Merge branch 'users/akataria/FTS_cosmos_nosql' of github.com:aayush30…

…11/langchain into users/akataria/FTS_cosmos_nosql

.github/workflows/check_diffs.yml

@@ -5,6 +5,7 @@ on:
   push:
     branches: [master]
   pull_request:
+  merge_group:
 
 # If another push to the same PR or branch happens while this workflow is still running,
 # cancel the earlier run in favor of the next run.

README.md

@@ -39,14 +39,16 @@ conda install langchain -c conda-forge
 For these applications, LangChain simplifies the entire application lifecycle:
 
 
-- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel), [components](https://python.langchain.com/docs/concepts/), and [third-party integrations](https://python.langchain.com/docs/integrations/providers/).
+- **Open-source libraries**: Build your applications using LangChain's open-source
+[components](https://python.langchain.com/docs/concepts/) and
+[third-party integrations](https://python.langchain.com/docs/integrations/providers/).
   Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
 - **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
 - **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/).
 
 ### Open-source libraries
 
-- **`langchain-core`**: Base abstractions and LangChain Expression Language.
+- **`langchain-core`**: Base abstractions.
 - **Integration packages** (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
 - **`langchain-community`**: Third-party integrations that are community maintained.
@@ -86,35 +88,32 @@ And much more! Head to the [Tutorials](https://python.langchain.com/docs/tutoria
 
 The main value props of the LangChain libraries are:
 
-1. **Components**: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
-2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks
-
-Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
-
-## LangChain Expression Language (LCEL)
-
-LCEL is a key part of LangChain, allowing you to build and organize chains of processes in a straightforward, declarative manner. It was designed to support taking prototypes directly into production without needing to alter any code. This means you can use LCEL to set up everything from basic "prompt + LLM" setups to intricate, multi-step workflows.
-
-- **[Overview](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel)**: LCEL and its benefits
-- **[Interface](https://python.langchain.com/docs/concepts/#runnable-interface)**: The standard Runnable interface for LCEL objects
-- **[Primitives](https://python.langchain.com/docs/how_to/#langchain-expression-language-lcel)**: More on the primitives LCEL includes
-- **[Cheatsheet](https://python.langchain.com/docs/how_to/lcel_cheatsheet/)**: Quick overview of the most common usage patterns
+1. **Components**: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not.
+2. **Easy orchestration with LangGraph**: [LangGraph](https://langchain-ai.github.io/langgraph/),
+built on top of `langchain-core`, has built-in support for [messages](https://python.langchain.com/docs/concepts/messages/), [tools](https://python.langchain.com/docs/concepts/tools/),
+and other LangChain abstractions. This makes it easy to combine components into
+production-ready applications with persistence, streaming, and other key features.
+Check out the LangChain [tutorials page](https://python.langchain.com/docs/tutorials/#orchestration) for examples.
 
 ## Components
 
 Components fall into the following **modules**:
 
 **📃 Model I/O**
 
-This includes [prompt management](https://python.langchain.com/docs/concepts/#prompt-templates), [prompt optimization](https://python.langchain.com/docs/concepts/#example-selectors), a generic interface for [chat models](https://python.langchain.com/docs/concepts/#chat-models) and [LLMs](https://python.langchain.com/docs/concepts/#llms), and common utilities for working with [model outputs](https://python.langchain.com/docs/concepts/#output-parsers).
+This includes [prompt management](https://python.langchain.com/docs/concepts/prompt_templates/)
+and a generic interface for [chat models](https://python.langchain.com/docs/concepts/chat_models/), including a consistent interface for [tool-calling](https://python.langchain.com/docs/concepts/tool_calling/) and [structured output](https://python.langchain.com/docs/concepts/structured_outputs/) across model providers.
 
 **📚 Retrieval**
 
-Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/concepts/#document-loaders) from a variety of sources, [preparing it](https://python.langchain.com/docs/concepts/#text-splitters), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/docs/concepts/#retrievers) it for use in the generation step.
+Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/concepts/document_loaders/) from a variety of sources, [preparing it](https://python.langchain.com/docs/concepts/text_splitters/), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/docs/concepts/retrievers/) it for use in the generation step.
 
 **🤖 Agents**
 
-Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
+Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. [LangGraph](https://langchain-ai.github.io/langgraph/) makes it easy to use
+LangChain components to build both [custom](https://langchain-ai.github.io/langgraph/tutorials/)
+and [built-in](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/)
+LLM agents.
 
