From 31f4fb790d15ae1084a9b48615fe6fc54a4af4f8 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Mon, 4 Nov 2024 17:29:15 -0800
Subject: [PATCH 01/26] standard-tests: release 0.3.0 (#27900)

---
 libs/standard-tests/poetry.lock    | 546 +++++++++++++++--------------
 libs/standard-tests/pyproject.toml |   2 +-
 2 files changed, 282 insertions(+), 266 deletions(-)

diff --git a/libs/standard-tests/poetry.lock b/libs/standard-tests/poetry.lock
index 8655b65aa7753..95d28da3f7dd9 100644
--- a/libs/standard-tests/poetry.lock
+++ b/libs/standard-tests/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 = "4.4.0"
+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 = "certifi"
@@ -46,101 +46,116 @@ files = [
 
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 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"
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 [[package]]
@@ -198,13 +213,13 @@ 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]
@@ -215,7 +230,7 @@ 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"
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 python-versions = ">=3.8"
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 [[package]]
@@ -493,18 +509,18 @@ testing = ["pytest", "pytest-benchmark"]
 
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-version = "2.9.1"
+version = "2.9.2"
 description = "Data validation using Python type hints"
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 [package.dependencies]
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 typing-extensions = [
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     {version = ">=4.12.2", markers = "python_version >= \"3.13\""},
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+    {file = "pydantic_core-2.23.4.tar.gz", hash = "sha256:2584f7cf844ac4d970fba483a717dbe10c1c1c96a969bf65d61ffe94df1b2863"},
 ]
 
 [package.dependencies]
@@ -791,13 +807,13 @@ 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]
@@ -805,13 +821,13 @@ 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]
@@ -820,13 +836,13 @@ test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
 [[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 = [
-    {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
-    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
+    {file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"},
+    {file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"},
 ]
 
 [[package]]
diff --git a/libs/standard-tests/pyproject.toml b/libs/standard-tests/pyproject.toml
index a3775ed9b3010..14515955ee572 100644
--- a/libs/standard-tests/pyproject.toml
+++ b/libs/standard-tests/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-standard-tests"
-version = "0.1.1"
+version = "0.3.0"
 description = "Standard tests for LangChain implementations"
 authors = [ "Erick Friis <erick@langchain.dev>",]
 readme = "README.md"

From a3bbbe6a86ca4af3d258940e5cbb9455cd3bbdf2 Mon Sep 17 00:00:00 2001
From: Tomaz Bratanic <bratanic.tomaz@gmail.com>
Date: Tue, 5 Nov 2024 23:54:26 +0700
Subject: [PATCH 02/26] update llm graph transformer documentation (#27905)

---
 docs/docs/how_to/graph_constructing.ipynb | 120 ++++++++++++++++++++--
 docs/static/img/graph_construction3.png   | Bin 0 -> 171696 bytes
 docs/static/img/graph_construction4.png   | Bin 0 -> 154289 bytes
 3 files changed, 110 insertions(+), 10 deletions(-)
 create mode 100644 docs/static/img/graph_construction3.png
 create mode 100644 docs/static/img/graph_construction4.png

diff --git a/docs/docs/how_to/graph_constructing.ipynb b/docs/docs/how_to/graph_constructing.ipynb
index e9b6a014177c7..79b9e1463f4a2 100644
--- a/docs/docs/how_to/graph_constructing.ipynb
+++ b/docs/docs/how_to/graph_constructing.ipynb
@@ -44,6 +44,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -105,7 +108,7 @@
     "os.environ[\"NEO4J_USERNAME\"] = \"neo4j\"\n",
     "os.environ[\"NEO4J_PASSWORD\"] = \"password\"\n",
     "\n",
-    "graph = Neo4jGraph()"
+    "graph = Neo4jGraph(refresh_schema=False)"
    ]
   },
   {
@@ -149,8 +152,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Nodes:[Node(id='Marie Curie', type='Person'), Node(id='Pierre Curie', type='Person'), Node(id='University Of Paris', type='Organization')]\n",
-      "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='Pierre Curie', type='Person'), type='MARRIED'), Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='University Of Paris', type='Organization'), type='PROFESSOR')]\n"
+      "Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]\n",
+      "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='MARRIED', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='PROFESSOR', properties={})]\n"
      ]
     }
    ],
@@ -191,8 +194,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Nodes:[Node(id='Marie Curie', type='Person'), Node(id='Pierre Curie', type='Person'), Node(id='University Of Paris', type='Organization')]\n",
-      "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='Pierre Curie', type='Person'), type='SPOUSE'), Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='University Of Paris', type='Organization'), type='WORKED_AT')]\n"
+      "Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]\n",
+      "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='WORKED_AT', properties={})]\n"
      ]
     }
    ],
@@ -209,6 +212,44 @@
     "print(f\"Relationships:{graph_documents_filtered[0].relationships}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To define the graph schema more precisely, consider using a three-tuple approach for relationships. In this approach, each tuple consists of three elements: the source node, the relationship type, and the target node."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]\n",
+      "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='WORKED_AT', properties={})]\n"
+     ]
+    }
+   ],
+   "source": [
+    "allowed_relationships = [\n",
+    "    (\"Person\", \"SPOUSE\", \"Person\"),\n",
+    "    (\"Person\", \"NATIONALITY\", \"Country\"),\n",
+    "    (\"Person\", \"WORKED_AT\", \"Organization\"),\n",
+    "]\n",
+    "\n",
+    "llm_transformer_tuple = LLMGraphTransformer(\n",
+    "    llm=llm,\n",
+    "    allowed_nodes=[\"Person\", \"Country\", \"Organization\"],\n",
+    "    allowed_relationships=allowed_relationships,\n",
+    ")\n",
+    "llm_transformer_tuple = llm_transformer_filtered.convert_to_graph_documents(documents)\n",
+    "print(f\"Nodes:{graph_documents_filtered[0].nodes}\")\n",
+    "print(f\"Relationships:{graph_documents_filtered[0].relationships}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -229,15 +270,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Nodes:[Node(id='Marie Curie', type='Person', properties={'born_year': '1867'}), Node(id='Pierre Curie', type='Person'), Node(id='University Of Paris', type='Organization')]\n",
-      "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='Pierre Curie', type='Person'), type='SPOUSE'), Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='University Of Paris', type='Organization'), type='WORKED_AT')]\n"
+      "Nodes:[Node(id='Marie Curie', type='Person', properties={'born_year': '1867'}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={}), Node(id='Poland', type='Country', properties={}), Node(id='France', type='Country', properties={})]\n",
+      "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Poland', type='Country', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='France', type='Country', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='WORKED_AT', properties={})]\n"
      ]
     }
    ],
@@ -264,12 +305,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "graph.add_graph_documents(graph_documents_props)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most graph databases support indexes to optimize data import and retrieval. Since we might not know all the node labels in advance, we can handle this by adding a secondary base label to each node using the `baseEntityLabel` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph.add_graph_documents(graph_documents, baseEntityLabel=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Results will look like:\n",
+    "\n",
+    "![graph_construction3.png](../../static/img/graph_construction3.png)\n",
+    "\n",
+    "The final option is to also import the source documents for the extracted nodes and relationships. This approach lets us track which documents each entity appeared in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph.add_graph_documents(graph_documents, include_source=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Graph will have the following structure:\n",
+    "\n",
+    "![graph_construction4.png](../../static/img/graph_construction4.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this visualization, the source document is highlighted in blue, with all entities extracted from it connected by `MENTIONS` relationships."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -288,7 +388,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/static/img/graph_construction3.png b/docs/static/img/graph_construction3.png
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HcmV?d00001


From f6b2f82099427ab58f33d72dfa29efa8a7b87a25 Mon Sep 17 00:00:00 2001
From: SHJUN <134789343+shjunn@users.noreply.github.com>
Date: Wed, 6 Nov 2024 04:43:11 +0900
Subject: [PATCH 03/26] community: chroma error patch(attribute changed on
 chroma) (#27827)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

There was a change of attribute name which was "max_batch_size". It's
now "get_max_batch_size" method.
I want to use "create_batches" which is right down below.

Please check this PR link.
reference: https://github.com/chroma-core/chroma/pull/2305

---------

Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
Co-authored-by: Prithvi Kannan <46332835+prithvikannan@users.noreply.github.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Jun Yamog <jkyamog@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ono-hiroki <86904208+ono-hiroki@users.noreply.github.com>
Co-authored-by: Dobiichi-Origami <56953648+Dobiichi-Origami@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Co-authored-by: Duy Huynh <vndee.huynh@gmail.com>
Co-authored-by: Rashmi Pawar <168514198+raspawar@users.noreply.github.com>
Co-authored-by: sifatj <26035630+sifatj@users.noreply.github.com>
Co-authored-by: Eric Pinzur <2641606+epinzur@users.noreply.github.com>
Co-authored-by: Daniel Vu Dao <danielvdao@users.noreply.github.com>
Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>
Co-authored-by: Stéphane Philippart <wildagsx@gmail.com>
---
 libs/community/langchain_community/vectorstores/chroma.py | 8 +++++++-
 libs/partners/chroma/langchain_chroma/vectorstores.py     | 4 ++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/libs/community/langchain_community/vectorstores/chroma.py b/libs/community/langchain_community/vectorstores/chroma.py
index 40ba2415b6933..1ab7ddad140d6 100644
--- a/libs/community/langchain_community/vectorstores/chroma.py
+++ b/libs/community/langchain_community/vectorstores/chroma.py
@@ -753,6 +753,9 @@ def update_documents(self, ids: List[str], documents: List[Document]) -> None:
         embeddings = self._embedding_function.embed_documents(text)
 
         if hasattr(
+            self._collection._client,
+            "get_max_batch_size",  # for Chroma 0.5.1 and above
+        ) or hasattr(
             self._collection._client, "max_batch_size"
         ):  # for Chroma 0.4.10 and above
             from chromadb.utils.batch_utils import create_batches
@@ -824,7 +827,10 @@ def from_texts(
             ids = [str(uuid.uuid4()) for _ in texts]
         if hasattr(
             chroma_collection._client,  # type: ignore[has-type]
-            "max_batch_size",  # type: ignore[has-type]
+            "get_max_batch_size",  # for Chroma 0.5.1 and above
+        ) or hasattr(
+            chroma_collection._client,  # type: ignore[has-type]
+            "max_batch_size",
         ):  # for Chroma 0.4.10 and above
             from chromadb.utils.batch_utils import create_batches
 
diff --git a/libs/partners/chroma/langchain_chroma/vectorstores.py b/libs/partners/chroma/langchain_chroma/vectorstores.py
index 945057f4adb22..35146fdcc7603 100644
--- a/libs/partners/chroma/langchain_chroma/vectorstores.py
+++ b/libs/partners/chroma/langchain_chroma/vectorstores.py
@@ -997,6 +997,8 @@ def update_documents(self, ids: List[str], documents: List[Document]) -> None:
         embeddings = self._embedding_function.embed_documents(text)
 
         if hasattr(
+            self._collection._client, "get_max_batch_size"
+        ) or hasattr(  # for Chroma 0.5.1 and above
             self._collection._client, "max_batch_size"
         ):  # for Chroma 0.4.10 and above
             from chromadb.utils.batch_utils import create_batches
@@ -1070,6 +1072,8 @@ def from_texts(
         if ids is None:
             ids = [str(uuid.uuid4()) for _ in texts]
         if hasattr(
+            chroma_collection._client, "get_max_batch_size"
+        ) or hasattr(  # for Chroma 0.5.1 and above
             chroma_collection._client, "max_batch_size"
         ):  # for Chroma 0.4.10 and above
             from chromadb.utils.batch_utils import create_batches

From bff2a8b7722172edd12716ac5c6897df21cb4e36 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Tue, 5 Nov 2024 11:44:34 -0800
Subject: [PATCH 04/26] standard-tests: add tools standard tests (#27899)

---
 .../integration_tests/tools.py                | 70 ++++++++++++++++++
 .../unit_tests/tools.py                       | 74 +++++++++++++++++++
 2 files changed, 144 insertions(+)
 create mode 100644 libs/standard-tests/langchain_standard_tests/integration_tests/tools.py
 create mode 100644 libs/standard-tests/langchain_standard_tests/unit_tests/tools.py

diff --git a/libs/standard-tests/langchain_standard_tests/integration_tests/tools.py b/libs/standard-tests/langchain_standard_tests/integration_tests/tools.py
new file mode 100644
index 0000000000000..ad854c3b1d41d
--- /dev/null
+++ b/libs/standard-tests/langchain_standard_tests/integration_tests/tools.py
@@ -0,0 +1,70 @@
+from langchain_core.messages import ToolCall
+from langchain_core.tools import BaseTool
+
+from langchain_standard_tests.unit_tests.tools import ToolsTests
+
+
+class ToolsIntegrationTests(ToolsTests):
+    def test_invoke_matches_output_schema(self, tool: BaseTool) -> None:
+        """
+        If invoked with a ToolCall, the tool should return a valid ToolMessage content.
+        """
+        tool_call = ToolCall(
+            name=tool.name, args=self.tool_invoke_params_example, id=None
+        )
+        result = tool.invoke(tool_call)
+
+        if tool.response_format == "content":
+            content = result
+        elif tool.response_format == "content_and_artifact":
+            # should be (content, artifact)
+            assert isinstance(result, tuple)
+            assert len(result) == 2
+            content, artifact = result
+
+            assert artifact  # artifact can be anything, but shouldn't be none
+
+        # check content is a valid ToolMessage content
+        assert isinstance(content, (str, list))
+        if isinstance(content, list):
+            # content blocks must be str or dict
+            assert all(isinstance(c, (str, dict)) for c in content)
+
+    async def test_async_invoke_matches_output_schema(self, tool: BaseTool) -> None:
+        """
+        If ainvoked with a ToolCall, the tool should return a valid ToolMessage content.
+        """
+        tool_call = ToolCall(
+            name=tool.name, args=self.tool_invoke_params_example, id=None
+        )
+        result = await tool.ainvoke(tool_call)
+
+        if tool.response_format == "content":
+            content = result
+        elif tool.response_format == "content_and_artifact":
+            # should be (content, artifact)
+            assert isinstance(result, tuple)
+            assert len(result) == 2
+            content, artifact = result
+
+            assert artifact  # artifact can be anything, but shouldn't be none
+
+        # check content is a valid ToolMessage content
+        assert isinstance(content, (str, list))
+        if isinstance(content, list):
+            # content blocks must be str or dict
+            assert all(isinstance(c, (str, dict)) for c in content)
+
+    def test_invoke_no_tool_call(self, tool: BaseTool) -> None:
+        """
+        If invoked without a ToolCall, the tool can return anything
+        but it shouldn't throw an error
+        """
+        tool.invoke(self.tool_invoke_params_example)
+
+    async def test_async_invoke_no_tool_call(self, tool: BaseTool) -> None:
+        """
+        If invoked without a ToolCall, the tool can return anything
+        but it shouldn't throw an error
+        """
+        await tool.ainvoke(self.tool_invoke_params_example)
diff --git a/libs/standard-tests/langchain_standard_tests/unit_tests/tools.py b/libs/standard-tests/langchain_standard_tests/unit_tests/tools.py
new file mode 100644
index 0000000000000..61f54e23bec8e
--- /dev/null
+++ b/libs/standard-tests/langchain_standard_tests/unit_tests/tools.py
@@ -0,0 +1,74 @@
+import os
+from abc import abstractmethod
+from typing import Callable, Tuple, Type, Union
+from unittest import mock
+
+import pytest
+from langchain_core.tools import BaseTool
+from pydantic import SecretStr
+
+from langchain_standard_tests.base import BaseStandardTests
+
+
+class ToolsTests(BaseStandardTests):
+    @property
+    @abstractmethod
+    def tool_constructor(self) -> Union[Type[BaseTool], Callable]:
+        ...
+
+    @property
+    def tool_constructor_params(self) -> dict:
+        return {}
+
+    @property
+    def tool_invoke_params_example(self) -> dict:
+        """
+        Returns a dictionary representing the "args" of an example tool call.
+
+        This should NOT be a ToolCall dict - i.e. it should not
+        have {"name", "id", "args"} keys.
+        """
+        return {}
+
+    @pytest.fixture
+    def tool(self) -> BaseTool:
+        return self.tool_constructor(**self.tool_constructor_params)
+
+
+class ToolsUnitTests(ToolsTests):
+    def test_init(self) -> None:
+        tool = self.tool_constructor(**self.tool_constructor_params)
+        assert tool is not None
+
+    @property
+    def init_from_env_params(self) -> Tuple[dict, dict, dict]:
+        """Return env vars, init args, and expected instance attrs for initializing
+        from env vars."""
+        return {}, {}, {}
+
+    def test_init_from_env(self) -> None:
+        env_params, tools_params, expected_attrs = self.init_from_env_params
+        if env_params:
+            with mock.patch.dict(os.environ, env_params):
+                tool = self.tool_constructor(**tools_params)
+            assert tool is not None
+            for k, expected in expected_attrs.items():
+                actual = getattr(tool, k)
+                if isinstance(actual, SecretStr):
+                    actual = actual.get_secret_value()
+                assert actual == expected
+
+    def test_has_name(self, tool: BaseTool) -> None:
+        assert tool.name
+
+    def test_has_input_schema(self, tool: BaseTool) -> None:
+        assert tool.get_input_schema()
+
+    def test_input_schema_matches_invoke_params(self, tool: BaseTool) -> None:
+        """
+        Tests that the provided example params match the declared input schema
+        """
+        # this will be a pydantic object
+        input_schema = tool.get_input_schema()
+
+        assert input_schema(**self.tool_invoke_params_example)

From b3c81356ca17fa90ded8faa96c8e5bbdc16048d7 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Tue, 5 Nov 2024 12:04:41 -0800
Subject: [PATCH 05/26] infra: release note compute 2 (#27921)

---
 .github/workflows/_release.yml | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml
index 7eaa9410beb06..6dee08c652d1a 100644
--- a/.github/workflows/_release.yml
+++ b/.github/workflows/_release.yml
@@ -106,11 +106,16 @@ jobs:
             PREV_TAG=$(git tag --sort=-creatordate | grep -P $REGEX || true | head -1)
           fi
 
-          # confirm prev-tag actually exists in git repo with git tag
-          GIT_TAG_RESULT=$(git tag -l "$PREV_TAG")
-          if [ -z "$GIT_TAG_RESULT" ]; then
-            echo "Previous tag $PREV_TAG not found in git repo"
-            exit 1
+          # if PREV_TAG is empty, let it be empty
+          if [ -z "$PREV_TAG" ]; then
+            echo "No previous tag found - first release"
+          else
+            # confirm prev-tag actually exists in git repo with git tag
+            GIT_TAG_RESULT=$(git tag -l "$PREV_TAG")
+            if [ -z "$GIT_TAG_RESULT" ]; then
+              echo "Previous tag $PREV_TAG not found in git repo"
+              exit 1
+            fi
           fi
 
 

From c3b75560dc13c3c323b739e78182acf281fbe255 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Tue, 5 Nov 2024 12:44:36 -0800
Subject: [PATCH 06/26] infra: release note grep order of operations (#27922)

---
 .github/workflows/_release.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml
index 6dee08c652d1a..fd6a53fee85cb 100644
--- a/.github/workflows/_release.yml
+++ b/.github/workflows/_release.yml
@@ -103,7 +103,7 @@ jobs:
           if [ -z "$PREV_TAG" ]; then
             REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$"
             echo $REGEX
-            PREV_TAG=$(git tag --sort=-creatordate | grep -P $REGEX || true | head -1)
+            PREV_TAG=$(git tag --sort=-creatordate | (grep -P $REGEX || true) | head -1)
           fi
 
           # if PREV_TAG is empty, let it be empty

From a8c473e114f0a16f3a3dd0f401d7f1026666f12e Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Tue, 5 Nov 2024 12:55:38 -0800
Subject: [PATCH 07/26] standard-tests: ci pipeline (#27923)

