Merge branch 'master' into redirect-new

more fixes for refs

.claude/settings.local.json

@@ -1,18 +0,0 @@
-{
-  "permissions": {
-    "allow": [
-      "Bash(uv run:*)",
-      "Bash(make:*)",
-      "WebSearch",
-      "WebFetch(domain:ai.pydantic.dev)",
-      "WebFetch(domain:openai.github.io)",
-      "Bash(uv run:*)",
-      "Bash(python3:*)",
-      "WebFetch(domain:github.com)",
-      "Bash(gh pr view:*)",
-      "Bash(gh pr diff:*)"
-    ],
-    "deny": [],
-    "ask": []
-  }
-}

.github/workflows/check_diffs.yml

@@ -186,7 +186,7 @@ jobs:
 
       # We have to use 3.12 as 3.13 is not yet supported
       - name: "📦 Install UV Package Manager"
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           python-version: "3.12"
 

.github/workflows/integration_tests.yml

@@ -23,10 +23,8 @@ permissions:
   contents: read
 
 env:
-  POETRY_VERSION: "1.8.4"
   UV_FROZEN: "true"
   DEFAULT_LIBS: '["libs/partners/openai", "libs/partners/anthropic", "libs/partners/fireworks", "libs/partners/groq", "libs/partners/mistralai", "libs/partners/xai", "libs/partners/google-vertexai", "libs/partners/google-genai", "libs/partners/aws"]'
-  POETRY_LIBS: ("libs/partners/aws")
 
 jobs:
   # Generate dynamic test matrix based on input parameters or defaults
@@ -60,7 +58,6 @@ jobs:
           echo $matrix
           echo "matrix=$matrix" >> $GITHUB_OUTPUT
   # Run integration tests against partner libraries with live API credentials
-  # Tests are run with Poetry or UV depending on the library's setup
   build:
     if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     name: "🐍 Python ${{ matrix.python-version }}: ${{ matrix.working-directory }}"
@@ -95,17 +92,7 @@ jobs:
           mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
           mv langchain-aws/libs/aws langchain/libs/partners/aws
 
-      - name: "🐍 Set up Python ${{ matrix.python-version }} + Poetry"
-        if: contains(env.POETRY_LIBS, matrix.working-directory)
-        uses: "./langchain/.github/actions/poetry_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: langchain/${{ matrix.working-directory }}
-          cache-key: scheduled
-
       - name: "🐍 Set up Python ${{ matrix.python-version }} + UV"
-        if: "!contains(env.POETRY_LIBS, matrix.working-directory)"
         uses: "./langchain/.github/actions/uv_setup"
         with:
           python-version: ${{ matrix.python-version }}
@@ -123,15 +110,7 @@ jobs:
           aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
           aws-region: ${{ secrets.AWS_REGION }}
 
-      - name: "📦 Install Dependencies (Poetry)"
-        if: contains(env.POETRY_LIBS, matrix.working-directory)
-        run: |
-          echo "Running scheduled tests, installing dependencies with poetry..."
-          cd langchain/${{ matrix.working-directory }}
-          poetry install --with=test_integration,test
-
-      - name: "📦 Install Dependencies (UV)"
-        if: "!contains(env.POETRY_LIBS, matrix.working-directory)"
+      - name: "📦 Install Dependencies"
         run: |
           echo "Running scheduled tests, installing dependencies with uv..."
           cd langchain/${{ matrix.working-directory }}

AGENTS.md

@@ -149,23 +149,25 @@ def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
     Args:
         to: The email address of the recipient.
         msg: The message body to send.
-        priority: Email priority level (`'low'`, ``'normal'``, `'high'`).
+        priority: Email priority level (`'low'`, `'normal'`, `'high'`).
 
     Returns:
-        True if email was sent successfully, False otherwise.
+        `True` if email was sent successfully, `False` otherwise.
 
     Raises:
-        InvalidEmailError: If the email address format is invalid.
-        SMTPConnectionError: If unable to connect to email server.
+        `InvalidEmailError`: If the email address format is invalid.
+        `SMTPConnectionError`: If unable to connect to email server.
     """
 ```
 
 **Documentation Guidelines:**
 
 - Types go in function signatures, NOT in docstrings
+  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
 - Focus on "why" rather than "what" in descriptions
 - Document all parameters, return values, and exceptions
 - Keep descriptions concise but clear
+- Ensure American English spelling (e.g., "behavior", not "behaviour")
 
 📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
 

CLAUDE.md

@@ -149,23 +149,25 @@ def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
     Args:
         to: The email address of the recipient.
         msg: The message body to send.
-        priority: Email priority level (`'low'`, ``'normal'``, `'high'`).
+        priority: Email priority level (`'low'`, `'normal'`, `'high'`).
 
     Returns:
-        True if email was sent successfully, False otherwise.
+        `True` if email was sent successfully, `False` otherwise.
 
     Raises:
-        InvalidEmailError: If the email address format is invalid.
-        SMTPConnectionError: If unable to connect to email server.
+        `InvalidEmailError`: If the email address format is invalid.
+        `SMTPConnectionError`: If unable to connect to email server.
     """
 ```
 
 **Documentation Guidelines:**
 
 - Types go in function signatures, NOT in docstrings
+  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
 - Focus on "why" rather than "what" in descriptions
 - Document all parameters, return values, and exceptions
 - Keep descriptions concise but clear
+- Ensure American English spelling (e.g., "behavior", not "behaviour")
 
 📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
 

README.md

@@ -12,13 +12,16 @@
 
 <p align="center">
   <a href="https://opensource.org/licenses/MIT" target="_blank">
-      <img src="https://img.shields.io/pypi/l/langchain-core?style=flat-square" alt="PyPI - License">
+      <img src="https://img.shields.io/pypi/l/langchain" alt="PyPI - License">
   </a>
-  <a href="https://pypistats.org/packages/langchain-core" target="_blank">
+  <a href="https://pypistats.org/packages/langchain" target="_blank">
       <img src="https://img.shields.io/pepy/dt/langchain" alt="PyPI - Downloads">
   </a>
+  <a href="https://pypi.org/project/langchain/#history" target="_blank">
+      <img src="https://img.shields.io/pypi/v/langchain?label=%20" alt="Version">
+  </a>
   <a href="https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain" target="_blank">
-      <img src="https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square" alt="Open in Dev Containers">
+      <img src="https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode" alt="Open in Dev Containers">
   </a>
   <a href="https://codespaces.new/langchain-ai/langchain" target="_blank">
       <img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20">
@@ -34,14 +37,14 @@
 LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development —  all while future-proofing decisions as the underlying technology evolves.
 
 ```bash
-pip install -U langchain
+pip install langchain
 ```
 
 ---
 
-**Documentation**: To learn more about LangChain, check out [the docs](https://docs.langchain.com/).
+**Documentation**: To learn more about LangChain, check out [the docs](https://docs.langchain.com/oss/python/langchain/overview).
 
-If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building controllable agent workflows.
+If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), our framework for building controllable agent workflows.
 
 > [!NOTE]
 > Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
@@ -62,16 +65,13 @@ While the LangChain framework can be used standalone, it also integrates seamles
 To improve your LLM application development, pair LangChain with:
 
 - [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
-- [LangGraph Platform](https://docs.langchain.com/langgraph-platform) - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
+- [LangGraph Platform](https://docs.langchain.com/langgraph-platform) - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio).
 
 ## Additional resources
 
-- [Conceptual Guides](https://docs.langchain.com/oss/python/langchain/overview): Explanations of key
-concepts behind the LangChain framework.
-- [Tutorials](https://docs.langchain.com/oss/python/learn): Simple walkthroughs with
-guided examples on getting started with LangChain.
-- [API Reference](https://reference.langchain.com/python/): Detailed reference on
+- [Learn](https://docs.langchain.com/oss/python/learn): Use cases, conceptual overviews, and more.
+- [API Reference](https://reference.langchain.com/python): Detailed reference on
 navigating base packages and integrations for LangChain.
-- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
-- [Chat LangChain](https://chat.langchain.com/): Ask questions & chat with our documentation.
+- [LangChain Forum](https://forum.langchain.com): Connect with the community and share all of your technical questions, ideas, and feedback.
+- [Chat LangChain](https://chat.langchain.com): Ask questions & chat with our documentation.

libs/cli/README.md

@@ -1,6 +1,30 @@
 # langchain-cli
 
-This package implements the official CLI for LangChain. Right now, it is most useful
-for getting started with LangChain Templates!
+[![PyPI - Version](https://img.shields.io/pypi/v/langchain-cli?label=%20)](https://pypi.org/project/langchain-cli/#history)
+[![PyPI - License](https://img.shields.io/pypi/l/langchain-cli)](https://opensource.org/licenses/MIT)
+[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-cli)](https://pypistats.org/packages/langchain-cli)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
+
+## Quick Install
+
+```bash
+pip install langchain-cli
+```
+
+## 🤔 What is this?
+
+This package implements the official CLI for LangChain. Right now, it is most useful for getting started with LangChain Templates!
+
+## 📖 Documentation
 
 [CLI Docs](https://github.com/langchain-ai/langchain/blob/master/libs/cli/DOCS.md)
+
+## 📕 Releases & Versioning
+
+See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
+
+## 💁 Contributing
+
+As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
+
+For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).

libs/cli/langchain_cli/integration_template/README.md

@@ -19,8 +19,8 @@ And you should configure credentials by setting the following environment variab
 ```python
 from __module_name__ import Chat__ModuleName__
 
-llm = Chat__ModuleName__()
-llm.invoke("Sing a ballad of LangChain.")
+model = Chat__ModuleName__()
+model.invoke("Sing a ballad of LangChain.")
 ```
 
