaHelper = algoliaSearchHelper(algoliaClient, indexName, {
+    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
+    // @ts-ignore: why errors happens after upgrading to TS 5.5 ?
+    hitsPerPage: 15,
+    advancedSyntax: true,
+    disjunctiveFacets,
+  });
+  algoliaHelper.on(
+    'result',
+    ({results: {query, hits, page, nbHits, nbPages}}) => {
+      if (query === '' || !Array.isArray(hits)) {
+        searchResultStateDispatcher({type: 'reset'});
+        return;
+      }
+      const sanitizeValue = (value) =>
+        value.replace(
+          /algolia-docsearch-suggestion--highlight/g,
+          'search-result-match',
+        );
+      const items = hits.map(
+        ({
+          url,
+          _highlightResult: {hierarchy},
+          _snippetResult: snippet = {},
+        }) => {
+          const titles = Object.keys(hierarchy).map((key) =>
+            sanitizeValue(hierarchy[key].value),
+          );
+          return {
+            title: titles.pop(),
+            url: processSearchResultUrl(url),
+            summary: snippet.content
+              ? `${sanitizeValue(snippet.content.value)}...`
+              : '',
+            breadcrumbs: titles,
+          };
+        },
+      );
+      searchResultStateDispatcher({
+        type: 'update',
+        value: {
+          items,
+          query,
+          totalResults: nbHits,
+          totalPages: nbPages,
+          lastPage: page,
+          hasMore: nbPages > page + 1,
+          loading: false,
+        },
+      });
+    },
+  );
+  const [loaderRef, setLoaderRef] = useState(null);
+  const prevY = useRef(0);
+  const observer = useRef(
+    ExecutionEnvironment.canUseIntersectionObserver &&
+      new IntersectionObserver(
+        (entries) => {
+          const {
+            isIntersecting,
+            boundingClientRect: {y: currentY},
+          } = entries[0];
+          if (isIntersecting && prevY.current > currentY) {
+            searchResultStateDispatcher({type: 'advance'});
+          }
+          prevY.current = currentY;
+        },
+        {threshold: 1},
+      ),
+  );
+  const getTitle = () =>
+    searchQuery
+      ? translate(
+          {
+            id: 'theme.SearchPage.existingResultsTitle',
+            message: 'Search results for "{query}"',
+            description: 'The search page title for non-empty query',
+          },
+          {
+            query: searchQuery,
+          },
+        )
+      : translate({
+          id: 'theme.SearchPage.emptyResultsTitle',
+          message: 'Search the documentation',
+          description: 'The search page title for empty query',
+        });
+  const makeSearch = useEvent((page = 0) => {
+    if (contextualSearch) {
+      algoliaHelper.addDisjunctiveFacetRefinement('docusaurus_tag', 'default');
+      algoliaHelper.addDisjunctiveFacetRefinement('language', currentLocale);
+      Object.entries(docsSearchVersionsHelpers.searchVersions).forEach(
+        ([pluginId, searchVersion]) => {
+          algoliaHelper.addDisjunctiveFacetRefinement(
+            'docusaurus_tag',
+            `docs-${pluginId}-${searchVersion}`,
+          );
+        },
+      );
+    }
+    algoliaHelper.setQuery(searchQuery).setPage(page).search();
+  });
+  useEffect(() => {
+    if (!loaderRef) {
+      return undefined;
+    }
+    const currentObserver = observer.current;
+    if (currentObserver) {
+      currentObserver.observe(loaderRef);
+      return () => currentObserver.unobserve(loaderRef);
+    }
+    return () => true;
+  }, [loaderRef]);
+  useEffect(() => {
+    searchResultStateDispatcher({type: 'reset'});
+    if (searchQuery) {
+      searchResultStateDispatcher({type: 'loading'});
+      setTimeout(() => {
+        makeSearch();
+      }, 300);
+    }
+  }, [searchQuery, docsSearchVersionsHelpers.searchVersions, makeSearch]);
+  useEffect(() => {
+    if (!searchResultState.lastPage || searchResultState.lastPage === 0) {
+      return;
+    }
+    makeSearch(searchResultState.lastPage);
+  }, [makeSearch, searchResultState.lastPage]);
+  return (
+    <Layout>
+      <Head>
+        <title>{useTitleFormatter(getTitle())}</title>
+        {/*
+         We should not index search pages
+          See https://github.com/facebook/docusaurus/pull/3233
+        */}
+        <meta property="robots" content="noindex, follow" />
+      </Head>
+
+      <div className="container margin-vert--lg">
+        <Heading as="h1">{getTitle()}</Heading>
+
+        <form className="row" onSubmit={(e) => e.preventDefault()}>
+          <div
+            className={clsx('col', styles.searchQueryColumn, {
+              'col--9': docsSearchVersionsHelpers.versioningEnabled,
+              'col--12': !docsSearchVersionsHelpers.versioningEnabled,
+            })}>
+            <input
+              type="search"
+              name="q"
+              className={styles.searchQueryInput}
+              placeholder={translate({
+                id: 'theme.SearchPage.inputPlaceholder',
+                message: 'Type your search here',
+                description: 'The placeholder for search page input',
+              })}
+              aria-label={translate({
+                id: 'theme.SearchPage.inputLabel',
+                message: 'Search',
+                description: 'The ARIA label for search page input',
+              })}
+              onChange={(e) => setSearchQuery(e.target.value)}
+              value={searchQuery}
+              autoComplete="off"
+              autoFocus
+            />
+          </div>
+
+          {contextualSearch && docsSearchVersionsHelpers.versioningEnabled && (
+            <SearchVersionSelectList
+              docsSearchVersionsHelpers={docsSearchVersionsHelpers}
+            />
+          )}
+        </form>
+
+        <div className="row">
+          <div className={clsx('col', 'col--8', styles.searchResultsColumn)}>
+            {!!searchResultState.totalResults &&
+              documentsFoundPlural(searchResultState.totalResults)}
+          </div>
+
+          <div
+            className={clsx(
+              'col',
+              'col--4',
+              'text--right',
+              styles.searchLogoColumn,
+            )}>
+            <Link
+              to="https://www.algolia.com/"
+              aria-label={translate({
+                id: 'theme.SearchPage.algoliaLabel',
+                message: 'Search by Algolia',
+                description: 'The ARIA label for Algolia mention',
+              })}>
+              <svg viewBox="0 0 168 24" className={styles.algoliaLogo}>
+                <g fill="none">
+                  <path
+                    className={styles.algoliaLogoPathFill}
+                    d="M120.925 18.804c-4.386.02-4.386-3.54-4.386-4.106l-.007-13.336 2.675-.424v13.254c0 .322 0 2.358 1.718 2.364v2.248zm-10.846-2.18c.821 0 1.43-.047 1.855-.129v-2.719a6.334 6.334 0 0 0-1.574-.199 5.7 5.7 0 0 0-.897.069 2.699 2.699 0 0 0-.814.24c-.24.116-.439.28-.582.491-.15.212-.219.335-.219.656 0 .628.219.991.616 1.23s.938.362 1.615.362zm-.233-9.7c.883 0 1.629.109 2.231.328.602.218 1.088.525 1.444.915.363.396.609.922.76 1.483.157.56.232 1.175.232 1.85v6.874a32.5 32.5 0 0 1-1.868.314c-.834.123-1.772.185-2.813.185-.69 0-1.327-.069-1.895-.198a4.001 4.001 0 0 1-1.471-.636 3.085 3.085 0 0 1-.951-1.134c-.226-.465-.343-1.12-.343-1.803 0-.656.13-1.073.384-1.525a3.24 3.24 0 0 1 1.047-1.106c.445-.287.95-.492 1.532-.615a8.8 8.8 0 0 1 1.82-.185 8.404 8.404 0 0 1 1.972.24v-.438c0-.307-.035-.6-.11-.874a1.88 1.88 0 0 0-.384-.73 1.784 1.784 0 0 0-.724-.493 3.164 3.164 0 0 0-1.143-.205c-.616 0-1.177.075-1.69.164a7.735 7.735 0 0 0-1.26.307l-.321-2.192c.335-.117.834-.233 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+                  />
+                  <path
+                    fill="#5468FF"
+                    d="M78.988.938h16.594a2.968 2.968 0 0 1 2.966 2.966V20.5a2.967 2.967 0 0 1-2.966 2.964H78.988a2.967 2.967 0 0 1-2.966-2.964V3.897A2.961 2.961 0 0 1 78.988.938z"
+                  />
+                  <path
+                    fill="white"
+                    d="M89.632 5.967v-.772a.978.978 0 0 0-.978-.977h-2.28a.978.978 0 0 0-.978.977v.793c0 .088.082.15.171.13a7.127 7.127 0 0 1 1.984-.28c.65 0 1.295.088 1.917.259.082.02.164-.04.164-.13m-6.248 1.01l-.39-.389a.977.977 0 0 0-1.382 0l-.465.465a.973.973 0 0 0 0 1.38l.383.383c.062.061.15.047.205-.014.226-.307.472-.601.746-.874.281-.28.568-.526.883-.751.068-.042.075-.137.02-.2m4.16 2.453v3.341c0 .096.104.165.192.117l2.97-1.537c.068-.034.089-.117.055-.184a3.695 3.695 0 0 0-3.08-1.866c-.068 0-.136.054-.136.13m0 8.048a4.489 4.489 0 0 1-4.49-4.482 4.488 4.488 0 0 1 4.49-4.482 4.488 4.488 0 0 1 4.489 4.482 4.484 4.484 0 0 1-4.49 4.482m0-10.85a6.363 6.363 0 1 0 0 12.729 6.37 6.37 0 0 0 6.372-6.368 6.358 6.358 0 0 0-6.371-6.36"
+                  />
+                </g>
+              </svg>
+            </Link>
+          </div>
+        </div>
+
+        {searchResultState.items.length > 0 ? (
+          <main>
+            {searchResultState.items.map(
+              ({title, url, summary, breadcrumbs}, i) => (
+                <article key={i} className={styles.searchResultItem}>
+                  <Heading as="h2" className={styles.searchResultItemHeading}>
+                    {url.includes('/api_reference/') ? <a href={url} dangerouslySetInnerHTML={{__html: title}} /> : <Link to={url} dangerouslySetInnerHTML={{__html: title}} />}
+                  </Heading>
+
+                  {breadcrumbs.length > 0 && (
+                    <nav aria-label="breadcrumbs">
+                      <ul
+                        className={clsx(
+                          'breadcrumbs',
+                          styles.searchResultItemPath,
+                        )}>
+                        {breadcrumbs.map((html, index) => (
+                          <li
+                            key={index}
+                            className="breadcrumbs__item"
+                            // Developer provided the HTML, so assume it's safe.
+                            // eslint-disable-next-line react/no-danger
+                            dangerouslySetInnerHTML={{__html: html}}
+                          />
+                        ))}
+                      </ul>
+                    </nav>
+                  )}
+
+                  {summary && (
+                    <p
+                      className={styles.searchResultItemSummary}
+                      // Developer provided the HTML, so assume it's safe.
+                      // eslint-disable-next-line react/no-danger
+                      dangerouslySetInnerHTML={{__html: summary}}
+                    />
+                  )}
+                </article>
+              ),
+            )}
+          </main>
+        ) : (
+          [
+            searchQuery && !searchResultState.loading && (
+              <p key="no-results">
+                <Translate
+                  id="theme.SearchPage.noResultsText"
+                  description="The paragraph for empty search result">
+                  No results were found
+                </Translate>
+              </p>
+            ),
+            !!searchResultState.loading && (
+              <div key="spinner" className={styles.loadingSpinner} />
+            ),
+          ]
+        )}
+
+        {searchResultState.hasMore && (
+          <div className={styles.loader} ref={setLoaderRef}>
+            <Translate
+              id="theme.SearchPage.fetchingNewResults"
+              description="The paragraph for fetching new search results">
+              Fetching new results...
+            </Translate>
+          </div>
+        )}
+      </div>
+    </Layout>
+  );
+}
+export default function SearchPage() {
+  return (
+    <HtmlClassNameProvider className="search-page-wrapper">
+      <SearchPageContent />
+    </HtmlClassNameProvider>
+  );
+}
diff --git a/docs/src/theme/SearchPage/styles.module.css b/docs/src/theme/SearchPage/styles.module.css
new file mode 100644
index 0000000000000..57de7498db663
--- /dev/null
+++ b/docs/src/theme/SearchPage/styles.module.css
@@ -0,0 +1,112 @@
+.searchQueryInput,
+.searchVersionInput {
+  border-radius: var(--ifm-global-radius);
+  border: 2px solid var(--ifm-toc-border-color);
+  font: var(--ifm-font-size-base) var(--ifm-font-family-base);
+  padding: 0.8rem;
+  width: 100%;
+  background: var(--docsearch-searchbox-focus-background);
+  color: var(--docsearch-text-color);
+  margin-bottom: 0.5rem;
+  transition: border var(--ifm-transition-fast) ease;
+}
+
+.searchQueryInput:focus,
+.searchVersionInput:focus {
+  border-color: var(--docsearch-primary-color);
+  outline: none;
+}
+
+.searchQueryInput::placeholder {
+  color: var(--docsearch-muted-color);
+}
+
+.searchResultsColumn {
+  font-size: 0.9rem;
+  font-weight: bold;
+}
+
+.algoliaLogo {
+  max-width: 150px;
+}
+
+.algoliaLogoPathFill {
+  fill: var(--ifm-font-color-base);
+}
+
+.searchResultItem {
+  padding: 1rem 0;
+  border-bottom: 1px solid var(--ifm-toc-border-color);
+}
+
+.searchResultItemHeading {
+  font-weight: 400;
+  margin-bottom: 0;
+}
+
+.searchResultItemPath {
+  font-size: 0.8rem;
+  color: var(--ifm-color-content-secondary);
+  --ifm-breadcrumb-separator-size-multiplier: 1;
+}
+
+.searchResultItemSummary {
+  margin: 0.5rem 0 0;
+  font-style: italic;
+}
+
+@media only screen and (max-width: 996px) {
+  .searchQueryColumn {
+    max-width: 60% !important;
+  }
+
+  .searchVersionColumn {
+    max-width: 40% !important;
+  }
+
+  .searchResultsColumn {
+    max-width: 60% !important;
+  }
+
+  .searchLogoColumn {
+    max-width: 40% !important;
+    padding-left: 0 !important;
+  }
+}
+
+@media screen and (max-width: 576px) {
+  .searchQueryColumn {
+    max-width: 100% !important;
+  }
+
+  .searchVersionColumn {
+    max-width: 100% !important;
+    padding-left: var(--ifm-spacing-horizontal) !important;
+  }
+}
+
+.loadingSpinner {
+  width: 3rem;
+  height: 3rem;
+  border: 0.4em solid #eee;
+  border-top-color: var(--ifm-color-primary);
+  border-radius: 50%;
+  animation: loading-spin 1s linear infinite;
+  margin: 0 auto;
+}
+
+@keyframes loading-spin {
+  100% {
+    transform: rotate(360deg);
+  }
+}
+
+.loader {
+  margin-top: 2rem;
+}
+
+:global(.search-result-match) {
+  color: var(--docsearch-hit-color);
+  background: rgb(255 215 142 / 25%);
+  padding: 0.09em 0;
+}

From 8f151223adee00c39df1b59cc800953ffde8b268 Mon Sep 17 00:00:00 2001
From: Andrew Effendi <84085709+AndrewEffendi@users.noreply.github.com>
Date: Wed, 23 Oct 2024 20:18:11 -0400
Subject: [PATCH 57/84] Community: Fix DuckDuckGo search tool Output Format
 (#27479)

**Issue:** : https://github.com/langchain-ai/langchain/issues/22961
   **Description:**

Previously, the documentation for `DuckDuckGoSearchResults` said that it
returns a JSON string, however the code returns a regular string that
can't be parsed as is.
for example running

```python
from langchain_community.tools import DuckDuckGoSearchResults

# Create a DuckDuckGo search instance
search = DuckDuckGoSearchResults()

# Invoke the search
result = search.invoke("Obama")

# Print the result
print(result)
# Print the type of the result
print("Result Type:", type(result))
```
will return
```
snippet: Harris will hold a campaign event with former President Barack Obama in Georgia next Thursday, the first time the pair has campaigned side by side, a senior campaign official said. A week from ..., title: Obamas to hit the campaign trail in first joint appearances with Harris, link: https://www.nbcnews.com/politics/2024-election/obamas-hit-campaign-trail-first-joint-appearances-harris-rcna176034, snippet: Item 1 of 3 Former U.S. first lady Michelle Obama and her husband, former U.S. President Barack Obama, stand on stage during Day 2 of the Democratic National Convention (DNC) in Chicago, Illinois ..., title: Obamas set to hit campaign trail with Kamala Harris for first time, link: https://www.reuters.com/world/us/obamas-set-hit-campaign-trail-with-kamala-harris-first-time-2024-10-18/, snippet: Barack and Michelle Obama will make their first campaign appearances alongside Kamala Harris at rallies in Georgia and Michigan. By Reid J. Epstein Reporting from Ashwaubenon, Wis. Here come the ..., title: Harris Will Join Michelle Obama and Barack Obama on Campaign Trail, link: https://www.nytimes.com/2024/10/18/us/politics/kamala-harris-michelle-obama-barack-obama.html, snippet: Obama's leaving office was "a turning point," Mirsky said. "That was the last time anybody felt normal." A few feet over, a 64-year-old physics professor named Eric Swanson who had grown ..., title: Obama's reemergence on the campaign trail for Harris comes as he ..., link: https://www.cnn.com/2024/10/13/politics/obama-campaign-trail-harris-biden/index.html
Result Type: <class 'str'>
```

After the change in this PR, `DuckDuckGoSearchResults` takes an
additional `output_format = "list" | "json" | "string"` ("string" =
current behavior, default). For example, invoking
`DuckDuckGoSearchResults(output_format="list")` return a list of
dictionaries in the format
```
[{'snippet': '...', 'title': '...', 'link': '...'}, ...]
```
e.g.

```
[{'snippet': "Obama has in a sense been wrestling with Trump's impact since the real estate magnate broke onto the political stage in 2015. Trump's victory the next year, defeating Obama's secretary of ...", 'title': "Obama's fears about Trump drive his stepped-up campaigning", 'link': 'https://www.washingtonpost.com/politics/2024/10/18/obama-trump-anxiety-harris-campaign/'}, {'snippet': 'Harris will hold a campaign event with former President Barack Obama in Georgia next Thursday, the first time the pair has campaigned side by side, a senior campaign official said. A week from ...', 'title': 'Obamas to hit the campaign trail in first joint appearances with Harris', 'link': 'https://www.nbcnews.com/politics/2024-election/obamas-hit-campaign-trail-first-joint-appearances-harris-rcna176034'}, {'snippet': 'Item 1 of 3 Former U.S. first lady Michelle Obama and her husband, former U.S. President Barack Obama, stand on stage during Day 2 of the Democratic National Convention (DNC) in Chicago, Illinois ...', 'title': 'Obamas set to hit campaign trail with Kamala Harris for first time', 'link': 'https://www.reuters.com/world/us/obamas-set-hit-campaign-trail-with-kamala-harris-first-time-2024-10-18/'}, {'snippet': 'Barack and Michelle Obama will make their first campaign appearances alongside Kamala Harris at rallies in Georgia and Michigan. By Reid J. Epstein Reporting from Ashwaubenon, Wis. Here come the ...', 'title': 'Harris Will Join Michelle Obama and Barack Obama on Campaign Trail', 'link': 'https://www.nytimes.com/2024/10/18/us/politics/kamala-harris-michelle-obama-barack-obama.html'}]
Result Type: <class 'list'>
```

---------

Co-authored-by: vbarda <vadym@langchain.dev>
---
 docs/docs/integrations/tools/ddg.ipynb        | 62 ++++++++++++++++---
 .../tools/ddg_search/tool.py                  | 53 +++++++++++-----
 2 files changed, 89 insertions(+), 26 deletions(-)

diff --git a/docs/docs/integrations/tools/ddg.ipynb b/docs/docs/integrations/tools/ddg.ipynb
index d436b349cd65b..703d25d5b35de 100644
--- a/docs/docs/integrations/tools/ddg.ipynb
+++ b/docs/docs/integrations/tools/ddg.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "21e46d4d",
    "metadata": {},
    "outputs": [],
@@ -31,7 +31,7 @@
     {
      "data": {
       "text/plain": [
-       "'When Ann Dunham and Barack Obama Sr. tied the knot, they kept the news to themselves. \"Nobody was invited,\" Neil Abercrombie, a college friend of Obama Sr., told Time in 2008. The wedding came as ... As the head of the government of the United States, the president is arguably the most powerful government official in the world. The president is elected to a four-year term via an electoral college system. Since the Twenty-second Amendment was adopted in 1951, the American presidency has been Most common names of U.S. presidents 1789-2021. Published by. Aaron O\\'Neill , Jul 4, 2024. The most common first name for a U.S. president is James, followed by John and then William. Six U.S ... Obama\\'s personal charisma, stirring oratory, and his campaign promise to bring change to the established political system resonated with many Democrats, especially young and minority voters. On January 3, 2008, Obama won a surprise victory in the first major nominating contest, the Iowa caucus, over Sen. Hillary Clinton, who was the overwhelming favorite to win the nomination. Former President Barack Obama released a letter about President Biden\\'s decision to drop out of the 2024 presidential race. Notably, Obama did not name or endorse Vice President Kamala Harris.'"
+       "\"The White House, official residence of the president of the United States, in July 2008. The president of the United States is the head of state and head of government of the United States, [1] indirectly elected to a four-year term via the Electoral College. [2] The officeholder leads the executive branch of the federal government and is the commander-in-chief of the United States Armed ... Here is a list of the presidents and vice presidents of the United States along with their parties and dates in office. ... Chester A Arthur: Twenty-First President of the United States. 10 Interesting Facts About James Buchanan. Martin Van Buren - Eighth President of the United States. Quotes From Harry S. Truman. 2 of 2. Barack Obama: timeline Key events in the life of Barack Obama. Barack Obama (born August 4, 1961, Honolulu, Hawaii, U.S.) is the 44th president of the United States (2009-17) and the first African American to hold the office. Before winning the presidency, Obama represented Illinois in the U.S. Senate (2005-08). Table of Contents As the head of the government of the United States, the president is arguably the most powerful government official in the world. The president is elected to a four-year term via an electoral college system. Since the Twenty-second Amendment was adopted in 1951, the American presidency has been limited to a maximum of two terms.. Click on a president below to learn more about ... Most common names of U.S. presidents 1789-2021. Published by. Aaron O'Neill, Aug 9, 2024. The most common first name for a U.S. president is James, followed by John and then William. Six U.S ...\""
       ]
      },
      "execution_count": 2,
@@ -64,7 +64,7 @@
     {
      "data": {
       "text/plain": [
-       "\"[snippet: Why Obama Hasn't Endorsed Harris. The former president has positioned himself as an impartial elder statesman above intraparty machinations and was neutral during the 2020 Democratic primaries., title: Why Obama Hasn't Endorsed Harris - The New York Times, link: https://www.nytimes.com/2024/07/21/us/politics/why-obama-hasnt-endorsed-harris.html], [snippet: Former President Barack Obama released a letter about President Biden's decision to drop out of the 2024 presidential race. Notably, Obama did not name or endorse Vice President Kamala Harris., title: Read Obama's full statement on Biden dropping out - CBS News, link: https://www.cbsnews.com/news/barack-obama-biden-dropping-out-2024-presidential-race-full-statement/], [snippet: USA TODAY. 0:04. 0:48. Former President Barack Obama recently said, in private, that President Joe Biden's chances at a successful presidential run in 2024 have declined and that he needs to ..., title: Did Obama tell Biden to quit? What the former president said - USA TODAY, link: https://www.usatoday.com/story/news/politics/elections/2024/07/18/did-obama-tell-biden-to-quit-what-the-former-president-said/74458139007/], [snippet: Many of the marquee names in Democratic politics began quickly lining up behind Vice President Kamala Harris on Sunday, but one towering presence in the party held back: Barack Obama. The former president has not yet endorsed Harris; in fact, he did not mention her once in an affectionate — if tautly written — tribute to President Joe Biden that was posted on Medium shortly after Biden ..., title: Why Obama Hasn't Endorsed Harris - Yahoo News, link: https://news.yahoo.com/news/why-obama-hasn-t-endorsed-114259092.html]\""
+       "\"snippet: He maintains a close friendship with Mr. Obama. He first weighed in on presidential politics to criticize President Reagan's re-election campaign, and has since supported Mr. Obama, Hillary ..., title: Bruce Springsteen to Appear With Harris and Obama at Atlanta and ..., link: https://www.nytimes.com/2024/10/22/us/politics/springsteen-harris-atlanta-obama-philly.html, snippet: Learn about the life and achievements of Barack Obama, the 44th president of the United States and the first African American to hold the office. Explore his early years, education, political career, books, awards, and more., title: Barack Obama | Biography, Parents, Education, Presidency, Books ..., link: https://www.britannica.com/biography/Barack-Obama, snippet: Obama's personal charisma, stirring oratory, and his campaign promise to bring change to the established political system resonated with many Democrats, especially young and minority voters. On January 3, 2008, Obama won a surprise victory in the first major nominating contest, the Iowa caucus, over Sen. Hillary Clinton, who was the overwhelming favorite to win the nomination., title: Barack Obama - 44th President, Political Career, Legacy | Britannica, link: https://www.britannica.com/biography/Barack-Obama/Politics-and-ascent-to-the-presidency, snippet: Former President Barack Obama endorsed Kamala Harris and Tim Walz for president and vice president in his keynote address at the 2024 Democratic National Convention. He criticized Donald Trump's rhetoric and policies, and praised Harris' empathy and decency., title: Watch: Barack Obama's full speech at the 2024 DNC, link: https://www.cbsnews.com/news/watch-barack-obamas-full-speech-2024-dnc-transcript/\""
       ]
      },
      "execution_count": 3,
@@ -80,6 +80,48 @@
     "search.invoke(\"Obama\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "aacc1186",
+   "metadata": {},
+   "source": [
+    "By default the results are returned as a comma-separated string of key-value pairs from the original search results. You can also choose to return the search results as a list by setting `output_format=\"list\"` or as a JSON string by setting `output_format=\"json\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5ac4602d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'snippet': 'Obama was headed to neighboring Michigan later Tuesday, among the several stops the former president is making in battleground states to encourage early voting. Harris has been spending a lot of time in the \" blue wall \" states of Wisconsin, Michigan and Pennsylvania in the final weeks of the campaign, including stops in Michigan and ...',\n",
+       "  'title': 'Obama and Walz host rally in Wisconsin as early voting kicks off | AP News',\n",
+       "  'link': 'https://apnews.com/article/wisconsin-voting-trump-harris-obama-walz-aeeff20ab17a54172263ee4778bed3dc'},\n",
+       " {'snippet': \"Obama has directed plenty of criticisms at Trump over the years, so some might perceive this as little more than the latest installment in a larger pattern. But let's not be too quick to rush ...\",\n",
+       "  'title': 'Why Obama slamming Trump on his response to Covid matters - MSNBC',\n",
+       "  'link': 'https://www.msnbc.com/rachel-maddow-show/maddowblog/obama-slamming-trump-response-covid-matters-rcna176624'},\n",
+       " {'snippet': 'Learn about the life and achievements of Barack Obama, the 44th president of the United States and the first African American to hold the office. Explore his early years, education, political career, books, awards, and more.',\n",
+       "  'title': 'Barack Obama | Biography, Parents, Education, Presidency, Books ...',\n",
+       "  'link': 'https://www.britannica.com/biography/Barack-Obama'},\n",
+       " {'snippet': \"He maintains a close friendship with Mr. Obama. He first weighed in on presidential politics to criticize President Reagan's re-election campaign, and has since supported Mr. Obama, Hillary ...\",\n",
+       "  'title': 'Bruce Springsteen to Appear With Harris and Obama at Atlanta and ...',\n",
+       "  'link': 'https://www.nytimes.com/2024/10/22/us/politics/springsteen-harris-atlanta-obama-philly.html'}]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search = DuckDuckGoSearchResults(output_format=\"list\")\n",
+    "\n",
+    "search.invoke(\"Obama\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e17ccfe7",
@@ -90,17 +132,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "21afe28d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'[snippet: Users on X have been widely comparing the boost of support felt for Kamala Harris\\' campaign to Barack Obama\\'s in 2008., title: Surging Support For Kamala Harris Compared To Obama-Era Energy, link: https://www.msn.com/en-us/news/politics/surging-support-for-kamala-harris-compared-to-obama-era-energy/ar-BB1qzdC0, date: 2024-07-24T18:27:01+00:00, source: Newsweek on MSN.com], [snippet: Harris tried to emulate Obama\\'s coalition in 2020 and failed. She may have a better shot at reaching young, Black, and Latino voters this time around., title: Harris May Follow Obama\\'s Path to the White House After All, link: https://www.msn.com/en-us/news/politics/harris-may-follow-obama-s-path-to-the-white-house-after-all/ar-BB1qv9d4, date: 2024-07-23T22:42:00+00:00, source: Intelligencer on MSN.com], [snippet: The Republican presidential candidate said in an interview on Fox News that he \"wouldn\\'t be worried\" about Michelle Obama running., title: Donald Trump Responds to Michelle Obama Threat, link: https://www.msn.com/en-us/news/politics/donald-trump-responds-to-michelle-obama-threat/ar-BB1qqtu5, date: 2024-07-22T18:26:00+00:00, source: Newsweek on MSN.com], [snippet: H eading into the weekend at his vacation home in Rehoboth Beach, Del., President Biden was reportedly stewing over Barack Obama\\'s role in the orchestrated campaign to force him, title: Opinion | Barack Obama Strikes Again, link: https://www.msn.com/en-us/news/politics/opinion-barack-obama-strikes-again/ar-BB1qrfiy, date: 2024-07-22T21:28:00+00:00, source: The Wall Street Journal on MSN.com]'"
+       "\"snippet: Springsteen, a longtime Democratic activist, will be joined by former President Barack Obama at both shows, with Harris scheduled to attend the Atlanta concert. Springsteen, a New Jersey native, has maintained a deep connection with Philadelphia throughout his career., title: Bruce Springsteen to hold battleground concerts with Kamala Harris, Barack Obama, link: https://www.statesman.com/story/news/politics/elections/2024/10/22/springsteen-obama-2024-concerts-harris/75791934007/, date: 2024-10-22T20:45:00+00:00, source: Austin American-Statesman, snippet: Obama roasts Trump's bible: 'He's Mr. Tough Guy on China except when it comes to making a few bucks', title: Obama roasts Trump's bible: 'He's Mr. Tough Guy on China except when it comes to making a few bucks', link: https://www.msnbc.com/deadline-white-house/watch/obama-roasts-trump-he-s-mr-tough-guy-on-china-except-when-it-comes-to-making-a-few-bucks-222399557750, date: 2024-10-22T20:28:00+00:00, source: MSNBC, snippet: Democratic nominee and Vice President Kamala Harris is sending a potentially powerful closer to Detroit today to shore up her standing in Michigan: Former President Barack Obama.             The campaign has been notably mum on exactly when and where Obama is speaking but as he hits the campaign trail on behalf of his fellow Democrat,, title: Watch live tonight: Former President Obama to rally for Kamala Harris in Detroit, link: https://www.msn.com/en-us/news/politics/watch-live-tonight-former-president-obama-to-rally-for-kamala-harris-in-detroit/ar-AA1sJpiB, date: 2024-10-22T18:32:26+00:00, source: Detroit Free Press on MSN.com, snippet: During a campaign rally in Wisconsin, former President Obama called into question former President Trump's mental fitness and joked about him ending a town hall to listen to music with attendees., title: 'Our playlist would be better': Obama jokes about Trump listening to music at town hall, link: https://www.msnbc.com/msnbc/watch/obama-jokes-about-trump-ending-town-hall-to-listen-to-music-222399045983, date: 2024-10-22T20:31:00+00:00, source: MSNBC\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -121,17 +163,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "c7ab3b55",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'[snippet: So war das zum Beispiel bei Barack Obama, als er sich für Joe Biden als Kandidat für die Vizepräsidentschaft entschied. USA-Expertin Rachel Tausendfreund erklärt, wie sich Kamala Harris als ..., title: Interview: „Kamala Harris sieht die Welt eher wie Barack Obama - nicht ..., link: https://www.handelsblatt.com/politik/interview-kamala-harris-sieht-die-welt-eher-wie-barack-obama-nicht-wie-joe-biden/100054923.html], [snippet: Disput um Klimapolitik Der seltsame Moment, als Kamala Harris sogar Obama vor Gericht zerrte. Teilen. 0. Chip Somodevilla/Getty Images US-Vizepräsidentin Kamala Harris (links) und Ex-Präsident ..., title: Der seltsame Moment, als Kamala Harris sogar Obama vor Gericht zerrte, link: https://www.focus.de/earth/analyse/disput-um-klimapolitik-der-seltsame-moment-als-kamala-harris-sogar-obama-vor-gericht-zerrte_id_260165157.html], [snippet: Kamala Harris «Auf den Spuren von Obama»: Harris\\' erste Rede überzeugt Experten. Kamala Harris hat ihre erste Rede als Präsidentschaftskandidatin gehalten. Zwei Experten sind sich einig: Sie ..., title: Kamala Harris\\' erste Wahlkampfrede überzeugt Experten, link: https://www.20min.ch/story/kamala-harris-auf-den-spuren-von-obama-harris-erste-rede-ueberzeugt-experten-103154550], [snippet: Harris hat ihre erste Rede als Präsidentschaftskandidatin gehalten. Experten sind sich einig: Sie hat das Potenzial, ein Feuer zu entfachen wie Obama., title: \"Auf Spuren von Obama\": Harris-Rede überzeugt Experten, link: https://www.heute.at/s/auf-spuren-von-obama-harris-rede-ueberzeugt-experten-120049557]'"
+       "'snippet: 22.10.2024, 18.21 Uhr. 1. Macht der Worte. Der beste Redner der US-Politik: Obama in Tucson. Foto: Mamta Popat / Arizona Daily Star / AP / dpa. Politik ist Sprache. Das wussten die Jakobiner im ..., title: News des Tages: Barack Obama und Donald Trump, Elon Musk, Herbert Kickl, link: https://www.spiegel.de/politik/deutschland/news-des-tages-barack-obama-und-donald-trump-elon-musk-herbert-kickl-a-c1a76de5-f9aa-4038-a1ad-f426b85267f8, snippet: Oct. 22, 2024, 12:38 p.m. ET. The rocker Bruce Springsteen will perform at a rally on Thursday in Atlanta, appearing alongside Vice President Kamala Harris and former President Barack Obama, as ..., title: Bruce Springsteen to Appear With Harris and Obama at Atlanta and ..., link: https://www.nytimes.com/2024/10/22/us/politics/springsteen-harris-atlanta-obama-philly.html, snippet: In-person early voting has kicked off across battleground Wisconsin, with former President Barack Obama and Democratic vice presidential nominee Tim Walz hosting a rally in liberal Madison, title: Watch live: Barack Obama and VP nominee Tim Walz hold rally in ..., link: https://news.sky.com/video/watch-live-barack-obama-and-and-vp-nominee-tim-walz-hold-rally-in-wisconsin-13239026, snippet: Barack Obama will speak to a campaign rally for Kamala Harris in Madison, Wisconsin this afternoon. The former US president was expected to be joined by Tim Walz, the Democratic vice presidential ..., title: Obama-Walz rally live: Former president campaigns for Harris in Madison, link: https://www.telegraph.co.uk/us/politics/2024/10/22/obama-walz-rally-campaigns-kamala-harris-madison-wisconsin/'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -173,7 +215,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.12.3"
   },
   "vscode": {
    "interpreter": {
diff --git a/libs/community/langchain_community/tools/ddg_search/tool.py b/libs/community/langchain_community/tools/ddg_search/tool.py
index f2889daa716b2..7db9b77da4273 100644
--- a/libs/community/langchain_community/tools/ddg_search/tool.py
+++ b/libs/community/langchain_community/tools/ddg_search/tool.py
@@ -1,7 +1,8 @@
 """Tool for the DuckDuckGo search API."""
 
+import json
 import warnings
-from typing import Any, List, Optional, Type
+from typing import Any, List, Literal, Optional, Type, Union
 
 from langchain_core.callbacks import CallbackManagerForToolRun
 from langchain_core.tools import BaseTool
@@ -74,14 +75,14 @@ def _run(
 
 
 class DuckDuckGoSearchResults(BaseTool):
-    """Tool that queries the DuckDuckGo search API and gets back json string."""
+    """Tool that queries the DuckDuckGo search API and
+    returns the results in `output_format`."""
 
     name: str = "duckduckgo_results_json"
     description: str = (
         "A wrapper around Duck Duck Go Search. "
         "Useful for when you need to answer questions about current events. "
-        "Input should be a search query. Output is a JSON string array of the "
-        "query results"
+        "Input should be a search query."
     )
     max_results: int = Field(alias="num_results", default=4)
     api_wrapper: DuckDuckGoSearchAPIWrapper = Field(
@@ -93,25 +94,45 @@ class DuckDuckGoSearchResults(BaseTool):
     """Which keys from each result to include. If None all keys are included."""
     results_separator: str = ", "
     """Character for separating results."""
+    output_format: Literal["string", "json", "list"] = "string"
+    """Output format of the search results.
+
+    - 'string': Return a concatenated string of the search results.
+    - 'json': Return a JSON string of the search results.
+    - 'list': Return a list of dictionaries of the search results.
+    """
+    response_format: Literal["content_and_artifact"] = "content_and_artifact"
 
     def _run(
         self,
         query: str,
         run_manager: Optional[CallbackManagerForToolRun] = None,
-    ) -> str:
+    ) -> tuple[Union[List[dict], str], List[dict]]:
         """Use the tool."""
-        res = self.api_wrapper.results(query, self.max_results, source=self.backend)
-        res_strs = [
-            ", ".join(
-                [
-                    f"{k}: {v}"
-                    for k, v in d.items()
-                    if not self.keys_to_include or k in self.keys_to_include
-                ]
-            )
-            for d in res
+        raw_results = self.api_wrapper.results(
+            query, self.max_results, source=self.backend
+        )
+        results = [
+            {
+                k: v
+                for k, v in d.items()
+                if not self.keys_to_include or k in self.keys_to_include
+            }
+            for d in raw_results
         ]
-        return self.results_separator.join(res_strs)
+
+        if self.output_format == "list":
+            return results, raw_results
+        elif self.output_format == "json":
+            return json.dumps(results), raw_results
+        elif self.output_format == "string":
+            res_strs = [", ".join([f"{k}: {v}" for k, v in d.items()]) for d in results]
+            return self.results_separator.join(res_strs), raw_results
+        else:
+            raise ValueError(
+                f"Invalid output_format: {self.output_format}. "
+                "Needs to be one of 'string', 'json', 'list'."
+            )
 
 
 def DuckDuckGoSearchTool(*args: Any, **kwargs: Any) -> DuckDuckGoSearchRun:

From f203229b513aa0f04704388081a501a7d8773eeb Mon Sep 17 00:00:00 2001
From: Lei Zhang <zhanglei@apache.org>
Date: Thu, 24 Oct 2024 09:17:10 +0800
Subject: [PATCH 58/84] community: Fix the failure of ChatSparkLLM after
 upgrading to Pydantic V2 (#27418)

**Description:**

The test_sparkllm.py can reproduce this issue.


https://github.com/langchain-ai/langchain/blob/master/libs/community/tests/integration_tests/chat_models/test_sparkllm.py#L66

```
Testing started at 18:27 ...
Launching pytest with arguments test_sparkllm.py::test_chat_spark_llm --no-header --no-summary -q in /Users/zhanglei/Work/github/langchain/libs/community/tests/integration_tests/chat_models

============================= test session starts ==============================
collecting ... collected 1 item

test_sparkllm.py::test_chat_spark_llm

============================== 1 failed in 0.45s ===============================
FAILED                             [100%]
tests/integration_tests/chat_models/test_sparkllm.py:65 (test_chat_spark_llm)
def test_chat_spark_llm() -> None:
>       chat = ChatSparkLLM(
            spark_app_id="your spark_app_id",
            spark_api_key="your spark_api_key",
            spark_api_secret="your spark_api_secret",
        )  # type: ignore[call-arg]

test_sparkllm.py:67:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../../../core/langchain_core/load/serializable.py:111: in __init__
    super().__init__(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

cls = <class 'langchain_community.chat_models.sparkllm.ChatSparkLLM'>
values = {'spark_api_key': 'your spark_api_key', 'spark_api_secret': 'your spark_api_secret', 'spark_api_url': 'wss://spark-api.xf-yun.com/v3.5/chat', 'spark_app_id': 'your spark_app_id', ...}

    @model_validator(mode="before")
    @classmethod
    def validate_environment(cls, values: Dict) -> Any:
        values["spark_app_id"] = get_from_dict_or_env(
            values,
            ["spark_app_id", "app_id"],
            "IFLYTEK_SPARK_APP_ID",
        )
        values["spark_api_key"] = get_from_dict_or_env(
            values,
            ["spark_api_key", "api_key"],
            "IFLYTEK_SPARK_API_KEY",
        )
        values["spark_api_secret"] = get_from_dict_or_env(
            values,
            ["spark_api_secret", "api_secret"],
            "IFLYTEK_SPARK_API_SECRET",
        )
        values["spark_api_url"] = get_from_dict_or_env(
            values,
            "spark_api_url",
            "IFLYTEK_SPARK_API_URL",
            SPARK_API_URL,
        )
        values["spark_llm_domain"] = get_from_dict_or_env(
            values,
            "spark_llm_domain",
            "IFLYTEK_SPARK_LLM_DOMAIN",
            SPARK_LLM_DOMAIN,
        )

        # put extra params into model_kwargs
        default_values = {
            name: field.default
            for name, field in get_fields(cls).items()
            if field.default is not None
        }
>       values["model_kwargs"]["temperature"] = default_values.get("temperature")
E       KeyError: 'model_kwargs'

../../../langchain_community/chat_models/sparkllm.py:368: KeyError
```

I found that when upgrading to Pydantic v2, @root_validator was changed
to @model_validator. When a class declares multiple
@model_validator(model=before), the execution order in V1 and V2 is
opposite. This is the reason for ChatSparkLLM's failure.

The correct execution order is to execute build_extra first.


https://github.com/langchain-ai/langchain/blob/langchain%3D%3D0.2.16/libs/community/langchain_community/chat_models/sparkllm.py#L302

And then execute validate_environment.


https://github.com/langchain-ai/langchain/blob/langchain%3D%3D0.2.16/libs/community/langchain_community/chat_models/sparkllm.py#L329

The Pydantic community also discusses it, but there hasn't been a
conclusion yet. https://github.com/pydantic/pydantic/discussions/7434

**Issus:** #27416

**Twitter handle:** coolbeevip

---------

Co-authored-by: vbarda <vadym@langchain.dev>
---
 libs/community/extended_testing_deps.txt      |  1 +
 .../chat_models/sparkllm.py                   | 60 ++++++++++---------
 .../unit_tests/chat_models/test_sparkllm.py   | 24 ++++++++
 3 files changed, 57 insertions(+), 28 deletions(-)

diff --git a/libs/community/extended_testing_deps.txt b/libs/community/extended_testing_deps.txt
index b2548b2219394..56caca04381cf 100644
--- a/libs/community/extended_testing_deps.txt
+++ b/libs/community/extended_testing_deps.txt
@@ -95,3 +95,4 @@ xmltodict>=0.13.0,<0.14
 nanopq==0.2.1
 mlflow[genai]>=2.14.0
 databricks-sdk>=0.30.0
+websocket>=0.2.1,<1
\ No newline at end of file
diff --git a/libs/community/langchain_community/chat_models/sparkllm.py b/libs/community/langchain_community/chat_models/sparkllm.py
index 996c8c2a2f67f..3b7d1d47d29cf 100644
--- a/libs/community/langchain_community/chat_models/sparkllm.py
+++ b/libs/community/langchain_community/chat_models/sparkllm.py
@@ -300,34 +300,6 @@ def lc_secrets(self) -> Dict[str, str]:
         populate_by_name=True,
     )
 
-    @model_validator(mode="before")
-    @classmethod
-    def build_extra(cls, values: Dict[str, Any]) -> Any:
-        """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = get_pydantic_field_names(cls)
-        extra = values.get("model_kwargs", {})
-        for field_name in list(values):
-            if field_name in extra:
-                raise ValueError(f"Found {field_name} supplied twice.")
-            if field_name not in all_required_field_names:
-                logger.warning(
-                    f"""WARNING! {field_name} is not default parameter.
-                    {field_name} was transferred to model_kwargs.
-                    Please confirm that {field_name} is what you intended."""
-                )
-                extra[field_name] = values.pop(field_name)
-
-        invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
-        if invalid_model_kwargs:
-            raise ValueError(
-                f"Parameters {invalid_model_kwargs} should be specified explicitly. "
-                f"Instead they were passed in as part of `model_kwargs` parameter."
-            )
-
-        values["model_kwargs"] = extra
-
-        return values
-
     @model_validator(mode="before")
     @classmethod
     def validate_environment(cls, values: Dict) -> Any:
@@ -378,6 +350,38 @@ def validate_environment(cls, values: Dict) -> Any:
         )
         return values
 
+    # When using Pydantic V2
+    # The execution order of multiple @model_validator decorators is opposite to
+    # their declaration order. https://github.com/pydantic/pydantic/discussions/7434
+
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
+        """Build extra kwargs from additional params that were passed in."""
+        all_required_field_names = get_pydantic_field_names(cls)
+        extra = values.get("model_kwargs", {})
+        for field_name in list(values):
+            if field_name in extra:
+                raise ValueError(f"Found {field_name} supplied twice.")
+            if field_name not in all_required_field_names:
+                logger.warning(
+                    f"""WARNING! {field_name} is not default parameter.
+                    {field_name} was transferred to model_kwargs.
+                    Please confirm that {field_name} is what you intended."""
+                )
+                extra[field_name] = values.pop(field_name)
+
+        invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
+        if invalid_model_kwargs:
+            raise ValueError(
+                f"Parameters {invalid_model_kwargs} should be specified explicitly. "
+                f"Instead they were passed in as part of `model_kwargs` parameter."
+            )
+
+        values["model_kwargs"] = extra
+
+        return values
+
     def _stream(
         self,
         messages: List[BaseMessage],
diff --git a/libs/community/tests/unit_tests/chat_models/test_sparkllm.py b/libs/community/tests/unit_tests/chat_models/test_sparkllm.py
index 6d7e4cf6aa868..f4c768a75d23b 100644
--- a/libs/community/tests/unit_tests/chat_models/test_sparkllm.py
+++ b/libs/community/tests/unit_tests/chat_models/test_sparkllm.py
@@ -1,3 +1,4 @@
+import pytest
 from langchain_core.messages import (
     AIMessage,
     HumanMessage,
@@ -8,6 +9,7 @@
 )
 
 from langchain_community.chat_models.sparkllm import (
+    ChatSparkLLM,
     convert_dict_to_message,
     convert_message_to_dict,
 )
@@ -83,3 +85,25 @@ def test__convert_message_to_dict_system() -> None:
     result = convert_message_to_dict(message)
     expected_output = {"role": "system", "content": "foo"}
     assert result == expected_output
+
+
+@pytest.mark.requires("websocket")
+def test__chat_spark_llm_initialization() -> None:
+    chat = ChatSparkLLM(
+        app_id="IFLYTEK_SPARK_APP_ID",
+        api_key="IFLYTEK_SPARK_API_KEY",
+        api_secret="IFLYTEK_SPARK_API_SECRET",
+        api_url="IFLYTEK_SPARK_API_URL",
+        model="IFLYTEK_SPARK_LLM_DOMAIN",
+        timeout=40,
+        temperature=0.1,
+        top_k=3,
+    )
+    assert chat.spark_app_id == "IFLYTEK_SPARK_APP_ID"
+    assert chat.spark_api_key == "IFLYTEK_SPARK_API_KEY"
+    assert chat.spark_api_secret == "IFLYTEK_SPARK_API_SECRET"
+    assert chat.spark_api_url == "IFLYTEK_SPARK_API_URL"
+    assert chat.spark_llm_domain == "IFLYTEK_SPARK_LLM_DOMAIN"
+    assert chat.request_timeout == 40
+    assert chat.temperature == 0.1
+    assert chat.top_k == 3

From d081a5400ab748a952b67a19f1d17758a215ebcf Mon Sep 17 00:00:00 2001
From: Eugene Yurtsev <eyurtsev@gmail.com>
Date: Wed, 23 Oct 2024 21:26:38 -0400
Subject: [PATCH 59/84] docs: fix more links (#27598)

Fix more links
---
 docs/docs/how_to/agent_executor.ipynb         |  2 +-
 .../how_to/example_selectors_langsmith.ipynb  |  2 +-
 docs/docs/how_to/few_shot_examples.ipynb      |  2 +-
 docs/docs/how_to/few_shot_examples_chat.ipynb |  2 +-
 docs/docs/how_to/index.mdx                    |  2 +-
 docs/docs/integrations/retrievers/index.mdx   |  2 +-
 docs/docs/integrations/vectorstores/index.mdx |  2 +-
 docs/docs/tutorials/llm_chain.ipynb           |  2 +-
 docs/docs/tutorials/local_rag.ipynb           |  2 +-
 docs/docs/tutorials/pdf_qa.ipynb              |  4 +-
 docs/docs/tutorials/qa_chat_history.ipynb     |  2 +-
 docs/docs/tutorials/query_analysis.ipynb      |  2 +-
 docs/docs/tutorials/rag.ipynb                 |  2 +-
 docs/scripts/append_related_links.py          | 82 +++++++++++--------
 14 files changed, 61 insertions(+), 49 deletions(-)

diff --git a/docs/docs/how_to/agent_executor.ipynb b/docs/docs/how_to/agent_executor.ipynb
index 995b631f17bb6..647e4c6a1176a 100644
--- a/docs/docs/how_to/agent_executor.ipynb
+++ b/docs/docs/how_to/agent_executor.ipynb
@@ -35,7 +35,7 @@
     "- Creating a [Retriever](/docs/concepts/retrievers) to expose specific information to our agent\n",
     "- Using a Search [Tool](/docs/concepts/tools) to look up things online\n",
     "- [`Chat History`](/docs/concepts/chat_history), which allows a chatbot to \"remember\" past interactions and take them into account when responding to follow-up questions. \n",
-    "- Debugging and tracing your application using [LangSmith](/docs/concepts/#langsmith)\n",
+    "- Debugging and tracing your application using [LangSmith](https://docs.smith.langchain.com/)\n",
     "\n",
     "## Setup\n",
     "\n",
diff --git a/docs/docs/how_to/example_selectors_langsmith.ipynb b/docs/docs/how_to/example_selectors_langsmith.ipynb
index 2c59ed313cdb1..efc9e2db46d3b 100644
--- a/docs/docs/how_to/example_selectors_langsmith.ipynb
+++ b/docs/docs/how_to/example_selectors_langsmith.ipynb
@@ -13,7 +13,7 @@
     "<Prerequisites titlesAndLinks={[\n",
     "  [\"Chat models\", \"/docs/concepts/chat_models\"],\n",
     "  [\"Few-shot-prompting\", \"/docs/concepts/few-shot-prompting\"],\n",
-    "  [\"LangSmith\", \"/docs/concepts/#langsmith\"],\n",
+    "  [\"LangSmith\", \"https://docs.smith.langchain.com/\"],\n",
     "]} />\n",
     "\n",
     "\n",
diff --git a/docs/docs/how_to/few_shot_examples.ipynb b/docs/docs/how_to/few_shot_examples.ipynb
index d583be400ab7b..6c8d0926f03e8 100644
--- a/docs/docs/how_to/few_shot_examples.ipynb
+++ b/docs/docs/how_to/few_shot_examples.ipynb
@@ -23,7 +23,7 @@
     "- [Prompt templates](/docs/concepts/prompt_templates)\n",
     "- [Example selectors](/docs/concepts/example_selectors)\n",
     "- [LLMs](/docs/concepts/text_llms)\n",
-    "- [Vectorstores](/docs/concepts/#vector-stores)\n",
+    "- [Vectorstores](/docs/concepts/vectorstores)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/few_shot_examples_chat.ipynb b/docs/docs/how_to/few_shot_examples_chat.ipynb
index 1192a211f1152..51e41f65e409e 100644
--- a/docs/docs/how_to/few_shot_examples_chat.ipynb
+++ b/docs/docs/how_to/few_shot_examples_chat.ipynb
@@ -23,7 +23,7 @@
     "- [Prompt templates](/docs/concepts/prompt_templates)\n",
     "- [Example selectors](/docs/concepts/example_selectors)\n",
     "- [Chat models](/docs/concepts/chat_models)\n",
-    "- [Vectorstores](/docs/concepts/#vector-stores)\n",
+    "- [Vectorstores](/docs/concepts/vectorstores)\n",
     "\n",
     ":::\n",
     "\n",
diff --git a/docs/docs/how_to/index.mdx b/docs/docs/how_to/index.mdx
index 3ca54fe42a52e..7e1b0535f09ee 100644
--- a/docs/docs/how_to/index.mdx
+++ b/docs/docs/how_to/index.mdx
@@ -159,7 +159,7 @@ What LangChain calls [LLMs](/docs/concepts/text_llms) are older forms of languag
 
 ### Vector stores
 
-[Vector stores](/docs/concepts/#vector-stores) are databases that can efficiently store and retrieve embeddings.
+[Vector stores](/docs/concepts/vectorstores) are databases that can efficiently store and retrieve embeddings.
 
 - [How to: use a vector store to retrieve data](/docs/how_to/vectorstores)
 
diff --git a/docs/docs/integrations/retrievers/index.mdx b/docs/docs/integrations/retrievers/index.mdx
index dee97cbb13e0c..4bbc458c563fa 100644
--- a/docs/docs/integrations/retrievers/index.mdx
+++ b/docs/docs/integrations/retrievers/index.mdx
@@ -16,7 +16,7 @@ Retrievers accept a string query as input and return a list of [Documents](https
 
 For specifics on how to use retrievers, see the [relevant how-to guides here](/docs/how_to/#retrievers).
 
-Note that all [vector stores](/docs/concepts/#vector-stores) can be [cast to retrievers](/docs/how_to/vectorstore_retriever/).
+Note that all [vector stores](/docs/concepts/vectorstores) can be [cast to retrievers](/docs/how_to/vectorstore_retriever/).
 Refer to the vector store [integration docs](/docs/integrations/vectorstores/) for available vector stores.
 This page lists custom retrievers, implemented via subclassing [BaseRetriever](/docs/how_to/custom_retriever/).
 
diff --git a/docs/docs/integrations/vectorstores/index.mdx b/docs/docs/integrations/vectorstores/index.mdx
index d6631ce945743..8b276057dad21 100644
--- a/docs/docs/integrations/vectorstores/index.mdx
+++ b/docs/docs/integrations/vectorstores/index.mdx
@@ -7,7 +7,7 @@ sidebar_class_name: hidden
 
 import { CategoryTable, IndexTable } from "@theme/FeatureTables";
 
-A [vector store](/docs/concepts/#vector-stores) stores [embedded](/docs/concepts/embedding_models) data and performs similarity search.
+A [vector store](/docs/concepts/vectorstores) stores [embedded](/docs/concepts/embedding_models) data and performs similarity search.
 
 <CategoryTable category="vectorstores" />
 
diff --git a/docs/docs/tutorials/llm_chain.ipynb b/docs/docs/tutorials/llm_chain.ipynb
index d9bd9500bce4c..0a04d91876f1a 100644
--- a/docs/docs/tutorials/llm_chain.ipynb
+++ b/docs/docs/tutorials/llm_chain.ipynb
@@ -27,7 +27,7 @@
     "\n",
     "- Using [LangChain Expression Language (LCEL)](/docs/concepts/lcel) to chain components together\n",
     "\n",
-    "- Debugging and tracing your application using [LangSmith](/docs/concepts/#langsmith)\n",
+    "- Debugging and tracing your application using [LangSmith](https://docs.smith.langchain.com/)\n",
     "\n",
     "- Deploying your application with [LangServe](/docs/concepts/#langserve)\n",
     "\n",
diff --git a/docs/docs/tutorials/local_rag.ipynb b/docs/docs/tutorials/local_rag.ipynb
index d98b07732a513..a43f764d4c19d 100644
--- a/docs/docs/tutorials/local_rag.ipynb
+++ b/docs/docs/tutorials/local_rag.ipynb
@@ -14,7 +14,7 @@
     "- [Chat Models](/docs/concepts/chat_models)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "- [Embeddings](/docs/concepts/embedding_models)\n",
-    "- [Vector stores](/docs/concepts/#vector-stores)\n",
+    "- [Vector stores](/docs/concepts/vectorstores)\n",
     "- [Retrieval-augmented generation](/docs/tutorials/rag/)\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/tutorials/pdf_qa.ipynb b/docs/docs/tutorials/pdf_qa.ipynb
index d5af436708abf..bdc5792eac8b9 100644
--- a/docs/docs/tutorials/pdf_qa.ipynb
+++ b/docs/docs/tutorials/pdf_qa.ipynb
@@ -26,7 +26,7 @@
     "- [Document loaders](/docs/concepts/document_loaders)\n",
     "- [Chat models](/docs/concepts/chat_models)\n",
     "- [Embeddings](/docs/concepts/embedding_models)\n",
-    "- [Vector stores](/docs/concepts/#vector-stores)\n",
+    "- [Vector stores](/docs/concepts/vectorstores)\n",
     "- [Retrieval-augmented generation](/docs/tutorials/rag/)\n",
     "\n",
     ":::\n",
@@ -117,7 +117,7 @@
     "\n",
     "## Question answering with RAG\n",
     "\n",
-    "Next, you'll prepare the loaded documents for later retrieval. Using a [text splitter](/docs/concepts/text_splitters), you'll split your loaded documents into smaller documents that can more easily fit into an LLM's context window, then load them into a [vector store](/docs/concepts/#vector-stores). You can then create a [retriever](/docs/concepts/retrievers) from the vector store for use in our RAG chain:\n",
+    "Next, you'll prepare the loaded documents for later retrieval. Using a [text splitter](/docs/concepts/text_splitters), you'll split your loaded documents into smaller documents that can more easily fit into an LLM's context window, then load them into a [vector store](/docs/concepts/vectorstores). You can then create a [retriever](/docs/concepts/retrievers) from the vector store for use in our RAG chain:\n",
     "\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
diff --git a/docs/docs/tutorials/qa_chat_history.ipynb b/docs/docs/tutorials/qa_chat_history.ipynb
index a74959d05b35d..8aff4c3a01420 100644
--- a/docs/docs/tutorials/qa_chat_history.ipynb
+++ b/docs/docs/tutorials/qa_chat_history.ipynb
@@ -24,7 +24,7 @@
     "- [Chat history](/docs/concepts/chat_history)\n",
     "- [Chat models](/docs/concepts/chat_models)\n",
     "- [Embeddings](/docs/concepts/embedding_models)\n",
-    "- [Vector stores](/docs/concepts/#vector-stores)\n",
+    "- [Vector stores](/docs/concepts/vectorstores)\n",
     "- [Retrieval-augmented generation](/docs/tutorials/rag/)\n",
     "- [Tools](/docs/concepts/tools)\n",
     "- [Agents](/docs/concepts/agents)\n",
diff --git a/docs/docs/tutorials/query_analysis.ipynb b/docs/docs/tutorials/query_analysis.ipynb
index 102febcf1aa4d..c5bf42f4ce8d0 100644
--- a/docs/docs/tutorials/query_analysis.ipynb
+++ b/docs/docs/tutorials/query_analysis.ipynb
@@ -24,7 +24,7 @@
     "- [Document loaders](/docs/concepts/document_loaders)\n",
     "- [Chat models](/docs/concepts/chat_models)\n",
     "- [Embeddings](/docs/concepts/embedding_models)\n",
-    "- [Vector stores](/docs/concepts/#vector-stores)\n",
+    "- [Vector stores](/docs/concepts/vectorstores)\n",
     "- [Retrieval](/docs/concepts/retrieval)\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/tutorials/rag.ipynb b/docs/docs/tutorials/rag.ipynb
index dd2299346df13..8eb07fbb5cfdd 100644
--- a/docs/docs/tutorials/rag.ipynb
+++ b/docs/docs/tutorials/rag.ipynb
@@ -41,7 +41,7 @@
     "### Indexing\n",
     "1. **Load**: First we need to load our data. This is done with [Document Loaders](/docs/concepts/document_loaders).\n",
     "2. **Split**: [Text splitters](/docs/concepts/text_splitters) break large `Documents` into smaller chunks. This is useful both for indexing data and for passing it in to a model, since large chunks are harder to search over and won't fit in a model's finite context window.\n",
-    "3. **Store**: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a [VectorStore](/docs/concepts/#vector-stores) and [Embeddings](/docs/concepts/embedding_models) model.\n",
+    "3. **Store**: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a [VectorStore](/docs/concepts/vectorstores) and [Embeddings](/docs/concepts/embedding_models) model.\n",
     "\n",
     "![index_diagram](../../static/img/rag_indexing.png)\n",
     "\n",
diff --git a/docs/scripts/append_related_links.py b/docs/scripts/append_related_links.py
index 49746bcc9e324..9689b432dcabc 100644
--- a/docs/scripts/append_related_links.py
+++ b/docs/scripts/append_related_links.py
@@ -3,49 +3,61 @@
 import re
 import sys
 from pathlib import Path
+from typing import Optional
 
+# List of 4-tuples (integration_name, display_name, concept_page, how_to_fragment)
+INTEGRATION_INFO = [
+    ("chat", "Chat model", "chat_models", "chat-models"),
+    ("llms", "LLM", "text_llms", "llms"),
+    ("text_embedding", "Embedding model", "embedding_models", "embedding-models"),
+    ("document_loaders", "Document loader", "document_loaders", "document-loaders"),
+    ("vectorstores", "Vector store", "vectorstores", "vector-stores"),
+    ("retrievers", "Retriever", "retrievers", "retrievers"),
+    ("tools", "Tool", "tools", "tools"),
+    # stores is a special case because there are no key-value store how-tos yet
+    # this is a placeholder for when we do have them
+    # for now the related links section will only contain the conceptual guide.
+    ("stores", "Key-value store", "key_value_stores", "key-value-stores"),
+]
 
-def _generate_related_links_section(integration_type: str, notebook_name: str):
-    concept_display_name = None
-    concept_heading = None
-    if integration_type == "chat":
-        concept_display_name = "Chat model"
-        concept_heading = "chat-models"
-    elif integration_type == "llms":
-        concept_display_name = "LLM"
-        concept_heading = "llms"
-    elif integration_type == "text_embedding":
-        concept_display_name = "Embedding model"
-        concept_heading = "embedding-models"
-    elif integration_type == "document_loaders":
-        concept_display_name = "Document loader"
-        concept_heading = "document-loaders"
-    elif integration_type == "vectorstores":
-        concept_display_name = "Vector store"
-        concept_heading = "vector-stores"
-    elif integration_type == "retrievers":
-        concept_display_name = "Retriever"
-        concept_heading = "retrievers"
-    elif integration_type == "tools":
-        concept_display_name = "Tool"
-        concept_heading = "tools"
-    elif integration_type == "stores":
-        concept_display_name = "Key-value store"
-        concept_heading = "key-value-stores"
-        # Special case because there are no key-value store how-tos yet
-        return f"""## Related
+# Create a dictionary with key being the first element (integration_name) and value being the rest of the tuple
+INTEGRATION_INFO_DICT = {
+    integration_name: rest for integration_name, *rest in INTEGRATION_INFO
+}
 
-- [{concept_display_name} conceptual guide](/docs/concepts/#{concept_heading})
+RELATED_LINKS_SECTION = """## Related
+- {concept_display_name} [conceptual guide](/docs/concepts/{concept_page})
+- {concept_display_name} [how-to guides](/docs/how_to/#{how_to_fragment})
 """
-    else:
-        return None
-    return f"""## Related
 
-- {concept_display_name} [conceptual guide](/docs/concepts/#{concept_heading})
-- {concept_display_name} [how-to guides](/docs/how_to/#{concept_heading})
+RELATED_LINKS_WITHOUT_HOW_TO_SECTION = """## Related
+- {concept_display_name} [conceptual guide](/docs/concepts/{concept_page})
 """
 
 
+def _generate_related_links_section(
+    integration_type: str, notebook_name: str
+) -> Optional[str]:
+    if integration_type not in INTEGRATION_INFO_DICT:
+        return None
+    concept_display_name, concept_page, how_to_fragment = INTEGRATION_INFO_DICT[
+        integration_type
+    ]
+
+    # Special case because there are no key-value store how-tos yet
+    if integration_type == "stores":
+        return RELATED_LINKS_WITHOUT_HOW_TO_SECTION.format(
+            concept_display_name=concept_display_name,
+            concept_page=concept_page,
+        )
+
+    return RELATED_LINKS_SECTION.format(
+        concept_display_name=concept_display_name,
+        concept_page=concept_page,
+        how_to_fragment=how_to_fragment,
+    )
+
+
 def _process_path(doc_path: Path):
     content = doc_path.read_text()
     pattern = r"/docs/integrations/([^/]+)/([^/]+).mdx?"

From 61897aef900ae02973298aefcc250d90586c2bcf Mon Sep 17 00:00:00 2001
From: hippopond <160084905+hippopond@users.noreply.github.com>
Date: Thu, 24 Oct 2024 08:04:18 -0700
Subject: [PATCH 60/84] docs: Fix for spelling mistake (#27599)

Fixes #26009

Thank you for contributing to LangChain!

- [x] **PR title**: "docs: Correcting spelling mistake"


- [x] **PR message**:
    - **Description:** Corrected spelling from "trianed" to "trained"
    - **Issue:** the issue #26009
    - **Dependencies:** NA
    - **Twitter handle:** NA


- [ ] **Add tests and docs**: NA


- [ ] **Lint and test**:

Co-authored-by: Libby Lin <libbylin@Libbys-MacBook-Pro.local>
---
 docs/docs/how_to/output_parser_xml.ipynb  | 2 +-
 docs/docs/how_to/output_parser_yaml.ipynb | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/docs/how_to/output_parser_xml.ipynb b/docs/docs/how_to/output_parser_xml.ipynb
index 0ae3d1ed0d41b..d01b5990feda0 100644
--- a/docs/docs/how_to/output_parser_xml.ipynb
+++ b/docs/docs/how_to/output_parser_xml.ipynb
@@ -18,7 +18,7 @@
     "\n",
     ":::\n",
     "\n",
-    "LLMs from different providers often have different strengths depending on the specific data they are trianed on. This also means that some may be \"better\" and more reliable at generating output in formats other than JSON.\n",
+    "LLMs from different providers often have different strengths depending on the specific data they are trained on. This also means that some may be \"better\" and more reliable at generating output in formats other than JSON.\n",
     "\n",
     "This guide shows you how to use the [`XMLOutputParser`](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html) to prompt models for XML output, then and parse that output into a usable format.\n",
     "\n",
diff --git a/docs/docs/how_to/output_parser_yaml.ipynb b/docs/docs/how_to/output_parser_yaml.ipynb
index fedc1f88fd803..392873538d3db 100644
--- a/docs/docs/how_to/output_parser_yaml.ipynb
+++ b/docs/docs/how_to/output_parser_yaml.ipynb
@@ -18,7 +18,7 @@
     "\n",
     ":::\n",
     "\n",
-    "LLMs from different providers often have different strengths depending on the specific data they are trianed on. This also means that some may be \"better\" and more reliable at generating output in formats other than JSON.\n",
+    "LLMs from different providers often have different strengths depending on the specific data they are trained on. This also means that some may be \"better\" and more reliable at generating output in formats other than JSON.\n",
     "\n",
     "This output parser allows users to specify an arbitrary schema and query LLMs for outputs that conform to that schema, using YAML to format their response.\n",
     "\n",

From 133c1b4f7601883294b5124359c3712fcb250db2 Mon Sep 17 00:00:00 2001
From: QiQi <57396324+laududu-cc@users.noreply.github.com>
Date: Thu, 24 Oct 2024 23:05:06 +0800
Subject: [PATCH 61/84] docs: Update passthrough.ipynb --  Grammar correction
 (#27601)

Grammar correction needed in passthrough.ipynb
The sentence is:

"Now you've learned how to pass data through your chains to help to help
format the data flowing through your chains."