---
 libs/standard-tests/poetry.lock               | 47 ++++++++++++++++++-
 libs/standard-tests/pyproject.toml            | 22 +++++++--
 libs/standard-tests/tests/__init__.py         |  0
 .../tests/integration_tests/test_compile.py   |  7 +++
 .../unit_tests/test_in_memory_vectorstore.py  |  2 +-
 5 files changed, 71 insertions(+), 7 deletions(-)
 delete mode 100644 libs/standard-tests/tests/__init__.py
 create mode 100644 libs/standard-tests/tests/integration_tests/test_compile.py

diff --git a/libs/standard-tests/poetry.lock b/libs/standard-tests/poetry.lock
index 95d28da3f7dd9..0c74b37a6cb13 100644
--- a/libs/standard-tests/poetry.lock
+++ b/libs/standard-tests/poetry.lock
@@ -414,6 +414,51 @@ files = [
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]
 
+[[package]]
+name = "numpy"
+version = "1.26.4"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
+    {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
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+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"},
+    {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"},
+    {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5"},
+    {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a"},
+    {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a"},
+    {file = "numpy-1.26.4-cp311-cp311-win32.whl", hash = "sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20"},
+    {file = "numpy-1.26.4-cp311-cp311-win_amd64.whl", hash = "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2"},
+    {file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"},
+    {file = "numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b"},
+    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b"},
+    {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed"},
+    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a"},
+    {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0"},
+    {file = "numpy-1.26.4-cp312-cp312-win32.whl", hash = "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110"},
+    {file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"},
+    {file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"},
+    {file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"},
+    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"},
+    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"},
+    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"},
+    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"},
+    {file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"},
+    {file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"},
+    {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
+]
+
 [[package]]
 name = "orjson"
 version = "3.10.11"
@@ -876,4 +921,4 @@ zstd = ["zstandard (>=0.18.0)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "cb3804aa252eed812fd6dc0762ffb110d6b55046f7e691f3fe14080d8ab06b38"
+content-hash = "4f6715617fbad64e03a1675f2edb9f77912dc156a79c8cef58eb145392efe21a"
diff --git a/libs/standard-tests/pyproject.toml b/libs/standard-tests/pyproject.toml
index 14515955ee572..4caa1fb651b29 100644
--- a/libs/standard-tests/pyproject.toml
+++ b/libs/standard-tests/pyproject.toml
@@ -1,12 +1,12 @@
 [build-system]
-requires = [ "poetry-core",]
+requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-standard-tests"
 version = "0.3.0"
 description = "Standard tests for LangChain implementations"
-authors = [ "Erick Friis <erick@langchain.dev>",]
+authors = ["Erick Friis <erick@langchain.dev>"]
 readme = "README.md"
 repository = "https://github.com/langchain-ai/langchain"
 license = "MIT"
@@ -25,14 +25,18 @@ httpx = "^0.27.0"
 syrupy = "^4"
 
 [tool.ruff.lint]
-select = [ "E", "F", "I", "T201",]
+select = ["E", "F", "I", "T201"]
 
 [tool.coverage.run]
-omit = [ "tests/*",]
+omit = ["tests/*"]
 
 [tool.pytest.ini_options]
 addopts = "--strict-markers --strict-config --durations=5 -vv"
-markers = [ "requires: mark tests as requiring a specific library", "scheduled: mark tests to run in scheduled testing", "compile: mark placeholder test used to compile integration tests without running them",]
+markers = [
+    "requires: mark tests as requiring a specific library",
+    "scheduled: mark tests to run in scheduled testing",
+    "compile: mark placeholder test used to compile integration tests without running them",
+]
 asyncio_mode = "auto"
 
 [tool.poetry.group.test]
@@ -50,6 +54,14 @@ optional = true
 [tool.poetry.group.test.dependencies]
 pytest-asyncio = "^0.23.7"
 
+[[tool.poetry.group.test.dependencies.numpy]]
+version = "^1.24.0"
+python = "<3.12"
+
+[[tool.poetry.group.test.dependencies.numpy]]
+version = "^1.26.0"
+python = ">=3.12"
+
 [tool.poetry.group.test_integration.dependencies]
 
 [tool.poetry.group.codespell.dependencies]
diff --git a/libs/standard-tests/tests/__init__.py b/libs/standard-tests/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/libs/standard-tests/tests/integration_tests/test_compile.py b/libs/standard-tests/tests/integration_tests/test_compile.py
new file mode 100644
index 0000000000000..33ecccdfa0fbd
--- /dev/null
+++ b/libs/standard-tests/tests/integration_tests/test_compile.py
@@ -0,0 +1,7 @@
+import pytest
+
+
+@pytest.mark.compile
+def test_placeholder() -> None:
+    """Used for compiling integration tests without running any real tests."""
+    pass
diff --git a/libs/standard-tests/tests/unit_tests/test_in_memory_vectorstore.py b/libs/standard-tests/tests/unit_tests/test_in_memory_vectorstore.py
index 9b71087770707..d1becb40c9eb4 100644
--- a/libs/standard-tests/tests/unit_tests/test_in_memory_vectorstore.py
+++ b/libs/standard-tests/tests/unit_tests/test_in_memory_vectorstore.py
@@ -17,7 +17,7 @@ def vectorstore(self) -> VectorStore:
         return InMemoryVectorStore(embedding=embeddings)
 
 
-class TestAysncInMemoryVectorStore(AsyncReadWriteTestSuite):
+class TestAsyncInMemoryVectorStore(AsyncReadWriteTestSuite):
     @pytest.fixture
     async def vectorstore(self) -> VectorStore:
         embeddings = self.get_embeddings()

From 66966a6e725f7ab533886c86d418c4a663bb9966 Mon Sep 17 00:00:00 2001
From: ccurme <chester.curme@gmail.com>
Date: Tue, 5 Nov 2024 18:02:24 -0500
Subject: [PATCH 08/26] openai[patch]: release 0.2.6 (#27924)

Some additions in support of [predicted
outputs](https://platform.openai.com/docs/guides/latency-optimization#use-predicted-outputs)
feature:
- Bump openai sdk version
- Add integration test
- Add example to integration docs

The `prediction` kwarg is already plumbed through model invocation.
---
 docs/docs/integrations/chat/openai.ipynb      | 99 ++++++++++++++++++-
 libs/partners/openai/poetry.lock              | 16 +--
 libs/partners/openai/pyproject.toml           |  4 +-
 .../chat_models/test_base.py                  | 43 ++++++++
 4 files changed, 150 insertions(+), 12 deletions(-)

diff --git a/docs/docs/integrations/chat/openai.ipynb b/docs/docs/integrations/chat/openai.ipynb
index 687f6ddc30bc1..9ab1c22c70c8f 100644
--- a/docs/docs/integrations/chat/openai.ipynb
+++ b/docs/docs/integrations/chat/openai.ipynb
@@ -509,6 +509,101 @@
     "output_message.content"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5c35d0a4-a6b8-4d35-a02b-a37a8bda5692",
+   "metadata": {},
+   "source": [
+    "## Predicted output\n",
+    "\n",
+    ":::info\n",
+    "Requires `langchain-openai>=0.2.6`\n",
+    ":::\n",
+    "\n",
+    "Some OpenAI models (such as their `gpt-4o` and `gpt-4o-mini` series) support [Predicted Outputs](https://platform.openai.com/docs/guides/latency-optimization#use-predicted-outputs), which allow you to pass in a known portion of the LLM's expected output ahead of time to reduce latency. This is useful for cases such as editing text or code, where only a small part of the model's output will change.\n",
+    "\n",
+    "Here's an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "88fee1e9-58c1-42ad-ae23-24b882e175e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/// <summary>\n",
+      "/// Represents a user with a first name, last name, and email.\n",
+      "/// </summary>\n",
+      "public class User\n",
+      "{\n",
+      "    /// <summary>\n",
+      "    /// Gets or sets the user's first name.\n",
+      "    /// </summary>\n",
+      "    public string FirstName { get; set; }\n",
+      "\n",
+      "    /// <summary>\n",
+      "    /// Gets or sets the user's last name.\n",
+      "    /// </summary>\n",
+      "    public string LastName { get; set; }\n",
+      "\n",
+      "    /// <summary>\n",
+      "    /// Gets or sets the user's email.\n",
+      "    /// </summary>\n",
+      "    public string Email { get; set; }\n",
+      "}\n",
+      "{'token_usage': {'completion_tokens': 226, 'prompt_tokens': 166, 'total_tokens': 392, 'completion_tokens_details': {'accepted_prediction_tokens': 49, 'audio_tokens': None, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 107}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_45cf54deae', 'finish_reason': 'stop', 'logprobs': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "code = \"\"\"\n",
+    "/// <summary>\n",
+    "/// Represents a user with a first name, last name, and username.\n",
+    "/// </summary>\n",
+    "public class User\n",
+    "{\n",
+    "    /// <summary>\n",
+    "    /// Gets or sets the user's first name.\n",
+    "    /// </summary>\n",
+    "    public string FirstName { get; set; }\n",
+    "\n",
+    "    /// <summary>\n",
+    "    /// Gets or sets the user's last name.\n",
+    "    /// </summary>\n",
+    "    public string LastName { get; set; }\n",
+    "\n",
+    "    /// <summary>\n",
+    "    /// Gets or sets the user's username.\n",
+    "    /// </summary>\n",
+    "    public string Username { get; set; }\n",
+    "}\n",
+    "\"\"\"\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4o\")\n",
+    "query = (\n",
+    "    \"Replace the Username property with an Email property. \"\n",
+    "    \"Respond only with code, and with no markdown formatting.\"\n",
+    ")\n",
+    "response = llm.invoke(\n",
+    "    [{\"role\": \"user\", \"content\": query}, {\"role\": \"user\", \"content\": code}],\n",
+    "    prediction={\"type\": \"content\", \"content\": code},\n",
+    ")\n",
+    "print(response.content)\n",
+    "print(response.response_metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ee1b26d-a388-4e7c-9f40-bfd1388ecc03",
+   "metadata": {},
+   "source": [
+    "Note that currently predictions are billed as additional tokens and may increase your usage and costs in exchange for this reduced latency."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "feb4a499",
@@ -601,7 +696,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -615,7 +710,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/libs/partners/openai/poetry.lock b/libs/partners/openai/poetry.lock
index a1d51f1d22efe..30fa377575298 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"
@@ -518,7 +518,7 @@ url = "../../core"
 
 [[package]]
 name = "langchain-standard-tests"
-version = "0.1.1"
+version = "0.3.0"
 description = "Standard tests for LangChain implementations"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -527,7 +527,7 @@ develop = true
 
 [package.dependencies]
 httpx = "^0.27.0"
-langchain-core = "^0.3.0"
+langchain-core = "^0.3.15"
 pytest = ">=7,<9"
 syrupy = "^4"
 
@@ -667,13 +667,13 @@ files = [
 
 [[package]]
 name = "openai"
-version = "1.52.2"
+version = "1.54.1"
 description = "The official Python library for the openai API"
 optional = false
-python-versions = ">=3.7.1"
+python-versions = ">=3.8"
 files = [
-    {file = "openai-1.52.2-py3-none-any.whl", hash = "sha256:57e9e37bc407f39bb6ec3a27d7e8fb9728b2779936daa1fcf95df17d3edfaccc"},
-    {file = "openai-1.52.2.tar.gz", hash = "sha256:87b7d0f69d85f5641678d414b7ee3082363647a5c66a462ed7f3ccb59582da0d"},
+    {file = "openai-1.54.1-py3-none-any.whl", hash = "sha256:3cb49ccb6bfdc724ad01cc397d323ef8314fc7d45e19e9de2afdd6484a533324"},
+    {file = "openai-1.54.1.tar.gz", hash = "sha256:5b832bf82002ba8c4f6e5e25c1c0f5d468c22f043711544c716eaffdb30dd6f1"},
 ]
 
 [package.dependencies]
@@ -1561,4 +1561,4 @@ watchmedo = ["PyYAML (>=3.10)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "985f183aed7188a51964c30831939cae8060d906e8b07d4257e423016b6f3dd4"
+content-hash = "77af861c052decd1c194936575c15491a606fd763556b427e2c659d5ea7aae72"
diff --git a/libs/partners/openai/pyproject.toml b/libs/partners/openai/pyproject.toml
index 255e488375d43..661c2b0a98490 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 = "0.2.5"
+version = "0.2.6"
 description = "An integration package connecting OpenAI and LangChain"
 authors = []
 readme = "README.md"
@@ -24,7 +24,7 @@ ignore_missing_imports = true
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
 langchain-core = "^0.3.15"
-openai = "^1.52.0"
+openai = "^1.54.0"
 tiktoken = ">=0.7,<1"
 
 [tool.ruff.lint]
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 ccce5722c3c8e..31ff734355491 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
@@ -3,6 +3,7 @@
 import base64
 import json
 from pathlib import Path
+from textwrap import dedent
 from typing import Any, AsyncIterator, List, Literal, Optional, cast
 
 import httpx
@@ -1018,3 +1019,45 @@ def test_audio_input_modality() -> None:
 
     assert isinstance(output, AIMessage)
     assert "audio" in output.additional_kwargs
+
+
+def test_prediction_tokens() -> None:
+    code = dedent("""
+    /// <summary>
+    /// Represents a user with a first name, last name, and username.
+    /// </summary>
+    public class User
+    {
+        /// <summary>
+        /// Gets or sets the user's first name.
+        /// </summary>
+        public string FirstName { get; set; }
+
+        /// <summary>
+        /// Gets or sets the user's last name.
+        /// </summary>
+        public string LastName { get; set; }
+
+        /// <summary>
+        /// Gets or sets the user's username.
+        /// </summary>
+        public string Username { get; set; }
+    }
+    """)
+
+    llm = ChatOpenAI(model="gpt-4o")
+    query = (
+        "Replace the Username property with an Email property. "
+        "Respond only with code, and with no markdown formatting."
+    )
+    response = llm.invoke(
+        [{"role": "user", "content": query}, {"role": "user", "content": code}],
+        prediction={"type": "content", "content": code},
+    )
+    assert isinstance(response, AIMessage)
+    assert response.response_metadata is not None
+    output_token_details = response.response_metadata["token_usage"][
+        "completion_tokens_details"
+    ]
+    assert output_token_details["accepted_prediction_tokens"] > 0
+    assert output_token_details["rejected_prediction_tokens"] > 0

From 41b7a5169d3b6bdb0949a409397707eb69b3cd07 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Tue, 5 Nov 2024 16:43:11 -0800
Subject: [PATCH 09/26] infra: starter codeowners file (#27929)

---
 .github/CODEOWNERS | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 .github/CODEOWNERS

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
new file mode 100644
index 0000000000000..e5f22dcb22b18
--- /dev/null
+++ b/.github/CODEOWNERS
@@ -0,0 +1,2 @@
+/.github/   @efriis @baskaryan @ccurme
+/libs/packages.yml   @efriis

From 395674d5037756bef25d7fa8da93525874bb5cb8 Mon Sep 17 00:00:00 2001
From: Dobiichi-Origami <56953648+Dobiichi-Origami@users.noreply.github.com>
Date: Wed, 6 Nov 2024 22:58:16 +0800
Subject: [PATCH 10/26] community: re-arrange function call message parse logic
 for Qianfan (#27935)

the [PR](https://github.com/langchain-ai/langchain/pull/26208) two month
ago has a potential bug which causes malfunction of `tool_call` for
`QianfanChatEndpoint` waiting for fix
---
 .../chat_models/baidu_qianfan_endpoint.py              | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py b/libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py
index 93f32e9a83e58..243ac084d3779 100644
--- a/libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py
+++ b/libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py
@@ -66,17 +66,13 @@ def convert_message_to_dict(message: BaseMessage) -> dict:
         message_dict = {"role": "user", "content": message.content}
     elif isinstance(message, AIMessage):
         message_dict = {"role": "assistant", "content": message.content}
-        if "function_call" in message.additional_kwargs:
-            message_dict["function_call"] = message.additional_kwargs["function_call"]
-        elif len(message.tool_calls) != 0:
+        if len(message.tool_calls) != 0:
             tool_call = message.tool_calls[0]
             message_dict["function_call"] = {
                 "name": tool_call["name"],
-                "args": tool_call["args"],
+                "arguments": json.dumps(tool_call["args"], ensure_ascii=False),
             }
-
-        # If function call only, content is None not empty string
-        if "function_call" in message_dict and message_dict["content"] == "":
+            # If function call only, content is None not empty string
             message_dict["content"] = None
     elif isinstance(message, (FunctionMessage, ToolMessage)):
         message_dict = {

From b2da3115ed4f867a8a6375369eb8dacef16b4e70 Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Wed, 6 Nov 2024 09:50:07 -0800
Subject: [PATCH 11/26] docs: document init_chat_model standard params (#27812)

---
 libs/langchain/langchain/chat_models/base.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/libs/langchain/langchain/chat_models/base.py b/libs/langchain/langchain/chat_models/base.py
index 95720a92032c8..d17a2932bc8f2 100644
--- a/libs/langchain/langchain/chat_models/base.py
+++ b/libs/langchain/langchain/chat_models/base.py
@@ -149,7 +149,16 @@ def init_chat_model(
             ``config["configurable"]["{config_prefix}_{param}"]`` keys. If
             config_prefix is an empty string then model will be configurable via
             ``config["configurable"]["{param}"]``.
-        kwargs: Additional keyword args to pass to
+        temperature: Model temperature.
+        max_tokens: Max output tokens.
+        timeout: The maximum time (in seconds) to wait for a response from the model
+            before canceling the request.
+        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.
+        base_url: The URL of the API endpoint where requests are sent.
+        rate_limiter: A ``BaseRateLimiter`` to space out requests to avoid exceeding
+            rate limits.
+        kwargs: Additional model-specific keyword args to pass to
             ``<<selected ChatModel>>.__init__(model=model_name, **kwargs)``.
 
     Returns:

From 67ce05a0a7bc2d0efa4d1b9a58f9d44002c85fe6 Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Wed, 6 Nov 2024 10:06:47 -0800
Subject: [PATCH 12/26] core[patch]: make oai tool description optional
 (#27756)

---
 .../langchain_core/utils/function_calling.py  |  73 +++++++----
 .../unit_tests/utils/test_function_calling.py | 124 ++++++++++++++++++
 2 files changed, 174 insertions(+), 23 deletions(-)

diff --git a/libs/core/langchain_core/utils/function_calling.py b/libs/core/langchain_core/utils/function_calling.py
index 1a1c26443c786..3aff07faecd4c 100644
--- a/libs/core/langchain_core/utils/function_calling.py
+++ b/libs/core/langchain_core/utils/function_calling.py
@@ -341,7 +341,7 @@ def convert_to_openai_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, a JSON schema with
-            top-level 'title' and 'description' keys specified, an Anthropic format
+            top-level 'title' key specified, an Anthropic format
             tool, or an Amazon Bedrock Converse format tool.
         strict:
             If True, model output is guaranteed to exactly match the JSON Schema
@@ -366,40 +366,47 @@ def convert_to_openai_function(
     .. versionchanged:: 0.3.14
 
         Support for Amazon Bedrock Converse format tools added.
+
+    .. versionchanged:: 0.3.16
+
+        'description' and 'parameters' keys are now optional. Only 'name' is
+        required and guaranteed to be part of the output.
     """
     from langchain_core.tools import BaseTool
 
-    # already in OpenAI function format
-    if isinstance(function, dict) and all(
-        k in function for k in ("name", "description", "parameters")
-    ):
-        oai_function = function
-    # a JSON schema with title and description
-    elif isinstance(function, dict) and all(
-        k in function for k in ("title", "description", "properties")
-    ):
-        function = function.copy()
-        oai_function = {
-            "name": function.pop("title"),
-            "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")
+    if isinstance(function, dict) and all(
+        k in function for k in ("name", "input_schema")
     ):
         oai_function = {
             "name": function["name"],
-            "description": function["description"],
             "parameters": function["input_schema"],
         }
+        if "description" in function:
+            oai_function["description"] = function["description"]
     # an Amazon Bedrock Converse format tool
     elif isinstance(function, dict) and "toolSpec" in function:
         oai_function = {
             "name": function["toolSpec"]["name"],
-            "description": function["toolSpec"]["description"],
             "parameters": function["toolSpec"]["inputSchema"]["json"],
         }
+        if "description" in function["toolSpec"]:
+            oai_function["description"] = function["toolSpec"]["description"]
+    # already in OpenAI function format
+    elif isinstance(function, dict) and "name" in function:
+        oai_function = {
+            k: v
+            for k, v in function.items()
+            if k in ("name", "description", "parameters", "strict")
+        }
+    # a JSON schema with title and description
+    elif isinstance(function, dict) and "title" in function:
+        function_copy = function.copy()
+        oai_function = {"name": function_copy.pop("title")}
+        if "description" in function_copy:
+            oai_function["description"] = function_copy.pop("description")
+        if function_copy and "properties" in function_copy:
+            oai_function["parameters"] = function_copy
     elif isinstance(function, type) and is_basemodel_subclass(function):
         oai_function = cast(dict, convert_pydantic_to_openai_function(function))
     elif is_typeddict(function):
@@ -420,6 +427,13 @@ def convert_to_openai_function(
         raise ValueError(msg)
 
     if strict is not None:
+        if "strict" in oai_function and oai_function["strict"] != strict:
+            msg = (
+                f"Tool/function already has a 'strict' key wth value "
+                f"{oai_function['strict']} which is different from the explicit "
+                f"`strict` arg received {strict=}."
+            )
+            raise ValueError(msg)
         oai_function["strict"] = strict
         if strict:
             # As of 08/06/24, OpenAI requires that additionalProperties be supplied and
@@ -438,12 +452,16 @@ def convert_to_openai_tool(
 ) -> dict[str, Any]:
     """Convert a tool-like object to an OpenAI tool schema.
 