 ## Embeddings
@@ -41,6 +41,6 @@ embeddings.embed_query("What is the meaning of life?")
 ```python
 from __module_name__ import __ModuleName__LLM
 
-llm = __ModuleName__LLM()
-llm.invoke("The meaning of life is")
+model = __ModuleName__LLM()
+model.invoke("The meaning of life is")
 ```

libs/cli/langchain_cli/integration_template/docs/chat.ipynb

@@ -1,262 +1,264 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__\n",
-    "---"
-   ]
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# Chat__ModuleName__\n",
+        "\n",
+        "- TODO: Make sure API reference link is correct.\n",
+        "\n",
+        "This will help you get started with __ModuleName__ [chat models](/docs/concepts/chat_models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/chat/openai/ for an example.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Fill in table features.\n",
+        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+        "- TODO: Make sure API reference links are correct.\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [Chat__ModuleName__](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package_name__](https://python.langchain.com/api_reference/__package_name_short_snake__/) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your __ModuleName__ API key: \"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU __package_name__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from __module_name__ import Chat__ModuleName__\n",
+        "\n",
+        "model = Chat__ModuleName__(\n",
+        "    model=\"model-name\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "- TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = model.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n",
+        "\n",
+        "- TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | model\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+      "metadata": {},
+      "source": [
+        "## TODO: Any functionality specific to this model provider\n",
+        "\n",
+        "E.g. creating/using finetuned models via this provider. Delete if not relevant."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# Chat__ModuleName__\n",
-    "\n",
-    "- TODO: Make sure API reference link is correct.\n",
-    "\n",
-    "This will help you get started with __ModuleName__ [chat models](/docs/concepts/chat_models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
-    "\n",
-    "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/chat/openai/ for an example.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Fill in table features.\n",
-    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-    "- TODO: Make sure API reference links are correct.\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [Chat__ModuleName__](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package_name__](https://python.langchain.com/api_reference/__package_name_short_snake__/) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your __ModuleName__ API key: \"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU __package_name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __module_name__ import Chat__ModuleName__\n",
-    "\n",
-    "llm = Chat__ModuleName__(\n",
-    "    model=\"model-name\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "- TODO: Run cells so output can be seen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n",
-    "\n",
-    "- TODO: Run cells so output can be seen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-   "metadata": {},
-   "source": [
-    "## TODO: Any functionality specific to this model provider\n",
-    "\n",
-    "E.g. creating/using finetuned models via this provider. Delete if not relevant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

libs/cli/langchain_cli/integration_template/docs/llms.ipynb

@@ -1,236 +1,238 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "67db2992",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__\n",
-    "---"
-   ]
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "67db2992",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9597802c",
+      "metadata": {},
+      "source": [
+        "# __ModuleName__LLM\n",
+        "\n",
+        "- [ ] TODO: Make sure API reference link is correct\n",
+        "\n",
+        "This will help you get started with __ModuleName__ completion models (LLMs) using LangChain. For detailed documentation on `__ModuleName__LLM` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Fill in table features.\n",
+        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+        "- TODO: Make sure API reference links are correct.\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__&label=%20) |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bc51e756",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your __ModuleName__ API key: \"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4b6e1ca6",
+      "metadata": {},
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "196c2b41",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "809c6577",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "59c710c4",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU __package_name__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0a760037",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0562a13",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from __module_name__ import __ModuleName__LLM\n",
+        "\n",
+        "model = __ModuleName__LLM(\n",
+        "    model=\"model-name\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0ee90032",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "- [ ] TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "035dea0f",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "input_text = \"__ModuleName__ is an AI company that \"\n",
+        "\n",
+        "completion = model.invoke(input_text)\n",
+        "completion"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "add38532",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
+        "\n",
+        "- TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "078e9db2",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import PromptTemplate\n",
+        "\n",
+        "prompt = PromptTemplate(\"How to say {input} in {output_language}:\\n\")\n",
+        "\n",
+        "chain = prompt | model\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e99eef30",
+      "metadata": {},
+      "source": [
+        "## TODO: Any functionality specific to this model provider\n",
+        "\n",
+        "E.g. creating/using finetuned models via this provider. Delete if not relevant"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e9bdfcef",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `__ModuleName__LLM` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.11.1 64-bit",
+      "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": "3.9.7"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+      }
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "9597802c",
-   "metadata": {},
-   "source": [
-    "# __ModuleName__LLM\n",
-    "\n",
-    "- [ ] TODO: Make sure API reference link is correct\n",
-    "\n",
-    "This will help you get started with __ModuleName__ completion models (LLMs) using LangChain. For detailed documentation on `__ModuleName__LLM` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Fill in table features.\n",
-    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-    "- TODO: Make sure API reference links are correct.\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bc51e756",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your __ModuleName__ API key: \"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b6e1ca6",
-   "metadata": {},
-   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "196c2b41",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "809c6577",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59c710c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU __package_name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a760037",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a0562a13",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __module_name__ import __ModuleName__LLM\n",
-    "\n",
-    "llm = __ModuleName__LLM(\n",
-    "    model=\"model-name\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ee90032",
-   "metadata": {},
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "- [ ] TODO: Run cells so output can be seen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "035dea0f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "input_text = \"__ModuleName__ is an AI company that \"\n",
-    "\n",
-    "completion = llm.invoke(input_text)\n",
-    "completion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "add38532",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
-    "\n",
-    "- TODO: Run cells so output can be seen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "078e9db2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
-    "\n",
-    "prompt = PromptTemplate(\"How to say {input} in {output_language}:\\n\")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e99eef30",
-   "metadata": {},
-   "source": [
-    "## TODO: Any functionality specific to this model provider\n",
-    "\n",
-    "E.g. creating/using finetuned models via this provider. Delete if not relevant"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9bdfcef",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `__ModuleName__LLM` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.11.1 64-bit",
-   "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": "3.9.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

libs/cli/langchain_cli/integration_template/docs/retrievers.ipynb

@@ -155,7 +155,7 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {
@@ -185,7 +185,7 @@
     "chain = (\n",
     "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
     "    | prompt\n",
-    "    | llm\n",
+    "    | model\n",
     "    | StrOutputParser()\n",
     ")"
    ]

libs/cli/langchain_cli/integration_template/docs/stores.ipynb

@@ -1,204 +1,204 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
+  "cells": [
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__ByteStore\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# __ModuleName__ByteStore\n",
+        "\n",
+        "- TODO: Make sure API reference link is correct.\n",
+        "\n",
+        "This will help you get started with __ModuleName__ [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all __ModuleName__ByteStore features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.__module_name__ByteStore.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/stores/in_memory/ for an example.\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "- TODO: (Optional) A short introduction to the underlying technology/API.\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Fill in table features.\n",
+        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+        "- TODO: Make sure API reference links are correct.\n",
+        "\n",
+        "| Class | Package | Local | [JS support](https://js.langchain.com/docs/integrations/stores/_package_name_) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
+        "| [__ModuleName__ByteStore](https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__&label=%20) |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "To create a __ModuleName__ byte store, you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "- TODO: Update with relevant info, or omit if the service does not require any credentials.\n",
+        "\n",
+        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your __ModuleName__ API key: \"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU __package_name__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our byte store:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from __module_name__ import __ModuleName__ByteStore\n",
+        "\n",
+        "kv_store = __ModuleName__ByteStore(\n",
+        "    # params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Usage\n",
+        "\n",
+        "- TODO: Run cells so output can be seen.\n",
+        "\n",
+        "You can set data under keys like this using the `mset` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "kv_store.mset(\n",
+        "    [\n",
+        "        [\"key1\", b\"value1\"],\n",
+        "        [\"key2\", b\"value2\"],\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "kv_store.mget(\n",
+        "    [\n",
+        "        \"key1\",\n",
+        "        \"key2\",\n",
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And you can delete data using the `mdelete` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "kv_store.mdelete(\n",
+        "    [\n",
+        "        \"key1\",\n",
+        "        \"key2\",\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "kv_store.mget(\n",
+        "    [\n",
+        "        \"key1\",\n",
+        "        \"key2\",\n",
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## TODO: Any functionality specific to this key-value store provider\n",
+        "\n",
+        "E.g. extra initialization. Delete if not relevant."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all __ModuleName__ByteStore features and configurations, head to the API reference: https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.5"
     }
-   },
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__ByteStore\n",
-    "---"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# __ModuleName__ByteStore\n",
-    "\n",
-    "- TODO: Make sure API reference link is correct.\n",
-    "\n",
-    "This will help you get started with __ModuleName__ [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all __ModuleName__ByteStore features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.__module_name__ByteStore.html).\n",
-    "\n",
-    "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/stores/in_memory/ for an example.\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "- TODO: (Optional) A short introduction to the underlying technology/API.\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Fill in table features.\n",
-    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-    "- TODO: Make sure API reference links are correct.\n",
-    "\n",
-    "| Class | Package | Local | [JS support](https://js.langchain.com/docs/integrations/stores/_package_name_) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
-    "| [__ModuleName__ByteStore](https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "To create a __ModuleName__ byte store, you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "- TODO: Update with relevant info, or omit if the service does not require any credentials.\n",
-    "\n",
-    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your __ModuleName__ API key: \"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU __package_name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our byte store:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __module_name__ import __ModuleName__ByteStore\n",
-    "\n",
-    "kv_store = __ModuleName__ByteStore(\n",
-    "    # params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "- TODO: Run cells so output can be seen.\n",
-    "\n",
-    "You can set data under keys like this using the `mset` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kv_store.mset(\n",
-    "    [\n",
-    "        [\"key1\", b\"value1\"],\n",
-    "        [\"key2\", b\"value2\"],\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "kv_store.mget(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can delete data using the `mdelete` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kv_store.mdelete(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "kv_store.mget(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## TODO: Any functionality specific to this key-value store provider\n",
-    "\n",
-    "E.g. extra initialization. Delete if not relevant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all __ModuleName__ByteStore features and configurations, head to the API reference: https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }

libs/cli/langchain_cli/integration_template/docs/tools.ipynb

@@ -1,271 +1,271 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "10238e62-3465-4973-9279-606cbb7ccf16",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__\n",
-    "---"
-   ]
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "10238e62-3465-4973-9279-606cbb7ccf16",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a6f91f20",
+      "metadata": {},
+      "source": [
+        "# __ModuleName__\n",
+        "\n",
+        "- TODO: Make sure API reference link is correct.\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with __ModuleName__ [tool](/docs/integrations/tools/). For detailed documentation of all __ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Make sure links and features are correct\n",
+        "\n",
+        "| Class | Package | Serializable | [JS support](https://js.langchain.com/docs/integrations/tools/__module_name__) |  Package latest |\n",
+        "| :--- | :--- | :---: | :---: | :---: |\n",
+        "| [__ModuleName__](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | beta/❌ | ✅/❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community&label=%20) |\n",
+        "\n",
+        "### Tool features\n",
+        "\n",
+        "- TODO: Add feature table if it makes sense\n",
+        "\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Add any additional deps\n",
+        "\n",
+        "The integration lives in the `langchain-community` package."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f85b4089",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install --quiet -U langchain-community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b15e9266",
+      "metadata": {},
+      "source": [
+        "### Credentials\n",
+        "\n",
+        "- TODO: Add any credentials that are needed"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "e0b178a2-8816-40ca-b57c-ccdd86dde9c9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "# if not os.environ.get(\"__MODULE_NAME___API_KEY\"):\n",
+        "#     os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"__MODULE_NAME__ API key:\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bc5ab717-fd27-4c59-b912-bdd099541478",
+      "metadata": {},
+      "source": [
+        "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "a6c2f136-6367-4f1f-825d-ae741e1bf281",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "- TODO: Fill in instantiation params\n",
+        "\n",
+        "Here we show how to instantiate an instance of the __ModuleName__ tool, with "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "8b3ddfe9-ca79-494c-a7ab-1f56d9407a64",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.tools import __ModuleName__\n",
+        "\n",
+        "\n",
+        "tool = __ModuleName__(...)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "74147a1a",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "### [Invoke directly with args](/docs/concepts/tools/#use-the-tool-directly)\n",
+        "\n",
+        "- TODO: Describe what the tool args are, fill them in, run cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "65310a8b-eb0c-4d9e-a618-4f4abe2414fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tool.invoke({...})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d6e73897",
+      "metadata": {},
+      "source": [
+        "### [Invoke with ToolCall](/docs/concepts/tool_calling/#tool-execution)\n",
+        "\n",
+        "We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:\n",
+        "\n",
+        "- TODO: Fill in tool args and run cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f90e33a7",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
+        "model_generated_tool_call = {\n",
+        "    \"args\": {...},  # TODO: FILL IN\n",
+        "    \"id\": \"1\",\n",
+        "    \"name\": tool.name,\n",
+        "    \"type\": \"tool_call\",\n",
+        "}\n",
+        "tool.invoke(model_generated_tool_call)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
+      "metadata": {},
+      "source": [
+        "## Use within an agent\n",
+        "\n",
+        "- TODO: Add user question and run cells\n",
+        "\n",
+        "We can use our tool in an [agent](/docs/concepts/agents/). For this we will need a LLM with [tool-calling](/docs/how_to/tool_calling/) capabilities:\n",
+        "\n",
+        "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+        "\n",
+        "<ChatModelTabs customVarName=\"llm\" />\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "af3123ad-7a02-40e5-b58e-7d56e23e5830",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# | output: false\n",
+        "# | echo: false\n",
+        "\n",
+        "# !pip install -qU langchain langchain-openai\n",
+        "from langchain.chat_models import init_chat_model\n",
+        "\n",
+        "model = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bea35fa1",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "tools = [tool]\n",
+        "agent = create_react_agent(model, tools)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fdbf35b5-3aaf-4947-9ec6-48c21533fb95",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "example_query = \"...\"\n",
+        "\n",
+        "events = agent.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": "4ac8146c",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all __ModuleName__ features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "poetry-venv-311",
+      "language": "python",
+      "name": "poetry-venv-311"
+    },
+    "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"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "a6f91f20",
-   "metadata": {},
-   "source": [
-    "# __ModuleName__\n",
-    "\n",
-    "- TODO: Make sure API reference link is correct.\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with __ModuleName__ [tool](/docs/integrations/tools/). For detailed documentation of all __ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html).\n",
-    "\n",
-    "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Make sure links and features are correct\n",
-    "\n",
-    "| Class | Package | Serializable | [JS support](https://js.langchain.com/docs/integrations/tools/__module_name__) |  Package latest |\n",
-    "| :--- | :--- | :---: | :---: | :---: |\n",
-    "| [__ModuleName__](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | beta/❌ | ✅/❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Tool features\n",
-    "\n",
-    "- TODO: Add feature table if it makes sense\n",
-    "\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Add any additional deps\n",
-    "\n",
-    "The integration lives in the `langchain-community` package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f85b4089",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --quiet -U langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b15e9266",
-   "metadata": {},
-   "source": [
-    "### Credentials\n",
-    "\n",
-    "- TODO: Add any credentials that are needed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e0b178a2-8816-40ca-b57c-ccdd86dde9c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "# if not os.environ.get(\"__MODULE_NAME___API_KEY\"):\n",
-    "#     os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"__MODULE_NAME__ API key:\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc5ab717-fd27-4c59-b912-bdd099541478",
-   "metadata": {},
-   "source": [
-    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a6c2f136-6367-4f1f-825d-ae741e1bf281",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "- TODO: Fill in instantiation params\n",
-    "\n",
-    "Here we show how to instantiate an instance of the __ModuleName__ tool, with "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "8b3ddfe9-ca79-494c-a7ab-1f56d9407a64",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.tools import __ModuleName__\n",
-    "\n",
-    "\n",
-    "tool = __ModuleName__(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74147a1a",
-   "metadata": {},
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "### [Invoke directly with args](/docs/concepts/tools/#use-the-tool-directly)\n",
-    "\n",
-    "- TODO: Describe what the tool args are, fill them in, run cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "65310a8b-eb0c-4d9e-a618-4f4abe2414fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tool.invoke({...})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6e73897",
-   "metadata": {},
-   "source": [
-    "### [Invoke with ToolCall](/docs/concepts/tool_calling/#tool-execution)\n",
-    "\n",
-    "We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:\n",
-    "\n",
-    "- TODO: Fill in tool args and run cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f90e33a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
-    "model_generated_tool_call = {\n",
-    "    \"args\": {...},  # TODO: FILL IN\n",
-    "    \"id\": \"1\",\n",
-    "    \"name\": tool.name,\n",
-    "    \"type\": \"tool_call\",\n",
-    "}\n",
-    "tool.invoke(model_generated_tool_call)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
-   "metadata": {},
-   "source": [
-    "## Use within an agent\n",
-    "\n",
-    "- TODO: Add user question and run cells\n",
-    "\n",
-    "We can use our tool in an [agent](/docs/concepts/agents/). For this we will need a LLM with [tool-calling](/docs/how_to/tool_calling/) capabilities:\n",
-    "\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "af3123ad-7a02-40e5-b58e-7d56e23e5830",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "# !pip install -qU langchain langchain-openai\n",
-    "from langchain.chat_models import init_chat_model\n",
-    "\n",
-    "llm = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bea35fa1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "tools = [tool]\n",
-    "agent = create_react_agent(llm, tools)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fdbf35b5-3aaf-4947-9ec6-48c21533fb95",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "example_query = \"...\"\n",
-    "\n",
-    "events = agent.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": "4ac8146c",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all __ModuleName__ features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv-311",
-   "language": "python",
-   "name": "poetry-venv-311"
-  },
-  "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": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

libs/cli/langchain_cli/integration_template/integration_template/chat_models.py

@@ -26,13 +26,13 @@ class Chat__ModuleName__(BaseChatModel):
 
     # TODO: Replace with relevant packages, env vars.
     Setup:
-        Install ``__package_name__`` and set environment variable
-        ``__MODULE_NAME___API_KEY``.
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
 
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
 
     # TODO: Populate with relevant params.
     Key init args — completion params:
@@ -57,216 +57,214 @@ class Chat__ModuleName__(BaseChatModel):
 
     # TODO: Replace with relevant init params.
     Instantiate:
-        .. code-block:: python
+        ```python
+        from __module_name__ import Chat__ModuleName__
 
-            from __module_name__ import Chat__ModuleName__
-
-            llm = Chat__ModuleName__(
-                model="...",
-                temperature=0,
-                max_tokens=None,
-                timeout=None,
-                max_retries=2,
-                # api_key="...",
-                # other params...
-            )
+        model = Chat__ModuleName__(
+            model="...",
+            temperature=0,
+            max_tokens=None,
+            timeout=None,
+            max_retries=2,
+            # api_key="...",
+            # other params...
+        )
+        ```
 
     Invoke:
-        .. code-block:: python
+        ```python
+        messages = [
+            ("system", "You are a helpful translator. Translate the user sentence to French."),
+            ("human", "I love programming."),
+        ]
+        model.invoke(messages)
+        ```
 
-            messages = [
-                ("system", "You are a helpful translator. Translate the user sentence to French."),
-                ("human", "I love programming."),
-            ]
-            llm.invoke(messages)
-
-        .. code-block:: python
-
-            # TODO: Example output.
+        ```python
+        # TODO: Example output.
+        ```
 
     # TODO: Delete if token-level streaming isn't supported.
     Stream:
-        .. code-block:: python
+        ```python
+        for chunk in model.stream(messages):
+            print(chunk.text, end="")
+        ```
 
-            for chunk in llm.stream(messages):
-                print(chunk.text, end="")
+        ```python
+        # TODO: Example output.
+        ```
 
-        .. code-block:: python
+        ```python
+        stream = model.stream(messages)
+        full = next(stream)
+        for chunk in stream:
+            full += chunk
+        full
+        ```
 
-            # TODO: Example output.
-
-        .. code-block:: python
-
-            stream = llm.stream(messages)
-            full = next(stream)
-            for chunk in stream:
-                full += chunk
-            full
-
-        .. code-block:: python
-
-            # TODO: Example output.
+        ```python
+        # TODO: Example output.
+        ```
 
     # TODO: Delete if native async isn't supported.
     Async:
-        .. code-block:: python
+        ```python
+        await model.ainvoke(messages)
 
-            await llm.ainvoke(messages)
+        # stream:
+        # async for chunk in (await model.astream(messages))
 
-            # stream:
-            # async for chunk in (await llm.astream(messages))
-
-            # batch:
-            # await llm.abatch([messages])
-
-        .. code-block:: python
-
-            # TODO: Example output.
+        # batch:
+        # await model.abatch([messages])
+        ```
 
+        ```python
+        # TODO: Example output.
+        ```
     # TODO: Delete if .bind_tools() isn't supported.
     Tool calling:
-        .. code-block:: python
+        ```python
+        from pydantic import BaseModel, Field
 
-            from pydantic import BaseModel, Field
+        class GetWeather(BaseModel):
+            '''Get the current weather in a given location'''
 
-            class GetWeather(BaseModel):
-                '''Get the current weather in a given location'''
+            location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
 
-                location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
+        class GetPopulation(BaseModel):
+            '''Get the current population in a given location'''
 
-            class GetPopulation(BaseModel):
-                '''Get the current population in a given location'''
+            location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
 
-                location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
+        model_with_tools = model.bind_tools([GetWeather, GetPopulation])
+        ai_msg = model_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
+        ai_msg.tool_calls
+        ```
 
-            llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
-            ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
-            ai_msg.tool_calls
+        ```python
+        # TODO: Example output.
+        ```
 
-        .. code-block:: python
-
-              # TODO: Example output.
-
-        See ``Chat__ModuleName__.bind_tools()`` method for more.
+        See `Chat__ModuleName__.bind_tools()` method for more.
 