There's a redundant "to help", and it could be more succinctly written
as:

"Now you've learned how to pass data through your chains to help format
the data flowing through your chains."
---
 docs/docs/how_to/passthrough.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/how_to/passthrough.ipynb b/docs/docs/how_to/passthrough.ipynb
index 6a1aa359187fd..ab4c22d06478b 100644
--- a/docs/docs/how_to/passthrough.ipynb
+++ b/docs/docs/how_to/passthrough.ipynb
@@ -153,7 +153,7 @@
     "\n",
     "## Next steps\n",
     "\n",
-    "Now you've learned how to pass data through your chains to help to help format the data flowing through your chains.\n",
+    "Now you've learned how to pass data through your chains to help format the data flowing through your chains.\n",
     "\n",
     "To learn more, see the other how-to guides on runnables in this section."
    ]

From da6b5267708ad8a77c832e8a56ff5ee01c8d9716 Mon Sep 17 00:00:00 2001
From: Daniel Vu Dao <danielvdao@users.noreply.github.com>
Date: Thu, 24 Oct 2024 08:05:43 -0700
Subject: [PATCH 62/84] docs: Update `Runnable` documentation (#27606)

**Description**
Adds better code formatting for one of the docs.
---
 docs/docs/how_to/output_parser_structured.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/how_to/output_parser_structured.ipynb b/docs/docs/how_to/output_parser_structured.ipynb
index aaff800f9549c..f205cd0b5504a 100644
--- a/docs/docs/how_to/output_parser_structured.ipynb
+++ b/docs/docs/how_to/output_parser_structured.ipynb
@@ -214,7 +214,7 @@
    "id": "3ca23082-c602-4ee8-af8c-a185b1f42bd1",
    "metadata": {},
    "source": [
-    "While the PydanticOutputParser cannot:"
+    "While the `PydanticOutputParser` cannot:"
    ]
   },
   {

From 2d58a8a08de7bd1ae0cd788d38d164d4b89ec356 Mon Sep 17 00:00:00 2001
From: Adarsh Sahu <118628700+yaarAdarsh@users.noreply.github.com>
Date: Thu, 24 Oct 2024 20:43:28 +0530
Subject: [PATCH 63/84] docs: Update structured_outputs.mdx (#27613)

`strightforward` => `straightforward`
`adavanced` => `advanced`
`There a few challenges` => `There are a few challenges`

Documentation Correction:

*
[`docs/docs/concepts/structured_output.mdx`]:
Corrected several typos in the sentence directing users to the API
reference.
---
 docs/docs/concepts/structured_outputs.mdx | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/docs/concepts/structured_outputs.mdx b/docs/docs/concepts/structured_outputs.mdx
index f58150d5c609d..a334ecc1276f4 100644
--- a/docs/docs/concepts/structured_outputs.mdx
+++ b/docs/docs/concepts/structured_outputs.mdx
@@ -66,7 +66,7 @@ Several more powerful methods that utilizes native features in the model provide
 
 Many [model providers support](/docs/integrations/chat/) tool calling, a concept discussed in more detail in our [tool calling guide](/docs/concepts/tool_calling/).
 In short, tool calling involves binding a tool to a model and, when appropriate, the model can *decide* to call this tool and ensure its response conforms to the tool's schema.
-With this in mind, the central concept is strightforward: *simply bind our schema to a model as a tool!*
+With this in mind, the central concept is straightforward: *simply bind our schema to a model as a tool!*
 Here is an example using the `ResponseFormatter` schema defined above:
 
 ```python
@@ -106,7 +106,7 @@ ai_msg.content
 ```
 
 One important point to flag: the model *still* returns a string, which needs to be parsed into a JSON object.
-This can, of course, simply use the `json` library or a JSON output parser if you need more adavanced functionality.
+This can, of course, simply use the `json` library or a JSON output parser if you need more advanced functionality.
 See this [how-to guide on the JSON output parser](/docs/how_to/output_parser_json) for more details.
 
 ```python
@@ -117,7 +117,7 @@ json_object = json.loads(ai_msg.content)
 
 ## Structured output method 
 
-There a few challenges when producing structured output with the above methods: 
+There are a few challenges when producing structured output with the above methods: 
 
 (1) If using tool calling, tool call arguments needs to be parsed from a dictionary back to the original schema.  
 
@@ -145,4 +145,4 @@ ResponseFormatter(answer="The powerhouse of the cell is the mitochondrion. Mitoc
 
 For more details on usage, see our [how-to guide](/docs/how_to/structured_output/#the-with_structured_output-method).
 
-:::
\ No newline at end of file
+:::

From 20b56a02331de1f4d7d18e79edec5b5c08b15274 Mon Sep 17 00:00:00 2001
From: Tibor Reiss <75096465+tibor-reiss@users.noreply.github.com>
Date: Thu, 24 Oct 2024 17:36:35 +0200
Subject: [PATCH 64/84] core[patch]: fix repr and str for Serializable (#26786)

Fixes #26499

---------

Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
---
 libs/core/langchain_core/load/serializable.py | 33 ++++++------
 .../unit_tests/load/test_serializable.py      | 52 +++++++++++++++----
 2 files changed, 61 insertions(+), 24 deletions(-)

diff --git a/libs/core/langchain_core/load/serializable.py b/libs/core/langchain_core/load/serializable.py
index b5e7d8b9150af..7655438be97af 100644
--- a/libs/core/langchain_core/load/serializable.py
+++ b/libs/core/langchain_core/load/serializable.py
@@ -10,6 +10,7 @@
 )
 
 from pydantic import BaseModel, ConfigDict
+from pydantic.fields import FieldInfo
 from typing_extensions import NotRequired
 
 
@@ -77,10 +78,23 @@ def try_neq_default(value: Any, key: str, model: BaseModel) -> bool:
     Raises:
         Exception: If the key is not in the model.
     """
+    field = model.model_fields[key]
+    return _try_neq_default(value, field)
+
+
+def _try_neq_default(value: Any, field: FieldInfo) -> bool:
+    # Handle edge case: inequality of two objects does not evaluate to a bool (e.g. two
+    # Pandas DataFrames).
     try:
-        return model.model_fields[key].get_default() != value
-    except Exception:
-        return True
+        return bool(field.get_default() != value)
+    except Exception as _:
+        try:
+            return all(field.get_default() != value)
+        except Exception as _:
+            try:
+                return value is not field.default
+            except Exception as _:
+                return False
 
 
 class Serializable(BaseModel, ABC):
@@ -297,18 +311,7 @@ def _is_field_useful(inst: Serializable, key: str, value: Any) -> bool:
     if field.default_factory is list and isinstance(value, list):
         return False
 
-    # Handle edge case: inequality of two objects does not evaluate to a bool (e.g. two
-    # Pandas DataFrames).
-    try:
-        value_neq_default = bool(field.get_default() != value)
-    except Exception as _:
-        try:
-            value_neq_default = all(field.get_default() != value)
-        except Exception as _:
-            try:
-                value_neq_default = value is not field.default
-            except Exception as _:
-                value_neq_default = False
+    value_neq_default = _try_neq_default(value, field)
 
     # If value is falsy and does not match the default
     return value_is_truthy or value_neq_default
diff --git a/libs/core/tests/unit_tests/load/test_serializable.py b/libs/core/tests/unit_tests/load/test_serializable.py
index 65040a6841b2a..1c8b6772f09ac 100644
--- a/libs/core/tests/unit_tests/load/test_serializable.py
+++ b/libs/core/tests/unit_tests/load/test_serializable.py
@@ -4,6 +4,22 @@
 from langchain_core.load.serializable import _is_field_useful
 
 
+class NonBoolObj:
+    def __bool__(self) -> bool:
+        msg = "Truthiness can't be determined"
+        raise ValueError(msg)
+
+    def __eq__(self, other: object) -> bool:
+        msg = "Equality can't be determined"
+        raise ValueError(msg)
+
+    def __str__(self) -> str:
+        return self.__class__.__name__
+
+    def __repr__(self) -> str:
+        return self.__class__.__name__
+
+
 def test_simple_serialization() -> None:
     class Foo(Serializable):
         bar: int
@@ -82,15 +98,6 @@ def __bool__(self) -> bool:
         def __eq__(self, other: object) -> bool:
             return self  # type: ignore[return-value]
 
-    class NonBoolObj:
-        def __bool__(self) -> bool:
-            msg = "Truthiness can't be determined"
-            raise ValueError(msg)
-
-        def __eq__(self, other: object) -> bool:
-            msg = "Equality can't be determined"
-            raise ValueError(msg)
-
     default_x = ArrayObj()
     default_y = NonBoolObj()
 
@@ -169,3 +176,30 @@ def test_simple_deserialization_with_additional_imports() -> None:
         },
     )
     assert isinstance(new_foo, Foo2)
+
+
+class Foo3(Serializable):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    content: str
+    non_bool: NonBoolObj
+
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return True
+
+
+def test_repr() -> None:
+    foo = Foo3(
+        content="repr",
+        non_bool=NonBoolObj(),
+    )
+    assert repr(foo) == "Foo3(content='repr', non_bool=NonBoolObj)"
+
+
+def test_str() -> None:
+    foo = Foo3(
+        content="str",
+        non_bool=NonBoolObj(),
+    )
+    assert str(foo) == "content='str' non_bool=NonBoolObj"

From 655ced84d762ccba70615fee607f1188d095de09 Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Thu, 24 Oct 2024 11:19:15 -0700
Subject: [PATCH 65/84] openai[patch]: accept json schema response format
 directly (#27623)

fix #25460

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../langchain_openai/chat_models/base.py      | 23 ++++++++--
 .../tests/unit_tests/chat_models/test_base.py | 44 +++++++++++++++++++
 2 files changed, 64 insertions(+), 3 deletions(-)

diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py
index a1b7e989e7181..52cb1e1912d37 100644
--- a/libs/partners/openai/langchain_openai/chat_models/base.py
+++ b/libs/partners/openai/langchain_openai/chat_models/base.py
@@ -1458,7 +1458,6 @@ class AnswerWithJustification(BaseModel):
                     "schema must be specified when method is not 'json_mode'. "
                     "Received None."
                 )
-            strict = strict if strict is not None else True
             response_format = _convert_to_openai_response_format(schema, strict=strict)
             llm = self.bind(response_format=response_format)
             if is_pydantic_schema:
@@ -2139,14 +2138,32 @@ def _resize(width: int, height: int) -> Tuple[int, int]:
 
 
 def _convert_to_openai_response_format(
-    schema: Union[Dict[str, Any], Type], strict: bool
+    schema: Union[Dict[str, Any], Type], *, strict: Optional[bool] = None
 ) -> Union[Dict, TypeBaseModel]:
     if isinstance(schema, type) and is_basemodel_subclass(schema):
         return schema
+
+    if "json_schema" in schema and schema.get("type") == "json_schema":
+        response_format = schema
+    elif "name" in schema and "schema" in schema:
+        response_format = {"type": "json_schema", "json_schema": schema}
     else:
+        strict = strict if strict is not None else True
         function = convert_to_openai_function(schema, strict=strict)
         function["schema"] = function.pop("parameters")
-        return {"type": "json_schema", "json_schema": function}
+        response_format = {"type": "json_schema", "json_schema": function}
+
+    if strict is not None and strict is not response_format["json_schema"].get(
+        "strict"
+    ):
+        msg = (
+            f"Output schema already has 'strict' value set to "
+            f"{schema['json_schema']['strict']} but 'strict' also passed in to "
+            f"with_structured_output as {strict}. Please make sure that "
+            f"'strict' is only specified in one place."
+        )
+        raise ValueError(msg)
+    return response_format
 
 
 @chain
diff --git a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
index c6118a414a489..8b96b89e08329 100644
--- a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
+++ b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
@@ -23,6 +23,7 @@
 from langchain_openai.chat_models.base import (
     _convert_dict_to_message,
     _convert_message_to_dict,
+    _convert_to_openai_response_format,
     _create_usage_metadata,
     _format_message_content,
 )
@@ -761,3 +762,46 @@ def test__create_usage_metadata() -> None:
         input_token_details={},
         output_token_details={},
     )
+
+
+def test__convert_to_openai_response_format() -> None:
+    # Test response formats that aren't tool-like.
+    response_format: dict = {
+        "type": "json_schema",
+        "json_schema": {
+            "name": "math_reasoning",
+            "schema": {
+                "type": "object",
+                "properties": {
+                    "steps": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "explanation": {"type": "string"},
+                                "output": {"type": "string"},
+                            },
+                            "required": ["explanation", "output"],
+                            "additionalProperties": False,
+                        },
+                    },
+                    "final_answer": {"type": "string"},
+                },
+                "required": ["steps", "final_answer"],
+                "additionalProperties": False,
+            },
+            "strict": True,
+        },
+    }
+
+    actual = _convert_to_openai_response_format(response_format)
+    assert actual == response_format
+
+    actual = _convert_to_openai_response_format(response_format["json_schema"])
+    assert actual == response_format
+
+    actual = _convert_to_openai_response_format(response_format, strict=True)
+    assert actual == response_format
+
+    with pytest.raises(ValueError):
+        _convert_to_openai_response_format(response_format, strict=False)

From 6227396e20aa36b8f1ff266c3ceb90e599beed80 Mon Sep 17 00:00:00 2001
From: Hyejun An <jagaldol.dev@gmail.com>
Date: Fri, 25 Oct 2024 05:49:43 +0900
Subject: [PATCH 66/84] partners/HuggingFacePipeline[stream]: Change to use
 `pipeline` instead of `pipeline.model.generate` in stream() (#26531)

## Description

I encountered an error while using the` gemma-2-2b-it model` with the
`HuggingFacePipeline` class and have implemented a fix to resolve this
issue.

### What is Problem

```python
model_id="google/gemma-2-2b-it"


gemma_2_model = AutoModelForCausalLM.from_pretrained(model_id)
gemma_2_tokenizer = AutoTokenizer.from_pretrained(model_id)

gen = pipeline(
    task='text-generation',
    model=gemma_2_model,
    tokenizer=gemma_2_tokenizer,
    max_new_tokens=1024,
    device=0 if torch.cuda.is_available() else -1,
    temperature=.5,
    top_p=0.7,
    repetition_penalty=1.1,
    do_sample=True,
    )

llm = HuggingFacePipeline(pipeline=gen)

for chunk in llm.stream("Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World."):
    print(chunk, end="", flush=True)
```

This code outputs the following error message:

```
/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1258: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
  warnings.warn(
Exception in thread Thread-19 (generate):
Traceback (most recent call last):
  File "/usr/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
    self.run()
  File "/usr/lib/python3.10/threading.py", line 953, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1874, in generate
    self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1266, in _validate_generated_length
    raise ValueError(
ValueError: Input length of input_ids is 31, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_length` or, better yet, setting `max_new_tokens`.
```

In addition, the following error occurs when the number of tokens is
reduced.

```python
for chunk in llm.stream("Hello World"):
    print(chunk, end="", flush=True)
```

```
/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1258: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1885: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.
  warnings.warn(
Exception in thread Thread-20 (generate):
Traceback (most recent call last):
  File "/usr/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
    self.run()
  File "/usr/lib/python3.10/threading.py", line 953, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 2024, in generate
    result = self._sample(
  File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 2982, in _sample
    outputs = self(**model_inputs, return_dict=True)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/gemma2/modeling_gemma2.py", line 994, in forward
    outputs = self.model(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/gemma2/modeling_gemma2.py", line 803, in forward
    inputs_embeds = self.embed_tokens(input_ids)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/sparse.py", line 164, in forward
    return F.embedding(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py", line 2267, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper_CUDA__index_select)
```

On the other hand, in the case of invoke, the output is normal:

```
llm.invoke("Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World.")
```
```
'Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World.\n\nThis is a simple program that prints the phrase "Hello World" to the console. \n\n**Here\'s how it works:**\n\n* **`print("Hello World")`**: This line of code uses the `print()` function, which is a built-in function in most programming languages (like Python). The `print()` function takes whatever you put inside its parentheses and displays it on the screen.\n* **`"Hello World"`**:  The text within the double quotes (`"`) is called a string. It represents the message we want to print.\n\n\nLet me know if you\'d like to explore other programming concepts or see more examples! \n'
```

### Problem Analysis

- Apparently, I put kwargs in while generating pipelines and it applied
to `invoke()`, but it's not applied in the `stream()`.
- When using the stream, `inputs = self.pipeline.tokenizer (prompt,
return_tensors = "pt")` enters cpu.
  - This can crash when the model is in gpu.

### Solution

Just use `self.pipeline` instead of `self.pipeline.model.generate`.

- **Original Code**

```python
stopping_criteria = StoppingCriteriaList([StopOnTokens()])

inputs = self.pipeline.tokenizer(prompt, return_tensors="pt")
streamer = TextIteratorStreamer(
    self.pipeline.tokenizer,
    timeout=60.0,
    skip_prompt=skip_prompt,
    skip_special_tokens=True,
)
generation_kwargs = dict(
    inputs,
    streamer=streamer,
    stopping_criteria=stopping_criteria,
    **pipeline_kwargs,
)
t1 = Thread(target=self.pipeline.model.generate, kwargs=generation_kwargs)
t1.start()
```

- **Updated Code**

```python
stopping_criteria = StoppingCriteriaList([StopOnTokens()])