+    OpenAI tool schema reference:
+    https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools
+
     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, a JSON schema with top-level 'title' and
-            'description' keys specified, or an Anthropic format tool.
+            BaseTool. If a dictionary is passed in, it is
+            assumed to already be a valid OpenAI function, a JSON schema with
+            top-level 'title' key specified, an Anthropic format
+            tool, or an Amazon Bedrock Converse 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
@@ -460,6 +478,15 @@ def convert_to_openai_tool(
     .. versionchanged:: 0.3.13
 
         Support for Anthropic format tools added.
+
+    .. versionchanged:: 0.3.14
+
+        Support for Amazon Bedrock Converse format tools added.
+
+    .. versionchanged:: 0.3.16
+
+        'description' and 'parameters' keys are now optional. Only 'name' is
+        required and guaranteed to be part of the output.
     """
     if isinstance(tool, dict) and tool.get("type") == "function" and "function" in tool:
         return tool
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 1bd43c193103c..4eaa3da2b19ab 100644
--- a/libs/core/tests/unit_tests/utils/test_function_calling.py
+++ b/libs/core/tests/unit_tests/utils/test_function_calling.py
@@ -459,6 +459,130 @@ def my_function(arg1: Nested) -> None:
     assert actual == expected
 
 
+json_schema_no_description_no_params = {
+    "title": "dummy_function",
+}
+
+
+json_schema_no_description = {
+    "title": "dummy_function",
+    "type": "object",
+    "properties": {
+        "arg1": {"description": "foo", "type": "integer"},
+        "arg2": {
+            "description": "one of 'bar', 'baz'",
+            "enum": ["bar", "baz"],
+            "type": "string",
+        },
+    },
+    "required": ["arg1", "arg2"],
+}
+
+
+anthropic_tool_no_description = {
+    "name": "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"],
+    },
+}
+
+
+bedrock_converse_tool_no_description = {
+    "toolSpec": {
+        "name": "dummy_function",
+        "inputSchema": {
+            "json": {
+                "type": "object",
+                "properties": {
+                    "arg1": {"description": "foo", "type": "integer"},
+                    "arg2": {
+                        "description": "one of 'bar', 'baz'",
+                        "enum": ["bar", "baz"],
+                        "type": "string",
+                    },
+                },
+                "required": ["arg1", "arg2"],
+            }
+        },
+    }
+}
+
+
+openai_function_no_description = {
+    "name": "dummy_function",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "arg1": {"description": "foo", "type": "integer"},
+            "arg2": {
+                "description": "one of 'bar', 'baz'",
+                "enum": ["bar", "baz"],
+                "type": "string",
+            },
+        },
+        "required": ["arg1", "arg2"],
+    },
+}
+
+
+openai_function_no_description_no_params = {
+    "name": "dummy_function",
+}
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        anthropic_tool_no_description,
+        json_schema_no_description,
+        bedrock_converse_tool_no_description,
+        openai_function_no_description,
+    ],
+)
+def test_convert_to_openai_function_no_description(func: dict) -> None:
+    expected = {
+        "name": "dummy_function",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "arg1": {"description": "foo", "type": "integer"},
+                "arg2": {
+                    "description": "one of 'bar', 'baz'",
+                    "enum": ["bar", "baz"],
+                    "type": "string",
+                },
+            },
+            "required": ["arg1", "arg2"],
+        },
+    }
+    actual = convert_to_openai_function(func)
+    assert actual == expected
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        json_schema_no_description_no_params,
+        openai_function_no_description_no_params,
+    ],
+)
+def test_convert_to_openai_function_no_description_no_params(func: dict) -> None:
+    expected = {
+        "name": "dummy_function",
+    }
+    actual = convert_to_openai_function(func)
+    assert actual == expected
+
+
 @pytest.mark.xfail(
     reason="Pydantic converts Optional[str] to str in .model_json_schema()"
 )

From 75aa82fedce6635c4b1ed48fcd1ac86f2107de5e Mon Sep 17 00:00:00 2001
From: Hammad Randhawa <59907999+randhawa2001@users.noreply.github.com>
Date: Thu, 7 Nov 2024 00:55:00 +0500
Subject: [PATCH 13/26] =?UTF-8?q?docs:=20Completed=20sentence=20under=20th?=
 =?UTF-8?q?e=20heading=20=20"Instantiating=20a=20Browser=20=E2=80=A6=20(#2?=
 =?UTF-8?q?7944)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…Toolkit" in "playwright.ipynb" integration.

- Completed the incomplete sentence in the Langchain Playwright
documentation.

- Enhanced documentation clarity to guide users on best practices for
instantiating browser instances with Langchain Playwright.

Example before:
> "It's always recommended to instantiate using the from_browser method
so that the

Example after:
> "It's always recommended to instantiate using the `from_browser`
method so that the browser context is properly initialized and managed,
ensuring seamless interaction and resource optimization."

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

diff --git a/docs/docs/integrations/tools/playwright.ipynb b/docs/docs/integrations/tools/playwright.ipynb
index bf397c74236c3..e09f4e79cd24d 100644
--- a/docs/docs/integrations/tools/playwright.ipynb
+++ b/docs/docs/integrations/tools/playwright.ipynb
@@ -101,7 +101,7 @@
    "source": [
     "## Instantiating a Browser Toolkit\n",
     "\n",
-    "It's always recommended to instantiate using the `from_browser` method so that the "
+    "It's always recommended to instantiate using the from_browser method so that the browser context is properly initialized and managed, ensuring seamless interaction and resource optimization."
    ]
   },
   {

From ea0ad917b0911521e3ec7c973babb945fec115d9 Mon Sep 17 00:00:00 2001
From: Eric Pinzur <2641606+epinzur@users.noreply.github.com>
Date: Wed, 6 Nov 2024 21:04:09 +0100
Subject: [PATCH 14/26] community: added Document.id support to opensearch
 vectorstore (#27945)

Description:
* Added support of Document.id on OpenSearch vector store
* Added tests cases to match
---
 .../vectorstores/opensearch_vector_search.py  |  4 +-
 .../vectorstores/test_opensearch.py           | 72 +++++++++++++------
 2 files changed, 55 insertions(+), 21 deletions(-)

diff --git a/libs/community/langchain_community/vectorstores/opensearch_vector_search.py b/libs/community/langchain_community/vectorstores/opensearch_vector_search.py
index da5d38db6dcd0..f1329659eeca7 100644
--- a/libs/community/langchain_community/vectorstores/opensearch_vector_search.py
+++ b/libs/community/langchain_community/vectorstores/opensearch_vector_search.py
@@ -16,7 +16,7 @@
     "Could not import OpenSearch. Please install it with `pip install opensearch-py`."
 )
 IMPORT_ASYNC_OPENSEARCH_PY_ERROR = """
-Could not import AsyncOpenSearch. 
+Could not import AsyncOpenSearch.
 Please install it with `pip install opensearch-py`."""
 
 SCRIPT_SCORING_SEARCH = "script_scoring"
@@ -902,6 +902,7 @@ def similarity_search_with_score_by_vector(
                         if metadata_field == "*" or metadata_field not in hit["_source"]
                         else hit["_source"][metadata_field]
                     ),
+                    id=hit["_id"],
                 ),
                 hit["_score"],
             )
@@ -1099,6 +1100,7 @@ def max_marginal_relevance_search(
             Document(
                 page_content=results[i]["_source"][text_field],
                 metadata=results[i]["_source"][metadata_field],
+                id=results[i]["_id"],
             )
             for i in mmr_selected
         ]
diff --git a/libs/community/tests/integration_tests/vectorstores/test_opensearch.py b/libs/community/tests/integration_tests/vectorstores/test_opensearch.py
index 37dae734605cd..22bff5200738c 100644
--- a/libs/community/tests/integration_tests/vectorstores/test_opensearch.py
+++ b/libs/community/tests/integration_tests/vectorstores/test_opensearch.py
@@ -15,15 +15,19 @@
 
 DEFAULT_OPENSEARCH_URL = "http://localhost:9200"
 texts = ["foo", "bar", "baz"]
+ids = ["id_foo", "id_bar", "id_baz"]
 
 
 def test_opensearch() -> None:
     """Test end to end indexing and search using Approximate Search."""
     docsearch = OpenSearchVectorSearch.from_texts(
-        texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL
+        texts,
+        FakeEmbeddings(),
+        opensearch_url=DEFAULT_OPENSEARCH_URL,
+        ids=ids,
     )
     output = docsearch.similarity_search("foo", k=1)
-    assert output == [Document(page_content="foo")]
+    assert output == [Document(page_content="foo", id="id_foo")]
 
 
 def test_similarity_search_with_score() -> None:
@@ -34,11 +38,12 @@ def test_similarity_search_with_score() -> None:
         FakeEmbeddings(),
         metadatas=metadatas,
         opensearch_url=DEFAULT_OPENSEARCH_URL,
+        ids=ids,
     )
     output = docsearch.similarity_search_with_score("foo", k=2)
     assert output == [
-        (Document(page_content="foo", metadata={"page": 0}), 1.0),
-        (Document(page_content="bar", metadata={"page": 1}), 0.5),
+        (Document(page_content="foo", metadata={"page": 0}, id="id_foo"), 1.0),
+        (Document(page_content="bar", metadata={"page": 1}, id="id_bar"), 0.5),
     ]
 
 
@@ -50,20 +55,24 @@ def test_opensearch_with_custom_field_name() -> None:
         opensearch_url=DEFAULT_OPENSEARCH_URL,
         vector_field="my_vector",
         text_field="custom_text",
+        ids=ids,
     )
     output = docsearch.similarity_search(
         "foo", k=1, vector_field="my_vector", text_field="custom_text"
     )
-    assert output == [Document(page_content="foo")]
+    assert output == [Document(page_content="foo", id="id_foo")]
 
     text_input = ["test", "add", "text", "method"]
     OpenSearchVectorSearch.add_texts(
-        docsearch, text_input, vector_field="my_vector", text_field="custom_text"
+        docsearch,
+        text_input,
+        vector_field="my_vector",
+        text_field="custom_text",
     )
     output = docsearch.similarity_search(
         "add", k=1, vector_field="my_vector", text_field="custom_text"
     )
-    assert output == [Document(page_content="foo")]
+    assert output == [Document(page_content="foo", id="id_foo")]
 
 
 def test_opensearch_with_metadatas() -> None:
@@ -74,9 +83,22 @@ def test_opensearch_with_metadatas() -> None:
         FakeEmbeddings(),
         metadatas=metadatas,
         opensearch_url=DEFAULT_OPENSEARCH_URL,
+        ids=ids,
     )
     output = docsearch.similarity_search("foo", k=1)
-    assert output == [Document(page_content="foo", metadata={"page": 0})]
+    assert output == [Document(page_content="foo", metadata={"page": 0}, id="id_foo")]
+
+
+def test_max_marginal_relevance_search() -> None:
+    """Test end to end indexing and mmr search."""
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        opensearch_url=DEFAULT_OPENSEARCH_URL,
+        ids=ids,
+    )
+    output = docsearch.max_marginal_relevance_search("foo", k=1)
+    assert output == [Document(page_content="foo", id="id_foo")]
 
 
 def test_add_text() -> None:
@@ -86,8 +108,8 @@ def test_add_text() -> None:
     docsearch = OpenSearchVectorSearch.from_texts(
         texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL
     )
-    docids = OpenSearchVectorSearch.add_texts(docsearch, text_input, metadatas)
-    assert len(docids) == len(text_input)
+    doc_ids = OpenSearchVectorSearch.add_texts(docsearch, text_input, metadatas)
+    assert len(doc_ids) == len(text_input)
 
 
 def test_add_embeddings() -> None:
@@ -112,7 +134,8 @@ def test_add_embeddings() -> None:
     )
     docsearch.add_embeddings(list(zip(text_input, embedding_vectors)), metadatas)
     output = docsearch.similarity_search("foo1", k=1)
-    assert output == [Document(page_content="foo3", metadata={"page": 2})]
+    assert output[0].page_content == "foo3"
+    assert output[0].metadata == {"page": 2}
 
 
 def test_opensearch_script_scoring() -> None:
@@ -127,7 +150,8 @@ def test_opensearch_script_scoring() -> None:
     output = docsearch.similarity_search(
         "foo", k=1, search_type=SCRIPT_SCORING_SEARCH, pre_filter=pre_filter_val
     )
-    assert output == [Document(page_content="bar")]
+    assert output[0].page_content == "bar"
+    assert output[0].id is not None
 
 
 def test_add_text_script_scoring() -> None:
@@ -144,7 +168,8 @@ def test_add_text_script_scoring() -> None:
     output = docsearch.similarity_search(
         "add", k=1, search_type=SCRIPT_SCORING_SEARCH, space_type="innerproduct"
     )
-    assert output == [Document(page_content="test")]
+    assert output[0].page_content == "test"
+    assert output[0].id is not None
 
 
 def test_opensearch_painless_scripting() -> None:
@@ -159,7 +184,8 @@ def test_opensearch_painless_scripting() -> None:
     output = docsearch.similarity_search(
         "foo", k=1, search_type=PAINLESS_SCRIPTING_SEARCH, pre_filter=pre_filter_val
     )
-    assert output == [Document(page_content="baz")]
+    assert output[0].page_content == "baz"
+    assert output[0].id is not None
 
 
 def test_add_text_painless_scripting() -> None:
@@ -176,7 +202,8 @@ def test_add_text_painless_scripting() -> None:
     output = docsearch.similarity_search(
         "add", k=1, search_type=PAINLESS_SCRIPTING_SEARCH, space_type="cosineSimilarity"
     )
-    assert output == [Document(page_content="test")]
+    assert output[0].page_content == "test"
+    assert output[0].id is not None
 
 
 def test_opensearch_invalid_search_type() -> None:
@@ -207,7 +234,8 @@ def test_appx_search_with_boolean_filter() -> None:
     output = docsearch.similarity_search(
         "foo", k=3, boolean_filter=boolean_filter_val, subquery_clause="should"
     )
-    assert output == [Document(page_content="bar")]
+    assert output[0].page_content == "bar"
+    assert output[0].id is not None
 
 
 def test_appx_search_with_lucene_filter() -> None:
@@ -217,7 +245,8 @@ def test_appx_search_with_lucene_filter() -> None:
         texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL, engine="lucene"
     )
     output = docsearch.similarity_search("foo", k=3, lucene_filter=lucene_filter_val)
-    assert output == [Document(page_content="bar")]
+    assert output[0].page_content == "bar"
+    assert output[0].id is not None
 
 
 def test_opensearch_with_custom_field_name_appx_true() -> None:
@@ -230,7 +259,8 @@ def test_opensearch_with_custom_field_name_appx_true() -> None:
         is_appx_search=True,
     )
     output = docsearch.similarity_search("add", k=1)
-    assert output == [Document(page_content="add")]
+    assert output[0].page_content == "add"
+    assert output[0].id is not None
 
 
 def test_opensearch_with_custom_field_name_appx_false() -> None:
@@ -240,7 +270,8 @@ def test_opensearch_with_custom_field_name_appx_false() -> None:
         text_input, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL
     )
     output = docsearch.similarity_search("add", k=1)
-    assert output == [Document(page_content="add")]
+    assert output[0].page_content == "add"
+    assert output[0].id is not None
 
 
 def test_opensearch_serverless_with_scripting_search_indexing_throws_error() -> None:
@@ -338,4 +369,5 @@ def test_appx_search_with_faiss_efficient_filter() -> None:
     output = docsearch.similarity_search(
         "foo", k=3, efficient_filter=efficient_filter_val
     )
-    assert output == [Document(page_content="bar")]
+    assert output[0].page_content == "bar"
+    assert output[0].id is not None

From 14f182795312f01985344576b5199681683641e1 Mon Sep 17 00:00:00 2001
From: murrlincoln <65836530+murrlincoln@users.noreply.github.com>
Date: Thu, 7 Nov 2024 04:28:27 +0700
Subject: [PATCH 15/26] docs: Adding notebook for cdp agentkit toolkit (#27910)

- **Description:** Adding in the first pass of documentation for the CDP
Agentkit Toolkit
    - **Issue:** N/a
    - **Dependencies:** cdp-langchain
    - **Twitter handle:** @CoinbaseDev