     # TODO: Delete if .with_structured_output() isn't supported.
     Structured output:
-        .. code-block:: python
+        ```python
+        from typing import Optional
 
-            from typing import Optional
+        from pydantic import BaseModel, Field
 
-            from pydantic import BaseModel, Field
+        class Joke(BaseModel):
+            '''Joke to tell user.'''
 
-            class Joke(BaseModel):
-                '''Joke to tell user.'''
+            setup: str = Field(description="The setup of the joke")
+            punchline: str = Field(description="The punchline to the joke")
+            rating: int | None = Field(description="How funny the joke is, from 1 to 10")
 
-                setup: str = Field(description="The setup of the joke")
-                punchline: str = Field(description="The punchline to the joke")
-                rating: int | None = Field(description="How funny the joke is, from 1 to 10")
+        structured_model = model.with_structured_output(Joke)
+        structured_model.invoke("Tell me a joke about cats")
+        ```
 
-            structured_llm = llm.with_structured_output(Joke)
-            structured_llm.invoke("Tell me a joke about cats")
+        ```python
+        # TODO: Example output.
+        ```
 
-        .. code-block:: python
-
-            # TODO: Example output.
-
-        See ``Chat__ModuleName__.with_structured_output()`` for more.
+        See `Chat__ModuleName__.with_structured_output()` for more.
 
     # TODO: Delete if JSON mode response format isn't supported.
     JSON mode:
-        .. code-block:: python
+        ```python
+        # TODO: Replace with appropriate bind arg.
+        json_model = model.bind(response_format={"type": "json_object"})
+        ai_msg = json_model.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
+        ai_msg.content
+        ```
 
-            # TODO: Replace with appropriate bind arg.
-            json_llm = llm.bind(response_format={"type": "json_object"})
-            ai_msg = json_llm.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
-            ai_msg.content
-
-        .. code-block:: python
-
-            # TODO: Example output.
+        ```python
+        # TODO: Example output.
+        ```
 
     # TODO: Delete if image inputs aren't supported.
     Image input:
-        .. code-block:: python
+        ```python
+        import base64
+        import httpx
+        from langchain_core.messages import HumanMessage
 
-            import base64
-            import httpx
-            from langchain_core.messages import HumanMessage
+        image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
+        image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
+        # TODO: Replace with appropriate message content format.
+        message = HumanMessage(
+            content=[
+                {"type": "text", "text": "describe the weather in this image"},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
+                },
+            ],
+        )
+        ai_msg = model.invoke([message])
+        ai_msg.content
+        ```
 
-            image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
-            image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
-            # TODO: Replace with appropriate message content format.
-            message = HumanMessage(
-                content=[
-                    {"type": "text", "text": "describe the weather in this image"},
-                    {
-                        "type": "image_url",
-                        "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
-                    },
-                ],
-            )
-            ai_msg = llm.invoke([message])
-            ai_msg.content
-
-        .. code-block:: python
-
-            # TODO: Example output.
+        ```python
+        # TODO: Example output.
+        ```
 
     # TODO: Delete if audio inputs aren't supported.
     Audio input:
-        .. code-block:: python
+        ```python
+        # TODO: Example input
+        ```
 
-            # TODO: Example input
-
-        .. code-block:: python
-
-            # TODO: Example output
+        ```python
+        # TODO: Example output
+        ```
 
     # TODO: Delete if video inputs aren't supported.
     Video input:
-        .. code-block:: python
+        ```python
+        # TODO: Example input
+        ```
 
-            # TODO: Example input
-
-        .. code-block:: python
-
-            # TODO: Example output
+        ```python
+        # TODO: Example output
+        ```
 
     # TODO: Delete if token usage metadata isn't supported.
     Token usage:
-        .. code-block:: python
+        ```python
+        ai_msg = model.invoke(messages)
+        ai_msg.usage_metadata
+        ```
 
-            ai_msg = llm.invoke(messages)
-            ai_msg.usage_metadata
-
-        .. code-block:: python
-
-            {'input_tokens': 28, 'output_tokens': 5, 'total_tokens': 33}
+        ```python
+        {'input_tokens': 28, 'output_tokens': 5, 'total_tokens': 33}
+        ```
 
     # TODO: Delete if logprobs aren't supported.
     Logprobs:
-        .. code-block:: python
-
-            # TODO: Replace with appropriate bind arg.
-            logprobs_llm = llm.bind(logprobs=True)
-            ai_msg = logprobs_llm.invoke(messages)
-            ai_msg.response_metadata["logprobs"]
-
-        .. code-block:: python
-
-              # TODO: Example output.
+        ```python
+        # TODO: Replace with appropriate bind arg.
+        logprobs_model = model.bind(logprobs=True)
+        ai_msg = logprobs_model.invoke(messages)
+        ai_msg.response_metadata["logprobs"]
+        ```
 
+        ```python
+        # TODO: Example output.
+        ```
     Response metadata
-        .. code-block:: python
+        ```python
+        ai_msg = model.invoke(messages)
+        ai_msg.response_metadata
+        ```
 
-            ai_msg = llm.invoke(messages)
-            ai_msg.response_metadata
-
-        .. code-block:: python
-
-             # TODO: Example output.
+        ```python
+        # TODO: Example output.
 
+        ```
     """  # noqa: E501
 
     model_name: str = Field(alias="model")
@@ -314,11 +312,11 @@ class Chat__ModuleName__(BaseChatModel):
         Args:
             messages: the prompt composed of a list of messages.
             stop: a list of strings on which the model should stop generating.
-                  If generation stops due to a stop token, the stop token itself
-                  SHOULD BE INCLUDED as part of the output. This is not enforced
-                  across models right now, but it's a good practice to follow since
-                  it makes it much easier to parse the output of the model
-                  downstream and understand why generation stopped.
+                If generation stops due to a stop token, the stop token itself
+                SHOULD BE INCLUDED as part of the output. This is not enforced
+                across models right now, but it's a good practice to follow since
+                it makes it much easier to parse the output of the model
+                downstream and understand why generation stopped.
             run_manager: A run manager with callbacks for the LLM.
         """
         # Replace this with actual logic to generate a response from a list
@@ -362,11 +360,11 @@ class Chat__ModuleName__(BaseChatModel):
         Args:
             messages: the prompt composed of a list of messages.
             stop: a list of strings on which the model should stop generating.
-                  If generation stops due to a stop token, the stop token itself
-                  SHOULD BE INCLUDED as part of the output. This is not enforced
-                  across models right now, but it's a good practice to follow since
-                  it makes it much easier to parse the output of the model
-                  downstream and understand why generation stopped.
+                If generation stops due to a stop token, the stop token itself
+                SHOULD BE INCLUDED as part of the output. This is not enforced
+                across models right now, but it's a good practice to follow since
+                it makes it much easier to parse the output of the model
+                downstream and understand why generation stopped.
             run_manager: A run manager with callbacks for the LLM.
         """
         last_message = messages[-1]

libs/cli/langchain_cli/integration_template/integration_template/document_loaders.py

@@ -14,55 +14,55 @@ class __ModuleName__Loader(BaseLoader):
 
     # TODO: Replace with relevant packages, env vars.
     Setup:
-        Install ``__package_name__`` and set environment variable
-        ``__MODULE_NAME___API_KEY``.
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
 
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
 
     # TODO: Replace with relevant init params.
     Instantiate:
-        .. code-block:: python
+        ```python
+        from langchain_community.document_loaders import __ModuleName__Loader
 
-            from langchain_community.document_loaders import __ModuleName__Loader
-
-            loader = __ModuleName__Loader(
-                # required params = ...
-                # other params = ...
-            )
+        loader = __ModuleName__Loader(
+            # required params = ...
+            # other params = ...
+        )
+        ```
 
     Lazy load:
-        .. code-block:: python
+        ```python
+        docs = []
+        docs_lazy = loader.lazy_load()
 
-            docs = []
-            docs_lazy = loader.lazy_load()
+        # async variant:
+        # docs_lazy = await loader.alazy_load()
 
-            # async variant:
-            # docs_lazy = await loader.alazy_load()
+        for doc in docs_lazy:
+            docs.append(doc)
+        print(docs[0].page_content[:100])
+        print(docs[0].metadata)
+        ```
 
-            for doc in docs_lazy:
-                docs.append(doc)
-            print(docs[0].page_content[:100])
-            print(docs[0].metadata)
-
-        .. code-block:: python
-
-            TODO: Example output
+        ```python
+        TODO: Example output
+        ```
 
     # TODO: Delete if async load is not implemented
     Async load:
-        .. code-block:: python
+        ```python
+        docs = await loader.aload()
+        print(docs[0].page_content[:100])
+        print(docs[0].metadata)
+        ```
 
-            docs = await loader.aload()
-            print(docs[0].page_content[:100])
-            print(docs[0].metadata)
-
-        .. code-block:: python
-
-            TODO: Example output
+        ```python
+        TODO: Example output
 
+        ```
     """
 
     # TODO: This method must be implemented to load documents.

libs/cli/langchain_cli/integration_template/integration_template/embeddings.py

@@ -8,13 +8,13 @@ class __ModuleName__Embeddings(Embeddings):
 
     # TODO: Replace with relevant packages, env vars.
     Setup:
-        Install ``__package_name__`` and set environment variable
-        ``__MODULE_NAME___API_KEY``.
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
 
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
 
     # TODO: Populate with relevant params.
     Key init args — completion params:
@@ -25,50 +25,50 @@ class __ModuleName__Embeddings(Embeddings):
 
     # TODO: Replace with relevant init params.
     Instantiate:
-        .. code-block:: python
+        ```python
+        from __module_name__ import __ModuleName__Embeddings
 
-            from __module_name__ import __ModuleName__Embeddings
-
-            embed = __ModuleName__Embeddings(
-                model="...",
-                # api_key="...",
-                # other params...
-            )
+        embed = __ModuleName__Embeddings(
+            model="...",
+            # api_key="...",
+            # other params...
+        )
+        ```
 
     Embed single text:
-        .. code-block:: python
+        ```python
+        input_text = "The meaning of life is 42"
+        embed.embed_query(input_text)
+        ```
 
-            input_text = "The meaning of life is 42"
-            embed.embed_query(input_text)
-
-        .. code-block:: python
-
-            # TODO: Example output.
+        ```python
+        # TODO: Example output.
+        ```
 
     # TODO: Delete if token-level streaming isn't supported.
     Embed multiple text:
-        .. code-block:: python
+        ```python
+        input_texts = ["Document 1...", "Document 2..."]
+        embed.embed_documents(input_texts)
+        ```
 
-             input_texts = ["Document 1...", "Document 2..."]
-            embed.embed_documents(input_texts)
-
-        .. code-block:: python
-
-            # TODO: Example output.
+        ```python
+        # TODO: Example output.
+        ```
 
     # TODO: Delete if native async isn't supported.
     Async:
-        .. code-block:: python
+        ```python
+        await embed.aembed_query(input_text)
 
-            await embed.aembed_query(input_text)
+        # multiple:
+        # await embed.aembed_documents(input_texts)
+        ```
 
-            # multiple:
-            # await embed.aembed_documents(input_texts)
-
-        .. code-block:: python
-
-            # TODO: Example output.
+        ```python
+        # TODO: Example output.
 