 ## 📖 Documentation
 

docs/Makefile

@@ -13,28 +13,21 @@ OUTPUT_NEW_DOCS_DIR = $(OUTPUT_NEW_DIR)/docs
 
 PYTHON = .venv/bin/python
 
-PARTNER_DEPS_LIST := $(shell find ../libs/partners -mindepth 1 -maxdepth 1 -type d -exec sh -c ' \
-for dir; do \
-    if find "$$dir" -maxdepth 1 -type f \( -name "pyproject.toml" -o -name "setup.py" \) | grep -q .; then \
-        echo "$$dir"; \
-    fi \
-done' sh {} + | grep -vE "airbyte|ibm|databricks" | tr '\n' ' ')
-
 PORT ?= 3001
 
 clean:
 	rm -rf build
 
 install-vercel-deps:
-	yum -y update
-	yum install gcc bzip2-devel libffi-devel zlib-devel wget tar gzip rsync -y
+	yum -y -q update
+	yum -y -q install gcc bzip2-devel libffi-devel zlib-devel wget tar gzip rsync -y
 
 install-py-deps:
 	python3 -m venv .venv
-	$(PYTHON) -m pip install --upgrade pip
-	$(PYTHON) -m pip install --upgrade uv
-	$(PYTHON) -m uv pip install --pre -r vercel_requirements.txt
-	$(PYTHON) -m uv pip install --pre --editable $(PARTNER_DEPS_LIST)
+	$(PYTHON) -m pip install -q --upgrade pip
+	$(PYTHON) -m pip install -q --upgrade uv
+	$(PYTHON) -m uv pip install -q --pre -r vercel_requirements.txt
+	$(PYTHON) -m uv pip install -q --pre $$($(PYTHON) scripts/partner_deps_list.py)
 
 generate-files:
 	mkdir -p $(INTERMEDIATE_DIR)

docs/api_reference/conf.py

@@ -87,6 +87,18 @@ def run(self):
 def setup(app):
     app.add_directive("example_links", ExampleLinksDirective)
     app.add_directive("beta", Beta)
+    app.connect("autodoc-skip-member", skip_private_members)
+
+
+def skip_private_members(app, what, name, obj, skip, options):
+    if skip:
+        return True
+    if hasattr(obj, "__doc__") and obj.__doc__ and ":private:" in obj.__doc__:
+        return True
+    if name == "__init__" and obj.__objclass__ is object:
+        # dont document default init
+        return True
+    return None
 
 
 # -- Project information -----------------------------------------------------
@@ -116,6 +128,7 @@ def setup(app):
     "_extensions.gallery_directive",
     "sphinx_design",
     "sphinx_copybutton",
+    "sphinxcontrib.googleanalytics",
 ]
 source_suffix = [".rst", ".md"]
 
@@ -255,6 +268,8 @@ def setup(app):
 # Set canonical URL from the Read the Docs Domain
 html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "")
 
+googleanalytics_id = "G-9B66JQQH2F"
+
 # Tell Jinja2 templates the build is running on Read the Docs
 if os.environ.get("READTHEDOCS", "") == "True":
     html_context["READTHEDOCS"] = True

docs/api_reference/requirements.txt

@@ -9,3 +9,4 @@ pyyaml
 sphinx-design
 sphinx-copybutton
 beautifulsoup4
+sphinxcontrib-googleanalytics

docs/docs/contributing/how_to/integrations/index.mdx

@@ -1,4 +1,5 @@
 ---
+pagination_prev: null
 pagination_next: contributing/how_to/integrations/package
 ---
 
@@ -11,7 +12,7 @@ LangChain provides standard interfaces for several different components (languag
 ## Why contribute an integration to LangChain?
 
 - **Discoverability:** LangChain is the most used framework for building LLM applications, with over 20 million monthly downloads. LangChain integrations are discoverable by a large community of GenAI builders.
-- **Interoptability:** LangChain components expose a standard interface, allowing developers to easily swap them for each other. If you implement a LangChain integration, any developer using a different component will easily be able to swap yours in.
+- **Interoperability:** LangChain components expose a standard interface, allowing developers to easily swap them for each other. If you implement a LangChain integration, any developer using a different component will easily be able to swap yours in.
 - **Best Practices:** Through their standard interface, LangChain components encourage and facilitate best practices (streaming, async, etc)
 
 
@@ -37,14 +38,14 @@ While any component can be integrated into LangChain, there are specific types o
         <li>Chat Models</li>
         <li>Tools/Toolkits</li>
         <li>Retrievers</li>
-        <li>Document Loaders</li>
         <li>Vector Stores</li>
         <li>Embedding Models</li>
       </ul>
     </td>
     <td>
       <ul>
         <li>LLMs (Text-Completion Models)</li>
+        <li>Document Loaders</li>
         <li>Key-Value Stores</li>
         <li>Document Transformers</li>
         <li>Model Caches</li>