streamer = TextIteratorStreamer(
    self.pipeline.tokenizer,
    timeout=60.0,
    skip_prompt=skip_prompt,
    skip_special_tokens=True,
)
generation_kwargs = dict(
    text_inputs= prompt,
    streamer=streamer,
    stopping_criteria=stopping_criteria,
    **pipeline_kwargs,
)
t1 = Thread(target=self.pipeline, kwargs=generation_kwargs)
t1.start()
```

By using the `pipeline` directly, the `kwargs` of the pipeline are
applied, and there is no need to consider the `device` of the `tensor`
made with the `tokenizer`.

> According to the change to use `pipeline`, it was modified to put
`text_inputs=prompts` directly into `generation_kwargs`.

## Issue

None

## Dependencies

None

## Twitter handle

None

---------

Co-authored-by: Vadym Barda <vadym@langchain.dev>
---
 .../langchain_huggingface/llms/huggingface_pipeline.py       | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py b/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py
index 9c8d5f7145a21..b1261bcfc4e4b 100644
--- a/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py
+++ b/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py
@@ -349,7 +349,6 @@ def __call__(
 
         stopping_criteria = StoppingCriteriaList([StopOnTokens()])
 
-        inputs = self.pipeline.tokenizer(prompt, return_tensors="pt")
         streamer = TextIteratorStreamer(
             self.pipeline.tokenizer,
             timeout=60.0,
@@ -357,12 +356,12 @@ def __call__(
             skip_special_tokens=True,
         )
         generation_kwargs = dict(
-            inputs,
+            text_inputs=prompt,
             streamer=streamer,
             stopping_criteria=stopping_criteria,
             **pipeline_kwargs,
         )
-        t1 = Thread(target=self.pipeline.model.generate, kwargs=generation_kwargs)
+        t1 = Thread(target=self.pipeline, kwargs=generation_kwargs)
         t1.start()
 
         for char in streamer:

From 846a75284fd40d6846f50ed679bc9be995fdcd4d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?CLOVA=20Studio=20=EA=B0=9C=EB=B0=9C?=
 <102506093+hyper-clova@users.noreply.github.com>
Date: Fri, 25 Oct 2024 05:54:13 +0900
Subject: [PATCH 67/84] community: Add Naver chat model & embeddings (#25162)

Reopened as a personal repo outside the organization.

## Description
- Naver HyperCLOVA X community package
  - Add chat model & embeddings
  - Add unit test & integration test
  - Add chat model & embeddings docs
- I changed partner
package(https://github.com/langchain-ai/langchain/pull/24252) to
community package on this PR
- Could this
embeddings(https://github.com/langchain-ai/langchain/pull/21890) be
deprecated? We are trying to replace it with embedding
model(**ClovaXEmbeddings**) in this PR.

Twitter handle: None. (if needed, contact with
joonha.jeon@navercorp.com)

---
you can check our previous discussion below:

> one question on namespaces - would it make sense to have these in
.clova namespaces instead of .naver?

I would like to keep it as is, unless it is essential to unify the
package name.
(ClovaX is a branding for the model, and I plan to add other models and
components. They need to be managed as separate classes.)

> also, could you clarify the difference between ClovaEmbeddings and
ClovaXEmbeddings?

There are 3 models that are being serviced by embedding, and all are
supported in the current PR. In addition, all the functionality of CLOVA
Studio that serves actual models, such as distinguishing between test
apps and service apps, is supported. The existing PR does not support
this content because it is hard-coded.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Vadym Barda <vadym@langchain.dev>
---
 docs/docs/integrations/chat/naver.ipynb       | 444 +++++++++++++++
 docs/docs/integrations/providers/naver.mdx    |  38 ++
 .../integrations/text_embedding/naver.ipynb   | 318 +++++++++++
 .../chat_models/__init__.py                   |   5 +
 .../langchain_community/chat_models/naver.py  | 524 ++++++++++++++++++
 .../embeddings/__init__.py                    |   5 +
 .../langchain_community/embeddings/clova.py   |   6 +
 .../langchain_community/embeddings/naver.py   | 192 +++++++
 libs/community/poetry.lock                    |  13 +-
 libs/community/pyproject.toml                 |   1 +
 .../chat_models/test_naver.py                 |  71 +++
 .../embeddings/test_naver.py                  |  37 ++
 .../unit_tests/chat_models/test_imports.py    |   1 +
 .../unit_tests/chat_models/test_naver.py      | 197 +++++++
 .../unit_tests/embeddings/test_imports.py     |   1 +
 .../tests/unit_tests/embeddings/test_naver.py |  18 +
 .../tests/unit_tests/test_dependencies.py     |   1 +
 17 files changed, 1871 insertions(+), 1 deletion(-)
 create mode 100644 docs/docs/integrations/chat/naver.ipynb
 create mode 100644 docs/docs/integrations/providers/naver.mdx
 create mode 100644 docs/docs/integrations/text_embedding/naver.ipynb
 create mode 100644 libs/community/langchain_community/chat_models/naver.py
 create mode 100644 libs/community/langchain_community/embeddings/naver.py
 create mode 100644 libs/community/tests/integration_tests/chat_models/test_naver.py
 create mode 100644 libs/community/tests/integration_tests/embeddings/test_naver.py
 create mode 100644 libs/community/tests/unit_tests/chat_models/test_naver.py
 create mode 100644 libs/community/tests/unit_tests/embeddings/test_naver.py

diff --git a/docs/docs/integrations/chat/naver.ipynb b/docs/docs/integrations/chat/naver.ipynb
new file mode 100644
index 0000000000000..64924cb56374a
--- /dev/null
+++ b/docs/docs/integrations/chat/naver.ipynb
@@ -0,0 +1,444 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Naver\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8444f1a-e907-4f07-b8b6-68fbedfb868e",
+   "metadata": {},
+   "source": [
+    "# ChatClovaX\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with Naver’s HyperCLOVA X [chat models](https://python.langchain.com/docs/concepts/#chat-models) via CLOVA Studio. For detailed documentation of all ChatClovaX features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html).\n",
+    "\n",
+    "[CLOVA Studio](http://clovastudio.ncloud.com/) has several chat models. You can find information about latest models and their costs, context windows, and supported input types in the CLOVA Studio API Guide [documentation](https://api.ncloud-docs.com/docs/clovastudio-chatcompletions).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- |:-----:| :---: |:------------------------------------------------------------------------:| :---: | :---: |\n",
+    "| [ChatClovaX](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) |   ❌   | ❌ |                                    ❌                                     | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "|:------------------------------------------:| :---: | :---: | :---: |  :---: | :---: |:-----------------------------------------------------:| :---: |:------------------------------------------------------:|:----------------------------------:|\n",
+    "|❌| ❌ | ❌ | ❌ | ❌ | ❌ |                          ✅                            | ✅ |                           ✅                            |                 ❌                  | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Before using the chat model, you must go through the three steps below.\n",
+    "\n",
+    "1. Creating [NAVER Cloud Platform](https://www.ncloud.com/) account \n",
+    "2. Apply to use [CLOVA Studio](https://www.ncloud.com/product/aiService/clovaStudio)\n",
+    "3. Find API Keys after creating CLOVA Studio Test App or Service App (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#테스트앱생성).)\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "CLOVA Studio requires 2 keys (`NCP_CLOVASTUDIO_API_KEY` and `NCP_APIGW_API_KEY`).\n",
+    "  - `NCP_CLOVASTUDIO_API_KEY` is issued per Test App or Service App\n",
+    "  - `NCP_APIGW_API_KEY` is issued per account, could be optional depending on the region you are using\n",
+    "\n",
+    "The two API Keys could be found by clicking `App Request Status` > `Service App, Test App List` > `‘Details’ button for each app` in [CLOVA Studio](https://clovastudio.ncloud.com/studio-application/service-app)\n",
+    "\n",
+    "You can add them to your environment variables as below:\n",
+    "\n",
+    "``` bash\n",
+    "export NCP_CLOVASTUDIO_API_KEY=\"your-api-key-here\"\n",
+    "export NCP_APIGW_API_KEY=\"your-api-key-here\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2def81b5-b023-4f40-a97b-b2c5ca59d6a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"NCP_CLOVASTUDIO_API_KEY\"):\n",
+    "    os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your NCP CLOVA Studio API Key: \"\n",
+    "    )\n",
+    "if not os.getenv(\"NCP_APIGW_API_KEY\"):\n",
+    "    os.environ[\"NCP_APIGW_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your NCP API Gateway API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c695442",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6151aeb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17bf9053-90c5-4955-b239-55a35cb07566",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Naver integration lives in the `langchain-community` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install package\n",
+    "!pip install -qU langchain-community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models import ChatClovaX\n",
+    "\n",
+    "chat = ChatClovaX(\n",
+    "    model=\"HCX-003\",\n",
+    "    max_tokens=100,\n",
+    "    temperature=0.5,\n",
+    "    # clovastudio_api_key=\"...\"    # set if you prefer to pass api key directly instead of using environment variables\n",
+    "    # task_id=\"...\"    # set if you want to use fine-tuned model\n",
+    "    # service_app=False    # set True if using Service App. Default value is False (means using Test App)\n",
+    "    # include_ai_filters=False     # set True if you want to detect inappropriate content. Default value is False\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47752b59",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "In addition to invoke, we also support batch and stream functionalities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 1112164354, 'ai_filter': None}, id='run-b57bc356-1148-4007-837d-cc409dbd57cc-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to Korean. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love using NAVER AI.\"),\n",
+    "]\n",
+    "\n",
+    "ai_msg = chat.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "24e7377f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "저는 네이버 AI를 사용하는 것이 좋아요.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 2575184681, 'ai_filter': None}, id='run-7014b330-eba3-4701-bb62-df73ce39b854-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}. Translate the user sentence.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"Korean\",\n",
+    "        \"input\": \"I love using NAVER AI.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66e69286",
+   "metadata": {},
+   "source": [
+    "## Streaming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2c07af21-dda5-4514-b4de-1f214c2cebcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Certainly! In Korean, \"Hi\" is pronounced as \"안녕\" (annyeong). The first syllable, \"안,\" sounds like the \"ahh\" sound in \"apple,\" while the second syllable, \"녕,\" sounds like the \"yuh\" sound in \"you.\" So when you put them together, it's like saying \"ahhyuh-nyuhng.\" Remember to pronounce each syllable clearly and separately for accurate pronunciation."
+     ]
+    }
+   ],
+   "source": [
+    "system = \"You are a helpful assistant that can teach Korean pronunciation.\"\n",
+    "human = \"Could you let me know how to say '{phrase}' in Korean?\"\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "\n",
+    "for chunk in chain.stream({\"phrase\": \"Hi\"}):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "metadata": {},
+   "source": [
+    "## Additional functionalities\n",
+    "\n",
+    "### Using fine-tuned models\n",
+    "\n",
+    "You can call fine-tuned models by passing in your corresponding `task_id` parameter. (You don’t need to specify the `model_name` parameter when calling fine-tuned model.)\n",
+    "\n",
+    "You can check `task_id` from corresponding Test App or Service App details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cb436788",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='저는 네이버 AI를 사용하는 것이 너무 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 15, 'seed': 52559061, 'ai_filter': None}, id='run-5bea8d4a-48f3-4c34-ae70-66e60dca5344-0', usage_metadata={'input_tokens': 25, 'output_tokens': 15, 'total_tokens': 40})"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fine_tuned_model = ChatClovaX(\n",
+    "    task_id=\"5s8egt3a\",  # set if you want to use fine-tuned model\n",
+    "    # other params...\n",
+    ")\n",
+    "\n",
+    "fine_tuned_model.invoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f428deaf",
+   "metadata": {},
+   "source": [
+    "### Service App\n",
+    "\n",
+    "When going live with production-level application using CLOVA Studio, you should apply for and use Service App. (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#서비스앱신청).)\n",
+    "\n",
+    "For a Service App, a corresponding `NCP_CLOVASTUDIO_API_KEY` is issued and can only be called with it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcf566df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update environment variables\n",
+    "\n",
+    "os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+    "    \"Enter NCP CLOVA Studio API Key for Service App: \"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cebe27ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat = ChatClovaX(\n",
+    "    service_app=True,  # True if you want to use your service app, default value is False.\n",
+    "    # clovastudio_api_key=\"...\"  # if you prefer to pass api key in directly instead of using env vars\n",
+    "    model=\"HCX-003\",\n",
+    "    # other params...\n",
+    ")\n",
+    "ai_msg = chat.invoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d73e7140",
+   "metadata": {},
+   "source": [
+    "### AI Filter\n",
+    "\n",
+    "AI Filter detects inappropriate output such as profanity from the test app (or service app included) created in Playground and informs the user. See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#AIFilter) for details. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32bfbc93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat = ChatClovaX(\n",
+    "    model=\"HCX-003\",\n",
+    "    include_ai_filters=True,  # True if you want to enable ai filter\n",
+    "    # other params...\n",
+    ")\n",
+    "\n",
+    "ai_msg = chat.invoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bd9e179",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(ai_msg.response_metadata[\"ai_filter\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatNaver features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/integrations/providers/naver.mdx b/docs/docs/integrations/providers/naver.mdx
new file mode 100644
index 0000000000000..f532766975963
--- /dev/null
+++ b/docs/docs/integrations/providers/naver.mdx
@@ -0,0 +1,38 @@
+# NAVER
+
+All functionality related to `Naver`, including HyperCLOVA X models, particularly those accessible through `Naver Cloud` [CLOVA Studio](https://clovastudio.ncloud.com/).
+
+> [Naver](https://navercorp.com/) is a global technology company with cutting-edge technologies and a diverse business portfolio including search, commerce, fintech, content, cloud, and AI.
+
+> [Naver Cloud](https://www.navercloudcorp.com/lang/en/) is the cloud computing arm of Naver, a leading cloud service provider offering a comprehensive suite of cloud services to businesses through its [Naver Cloud Platform (NCP)](https://www.ncloud.com/).
+
+Please refer to [NCP User Guide](https://guide.ncloud-docs.com/docs/clovastudio-overview) for more detailed instructions (also in Korean).
+
+## Installation and Setup
+
+- Get both CLOVA Studio API Key and API Gateway Key by [creating your app](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#create-test-app) and set them as environment variables respectively (`NCP_CLOVASTUDIO_API_KEY`, `NCP_APIGW_API_KEY`).
+- Install the integration Python package with:
+
+```bash
+pip install -U langchain-community
+```
+
+## Chat models
+
+### ChatClovaX 
+
+See a [usage example](/docs/integrations/chat/naver).
+
+```python
+from langchain_community.chat_models import ChatClovaX
+```
+
+## Embedding models
+
+### ClovaXEmbeddings
+
+See a [usage example](/docs/integrations/text_embedding/naver).
+
+```python
+from langchain_community.embeddings import ClovaXEmbeddings
+```
\ No newline at end of file
diff --git a/docs/docs/integrations/text_embedding/naver.ipynb b/docs/docs/integrations/text_embedding/naver.ipynb
new file mode 100644
index 0000000000000..05236288f4763
--- /dev/null
+++ b/docs/docs/integrations/text_embedding/naver.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Naver\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ClovaXEmbeddings\n",
+    "\n",
+    "This notebook covers how to get started with embedding models provided by CLOVA Studio. For detailed documentation on `ClovaXEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/community/embeddings/langchain_community.naver.ClovaXEmbeddings.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Provider | Package |\n",
+    "|:--------:|:-------:|\n",
+    "| [Naver](/docs/integrations/providers/naver.mdx) | [langchain-community](https://python.langchain.com/api_reference/community/embeddings/langchain_community.naver.ClovaXEmbeddings.html) |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Before using embedding models provided by CLOVA Studio, you must go through the three steps below.\n",
+    "\n",
+    "1. Creating [NAVER Cloud Platform](https://www.ncloud.com/) account \n",
+    "2. Apply to use [CLOVA Studio](https://www.ncloud.com/product/aiService/clovaStudio)\n",
+    "3. Find API Keys after creating CLOVA Studio Test App or Service App (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#테스트앱생성).)\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "CLOVA Studio requires 3 keys (`NCP_CLOVASTUDIO_API_KEY`, `NCP_APIGW_API_KEY` and `NCP_CLOVASTUDIO_APP_ID`) for embeddings.\n",
+    "- `NCP_CLOVASTUDIO_API_KEY` and `NCP_CLOVASTUDIO_APP_ID` is issued per serviceApp or testApp\n",
+    "- `NCP_APIGW_API_KEY` is issued per account\n",
+    "\n",
+    "The two API Keys could be found by clicking `App Request Status` > `Service App, Test App List` > `‘Details’ button for each app` in [CLOVA Studio](https://clovastudio.ncloud.com/studio-application/service-app)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c52e8a50-3e67-4272-bc80-3954d98f8dea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"NCP_CLOVASTUDIO_API_KEY\"):\n",
+    "    os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter NCP CLOVA Studio API Key: \"\n",
+    "    )\n",
+    "if not os.getenv(\"NCP_APIGW_API_KEY\"):\n",
+    "    os.environ[\"NCP_APIGW_API_KEY\"] = getpass.getpass(\"Enter NCP API Gateway API Key: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83520d8e-ecf8-4e47-b3bc-1ac205b3a2ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"NCP_CLOVASTUDIO_APP_ID\"] = input(\"Enter NCP CLOVA Studio App ID: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff00653e",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "ClovaXEmbeddings integration lives in the `langchain_community` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99400c9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install package\n",
+    "!pip install -U langchain-community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2651e611-9d5b-4315-9bbd-f99f56be4e19",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our embeddings object and embed query or document:\n",
+    "\n",
+    "- There are several embedding models available in CLOVA Studio. Please refer [here](https://guide.ncloud-docs.com/docs/en/clovastudio-explorer03#임베딩API) for further details.\n",
+    "- Note that you might need to normalize the embeddings depending on your specific use case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "62e0dbc3",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.embeddings import ClovaXEmbeddings\n",
+    "\n",
+    "embeddings = ClovaXEmbeddings(\n",
+    "    model=\"clir-emb-dolphin\",  # set with the model name of corresponding app id. Default is `clir-emb-dolphin`\n",
+    "    # app_id=\"...\"  # set if you prefer to pass app id directly instead of using environment variables\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0493b4a8",
+   "metadata": {},
+   "source": [
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4d59653",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'CLOVA Studio is an AI development tool that allows you to customize your own HyperCLOVA X models.'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"CLOVA Studio is an AI development tool that allows you to customize your own HyperCLOVA X models.\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is CLOVA Studio?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1a249e1",
+   "metadata": {},
+   "source": [
+    "## Direct Usage\n",
+    "\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+    "\n",
+    "You can directly call these methods to get embeddings for your own use cases.\n",
+    "\n",
+    "### Embed single texts\n",
+    "\n",
+    "You can embed single texts or documents with `embed_query`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "12fcfb4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.094717406, -0.4077411, -0.5513184, 1.6024436, -1.3235079, -1.0720996, -0.44471845, 1.3665184, 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "single_vector = embeddings.embed_query(text)\n",
+    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b383b53",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1f2e6104",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.094717406, -0.4077411, -0.5513184, 1.6024436, -1.3235079, -1.0720996, -0.44471845, 1.3665184, 0.\n",
+      "[-0.25525448, -0.84877056, -0.6928286, 1.5867524, -1.2930486, -0.8166254, -0.17934391, 1.4236152, 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "text2 = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
+    "for vector in two_vectors:\n",
+    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eee40d32367cc5c4",
+   "metadata": {},
+   "source": [
+    "## Additional functionalities\n",
+    "\n",
+    "### Service App\n",
+    "\n",
+    "When going live with production-level application using CLOVA Studio, you should apply for and use Service App. (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#서비스앱신청).)\n",
+    "\n",
+    "For a Service App, corresponding `NCP_CLOVASTUDIO_API_KEY` and `NCP_CLOVASTUDIO_APP_ID` are issued and can only be called with them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08f9f44e-c6a4-4163-8caf-27a0cda345b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update environment variables\n",
+    "\n",
+    "os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+    "    \"Enter NCP CLOVA Studio API Key for Service App: \"\n",
+    ")\n",
+    "os.environ[\"NCP_CLOVASTUDIO_APP_ID\"] = input(\"Enter NCP CLOVA Studio Service App ID: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86f59698-b3f4-4b19-a9d4-4facfcea304b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = ClovaXEmbeddings(\n",
+    "    service_app=True,\n",
+    "    model=\"clir-emb-dolphin\",  # set with the model name of corresponding app id of your Service App\n",
+    "    # app_id=\"...\"  # set if you prefer to pass app id directly instead of using environment variables\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ddeaee9",
+   "metadata": {},
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `ClovaXEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/latest/api_reference/community/embeddings/langchain_community.embeddings.naver.ClovaXEmbeddings.html)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/libs/community/langchain_community/chat_models/__init__.py b/libs/community/langchain_community/chat_models/__init__.py
index 88df9d4330fe2..0cff776c786d7 100644
--- a/libs/community/langchain_community/chat_models/__init__.py
+++ b/libs/community/langchain_community/chat_models/__init__.py
@@ -125,6 +125,9 @@
     from langchain_community.chat_models.moonshot import (
         MoonshotChat,
     )
+    from langchain_community.chat_models.naver import (
+        ChatClovaX,
+    )
     from langchain_community.chat_models.oci_data_science import (
         ChatOCIModelDeployment,
         ChatOCIModelDeploymentTGI,
@@ -193,6 +196,7 @@
     "ChatAnthropic",
     "ChatAnyscale",
     "ChatBaichuan",
+    "ChatClovaX",
     "ChatCohere",
     "ChatCoze",
     "ChatOctoAI",
@@ -257,6 +261,7 @@
     "ChatAnthropic": "langchain_community.chat_models.anthropic",
     "ChatAnyscale": "langchain_community.chat_models.anyscale",
     "ChatBaichuan": "langchain_community.chat_models.baichuan",
+    "ChatClovaX": "langchain_community.chat_models.naver",
     "ChatCohere": "langchain_community.chat_models.cohere",
     "ChatCoze": "langchain_community.chat_models.coze",
     "ChatDatabricks": "langchain_community.chat_models.databricks",
diff --git a/libs/community/langchain_community/chat_models/naver.py b/libs/community/langchain_community/chat_models/naver.py
new file mode 100644
index 0000000000000..8b570265393b8
--- /dev/null
+++ b/libs/community/langchain_community/chat_models/naver.py
@@ -0,0 +1,524 @@
+import logging
+from typing import (
+    Any,
+    AsyncContextManager,
+    AsyncIterator,
+    Callable,
+    Dict,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+    cast,
+)
+
+import httpx
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams
+from langchain_core.language_models.llms import create_base_retry_decorator
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    BaseMessageChunk,
+    ChatMessage,
+    ChatMessageChunk,
+    HumanMessage,
+    HumanMessageChunk,
+    SystemMessage,
+    SystemMessageChunk,
+)
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_core.utils import convert_to_secret_str, get_from_env
+from pydantic import AliasChoices, Field, SecretStr, model_validator
+from typing_extensions import Self
+
+_DEFAULT_BASE_URL = "https://clovastudio.stream.ntruss.com"
+
+logger = logging.getLogger(__name__)
+
+
+def _convert_chunk_to_message_chunk(
+    sse: Any, default_class: Type[BaseMessageChunk]
+) -> BaseMessageChunk:
+    sse_data = sse.json()
+    message = sse_data.get("message")
+    role = message.get("role")
+    content = message.get("content") or ""
+
+    if role == "user" or default_class == HumanMessageChunk:
+        return HumanMessageChunk(content=content)
+    elif role == "assistant" or default_class == AIMessageChunk:
+        return AIMessageChunk(content=content)
+    elif role == "system" or default_class == SystemMessageChunk:
+        return SystemMessageChunk(content=content)
+    elif role or default_class == ChatMessageChunk:
+        return ChatMessageChunk(content=content, role=role)
+    else:
+        return default_class(content=content)  # type: ignore[call-arg]
+
+
+def _convert_message_to_naver_chat_message(
+    message: BaseMessage,
+) -> Dict:
+    if isinstance(message, ChatMessage):
+        return dict(role=message.role, content=message.content)
+    elif isinstance(message, HumanMessage):
+        return dict(role="user", content=message.content)
+    elif isinstance(message, SystemMessage):
+        return dict(role="system", content=message.content)
+    elif isinstance(message, AIMessage):
+        return dict(role="assistant", content=message.content)
+    else:
+        logger.warning(
+            "FunctionMessage, ToolMessage not yet supported "
+            "(https://api.ncloud-docs.com/docs/clovastudio-chatcompletions)"
+        )
+        raise ValueError(f"Got unknown type {message}")
+
+
+def _convert_naver_chat_message_to_message(
+    _message: Dict,
+) -> BaseMessage:
+    role = _message["role"]
+    assert role in (
+        "assistant",
+        "system",
+        "user",
+    ), f"Expected role to be 'assistant', 'system', 'user', got {role}"
+    content = cast(str, _message["content"])
+    additional_kwargs: Dict = {}
+
+    if role == "user":
+        return HumanMessage(
+            content=content,
+            additional_kwargs=additional_kwargs,
+        )
+    elif role == "system":
+        return SystemMessage(
+            content=content,
+            additional_kwargs=additional_kwargs,
+        )
+    elif role == "assistant":
+        return AIMessage(
+            content=content,
+            additional_kwargs=additional_kwargs,
+        )
+    else:
+        logger.warning("Got unknown role %s", role)
+        raise ValueError(f"Got unknown role {role}")
+
+
+async def _aiter_sse(
+    event_source_mgr: AsyncContextManager[Any],
+) -> AsyncIterator[Dict]:
+    """Iterate over the server-sent events."""
+    async with event_source_mgr as event_source:
+        await _araise_on_error(event_source.response)
+        async for sse in event_source.aiter_sse():
+            event_data = sse.json()
+            if sse.event == "signal" and event_data.get("data", {}) == "[DONE]":
+                return
+            if sse.event == "result":
+                return
+            yield sse
+
+
+def _raise_on_error(response: httpx.Response) -> None:
+    """Raise an error if the response is an error."""
+    if httpx.codes.is_error(response.status_code):
+        error_message = response.read().decode("utf-8")
+        raise httpx.HTTPStatusError(
+            f"Error response {response.status_code} "
+            f"while fetching {response.url}: {error_message}",
+            request=response.request,
+            response=response,
+        )
+
+
+async def _araise_on_error(response: httpx.Response) -> None:
+    """Raise an error if the response is an error."""
+    if httpx.codes.is_error(response.status_code):
+        error_message = (await response.aread()).decode("utf-8")
+        raise httpx.HTTPStatusError(
+            f"Error response {response.status_code} "
+            f"while fetching {response.url}: {error_message}",
+            request=response.request,
+            response=response,
+        )
+
+
+class ChatClovaX(BaseChatModel):
+    """`NCP ClovaStudio` Chat Completion API.
+
+    following environment variables set or passed in constructor in lower case:
+    - ``NCP_CLOVASTUDIO_API_KEY``
+    - ``NCP_APIGW_API_KEY``
+
+    Example:
+        .. code-block:: python
+
+            from langchain_core.messages import HumanMessage
+
+            from langchain_community import ChatClovaX
+
+            model = ChatClovaX()
+            model.invoke([HumanMessage(content="Come up with 10 names for a song about parrots.")])
+    """  # noqa: E501
+
+    client: httpx.Client = Field(default=None)  #: :meta private:
+    async_client: httpx.AsyncClient = Field(default=None)  #: :meta private:
+
+    model_name: str = Field(
+        default="HCX-003",
+        validation_alias=AliasChoices("model_name", "model"),
+        description="NCP ClovaStudio chat model name",
+    )
+    task_id: Optional[str] = Field(
+        default=None, description="NCP Clova Studio chat model tuning task ID"
+    )
+    service_app: bool = Field(
+        default=False,
+        description="false: use testapp, true: use service app on NCP Clova Studio",
+    )
+
+    ncp_clovastudio_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
+    """Automatically inferred from env are `NCP_CLOVASTUDIO_API_KEY` if not provided."""
+
+    ncp_apigw_api_key: Optional[SecretStr] = Field(default=None, alias="apigw_api_key")
+    """Automatically inferred from env are `NCP_APIGW_API_KEY` if not provided."""
+
+    base_url: str = Field(default=None, alias="base_url")
+    """
+    Automatically inferred from env are `NCP_CLOVASTUDIO_API_BASE_URL` if not provided.
+    """
+
+    temperature: Optional[float] = Field(gt=0.0, le=1.0, default=0.5)
+    top_k: Optional[int] = Field(ge=0, le=128, default=0)
+    top_p: Optional[float] = Field(ge=0, le=1.0, default=0.8)
+    repeat_penalty: Optional[float] = Field(gt=0.0, le=10, default=5.0)
+    max_tokens: Optional[int] = Field(ge=0, le=4096, default=100)
+    stop_before: Optional[list[str]] = Field(default=None, alias="stop")
+    include_ai_filters: Optional[bool] = Field(default=False)
+    seed: Optional[int] = Field(ge=0, le=4294967295, default=0)
+
+    timeout: int = Field(gt=0, default=90)
+    max_retries: int = Field(ge=1, default=2)
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        populate_by_name = True
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling the API."""
+        defaults = {
+            "temperature": self.temperature,
+            "topK": self.top_k,
+            "topP": self.top_p,
+            "repeatPenalty": self.repeat_penalty,
+            "maxTokens": self.max_tokens,
+            "stopBefore": self.stop_before,
+            "includeAiFilters": self.include_ai_filters,
+            "seed": self.seed,
+        }
+        filtered = {k: v for k, v in defaults.items() if v is not None}
+        return filtered
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        self._default_params["model_name"] = self.model_name
+        return self._default_params
+
+    @property
+    def lc_secrets(self) -> Dict[str, str]:
+        return {
+            "ncp_clovastudio_api_key": "NCP_CLOVASTUDIO_API_KEY",
+            "ncp_apigw_api_key": "NCP_APIGW_API_KEY",
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of chat model."""
+        return "chat-naver"
+
+    def _get_ls_params(
+        self, stop: Optional[List[str]] = None, **kwargs: Any
+    ) -> LangSmithParams:
+        """Get the parameters used to invoke the model."""
+        params = super()._get_ls_params(stop=stop, **kwargs)
+        params["ls_provider"] = "naver"
+        return params
+
+    @property
+    def _client_params(self) -> Dict[str, Any]:
+        """Get the parameters used for the client."""
+        return self._default_params
+
+    @property
+    def _api_url(self) -> str:
+        """GET chat completion api url"""
+        app_type = "serviceapp" if self.service_app else "testapp"
+
+        if self.task_id:
+            return (
+                f"{self.base_url}/{app_type}/v1/tasks/{self.task_id}/chat-completions"
+            )
+        else:
+            return f"{self.base_url}/{app_type}/v1/chat-completions/{self.model_name}"
+
+    @model_validator(mode="after")
+    def validate_model_after(self) -> Self:
+        if not (self.model_name or self.task_id):
+            raise ValueError("either model_name or task_id must be assigned a value.")
+
+        if not self.ncp_clovastudio_api_key:
+            self.ncp_clovastudio_api_key = convert_to_secret_str(
+                get_from_env("ncp_clovastudio_api_key", "NCP_CLOVASTUDIO_API_KEY")
+            )
+
+        if not self.ncp_apigw_api_key:
+            self.ncp_apigw_api_key = convert_to_secret_str(
+                get_from_env("ncp_apigw_api_key", "NCP_APIGW_API_KEY")
+            )
+
+        if not self.base_url:
+            self.base_url = get_from_env(
+                "base_url", "NCP_CLOVASTUDIO_API_BASE_URL", _DEFAULT_BASE_URL
+            )
+
+        if not self.client:
+            self.client = httpx.Client(
+                base_url=self.base_url,
+                headers=self.default_headers(),
+                timeout=self.timeout,
+            )
+
+        if not self.async_client:
+            self.async_client = httpx.AsyncClient(
+                base_url=self.base_url,
+                headers=self.default_headers(),
+                timeout=self.timeout,
+            )
+
+        return self
+
+    def default_headers(self) -> Dict[str, Any]:
+        clovastudio_api_key = (
+            self.ncp_clovastudio_api_key.get_secret_value()
+            if self.ncp_clovastudio_api_key
+            else None
+        )
+        apigw_api_key = (
+            self.ncp_apigw_api_key.get_secret_value()
+            if self.ncp_apigw_api_key
+            else None
+        )
+        return {
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+            "X-NCP-CLOVASTUDIO-API-KEY": clovastudio_api_key,
+            "X-NCP-APIGW-API-KEY": apigw_api_key,
+        }
+
+    def _create_message_dicts(
+        self, messages: List[BaseMessage], stop: Optional[List[str]]
+    ) -> Tuple[List[Dict], Dict[str, Any]]:
+        params = self._client_params
+        if stop is not None and "stopBefore" in params:
+            params["stopBefore"] = stop
+
+        message_dicts = [_convert_message_to_naver_chat_message(m) for m in messages]
+        return message_dicts, params
+
+    def _completion_with_retry(self, **kwargs: Any) -> Any:
+        from httpx_sse import (
+            ServerSentEvent,
+            SSEError,
+            connect_sse,
+        )
+
+        if "stream" not in kwargs:
+            kwargs["stream"] = False
+
+        stream = kwargs["stream"]
+        if stream:
+
+            def iter_sse() -> Iterator[ServerSentEvent]:
+                with connect_sse(
+                    self.client, "POST", self._api_url, json=kwargs
+                ) as event_source:
+                    _raise_on_error(event_source.response)
+                    for sse in event_source.iter_sse():
+                        event_data = sse.json()
+                        if (
+                            sse.event == "signal"
+                            and event_data.get("data", {}) == "[DONE]"
+                        ):
+                            return
+                        if sse.event == "result":
+                            return
+                        if sse.event == "error":
+                            raise SSEError(message=sse.data)
+                        yield sse
+
+            return iter_sse()
+        else:
+            response = self.client.post(url=self._api_url, json=kwargs)
+            _raise_on_error(response)
+            return response.json()
+
+    async def _acompletion_with_retry(
+        self,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Any:
+        from httpx_sse import aconnect_sse
+
+        """Use tenacity to retry the async completion call."""
+        retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
+
+        @retry_decorator
+        async def _completion_with_retry(**kwargs: Any) -> Any:
+            if "stream" not in kwargs:
+                kwargs["stream"] = False
+            stream = kwargs["stream"]
+            if stream:
+                event_source = aconnect_sse(
+                    self.async_client, "POST", self._api_url, json=kwargs
+                )
+                return _aiter_sse(event_source)
+            else:
+                response = await self.async_client.post(url=self._api_url, json=kwargs)
+                await _araise_on_error(response)
+                return response.json()
+
+        return await _completion_with_retry(**kwargs)
+
+    def _create_chat_result(self, response: Dict) -> ChatResult:
+        generations = []
+        result = response.get("result", {})
+        msg = result.get("message", {})
+        message = _convert_naver_chat_message_to_message(msg)
+
+        if isinstance(message, AIMessage):
+            message.usage_metadata = {
+                "input_tokens": result.get("inputLength"),
+                "output_tokens": result.get("outputLength"),
+                "total_tokens": result.get("inputLength") + result.get("outputLength"),
+            }
+
+        gen = ChatGeneration(
+            message=message,
+        )
+        generations.append(gen)
+
+        llm_output = {
+            "stop_reason": result.get("stopReason"),
+            "input_length": result.get("inputLength"),
+            "output_length": result.get("outputLength"),
+            "seed": result.get("seed"),
+            "ai_filter": result.get("aiFilter"),
+        }
+        return ChatResult(generations=generations, llm_output=llm_output)
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs}
+
+        response = self._completion_with_retry(messages=message_dicts, **params)
+
+        return self._create_chat_result(response)
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs, "stream": True}
+
+        default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
+        for sse in self._completion_with_retry(
+            messages=message_dicts, run_manager=run_manager, **params
+        ):
+            new_chunk = _convert_chunk_to_message_chunk(sse, default_chunk_class)
+            default_chunk_class = new_chunk.__class__
+            gen_chunk = ChatGenerationChunk(message=new_chunk)
+
+            if run_manager:
+                run_manager.on_llm_new_token(
+                    token=cast(str, new_chunk.content), chunk=gen_chunk
+                )
+
+            yield gen_chunk
+
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs}
+
+        response = await self._acompletion_with_retry(
+            messages=message_dicts, run_manager=run_manager, **params
+        )
+
+        return self._create_chat_result(response)
+
+    async def _astream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs, "stream": True}
+
+        default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
+        async for chunk in await self._acompletion_with_retry(
+            messages=message_dicts, run_manager=run_manager, **params
+        ):
+            new_chunk = _convert_chunk_to_message_chunk(chunk, default_chunk_class)
+            default_chunk_class = new_chunk.__class__
+            gen_chunk = ChatGenerationChunk(message=new_chunk)
+
+            if run_manager:
+                await run_manager.on_llm_new_token(
+                    token=cast(str, new_chunk.content), chunk=gen_chunk
+                )
+
+            yield gen_chunk
+
+
+def _create_retry_decorator(
+    llm: ChatClovaX,
+    run_manager: Optional[
+        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
+    ] = None,
+) -> Callable[[Any], Any]:
+    """Returns a tenacity retry decorator, preconfigured to handle exceptions"""
+
+    errors = [httpx.RequestError, httpx.StreamError]
+    return create_base_retry_decorator(
+        error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
+    )
diff --git a/libs/community/langchain_community/embeddings/__init__.py b/libs/community/langchain_community/embeddings/__init__.py
index 845d437e1ece4..38c7d5a76bc1d 100644
--- a/libs/community/langchain_community/embeddings/__init__.py
+++ b/libs/community/langchain_community/embeddings/__init__.py
@@ -151,6 +151,9 @@
     from langchain_community.embeddings.mosaicml import (
         MosaicMLInstructorEmbeddings,
     )
+    from langchain_community.embeddings.naver import (
+        ClovaXEmbeddings,
+    )
     from langchain_community.embeddings.nemo import (
         NeMoEmbeddings,
     )
@@ -250,6 +253,7 @@
     "BookendEmbeddings",
     "ClarifaiEmbeddings",
     "ClovaEmbeddings",
+    "ClovaXEmbeddings",
     "CohereEmbeddings",
     "DashScopeEmbeddings",
     "DatabricksEmbeddings",
@@ -332,6 +336,7 @@
     "BookendEmbeddings": "langchain_community.embeddings.bookend",
     "ClarifaiEmbeddings": "langchain_community.embeddings.clarifai",
     "ClovaEmbeddings": "langchain_community.embeddings.clova",
+    "ClovaXEmbeddings": "langchain_community.embeddings.naver",
     "CohereEmbeddings": "langchain_community.embeddings.cohere",
     "DashScopeEmbeddings": "langchain_community.embeddings.dashscope",
     "DatabricksEmbeddings": "langchain_community.embeddings.databricks",
diff --git a/libs/community/langchain_community/embeddings/clova.py b/libs/community/langchain_community/embeddings/clova.py
index 551dc63526192..d6d3d77b74d0b 100644
--- a/libs/community/langchain_community/embeddings/clova.py
+++ b/libs/community/langchain_community/embeddings/clova.py
@@ -3,11 +3,17 @@
 from typing import Any, Dict, List, Optional, cast
 
 import requests
+from langchain_core._api.deprecation import deprecated
 from langchain_core.embeddings import Embeddings
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 from pydantic import BaseModel, ConfigDict, SecretStr, model_validator
 
 
+@deprecated(
+    since="0.3.4",
+    removal="1.0.0",
+    alternative_import="langchain_community.ClovaXEmbeddings",
+)
 class ClovaEmbeddings(BaseModel, Embeddings):
     """
     Clova's embedding service.
diff --git a/libs/community/langchain_community/embeddings/naver.py b/libs/community/langchain_community/embeddings/naver.py
new file mode 100644
index 0000000000000..af7003c70c23d
--- /dev/null
+++ b/libs/community/langchain_community/embeddings/naver.py
@@ -0,0 +1,192 @@
+import logging
+from typing import Any, Dict, List, Optional
+
+import httpx
+from langchain_core.embeddings import Embeddings
+from langchain_core.utils import convert_to_secret_str, get_from_env
+from pydantic import (
+    AliasChoices,
+    BaseModel,
+    Field,
+    SecretStr,
+    model_validator,
+)
+from typing_extensions import Self
+
+_DEFAULT_BASE_URL = "https://clovastudio.apigw.ntruss.com"
+
+logger = logging.getLogger(__name__)
+
+
+def _raise_on_error(response: httpx.Response) -> None:
+    """Raise an error if the response is an error."""
+    if httpx.codes.is_error(response.status_code):
+        error_message = response.read().decode("utf-8")
+        raise httpx.HTTPStatusError(
+            f"Error response {response.status_code} "
+            f"while fetching {response.url}: {error_message}",
+            request=response.request,
+            response=response,
+        )
+
+
+async def _araise_on_error(response: httpx.Response) -> None:
+    """Raise an error if the response is an error."""
+    if httpx.codes.is_error(response.status_code):
+        error_message = (await response.aread()).decode("utf-8")
+        raise httpx.HTTPStatusError(
+            f"Error response {response.status_code} "
+            f"while fetching {response.url}: {error_message}",
+            request=response.request,
+            response=response,
+        )
+
+
+class ClovaXEmbeddings(BaseModel, Embeddings):
+    """`NCP ClovaStudio` Embedding API.
+
+    following environment variables set or passed in constructor in lower case:
+    - ``NCP_CLOVASTUDIO_API_KEY``
+    - ``NCP_APIGW_API_KEY``
+    - ``NCP_CLOVASTUDIO_APP_ID``
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community import ClovaXEmbeddings
+
+            model = ClovaXEmbeddings(model="clir-emb-dolphin")
+            output = embedding.embed_documents(documents)
+    """  # noqa: E501
+
+    client: httpx.Client = Field(default=None)  #: :meta private:
+    async_client: httpx.AsyncClient = Field(default=None)  #: :meta private:
+
+    ncp_clovastudio_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
+    """Automatically inferred from env are `NCP_CLOVASTUDIO_API_KEY` if not provided."""
+
+    ncp_apigw_api_key: Optional[SecretStr] = Field(default=None, alias="apigw_api_key")
+    """Automatically inferred from env are `NCP_APIGW_API_KEY` if not provided."""
+
+    base_url: str = Field(default=None, alias="base_url")
+    """
+    Automatically inferred from env are  `NCP_CLOVASTUDIO_API_BASE_URL` if not provided.
+    """
+
+    app_id: Optional[str] = Field(default=None)
+    service_app: bool = Field(
+        default=False,
+        description="false: use testapp, true: use service app on NCP Clova Studio",
+    )
+    model_name: str = Field(
+        default="clir-emb-dolphin",
+        validation_alias=AliasChoices("model_name", "model"),
+        description="NCP ClovaStudio embedding model name",
+    )
+
+    timeout: int = Field(gt=0, default=60)
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    @property
+    def lc_secrets(self) -> Dict[str, str]:
+        return {
+            "ncp_clovastudio_api_key": "NCP_CLOVASTUDIO_API_KEY",
+            "ncp_apigw_api_key": "NCP_APIGW_API_KEY",
+        }
+
+    @property
+    def _api_url(self) -> str:
+        """GET embedding api url"""
+        app_type = "serviceapp" if self.service_app else "testapp"
+        model_name = self.model_name if self.model_name != "bge-m3" else "v2"
+        return (
+            f"{self.base_url}/{app_type}"
+            f"/v1/api-tools/embedding/{model_name}/{self.app_id}"
+        )
+
+    @model_validator(mode="after")
+    def validate_model_after(self) -> Self:
+        if not self.ncp_clovastudio_api_key:
+            self.ncp_clovastudio_api_key = convert_to_secret_str(
+                get_from_env("ncp_clovastudio_api_key", "NCP_CLOVASTUDIO_API_KEY")
+            )
+
+        if not self.ncp_apigw_api_key:
+            self.ncp_apigw_api_key = convert_to_secret_str(
+                get_from_env("ncp_apigw_api_key", "NCP_APIGW_API_KEY")
+            )
+
+        if not self.base_url:
+            self.base_url = get_from_env(
+                "base_url", "NCP_CLOVASTUDIO_API_BASE_URL", _DEFAULT_BASE_URL
+            )
+
+        if not self.app_id:
+            self.app_id = get_from_env("app_id", "NCP_CLOVASTUDIO_APP_ID")
+
+        if not self.client:
+            self.client = httpx.Client(
+                base_url=self.base_url,
+                headers=self.default_headers(),
+                timeout=self.timeout,
+            )
+
+        if not self.async_client:
+            self.async_client = httpx.AsyncClient(
+                base_url=self.base_url,
+                headers=self.default_headers(),
+                timeout=self.timeout,
+            )
+
+        return self
+
+    def default_headers(self) -> Dict[str, Any]:
+        clovastudio_api_key = (
+            self.ncp_clovastudio_api_key.get_secret_value()
+            if self.ncp_clovastudio_api_key
+            else None
+        )
+        apigw_api_key = (
+            self.ncp_apigw_api_key.get_secret_value()
+            if self.ncp_apigw_api_key
+            else None
+        )
+        return {
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+            "X-NCP-CLOVASTUDIO-API-KEY": clovastudio_api_key,
+            "X-NCP-APIGW-API-KEY": apigw_api_key,
+        }
+
+    def _embed_text(self, text: str) -> List[float]:
+        payload = {"text": text}
+        response = self.client.post(url=self._api_url, json=payload)
+        _raise_on_error(response)
+        return response.json()["result"]["embedding"]
+
+    async def _aembed_text(self, text: str) -> List[float]:
+        payload = {"text": text}
+        response = await self.async_client.post(url=self._api_url, json=payload)
+        await _araise_on_error(response)
+        return response.json()["result"]["embedding"]
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        embeddings = []
+        for text in texts:
+            embeddings.append(self._embed_text(text))
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        return self._embed_text(text)
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        embeddings = []
+        for text in texts:
+            embedding = await self._aembed_text(text)
+            embeddings.append(embedding)
+        return embeddings
+
+    async def aembed_query(self, text: str) -> List[float]:
+        return await self._aembed_text(text)
diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index 18c816b646533..1072eafe6ec6e 100644
--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -1267,6 +1267,17 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "httpx-sse"
+version = "0.4.0"
+description = "Consume Server-Sent Event (SSE) messages with HTTPX."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpx-sse-0.4.0.tar.gz", hash = "sha256:1e81a3a3070ce322add1d3529ed42eb5f70817f45ed6ec915ab753f961139721"},
+    {file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
+]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -4557,4 +4568,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "5c436a9ba9a1695c5c456c1ad8a81c9772a2ba0248624278c6cb606dd019b338"
+content-hash = "5c36c0453948190412f8ea627e6a10e02c56a1ea732ca215834dad3f1d785f4c"
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index 41a3cb4d9cc99..8ecb5a591690b 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -43,6 +43,7 @@ tenacity = ">=8.1.0,!=8.4.0,<10"
 dataclasses-json = ">= 0.5.7, < 0.7"
 pydantic-settings = "^2.4.0"
 langsmith = "^0.1.125"
+httpx-sse = "^0.4.0"
 
 [[tool.poetry.dependencies.numpy]]
 version = "^1"
diff --git a/libs/community/tests/integration_tests/chat_models/test_naver.py b/libs/community/tests/integration_tests/chat_models/test_naver.py
new file mode 100644
index 0000000000000..3572db8046fdf
--- /dev/null
+++ b/libs/community/tests/integration_tests/chat_models/test_naver.py
@@ -0,0 +1,71 @@
+"""Test ChatNaver chat model."""
+
+from langchain_core.messages import AIMessage, AIMessageChunk
+
+from langchain_community.chat_models import ChatClovaX
+
+
+def test_stream() -> None:
+    """Test streaming tokens from ChatClovaX."""
+    llm = ChatClovaX()
+
+    for token in llm.stream("I'm Clova"):
+        assert isinstance(token, AIMessageChunk)
+        assert isinstance(token.content, str)
+
+
+async def test_astream() -> None:
+    """Test streaming tokens from ChatClovaX."""
+    llm = ChatClovaX()
+
+    async for token in llm.astream("I'm Clova"):
+        assert isinstance(token, AIMessageChunk)
+        assert isinstance(token.content, str)
+
+
+async def test_abatch() -> None:
+    """Test streaming tokens from ChatClovaX."""
+    llm = ChatClovaX()
+
+    result = await llm.abatch(["I'm Clova", "I'm not Clova"])
+    for token in result:
+        assert isinstance(token, AIMessage)
+        assert isinstance(token.content, str)
+
+
+async def test_abatch_tags() -> None:
+    """Test batch tokens from ChatClovaX."""
+    llm = ChatClovaX()
+
+    result = await llm.abatch(["I'm Clova", "I'm not Clova"], config={"tags": ["foo"]})
+    for token in result:
+        assert isinstance(token, AIMessage)
+        assert isinstance(token.content, str)
+
+
+def test_batch() -> None:
+    """Test batch tokens from ChatClovaX."""
+    llm = ChatClovaX()
+
+    result = llm.batch(["I'm Clova", "I'm not Clova"])
+    for token in result:
+        assert isinstance(token, AIMessage)
+        assert isinstance(token.content, str)
+
+
+async def test_ainvoke() -> None:
+    """Test invoke tokens from ChatClovaX."""
+    llm = ChatClovaX()
+
+    result = await llm.ainvoke("I'm Clova", config={"tags": ["foo"]})
+    assert isinstance(result, AIMessage)
+    assert isinstance(result.content, str)
+
+
+def test_invoke() -> None:
+    """Test invoke tokens from ChatClovaX."""
+    llm = ChatClovaX()
+
+    result = llm.invoke("I'm Clova", config=dict(tags=["foo"]))
+    assert isinstance(result, AIMessage)
+    assert isinstance(result.content, str)
diff --git a/libs/community/tests/integration_tests/embeddings/test_naver.py b/libs/community/tests/integration_tests/embeddings/test_naver.py
new file mode 100644
index 0000000000000..a2c836c7ac287
--- /dev/null
+++ b/libs/community/tests/integration_tests/embeddings/test_naver.py
@@ -0,0 +1,37 @@
+"""Test Naver embeddings."""
+
+from langchain_community.embeddings import ClovaXEmbeddings
+
+
+def test_embedding_documents() -> None:
+    """Test cohere embeddings."""
+    documents = ["foo bar"]
+    embedding = ClovaXEmbeddings()
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) > 0
+
+
+async def test_aembedding_documents() -> None:
+    """Test cohere embeddings."""
+    documents = ["foo bar"]
+    embedding = ClovaXEmbeddings()
+    output = await embedding.aembed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) > 0
+
+
+def test_embedding_query() -> None:
+    """Test cohere embeddings."""
+    document = "foo bar"
+    embedding = ClovaXEmbeddings()
+    output = embedding.embed_query(document)
+    assert len(output) > 0
+
+
+async def test_aembedding_query() -> None:
+    """Test cohere embeddings."""
+    document = "foo bar"
+    embedding = ClovaXEmbeddings()
+    output = await embedding.aembed_query(document)
+    assert len(output) > 0
diff --git a/libs/community/tests/unit_tests/chat_models/test_imports.py b/libs/community/tests/unit_tests/chat_models/test_imports.py
index 035ecbca8b1c6..ee3240168e01e 100644
--- a/libs/community/tests/unit_tests/chat_models/test_imports.py
+++ b/libs/community/tests/unit_tests/chat_models/test_imports.py
@@ -6,6 +6,7 @@
     "ChatAnthropic",
     "ChatAnyscale",
     "ChatBaichuan",
+    "ChatClovaX",
     "ChatCohere",
     "ChatCoze",
     "ChatDatabricks",
diff --git a/libs/community/tests/unit_tests/chat_models/test_naver.py b/libs/community/tests/unit_tests/chat_models/test_naver.py
new file mode 100644
index 0000000000000..ea0d2e7a42de9
--- /dev/null
+++ b/libs/community/tests/unit_tests/chat_models/test_naver.py
@@ -0,0 +1,197 @@
+"""Test chat model integration."""
+
+import json
+import os
+from typing import Any, AsyncGenerator, Generator, cast
+from unittest.mock import patch
+
+import pytest
+from langchain_core.callbacks import BaseCallbackHandler
+from langchain_core.messages import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage,
+)
+from pydantic import SecretStr
+
+from langchain_community.chat_models import ChatClovaX
+from langchain_community.chat_models.naver import (
+    _convert_message_to_naver_chat_message,
+    _convert_naver_chat_message_to_message,
+)
+
+os.environ["NCP_CLOVASTUDIO_API_KEY"] = "test_api_key"
+os.environ["NCP_APIGW_API_KEY"] = "test_gw_key"
+
+
+def test_initialization_api_key() -> None:
+    """Test chat model initialization."""
+    chat_model = ChatClovaX(api_key="foo", apigw_api_key="bar")  # type: ignore[arg-type]
+    assert (
+        cast(SecretStr, chat_model.ncp_clovastudio_api_key).get_secret_value() == "foo"
+    )
+    assert cast(SecretStr, chat_model.ncp_apigw_api_key).get_secret_value() == "bar"
+
+
+def test_initialization_model_name() -> None:
+    llm = ChatClovaX(model="HCX-DASH-001")  # type: ignore[call-arg]
+    assert llm.model_name == "HCX-DASH-001"
+    llm = ChatClovaX(model_name="HCX-DASH-001")
+    assert llm.model_name == "HCX-DASH-001"
+
+
+def test_convert_dict_to_message_human() -> None:
+    message = {"role": "user", "content": "foo"}
+    result = _convert_naver_chat_message_to_message(message)
+    expected_output = HumanMessage(content="foo")
+    assert result == expected_output
+    assert _convert_message_to_naver_chat_message(expected_output) == message
+
+
+def test_convert_dict_to_message_ai() -> None:
+    message = {"role": "assistant", "content": "foo"}
+    result = _convert_naver_chat_message_to_message(message)
+    expected_output = AIMessage(content="foo")
+    assert result == expected_output
+    assert _convert_message_to_naver_chat_message(expected_output) == message
+
+
+def test_convert_dict_to_message_system() -> None:
+    message = {"role": "system", "content": "foo"}
+    result = _convert_naver_chat_message_to_message(message)
+    expected_output = SystemMessage(content="foo")
+    assert result == expected_output
+    assert _convert_message_to_naver_chat_message(expected_output) == message
+
+
+@pytest.fixture
+def mock_chat_completion_response() -> dict:
+    return {
+        "status": {"code": "20000", "message": "OK"},
+        "result": {
+            "message": {
+                "role": "assistant",
+                "content": "Phrases: Record what happened today and prepare "
+                "for tomorrow. "
+                "The diary will make your life richer.",
+            },
+            "stopReason": "LENGTH",
+            "inputLength": 100,
+            "outputLength": 10,
+            "aiFilter": [
+                {"groupName": "curse", "name": "insult", "score": "1"},
+                {"groupName": "curse", "name": "discrimination", "score": "0"},
+                {
+                    "groupName": "unsafeContents",
+                    "name": "sexualHarassment",
+                    "score": "2",
+                },
+            ],
+        },
+    }
+
+
+def test_naver_invoke(mock_chat_completion_response: dict) -> None:
+    llm = ChatClovaX()
+    completed = False
+
+    def mock_completion_with_retry(*args: Any, **kwargs: Any) -> Any:
+        nonlocal completed
+        completed = True
+        return mock_chat_completion_response
+
+    with patch.object(ChatClovaX, "_completion_with_retry", mock_completion_with_retry):
+        res = llm.invoke("Let's test it.")
+        assert (
+            res.content
+            == "Phrases: Record what happened today and prepare for tomorrow. "
+            "The diary will make your life richer."
+        )
+    assert completed
+
+
+async def test_naver_ainvoke(mock_chat_completion_response: dict) -> None:
+    llm = ChatClovaX()
+    completed = False
+
+    async def mock_acompletion_with_retry(*args: Any, **kwargs: Any) -> Any:
+        nonlocal completed
+        completed = True
+        return mock_chat_completion_response
+
+    with patch.object(
+        ChatClovaX, "_acompletion_with_retry", mock_acompletion_with_retry
+    ):
+        res = await llm.ainvoke("Let's test it.")
+        assert (
+            res.content
+            == "Phrases: Record what happened today and prepare for tomorrow. "
+            "The diary will make your life richer."
+        )
+    assert completed
+
+
+def _make_completion_response_from_token(token: str):  # type: ignore[no-untyped-def]
+    from httpx_sse import ServerSentEvent
+
+    return ServerSentEvent(
+        event="token",
+        data=json.dumps(
+            dict(
+                index=0,
+                inputLength=89,
+                outputLength=1,
+                message=dict(
+                    content=token,
+                    role="assistant",
+                ),
+            )
+        ),
+    )
+
+
+def mock_chat_stream(*args: Any, **kwargs: Any) -> Generator:
+    def it() -> Generator:
+        for token in ["Hello", " how", " can", " I", " help", "?"]:
+            yield _make_completion_response_from_token(token)
+
+    return it()
+
+
+async def mock_chat_astream(*args: Any, **kwargs: Any) -> AsyncGenerator:
+    async def it() -> AsyncGenerator:
+        for token in ["Hello", " how", " can", " I", " help", "?"]:
+            yield _make_completion_response_from_token(token)
+
+    return it()
+
+
+class MyCustomHandler(BaseCallbackHandler):
+    last_token: str = ""
+
+    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        self.last_token = token
+
+
+@patch(
+    "langchain_community.chat_models.ChatClovaX._completion_with_retry",
+    new=mock_chat_stream,
+)
+@pytest.mark.requires("httpx_sse")
+def test_stream_with_callback() -> None:
+    callback = MyCustomHandler()
+    chat = ChatClovaX(callbacks=[callback])
+    for token in chat.stream("Hello"):
+        assert callback.last_token == token.content
+
+
+@patch(
+    "langchain_community.chat_models.ChatClovaX._acompletion_with_retry",
+    new=mock_chat_astream,
+)
+@pytest.mark.requires("httpx_sse")
+async def test_astream_with_callback() -> None:
+    callback = MyCustomHandler()
+    chat = ChatClovaX(callbacks=[callback])
+    async for token in chat.astream("Hello"):
+        assert callback.last_token == token.content
diff --git a/libs/community/tests/unit_tests/embeddings/test_imports.py b/libs/community/tests/unit_tests/embeddings/test_imports.py
index 6298b43464aa5..a6f26ce0c3fa6 100644
--- a/libs/community/tests/unit_tests/embeddings/test_imports.py
+++ b/libs/community/tests/unit_tests/embeddings/test_imports.py
@@ -7,6 +7,7 @@
     "AzureOpenAIEmbeddings",
     "BaichuanTextEmbeddings",
     "ClarifaiEmbeddings",
+    "ClovaXEmbeddings",
     "CohereEmbeddings",
     "DatabricksEmbeddings",
     "ElasticsearchEmbeddings",
diff --git a/libs/community/tests/unit_tests/embeddings/test_naver.py b/libs/community/tests/unit_tests/embeddings/test_naver.py
new file mode 100644
index 0000000000000..39f072f28af94
--- /dev/null
+++ b/libs/community/tests/unit_tests/embeddings/test_naver.py
@@ -0,0 +1,18 @@
+"""Test embedding model integration."""
+
+import os
+from typing import cast
+
+from pydantic import SecretStr
+
+from langchain_community.embeddings import ClovaXEmbeddings
+
+os.environ["NCP_CLOVASTUDIO_API_KEY"] = "test_api_key"
+os.environ["NCP_APIGW_API_KEY"] = "test_gw_key"
+os.environ["NCP_CLOVASTUDIO_APP_ID"] = "test_app_id"
+
+
+def test_initialization_api_key() -> None:
+    llm = ClovaXEmbeddings(api_key="foo", apigw_api_key="bar")  # type: ignore[arg-type]
+    assert cast(SecretStr, llm.ncp_clovastudio_api_key).get_secret_value() == "foo"
+    assert cast(SecretStr, llm.ncp_apigw_api_key).get_secret_value() == "bar"
diff --git a/libs/community/tests/unit_tests/test_dependencies.py b/libs/community/tests/unit_tests/test_dependencies.py
index 8fe12f140f164..6ebc2d3d9af63 100644
--- a/libs/community/tests/unit_tests/test_dependencies.py
+++ b/libs/community/tests/unit_tests/test_dependencies.py
@@ -43,6 +43,7 @@ def test_required_dependencies(poetry_conf: Mapping[str, Any]) -> None:
             "SQLAlchemy",
             "aiohttp",
             "dataclasses-json",
+            "httpx-sse",
             "langchain-core",
             "langsmith",
             "numpy",

From 7345470669d302ba3b3d17252605a777a9ccec53 Mon Sep 17 00:00:00 2001
From: Ed Branch <140059108+ebranchy@users.noreply.github.com>
Date: Thu, 24 Oct 2024 22:23:32 +0100
Subject: [PATCH 68/84] docs: add aws support to how-to-guides (#27450)

This PR adds support to the how-to documentation for using AWS Bedrock
and Sagemaker Endpoints.

Because AWS services above dont presently use API keys to access LLMs
I've amended more of the source code than would normally be expected.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/src/theme/ChatModelTabs.js | 30 +++++++++++++++++++++++++++---
 1 file changed, 27 insertions(+), 3 deletions(-)

diff --git a/docs/src/theme/ChatModelTabs.js b/docs/src/theme/ChatModelTabs.js
index 9427b40396c20..b6b403c2f8d75 100644
--- a/docs/src/theme/ChatModelTabs.js
+++ b/docs/src/theme/ChatModelTabs.js
@@ -15,6 +15,7 @@ import CodeBlock from "@theme-original/CodeBlock";
  * @property {string} [googleParams] - Parameters for Google chat model. Defaults to `model="gemini-pro"`
  * @property {string} [togetherParams] - Parameters for Together chat model. Defaults to `model="mistralai/Mixtral-8x7B-Instruct-v0.1"`
  * @property {string} [nvidiaParams] - Parameters for Nvidia NIM model. Defaults to `model="meta/llama3-70b-instruct"`
+ * @property {string} [awsBedrockParams] - Parameters for AWS Bedrock chat model.
  * @property {boolean} [hideOpenai] - Whether or not to hide OpenAI chat model.
  * @property {boolean} [hideAnthropic] - Whether or not to hide Anthropic chat model.
  * @property {boolean} [hideCohere] - Whether or not to hide Cohere chat model.
@@ -25,6 +26,7 @@ import CodeBlock from "@theme-original/CodeBlock";
  * @property {boolean} [hideTogether] - Whether or not to hide Together chat model.
  * @property {boolean} [hideAzure] - Whether or not to hide Microsoft Azure OpenAI chat model.
  * @property {boolean} [hideNvidia] - Whether or not to hide NVIDIA NIM model.
+ * @property {boolean} [hideAWS] - Whether or not to hide AWS models.
  * @property {string} [customVarName] - Custom variable name for the model. Defaults to `model`.
  */
 
@@ -43,6 +45,7 @@ export default function ChatModelTabs(props) {
     togetherParams,
     azureParams,
     nvidiaParams,
+    awsBedrockParams,
     hideOpenai,
     hideAnthropic,
     hideCohere,
@@ -53,6 +56,7 @@ export default function ChatModelTabs(props) {
     hideTogether,
     hideAzure,
     hideNvidia,
+    hideAWS,
     customVarName,
   } = props;
 
@@ -74,6 +78,7 @@ export default function ChatModelTabs(props) {
     azureParams ??
     `\n    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],\n    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],\n    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],\n`;
   const nvidiaParamsOrDefault = nvidiaParams ?? `model="meta/llama3-70b-instruct"`
+  const awsBedrockParamsOrDefault = awsBedrockParams ?? `model_id="anthropic.claude-3-5-sonnet-20240620-v1:0"`;
 
   const llmVarName = customVarName ?? "model";
 
@@ -168,6 +173,15 @@ export default function ChatModelTabs(props) {
       default: false,
       shouldHide: hideTogether,
     },
+    {
+      value: "AWS",
+      label: "AWS",
+      text: `from langchain_aws import ChatBedrock\n\n${llmVarName} = ChatBedrock(${awsBedrockParamsOrDefault})`,
+      apiKeyText: "# Ensure your AWS credentials are configured",
+      packageName: "langchain-aws",
+      default: false,
+      shouldHide: hideAWS,
+    },
   ];
 
   return (
@@ -175,10 +189,16 @@ export default function ChatModelTabs(props) {
       {tabItems
         .filter((tabItem) => !tabItem.shouldHide)
         .map((tabItem) => {
-          const apiKeyText = `import getpass
+          let apiKeyText = "";
+          if (tabItem.apiKeyName) {
+            apiKeyText = `import getpass
 import os
 
 os.environ["${tabItem.apiKeyName}"] = getpass.getpass()`;
+          } else if (tabItem.apiKeyText) {
+            apiKeyText = tabItem.apiKeyText;
+          }
+
           return (
             <TabItem
               key={tabItem.value}
@@ -186,8 +206,12 @@ os.environ["${tabItem.apiKeyName}"] = getpass.getpass()`;
               label={tabItem.label}
               default={tabItem.default}
             >
-              <CodeBlock language="bash">{`pip install -qU ${tabItem.packageName}`}</CodeBlock>              
-              <CodeBlock language="python">{apiKeyText + "\n\n" + tabItem.text}</CodeBlock>
+              <CodeBlock language="bash">
+                {`pip install -qU ${tabItem.packageName}`}
+              </CodeBlock>
+              <CodeBlock language="python">
+                {apiKeyText ? apiKeyText + "\n\n" + tabItem.text : tabItem.text}
+              </CodeBlock>
             </TabItem>
           );
         })

From 455ab7d714a0bf971fbd1e3d3666e7bf8f81bcce Mon Sep 17 00:00:00 2001
From: ZhangShenao <15201440436@163.com>
Date: Fri, 25 Oct 2024 05:42:16 +0800
Subject: [PATCH 69/84] Improvement[Community] Improve Document Loaders and
 Splitters (#27568)

- Fix word spelling error
- Add static method decorator
- Fix language splitter

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 libs/community/langchain_community/document_loaders/pdf.py | 5 +++--
 libs/text-splitters/langchain_text_splitters/character.py  | 2 +-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/libs/community/langchain_community/document_loaders/pdf.py b/libs/community/langchain_community/document_loaders/pdf.py
index b01ffea8efcaa..528d34c43b92a 100644
--- a/libs/community/langchain_community/document_loaders/pdf.py
+++ b/libs/community/langchain_community/document_loaders/pdf.py
@@ -178,7 +178,7 @@ class PyPDFLoader(BasePDFLoader):
 
             loader = PyPDFLoader(
                 file_path = "./example_data/layout-parser-paper.pdf",
-                password = "my-pasword",
+                password = "my-password",
                 extract_images = True,
                 # headers = None
                 # extraction_mode = "plain",
@@ -572,7 +572,8 @@ def get_processed_pdf(self, pdf_id: str) -> str:
         response = requests.get(url, headers=self._mathpix_headers)
         return response.content.decode("utf-8")
 
-    def clean_pdf(self, contents: str) -> str:
+    @staticmethod
+    def clean_pdf(contents: str) -> str:
         """Clean the PDF file.
 
         Args:
diff --git a/libs/text-splitters/langchain_text_splitters/character.py b/libs/text-splitters/langchain_text_splitters/character.py
index 85124b39de924..f65c38869d394 100644
--- a/libs/text-splitters/langchain_text_splitters/character.py
+++ b/libs/text-splitters/langchain_text_splitters/character.py
@@ -465,7 +465,7 @@ def get_separators_for_language(language: Language) -> List[str]:
                 "\n\\\\begin{verse}",
                 "\n\\\\begin{verbatim}",
                 # Now split by math environments
-                "\n\\\begin{align}",
+                "\n\\\\begin{align}",
                 "$$",
                 "$",
                 # Now split by the normal type of lines

From 0623c74560d18570ecea55d39566fa7eb53823e7 Mon Sep 17 00:00:00 2001
From: Nithish Raghunandanan <nithishr@gmail.com>
Date: Thu, 24 Oct 2024 23:47:36 +0200
Subject: [PATCH 70/84] couchbase: Add document id to vector search results
 (#27622)

**Description:** Returns the document id along with the Vector Search
results

**Issue:** Fixes https://github.com/langchain-ai/langchain/issues/26860
for CouchbaseVectorStore


- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified.

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../langchain_couchbase/vectorstores.py       |  3 +-
 libs/partners/couchbase/pyproject.toml        |  2 +-
 .../integration_tests/test_vector_store.py    | 30 +++++++++++++++++++
 3 files changed, 33 insertions(+), 2 deletions(-)

diff --git a/libs/partners/couchbase/langchain_couchbase/vectorstores.py b/libs/partners/couchbase/langchain_couchbase/vectorstores.py
index 069cc12550622..e76a206bbd3d7 100644
--- a/libs/partners/couchbase/langchain_couchbase/vectorstores.py
+++ b/libs/partners/couchbase/langchain_couchbase/vectorstores.py
@@ -559,12 +559,13 @@ def similarity_search_with_score_by_vector(
             # Parse the results
             for row in search_iter.rows():
                 text = row.fields.pop(self._text_key, "")
+                id = row.id
 
                 # Format the metadata from Couchbase
                 metadata = self._format_metadata(row.fields)
 
                 score = row.score
-                doc = Document(page_content=text, metadata=metadata)
+                doc = Document(id=id, page_content=text, metadata=metadata)
                 docs_with_score.append((doc, score))
 
         except Exception as e:
diff --git a/libs/partners/couchbase/pyproject.toml b/libs/partners/couchbase/pyproject.toml
index f051b4336d468..04c594754f988 100644
--- a/libs/partners/couchbase/pyproject.toml
+++ b/libs/partners/couchbase/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-couchbase"
-version = "0.2.0"
+version = "0.2.1"
 description = "An integration package connecting Couchbase and LangChain"
 authors = []
 readme = "README.md"
diff --git a/libs/partners/couchbase/tests/integration_tests/test_vector_store.py b/libs/partners/couchbase/tests/integration_tests/test_vector_store.py
index 4cad73481d032..ebc6c93f6c873 100644
--- a/libs/partners/couchbase/tests/integration_tests/test_vector_store.py
+++ b/libs/partners/couchbase/tests/integration_tests/test_vector_store.py
@@ -193,6 +193,7 @@ def test_add_texts_with_ids_and_metadatas(self, cluster: Any) -> None:
         time.sleep(SLEEP_DURATION)
 
         output = vectorstore.similarity_search("foo", k=1)
+        assert output[0].id == "a"
         assert output[0].page_content == "foo"
         assert output[0].metadata["a"] == 1
 
@@ -364,3 +365,32 @@ def test_hybrid_search(self, cluster: Any) -> None:
 
         assert result == hybrid_result
         assert score <= hybrid_score
+
+    def test_id_in_results(self, cluster: Any) -> None:
+        """Test that the id is returned in the result documents."""
+
+        texts = [
+            "foo",
+            "bar",
+            "baz",
+        ]
+
+        metadatas = [{"a": 1}, {"b": 2}, {"c": 3}]
+
+        vectorstore = CouchbaseVectorStore(
+            cluster=cluster,
+            embedding=ConsistentFakeEmbeddings(),
+            index_name=INDEX_NAME,
+            bucket_name=BUCKET_NAME,
+            scope_name=SCOPE_NAME,
+            collection_name=COLLECTION_NAME,
+        )
+
+        ids = vectorstore.add_texts(texts, metadatas=metadatas)
+        assert len(ids) == len(texts)
+
+        # Wait for the documents to be indexed
+        time.sleep(SLEEP_DURATION)
+
+        output = vectorstore.similarity_search("foo", k=1)
+        assert output[0].id == ids[0]

From bcff458ae3c014a1550848091ec179aaef1491c3 Mon Sep 17 00:00:00 2001
From: hippopond <160084905+hippopond@users.noreply.github.com>
Date: Thu, 24 Oct 2024 14:54:12 -0700
Subject: [PATCH 71/84] DOC: Added notes in ipynb file to advise user to
 upgrade package langchain_openai.   For issue:
 https://github.com/langchain-ai/langchain/issues/26616 (#27621)

Thank you for contributing to LangChain!

- [X] **PR title**: DOC: Added notes in ipynb file to advice user to
upgrade package langchain_openai.


- [X]

Added notes from the issue report: to advise the user to upgrade
langchain_openai


Issue:
https://github.com/langchain-ai/langchain/issues/26616

- [ ] **Add tests and docs**:

- [ ] **Lint and test**:

- [ ]

---------

Co-authored-by: Libby Lin <libbylin@Libbys-MacBook-Pro.local>
Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/docs/integrations/tools/pandas.ipynb | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/docs/docs/integrations/tools/pandas.ipynb b/docs/docs/integrations/tools/pandas.ipynb
index 9ad8ec30df677..2647140f54231 100644
--- a/docs/docs/integrations/tools/pandas.ipynb
+++ b/docs/docs/integrations/tools/pandas.ipynb
@@ -9,7 +9,13 @@
     "\n",
     "This notebook shows how to use agents to interact with a `Pandas DataFrame`. It is mostly optimized for question answering.\n",
     "\n",
-    "**NOTE: this agent calls the `Python` agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**"
+    "**NOTE: this agent calls the `Python` agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**\n",
+    "\n",
+    "**NOTE: Since langchain migrated to v0.3 you should upgrade langchain_openai and langchain.   This would avoid import errors.**\n",
+    "\n",
+    "\n",
+    "pip install --upgrade langchain_openai\n",
+    "pip install --upgrade langchain\n"
    ]
   },
   {
@@ -294,7 +300,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

From 265e0a164a84ceeed633c179bef77724980f5b25 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Thu, 24 Oct 2024 15:33:41 -0700
Subject: [PATCH 72/84] core: add flake8-bandit (S) ruff rules to core (#27368)

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
---
 libs/core/langchain_core/indexing/api.py            |  4 ++--
 libs/core/langchain_core/prompts/string.py          |  3 ++-
 libs/core/langchain_core/runnables/graph_mermaid.py |  2 +-
 libs/core/pyproject.toml                            | 12 ++++++------
 libs/core/scripts/check_imports.py                  |  3 ++-
 5 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/libs/core/langchain_core/indexing/api.py b/libs/core/langchain_core/indexing/api.py
index 63226ba366a6f..ab729314cfc99 100644
--- a/libs/core/langchain_core/indexing/api.py
+++ b/libs/core/langchain_core/indexing/api.py
@@ -36,14 +36,14 @@
 
 def _hash_string_to_uuid(input_string: str) -> uuid.UUID:
     """Hashes a string and returns the corresponding UUID."""
-    hash_value = hashlib.sha1(input_string.encode("utf-8")).hexdigest()
+    hash_value = hashlib.sha1(input_string.encode("utf-8")).hexdigest()  # noqa: S324
     return uuid.uuid5(NAMESPACE_UUID, hash_value)
 
 
 def _hash_nested_dict_to_uuid(data: dict[Any, Any]) -> uuid.UUID:
     """Hashes a nested dictionary and returns the corresponding UUID."""
     serialized_data = json.dumps(data, sort_keys=True)
-    hash_value = hashlib.sha1(serialized_data.encode("utf-8")).hexdigest()
+    hash_value = hashlib.sha1(serialized_data.encode("utf-8")).hexdigest()  # noqa: S324
     return uuid.uuid5(NAMESPACE_UUID, hash_value)
 
 
diff --git a/libs/core/langchain_core/prompts/string.py b/libs/core/langchain_core/prompts/string.py
index 0040f4b712279..f9128147e2b1a 100644
--- a/libs/core/langchain_core/prompts/string.py
+++ b/libs/core/langchain_core/prompts/string.py
@@ -97,7 +97,8 @@ def _get_jinja2_variables_from_template(template: str) -> set[str]:
             "Please install it with `pip install jinja2`."
         )
         raise ImportError(msg) from e
-    env = Environment()
+    # noqa for insecure warning elsewhere
+    env = Environment()  # noqa: S701
     ast = env.parse(template)
     variables = meta.find_undeclared_variables(ast)
     return variables
diff --git a/libs/core/langchain_core/runnables/graph_mermaid.py b/libs/core/langchain_core/runnables/graph_mermaid.py
index 2c8319f027bf0..b0e7f3dd5de5e 100644
--- a/libs/core/langchain_core/runnables/graph_mermaid.py
+++ b/libs/core/langchain_core/runnables/graph_mermaid.py
@@ -331,7 +331,7 @@ def _render_mermaid_using_api(
     image_url = (
         f"https://mermaid.ink/img/{mermaid_syntax_encoded}?bgColor={background_color}"
     )
-    response = requests.get(image_url)
+    response = requests.get(image_url, timeout=10)
     if response.status_code == 200:
         img_bytes = response.content
         if output_file_path is not None:
diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml
index 4a26b7f35a16f..92c0b0659ae38 100644
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -72,6 +72,7 @@ select = [
     "PIE",
     "Q",
     "RSE",
+    "S", # https://docs.astral.sh/ruff/rules/#flake8-bandit-s
     "SIM",
     "SLOT",
     "T10",
@@ -85,6 +86,9 @@ ignore = [
     "COM812", # Messes with the formatter
     "UP007",  # Incompatible with pydantic + Python 3.9
     "W293",   # 
+    "S101",   # allow assert - TODO remove
+    "S110",   # allow try/except/pass - TODO remove
+    "S112",   # allow try/except/continue - TODO remove
 ]
 
 [tool.coverage.run]
@@ -128,12 +132,13 @@ classmethod-decorators = [
 "tests/unit_tests/prompts/test_chat.py" = ["E501"]
 "tests/unit_tests/runnables/test_runnable.py" = ["E501"]
 "tests/unit_tests/runnables/test_graph.py" = ["E501"]
+"tests/**" = ["S"]                                       # Ignore flake8-bandit rules in tests
+"scripts/**" = ["S"]                                     # Ignore flake8-bandit rules in scripts
 
 [tool.poetry.group.lint.dependencies]
 ruff = "^0.5"
 
 
-
 [tool.poetry.group.typing.dependencies]
 mypy = ">=1.10,<1.11"
 types-pyyaml = "^6.0.12.2"
@@ -142,14 +147,12 @@ types-jinja2 = "^2.11.9"
 simsimd = "^5.0.0"
 
 
-
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"
 setuptools = "^67.6.1"
 grandalf = "^0.8"
 
 
-
 [tool.poetry.group.test.dependencies]
 pytest = "^7.3.0"
 freezegun = "^1.2.2"
@@ -169,17 +172,14 @@ version = "^1.26.0"
 python = ">=3.12"
 
 
-
 [tool.poetry.group.test_integration.dependencies]
 
 
-
 [tool.poetry.group.typing.dependencies.langchain-text-splitters]
 path = "../text-splitters"
 develop = true
 
 
-
 [tool.poetry.group.test.dependencies.langchain-standard-tests]
 path = "../standard-tests"
 develop = true
diff --git a/libs/core/scripts/check_imports.py b/libs/core/scripts/check_imports.py
index 825bea5b48737..fa189a7117534 100644
--- a/libs/core/scripts/check_imports.py
+++ b/libs/core/scripts/check_imports.py
@@ -10,7 +10,8 @@
     for file in files:
         try:
             module_name = "".join(
-                random.choice(string.ascii_letters) for _ in range(20)
+                random.choice(string.ascii_letters)
+                for _ in range(20)  # noqa: S311
             )
             SourceFileLoader(module_name, file).load_module()
         except Exception:

From 7667ee126fdf3882ad5f2bd23cd05353b23cfc63 Mon Sep 17 00:00:00 2001
From: Eugene Yurtsev <eyurtsev@gmail.com>
Date: Thu, 24 Oct 2024 22:12:49 -0400
Subject: [PATCH 73/84] core: remove mustache in extended deps (#27629)

Remove mustache from extended deps -- we vendor the mustache
implementation
---
 libs/core/extended_testing_deps.txt             | 1 -
 libs/core/tests/unit_tests/prompts/test_chat.py | 1 -
 2 files changed, 2 deletions(-)

diff --git a/libs/core/extended_testing_deps.txt b/libs/core/extended_testing_deps.txt
index 0e216a61157d8..5ad9c8930daf9 100644
--- a/libs/core/extended_testing_deps.txt
+++ b/libs/core/extended_testing_deps.txt
@@ -1,2 +1 @@
 jinja2>=3,<4
-mustache>=0.1.4,<1
diff --git a/libs/core/tests/unit_tests/prompts/test_chat.py b/libs/core/tests/unit_tests/prompts/test_chat.py
index 95cbdc5a30c68..ec9d05c6de5fc 100644
--- a/libs/core/tests/unit_tests/prompts/test_chat.py
+++ b/libs/core/tests/unit_tests/prompts/test_chat.py
@@ -328,7 +328,6 @@ def test_chat_prompt_template_from_messages_jinja2() -> None:
 
 
 @pytest.mark.requires("jinja2")
-@pytest.mark.requires("mustache")
 @pytest.mark.parametrize(
     "template_format,image_type_placeholder,image_data_placeholder",
     [

From 24605bcdb694f4c72ee53c0d3f503e20f865f643 Mon Sep 17 00:00:00 2001
From: Steve Moss <gawbul@users.noreply.github.com>
Date: Fri, 25 Oct 2024 03:16:26 +0100
Subject: [PATCH 74/84] community[patch]: Fix missing protected_namespaces().
 (#27610)

- [x] **PR message**:
- **Description:** Fixes warning messages raised due to missing
`protected_namespaces` parameter in `ConfigDict`.
    - **Issue:** https://github.com/langchain-ai/langchain/issues/27609
    - **Dependencies:** No dependencies
    - **Twitter handle:** @gawbul
---
 libs/community/langchain_community/embeddings/ascend.py       | 4 +++-
 .../langchain_community/embeddings/baidu_qianfan_endpoint.py  | 4 +++-
 libs/community/langchain_community/embeddings/bookend.py      | 4 +++-
 libs/community/langchain_community/embeddings/ernie.py        | 4 +++-
 libs/community/langchain_community/embeddings/google_palm.py  | 4 +++-
 libs/community/langchain_community/embeddings/gpt4all.py      | 4 +++-
 .../langchain_community/embeddings/infinity_local.py          | 1 +
 libs/community/langchain_community/embeddings/itrex.py        | 1 +
 libs/community/langchain_community/embeddings/jina.py         | 4 +++-
 libs/community/langchain_community/embeddings/llamacpp.py     | 1 +
 libs/community/langchain_community/embeddings/nlpcloud.py     | 4 +++-
 .../community/langchain_community/embeddings/optimum_intel.py | 1 +
 libs/community/langchain_community/embeddings/sambanova.py    | 4 +++-
 .../langchain_community/embeddings/tensorflow_hub.py          | 1 +
 libs/community/langchain_community/embeddings/text2vec.py     | 4 +++-
 15 files changed, 35 insertions(+), 10 deletions(-)

diff --git a/libs/community/langchain_community/embeddings/ascend.py b/libs/community/langchain_community/embeddings/ascend.py
index dd8c9c67fd94c..d01f7967491d8 100644
--- a/libs/community/langchain_community/embeddings/ascend.py
+++ b/libs/community/langchain_community/embeddings/ascend.py
@@ -2,7 +2,7 @@
 from typing import Any, Dict, List, Optional
 
 from langchain_core.embeddings import Embeddings
-from pydantic import BaseModel, model_validator
+from pydantic import BaseModel, ConfigDict, model_validator
 
 
 class AscendEmbeddings(Embeddings, BaseModel):
@@ -33,6 +33,8 @@ class AscendEmbeddings(Embeddings, BaseModel):
     model: Any
     tokenizer: Any
 
+    model_config = ConfigDict(protected_namespaces=())
+
     def __init__(self, *args: Any, **kwargs: Any) -> None:
         super().__init__(*args, **kwargs)
         try:
diff --git a/libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py b/libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py
index 14e2f35a4ea40..aaba2f3487a46 100644
--- a/libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py
+++ b/libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py
@@ -5,7 +5,7 @@
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
-from pydantic import BaseModel, Field, SecretStr
+from pydantic import BaseModel, ConfigDict, Field, SecretStr
 
 logger = logging.getLogger(__name__)
 
@@ -81,6 +81,8 @@ class QianfanEmbeddingsEndpoint(BaseModel, Embeddings):
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """extra params for model invoke using with `do`."""
 
+    model_config = ConfigDict(protected_namespaces=())
+
     @pre_init
     def validate_environment(cls, values: Dict) -> Dict:
         """
diff --git a/libs/community/langchain_community/embeddings/bookend.py b/libs/community/langchain_community/embeddings/bookend.py
index c5390e19e71f0..8e977834a47bf 100644
--- a/libs/community/langchain_community/embeddings/bookend.py
+++ b/libs/community/langchain_community/embeddings/bookend.py
@@ -5,7 +5,7 @@
 
 import requests
 from langchain_core.embeddings import Embeddings
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 API_URL = "https://api.bookend.ai/"
 DEFAULT_TASK = "embeddings"
@@ -42,6 +42,8 @@ class BookendEmbeddings(BaseModel, Embeddings):
     """Embeddings model ID to use."""
     auth_header: dict = Field(default_factory=dict)
 
+    model_config = ConfigDict(protected_namespaces=())
+
     def __init__(self, **kwargs: Any):
         super().__init__(**kwargs)
         self.auth_header = {"Authorization": "Basic {}".format(self.api_token)}
diff --git a/libs/community/langchain_community/embeddings/ernie.py b/libs/community/langchain_community/embeddings/ernie.py
index 42a49e2c6ca6f..34758c58b4c20 100644
--- a/libs/community/langchain_community/embeddings/ernie.py
+++ b/libs/community/langchain_community/embeddings/ernie.py
@@ -8,7 +8,7 @@
 from langchain_core.embeddings import Embeddings
 from langchain_core.runnables.config import run_in_executor
 from langchain_core.utils import get_from_dict_or_env, pre_init
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 logger = logging.getLogger(__name__)
 
@@ -31,6 +31,8 @@ class ErnieEmbeddings(BaseModel, Embeddings):
 
     _lock = threading.Lock()
 