+        ```
     """
 
     def __init__(self, model: str):

libs/cli/langchain_cli/integration_template/integration_template/retrievers.py

@@ -14,13 +14,13 @@ class __ModuleName__Retriever(BaseRetriever):
 
     # TODO: Replace with relevant packages, env vars, etc.
     Setup:
-        Install ``__package_name__`` and set environment variable
-        ``__MODULE_NAME___API_KEY``.
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
 
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
 
     # TODO: Populate with relevant params.
     Key init args:
@@ -31,58 +31,58 @@ class __ModuleName__Retriever(BaseRetriever):
 
     # TODO: Replace with relevant init params.
     Instantiate:
-        .. code-block:: python
+        ```python
+        from __package_name__ import __ModuleName__Retriever
 
-            from __package_name__ import __ModuleName__Retriever
-
-            retriever = __ModuleName__Retriever(
-                # ...
-            )
+        retriever = __ModuleName__Retriever(
+            # ...
+        )
+        ```
 
     Usage:
-        .. code-block:: python
+        ```python
+        query = "..."
 
-            query = "..."
+        retriever.invoke(query)
+        ```
 
-            retriever.invoke(query)
-
-        .. code-block::
-
-            # TODO: Example output.
+        ```txt
+        # TODO: Example output.
+        ```
 
     Use within a chain:
-        .. code-block:: python
+        ```python
+        from langchain_core.output_parsers import StrOutputParser
+        from langchain_core.prompts import ChatPromptTemplate
+        from langchain_core.runnables import RunnablePassthrough
+        from langchain_openai import ChatOpenAI
 
-            from langchain_core.output_parsers import StrOutputParser
-            from langchain_core.prompts import ChatPromptTemplate
-            from langchain_core.runnables import RunnablePassthrough
-            from langchain_openai import ChatOpenAI
+        prompt = ChatPromptTemplate.from_template(
+            \"\"\"Answer the question based only on the context provided.
 
-            prompt = ChatPromptTemplate.from_template(
-                \"\"\"Answer the question based only on the context provided.
+        Context: {context}
 
-            Context: {context}
+        Question: {question}\"\"\"
+        )
 
-            Question: {question}\"\"\"
-            )
+        model = ChatOpenAI(model="gpt-3.5-turbo-0125")
 
-            llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
+        def format_docs(docs):
+            return "\\n\\n".join(doc.page_content for doc in docs)
 
-            def format_docs(docs):
-                return "\\n\\n".join(doc.page_content for doc in docs)
+        chain = (
+            {"context": retriever | format_docs, "question": RunnablePassthrough()}
+            | prompt
+            | model
+            | StrOutputParser()
+        )
 
-            chain = (
-                {"context": retriever | format_docs, "question": RunnablePassthrough()}
-                | prompt
-                | llm
-                | StrOutputParser()
-            )
+        chain.invoke("...")
+        ```
 
-            chain.invoke("...")
-
-        .. code-block::
-
-             # TODO: Example output.
+        ```
+        # TODO: Example output.
+        ```
 
     """
 

libs/cli/langchain_cli/integration_template/integration_template/toolkits.py

@@ -12,13 +12,13 @@ class __ModuleName__Toolkit(BaseToolkit):
 
     # TODO: Replace with relevant packages, env vars, etc.
     Setup:
-        Install ``__package_name__`` and set environment variable
-        ``__MODULE_NAME___API_KEY``.
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
 
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
 
     # TODO: Populate with relevant params.
     Key init args:
@@ -29,42 +29,42 @@ class __ModuleName__Toolkit(BaseToolkit):
 
     # TODO: Replace with relevant init params.
     Instantiate:
-        .. code-block:: python
+        ```python
+        from __package_name__ import __ModuleName__Toolkit
 
-            from __package_name__ import __ModuleName__Toolkit
-
-            toolkit = __ModuleName__Toolkit(
-                # ...
-            )
+        toolkit = __ModuleName__Toolkit(
+            # ...
+        )
+        ```
 
     Tools:
-        .. code-block:: python
+        ```python
+        toolkit.get_tools()
+        ```
 
-            toolkit.get_tools()
-
-        .. code-block::
-
-            # TODO: Example output.
+        ```txt
+        # TODO: Example output.
+        ```
 
     Use within an agent:
-        .. code-block:: python
+        ```python
+        from langgraph.prebuilt import create_react_agent
 
-            from langgraph.prebuilt import create_react_agent
+        agent_executor = create_react_agent(llm, tools)
 
-            agent_executor = create_react_agent(llm, tools)
+        example_query = "..."
 
-            example_query = "..."
+        events = agent_executor.stream(
+            {"messages": [("user", example_query)]},
+            stream_mode="values",
+        )
+        for event in events:
+            event["messages"][-1].pretty_print()
+        ```
 
-            events = agent_executor.stream(
-                {"messages": [("user", example_query)]},
-                stream_mode="values",
-            )
-            for event in events:
-                event["messages"][-1].pretty_print()
-
-        .. code-block::
-
-             # TODO: Example output.
+        ```txt
+        # TODO: Example output.
+        ```
 
     """
 

libs/cli/langchain_cli/integration_template/integration_template/tools.py

@@ -27,42 +27,42 @@ class __ModuleName__Tool(BaseTool):  # type: ignore[override]
 
     Setup:
         # TODO: Replace with relevant packages, env vars.
-        Install ``__package_name__`` and set environment variable
-        ``__MODULE_NAME___API_KEY``.
+        Install `__package_name__` and set environment variable
+        `__MODULE_NAME___API_KEY`.
 
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
 
     Instantiation:
-        .. code-block:: python
-
-            tool = __ModuleName__Tool(
-                # TODO: init params
-            )
+        ```python
+        tool = __ModuleName__Tool(
+            # TODO: init params
+        )
+        ```
 
     Invocation with args:
-        .. code-block:: python
+        ```python
+        # TODO: invoke args
+        tool.invoke({...})
+        ```
 
-            # TODO: invoke args
-            tool.invoke({...})
-
-        .. code-block:: python
-
-            # TODO: output of invocation
+        ```python
+        # TODO: output of invocation
+        ```
 
     Invocation with ToolCall:
 
-        .. code-block:: python
+        ```python
+        # TODO: invoke args
+        tool.invoke({"args": {...}, "id": "1", "name": tool.name, "type": "tool_call"})
+        ```
 
-            # TODO: invoke args
-            tool.invoke({"args": {...}, "id": "1", "name": tool.name, "type": "tool_call"})
-
-        .. code-block:: python
-
-            # TODO: output of invocation
+        ```python
+        # TODO: output of invocation
 
+        ```
     """  # noqa: E501
 
     # TODO: Set tool name and description

libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py

@@ -28,12 +28,12 @@ class __ModuleName__VectorStore(VectorStore):
 
     # TODO: Replace with relevant packages, env vars.
     Setup:
-        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
+        Install `__package_name__` and set environment variable `__MODULE_NAME___API_KEY`.
 
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
+        ```bash
+        pip install -U __package_name__
+        export __MODULE_NAME___API_KEY="your-api-key"
+        ```
 
     # TODO: Populate with relevant params.
     Key init args — indexing params:
@@ -51,110 +51,110 @@ class __ModuleName__VectorStore(VectorStore):
 
     # TODO: Replace with relevant init params.
     Instantiate:
-        .. code-block:: python
+        ```python
+        from __module_name__.vectorstores import __ModuleName__VectorStore
+        from langchain_openai import OpenAIEmbeddings
 
-            from __module_name__.vectorstores import __ModuleName__VectorStore
-            from langchain_openai import OpenAIEmbeddings
-
-            vector_store = __ModuleName__VectorStore(
-                collection_name="foo",
-                embedding_function=OpenAIEmbeddings(),
-                connection_args={"uri": "./foo.db"},
-                # other params...
-            )
+        vector_store = __ModuleName__VectorStore(
+            collection_name="foo",
+            embedding_function=OpenAIEmbeddings(),
+            connection_args={"uri": "./foo.db"},
+            # other params...
+        )
+        ```
 
     # TODO: Populate with relevant variables.
     Add Documents:
-        .. code-block:: python
+        ```python
+        from langchain_core.documents import Document
 
-            from langchain_core.documents import Document
+        document_1 = Document(page_content="foo", metadata={"baz": "bar"})
+        document_2 = Document(page_content="thud", metadata={"bar": "baz"})
+        document_3 = Document(page_content="i will be deleted :(")
 
-            document_1 = Document(page_content="foo", metadata={"baz": "bar"})
-            document_2 = Document(page_content="thud", metadata={"bar": "baz"})
-            document_3 = Document(page_content="i will be deleted :(")
-
-            documents = [document_1, document_2, document_3]
-            ids = ["1", "2", "3"]
-            vector_store.add_documents(documents=documents, ids=ids)
+        documents = [document_1, document_2, document_3]
+        ids = ["1", "2", "3"]
+        vector_store.add_documents(documents=documents, ids=ids)
+        ```
 