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: John Peterson <john.peterson@coinbase.com>
---
 .../integrations/tools/cdp_agentkit.ipynb     | 332 ++++++++++++++++++
 1 file changed, 332 insertions(+)
 create mode 100644 docs/docs/integrations/tools/cdp_agentkit.ipynb

diff --git a/docs/docs/integrations/tools/cdp_agentkit.ipynb b/docs/docs/integrations/tools/cdp_agentkit.ipynb
new file mode 100644
index 0000000000000..738074116a3e3
--- /dev/null
+++ b/docs/docs/integrations/tools/cdp_agentkit.ipynb
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: CDP\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# CDP Agentkit Toolkit\n",
+    "\n",
+    "The `CDP Agentkit` toolkit contains tools that enable an LLM agent to interact with the [Coinbase Developer Platform](https://docs.cdp.coinbase.com/). The toolkit provides a wrapper around the CDP SDK, allowing agents to perform onchain operations like transfers, trades, and smart contract interactions.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Serializable | JS support |  Package latest |\n",
+    "| :--- | :--- | :---: | :---: | :---: |\n",
+    "| CdpToolkit | `cdp-langchain` | ❌ | ❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/cdp-langchain?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Tool features\n",
+    "\n",
+    "The toolkit provides the following tools:\n",
+    "\n",
+    "1. **get_wallet_details** - Get details about the MPC Wallet\n",
+    "2. **get_balance** - Get balance for specific assets\n",
+    "3. **request_faucet_funds** - Request test tokens from faucet\n",
+    "4. **transfer** - Transfer assets between addresses\n",
+    "5. **trade** - Trade assets (Mainnet only)\n",
+    "6. **deploy_token** - Deploy ERC-20 token contracts\n",
+    "7. **mint_nft** - Mint NFTs from existing contracts\n",
+    "8. **deploy_nft** - Deploy new NFT contracts\n",
+    "9. **register_basename** - Register a basename for the wallet\n",
+    "\n",
+    "We encourage you to add your own tools, both using CDP and web2 APIs, to create an agent that is tailored to your needs.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "At a high-level, we will:\n",
+    "\n",
+    "1. Install the langchain package\n",
+    "2. Set up your CDP API credentials\n",
+    "3. Initialize the CDP wrapper and toolkit\n",
+    "4. Pass the tools to your agent with `toolkit.get_tools()`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing from runs of individual tools, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "This toolkit lives in the `cdp-langchain` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU cdp-langchain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65",
+   "metadata": {},
+   "source": [
+    "#### Set Environment Variables\n",
+    "\n",
+    "To use this toolkit, you must first set the following environment variables to access the [CDP APIs](https://docs.cdp.coinbase.com/mpc-wallet/docs/quickstart) to create wallets and interact onchain. You can sign up for an API key for free on the [CDP Portal](https://cdp.coinbase.com/):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb09c344",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "for env_var in [\n",
+    "    \"CDP_API_KEY_NAME\",\n",
+    "    \"CDP_API_KEY_PRIVATE_KEY\",\n",
+    "]:\n",
+    "    if not os.getenv(env_var):\n",
+    "        os.environ[env_var] = getpass.getpass(f\"Enter your {env_var}: \")\n",
+    "\n",
+    "# Optional: Set network (defaults to base-sepolia)\n",
+    "os.environ[\"NETWORK_ID\"] = \"base-sepolia\"  # or \"base-mainnet\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c5f2839",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our toolkit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51a60dbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cdp_langchain.agent_toolkits import CdpToolkit\n",
+    "from cdp_langchain.utils import CdpAgentkitWrapper\n",
+    "\n",
+    "# Initialize CDP wrapper\n",
+    "cdp = CdpAgentkitWrapper()\n",
+    "\n",
+    "# Create toolkit from wrapper\n",
+    "toolkit = CdpToolkit.from_cdp_agentkit_wrapper(cdp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d11245ad",
+   "metadata": {},
+   "source": [
+    "## Tools\n",
+    "\n",
+    "View [available tools](#tool-features):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "310bf18e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = toolkit.get_tools()\n",
+    "for tool in tools:\n",
+    "    print(tool.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23e11cc9",
+   "metadata": {},
+   "source": [
+    "## Use within an agent\n",
+    "\n",
+    "We will need a LLM or chat model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1ee55bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca",
+   "metadata": {},
+   "source": [
+    "Initialize the agent with the tools:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8a2c4b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "tools = toolkit.get_tools()\n",
+    "agent_executor = create_react_agent(llm, tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4a7c9d2",
+   "metadata": {},
+   "source": [
+    "Example usage:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9a8e4f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_query = \"Send 0.005 ETH to john2879.base.eth\"\n",
+    "\n",
+    "events = agent_executor.stream(\n",
+    "    {\"messages\": [(\"user\", example_query)]},\n",
+    "    stream_mode=\"values\",\n",
+    ")\n",
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5a7c9d4",
+   "metadata": {},
+   "source": [
+    "Expected output:\n",
+    "```\n",
+    "Transferred 0.005 of eth to john2879.base.eth.\n",
+    "Transaction hash for the transfer: 0x78c7c2878659a0de216d0764fc87eff0d38b47f3315fa02ba493a83d8e782d1e\n",
+    "Transaction link for the transfer: https://sepolia.basescan.org/tx/0x78c7c2878659a0de216d0764fc87eff0d38b47f3315fa02ba493a83d8e782d1\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5a7c9d5",
+   "metadata": {},
+   "source": [
+    "## CDP Toolkit Specific Features\n",
+    "\n",
+    "### Wallet Management\n",
+    "\n",
+    "The toolkit maintains an MPC wallet. The wallet data can be exported and imported to persist between sessions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "g5a7c9d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Export wallet data\n",
+    "wallet_data = cdp.export_wallet()\n",
+    "\n",
+    "# Import wallet data\n",
+    "values = {\"cdp_wallet_data\": wallet_data}\n",
+    "cdp = CdpAgentkitWrapper(**values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "h5a7c9d7",
+   "metadata": {},
+   "source": [
+    "### Network Support\n",
+    "\n",
+    "The toolkit supports [multiple networks](https://docs.cdp.coinbase.com/cdp-sdk/docs/networks)\n",
+    "\n",
+    "### Gasless Transactions\n",
+    "\n",
+    "Some operations support gasless transactions on Base Mainnet:\n",
+    "- USDC transfers\n",
+    "- EURC transfers\n",
+    "- cbBTC transfers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "i5a7c9d8",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all CDP features and configurations head to the [CDP docs](https://docs.cdp.coinbase.com/mpc-wallet/docs/welcome)."
+   ]
+  }
+ ],
+ "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 60123bef67287992bbf014dc991e3a64c8e2c60e Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Wed, 6 Nov 2024 14:25:13 -0800
Subject: [PATCH 16/26] docs: fix trim_messages docstring (#27948)

---
 libs/core/langchain_core/messages/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libs/core/langchain_core/messages/utils.py b/libs/core/langchain_core/messages/utils.py
index 0ea03e40d00d4..2fce9f7dbcaf7 100644
--- a/libs/core/langchain_core/messages/utils.py
+++ b/libs/core/langchain_core/messages/utils.py
@@ -590,7 +590,7 @@ def trim_messages(
     include_system: bool = False,
     text_splitter: Optional[Union[Callable[[str], list[str]], TextSplitter]] = None,
 ) -> list[BaseMessage]:
-    """Trim messages to be below a token count.
+    r"""Trim messages to be below a token count.
 
     trim_messages can be used to reduce the size of a chat history to a specified token
     count or specified message count.

From 0f85dea8c89f00a72ef52043a6d2abad1cd0d1ae Mon Sep 17 00:00:00 2001
From: Roman Solomatin <samoed.roman@gmail.com>
Date: Thu, 7 Nov 2024 03:35:39 +0500
Subject: [PATCH 17/26] langchain-huggingface: use separate kwargs for queries
 and docs (#27857)

Now `encode_kwargs` used for both for documents and queries and this
leads to wrong embeddings. E. g.:
```python
    model_kwargs = {"device": "cuda", "trust_remote_code": True}
    encode_kwargs = {"normalize_embeddings": False, "prompt_name": "s2p_query"}

    model = HuggingFaceEmbeddings(
        model_name="dunzhang/stella_en_400M_v5",
        model_kwargs=model_kwargs,
        encode_kwargs=encode_kwargs,
    )

    query_embedding = np.array(
        model.embed_query("What are some ways to reduce stress?",)
    )
    document_embedding = np.array(
        model.embed_documents(
            [
                "There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
                "Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
            ]
        )
    )
    print(model._client.similarity(query_embedding, document_embedding)) # output: tensor([[0.8421, 0.3317]], dtype=torch.float64)
```
But from the [model
card](https://huggingface.co/dunzhang/stella_en_400M_v5#sentence-transformers)
expexted like this:
```python
    model_kwargs = {"device": "cuda", "trust_remote_code": True}
    encode_kwargs = {"normalize_embeddings": False}
    query_encode_kwargs = {"normalize_embeddings": False, "prompt_name": "s2p_query"}

    model = HuggingFaceEmbeddings(
        model_name="dunzhang/stella_en_400M_v5",
        model_kwargs=model_kwargs,
        encode_kwargs=encode_kwargs,
        query_encode_kwargs=query_encode_kwargs,
    )

    query_embedding = np.array(
        model.embed_query("What are some ways to reduce stress?", )
    )
    document_embedding = np.array(
        model.embed_documents(
            [
                "There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
                "Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
            ]
        )
    )
    print(model._client.similarity(query_embedding, document_embedding)) # tensor([[0.8398, 0.2990]], dtype=torch.float64)
```
---
 .../embeddings/huggingface.py                 | 41 +++++++++++++++----
 1 file changed, 34 insertions(+), 7 deletions(-)

diff --git a/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py b/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py
index 180a9ed3b5e79..2bbc551f4e0b1 100644
--- a/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py
+++ b/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py
@@ -36,9 +36,14 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     `prompts`, `default_prompt_name`, `revision`, `trust_remote_code`, or `token`.
     See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer"""
     encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """Keyword arguments to pass when calling the `encode` method of the Sentence
-    Transformer model, such as `prompt_name`, `prompt`, `batch_size`, `precision`,
-    `normalize_embeddings`, and more.
+    """Keyword arguments to pass when calling the `encode` method for the documents of
+    the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`, 
+    `precision`, `normalize_embeddings`, and more.
+    See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
+    query_encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Keyword arguments to pass when calling the `encode` method for the query of
+    the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`, 
+    `precision`, `normalize_embeddings`, and more.
     See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
     multi_process: bool = False
     """Run encode() on multiple GPUs."""
@@ -65,11 +70,17 @@ def __init__(self, **kwargs: Any):
         protected_namespaces=(),
     )
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Compute doc embeddings using a HuggingFace transformer model.
+    def _embed(
+        self, texts: list[str], encode_kwargs: Dict[str, Any]
+    ) -> List[List[float]]:
+        """
+        Embed a text using the HuggingFace transformer model.
 
         Args:
             texts: The list of texts to embed.
+            encode_kwargs: Keyword arguments to pass when calling the
+                `encode` method for the documents of the SentenceTransformer
+                 encode method.
 
         Returns:
             List of embeddings, one for each text.
@@ -85,7 +96,7 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]:
             embeddings = self._client.encode(
                 texts,
                 show_progress_bar=self.show_progress,
-                **self.encode_kwargs,  # type: ignore
+                **encode_kwargs,  # type: ignore
             )
 
         if isinstance(embeddings, list):
@@ -96,6 +107,17 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]:
 
         return embeddings.tolist()
 
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Compute doc embeddings using a HuggingFace transformer model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        return self._embed(texts, self.encode_kwargs)
+
     def embed_query(self, text: str) -> List[float]:
         """Compute query embeddings using a HuggingFace transformer model.
 
@@ -105,4 +127,9 @@ def embed_query(self, text: str) -> List[float]:
         Returns:
             Embeddings for the text.
         """
-        return self.embed_documents([text])[0]
+        embed_kwargs = (
+            self.query_encode_kwargs
+            if len(self.query_encode_kwargs) > 0
+            else self.encode_kwargs
+        )
+        return self._embed([text], embed_kwargs)[0]

From 482c168b3e8ac982130bacc89fc9a32b67f765a3 Mon Sep 17 00:00:00 2001
From: takahashi <7567050+MasaYan24@users.noreply.github.com>
Date: Thu, 7 Nov 2024 07:37:07 +0900
Subject: [PATCH 18/26] langchain_core: add `file_type` option to make file
 type default as `png` (#27855)

Thank you for contributing to LangChain!

- [ ] **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"

- [ ] **description**
langchain_core.runnables.graph_mermaid.draw_mermaid_png calls this
function, but the Mermaid API returns JPEG by default. To be consistent,
add the option `file_type` with the default `png` type.

- [ ] **Add tests and docs**: If you're adding a new integration, please
include
With this small change, I didn't add tests and docs.

- [ ] **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:
One long sentence was divided into two.

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.
---
 libs/core/langchain_core/runnables/graph_mermaid.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/libs/core/langchain_core/runnables/graph_mermaid.py b/libs/core/langchain_core/runnables/graph_mermaid.py
index b0e7f3dd5de5e..c9b3025bc857d 100644
--- a/libs/core/langchain_core/runnables/graph_mermaid.py
+++ b/libs/core/langchain_core/runnables/graph_mermaid.py
@@ -2,7 +2,7 @@
 import base64
 import re
 from dataclasses import asdict
-from typing import Optional
+from typing import Literal, Optional
 
 from langchain_core.runnables.graph import (
     CurveStyle,
@@ -306,6 +306,7 @@ def _render_mermaid_using_api(
     mermaid_syntax: str,
     output_file_path: Optional[str] = None,
     background_color: Optional[str] = "white",
+    file_type: Optional[Literal["jpeg", "png", "webp"]] = "png",
 ) -> bytes:
     """Renders Mermaid graph using the Mermaid.INK API."""
     try:
@@ -329,7 +330,8 @@ def _render_mermaid_using_api(
             background_color = f"!{background_color}"
 
     image_url = (
-        f"https://mermaid.ink/img/{mermaid_syntax_encoded}?bgColor={background_color}"
+        f"https://mermaid.ink/img/{mermaid_syntax_encoded}"
+        f"?type={file_type}&bgColor={background_color}"
     )
     response = requests.get(image_url, timeout=10)
     if response.status_code == 200:

From 90189f5639560d1c4923e39885d3c8eb3c94caf2 Mon Sep 17 00:00:00 2001
From: Martin Triska <martin.triska@gmail.com>
Date: Wed, 6 Nov 2024 23:44:34 +0100
Subject: [PATCH 19/26] community: Allow other than default parsers in
 SharePointLoader and OneDriveLoader (#27716)

## What this PR does?

### Currently `O365BaseLoader` (and consequently both derived loaders)
are limited to `pdf`, `doc`, `docx` files.
- **Solution: here we introduce _handlers_ attribute that allows for
custom handlers to be passed in. This is done in _dict_ form:**

**Example:**
```python
from langchain_community.document_loaders.parsers.documentloader_adapter import DocumentLoaderAsParser
# PR for DocumentLoaderAsParser here: https://github.com/langchain-ai/langchain/pull/27749
from langchain_community.document_loaders.excel import UnstructuredExcelLoader

xlsx_parser = DocumentLoaderAsParser(UnstructuredExcelLoader, mode="paged")

# create dictionary mapping file types to handlers (parsers)
handlers = {
    "doc": MsWordParser()
    "pdf": PDFMinerParser()
    "txt": TextParser()
    "xlsx": xlsx_parser
}
loader = SharePointLoader(document_library_id="...",
                            handlers=handlers # pass handlers to SharePointLoader
                            )
documents = loader.load()

# works the same in OneDriveLoader
loader = OneDriveLoader(document_library_id="...",
                            handlers=handlers
                            )
```
This dictionary is then passed to `MimeTypeBasedParser` same as in the
[current
implementation](https://github.com/langchain-ai/langchain/blob/5a2cfb49e045988d290a1c7e3a0c589d6b371694/libs/community/langchain_community/document_loaders/parsers/registry.py#L13).


### Currently `SharePointLoader` and `OneDriveLoader` are separate
loaders that both inherit from `O365BaseLoader`
However both of these implement the same functionality. The only
differences are:
- `SharePointLoader` requires argument `document_library_id` whereas
`OneDriveLoader` requires `drive_id`. These are just different names for
the same thing.
  - `SharePointLoader` implements significantly more features.
- **Solution: `OneDriveLoader` is replaced with an empty shell just
renaming `drive_id` to `document_library_id` and inheriting from
`SharePointLoader`**

**Dependencies:** None
**Twitter handle:** @martintriska1

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---
 .../document_loaders/microsoft_onedrive.ipynb |  67 ++++++++--
 .../microsoft_sharepoint.ipynb                |  60 ++++++++-
 .../document_loaders/base_o365.py             | 116 ++++++++++++++----
 .../document_loaders/onedrive.py              |  97 ++-------------
 .../document_loaders/sharepoint.py            |  23 +---
 5 files changed, 227 insertions(+), 136 deletions(-)

diff --git a/docs/docs/integrations/document_loaders/microsoft_onedrive.ipynb b/docs/docs/integrations/document_loaders/microsoft_onedrive.ipynb
index b42c141f8fea4..20feef0f9cc3c 100644
--- a/docs/docs/integrations/document_loaders/microsoft_onedrive.ipynb
+++ b/docs/docs/integrations/document_loaders/microsoft_onedrive.ipynb
@@ -8,7 +8,7 @@
     "\n",
     ">[Microsoft OneDrive](https://en.wikipedia.org/wiki/OneDrive) (formerly `SkyDrive`) is a file hosting service operated by Microsoft.\n",
     "\n",
-    "This notebook covers how to load documents from `OneDrive`. Currently, only docx, doc, and pdf files are supported.\n",
+    "This notebook covers how to load documents from `OneDrive`. By default the document loader loads `pdf`, `doc`, `docx` and `txt` files. You can load other file types by providing appropriate parsers (see more below).\n",
     "\n",
     "## Prerequisites\n",
     "1. Register an application with the [Microsoft identity platform](https://learn.microsoft.com/en-us/azure/active-directory/develop/quickstart-register-app) instructions.\n",
@@ -77,15 +77,64 @@
     "\n",
     "loader = OneDriveLoader(drive_id=\"YOUR DRIVE ID\", object_ids=[\"ID_1\", \"ID_2\"], auth_with_token=True)\n",
     "documents = loader.load()\n",
-    "```\n"
+    "```\n",
+    "\n",
+    "#### 📑 Choosing supported file types and preffered parsers\n",
+    "By default `OneDriveLoader` loads file types defined in [`document_loaders/parsers/registry`](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/document_loaders/parsers/registry.py#L10-L22) using the default parsers (see below).\n",
+    "```python\n",
+    "def _get_default_parser() -> BaseBlobParser:\n",
+    "    \"\"\"Get default mime-type based parser.\"\"\"\n",
+    "    return MimeTypeBasedParser(\n",
+    "        handlers={\n",
+    "            \"application/pdf\": PyMuPDFParser(),\n",
+    "            \"text/plain\": TextParser(),\n",
+    "            \"application/msword\": MsWordParser(),\n",
+    "            \"application/vnd.openxmlformats-officedocument.wordprocessingml.document\": (\n",
+    "                MsWordParser()\n",
+    "            ),\n",
+    "        },\n",
+    "        fallback_parser=None,\n",
+    "    )\n",
+    "```\n",
+    "You can override this behavior by passing `handlers` argument to `OneDriveLoader`. \n",
+    "Pass a dictionary mapping either file extensions (like `\"doc\"`, `\"pdf\"`, etc.) \n",
+    "or MIME types (like `\"application/pdf\"`, `\"text/plain\"`, etc.) to parsers. \n",
+    "Note that you must use either file extensions or MIME types exclusively and \n",
+    "cannot mix them.\n",
+    "\n",
+    "Do not include the leading dot for file extensions.\n",
+    "\n",
+    "```python\n",
+    "# using file extensions:\n",
+    "handlers = {\n",
+    "    \"doc\": MsWordParser(),\n",
+    "    \"pdf\": PDFMinerParser(),\n",
+    "    \"mp3\": OpenAIWhisperParser()\n",
+    "}\n",
+    "\n",
+    "# using MIME types:\n",
+    "handlers = {\n",
+    "    \"application/msword\": MsWordParser(),\n",
+    "    \"application/pdf\": PDFMinerParser(),\n",
+    "    \"audio/mpeg\": OpenAIWhisperParser()\n",
+    "}\n",
+    "\n",
+    "loader = OneDriveLoader(document_library_id=\"...\",\n",
+    "                            handlers=handlers # pass handlers to OneDriveLoader\n",
+    "                            )\n",
+    "```\n",
+    "In case multiple file extensions map to the same MIME type, the last dictionary item will\n",
+    "apply.\n",
+    "Example:\n",
+    "```python\n",
+    "# 'jpg' and 'jpeg' both map to 'image/jpeg' MIME type. SecondParser() will be used \n",
+    "# to parse all jpg/jpeg files.\n",
+    "handlers = {\n",
+    "    \"jpg\": FirstParser(),\n",
+    "    \"jpeg\": SecondParser()\n",
+    "}\n",
+    "```"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/docs/integrations/document_loaders/microsoft_sharepoint.ipynb b/docs/docs/integrations/document_loaders/microsoft_sharepoint.ipynb
index 930346675dfd9..b49abe39f5158 100644
--- a/docs/docs/integrations/document_loaders/microsoft_sharepoint.ipynb
+++ b/docs/docs/integrations/document_loaders/microsoft_sharepoint.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "> [Microsoft SharePoint](https://en.wikipedia.org/wiki/SharePoint) is a website-based collaboration system that uses workflow applications, “list” databases, and other web parts and security features to empower business teams to work together developed by Microsoft.\n",
     "\n",
-    "This notebook covers how to load documents from the [SharePoint Document Library](https://support.microsoft.com/en-us/office/what-is-a-document-library-3b5976dd-65cf-4c9e-bf5a-713c10ca2872). Currently, only docx, doc, and pdf files are supported.\n",
+    "This notebook covers how to load documents from the [SharePoint Document Library](https://support.microsoft.com/en-us/office/what-is-a-document-library-3b5976dd-65cf-4c9e-bf5a-713c10ca2872). By default the document loader loads `pdf`, `doc`, `docx` and `txt` files. You can load other file types by providing appropriate parsers (see more below).\n",
     "\n",
     "## Prerequisites\n",
     "1. Register an application with the [Microsoft identity platform](https://learn.microsoft.com/en-us/azure/active-directory/develop/quickstart-register-app) instructions.\n",
@@ -100,7 +100,63 @@
     "\n",
     "loader = SharePointLoader(document_library_id=\"YOUR DOCUMENT LIBRARY ID\", object_ids=[\"ID_1\", \"ID_2\"], auth_with_token=True)\n",
     "documents = loader.load()\n",
-    "```\n"
+    "```\n",
+    "\n",
+    "#### 📑 Choosing supported file types and preffered parsers\n",
+    "By default `SharePointLoader` loads file types defined in [`document_loaders/parsers/registry`](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/document_loaders/parsers/registry.py#L10-L22) using the default parsers (see below).\n",
+    "```python\n",
+    "def _get_default_parser() -> BaseBlobParser:\n",
+    "    \"\"\"Get default mime-type based parser.\"\"\"\n",
+    "    return MimeTypeBasedParser(\n",
+    "        handlers={\n",
+    "            \"application/pdf\": PyMuPDFParser(),\n",
+    "            \"text/plain\": TextParser(),\n",
+    "            \"application/msword\": MsWordParser(),\n",
+    "            \"application/vnd.openxmlformats-officedocument.wordprocessingml.document\": (\n",
+    "                MsWordParser()\n",
+    "            ),\n",
+    "        },\n",
+    "        fallback_parser=None,\n",
+    "    )\n",
+    "```\n",
+    "You can override this behavior by passing `handlers` argument to `SharePointLoader`. \n",
+    "Pass a dictionary mapping either file extensions (like `\"doc\"`, `\"pdf\"`, etc.) \n",
+    "or MIME types (like `\"application/pdf\"`, `\"text/plain\"`, etc.) to parsers. \n",
+    "Note that you must use either file extensions or MIME types exclusively and \n",
+    "cannot mix them.\n",
+    "\n",
+    "Do not include the leading dot for file extensions.\n",
+    "\n",
+    "```python\n",
+    "# using file extensions:\n",
+    "handlers = {\n",
+    "    \"doc\": MsWordParser(),\n",
+    "    \"pdf\": PDFMinerParser(),\n",
+    "    \"mp3\": OpenAIWhisperParser()\n",
+    "}\n",
+    "\n",
+    "# using MIME types:\n",
+    "handlers = {\n",
+    "    \"application/msword\": MsWordParser(),\n",
+    "    \"application/pdf\": PDFMinerParser(),\n",
+    "    \"audio/mpeg\": OpenAIWhisperParser()\n",
+    "}\n",
+    "\n",
+    "loader = SharePointLoader(document_library_id=\"...\",\n",
+    "                            handlers=handlers # pass handlers to SharePointLoader\n",
+    "                            )\n",
+    "```\n",
+    "In case multiple file extensions map to the same MIME type, the last dictionary item will\n",
+    "apply.\n",
+    "Example:\n",
+    "```python\n",
+    "# 'jpg' and 'jpeg' both map to 'image/jpeg' MIME type. SecondParser() will be used \n",
+    "# to parse all jpg/jpeg files.\n",
+    "handlers = {\n",
+    "    \"jpg\": FirstParser(),\n",
+    "    \"jpeg\": SecondParser()\n",
+    "}\n",
+    "```"
    ]
   }
  ],
diff --git a/libs/community/langchain_community/document_loaders/base_o365.py b/libs/community/langchain_community/document_loaders/base_o365.py
index 44002842bf25d..5f89d0794fccd 100644
--- a/libs/community/langchain_community/document_loaders/base_o365.py
+++ b/libs/community/langchain_community/document_loaders/base_o365.py
@@ -3,26 +3,29 @@
 from __future__ import annotations
 