+    model_config = ConfigDict(protected_namespaces=())
+
     @pre_init
     def validate_environment(cls, values: Dict) -> Dict:
         values["ernie_api_base"] = get_from_dict_or_env(
diff --git a/libs/community/langchain_community/embeddings/google_palm.py b/libs/community/langchain_community/embeddings/google_palm.py
index 363214caead72..d058bc46add0d 100644
--- a/libs/community/langchain_community/embeddings/google_palm.py
+++ b/libs/community/langchain_community/embeddings/google_palm.py
@@ -5,7 +5,7 @@
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.utils import get_from_dict_or_env, pre_init
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 from tenacity import (
     before_sleep_log,
     retry,
@@ -62,6 +62,8 @@ class GooglePalmEmbeddings(BaseModel, Embeddings):
     show_progress_bar: bool = False
     """Whether to show a tqdm progress bar. Must have `tqdm` installed."""
 
+    model_config = ConfigDict(protected_namespaces=())
+
     @pre_init
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate api key, python package exists."""
diff --git a/libs/community/langchain_community/embeddings/gpt4all.py b/libs/community/langchain_community/embeddings/gpt4all.py
index ce92ab84b6d23..5183cbb08bd84 100644
--- a/libs/community/langchain_community/embeddings/gpt4all.py
+++ b/libs/community/langchain_community/embeddings/gpt4all.py
@@ -1,7 +1,7 @@
 from typing import Any, Dict, List, Optional
 
 from langchain_core.embeddings import Embeddings
-from pydantic import BaseModel, model_validator
+from pydantic import BaseModel, ConfigDict, model_validator
 
 
 class GPT4AllEmbeddings(BaseModel, Embeddings):
@@ -28,6 +28,8 @@ class GPT4AllEmbeddings(BaseModel, Embeddings):
     gpt4all_kwargs: Optional[dict] = {}
     client: Any  #: :meta private:
 
+    model_config = ConfigDict(protected_namespaces=())
+
     @model_validator(mode="before")
     @classmethod
     def validate_environment(cls, values: Dict) -> Any:
diff --git a/libs/community/langchain_community/embeddings/infinity_local.py b/libs/community/langchain_community/embeddings/infinity_local.py
index 610ccaf3e88f0..075d028c9c758 100644
--- a/libs/community/langchain_community/embeddings/infinity_local.py
+++ b/libs/community/langchain_community/embeddings/infinity_local.py
@@ -60,6 +60,7 @@ class InfinityEmbeddingsLocal(BaseModel, Embeddings):
     # LLM call kwargs
     model_config = ConfigDict(
         extra="forbid",
+        protected_namespaces=(),
     )
 
     @model_validator(mode="after")
diff --git a/libs/community/langchain_community/embeddings/itrex.py b/libs/community/langchain_community/embeddings/itrex.py
index 2fcd0afae3d7c..1f9a8e0731bfd 100644
--- a/libs/community/langchain_community/embeddings/itrex.py
+++ b/libs/community/langchain_community/embeddings/itrex.py
@@ -120,6 +120,7 @@ def load_model(self) -> None:
 
     model_config = ConfigDict(
         extra="allow",
+        protected_namespaces=(),
     )
 
     def _embed(self, inputs: Any) -> Any:
diff --git a/libs/community/langchain_community/embeddings/jina.py b/libs/community/langchain_community/embeddings/jina.py
index 718345f1afd23..b46ae026397e9 100644
--- a/libs/community/langchain_community/embeddings/jina.py
+++ b/libs/community/langchain_community/embeddings/jina.py
@@ -6,7 +6,7 @@
 import requests
 from langchain_core.embeddings import Embeddings
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
-from pydantic import BaseModel, SecretStr, model_validator
+from pydantic import BaseModel, ConfigDict, SecretStr, model_validator
 
 JINA_API_URL: str = "https://api.jina.ai/v1/embeddings"
 
@@ -46,6 +46,8 @@ class JinaEmbeddings(BaseModel, Embeddings):
     model_name: str = "jina-embeddings-v2-base-en"
     jina_api_key: Optional[SecretStr] = None
 
+    model_config = ConfigDict(protected_namespaces=())
+
     @model_validator(mode="before")
     @classmethod
     def validate_environment(cls, values: Dict) -> Any:
diff --git a/libs/community/langchain_community/embeddings/llamacpp.py b/libs/community/langchain_community/embeddings/llamacpp.py
index 86a029ea753f0..6487312fd31d0 100644
--- a/libs/community/langchain_community/embeddings/llamacpp.py
+++ b/libs/community/langchain_community/embeddings/llamacpp.py
@@ -63,6 +63,7 @@ class LlamaCppEmbeddings(BaseModel, Embeddings):
 
     model_config = ConfigDict(
         extra="forbid",
+        protected_namespaces=(),
     )
 
     @model_validator(mode="after")
diff --git a/libs/community/langchain_community/embeddings/nlpcloud.py b/libs/community/langchain_community/embeddings/nlpcloud.py
index decf8ac2b803e..7e13f9cbbaf93 100644
--- a/libs/community/langchain_community/embeddings/nlpcloud.py
+++ b/libs/community/langchain_community/embeddings/nlpcloud.py
@@ -2,7 +2,7 @@
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.utils import get_from_dict_or_env, pre_init
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 
 class NLPCloudEmbeddings(BaseModel, Embeddings):
@@ -22,6 +22,8 @@ class NLPCloudEmbeddings(BaseModel, Embeddings):
     gpu: bool  # Define gpu as a class attribute
     client: Any  #: :meta private:
 
+    model_config = ConfigDict(protected_namespaces=())
+
     def __init__(
         self,
         model_name: str = "paraphrase-multilingual-mpnet-base-v2",
diff --git a/libs/community/langchain_community/embeddings/optimum_intel.py b/libs/community/langchain_community/embeddings/optimum_intel.py
index 657cba32c78c4..05de5cca3a963 100644
--- a/libs/community/langchain_community/embeddings/optimum_intel.py
+++ b/libs/community/langchain_community/embeddings/optimum_intel.py
@@ -102,6 +102,7 @@ def load_model(self) -> None:
 
     model_config = ConfigDict(
         extra="allow",
+        protected_namespaces=(),
     )
 
     def _embed(self, inputs: Any) -> Any:
diff --git a/libs/community/langchain_community/embeddings/sambanova.py b/libs/community/langchain_community/embeddings/sambanova.py
index 0daab18ef6620..e23760b43182c 100644
--- a/libs/community/langchain_community/embeddings/sambanova.py
+++ b/libs/community/langchain_community/embeddings/sambanova.py
@@ -4,7 +4,7 @@
 import requests
 from langchain_core.embeddings import Embeddings
 from langchain_core.utils import get_from_dict_or_env, pre_init
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 
 class SambaStudioEmbeddings(BaseModel, Embeddings):
@@ -64,6 +64,8 @@ class SambaStudioEmbeddings(BaseModel, Embeddings):
     batch_size: int = 32
     """Batch size for the embedding models"""
 
+    model_config = ConfigDict(protected_namespaces=())
+
     @pre_init
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
diff --git a/libs/community/langchain_community/embeddings/tensorflow_hub.py b/libs/community/langchain_community/embeddings/tensorflow_hub.py
index a9c2edf419b9d..270c9f17cb36e 100644
--- a/libs/community/langchain_community/embeddings/tensorflow_hub.py
+++ b/libs/community/langchain_community/embeddings/tensorflow_hub.py
@@ -45,6 +45,7 @@ def __init__(self, **kwargs: Any):
 
     model_config = ConfigDict(
         extra="forbid",
+        protected_namespaces=(),
     )
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
diff --git a/libs/community/langchain_community/embeddings/text2vec.py b/libs/community/langchain_community/embeddings/text2vec.py
index 2d55f5585b5ea..4b8cc77192ad5 100644
--- a/libs/community/langchain_community/embeddings/text2vec.py
+++ b/libs/community/langchain_community/embeddings/text2vec.py
@@ -3,7 +3,7 @@
 from typing import Any, List, Optional
 
 from langchain_core.embeddings import Embeddings
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 
 class Text2vecEmbeddings(Embeddings, BaseModel):
@@ -33,6 +33,8 @@ class Text2vecEmbeddings(Embeddings, BaseModel):
     device: Optional[str] = None
     model: Any = None
 
+    model_config = ConfigDict(protected_namespaces=())
+
     def __init__(
         self,
         *,

From 83eebf549fa14ff00d93c71c36fd8f4560b71156 Mon Sep 17 00:00:00 2001
From: Rashmi Pawar <168514198+raspawar@users.noreply.github.com>
Date: Fri, 25 Oct 2024 21:51:22 +0530
Subject: [PATCH 75/84] docs: Add NVIDIA as provider in v3 integrations
 (#27254)

### Add NVIDIA as provider in langchain v3 integrations

cc: @sumitkbh @mattf @dglogo

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb | 2 +-
 docs/docs/versions/v0_3/index.mdx                     | 1 +
 docs/src/theme/FeatureTables.js                       | 4 ++--
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
index 322c2955f89d4..4127fb25fae5a 100644
--- a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
+++ b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
@@ -46,7 +46,7 @@
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
     "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | \n",
+    "| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
     "\n",
     "## Setup\n",
     "\n",
diff --git a/docs/docs/versions/v0_3/index.mdx b/docs/docs/versions/v0_3/index.mdx
index 25a3874b57302..cd52380575480 100644
--- a/docs/docs/versions/v0_3/index.mdx
+++ b/docs/docs/versions/v0_3/index.mdx
@@ -78,6 +78,7 @@ well as updating the `langchain_core.pydantic_v1` and `langchain.pydantic_v1` im
 | langchain-mistralai                    | 0.2.0   | >=0.2,&lt;0.3                 |
 | langchain-mongodb                      | 0.2.0   | >=0.2,&lt;0.3                 |
 | langchain-nomic                        | 0.1.3   | >=0.1.3,&lt;0.2               |
+| langchain-nvidia                       | 0.3.0   | >=0.3,&lt;0.4                 |
 | langchain-ollama                       | 0.2.0   | >=0.2,&lt;0.3                 |
 | langchain-openai                       | 0.2.0   | >=0.2,&lt;0.3                 |
 | langchain-pinecone                     | 0.2.0   | >=0.2,&lt;0.3                 |
diff --git a/docs/src/theme/FeatureTables.js b/docs/src/theme/FeatureTables.js
index 2016c7c5454b2..095d45dbbb826 100644
--- a/docs/src/theme/FeatureTables.js
+++ b/docs/src/theme/FeatureTables.js
@@ -156,8 +156,8 @@ const FEATURE_TABLES = {
                 "link": "nvidia_ai_endpoints/",
                 "structured_output": true,
                 "tool_calling": true,
-                "json_mode": false,
-                "multimodal": false,
+                "json_mode": true,
+                "multimodal": true,
                 "local": true,
                 "apiLink": "https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html"
             },

From 2683f814f4c03c4bb402cc0b748f1b64bb95f5be Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 25 Oct 2024 10:06:55 -0700
Subject: [PATCH 76/84] docs: contributing index page (#27647)

---
 docs/docs/contributing/index.mdx | 32 +++++++++++++++-----------------
 1 file changed, 15 insertions(+), 17 deletions(-)

diff --git a/docs/docs/contributing/index.mdx b/docs/docs/contributing/index.mdx
index e8f7b892a0578..68736123b502b 100644
--- a/docs/docs/contributing/index.mdx
+++ b/docs/docs/contributing/index.mdx
@@ -6,16 +6,23 @@ sidebar_position: 0
 Hi there! Thank you for your interest in contributing to LangChain.
 As an open-source project in a fast developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
 
-## 🗺️ Guidelines
+## Tutorials
 
-### 👩‍💻 Ways to contribute
+Coming soon! We are working on tutorials to help you make your first contribution to the project.
 
-There are many ways to contribute to LangChain. Here are some common ways people contribute:
+## How-to Guides
 
 - [**Documentation**](/docs/contributing/documentation/): Help improve our docs, including this one!
 - [**Code**](/docs/contributing/code/): Help us write code, fix bugs, or improve our infrastructure.
 - [**Integrations**](integrations.mdx): Help us integrate with your favorite vendors and tools.
-- [**Discussions**](https://github.com/langchain-ai/langchain/discussions): Help answer usage questions and discuss issues with users.
+
+## Community
+
+### 💭 GitHub Discussions
+
+We have a [discussions](https://github.com/langchain-ai/langchain/discussions) page where users can ask usage questions, discuss design decisions, and propose new features.
+
+If you are able to help answer questions, please do so! This will allow the maintainers to spend more time focused on development and bug fixing.
 
 ### 🚩 GitHub Issues
 
@@ -32,23 +39,14 @@ We will try to keep these issues as up-to-date as possible, though
 with the rapid rate of development in this field some may get out of date.
 If you notice this happening, please let us know.
 
-### 💭 GitHub Discussions
-
-We have a [discussions](https://github.com/langchain-ai/langchain/discussions) page where users can ask usage questions, discuss design decisions, and propose new features.
+### 📢 Community Slack
 
-If you are able to help answer questions, please do so! This will allow the maintainers to spend more time focused on development and bug fixing.
+We have a [community slack](https://www.langchain.com/join-community) where you can ask questions, get help, and discuss the project with other contributors and users.
 
 ### 🙋 Getting Help
 
 Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
-contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is
-smooth for future contributors.
+ask in [community slack](https://www.langchain.com/join-community) or open a [discussion on GitHub](https://github.com/langchain-ai/langchain/discussions).
 
 In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
-If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
-we do not want these to get in the way of getting good code into the codebase.
-
-### 🌟 Recognition
-
-If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
-If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.
+If you are finding these difficult (or even just annoying) to work with, feel free to ask in [community slack](https://www.langchain.com/join-community)!

From 06df15c9c05e937b6cf578445c210d2389ac855f Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Fri, 25 Oct 2024 12:22:44 -0700
Subject: [PATCH 77/84] core[patch]: Release 0.3.13 (#27651)

---
 libs/core/pyproject.toml | 90 +++++++---------------------------------
 1 file changed, 15 insertions(+), 75 deletions(-)

diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml
index 92c0b0659ae38..81604038c8b0b 100644
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -1,10 +1,10 @@
 [build-system]
-requires = ["poetry-core>=1.0.0"]
+requires = [ "poetry-core>=1.0.0",]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-core"
-version = "0.3.12"
+version = "0.3.13"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -12,16 +12,10 @@ readme = "README.md"
 repository = "https://github.com/langchain-ai/langchain"
 
 [tool.mypy]
-exclude = [
-    "notebooks",
-    "examples",
-    "example_data",
-    "langchain_core/pydantic",
-    "tests/unit_tests/utils/test_function_calling.py",
-]
+exclude = [ "notebooks", "examples", "example_data", "langchain_core/pydantic", "tests/unit_tests/utils/test_function_calling.py",]
 disallow_untyped_defs = "True"
 [[tool.mypy.overrides]]
-module = ["numpy", "pytest"]
+module = [ "numpy", "pytest",]
 ignore_missing_imports = true
 
 [tool.ruff]
@@ -50,60 +44,17 @@ python = ">=3.12.4"
 [tool.poetry.extras]
 
 [tool.ruff.lint]
-select = [
-    "ASYNC",
-    "B",
-    "C4",
-    "COM",
-    "DJ",
-    "E",
-    "EM",
-    "EXE",
-    "F",
-    "FLY",
-    "FURB",
-    "I",
-    "ICN",
-    "INT",
-    "LOG",
-    "N",
-    "NPY",
-    "PD",
-    "PIE",
-    "Q",
-    "RSE",
-    "S", # https://docs.astral.sh/ruff/rules/#flake8-bandit-s
-    "SIM",
-    "SLOT",
-    "T10",
-    "T201",
-    "TID",
-    "UP",
-    "W",
-    "YTT",
-]
-ignore = [
-    "COM812", # Messes with the formatter
-    "UP007",  # Incompatible with pydantic + Python 3.9
-    "W293",   # 
-    "S101",   # allow assert - TODO remove
-    "S110",   # allow try/except/pass - TODO remove
-    "S112",   # allow try/except/continue - TODO remove
-]
+select = [ "ASYNC", "B", "C4", "COM", "DJ", "E", "EM", "EXE", "F", "FLY", "FURB", "I", "ICN", "INT", "LOG", "N", "NPY", "PD", "PIE", "Q", "RSE", "S", "SIM", "SLOT", "T10", "T201", "TID", "UP", "W", "YTT",]
+ignore = [ "COM812", "UP007", "W293", "S101", "S110", "S112",]
 
 [tool.coverage.run]
-omit = ["tests/*"]
+omit = [ "tests/*",]
 
 [tool.pytest.ini_options]
 addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
-markers = [
-    "requires: mark tests as requiring a specific library",
-    "compile: mark placeholder test used to compile integration tests without running them",
-]
+markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them",]
 asyncio_mode = "auto"
-filterwarnings = [
-    "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning",
-]
+filterwarnings = [ "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning",]
 
 [tool.poetry.group.lint]
 optional = true
@@ -121,24 +72,18 @@ optional = true
 optional = true
 
 [tool.ruff.lint.pep8-naming]
-classmethod-decorators = [
-    "classmethod",
-    "langchain_core.utils.pydantic.pre_init",
-    "pydantic.field_validator",
-    "pydantic.v1.root_validator",
-]
+classmethod-decorators = [ "classmethod", "langchain_core.utils.pydantic.pre_init", "pydantic.field_validator", "pydantic.v1.root_validator",]
 
 [tool.ruff.lint.per-file-ignores]
-"tests/unit_tests/prompts/test_chat.py" = ["E501"]
-"tests/unit_tests/runnables/test_runnable.py" = ["E501"]
-"tests/unit_tests/runnables/test_graph.py" = ["E501"]
-"tests/**" = ["S"]                                       # Ignore flake8-bandit rules in tests
-"scripts/**" = ["S"]                                     # Ignore flake8-bandit rules in scripts
+"tests/unit_tests/prompts/test_chat.py" = [ "E501",]
+"tests/unit_tests/runnables/test_runnable.py" = [ "E501",]
+"tests/unit_tests/runnables/test_graph.py" = [ "E501",]
+"tests/**" = [ "S",]
+"scripts/**" = [ "S",]
 
 [tool.poetry.group.lint.dependencies]
 ruff = "^0.5"
 
-
 [tool.poetry.group.typing.dependencies]
 mypy = ">=1.10,<1.11"
 types-pyyaml = "^6.0.12.2"
@@ -146,13 +91,11 @@ types-requests = "^2.28.11.5"
 types-jinja2 = "^2.11.9"
 simsimd = "^5.0.0"
 
-
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"
 setuptools = "^67.6.1"
 grandalf = "^0.8"
 
-
 [tool.poetry.group.test.dependencies]
 pytest = "^7.3.0"
 freezegun = "^1.2.2"
@@ -171,15 +114,12 @@ python = "<3.12"
 version = "^1.26.0"
 python = ">=3.12"
 
-
 [tool.poetry.group.test_integration.dependencies]
 
-
 [tool.poetry.group.typing.dependencies.langchain-text-splitters]
 path = "../text-splitters"
 develop = true
 
-
 [tool.poetry.group.test.dependencies.langchain-standard-tests]
 path = "../standard-tests"
 develop = true

From 600b7bdd610267bdf322fe871e939c54f416d7c2 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 25 Oct 2024 12:56:58 -0700
Subject: [PATCH 78/84] all: test 3.13 ci (#27197)

Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
---
 .github/scripts/check_diff.py                 |    9 +-
 .../agent_toolkits/nla/tool.py                |    2 +-
 .../agent_toolkits/openapi/planner.py         |   10 +-
 .../agents/openai_assistant/base.py           |    4 +-
 .../chat_models/anyscale.py                   |    2 +-
 .../chat_models/azure_openai.py               |    2 +-
 .../chat_models/azureml_endpoint.py           |   37 +-
 .../chat_models/everlyai.py                   |    2 +-
 .../chat_models/jinachat.py                   |   22 +-
 .../langchain_community/chat_models/konko.py  |    2 +-
 .../chat_models/moonshot.py                   |    2 +-
 .../langchain_community/chat_models/octoai.py |    2 +-
 .../langchain_community/chat_models/openai.py |   14 +-
 .../langchain_community/chat_models/solar.py  |    2 +-
 .../chat_models/vertexai.py                   |    2 +-
 .../blob_loaders/cloud_blob_loader.py         |    2 +-
 .../document_loaders/csv_loader.py            |    2 +-
 .../document_loaders/email.py                 |    6 +-
 .../document_loaders/epub.py                  |    2 +-
 .../document_loaders/excel.py                 |    2 +-
 .../document_loaders/git.py                   |    2 +-
 .../document_loaders/html.py                  |    2 +-
 .../document_loaders/image.py                 |    2 +-
 .../document_loaders/image_captions.py        |    6 +-
 .../document_loaders/markdown.py              |    2 +-
 .../document_loaders/odt.py                   |    2 +-
 .../document_loaders/org_mode.py              |    2 +-
 .../document_loaders/parsers/audio.py         |    2 +-
 .../document_loaders/parsers/msword.py        |    2 +-
 .../document_loaders/parsers/pdf.py           |    5 +-
 .../document_loaders/pdf.py                   |    2 +-
 .../document_loaders/powerpoint.py            |    6 +-
 .../document_loaders/rst.py                   |    2 +-
 .../document_loaders/rtf.py                   |    2 +-
 .../document_loaders/tsv.py                   |    2 +-
 .../document_loaders/word_document.py         |    6 +-
 .../document_loaders/xml.py                   |    2 +-
 .../document_transformers/google_translate.py |    4 +-
 .../embeddings/anyscale.py                    |    2 +-
 .../embeddings/azure_openai.py                |    8 +-
 .../langchain_community/embeddings/localai.py |   26 +-
 .../embeddings/octoai_embeddings.py           |    4 +-
 .../langchain_community/embeddings/openai.py  |   26 +-
 .../embeddings/spacy_embeddings.py            |    2 +-
 .../embeddings/vertexai.py                    |    5 +-
 .../langchain_community/graphs/neo4j_graph.py |    8 +-
 .../langchain_community/llms/anyscale.py      |    4 +-
 .../langchain_community/llms/beam.py          |    2 +-
 .../langchain_community/llms/gooseai.py       |    4 +-
 .../llms/octoai_endpoint.py                   |    4 +-
 .../langchain_community/llms/openai.py        |   22 +-
 .../langchain_community/llms/vertexai.py      |   14 +-
 .../memory/zep_cloud_memory.py                |    2 +-
 .../langchain_community/memory/zep_memory.py  |    2 +-
 .../tools/ainetwork/app.py                    |    2 +-
 .../tools/ainetwork/base.py                   |    2 +-
 .../tools/ainetwork/owner.py                  |    2 +-
 .../tools/ainetwork/rule.py                   |    2 +-
 .../tools/ainetwork/transfer.py               |    2 +-
 .../tools/ainetwork/value.py                  |    2 +-
 .../langchain_community/tools/amadeus/base.py |    2 +-
 .../tools/amadeus/closest_airport.py          |    2 +-
 .../tools/amadeus/flight_search.py            |    2 +-
 .../langchain_community/tools/arxiv/tool.py   |    2 +-
 .../langchain_community/tools/asknews/tool.py |    8 +-
 .../huggingface_text_to_speech_inference.py   |    2 +-
 .../document_intelligence.py                  |    2 +-
 .../tools/azure_ai_services/image_analysis.py |    2 +-
 .../tools/azure_ai_services/speech_to_text.py |    2 +-
 .../text_analytics_for_health.py              |    2 +-
 .../tools/azure_ai_services/text_to_speech.py |    2 +-
 .../form_recognizer.py                        |    2 +-
 .../image_analysis.py                         |    2 +-
 .../azure_cognitive_services/speech2text.py   |    2 +-
 .../azure_cognitive_services/text2speech.py   |    2 +-
 .../text_analytics_health.py                  |    2 +-
 .../tools/bing_search/tool.py                 |    4 +-
 .../tools/brave_search/tool.py                |    2 +-
 .../tools/cassandra_database/tool.py          |    6 +-
 .../langchain_community/tools/clickup/tool.py |    2 +-
 .../tools/cogniswitch/tool.py                 |    8 +-
 .../langchain_community/tools/connery/tool.py |    2 +-
 .../tools/dataforseo_api_search/tool.py       |    4 +-
 .../tools/dataherald/tool.py                  |    2 +-
 .../tools/ddg_search/tool.py                  |    4 +-
 .../tools/e2b_data_analysis/tool.py           |    2 +-
 .../tools/edenai/audio_speech_to_text.py      |    2 +-
 .../tools/edenai/audio_text_to_speech.py      |    2 +-
 .../tools/edenai/edenai_base_tool.py          |    2 +-
 .../tools/edenai/image_explicitcontent.py     |    2 +-
 .../tools/edenai/image_objectdetection.py     |    2 +-
 .../tools/edenai/ocr_identityparser.py        |    2 +-
 .../tools/edenai/ocr_invoiceparser.py         |    2 +-
 .../tools/edenai/text_moderation.py           |    2 +-
 .../tools/eleven_labs/text2speech.py          |    2 +-
 .../tools/file_management/copy.py             |    2 +-
 .../tools/file_management/delete.py           |    2 +-
 .../tools/file_management/file_search.py      |    2 +-
 .../tools/file_management/list_dir.py         |    2 +-
 .../tools/file_management/move.py             |    2 +-
 .../tools/file_management/read.py             |    2 +-
 .../tools/file_management/write.py            |    2 +-
 .../financial_datasets/balance_sheets.py      |    2 +-
 .../cash_flow_statements.py                   |    2 +-
 .../financial_datasets/income_statements.py   |    2 +-
 .../langchain_community/tools/github/tool.py  |    2 +-
 .../langchain_community/tools/gitlab/tool.py  |    2 +-
 .../langchain_community/tools/gmail/base.py   |    2 +-
 .../tools/gmail/create_draft.py               |    2 +-
 .../tools/gmail/get_message.py                |    2 +-
 .../tools/gmail/get_thread.py                 |    2 +-
 .../langchain_community/tools/gmail/search.py |    2 +-
 .../tools/gmail/send_message.py               |    2 +-
 .../langchain_community/tools/gmail/utils.py  |    2 +-
 .../tools/golden_query/tool.py                |    2 +-
 .../tools/google_cloud/texttospeech.py        |    6 +-
 .../tools/google_finance/tool.py              |    2 +-
 .../tools/google_jobs/tool.py                 |    2 +-
 .../tools/google_lens/tool.py                 |    2 +-
 .../tools/google_places/tool.py               |    2 +-
 .../tools/google_scholar/tool.py              |    2 +-
 .../tools/google_search/tool.py               |    4 +-
 .../tools/google_serper/tool.py               |    4 +-
 .../tools/google_trends/tool.py               |    2 +-
 .../langchain_community/tools/graphql/tool.py |    2 +-
 .../langchain_community/tools/human/tool.py   |    2 +-
 .../langchain_community/tools/ifttt.py        |    2 +-
 .../tools/jina_search/tool.py                 |    2 +-
 .../langchain_community/tools/jira/tool.py    |    2 +-
 .../langchain_community/tools/json/tool.py    |    4 +-
 .../tools/memorize/tool.py                    |    2 +-
 .../tools/merriam_webster/tool.py             |    2 +-
 .../tools/metaphor_search/tool.py             |    2 +-
 .../tools/mojeek_search/tool.py               |    2 +-
 .../tools/multion/close_session.py            |    2 +-
 .../tools/multion/create_session.py           |    2 +-
 .../tools/multion/update_session.py           |    2 +-
 .../langchain_community/tools/nasa/tool.py    |    2 +-
 .../langchain_community/tools/nuclia/tool.py  |    2 +-
 .../tools/office365/base.py                   |    2 +-
 .../tools/office365/create_draft_message.py   |    2 +-
 .../tools/office365/events_search.py          |    2 +-
 .../tools/office365/messages_search.py        |    2 +-
 .../tools/office365/send_event.py             |    2 +-
 .../tools/office365/send_message.py           |    2 +-
 .../openai_dalle_image_generation/tool.py     |    2 +-
 .../tools/openweathermap/tool.py              |    2 +-
 .../tools/passio_nutrition_ai/tool.py         |    2 +-
 .../tools/playwright/base.py                  |    2 +-
 .../tools/playwright/click.py                 |    2 +-
 .../tools/playwright/current_page.py          |    2 +-
 .../tools/playwright/extract_hyperlinks.py    |    2 +-
 .../tools/playwright/extract_text.py          |    2 +-
 .../tools/playwright/get_elements.py          |    2 +-
 .../tools/playwright/navigate.py              |    2 +-
 .../tools/playwright/navigate_back.py         |    2 +-
 .../langchain_community/tools/plugin.py       |    2 +-
 .../tools/polygon/aggregates.py               |    2 +-
 .../tools/polygon/financials.py               |    2 +-
 .../tools/polygon/last_quote.py               |    2 +-
 .../tools/polygon/ticker_news.py              |    2 +-
 .../langchain_community/tools/powerbi/tool.py |    6 +-
 .../langchain_community/tools/pubmed/tool.py  |    2 +-
 .../tools/reddit_search/tool.py               |    2 +-
 .../tools/requests/tool.py                    |   10 +-
 .../langchain_community/tools/riza/command.py |    4 +-
 .../tools/scenexplain/tool.py                 |    2 +-
 .../tools/searchapi/tool.py                   |    4 +-
 .../tools/searx_search/tool.py                |    4 +-
 .../tools/semanticscholar/tool.py             |    2 +-
 .../langchain_community/tools/shell/tool.py   |    2 +-
 .../langchain_community/tools/slack/base.py   |    2 +-
 .../tools/slack/get_channel.py                |    2 +-
 .../tools/slack/get_message.py                |    2 +-
 .../tools/slack/schedule_message.py           |    2 +-
 .../tools/slack/send_message.py               |    2 +-
 .../langchain_community/tools/sleep/tool.py   |    2 +-
 .../tools/spark_sql/tool.py                   |    8 +-
 .../tools/sql_database/tool.py                |    8 +-
 .../tools/stackexchange/tool.py               |    2 +-
 .../langchain_community/tools/steam/tool.py   |    2 +-
 .../tools/steamship_image_generation/tool.py  |    2 +-
 .../tools/tavily_search/tool.py               |    4 +-
 .../tools/vectorstore/tool.py                 |    4 +-
 .../tools/wikidata/tool.py                    |    2 +-
 .../tools/wikipedia/tool.py                   |    2 +-
 .../tools/wolfram_alpha/tool.py               |    2 +-
 .../tools/yahoo_finance_news.py               |    2 +-
 .../langchain_community/tools/you/tool.py     |    2 +-
 .../tools/youtube/search.py                   |    2 +-
 .../langchain_community/tools/zapier/tool.py  |    4 +-
 .../tools/zenguard/tool.py                    |    2 +-
 .../utilities/dalle_image_generator.py        |    6 +-
 .../utilities/duckduckgo_search.py            |    4 +-
 .../utilities/nvidia_riva.py                  |    4 +-
 .../vectorstores/chroma.py                    |   59 +-
 .../vectorstores/llm_rails.py                 |    2 +-
 .../vectorstores/matching_engine.py           |   13 +-
 .../vectorstores/neo4j_vector.py              |    8 +-
 .../vectorstores/redis/base.py                |    2 +-
 .../vectorstores/redis/schema.py              |    4 +-
 .../langchain_community/vectorstores/vdms.py  |    2 +-
 .../vectorstores/vectara.py                   |    2 +-
 libs/community/poetry.lock                    | 1352 +++++-----
 libs/community/pyproject.toml                 |    2 +-
 .../integration_tests/adapters/test_openai.py |    8 +-
 .../chains/test_ontotext_graphdb_qa.py        |    2 +-
 .../embeddings/test_azure_openai.py           |    2 +-
 .../embeddings/test_openai.py                 |    2 +-
 .../vectorstores/test_chroma.py               |    8 +-
 .../vectorstores/test_singlestoredb.py        |    2 +-
 .../core/langchain_core/vectorstores/utils.py |    2 +-
 libs/core/poetry.lock                         |  462 ++--
 libs/core/pyproject.toml                      |    4 +-
 .../tests/unit_tests/prompts/test_chat.py     |    4 +-
 .../__snapshots__/test_runnable.ambr          |    6 -
 .../unit_tests/runnables/test_runnable.py     |   10 +-
 .../tests/unit_tests/utils/test_pydantic.py   |   13 +-
 libs/langchain/langchain/agents/agent.py      |    2 +-
 .../agent_token_buffer_memory.py              |    2 +-
 .../langchain/agents/output_parsers/tools.py  |    2 +-
 libs/langchain/langchain/agents/tools.py      |    2 +-
 .../langchain/chains/conversation/base.py     |    2 +-
 .../chains/router/multi_retrieval_qa.py       |    2 +-
 .../evaluation/comparison/eval_chain.py       |    6 +-
 .../evaluation/criteria/eval_chain.py         |    4 +-
 .../evaluation/scoring/eval_chain.py          |    4 +-
 .../document_compressors/chain_extract.py     |    9 +-
 .../smith/evaluation/string_run_evaluator.py  |    2 +-
 libs/langchain/poetry.lock                    | 1093 ++++----
 libs/langchain/pyproject.toml                 |    2 +-
 .../evaluation/parsing/test_base.py           |    3 -
 .../test_chain_extract.py                     |    2 +-
 libs/text-splitters/poetry.lock               | 2231 +++++-----------
 libs/text-splitters/pyproject.toml            |    9 +-
 poetry.lock                                   | 2272 ++++++++---------
 pyproject.toml                                |   30 +-
 237 files changed, 3668 insertions(+), 4656 deletions(-)

diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py
index 9ed13da9a0586..71a189124c716 100644
--- a/.github/scripts/check_diff.py
+++ b/.github/scripts/check_diff.py
@@ -110,7 +110,7 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
         return _get_pydantic_test_configs(dir_)
 
     if dir_ == "libs/core":
-        py_versions = ["3.9", "3.10", "3.11", "3.12"]
+        py_versions = ["3.9", "3.10", "3.11", "3.12", "3.13"]
     # custom logic for specific directories
     elif dir_ == "libs/partners/milvus":
         # milvus poetry doesn't allow 3.12 because they
@@ -125,8 +125,11 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     elif dir_ == "libs/community" and job == "compile-integration-tests":
         # community integration deps are slow in 3.12
         py_versions = ["3.9", "3.11"]
-    else:
+    elif dir_ == ".":
+        # unable to install with 3.13 because tokenizers doesn't support 3.13 yet
         py_versions = ["3.9", "3.12"]
+    else:
+        py_versions = ["3.9", "3.13"]
 
     return [{"working-directory": dir_, "python-version": py_v} for py_v in py_versions]
 
@@ -305,7 +308,7 @@ def _get_configs_for_multi_dirs(
         ]
     }
     map_job_to_configs["test-doc-imports"] = (
-        [{"python-version": "3.12"}] if docs_edited else []
+        [{"python-version": "3.13"}] if docs_edited else []
     )
 
     for key, value in map_job_to_configs.items():
diff --git a/libs/community/langchain_community/agent_toolkits/nla/tool.py b/libs/community/langchain_community/agent_toolkits/nla/tool.py
index 3e49748e4be8e..47f25d13b687b 100644
--- a/libs/community/langchain_community/agent_toolkits/nla/tool.py
+++ b/libs/community/langchain_community/agent_toolkits/nla/tool.py
@@ -13,7 +13,7 @@
 from langchain_community.utilities.requests import Requests
 
 
-class NLATool(Tool):
+class NLATool(Tool):  # type: ignore[override]
     """Natural Language API Tool."""
 
     @classmethod
diff --git a/libs/community/langchain_community/agent_toolkits/openapi/planner.py b/libs/community/langchain_community/agent_toolkits/openapi/planner.py
index eb2c0efe4c606..3d49a22537083 100644
--- a/libs/community/langchain_community/agent_toolkits/openapi/planner.py
+++ b/libs/community/langchain_community/agent_toolkits/openapi/planner.py
@@ -64,7 +64,7 @@ def _get_default_llm_chain_factory(
     return partial(_get_default_llm_chain, prompt)
 
 
-class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
+class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Requests GET tool with LLM-instructed extraction of truncated responses."""
 
     name: str = "requests_get"
@@ -98,7 +98,7 @@ async def _arun(self, text: str) -> str:
         raise NotImplementedError()
 
 
-class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
+class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Requests POST tool with LLM-instructed extraction of truncated responses."""
 
     name: str = "requests_post"
@@ -129,7 +129,7 @@ async def _arun(self, text: str) -> str:
         raise NotImplementedError()
 
 
-class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
+class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Requests PATCH tool with LLM-instructed extraction of truncated responses."""
 
     name: str = "requests_patch"
@@ -162,7 +162,7 @@ async def _arun(self, text: str) -> str:
         raise NotImplementedError()
 
 
-class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool):
+class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Requests PUT tool with LLM-instructed extraction of truncated responses."""
 
     name: str = "requests_put"
@@ -193,7 +193,7 @@ async def _arun(self, text: str) -> str:
         raise NotImplementedError()
 
 
-class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
+class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Tool that sends a DELETE request and parses the response."""
 
     name: str = "requests_delete"
diff --git a/libs/community/langchain_community/agents/openai_assistant/base.py b/libs/community/langchain_community/agents/openai_assistant/base.py
index d2b1b7c02ace3..ff3e670caae08 100644
--- a/libs/community/langchain_community/agents/openai_assistant/base.py
+++ b/libs/community/langchain_community/agents/openai_assistant/base.py
@@ -255,7 +255,7 @@ def create_assistant(
             name=name,
             instructions=instructions,
             tools=[_get_assistants_tool(tool) for tool in tools],  # type: ignore
-            tool_resources=tool_resources,
+            tool_resources=tool_resources,  # type: ignore[arg-type]
             model=model,
         )
         return cls(assistant_id=assistant.id, client=client, **kwargs)
@@ -394,7 +394,7 @@ async def acreate_assistant(
             name=name,
             instructions=instructions,
             tools=openai_tools,  # type: ignore
-            tool_resources=tool_resources,
+            tool_resources=tool_resources,  # type: ignore[arg-type]
             model=model,
         )
         return cls(assistant_id=assistant.id, async_client=async_client, **kwargs)
diff --git a/libs/community/langchain_community/chat_models/anyscale.py b/libs/community/langchain_community/chat_models/anyscale.py
index bb1f83ad1b257..bb1adff0ca3b6 100644
--- a/libs/community/langchain_community/chat_models/anyscale.py
+++ b/libs/community/langchain_community/chat_models/anyscale.py
@@ -155,7 +155,7 @@ def validate_environment(cls, values: dict) -> Any:
             else:
                 values["openai_api_base"] = values["anyscale_api_base"]
                 values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
-                values["client"] = openai.ChatCompletion
+                values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         except AttributeError as exc:
             raise ValueError(
                 "`openai` has no `ChatCompletion` attribute, this is likely "
diff --git a/libs/community/langchain_community/chat_models/azure_openai.py b/libs/community/langchain_community/chat_models/azure_openai.py
index 182e10f6f3b55..2f2711f4625c4 100644
--- a/libs/community/langchain_community/chat_models/azure_openai.py
+++ b/libs/community/langchain_community/chat_models/azure_openai.py
@@ -224,7 +224,7 @@ def validate_environment(cls, values: Dict) -> Dict:
                 **client_params
             ).chat.completions
         else:
-            values["client"] = openai.ChatCompletion
+            values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         return values
 
     @property
diff --git a/libs/community/langchain_community/chat_models/azureml_endpoint.py b/libs/community/langchain_community/chat_models/azureml_endpoint.py
index c42d8a610a963..a02919d760a26 100644
--- a/libs/community/langchain_community/chat_models/azureml_endpoint.py
+++ b/libs/community/langchain_community/chat_models/azureml_endpoint.py
@@ -304,7 +304,7 @@ def _stream(
             "http_client": None,
         }
 
-        client = openai.OpenAI(**client_params)
+        client = openai.OpenAI(**client_params)  # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
         message_dicts = [
             CustomOpenAIChatContentFormatter._convert_message_to_dict(m)
             for m in messages
@@ -312,28 +312,30 @@ def _stream(
         params = {"stream": True, "stop": stop, "model": None, **kwargs}
 
         default_chunk_class = AIMessageChunk
-        for chunk in client.chat.completions.create(messages=message_dicts, **params):
+        for chunk in client.chat.completions.create(messages=message_dicts, **params):  # type: ignore[arg-type]
             if not isinstance(chunk, dict):
-                chunk = chunk.dict()
-            if len(chunk["choices"]) == 0:
+                chunk = chunk.dict()  # type: ignore[attr-defined]
+            if len(chunk["choices"]) == 0:  # type: ignore[call-overload]
                 continue
-            choice = chunk["choices"][0]
-            chunk = _convert_delta_to_message_chunk(
-                choice["delta"], default_chunk_class
+            choice = chunk["choices"][0]  # type: ignore[call-overload]
+            chunk = _convert_delta_to_message_chunk(  # type: ignore[assignment]
+                choice["delta"],  # type: ignore[arg-type, index]
+                default_chunk_class,  # type: ignore[arg-type, index]
             )
             generation_info = {}
-            if finish_reason := choice.get("finish_reason"):
+            if finish_reason := choice.get("finish_reason"):  # type: ignore[union-attr]
                 generation_info["finish_reason"] = finish_reason
-            logprobs = choice.get("logprobs")
+            logprobs = choice.get("logprobs")  # type: ignore[union-attr]
             if logprobs:
                 generation_info["logprobs"] = logprobs
-            default_chunk_class = chunk.__class__
-            chunk = ChatGenerationChunk(
-                message=chunk, generation_info=generation_info or None
+            default_chunk_class = chunk.__class__  # type: ignore[assignment]
+            chunk = ChatGenerationChunk(  # type: ignore[assignment]
+                message=chunk,  # type: ignore[arg-type]
+                generation_info=generation_info or None,  # type: ignore[arg-type]
             )
             if run_manager:
-                run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
-            yield chunk
+                run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)  # type: ignore[attr-defined, arg-type]
+            yield chunk  # type: ignore[misc]
 
     async def _astream(
         self,
@@ -357,7 +359,7 @@ async def _astream(
             "http_client": None,
         }
 
-        async_client = openai.AsyncOpenAI(**client_params)
+        async_client = openai.AsyncOpenAI(**client_params)  # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
         message_dicts = [
             CustomOpenAIChatContentFormatter._convert_message_to_dict(m)
             for m in messages
@@ -365,8 +367,9 @@ async def _astream(
         params = {"stream": True, "stop": stop, "model": None, **kwargs}
 
         default_chunk_class = AIMessageChunk
-        async for chunk in await async_client.chat.completions.create(
-            messages=message_dicts, **params
+        async for chunk in await async_client.chat.completions.create(  # type: ignore[attr-defined]
+            messages=message_dicts,  # type: ignore[arg-type]
+            **params,  # type: ignore[arg-type]
         ):
             if not isinstance(chunk, dict):
                 chunk = chunk.dict()
diff --git a/libs/community/langchain_community/chat_models/everlyai.py b/libs/community/langchain_community/chat_models/everlyai.py
index 160389b57c885..f7de8d3a34f87 100644
--- a/libs/community/langchain_community/chat_models/everlyai.py
+++ b/libs/community/langchain_community/chat_models/everlyai.py
@@ -98,7 +98,7 @@ def validate_environment_override(cls, values: dict) -> Any:
                 "Please install it with `pip install openai`.",
             ) from e
         try:
-            values["client"] = openai.ChatCompletion
+            values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         except AttributeError as exc:
             raise ValueError(
                 "`openai` has no `ChatCompletion` attribute, this is likely "
diff --git a/libs/community/langchain_community/chat_models/jinachat.py b/libs/community/langchain_community/chat_models/jinachat.py
index 72c962d935493..9a3c8f8c037d2 100644
--- a/libs/community/langchain_community/chat_models/jinachat.py
+++ b/libs/community/langchain_community/chat_models/jinachat.py
@@ -70,11 +70,11 @@ def _create_retry_decorator(llm: JinaChat) -> Callable[[Any], Any]:
         stop=stop_after_attempt(llm.max_retries),
         wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
         retry=(
-            retry_if_exception_type(openai.error.Timeout)
-            | retry_if_exception_type(openai.error.APIError)
-            | retry_if_exception_type(openai.error.APIConnectionError)
-            | retry_if_exception_type(openai.error.RateLimitError)
-            | retry_if_exception_type(openai.error.ServiceUnavailableError)
+            retry_if_exception_type(openai.error.Timeout)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIConnectionError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.RateLimitError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.ServiceUnavailableError)  # type: ignore[attr-defined]
         ),
         before_sleep=before_sleep_log(logger, logging.WARNING),
     )
@@ -234,7 +234,7 @@ def validate_environment(cls, values: Dict) -> Dict:
                 "Please install it with `pip install openai`."
             )
         try:
-            values["client"] = openai.ChatCompletion
+            values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         except AttributeError:
             raise ValueError(
                 "`openai` has no `ChatCompletion` attribute, this is likely "
@@ -266,11 +266,11 @@ def _create_retry_decorator(self) -> Callable[[Any], Any]:
             stop=stop_after_attempt(self.max_retries),
             wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
             retry=(
-                retry_if_exception_type(openai.error.Timeout)
-                | retry_if_exception_type(openai.error.APIError)
-                | retry_if_exception_type(openai.error.APIConnectionError)
-                | retry_if_exception_type(openai.error.RateLimitError)
-                | retry_if_exception_type(openai.error.ServiceUnavailableError)
+                retry_if_exception_type(openai.error.Timeout)  # type: ignore[attr-defined]
+                | retry_if_exception_type(openai.error.APIError)  # type: ignore[attr-defined]
+                | retry_if_exception_type(openai.error.APIConnectionError)  # type: ignore[attr-defined]
+                | retry_if_exception_type(openai.error.RateLimitError)  # type: ignore[attr-defined]
+                | retry_if_exception_type(openai.error.ServiceUnavailableError)  # type: ignore[attr-defined]
             ),
             before_sleep=before_sleep_log(logger, logging.WARNING),
         )
diff --git a/libs/community/langchain_community/chat_models/konko.py b/libs/community/langchain_community/chat_models/konko.py
index a2d16e51179e6..3aec395cf5304 100644
--- a/libs/community/langchain_community/chat_models/konko.py
+++ b/libs/community/langchain_community/chat_models/konko.py
@@ -42,7 +42,7 @@
 logger = logging.getLogger(__name__)
 
 
-class ChatKonko(ChatOpenAI):
+class ChatKonko(ChatOpenAI):  # type: ignore[override]
     """`ChatKonko` Chat large language models API.
 
     To use, you should have the ``konko`` python package installed, and the
diff --git a/libs/community/langchain_community/chat_models/moonshot.py b/libs/community/langchain_community/chat_models/moonshot.py
index cd9de1c51dd5e..7290c52b76e8b 100644
--- a/libs/community/langchain_community/chat_models/moonshot.py
+++ b/libs/community/langchain_community/chat_models/moonshot.py
@@ -12,7 +12,7 @@
 from langchain_community.llms.moonshot import MOONSHOT_SERVICE_URL_BASE, MoonshotCommon
 
 
-class MoonshotChat(MoonshotCommon, ChatOpenAI):  # type: ignore[misc]
+class MoonshotChat(MoonshotCommon, ChatOpenAI):  # type: ignore[misc, override, override]
     """Moonshot large language models.
 
     To use, you should have the ``openai`` python package installed, and the
diff --git a/libs/community/langchain_community/chat_models/octoai.py b/libs/community/langchain_community/chat_models/octoai.py
index cbe1ee4ca8853..4f7120c2c8398 100644
--- a/libs/community/langchain_community/chat_models/octoai.py
+++ b/libs/community/langchain_community/chat_models/octoai.py
@@ -98,7 +98,7 @@ def validate_environment(cls, values: Dict) -> Dict:
             else:
                 values["openai_api_base"] = values["octoai_api_base"]
                 values["openai_api_key"] = values["octoai_api_token"].get_secret_value()
-                values["client"] = openai.ChatCompletion
+                values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         except ImportError:
             raise ImportError(
                 "Could not import openai python package. "
diff --git a/libs/community/langchain_community/chat_models/openai.py b/libs/community/langchain_community/chat_models/openai.py
index 0ad982eec7022..a2b3a057295b2 100644
--- a/libs/community/langchain_community/chat_models/openai.py
+++ b/libs/community/langchain_community/chat_models/openai.py
@@ -87,11 +87,11 @@ def _create_retry_decorator(
     import openai
 
     errors = [
-        openai.error.Timeout,
-        openai.error.APIError,
-        openai.error.APIConnectionError,
-        openai.error.RateLimitError,
-        openai.error.ServiceUnavailableError,
+        openai.error.Timeout,  # type: ignore[attr-defined]
+        openai.error.APIError,  # type: ignore[attr-defined]
+        openai.error.APIConnectionError,  # type: ignore[attr-defined]
+        openai.error.RateLimitError,  # type: ignore[attr-defined]
+        openai.error.ServiceUnavailableError,  # type: ignore[attr-defined]
     ]
     return create_base_retry_decorator(
         error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
@@ -357,7 +357,7 @@ def validate_environment(cls, values: Dict) -> Dict:
                     **client_params
                 ).chat.completions
         elif not values.get("client"):
-            values["client"] = openai.ChatCompletion
+            values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         else:
             pass
         return values
@@ -594,7 +594,7 @@ def _client_params(self) -> Dict[str, Any]:
         if self.openai_proxy:
             import openai
 
-            openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}
+            openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}  # type: ignore[attr-defined]
         return {**self._default_params, **openai_creds}
 
     def _get_invocation_params(
diff --git a/libs/community/langchain_community/chat_models/solar.py b/libs/community/langchain_community/chat_models/solar.py
index 10eba5da5a7a7..1f2da3244c8c1 100644
--- a/libs/community/langchain_community/chat_models/solar.py
+++ b/libs/community/langchain_community/chat_models/solar.py
@@ -13,7 +13,7 @@
 @deprecated(  # type: ignore[arg-type]
     since="0.0.34", removal="1.0", alternative_import="langchain_upstage.ChatUpstage"
 )
-class SolarChat(SolarCommon, ChatOpenAI):
+class SolarChat(SolarCommon, ChatOpenAI):  # type: ignore[override, override]
     """Wrapper around Solar large language models.
     To use, you should have the ``openai`` python package installed, and the
     environment variable ``SOLAR_API_KEY`` set with your API key.
diff --git a/libs/community/langchain_community/chat_models/vertexai.py b/libs/community/langchain_community/chat_models/vertexai.py
index eeb638e2dc074..6fae0513f944c 100644
--- a/libs/community/langchain_community/chat_models/vertexai.py
+++ b/libs/community/langchain_community/chat_models/vertexai.py
@@ -209,7 +209,7 @@ def _get_question(messages: List[BaseMessage]) -> HumanMessage:
     removal="1.0",
     alternative_import="langchain_google_vertexai.ChatVertexAI",
 )
-class ChatVertexAI(_VertexAICommon, BaseChatModel):
+class ChatVertexAI(_VertexAICommon, BaseChatModel):  # type: ignore[override]
     """`Vertex AI` Chat large language models API."""
 
     model_name: str = "chat-bison"
diff --git a/libs/community/langchain_community/document_loaders/blob_loaders/cloud_blob_loader.py b/libs/community/langchain_community/document_loaders/blob_loaders/cloud_blob_loader.py
index f019b57d3cd08..0a5efbab54c40 100644
--- a/libs/community/langchain_community/document_loaders/blob_loaders/cloud_blob_loader.py
+++ b/libs/community/langchain_community/document_loaders/blob_loaders/cloud_blob_loader.py
@@ -224,7 +224,7 @@ def _yield_paths(self) -> Iterable["AnyPath"]:
             yield self.path
             return
 
-        paths = self.path.glob(self.glob)
+        paths = self.path.glob(self.glob)  # type: ignore[attr-defined]
         for path in paths:
             if self.exclude:
                 if any(path.match(glob) for glob in self.exclude):
diff --git a/libs/community/langchain_community/document_loaders/csv_loader.py b/libs/community/langchain_community/document_loaders/csv_loader.py
index 8b0e6b3ab9cd0..1e126709d4add 100644
--- a/libs/community/langchain_community/document_loaders/csv_loader.py
+++ b/libs/community/langchain_community/document_loaders/csv_loader.py
@@ -219,4 +219,4 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.partition.csv import partition_csv
 
-        return partition_csv(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_csv(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/email.py b/libs/community/langchain_community/document_loaders/email.py
index 24716c55a9fd7..c7e7cee54bbaf 100644
--- a/libs/community/langchain_community/document_loaders/email.py
+++ b/libs/community/langchain_community/document_loaders/email.py
@@ -60,16 +60,16 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.file_utils.filetype import FileType, detect_filetype
 
-        filetype = detect_filetype(self.file_path)
+        filetype = detect_filetype(self.file_path)  # type: ignore[arg-type]
 
         if filetype == FileType.EML:
             from unstructured.partition.email import partition_email
 
-            return partition_email(filename=self.file_path, **self.unstructured_kwargs)
+            return partition_email(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
         elif satisfies_min_unstructured_version("0.5.8") and filetype == FileType.MSG:
             from unstructured.partition.msg import partition_msg
 
-            return partition_msg(filename=self.file_path, **self.unstructured_kwargs)
+            return partition_msg(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
         else:
             raise ValueError(
                 f"Filetype {filetype} is not supported in UnstructuredEmailLoader."
diff --git a/libs/community/langchain_community/document_loaders/epub.py b/libs/community/langchain_community/document_loaders/epub.py
index fbef21b9b5afa..6a3160691273f 100644
--- a/libs/community/langchain_community/document_loaders/epub.py
+++ b/libs/community/langchain_community/document_loaders/epub.py
@@ -39,4 +39,4 @@ def _get_elements(self) -> List:
             )
         from unstructured.partition.epub import partition_epub
 
-        return partition_epub(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_epub(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/excel.py b/libs/community/langchain_community/document_loaders/excel.py
index 4c93cde008b74..ace4eeee6467c 100644
--- a/libs/community/langchain_community/document_loaders/excel.py
+++ b/libs/community/langchain_community/document_loaders/excel.py
@@ -48,4 +48,4 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.partition.xlsx import partition_xlsx
 
-        return partition_xlsx(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_xlsx(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/git.py b/libs/community/langchain_community/document_loaders/git.py
index b37f575eacbb8..3b380b275f592 100644
--- a/libs/community/langchain_community/document_loaders/git.py
+++ b/libs/community/langchain_community/document_loaders/git.py
@@ -74,7 +74,7 @@ def lazy_load(self) -> Iterator[Document]:
 
             file_path = os.path.join(self.repo_path, item.path)
 
-            ignored_files = repo.ignored([file_path])
+            ignored_files = repo.ignored([file_path])  # type: ignore[arg-type]
             if len(ignored_files):
                 continue
 
diff --git a/libs/community/langchain_community/document_loaders/html.py b/libs/community/langchain_community/document_loaders/html.py
index 77a2ef3d24df8..857142bce96ae 100644
--- a/libs/community/langchain_community/document_loaders/html.py
+++ b/libs/community/langchain_community/document_loaders/html.py
@@ -30,4 +30,4 @@ class UnstructuredHTMLLoader(UnstructuredFileLoader):
     def _get_elements(self) -> List:
         from unstructured.partition.html import partition_html
 
-        return partition_html(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_html(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/image.py b/libs/community/langchain_community/document_loaders/image.py
index c7b2e29b4c6f2..c574226c753ab 100644
--- a/libs/community/langchain_community/document_loaders/image.py
+++ b/libs/community/langchain_community/document_loaders/image.py
@@ -30,4 +30,4 @@ class UnstructuredImageLoader(UnstructuredFileLoader):
     def _get_elements(self) -> List:
         from unstructured.partition.image import partition_image
 
-        return partition_image(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_image(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/image_captions.py b/libs/community/langchain_community/document_loaders/image_captions.py
index 568fa0a25aacb..aeb1580cb3980 100644
--- a/libs/community/langchain_community/document_loaders/image_captions.py
+++ b/libs/community/langchain_community/document_loaders/image_captions.py
@@ -76,13 +76,13 @@ def _get_captions_and_metadata(
 
         try:
             if isinstance(image, bytes):
-                image = Image.open(BytesIO(image)).convert("RGB")
+                image = Image.open(BytesIO(image)).convert("RGB")  # type: ignore[assignment]
             elif isinstance(image, str) and (
                 image.startswith("http://") or image.startswith("https://")
             ):
-                image = Image.open(requests.get(image, stream=True).raw).convert("RGB")
+                image = Image.open(requests.get(image, stream=True).raw).convert("RGB")  # type: ignore[assignment, arg-type]
             else:
-                image = Image.open(image).convert("RGB")
+                image = Image.open(image).convert("RGB")  # type: ignore[assignment]
         except Exception:
             if isinstance(image_source, bytes):
                 msg = "Could not get image data from bytes"
diff --git a/libs/community/langchain_community/document_loaders/markdown.py b/libs/community/langchain_community/document_loaders/markdown.py
index e9cdd44887012..f204b0bdc0349 100644
--- a/libs/community/langchain_community/document_loaders/markdown.py
+++ b/libs/community/langchain_community/document_loaders/markdown.py
@@ -83,4 +83,4 @@ def _get_elements(self) -> List:
                 "Partitioning markdown files is only supported in unstructured>=0.4.16."
             )
 
-        return partition_md(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_md(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/odt.py b/libs/community/langchain_community/document_loaders/odt.py
index 171cede5caf53..a803aeafcbeca 100644
--- a/libs/community/langchain_community/document_loaders/odt.py
+++ b/libs/community/langchain_community/document_loaders/odt.py
@@ -51,4 +51,4 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.partition.odt import partition_odt
 
-        return partition_odt(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_odt(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/org_mode.py b/libs/community/langchain_community/document_loaders/org_mode.py
index 56331e2db07d3..106bf58da48d7 100644
--- a/libs/community/langchain_community/document_loaders/org_mode.py
+++ b/libs/community/langchain_community/document_loaders/org_mode.py
@@ -51,4 +51,4 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.partition.org import partition_org
 
-        return partition_org(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_org(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/parsers/audio.py b/libs/community/langchain_community/document_loaders/parsers/audio.py
index 9741a32f3de17..abc08e561a578 100644
--- a/libs/community/langchain_community/document_loaders/parsers/audio.py
+++ b/libs/community/langchain_community/document_loaders/parsers/audio.py
@@ -118,7 +118,7 @@ def lazy_parse(self, blob: Blob) -> Iterator[Document]:
                             model="whisper-1", file=file_obj, **self._create_params
                         )
                     else:
-                        transcript = openai.Audio.transcribe("whisper-1", file_obj)
+                        transcript = openai.Audio.transcribe("whisper-1", file_obj)  # type: ignore[attr-defined]
                     break
                 except Exception as e:
                     attempts += 1
diff --git a/libs/community/langchain_community/document_loaders/parsers/msword.py b/libs/community/langchain_community/document_loaders/parsers/msword.py
index a99672e285028..c366a8ce1af96 100644
--- a/libs/community/langchain_community/document_loaders/parsers/msword.py
+++ b/libs/community/langchain_community/document_loaders/parsers/msword.py
@@ -39,7 +39,7 @@ def lazy_parse(self, blob: Blob) -> Iterator[Document]:  # type: ignore[valid-ty
         ):
             raise ValueError("This blob type is not supported for this parser.")
         with blob.as_bytes_io() as word_document:  # type: ignore[attr-defined]
-            elements = mime_type_parser[blob.mimetype](file=word_document)  # type: ignore[attr-defined]
+            elements = mime_type_parser[blob.mimetype](file=word_document)  # type: ignore[attr-defined]  # type: ignore[operator]  # type: ignore[operator]  # type: ignore[operator]  # type: ignore[operator]  # type: ignore[operator]  # type: ignore[operator]
             text = "\n\n".join([str(el) for el in elements])
             metadata = {"source": blob.source}  # type: ignore[attr-defined]
             yield Document(page_content=text, metadata=metadata)
diff --git a/libs/community/langchain_community/document_loaders/parsers/pdf.py b/libs/community/langchain_community/document_loaders/parsers/pdf.py
index d0329b7cc1bea..ef9ca7561abd6 100644
--- a/libs/community/langchain_community/document_loaders/parsers/pdf.py
+++ b/libs/community/langchain_community/document_loaders/parsers/pdf.py
@@ -115,7 +115,8 @@ def _extract_text_from_page(page: "PageObject") -> str:
                 return page.extract_text()
             else:
                 return page.extract_text(
-                    extraction_mode=self.extraction_mode, **self.extraction_kwargs
+                    extraction_mode=self.extraction_mode,  # type: ignore[arg-type]
+                    **self.extraction_kwargs,  # type: ignore[arg-type]
                 )
 
         with blob.as_bytes_io() as pdf_file_obj:  # type: ignore[attr-defined]
@@ -132,7 +133,7 @@ def _extract_text_from_page(page: "PageObject") -> str:
 
     def _extract_images_from_page(self, page: pypdf._page.PageObject) -> str:
         """Extract images from page and get the text with RapidOCR."""
-        if not self.extract_images or "/XObject" not in page["/Resources"].keys():
+        if not self.extract_images or "/XObject" not in page["/Resources"].keys():  # type: ignore[attr-defined]
             return ""
 
         xObject = page["/Resources"]["/XObject"].get_object()  # type: ignore
diff --git a/libs/community/langchain_community/document_loaders/pdf.py b/libs/community/langchain_community/document_loaders/pdf.py
index 528d34c43b92a..10261dd408b6b 100644
--- a/libs/community/langchain_community/document_loaders/pdf.py
+++ b/libs/community/langchain_community/document_loaders/pdf.py
@@ -71,7 +71,7 @@ class UnstructuredPDFLoader(UnstructuredFileLoader):
     def _get_elements(self) -> List:
         from unstructured.partition.pdf import partition_pdf
 
-        return partition_pdf(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_pdf(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
 
 
 class BasePDFLoader(BaseLoader, ABC):
diff --git a/libs/community/langchain_community/document_loaders/powerpoint.py b/libs/community/langchain_community/document_loaders/powerpoint.py
index 6f5598815514d..ed360e94f903d 100644
--- a/libs/community/langchain_community/document_loaders/powerpoint.py
+++ b/libs/community/langchain_community/document_loaders/powerpoint.py
@@ -42,7 +42,7 @@ def _get_elements(self) -> List:
         try:
             import magic  # noqa: F401
 
-            is_ppt = detect_filetype(self.file_path) == FileType.PPT
+            is_ppt = detect_filetype(self.file_path) == FileType.PPT  # type: ignore[arg-type]
         except ImportError:
             _, extension = os.path.splitext(str(self.file_path))
             is_ppt = extension == ".ppt"
@@ -57,8 +57,8 @@ def _get_elements(self) -> List:
         if is_ppt:
             from unstructured.partition.ppt import partition_ppt
 
-            return partition_ppt(filename=self.file_path, **self.unstructured_kwargs)
+            return partition_ppt(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
         else:
             from unstructured.partition.pptx import partition_pptx
 
-            return partition_pptx(filename=self.file_path, **self.unstructured_kwargs)
+            return partition_pptx(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/rst.py b/libs/community/langchain_community/document_loaders/rst.py
index 5e9feb7181a44..310f1bb7c6086 100644
--- a/libs/community/langchain_community/document_loaders/rst.py
+++ b/libs/community/langchain_community/document_loaders/rst.py
@@ -55,4 +55,4 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.partition.rst import partition_rst
 
-        return partition_rst(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_rst(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/rtf.py b/libs/community/langchain_community/document_loaders/rtf.py
index c5c94934fa6df..63128aa837224 100644
--- a/libs/community/langchain_community/document_loaders/rtf.py
+++ b/libs/community/langchain_community/document_loaders/rtf.py
@@ -61,4 +61,4 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.partition.rtf import partition_rtf
 
-        return partition_rtf(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_rtf(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/tsv.py b/libs/community/langchain_community/document_loaders/tsv.py
index 7a06b672825dc..f6d4a085ce7af 100644
--- a/libs/community/langchain_community/document_loaders/tsv.py
+++ b/libs/community/langchain_community/document_loaders/tsv.py
@@ -38,4 +38,4 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.partition.tsv import partition_tsv
 
-        return partition_tsv(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_tsv(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/word_document.py b/libs/community/langchain_community/document_loaders/word_document.py
index 502ecd0e25a83..c7c50575cc7ef 100644
--- a/libs/community/langchain_community/document_loaders/word_document.py
+++ b/libs/community/langchain_community/document_loaders/word_document.py
@@ -104,7 +104,7 @@ def _get_elements(self) -> List:
         try:
             import magic  # noqa: F401
 
-            is_doc = detect_filetype(self.file_path) == FileType.DOC
+            is_doc = detect_filetype(self.file_path) == FileType.DOC  # type: ignore[arg-type]
         except ImportError:
             _, extension = os.path.splitext(str(self.file_path))
             is_doc = extension == ".doc"
@@ -119,8 +119,8 @@ def _get_elements(self) -> List:
         if is_doc:
             from unstructured.partition.doc import partition_doc
 
-            return partition_doc(filename=self.file_path, **self.unstructured_kwargs)
+            return partition_doc(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
         else:
             from unstructured.partition.docx import partition_docx
 
-            return partition_docx(filename=self.file_path, **self.unstructured_kwargs)
+            return partition_docx(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_loaders/xml.py b/libs/community/langchain_community/document_loaders/xml.py
index a4757f222b884..1541e3b897ef3 100644
--- a/libs/community/langchain_community/document_loaders/xml.py
+++ b/libs/community/langchain_community/document_loaders/xml.py
@@ -46,4 +46,4 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.partition.xml import partition_xml
 
-        return partition_xml(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_xml(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/document_transformers/google_translate.py b/libs/community/langchain_community/document_transformers/google_translate.py
index 613ab0bdfc1ff..f5a95ab85107e 100644
--- a/libs/community/langchain_community/document_transformers/google_translate.py
+++ b/libs/community/langchain_community/document_transformers/google_translate.py
@@ -33,7 +33,7 @@ def __init__(
         """
         try:
             from google.api_core.client_options import ClientOptions
-            from google.cloud import translate
+            from google.cloud import translate  # type: ignore[attr-defined]
         except ImportError as exc:
             raise ImportError(
                 "Install Google Cloud Translate to use this parser."
@@ -76,7 +76,7 @@ def transform_documents(
                 Options: `text/plain`, `text/html`
         """
         try:
-            from google.cloud import translate
+            from google.cloud import translate  # type: ignore[attr-defined]
         except ImportError as exc:
             raise ImportError(
                 "Install Google Cloud Translate to use this parser."
diff --git a/libs/community/langchain_community/embeddings/anyscale.py b/libs/community/langchain_community/embeddings/anyscale.py
index 76ba0132a1ec6..82a87c46debbc 100644
--- a/libs/community/langchain_community/embeddings/anyscale.py
+++ b/libs/community/langchain_community/embeddings/anyscale.py
@@ -68,7 +68,7 @@ def validate_environment(cls, values: dict) -> dict:
         else:
             values["openai_api_base"] = values["anyscale_api_base"]
             values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
-            values["client"] = openai.Embedding
+            values["client"] = openai.Embedding  # type: ignore[attr-defined]
         return values
 
     @property
diff --git a/libs/community/langchain_community/embeddings/azure_openai.py b/libs/community/langchain_community/embeddings/azure_openai.py
index b65422320a505..61d1b4d4d3fb7 100644
--- a/libs/community/langchain_community/embeddings/azure_openai.py
+++ b/libs/community/langchain_community/embeddings/azure_openai.py
@@ -20,7 +20,7 @@
     removal="1.0",
     alternative_import="langchain_openai.AzureOpenAIEmbeddings",
 )
-class AzureOpenAIEmbeddings(OpenAIEmbeddings):
+class AzureOpenAIEmbeddings(OpenAIEmbeddings):  # type: ignore[override]
     """`Azure OpenAI` Embeddings API."""
 
     azure_endpoint: Union[str, None] = None
@@ -167,16 +167,16 @@ def post_init_validator(self) -> Self:
                 "default_query": self.default_query,
                 "http_client": self.http_client,
             }
-            self.client = openai.AzureOpenAI(**client_params).embeddings
+            self.client = openai.AzureOpenAI(**client_params).embeddings  # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
 
             if self.azure_ad_async_token_provider:
                 client_params["azure_ad_token_provider"] = (
                     self.azure_ad_async_token_provider
                 )
 
-            self.async_client = openai.AsyncAzureOpenAI(**client_params).embeddings
+            self.async_client = openai.AsyncAzureOpenAI(**client_params).embeddings  # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
         else:
-            self.client = openai.Embedding
+            self.client = openai.Embedding  # type: ignore[attr-defined]
         return self
 
     @property
diff --git a/libs/community/langchain_community/embeddings/localai.py b/libs/community/langchain_community/embeddings/localai.py
index 924f162128ec2..d163bb284fb9c 100644
--- a/libs/community/langchain_community/embeddings/localai.py
+++ b/libs/community/langchain_community/embeddings/localai.py
@@ -46,11 +46,11 @@ def _create_retry_decorator(embeddings: LocalAIEmbeddings) -> Callable[[Any], An
         stop=stop_after_attempt(embeddings.max_retries),
         wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
         retry=(
-            retry_if_exception_type(openai.error.Timeout)
-            | retry_if_exception_type(openai.error.APIError)
-            | retry_if_exception_type(openai.error.APIConnectionError)
-            | retry_if_exception_type(openai.error.RateLimitError)
-            | retry_if_exception_type(openai.error.ServiceUnavailableError)
+            retry_if_exception_type(openai.error.Timeout)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIConnectionError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.RateLimitError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.ServiceUnavailableError)  # type: ignore[attr-defined]
         ),
         before_sleep=before_sleep_log(logger, logging.WARNING),
     )
@@ -68,11 +68,11 @@ def _async_retry_decorator(embeddings: LocalAIEmbeddings) -> Any:
         stop=stop_after_attempt(embeddings.max_retries),
         wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
         retry=(
-            retry_if_exception_type(openai.error.Timeout)
-            | retry_if_exception_type(openai.error.APIError)
-            | retry_if_exception_type(openai.error.APIConnectionError)
-            | retry_if_exception_type(openai.error.RateLimitError)
-            | retry_if_exception_type(openai.error.ServiceUnavailableError)
+            retry_if_exception_type(openai.error.Timeout)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIConnectionError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.RateLimitError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.ServiceUnavailableError)  # type: ignore[attr-defined]
         ),
         before_sleep=before_sleep_log(logger, logging.WARNING),
     )
@@ -93,7 +93,7 @@ def _check_response(response: dict) -> dict:
     if any(len(d["embedding"]) == 1 for d in response["data"]):
         import openai
 
-        raise openai.error.APIError("LocalAI API returned an empty embedding")
+        raise openai.error.APIError("LocalAI API returned an empty embedding")  # type: ignore[attr-defined]
     return response
 
 
@@ -230,7 +230,7 @@ def validate_environment(cls, values: Dict) -> Dict:
         try:
             import openai
 
-            values["client"] = openai.Embedding
+            values["client"] = openai.Embedding  # type: ignore[attr-defined]
         except ImportError:
             raise ImportError(
                 "Could not import openai python package. "
@@ -253,7 +253,7 @@ def _invocation_params(self) -> Dict:
         if self.openai_proxy:
             import openai
 
-            openai.proxy = {
+            openai.proxy = {  # type: ignore[attr-defined]
                 "http": self.openai_proxy,
                 "https": self.openai_proxy,
             }  # type: ignore[assignment]
diff --git a/libs/community/langchain_community/embeddings/octoai_embeddings.py b/libs/community/langchain_community/embeddings/octoai_embeddings.py
index 102ba313f54f8..3cbfe01449ae4 100644
--- a/libs/community/langchain_community/embeddings/octoai_embeddings.py
+++ b/libs/community/langchain_community/embeddings/octoai_embeddings.py
@@ -74,8 +74,8 @@ def validate_environment(cls, values: dict) -> dict:
             else:
                 values["openai_api_base"] = values["endpoint_url"]
                 values["openai_api_key"] = values["octoai_api_token"].get_secret_value()
-                values["client"] = openai.Embedding
-                values["async_client"] = openai.Embedding
+                values["client"] = openai.Embedding  # type: ignore[attr-defined]
+                values["async_client"] = openai.Embedding  # type: ignore[attr-defined]
 
         except ImportError:
             raise ImportError(
diff --git a/libs/community/langchain_community/embeddings/openai.py b/libs/community/langchain_community/embeddings/openai.py
index 770bb765e8151..126fe564ae5ef 100644
--- a/libs/community/langchain_community/embeddings/openai.py
+++ b/libs/community/langchain_community/embeddings/openai.py
@@ -58,11 +58,11 @@ def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any
             max=embeddings.retry_max_seconds,
         ),
         retry=(
-            retry_if_exception_type(openai.error.Timeout)
-            | retry_if_exception_type(openai.error.APIError)
-            | retry_if_exception_type(openai.error.APIConnectionError)
-            | retry_if_exception_type(openai.error.RateLimitError)
-            | retry_if_exception_type(openai.error.ServiceUnavailableError)
+            retry_if_exception_type(openai.error.Timeout)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIConnectionError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.RateLimitError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.ServiceUnavailableError)  # type: ignore[attr-defined]
         ),
         before_sleep=before_sleep_log(logger, logging.WARNING),
     )
@@ -85,11 +85,11 @@ def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any:
             max=embeddings.retry_max_seconds,
         ),
         retry=(
-            retry_if_exception_type(openai.error.Timeout)
-            | retry_if_exception_type(openai.error.APIError)
-            | retry_if_exception_type(openai.error.APIConnectionError)
-            | retry_if_exception_type(openai.error.RateLimitError)
-            | retry_if_exception_type(openai.error.ServiceUnavailableError)
+            retry_if_exception_type(openai.error.Timeout)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIConnectionError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.RateLimitError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.ServiceUnavailableError)  # type: ignore[attr-defined]
         ),
         before_sleep=before_sleep_log(logger, logging.WARNING),
     )
@@ -110,7 +110,7 @@ def _check_response(response: dict, skip_empty: bool = False) -> dict:
     if any(len(d["embedding"]) == 1 for d in response["data"]) and not skip_empty:
         import openai
 
-        raise openai.error.APIError("OpenAI API returned an empty embedding")
+        raise openai.error.APIError("OpenAI API returned an empty embedding")  # type: ignore[attr-defined]
     return response
 
 
@@ -357,7 +357,7 @@ def validate_environment(cls, values: Dict) -> Dict:
                         **client_params
                     ).embeddings
             elif not values.get("client"):
-                values["client"] = openai.Embedding
+                values["client"] = openai.Embedding  # type: ignore[attr-defined]
             else:
                 pass
         return values
@@ -390,7 +390,7 @@ def _invocation_params(self) -> Dict[str, Any]:
                         "Please install it with `pip install openai`."
                     )
 
-                openai.proxy = {
+                openai.proxy = {  # type: ignore[attr-defined]
                     "http": self.openai_proxy,
                     "https": self.openai_proxy,
                 }  # type: ignore[assignment]
diff --git a/libs/community/langchain_community/embeddings/spacy_embeddings.py b/libs/community/langchain_community/embeddings/spacy_embeddings.py
index 0413741c4df5d..1d7c6ed4e95ad 100644
--- a/libs/community/langchain_community/embeddings/spacy_embeddings.py
+++ b/libs/community/langchain_community/embeddings/spacy_embeddings.py
@@ -55,7 +55,7 @@ def validate_environment(cls, values: Dict) -> Any:
             # Try to load the spaCy model
             import spacy
 
-            values["nlp"] = spacy.load(model_name)
+            values["nlp"] = spacy.load(model_name)  # type: ignore[arg-type]
         except OSError:
             # If the model is not found, raise a ValueError
             raise ValueError(
diff --git a/libs/community/langchain_community/embeddings/vertexai.py b/libs/community/langchain_community/embeddings/vertexai.py
index 5cf409e53cb7a..ea625c216b118 100644
--- a/libs/community/langchain_community/embeddings/vertexai.py
+++ b/libs/community/langchain_community/embeddings/vertexai.py
@@ -25,7 +25,7 @@
     removal="1.0",
     alternative_import="langchain_google_vertexai.VertexAIEmbeddings",
 )
-class VertexAIEmbeddings(_VertexAICommon, Embeddings):
+class VertexAIEmbeddings(_VertexAICommon, Embeddings):  # type: ignore[override]
     """Google Cloud VertexAI embedding models."""
 
     # Instance context
@@ -163,7 +163,8 @@ def _get_embeddings_with_retry(
             DeadlineExceeded,
         ]
         retry_decorator = create_base_retry_decorator(
-            error_types=errors, max_retries=self.max_retries
+            error_types=errors,  # type: ignore[arg-type]
+            max_retries=self.max_retries,  # type: ignore[arg-type]
         )
 
         @retry_decorator
diff --git a/libs/community/langchain_community/graphs/neo4j_graph.py b/libs/community/langchain_community/graphs/neo4j_graph.py
index baca74bb3e4ed..d3e8860c89131 100644
--- a/libs/community/langchain_community/graphs/neo4j_graph.py
+++ b/libs/community/langchain_community/graphs/neo4j_graph.py
@@ -445,20 +445,20 @@ def query(
                         or e.code
                         == "Neo.DatabaseError.Transaction.TransactionStartFailed"
                     )
-                    and "in an implicit transaction" in e.message
+                    and "in an implicit transaction" in e.message  # type: ignore[operator]
                 )
                 or (  # isPeriodicCommitError
                     e.code == "Neo.ClientError.Statement.SemanticError"
                     and (
-                        "in an open transaction is not possible" in e.message
-                        or "tried to execute in an explicit transaction" in e.message
+                        "in an open transaction is not possible" in e.message  # type: ignore[operator]
+                        or "tried to execute in an explicit transaction" in e.message  # type: ignore[operator]
                     )
                 )
             ):
                 raise
         # fallback to allow implicit transactions
         with self._driver.session(database=self._database) as session:
-            data = session.run(Query(text=query, timeout=self.timeout), params)
+            data = session.run(Query(text=query, timeout=self.timeout), params)  # type: ignore[assignment]
             json_data = [r.data() for r in data]
             if self.sanitize:
                 json_data = [value_sanitize(el) for el in json_data]
diff --git a/libs/community/langchain_community/llms/anyscale.py b/libs/community/langchain_community/llms/anyscale.py
index 2ae364c390630..60e3e2526d0cc 100644
--- a/libs/community/langchain_community/llms/anyscale.py
+++ b/libs/community/langchain_community/llms/anyscale.py
@@ -62,7 +62,7 @@ def create_llm_result(
     return LLMResult(generations=generations, llm_output=llm_output)
 
 
-class Anyscale(BaseOpenAI):
+class Anyscale(BaseOpenAI):  # type: ignore[override]
     """Anyscale large language models.
 
     To use, you should have the environment variable ``ANYSCALE_API_KEY``set with your
@@ -136,7 +136,7 @@ def validate_environment(cls, values: Dict) -> Dict:
             else:
                 values["openai_api_base"] = values["anyscale_api_base"]
                 values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
-                values["client"] = openai.Completion
+                values["client"] = openai.Completion  # type: ignore[attr-defined]
         except ImportError:
             raise ImportError(
                 "Could not import openai python package. "
diff --git a/libs/community/langchain_community/llms/beam.py b/libs/community/langchain_community/llms/beam.py
index fe8bf5f5ff8c0..2e7fdcd93883b 100644
--- a/libs/community/langchain_community/llms/beam.py
+++ b/libs/community/langchain_community/llms/beam.py
@@ -19,7 +19,7 @@
 DEFAULT_SLEEP_TIME = 4
 
 
-class Beam(LLM):
+class Beam(LLM):  # type: ignore[override, override, override, override]
     """Beam API for gpt2 large language model.
 
     To use, you should have the ``beam-sdk`` python package installed,
diff --git a/libs/community/langchain_community/llms/gooseai.py b/libs/community/langchain_community/llms/gooseai.py
index 990dee8758cc1..e8dc5ec77efeb 100644
--- a/libs/community/langchain_community/llms/gooseai.py
+++ b/libs/community/langchain_community/llms/gooseai.py
@@ -97,8 +97,8 @@ def build_extra(cls, values: Dict[str, Any]) -> Any:
             import openai
 
             openai.api_key = gooseai_api_key.get_secret_value()
-            openai.api_base = "https://api.goose.ai/v1"
-            values["client"] = openai.Completion
+            openai.api_base = "https://api.goose.ai/v1"  # type: ignore[attr-defined]
+            values["client"] = openai.Completion  # type: ignore[attr-defined]
         except ImportError:
             raise ImportError(
                 "Could not import openai python package. "
diff --git a/libs/community/langchain_community/llms/octoai_endpoint.py b/libs/community/langchain_community/llms/octoai_endpoint.py
index 7a30c7c65b4e5..38abe0813f73b 100644
--- a/libs/community/langchain_community/llms/octoai_endpoint.py
+++ b/libs/community/langchain_community/llms/octoai_endpoint.py
@@ -10,7 +10,7 @@
 DEFAULT_MODEL = "codellama-7b-instruct"
 
 
-class OctoAIEndpoint(BaseOpenAI):
+class OctoAIEndpoint(BaseOpenAI):  # type: ignore[override]
     """OctoAI LLM Endpoints - OpenAI compatible.
 
     OctoAIEndpoint is a class to interact with OctoAI Compute Service large
@@ -102,7 +102,7 @@ def validate_environment(cls, values: Dict) -> Dict:
             else:
                 values["openai_api_base"] = values["octoai_api_base"]
                 values["openai_api_key"] = values["octoai_api_token"].get_secret_value()
-                values["client"] = openai.Completion
+                values["client"] = openai.Completion  # type: ignore[attr-defined]
         except ImportError:
             raise ImportError(
                 "Could not import openai python package. "
diff --git a/libs/community/langchain_community/llms/openai.py b/libs/community/langchain_community/llms/openai.py
index 8a2b5d2e0a457..fe74a55105a2f 100644
--- a/libs/community/langchain_community/llms/openai.py
+++ b/libs/community/langchain_community/llms/openai.py
@@ -100,11 +100,11 @@ def _create_retry_decorator(
     import openai
 
     errors = [
-        openai.error.Timeout,
-        openai.error.APIError,
-        openai.error.APIConnectionError,
-        openai.error.RateLimitError,
-        openai.error.ServiceUnavailableError,
+        openai.error.Timeout,  # type: ignore[attr-defined]
+        openai.error.APIError,  # type: ignore[attr-defined]
+        openai.error.APIConnectionError,  # type: ignore[attr-defined]
+        openai.error.RateLimitError,  # type: ignore[attr-defined]
+        openai.error.ServiceUnavailableError,  # type: ignore[attr-defined]
     ]
     return create_base_retry_decorator(
         error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
@@ -323,7 +323,7 @@ def validate_environment(cls, values: Dict) -> Dict:
             if not values.get("async_client"):
                 values["async_client"] = openai.AsyncOpenAI(**client_params).completions
         elif not values.get("client"):
-            values["client"] = openai.Completion
+            values["client"] = openai.Completion  # type: ignore[attr-defined]
         else:
             pass
 
@@ -607,7 +607,7 @@ def _invocation_params(self) -> Dict[str, Any]:
         if self.openai_proxy:
             import openai
 
-            openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}  # type: ignore[assignment]
+            openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}  # type: ignore[assignment]  # type: ignore[attr-defined]  # type: ignore[attr-defined]  # type: ignore[attr-defined]  # type: ignore[attr-defined]  # type: ignore[attr-defined]  # type: ignore[attr-defined]
         return {**openai_creds, **self._default_params}
 
     @property
@@ -940,7 +940,7 @@ def validate_environment(cls, values: Dict) -> Dict:
             ).completions
 
         else:
-            values["client"] = openai.Completion
+            values["client"] = openai.Completion  # type: ignore[attr-defined]
 
         return values
 
@@ -1065,18 +1065,18 @@ def validate_environment(cls, values: Dict) -> Dict:
 
             openai.api_key = openai_api_key
             if openai_api_base:
-                openai.api_base = openai_api_base
+                openai.api_base = openai_api_base  # type: ignore[attr-defined]
             if openai_organization:
                 openai.organization = openai_organization
             if openai_proxy:
-                openai.proxy = {"http": openai_proxy, "https": openai_proxy}  # type: ignore[assignment]
+                openai.proxy = {"http": openai_proxy, "https": openai_proxy}  # type: ignore[assignment]  # type: ignore[attr-defined]  # type: ignore[attr-defined]  # type: ignore[attr-defined]  # type: ignore[attr-defined]  # type: ignore[attr-defined]  # type: ignore[attr-defined]