     # TODO: Populate with relevant variables.
     Delete Documents:
-        .. code-block:: python
-
-            vector_store.delete(ids=["3"])
+        ```python
+        vector_store.delete(ids=["3"])
+        ```
 
     # TODO: Fill out with relevant variables and example output.
     Search:
-        .. code-block:: python
+        ```python
+        results = vector_store.similarity_search(query="thud",k=1)
+        for doc in results:
+            print(f"* {doc.page_content} [{doc.metadata}]")
+        ```
 
-            results = vector_store.similarity_search(query="thud",k=1)
-            for doc in results:
-                print(f"* {doc.page_content} [{doc.metadata}]")
-
-        .. code-block:: python
-
-            # TODO: Example output
+        ```python
+        # TODO: Example output
+        ```
 
     # TODO: Fill out with relevant variables and example output.
     Search with filter:
-        .. code-block:: python
+        ```python
+        results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
+        for doc in results:
+            print(f"* {doc.page_content} [{doc.metadata}]")
+        ```
 
-            results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
-            for doc in results:
-                print(f"* {doc.page_content} [{doc.metadata}]")
-
-        .. code-block:: python
-
-            # TODO: Example output
+        ```python
+        # TODO: Example output
+        ```
 
     # TODO: Fill out with relevant variables and example output.
     Search with score:
-        .. code-block:: python
+        ```python
+        results = vector_store.similarity_search_with_score(query="qux",k=1)
+        for doc, score in results:
+            print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
+        ```
 
-            results = vector_store.similarity_search_with_score(query="qux",k=1)
-            for doc, score in results:
-                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
-
-        .. code-block:: python
-
-            # TODO: Example output
+        ```python
+        # TODO: Example output
+        ```
 
     # TODO: Fill out with relevant variables and example output.
     Async:
-        .. code-block:: python
+        ```python
+        # add documents
+        # await vector_store.aadd_documents(documents=documents, ids=ids)
 
-            # add documents
-            # await vector_store.aadd_documents(documents=documents, ids=ids)
+        # delete documents
+        # await vector_store.adelete(ids=["3"])
 
-            # delete documents
-            # await vector_store.adelete(ids=["3"])
+        # search
+        # results = vector_store.asimilarity_search(query="thud",k=1)
 
-            # search
-            # results = vector_store.asimilarity_search(query="thud",k=1)
+        # search with score
+        results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
+        for doc,score in results:
+            print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
+        ```
 
-            # search with score
-            results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
-            for doc,score in results:
-                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
-
-        .. code-block:: python
-
-            # TODO: Example output
+        ```python
+        # TODO: Example output
+        ```
 
     # TODO: Fill out with relevant variables and example output.
     Use as Retriever:
-        .. code-block:: python
+        ```python
+        retriever = vector_store.as_retriever(
+            search_type="mmr",
+            search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
+        )
+        retriever.invoke("thud")
+        ```
 
-            retriever = vector_store.as_retriever(
-                search_type="mmr",
-                search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
-            )
-            retriever.invoke("thud")
-
-        .. code-block:: python
-
-            # TODO: Example output
+        ```python
+        # TODO: Example output
 
+        ```
     """  # noqa: E501
 
     def __init__(self, embedding: Embeddings) -> None:

libs/cli/langchain_cli/namespaces/migrate/generate/partner.py

@@ -24,7 +24,7 @@ def get_migrations_for_partner_package(pkg_name: str) -> list[tuple[str, str]]:
     This code works
 
     Args:
-        pkg_name (str): The name of the partner package.
+        pkg_name: The name of the partner package.
 
     Returns:
         List of 2-tuples containing old and new import paths.

libs/cli/langchain_cli/namespaces/migrate/generate/utils.py

@@ -65,7 +65,7 @@ def is_subclass(class_obj: type, classes_: list[type]) -> bool:
         classes_: A list of classes to check against.
 
     Returns:
-        True if `class_obj` is a subclass of any class in `classes_`, False otherwise.
+        True if `class_obj` is a subclass of any class in `classes_`, `False` otherwise.
     """
     return any(
         issubclass(class_obj, kls)

libs/core/README.md

@@ -1,7 +1,14 @@
 # 🦜🍎️ LangChain Core
 
-[![PyPI - License](https://img.shields.io/pypi/l/langchain-core?style=flat-square)](https://opensource.org/licenses/MIT)
+[![PyPI - Version](https://img.shields.io/pypi/v/langchain-core?label=%20)](https://pypi.org/project/langchain-core/#history)
+[![PyPI - License](https://img.shields.io/pypi/l/langchain-core)](https://opensource.org/licenses/MIT)
 [![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-core)](https://pypistats.org/packages/langchain-core)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
+
+Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
+
+To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
+[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.
 