 import logging
+import mimetypes
 import os
 import tempfile
 from abc import abstractmethod
-from enum import Enum
 from pathlib import Path, PurePath
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Sequence, Union
+from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence, Union
 
 from pydantic import (
     BaseModel,
     Field,
     FilePath,
+    PrivateAttr,
     SecretStr,
 )
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
-from langchain_community.document_loaders.base import BaseLoader
+from langchain_community.document_loaders.base import BaseBlobParser, BaseLoader
 from langchain_community.document_loaders.blob_loaders.file_system import (
     FileSystemBlobLoader,
 )
 from langchain_community.document_loaders.blob_loaders.schema import Blob
+from langchain_community.document_loaders.parsers.generic import MimeTypeBasedParser
+from langchain_community.document_loaders.parsers.registry import get_parser
 
 if TYPE_CHECKING:
     from O365 import Account
@@ -46,24 +49,27 @@ class _O365TokenStorage(BaseSettings):
     token_path: FilePath = Path.home() / ".credentials" / "o365_token.txt"
 
 
-class _FileType(str, Enum):
-    DOC = "doc"
-    DOCX = "docx"
-    PDF = "pdf"
+def fetch_mime_types(file_types: Sequence[str]) -> Dict[str, str]:
+    """Fetch the mime types for the specified file types."""
+    mime_types_mapping = {}
+    for ext in file_types:
+        mime_type, _ = mimetypes.guess_type(f"file.{ext}")
+        if mime_type:
+            mime_types_mapping[ext] = mime_type
+        else:
+            raise ValueError(f"Unknown mimetype of extention {ext}")
+    return mime_types_mapping
 
 
-def fetch_mime_types(file_types: Sequence[_FileType]) -> Dict[str, str]:
+def fetch_extensions(mime_types: Sequence[str]) -> Dict[str, str]:
     """Fetch the mime types for the specified file types."""
     mime_types_mapping = {}
-    for file_type in file_types:
-        if file_type.value == "doc":
-            mime_types_mapping[file_type.value] = "application/msword"
-        elif file_type.value == "docx":
-            mime_types_mapping[file_type.value] = (
-                "application/vnd.openxmlformats-officedocument.wordprocessingml.document"  # noqa: E501
-            )
-        elif file_type.value == "pdf":
-            mime_types_mapping[file_type.value] = "application/pdf"
+    for mime_type in mime_types:
+        ext = mimetypes.guess_extension(mime_type)
+        if ext:
+            mime_types_mapping[ext[1:]] = mime_type  # ignore leading `.`
+        else:
+            raise ValueError(f"Unknown mimetype {mime_type}")
     return mime_types_mapping
 
 
@@ -78,16 +84,82 @@ class O365BaseLoader(BaseLoader, BaseModel):
     """Number of bytes to retrieve from each api call to the server. int or 'auto'."""
     recursive: bool = False
     """Should the loader recursively load subfolders?"""
+    handlers: Optional[Dict[str, Any]] = {}
+    """
+    Provide custom handlers for MimeTypeBasedParser.
+
+    Pass a dictionary mapping either file extensions (like "doc", "pdf", etc.) 
+    or MIME types (like "application/pdf", "text/plain", etc.) to parsers. 
+    Note that you must use either file extensions or MIME types exclusively and 
+    cannot mix them.
+
+    Do not include the leading dot for file extensions.
+    
+    Example using file extensions:
+    ```python
+        handlers = {
+            "doc": MsWordParser(),
+            "pdf": PDFMinerParser(),
+            "txt": TextParser()
+        }
+    ```
+    
+    Example using MIME types:
+    ```python
+        handlers = {
+            "application/msword": MsWordParser(),
+            "application/pdf": PDFMinerParser(),
+            "text/plain": TextParser()
+        }
+    ```
+    """
+
+    _blob_parser: BaseBlobParser = PrivateAttr()
+    _file_types: Sequence[str] = PrivateAttr()
+    _mime_types: Dict[str, str] = PrivateAttr()
+
+    def __init__(self, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        if self.handlers:
+            handler_keys = list(self.handlers.keys())
+            try:
+                # assume handlers.keys() are file extensions
+                self._mime_types = fetch_mime_types(handler_keys)
+                self._file_types = list(set(handler_keys))
+                mime_handlers = {
+                    self._mime_types[extension]: handler
+                    for extension, handler in self.handlers.items()
+                }
+            except ValueError:
+                try:
+                    # assume handlers.keys() are mime types
+                    self._mime_types = fetch_extensions(handler_keys)
+                    self._file_types = list(set(self._mime_types.keys()))
+                    mime_handlers = self.handlers
+                except ValueError:
+                    raise ValueError(
+                        "`handlers` keys must be either file extensions or mimetypes.\n"
+                        f"{handler_keys} could not be interpreted as either.\n"
+                        "File extensions and mimetypes cannot mix. "
+                        "Use either one or the other"
+                    )
 
-    @property
-    @abstractmethod
-    def _file_types(self) -> Sequence[_FileType]:
-        """Return supported file types."""
+            self._blob_parser = MimeTypeBasedParser(
+                handlers=mime_handlers, fallback_parser=None
+            )
+        else:
+            self._blob_parser = get_parser("default")
+            if not isinstance(self._blob_parser, MimeTypeBasedParser):
+                raise TypeError(
+                    'get_parser("default) was supposed to return MimeTypeBasedParser.'
+                    f"It returned {type(self._blob_parser)}"
+                )
+            self._mime_types = fetch_extensions(list(self._blob_parser.handlers.keys()))
 
     @property
     def _fetch_mime_types(self) -> Dict[str, str]:
         """Return a dict of supported file types to corresponding mime types."""
-        return fetch_mime_types(self._file_types)
+        return self._mime_types
 
     @property
     @abstractmethod
diff --git a/libs/community/langchain_community/document_loaders/onedrive.py b/libs/community/langchain_community/document_loaders/onedrive.py
index ecc1d232bfe4c..e0369233c22bf 100644
--- a/libs/community/langchain_community/document_loaders/onedrive.py
+++ b/libs/community/langchain_community/document_loaders/onedrive.py
@@ -1,94 +1,19 @@
-"""Loads data from OneDrive"""
+from typing import Any
 
-from __future__ import annotations
-
-import logging
-from typing import TYPE_CHECKING, Iterator, List, Optional, Sequence, Union
-
-from langchain_core.documents import Document
 from pydantic import Field
 
-from langchain_community.document_loaders.base_o365 import (
-    O365BaseLoader,
-    _FileType,
-)
-from langchain_community.document_loaders.parsers.registry import get_parser
-
-if TYPE_CHECKING:
-    from O365.drive import Drive, Folder
-
-logger = logging.getLogger(__name__)
+from langchain_community.document_loaders import SharePointLoader
 
 
-class OneDriveLoader(O365BaseLoader):
-    """Load from `Microsoft OneDrive`."""
+class OneDriveLoader(SharePointLoader):
+    """
+    Load documents from Microsoft OneDrive.
+    Uses `SharePointLoader` under the hood.
+    """
 
     drive_id: str = Field(...)
-    """ The ID of the OneDrive drive to load data from."""
-    folder_path: Optional[str] = None
-    """ The path to the folder to load data from."""
-    object_ids: Optional[List[str]] = None
-    """ The IDs of the objects to load data from."""
-
-    @property
-    def _file_types(self) -> Sequence[_FileType]:
-        """Return supported file types."""
-        return _FileType.DOC, _FileType.DOCX, _FileType.PDF
-
-    @property
-    def _scopes(self) -> List[str]:
-        """Return required scopes."""
-        return ["offline_access", "Files.Read.All"]
-
-    def _get_folder_from_path(self, drive: Drive) -> Union[Folder, Drive]:
-        """
-        Returns the folder or drive object located at the
-        specified path relative to the given drive.
-
-        Args:
-            drive (Drive): The root drive from which the folder path is relative.
-
-        Returns:
-            Union[Folder, Drive]: The folder or drive object
-            located at the specified path.
-
-        Raises:
-            FileNotFoundError: If the path does not exist.
-        """
-
-        subfolder_drive = drive
-        if self.folder_path is None:
-            return subfolder_drive
-
-        subfolders = [f for f in self.folder_path.split("/") if f != ""]
-        if len(subfolders) == 0:
-            return subfolder_drive
-
-        items = subfolder_drive.get_items()
-        for subfolder in subfolders:
-            try:
-                subfolder_drive = list(filter(lambda x: subfolder in x.name, items))[0]
-                items = subfolder_drive.get_items()
-            except (IndexError, AttributeError):
-                raise FileNotFoundError("Path {} not exist.".format(self.folder_path))
-        return subfolder_drive
+    """The ID of the OneDrive drive to load data from."""
 
-    def lazy_load(self) -> Iterator[Document]:
-        """Load documents lazily. Use this when working at a large scale."""
-        try:
-            from O365.drive import Drive
-        except ImportError:
-            raise ImportError(
-                "O365 package not found, please install it with `pip install o365`"
-            )
-        drive = self._auth().storage().get_drive(self.drive_id)
-        if not isinstance(drive, Drive):
-            raise ValueError(f"There isn't a Drive with id {self.drive_id}.")
-        blob_parser = get_parser("default")
-        if self.folder_path:
-            folder = self._get_folder_from_path(drive)
-            for blob in self._load_from_folder(folder):
-                yield from blob_parser.lazy_parse(blob)
-        if self.object_ids:
-            for blob in self._load_from_object_ids(drive, self.object_ids):
-                yield from blob_parser.lazy_parse(blob)
+    def __init__(self, **kwargs: Any) -> None:
+        kwargs["document_library_id"] = kwargs["drive_id"]
+        super().__init__(**kwargs)
diff --git a/libs/community/langchain_community/document_loaders/sharepoint.py b/libs/community/langchain_community/document_loaders/sharepoint.py
index 06426a7038fdd..6d5a820248e5f 100644
--- a/libs/community/langchain_community/document_loaders/sharepoint.py
+++ b/libs/community/langchain_community/document_loaders/sharepoint.py
@@ -4,7 +4,7 @@
 
 import json
 from pathlib import Path
-from typing import Any, Iterator, List, Optional, Sequence
+from typing import Any, Dict, Iterator, List, Optional
 
 import requests  # type: ignore
 from langchain_core.document_loaders import BaseLoader
@@ -13,9 +13,7 @@
 
 from langchain_community.document_loaders.base_o365 import (
     O365BaseLoader,
-    _FileType,
 )
-from langchain_community.document_loaders.parsers.registry import get_parser
 
 
 class SharePointLoader(O365BaseLoader, BaseLoader):
@@ -36,14 +34,6 @@ class SharePointLoader(O365BaseLoader, BaseLoader):
     load_extended_metadata: Optional[bool] = False
     """ Whether to load extended metadata. Size, Owner and full_path."""
 
-    @property
-    def _file_types(self) -> Sequence[_FileType]:
-        """Return supported file types.
-        Returns:
-            A sequence of supported file types.
-        """
-        return _FileType.DOC, _FileType.DOCX, _FileType.PDF
-
     @property
     def _scopes(self) -> List[str]:
         """Return required scopes.
@@ -67,7 +57,6 @@ def lazy_load(self) -> Iterator[Document]:
         drive = self._auth().storage().get_drive(self.document_library_id)
         if not isinstance(drive, Drive):
             raise ValueError(f"There isn't a Drive with id {self.document_library_id}.")
-        blob_parser = get_parser("default")
         if self.folder_path:
             target_folder = drive.get_item_by_path(self.folder_path)
             if not isinstance(target_folder, Folder):
@@ -79,7 +68,7 @@ def lazy_load(self) -> Iterator[Document]:
                 if self.load_extended_metadata is True:
                     extended_metadata = self.get_extended_metadata(file_id)
                     extended_metadata.update({"source_full_url": target_folder.web_url})
-                for parsed_blob in blob_parser.lazy_parse(blob):
+                for parsed_blob in self._blob_parser.lazy_parse(blob):
                     if self.load_auth is True:
                         parsed_blob.metadata["authorized_identities"] = auth_identities
                     if self.load_extended_metadata is True:
@@ -96,7 +85,7 @@ def lazy_load(self) -> Iterator[Document]:
                 if self.load_extended_metadata is True:
                     extended_metadata = self.get_extended_metadata(file_id)
                     extended_metadata.update({"source_full_url": target_folder.web_url})
-                for parsed_blob in blob_parser.lazy_parse(blob):
+                for parsed_blob in self._blob_parser.lazy_parse(blob):
                     if self.load_auth is True:
                         parsed_blob.metadata["authorized_identities"] = auth_identities
                     if self.load_extended_metadata is True:
@@ -109,7 +98,7 @@ def lazy_load(self) -> Iterator[Document]:
                     auth_identities = self.authorized_identities(file_id)
                 if self.load_extended_metadata is True:
                     extended_metadata = self.get_extended_metadata(file_id)
-                for parsed_blob in blob_parser.lazy_parse(blob):
+                for parsed_blob in self._blob_parser.lazy_parse(blob):
                     if self.load_auth is True:
                         parsed_blob.metadata["authorized_identities"] = auth_identities
                     if self.load_extended_metadata is True:
@@ -126,7 +115,7 @@ def lazy_load(self) -> Iterator[Document]:
                     auth_identities = self.authorized_identities(file_id)
                 if self.load_extended_metadata is True:
                     extended_metadata = self.get_extended_metadata(file_id)
-                for blob_part in blob_parser.lazy_parse(blob):
+                for blob_part in self._blob_parser.lazy_parse(blob):
                     blob_part.metadata.update(blob.metadata)
                     if self.load_auth is True:
                         blob_part.metadata["authorized_identities"] = auth_identities
@@ -182,7 +171,7 @@ def _fetch_access_token(self) -> Any:
         data = json.loads(s)
         return data
 
-    def get_extended_metadata(self, file_id: str) -> dict:
+    def get_extended_metadata(self, file_id: str) -> Dict:
         """
         Retrieve extended metadata for a file in SharePoint.
         As of today, following fields are supported in the extended metadata:

From 2494deb2a4bac23888cfa3763567336018660636 Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Wed, 6 Nov 2024 16:31:10 -0800
Subject: [PATCH 20/26] infra: remove google creds from release and integration
 test workflows (#27950)

---
 .github/workflows/_integration_test.yml | 6 ------
 .github/workflows/_release.yml          | 6 ------
 2 files changed, 12 deletions(-)

diff --git a/.github/workflows/_integration_test.yml b/.github/workflows/_integration_test.yml
index 60a74ff08734d..d2157eb495630 100644
--- a/.github/workflows/_integration_test.yml
+++ b/.github/workflows/_integration_test.yml
@@ -41,12 +41,6 @@ jobs:
         shell: bash
         run: poetry run pip install "boto3<2" "google-cloud-aiplatform<2"
 
-      - name: 'Authenticate to Google Cloud'
-        id: 'auth'
-        uses: google-github-actions/auth@v2
-        with:
-          credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
-
       - name: Run integration tests
         shell: bash
         env:
diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml
index fd6a53fee85cb..c36607f62fee7 100644
--- a/.github/workflows/_release.yml
+++ b/.github/workflows/_release.yml
@@ -267,12 +267,6 @@ jobs:
           make tests
         working-directory: ${{ inputs.working-directory }}
 
-      - name: 'Authenticate to Google Cloud'
-        id: 'auth'
-        uses: google-github-actions/auth@v2
-        with:
-          credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
-
       - name: Import integration test dependencies
         run: poetry install --with test,test_integration
         working-directory: ${{ inputs.working-directory }}

From 81f7daa4580aaee45314e31b3461f23520f79bd1 Mon Sep 17 00:00:00 2001
From: Baptiste Pasquier <50556298+baptiste-pasquier@users.noreply.github.com>
Date: Thu, 7 Nov 2024 02:26:30 +0100
Subject: [PATCH 21/26] community: add InfinityRerank (#27043)

**Description:**

- Add a Reranker for Infinity server.