         except ImportError:
             raise ImportError(
                 "Could not import openai python package. "
                 "Please install it with `pip install openai`."
             )
         try:
-            values["client"] = openai.ChatCompletion
+            values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         except AttributeError:
             raise ValueError(
                 "`openai` has no `ChatCompletion` attribute, this is likely "
diff --git a/libs/community/langchain_community/llms/vertexai.py b/libs/community/langchain_community/llms/vertexai.py
index 0b42bd535ef83..22a1bca8e190d 100644
--- a/libs/community/langchain_community/llms/vertexai.py
+++ b/libs/community/langchain_community/llms/vertexai.py
@@ -124,7 +124,7 @@ def _get_task_executor(cls, request_parallelism: int = 5) -> Executor:
         return cls.task_executor
 
 
-class _VertexAICommon(_VertexAIBase):
+class _VertexAICommon(_VertexAIBase):  # type: ignore[override]
     client: "_LanguageModel" = None  #: :meta private:
     client_preview: "_LanguageModel" = None  #: :meta private:
     model_name: str
@@ -208,7 +208,7 @@ def _prepare_params(
     removal="1.0",
     alternative_import="langchain_google_vertexai.VertexAI",
 )
-class VertexAI(_VertexAICommon, BaseLLM):
+class VertexAI(_VertexAICommon, BaseLLM):  # type: ignore[override]
     """Google Vertex AI large language models."""
 
     model_name: str = "text-bison"
@@ -401,8 +401,12 @@ def _stream(
 class VertexAIModelGarden(_VertexAIBase, BaseLLM):
     """Vertex AI Model Garden large language models."""
 
-    client: "PredictionServiceClient" = None  #: :meta private:
-    async_client: "PredictionServiceAsyncClient" = None  #: :meta private:
+    client: "PredictionServiceClient" = (
+        None  #: :meta private:  # type: ignore[assignment]
+    )
+    async_client: "PredictionServiceAsyncClient" = (
+        None  #: :meta private:  # type: ignore[assignment]
+    )
     endpoint_id: str
     "A name of an endpoint where the model has been deployed."
     allowed_model_args: Optional[List[str]] = None
@@ -444,7 +448,7 @@ def validate_environment(cls, values: Dict) -> Dict:
     @property
     def endpoint_path(self) -> str:
         return self.client.endpoint_path(
-            project=self.project,
+            project=self.project,  # type: ignore[arg-type]
             location=self.location,
             endpoint=self.endpoint_id,
         )
diff --git a/libs/community/langchain_community/memory/zep_cloud_memory.py b/libs/community/langchain_community/memory/zep_cloud_memory.py
index 24ddb04677c94..cc65c11c8a79d 100644
--- a/libs/community/langchain_community/memory/zep_cloud_memory.py
+++ b/libs/community/langchain_community/memory/zep_cloud_memory.py
@@ -8,7 +8,7 @@
     from langchain.memory import ConversationBufferMemory
     from zep_cloud import MemoryGetRequestMemoryType
 
-    class ZepCloudMemory(ConversationBufferMemory):
+    class ZepCloudMemory(ConversationBufferMemory):  # type: ignore[override]
         """Persist your chain history to the Zep MemoryStore.
 
         Documentation: https://help.getzep.com
diff --git a/libs/community/langchain_community/memory/zep_memory.py b/libs/community/langchain_community/memory/zep_memory.py
index 80d7caad0cb8a..3f05febc3ae6c 100644
--- a/libs/community/langchain_community/memory/zep_memory.py
+++ b/libs/community/langchain_community/memory/zep_memory.py
@@ -7,7 +7,7 @@
 try:
     from langchain.memory import ConversationBufferMemory
 
-    class ZepMemory(ConversationBufferMemory):
+    class ZepMemory(ConversationBufferMemory):  # type: ignore[override]
         """Persist your chain history to the Zep MemoryStore.
 
         The number of messages returned by Zep and when the Zep server summarizes chat
diff --git a/libs/community/langchain_community/tools/ainetwork/app.py b/libs/community/langchain_community/tools/ainetwork/app.py
index 8175a210b7067..98f83c5d366f1 100644
--- a/libs/community/langchain_community/tools/ainetwork/app.py
+++ b/libs/community/langchain_community/tools/ainetwork/app.py
@@ -30,7 +30,7 @@ class AppSchema(BaseModel):
     )
 
 
-class AINAppOps(AINBaseTool):
+class AINAppOps(AINBaseTool):  # type: ignore[override, override]
     """Tool for app operations."""
 
     name: str = "AINappOps"
diff --git a/libs/community/langchain_community/tools/ainetwork/base.py b/libs/community/langchain_community/tools/ainetwork/base.py
index 00e4fc7f7a298..a973412bd6ff2 100644
--- a/libs/community/langchain_community/tools/ainetwork/base.py
+++ b/libs/community/langchain_community/tools/ainetwork/base.py
@@ -22,7 +22,7 @@ class OperationType(str, Enum):
     GET = "GET"
 
 
-class AINBaseTool(BaseTool):
+class AINBaseTool(BaseTool):  # type: ignore[override]
     """Base class for the AINetwork tools."""
 
     interface: Ain = Field(default_factory=authenticate)
diff --git a/libs/community/langchain_community/tools/ainetwork/owner.py b/libs/community/langchain_community/tools/ainetwork/owner.py
index 13d41d9327350..fdd15ed65a1ed 100644
--- a/libs/community/langchain_community/tools/ainetwork/owner.py
+++ b/libs/community/langchain_community/tools/ainetwork/owner.py
@@ -30,7 +30,7 @@ class RuleSchema(BaseModel):
     )
 
 
-class AINOwnerOps(AINBaseTool):
+class AINOwnerOps(AINBaseTool):  # type: ignore[override, override]
     """Tool for owner operations."""
 
     name: str = "AINownerOps"
diff --git a/libs/community/langchain_community/tools/ainetwork/rule.py b/libs/community/langchain_community/tools/ainetwork/rule.py
index 5a24c9e5aba79..9a2105017531e 100644
--- a/libs/community/langchain_community/tools/ainetwork/rule.py
+++ b/libs/community/langchain_community/tools/ainetwork/rule.py
@@ -16,7 +16,7 @@ class RuleSchema(BaseModel):
     eval: Optional[str] = Field(None, description="eval string to determine permission")
 
 
-class AINRuleOps(AINBaseTool):
+class AINRuleOps(AINBaseTool):  # type: ignore[override, override]
     """Tool for owner operations."""
 
     name: str = "AINruleOps"
diff --git a/libs/community/langchain_community/tools/ainetwork/transfer.py b/libs/community/langchain_community/tools/ainetwork/transfer.py
index 81d630af2e3bd..ff8d307948b12 100644
--- a/libs/community/langchain_community/tools/ainetwork/transfer.py
+++ b/libs/community/langchain_community/tools/ainetwork/transfer.py
@@ -14,7 +14,7 @@ class TransferSchema(BaseModel):
     amount: int = Field(..., description="Amount of AIN to transfer")
 
 
-class AINTransfer(AINBaseTool):
+class AINTransfer(AINBaseTool):  # type: ignore[override, override]
     """Tool for transfer operations."""
 
     name: str = "AINtransfer"
diff --git a/libs/community/langchain_community/tools/ainetwork/value.py b/libs/community/langchain_community/tools/ainetwork/value.py
index be6414727f53f..cd52115431837 100644
--- a/libs/community/langchain_community/tools/ainetwork/value.py
+++ b/libs/community/langchain_community/tools/ainetwork/value.py
@@ -18,7 +18,7 @@ class ValueSchema(BaseModel):
     )
 
 
-class AINValueOps(AINBaseTool):
+class AINValueOps(AINBaseTool):  # type: ignore[override, override]
     """Tool for value operations."""
 
     name: str = "AINvalueOps"
diff --git a/libs/community/langchain_community/tools/amadeus/base.py b/libs/community/langchain_community/tools/amadeus/base.py
index 3fd3f377ce2dd..9a8d5473ade11 100644
--- a/libs/community/langchain_community/tools/amadeus/base.py
+++ b/libs/community/langchain_community/tools/amadeus/base.py
@@ -13,7 +13,7 @@
     from amadeus import Client
 
 
-class AmadeusBaseTool(BaseTool):
+class AmadeusBaseTool(BaseTool):  # type: ignore[override]
     """Base Tool for Amadeus."""
 
     client: Client = Field(default_factory=authenticate)
diff --git a/libs/community/langchain_community/tools/amadeus/closest_airport.py b/libs/community/langchain_community/tools/amadeus/closest_airport.py
index 9523f73afbb6d..71af3c2b08bb4 100644
--- a/libs/community/langchain_community/tools/amadeus/closest_airport.py
+++ b/libs/community/langchain_community/tools/amadeus/closest_airport.py
@@ -27,7 +27,7 @@ class ClosestAirportSchema(BaseModel):
     )
 
 
-class AmadeusClosestAirport(AmadeusBaseTool):
+class AmadeusClosestAirport(AmadeusBaseTool):  # type: ignore[override, override, override]
     """Tool for finding the closest airport to a particular location."""
 
     name: str = "closest_airport"
diff --git a/libs/community/langchain_community/tools/amadeus/flight_search.py b/libs/community/langchain_community/tools/amadeus/flight_search.py
index c3cd8fe7bb9d2..5dd32047c1dcd 100644
--- a/libs/community/langchain_community/tools/amadeus/flight_search.py
+++ b/libs/community/langchain_community/tools/amadeus/flight_search.py
@@ -51,7 +51,7 @@ class FlightSearchSchema(BaseModel):
     )
 
 
-class AmadeusFlightSearch(AmadeusBaseTool):
+class AmadeusFlightSearch(AmadeusBaseTool):  # type: ignore[override, override]
     """Tool for searching for a single flight between two airports."""
 
     name: str = "single_flight_search"
diff --git a/libs/community/langchain_community/tools/arxiv/tool.py b/libs/community/langchain_community/tools/arxiv/tool.py
index 601022a1325c3..1d2fb5aa8312b 100644
--- a/libs/community/langchain_community/tools/arxiv/tool.py
+++ b/libs/community/langchain_community/tools/arxiv/tool.py
@@ -15,7 +15,7 @@ class ArxivInput(BaseModel):
     query: str = Field(description="search query to look up")
 
 
-class ArxivQueryRun(BaseTool):
+class ArxivQueryRun(BaseTool):  # type: ignore[override, override]
     """Tool that searches the Arxiv API."""
 
     name: str = "arxiv"
diff --git a/libs/community/langchain_community/tools/asknews/tool.py b/libs/community/langchain_community/tools/asknews/tool.py
index 7589d11078bd6..60cc7bf34c730 100644
--- a/libs/community/langchain_community/tools/asknews/tool.py
+++ b/libs/community/langchain_community/tools/asknews/tool.py
@@ -6,7 +6,7 @@
     ASKNEWS_CLIENT_SECRET
 """
 
-from typing import Optional, Type
+from typing import Any, Optional, Type
 
 from langchain_core.callbacks import (
     AsyncCallbackManagerForToolRun,
@@ -34,7 +34,7 @@ class SearchInput(BaseModel):
     )
 
 
-class AskNewsSearch(BaseTool):
+class AskNewsSearch(BaseTool):  # type: ignore[override]
     """Tool that searches the AskNews API."""
 
     name: str = "asknews_search"
@@ -45,13 +45,14 @@ class AskNewsSearch(BaseTool):
     )
     api_wrapper: AskNewsAPIWrapper = Field(default_factory=AskNewsAPIWrapper)  # type: ignore[arg-type]
     max_results: int = 10
-    args_schema: Type[BaseModel] = SearchInput
+    args_schema: Optional[Type[BaseModel]] = SearchInput
 
     def _run(
         self,
         query: str,
         hours_back: int = 0,
         run_manager: Optional[CallbackManagerForToolRun] = None,
+        **kwargs: Any,
     ) -> str:
         """Use the tool."""
         try:
@@ -68,6 +69,7 @@ async def _arun(
         query: str,
         hours_back: int = 0,
         run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+        **kwargs: Any,
     ) -> str:
         """Use the tool asynchronously."""
         try:
diff --git a/libs/community/langchain_community/tools/audio/huggingface_text_to_speech_inference.py b/libs/community/langchain_community/tools/audio/huggingface_text_to_speech_inference.py
index bbe5f7d47c295..bce7c841306e6 100644
--- a/libs/community/langchain_community/tools/audio/huggingface_text_to_speech_inference.py
+++ b/libs/community/langchain_community/tools/audio/huggingface_text_to_speech_inference.py
@@ -12,7 +12,7 @@
 logger = logging.getLogger(__name__)
 
 
-class HuggingFaceTextToSpeechModelInference(BaseTool):
+class HuggingFaceTextToSpeechModelInference(BaseTool):  # type: ignore[override]
     """HuggingFace Text-to-Speech Model Inference.
 
     Requirements:
diff --git a/libs/community/langchain_community/tools/azure_ai_services/document_intelligence.py b/libs/community/langchain_community/tools/azure_ai_services/document_intelligence.py
index cd0ac25018ee4..994fd13e64693 100644
--- a/libs/community/langchain_community/tools/azure_ai_services/document_intelligence.py
+++ b/libs/community/langchain_community/tools/azure_ai_services/document_intelligence.py
@@ -15,7 +15,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AzureAiServicesDocumentIntelligenceTool(BaseTool):
+class AzureAiServicesDocumentIntelligenceTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Azure AI Services Document Intelligence API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/azure_ai_services/image_analysis.py b/libs/community/langchain_community/tools/azure_ai_services/image_analysis.py
index 0a1e206e45e7d..856233360c5bf 100644
--- a/libs/community/langchain_community/tools/azure_ai_services/image_analysis.py
+++ b/libs/community/langchain_community/tools/azure_ai_services/image_analysis.py
@@ -15,7 +15,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AzureAiServicesImageAnalysisTool(BaseTool):
+class AzureAiServicesImageAnalysisTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Azure AI Services Image Analysis API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/azure_ai_services/speech_to_text.py b/libs/community/langchain_community/tools/azure_ai_services/speech_to_text.py
index 15e08d27222e1..b10c0323427a0 100644
--- a/libs/community/langchain_community/tools/azure_ai_services/speech_to_text.py
+++ b/libs/community/langchain_community/tools/azure_ai_services/speech_to_text.py
@@ -17,7 +17,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AzureAiServicesSpeechToTextTool(BaseTool):
+class AzureAiServicesSpeechToTextTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Azure AI Services Speech to Text API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/azure_ai_services/text_analytics_for_health.py b/libs/community/langchain_community/tools/azure_ai_services/text_analytics_for_health.py
index 6447c5de4185e..b47b29210fde6 100644
--- a/libs/community/langchain_community/tools/azure_ai_services/text_analytics_for_health.py
+++ b/libs/community/langchain_community/tools/azure_ai_services/text_analytics_for_health.py
@@ -11,7 +11,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AzureAiServicesTextAnalyticsForHealthTool(BaseTool):
+class AzureAiServicesTextAnalyticsForHealthTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Azure AI Services Text Analytics for Health API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/azure_ai_services/text_to_speech.py b/libs/community/langchain_community/tools/azure_ai_services/text_to_speech.py
index 1291e2dac5641..605a97998e3a2 100644
--- a/libs/community/langchain_community/tools/azure_ai_services/text_to_speech.py
+++ b/libs/community/langchain_community/tools/azure_ai_services/text_to_speech.py
@@ -12,7 +12,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AzureAiServicesTextToSpeechTool(BaseTool):
+class AzureAiServicesTextToSpeechTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Azure AI Services Text to Speech API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/azure_cognitive_services/form_recognizer.py b/libs/community/langchain_community/tools/azure_cognitive_services/form_recognizer.py
index 937b1fc7930f3..6789787bcc9e4 100644
--- a/libs/community/langchain_community/tools/azure_cognitive_services/form_recognizer.py
+++ b/libs/community/langchain_community/tools/azure_cognitive_services/form_recognizer.py
@@ -15,7 +15,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AzureCogsFormRecognizerTool(BaseTool):
+class AzureCogsFormRecognizerTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Azure Cognitive Services Form Recognizer API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/azure_cognitive_services/image_analysis.py b/libs/community/langchain_community/tools/azure_cognitive_services/image_analysis.py
index ce076243a7b14..c4223d24a7c6d 100644
--- a/libs/community/langchain_community/tools/azure_cognitive_services/image_analysis.py
+++ b/libs/community/langchain_community/tools/azure_cognitive_services/image_analysis.py
@@ -15,7 +15,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AzureCogsImageAnalysisTool(BaseTool):
+class AzureCogsImageAnalysisTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Azure Cognitive Services Image Analysis API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/azure_cognitive_services/speech2text.py b/libs/community/langchain_community/tools/azure_cognitive_services/speech2text.py
index 125c910df1ba7..3a80955060071 100644
--- a/libs/community/langchain_community/tools/azure_cognitive_services/speech2text.py
+++ b/libs/community/langchain_community/tools/azure_cognitive_services/speech2text.py
@@ -17,7 +17,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AzureCogsSpeech2TextTool(BaseTool):
+class AzureCogsSpeech2TextTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Azure Cognitive Services Speech2Text API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/azure_cognitive_services/text2speech.py b/libs/community/langchain_community/tools/azure_cognitive_services/text2speech.py
index 343653fe9c3e1..c669256180655 100644
--- a/libs/community/langchain_community/tools/azure_cognitive_services/text2speech.py
+++ b/libs/community/langchain_community/tools/azure_cognitive_services/text2speech.py
@@ -12,7 +12,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AzureCogsText2SpeechTool(BaseTool):
+class AzureCogsText2SpeechTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Azure Cognitive Services Text2Speech API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/azure_cognitive_services/text_analytics_health.py b/libs/community/langchain_community/tools/azure_cognitive_services/text_analytics_health.py
index 1de8566528fe4..0827cb9b93c18 100644
--- a/libs/community/langchain_community/tools/azure_cognitive_services/text_analytics_health.py
+++ b/libs/community/langchain_community/tools/azure_cognitive_services/text_analytics_health.py
@@ -11,7 +11,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AzureCogsTextAnalyticsHealthTool(BaseTool):
+class AzureCogsTextAnalyticsHealthTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Azure Cognitive Services Text Analytics for Health API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/bing_search/tool.py b/libs/community/langchain_community/tools/bing_search/tool.py
index 9c05405f82510..413b1f70cf81b 100644
--- a/libs/community/langchain_community/tools/bing_search/tool.py
+++ b/libs/community/langchain_community/tools/bing_search/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.bing_search import BingSearchAPIWrapper
 
 
-class BingSearchRun(BaseTool):
+class BingSearchRun(BaseTool):  # type: ignore[override]
     """Tool that queries the Bing search API."""
 
     name: str = "bing_search"
@@ -28,7 +28,7 @@ def _run(
         return self.api_wrapper.run(query)
 
 
-class BingSearchResults(BaseTool):
+class BingSearchResults(BaseTool):  # type: ignore[override, override]
     """Bing Search tool.
 
     Setup:
diff --git a/libs/community/langchain_community/tools/brave_search/tool.py b/libs/community/langchain_community/tools/brave_search/tool.py
index 4ca8d68501e19..be8d8ae41d13e 100644
--- a/libs/community/langchain_community/tools/brave_search/tool.py
+++ b/libs/community/langchain_community/tools/brave_search/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.brave_search import BraveSearchWrapper
 
 
-class BraveSearch(BaseTool):
+class BraveSearch(BaseTool):  # type: ignore[override]
     """Tool that queries the BraveSearch."""
 
     name: str = "brave_search"
diff --git a/libs/community/langchain_community/tools/cassandra_database/tool.py b/libs/community/langchain_community/tools/cassandra_database/tool.py
index ab6e502fb0846..b86e2a79bd3a2 100644
--- a/libs/community/langchain_community/tools/cassandra_database/tool.py
+++ b/libs/community/langchain_community/tools/cassandra_database/tool.py
@@ -29,7 +29,7 @@ class _QueryCassandraDatabaseToolInput(BaseModel):
     query: str = Field(..., description="A detailed and correct CQL query.")
 
 
-class QueryCassandraDatabaseTool(BaseCassandraDatabaseTool, BaseTool):
+class QueryCassandraDatabaseTool(BaseCassandraDatabaseTool, BaseTool):  # type: ignore[override, override]
     """Tool for querying an Apache Cassandra database with provided CQL."""
 
     name: str = "cassandra_db_query"
@@ -60,7 +60,7 @@ class _GetSchemaCassandraDatabaseToolInput(BaseModel):
     )
 
 
-class GetSchemaCassandraDatabaseTool(BaseCassandraDatabaseTool, BaseTool):
+class GetSchemaCassandraDatabaseTool(BaseCassandraDatabaseTool, BaseTool):  # type: ignore[override, override]
     """Tool for getting the schema of a keyspace in an Apache Cassandra database."""
 
     name: str = "cassandra_db_schema"
@@ -107,7 +107,7 @@ class _GetTableDataCassandraDatabaseToolInput(BaseModel):
     )
 
 
-class GetTableDataCassandraDatabaseTool(BaseCassandraDatabaseTool, BaseTool):
+class GetTableDataCassandraDatabaseTool(BaseCassandraDatabaseTool, BaseTool):  # type: ignore[override, override]
     """
     Tool for getting data from a table in an Apache Cassandra database.
     Use the WHERE clause to specify the predicate for the query that uses the
diff --git a/libs/community/langchain_community/tools/clickup/tool.py b/libs/community/langchain_community/tools/clickup/tool.py
index 03b3fde586c18..7b0cac67ced16 100644
--- a/libs/community/langchain_community/tools/clickup/tool.py
+++ b/libs/community/langchain_community/tools/clickup/tool.py
@@ -26,7 +26,7 @@
 from langchain_community.utilities.clickup import ClickupAPIWrapper
 
 
-class ClickupAction(BaseTool):
+class ClickupAction(BaseTool):  # type: ignore[override]
     """Tool that queries the  Clickup API."""
 
     api_wrapper: ClickupAPIWrapper = Field(default_factory=ClickupAPIWrapper)
diff --git a/libs/community/langchain_community/tools/cogniswitch/tool.py b/libs/community/langchain_community/tools/cogniswitch/tool.py
index 41c9663fb5eb0..1dc62b9e5c270 100644
--- a/libs/community/langchain_community/tools/cogniswitch/tool.py
+++ b/libs/community/langchain_community/tools/cogniswitch/tool.py
@@ -7,7 +7,7 @@
 from langchain_core.tools import BaseTool
 
 
-class CogniswitchKnowledgeRequest(BaseTool):
+class CogniswitchKnowledgeRequest(BaseTool):  # type: ignore[override]
     """Tool that uses the Cogniswitch service to answer questions.
 
     name: str = "cogniswitch_knowledge_request"
@@ -80,7 +80,7 @@ def answer_cs(self, cs_token: str, OAI_token: str, query: str, apiKey: str) -> d
         return response.json()
 
 
-class CogniswitchKnowledgeStatus(BaseTool):
+class CogniswitchKnowledgeStatus(BaseTool):  # type: ignore[override]
     """Tool that uses the Cogniswitch services to get the
      status of the document or url uploaded.
 
@@ -180,7 +180,7 @@ def knowledge_status(self, document_name: str) -> dict:
             }
 
 
-class CogniswitchKnowledgeSourceFile(BaseTool):
+class CogniswitchKnowledgeSourceFile(BaseTool):  # type: ignore[override]
     """Tool that uses the Cogniswitch services to store data from file.
 
     name: str = "cogniswitch_knowledge_source_file"
@@ -293,7 +293,7 @@ def store_data(
             return {"message": "Bad Request"}
 
 
-class CogniswitchKnowledgeSourceURL(BaseTool):
+class CogniswitchKnowledgeSourceURL(BaseTool):  # type: ignore[override]
     """Tool that uses the Cogniswitch services to store data from a URL.
 
     name: str = "cogniswitch_knowledge_source_url"
diff --git a/libs/community/langchain_community/tools/connery/tool.py b/libs/community/langchain_community/tools/connery/tool.py
index d74bfc1743e87..2bb0713493b7d 100644
--- a/libs/community/langchain_community/tools/connery/tool.py
+++ b/libs/community/langchain_community/tools/connery/tool.py
@@ -12,7 +12,7 @@
 from langchain_community.tools.connery.models import Action, Parameter
 
 
-class ConneryAction(BaseTool):
+class ConneryAction(BaseTool):  # type: ignore[override, override]
     """Connery Action tool."""
 
     name: str
diff --git a/libs/community/langchain_community/tools/dataforseo_api_search/tool.py b/libs/community/langchain_community/tools/dataforseo_api_search/tool.py
index 65bf1ec22cd75..8adc8b9ab86ab 100644
--- a/libs/community/langchain_community/tools/dataforseo_api_search/tool.py
+++ b/libs/community/langchain_community/tools/dataforseo_api_search/tool.py
@@ -12,7 +12,7 @@
 from langchain_community.utilities.dataforseo_api_search import DataForSeoAPIWrapper
 
 
-class DataForSeoAPISearchRun(BaseTool):
+class DataForSeoAPISearchRun(BaseTool):  # type: ignore[override]
     """Tool that queries the DataForSeo Google search API."""
 
     name: str = "dataforseo_api_search"
@@ -40,7 +40,7 @@ async def _arun(
         return (await self.api_wrapper.arun(query)).__str__()
 
 
-class DataForSeoAPISearchResults(BaseTool):
+class DataForSeoAPISearchResults(BaseTool):  # type: ignore[override]
     """Tool that queries the DataForSeo Google Search API
     and get back json."""
 
diff --git a/libs/community/langchain_community/tools/dataherald/tool.py b/libs/community/langchain_community/tools/dataherald/tool.py
index 2a2546a328378..56b02e7018631 100644
--- a/libs/community/langchain_community/tools/dataherald/tool.py
+++ b/libs/community/langchain_community/tools/dataherald/tool.py
@@ -15,7 +15,7 @@ class DataheraldTextToSQLInput(BaseModel):
     )
 
 
-class DataheraldTextToSQL(BaseTool):
+class DataheraldTextToSQL(BaseTool):  # type: ignore[override, override]
     """Tool that queries using the Dataherald SDK."""
 
     name: str = "dataherald"
diff --git a/libs/community/langchain_community/tools/ddg_search/tool.py b/libs/community/langchain_community/tools/ddg_search/tool.py
index 7db9b77da4273..e5c28cb1f6899 100644
--- a/libs/community/langchain_community/tools/ddg_search/tool.py
+++ b/libs/community/langchain_community/tools/ddg_search/tool.py
@@ -17,7 +17,7 @@ class DDGInput(BaseModel):
     query: str = Field(description="search query to look up")
 
 
-class DuckDuckGoSearchRun(BaseTool):
+class DuckDuckGoSearchRun(BaseTool):  # type: ignore[override, override]
     """DuckDuckGo tool.
 
     Setup:
@@ -74,7 +74,7 @@ def _run(
         return self.api_wrapper.run(query)
 
 
-class DuckDuckGoSearchResults(BaseTool):
+class DuckDuckGoSearchResults(BaseTool):  # type: ignore[override, override]
     """Tool that queries the DuckDuckGo search API and
     returns the results in `output_format`."""
 
diff --git a/libs/community/langchain_community/tools/e2b_data_analysis/tool.py b/libs/community/langchain_community/tools/e2b_data_analysis/tool.py
index f9705086a9800..0078637d96c3d 100644
--- a/libs/community/langchain_community/tools/e2b_data_analysis/tool.py
+++ b/libs/community/langchain_community/tools/e2b_data_analysis/tool.py
@@ -93,7 +93,7 @@ class E2BDataAnalysisToolArguments(BaseModel):
     )
 
 
-class E2BDataAnalysisTool(BaseTool):
+class E2BDataAnalysisTool(BaseTool):  # type: ignore[override, override]
     """Tool for running python code in a sandboxed environment for data analysis."""
 
     name: str = "e2b_data_analysis"
diff --git a/libs/community/langchain_community/tools/edenai/audio_speech_to_text.py b/libs/community/langchain_community/tools/edenai/audio_speech_to_text.py
index c0fc870ef2d94..b4a218dc3daa6 100644
--- a/libs/community/langchain_community/tools/edenai/audio_speech_to_text.py
+++ b/libs/community/langchain_community/tools/edenai/audio_speech_to_text.py
@@ -18,7 +18,7 @@ class SpeechToTextInput(BaseModel):
     query: HttpUrl = Field(description="url of the audio to analyze")
 
 
-class EdenAiSpeechToTextTool(EdenaiTool):
+class EdenAiSpeechToTextTool(EdenaiTool):  # type: ignore[override, override, override]
     """Tool that queries the Eden AI Speech To Text API.
 
     for api reference check edenai documentation:
diff --git a/libs/community/langchain_community/tools/edenai/audio_text_to_speech.py b/libs/community/langchain_community/tools/edenai/audio_text_to_speech.py
index d17c0854f9eab..8c88060b75a67 100644
--- a/libs/community/langchain_community/tools/edenai/audio_text_to_speech.py
+++ b/libs/community/langchain_community/tools/edenai/audio_text_to_speech.py
@@ -16,7 +16,7 @@ class TextToSpeechInput(BaseModel):
     query: str = Field(description="text to generate audio from")
 
 
-class EdenAiTextToSpeechTool(EdenaiTool):
+class EdenAiTextToSpeechTool(EdenaiTool):  # type: ignore[override, override, override]
     """Tool that queries the Eden AI Text to speech API.
     for api reference check edenai documentation:
     https://docs.edenai.co/reference/audio_text_to_speech_create.
diff --git a/libs/community/langchain_community/tools/edenai/edenai_base_tool.py b/libs/community/langchain_community/tools/edenai/edenai_base_tool.py
index c4f5547999234..6c802bbf72a3c 100644
--- a/libs/community/langchain_community/tools/edenai/edenai_base_tool.py
+++ b/libs/community/langchain_community/tools/edenai/edenai_base_tool.py
@@ -13,7 +13,7 @@
 logger = logging.getLogger(__name__)
 
 
-class EdenaiTool(BaseTool):
+class EdenaiTool(BaseTool):  # type: ignore[override]
     """
     the base tool for all the EdenAI Tools .
     you should have
diff --git a/libs/community/langchain_community/tools/edenai/image_explicitcontent.py b/libs/community/langchain_community/tools/edenai/image_explicitcontent.py
index 50f9f24338a3f..11dfa02df0e4f 100644
--- a/libs/community/langchain_community/tools/edenai/image_explicitcontent.py
+++ b/libs/community/langchain_community/tools/edenai/image_explicitcontent.py
@@ -15,7 +15,7 @@ class ExplicitImageInput(BaseModel):
     query: HttpUrl = Field(description="url of the image to analyze")
 
 
-class EdenAiExplicitImageTool(EdenaiTool):
+class EdenAiExplicitImageTool(EdenaiTool):  # type: ignore[override, override, override]
     """Tool that queries the Eden AI Explicit image detection.
 
     for api reference check edenai documentation:
diff --git a/libs/community/langchain_community/tools/edenai/image_objectdetection.py b/libs/community/langchain_community/tools/edenai/image_objectdetection.py
index aedfe56b88389..cb8209f577cbc 100644
--- a/libs/community/langchain_community/tools/edenai/image_objectdetection.py
+++ b/libs/community/langchain_community/tools/edenai/image_objectdetection.py
@@ -15,7 +15,7 @@ class ObjectDetectionInput(BaseModel):
     query: HttpUrl = Field(description="url of the image to analyze")
 
 
-class EdenAiObjectDetectionTool(EdenaiTool):
+class EdenAiObjectDetectionTool(EdenaiTool):  # type: ignore[override, override, override]
     """Tool that queries the Eden AI Object detection API.
 
     for api reference check edenai documentation:
diff --git a/libs/community/langchain_community/tools/edenai/ocr_identityparser.py b/libs/community/langchain_community/tools/edenai/ocr_identityparser.py
index f2270345917b4..74b0e9cefc206 100644
--- a/libs/community/langchain_community/tools/edenai/ocr_identityparser.py
+++ b/libs/community/langchain_community/tools/edenai/ocr_identityparser.py
@@ -15,7 +15,7 @@ class IDParsingInput(BaseModel):
     query: HttpUrl = Field(description="url of the document to parse")
 
 
-class EdenAiParsingIDTool(EdenaiTool):
+class EdenAiParsingIDTool(EdenaiTool):  # type: ignore[override, override, override]
     """Tool that queries the Eden AI  Identity parsing API.
 
     for api reference check edenai documentation:
diff --git a/libs/community/langchain_community/tools/edenai/ocr_invoiceparser.py b/libs/community/langchain_community/tools/edenai/ocr_invoiceparser.py
index d5266476784c9..e84d514ccacce 100644
--- a/libs/community/langchain_community/tools/edenai/ocr_invoiceparser.py
+++ b/libs/community/langchain_community/tools/edenai/ocr_invoiceparser.py
@@ -15,7 +15,7 @@ class InvoiceParsingInput(BaseModel):
     query: HttpUrl = Field(description="url of the document to parse")
 
 
-class EdenAiParsingInvoiceTool(EdenaiTool):
+class EdenAiParsingInvoiceTool(EdenaiTool):  # type: ignore[override, override, override]
     """Tool that queries the Eden AI Invoice parsing API.
 
     for api reference check edenai documentation:
diff --git a/libs/community/langchain_community/tools/edenai/text_moderation.py b/libs/community/langchain_community/tools/edenai/text_moderation.py
index f5f8497ff3ca6..319f87702a6a1 100644
--- a/libs/community/langchain_community/tools/edenai/text_moderation.py
+++ b/libs/community/langchain_community/tools/edenai/text_moderation.py
@@ -15,7 +15,7 @@ class TextModerationInput(BaseModel):
     query: str = Field(description="Text to moderate")
 
 
-class EdenAiTextModerationTool(EdenaiTool):
+class EdenAiTextModerationTool(EdenaiTool):  # type: ignore[override, override, override]
     """Tool that queries the Eden AI Explicit text detection.
 
     for api reference check edenai documentation:
diff --git a/libs/community/langchain_community/tools/eleven_labs/text2speech.py b/libs/community/langchain_community/tools/eleven_labs/text2speech.py
index 333dfb6bb6432..9f56647caf5d7 100644
--- a/libs/community/langchain_community/tools/eleven_labs/text2speech.py
+++ b/libs/community/langchain_community/tools/eleven_labs/text2speech.py
@@ -25,7 +25,7 @@ class ElevenLabsModel(str, Enum):
     MONO_LINGUAL = "eleven_monolingual_v1"
 
 
-class ElevenLabsText2SpeechTool(BaseTool):
+class ElevenLabsText2SpeechTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Eleven Labs Text2Speech API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/file_management/copy.py b/libs/community/langchain_community/tools/file_management/copy.py
index 7679e3c43bee0..209f217b751e4 100644
--- a/libs/community/langchain_community/tools/file_management/copy.py
+++ b/libs/community/langchain_community/tools/file_management/copy.py
@@ -19,7 +19,7 @@ class FileCopyInput(BaseModel):
     destination_path: str = Field(..., description="Path to save the copied file")
 
 
-class CopyFileTool(BaseFileToolMixin, BaseTool):
+class CopyFileTool(BaseFileToolMixin, BaseTool):  # type: ignore[override, override]
     """Tool that copies a file."""
 
     name: str = "copy_file"
diff --git a/libs/community/langchain_community/tools/file_management/delete.py b/libs/community/langchain_community/tools/file_management/delete.py
index 33f4b70b28dd6..e840875807ded 100644
--- a/libs/community/langchain_community/tools/file_management/delete.py
+++ b/libs/community/langchain_community/tools/file_management/delete.py
@@ -18,7 +18,7 @@ class FileDeleteInput(BaseModel):
     file_path: str = Field(..., description="Path of the file to delete")
 
 
-class DeleteFileTool(BaseFileToolMixin, BaseTool):
+class DeleteFileTool(BaseFileToolMixin, BaseTool):  # type: ignore[override, override]
     """Tool that deletes a file."""
 
     name: str = "file_delete"
diff --git a/libs/community/langchain_community/tools/file_management/file_search.py b/libs/community/langchain_community/tools/file_management/file_search.py
index a00aee40b4ba7..02b2e1dd44bdf 100644
--- a/libs/community/langchain_community/tools/file_management/file_search.py
+++ b/libs/community/langchain_community/tools/file_management/file_search.py
@@ -26,7 +26,7 @@ class FileSearchInput(BaseModel):
     )
 
 
-class FileSearchTool(BaseFileToolMixin, BaseTool):
+class FileSearchTool(BaseFileToolMixin, BaseTool):  # type: ignore[override, override]
     """Tool that searches for files in a subdirectory that match a regex pattern."""
 
     name: str = "file_search"
diff --git a/libs/community/langchain_community/tools/file_management/list_dir.py b/libs/community/langchain_community/tools/file_management/list_dir.py
index a8bfdc8e3abe1..a2ffc6494f039 100644
--- a/libs/community/langchain_community/tools/file_management/list_dir.py
+++ b/libs/community/langchain_community/tools/file_management/list_dir.py
@@ -18,7 +18,7 @@ class DirectoryListingInput(BaseModel):
     dir_path: str = Field(default=".", description="Subdirectory to list.")
 
 
-class ListDirectoryTool(BaseFileToolMixin, BaseTool):
+class ListDirectoryTool(BaseFileToolMixin, BaseTool):  # type: ignore[override, override]
     """Tool that lists files and directories in a specified folder."""
 
     name: str = "list_directory"
diff --git a/libs/community/langchain_community/tools/file_management/move.py b/libs/community/langchain_community/tools/file_management/move.py
index 935625172e9a3..1d9bce338eafb 100644
--- a/libs/community/langchain_community/tools/file_management/move.py
+++ b/libs/community/langchain_community/tools/file_management/move.py
@@ -19,7 +19,7 @@ class FileMoveInput(BaseModel):
     destination_path: str = Field(..., description="New path for the moved file")
 
 
-class MoveFileTool(BaseFileToolMixin, BaseTool):
+class MoveFileTool(BaseFileToolMixin, BaseTool):  # type: ignore[override, override]
     """Tool that moves a file."""
 
     name: str = "move_file"
diff --git a/libs/community/langchain_community/tools/file_management/read.py b/libs/community/langchain_community/tools/file_management/read.py
index 9f746ed16c105..92572ee85aba7 100644
--- a/libs/community/langchain_community/tools/file_management/read.py
+++ b/libs/community/langchain_community/tools/file_management/read.py
@@ -17,7 +17,7 @@ class ReadFileInput(BaseModel):
     file_path: str = Field(..., description="name of file")
 
 
-class ReadFileTool(BaseFileToolMixin, BaseTool):
+class ReadFileTool(BaseFileToolMixin, BaseTool):  # type: ignore[override, override]
     """Tool that reads a file."""
 
     name: str = "read_file"
diff --git a/libs/community/langchain_community/tools/file_management/write.py b/libs/community/langchain_community/tools/file_management/write.py
index 1d62065bb74e4..647f845ecd106 100644
--- a/libs/community/langchain_community/tools/file_management/write.py
+++ b/libs/community/langchain_community/tools/file_management/write.py
@@ -21,7 +21,7 @@ class WriteFileInput(BaseModel):
     )
 
 
-class WriteFileTool(BaseFileToolMixin, BaseTool):
+class WriteFileTool(BaseFileToolMixin, BaseTool):  # type: ignore[override, override]
     """Tool that writes a file to disk."""
 
     name: str = "write_file"
diff --git a/libs/community/langchain_community/tools/financial_datasets/balance_sheets.py b/libs/community/langchain_community/tools/financial_datasets/balance_sheets.py
index a2374e35c7cbb..0b02b549ad6c2 100644
--- a/libs/community/langchain_community/tools/financial_datasets/balance_sheets.py
+++ b/libs/community/langchain_community/tools/financial_datasets/balance_sheets.py
@@ -24,7 +24,7 @@ class BalanceSheetsSchema(BaseModel):
     )
 
 
-class BalanceSheets(BaseTool):
+class BalanceSheets(BaseTool):  # type: ignore[override, override]
     """
     Tool that gets balance sheets for a given ticker over a given period.
     """
diff --git a/libs/community/langchain_community/tools/financial_datasets/cash_flow_statements.py b/libs/community/langchain_community/tools/financial_datasets/cash_flow_statements.py
index e8ac4a5b2bff6..a627300a3a433 100644
--- a/libs/community/langchain_community/tools/financial_datasets/cash_flow_statements.py
+++ b/libs/community/langchain_community/tools/financial_datasets/cash_flow_statements.py
@@ -24,7 +24,7 @@ class CashFlowStatementsSchema(BaseModel):
     )
 
 
-class CashFlowStatements(BaseTool):
+class CashFlowStatements(BaseTool):  # type: ignore[override, override]
     """
     Tool that gets cash flow statements for a given ticker over a given period.
     """
diff --git a/libs/community/langchain_community/tools/financial_datasets/income_statements.py b/libs/community/langchain_community/tools/financial_datasets/income_statements.py
index e9db4e5cc1626..dc06d358df117 100644
--- a/libs/community/langchain_community/tools/financial_datasets/income_statements.py
+++ b/libs/community/langchain_community/tools/financial_datasets/income_statements.py
@@ -24,7 +24,7 @@ class IncomeStatementsSchema(BaseModel):
     )
 
 
-class IncomeStatements(BaseTool):
+class IncomeStatements(BaseTool):  # type: ignore[override, override]
     """
     Tool that gets income statements for a given ticker over a given period.
     """
diff --git a/libs/community/langchain_community/tools/github/tool.py b/libs/community/langchain_community/tools/github/tool.py
index af6933fe9b80e..1c605c953653d 100644
--- a/libs/community/langchain_community/tools/github/tool.py
+++ b/libs/community/langchain_community/tools/github/tool.py
@@ -17,7 +17,7 @@
 from langchain_community.utilities.github import GitHubAPIWrapper
 
 
-class GitHubAction(BaseTool):
+class GitHubAction(BaseTool):  # type: ignore[override]
     """Tool for interacting with the GitHub API."""
 
     api_wrapper: GitHubAPIWrapper = Field(default_factory=GitHubAPIWrapper)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/tools/gitlab/tool.py b/libs/community/langchain_community/tools/gitlab/tool.py
index 8dfe2bb32fdbd..ac9d27b7dfdd6 100644
--- a/libs/community/langchain_community/tools/gitlab/tool.py
+++ b/libs/community/langchain_community/tools/gitlab/tool.py
@@ -17,7 +17,7 @@
 from langchain_community.utilities.gitlab import GitLabAPIWrapper
 
 
-class GitLabAction(BaseTool):
+class GitLabAction(BaseTool):  # type: ignore[override]
     """Tool for interacting with the GitLab API."""
 
     api_wrapper: GitLabAPIWrapper = Field(default_factory=GitLabAPIWrapper)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/tools/gmail/base.py b/libs/community/langchain_community/tools/gmail/base.py
index d55b0d30f8a45..82e44fbc002e3 100644
--- a/libs/community/langchain_community/tools/gmail/base.py
+++ b/libs/community/langchain_community/tools/gmail/base.py
@@ -20,7 +20,7 @@
         pass
 
 
-class GmailBaseTool(BaseTool):
+class GmailBaseTool(BaseTool):  # type: ignore[override]
     """Base class for Gmail tools."""
 
     api_resource: Resource = Field(default_factory=build_resource_service)
diff --git a/libs/community/langchain_community/tools/gmail/create_draft.py b/libs/community/langchain_community/tools/gmail/create_draft.py
index 2fdfcf37dd2c8..b35b0f8758025 100644
--- a/libs/community/langchain_community/tools/gmail/create_draft.py
+++ b/libs/community/langchain_community/tools/gmail/create_draft.py
@@ -33,7 +33,7 @@ class CreateDraftSchema(BaseModel):
     )
 
 
-class GmailCreateDraft(GmailBaseTool):
+class GmailCreateDraft(GmailBaseTool):  # type: ignore[override, override]
     """Tool that creates a draft email for Gmail."""
 
     name: str = "create_gmail_draft"
diff --git a/libs/community/langchain_community/tools/gmail/get_message.py b/libs/community/langchain_community/tools/gmail/get_message.py
index 6155cb499f4d9..0145d37b65f87 100644
--- a/libs/community/langchain_community/tools/gmail/get_message.py
+++ b/libs/community/langchain_community/tools/gmail/get_message.py
@@ -18,7 +18,7 @@ class SearchArgsSchema(BaseModel):
     )
 
 
-class GmailGetMessage(GmailBaseTool):
+class GmailGetMessage(GmailBaseTool):  # type: ignore[override, override]
     """Tool that gets a message by ID from Gmail."""
 
     name: str = "get_gmail_message"
diff --git a/libs/community/langchain_community/tools/gmail/get_thread.py b/libs/community/langchain_community/tools/gmail/get_thread.py
index 5e61bd8bb98ab..266b81115c296 100644
--- a/libs/community/langchain_community/tools/gmail/get_thread.py
+++ b/libs/community/langchain_community/tools/gmail/get_thread.py
@@ -16,7 +16,7 @@ class GetThreadSchema(BaseModel):
     )
 
 
-class GmailGetThread(GmailBaseTool):
+class GmailGetThread(GmailBaseTool):  # type: ignore[override, override]
     """Tool that gets a thread by ID from Gmail."""
 
     name: str = "get_gmail_thread"
diff --git a/libs/community/langchain_community/tools/gmail/search.py b/libs/community/langchain_community/tools/gmail/search.py
index eb61968429520..f0b4fe1df447b 100644
--- a/libs/community/langchain_community/tools/gmail/search.py
+++ b/libs/community/langchain_community/tools/gmail/search.py
@@ -43,7 +43,7 @@ class SearchArgsSchema(BaseModel):
     )
 
 
-class GmailSearch(GmailBaseTool):
+class GmailSearch(GmailBaseTool):  # type: ignore[override, override]
     """Tool that searches for messages or threads in Gmail."""
 
     name: str = "search_gmail"
diff --git a/libs/community/langchain_community/tools/gmail/send_message.py b/libs/community/langchain_community/tools/gmail/send_message.py
index 3ccd2185048e6..0d9ab4634993b 100644
--- a/libs/community/langchain_community/tools/gmail/send_message.py
+++ b/libs/community/langchain_community/tools/gmail/send_message.py
@@ -36,7 +36,7 @@ class SendMessageSchema(BaseModel):
     )
 
 
-class GmailSendMessage(GmailBaseTool):
+class GmailSendMessage(GmailBaseTool):  # type: ignore[override, override]
     """Tool that sends a message to Gmail."""
 
     name: str = "send_gmail_message"
diff --git a/libs/community/langchain_community/tools/gmail/utils.py b/libs/community/langchain_community/tools/gmail/utils.py
index ef9dfc91b9b42..e8bb4d468b3f3 100644
--- a/libs/community/langchain_community/tools/gmail/utils.py
+++ b/libs/community/langchain_community/tools/gmail/utils.py
@@ -83,7 +83,7 @@ def get_gmail_credentials(
     # If there are no (valid) credentials available, let the user log in.
     if not creds or not creds.valid:
         if creds and creds.expired and creds.refresh_token:
-            creds.refresh(Request())
+            creds.refresh(Request())  # type: ignore[call-arg]
         else:
             # https://developers.google.com/gmail/api/quickstart/python#authorize_credentials_for_a_desktop_application # noqa
             flow = InstalledAppFlow.from_client_secrets_file(
diff --git a/libs/community/langchain_community/tools/golden_query/tool.py b/libs/community/langchain_community/tools/golden_query/tool.py
index 7cc5c72234cc3..1f9664aa158de 100644
--- a/libs/community/langchain_community/tools/golden_query/tool.py
+++ b/libs/community/langchain_community/tools/golden_query/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.golden_query import GoldenQueryAPIWrapper
 
 
-class GoldenQueryRun(BaseTool):
+class GoldenQueryRun(BaseTool):  # type: ignore[override]
     """Tool that adds the capability to query using the Golden API and get back JSON."""
 
     name: str = "golden_query"
diff --git a/libs/community/langchain_community/tools/google_cloud/texttospeech.py b/libs/community/langchain_community/tools/google_cloud/texttospeech.py
index 02a24e9cf1a95..7984ae328f1f4 100644
--- a/libs/community/langchain_community/tools/google_cloud/texttospeech.py
+++ b/libs/community/langchain_community/tools/google_cloud/texttospeech.py
@@ -10,12 +10,12 @@
 from langchain_community.utilities.vertexai import get_client_info
 
 if TYPE_CHECKING:
-    from google.cloud import texttospeech
+    from google.cloud import texttospeech  # type: ignore[attr-defined]
 
 
 def _import_google_cloud_texttospeech() -> Any:
     try:
-        from google.cloud import texttospeech
+        from google.cloud import texttospeech  # type: ignore[attr-defined]
     except ImportError as e:
         raise ImportError(
             "Cannot import google.cloud.texttospeech, please install "
@@ -42,7 +42,7 @@ def _encoding_file_extension_map(encoding: texttospeech.AudioEncoding) -> Option
     removal="1.0",
     alternative_import="langchain_google_community.TextToSpeechTool",
 )
-class GoogleCloudTextToSpeechTool(BaseTool):
+class GoogleCloudTextToSpeechTool(BaseTool):  # type: ignore[override]
     """Tool that queries the Google Cloud Text to Speech API.
 
     In order to set this up, follow instructions at:
diff --git a/libs/community/langchain_community/tools/google_finance/tool.py b/libs/community/langchain_community/tools/google_finance/tool.py
index 82eb82de318b6..15bbc1da2b489 100644
--- a/libs/community/langchain_community/tools/google_finance/tool.py
+++ b/libs/community/langchain_community/tools/google_finance/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper
 
 
-class GoogleFinanceQueryRun(BaseTool):
+class GoogleFinanceQueryRun(BaseTool):  # type: ignore[override]
     """Tool that queries the Google Finance API."""
 
     name: str = "google_finance"
diff --git a/libs/community/langchain_community/tools/google_jobs/tool.py b/libs/community/langchain_community/tools/google_jobs/tool.py
index 6a83b3043d9f3..98211d4351c45 100644
--- a/libs/community/langchain_community/tools/google_jobs/tool.py
+++ b/libs/community/langchain_community/tools/google_jobs/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.google_jobs import GoogleJobsAPIWrapper
 
 
-class GoogleJobsQueryRun(BaseTool):
+class GoogleJobsQueryRun(BaseTool):  # type: ignore[override]
     """Tool that queries the Google Jobs API."""
 
     name: str = "google_jobs"
diff --git a/libs/community/langchain_community/tools/google_lens/tool.py b/libs/community/langchain_community/tools/google_lens/tool.py
index 38a4b847e2114..677e11babd3d0 100644
--- a/libs/community/langchain_community/tools/google_lens/tool.py
+++ b/libs/community/langchain_community/tools/google_lens/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.google_lens import GoogleLensAPIWrapper
 
 
-class GoogleLensQueryRun(BaseTool):
+class GoogleLensQueryRun(BaseTool):  # type: ignore[override]
     """Tool that queries the Google Lens API."""
 
     name: str = "google_lens"
diff --git a/libs/community/langchain_community/tools/google_places/tool.py b/libs/community/langchain_community/tools/google_places/tool.py
index b97705c9742dd..9328ec9050ba7 100644
--- a/libs/community/langchain_community/tools/google_places/tool.py
+++ b/libs/community/langchain_community/tools/google_places/tool.py
@@ -21,7 +21,7 @@ class GooglePlacesSchema(BaseModel):
     removal="1.0",
     alternative_import="langchain_google_community.GooglePlacesTool",
 )
-class GooglePlacesTool(BaseTool):
+class GooglePlacesTool(BaseTool):  # type: ignore[override, override]
     """Tool that queries the Google places API."""
 
     name: str = "google_places"
diff --git a/libs/community/langchain_community/tools/google_scholar/tool.py b/libs/community/langchain_community/tools/google_scholar/tool.py
index 49f8769696ff0..05bf590b9b875 100644
--- a/libs/community/langchain_community/tools/google_scholar/tool.py
+++ b/libs/community/langchain_community/tools/google_scholar/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.google_scholar import GoogleScholarAPIWrapper
 
 
-class GoogleScholarQueryRun(BaseTool):
+class GoogleScholarQueryRun(BaseTool):  # type: ignore[override]
     """Tool that queries the Google search API."""
 
     name: str = "google_scholar"
diff --git a/libs/community/langchain_community/tools/google_search/tool.py b/libs/community/langchain_community/tools/google_search/tool.py
index 3ba05079df841..fe9eac16be528 100644
--- a/libs/community/langchain_community/tools/google_search/tool.py
+++ b/libs/community/langchain_community/tools/google_search/tool.py
@@ -14,7 +14,7 @@
     removal="1.0",
     alternative_import="langchain_google_community.GoogleSearchRun",
 )
-class GoogleSearchRun(BaseTool):
+class GoogleSearchRun(BaseTool):  # type: ignore[override]
     """Tool that queries the Google search API."""
 
     name: str = "google_search"
@@ -39,7 +39,7 @@ def _run(
     removal="1.0",
     alternative_import="langchain_google_community.GoogleSearchResults",
 )
-class GoogleSearchResults(BaseTool):
+class GoogleSearchResults(BaseTool):  # type: ignore[override]
     """Tool that queries the Google Search API and gets back json."""
 
     name: str = "google_search_results_json"
diff --git a/libs/community/langchain_community/tools/google_serper/tool.py b/libs/community/langchain_community/tools/google_serper/tool.py
index 562dd012a1ac6..2b277790fbd11 100644
--- a/libs/community/langchain_community/tools/google_serper/tool.py
+++ b/libs/community/langchain_community/tools/google_serper/tool.py
@@ -12,7 +12,7 @@
 from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
 
 
-class GoogleSerperRun(BaseTool):
+class GoogleSerperRun(BaseTool):  # type: ignore[override]
     """Tool that queries the Serper.dev Google search API."""
 
     name: str = "google_serper"
@@ -40,7 +40,7 @@ async def _arun(
         return (await self.api_wrapper.arun(query)).__str__()
 
 
-class GoogleSerperResults(BaseTool):
+class GoogleSerperResults(BaseTool):  # type: ignore[override]
     """Tool that queries the Serper.dev Google Search API
     and get back json."""
 
diff --git a/libs/community/langchain_community/tools/google_trends/tool.py b/libs/community/langchain_community/tools/google_trends/tool.py
index 8b2b5dd8bfbd1..f830cf871ea4e 100644
--- a/libs/community/langchain_community/tools/google_trends/tool.py
+++ b/libs/community/langchain_community/tools/google_trends/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper
 
 
-class GoogleTrendsQueryRun(BaseTool):
+class GoogleTrendsQueryRun(BaseTool):  # type: ignore[override]
     """Tool that queries the Google trends API."""
 
     name: str = "google_trends"
diff --git a/libs/community/langchain_community/tools/graphql/tool.py b/libs/community/langchain_community/tools/graphql/tool.py
index 0530f8cae07fe..939c6a09c616c 100644
--- a/libs/community/langchain_community/tools/graphql/tool.py
+++ b/libs/community/langchain_community/tools/graphql/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.graphql import GraphQLAPIWrapper
 
 
-class BaseGraphQLTool(BaseTool):
+class BaseGraphQLTool(BaseTool):  # type: ignore[override]
     """Base tool for querying a GraphQL API."""
 
     graphql_wrapper: GraphQLAPIWrapper
diff --git a/libs/community/langchain_community/tools/human/tool.py b/libs/community/langchain_community/tools/human/tool.py
index d9e238b93c97b..0262039dc922b 100644
--- a/libs/community/langchain_community/tools/human/tool.py
+++ b/libs/community/langchain_community/tools/human/tool.py
@@ -12,7 +12,7 @@ def _print_func(text: str) -> None:
     print(text)  # noqa: T201
 
 
-class HumanInputRun(BaseTool):
+class HumanInputRun(BaseTool):  # type: ignore[override]
     """Tool that asks user for input."""
 
     name: str = "human"
diff --git a/libs/community/langchain_community/tools/ifttt.py b/libs/community/langchain_community/tools/ifttt.py
index 40bbe76fdad12..c28b0781488f9 100644
--- a/libs/community/langchain_community/tools/ifttt.py
+++ b/libs/community/langchain_community/tools/ifttt.py
@@ -40,7 +40,7 @@
 from langchain_core.tools import BaseTool
 
 
-class IFTTTWebhook(BaseTool):
+class IFTTTWebhook(BaseTool):  # type: ignore[override]
     """IFTTT Webhook.
 
     Args:
diff --git a/libs/community/langchain_community/tools/jina_search/tool.py b/libs/community/langchain_community/tools/jina_search/tool.py
index a6961e685b382..42a2a5e7e7a59 100644
--- a/libs/community/langchain_community/tools/jina_search/tool.py
+++ b/libs/community/langchain_community/tools/jina_search/tool.py
@@ -15,7 +15,7 @@ class JinaInput(BaseModel):
     query: str = Field(description="search query to look up")
 
 
-class JinaSearch(BaseTool):
+class JinaSearch(BaseTool):  # type: ignore[override]
     """Tool that queries the JinaSearch.
 
     ..versionadded:: 0.2.16
diff --git a/libs/community/langchain_community/tools/jira/tool.py b/libs/community/langchain_community/tools/jira/tool.py
index 70868947004ac..caa1a34c8c96f 100644
--- a/libs/community/langchain_community/tools/jira/tool.py
+++ b/libs/community/langchain_community/tools/jira/tool.py
@@ -29,7 +29,7 @@
 from langchain_community.utilities.jira import JiraAPIWrapper
 
 
-class JiraAction(BaseTool):
+class JiraAction(BaseTool):  # type: ignore[override]
     """Tool that queries the Atlassian Jira API."""
 
     api_wrapper: JiraAPIWrapper = Field(default_factory=JiraAPIWrapper)  # type: ignore[arg-type]
diff --git a/libs/community/langchain_community/tools/json/tool.py b/libs/community/langchain_community/tools/json/tool.py
index 6e7fddff6d7d6..c9957ae33ab71 100644
--- a/libs/community/langchain_community/tools/json/tool.py
+++ b/libs/community/langchain_community/tools/json/tool.py
@@ -82,7 +82,7 @@ def value(self, text: str) -> str:
             return repr(e)
 
 
-class JsonListKeysTool(BaseTool):
+class JsonListKeysTool(BaseTool):  # type: ignore[override]
     """Tool for listing keys in a JSON spec."""
 
     name: str = "json_spec_list_keys"
@@ -108,7 +108,7 @@ async def _arun(
         return self._run(tool_input)
 
 
-class JsonGetValueTool(BaseTool):
+class JsonGetValueTool(BaseTool):  # type: ignore[override]
     """Tool for getting a value in a JSON spec."""
 
     name: str = "json_spec_get_value"
diff --git a/libs/community/langchain_community/tools/memorize/tool.py b/libs/community/langchain_community/tools/memorize/tool.py
index 87badf9ac31f1..fe1ec52ac4a99 100644
--- a/libs/community/langchain_community/tools/memorize/tool.py
+++ b/libs/community/langchain_community/tools/memorize/tool.py
@@ -30,7 +30,7 @@ async def atrain_unsupervised(
     ) -> TrainResult: ...
 
 
-class Memorize(BaseTool):
+class Memorize(BaseTool):  # type: ignore[override]
     """Tool that trains a language model."""
 
     name: str = "memorize"
diff --git a/libs/community/langchain_community/tools/merriam_webster/tool.py b/libs/community/langchain_community/tools/merriam_webster/tool.py
index 9cf4e9f21ca69..b6f9209477fb3 100644
--- a/libs/community/langchain_community/tools/merriam_webster/tool.py
+++ b/libs/community/langchain_community/tools/merriam_webster/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.merriam_webster import MerriamWebsterAPIWrapper
 
 
-class MerriamWebsterQueryRun(BaseTool):
+class MerriamWebsterQueryRun(BaseTool):  # type: ignore[override]
     """Tool that searches the Merriam-Webster API."""
 
     name: str = "merriam_webster"
diff --git a/libs/community/langchain_community/tools/metaphor_search/tool.py b/libs/community/langchain_community/tools/metaphor_search/tool.py
index 98e932e8d2f48..03af3636ba05a 100644
--- a/libs/community/langchain_community/tools/metaphor_search/tool.py
+++ b/libs/community/langchain_community/tools/metaphor_search/tool.py
@@ -17,7 +17,7 @@
     removal="1.0",
     alternative="langchain_exa.ExaSearchResults",
 )
-class MetaphorSearchResults(BaseTool):
+class MetaphorSearchResults(BaseTool):  # type: ignore[override]
     """Tool that queries the Metaphor Search API and gets back json."""
 
     name: str = "metaphor_search_results_json"
diff --git a/libs/community/langchain_community/tools/mojeek_search/tool.py b/libs/community/langchain_community/tools/mojeek_search/tool.py
index 9112e1afe65e2..8172475cb38b7 100644
--- a/libs/community/langchain_community/tools/mojeek_search/tool.py
+++ b/libs/community/langchain_community/tools/mojeek_search/tool.py
@@ -11,7 +11,7 @@
 from langchain_community.utilities.mojeek_search import MojeekSearchAPIWrapper
 
 
-class MojeekSearch(BaseTool):
+class MojeekSearch(BaseTool):  # type: ignore[override]
     name: str = "mojeek_search"
     description: str = (
         "A wrapper around Mojeek Search. "
diff --git a/libs/community/langchain_community/tools/multion/close_session.py b/libs/community/langchain_community/tools/multion/close_session.py
index 28f0abd013ba7..03a777f00c9c7 100644
--- a/libs/community/langchain_community/tools/multion/close_session.py
+++ b/libs/community/langchain_community/tools/multion/close_session.py
@@ -27,7 +27,7 @@ class CloseSessionSchema(BaseModel):
     )
 
 
-class MultionCloseSession(BaseTool):
+class MultionCloseSession(BaseTool):  # type: ignore[override, override]
     """Tool that closes an existing Multion Browser Window with provided fields.
 
     Attributes:
diff --git a/libs/community/langchain_community/tools/multion/create_session.py b/libs/community/langchain_community/tools/multion/create_session.py
index 53388a5a973f7..3feb83815dae8 100644
--- a/libs/community/langchain_community/tools/multion/create_session.py
+++ b/libs/community/langchain_community/tools/multion/create_session.py
@@ -31,7 +31,7 @@ class CreateSessionSchema(BaseModel):
     )
 
 
-class MultionCreateSession(BaseTool):
+class MultionCreateSession(BaseTool):  # type: ignore[override]
     """Tool that creates a new Multion Browser Window with provided fields.
 
     Attributes:
diff --git a/libs/community/langchain_community/tools/multion/update_session.py b/libs/community/langchain_community/tools/multion/update_session.py
index b535861e2ac76..4a2abddd35ba6 100644
--- a/libs/community/langchain_community/tools/multion/update_session.py
+++ b/libs/community/langchain_community/tools/multion/update_session.py
@@ -36,7 +36,7 @@ class UpdateSessionSchema(BaseModel):
     )
 
 
-class MultionUpdateSession(BaseTool):
+class MultionUpdateSession(BaseTool):  # type: ignore[override, override]
     """Tool that updates an existing Multion Browser Window with provided fields.
 
     Attributes:
diff --git a/libs/community/langchain_community/tools/nasa/tool.py b/libs/community/langchain_community/tools/nasa/tool.py
index b9f2caa455584..ee2c5f2a10d1a 100644
--- a/libs/community/langchain_community/tools/nasa/tool.py
+++ b/libs/community/langchain_community/tools/nasa/tool.py
@@ -12,7 +12,7 @@
 from langchain_community.utilities.nasa import NasaAPIWrapper
 
 
-class NasaAction(BaseTool):
+class NasaAction(BaseTool):  # type: ignore[override]
     """Tool that queries the Atlassian Jira API."""
 
     api_wrapper: NasaAPIWrapper = Field(default_factory=NasaAPIWrapper)
diff --git a/libs/community/langchain_community/tools/nuclia/tool.py b/libs/community/langchain_community/tools/nuclia/tool.py
index a575f7d1d054d..02cb335367a53 100644
--- a/libs/community/langchain_community/tools/nuclia/tool.py
+++ b/libs/community/langchain_community/tools/nuclia/tool.py
@@ -54,7 +54,7 @@ class NUASchema(BaseModel):
     )
 
 
-class NucliaUnderstandingAPI(BaseTool):
+class NucliaUnderstandingAPI(BaseTool):  # type: ignore[override, override]
     """Tool to process files with the Nuclia Understanding API."""
 
     name: str = "nuclia_understanding_api"
diff --git a/libs/community/langchain_community/tools/office365/base.py b/libs/community/langchain_community/tools/office365/base.py
index 55160bd5e509e..a1d13cca38e3e 100644
--- a/libs/community/langchain_community/tools/office365/base.py
+++ b/libs/community/langchain_community/tools/office365/base.py
@@ -13,7 +13,7 @@
     from O365 import Account
 
 
-class O365BaseTool(BaseTool):
+class O365BaseTool(BaseTool):  # type: ignore[override]
     """Base class for the Office 365 tools."""
 
     account: Account = Field(default_factory=authenticate)
diff --git a/libs/community/langchain_community/tools/office365/create_draft_message.py b/libs/community/langchain_community/tools/office365/create_draft_message.py
index 02915ffedcf86..0abac1ab2989d 100644
--- a/libs/community/langchain_community/tools/office365/create_draft_message.py
+++ b/libs/community/langchain_community/tools/office365/create_draft_message.py
@@ -31,7 +31,7 @@ class CreateDraftMessageSchema(BaseModel):
     )
 
 
-class O365CreateDraftMessage(O365BaseTool):
+class O365CreateDraftMessage(O365BaseTool):  # type: ignore[override, override]
     """Tool for creating a draft email in Office 365."""
 
     name: str = "create_email_draft"
diff --git a/libs/community/langchain_community/tools/office365/events_search.py b/libs/community/langchain_community/tools/office365/events_search.py
index f23dd86b0870c..9615b27e8bf8b 100644
--- a/libs/community/langchain_community/tools/office365/events_search.py
+++ b/libs/community/langchain_community/tools/office365/events_search.py
@@ -53,7 +53,7 @@ class SearchEventsInput(BaseModel):
     )
 
 
-class O365SearchEvents(O365BaseTool):
+class O365SearchEvents(O365BaseTool):  # type: ignore[override, override]
     """Search calendar events in Office 365.
 
     Free, but setup is required
diff --git a/libs/community/langchain_community/tools/office365/messages_search.py b/libs/community/langchain_community/tools/office365/messages_search.py
index 5b66dc63644b3..a320844356941 100644
--- a/libs/community/langchain_community/tools/office365/messages_search.py
+++ b/libs/community/langchain_community/tools/office365/messages_search.py
@@ -52,7 +52,7 @@ class SearchEmailsInput(BaseModel):
     )
 
 
-class O365SearchEmails(O365BaseTool):
+class O365SearchEmails(O365BaseTool):  # type: ignore[override, override]
     """Search email messages in Office 365.
 
     Free, but setup is required.
diff --git a/libs/community/langchain_community/tools/office365/send_event.py b/libs/community/langchain_community/tools/office365/send_event.py
index 6a824deef759d..b6916d9ae144e 100644
--- a/libs/community/langchain_community/tools/office365/send_event.py
+++ b/libs/community/langchain_community/tools/office365/send_event.py
@@ -47,7 +47,7 @@ class SendEventSchema(BaseModel):
     )
 
 
-class O365SendEvent(O365BaseTool):
+class O365SendEvent(O365BaseTool):  # type: ignore[override, override]
     """Tool for sending calendar events in Office 365."""
 
     name: str = "send_event"
diff --git a/libs/community/langchain_community/tools/office365/send_message.py b/libs/community/langchain_community/tools/office365/send_message.py
index 6ebc8883714ed..c7e180e9b73b8 100644
--- a/libs/community/langchain_community/tools/office365/send_message.py
+++ b/libs/community/langchain_community/tools/office365/send_message.py
@@ -31,7 +31,7 @@ class SendMessageSchema(BaseModel):
     )
 
 
-class O365SendMessage(O365BaseTool):
+class O365SendMessage(O365BaseTool):  # type: ignore[override, override]
     """Send an email in Office 365."""
 
     name: str = "send_email"
diff --git a/libs/community/langchain_community/tools/openai_dalle_image_generation/tool.py b/libs/community/langchain_community/tools/openai_dalle_image_generation/tool.py
index 36374e887f74b..6d0583b6f9967 100644
--- a/libs/community/langchain_community/tools/openai_dalle_image_generation/tool.py
+++ b/libs/community/langchain_community/tools/openai_dalle_image_generation/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper
 
 
-class OpenAIDALLEImageGenerationTool(BaseTool):
+class OpenAIDALLEImageGenerationTool(BaseTool):  # type: ignore[override]
     """Tool that generates an image using OpenAI DALLE."""
 
     name: str = "openai_dalle"
diff --git a/libs/community/langchain_community/tools/openweathermap/tool.py b/libs/community/langchain_community/tools/openweathermap/tool.py
index f1e7758d2d0c2..cbeb552da2bb0 100644
--- a/libs/community/langchain_community/tools/openweathermap/tool.py
+++ b/libs/community/langchain_community/tools/openweathermap/tool.py
@@ -9,7 +9,7 @@
 from langchain_community.utilities.openweathermap import OpenWeatherMapAPIWrapper
 
 
-class OpenWeatherMapQueryRun(BaseTool):
+class OpenWeatherMapQueryRun(BaseTool):  # type: ignore[override]
     """Tool that queries the OpenWeatherMap API."""
 
     api_wrapper: OpenWeatherMapAPIWrapper = Field(
diff --git a/libs/community/langchain_community/tools/passio_nutrition_ai/tool.py b/libs/community/langchain_community/tools/passio_nutrition_ai/tool.py
index 939e1a41bc9fe..464caf6f9a2b3 100644
--- a/libs/community/langchain_community/tools/passio_nutrition_ai/tool.py
+++ b/libs/community/langchain_community/tools/passio_nutrition_ai/tool.py
@@ -17,7 +17,7 @@ class NutritionAIInputs(BaseModel):
     )
 
 
-class NutritionAI(BaseTool):
+class NutritionAI(BaseTool):  # type: ignore[override, override]
     """Tool that queries the Passio Nutrition AI API."""
 
     name: str = "nutritionai_advanced_search"
diff --git a/libs/community/langchain_community/tools/playwright/base.py b/libs/community/langchain_community/tools/playwright/base.py
index e85cc8479360c..efed3bc8deb86 100644
--- a/libs/community/langchain_community/tools/playwright/base.py
+++ b/libs/community/langchain_community/tools/playwright/base.py
@@ -32,7 +32,7 @@ def lazy_import_playwright_browsers() -> Tuple[Type[AsyncBrowser], Type[SyncBrow
     )
 
 
-class BaseBrowserTool(BaseTool):
+class BaseBrowserTool(BaseTool):  # type: ignore[override]
     """Base class for browser tools."""
 
     sync_browser: Optional["SyncBrowser"] = None
diff --git a/libs/community/langchain_community/tools/playwright/click.py b/libs/community/langchain_community/tools/playwright/click.py
index 22c6a23bf9c33..07490e45fbf3b 100644
--- a/libs/community/langchain_community/tools/playwright/click.py
+++ b/libs/community/langchain_community/tools/playwright/click.py
@@ -21,7 +21,7 @@ class ClickToolInput(BaseModel):
     selector: str = Field(..., description="CSS selector for the element to click")
 
 
-class ClickTool(BaseBrowserTool):
+class ClickTool(BaseBrowserTool):  # type: ignore[override, override, override]
     """Tool for clicking on an element with the given CSS selector."""
 
     name: str = "click_element"
diff --git a/libs/community/langchain_community/tools/playwright/current_page.py b/libs/community/langchain_community/tools/playwright/current_page.py
index 94abcaf1f1c25..50033d7f37878 100644
--- a/libs/community/langchain_community/tools/playwright/current_page.py
+++ b/libs/community/langchain_community/tools/playwright/current_page.py
@@ -15,7 +15,7 @@
 )
 
 
-class CurrentWebPageTool(BaseBrowserTool):
+class CurrentWebPageTool(BaseBrowserTool):  # type: ignore[override, override]
     """Tool for getting the URL of the current webpage."""
 
     name: str = "current_webpage"
diff --git a/libs/community/langchain_community/tools/playwright/extract_hyperlinks.py b/libs/community/langchain_community/tools/playwright/extract_hyperlinks.py
index 00a5e290274b5..18c2a1180bf3a 100644
--- a/libs/community/langchain_community/tools/playwright/extract_hyperlinks.py
+++ b/libs/community/langchain_community/tools/playwright/extract_hyperlinks.py
@@ -28,7 +28,7 @@ class ExtractHyperlinksToolInput(BaseModel):
     )
 
 
-class ExtractHyperlinksTool(BaseBrowserTool):
+class ExtractHyperlinksTool(BaseBrowserTool):  # type: ignore[override, override]
     """Extract all hyperlinks on the page."""
 
     name: str = "extract_hyperlinks"
diff --git a/libs/community/langchain_community/tools/playwright/extract_text.py b/libs/community/langchain_community/tools/playwright/extract_text.py
index b04e367952afa..dc77f1297f388 100644
--- a/libs/community/langchain_community/tools/playwright/extract_text.py
+++ b/libs/community/langchain_community/tools/playwright/extract_text.py
@@ -15,7 +15,7 @@
 )
 
 
-class ExtractTextTool(BaseBrowserTool):
+class ExtractTextTool(BaseBrowserTool):  # type: ignore[override, override]
     """Tool for extracting all the text on the current webpage."""
 
     name: str = "extract_text"
diff --git a/libs/community/langchain_community/tools/playwright/get_elements.py b/libs/community/langchain_community/tools/playwright/get_elements.py
index 11e43c01696aa..9708dcb39040e 100644
--- a/libs/community/langchain_community/tools/playwright/get_elements.py
+++ b/libs/community/langchain_community/tools/playwright/get_elements.py
@@ -73,7 +73,7 @@ def _get_elements(
     return results
 
 
-class GetElementsTool(BaseBrowserTool):
+class GetElementsTool(BaseBrowserTool):  # type: ignore[override, override]
     """Tool for getting elements in the current web page matching a CSS selector."""
 
     name: str = "get_elements"
diff --git a/libs/community/langchain_community/tools/playwright/navigate.py b/libs/community/langchain_community/tools/playwright/navigate.py
index 2bfe2be4fd7d8..0cd7658598216 100644
--- a/libs/community/langchain_community/tools/playwright/navigate.py
+++ b/libs/community/langchain_community/tools/playwright/navigate.py
@@ -32,7 +32,7 @@ def validate_url_scheme(cls, values: dict) -> dict:
         return values
 
 
-class NavigateTool(BaseBrowserTool):
+class NavigateTool(BaseBrowserTool):  # type: ignore[override, override]
     """Tool for navigating a browser to a URL.
 
     **Security Note**: This tool provides code to control web-browser navigation.
diff --git a/libs/community/langchain_community/tools/playwright/navigate_back.py b/libs/community/langchain_community/tools/playwright/navigate_back.py
index 908e20bd57100..688b135a23e7a 100644
--- a/libs/community/langchain_community/tools/playwright/navigate_back.py
+++ b/libs/community/langchain_community/tools/playwright/navigate_back.py
@@ -15,7 +15,7 @@
 )
 
 
-class NavigateBackTool(BaseBrowserTool):
+class NavigateBackTool(BaseBrowserTool):  # type: ignore[override, override]
     """Navigate back to the previous page in the browser history."""
 
     name: str = "previous_webpage"
diff --git a/libs/community/langchain_community/tools/plugin.py b/libs/community/langchain_community/tools/plugin.py
index 102451e72dade..440ea8d0ae61d 100644
--- a/libs/community/langchain_community/tools/plugin.py
+++ b/libs/community/langchain_community/tools/plugin.py
@@ -63,7 +63,7 @@ class AIPluginToolSchema(BaseModel):
     tool_input: Optional[str] = ""
 
 
-class AIPluginTool(BaseTool):
+class AIPluginTool(BaseTool):  # type: ignore[override, override]
     """Tool for getting the OpenAPI spec for an AI Plugin."""
 
     plugin: AIPlugin
diff --git a/libs/community/langchain_community/tools/polygon/aggregates.py b/libs/community/langchain_community/tools/polygon/aggregates.py
index 26cb62d4677de..74f0b85f5f467 100644
--- a/libs/community/langchain_community/tools/polygon/aggregates.py
+++ b/libs/community/langchain_community/tools/polygon/aggregates.py
@@ -39,7 +39,7 @@ class PolygonAggregatesSchema(BaseModel):
     )
 
 
-class PolygonAggregates(BaseTool):
+class PolygonAggregates(BaseTool):  # type: ignore[override, override]
     """
     Tool that gets aggregate bars (stock prices) over a
     given date range for a given ticker from Polygon.
diff --git a/libs/community/langchain_community/tools/polygon/financials.py b/libs/community/langchain_community/tools/polygon/financials.py
index 8400e7498b469..bf044a5f898e9 100644
--- a/libs/community/langchain_community/tools/polygon/financials.py
+++ b/libs/community/langchain_community/tools/polygon/financials.py
@@ -13,7 +13,7 @@ class Inputs(BaseModel):
     query: str
 
 
-class PolygonFinancials(BaseTool):
+class PolygonFinancials(BaseTool):  # type: ignore[override, override]
     """Tool that gets the financials of a ticker from Polygon"""
 
     mode: str = "get_financials"
diff --git a/libs/community/langchain_community/tools/polygon/last_quote.py b/libs/community/langchain_community/tools/polygon/last_quote.py
index 76c768113b3b5..2b9816683c633 100644
--- a/libs/community/langchain_community/tools/polygon/last_quote.py
+++ b/libs/community/langchain_community/tools/polygon/last_quote.py
@@ -13,7 +13,7 @@ class Inputs(BaseModel):
     query: str
 
 
-class PolygonLastQuote(BaseTool):
+class PolygonLastQuote(BaseTool):  # type: ignore[override, override]
     """Tool that gets the last quote of a ticker from Polygon"""
 
     mode: str = "get_last_quote"
diff --git a/libs/community/langchain_community/tools/polygon/ticker_news.py b/libs/community/langchain_community/tools/polygon/ticker_news.py
index d4c4a2017abbd..4a602d54acf85 100644
--- a/libs/community/langchain_community/tools/polygon/ticker_news.py
+++ b/libs/community/langchain_community/tools/polygon/ticker_news.py
@@ -13,7 +13,7 @@ class Inputs(BaseModel):
     query: str
 
 
-class PolygonTickerNews(BaseTool):
+class PolygonTickerNews(BaseTool):  # type: ignore[override, override]
     """Tool that gets the latest news for a given ticker from Polygon"""
 
     mode: str = "get_ticker_news"
diff --git a/libs/community/langchain_community/tools/powerbi/tool.py b/libs/community/langchain_community/tools/powerbi/tool.py
index c5ec51e5b52e6..cf930b3bafcc6 100644
--- a/libs/community/langchain_community/tools/powerbi/tool.py
+++ b/libs/community/langchain_community/tools/powerbi/tool.py
@@ -22,7 +22,7 @@
 logger = logging.getLogger(__name__)
 
 
-class QueryPowerBITool(BaseTool):
+class QueryPowerBITool(BaseTool):  # type: ignore[override]
     """Tool for querying a Power BI Dataset."""
 
     name: str = "query_powerbi"
@@ -216,7 +216,7 @@ def _result_too_large(self, result: str) -> Tuple[bool, int]:
         return False, 0
 
 
-class InfoPowerBITool(BaseTool):
+class InfoPowerBITool(BaseTool):  # type: ignore[override]
     """Tool for getting metadata about a PowerBI Dataset."""
 
     name: str = "schema_powerbi"
@@ -248,7 +248,7 @@ async def _arun(
         return await self.powerbi.aget_table_info(tool_input.split(", "))
 
 
-class ListPowerBITool(BaseTool):
+class ListPowerBITool(BaseTool):  # type: ignore[override]
     """Tool for getting tables names."""
 
     name: str = "list_tables_powerbi"
diff --git a/libs/community/langchain_community/tools/pubmed/tool.py b/libs/community/langchain_community/tools/pubmed/tool.py
index fe8338635449f..543c02a33bd71 100644
--- a/libs/community/langchain_community/tools/pubmed/tool.py
+++ b/libs/community/langchain_community/tools/pubmed/tool.py
@@ -7,7 +7,7 @@
 from langchain_community.utilities.pubmed import PubMedAPIWrapper
 
 
-class PubmedQueryRun(BaseTool):
+class PubmedQueryRun(BaseTool):  # type: ignore[override]
     """Tool that searches the PubMed API."""
 
     name: str = "pub_med"
diff --git a/libs/community/langchain_community/tools/reddit_search/tool.py b/libs/community/langchain_community/tools/reddit_search/tool.py
index fc823c2b23dcd..9a9ce882bbcd0 100644
--- a/libs/community/langchain_community/tools/reddit_search/tool.py
+++ b/libs/community/langchain_community/tools/reddit_search/tool.py
@@ -34,7 +34,7 @@ class RedditSearchSchema(BaseModel):
     )
 
 
-class RedditSearchRun(BaseTool):
+class RedditSearchRun(BaseTool):  # type: ignore[override, override]
     """Tool that queries for posts on a subreddit."""
 
     name: str = "reddit_search"
diff --git a/libs/community/langchain_community/tools/requests/tool.py b/libs/community/langchain_community/tools/requests/tool.py
index c91b348053f0e..bdfd63ddcc42a 100644
--- a/libs/community/langchain_community/tools/requests/tool.py
+++ b/libs/community/langchain_community/tools/requests/tool.py
@@ -47,7 +47,7 @@ def __init__(self, **kwargs: Any):
         super().__init__(**kwargs)
 
 
-class RequestsGetTool(BaseRequestsTool, BaseTool):
+class RequestsGetTool(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Tool for making a GET request to an API endpoint."""
 
     name: str = "requests_get"
@@ -71,7 +71,7 @@ async def _arun(
         return await self.requests_wrapper.aget(_clean_url(url))
 
 
-class RequestsPostTool(BaseRequestsTool, BaseTool):
+class RequestsPostTool(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Tool for making a POST request to an API endpoint."""
 
     name: str = "requests_post"
@@ -108,7 +108,7 @@ async def _arun(
             return repr(e)
 
 
-class RequestsPatchTool(BaseRequestsTool, BaseTool):
+class RequestsPatchTool(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Tool for making a PATCH request to an API endpoint."""
 
     name: str = "requests_patch"
@@ -145,7 +145,7 @@ async def _arun(
             return repr(e)
 
 
-class RequestsPutTool(BaseRequestsTool, BaseTool):
+class RequestsPutTool(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Tool for making a PUT request to an API endpoint."""
 
     name: str = "requests_put"
@@ -182,7 +182,7 @@ async def _arun(
             return repr(e)
 
 
-class RequestsDeleteTool(BaseRequestsTool, BaseTool):
+class RequestsDeleteTool(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Tool for making a DELETE request to an API endpoint."""
 
     name: str = "requests_delete"
diff --git a/libs/community/langchain_community/tools/riza/command.py b/libs/community/langchain_community/tools/riza/command.py
index f0fbe382d46cd..f3f618c5ce221 100644
--- a/libs/community/langchain_community/tools/riza/command.py
+++ b/libs/community/langchain_community/tools/riza/command.py
@@ -18,7 +18,7 @@ class ExecPythonInput(BaseModel):
     code: str = Field(description="the Python code to execute")
 
 
-class ExecPython(BaseTool):
+class ExecPython(BaseTool):  # type: ignore[override, override]
     """Riza Code tool.
 
     Setup:
@@ -94,7 +94,7 @@ class ExecJavaScriptInput(BaseModel):
     code: str = Field(description="the JavaScript code to execute")
 
 
-class ExecJavaScript(BaseTool):
+class ExecJavaScript(BaseTool):  # type: ignore[override, override]
     """A tool implementation to execute JavaScript via Riza's Code Interpreter API."""
 
     name: str = "riza_exec_javascript"
diff --git a/libs/community/langchain_community/tools/scenexplain/tool.py b/libs/community/langchain_community/tools/scenexplain/tool.py
index fdc085ab1c8fd..fb3914470da19 100644
--- a/libs/community/langchain_community/tools/scenexplain/tool.py
+++ b/libs/community/langchain_community/tools/scenexplain/tool.py
@@ -15,7 +15,7 @@ class SceneXplainInput(BaseModel):
     query: str = Field(..., description="The link to the image to explain")
 
 
-class SceneXplainTool(BaseTool):
+class SceneXplainTool(BaseTool):  # type: ignore[override]
     """Tool that explains images."""
 
     name: str = "image_explainer"
diff --git a/libs/community/langchain_community/tools/searchapi/tool.py b/libs/community/langchain_community/tools/searchapi/tool.py
index 205d59880deaa..8b92679bf6aa2 100644
--- a/libs/community/langchain_community/tools/searchapi/tool.py
+++ b/libs/community/langchain_community/tools/searchapi/tool.py
@@ -12,7 +12,7 @@
 from langchain_community.utilities.searchapi import SearchApiAPIWrapper
 
 
-class SearchAPIRun(BaseTool):
+class SearchAPIRun(BaseTool):  # type: ignore[override]
     """Tool that queries the SearchApi.io search API."""
 
     name: str = "searchapi"
@@ -40,7 +40,7 @@ async def _arun(
         return await self.api_wrapper.arun(query)
 
 
-class SearchAPIResults(BaseTool):
+class SearchAPIResults(BaseTool):  # type: ignore[override]
     """Tool that queries the SearchApi.io search API and returns JSON."""
 
     name: str = "searchapi_results_json"
diff --git a/libs/community/langchain_community/tools/searx_search/tool.py b/libs/community/langchain_community/tools/searx_search/tool.py
index d16739e88f2f1..3f752785ae783 100644
--- a/libs/community/langchain_community/tools/searx_search/tool.py
+++ b/libs/community/langchain_community/tools/searx_search/tool.py
@@ -18,7 +18,7 @@ class SearxSearchQueryInput(BaseModel):
     query: str = Field(description="query to look up on searx")
 
 
-class SearxSearchRun(BaseTool):
+class SearxSearchRun(BaseTool):  # type: ignore[override, override]
     """Tool that queries a Searx instance."""
 
     name: str = "searx_search"
@@ -48,7 +48,7 @@ async def _arun(
         return await self.wrapper.arun(query, **self.kwargs)
 
 
-class SearxSearchResults(BaseTool):
+class SearxSearchResults(BaseTool):  # type: ignore[override, override]
     """Tool that queries a Searx instance and gets back json."""
 
     name: str = "searx_search_results"
diff --git a/libs/community/langchain_community/tools/semanticscholar/tool.py b/libs/community/langchain_community/tools/semanticscholar/tool.py
index ce53fa4bab525..b5999eb22d7da 100644
--- a/libs/community/langchain_community/tools/semanticscholar/tool.py
+++ b/libs/community/langchain_community/tools/semanticscholar/tool.py
@@ -15,7 +15,7 @@ class SemantscholarInput(BaseModel):
     query: str = Field(description="search query to look up")
 
 
-class SemanticScholarQueryRun(BaseTool):
+class SemanticScholarQueryRun(BaseTool):  # type: ignore[override, override]
     """Tool that searches the semanticscholar API."""
 
     name: str = "semanticscholar"
diff --git a/libs/community/langchain_community/tools/shell/tool.py b/libs/community/langchain_community/tools/shell/tool.py
index c4ff4d1b60596..75481536df367 100644
--- a/libs/community/langchain_community/tools/shell/tool.py
+++ b/libs/community/langchain_community/tools/shell/tool.py
@@ -57,7 +57,7 @@ def _get_platform() -> str:
     return system
 
 
-class ShellTool(BaseTool):
+class ShellTool(BaseTool):  # type: ignore[override, override]
     """Tool to run shell commands."""
 
     process: Any = Field(default_factory=_get_default_bash_process)
diff --git a/libs/community/langchain_community/tools/slack/base.py b/libs/community/langchain_community/tools/slack/base.py
index 6579e6ce44e30..23a3fa6c99f94 100644
--- a/libs/community/langchain_community/tools/slack/base.py
+++ b/libs/community/langchain_community/tools/slack/base.py
@@ -13,7 +13,7 @@
     from slack_sdk import WebClient
 
 
-class SlackBaseTool(BaseTool):
+class SlackBaseTool(BaseTool):  # type: ignore[override]
     """Base class for Slack tools."""
 
     client: WebClient = Field(default_factory=login)
diff --git a/libs/community/langchain_community/tools/slack/get_channel.py b/libs/community/langchain_community/tools/slack/get_channel.py
index 4cee16a9350b3..60419b6676856 100644
--- a/libs/community/langchain_community/tools/slack/get_channel.py
+++ b/libs/community/langchain_community/tools/slack/get_channel.py
@@ -7,7 +7,7 @@
 from langchain_community.tools.slack.base import SlackBaseTool
 
 
-class SlackGetChannel(SlackBaseTool):
+class SlackGetChannel(SlackBaseTool):  # type: ignore[override]
     """Tool that gets Slack channel information."""
 
     name: str = "get_channelid_name_dict"
diff --git a/libs/community/langchain_community/tools/slack/get_message.py b/libs/community/langchain_community/tools/slack/get_message.py
index 733f16979e428..b756f3124c3ca 100644
--- a/libs/community/langchain_community/tools/slack/get_message.py
+++ b/libs/community/langchain_community/tools/slack/get_message.py
@@ -17,7 +17,7 @@ class SlackGetMessageSchema(BaseModel):
     )
 
 
-class SlackGetMessage(SlackBaseTool):
+class SlackGetMessage(SlackBaseTool):  # type: ignore[override, override]
     """Tool that gets Slack messages."""
 
     name: str = "get_messages"
diff --git a/libs/community/langchain_community/tools/slack/schedule_message.py b/libs/community/langchain_community/tools/slack/schedule_message.py
index c4a561f5aae17..2e4e2c060e192 100644
--- a/libs/community/langchain_community/tools/slack/schedule_message.py
+++ b/libs/community/langchain_community/tools/slack/schedule_message.py
@@ -33,7 +33,7 @@ class ScheduleMessageSchema(BaseModel):
     )
 
 
-class SlackScheduleMessage(SlackBaseTool):
+class SlackScheduleMessage(SlackBaseTool):  # type: ignore[override, override]
     """Tool for scheduling a message in Slack."""
 
     name: str = "schedule_message"
diff --git a/libs/community/langchain_community/tools/slack/send_message.py b/libs/community/langchain_community/tools/slack/send_message.py
index 87223830d431a..d56de486047e8 100644
--- a/libs/community/langchain_community/tools/slack/send_message.py
+++ b/libs/community/langchain_community/tools/slack/send_message.py
@@ -19,7 +19,7 @@ class SendMessageSchema(BaseModel):
     )
 
 
-class SlackSendMessage(SlackBaseTool):
+class SlackSendMessage(SlackBaseTool):  # type: ignore[override, override]
     """Tool for sending a message in Slack."""
 
     name: str = "send_message"
diff --git a/libs/community/langchain_community/tools/sleep/tool.py b/libs/community/langchain_community/tools/sleep/tool.py
index e39a272a79f79..f3c7060a522df 100644
--- a/libs/community/langchain_community/tools/sleep/tool.py
+++ b/libs/community/langchain_community/tools/sleep/tool.py
@@ -18,7 +18,7 @@ class SleepInput(BaseModel):
     sleep_time: int = Field(..., description="Time to sleep in seconds")
 
 
-class SleepTool(BaseTool):
+class SleepTool(BaseTool):  # type: ignore[override]
     """Tool that adds the capability to sleep."""
 
     name: str = "sleep"
diff --git a/libs/community/langchain_community/tools/spark_sql/tool.py b/libs/community/langchain_community/tools/spark_sql/tool.py
index 91bbc87f29549..421e507cd2e47 100644
--- a/libs/community/langchain_community/tools/spark_sql/tool.py
+++ b/libs/community/langchain_community/tools/spark_sql/tool.py
@@ -26,7 +26,7 @@ class BaseSparkSQLTool(BaseModel):
     )
 
 
-class QuerySparkSQLTool(BaseSparkSQLTool, BaseTool):
+class QuerySparkSQLTool(BaseSparkSQLTool, BaseTool):  # type: ignore[override]
     """Tool for querying a Spark SQL."""
 
     name: str = "query_sql_db"
@@ -45,7 +45,7 @@ def _run(
         return self.db.run_no_throw(query)
 
 
-class InfoSparkSQLTool(BaseSparkSQLTool, BaseTool):
+class InfoSparkSQLTool(BaseSparkSQLTool, BaseTool):  # type: ignore[override]
     """Tool for getting metadata about a Spark SQL."""
 
     name: str = "schema_sql_db"
@@ -65,7 +65,7 @@ def _run(
         return self.db.get_table_info_no_throw(table_names.split(", "))
 
 
-class ListSparkSQLTool(BaseSparkSQLTool, BaseTool):
+class ListSparkSQLTool(BaseSparkSQLTool, BaseTool):  # type: ignore[override]
     """Tool for getting tables names."""
 
     name: str = "list_tables_sql_db"
@@ -80,7 +80,7 @@ def _run(
         return ", ".join(self.db.get_usable_table_names())
 
 
-class QueryCheckerTool(BaseSparkSQLTool, BaseTool):
+class QueryCheckerTool(BaseSparkSQLTool, BaseTool):  # type: ignore[override]
     """Use an LLM to check if a query is correct.
     Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/"""
 
diff --git a/libs/community/langchain_community/tools/sql_database/tool.py b/libs/community/langchain_community/tools/sql_database/tool.py
index 1bc119860d6ed..42cee9e7ed8af 100644
--- a/libs/community/langchain_community/tools/sql_database/tool.py
+++ b/libs/community/langchain_community/tools/sql_database/tool.py
@@ -32,7 +32,7 @@ class _QuerySQLDataBaseToolInput(BaseModel):
     query: str = Field(..., description="A detailed and correct SQL query.")
 
 
-class QuerySQLDataBaseTool(BaseSQLDatabaseTool, BaseTool):
+class QuerySQLDataBaseTool(BaseSQLDatabaseTool, BaseTool):  # type: ignore[override, override]
     """Tool for querying a SQL database."""
 
     name: str = "sql_db_query"
@@ -62,7 +62,7 @@ class _InfoSQLDatabaseToolInput(BaseModel):
     )
 
 
-class InfoSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
+class InfoSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):  # type: ignore[override, override]