 ## Quick Install
 
@@ -9,16 +16,14 @@
 pip install langchain-core
 ```
 
-## What is it?
+## 🤔 What is this?
 
-LangChain Core contains the base abstractions that power the the LangChain ecosystem.
+LangChain Core contains the base abstractions that power the LangChain ecosystem.
 
 These abstractions are designed to be as modular and simple as possible.
 
 The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.
 
-For full documentation see the [API reference](https://reference.langchain.com/python/).
-
 ## ⛰️ Why build on top of LangChain Core?
 
 The LangChain ecosystem is built on top of `langchain-core`. Some of the benefits:
@@ -27,12 +32,16 @@ The LangChain ecosystem is built on top of `langchain-core`. Some of the benefit
 - **Stability**: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
 - **Battle-tested**: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.
 
+## 📖 Documentation
+
+For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_core/).
+
 ## 📕 Releases & Versioning
 
-See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning Policy](https://docs.langchain.com/oss/python/versioning).
+See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
 
 ## 💁 Contributing
 
 As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
 
-For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing).
+For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).

libs/core/langchain_core/_api/__init__.py

@@ -6,7 +6,6 @@ This module is only relevant for LangChain developers, not for users.
 
     This module and its submodules are for internal use only. Do not use them in your
     own code.  We may change the API at any time with no warning.
-
 """
 
 from typing import TYPE_CHECKING

libs/core/langchain_core/_api/beta_decorator.py

@@ -51,29 +51,26 @@ def beta(
     own (annotation-emitting) `C.__init__`).
 
     Args:
-        message : str, optional
+        message:
             Override the default beta message. The %(since)s,
             %(name)s, %(alternative)s, %(obj_type)s, %(addendum)s,
             and %(removal)s format specifiers will be replaced by the
             values of the respective arguments passed to this function.
-        name : str, optional
+        name:
             The name of the beta object.
-        obj_type : str, optional
+        obj_type:
             The object type being beta.
-        addendum : str, optional
+        addendum:
             Additional text appended directly to the final message.
 
     Returns:
         A decorator which can be used to mark functions or classes as beta.
 
-    Examples:
-
-        .. code-block:: python
-
-            @beta
-            def the_function_to_annotate():
-                pass
-
+    ```python
+    @beta
+    def the_function_to_annotate():
+        pass
+    ```
     """
 
     def beta(

libs/core/langchain_core/_api/deprecation.py

@@ -97,47 +97,44 @@ def deprecated(
     property, and 'function' otherwise.
 
     Args:
-        since : str
+        since:
             The release at which this API became deprecated.
-        message : str, optional
+        message:
             Override the default deprecation message. The %(since)s,
             %(name)s, %(alternative)s, %(obj_type)s, %(addendum)s,
             and %(removal)s format specifiers will be replaced by the
             values of the respective arguments passed to this function.
-        name : str, optional
+        name:
             The name of the deprecated object.
-        alternative : str, optional
+        alternative:
             An alternative API that the user may use in place of the
             deprecated API. The deprecation warning will tell the user
             about this alternative if provided.
-        alternative_import: str, optional
+        alternative_import:
             An alternative import that the user may use instead.
-        pending : bool, optional
+        pending:
             If `True`, uses a `PendingDeprecationWarning` instead of a
             DeprecationWarning. Cannot be used together with removal.
-        obj_type : str, optional
+        obj_type:
             The object type being deprecated.
-        addendum : str, optional
+        addendum:
             Additional text appended directly to the final message.
-        removal : str, optional
+        removal:
             The expected removal version. With the default (an empty
             string), a removal version is automatically computed from
             since. Set to other Falsy values to not schedule a removal
             date. Cannot be used together with pending.
-        package: str, optional
+        package:
             The package of the deprecated object.
 
     Returns:
         A decorator to mark a function or class as deprecated.
 
-    Examples:
-
-        .. code-block:: python
-
-            @deprecated("1.4.0")
-            def the_function_to_deprecate():
-                pass
-
+    ```python
+    @deprecated("1.4.0")
+    def the_function_to_deprecate():
+        pass
+    ```
     """
     _validate_deprecation_params(
         removal, alternative, alternative_import, pending=pending
@@ -550,12 +547,10 @@ def rename_parameter(
         A decorator indicating that a parameter was renamed.
 
     Example:
-
-        .. code-block:: python
-
-            @_api.rename_parameter("3.1", "bad_name", "good_name")
-            def func(good_name): ...
-
+        ```python
+        @_api.rename_parameter("3.1", "bad_name", "good_name")
+        def func(good_name): ...
+        ```
     """
 
     def decorator(f: Callable[_P, _R]) -> Callable[_P, _R]:

libs/core/langchain_core/agents.py

@@ -1,6 +1,7 @@
 """Schema definitions for representing agent actions, observations, and return values.
 
-**ATTENTION** The schema definitions are provided for backwards compatibility.
+!!! warning
+    The schema definitions are provided for backwards compatibility.
 
 !!! warning
     New agents should be built using the
@@ -16,10 +17,10 @@ Agents use language models to choose a sequence of actions to take.
 A basic agent works in the following manner:
 
 1. Given a prompt an agent uses an LLM to request an action to take
-   (e.g., a tool to run).
+    (e.g., a tool to run).
 2. The agent executes the action (e.g., runs the tool), and receives an observation.
 3. The agent returns the observation to the LLM, which can then be used to generate
-   the next action.
+    the next action.
 4. When the agent reaches a stopping condition, it returns a final return value.
 
 The schemas for the agents themselves are defined in langchain.agents.agent.
@@ -83,10 +84,10 @@ class AgentAction(Serializable):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "schema", "agent"]``
+            `["langchain", "schema", "agent"]`
         """
         return ["langchain", "schema", "agent"]
 
@@ -111,7 +112,7 @@ class AgentActionMessageLog(AgentAction):
     if (tool, tool_input) cannot be used to fully recreate the LLM
     prediction, and you need that LLM prediction (for future agent iteration).
     Compared to `log`, this is useful when the underlying LLM is a
-    ChatModel (and therefore returns messages rather than a string)."""
+    chat model (and therefore returns messages rather than a string)."""
     # Ignoring type because we're overriding the type from AgentAction.
     # And this is the correct thing to do in this case.
     # The type literal is used for serialization purposes.
@@ -160,10 +161,10 @@ class AgentFinish(Serializable):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "schema", "agent"]``
+            `["langchain", "schema", "agent"]`
         """
         return ["langchain", "schema", "agent"]
 

libs/core/langchain_core/caches.py

@@ -1,24 +1,15 @@
-"""Cache classes.
+"""`caches` provides an optional caching layer for language models.
 
 !!! warning
-  Beta Feature!
+    This is a beta feature! Please be wary of deploying experimental code to production
+    unless you've taken appropriate precautions.
 
-**Cache** provides an optional caching layer for LLMs.
+A cache is useful for two reasons:
 
-Cache is useful for two reasons:
-
-- It can save you money by reducing the number of API calls you make to the LLM
-  provider if you're often requesting the same completion multiple times.
-- It can speed up your application by reducing the number of API calls you make
-  to the LLM provider.
-
-Cache directly competes with Memory. See documentation for Pros and Cons.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseCache --> <name>Cache  # Examples: InMemoryCache, RedisCache, GPTCache
+1. It can save you money by reducing the number of API calls you make to the LLM
+    provider if you're often requesting the same completion multiple times.
+2. It can speed up your application by reducing the number of API calls you make to the
+    LLM provider.
 """
 
 from __future__ import annotations
@@ -40,8 +31,8 @@ class BaseCache(ABC):
 
     The cache interface consists of the following methods:
 
-    - lookup: Look up a value based on a prompt and llm_string.
-    - update: Update the cache based on a prompt and llm_string.
+    - lookup: Look up a value based on a prompt and `llm_string`.
+    - update: Update the cache based on a prompt and `llm_string`.
     - clear: Clear the cache.
 
     In addition, the cache interface provides an async version of each method.
@@ -53,14 +44,14 @@ class BaseCache(ABC):
 
     @abstractmethod
     def lookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Look up based on prompt and llm_string.
+        """Look up based on `prompt` and `llm_string`.
 
         A cache implementation is expected to generate a key from the 2-tuple
         of prompt and llm_string (e.g., by concatenating them with a delimiter).
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
                 This is used to capture the invocation parameters of the LLM
@@ -69,27 +60,27 @@ class BaseCache(ABC):
                 representation.
 
         Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
-            The cached value is a list of Generations (or subclasses).
+            On a cache miss, return `None`. On a cache hit, return the cached value.
+            The cached value is a list of `Generation` (or subclasses).
         """
 
     @abstractmethod
     def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
-        """Update cache based on prompt and llm_string.
+        """Update cache based on `prompt` and `llm_string`.
 
         The prompt and llm_string are used to generate a key for the cache.
         The key should match that of the lookup method.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
                 This is used to capture the invocation parameters of the LLM
                 (e.g., model name, temperature, stop tokens, max tokens, etc.).
                 These invocation parameters are serialized into a string
                 representation.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                 (or subclasses).
         """
 
@@ -98,14 +89,14 @@ class BaseCache(ABC):
         """Clear cache that can take additional keyword arguments."""
 
     async def alookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Async look up based on prompt and llm_string.
+        """Async look up based on `prompt` and `llm_string`.
 
         A cache implementation is expected to generate a key from the 2-tuple
         of prompt and llm_string (e.g., by concatenating them with a delimiter).
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
                 This is used to capture the invocation parameters of the LLM
@@ -114,29 +105,29 @@ class BaseCache(ABC):
                 representation.
 
         Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
-            The cached value is a list of Generations (or subclasses).
+            On a cache miss, return `None`. On a cache hit, return the cached value.
+            The cached value is a list of `Generation` (or subclasses).
         """
         return await run_in_executor(None, self.lookup, prompt, llm_string)
 
     async def aupdate(
         self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
     ) -> None:
-        """Async update cache based on prompt and llm_string.
+        """Async update cache based on `prompt` and `llm_string`.
 
         The prompt and llm_string are used to generate a key for the cache.
         The key should match that of the look up method.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
                 This is used to capture the invocation parameters of the LLM
                 (e.g., model name, temperature, stop tokens, max tokens, etc.).
                 These invocation parameters are serialized into a string
                 representation.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                 (or subclasses).
         """
         return await run_in_executor(None, self.update, prompt, llm_string, return_val)
@@ -156,10 +147,9 @@ class InMemoryCache(BaseCache):
             maxsize: The maximum number of items to store in the cache.
                 If `None`, the cache has no maximum size.
                 If the cache exceeds the maximum size, the oldest items are removed.
-                Default is None.
 
         Raises:
-            ValueError: If maxsize is less than or equal to 0.
+            ValueError: If `maxsize` is less than or equal to `0`.
         """
         self._cache: dict[tuple[str, str], RETURN_VAL_TYPE] = {}
         if maxsize is not None and maxsize <= 0:
@@ -168,28 +158,28 @@ class InMemoryCache(BaseCache):
         self._maxsize = maxsize
 
     def lookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Look up based on prompt and llm_string.
+        """Look up based on `prompt` and `llm_string`.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
 
         Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
+            On a cache miss, return `None`. On a cache hit, return the cached value.
         """
         return self._cache.get((prompt, llm_string), None)
 
     def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
-        """Update cache based on prompt and llm_string.
+        """Update cache based on `prompt` and `llm_string`.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                 (or subclasses).
         """
         if self._maxsize is not None and len(self._cache) == self._maxsize:
@@ -202,30 +192,30 @@ class InMemoryCache(BaseCache):
         self._cache = {}
 
     async def alookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Async look up based on prompt and llm_string.
+        """Async look up based on `prompt` and `llm_string`.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
 
         Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
+            On a cache miss, return `None`. On a cache hit, return the cached value.
         """
         return self.lookup(prompt, llm_string)
 
     async def aupdate(
         self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
     ) -> None:
-        """Async update cache based on prompt and llm_string.
+        """Async update cache based on `prompt` and `llm_string`.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                 (or subclasses).
         """
         self.update(prompt, llm_string, return_val)

libs/core/langchain_core/callbacks/__init__.py

@@ -1,11 +1,4 @@
-"""**Callback handlers** allow listening to events in LangChain.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseCallbackHandler --> <name>CallbackHandler  # Example: AimCallbackHandler
-"""
+"""**Callback handlers** allow listening to events in LangChain."""
 
 from typing import TYPE_CHECKING
 

libs/core/langchain_core/callbacks/base.py

@@ -35,10 +35,10 @@ class RetrieverManagerMixin:
         """Run when Retriever errors.
 
         Args:
-            error (BaseException): The error that occurred.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            error: The error that occurred.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_retriever_end(
@@ -52,10 +52,10 @@ class RetrieverManagerMixin:
         """Run when Retriever ends running.
 
         Args:
-            documents (Sequence[Document]): The documents retrieved.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            documents: The documents retrieved.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
 
@@ -76,12 +76,11 @@ class LLMManagerMixin:
         For both chat models and non-chat models (legacy LLMs).
 
         Args:
-            token (str): The new token.
-            chunk (GenerationChunk | ChatGenerationChunk): The new generated chunk,
-              containing content and other information.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            token: The new token.
+            chunk: The new generated chunk, containing content and other information.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_llm_end(
@@ -95,10 +94,10 @@ class LLMManagerMixin:
         """Run when LLM ends running.
 
         Args:
-            response (LLMResult): The response which was generated.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            response: The response which was generated.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_llm_error(
@@ -112,10 +111,10 @@ class LLMManagerMixin:
         """Run when LLM errors.
 
         Args:
-            error (BaseException): The error that occurred.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            error: The error that occurred.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
 
@@ -133,10 +132,10 @@ class ChainManagerMixin:
         """Run when chain ends running.
 
         Args:
-            outputs (dict[str, Any]): The outputs of the chain.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            outputs: The outputs of the chain.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_chain_error(
@@ -150,10 +149,10 @@ class ChainManagerMixin:
         """Run when chain errors.
 
         Args:
-            error (BaseException): The error that occurred.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            error: The error that occurred.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_agent_action(
@@ -167,10 +166,10 @@ class ChainManagerMixin:
         """Run on agent action.
 
         Args:
-            action (AgentAction): The agent action.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            action: The agent action.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_agent_finish(
@@ -184,10 +183,10 @@ class ChainManagerMixin:
         """Run on the agent end.
 
         Args:
-            finish (AgentFinish): The agent finish.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            finish: The agent finish.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
 
@@ -205,10 +204,10 @@ class ToolManagerMixin:
         """Run when the tool ends running.
 
         Args:
-            output (Any): The output of the tool.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            output: The output of the tool.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_tool_error(
@@ -222,10 +221,10 @@ class ToolManagerMixin:
         """Run when tool errors.
 
         Args:
-            error (BaseException): The error that occurred.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            error: The error that occurred.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
 
@@ -248,16 +247,16 @@ class CallbackManagerMixin:
         !!! warning
             This method is called for non-chat models (regular LLMs). If you're
             implementing a handler for a chat model, you should use
-            ``on_chat_model_start`` instead.
+            `on_chat_model_start` instead.
 
         Args:
-            serialized (dict[str, Any]): The serialized LLM.
-            prompts (list[str]): The prompts.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            metadata (dict[str, Any] | None): The metadata.
-            kwargs (Any): Additional keyword arguments.
+            serialized: The serialized LLM.
+            prompts: The prompts.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            metadata: The metadata.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_chat_model_start(
@@ -275,16 +274,16 @@ class CallbackManagerMixin:
 
         !!! warning
             This method is called for chat models. If you're implementing a handler for
-            a non-chat model, you should use ``on_llm_start`` instead.
+            a non-chat model, you should use `on_llm_start` instead.
 
         Args:
-            serialized (dict[str, Any]): The serialized chat model.
-            messages (list[list[BaseMessage]]): The messages.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            metadata (dict[str, Any] | None): The metadata.
-            kwargs (Any): Additional keyword arguments.
+            serialized: The serialized chat model.
+            messages: The messages.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            metadata: The metadata.
+            **kwargs: Additional keyword arguments.
         """
         # NotImplementedError is thrown intentionally
         # Callback handler will fall back to on_llm_start if this is exception is thrown
@@ -305,13 +304,13 @@ class CallbackManagerMixin:
         """Run when the Retriever starts running.
 
         Args:
-            serialized (dict[str, Any]): The serialized Retriever.
-            query (str): The query.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            metadata (dict[str, Any] | None): The metadata.
-            kwargs (Any): Additional keyword arguments.
+            serialized: The serialized Retriever.
+            query: The query.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            metadata: The metadata.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_chain_start(
@@ -328,13 +327,13 @@ class CallbackManagerMixin:
         """Run when a chain starts running.
 
         Args:
-            serialized (dict[str, Any]): The serialized chain.
-            inputs (dict[str, Any]): The inputs.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            metadata (dict[str, Any] | None): The metadata.
-            kwargs (Any): Additional keyword arguments.
+            serialized: The serialized chain.
+            inputs: The inputs.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            metadata: The metadata.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_tool_start(
@@ -352,14 +351,14 @@ class CallbackManagerMixin:
         """Run when the tool starts running.
 
         Args:
-            serialized (dict[str, Any]): The serialized tool.
-            input_str (str): The input string.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            metadata (dict[str, Any] | None): The metadata.
-            inputs (dict[str, Any] | None): The inputs.
-            kwargs (Any): Additional keyword arguments.
+            serialized: The serialized chain.
+            input_str: The input string.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            metadata: The metadata.
+            inputs: The inputs.
+            **kwargs: Additional keyword arguments.
         """
 
 
@@ -377,10 +376,10 @@ class RunManagerMixin:
         """Run on an arbitrary text.
 
         Args:
-            text (str): The text.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            text: The text.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_retry(
@@ -394,10 +393,10 @@ class RunManagerMixin:
         """Run on a retry event.
 
         Args:
-            retry_state (RetryCallState): The retry state.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            retry_state: The retry state.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_custom_event(
@@ -415,7 +414,7 @@ class RunManagerMixin:
         Args:
             name: The name of the custom event.
             data: The data for the custom event. Format will match
-                  the format specified by the user.
+                the format specified by the user.
             run_id: The ID of the run.
             tags: The tags associated with the custom event
                 (includes inherited tags).
@@ -497,16 +496,16 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         !!! warning
             This method is called for non-chat models (regular LLMs). If you're
             implementing a handler for a chat model, you should use
-            ``on_chat_model_start`` instead.
+            `on_chat_model_start` instead.
 
         Args:
-            serialized (dict[str, Any]): The serialized LLM.
-            prompts (list[str]): The prompts.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            metadata (dict[str, Any] | None): The metadata.
-            kwargs (Any): Additional keyword arguments.
+            serialized: The serialized LLM.
+            prompts: The prompts.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            metadata: The metadata.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_chat_model_start(
@@ -524,16 +523,16 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         !!! warning
             This method is called for chat models. If you're implementing a handler for
-            a non-chat model, you should use ``on_llm_start`` instead.
+            a non-chat model, you should use `on_llm_start` instead.
 
         Args:
-            serialized (dict[str, Any]): The serialized chat model.
-            messages (list[list[BaseMessage]]): The messages.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            metadata (dict[str, Any] | None): The metadata.
-            kwargs (Any): Additional keyword arguments.
+            serialized: The serialized chat model.
+            messages: The messages.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            metadata: The metadata.
+            **kwargs: Additional keyword arguments.
         """
         # NotImplementedError is thrown intentionally
         # Callback handler will fall back to on_llm_start if this is exception is thrown
@@ -555,13 +554,12 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         For both chat models and non-chat models (legacy LLMs).
 
         Args:
-            token (str): The new token.
-            chunk (GenerationChunk | ChatGenerationChunk): The new generated chunk,
-              containing content and other information.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            token: The new token.
+            chunk: The new generated chunk, containing content and other information.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_llm_end(
@@ -576,11 +574,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run when the model ends running.
 
         Args:
-            response (LLMResult): The response which was generated.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            response: The response which was generated.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_llm_error(
@@ -599,7 +597,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
             run_id: The run ID. This is the ID of the current run.
             parent_run_id: The parent run ID. This is the ID of the parent run.
             tags: The tags.
-            kwargs (Any): Additional keyword arguments.
+            **kwargs: Additional keyword arguments.
                 - response (LLMResult): The response which was generated before
                     the error occurred.
         """
@@ -618,13 +616,13 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run when a chain starts running.
 
         Args:
-            serialized (dict[str, Any]): The serialized chain.
-            inputs (dict[str, Any]): The inputs.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            metadata (dict[str, Any] | None): The metadata.
-            kwargs (Any): Additional keyword arguments.
+            serialized: The serialized chain.
+            inputs: The inputs.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            metadata: The metadata.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_chain_end(
@@ -639,11 +637,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run when a chain ends running.
 
         Args:
-            outputs (dict[str, Any]): The outputs of the chain.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            outputs: The outputs of the chain.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_chain_error(
@@ -658,11 +656,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run when chain errors.
 
         Args:
-            error (BaseException): The error that occurred.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            error: The error that occurred.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_tool_start(
@@ -680,14 +678,14 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run when the tool starts running.
 
         Args:
-            serialized (dict[str, Any]): The serialized tool.
-            input_str (str): The input string.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            metadata (dict[str, Any] | None): The metadata.
-            inputs (dict[str, Any] | None): The inputs.
-            kwargs (Any): Additional keyword arguments.
+            serialized: The serialized tool.
+            input_str: The input string.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            metadata: The metadata.
+            inputs: The inputs.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_tool_end(
@@ -702,11 +700,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run when the tool ends running.
 
         Args:
-            output (Any): The output of the tool.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            output: The output of the tool.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_tool_error(
@@ -721,11 +719,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run when tool errors.
 
         Args:
-            error (BaseException): The error that occurred.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            error: The error that occurred.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_text(
@@ -740,11 +738,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run on an arbitrary text.
 
         Args:
-            text (str): The text.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            text: The text.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_retry(
@@ -758,10 +756,10 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run on a retry event.
 
         Args:
-            retry_state (RetryCallState): The retry state.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            kwargs (Any): Additional keyword arguments.
+            retry_state: The retry state.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_agent_action(
@@ -776,11 +774,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run on agent action.
 
         Args:
-            action (AgentAction): The agent action.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            action: The agent action.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_agent_finish(
@@ -795,11 +793,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run on the agent end.
 
         Args:
-            finish (AgentFinish): The agent finish.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            finish: The agent finish.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_retriever_start(
@@ -816,13 +814,13 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run on the retriever start.
 
         Args:
-            serialized (dict[str, Any]): The serialized retriever.
-            query (str): The query.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            metadata (dict[str, Any] | None): The metadata.
-            kwargs (Any): Additional keyword arguments.
+            serialized: The serialized retriever.
+            query: The query.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            metadata: The metadata.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_retriever_end(
@@ -837,11 +835,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run on the retriever end.
 
         Args:
-            documents (Sequence[Document]): The documents retrieved.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            documents: The documents retrieved.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_retriever_error(
@@ -856,11 +854,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         """Run on retriever error.
 
         Args:
-            error (BaseException): The error that occurred.
-            run_id (UUID): The run ID. This is the ID of the current run.
-            parent_run_id (UUID): The parent run ID. This is the ID of the parent run.
-            tags (list[str] | None): The tags.
-            kwargs (Any): Additional keyword arguments.
+            error: The error that occurred.
+            run_id: The run ID. This is the ID of the current run.
+            parent_run_id: The parent run ID. This is the ID of the parent run.
+            tags: The tags.
+            **kwargs: Additional keyword arguments.
         """
 
     async def on_custom_event(
@@ -878,7 +876,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             name: The name of the custom event.
             data: The data for the custom event. Format will match
-                  the format specified by the user.
+                the format specified by the user.
             run_id: The ID of the run.
             tags: The tags associated with the custom event
                 (includes inherited tags).
@@ -943,35 +941,29 @@ class BaseCallbackManager(CallbackManagerMixin):
         within merge_configs.
 
         Returns:
-            BaseCallbackManager: The merged callback manager of the same type
-                as the current object.
+            The merged callback manager of the same type as the current object.
 
         Example: Merging two callback managers.
 
-            .. code-block:: python
+            ```python
+            from langchain_core.callbacks.manager import (
+                CallbackManager,
+                trace_as_chain_group,
+            )
+            from langchain_core.callbacks.stdout import StdOutCallbackHandler
 