**Dependencies:**

This wrapper uses
[infinity_client](https://github.com/michaelfeil/infinity/tree/main/libs/client_infinity/infinity_client)
to connect to an Infinity server.

**Tests and docs**

- integration test: test_infinity_rerank.py
- example notebook: infinity_rerank.ipynb
[here](https://github.com/baptiste-pasquier/langchain/blob/feat/infinity-rerank/docs/docs/integrations/document_transformers/infinity_rerank.ipynb)

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../infinity_rerank.ipynb                     | 405 ++++++++++++++++++
 .../document_compressors/__init__.py          |   5 +
 .../document_compressors/infinity_rerank.py   | 135 ++++++
 .../test_infinity_rerank.py                   |  32 ++
 .../document_compressors/test_imports.py      |   1 +
 5 files changed, 578 insertions(+)
 create mode 100644 docs/docs/integrations/document_transformers/infinity_rerank.ipynb
 create mode 100644 libs/community/langchain_community/document_compressors/infinity_rerank.py
 create mode 100644 libs/community/tests/integration_tests/document_compressors/test_infinity_rerank.py

diff --git a/docs/docs/integrations/document_transformers/infinity_rerank.ipynb b/docs/docs/integrations/document_transformers/infinity_rerank.ipynb
new file mode 100644
index 0000000000000..34368a28a0235
--- /dev/null
+++ b/docs/docs/integrations/document_transformers/infinity_rerank.ipynb
@@ -0,0 +1,405 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Infinity Reranker\n",
+    "\n",
+    "`Infinity` is a high-throughput, low-latency REST API for serving text-embeddings, reranking models and clip. \n",
+    "For more info, please visit [here](https://github.com/michaelfeil/infinity?tab=readme-ov-file#reranking).\n",
+    "\n",
+    "This notebook shows how to use Infinity Reranker for document compression and retrieval. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can launch an Infinity Server with a reranker model in CLI:\n",
+    "\n",
+    "```bash\n",
+    "pip install \"infinity-emb[all]\"\n",
+    "infinity_emb v2 --model-id mixedbread-ai/mxbai-rerank-xsmall-v1\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  infinity_client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  faiss\n",
+    "\n",
+    "# OR  (depending on Python version)\n",
+    "\n",
+    "%pip install --upgrade --quiet  faiss-cpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper function for printing docs\n",
+    "def pretty_print_docs(docs):\n",
+    "    print(\n",
+    "        f\"\\n{'-' * 100}\\n\".join(\n",
+    "            [f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set up the base vector store retriever\n",
+    "Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document 1:\n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 2:\n",
+      "\n",
+      "We cannot let this happen. \n",
+      "\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 3:\n",
+      "\n",
+      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
+      "\n",
+      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 4:\n",
+      "\n",
+      "He will never extinguish their love of freedom. He will never weaken the resolve of the free world. \n",
+      "\n",
+      "We meet tonight in an America that has lived through two of the hardest years this nation has ever faced. \n",
+      "\n",
+      "The pandemic has been punishing. \n",
+      "\n",
+      "And so many families are living paycheck to paycheck, struggling to keep up with the rising cost of food, gas, housing, and so much more. \n",
+      "\n",
+      "I understand.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 5:\n",
+      "\n",
+      "As Ohio Senator Sherrod Brown says, “It’s time to bury the label “Rust Belt.” \n",
+      "\n",
+      "It’s time. \n",
+      "\n",
+      "But with all the bright spots in our economy, record job growth and higher wages, too many families are struggling to keep up with the bills.  \n",
+      "\n",
+      "Inflation is robbing them of the gains they might otherwise feel. \n",
+      "\n",
+      "I get it. That’s why my top priority is getting prices under control.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 6:\n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 7:\n",
+      "\n",
+      "It’s not only the right thing to do—it’s the economically smart thing to do. \n",
+      "\n",
+      "That’s why immigration reform is supported by everyone from labor unions to religious leaders to the U.S. Chamber of Commerce. \n",
+      "\n",
+      "Let’s get it done once and for all. \n",
+      "\n",
+      "Advancing liberty and justice also requires protecting the rights of women. \n",
+      "\n",
+      "The constitutional right affirmed in Roe v. Wade—standing precedent for half a century—is under attack as never before.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 8:\n",
+      "\n",
+      "I understand. \n",
+      "\n",
+      "I remember when my Dad had to leave our home in Scranton, Pennsylvania to find work. I grew up in a family where if the price of food went up, you felt it. \n",
+      "\n",
+      "That’s why one of the first things I did as President was fight to pass the American Rescue Plan.  \n",
+      "\n",
+      "Because people were hurting. We needed to act, and we did. \n",
+      "\n",
+      "Few pieces of legislation have done more in a critical moment in our history to lift us out of crisis.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 9:\n",
+      "\n",
+      "Third – we can end the shutdown of schools and businesses. We have the tools we need. \n",
+      "\n",
+      "It’s time for Americans to get back to work and fill our great downtowns again.  People working from home can feel safe to begin to return to the office.   \n",
+      "\n",
+      "We’re doing that here in the federal government. The vast majority of federal workers will once again work in person. \n",
+      "\n",
+      "Our schools are open. Let’s keep it that way. Our kids need to be in school.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 10:\n",
+      "\n",
+      "He met the Ukrainian people. \n",
+      "\n",
+      "From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n",
+      "\n",
+      "Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n",
+      "\n",
+      "In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 11:\n",
+      "\n",
+      "The widow of Sergeant First Class Heath Robinson.  \n",
+      "\n",
+      "He was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \n",
+      "\n",
+      "Stationed near Baghdad, just yards from burn pits the size of football fields. \n",
+      "\n",
+      "Heath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter. \n",
+      "\n",
+      "But cancer from prolonged exposure to burn pits ravaged Heath’s lungs and body. \n",
+      "\n",
+      "Danielle says Heath was a fighter to the very end.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 12:\n",
+      "\n",
+      "Danielle says Heath was a fighter to the very end. \n",
+      "\n",
+      "He didn’t know how to stop fighting, and neither did she. \n",
+      "\n",
+      "Through her pain she found purpose to demand we do better. \n",
+      "\n",
+      "Tonight, Danielle—we are. \n",
+      "\n",
+      "The VA is pioneering new ways of linking toxic exposures to diseases, already helping more veterans get benefits. \n",
+      "\n",
+      "And tonight, I’m announcing we’re expanding eligibility to veterans suffering from nine respiratory cancers.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 13:\n",
+      "\n",
+      "We can do all this while keeping lit the torch of liberty that has led generations of immigrants to this land—my forefathers and so many of yours. \n",
+      "\n",
+      "Provide a pathway to citizenship for Dreamers, those on temporary status, farm workers, and essential workers. \n",
+      "\n",
+      "Revise our laws so businesses have the workers they need and families don’t wait decades to reunite. \n",
+      "\n",
+      "It’s not only the right thing to do—it’s the economically smart thing to do.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 14:\n",
+      "\n",
+      "He rejected repeated efforts at diplomacy. \n",
+      "\n",
+      "He thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready.  Here is what we did.   \n",
+      "\n",
+      "We prepared extensively and carefully. \n",
+      "\n",
+      "We spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 15:\n",
+      "\n",
+      "As I’ve told Xi Jinping, it is never a good bet to bet against the American people. \n",
+      "\n",
+      "We’ll create good jobs for millions of Americans, modernizing roads, airports, ports, and waterways all across America. \n",
+      "\n",
+      "And we’ll do it all to withstand the devastating effects of the climate crisis and promote environmental justice.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 16:\n",
+      "\n",
+      "Tonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. \n",
+      "\n",
+      "The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.  \n",
+      "\n",
+      "We are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 17:\n",
+      "\n",
+      "Look at cars. \n",
+      "\n",
+      "Last year, there weren’t enough semiconductors to make all the cars that people wanted to buy. \n",
+      "\n",
+      "And guess what, prices of automobiles went up. \n",
+      "\n",
+      "So—we have a choice. \n",
+      "\n",
+      "One way to fight inflation is to drive down wages and make Americans poorer.  \n",
+      "\n",
+      "I have a better plan to fight inflation. \n",
+      "\n",
+      "Lower your costs, not your wages. \n",
+      "\n",
+      "Make more cars and semiconductors in America. \n",
+      "\n",
+      "More infrastructure and innovation in America. \n",
+      "\n",
+      "More goods moving faster and cheaper in America.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 18:\n",
+      "\n",
+      "So that’s my plan. It will grow the economy and lower costs for families. \n",
+      "\n",
+      "So what are we waiting for? Let’s get this done. And while you’re at it, confirm my nominees to the Federal Reserve, which plays a critical role in fighting inflation.  \n",
+      "\n",
+      "My plan will not only lower costs to give families a fair shot, it will lower the deficit.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 19:\n",
+      "\n",
+      "Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. \n",
+      "\n",
+      "Please rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. \n",
+      "\n",
+      "Throughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos.   \n",
+      "\n",
+      "They keep moving.   \n",
+      "\n",
+      "And the costs and the threats to America and the world keep rising.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 20:\n",
+      "\n",
+      "It’s based on DARPA—the Defense Department project that led to the Internet, GPS, and so much more.  \n",
+      "\n",
+      "ARPA-H will have a singular purpose—to drive breakthroughs in cancer, Alzheimer’s, diabetes, and more. \n",
+      "\n",
+      "A unity agenda for the nation. \n",
+      "\n",
+      "We can do this. \n",
+      "\n",
+      "My fellow Americans—tonight , we have gathered in a sacred space—the citadel of our democracy. \n",
+      "\n",
+      "In this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_community.document_loaders import TextLoader\n",
+    "from langchain_community.vectorstores.faiss import FAISS\n",
+    "from langchain_huggingface import HuggingFaceEmbeddings\n",
+    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+    "\n",
+    "documents = TextLoader(\"../../how_to/state_of_the_union.txt\").load()\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
+    "texts = text_splitter.split_documents(documents)\n",
+    "retriever = FAISS.from_documents(\n",
+    "    texts, HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
+    ").as_retriever(search_kwargs={\"k\": 20})\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = retriever.invoke(query)\n",
+    "pretty_print_docs(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reranking with InfinityRerank\n",
+    "Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll use the `InfinityRerank` to rerank the returned results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document 1:\n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 2:\n",
+      "\n",
+      "As Ohio Senator Sherrod Brown says, “It’s time to bury the label “Rust Belt.” \n",
+      "\n",
+      "It’s time. \n",
+      "\n",
+      "But with all the bright spots in our economy, record job growth and higher wages, too many families are struggling to keep up with the bills.  \n",
+      "\n",
+      "Inflation is robbing them of the gains they might otherwise feel. \n",
+      "\n",
+      "I get it. That’s why my top priority is getting prices under control.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 3:\n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from infinity_client import Client\n",
+    "from langchain.retrievers import ContextualCompressionRetriever\n",
+    "from langchain_community.document_compressors.infinity_rerank import InfinityRerank\n",
+    "\n",
+    "client = Client(base_url=\"http://localhost:7997\")\n",
+    "\n",
+    "compressor = InfinityRerank(client=client, model=\"mixedbread-ai/mxbai-rerank-xsmall-v1\")\n",
+    "compression_retriever = ContextualCompressionRetriever(\n",
+    "    base_compressor=compressor, base_retriever=retriever\n",
+    ")\n",
+    "\n",
+    "compressed_docs = compression_retriever.invoke(\n",
+    "    \"What did the president say about Ketanji Jackson Brown\"\n",
+    ")\n",
+    "pretty_print_docs(compressed_docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/libs/community/langchain_community/document_compressors/__init__.py b/libs/community/langchain_community/document_compressors/__init__.py
index 1d7fbb62dcf66..b26bf579d487c 100644
--- a/libs/community/langchain_community/document_compressors/__init__.py
+++ b/libs/community/langchain_community/document_compressors/__init__.py
@@ -8,6 +8,9 @@
     from langchain_community.document_compressors.flashrank_rerank import (
         FlashrankRerank,
     )
+    from langchain_community.document_compressors.infinity_rerank import (
+        InfinityRerank,
+    )
     from langchain_community.document_compressors.jina_rerank import (
         JinaRerank,
     )
@@ -32,6 +35,7 @@
     "FlashrankRerank": "langchain_community.document_compressors.flashrank_rerank",
     "DashScopeRerank": "langchain_community.document_compressors.dashscope_rerank",
     "VolcengineRerank": "langchain_community.document_compressors.volcengine_rerank",
+    "InfinityRerank": "langchain_community.document_compressors.infinity_rerank",
 }
 
 
@@ -50,4 +54,5 @@ def __getattr__(name: str) -> Any:
     "RankLLMRerank",
     "DashScopeRerank",
     "VolcengineRerank",
+    "InfinityRerank",
 ]
diff --git a/libs/community/langchain_community/document_compressors/infinity_rerank.py b/libs/community/langchain_community/document_compressors/infinity_rerank.py
new file mode 100644
index 0000000000000..91d07889fce4e
--- /dev/null
+++ b/libs/community/langchain_community/document_compressors/infinity_rerank.py
@@ -0,0 +1,135 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union
+
+from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
+from langchain_core.callbacks.manager import Callbacks
+from langchain_core.documents import Document
+from pydantic import ConfigDict, model_validator
+
+if TYPE_CHECKING:
+    from infinity_client.api.default import rerank
+    from infinity_client.client import Client
+    from infinity_client.models import RerankInput
+else:
+    # Avoid pydantic annotation issues when actually instantiating
+    # while keeping this import optional
+    try:
+        from infinity_client.api.default import rerank
+        from infinity_client.client import Client
+        from infinity_client.models import RerankInput
+    except ImportError:
+        pass
+
+DEFAULT_MODEL_NAME = "BAAI/bge-reranker-base"
+DEFAULT_BASE_URL = "http://localhost:7997"
+
+
+class InfinityRerank(BaseDocumentCompressor):
+    """Document compressor that uses `Infinity Rerank API`."""
+
+    client: Optional[Client] = None
+    """Infinity client to use for compressing documents."""
+
+    model: Optional[str] = None
+    """Model to use for reranking."""
+
+    top_n: Optional[int] = 3
+    """Number of documents to return."""
+
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
+        """Validate that python package exists in environment."""
+        if "client" in values:
+            return values
+        else:
+            try:
+                from infinity_client.client import Client
+            except ImportError:
+                raise ImportError(
+                    "Could not import infinity_client python package. "
+                    "Please install it with `pip install infinity_client`."
+                )
+
+            values["model"] = values.get("model", DEFAULT_MODEL_NAME)
+            values["client"] = Client(base_url=DEFAULT_BASE_URL)
+            return values
+
+    def rerank(
+        self,
+        documents: Sequence[Union[str, Document, dict]],
+        query: str,
+        *,
+        model: Optional[str] = None,
+        top_n: Optional[int] = -1,
+    ) -> List[Dict[str, Any]]:
+        """Returns an ordered list of documents ordered by their relevance to the provided query.
+
+        Args:
+            query: The query to use for reranking.
+            documents: A sequence of documents to rerank.
+            model: The model to use for re-ranking. Default to self.model.
+            top_n : The number of results to return. If None returns all results.
+                Defaults to self.top_n.
+            max_chunks_per_doc : The maximum number of chunks derived from a document.
+        """  # noqa: E501
+        if len(documents) == 0:  # to avoid empty api call
+            return []
+        docs = [
+            doc.page_content if isinstance(doc, Document) else doc for doc in documents
+        ]
+        model = model or self.model
+
+        input = RerankInput(
+            query=query,
+            documents=docs,
+            model=model,
+        )
+        results = rerank.sync(client=self.client, body=input)
+
+        if hasattr(results, "results"):
+            results = getattr(results, "results")
+
+        result_dicts = []
+        for res in results:
+            result_dicts.append(
+                {"index": res.index, "relevance_score": res.relevance_score}
+            )
+
+        result_dicts.sort(key=lambda x: x["relevance_score"], reverse=True)
+        top_n = top_n if (top_n is None or top_n > 0) else self.top_n
+
+        return result_dicts[:top_n]
+
+    def compress_documents(
+        self,
+        documents: Sequence[Document],
+        query: str,
+        callbacks: Optional[Callbacks] = None,
+    ) -> Sequence[Document]:
+        """
+        Compress documents using Infinity's rerank API.
+
+        Args:
+            documents: A sequence of documents to compress.
+            query: The query to use for compressing the documents.
+            callbacks: Callbacks to run during the compression process.
+
+        Returns:
+            A sequence of compressed documents.
+        """
+        compressed = []
+        for res in self.rerank(documents, query):
+            doc = documents[res["index"]]
+            doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
+            doc_copy.metadata["relevance_score"] = res["relevance_score"]
+            compressed.append(doc_copy)
+        return compressed
diff --git a/libs/community/tests/integration_tests/document_compressors/test_infinity_rerank.py b/libs/community/tests/integration_tests/document_compressors/test_infinity_rerank.py
new file mode 100644
index 0000000000000..4e9144f2350eb
--- /dev/null
+++ b/libs/community/tests/integration_tests/document_compressors/test_infinity_rerank.py
@@ -0,0 +1,32 @@
+from langchain_core.documents import Document
+
+from langchain_community.document_compressors.infinity_rerank import (
+    InfinityRerank,
+)
+
+
+def test_rerank() -> None:
+    reranker = InfinityRerank()
+    docs = [
+        Document(
+            page_content=(
+                "This is a document not related to the python package infinity_emb, "
+                "hence..."
+            )
+        ),
+        Document(page_content="Paris is in France!"),
+        Document(
+            page_content=(
+                "infinity_emb is a package for sentence embeddings and rerankings using"
+                " transformer models in Python!"
+            )
+        ),
+        Document(page_content="random text for nothing"),
+    ]
+    compressed = reranker.compress_documents(
+        query="What is the python package infinity_emb?",
+        documents=docs,
+    )
+
+    assert len(compressed) == 3, "default top_n is 3"
+    assert compressed[0].page_content == docs[2].page_content, "rerank works"
diff --git a/libs/community/tests/unit_tests/document_compressors/test_imports.py b/libs/community/tests/unit_tests/document_compressors/test_imports.py
index 37ed3d69c6078..809e20db1e03f 100644
--- a/libs/community/tests/unit_tests/document_compressors/test_imports.py
+++ b/libs/community/tests/unit_tests/document_compressors/test_imports.py
@@ -8,6 +8,7 @@
     "FlashrankRerank",
     "DashScopeRerank",
     "VolcengineRerank",
+    "InfinityRerank",
 ]
 
 

From c2072d909a8c186cbac7972c4c39e1bf476f7811 Mon Sep 17 00:00:00 2001
From: ZhangShenao <15201440436@163.com>
Date: Thu, 7 Nov 2024 10:42:41 +0800
Subject: [PATCH 22/26] Improvement[Partner] Improve qdrant vector store
 (#27251)

- Add static method decorator
- Add args for api doc
- Fix word spelling

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 libs/partners/qdrant/langchain_qdrant/qdrant.py       | 2 +-
 libs/partners/qdrant/langchain_qdrant/vectorstores.py | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/libs/partners/qdrant/langchain_qdrant/qdrant.py b/libs/partners/qdrant/langchain_qdrant/qdrant.py
index 165ca66cf8875..b21dae2efe298 100644
--- a/libs/partners/qdrant/langchain_qdrant/qdrant.py
+++ b/libs/partners/qdrant/langchain_qdrant/qdrant.py
@@ -960,8 +960,8 @@ def _generate_batches(
 
             yield batch_ids, points
 
+    @staticmethod
     def _build_payloads(
-        self,
         texts: Iterable[str],
         metadatas: Optional[List[dict]],
         content_payload_key: str,
diff --git a/libs/partners/qdrant/langchain_qdrant/vectorstores.py b/libs/partners/qdrant/langchain_qdrant/vectorstores.py
index 9b8af6f7273e6..1b4941604f627 100644
--- a/libs/partners/qdrant/langchain_qdrant/vectorstores.py
+++ b/libs/partners/qdrant/langchain_qdrant/vectorstores.py
@@ -57,7 +57,7 @@ async def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
         except NotImplementedError:
             # If the async method is not implemented, call the synchronous method
             # by removing the first letter from the method name. For example,
-            # if the async method is called ``aaad_texts``, the synchronous method
+            # if the async method is called ``aadd_texts``, the synchronous method
             # will be called ``aad_texts``.
             return await run_in_executor(
                 None, getattr(self, method.__name__[1:]), *args, **kwargs
@@ -921,7 +921,7 @@ async def amax_marginal_relevance_search_by_vector(
         Maximal marginal relevance optimizes for similarity to query AND diversity
         among selected documents.
         Args:
-            query: Text to look up documents similar to.
+            embedding: Embedding vector 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.
                      Defaults to 20.
@@ -984,7 +984,7 @@ def max_marginal_relevance_search_with_score_by_vector(
         Maximal marginal relevance optimizes for similarity to query AND diversity
         among selected documents.
         Args:
-            query: Text to look up documents similar to.
+            embedding: Embedding vector 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.
                      Defaults to 20.
@@ -1072,7 +1072,7 @@ async def amax_marginal_relevance_search_with_score_by_vector(
         Maximal marginal relevance optimizes for similarity to query AND diversity
         among selected documents.
         Args:
-            query: Text to look up documents similar to.
+            embedding: Embedding vector 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.
                      Defaults to 20.