     """Tool for getting metadata about a SQL database."""
 
     name: str = "sql_db_schema"
@@ -84,7 +84,7 @@ class _ListSQLDataBaseToolInput(BaseModel):
     tool_input: str = Field("", description="An empty string")
 
 
-class ListSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
+class ListSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):  # type: ignore[override, override]
     """Tool for getting tables names."""
 
     name: str = "sql_db_list_tables"
@@ -104,7 +104,7 @@ class _QuerySQLCheckerToolInput(BaseModel):
     query: str = Field(..., description="A detailed and SQL query to be checked.")
 
 
-class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool):
+class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool):  # type: ignore[override, override]
     """Use an LLM to check if a query is correct.
     Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/"""
 
diff --git a/libs/community/langchain_community/tools/stackexchange/tool.py b/libs/community/langchain_community/tools/stackexchange/tool.py
index 2060eada6e7e0..fe4a48bd1e26f 100644
--- a/libs/community/langchain_community/tools/stackexchange/tool.py
+++ b/libs/community/langchain_community/tools/stackexchange/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.stackexchange import StackExchangeAPIWrapper
 
 
-class StackExchangeTool(BaseTool):
+class StackExchangeTool(BaseTool):  # type: ignore[override]
     """Tool that uses StackExchange"""
 
     name: str = "stack_exchange"
diff --git a/libs/community/langchain_community/tools/steam/tool.py b/libs/community/langchain_community/tools/steam/tool.py
index 3e71dddc0b4ea..f03f0c26fb9e0 100644
--- a/libs/community/langchain_community/tools/steam/tool.py
+++ b/libs/community/langchain_community/tools/steam/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.steam import SteamWebAPIWrapper
 
 
-class SteamWebAPIQueryRun(BaseTool):
+class SteamWebAPIQueryRun(BaseTool):  # type: ignore[override]
     """Tool that searches the Steam Web API."""
 
     mode: str
diff --git a/libs/community/langchain_community/tools/steamship_image_generation/tool.py b/libs/community/langchain_community/tools/steamship_image_generation/tool.py
index ea1c4b6860df0..ce219236d52c9 100644
--- a/libs/community/langchain_community/tools/steamship_image_generation/tool.py
+++ b/libs/community/langchain_community/tools/steamship_image_generation/tool.py
@@ -41,7 +41,7 @@ class ModelName(str, Enum):
 }
 
 
-class SteamshipImageGenerationTool(BaseTool):
+class SteamshipImageGenerationTool(BaseTool):  # type: ignore[override]
     """Tool used to generate images from a text-prompt."""
 
     model_name: ModelName
diff --git a/libs/community/langchain_community/tools/tavily_search/tool.py b/libs/community/langchain_community/tools/tavily_search/tool.py
index 53164d9340698..1527458f30a5f 100644
--- a/libs/community/langchain_community/tools/tavily_search/tool.py
+++ b/libs/community/langchain_community/tools/tavily_search/tool.py
@@ -18,7 +18,7 @@ class TavilyInput(BaseModel):
     query: str = Field(description="search query to look up")
 
 
-class TavilySearchResults(BaseTool):
+class TavilySearchResults(BaseTool):  # type: ignore[override, override]
     """Tool that queries the Tavily Search API and gets back json.
 
     Setup:
@@ -190,7 +190,7 @@ async def _arun(
         return self.api_wrapper.clean_results(raw_results["results"]), raw_results
 
 
-class TavilyAnswer(BaseTool):
+class TavilyAnswer(BaseTool):  # type: ignore[override, override]
     """Tool that queries the Tavily Search API and gets back an answer."""
 
     name: str = "tavily_answer"
diff --git a/libs/community/langchain_community/tools/vectorstore/tool.py b/libs/community/langchain_community/tools/vectorstore/tool.py
index e2479929df6ba..269d00ac48640 100644
--- a/libs/community/langchain_community/tools/vectorstore/tool.py
+++ b/libs/community/langchain_community/tools/vectorstore/tool.py
@@ -31,7 +31,7 @@ def _create_description_from_template(values: Dict[str, Any]) -> Dict[str, Any]:
     return values
 
 
-class VectorStoreQATool(BaseVectorStoreTool, BaseTool):
+class VectorStoreQATool(BaseVectorStoreTool, BaseTool):  # type: ignore[override]
     """Tool for the VectorDBQA chain. To be initialized with name and chain."""
 
     @staticmethod
@@ -79,7 +79,7 @@ async def _arun(
         )[chain.output_key]
 
 
-class VectorStoreQAWithSourcesTool(BaseVectorStoreTool, BaseTool):
+class VectorStoreQAWithSourcesTool(BaseVectorStoreTool, BaseTool):  # type: ignore[override]
     """Tool for the VectorDBQAWithSources chain."""
 
     @staticmethod
diff --git a/libs/community/langchain_community/tools/wikidata/tool.py b/libs/community/langchain_community/tools/wikidata/tool.py
index c34096cf0199f..eb2ad9a06f126 100644
--- a/libs/community/langchain_community/tools/wikidata/tool.py
+++ b/libs/community/langchain_community/tools/wikidata/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.wikidata import WikidataAPIWrapper
 
 
-class WikidataQueryRun(BaseTool):
+class WikidataQueryRun(BaseTool):  # type: ignore[override]
     """Tool that searches the Wikidata API."""
 
     name: str = "Wikidata"
diff --git a/libs/community/langchain_community/tools/wikipedia/tool.py b/libs/community/langchain_community/tools/wikipedia/tool.py
index 127117f89d642..5d4f6e3540e25 100644
--- a/libs/community/langchain_community/tools/wikipedia/tool.py
+++ b/libs/community/langchain_community/tools/wikipedia/tool.py
@@ -15,7 +15,7 @@ class WikipediaQueryInput(BaseModel):
     query: str = Field(description="query to look up on wikipedia")
 
 
-class WikipediaQueryRun(BaseTool):
+class WikipediaQueryRun(BaseTool):  # type: ignore[override, override]
     """Tool that searches the Wikipedia API."""
 
     name: str = "wikipedia"
diff --git a/libs/community/langchain_community/tools/wolfram_alpha/tool.py b/libs/community/langchain_community/tools/wolfram_alpha/tool.py
index e4364e669a066..cc5c8cf77d8fc 100644
--- a/libs/community/langchain_community/tools/wolfram_alpha/tool.py
+++ b/libs/community/langchain_community/tools/wolfram_alpha/tool.py
@@ -8,7 +8,7 @@
 from langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper
 
 
-class WolframAlphaQueryRun(BaseTool):
+class WolframAlphaQueryRun(BaseTool):  # type: ignore[override]
     """Tool that queries using the Wolfram Alpha SDK."""
 
     name: str = "wolfram_alpha"
diff --git a/libs/community/langchain_community/tools/yahoo_finance_news.py b/libs/community/langchain_community/tools/yahoo_finance_news.py
index 7c844f0c59052..7a4f7d7f7660a 100644
--- a/libs/community/langchain_community/tools/yahoo_finance_news.py
+++ b/libs/community/langchain_community/tools/yahoo_finance_news.py
@@ -16,7 +16,7 @@ class YahooFinanceNewsInput(BaseModel):
     query: str = Field(description="company ticker query to look up")
 
 
-class YahooFinanceNewsTool(BaseTool):
+class YahooFinanceNewsTool(BaseTool):  # type: ignore[override, override]
     """Tool that searches financial news on Yahoo Finance."""
 
     name: str = "yahoo_finance_news"
diff --git a/libs/community/langchain_community/tools/you/tool.py b/libs/community/langchain_community/tools/you/tool.py
index 59990e71ff545..923ad41ebf36c 100644
--- a/libs/community/langchain_community/tools/you/tool.py
+++ b/libs/community/langchain_community/tools/you/tool.py
@@ -17,7 +17,7 @@ class YouInput(BaseModel):
     query: str = Field(description="should be a search query")
 
 
-class YouSearchTool(BaseTool):
+class YouSearchTool(BaseTool):  # type: ignore[override, override]
     """Tool that searches the you.com API."""
 
     name: str = "you_search"
diff --git a/libs/community/langchain_community/tools/youtube/search.py b/libs/community/langchain_community/tools/youtube/search.py
index 497fa542adf7b..cc68055b1b944 100644
--- a/libs/community/langchain_community/tools/youtube/search.py
+++ b/libs/community/langchain_community/tools/youtube/search.py
@@ -16,7 +16,7 @@
 from langchain_core.tools import BaseTool
 
 
-class YouTubeSearchTool(BaseTool):
+class YouTubeSearchTool(BaseTool):  # type: ignore[override]
     """Tool that queries YouTube."""
 
     name: str = "youtube_search"
diff --git a/libs/community/langchain_community/tools/zapier/tool.py b/libs/community/langchain_community/tools/zapier/tool.py
index 44d7fb04c0af7..cc7f0f4689094 100644
--- a/libs/community/langchain_community/tools/zapier/tool.py
+++ b/libs/community/langchain_community/tools/zapier/tool.py
@@ -83,7 +83,7 @@
 from langchain_community.utilities.zapier import ZapierNLAWrapper
 
 
-class ZapierNLARunAction(BaseTool):
+class ZapierNLARunAction(BaseTool):  # type: ignore[override]
     """Tool to run a specific action from the user's exposed actions.
 
     Params:
@@ -169,7 +169,7 @@ async def _arun(
 # other useful actions
 
 
-class ZapierNLAListActions(BaseTool):
+class ZapierNLAListActions(BaseTool):  # type: ignore[override]
     """Tool to list all exposed actions for the user."""
 
     name: str = "ZapierNLA_list_actions"
diff --git a/libs/community/langchain_community/tools/zenguard/tool.py b/libs/community/langchain_community/tools/zenguard/tool.py
index f577b07984708..e54590079bd91 100644
--- a/libs/community/langchain_community/tools/zenguard/tool.py
+++ b/libs/community/langchain_community/tools/zenguard/tool.py
@@ -44,7 +44,7 @@ class ZenGuardInput(BaseModel):
     )
 
 
-class ZenGuardTool(BaseTool):
+class ZenGuardTool(BaseTool):  # type: ignore[override, override]
     name: str = "ZenGuard"
     description: str = (
         "ZenGuard AI integration package. ZenGuard AI - the fastest GenAI guardrails."
diff --git a/libs/community/langchain_community/utilities/dalle_image_generator.py b/libs/community/langchain_community/utilities/dalle_image_generator.py
index fea18c3958da5..ed34231b2bfe3 100644
--- a/libs/community/langchain_community/utilities/dalle_image_generator.py
+++ b/libs/community/langchain_community/utilities/dalle_image_generator.py
@@ -129,11 +129,11 @@ def validate_environment(self) -> Self:
             }
 
             if not self.client:
-                self.client = openai.OpenAI(**client_params).images
+                self.client = openai.OpenAI(**client_params).images  # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
             if not self.async_client:
-                self.async_client = openai.AsyncOpenAI(**client_params).images
+                self.async_client = openai.AsyncOpenAI(**client_params).images  # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
         elif not self.client:
-            self.client = openai.Image
+            self.client = openai.Image  # type: ignore[attr-defined]
         else:
             pass
         return self
diff --git a/libs/community/langchain_community/utilities/duckduckgo_search.py b/libs/community/langchain_community/utilities/duckduckgo_search.py
index d8017c28ae661..ede5c12298710 100644
--- a/libs/community/langchain_community/utilities/duckduckgo_search.py
+++ b/libs/community/langchain_community/utilities/duckduckgo_search.py
@@ -63,7 +63,7 @@ def _ddgs_text(
         with DDGS() as ddgs:
             ddgs_gen = ddgs.text(
                 query,
-                region=self.region,
+                region=self.region,  # type: ignore[arg-type]
                 safesearch=self.safesearch,
                 timelimit=self.time,
                 max_results=max_results or self.max_results,
@@ -82,7 +82,7 @@ def _ddgs_news(
         with DDGS() as ddgs:
             ddgs_gen = ddgs.news(
                 query,
-                region=self.region,
+                region=self.region,  # type: ignore[arg-type]
                 safesearch=self.safesearch,
                 timelimit=self.time,
                 max_results=max_results or self.max_results,
diff --git a/libs/community/langchain_community/utilities/nvidia_riva.py b/libs/community/langchain_community/utilities/nvidia_riva.py
index 40a788a85f70e..74afba23d2d54 100644
--- a/libs/community/langchain_community/utilities/nvidia_riva.py
+++ b/libs/community/langchain_community/utilities/nvidia_riva.py
@@ -401,7 +401,7 @@ def worker() -> None:
 ASROutputType = str
 
 
-class RivaASR(
+class RivaASR(  # type: ignore[override]
     RivaAuthMixin,
     RivaCommonConfigMixin,
     RunnableSerializable[ASRInputType, ASROutputType],
@@ -509,7 +509,7 @@ def invoke(
 TTSOutputType = bytes
 
 
-class RivaTTS(
+class RivaTTS(  # type: ignore[override]
     RivaAuthMixin,
     RivaCommonConfigMixin,
     RunnableSerializable[TTSInputType, TTSOutputType],
diff --git a/libs/community/langchain_community/vectorstores/chroma.py b/libs/community/langchain_community/vectorstores/chroma.py
index 248dff00d53e5..40ba2415b6933 100644
--- a/libs/community/langchain_community/vectorstores/chroma.py
+++ b/libs/community/langchain_community/vectorstores/chroma.py
@@ -75,7 +75,7 @@ def __init__(
         persist_directory: Optional[str] = None,
         client_settings: Optional[chromadb.config.Settings] = None,
         collection_metadata: Optional[Dict] = None,
-        client: Optional[chromadb.Client] = None,
+        client: Optional[chromadb.Client] = None,  # type: ignore[valid-type]
         relevance_score_fn: Optional[Callable[[float], float]] = None,
     ) -> None:
         """Initialize with a Chroma client."""
@@ -118,14 +118,14 @@ def __init__(
                 _client_settings.persist_directory = persist_directory
             else:
                 _client_settings = chromadb.config.Settings()
-            self._client_settings = _client_settings
-            self._client = chromadb.Client(_client_settings)
-            self._persist_directory = (
+            self._client_settings = _client_settings  # type: ignore[has-type]
+            self._client = chromadb.Client(_client_settings)  # type: ignore[has-type]
+            self._persist_directory = (  # type: ignore[has-type]
                 _client_settings.persist_directory or persist_directory
             )
 
         self._embedding_function = embedding_function
-        self._collection = self._client.get_or_create_collection(
+        self._collection = self._client.get_or_create_collection(  # type: ignore[has-type]
             name=collection_name,
             embedding_function=None,
             metadata=collection_metadata,
@@ -154,12 +154,12 @@ def __query_collection(
                 "Could not import chromadb python package. "
                 "Please install it with `pip install chromadb`."
             )
-        return self._collection.query(
+        return self._collection.query(  # type: ignore[return-value]
             query_texts=query_texts,
-            query_embeddings=query_embeddings,
+            query_embeddings=query_embeddings,  # type: ignore[arg-type]
             n_results=n_results,
-            where=where,
-            where_document=where_document,
+            where=where,  # type: ignore[arg-type]
+            where_document=where_document,  # type: ignore[arg-type]
             **kwargs,
         )
 
@@ -218,7 +218,7 @@ def add_images(
                 ids_with_metadata = [ids[idx] for idx in non_empty_ids]
                 try:
                     self._collection.upsert(
-                        metadatas=metadatas,
+                        metadatas=metadatas,  # type: ignore[arg-type]
                         embeddings=embeddings_with_metadatas,
                         documents=images_with_metadatas,
                         ids=ids_with_metadata,
@@ -297,8 +297,8 @@ def add_texts(
                 ids_with_metadata = [ids[idx] for idx in non_empty_ids]
                 try:
                     self._collection.upsert(
-                        metadatas=metadatas,
-                        embeddings=embeddings_with_metadatas,
+                        metadatas=metadatas,  # type: ignore[arg-type]
+                        embeddings=embeddings_with_metadatas,  # type: ignore[arg-type]
                         documents=texts_with_metadatas,
                         ids=ids_with_metadata,
                     )
@@ -318,13 +318,13 @@ def add_texts(
                 )
                 ids_without_metadatas = [ids[j] for j in empty_ids]
                 self._collection.upsert(
-                    embeddings=embeddings_without_metadatas,
+                    embeddings=embeddings_without_metadatas,  # type: ignore[arg-type]
                     documents=texts_without_metadatas,
                     ids=ids_without_metadatas,
                 )
         else:
             self._collection.upsert(
-                embeddings=embeddings,
+                embeddings=embeddings,  # type: ignore[arg-type]
                 documents=texts,
                 ids=ids,
             )
@@ -659,7 +659,7 @@ def max_marginal_relevance_search(
 
     def delete_collection(self) -> None:
         """Delete the collection."""
-        self._client.delete_collection(self._collection.name)
+        self._client.delete_collection(self._collection.name)  # type: ignore[has-type]
 
     def get(
         self,
@@ -697,7 +697,7 @@ def get(
         if include is not None:
             kwargs["include"] = include
 
-        return self._collection.get(**kwargs)
+        return self._collection.get(**kwargs)  # type: ignore[return-value, arg-type, arg-type, arg-type, arg-type, arg-type]
 
     @deprecated(
         since="0.1.17",
@@ -716,7 +716,7 @@ def persist(self) -> None:
         Since Chroma 0.4.x the manual persistence method is no longer
         supported as docs are automatically persisted.
         """
-        if self._persist_directory is None:
+        if self._persist_directory is None:  # type: ignore[has-type]
             raise ValueError(
                 "You must specify a persist_directory on"
                 "creation to persist the collection."
@@ -726,7 +726,7 @@ def persist(self) -> None:
         # Maintain backwards compatibility with chromadb < 0.4.0
         major, minor, _ = chromadb.__version__.split(".")
         if int(major) == 0 and int(minor) < 4:
-            self._client.persist()
+            self._client.persist()  # type: ignore[has-type]
 
     def update_document(self, document_id: str, document: Document) -> None:
         """Update a document in the collection.
@@ -760,9 +760,9 @@ def update_documents(self, ids: List[str], documents: List[Document]) -> None:
             for batch in create_batches(
                 api=self._collection._client,
                 ids=ids,
-                metadatas=metadata,
+                metadatas=metadata,  # type: ignore[arg-type]
                 documents=text,
-                embeddings=embeddings,
+                embeddings=embeddings,  # type: ignore[arg-type]
             ):
                 self._collection.update(
                     ids=batch[0],
@@ -773,9 +773,9 @@ def update_documents(self, ids: List[str], documents: List[Document]) -> None:
         else:
             self._collection.update(
                 ids=ids,
-                embeddings=embeddings,
+                embeddings=embeddings,  # type: ignore[arg-type]
                 documents=text,
-                metadatas=metadata,
+                metadatas=metadata,  # type: ignore[arg-type]
             )
 
     @classmethod
@@ -788,7 +788,7 @@ def from_texts(
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
         persist_directory: Optional[str] = None,
         client_settings: Optional[chromadb.config.Settings] = None,
-        client: Optional[chromadb.Client] = None,
+        client: Optional[chromadb.Client] = None,  # type: ignore[valid-type]
         collection_metadata: Optional[Dict] = None,
         **kwargs: Any,
     ) -> Chroma:
@@ -823,19 +823,20 @@ def from_texts(
         if ids is None:
             ids = [str(uuid.uuid4()) for _ in texts]
         if hasattr(
-            chroma_collection._client, "max_batch_size"
+            chroma_collection._client,  # type: ignore[has-type]
+            "max_batch_size",  # type: ignore[has-type]
         ):  # for Chroma 0.4.10 and above
             from chromadb.utils.batch_utils import create_batches
 
             for batch in create_batches(
-                api=chroma_collection._client,
+                api=chroma_collection._client,  # type: ignore[has-type]
                 ids=ids,
-                metadatas=metadatas,
+                metadatas=metadatas,  # type: ignore[arg-type]
                 documents=texts,
             ):
                 chroma_collection.add_texts(
                     texts=batch[3] if batch[3] else [],
-                    metadatas=batch[2] if batch[2] else None,
+                    metadatas=batch[2] if batch[2] else None,  # type: ignore[arg-type]
                     ids=batch[0],
                 )
         else:
@@ -851,7 +852,9 @@ def from_documents(
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
         persist_directory: Optional[str] = None,
         client_settings: Optional[chromadb.config.Settings] = None,
-        client: Optional[chromadb.Client] = None,  # Add this line
+        client: Optional[  # type: ignore[valid-type]
+            chromadb.Client
+        ] = None,  # Add this line  # type: ignore[valid-type]
         collection_metadata: Optional[Dict] = None,
         **kwargs: Any,
     ) -> Chroma:
diff --git a/libs/community/langchain_community/vectorstores/llm_rails.py b/libs/community/langchain_community/vectorstores/llm_rails.py
index 16277161280a0..2238d15fab086 100644
--- a/libs/community/langchain_community/vectorstores/llm_rails.py
+++ b/libs/community/langchain_community/vectorstores/llm_rails.py
@@ -226,7 +226,7 @@ def as_retriever(self, **kwargs: Any) -> LLMRailsRetriever:
         return LLMRailsRetriever(vectorstore=self, **kwargs)
 
 
-class LLMRailsRetriever(VectorStoreRetriever):
+class LLMRailsRetriever(VectorStoreRetriever):  # type: ignore[override]
     """Retriever for LLMRails."""
 
     vectorstore: LLMRails
diff --git a/libs/community/langchain_community/vectorstores/matching_engine.py b/libs/community/langchain_community/vectorstores/matching_engine.py
index 8b55d6262677a..86f2ea27e051e 100644
--- a/libs/community/langchain_community/vectorstores/matching_engine.py
+++ b/libs/community/langchain_community/vectorstores/matching_engine.py
@@ -284,7 +284,7 @@ def similarity_search_by_vector_with_score(
                 page_content=page_content,
                 metadata=metadata,
             )
-            docs.append((document, result.distance))
+            docs.append((document, result.distance))  # type: ignore[arg-type]
 
         logger.debug("Downloaded documents for query.")
 
@@ -426,13 +426,16 @@ def from_components(
         """
         gcs_bucket_name = cls._validate_gcs_bucket(gcs_bucket_name)
         credentials = cls._create_credentials_from_file(credentials_path)
-        index = cls._create_index_by_id(index_id, project_id, region, credentials)
+        index = cls._create_index_by_id(index_id, project_id, region, credentials)  # type: ignore[arg-type]
         endpoint = cls._create_endpoint_by_id(
-            endpoint_id, project_id, region, credentials
+            endpoint_id,
+            project_id,
+            region,
+            credentials,  # type: ignore[arg-type]
         )
 
-        gcs_client = cls._get_gcs_client(credentials, project_id)
-        cls._init_aiplatform(project_id, region, gcs_bucket_name, credentials)
+        gcs_client = cls._get_gcs_client(credentials, project_id)  # type: ignore[arg-type]
+        cls._init_aiplatform(project_id, region, gcs_bucket_name, credentials)  # type: ignore[arg-type]
 
         return cls(
             project_id=project_id,
diff --git a/libs/community/langchain_community/vectorstores/neo4j_vector.py b/libs/community/langchain_community/vectorstores/neo4j_vector.py
index bb0be02afdd74..a17444c6907a2 100644
--- a/libs/community/langchain_community/vectorstores/neo4j_vector.py
+++ b/libs/community/langchain_community/vectorstores/neo4j_vector.py
@@ -634,20 +634,20 @@ def query(
                         or e.code
                         == "Neo.DatabaseError.Transaction.TransactionStartFailed"
                     )
-                    and "in an implicit transaction" in e.message
+                    and "in an implicit transaction" in e.message  # type: ignore[operator]
                 )
                 or (  # isPeriodicCommitError
                     e.code == "Neo.ClientError.Statement.SemanticError"
                     and (
-                        "in an open transaction is not possible" in e.message
-                        or "tried to execute in an explicit transaction" in e.message
+                        "in an open transaction is not possible" in e.message  # type: ignore[operator]
+                        or "tried to execute in an explicit transaction" in e.message  # type: ignore[operator]
                     )
                 )
             ):
                 raise
         # Fallback to allow implicit transactions
         with self._driver.session(database=self._database) as session:
-            data = session.run(Query(text=query), params)
+            data = session.run(Query(text=query), params)  # type: ignore[assignment]
             return [r.data() for r in data]
 
     def verify_version(self) -> None:
diff --git a/libs/community/langchain_community/vectorstores/redis/base.py b/libs/community/langchain_community/vectorstores/redis/base.py
index 5a50b8dd310b2..e88938ce2c650 100644
--- a/libs/community/langchain_community/vectorstores/redis/base.py
+++ b/libs/community/langchain_community/vectorstores/redis/base.py
@@ -1425,7 +1425,7 @@ def raise_error(key: str, value: Any) -> None:
     return clean_meta
 
 
-class RedisVectorStoreRetriever(VectorStoreRetriever):
+class RedisVectorStoreRetriever(VectorStoreRetriever):  # type: ignore[override]
     """Retriever for Redis VectorStore."""
 
     vectorstore: Redis
diff --git a/libs/community/langchain_community/vectorstores/redis/schema.py b/libs/community/langchain_community/vectorstores/redis/schema.py
index 2cf1ee2d927cd..413951e1fffb4 100644
--- a/libs/community/langchain_community/vectorstores/redis/schema.py
+++ b/libs/community/langchain_community/vectorstores/redis/schema.py
@@ -124,7 +124,7 @@ def _fields(self) -> Dict[str, Any]:
         return field_data
 
 
-class FlatVectorField(RedisVectorField):
+class FlatVectorField(RedisVectorField):  # type: ignore[override]
     """Schema for flat vector fields in Redis."""
 
     algorithm: Literal["FLAT"] = "FLAT"
@@ -139,7 +139,7 @@ def as_field(self) -> VectorField:
         return VectorField(self.name, self.algorithm, field_data)
 
 
-class HNSWVectorField(RedisVectorField):
+class HNSWVectorField(RedisVectorField):  # type: ignore[override]
     """Schema for HNSW vector fields in Redis."""
 
     algorithm: Literal["HNSW"] = "HNSW"
diff --git a/libs/community/langchain_community/vectorstores/vdms.py b/libs/community/langchain_community/vectorstores/vdms.py
index 8f1a2422bd0c9..ed50d01462623 100644
--- a/libs/community/langchain_community/vectorstores/vdms.py
+++ b/libs/community/langchain_community/vectorstores/vdms.py
@@ -108,7 +108,7 @@ def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
                     (Document(page_content=txt_contents, metadata=props), distance)
                 )
     except Exception as e:
-        logger.warn(f"No results returned. Error while parsing results: {e}")
+        logger.warning(f"No results returned. Error while parsing results: {e}")
     return final_res
 
 
diff --git a/libs/community/langchain_community/vectorstores/vectara.py b/libs/community/langchain_community/vectorstores/vectara.py
index 8683e22e2d8cf..b71e7d12cff8b 100644
--- a/libs/community/langchain_community/vectorstores/vectara.py
+++ b/libs/community/langchain_community/vectorstores/vectara.py
@@ -723,7 +723,7 @@ def as_retriever(self, **kwargs: Any) -> VectaraRetriever:
         )
 
 
-class VectaraRetriever(VectorStoreRetriever):
+class VectaraRetriever(VectorStoreRetriever):  # type: ignore[override]
     """Vectara Retriever class."""
 
     vectorstore: Vectara
diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index 1072eafe6ec6e..79a7c2b80b5b3 100644
--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -13,102 +13,102 @@ files = [
 
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 [package.dependencies]
@@ -150,13 +150,13 @@ files = [
 
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 [package.extras]
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 trio = ["trio (>=0.26.1)"]
 
 [[package]]
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 [package.dependencies]
@@ -779,38 +779,38 @@ toml = ["tomli"]
 
 [[package]]
 name = "cryptography"
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 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
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 [package.dependencies]
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 ssh = ["bcrypt (>=3.1.5)"]
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 test-randomorder = ["pytest-randomly"]
 
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 [[package]]
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 [[package]]
@@ -1040,88 +1044,103 @@ python-dateutil = ">=2.7"
 
 [[package]]
 name = "frozenlist"
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 description = "A list-like structure which implements collections.abc.MutableSequence"
 optional = false
 python-versions = ">=3.8"
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 [[package]]
@@ -1901,13 +1920,13 @@ url = "../text-splitters"
 
 [[package]]
 name = "langsmith"
-version = "0.1.132"
+version = "0.1.137"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
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 [package.dependencies]
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 [[package]]
 name = "markupsafe"
-version = "3.0.1"
+version = "3.0.2"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.9"
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 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
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 [package.dependencies]
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+tests = ["pytest", "simplejson"]
 
 [[package]]
 name = "matplotlib-inline"
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 ]
 
 [package.dependencies]
@@ -2203,6 +2222,7 @@ typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
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 install-types = ["pip"]
 mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
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+    {file = "yarl-1.16.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:ab2b2ac232110a1fdb0d3ffcd087783edd3d4a6ced432a1bf75caf7b7be70916"},
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+    {file = "yarl-1.16.0-cp39-cp39-win32.whl", hash = "sha256:e9951afe6557c75a71045148890052cb942689ee4c9ec29f5436240e1fcc73b7"},
+    {file = "yarl-1.16.0-cp39-cp39-win_amd64.whl", hash = "sha256:7d7aaa8ff95d0840e289423e7dc35696c2b058d635f945bf05b5cd633146b027"},
+    {file = "yarl-1.16.0-py3-none-any.whl", hash = "sha256:e6980a558d8461230c457218bd6c92dfc1d10205548215c2c21d79dc8d0a96f3"},
+    {file = "yarl-1.16.0.tar.gz", hash = "sha256:b6f687ced5510a9a2474bbae96a4352e5ace5fa34dc44a217b0537fec1db00b4"},
 ]
 
 [package.dependencies]
@@ -4568,4 +4580,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "5c36c0453948190412f8ea627e6a10e02c56a1ea732ca215834dad3f1d785f4c"
+content-hash = "e491e87aa2f1cee1769e24a9fb4a8a8c412d53aa382ed1ba092bfcfd26a5a61c"
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index 8ecb5a591690b..092795bf06ab1 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -89,7 +89,7 @@ optional = true
 optional = true
 
 [tool.poetry.group.test.dependencies]
-pytest = "^7.3.0"
+pytest = "^7.4.4"
 pytest-cov = "^4.1.0"
 pytest-dotenv = "^0.5.2"
 duckdb-engine = "^0.11.0"
diff --git a/libs/community/tests/integration_tests/adapters/test_openai.py b/libs/community/tests/integration_tests/adapters/test_openai.py
index 3f8f6c01c8dff..644f3cc7ffef6 100644
--- a/libs/community/tests/integration_tests/adapters/test_openai.py
+++ b/libs/community/tests/integration_tests/adapters/test_openai.py
@@ -6,7 +6,7 @@
 def _test_no_stream(**kwargs: Any) -> None:
     import openai
 
-    result = openai.ChatCompletion.create(**kwargs)
+    result = openai.ChatCompletion.create(**kwargs)  # type: ignore[attr-defined]
     lc_result = lcopenai.ChatCompletion.create(**kwargs)
     if isinstance(lc_result, dict):
         if isinstance(result, dict):
@@ -20,7 +20,7 @@ def _test_stream(**kwargs: Any) -> None:
     import openai
 
     result = []
-    for c in openai.ChatCompletion.create(**kwargs):
+    for c in openai.ChatCompletion.create(**kwargs):  # type: ignore[attr-defined]
         result.append(c["choices"][0]["delta"].to_dict_recursive())
 
     lc_result = []
@@ -32,7 +32,7 @@ def _test_stream(**kwargs: Any) -> None:
 async def _test_async(**kwargs: Any) -> None:
     import openai
 
-    result = await openai.ChatCompletion.acreate(**kwargs)
+    result = await openai.ChatCompletion.acreate(**kwargs)  # type: ignore[attr-defined]
     lc_result = await lcopenai.ChatCompletion.acreate(**kwargs)
     if isinstance(lc_result, dict):
         if isinstance(result, dict):
@@ -46,7 +46,7 @@ async def _test_astream(**kwargs: Any) -> None:
     import openai
 
     result = []
-    async for c in await openai.ChatCompletion.acreate(**kwargs):
+    async for c in await openai.ChatCompletion.acreate(**kwargs):  # type: ignore[attr-defined]
         result.append(c["choices"][0]["delta"].to_dict_recursive())
 
     lc_result = []
diff --git a/libs/community/tests/integration_tests/chains/test_ontotext_graphdb_qa.py b/libs/community/tests/integration_tests/chains/test_ontotext_graphdb_qa.py
index 34955b6643195..85785f660b582 100644
--- a/libs/community/tests/integration_tests/chains/test_ontotext_graphdb_qa.py
+++ b/libs/community/tests/integration_tests/chains/test_ontotext_graphdb_qa.py
@@ -375,7 +375,7 @@ def test_chain(model_name: str, question: str) -> None:
         "FROM <https://swapi.co/ontology/> WHERE {?s ?p ?o}",
     )
     chain = OntotextGraphDBQAChain.from_llm(
-        ChatOpenAI(temperature=0, model_name=model_name),
+        ChatOpenAI(temperature=0, model_name=model_name),  # type: ignore[call-arg]
         graph=graph,
         verbose=True,  # type: ignore[call-arg]
     )
diff --git a/libs/community/tests/integration_tests/embeddings/test_azure_openai.py b/libs/community/tests/integration_tests/embeddings/test_azure_openai.py
index 7f895055ea23c..5f304551b2224 100644
--- a/libs/community/tests/integration_tests/embeddings/test_azure_openai.py
+++ b/libs/community/tests/integration_tests/embeddings/test_azure_openai.py
@@ -105,7 +105,7 @@ def test_azure_openai_embedding_with_empty_string() -> None:
     output = embedding.embed_documents(document)
     assert len(output) == 2
     assert len(output[0]) == 1536
-    expected_output = openai.Embedding.create(input="", model="text-embedding-ada-002")[
+    expected_output = openai.Embedding.create(input="", model="text-embedding-ada-002")[  # type: ignore[attr-defined]
         "data"
     ][0]["embedding"]
     assert np.allclose(output[0], expected_output)
diff --git a/libs/community/tests/integration_tests/embeddings/test_openai.py b/libs/community/tests/integration_tests/embeddings/test_openai.py
index ac6c0a6f8ba5c..739b12593f8a3 100644
--- a/libs/community/tests/integration_tests/embeddings/test_openai.py
+++ b/libs/community/tests/integration_tests/embeddings/test_openai.py
@@ -71,7 +71,7 @@ def test_openai_embedding_with_empty_string() -> None:
     output = embedding.embed_documents(document)
     assert len(output) == 2
     assert len(output[0]) == 1536
-    expected_output = openai.Embedding.create(input="", model="text-embedding-ada-002")[
+    expected_output = openai.Embedding.create(input="", model="text-embedding-ada-002")[  # type: ignore[attr-defined]
         "data"
     ][0]["embedding"]
     assert np.allclose(output[0], expected_output)
diff --git a/libs/community/tests/integration_tests/vectorstores/test_chroma.py b/libs/community/tests/integration_tests/vectorstores/test_chroma.py
index 6bc38cb960a8e..a41c983a16fde 100644
--- a/libs/community/tests/integration_tests/vectorstores/test_chroma.py
+++ b/libs/community/tests/integration_tests/vectorstores/test_chroma.py
@@ -206,7 +206,7 @@ def test_chroma_update_document() -> None:
         embedding=embedding,
         ids=[document_id],
     )
-    old_embedding = docsearch._collection.peek()["embeddings"][
+    old_embedding = docsearch._collection.peek()["embeddings"][  # type: ignore[index]
         docsearch._collection.peek()["ids"].index(document_id)
     ]
 
@@ -226,7 +226,7 @@ def test_chroma_update_document() -> None:
     assert output == [Document(page_content=updated_content, metadata={"page": "0"})]
 
     # Assert that the new embedding is correct
-    new_embedding = docsearch._collection.peek()["embeddings"][
+    new_embedding = docsearch._collection.peek()["embeddings"][  # type: ignore[index]
         docsearch._collection.peek()["ids"].index(document_id)
     ]
     assert new_embedding == embedding.embed_documents([updated_content])[0]
@@ -344,7 +344,7 @@ def test_chroma_large_batch() -> None:
         "my_collection",
         embedding_function=embedding_function.embed_documents,  # type: ignore
     )
-    docs = ["This is a test document"] * (client.max_batch_size + 100)
+    docs = ["This is a test document"] * (client.max_batch_size + 100)  # type: ignore[attr-defined]
     Chroma.from_texts(
         client=client,
         collection_name=col.name,
@@ -372,7 +372,7 @@ def test_chroma_large_batch_update() -> None:
         "my_collection",
         embedding_function=embedding_function.embed_documents,  # type: ignore
     )
-    docs = ["This is a test document"] * (client.max_batch_size + 100)
+    docs = ["This is a test document"] * (client.max_batch_size + 100)  # type: ignore[attr-defined]
     ids = [str(uuid.uuid4()) for _ in range(len(docs))]
     db = Chroma.from_texts(
         client=client,
diff --git a/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py b/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py
index b59ac2dee8f99..fe8e3ca3004bf 100644
--- a/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py
+++ b/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py
@@ -557,7 +557,7 @@ def test_singestoredb_add_image2() -> None:
     table_name = "test_singlestoredb_add_images"
     drop(table_name)
     docsearch = SingleStoreDB(
-        OpenCLIPEmbeddings(),
+        OpenCLIPEmbeddings(),  # type: ignore[call-arg, call-arg, call-arg]
         table_name=table_name,
         host=TEST_SINGLESTOREDB_URL,
     )
diff --git a/libs/core/langchain_core/vectorstores/utils.py b/libs/core/langchain_core/vectorstores/utils.py
index 6a8f6758f999a..add5e2ef4ab4f 100644
--- a/libs/core/langchain_core/vectorstores/utils.py
+++ b/libs/core/langchain_core/vectorstores/utils.py
@@ -53,7 +53,7 @@ def _cosine_similarity(x: Matrix, y: Matrix) -> np.ndarray:
         )
         raise ValueError(msg)
     try:
-        import simsimd as simd  # type: ignore[import-not-found]
+        import simsimd as simd  # type: ignore
 
         x = np.array(x, dtype=np.float32)
         y = np.array(y, dtype=np.float32)
diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock
index 6b960860db2cd..c8441c5735dcf 100644
--- a/libs/core/poetry.lock
+++ b/libs/core/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -580,17 +580,6 @@ files = [
 [package.dependencies]
 python-dateutil = ">=2.7"
 
-[[package]]
-name = "gprof2dot"
-version = "2024.6.6"
-description = "Generate a dot graph from the output of several profilers."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "gprof2dot-2024.6.6-py2.py3-none-any.whl", hash = "sha256:45b14ad7ce64e299c8f526881007b9eb2c6b75505d5613e96e66ee4d5ab33696"},
-    {file = "gprof2dot-2024.6.6.tar.gz", hash = "sha256:fa1420c60025a9eb7734f65225b4da02a10fc6dd741b37fa129bc6b41951e5ab"},
-]
-
 [[package]]
 name = "grandalf"
 version = "0.8"
@@ -1234,13 +1223,13 @@ url = "../text-splitters"
 
 [[package]]
 name = "langsmith"
-version = "0.1.135"
+version = "0.1.137"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.135-py3-none-any.whl", hash = "sha256:b1d1ca3bad483a4239745c57e9b9157b4d099fbf3149be21e3d112c94ede06ac"},
-    {file = "langsmith-0.1.135.tar.gz", hash = "sha256:7abed7e141386af99a2177f0b3600b124ae3ad1b482879ba0724ce92ef998a11"},
+    {file = "langsmith-0.1.137-py3-none-any.whl", hash = "sha256:4256d5c61133749890f7b5c88321dbb133ce0f440c621ea28e76513285859b81"},
+    {file = "langsmith-0.1.137.tar.gz", hash = "sha256:56cdfcc6c74cb20a3f437d5bd144feb5bf93f54c5a2918d1e568cbd084a372d4"},
 ]
 
 [package.dependencies]
@@ -1255,72 +1244,72 @@ requests-toolbelt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "markupsafe"
-version = "3.0.1"
+version = "3.0.2"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "MarkupSafe-3.0.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:db842712984e91707437461930e6011e60b39136c7331e971952bb30465bc1a1"},
-    {file = "MarkupSafe-3.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3ffb4a8e7d46ed96ae48805746755fadd0909fea2306f93d5d8233ba23dda12a"},
-    {file = "MarkupSafe-3.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:67c519635a4f64e495c50e3107d9b4075aec33634272b5db1cde839e07367589"},
-    {file = "MarkupSafe-3.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:48488d999ed50ba8d38c581d67e496f955821dc183883550a6fbc7f1aefdc170"},
-    {file = "MarkupSafe-3.0.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f31ae06f1328595d762c9a2bf29dafd8621c7d3adc130cbb46278079758779ca"},
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 [[package]]
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 [[package]]
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-version = "6.0.0"
+version = "6.1.0"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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 ]
 
 [package.extras]
-test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+dev = ["black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "wheel"]
+test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
 name = "ptyprocess"
@@ -1989,13 +1980,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "7.4.4"
+version = "8.3.3"
 description = "pytest: simple powerful testing with Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -2003,11 +1994,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+pluggy = ">=1.5,<2"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
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+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
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 name = "pytest-asyncio"
@@ -2044,25 +2035,6 @@ pytest = ">=6.2.5"
 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]
 
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-six = "*"
-
-[package.extras]
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-
 [[package]]
 name = "pytest-watcher"
 version = "0.3.5"
@@ -2132,17 +2104,17 @@ files = [
 
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-    {file = "simsimd-5.7.2.tar.gz", hash = "sha256:a1a0f2b025484044864b62ca6ad64afc1f9e5a8b020963d1db1d4a5a8f105616"},
-]
-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -2805,13 +2669,13 @@ typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
 [[package]]
 name = "tinycss2"
-version = "1.3.0"
+version = "1.4.0"
 description = "A tiny CSS parser"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tinycss2-1.3.0-py3-none-any.whl", hash = "sha256:54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7"},
-    {file = "tinycss2-1.3.0.tar.gz", hash = "sha256:152f9acabd296a8375fbca5b84c961ff95971fcfc32e79550c8df8e29118c54d"},
+    {file = "tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289"},
+    {file = "tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7"},
 ]
 
 [package.dependencies]
@@ -3098,4 +2962,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "553cec5042402574196ae21460a8f8915ddcd9c0190196bd9a666ef8c3a112a8"
+content-hash = "06bd735e73dd0eff94b3fbf2c429280a5908e01c9b7f0273865be39c02458874"
diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml
index 81604038c8b0b..0a840852b20b5 100644
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -89,7 +89,6 @@ mypy = ">=1.10,<1.11"
 types-pyyaml = "^6.0.12.2"
 types-requests = "^2.28.11.5"
 types-jinja2 = "^2.11.9"
-simsimd = "^5.0.0"
 
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"
@@ -97,14 +96,13 @@ setuptools = "^67.6.1"
 grandalf = "^0.8"
 
 [tool.poetry.group.test.dependencies]
-pytest = "^7.3.0"
+pytest = "^8"
 freezegun = "^1.2.2"
 pytest-mock = "^3.10.0"
 syrupy = "^4.0.2"
 pytest-watcher = "^0.3.4"
 pytest-asyncio = "^0.21.1"
 grandalf = "^0.8"
-pytest-profiling = "^1.7.0"
 responses = "^0.25.0"
 [[tool.poetry.group.test.dependencies.numpy]]
 version = "^1.24.0"
diff --git a/libs/core/tests/unit_tests/prompts/test_chat.py b/libs/core/tests/unit_tests/prompts/test_chat.py
index ec9d05c6de5fc..eee1ac498ec47 100644
--- a/libs/core/tests/unit_tests/prompts/test_chat.py
+++ b/libs/core/tests/unit_tests/prompts/test_chat.py
@@ -1,5 +1,6 @@
 import base64
 import tempfile
+import warnings
 from pathlib import Path
 from typing import Any, Union, cast
 
@@ -947,7 +948,8 @@ def test_chat_prompt_template_variable_names() -> None:
 
     Verify that no run time warnings are raised.
     """
-    with pytest.warns(None) as record:  # type: ignore
+    with warnings.catch_warnings(record=True) as record:
+        warnings.simplefilter("always")  # Cause all warnings to always be triggered
         prompt = ChatPromptTemplate([("system", "{schema}")])
         prompt.get_input_schema()
 
diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
index e74caea6312b5..c8312104b0d1d 100644
--- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
+++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
@@ -1725,12 +1725,6 @@
     RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakestreaminglist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])", 'name': 'FakeStreamingListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bear, dog, cat', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'bear, dog, cat'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')),
   ])
 # ---
-# name: test_prompt_with_llm_parser.2
-  list([
-    RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['tomato', 'lettuce', 'onion']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakestreaminglist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])", 'name': 'FakeStreamingListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'tomato, lettuce, onion', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'tomato, lettuce, onion'}, outputs={'output': ['tomato', 'lettuce', 'onion']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')),
-    RunTree(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your favorite color?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000005', trace_id=UUID('00000000-0000-4000-8000-000000000004')), RunTree(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakestreaminglist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])", 'name': 'FakeStreamingListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'bear, dog, cat', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000006', trace_id=UUID('00000000-0000-4000-8000-000000000004')), RunTree(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'bear, dog, cat'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000007', trace_id=UUID('00000000-0000-4000-8000-000000000004'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004', trace_id=UUID('00000000-0000-4000-8000-000000000004')),
-  ])
-# ---
 # name: test_router_runnable
   '''
   {
diff --git a/libs/core/tests/unit_tests/runnables/test_runnable.py b/libs/core/tests/unit_tests/runnables/test_runnable.py
index a58e81dc36275..7b03b53c21236 100644
--- a/libs/core/tests/unit_tests/runnables/test_runnable.py
+++ b/libs/core/tests/unit_tests/runnables/test_runnable.py
@@ -1307,7 +1307,7 @@ async def test_with_config(mocker: MockerFixture) -> None:
         5,
         7,
     ]
-    assert spy.call_args_list == [
+    assert sorted(spy.call_args_list) == [
         mocker.call(
             "hello",
             {
@@ -2161,7 +2161,13 @@ async def test_prompt_with_llm_parser(
         "tomato, lettuce, onion",
         "bear, dog, cat",
     ]
-    assert tracer.runs == snapshot
+    assert len(tracer.runs) == 2
+    assert all(
+        run.name == "RunnableSequence"
+        and run.run_type == "chain"
+        and len(run.child_runs) == 3
+        for run in tracer.runs
+    )
     mocker.stop(prompt_spy)
     mocker.stop(llm_spy)
     mocker.stop(parser_spy)
diff --git a/libs/core/tests/unit_tests/utils/test_pydantic.py b/libs/core/tests/unit_tests/utils/test_pydantic.py
index 01e5657866f72..fbe748a227dfc 100644
--- a/libs/core/tests/unit_tests/utils/test_pydantic.py
+++ b/libs/core/tests/unit_tests/utils/test_pydantic.py
@@ -1,5 +1,6 @@
 """Test for some custom pydantic decorators."""
 
+import warnings
 from typing import Any, Optional
 
 import pytest
@@ -202,27 +203,31 @@ class Foo(BaseModel):
 def test_create_model_v2() -> None:
     """Test that create model v2 works as expected."""
 
-    with pytest.warns(None) as record:  # type: ignore
+    with warnings.catch_warnings(record=True) as record:
+        warnings.simplefilter("always")  # Cause all warnings to always be triggered
         foo = create_model_v2("Foo", field_definitions={"a": (int, None)})
         foo.model_json_schema()
 
     assert list(record) == []
 
     # schema is used by pydantic, but OK to re-use
-    with pytest.warns(None) as record:  # type: ignore
+    with warnings.catch_warnings(record=True) as record:
+        warnings.simplefilter("always")  # Cause all warnings to always be triggered
         foo = create_model_v2("Foo", field_definitions={"schema": (int, None)})
         foo.model_json_schema()
 
     assert list(record) == []
 
     # From protected namespaces, but definitely OK to use.
-    with pytest.warns(None) as record:  # type: ignore
+    with warnings.catch_warnings(record=True) as record:
+        warnings.simplefilter("always")  # Cause all warnings to always be triggered
         foo = create_model_v2("Foo", field_definitions={"model_id": (int, None)})
         foo.model_json_schema()
 
     assert list(record) == []
 
-    with pytest.warns(None) as record:  # type: ignore
+    with warnings.catch_warnings(record=True) as record:
+        warnings.simplefilter("always")  # Cause all warnings to always be triggered
         # Verify that we can use non-English characters
         field_name = "もしもし"
         foo = create_model_v2("Foo", field_definitions={field_name: (int, None)})
diff --git a/libs/langchain/langchain/agents/agent.py b/libs/langchain/langchain/agents/agent.py
index e5c3175a5fea1..7cce580391438 100644
--- a/libs/langchain/langchain/agents/agent.py
+++ b/libs/langchain/langchain/agents/agent.py
@@ -1022,7 +1022,7 @@ def tool_run_logging_kwargs(self) -> Dict:
         }
 
 
-class ExceptionTool(BaseTool):
+class ExceptionTool(BaseTool):  # type: ignore[override]
     """Tool that just returns the query."""
 
     name: str = "_Exception"
diff --git a/libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py b/libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py
index f6e53ee26a1ad..9b5fc009ae741 100644
--- a/libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py
+++ b/libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py
@@ -12,7 +12,7 @@
 from langchain.memory.chat_memory import BaseChatMemory
 
 
-class AgentTokenBufferMemory(BaseChatMemory):
+class AgentTokenBufferMemory(BaseChatMemory):  # type: ignore[override]
     """Memory used to save agent output AND intermediate steps.
 
     Parameters:
diff --git a/libs/langchain/langchain/agents/output_parsers/tools.py b/libs/langchain/langchain/agents/output_parsers/tools.py
index 850fdb42af684..a72b9441fb225 100644
--- a/libs/langchain/langchain/agents/output_parsers/tools.py
+++ b/libs/langchain/langchain/agents/output_parsers/tools.py
@@ -14,7 +14,7 @@
 from langchain.agents.agent import MultiActionAgentOutputParser
 
 
-class ToolAgentAction(AgentActionMessageLog):
+class ToolAgentAction(AgentActionMessageLog):  # type: ignore[override]
     tool_call_id: str
     """Tool call that this message is responding to."""
 
diff --git a/libs/langchain/langchain/agents/tools.py b/libs/langchain/langchain/agents/tools.py
index 4a36fb1ee455b..41763dc9f42d2 100644
--- a/libs/langchain/langchain/agents/tools.py
+++ b/libs/langchain/langchain/agents/tools.py
@@ -9,7 +9,7 @@
 from langchain_core.tools import BaseTool, tool
 
 
-class InvalidTool(BaseTool):
+class InvalidTool(BaseTool):  # type: ignore[override]
     """Tool that is run when invalid tool name is encountered by agent."""
 
     name: str = "invalid_tool"
diff --git a/libs/langchain/langchain/chains/conversation/base.py b/libs/langchain/langchain/chains/conversation/base.py
index 2399d963543da..0756234a71986 100644
--- a/libs/langchain/langchain/chains/conversation/base.py
+++ b/libs/langchain/langchain/chains/conversation/base.py
@@ -21,7 +21,7 @@
     ),
     removal="1.0",
 )
-class ConversationChain(LLMChain):
+class ConversationChain(LLMChain):  # type: ignore[override, override]
     """Chain to have a conversation and load context from memory.
 
     This class is deprecated in favor of ``RunnableWithMessageHistory``. Please refer
diff --git a/libs/langchain/langchain/chains/router/multi_retrieval_qa.py b/libs/langchain/langchain/chains/router/multi_retrieval_qa.py
index 77c5253851b85..848d4b1862ed5 100644
--- a/libs/langchain/langchain/chains/router/multi_retrieval_qa.py
+++ b/libs/langchain/langchain/chains/router/multi_retrieval_qa.py
@@ -19,7 +19,7 @@
 )
 
 
-class MultiRetrievalQAChain(MultiRouteChain):
+class MultiRetrievalQAChain(MultiRouteChain):  # type: ignore[override]
     """A multi-route chain that uses an LLM router chain to choose amongst retrieval
     qa chains."""
 
diff --git a/libs/langchain/langchain/evaluation/comparison/eval_chain.py b/libs/langchain/langchain/evaluation/comparison/eval_chain.py
index 46b9001e48fd7..79780fc801c5f 100644
--- a/libs/langchain/langchain/evaluation/comparison/eval_chain.py
+++ b/libs/langchain/langchain/evaluation/comparison/eval_chain.py
@@ -95,7 +95,7 @@ def resolve_pairwise_criteria(
     return criteria_
 
 
-class PairwiseStringResultOutputParser(BaseOutputParser[dict]):
+class PairwiseStringResultOutputParser(BaseOutputParser[dict]):  # type: ignore[override]
     """A parser for the output of the PairwiseStringEvalChain.
 
     Attributes:
@@ -151,7 +151,7 @@ def parse(self, text: str) -> Dict[str, Any]:
         }
 
 
-class PairwiseStringEvalChain(PairwiseStringEvaluator, LLMEvalChain, LLMChain):
+class PairwiseStringEvalChain(PairwiseStringEvaluator, LLMEvalChain, LLMChain):  # type: ignore[override]
     """A chain for comparing two outputs, such as the outputs
      of two models, prompts, or outputs of a single model on similar inputs.
 
@@ -391,7 +391,7 @@ async def _aevaluate_string_pairs(
         return self._prepare_output(result)
 
 
-class LabeledPairwiseStringEvalChain(PairwiseStringEvalChain):
+class LabeledPairwiseStringEvalChain(PairwiseStringEvalChain):  # type: ignore[override]
     """A chain for comparing two outputs, such as the outputs
      of two models, prompts, or outputs of a single model on similar inputs,
      with labeled preferences.
diff --git a/libs/langchain/langchain/evaluation/criteria/eval_chain.py b/libs/langchain/langchain/evaluation/criteria/eval_chain.py
index 896b1efceabc2..6daec73848248 100644
--- a/libs/langchain/langchain/evaluation/criteria/eval_chain.py
+++ b/libs/langchain/langchain/evaluation/criteria/eval_chain.py
@@ -164,7 +164,7 @@ def resolve_criteria(
     return criteria_
 
 
-class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
+class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):  # type: ignore[override]
     """LLM Chain for evaluating runs against criteria.
 
     Parameters
@@ -508,7 +508,7 @@ async def _aevaluate_strings(
         return self._prepare_output(result)
 
 
-class LabeledCriteriaEvalChain(CriteriaEvalChain):
+class LabeledCriteriaEvalChain(CriteriaEvalChain):  # type: ignore[override]
     """Criteria evaluation chain that requires references."""
 
     @classmethod
diff --git a/libs/langchain/langchain/evaluation/scoring/eval_chain.py b/libs/langchain/langchain/evaluation/scoring/eval_chain.py
index a8a84a05813d6..7a6e3de78f56f 100644
--- a/libs/langchain/langchain/evaluation/scoring/eval_chain.py
+++ b/libs/langchain/langchain/evaluation/scoring/eval_chain.py
@@ -144,7 +144,7 @@ def parse(self, text: str) -> Dict[str, Any]:
         }
 
 
-class ScoreStringEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
+class ScoreStringEvalChain(StringEvaluator, LLMEvalChain, LLMChain):  # type: ignore[override]
     """A chain for scoring on a scale of 1-10 the output of a model.
 
     Attributes:
@@ -396,7 +396,7 @@ async def _aevaluate_string_pairs(
         return self._prepare_output(result)
 
 
-class LabeledScoreStringEvalChain(ScoreStringEvalChain):
+class LabeledScoreStringEvalChain(ScoreStringEvalChain):  # type: ignore[override]
     """A chain for scoring the output of a model on a scale of 1-10.
 
     Attributes:
diff --git a/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py b/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py
index 9933319ad0485..9e145d1dcc998 100644
--- a/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py
+++ b/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py
@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import asyncio
 from typing import Any, Callable, Dict, Optional, Sequence, cast
 
 from langchain_core.callbacks.manager import Callbacks
@@ -92,12 +91,8 @@ async def acompress_documents(
         callbacks: Optional[Callbacks] = None,
     ) -> Sequence[Document]:
         """Compress page content of raw documents asynchronously."""
-        outputs = await asyncio.gather(
-            *[
-                self.llm_chain.ainvoke(self.get_input(query, doc), callbacks=callbacks)
-                for doc in documents
-            ]
-        )
+        inputs = [self.get_input(query, doc) for doc in documents]
+        outputs = await self.llm_chain.abatch(inputs, {"callbacks": callbacks})
         compressed_docs = []
         for i, doc in enumerate(documents):
             if len(outputs[i]) == 0:
diff --git a/libs/langchain/langchain/smith/evaluation/string_run_evaluator.py b/libs/langchain/langchain/smith/evaluation/string_run_evaluator.py
index 4bc95504d13c3..b3503b0faf2ac 100644
--- a/libs/langchain/langchain/smith/evaluation/string_run_evaluator.py
+++ b/libs/langchain/langchain/smith/evaluation/string_run_evaluator.py
@@ -239,7 +239,7 @@ def __call__(self, example: Example) -> Dict[str, str]:
         return self.map(example)
 
 
-class StringRunEvaluatorChain(Chain, RunEvaluator):
+class StringRunEvaluatorChain(Chain, RunEvaluator):  # type: ignore[override, override]
     """Evaluate Run and optional examples."""
 
     run_mapper: StringRunMapper
diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock
index 26982ada7beae..7d952f6e23ebb 100644
--- a/libs/langchain/poetry.lock
+++ b/libs/langchain/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -779,73 +779,73 @@ test = ["pytest"]
 
 [[package]]
 name = "coverage"
-version = "7.6.3"
+version = "7.6.4"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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-    {file = "coverage-7.6.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c222958f59b0ae091f4535851cbb24eb57fc0baea07ba675af718fb5302dddb2"},
-    {file = "coverage-7.6.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ab84a8b698ad5a6c365b08061920138e7a7dd9a04b6feb09ba1bfae68346ce6d"},
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 ]
 
 [package.dependencies]
@@ -856,38 +856,38 @@ toml = ["tomli"]
 
 [[package]]
 name = "cryptography"
-version = "43.0.1"
+version = "43.0.3"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -900,7 +900,7 @@ nox = ["nox"]
 pep8test = ["check-sdist", "click", "mypy", "ruff"]
 sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["certifi", "cryptography-vectors (==43.0.1)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test = ["certifi", "cryptography-vectors (==43.0.3)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -1116,88 +1116,103 @@ python-dateutil = ">=2.7"
 
 [[package]]
 name = "frozenlist"
-version = "1.4.1"
+version = "1.5.0"
 description = "A list-like structure which implements collections.abc.MutableSequence"
 optional = false
 python-versions = ">=3.8"
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 [[package]]
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 [package.extras]
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 install-types = ["pip"]
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 reports = ["lxml"]
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 [[package]]
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 [[package]]
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+version = "8.3.3"
 description = "pytest: simple powerful testing with Python"
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 iniconfig = "*"
 packaging = "*"
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 [[package]]
 name = "pytest-asyncio"
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 python-versions = ">=3.7"
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@@ -4201,13 +4280,13 @@ blobfile = ["blobfile (>=2)"]
 
 [[package]]
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 description = "A tiny CSS parser"
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -4423,13 +4502,13 @@ urllib3 = ">=2"
 
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+    {file = "yarl-1.16.0-py3-none-any.whl", hash = "sha256:e6980a558d8461230c457218bd6c92dfc1d10205548215c2c21d79dc8d0a96f3"},
+    {file = "yarl-1.16.0.tar.gz", hash = "sha256:b6f687ced5510a9a2474bbae96a4352e5ace5fa34dc44a217b0537fec1db00b4"},
 ]
 
 [package.dependencies]
@@ -4849,4 +4928,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "ed5b8b9206f8b9af193bdfac048d7084d7c5a8aeaef4fbdf6d90176c5f183985"
+content-hash = "40da6b8dcd1e7331230e9fd4e572ca5f2aa29a2732b26ebd5ad59b8530e2f6eb"
diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml
index 051099d208166..42aed52f2a9a2 100644
--- a/libs/langchain/pyproject.toml
+++ b/libs/langchain/pyproject.toml
@@ -85,7 +85,7 @@ optional = true
 optional = true
 
 [tool.poetry.group.test.dependencies]
-pytest = "^7.3.0"
+pytest = "^8"
 pytest-cov = "^4.0.0"
 pytest-dotenv = "^0.5.2"
 duckdb-engine = "^0.9.2"
diff --git a/libs/langchain/tests/unit_tests/evaluation/parsing/test_base.py b/libs/langchain/tests/unit_tests/evaluation/parsing/test_base.py
index 9a10224f16011..a4d4230788e96 100644
--- a/libs/langchain/tests/unit_tests/evaluation/parsing/test_base.py
+++ b/libs/langchain/tests/unit_tests/evaluation/parsing/test_base.py
@@ -45,9 +45,6 @@ def test_json_validity_evaluator_evaluate_invalid_json(
     prediction = '{"name": "John", "age": 30, "city": "New York",}'
     result = json_validity_evaluator.evaluate_strings(prediction=prediction)
     assert result["score"] == 0
-    assert result["reasoning"].startswith(
-        "Expecting property name enclosed in double quotes"
-    )
 
 
 @pytest.fixture
diff --git a/libs/langchain/tests/unit_tests/retrievers/document_compressors/test_chain_extract.py b/libs/langchain/tests/unit_tests/retrievers/document_compressors/test_chain_extract.py
index 1e4afed1eec98..87b9898b9b5fb 100644
--- a/libs/langchain/tests/unit_tests/retrievers/document_compressors/test_chain_extract.py
+++ b/libs/langchain/tests/unit_tests/retrievers/document_compressors/test_chain_extract.py
@@ -72,7 +72,7 @@ async def test_llm_chain_extractor_async() -> None:
     output = await doc_compressor.acompress_documents(
         documents, "Tell me about Candlepin bowling."
     )
-    expected = documents = [
+    expected = [
         Document(
             page_content="Candlepin bowling is popular in New England.",
             metadata={"a": 1},
diff --git a/libs/text-splitters/poetry.lock b/libs/text-splitters/poetry.lock
index 07e01c0c3f216..4a68af4a76cc2 100644
--- a/libs/text-splitters/poetry.lock
+++ b/libs/text-splitters/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -13,13 +13,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.4.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
-    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
+    {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
+    {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
 ]
 
 [package.dependencies]
@@ -29,9 +29,9 @@ sniffio = ">=1.1"
 typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.23)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "appnope"
@@ -224,63 +224,6 @@ webencodings = "*"
 [package.extras]
 css = ["tinycss2 (>=1.1.0,<1.3)"]
 
-[[package]]
-name = "blis"
-version = "0.7.11"
-description = "The Blis BLAS-like linear algebra library, as a self-contained C-extension."
-optional = false
-python-versions = "*"
-files = [
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-    {file = "blis-0.7.11-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d06883f83d4c8de8264154f7c4a420b4af323050ed07398c1ff201c34c25c0d2"},
-    {file = "blis-0.7.11-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ee493683e3043650d4413d531e79e580d28a3c7bdd184f1b9cfa565497bda1e7"},
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 [package.extras]
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 ]
 
 [[package]]
@@ -709,17 +580,6 @@ files = [
 [package.dependencies]
 python-dateutil = ">=2.7"
 
-[[package]]
-name = "gprof2dot"
-version = "2024.6.6"
-description = "Generate a dot graph from the output of several profilers."
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -733,13 +593,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.5"
+version = "1.0.6"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
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+    {file = "httpcore-1.0.6.tar.gz", hash = "sha256:73f6dbd6eb8c21bbf7ef8efad555481853f5f6acdeaff1edb0694289269ee17f"},
 ]
 
 [package.dependencies]
@@ -750,7 +610,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.26.0)"]
+trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httpx"
@@ -779,33 +639,40 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "idna"
-version = "3.8"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "idna-3.8-py3-none-any.whl", hash = "sha256:050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac"},
-    {file = "idna-3.8.tar.gz", hash = "sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603"},
+    {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
+    {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
 ]
 
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
 [[package]]
 name = "importlib-metadata"
-version = "8.4.0"
+version = "8.5.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-zipp = ">=0.5"
+zipp = ">=3.20"
 
 [package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "iniconfig"
@@ -1026,13 +893,13 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2023.12.1"
+version = "2024.10.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
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+    {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
+    {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
 ]
 
 [package.dependencies]
@@ -1059,13 +926,13 @@ notebook = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.2"
+version = "8.6.3"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.6.2-py3-none-any.whl", hash = "sha256:50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f"},
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+    {file = "jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f"},
+    {file = "jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419"},
 ]
 
 [package.dependencies]
@@ -1304,7 +1171,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0"
+version = "0.3.12"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1313,47 +1180,29 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.117"
+langsmith = "^0.1.125"
 packaging = ">=23.2,<25"
 pydantic = [
     {version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 PyYAML = ">=5.3"
-tenacity = "^8.1.0,!=8.4.0"
+tenacity = ">=8.1.0,!=8.4.0,<10.0.0"
 typing-extensions = ">=4.7"
 
 [package.source]
 type = "directory"
 url = "../core"
 
-[[package]]
-name = "langcodes"
-version = "3.4.0"
-description = "Tools for labeling human languages with IETF language tags"
-optional = false
-python-versions = ">=3.8"
-files = [
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-    {file = "langcodes-3.4.0.tar.gz", hash = "sha256:ae5a77d1a01d0d1e91854a671890892b7ce9abb601ab7327fc5c874f899e1979"},
-]
-
-[package.dependencies]
-language-data = ">=1.2"
-
-[package.extras]
-build = ["build", "twine"]
-test = ["pytest", "pytest-cov"]
-
 [[package]]
 name = "langsmith"
-version = "0.1.120"
+version = "0.1.137"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.120-py3-none-any.whl", hash = "sha256:54d2785e301646c0988e0a69ebe4d976488c87b41928b358cb153b6ddd8db62b"},
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+    {file = "langsmith-0.1.137-py3-none-any.whl", hash = "sha256:4256d5c61133749890f7b5c88321dbb133ce0f440c621ea28e76513285859b81"},
+    {file = "langsmith-0.1.137.tar.gz", hash = "sha256:56cdfcc6c74cb20a3f437d5bd144feb5bf93f54c5a2918d1e568cbd084a372d4"},
 ]
 
 [package.dependencies]
@@ -1364,24 +1213,7 @@ pydantic = [
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 requests = ">=2,<3"
-
-[[package]]
-name = "language-data"
-version = "1.2.0"
-description = "Supplementary data about languages used by the langcodes module"
-optional = false
-python-versions = "*"
-files = [
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-]
-
-[package.dependencies]
-marisa-trie = ">=0.7.7"
-
-[package.extras]
-build = ["build", "twine"]
-test = ["pytest", "pytest-cov"]
+requests-toolbelt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "lxml-stubs"
@@ -1397,200 +1229,74 @@ files = [
 [package.extras]
 test = ["coverage[toml] (>=7.2.5)", "mypy (>=1.2.0)", "pytest (>=7.3.0)", "pytest-mypy-plugins (>=1.10.1)"]
 
-[[package]]
-name = "marisa-trie"
-version = "1.2.0"
-description = "Static memory-efficient and fast Trie-like structures for Python."
-optional = false
-python-versions = ">=3.7"
-files = [
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 [package.dependencies]
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 [package.extras]
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 [[package]]
@@ -2036,19 +1651,19 @@ ptyprocess = ">=0.5"
 
 [[package]]
 name = "platformdirs"
-version = "4.2.2"
+version = "4.3.6"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
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-type = ["mypy (>=1.8)"]
+docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
+type = ["mypy (>=1.11.2)"]
 
 [[package]]
 name = "pluggy"
@@ -2065,61 +1680,15 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
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-murmurhash = ">=0.28.0,<1.1.0"
-
 [[package]]
 name = "prometheus-client"
-version = "0.20.0"
+version = "0.21.0"
 description = "Python client for the Prometheus monitoring system."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -2127,13 +1696,13 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.47"
+version = "3.0.48"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2141,32 +1710,33 @@ wcwidth = "*"
 
 [[package]]
 name = "psutil"
-version = "6.0.0"
+version = "6.1.0"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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+test = ["pytest", "pytest-xdist", "setuptools"]
 
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 name = "ptyprocess"
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@@ -2344,13 +1914,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pytest"
-version = "7.4.4"
+version = "8.3.3"
 description = "pytest: simple powerful testing with Python"
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@@ -2358,11 +1928,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
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 iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<2.0"
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+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-asyncio"
@@ -2399,25 +1969,6 @@ pytest = ">=6.2.5"
 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]
 
-[[package]]
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-six = "*"
-
-[package.extras]
-tests = ["pytest-virtualenv"]
-
 [[package]]
 name = "pytest-watcher"
 version = "0.3.5"
@@ -2460,40 +2011,44 @@ files = [
 
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 ]
 
 [[package]]
@@ -2696,90 +2251,105 @@ rpds-py = ">=0.7.0"
 
 [[package]]
 name = "regex"
-version = "2024.7.24"
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 description = "Alternative regular expression module, to replace re."
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 python-versions = ">=3.8"
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 ]
 
 [[package]]
@@ -2803,6 +2373,20 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "requests-toolbelt"
+version = "1.0.0"
+description = "A utility belt for advanced users of python-requests"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"},
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+]
+
+[package.dependencies]
+requests = ">=2.0.1,<3.0.0"
+
 [[package]]
 name = "rfc3339-validator"
 version = "0.1.4"
@@ -2828,24 +2412,6 @@ files = [
     {file = "rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055"},
 ]
 
-[[package]]
-name = "rich"
-version = "13.8.0"
-description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
-optional = false
-python-versions = ">=3.7.0"
-files = [
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-]
-
-[package.dependencies]
-markdown-it-py = ">=2.2.0"
-pygments = ">=2.13.0,<3.0.0"
-
-[package.extras]
-jupyter = ["ipywidgets (>=7.5.1,<9)"]
-
 [[package]]
 name = "rpds-py"
 version = "0.20.0"
@@ -3003,35 +2569,24 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "setuptools"
-version = "74.1.2"
+version = "75.2.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
 check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.5.2)"]
-core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.text (>=3.7)", "more-itertools (>=8.8)", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.collections", "jaraco.functools", "jaraco.text (>=3.7)", "more-itertools", "more-itertools (>=8.8)", "packaging", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 enabler = ["pytest-enabler (>=2.2)"]
 test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
 type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.11.*)", "pytest-mypy"]
 
-[[package]]
-name = "shellingham"
-version = "1.5.4"
-description = "Tool to Detect Surrounding Shell"
-optional = false
-python-versions = ">=3.7"
-files = [
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-]
-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -3043,31 +2598,6 @@ files = [
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 ]
 
-[[package]]
-name = "smart-open"
-version = "7.0.4"
-description = "Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)"
-optional = false
-python-versions = "<4.0,>=3.7"
-files = [
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-]
-
-[package.dependencies]
-wrapt = "*"
-
-[package.extras]
-all = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "paramiko", "requests", "zstandard"]
-azure = ["azure-common", "azure-core", "azure-storage-blob"]
-gcs = ["google-cloud-storage (>=2.6.0)"]
-http = ["requests"]
-s3 = ["boto3"]
-ssh = ["paramiko"]
-test = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "moto[server]", "paramiko", "pytest", "pytest-rerunfailures", "requests", "responses", "zstandard"]
-webhdfs = ["requests"]
-zst = ["zstandard"]
-
 [[package]]
 name = "sniffio"
 version = "1.3.1"
@@ -3090,148 +2620,6 @@ files = [
     {file = "soupsieve-2.6.tar.gz", hash = "sha256:e2e68417777af359ec65daac1057404a3c8a5455bb8abc36f1a9866ab1a51abb"},
 ]
 
-[[package]]
-name = "spacy"
-version = "3.7.6"
-description = "Industrial-strength Natural Language Processing (NLP) in Python"
-optional = false
-python-versions = ">=3.7"
-files = [
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 ]
 
 [package.dependencies]
@@ -3410,13 +2724,13 @@ blobfile = ["blobfile (>=2)"]
 
 [[package]]
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+version = "1.4.0"
 description = "A tiny CSS parser"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3428,13 +2742,13 @@ test = ["pytest", "ruff"]
 
 [[package]]
 name = "tomli"
-version = "2.0.1"
+version = "2.0.2"
 description = "A lil' TOML parser"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -3457,26 +2771,6 @@ files = [
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 ]
 
-[[package]]
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-description = "Fast, Extensible Progress Meter"
-optional = false
-python-versions = ">=3.7"
-files = [
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-slack = ["slack-sdk"]
-telegram = ["requests"]
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-
 [[package]]
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 python-versions = ">=3.9"
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 [package.extras]
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-
 [[package]]
 name = "webcolors"
 version = "24.8.0"
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-    {file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"},
-]
-
 [[package]]
 name = "zipp"
-version = "3.20.1"
+version = "3.20.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.20.1-py3-none-any.whl", hash = "sha256:9960cd8967c8f85a56f920d5d507274e74f9ff813a0ab8889a5b5be2daf44064"},
-    {file = "zipp-3.20.1.tar.gz", hash = "sha256:c22b14cc4763c5a5b04134207736c107db42e9d3ef2d9779d465f5f1bcba572b"},
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
+    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
 ]
 
 [package.extras]
@@ -3830,4 +2981,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "e68f3e7fd161d5982616fa33a1cfcdd79ee0ade42ab0012ba4402f3ad2256621"
+content-hash = "924c618410ef2ae8b7fa6003f53988a0d81546f9e249436ab334892a94b2be93"
diff --git a/libs/text-splitters/pyproject.toml b/libs/text-splitters/pyproject.toml
index a23a8b09fba5d..fb3cf11d948f9 100644
--- a/libs/text-splitters/pyproject.toml
+++ b/libs/text-splitters/pyproject.toml
@@ -21,6 +21,9 @@ module = [
     "konlpy.tag",
     "bs4",
     "pytest",
+    "spacy",
+    "spacy.lang.en",
+    "numpy",
 ]
 ignore_missing_imports = "True"
 
@@ -65,19 +68,17 @@ ruff = "^0.5"
 mypy = "^1.10"
 lxml-stubs = "^0.5.1"
 types-requests = "^2.31.0.20240218"
-tiktoken = "^0.6.0"
-spacy = "^3.7.4"
+tiktoken = "^0.8.0"
 
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"
 
 [tool.poetry.group.test.dependencies]
-pytest = "^7.3.0"
+pytest = "^8"
 freezegun = "^1.2.2"
 pytest-mock = "^3.10.0"
 pytest-watcher = "^0.3.4"
 pytest-asyncio = "^0.21.1"
-pytest-profiling = "^1.7.0"
 
 [tool.poetry.group.test_integration.dependencies]
 
diff --git a/poetry.lock b/poetry.lock
index c23df188e38f2..2c21882bb63c3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,132 +1,114 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
-
-[[package]]
-name = "accessible-pygments"
-version = "0.0.5"
-description = "A collection of accessible pygments styles"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7"},
-    {file = "accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872"},
-]
-
-[package.dependencies]
-pygments = ">=1.5"
-
-[package.extras]
-dev = ["pillow", "pkginfo (>=1.10)", "playwright", "pre-commit", "setuptools", "twine (>=5.0)"]
-tests = ["hypothesis", "pytest"]
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.0"
+version = "2.4.3"
 description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"},
+    {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
+    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.10.6"
+version = "3.10.10"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
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-
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -179,13 +150,13 @@ files = [
 
 [[package]]
 name = "anthropic"
-version = "0.36.0"
+version = "0.37.1"
 description = "The official Python library for the anthropic API"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -204,13 +175,13 @@ vertex = ["google-auth (>=2,<3)"]
 
 [[package]]
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-version = "4.6.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
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 [package.dependencies]
@@ -221,7 +192,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
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+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -390,29 +361,6 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi
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 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
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-version = "2.2.0"
-description = "Seamlessly integrate pydantic models in your Sphinx documentation."
-optional = false
-python-versions = "<4.0.0,>=3.8.1"
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-pydantic-settings = ">=2.0,<3.0.0"
-Sphinx = ">=4.0"
-
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-
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 name = "babel"
 version = "2.16.0"
@@ -645,101 +593,116 @@ pycparser = "*"
 
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-
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 typing-extensions = ">=4.8.0"
 
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 [package.extras]
@@ -1405,70 +1376,70 @@ test = ["objgraph", "psutil"]
 
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 [package.extras]
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 [[package]]
 name = "h11"
@@ -1483,13 +1454,13 @@ files = [
 
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 [package.dependencies]
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 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
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@@ -1595,13 +1566,13 @@ files = [
 
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 python-versions = ">=3.8.0"
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 typing-extensions = ">=3.7.4.3"
 
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 ]
 
 [[package]]
@@ -2290,7 +2262,7 @@ adal = ["adal (>=1.0.2)"]
 
 [[package]]
 name = "langchain"
-version = "0.3.3"
+version = "0.3.4"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -2300,7 +2272,7 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-langchain-core = "^0.3.10"
+langchain-core = "^0.3.12"
 langchain-text-splitters = "^0.3.0"
 langsmith = "^0.1.17"
 numpy = [
@@ -2358,7 +2330,7 @@ url = "libs/partners/chroma"
 
 [[package]]
 name = "langchain-community"
-version = "0.3.2"
+version = "0.3.3"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -2368,8 +2340,9 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 dataclasses-json = ">= 0.5.7, < 0.7"
-langchain = "^0.3.3"
-langchain-core = "^0.3.10"
+httpx-sse = "^0.4.0"
+langchain = "^0.3.4"
+langchain-core = "^0.3.12"
 langsmith = "^0.1.125"
 numpy = [
     {version = ">=1,<2", markers = "python_version < \"3.12\""},
@@ -2387,7 +2360,7 @@ url = "libs/community"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.10"
+version = "0.3.12"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -2427,7 +2400,7 @@ langchain-core = "^0.3.6"
 type = "git"
 url = "https://github.com/langchain-ai/langchain-experimental.git"
 reference = "HEAD"
-resolved_reference = "6fdfdbe2d66325a2e81555890715e69946e7e26b"
+resolved_reference = "ebd1933cb3945a82a8acfc0071176b82dce92705"
 subdirectory = "libs/experimental"
 
 [[package]]
@@ -2452,7 +2425,7 @@ url = "libs/partners/mistralai"
 
 [[package]]
 name = "langchain-openai"
-version = "0.2.2"
+version = "0.2.3"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -2460,8 +2433,8 @@ files = []
 develop = true
 
 [package.dependencies]
-langchain-core = "^0.3.9"
-openai = "^1.40.0"
+langchain-core = "^0.3.12"
+openai = "^1.52.0"
 tiktoken = ">=0.7,<1"
 
 [package.source]
@@ -2486,7 +2459,7 @@ url = "libs/text-splitters"
 
 [[package]]
 name = "langgraph"
-version = "0.2.36"
+version = "0.2.39"
 description = "Building stateful, multi-actor applications with LLMs"
 optional = false
 python-versions = ">=3.9.0,<4.0"
@@ -2502,18 +2475,18 @@ langgraph-sdk = "^0.1.32"
 type = "git"
 url = "https://github.com/langchain-ai/langgraph.git"
 reference = "HEAD"
-resolved_reference = "f2dc537696fcf9b86fb7723a7939d6e891e64136"
+resolved_reference = "18b91357701563cef265fe97851fbee27f948ecc"
 subdirectory = "libs/langgraph"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.1"
+version = "2.0.2"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "<4.0.0,>=3.9.0"
 files = [
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+    {file = "langgraph_checkpoint-2.0.2-py3-none-any.whl", hash = "sha256:6e5dfd90e1fc71b91ccff75939ada1114e5d7f824df5f24c62d39bed69039ee2"},
+    {file = "langgraph_checkpoint-2.0.2.tar.gz", hash = "sha256:c1d033e4e4855f580fa56830327eb86513b64ab5be527245363498e76b19a0b9"},
 ]
 
 [package.dependencies]
@@ -2522,13 +2495,13 @@ msgpack = ">=1.1.0,<2.0.0"
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.33"
+version = "0.1.34"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "<4.0.0,>=3.9.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2538,13 +2511,13 @@ orjson = ">=3.10.1"
 
 [[package]]
 name = "langsmith"
-version = "0.1.128"
+version = "0.1.137"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -2555,6 +2528,7 @@ pydantic = [
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 requests = ">=2,<3"
+requests-toolbelt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "markdown-it-py"
@@ -2582,91 +2556,92 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
 [[package]]
 name = "markupsafe"
-version = "2.1.5"
+version = "3.0.2"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
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 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
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 [package.dependencies]
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 [package.extras]
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+tests = ["pytest", "simplejson"]
 
 [[package]]
 name = "matplotlib-inline"
@@ -3270,13 +3245,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.48.0"
+version = "1.52.2"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -3457,68 +3432,69 @@ files = [
 
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 [[package]]
@@ -3650,6 +3626,113 @@ files = [
 [package.dependencies]
 wcwidth = "*"
 
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 name = "ptyprocess"
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 toml = ["tomli (>=2.0.1)"]
 yaml = ["pyyaml (>=6.0.1)"]
 
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 [[package]]
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@@ -4419,6 +4479,20 @@ requests = ">=2.0.0"
 [package.extras]
 rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
 
+[[package]]
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+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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+
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@@ -4446,13 +4520,13 @@ files = [
 
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@@ -4634,13 +4708,13 @@ win32 = ["pywin32"]
 
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 mssql-pymssql = ["pymssql"]
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@@ -5081,13 +5004,13 @@ blobfile = ["blobfile (>=2)"]
 
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 description = "A tiny CSS parser"
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 python-versions = ">=3.8"
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 docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
 testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
 
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 [[package]]
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 [[package]]
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+    {file = "yarl-1.16.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3ab3ed42c78275477ea8e917491365e9a9b69bb615cb46169020bd0aa5e2d6d3"},
+    {file = "yarl-1.16.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:707ae579ccb3262dfaef093e202b4c3fb23c3810e8df544b1111bd2401fd7b09"},
+    {file = "yarl-1.16.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad7a852d1cd0b8d8b37fc9d7f8581152add917a98cfe2ea6e241878795f917ae"},
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+    {file = "yarl-1.16.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:5ff96da263740779b0893d02b718293cc03400c3a208fc8d8cd79d9b0993e532"},