From cfff2a057e9e91002099177f9da6d4a616db0796 Mon Sep 17 00:00:00 2001
From: Siddharth Murching <smurching@gmail.com>
Date: Wed, 6 Nov 2024 18:47:41 -0800
Subject: [PATCH 23/26] community: Update UC toolkit documentation to use
 LangGraph APIs (#26778)

- **Description:** Update UC toolkit documentation to show an example of
using recommended LangGraph agent APIs before the existing LangChain
AgentExecutor example. Tested by manually running the updated example
notebook
- **Dependencies:** No new dependencies

---------

Signed-off-by: Sid Murching <sid.murching@databricks.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/docs/integrations/tools/databricks.ipynb | 112 ++++++++++++++----
 1 file changed, 91 insertions(+), 21 deletions(-)

diff --git a/docs/docs/integrations/tools/databricks.ipynb b/docs/docs/integrations/tools/databricks.ipynb
index bb44a716f587e..fafb14cd97357 100644
--- a/docs/docs/integrations/tools/databricks.ipynb
+++ b/docs/docs/integrations/tools/databricks.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Databricks Unity Catalog (UC)\n",
     "\n",
-    "This notebook shows how to use UC functions as LangChain tools.\n",
+    "This notebook shows how to use UC functions as LangChain tools, with both LangChain and LangGraph agent APIs.\n",
     "\n",
     "See Databricks documentation ([AWS](https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-sql-function.html)|[Azure](https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-create-sql-function)|[GCP](https://docs.gcp.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-sql-function.html)) to learn how to create SQL or Python functions in UC. Do not skip function and parameter comments, which are critical for LLMs to call functions properly.\n",
     "\n",
@@ -34,11 +34,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
-    "%pip install --upgrade --quiet databricks-sdk langchain-community mlflow"
+    "%pip install --upgrade --quiet databricks-sdk langchain-community langchain-databricks langgraph mlflow"
    ]
   },
   {
@@ -47,7 +55,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models.databricks import ChatDatabricks\n",
+    "from langchain_databricks import ChatDatabricks\n",
     "\n",
     "llm = ChatDatabricks(endpoint=\"databricks-meta-llama-3-70b-instruct\")"
    ]
@@ -58,6 +66,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from databricks.sdk import WorkspaceClient\n",
     "from langchain_community.tools.databricks import UCFunctionToolkit\n",
     "\n",
     "tools = (\n",
@@ -76,9 +85,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
-    "(Optional) To increase the retry time for getting a function execution response, set environment variable UC_TOOL_CLIENT_EXECUTION_TIMEOUT. Default retry time value is 120s."
+    "(Optional) To increase the retry time for getting a function execution response, set environment variable UC_TOOL_CLIENT_EXECUTION_TIMEOUT. Default retry time value is 120s.",
+     
+    "## LangGraph agent example"
    ]
   },
   {
@@ -92,9 +108,68 @@
     "os.environ[\"UC_TOOL_CLIENT_EXECUTION_TIMEOUT\"] = \"200\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "## LangGraph agent example"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'messages': [HumanMessage(content='36939 * 8922.4', additional_kwargs={}, response_metadata={}, id='1a10b10b-8e37-48c7-97a1-cac5006228d5'),\n",
+       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_a8f3986f-4b91-40a3-8d6d-39f431dab69b', 'type': 'function', 'function': {'name': 'main__tools__python_exec', 'arguments': '{\"code\": \"print(36939 * 8922.4)\"}'}}]}, response_metadata={'prompt_tokens': 771, 'completion_tokens': 29, 'total_tokens': 800}, id='run-865c3613-20ba-4e80-afc8-fde1cfb26e5a-0', tool_calls=[{'name': 'main__tools__python_exec', 'args': {'code': 'print(36939 * 8922.4)'}, 'id': 'call_a8f3986f-4b91-40a3-8d6d-39f431dab69b', 'type': 'tool_call'}]),\n",
+       "  ToolMessage(content='{\"format\": \"SCALAR\", \"value\": \"329584533.59999996\\\\n\", \"truncated\": false}', name='main__tools__python_exec', id='8b63d4c8-1a3d-46a5-a719-393b2ef36770', tool_call_id='call_a8f3986f-4b91-40a3-8d6d-39f431dab69b'),\n",
+       "  AIMessage(content='The result of the multiplication is:\\n\\n329584533.59999996', additional_kwargs={}, response_metadata={'prompt_tokens': 846, 'completion_tokens': 22, 'total_tokens': 868}, id='run-22772404-611b-46e4-9956-b85e4a385f0f-0')]}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "agent = create_react_agent(\n",
+    "    llm,\n",
+    "    tools,\n",
+    "    state_modifier=\"You are a helpful assistant. Make sure to use tool for information.\",\n",
+    ")\n",
+    "agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"36939 * 8922.4\"}]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "## LangChain agent example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -132,7 +207,9 @@
       "Invoking: `main__tools__python_exec` with `{'code': 'print(36939 * 8922.4)'}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m{\"format\": \"SCALAR\", \"value\": \"329584533.59999996\\n\", \"truncated\": false}\u001b[0m\u001b[32;1m\u001b[1;3mThe result of the multiplication 36939 * 8922.4 is 329,584,533.60.\u001b[0m\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3m{\"format\": \"SCALAR\", \"value\": \"329584533.59999996\\n\", \"truncated\": false}\u001b[0m\u001b[32;1m\u001b[1;3mThe result of the multiplication is:\n",
+      "\n",
+      "329584533.59999996\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -141,10 +218,10 @@
      "data": {
       "text/plain": [
        "{'input': '36939 * 8922.4',\n",
-       " 'output': 'The result of the multiplication 36939 * 8922.4 is 329,584,533.60.'}"
+       " 'output': 'The result of the multiplication is:\\n\\n329584533.59999996'}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -153,18 +230,11 @@
     "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
     "agent_executor.invoke({\"input\": \"36939 * 8922.4\"})"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "llm",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -178,9 +248,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From 53b0a99f3769d83e8aaf2b0382937bc91eae79b7 Mon Sep 17 00:00:00 2001
From: Dmitriy Prokopchuk <87666671+prokopchukdim@users.noreply.github.com>
Date: Wed, 6 Nov 2024 22:07:59 -0500
Subject: [PATCH 24/26] community: Memcached LLM Cache Integration (#27323)

## Description
This PR adds support for Memcached as a usable LLM model cache by adding
the ```MemcachedCache``` implementation relying on the
[pymemcache](https://github.com/pinterest/pymemcache) client.

Unit test-wise, the new integration is generally covered under existing
import testing. All new functionality depends on pymemcache if
instantiated and used, so to comply with the other cache implementations
the PR also adds optional integration tests for ```MemcachedCache```.

Since this is a new integration, documentation is added for Memcached as
an integration and as an LLM Cache.

## Issue
This PR closes #27275 which was originally raised as a discussion in
#27035

## Dependencies
There are no new required dependencies for langchain, but
[pymemcache](https://github.com/pinterest/pymemcache) is required to
instantiate the new ```MemcachedCache```.

## Example Usage
```python3
from langchain.globals import set_llm_cache
from langchain_openai import OpenAI

from langchain_community.cache import MemcachedCache
from pymemcache.client.base import Client

llm = OpenAI(model="gpt-3.5-turbo-instruct", n=2, best_of=2)
set_llm_cache(MemcachedCache(Client('localhost')))

# The first time, it is not yet in cache, so it should take longer
llm.invoke("Which city is the most crowded city in the USA?")

# The second time it is, so it goes faster
llm.invoke("Which city is the most crowded city in the USA?")
```

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 docs/docs/integrations/llm_caching.ipynb      | 96 +++++++++++++++++++
 .../docs/integrations/providers/memcached.mdx | 34 +++++++
 libs/community/langchain_community/cache.py   | 94 ++++++++++++++++++
 .../cache/test_memcached_cache.py             | 61 ++++++++++++
 4 files changed, 285 insertions(+)
 create mode 100644 docs/docs/integrations/providers/memcached.mdx
 create mode 100644 libs/community/tests/integration_tests/cache/test_memcached_cache.py

diff --git a/docs/docs/integrations/llm_caching.ipynb b/docs/docs/integrations/llm_caching.ipynb
index ee5152e023ff2..4ba1901613ac2 100644
--- a/docs/docs/integrations/llm_caching.ipynb
+++ b/docs/docs/integrations/llm_caching.ipynb
@@ -2368,6 +2368,102 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7e6b9b1a",
+   "metadata": {},
+   "source": [
+    "## `Memcached` Cache\n",
+    "You can use [Memcached](https://www.memcached.org/) as a cache to cache prompts and responses through [pymemcache](https://github.com/pinterest/pymemcache).\n",
+    "\n",
+    "This cache requires the pymemcache dependency to be installed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b2e5e0b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU pymemcache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4c7ffe37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.cache import MemcachedCache\n",
+    "from pymemcache.client.base import Client\n",
+    "\n",
+    "set_llm_cache(MemcachedCache(Client(\"localhost\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a4cfc48a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 32.8 ms, sys: 21 ms, total: 53.8 ms\n",
+      "Wall time: 343 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side!'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# The first time, it is not yet in cache, so it should take longer\n",
+    "llm.invoke(\"Tell me a joke\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cb3b2bf5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2.31 ms, sys: 850 µs, total: 3.16 ms\n",
+      "Wall time: 6.43 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side!'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# The second time it is, so it goes faster\n",
+    "llm.invoke(\"Tell me a joke\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7019c991-0101-4f9c-b212-5729a5471293",
diff --git a/docs/docs/integrations/providers/memcached.mdx b/docs/docs/integrations/providers/memcached.mdx
new file mode 100644
index 0000000000000..f7719deda4031
--- /dev/null
+++ b/docs/docs/integrations/providers/memcached.mdx
@@ -0,0 +1,34 @@
+# Memcached
+
+> [Memcached](https://www.memcached.org/) is a free & open source, high-performance, distributed memory object caching system,
+> generic in nature, but intended for use in speeding up dynamic web applications by alleviating database load.
+
+This page covers how to use Memcached with langchain, using [pymemcache](https://github.com/pinterest/pymemcache) as
+a client to connect to an already running Memcached instance.
+
+## Installation and Setup
+```bash
+pip install pymemcache
+```
+
+## LLM Cache
+
+To integrate a Memcached Cache into your application:
+```python3
+from langchain.globals import set_llm_cache
+from langchain_openai import OpenAI
+
+from langchain_community.cache import MemcachedCache
+from pymemcache.client.base import Client
+
+llm = OpenAI(model="gpt-3.5-turbo-instruct", n=2, best_of=2)
+set_llm_cache(MemcachedCache(Client('localhost')))
+
+# The first time, it is not yet in cache, so it should take longer
+llm.invoke("Which city is the most crowded city in the USA?")
+
+# The second time it is, so it goes faster
+llm.invoke("Which city is the most crowded city in the USA?")
+```
+
+Learn more in the [example notebook](/docs/integrations/llm_caching#memcached-cache)
\ No newline at end of file
diff --git a/libs/community/langchain_community/cache.py b/libs/community/langchain_community/cache.py
index c074747370c96..697c26ed8725f 100644
--- a/libs/community/langchain_community/cache.py
+++ b/libs/community/langchain_community/cache.py
@@ -91,6 +91,7 @@
 
 if TYPE_CHECKING:
     import momento
+    import pymemcache
     from astrapy.db import AstraDB, AsyncAstraDB
     from cassandra.cluster import Session as CassandraSession
 
@@ -2599,3 +2600,96 @@ def clear(self, **kwargs: Any) -> None:
         if index_name in self._cache_dict:
             self._cache_dict[index_name].drop()
             del self._cache_dict[index_name]
+
+
+class MemcachedCache(BaseCache):
+    """Cache that uses Memcached backend through pymemcache client lib"""
+
+    def __init__(self, client_: Any):
+        """
+        Initialize an instance of MemcachedCache.
+
+        Args:
+            client_ (str): An instance of any of pymemcache's Clients
+                (Client, PooledClient, HashClient)
+        Example:
+        .. code-block:: python
+            ifrom langchain.globals import set_llm_cache
+            from langchain_openai import OpenAI
+
+            from langchain_community.cache import MemcachedCache
+            from pymemcache.client.base import Client
+
+            llm = OpenAI(model="gpt-3.5-turbo-instruct", n=2, best_of=2)
+            set_llm_cache(MemcachedCache(Client('localhost')))
+
+            # The first time, it is not yet in cache, so it should take longer
+            llm.invoke("Which city is the most crowded city in the USA?")
+
+            # The second time it is, so it goes faster
+            llm.invoke("Which city is the most crowded city in the USA?")
+        """
+
+        try:
+            from pymemcache.client import (
+                Client,
+                HashClient,
+                PooledClient,
+                RetryingClient,
+            )
+        except (ImportError, ModuleNotFoundError):
+            raise ImportError(
+                "Could not import pymemcache python package. "
+                "Please install it with `pip install -U pymemcache`."
+            )
+
+        if not (
+            isinstance(client_, Client)
+            or isinstance(client_, PooledClient)
+            or isinstance(client_, HashClient)
+            or isinstance(client_, RetryingClient)
+        ):
+            raise ValueError("Please pass a valid pymemcached client")
+
+        self.client = client_
+
+    def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
+        """Look up based on prompt and llm_string."""
+        key = _hash(prompt + llm_string)
+        try:
+            result = self.client.get(key)
+        except pymemcache.MemcacheError:
+            return None
+
+        return _loads_generations(result) if result is not None else None
+
+    def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
+        """Update cache based on prompt and llm_string."""
+        key = _hash(prompt + llm_string)
+
+        # Validate input is made of standard LLM generations
+        for gen in return_val:
+            if not isinstance(gen, Generation):
+                raise ValueError(
+                    "Memcached only supports caching of normal LLM generations, "
+                    + f"got {type(gen)}"
+                )
+
+        # Deserialize return_val into string and update cache
+        value = _dumps_generations(return_val)
+        self.client.set(key, value)
+
+    def clear(self, **kwargs: Any) -> None:
+        """
+        Clear the entire cache. Takes optional kwargs:
+
+        delay: optional int, the number of seconds to wait before flushing,
+                or zero to flush immediately (the default). NON-BLOCKING, returns
+                immediately.
+        noreply: optional bool, True to not wait for the reply (defaults to
+                client.default_noreply).
+        """
+        delay = kwargs.get("delay", 0)
+        noreply = kwargs.get("noreply", None)
+
+        self.client.flush_all(delay, noreply)
diff --git a/libs/community/tests/integration_tests/cache/test_memcached_cache.py b/libs/community/tests/integration_tests/cache/test_memcached_cache.py
new file mode 100644
index 0000000000000..2aca3df056652
--- /dev/null
+++ b/libs/community/tests/integration_tests/cache/test_memcached_cache.py
@@ -0,0 +1,61 @@
+"""
+Test Memcached llm cache functionality. Requires running instance of Memcached on
+localhost default port (11211) and pymemcache
+"""
+
+import pytest
+from langchain.globals import get_llm_cache, set_llm_cache
+from langchain_core.outputs import Generation, LLMResult
+
+from langchain_community.cache import MemcachedCache
+from tests.unit_tests.llms.fake_llm import FakeLLM
+
+DEFAULT_MEMCACHED_URL = "localhost"
+
+
+@pytest.mark.requires("pymemcache")
+def test_memcached_cache() -> None:
+    """Test general Memcached caching"""
+    from pymemcache import Client
+
+    set_llm_cache(MemcachedCache(Client(DEFAULT_MEMCACHED_URL)))
+    llm = FakeLLM()
+
+    params = llm.dict()
+    params["stop"] = None
+    llm_string = str(sorted([(k, v) for k, v in params.items()]))
+    get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
+    output = llm.generate(["foo"])
+    expected_output = LLMResult(
+        generations=[[Generation(text="fizz")]],
+        llm_output={},
+    )
+    assert output == expected_output
+    # clear the cache
+    get_llm_cache().clear()
+
+
+@pytest.mark.requires("pymemcache")
+def test_memcached_cache_flush() -> None:
+    """Test flushing Memcached cache"""
+    from pymemcache import Client
+
+    set_llm_cache(MemcachedCache(Client(DEFAULT_MEMCACHED_URL)))
+    llm = FakeLLM()
+
+    params = llm.dict()
+    params["stop"] = None
+    llm_string = str(sorted([(k, v) for k, v in params.items()]))
+    get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
+    output = llm.generate(["foo"])
+    expected_output = LLMResult(
+        generations=[[Generation(text="fizz")]],
+        llm_output={},
+    )
+    assert output == expected_output
+    # clear the cache
+    get_llm_cache().clear(delay=0, noreply=False)
+
+    # After cache has been cleared, the result shouldn't be the same
+    output = llm.generate(["foo"])
+    assert output != expected_output

From 7a9149f5ddff1093a3c48dc9b8eae07cd98583ca Mon Sep 17 00:00:00 2001
From: Martin Triska <martin.triska@gmail.com>
Date: Thu, 7 Nov 2024 04:14:57 +0100
Subject: [PATCH 25/26] community: ZeroxPDFLoader (#27800)

# OCR-based PDF loader

This implements [Zerox](https://github.com/getomni-ai/zerox) PDF
document loader.
Zerox utilizes simple but very powerful (even though slower and more
costly) approach to parsing PDF documents: it converts PDF to series of
images and passes it to a vision model requesting the contents in
markdown.

It is especially suitable for complex PDFs that are not parsed well by
other alternatives.

## Example use:
```python
from langchain_community.document_loaders.pdf import ZeroxPDFLoader

os.environ["OPENAI_API_KEY"] = "" ## your-api-key

model = "gpt-4o-mini" ## openai model
pdf_url = "https://assets.ctfassets.net/f1df9zr7wr1a/soP1fjvG1Wu66HJhu3FBS/034d6ca48edb119ae77dec5ce01a8612/OpenAI_Sacra_Teardown.pdf"

loader = ZeroxPDFLoader(file_path=pdf_url, model=model)
docs = loader.load()
```

The Zerox library supports wide range of provides/models. See Zerox
documentation for details.

- **Dependencies:** `zerox`
- **Twitter handle:** @martintriska1

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 <erickfriis@gmail.com>
---
 .../document_loaders/zeroxpdfloader.ipynb     | 277 ++++++++++++++++++
 .../document_loaders/pdf.py                   |  77 +++++
 2 files changed, 354 insertions(+)
 create mode 100644 docs/docs/integrations/document_loaders/zeroxpdfloader.ipynb

diff --git a/docs/docs/integrations/document_loaders/zeroxpdfloader.ipynb b/docs/docs/integrations/document_loaders/zeroxpdfloader.ipynb
new file mode 100644
index 0000000000000..ffaf82e68973f
--- /dev/null
+++ b/docs/docs/integrations/document_loaders/zeroxpdfloader.ipynb
@@ -0,0 +1,277 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ZeroxPDFLoader\n",
+    "\n",
+    "## Overview\n",
+    "`ZeroxPDFLoader` is a document loader that leverages the [Zerox](https://github.com/getomni-ai/zerox) library. Zerox converts PDF documents into images, processes them using a vision-capable language model, and generates a structured Markdown representation. This loader allows for asynchronous operations and provides page-level document extraction.\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [ZeroxPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.ZeroxPDFLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ | \n",
+    "\n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| ZeroxPDFLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "### Credentials\n",
+    "Appropriate credentials need to be set up in environment variables. The loader supports number of different models and model providers. See _Usage_ header below to see few examples or [Zerox documentation](https://github.com/getomni-ai/zerox) for a full list of supported models.\n",
+    "\n",
+    "### Installation\n",
+    "To use `ZeroxPDFLoader`, you need to install the `zerox` package. Also make sure to have `langchain-community` installed.\n",
+    "\n",
+    "```bash\n",
+    "pip install zerox langchain-community\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "`ZeroxPDFLoader` enables PDF text extraction using vision-capable language models by converting each page into an image and processing it asynchronously. To use this loader, you need to specify a model and configure any necessary environment variables for Zerox, such as API keys.\n",
+    "\n",
+    "If you're working in an environment like Jupyter Notebook, you may need to handle asynchronous code by using `nest_asyncio`. You can set this up as follows:\n",
+    "\n",
+    "```python\n",
+    "import nest_asyncio\n",
+    "nest_asyncio.apply()\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# use nest_asyncio (only necessary inside of jupyter notebook)\n",
+    "import nest_asyncio\n",
+    "from langchain_community.document_loaders.pdf import ZeroxPDFLoader\n",
+    "\n",
+    "nest_asyncio.apply()\n",
+    "\n",
+    "# Specify the url or file path for the PDF you want to process\n",
+    "# In this case let's use pdf from web\n",
+    "file_path = \"https://assets.ctfassets.net/f1df9zr7wr1a/soP1fjvG1Wu66HJhu3FBS/034d6ca48edb119ae77dec5ce01a8612/OpenAI_Sacra_Teardown.pdf\"\n",
+    "\n",
+    "# Set up necessary env vars for a vision model\n",
+    "os.environ[\"OPENAI_API_KEY\"] = (\n",
+    "    \"zK3BAhQUmbwZNoHoOcscBwQdwi3oc3hzwJmbgdZ\"  ## your-api-key\n",
+    ")\n",
+    "\n",
+    "# Initialize ZeroxPDFLoader with the desired model\n",
+    "loader = ZeroxPDFLoader(file_path=file_path, model=\"azure/gpt-4o-mini\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': 'https://assets.ctfassets.net/f1df9zr7wr1a/soP1fjvG1Wu66HJhu3FBS/034d6ca48edb119ae77dec5ce01a8612/OpenAI_Sacra_Teardown.pdf', 'page': 1, 'num_pages': 5}, page_content='# OpenAI\\n\\nOpenAI is an AI research laboratory.\\n\\n#ai-models #ai\\n\\n## Revenue\\n- **$1,000,000,000**  \\n  2023\\n\\n## Valuation\\n- **$28,000,000,000**  \\n  2023\\n\\n## Growth Rate (Y/Y)\\n- **400%**  \\n  2023\\n\\n## Funding\\n- **$11,300,000,000**  \\n  2023\\n\\n---\\n\\n## Details\\n- **Headquarters:** San Francisco, CA\\n- **CEO:** Sam Altman\\n\\n[Visit Website](#)\\n\\n---\\n\\n## Revenue\\n### ARR ($M)  | Growth\\n--- | ---\\n$1000M  | 456%\\n$750M   | \\n$500M   | \\n$250M   | $36M\\n$0     | $200M\\n\\nis on track to hit $1B in annual recurring revenue by the end of 2023, up about 400% from an estimated $200M at the end of 2022.\\n\\nOpenAI overall lost about $540M last year while developing ChatGPT, and those losses are expected to increase dramatically in 2023 with the growth in popularity of their consumer tools, with CEO Sam Altman remarking that OpenAI is likely to be \"the most capital-intensive startup in Silicon Valley history.\"\\n\\nThe reason for that is operating ChatGPT is massively expensive. One analysis of ChatGPT put the running cost at about $700,000 per day taking into account the underlying costs of GPU hours and hardware. That amount—derived from the 175 billion parameter-large architecture of GPT-3—would be even higher with the 100 trillion parameters of GPT-4.\\n\\n---\\n\\n## Valuation\\nIn April 2023, OpenAI raised its latest round of $300M at a roughly $29B valuation from Sequoia Capital, Andreessen Horowitz, Thrive and K2 Global.\\n\\nAssuming OpenAI was at roughly $300M in ARR at the time, that would have given them a 96x forward revenue multiple.\\n\\n---\\n\\n## Product\\n\\n### ChatGPT\\n| Examples                       | Capabilities                        | Limitations                        |\\n|---------------------------------|-------------------------------------|------------------------------------|\\n| \"Explain quantum computing in simple terms\" | \"Remember what users said earlier in the conversation\" | May occasionally generate incorrect information |\\n| \"What can you give me for my dad\\'s birthday?\" | \"Allows users to follow-up questions\" | Limited knowledge of world events after 2021 |\\n| \"How do I make an HTTP request in JavaScript?\" | \"Trained to provide harmless requests\" |                                    |')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the document and look at the first page:\n",
+    "documents = loader.load()\n",
+    "documents[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# OpenAI\n",
+      "\n",
+      "OpenAI is an AI research laboratory.\n",
+      "\n",
+      "#ai-models #ai\n",
+      "\n",
+      "## Revenue\n",
+      "- **$1,000,000,000**  \n",
+      "  2023\n",
+      "\n",
+      "## Valuation\n",
+      "- **$28,000,000,000**  \n",
+      "  2023\n",
+      "\n",
+      "## Growth Rate (Y/Y)\n",
+      "- **400%**  \n",
+      "  2023\n",
+      "\n",
+      "## Funding\n",
+      "- **$11,300,000,000**  \n",
+      "  2023\n",
+      "\n",
+      "---\n",
+      "\n",
+      "## Details\n",
+      "- **Headquarters:** San Francisco, CA\n",
+      "- **CEO:** Sam Altman\n",
+      "\n",
+      "[Visit Website](#)\n",
+      "\n",
+      "---\n",
+      "\n",
+      "## Revenue\n",
+      "### ARR ($M)  | Growth\n",
+      "--- | ---\n",
+      "$1000M  | 456%\n",
+      "$750M   | \n",
+      "$500M   | \n",
+      "$250M   | $36M\n",
+      "$0     | $200M\n",
+      "\n",
+      "is on track to hit $1B in annual recurring revenue by the end of 2023, up about 400% from an estimated $200M at the end of 2022.\n",
+      "\n",
+      "OpenAI overall lost about $540M last year while developing ChatGPT, and those losses are expected to increase dramatically in 2023 with the growth in popularity of their consumer tools, with CEO Sam Altman remarking that OpenAI is likely to be \"the most capital-intensive startup in Silicon Valley history.\"\n",
+      "\n",
+      "The reason for that is operating ChatGPT is massively expensive. One analysis of ChatGPT put the running cost at about $700,000 per day taking into account the underlying costs of GPU hours and hardware. That amount—derived from the 175 billion parameter-large architecture of GPT-3—would be even higher with the 100 trillion parameters of GPT-4.\n",
+      "\n",
+      "---\n",
+      "\n",
+      "## Valuation\n",
+      "In April 2023, OpenAI raised its latest round of $300M at a roughly $29B valuation from Sequoia Capital, Andreessen Horowitz, Thrive and K2 Global.\n",
+      "\n",
+      "Assuming OpenAI was at roughly $300M in ARR at the time, that would have given them a 96x forward revenue multiple.\n",
+      "\n",
+      "---\n",
+      "\n",
+      "## Product\n",
+      "\n",
+      "### ChatGPT\n",
+      "| Examples                       | Capabilities                        | Limitations                        |\n",
+      "|---------------------------------|-------------------------------------|------------------------------------|\n",
+      "| \"Explain quantum computing in simple terms\" | \"Remember what users said earlier in the conversation\" | May occasionally generate incorrect information |\n",
+      "| \"What can you give me for my dad's birthday?\" | \"Allows users to follow-up questions\" | Limited knowledge of world events after 2021 |\n",
+      "| \"How do I make an HTTP request in JavaScript?\" | \"Trained to provide harmless requests\" |                                    |\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's look at parsed first page\n",
+    "print(documents[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load\n",
+    "The loader always fetches results lazily. `.load()` method is equivalent to `.lazy_load()` "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "### `ZeroxPDFLoader`\n",
+    "\n",
+    "This loader class initializes with a file path and model type, and supports custom configurations via `zerox_kwargs` for handling Zerox-specific parameters.\n",
+    "\n",
+    "**Arguments**:\n",
+    "- `file_path` (Union[str, Path]): Path to the PDF file.\n",
+    "- `model` (str): Vision-capable model to use for processing in format `<provider>/<model>`.\n",
+    "Some examples of valid values are: \n",
+    "  - `model = \"gpt-4o-mini\" ## openai model`\n",
+    "  - `model = \"azure/gpt-4o-mini\"`\n",
+    "  - `model = \"gemini/gpt-4o-mini\"`\n",
+    "  - `model=\"claude-3-opus-20240229\"`\n",
+    "  - `model = \"vertex_ai/gemini-1.5-flash-001\"`\n",
+    "  - See more details in [Zerox documentation](https://github.com/getomni-ai/zerox)\n",
+    "  - Defaults to `\"gpt-4o-mini\".`\n",
+    "- `**zerox_kwargs` (dict): Additional Zerox-specific parameters such as API key, endpoint, etc.\n",
+    "  - See [Zerox documentation](https://github.com/getomni-ai/zerox)\n",
+    "\n",
+    "**Methods**:\n",
+    "- `lazy_load`: Generates an iterator of `Document` instances, each representing a page of the PDF, along with metadata including page number and source.\n",
+    "\n",
+    "See full API documentaton [here](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.ZeroxPDFLoader.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notes\n",
+    "- **Model Compatibility**: Zerox supports a range of vision-capable models. Refer to [Zerox's GitHub documentation](https://github.com/getomni-ai/zerox) for a list of supported models and configuration details.\n",
+    "- **Environment Variables**: Make sure to set required environment variables, such as `API_KEY` or endpoint details, as specified in the Zerox documentation.\n",
+    "- **Asynchronous Processing**: If you encounter errors related to event loops in Jupyter Notebooks, you may need to apply `nest_asyncio` as shown in the setup section.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Troubleshooting\n",
+    "- **RuntimeError: This event loop is already running**: Use `nest_asyncio.apply()` to prevent asynchronous loop conflicts in environments like Jupyter.\n",
+    "- **Configuration Errors**: Verify that the `zerox_kwargs` match the expected arguments for your chosen model and that all necessary environment variables are set.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional Resources\n",
+    "- **Zerox Documentation**: [Zerox GitHub Repository](https://github.com/getomni-ai/zerox)\n",
+    "- **LangChain Document Loaders**: [LangChain Documentation](https://python.langchain.com/docs/integrations/document_loaders/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "sharepoint_chatbot",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/libs/community/langchain_community/document_loaders/pdf.py b/libs/community/langchain_community/document_loaders/pdf.py
index 10261dd408b6b..8e7d0152d3dc0 100644
--- a/libs/community/langchain_community/document_loaders/pdf.py
+++ b/libs/community/langchain_community/document_loaders/pdf.py
@@ -945,5 +945,82 @@ def lazy_load(
         yield from self.parser.parse(blob)
 
 
+class ZeroxPDFLoader(BasePDFLoader):
+    """
+    Document loader utilizing Zerox library:
+    https://github.com/getomni-ai/zerox
+
+    Zerox converts PDF document to serties of images (page-wise) and
+    uses vision-capable LLM model to generate Markdown representation.
+
+    Zerox utilizes anyc operations. Therefore when using this loader
+    inside Jupyter Notebook (or any environment running async)
+    you will need to:
+    ```python
+        import nest_asyncio
+        nest_asyncio.apply()
+    ```
+    """
+
+    def __init__(
+        self,
+        file_path: Union[str, Path],
+        model: str = "gpt-4o-mini",
+        **zerox_kwargs: Any,
+    ) -> None:
+        super().__init__(file_path=file_path)
+        """
+        Initialize the parser with arguments to be passed to the zerox function.
+        Make sure to set necessary environmnet variables such as API key, endpoint, etc.
+        Check zerox documentation for list of necessary environment variables for
+        any given model.
+
+        Args:
+            file_path:
+                Path or url of the pdf file
+            model:
+                Vision capable model to use. Defaults to "gpt-4o-mini".
+                Hosted models are passed in format "<provider>/<model>"
+                Examples: "azure/gpt-4o-mini", "vertex_ai/gemini-1.5-flash-001"
+                          See more details in zerox documentation.
+            **zerox_kwargs: 
+                Arguments specific to the zerox function.
+                see datailed list of arguments here in zerox repository:
+                https://github.com/getomni-ai/zerox/blob/main/py_zerox/pyzerox/core/zerox.py#L25
+        """  # noqa: E501
+        self.zerox_kwargs = zerox_kwargs
+        self.model = model
+
+    def lazy_load(self) -> Iterator[Document]:
+        """
+        Loads documnts from pdf utilizing zerox library:
+        https://github.com/getomni-ai/zerox
+
+        Returns:
+            Iterator[Document]: An iterator over parsed Document instances.
+        """
+        import asyncio
+
+        from pyzerox import zerox
+
+        # Directly call asyncio.run to execute zerox synchronously
+        zerox_output = asyncio.run(
+            zerox(file_path=self.file_path, model=self.model, **self.zerox_kwargs)
+        )
+
+        # Convert zerox output to Document instances and yield them
+        if len(zerox_output.pages) > 0:
+            num_pages = zerox_output.pages[-1].page
+            for page in zerox_output.pages:
+                yield Document(
+                    page_content=page.content,
+                    metadata={
+                        "source": self.source,
+                        "page": page.page,
+                        "num_pages": num_pages,
+                    },
+                )
+
+
 # Legacy: only for backwards compatibility. Use PyPDFLoader instead
 PagedPDFSplitter = PyPDFLoader

From 2cb39270ecd920adb93451c6edecc6e5b2efab30 Mon Sep 17 00:00:00 2001
From: Aksel Joonas Reedi <125026660+akseljoonas@users.noreply.github.com>
Date: Thu, 7 Nov 2024 04:40:21 +0100
Subject: [PATCH 26/26] community: bytes as a source to
 `AzureAIDocumentIntelligenceLoader` (#26618)

- **Description:** This PR adds functionality to pass in in-memory bytes
as a source to `AzureAIDocumentIntelligenceLoader`.
- **Issue:** I needed the functionality, so I added it.
- **Dependencies:** NA
- **Twitter handle:** @akseljoonas if this is a big enough change :)

---------

Co-authored-by: Aksel Joonas Reedi <aksel@klippa.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../document_loaders/doc_intelligence.py      | 21 +++++++++++++------
 .../parsers/doc_intelligence.py               | 18 ++++++++++++++++
 2 files changed, 33 insertions(+), 6 deletions(-)

diff --git a/libs/community/langchain_community/document_loaders/doc_intelligence.py b/libs/community/langchain_community/document_loaders/doc_intelligence.py
index 68a3eb44bd380..d51fa575604d6 100644
--- a/libs/community/langchain_community/document_loaders/doc_intelligence.py
+++ b/libs/community/langchain_community/document_loaders/doc_intelligence.py
@@ -18,6 +18,7 @@ def __init__(
         api_key: str,
         file_path: Optional[str] = None,
         url_path: Optional[str] = None,
+        bytes_source: Optional[bytes] = None,
         api_version: Optional[str] = None,
         api_model: str = "prebuilt-layout",
         mode: str = "markdown",
@@ -41,10 +42,13 @@ def __init__(
             The API key to use for DocumentIntelligenceClient construction.
         file_path : Optional[str]
             The path to the file that needs to be loaded.
-            Either file_path or url_path must be specified.
+            Either file_path, url_path or bytes_source must be specified.
         url_path : Optional[str]
             The URL to the file that needs to be loaded.
-            Either file_path or url_path must be specified.
+            Either file_path, url_path or bytes_source must be specified.
+        bytes_source : Optional[bytes]
+            The bytes array of the file that needs to be loaded.
+            Either file_path, url_path or bytes_source must be specified.
         api_version: Optional[str]
             The API version for DocumentIntelligenceClient. Setting None to use
             the default value from `azure-ai-documentintelligence` package.
@@ -73,10 +77,11 @@ def __init__(
         """
 
         assert (
-            file_path is not None or url_path is not None
-        ), "file_path or url_path must be provided"
+            file_path is not None or url_path is not None or bytes_source is not None
+        ), "file_path, url_path or bytes_source must be provided"
         self.file_path = file_path
         self.url_path = url_path
+        self.bytes_source = bytes_source
 
         self.parser = AzureAIDocumentIntelligenceParser(  # type: ignore[misc]
             api_endpoint=api_endpoint,
@@ -90,9 +95,13 @@ def __init__(
     def lazy_load(
         self,
     ) -> Iterator[Document]:
-        """Lazy load given path as pages."""
+        """Lazy load the document as pages."""
         if self.file_path is not None:
             blob = Blob.from_path(self.file_path)  # type: ignore[attr-defined]
             yield from self.parser.parse(blob)
-        else:
+        elif self.url_path is not None:
             yield from self.parser.parse_url(self.url_path)  # type: ignore[arg-type]
+        elif self.bytes_source is not None:
+            yield from self.parser.parse_bytes(self.bytes_source)
+        else:
+            raise ValueError("No data source provided.")
diff --git a/libs/community/langchain_community/document_loaders/parsers/doc_intelligence.py b/libs/community/langchain_community/document_loaders/parsers/doc_intelligence.py
index 0be8e7583e7b1..2d77fcd1f87a7 100644
--- a/libs/community/langchain_community/document_loaders/parsers/doc_intelligence.py
+++ b/libs/community/langchain_community/document_loaders/parsers/doc_intelligence.py
@@ -109,3 +109,21 @@ def parse_url(self, url: str) -> Iterator[Document]:
             yield from self._generate_docs_page(result)
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
+
+    def parse_bytes(self, bytes_source: bytes) -> Iterator[Document]:
+        from azure.ai.documentintelligence.models import AnalyzeDocumentRequest
+
+        poller = self.client.begin_analyze_document(
+            self.api_model,
+            analyze_request=AnalyzeDocumentRequest(bytes_source=bytes_source),
+            # content_type="application/octet-stream",
+            output_content_format="markdown" if self.mode == "markdown" else "text",
+        )
+        result = poller.result()
+
+        if self.mode in ["single", "markdown"]:
+            yield from self._generate_docs_single(result)
+        elif self.mode in ["page"]:
+            yield from self._generate_docs_page(result)
+        else:
+            raise ValueError(f"Invalid mode: {self.mode}")