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+    {file = "yarl-1.16.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:62c7da0ad93a07da048b500514ca47b759459ec41924143e2ddb5d7e20fd3db5"},
+    {file = "yarl-1.16.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:147b0fcd0ee33b4b5f6edfea80452d80e419e51b9a3f7a96ce98eaee145c1581"},
+    {file = "yarl-1.16.0-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:504e1fe1cc4f170195320eb033d2b0ccf5c6114ce5bf2f617535c01699479bca"},
+    {file = "yarl-1.16.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:bdcf667a5dec12a48f669e485d70c54189f0639c2157b538a4cffd24a853624f"},
+    {file = "yarl-1.16.0-cp39-cp39-win32.whl", hash = "sha256:e9951afe6557c75a71045148890052cb942689ee4c9ec29f5436240e1fcc73b7"},
+    {file = "yarl-1.16.0-cp39-cp39-win_amd64.whl", hash = "sha256:7d7aaa8ff95d0840e289423e7dc35696c2b058d635f945bf05b5cd633146b027"},
+    {file = "yarl-1.16.0-py3-none-any.whl", hash = "sha256:e6980a558d8461230c457218bd6c92dfc1d10205548215c2c21d79dc8d0a96f3"},
+    {file = "yarl-1.16.0.tar.gz", hash = "sha256:b6f687ced5510a9a2474bbae96a4352e5ace5fa34dc44a217b0537fec1db00b4"},
 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
+propcache = ">=0.2.0"
 
 [[package]]
 name = "zipp"
@@ -5945,4 +5855,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "18d3fafdb2b5509b9f476aa2ed52de9aeac9ef1990ab7e85c91240c1caec0d9f"
+content-hash = "d61c2a3661edfbde1df43d30086496036df2afcc42535d4083df4d9d019cf965"
diff --git a/pyproject.toml b/pyproject.toml
index e63de41890dee..de20fd225aeb0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,24 +11,8 @@ repository = "https://www.github.com/langchain-ai/langchain"
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
 
-[tool.poetry.group.docs.dependencies]
-autodoc_pydantic = "^2"
-sphinx = ">=7"
-sphinx-autobuild = ">=2024"
-pydata-sphinx-theme = ">=0.15"
-toml = "^0.10.2"
-
 [tool.poetry.group.lint.dependencies]
 ruff = "^0.5.0"
-langchain-core = { path = "libs/core/", develop = true }
-langchain-text-splitters = { path = "libs/text-splitters", develop = true }
-langchain-community = { path = "libs/community/", develop = true }
-langchain = { path = "libs/langchain/", develop = true }
-langchain-openai = { path = "libs/partners/openai", develop = true }
-
-[tool.poetry.group.codespell.dependencies]
-codespell = "^2.2.0"
-
 
 [tool.poetry.group.dev.dependencies]
 langchain-core = { path = "libs/core/", develop = true }
@@ -38,12 +22,10 @@ langchain = { path = "libs/langchain/", develop = true }
 langchain-openai = { path = "libs/partners/openai", develop = true }
 ipykernel = "^6.29.2"
 
-# Support Python 3.8 and 3.12+.
-# Can be removed once the numpy version is fixed in langchain-community.
-numpy = [
-    { version = "^1.24.0", python = "<3.12" },
-    { version = "^1.26.0", python = ">=3.12" },
-]
+[tool.poetry.group.codespell.dependencies]
+codespell = "^2.2.0"
+
+[tool.poetry.group.typing.dependencies]
 
 [tool.poetry.group.test.dependencies]
 langchain-experimental = { git = "https://github.com/langchain-ai/langchain-experimental.git", subdirectory = "libs/experimental" }
@@ -57,10 +39,6 @@ faiss-cpu = "^1.7.4"
 pypdf = "^5.0.0"
 vcrpy = "^6.0.1"
 
-[tool.poetry.group.test_integration.dependencies]
-
-[tool.poetry.group.typing.dependencies]
-
 [tool.codespell]
 skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,example_data,_dist,examples,templates,*.trig'
 # Ignore latin etc

From 247d6bb09debe261d9cb5cf4fdad0c652a2e861e Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 25 Oct 2024 13:23:06 -0700
Subject: [PATCH 79/84] infra: test doc imports 3.12 (#27653)

---
 .github/scripts/check_diff.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py
index 71a189124c716..f89995a84458e 100644
--- a/.github/scripts/check_diff.py
+++ b/.github/scripts/check_diff.py
@@ -308,7 +308,7 @@ def _get_configs_for_multi_dirs(
         ]
     }
     map_job_to_configs["test-doc-imports"] = (
-        [{"python-version": "3.13"}] if docs_edited else []
+        [{"python-version": "3.12"}] if docs_edited else []
     )
 
     for key, value in map_job_to_configs.items():

From d5306899d3ad70debd5ecce84fa4cf26dfa5ca43 Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Fri, 25 Oct 2024 13:26:21 -0700
Subject: [PATCH 80/84] openai[patch]: Release 0.2.4 (#27652)

---
 .../langchain_openai/embeddings/azure.py      |   2 +-
 libs/partners/openai/poetry.lock              | 328 +++++++++---------
 libs/partners/openai/pyproject.toml           |  20 +-
 3 files changed, 173 insertions(+), 177 deletions(-)

diff --git a/libs/partners/openai/langchain_openai/embeddings/azure.py b/libs/partners/openai/langchain_openai/embeddings/azure.py
index 0341c908fcd2f..3a83abc7d3957 100644
--- a/libs/partners/openai/langchain_openai/embeddings/azure.py
+++ b/libs/partners/openai/langchain_openai/embeddings/azure.py
@@ -12,7 +12,7 @@
 from langchain_openai.embeddings.base import OpenAIEmbeddings
 
 
-class AzureOpenAIEmbeddings(OpenAIEmbeddings):
+class AzureOpenAIEmbeddings(OpenAIEmbeddings):  # type: ignore[override]
     """AzureOpenAI embedding model integration.
 
     Setup:
diff --git a/libs/partners/openai/poetry.lock b/libs/partners/openai/poetry.lock
index 00d9445245c76..a4624a55c75b4 100644
--- a/libs/partners/openai/poetry.lock
+++ b/libs/partners/openai/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -188,73 +188,73 @@ files = [
 
 [[package]]
 name = "coverage"
-version = "7.6.3"
+version = "7.6.4"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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-    {file = "coverage-7.6.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c222958f59b0ae091f4535851cbb24eb57fc0baea07ba675af718fb5302dddb2"},
-    {file = "coverage-7.6.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ab84a8b698ad5a6c365b08061920138e7a7dd9a04b6feb09ba1bfae68346ce6d"},
-    {file = "coverage-7.6.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:70a6756ce66cd6fe8486c775b30889f0dc4cb20c157aa8c35b45fd7868255c5c"},
-    {file = "coverage-7.6.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c2e6fa98032fec8282f6b27e3f3986c6e05702828380618776ad794e938f53a"},
-    {file = "coverage-7.6.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:921fbe13492caf6a69528f09d5d7c7d518c8d0e7b9f6701b7719715f29a71e6e"},
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 [package.dependencies]
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 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
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 [[package]]
 name = "langsmith"
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+version = "0.1.137"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
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 [package.dependencies]
@@ -604,6 +604,7 @@ typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
+faster-cache = ["orjson"]
 install-types = ["pip"]
 mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
@@ -666,13 +667,13 @@ files = [
 
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 optional = false
 python-versions = ">=3.7.1"
 files = [
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 [package.dependencies]
@@ -690,68 +691,69 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
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 python-versions = ">=3.8"
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+    {file = "orjson-3.10.10-cp39-cp39-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:5a059afddbaa6dd733b5a2d76a90dbc8af790b993b1b5cb97a1176ca713b5df8"},
+    {file = "orjson-3.10.10-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6f9b5c59f7e2a1a410f971c5ebc68f1995822837cd10905ee255f96074537ee6"},
+    {file = "orjson-3.10.10-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:d5ef198bafdef4aa9d49a4165ba53ffdc0a9e1c7b6f76178572ab33118afea25"},
+    {file = "orjson-3.10.10-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aaf29ce0bb5d3320824ec3d1508652421000ba466abd63bdd52c64bcce9eb1fa"},
+    {file = "orjson-3.10.10-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:dddd5516bcc93e723d029c1633ae79c4417477b4f57dad9bfeeb6bc0315e654a"},
+    {file = "orjson-3.10.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a12f2003695b10817f0fa8b8fca982ed7f5761dcb0d93cff4f2f9f6709903fd7"},
+    {file = "orjson-3.10.10-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:672f9874a8a8fb9bb1b771331d31ba27f57702c8106cdbadad8bda5d10bc1019"},
+    {file = "orjson-3.10.10-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:1dcbb0ca5fafb2b378b2c74419480ab2486326974826bbf6588f4dc62137570a"},
+    {file = "orjson-3.10.10-cp39-none-win32.whl", hash = "sha256:d9bbd3a4b92256875cb058c3381b782649b9a3c68a4aa9a2fff020c2f9cfc1be"},
+    {file = "orjson-3.10.10-cp39-none-win_amd64.whl", hash = "sha256:766f21487a53aee8524b97ca9582d5c6541b03ab6210fbaf10142ae2f3ced2aa"},
+    {file = "orjson-3.10.10.tar.gz", hash = "sha256:37949383c4df7b4337ce82ee35b6d7471e55195efa7dcb45ab8226ceadb0fe3b"},
 ]
 
 [[package]]
@@ -1559,4 +1561,4 @@ watchmedo = ["PyYAML (>=3.10)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "8a60367ec11be17e65f368842c2da805aa6d450d4330b23eb6828964be2ad16c"
+content-hash = "3362b445a24e614b105f82e09d06ca1c914004df2b017eae705c969810678b76"
diff --git a/libs/partners/openai/pyproject.toml b/libs/partners/openai/pyproject.toml
index b889892039f00..f60414d84d612 100644
--- a/libs/partners/openai/pyproject.toml
+++ b/libs/partners/openai/pyproject.toml
@@ -1,10 +1,10 @@
 [build-system]
-requires = ["poetry-core>=1.0.0"]
+requires = [ "poetry-core>=1.0.0",]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-openai"
-version = "0.2.3"
+version = "0.2.4"
 description = "An integration package connecting OpenAI and LangChain"
 authors = []
 readme = "README.md"
@@ -23,31 +23,25 @@ ignore_missing_imports = true
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.3.12"
+langchain-core = "^0.3.13"
 openai = "^1.52.0"
 tiktoken = ">=0.7,<1"
 
 [tool.ruff.lint]
-select = ["E", "F", "I", "T201"]
+select = [ "E", "F", "I", "T201",]
 
 [tool.ruff.format]
 docstring-code-format = true
 skip-magic-trailing-comma = true
 
 [tool.coverage.run]
-omit = ["tests/*"]
+omit = [ "tests/*",]
 
 [tool.pytest.ini_options]
 addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5 --cov=langchain_openai"
-markers = [
-    "requires: mark tests as requiring a specific library",
-    "compile: mark placeholder test used to compile integration tests without running them",
-    "scheduled: mark tests to run in scheduled testing",
-]
+markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them", "scheduled: mark tests to run in scheduled testing",]
 asyncio_mode = "auto"
-filterwarnings = [
-    "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning",
-]
+filterwarnings = [ "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning",]
 
 [tool.poetry.group.test]
 optional = true

From 7bc4e320f1b41b03208af863dd7d9234bf4c3bde Mon Sep 17 00:00:00 2001
From: Vincent Min <93780551+VMinB12@users.noreply.github.com>
Date: Fri, 25 Oct 2024 23:07:04 +0200
Subject: [PATCH 81/84] core[patch]: improve performance of InMemoryVectorStore
 (#27538)

**Description:** We improve the performance of the InMemoryVectorStore.
**Isue:** Originally, similarity was computed document by document:
```
for doc in self.store.values():
            vector = doc["vector"]
            similarity = float(cosine_similarity([embedding], [vector]).item(0))
```
This is inefficient and does not make use of numpy vectorization.
This PR computes the similarity in one vectorized go:
```
docs = list(self.store.values())
similarity = cosine_similarity([embedding], [doc["vector"] for doc in docs])
```
**Dependencies:** None
**Twitter handle:** @b12_consulting, @Vincent_Min

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
---
 .../langchain_core/vectorstores/in_memory.py  | 47 ++++++++++++-------
 1 file changed, 31 insertions(+), 16 deletions(-)

diff --git a/libs/core/langchain_core/vectorstores/in_memory.py b/libs/core/langchain_core/vectorstores/in_memory.py
index 06aa13f785a24..d6eb978d64304 100644
--- a/libs/core/langchain_core/vectorstores/in_memory.py
+++ b/libs/core/langchain_core/vectorstores/in_memory.py
@@ -329,23 +329,38 @@ def _similarity_search_with_score_by_vector(
         filter: Optional[Callable[[Document], bool]] = None,
         **kwargs: Any,
     ) -> list[tuple[Document, float, list[float]]]:
-        result = []
-        for doc in self.store.values():
-            vector = doc["vector"]
-            similarity = float(cosine_similarity([embedding], [vector]).item(0))
-            result.append(
-                (
-                    Document(
-                        id=doc["id"], page_content=doc["text"], metadata=doc["metadata"]
-                    ),
-                    similarity,
-                    vector,
-                )
-            )
-        result.sort(key=lambda x: x[1], reverse=True)
+        # get all docs with fixed order in list
+        docs = list(self.store.values())
+
         if filter is not None:
-            result = [r for r in result if filter(r[0])]
-        return result[:k]
+            docs = [
+                doc
+                for doc in docs
+                if filter(Document(page_content=doc["text"], metadata=doc["metadata"]))
+            ]
+
+        if not docs:
+            return []
+
+        similarity = cosine_similarity([embedding], [doc["vector"] for doc in docs])[0]
+
+        # get the indices ordered by similarity score
+        top_k_idx = similarity.argsort()[::-1][:k]
+
+        return [
+            (
+                Document(
+                    id=doc_dict["id"],
+                    page_content=doc_dict["text"],
+                    metadata=doc_dict["metadata"],
+                ),
+                float(similarity[idx].item()),
+                doc_dict["vector"],
+            )
+            for idx in top_k_idx
+            # Assign using walrus operator to avoid multiple lookups
+            if (doc_dict := docs[idx])
+        ]
 
     def similarity_search_with_score_by_vector(
         self,

From ef27ce7a45fc79d62ccdeba908d2e305639cc2c6 Mon Sep 17 00:00:00 2001
From: Gabriel Faundez <gabriel.faundez@gmail.com>
Date: Fri, 25 Oct 2024 18:14:40 -0300
Subject: [PATCH 82/84] docs: add missing import for tools docs (#27650)

## Description

Added missing import from `pydantic` in the tools docs
---
 docs/docs/how_to/custom_tools.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/how_to/custom_tools.ipynb b/docs/docs/how_to/custom_tools.ipynb
index 49d54908ff238..487af8d5260d1 100644
--- a/docs/docs/how_to/custom_tools.ipynb
+++ b/docs/docs/how_to/custom_tools.ipynb
@@ -511,7 +511,7 @@
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
-    "from pydantic import BaseModel\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class CalculatorInput(BaseModel):\n",

From fbfc6bdade1951568dca36592353ad8a763dfe0f Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 25 Oct 2024 15:06:59 -0700
Subject: [PATCH 83/84] core: test runner improvements (#27654)

when running core tests locally this
- prevents langsmith tracing from being enabled by env vars
- prevents network calls
---
 libs/core/Makefile       | 21 ++++++++++++++-------
 libs/core/poetry.lock    | 18 ++++++++++++++++--
 libs/core/pyproject.toml |  7 +++++++
 3 files changed, 37 insertions(+), 9 deletions(-)

diff --git a/libs/core/Makefile b/libs/core/Makefile
index d6d84f0911759..61ac2dcc6971e 100644
--- a/libs/core/Makefile
+++ b/libs/core/Makefile
@@ -6,14 +6,21 @@ all: help
 # Define a variable for the test file path.
 TEST_FILE ?= tests/unit_tests/
 
-test:
-	poetry run pytest $(TEST_FILE)
-
-tests:
-	poetry run pytest $(TEST_FILE)
+test tests:
+	env \
+	-u LANGCHAIN_TRACING_V2 \
+	-u LANGCHAIN_API_KEY \
+	-u LANGSMITH_TRACING \
+	-u LANGCHAIN_PROJECT \
+	poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
 
 test_watch:
-	poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
+	env \
+	-u LANGCHAIN_TRACING_V2 \
+	-u LANGCHAIN_API_KEY \
+	-u LANGSMITH_TRACING \
+	-u LANGCHAIN_PROJECT \
+	poetry run ptw --snapshot-update --now . --disable-socket --allow-unix-socket -- -vv $(TEST_FILE)
 
 test_profile:
 	poetry run pytest -vv tests/unit_tests/ --profile-svg
@@ -22,7 +29,7 @@ check_imports: $(shell find langchain_core -name '*.py')
 	poetry run python ./scripts/check_imports.py $^
 
 extended_tests:
-	poetry run pytest --only-extended $(TEST_FILE)
+	poetry run pytest --only-extended --disable-socket --allow-unix-socket $(TEST_FILE)
 
 
 ######################
diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock
index c8441c5735dcf..3add0d339c5c8 100644
--- a/libs/core/poetry.lock
+++ b/libs/core/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -2035,6 +2035,20 @@ pytest = ">=6.2.5"
 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]
 
+[[package]]
+name = "pytest-socket"
+version = "0.7.0"
+description = "Pytest Plugin to disable socket calls during tests"
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
+    {file = "pytest_socket-0.7.0-py3-none-any.whl", hash = "sha256:7e0f4642177d55d317bbd58fc68c6bd9048d6eadb2d46a89307fa9221336ce45"},
+    {file = "pytest_socket-0.7.0.tar.gz", hash = "sha256:71ab048cbbcb085c15a4423b73b619a8b35d6a307f46f78ea46be51b1b7e11b3"},
+]
+
+[package.dependencies]
+pytest = ">=6.2.5"
+
 [[package]]
 name = "pytest-watcher"
 version = "0.3.5"
@@ -2962,4 +2976,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "06bd735e73dd0eff94b3fbf2c429280a5908e01c9b7f0273865be39c02458874"
+content-hash = "3c680384ebb62b873c70201979927665c8fcce32dbf1df7457d12e83fba15ac2"
diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml
index 0a840852b20b5..02e13e1838d9c 100644
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -84,17 +84,20 @@ classmethod-decorators = [ "classmethod", "langchain_core.utils.pydantic.pre_ini
 [tool.poetry.group.lint.dependencies]
 ruff = "^0.5"
 
+
 [tool.poetry.group.typing.dependencies]
 mypy = ">=1.10,<1.11"
 types-pyyaml = "^6.0.12.2"
 types-requests = "^2.28.11.5"
 types-jinja2 = "^2.11.9"
 
+
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"
 setuptools = "^67.6.1"
 grandalf = "^0.8"
 
+
 [tool.poetry.group.test.dependencies]
 pytest = "^8"
 freezegun = "^1.2.2"
@@ -104,6 +107,7 @@ pytest-watcher = "^0.3.4"
 pytest-asyncio = "^0.21.1"
 grandalf = "^0.8"
 responses = "^0.25.0"
+pytest-socket = "^0.7.0"
 [[tool.poetry.group.test.dependencies.numpy]]
 version = "^1.24.0"
 python = "<3.12"
@@ -112,12 +116,15 @@ python = "<3.12"
 version = "^1.26.0"
 python = ">=3.12"
 
+
 [tool.poetry.group.test_integration.dependencies]
 
+
 [tool.poetry.group.typing.dependencies.langchain-text-splitters]
 path = "../text-splitters"
 develop = true
 
+
 [tool.poetry.group.test.dependencies.langchain-standard-tests]
 path = "../standard-tests"
 develop = true

From cdb4b1980a7f3d93accae81613f45f501ccc4616 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Fri, 25 Oct 2024 15:41:54 -0700
Subject: [PATCH 84/84] docs: reorganize contributing docs (#27649)

---
 docs/README.md                                |  2 +-
 docs/api_reference/guide_imports.json         |  2 +-
 .../{ => how_to}/code/guidelines.mdx          |  4 +-
 .../contributing/{ => how_to}/code/index.mdx  |  4 +-
 .../contributing/{ => how_to}/code/setup.mdx  |  2 +-
 .../{ => how_to}/documentation/index.mdx      |  4 +-
 .../{ => how_to}/documentation/setup.mdx      |  0
 .../documentation/style_guide.mdx             |  6 +--
 docs/docs/contributing/how_to/index.mdx       |  5 ++
 .../{ => how_to}/integrations.mdx             |  4 +-
 .../contributing/{ => how_to}/testing.mdx     |  0
 docs/docs/contributing/index.mdx              | 16 ++++--
 .../docs/contributing/{ => reference}/faq.mdx |  4 +-
 docs/docs/contributing/reference/index.mdx    |  5 ++
 .../{ => reference}/repo_structure.mdx        |  8 +--
 .../{ => reference}/review_process.mdx        |  0
 docs/docs/contributing/tutorials/docs.mdx     | 51 +++++++++++++++++++
 docs/docs/contributing/tutorials/index.mdx    |  5 ++
 docs/docs/integrations/chat/index.mdx         |  2 +-
 docs/docs/integrations/llms/index.mdx         |  2 +-
 docs/docs/introduction.mdx                    |  4 +-
 docs/scripts/document_loader_feat_table.py    |  2 +-
 docs/scripts/kv_store_feat_table.py           |  2 +-
 docs/scripts/partner_pkg_table.py             |  2 +-
 docs/scripts/tool_feat_table.py               |  2 +-
 docs/sidebars.js                              | 49 ++++++++++++------
 docs/src/theme/NotFound/Content/index.js      |  4 +-
 docs/vercel.json                              |  8 +++
 28 files changed, 150 insertions(+), 49 deletions(-)
 rename docs/docs/contributing/{ => how_to}/code/guidelines.mdx (87%)
 rename docs/docs/contributing/{ => how_to}/code/index.mdx (58%)
 rename docs/docs/contributing/{ => how_to}/code/setup.mdx (98%)
 rename docs/docs/contributing/{ => how_to}/documentation/index.mdx (65%)
 rename docs/docs/contributing/{ => how_to}/documentation/setup.mdx (100%)
 rename docs/docs/contributing/{ => how_to}/documentation/style_guide.mdx (96%)
 create mode 100644 docs/docs/contributing/how_to/index.mdx
 rename docs/docs/contributing/{ => how_to}/integrations.mdx (98%)
 rename docs/docs/contributing/{ => how_to}/testing.mdx (100%)
 rename docs/docs/contributing/{ => reference}/faq.mdx (90%)
 create mode 100644 docs/docs/contributing/reference/index.mdx
 rename docs/docs/contributing/{ => reference}/repo_structure.mdx (86%)
 rename docs/docs/contributing/{ => reference}/review_process.mdx (100%)
 create mode 100644 docs/docs/contributing/tutorials/docs.mdx
 create mode 100644 docs/docs/contributing/tutorials/index.mdx

diff --git a/docs/README.md b/docs/README.md
index 7593716a9eacd..7d99bcbf19479 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,3 +1,3 @@
 # LangChain Documentation
 
-For more information on contributing to our documentation, see the [Documentation Contributing Guide](https://python.langchain.com/docs/contributing/documentation)
+For more information on contributing to our documentation, see the [Documentation Contributing Guide](https://python.langchain.com/docs/contributing/how_to/documentation)
diff --git a/docs/api_reference/guide_imports.json b/docs/api_reference/guide_imports.json
index 4f372db195a11..78faefefebca2 100644
--- a/docs/api_reference/guide_imports.json
+++ b/docs/api_reference/guide_imports.json
@@ -1 +1 @@
-{"ChatPromptTemplate": {"\ud83e\udd9c\ufe0f\ud83c\udfd3 LangServe": "https://python.langchain.com/docs/langserve/", "Conceptual guide": "https://python.langchain.com/docs/concepts/", "# Example": "https://python.langchain.com/docs/versions/migrating_chains/map_rerank_docs_chain/", "# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_router_chain/", "Load docs": "https://python.langchain.com/docs/versions/migrating_chains/conversation_retrieval_chain/", "# Basic example (short documents)": "https://python.langchain.com/docs/versions/migrating_chains/map_reduce_chain/", "How to add a semantic layer over graph database": "https://python.langchain.com/docs/how_to/graph_semantic/", "How to handle long text when doing extraction": "https://python.langchain.com/docs/how_to/extraction_long_text/", "How to add values to a chain's state": "https://python.langchain.com/docs/how_to/assign/", "How to do per-user retrieval": "https://python.langchain.com/docs/how_to/qa_per_user/", "How to track token usage in ChatModels": "https://python.langchain.com/docs/how_to/chat_token_usage_tracking/", "How to create a custom LLM class": "https://python.langchain.com/docs/how_to/custom_llm/", "How to inspect runnables": "https://python.langchain.com/docs/how_to/inspect/", "How to handle cases where no queries are generated": "https://python.langchain.com/docs/how_to/query_no_queries/", "How to use few shot examples in chat models": "https://python.langchain.com/docs/how_to/few_shot_examples_chat/", "How to summarize text through iterative refinement": "https://python.langchain.com/docs/how_to/summarize_refine/", "How to do tool/function calling": "https://python.langchain.com/docs/how_to/function_calling/", "How to create tools": "https://python.langchain.com/docs/how_to/custom_tools/", "How to use prompting alone (no tool calling) to do extraction": "https://python.langchain.com/docs/how_to/extraction_parse/", "How to deal with large databases when doing SQL question-answering": "https://python.langchain.com/docs/how_to/sql_large_db/", "How to use reference examples when doing extraction": "https://python.langchain.com/docs/how_to/extraction_examples/", "How to handle multiple queries when doing query analysis": "https://python.langchain.com/docs/how_to/query_multiple_queries/", "How to add fallbacks to a runnable": "https://python.langchain.com/docs/how_to/fallbacks/", "How to propagate callbacks  constructor": "https://python.langchain.com/docs/how_to/callbacks_constructor/", "How to map values to a graph database": "https://python.langchain.com/docs/how_to/graph_mapping/", "How to save and load LangChain objects": "https://python.langchain.com/docs/how_to/serialization/", "How to do question answering over CSVs": "https://python.langchain.com/docs/how_to/sql_csv/", "How to stream results from your RAG application": "https://python.langchain.com/docs/how_to/qa_streaming/", "How to get your RAG application to return sources": "https://python.langchain.com/docs/how_to/qa_sources/", "How to summarize text through parallelization": "https://python.langchain.com/docs/how_to/summarize_map_reduce/", "How to attach callbacks to a runnable": "https://python.langchain.com/docs/how_to/callbacks_attach/", "How to handle tool errors": "https://python.langchain.com/docs/how_to/tools_error/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to add default invocation args to a Runnable": "https://python.langchain.com/docs/how_to/binding/", "How to convert Runnables as Tools": "https://python.langchain.com/docs/how_to/convert_runnable_to_tool/", "How to stream events from a tool": "https://python.langchain.com/docs/how_to/tool_stream_events/", "How to create a dynamic (self-constructing) chain": "https://python.langchain.com/docs/how_to/dynamic_chain/", "How to create custom callback handlers": "https://python.langchain.com/docs/how_to/custom_callbacks/", "How to stream runnables": "https://python.langchain.com/docs/how_to/streaming/", "How to invoke runnables in parallel": "https://python.langchain.com/docs/how_to/parallel/", "How to pass through arguments from one step to the next": "https://python.langchain.com/docs/how_to/passthrough/", "How to retrieve using multiple vectors per document": "https://python.langchain.com/docs/how_to/multi_vector/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add message history": "https://python.langchain.com/docs/how_to/message_history/", "How to add retrieval to chatbots": "https://python.langchain.com/docs/how_to/chatbots_retrieval/", "How to handle multiple retrievers when doing query analysis": "https://python.langchain.com/docs/how_to/query_multiple_retrievers/", "How to get a RAG application to add citations": "https://python.langchain.com/docs/how_to/qa_citations/", "How to 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"ChatOllama": "https://python.langchain.com/docs/integrations/chat/ollama/", "Llama.cpp": "https://python.langchain.com/docs/integrations/chat/llamacpp/", "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere/", "Eden AI": "https://python.langchain.com/docs/integrations/chat/edenai/", "ChatTongyi": "https://python.langchain.com/docs/integrations/chat/tongyi/", "ChatPremAI": "https://python.langchain.com/docs/integrations/chat/premai/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor/", "PremAI": "https://python.langchain.com/docs/integrations/providers/premai/", "Log, Trace, and Monitor": "https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey/", "Portkey": "https://python.langchain.com/docs/integrations/providers/portkey/index/", "JSONFormer": "https://python.langchain.com/docs/integrations/llms/jsonformer_experimental/"}, "MultiPromptChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/multi_prompt_chain/"}, "ConversationChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/"}, "ConversationBufferMemory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Memorize": "https://python.langchain.com/docs/integrations/tools/memorize/", "Gradio": "https://python.langchain.com/docs/integrations/tools/gradio_tools/", "SceneXplain": "https://python.langchain.com/docs/integrations/tools/sceneXplain/", "Reddit Search ": "https://python.langchain.com/docs/integrations/tools/reddit_search/", "Xata": "https://python.langchain.com/docs/integrations/memory/xata_chat_message_history/", "Llama2Chat": "https://python.langchain.com/docs/integrations/chat/llama2_chat/", "SAP HANA Cloud Vector Engine": "https://python.langchain.com/docs/integrations/vectorstores/sap_hanavector/", "Bittensor": "https://python.langchain.com/docs/integrations/llms/bittensor/"}, "InMemoryChatMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/"}, "RunnableWithMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add message history": "https://python.langchain.com/docs/how_to/message_history/", "How to add memory to chatbots": "https://python.langchain.com/docs/how_to/chatbots_memory/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "Redis": "https://python.langchain.com/docs/integrations/memory/redis_chat_message_history/", "Google SQL for MySQL": "https://python.langchain.com/docs/integrations/memory/google_sql_mysql/", "Google AlloyDB for PostgreSQL": "https://python.langchain.com/docs/integrations/memory/google_alloydb/", "ZepCloudChatMessageHistory": "https://python.langchain.com/docs/integrations/memory/zep_cloud_chat_message_history/", "AWS DynamoDB": "https://python.langchain.com/docs/integrations/memory/aws_dynamodb/", "Couchbase": "https://python.langchain.com/docs/integrations/memory/couchbase_chat_message_history/", "MongoDB": "https://python.langchain.com/docs/integrations/memory/mongodb_chat_message_history/", "SQL (SQLAlchemy)": "https://python.langchain.com/docs/integrations/memory/sql_chat_message_history/", "Streamlit": "https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history/", "Google El Carro Oracle": "https://python.langchain.com/docs/integrations/memory/google_el_carro/", "SQLite": "https://python.langchain.com/docs/integrations/memory/sqlite/", "Google SQL for PostgreSQL": "https://python.langchain.com/docs/integrations/memory/google_sql_pg/", "Google SQL for SQL Server": "https://python.langchain.com/docs/integrations/memory/google_sql_mssql/", "TiDB": "https://python.langchain.com/docs/integrations/memory/tidb_chat_message_history/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/"}, "BaseChatMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/"}, "ConstitutionalChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "ConstitutionalPrinciple": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "OpenAI": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/", "# Example": "https://python.langchain.com/docs/versions/migrating_chains/map_rerank_docs_chain/", "How to use output parsers to parse an LLM response into structured format": "https://python.langchain.com/docs/how_to/output_parser_structured/", "How to reorder retrieved results to mitigate the \"lost in the middle\" effect": "https://python.langchain.com/docs/how_to/long_context_reorder/", "How to add fallbacks to a runnable": "https://python.langchain.com/docs/how_to/fallbacks/", "How to do retrieval with contextual compression": "https://python.langchain.com/docs/how_to/contextual_compression/", "How to retry when a parsing error occurs": "https://python.langchain.com/docs/how_to/output_parser_retry/", "How to stream responses from an LLM": "https://python.langchain.com/docs/how_to/streaming_llm/", "How to cache LLM responses": "https://python.langchain.com/docs/how_to/llm_caching/", "How to track token usage for LLMs": "https://python.langchain.com/docs/how_to/llm_token_usage_tracking/", "Model caches": "https://python.langchain.com/docs/integrations/llm_caching/", "Milvus": "https://python.langchain.com/docs/integrations/retrievers/self_query/milvus_self_query/", "PGVector (Postgres)": "https://python.langchain.com/docs/integrations/retrievers/self_query/pgvector_self_query/", "Weaviate": "https://python.langchain.com/docs/integrations/vectorstores/weaviate/", "Databricks Vector Search": "https://python.langchain.com/docs/integrations/retrievers/self_query/databricks_vector_search/", "DingoDB": "https://python.langchain.com/docs/integrations/retrievers/self_query/dingo/", "OpenSearch": "https://python.langchain.com/docs/integrations/retrievers/self_query/opensearch_self_query/", "Elasticsearch": "https://python.langchain.com/docs/integrations/retrievers/self_query/elasticsearch_self_query/", "Chroma": "https://python.langchain.com/docs/integrations/retrievers/self_query/chroma_self_query/", "Timescale Vector (Postgres) ": "https://python.langchain.com/docs/integrations/retrievers/self_query/timescalevector_self_query/", "Astra DB (Cassandra)": "https://python.langchain.com/docs/integrations/retrievers/self_query/astradb/", "Pinecone": "https://python.langchain.com/docs/integrations/retrievers/self_query/pinecone/", "Supabase (Postgres)": "https://python.langchain.com/docs/integrations/retrievers/self_query/supabase_self_query/", "Redis": "https://python.langchain.com/docs/integrations/retrievers/self_query/redis_self_query/", "Deep Lake": "https://python.langchain.com/docs/integrations/retrievers/self_query/activeloop_deeplake_self_query/", "MongoDB Atlas": "https://python.langchain.com/docs/integrations/retrievers/self_query/mongodb_atlas/", "Qdrant": "https://python.langchain.com/docs/integrations/retrievers/self_query/qdrant_self_query/", "OpenAI": "https://python.langchain.com/docs/integrations/llms/openai/", "Jira Toolkit": "https://python.langchain.com/docs/integrations/tools/jira/", "Google Jobs": "https://python.langchain.com/docs/integrations/tools/google_jobs/", "Google Serper": "https://python.langchain.com/docs/integrations/tools/google_serper/", "Azure Cognitive Services Toolkit": "https://python.langchain.com/docs/integrations/tools/azure_cognitive_services/", "Human as a tool": "https://python.langchain.com/docs/integrations/tools/human_tools/", "Natural Language API Toolkits": "https://python.langchain.com/docs/integrations/tools/openapi_nla/", "Steam Toolkit": "https://python.langchain.com/docs/integrations/tools/steam/", "JSON Toolkit": "https://python.langchain.com/docs/integrations/tools/json/", "Google Finance": "https://python.langchain.com/docs/integrations/tools/google_finance/", "ClickUp Toolkit": "https://python.langchain.com/docs/integrations/tools/clickup/", "AWS Lambda": "https://python.langchain.com/docs/integrations/tools/awslambda/", "Google Drive": "https://python.langchain.com/docs/integrations/tools/google_drive/", "OpenWeatherMap": "https://python.langchain.com/docs/integrations/tools/openweathermap/", "Eleven Labs Text2Speech": "https://python.langchain.com/docs/integrations/tools/eleven_labs_tts/", "Office365 Toolkit": "https://python.langchain.com/docs/integrations/tools/office365/", "Pandas Dataframe": "https://python.langchain.com/docs/integrations/tools/pandas/", "Zapier Natural Language Actions": "https://python.langchain.com/docs/integrations/tools/zapier/", "Lemon Agent": "https://python.langchain.com/docs/integrations/tools/lemonai/", "NASA Toolkit": "https://python.langchain.com/docs/integrations/tools/nasa/", "GraphQL": "https://python.langchain.com/docs/integrations/tools/graphql/", "SearchApi": "https://python.langchain.com/docs/integrations/providers/searchapi/", "Gradio": "https://python.langchain.com/docs/integrations/tools/gradio_tools/", "SceneXplain": "https://python.langchain.com/docs/integrations/tools/sceneXplain/", "Azure AI Services Toolkit": "https://python.langchain.com/docs/integrations/tools/azure_ai_services/", "OpenAPI Toolkit": "https://python.langchain.com/docs/integrations/tools/openapi/", "Dall-E Image Generator": "https://python.langchain.com/docs/integrations/tools/dalle_image_generator/", "Gitlab Toolkit": "https://python.langchain.com/docs/integrations/tools/gitlab/", "Ionic Shopping Tool": "https://python.langchain.com/docs/integrations/tools/ionic_shopping/", "Zep Open Source Memory": "https://python.langchain.com/docs/integrations/memory/zep_memory/", "Mot\u00f6rhead": "https://python.langchain.com/docs/integrations/memory/motorhead_memory/", "Zep Cloud Memory": "https://python.langchain.com/docs/integrations/memory/zep_memory_cloud/", "Confident": "https://python.langchain.com/docs/integrations/callbacks/confident/", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor/", "Fiddler": "https://python.langchain.com/docs/integrations/callbacks/fiddler/", "SageMaker Tracking": "https://python.langchain.com/docs/integrations/callbacks/sagemaker_tracking/", "Label Studio": "https://python.langchain.com/docs/integrations/callbacks/labelstudio/", "Comet Tracing": "https://python.langchain.com/docs/integrations/callbacks/comet_tracing/", "Argilla": "https://python.langchain.com/docs/integrations/callbacks/argilla/", "PromptLayer": "https://python.langchain.com/docs/integrations/callbacks/promptlayer/", "Streamlit": "https://python.langchain.com/docs/integrations/callbacks/streamlit/", "Trubrics": "https://python.langchain.com/docs/integrations/callbacks/trubrics/", "Infino": "https://python.langchain.com/docs/integrations/callbacks/infino/", "DSPy": "https://python.langchain.com/docs/integrations/providers/dspy/", "Comet": "https://python.langchain.com/docs/integrations/providers/comet_tracking/", "Aim": "https://python.langchain.com/docs/integrations/providers/aim_tracking/", "Weights & Biases": "https://python.langchain.com/docs/integrations/providers/wandb_tracking/", "Log10": "https://python.langchain.com/docs/integrations/providers/log10/", "LangChain Decorators \u2728": "https://python.langchain.com/docs/integrations/providers/langchain_decorators/", "Rebuff": "https://python.langchain.com/docs/integrations/providers/rebuff/", "Serper - Google Search API": "https://python.langchain.com/docs/integrations/providers/google_serper/", "Helicone": "https://python.langchain.com/docs/integrations/providers/helicone/", "Shale Protocol": "https://python.langchain.com/docs/integrations/providers/shaleprotocol/", "WhyLabs": "https://python.langchain.com/docs/integrations/providers/whylabs_profiling/", "WandB Tracing": "https://python.langchain.com/docs/integrations/providers/wandb_tracing/", "ClearML": "https://python.langchain.com/docs/integrations/providers/clearml_tracking/", "Ray Serve": "https://python.langchain.com/docs/integrations/providers/ray_serve/", "Identity-enabled RAG using PebbloRetrievalQA": "https://python.langchain.com/docs/integrations/providers/pebblo/pebblo_retrieval_qa/", "Apache Doris": "https://python.langchain.com/docs/integrations/vectorstores/apache_doris/", "Jaguar Vector Database": "https://python.langchain.com/docs/integrations/vectorstores/jaguar/", "StarRocks": "https://python.langchain.com/docs/integrations/vectorstores/starrocks/", "Marqo": "https://python.langchain.com/docs/integrations/vectorstores/marqo/", "Timescale Vector (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/timescalevector/", "Amazon Document DB": "https://python.langchain.com/docs/integrations/vectorstores/documentdb/", "VoyageAI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/voyageai-reranker/", "Apify Dataset": "https://python.langchain.com/docs/integrations/document_loaders/apify_dataset/", "Psychic": "https://python.langchain.com/docs/integrations/document_loaders/psychic/", "Docugami": "https://python.langchain.com/docs/integrations/document_loaders/docugami/", "Amazon Textract ": "https://python.langchain.com/docs/integrations/document_loaders/amazon_textract/", "NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/", "OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/", "Layerup Security": "https://python.langchain.com/docs/integrations/llms/layerup_security/", "Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "CRITIQUE_PROMPT": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "REVISION_PROMPT": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "WebBaseLoader": {"Load docs": "https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/", "# Basic example (short documents)": "https://python.langchain.com/docs/versions/migrating_chains/map_reduce_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to stream results from your RAG application": "https://python.langchain.com/docs/how_to/qa_streaming/", "How to get your RAG application to return sources": "https://python.langchain.com/docs/how_to/qa_sources/", "How to summarize text through parallelization": "https://python.langchain.com/docs/how_to/summarize_map_reduce/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add retrieval to chatbots": "https://python.langchain.com/docs/how_to/chatbots_retrieval/", "How to use the MultiQueryRetriever": "https://python.langchain.com/docs/how_to/MultiQueryRetriever/", "RePhraseQuery": "https://python.langchain.com/docs/integrations/retrievers/re_phrase/", "Infino": "https://python.langchain.com/docs/integrations/callbacks/infino/", "ApertureDB": "https://python.langchain.com/docs/integrations/vectorstores/aperturedb/", "Zep Cloud": "https://python.langchain.com/docs/integrations/vectorstores/zep_cloud/", "Zep": "https://python.langchain.com/docs/integrations/vectorstores/zep/", "Google Cloud Vertex AI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/google_cloud_vertexai_rerank/", "Merge Documents Loader": "https://python.langchain.com/docs/integrations/document_loaders/merge_doc/", "WebBaseLoader": "https://python.langchain.com/docs/integrations/document_loaders/web_base/", "Build a Retrieval Augmented Generation (RAG) App": "https://python.langchain.com/docs/tutorials/rag/", "Build a Local RAG Application": "https://python.langchain.com/docs/tutorials/local_rag/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/"}, "FAISS": {"Load docs": "https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to handle long text when doing extraction": "https://python.langchain.com/docs/how_to/extraction_long_text/", "How to load PDFs": "https://python.langchain.com/docs/how_to/document_loader_pdf/", "How to better prompt when doing SQL question-answering": "https://python.langchain.com/docs/how_to/sql_prompting/", "How to add values to a chain's state": "https://python.langchain.com/docs/how_to/assign/", "How to inspect runnables": "https://python.langchain.com/docs/how_to/inspect/", "How to deal with large databases when doing SQL question-answering": "https://python.langchain.com/docs/how_to/sql_large_db/", "How to stream runnables": "https://python.langchain.com/docs/how_to/streaming/", "How to invoke runnables in parallel": "https://python.langchain.com/docs/how_to/parallel/", "How to pass through arguments from one step to the next": "https://python.langchain.com/docs/how_to/passthrough/", "How to do retrieval with contextual compression": "https://python.langchain.com/docs/how_to/contextual_compression/", "How to use a time-weighted vector store retriever": "https://python.langchain.com/docs/how_to/time_weighted_vectorstore/", "How to create and query vector stores": "https://python.langchain.com/docs/how_to/vectorstores/", "How to use a vectorstore as a retriever": "https://python.langchain.com/docs/how_to/vectorstore_retriever/", "Caching": "https://python.langchain.com/docs/how_to/caching_embeddings/", "How to combine results from multiple retrievers": "https://python.langchain.com/docs/how_to/ensemble_retriever/", "How to select examples by maximal marginal relevance (MMR)": "https://python.langchain.com/docs/how_to/example_selectors_mmr/", "NVIDIA NIMs ": "https://python.langchain.com/docs/integrations/text_embedding/nvidia_ai_endpoints/", "Fleet AI Context": "https://python.langchain.com/docs/integrations/retrievers/fleet_context/", "LLMLingua Document Compressor": "https://python.langchain.com/docs/integrations/retrievers/llmlingua/", "Cohere reranker": "https://python.langchain.com/docs/integrations/retrievers/cohere-reranker/", "FlashRank reranker": "https://python.langchain.com/docs/integrations/retrievers/flashrank-reranker/", "UpTrain": "https://python.langchain.com/docs/integrations/callbacks/uptrain/", "RAGatouille": "https://python.langchain.com/docs/integrations/providers/ragatouille/", "Facebook - Meta": "https://python.langchain.com/docs/integrations/providers/facebook/", "Faiss (Async)": "https://python.langchain.com/docs/integrations/vectorstores/faiss_async/", "Faiss": "https://python.langchain.com/docs/integrations/vectorstores/faiss/", "VoyageAI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/voyageai-reranker/", "RankLLM Reranker": "https://python.langchain.com/docs/integrations/document_transformers/rankllm-reranker/", "Volcengine Reranker": "https://python.langchain.com/docs/integrations/document_transformers/volcengine_rerank/", "OpenVINO Reranker": "https://python.langchain.com/docs/integrations/document_transformers/openvino_rerank/", "Cross Encoder Reranker": "https://python.langchain.com/docs/integrations/document_transformers/cross_encoder_reranker/", "Jina Reranker": "https://python.langchain.com/docs/integrations/document_transformers/jina_rerank/", "DashScope Reranker": "https://python.langchain.com/docs/integrations/document_transformers/dashscope_rerank/", "YouTube audio": "https://python.langchain.com/docs/integrations/document_loaders/youtube_audio/", "Build a Question/Answering system over SQL data": "https://python.langchain.com/docs/tutorials/sql_qa/"}, "OpenAIEmbeddings": {"Load docs": "https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to handle long text when doing extraction": "https://python.langchain.com/docs/how_to/extraction_long_text/", "How to load PDFs": "https://python.langchain.com/docs/how_to/document_loader_pdf/", "How to better prompt when doing SQL question-answering": "https://python.langchain.com/docs/how_to/sql_prompting/", "How to add values to a chain's state": "https://python.langchain.com/docs/how_to/assign/", "How to route between sub-chains": "https://python.langchain.com/docs/how_to/routing/", "How to do per-user retrieval": "https://python.langchain.com/docs/how_to/qa_per_user/", "How to use few shot examples": "https://python.langchain.com/docs/how_to/few_shot_examples/", "How to inspect runnables": "https://python.langchain.com/docs/how_to/inspect/", "How to handle cases where no queries are generated": "https://python.langchain.com/docs/how_to/query_no_queries/", "How to use few shot examples in chat models": "https://python.langchain.com/docs/how_to/few_shot_examples_chat/", "How to select examples by similarity": 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"https://python.langchain.com/docs/integrations/vectorstores/neo4jvector/", "Lantern": "https://python.langchain.com/docs/integrations/vectorstores/lantern/", "Tair": "https://python.langchain.com/docs/integrations/vectorstores/tair/", "DuckDB": "https://python.langchain.com/docs/integrations/vectorstores/duckdb/", "Alibaba Cloud OpenSearch": "https://python.langchain.com/docs/integrations/vectorstores/alibabacloud_opensearch/", "Clarifai": "https://python.langchain.com/docs/integrations/vectorstores/clarifai/", "scikit-learn": "https://python.langchain.com/docs/integrations/vectorstores/sklearn/", "Tencent Cloud VectorDB": "https://python.langchain.com/docs/integrations/vectorstores/tencentvectordb/", "DocArray HnswSearch": "https://python.langchain.com/docs/integrations/vectorstores/docarray_hnsw/", "MyScale": "https://python.langchain.com/docs/integrations/vectorstores/myscale/", "TileDB": "https://python.langchain.com/docs/integrations/vectorstores/tiledb/", "Google Memorystore for Redis": "https://python.langchain.com/docs/integrations/vectorstores/google_memorystore_redis/", "Tigris": "https://python.langchain.com/docs/integrations/vectorstores/tigris/", "China Mobile ECloud ElasticSearch VectorSearch": "https://python.langchain.com/docs/integrations/vectorstores/ecloud_vector_search/", "Bagel": "https://python.langchain.com/docs/integrations/vectorstores/bagel/", "Baidu Cloud ElasticSearch VectorSearch": "https://python.langchain.com/docs/integrations/vectorstores/baiducloud_vector_search/", "AwaDB": "https://python.langchain.com/docs/integrations/vectorstores/awadb/", "Supabase (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/supabase/", "SurrealDB": "https://python.langchain.com/docs/integrations/vectorstores/surrealdb/", "OpenSearch": "https://python.langchain.com/docs/integrations/vectorstores/opensearch/", "Faiss (Async)": "https://python.langchain.com/docs/integrations/vectorstores/faiss_async/", "BagelDB": "https://python.langchain.com/docs/integrations/vectorstores/bageldb/", "ManticoreSearch VectorStore": "https://python.langchain.com/docs/integrations/vectorstores/manticore_search/", "Azure AI Search": "https://python.langchain.com/docs/integrations/vectorstores/azuresearch/", "USearch": "https://python.langchain.com/docs/integrations/vectorstores/usearch/", "PGVecto.rs": "https://python.langchain.com/docs/integrations/vectorstores/pgvecto_rs/", "Marqo": "https://python.langchain.com/docs/integrations/vectorstores/marqo/", "DocArray InMemorySearch": "https://python.langchain.com/docs/integrations/vectorstores/docarray_in_memory/", "Postgres Embedding": "https://python.langchain.com/docs/integrations/vectorstores/pgembedding/", "Intel's Visual Data Management System (VDMS)": "https://python.langchain.com/docs/integrations/vectorstores/vdms/", "Timescale Vector (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/timescalevector/", "Epsilla": "https://python.langchain.com/docs/integrations/vectorstores/epsilla/", "Amazon Document DB": "https://python.langchain.com/docs/integrations/vectorstores/documentdb/", "SemaDB": "https://python.langchain.com/docs/integrations/vectorstores/semadb/", "AnalyticDB": "https://python.langchain.com/docs/integrations/vectorstores/analyticdb/", "Hologres": "https://python.langchain.com/docs/integrations/vectorstores/hologres/", "Baidu VectorDB": "https://python.langchain.com/docs/integrations/vectorstores/baiduvectordb/", "Meilisearch": "https://python.langchain.com/docs/integrations/vectorstores/meilisearch/", "Psychic": "https://python.langchain.com/docs/integrations/document_loaders/psychic/", "Manifest": "https://python.langchain.com/docs/integrations/llms/manifest/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/"}, "acollapse_docs": {"# Basic example (short documents)": "https://python.langchain.com/docs/versions/migrating_chains/map_reduce_chain/", "How to summarize text through parallelization": "https://python.langchain.com/docs/how_to/summarize_map_reduce/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/"}, "split_list_of_docs": {"# Basic example (short documents)": "https://python.langchain.com/docs/versions/migrating_chains/map_reduce_chain/", "How to summarize text through parallelization": "https://python.langchain.com/docs/how_to/summarize_map_reduce/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/"}, "RefineDocumentsChain": {"# Example": "https://python.langchain.com/docs/versions/migrating_chains/refine_docs_chain/"}, "RetrievalQA": {"Load docs": "https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/", "LLMLingua Document Compressor": "https://python.langchain.com/docs/integrations/retrievers/llmlingua/", "Bedrock (Knowledge Bases) Retriever": "https://python.langchain.com/docs/integrations/retrievers/bedrock/", "Cohere reranker": "https://python.langchain.com/docs/integrations/retrievers/cohere-reranker/", "Activeloop Deep Memory": "https://python.langchain.com/docs/integrations/retrievers/activeloop/", "FlashRank reranker": "https://python.langchain.com/docs/integrations/retrievers/flashrank-reranker/", "Confident": "https://python.langchain.com/docs/integrations/callbacks/confident/", "UpTrain": "https://python.langchain.com/docs/integrations/callbacks/uptrain/", "Apache Doris": "https://python.langchain.com/docs/integrations/vectorstores/apache_doris/", "ScaNN": "https://python.langchain.com/docs/integrations/vectorstores/scann/", "Google Vertex AI Vector Search": "https://python.langchain.com/docs/integrations/vectorstores/google_vertex_ai_vector_search/", "Momento Vector Index (MVI)": "https://python.langchain.com/docs/integrations/vectorstores/momento_vector_index/", "Activeloop Deep Lake": "https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake/", "StarRocks": "https://python.langchain.com/docs/integrations/vectorstores/starrocks/", "KDB.AI": "https://python.langchain.com/docs/integrations/vectorstores/kdbai/", "Timescale Vector (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/timescalevector/", "Amazon Document DB": "https://python.langchain.com/docs/integrations/vectorstores/documentdb/", "VoyageAI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/voyageai-reranker/", "RankLLM Reranker": "https://python.langchain.com/docs/integrations/document_transformers/rankllm-reranker/", "YouTube audio": "https://python.langchain.com/docs/integrations/document_loaders/youtube_audio/", "Docugami": "https://python.langchain.com/docs/integrations/document_loaders/docugami/"}, "RunnablePassthrough": {"Load docs": "https://python.langchain.com/docs/versions/migrating_chains/retrieval_qa/", "# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_router_chain/", "How to add values to a chain's state": "https://python.langchain.com/docs/how_to/assign/", "How to route between sub-chains": "https://python.langchain.com/docs/how_to/routing/", "How to do per-user retrieval": "https://python.langchain.com/docs/how_to/qa_per_user/", "How to inspect runnables": "https://python.langchain.com/docs/how_to/inspect/", "How to handle cases where no queries are generated": "https://python.langchain.com/docs/how_to/query_no_queries/", "How to do tool/function calling": "https://python.langchain.com/docs/how_to/function_calling/", "How to add a human-in-the-loop for tools": "https://python.langchain.com/docs/how_to/tools_human/", "How to deal with large databases when doing SQL question-answering": "https://python.langchain.com/docs/how_to/sql_large_db/", "How to handle multiple queries when doing query analysis": "https://python.langchain.com/docs/how_to/query_multiple_queries/", "How to map values to a graph database": "https://python.langchain.com/docs/how_to/graph_mapping/", "How to do question answering over CSVs": "https://python.langchain.com/docs/how_to/sql_csv/", "How to get your RAG application to return sources": "https://python.langchain.com/docs/how_to/qa_sources/", "How to add default invocation args to a Runnable": "https://python.langchain.com/docs/how_to/binding/", "How to convert Runnables as Tools": "https://python.langchain.com/docs/how_to/convert_runnable_to_tool/", "How to create a dynamic (self-constructing) chain": "https://python.langchain.com/docs/how_to/dynamic_chain/", "How to stream runnables": "https://python.langchain.com/docs/how_to/streaming/", "How to invoke runnables in parallel": "https://python.langchain.com/docs/how_to/parallel/", "How to pass through arguments from one step to the next": "https://python.langchain.com/docs/how_to/passthrough/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add retrieval to chatbots": "https://python.langchain.com/docs/how_to/chatbots_retrieval/", "How to handle multiple retrievers when doing query analysis": "https://python.langchain.com/docs/how_to/query_multiple_retrievers/", "How to get a RAG application to add citations": "https://python.langchain.com/docs/how_to/qa_citations/", "How to add memory to chatbots": "https://python.langchain.com/docs/how_to/chatbots_memory/", "How deal with high cardinality categoricals when doing query analysis": "https://python.langchain.com/docs/how_to/query_high_cardinality/", "How to add ad-hoc tool calling capability to LLMs and Chat Models": "https://python.langchain.com/docs/how_to/tools_prompting/", "LangChain Expression Language Cheatsheet": "https://python.langchain.com/docs/how_to/lcel_cheatsheet/", "Hybrid Search": "https://python.langchain.com/docs/how_to/hybrid/", "How to use few-shot prompting with tool calling": "https://python.langchain.com/docs/how_to/tools_few_shot/", "How to add examples to the prompt for query analysis": "https://python.langchain.com/docs/how_to/query_few_shot/", "NVIDIA NIMs ": "https://python.langchain.com/docs/integrations/text_embedding/nvidia_ai_endpoints/", "AzureAISearchRetriever": "https://python.langchain.com/docs/integrations/retrievers/azure_ai_search/", "You.com": "https://python.langchain.com/docs/integrations/retrievers/you-retriever/", "Fleet AI Context": "https://python.langchain.com/docs/integrations/retrievers/fleet_context/", "AskNews": "https://python.langchain.com/docs/integrations/retrievers/asknews/", "WikipediaRetriever": "https://python.langchain.com/docs/integrations/retrievers/wikipedia/", "TavilySearchAPIRetriever": "https://python.langchain.com/docs/integrations/retrievers/tavily/", "ArxivRetriever": "https://python.langchain.com/docs/integrations/retrievers/arxiv/", "ElasticsearchRetriever": "https://python.langchain.com/docs/integrations/retrievers/elasticsearch_retriever/", "Milvus Hybrid Search Retriever": "https://python.langchain.com/docs/integrations/retrievers/milvus_hybrid_search/", "Google Vertex AI Search": "https://python.langchain.com/docs/integrations/retrievers/google_vertex_ai_search/", "UpTrain": "https://python.langchain.com/docs/integrations/callbacks/uptrain/", "DSPy": "https://python.langchain.com/docs/integrations/providers/dspy/", "Weaviate": "https://python.langchain.com/docs/integrations/vectorstores/weaviate/", "Jaguar Vector Database": "https://python.langchain.com/docs/integrations/vectorstores/jaguar/", "Apache Cassandra": "https://python.langchain.com/docs/integrations/vectorstores/cassandra/", "Google Cloud Vertex AI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/google_cloud_vertexai_rerank/", "OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/", "Build a Retrieval Augmented Generation (RAG) App": "https://python.langchain.com/docs/tutorials/rag/", "Build a Local RAG Application": "https://python.langchain.com/docs/tutorials/local_rag/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/", "Build a Query Analysis System": "https://python.langchain.com/docs/tutorials/query_analysis/", "Build a Question/Answering system over SQL data": "https://python.langchain.com/docs/tutorials/sql_qa/", "Vector stores and retrievers": "https://python.langchain.com/docs/tutorials/retrievers/"}, "LLMRouterChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_router_chain/"}, "RouterOutputParser": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_router_chain/"}, "MapRerankDocumentsChain": {"# Example": "https://python.langchain.com/docs/versions/migrating_chains/map_rerank_docs_chain/"}, "RegexParser": {"# Example": "https://python.langchain.com/docs/versions/migrating_chains/map_rerank_docs_chain/"}, "TavilySearchResults": {"Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to debug your LLM apps": "https://python.langchain.com/docs/how_to/debugging/", "Tavily Search": "https://python.langchain.com/docs/integrations/tools/tavily_search/", "ZHIPU AI": "https://python.langchain.com/docs/integrations/chat/zhipuai/", "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere/", "Build an Agent": "https://python.langchain.com/docs/tutorials/agents/"}, "create_retriever_tool": {"Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "Xata": "https://python.langchain.com/docs/integrations/memory/xata_chat_message_history/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/", "Build a Question/Answering system over SQL data": "https://python.langchain.com/docs/tutorials/sql_qa/"}, "create_tool_calling_agent": {"Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to use tools in a chain": "https://python.langchain.com/docs/how_to/tools_chain/", "How to track token usage in ChatModels": "https://python.langchain.com/docs/how_to/chat_token_usage_tracking/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to debug your LLM apps": "https://python.langchain.com/docs/how_to/debugging/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "Azure Container Apps dynamic sessions": "https://python.langchain.com/docs/integrations/tools/azure_dynamic_sessions/", "FinancialDatasets Toolkit": "https://python.langchain.com/docs/integrations/tools/financial_datasets/", "Databricks Unity Catalog (UC)": "https://python.langchain.com/docs/integrations/tools/databricks/", "Riza Code Interpreter": "https://python.langchain.com/docs/integrations/tools/riza/", "Bing Search": "https://python.langchain.com/docs/integrations/tools/bing_search/"}, "AgentExecutor": {"Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add a semantic layer over graph database": "https://python.langchain.com/docs/how_to/graph_semantic/", "How to use tools in a chain": "https://python.langchain.com/docs/how_to/tools_chain/", "How to track token usage in ChatModels": "https://python.langchain.com/docs/how_to/chat_token_usage_tracking/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to debug your LLM apps": "https://python.langchain.com/docs/how_to/debugging/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "Infobip": "https://python.langchain.com/docs/integrations/tools/infobip/", "AskNews": "https://python.langchain.com/docs/integrations/tools/asknews/", "Azure Container Apps dynamic sessions": "https://python.langchain.com/docs/integrations/tools/azure_dynamic_sessions/", "FinancialDatasets Toolkit": "https://python.langchain.com/docs/integrations/tools/financial_datasets/", "Cassandra Database Toolkit": "https://python.langchain.com/docs/integrations/tools/cassandra_database/", "Polygon IO Toolkit": "https://python.langchain.com/docs/integrations/tools/polygon_toolkit/", "Semantic Scholar API Tool": "https://python.langchain.com/docs/integrations/tools/semanticscholar/", "Databricks Unity Catalog (UC)": "https://python.langchain.com/docs/integrations/tools/databricks/", "Passio NutritionAI": "https://python.langchain.com/docs/integrations/tools/passio_nutrition_ai/", "Memorize": "https://python.langchain.com/docs/integrations/tools/memorize/", "Riza Code Interpreter": "https://python.langchain.com/docs/integrations/tools/riza/", "ArXiv": "https://python.langchain.com/docs/integrations/tools/arxiv/", "Robocorp Toolkit": "https://python.langchain.com/docs/integrations/tools/robocorp/", "MultiOn Toolkit": "https://python.langchain.com/docs/integrations/tools/multion/", "Exa Search": "https://python.langchain.com/docs/integrations/tools/exa_search/", "Amadeus Toolkit": "https://python.langchain.com/docs/integrations/tools/amadeus/", "Reddit Search ": "https://python.langchain.com/docs/integrations/tools/reddit_search/", "You.com Search": "https://python.langchain.com/docs/integrations/tools/you/", "Bing Search": "https://python.langchain.com/docs/integrations/tools/bing_search/", "Azure AI Services Toolkit": "https://python.langchain.com/docs/integrations/tools/azure_ai_services/", "Polygon IO Toolkit and Tools": "https://python.langchain.com/docs/integrations/tools/polygon/", "Ionic Shopping Tool": "https://python.langchain.com/docs/integrations/tools/ionic_shopping/", "MLX": "https://python.langchain.com/docs/integrations/chat/mlx/", "ZHIPU AI": "https://python.langchain.com/docs/integrations/chat/zhipuai/", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor/", "Streamlit": "https://python.langchain.com/docs/integrations/callbacks/streamlit/", "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere/", "Dataherald": "https://python.langchain.com/docs/integrations/providers/dataherald/", "Log, Trace, and Monitor": "https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey/", "Portkey": "https://python.langchain.com/docs/integrations/providers/portkey/index/", "Bittensor": "https://python.langchain.com/docs/integrations/llms/bittensor/"}, "AIMessage": {"Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add a semantic layer over graph database": "https://python.langchain.com/docs/how_to/graph_semantic/", "How to merge consecutive messages of the same type": "https://python.langchain.com/docs/how_to/merge_message_runs/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to do tool/function calling": "https://python.langchain.com/docs/how_to/function_calling/", "How to add a human-in-the-loop for tools": "https://python.langchain.com/docs/how_to/tools_human/", "How to use prompting alone (no tool calling) to do extraction": "https://python.langchain.com/docs/how_to/extraction_parse/", "How to use reference examples when doing extraction": "https://python.langchain.com/docs/how_to/extraction_examples/", "How to create a custom chat model class": "https://python.langchain.com/docs/how_to/custom_chat_model/", "How to filter messages": "https://python.langchain.com/docs/how_to/filter_messages/", "How to handle tool errors": "https://python.langchain.com/docs/how_to/tools_error/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add retrieval to chatbots": "https://python.langchain.com/docs/how_to/chatbots_retrieval/", "How to create a custom Output Parser": "https://python.langchain.com/docs/how_to/output_parser_custom/", "How to return structured data from a model": "https://python.langchain.com/docs/how_to/structured_output/", "How to compose prompts together": "https://python.langchain.com/docs/how_to/prompts_composition/", "How to use few-shot prompting with tool calling": "https://python.langchain.com/docs/how_to/tools_few_shot/", "How to add examples to the prompt for query analysis": "https://python.langchain.com/docs/how_to/query_few_shot/", "Twitter (via Apify)": "https://python.langchain.com/docs/integrations/chat_loaders/twitter/", "Zep Open Source": 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"https://python.langchain.com/docs/integrations/llms/nlpcloud/", "ForefrontAI": "https://python.langchain.com/docs/integrations/llms/forefrontai/", "MosaicML": "https://python.langchain.com/docs/integrations/llms/mosaicml/", "CerebriumAI": "https://python.langchain.com/docs/integrations/llms/cerebriumai/", "Predibase": "https://python.langchain.com/docs/integrations/llms/predibase/", "GigaChat": "https://python.langchain.com/docs/integrations/llms/gigachat/", "OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/", "Aphrodite Engine": "https://python.langchain.com/docs/integrations/llms/aphrodite/", "Eden AI": "https://python.langchain.com/docs/integrations/llms/edenai/", "Javelin AI Gateway Tutorial": "https://python.langchain.com/docs/integrations/llms/javelin/", "C Transformers": "https://python.langchain.com/docs/integrations/llms/ctransformers/", "vLLM": "https://python.langchain.com/docs/integrations/llms/vllm/", "Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/", "Minimax": "https://python.langchain.com/docs/integrations/llms/minimax/", "Yuan2.0": "https://python.langchain.com/docs/integrations/llms/yuan2/", "Huggingface Endpoints": "https://python.langchain.com/docs/integrations/llms/huggingface_endpoint/", "Runhouse": "https://python.langchain.com/docs/integrations/llms/runhouse/", "Anyscale": "https://python.langchain.com/docs/integrations/llms/anyscale/", "YandexGPT": "https://python.langchain.com/docs/integrations/llms/yandex/", "GooseAI": "https://python.langchain.com/docs/integrations/llms/gooseai/", "OpenLM": "https://python.langchain.com/docs/integrations/llms/openlm/", "Cloudflare Workers AI": "https://python.langchain.com/docs/integrations/llms/cloudflare_workersai/", "CTranslate2": "https://python.langchain.com/docs/integrations/llms/ctranslate2/", "ChatGLM": "https://python.langchain.com/docs/integrations/llms/chatglm/", "Gradient": 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"https://python.langchain.com/docs/tutorials/summarization/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/", "Build a PDF ingestion and Question/Answering system": "https://python.langchain.com/docs/tutorials/pdf_qa/"}, "LLMMathChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_math_chain/"}, "BaseMessage": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_math_chain/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to use reference examples when doing extraction": "https://python.langchain.com/docs/how_to/extraction_examples/", "How to propagate callbacks  constructor": "https://python.langchain.com/docs/how_to/callbacks_constructor/", "How to attach callbacks to a runnable": "https://python.langchain.com/docs/how_to/callbacks_attach/", "How to create a custom chat model class": "https://python.langchain.com/docs/how_to/custom_chat_model/", "How to pass callbacks in at runtime": "https://python.langchain.com/docs/how_to/callbacks_runtime/", "How to add examples to the prompt for query analysis": "https://python.langchain.com/docs/how_to/query_few_shot/", "WeChat": "https://python.langchain.com/docs/integrations/chat_loaders/wechat/", "Discord": "https://python.langchain.com/docs/integrations/chat_loaders/discord/", "Chat Bot Feedback Template": "https://python.langchain.com/docs/templates/chat-bot-feedback/"}, "RunnableConfig": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/multi_prompt_chain/", "# Example": "https://python.langchain.com/docs/versions/migrating_chains/refine_docs_chain/", "How to access the RunnableConfig from a tool": "https://python.langchain.com/docs/how_to/tool_configure/", "How to summarize text through iterative refinement": "https://python.langchain.com/docs/how_to/summarize_refine/", "How to handle tool errors": "https://python.langchain.com/docs/how_to/tools_error/", "How to stream events from a tool": "https://python.langchain.com/docs/how_to/tool_stream_events/", "How to run custom functions": "https://python.langchain.com/docs/how_to/functions/", "How to add ad-hoc tool calling capability to LLMs and Chat Models": "https://python.langchain.com/docs/how_to/tools_prompting/", "LangChain Expression Language Cheatsheet": "https://python.langchain.com/docs/how_to/lcel_cheatsheet/", "How to dispatch custom callback events": "https://python.langchain.com/docs/how_to/callbacks_custom_events/", "How to pass runtime secrets to runnables": "https://python.langchain.com/docs/how_to/runnable_runtime_secrets/", "Tavily Search": "https://python.langchain.com/docs/integrations/tools/tavily_search/"}, "tool": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_math_chain/", "How to disable parallel tool calling": "https://python.langchain.com/docs/how_to/tool_calling_parallel/", "How to use tools in a chain": 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"https://python.langchain.com/docs/how_to/streaming/", "How to pass tool outputs to chat models": "https://python.langchain.com/docs/how_to/tool_results_pass_to_model/", "How to add ad-hoc tool calling capability to LLMs and Chat Models": "https://python.langchain.com/docs/how_to/tools_prompting/", "How to return artifacts from a tool": "https://python.langchain.com/docs/how_to/tool_artifacts/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "How to stream tool calls": "https://python.langchain.com/docs/how_to/tool_streaming/", "How to pass runtime secrets to runnables": "https://python.langchain.com/docs/how_to/runnable_runtime_secrets/", "How to use few-shot prompting with tool calling": "https://python.langchain.com/docs/how_to/tools_few_shot/", "FinancialDatasets Toolkit": "https://python.langchain.com/docs/integrations/tools/financial_datasets/", "Exa Search": "https://python.langchain.com/docs/integrations/tools/exa_search/", "DeepInfra": "https://python.langchain.com/docs/integrations/chat/deepinfra/", "ChatOllama": "https://python.langchain.com/docs/integrations/chat/ollama/", "Llama.cpp": "https://python.langchain.com/docs/integrations/chat/llamacpp/", "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere/", "Eden AI": "https://python.langchain.com/docs/integrations/chat/edenai/", "ChatTongyi": "https://python.langchain.com/docs/integrations/chat/tongyi/", "ChatPremAI": "https://python.langchain.com/docs/integrations/chat/premai/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor/", "PremAI": "https://python.langchain.com/docs/integrations/providers/premai/", "Log, Trace, and Monitor": "https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey/", "Portkey": 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"https://python.langchain.com/docs/integrations/chat/llama2_chat/", "SAP HANA Cloud Vector Engine": "https://python.langchain.com/docs/integrations/vectorstores/sap_hanavector/", "Bittensor": "https://python.langchain.com/docs/integrations/llms/bittensor/"}, "InMemoryChatMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/"}, "RunnableWithMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to trim 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"https://python.langchain.com/docs/integrations/document_loaders/youtube_transcript/"}, "ScrapingAntLoader": {"ScrapingAnt": "https://python.langchain.com/docs/integrations/document_loaders/scrapingant/"}, "AirbyteSalesforceLoader": {"Airbyte Salesforce (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_salesforce/"}, "AirbyteCDKLoader": {"Airbyte CDK (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_cdk/"}, "Docx2txtLoader": {"Microsoft Word": "https://python.langchain.com/docs/integrations/document_loaders/microsoft_word/"}, "RSpaceLoader": {"# replace these ids with some from your own research notes.": "https://python.langchain.com/docs/integrations/document_loaders/rspace/"}, "SeleniumURLLoader": {"URL": "https://python.langchain.com/docs/integrations/document_loaders/url/"}, "PlaywrightURLLoader": {"URL": "https://python.langchain.com/docs/integrations/document_loaders/url/"}, "AirbyteJSONLoader": {"Airbyte JSON (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_json/"}, "AirbyteStripeLoader": {"Airbyte Stripe (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_stripe/"}, "GeoDataFrameLoader": {"Geopandas": "https://python.langchain.com/docs/integrations/document_loaders/geopandas/"}, "VectorstoreIndexCreator": {"HuggingFace dataset": "https://python.langchain.com/docs/integrations/document_loaders/hugging_face_dataset/", "Spreedly": "https://python.langchain.com/docs/integrations/document_loaders/spreedly/", "Figma": "https://python.langchain.com/docs/integrations/document_loaders/figma/", "Apify Dataset": "https://python.langchain.com/docs/integrations/document_loaders/apify_dataset/", "Iugu": "https://python.langchain.com/docs/integrations/document_loaders/iugu/", "Stripe": "https://python.langchain.com/docs/integrations/document_loaders/stripe/", "Modern Treasury": "https://python.langchain.com/docs/integrations/document_loaders/modern_treasury/"}, "AirbyteTypeformLoader": {"Airbyte Typeform (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_typeform/"}, "MHTMLLoader": {"mhtml": "https://python.langchain.com/docs/integrations/document_loaders/mhtml/"}, "SpiderLoader": {"Spider": "https://python.langchain.com/docs/integrations/document_loaders/spider/"}, "NewsURLLoader": {"News URL": "https://python.langchain.com/docs/integrations/document_loaders/news/"}, "ImageCaptionLoader": {"Image captions": "https://python.langchain.com/docs/integrations/document_loaders/image_captions/"}, "LLMSherpaFileLoader": {"LLM Sherpa": "https://python.langchain.com/docs/integrations/document_loaders/llmsherpa/"}, "PyMuPDFLoader": {"PyMuPDF": "https://python.langchain.com/docs/integrations/document_loaders/pymupdf/"}, "ScrapflyLoader": {"# ScrapFly": "https://python.langchain.com/docs/integrations/document_loaders/scrapfly/"}, "TomlLoader": {"TOML": "https://python.langchain.com/docs/integrations/document_loaders/toml/"}, "PsychicLoader": {"Psychic": "https://python.langchain.com/docs/integrations/document_loaders/psychic/"}, "FireCrawlLoader": {"FireCrawl": "https://python.langchain.com/docs/integrations/document_loaders/firecrawl/"}, "LarkSuiteWikiLoader": {"LarkSuite (FeiShu)": "https://python.langchain.com/docs/integrations/document_loaders/larksuite/"}, "FakeListLLM": {"LarkSuite (FeiShu)": "https://python.langchain.com/docs/integrations/document_loaders/larksuite/"}, "MergedDataLoader": {"Merge Documents Loader": "https://python.langchain.com/docs/integrations/document_loaders/merge_doc/"}, "RecursiveUrlLoader": {"Recursive URL": "https://python.langchain.com/docs/integrations/document_loaders/recursive_url/"}, "PDFPlumberLoader": {"PDFPlumber": "https://python.langchain.com/docs/integrations/document_loaders/pdfplumber/"}, "PyPDFium2Loader": {"PyPDFium2Loader": "https://python.langchain.com/docs/integrations/document_loaders/pypdfium2/"}, "AirbyteHubspotLoader": {"Airbyte Hubspot (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_hubspot/"}, "AirbyteGongLoader": {"Airbyte Gong (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_gong/"}, "AstraDBLoader": {"AstraDB": "https://python.langchain.com/docs/integrations/document_loaders/astradb/"}, "ReadTheDocsLoader": {"ReadTheDocs Documentation": "https://python.langchain.com/docs/integrations/document_loaders/readthedocs_documentation/"}, "MathpixPDFLoader": {"MathPixPDFLoader": "https://python.langchain.com/docs/integrations/document_loaders/mathpix/"}, "PolarsDataFrameLoader": {"Polars DataFrame": "https://python.langchain.com/docs/integrations/document_loaders/polars_dataframe/"}, "DataFrameLoader": {"Pandas DataFrame": "https://python.langchain.com/docs/integrations/document_loaders/pandas_dataframe/"}, "SurrealDBLoader": {"SurrealDB": "https://python.langchain.com/docs/integrations/document_loaders/surrealdb/"}, "DedocFileLoader": {"Dedoc": "https://python.langchain.com/docs/integrations/document_loaders/dedoc/"}, "DedocPDFLoader": {"Dedoc": "https://python.langchain.com/docs/integrations/document_loaders/dedoc/"}, "DedocAPIFileLoader": {"Dedoc": "https://python.langchain.com/docs/integrations/document_loaders/dedoc/"}, "GoogleApiClient": {"YouTube transcripts": "https://python.langchain.com/docs/integrations/document_loaders/youtube_transcript/"}, "ConcurrentLoader": {"Concurrent Loader": "https://python.langchain.com/docs/integrations/document_loaders/concurrent/"}, "RSSFeedLoader": {"RSS Feeds": "https://python.langchain.com/docs/integrations/document_loaders/rss/"}, "PebbloSafeLoader": {"Pebblo Safe DocumentLoader": "https://python.langchain.com/docs/integrations/document_loaders/pebblo/"}, "VsdxLoader": {"Vsdx": "https://python.langchain.com/docs/integrations/document_loaders/vsdx/"}, "NotebookLoader": {"Jupyter Notebook": "https://python.langchain.com/docs/integrations/document_loaders/jupyter_notebook/"}, "OracleAutonomousDatabaseLoader": {"Oracle Autonomous Database": "https://python.langchain.com/docs/integrations/document_loaders/oracleadb_loader/"}, "LanguageParser": {"Source Code": "https://python.langchain.com/docs/integrations/document_loaders/source_code/"}, "SRTLoader": {"Subtitle": "https://python.langchain.com/docs/integrations/document_loaders/subtitle/"}, "MastodonTootsLoader": {"Mastodon": "https://python.langchain.com/docs/integrations/document_loaders/mastodon/"}, "AirbyteShopifyLoader": {"Airbyte Shopify (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_shopify/"}, "PyPDFDirectoryLoader": {"PyPDFDirectoryLoader": "https://python.langchain.com/docs/integrations/document_loaders/pypdfdirectory/"}, "PySparkDataFrameLoader": {"PySpark": "https://python.langchain.com/docs/integrations/document_loaders/pyspark_dataframe/"}, "AirbyteZendeskSupportLoader": {"Airbyte Zendesk Support (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_zendesk_support/"}, "CoNLLULoader": {"CoNLL-U": "https://python.langchain.com/docs/integrations/document_loaders/conll-u/"}, "MongodbLoader": {"MongoDB": "https://python.langchain.com/docs/integrations/document_loaders/mongodb/"}, "SitemapLoader": {"Sitemap": "https://python.langchain.com/docs/integrations/document_loaders/sitemap/"}, "YuqueLoader": {"Yuque": "https://python.langchain.com/docs/integrations/document_loaders/yuque/"}, "PDFMinerLoader": {"PDFMiner": "https://python.langchain.com/docs/integrations/document_loaders/pdfminer/"}, "PDFMinerPDFasHTMLLoader": {"PDFMiner": "https://python.langchain.com/docs/integrations/document_loaders/pdfminer/"}, "QuipLoader": {"Quip": "https://python.langchain.com/docs/integrations/document_loaders/quip/"}, "LangSmithLoader": {"LangSmithLoader": "https://python.langchain.com/docs/integrations/document_loaders/langsmith/"}, "MemgraphGraph": {"Memgraph": "https://python.langchain.com/docs/integrations/graphs/memgraph/"}, "GraphSparqlQAChain": {"RDFLib": "https://python.langchain.com/docs/integrations/graphs/rdflib_sparql/"}, "RdfGraph": {"RDFLib": "https://python.langchain.com/docs/integrations/graphs/rdflib_sparql/"}, "NebulaGraphQAChain": {"NebulaGraph": "https://python.langchain.com/docs/integrations/graphs/nebula_graph/"}, "NebulaGraph": {"NebulaGraph": "https://python.langchain.com/docs/integrations/graphs/nebula_graph/"}, "GremlinQAChain": {"Azure Cosmos DB for Apache Gremlin": "https://python.langchain.com/docs/integrations/graphs/azure_cosmosdb_gremlin/"}, "GraphIndexCreator": {"NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/"}, "GraphQAChain": {"NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/"}, "NetworkxEntityGraph": {"NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/"}, "HugeGraphQAChain": {"HugeGraph": "https://python.langchain.com/docs/integrations/graphs/hugegraph/"}, "HugeGraph": {"HugeGraph": "https://python.langchain.com/docs/integrations/graphs/hugegraph/"}, "AGEGraph": {"Apache AGE": "https://python.langchain.com/docs/integrations/graphs/apache_age/"}, "KuzuQAChain": {"Kuzu": "https://python.langchain.com/docs/integrations/graphs/kuzu_db/"}, "KuzuGraph": {"Kuzu": "https://python.langchain.com/docs/integrations/graphs/kuzu_db/"}, "FalkorDBQAChain": {"FalkorDB": "https://python.langchain.com/docs/integrations/graphs/falkordb/"}, "FalkorDBGraph": {"FalkorDB": "https://python.langchain.com/docs/integrations/graphs/falkordb/"}, "ConversationBufferWindowMemory": {"Baseten": "https://python.langchain.com/docs/integrations/llms/baseten/", "OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/"}, "Solar": {"Solar": "https://python.langchain.com/docs/integrations/llms/solar/"}, "GoogleSearchAPIWrapper": {"Bittensor": "https://python.langchain.com/docs/integrations/llms/bittensor/"}, "IpexLLM": {"IPEX-LLM": "https://python.langchain.com/docs/integrations/llms/ipex_llm/"}, "LLMContentHandler": {"SageMakerEndpoint": "https://python.langchain.com/docs/integrations/llms/sagemaker/"}, "TextGen": {"TextGen": "https://python.langchain.com/docs/integrations/llms/textgen/"}, "MosaicML": {"MosaicML": "https://python.langchain.com/docs/integrations/llms/mosaicml/"}, "VolcEngineMaasLLM": {"Volc Engine Maas": "https://python.langchain.com/docs/integrations/llms/volcengine_maas/"}, "KoboldApiLLM": {"KoboldAI API": "https://python.langchain.com/docs/integrations/llms/koboldai/"}, "Konko": {"Konko": "https://python.langchain.com/docs/integrations/llms/konko/"}, "OpaquePrompts": {"OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/"}, "TitanTakeoff": {"Titan Takeoff": "https://python.langchain.com/docs/integrations/llms/titan_takeoff/"}, "Friendli": {"Friendli": "https://python.langchain.com/docs/integrations/llms/friendli/"}, "Databricks": {"Databricks": "https://python.langchain.com/docs/integrations/llms/databricks/"}, "LMFormatEnforcer": {"LM Format Enforcer": "https://python.langchain.com/docs/integrations/llms/lmformatenforcer_experimental/"}, "VLLM": {"vLLM": "https://python.langchain.com/docs/integrations/llms/vllm/"}, "VLLMOpenAI": {"vLLM": "https://python.langchain.com/docs/integrations/llms/vllm/"}, "CustomOpenAIContentFormatter": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "ContentFormatterBase": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "DollyContentFormatter": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "load_llm": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "MapReduceChain": {"Manifest": "https://python.langchain.com/docs/integrations/llms/manifest/"}, "ModelLaboratory": {"Manifest": "https://python.langchain.com/docs/integrations/llms/manifest/"}, "ExLlamaV2": {"ExLlamaV2": "https://python.langchain.com/docs/integrations/llms/exllamav2/"}, "RELLM": {"RELLM": "https://python.langchain.com/docs/integrations/llms/rellm_experimental/"}, "Moonshot": {"MoonshotChat": "https://python.langchain.com/docs/integrations/llms/moonshot/"}, "OpenLM": {"OpenLM": "https://python.langchain.com/docs/integrations/llms/openlm/"}, "CloudflareWorkersAI": {"Cloudflare Workers AI": "https://python.langchain.com/docs/integrations/llms/cloudflare_workersai/"}, "ChatGLM3": {"ChatGLM": "https://python.langchain.com/docs/integrations/llms/chatglm/"}, "ChatGLM": {"ChatGLM": "https://python.langchain.com/docs/integrations/llms/chatglm/"}, "Sambaverse": {"SambaNova": "https://python.langchain.com/docs/integrations/llms/sambanova/"}, "SambaStudio": {"SambaNova": "https://python.langchain.com/docs/integrations/llms/sambanova/"}, "LayerupSecurity": {"Layerup Security": "https://python.langchain.com/docs/integrations/llms/layerup_security/"}, "JsonFormer": {"JSONFormer": "https://python.langchain.com/docs/integrations/llms/jsonformer_experimental/"}, "WeightOnlyQuantPipeline": {"Intel Weight-Only Quantization": "https://python.langchain.com/docs/integrations/llms/weight_only_quantization/"}, "Replicate": {"Replicate": "https://python.langchain.com/docs/integrations/llms/replicate/"}, "tracing_v2_enabled": {"Chat Bot Feedback Template": "https://python.langchain.com/docs/templates/chat-bot-feedback/"}, "QuerySQLDataBaseTool": {"Build a Question/Answering system over SQL data": "https://python.langchain.com/docs/tutorials/sql_qa/"}, "OPENAI_TEMPLATE": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "create_openai_data_generator": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "DatasetGenerator": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "create_data_generation_chain": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "create_extraction_chain_pydantic": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}}
\ No newline at end of file
diff --git a/docs/docs/contributing/code/guidelines.mdx b/docs/docs/contributing/how_to/code/guidelines.mdx
similarity index 87%
rename from docs/docs/contributing/code/guidelines.mdx
rename to docs/docs/contributing/how_to/code/guidelines.mdx
index 7f75199b1e1bc..175290a6428c2 100644
--- a/docs/docs/contributing/code/guidelines.mdx
+++ b/docs/docs/contributing/how_to/code/guidelines.mdx
@@ -6,7 +6,7 @@ Here are some things to keep in mind for all types of contributions:
 - Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
 - Ensure your PR passes formatting, linting, and testing checks before requesting a review.
   - If you would like comments or feedback on your current progress, please open an issue or discussion and tag a maintainer.
-  - See the sections on [Testing](/docs/contributing/code/setup#testing) and [Formatting and Linting](/docs/contributing/code/setup#formatting-and-linting) for how to run these checks locally.
+  - See the sections on [Testing](setup.mdx#testing) and [Formatting and Linting](setup.mdx#formatting-and-linting) for how to run these checks locally.
 - Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
 - Look for duplicate PRs or issues that have already been opened before opening a new one.
 - Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
@@ -29,7 +29,7 @@ or new agents/chains from outside contributors without an existing GitHub discus
 
 - New features must come with docs, unit tests, and (if appropriate) integration tests.
 - New integrations must come with docs, unit tests, and (if appropriate) integration tests.
-  - See [this page](/docs/contributing/integrations) for more details on contributing new integrations.
+  - See [this page](../integrations.mdx) for more details on contributing new integrations.
 - New functionality should not inherit from or use deprecated methods or classes.
 - We will reject features that are likely to lead to security vulnerabilities or reports.
 - Do not add any hard dependencies. Integrations may add optional dependencies.
diff --git a/docs/docs/contributing/code/index.mdx b/docs/docs/contributing/how_to/code/index.mdx
similarity index 58%
rename from docs/docs/contributing/code/index.mdx
rename to docs/docs/contributing/how_to/code/index.mdx
index 43b90785567b2..cd3889bed0cf4 100644
--- a/docs/docs/contributing/code/index.mdx
+++ b/docs/docs/contributing/how_to/code/index.mdx
@@ -2,5 +2,5 @@
 
 If you would like to add a new feature or update an existing one, please read the resources below before getting started:
 
-- [General guidelines](/docs/contributing/code/guidelines/)
-- [Setup](/docs/contributing/code/setup/)
+- [General guidelines](guidelines.mdx)
+- [Setup](setup.mdx)
diff --git a/docs/docs/contributing/code/setup.mdx b/docs/docs/contributing/how_to/code/setup.mdx
similarity index 98%
rename from docs/docs/contributing/code/setup.mdx
rename to docs/docs/contributing/how_to/code/setup.mdx
index c5ad124f5916d..77dc1741adc06 100644
--- a/docs/docs/contributing/code/setup.mdx
+++ b/docs/docs/contributing/how_to/code/setup.mdx
@@ -71,7 +71,7 @@ To run unit tests in Docker:
 make docker_tests
 ```
 
-There are also [integration tests and code-coverage](/docs/contributing/testing/) available.
+There are also [integration tests and code-coverage](../testing.mdx) available.
 
 ### Only develop langchain_core or langchain_community
 
diff --git a/docs/docs/contributing/documentation/index.mdx b/docs/docs/contributing/how_to/documentation/index.mdx
similarity index 65%
rename from docs/docs/contributing/documentation/index.mdx
rename to docs/docs/contributing/how_to/documentation/index.mdx
index 564edf6d53498..53e3cb052717c 100644
--- a/docs/docs/contributing/documentation/index.mdx
+++ b/docs/docs/contributing/how_to/documentation/index.mdx
@@ -3,5 +3,5 @@
 Documentation is a vital part of LangChain. We welcome both new documentation for new features and 
 community improvements to our current documentation. Please read the resources below before getting started:
 
-- [Documentation style guide](/docs/contributing/documentation/style_guide/)
-- [Setup](/docs/contributing/documentation/setup/)
+- [Documentation style guide](style_guide.mdx)
+- [Setup](setup.mdx)
diff --git a/docs/docs/contributing/documentation/setup.mdx b/docs/docs/contributing/how_to/documentation/setup.mdx
similarity index 100%
rename from docs/docs/contributing/documentation/setup.mdx
rename to docs/docs/contributing/how_to/documentation/setup.mdx
diff --git a/docs/docs/contributing/documentation/style_guide.mdx b/docs/docs/contributing/how_to/documentation/style_guide.mdx
similarity index 96%
rename from docs/docs/contributing/documentation/style_guide.mdx
rename to docs/docs/contributing/how_to/documentation/style_guide.mdx
index 76a6ab837b361..437977573c09f 100644
--- a/docs/docs/contributing/documentation/style_guide.mdx
+++ b/docs/docs/contributing/how_to/documentation/style_guide.mdx
@@ -11,9 +11,9 @@ organization and structure.
 ## Philosophy
 
 LangChain's documentation follows the [Diataxis framework](https://diataxis.fr).
-Under this framework, all documentation falls under one of four categories: [Tutorials](/docs/contributing/documentation/style_guide/#tutorials),
-[How-to guides](/docs/contributing/documentation/style_guide/#how-to-guides),
-[References](/docs/contributing/documentation/style_guide/#references), and [Explanations](/docs/contributing/documentation/style_guide/#conceptual-guide).
+Under this framework, all documentation falls under one of four categories: [Tutorials](#tutorials),
+[How-to guides](#how-to-guides),
+[References](#references), and [Explanations](#conceptual-guide).
 
 ### Tutorials
 
diff --git a/docs/docs/contributing/how_to/index.mdx b/docs/docs/contributing/how_to/index.mdx
new file mode 100644
index 0000000000000..820c20c97a5e6
--- /dev/null
+++ b/docs/docs/contributing/how_to/index.mdx
@@ -0,0 +1,5 @@
+# How-to Guides
+
+- [**Documentation**](documentation/index.mdx): Help improve our docs, including this one!
+- [**Code**](code/index.mdx): Help us write code, fix bugs, or improve our infrastructure.
+- [**Integrations**](integrations.mdx): Help us integrate with your favorite vendors and tools.
\ No newline at end of file
diff --git a/docs/docs/contributing/integrations.mdx b/docs/docs/contributing/how_to/integrations.mdx
similarity index 98%
rename from docs/docs/contributing/integrations.mdx
rename to docs/docs/contributing/how_to/integrations.mdx
index 0c60ca739ddd4..ed2cc9ec449c6 100644
--- a/docs/docs/contributing/integrations.mdx
+++ b/docs/docs/contributing/how_to/integrations.mdx
@@ -4,7 +4,7 @@ sidebar_position: 5
 
 # Contribute Integrations
 
-To begin, make sure you have all the dependencies outlined in guide on [Contributing Code](/docs/contributing/code/).
+To begin, make sure you have all the dependencies outlined in guide on [Contributing Code](code/index.mdx).
 
 There are a few different places you can contribute integrations for LangChain:
 
@@ -138,7 +138,7 @@ By default, this will include stubs for a Chat Model, an LLM, and/or a Vector St
 
 Some basic tests are presented in the `tests/` directory. You should add more tests to cover your package's functionality.
 
-For information on running and implementing tests, see the [Testing guide](/docs/contributing/testing/).
+For information on running and implementing tests, see the [Testing guide](testing.mdx).
 
 ### Write documentation
 
diff --git a/docs/docs/contributing/testing.mdx b/docs/docs/contributing/how_to/testing.mdx
similarity index 100%
rename from docs/docs/contributing/testing.mdx
rename to docs/docs/contributing/how_to/testing.mdx
diff --git a/docs/docs/contributing/index.mdx b/docs/docs/contributing/index.mdx
index 68736123b502b..e567ee75a71f7 100644
--- a/docs/docs/contributing/index.mdx
+++ b/docs/docs/contributing/index.mdx
@@ -8,13 +8,21 @@ As an open-source project in a fast developing field, we are extremely open to c
 
 ## Tutorials
 
-Coming soon! We are working on tutorials to help you make your first contribution to the project.
+More coming soon! We are working on tutorials to help you make your first contribution to the project.
+
+- [**Make your first docs PR**](tutorials/docs.mdx)
 
 ## How-to Guides
 
-- [**Documentation**](/docs/contributing/documentation/): Help improve our docs, including this one!
-- [**Code**](/docs/contributing/code/): Help us write code, fix bugs, or improve our infrastructure.
-- [**Integrations**](integrations.mdx): Help us integrate with your favorite vendors and tools.
+- [**Documentation**](how_to/documentation/index.mdx): Help improve our docs, including this one!
+- [**Code**](how_to/code/index.mdx): Help us write code, fix bugs, or improve our infrastructure.
+- [**Integrations**](how_to/integrations.mdx): Help us integrate with your favorite vendors and tools.
+
+## Reference
+
+- [**Repository Structure**](reference/repo_structure.mdx): Understand the high level structure of the repository.
+- [**Review Process**](reference/review_process.mdx): Learn about the review process for pull requests.
+- [**Frequently Asked Questions (FAQ)**](reference/faq.mdx): Get answers to common questions about contributing.
 
 ## Community
 
diff --git a/docs/docs/contributing/faq.mdx b/docs/docs/contributing/reference/faq.mdx
similarity index 90%
rename from docs/docs/contributing/faq.mdx
rename to docs/docs/contributing/reference/faq.mdx
index e1993e1a83f0c..d546d95b634d7 100644
--- a/docs/docs/contributing/faq.mdx
+++ b/docs/docs/contributing/reference/faq.mdx
@@ -27,11 +27,11 @@ fork in order to enable this setting.
 
 ### Why hasn't my PR been reviewed?
 
-Please reference our [Review Process](/docs/contributing/review_process/).
+Please reference our [Review Process](review_process.mdx).
 
 ### Why was my PR closed?
 
-Please reference our [Review Process](/docs/contributing/review_process/).
+Please reference our [Review Process](review_process.mdx).
 
 ### I think my PR was closed in a way that didn't follow the review process. What should I do?
 
diff --git a/docs/docs/contributing/reference/index.mdx b/docs/docs/contributing/reference/index.mdx
new file mode 100644
index 0000000000000..4311a01cda17c
--- /dev/null
+++ b/docs/docs/contributing/reference/index.mdx
@@ -0,0 +1,5 @@
+# Reference
+
+- [**Repository Structure**](repo_structure.mdx): Understand the high level structure of the repository.
+- [**Review Process**](review_process.mdx): Learn about the review process for pull requests.
+- [**Frequently Asked Questions (FAQ)**](faq.mdx): Get answers to common questions about contributing.
\ No newline at end of file
diff --git a/docs/docs/contributing/repo_structure.mdx b/docs/docs/contributing/reference/repo_structure.mdx
similarity index 86%
rename from docs/docs/contributing/repo_structure.mdx
rename to docs/docs/contributing/reference/repo_structure.mdx
index 30459d55784e8..f7df2f11423ab 100644
--- a/docs/docs/contributing/repo_structure.mdx
+++ b/docs/docs/contributing/reference/repo_structure.mdx
@@ -52,7 +52,7 @@ There are other files in the root directory level, but their presence should be
 The `/docs` directory contains the content for the documentation that is shown
 at https://python.langchain.com/ and the associated API Reference https://python.langchain.com/api_reference/langchain/index.html.
 
-See the [documentation](/docs/contributing/documentation/) guidelines to learn how to contribute to the documentation.
+See the [documentation](../how_to/documentation/index.mdx) guidelines to learn how to contribute to the documentation.
 
 ## Code
 
@@ -60,6 +60,6 @@ The `/libs` directory contains the code for the LangChain packages.
 
 To learn more about how to contribute code see the following guidelines:
 
-- [Code](/docs/contributing/code/): Learn how to develop in the LangChain codebase.
-- [Integrations](./integrations.mdx): Learn how to contribute to third-party integrations to `langchain-community` or to start a new partner package.
-- [Testing](./testing.mdx): Guidelines to learn how to write tests for the packages.
+- [Code](../how_to/code/index.mdx): Learn how to develop in the LangChain codebase.
+- [Integrations](../how_to/integrations.mdx): Learn how to contribute to third-party integrations to `langchain-community` or to start a new partner package.
+- [Testing](../how_to/testing.mdx): Guidelines to learn how to write tests for the packages.
diff --git a/docs/docs/contributing/review_process.mdx b/docs/docs/contributing/reference/review_process.mdx
similarity index 100%
rename from docs/docs/contributing/review_process.mdx
rename to docs/docs/contributing/reference/review_process.mdx
diff --git a/docs/docs/contributing/tutorials/docs.mdx b/docs/docs/contributing/tutorials/docs.mdx
new file mode 100644
index 0000000000000..423163d14438f
--- /dev/null
+++ b/docs/docs/contributing/tutorials/docs.mdx
@@ -0,0 +1,51 @@
+# Make your first docs PR
+
+This tutorial will guide you through making a simple documentation edit, like correcting a typo.
+
+### **Prerequisites**
+- GitHub account.
+- Familiarity with GitHub pull requests (basic understanding).
+
+---
+
+## Editing a Documentation Page on GitHub**
+
+Sometimes you want to make a small change, like fixing a typo, and the easiest way to do this is to use GitHub's editor directly.
+
+### **Steps**
+
+1. **Navigate to the documentation page in the LangChain docs:**
+   - On the documentation page, find the green "Edit this page" link at the bottom of the page.
+   - Click the button to be directed to the GitHub editor.
+   - If the file you're editing is a Jupyter Notebook (.ipynb) instead of a Markdown (.md, .mdx)
+        file, we recommend following the steps in section 3.
+
+2. **Fork the repository:**
+   - If you haven't already, GitHub will prompt you to fork the repository to your account.
+   - Make sure to fork the repository into your **personal account and not an organization** ([why?](../reference/faq.mdx#how-do-i-allow-maintainers-to-edit-my-pr)).
+   - Click the "Fork this repository" button to create a copy of the repository under your account.
+   - After forking, you'll automatically be redirected to the correct editor.
+
+3. **Make your changes:**
+   - Correct the typo directly in the GitHub editor.
+
+4. **Commit your changes:**
+   - Click the "Commit changes..." button at the top-right corner of the page.
+   - Give your commit a title like "Fix typo in X section."
+   - Optionally, write an extended commit description.
+   - Click "Propose changes"
+
+5. **Submit a pull request (PR):**
+   - GitHub will redirect you to a page where you can create a pull request.
+   - First, review your proposed changes to ensure they are correct.
+   - Click **Create pull request**.
+   - Give your PR a title like `docs: Fix typo in X section`.
+   - Follow the checklist in the PR description template.
+
+## Getting a Review
+
+Once you've submitted the pull request, it will be reviewed by the maintainers. You may receive feedback or requests for changes. Keep an eye on the PR to address any comments.
+
+Docs PRs are typically reviewed within a few days, but it may take longer depending on the complexity of the change and the availability of maintainers.
+
+For more information on reviews, see the [Review Process](../reference/review_process.mdx).
diff --git a/docs/docs/contributing/tutorials/index.mdx b/docs/docs/contributing/tutorials/index.mdx
new file mode 100644
index 0000000000000..fc55773618958
--- /dev/null
+++ b/docs/docs/contributing/tutorials/index.mdx
@@ -0,0 +1,5 @@
+# Tutorials
+
+More coming soon! We are working on tutorials to help you make your first contribution to the project.
+
+- [**Make your first docs PR**](docs.mdx)
diff --git a/docs/docs/integrations/chat/index.mdx b/docs/docs/integrations/chat/index.mdx
index 557c11c34ae24..e2b0be6da4dda 100644
--- a/docs/docs/integrations/chat/index.mdx
+++ b/docs/docs/integrations/chat/index.mdx
@@ -11,7 +11,7 @@ keywords: [compatibility]
 :::info
 
 If you'd like to write your own chat model, see [this how-to](/docs/how_to/custom_chat_model/).
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
+If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/how_to/integrations/).
 
 :::
 
diff --git a/docs/docs/integrations/llms/index.mdx b/docs/docs/integrations/llms/index.mdx
index b902bf7ea7f74..1831274c45555 100644
--- a/docs/docs/integrations/llms/index.mdx
+++ b/docs/docs/integrations/llms/index.mdx
@@ -17,7 +17,7 @@ Unless you are specifically using more advanced prompting techniques, you are pr
 :::info
 
 If you'd like to write your own LLM, see [this how-to](/docs/how_to/custom_llm/).
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
+If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/how_to/integrations/).
 
 :::
 
diff --git a/docs/docs/introduction.mdx b/docs/docs/introduction.mdx
index 039cbca49db82..7f5afbee628c8 100644
--- a/docs/docs/introduction.mdx
+++ b/docs/docs/introduction.mdx
@@ -91,8 +91,8 @@ See what changed in v0.3, learn how to migrate legacy code, read up on our versi
 ### [Security](/docs/security)
 Read up on [security](/docs/security) best practices to make sure you're developing safely with LangChain.
 
-### [Integrations](/docs/integrations/providers/)
+### [Integrations](integrations/providers/index.mdx)
 LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/providers/).
 
-### [Contributing](/docs/contributing)
+### [Contributing](contributing/index.mdx)
 Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.
diff --git a/docs/scripts/document_loader_feat_table.py b/docs/scripts/document_loader_feat_table.py
index 44e070dcd6ac6..b18ca200482d5 100644
--- a/docs/scripts/document_loader_feat_table.py
+++ b/docs/scripts/document_loader_feat_table.py
@@ -18,7 +18,7 @@
 :::info
 
 If you'd like to write your own document loader, see [this how-to](/docs/how_to/document_loader_custom/).
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
+If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/how_to/integrations/).
 
 :::
 
diff --git a/docs/scripts/kv_store_feat_table.py b/docs/scripts/kv_store_feat_table.py
index 55adb2ac87a2f..dd7fc1ca203a8 100644
--- a/docs/scripts/kv_store_feat_table.py
+++ b/docs/scripts/kv_store_feat_table.py
@@ -18,7 +18,7 @@
 
 :::info
 
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
+If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/how_to/integrations/).
 
 :::
 
diff --git a/docs/scripts/partner_pkg_table.py b/docs/scripts/partner_pkg_table.py
index 00219fe23f9a2..36822da3bb968 100644
--- a/docs/scripts/partner_pkg_table.py
+++ b/docs/scripts/partner_pkg_table.py
@@ -122,7 +122,7 @@ def doc() -> str:
 :::info
 
 If you'd like to write your own integration, see [Extending LangChain](/docs/how_to/#custom).
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
+If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/how_to/integrations/).
 
 :::
 
diff --git a/docs/scripts/tool_feat_table.py b/docs/scripts/tool_feat_table.py
index a71843a75286e..981fe2f706525 100644
--- a/docs/scripts/tool_feat_table.py
+++ b/docs/scripts/tool_feat_table.py
@@ -189,7 +189,7 @@
 :::info
 
 If you'd like to write your own tool, see [this how-to](/docs/how_to/custom_tools/).
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
+If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/how_to/integrations/).
 
 :::
 
diff --git a/docs/sidebars.js b/docs/sidebars.js
index 6f02bf20fdcfb..94daa97f1f709 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -427,24 +427,43 @@ module.exports = {
     },
   ],
   contributing: [
+    {
+      type: "doc",
+      label: "Welcome Contributors",
+      id: "contributing/index",
+    },
     {
       type: "category",
-      label: "Contributing",
-      items: [
-        "contributing/index",
-        "contributing/repo_structure",
-        "contributing/code/index",
-        { type: "doc", id: "contributing/code/guidelines", className: "hidden" },
-        { type: "doc", id: "contributing/code/setup", className: "hidden" },
-        "contributing/integrations",
-        "contributing/documentation/index",
-        { type: "doc", id: "contributing/documentation/style_guide", className: "hidden" },
-        { type: "doc", id: "contributing/documentation/setup", className: "hidden" },
-        "contributing/testing",
-        "contributing/review_process",
-        "contributing/faq",
-      ],
+      link: {type: 'doc', id: 'contributing/tutorials/index'},
+      label: "Tutorials",
       collapsible: false,
+      items: [{
+        type: 'autogenerated',
+        dirName: 'contributing/tutorials',
+        className: 'hidden',
+      }],
+    },
+    {
+      type: "category",
+      link: {type: 'doc', id: 'contributing/how_to/index'},
+      label: "How-to guides",
+      collapsible: false,
+      items: [{
+        type: 'autogenerated',
+        dirName: 'contributing/how_to',
+        className: 'hidden',
+      }],
+    },
+    {
+      type: "category",
+      link: {type: 'doc', id: 'contributing/reference/index'},
+      label: "Reference & FAQ",
+      collapsible: false,
+      items: [{
+        type: 'autogenerated',
+        dirName: 'contributing/reference',
+        className: 'hidden',
+      }],
     },
   ],
 };
diff --git a/docs/src/theme/NotFound/Content/index.js b/docs/src/theme/NotFound/Content/index.js
index aad591a8d477c..8b7fc2f9690f9 100644
--- a/docs/src/theme/NotFound/Content/index.js
+++ b/docs/src/theme/NotFound/Content/index.js
@@ -67,7 +67,7 @@ const suggestedLinks = {
     ]
   },
   "/docs/contributing/documentation/technical_logistics/": {
-    "canonical": "/docs/contributing/documentation/",
+    "canonical": "/docs/contributing/how_to/documentation/",
     "alternative": [
       "/v0.1/docs/contributing/documentation/technical_logistics/"
     ]
@@ -2894,7 +2894,7 @@ const suggestedLinks = {
   "/docs/expression_language/how_to/binding/": {"canonical": "/docs/how_to/binding/", "alternative": ["/v0.1/docs/expression_language/primitives/binding/"]},
   "/docs/expression_language/how_to/configure/": {"canonical": "/docs/how_to/configure/", "alternative": ["/v0.1/docs/expression_language/primitives/configure/"]},
   "/docs/expression_language/cookbook/prompt_llm_parser/": {"canonical": "/docs/how_to/sequence/", "alternative": ["/v0.1/docs/expression_language/get_started/"]},
-  "/docs/contributing/documentation/": {"canonical": "/docs/contributing/documentation/", "alternative": ["/v0.1/docs/contributing/documentation/technical_logistics/"]},
+  "/docs/contributing/documentation/": {"canonical": "/docs/contributing/how_to/documentation/", "alternative": ["/v0.1/docs/contributing/documentation/technical_logistics/"]},
   "/docs/expression_language/cookbook/": {"canonical": "/docs/how_to/#langchain-expression-language-lcel", "alternative": ["/v0.1/docs/expression_language/"]},
   "/docs/integrations/text_embedding/solar/": {"canonical": "/docs/integrations/text_embedding/upstage/"},
   "/docs/integrations/chat/solar/": {"canonical": "/docs/integrations/chat/upstage/"},
diff --git a/docs/vercel.json b/docs/vercel.json
index d8a94fd108318..1f2aa98c786e8 100644
--- a/docs/vercel.json
+++ b/docs/vercel.json
@@ -89,6 +89,14 @@
     {
       "source": "/docs/troubleshooting/errors/:path((?:GRAPH_RECURSION_LIMIT|INVALID_CONCURRENT_GRAPH_UPDATE|INVALID_GRAPH_NODE_RETURN_VALUE|MULTIPLE_SUBGRAPHS)/?)*",
       "destination": "https://langchain-ai.github.io/langgraph/troubleshooting/errors/:path*"
+    },
+    {
+      "source": "/docs/contributing/:path((?:code|documentation|integrations|testing)(?:/|/.*/?)?)",
+      "destination": "/docs/contributing/how_to/:path"
+    },
+    {
+      "source": "/docs/contributing/:path((?:faq|repo_structure|review_process)/?)",
+      "destination": "/docs/contributing/reference/:path"
     }
   ]
 }