-                from langchain_core.callbacks.manager import (
-                    CallbackManager,
-                    trace_as_chain_group,
-                )
-                from langchain_core.callbacks.stdout import StdOutCallbackHandler
-
-                manager = CallbackManager(
-                    handlers=[StdOutCallbackHandler()], tags=["tag2"]
-                )
-                with trace_as_chain_group(
-                    "My Group Name", tags=["tag1"]
-                ) as group_manager:
-                    merged_manager = group_manager.merge(manager)
-                    print(merged_manager.handlers)
-                    # [
-                    #    <langchain_core.callbacks.stdout.StdOutCallbackHandler object at ...>,
-                    #    <langchain_core.callbacks.streaming_stdout.StreamingStdOutCallbackHandler object at ...>,
-                    # ]
-
-                    print(merged_manager.tags)
-                    #    ['tag2', 'tag1']
+            manager = CallbackManager(handlers=[StdOutCallbackHandler()], tags=["tag2"])
+            with trace_as_chain_group("My Group Name", tags=["tag1"]) as group_manager:
+                merged_manager = group_manager.merge(manager)
+                print(merged_manager.handlers)
+                # [
+                #    <langchain_core.callbacks.stdout.StdOutCallbackHandler object at ...>,
+                #    <langchain_core.callbacks.streaming_stdout.StreamingStdOutCallbackHandler object at ...>,
+                # ]
 
+                print(merged_manager.tags)
+                #    ['tag2', 'tag1']
+            ```
         """  # noqa: E501
         manager = self.__class__(
             parent_run_id=self.parent_run_id or other.parent_run_id,
@@ -1008,8 +1000,8 @@ class BaseCallbackManager(CallbackManagerMixin):
         """Add a handler to the callback manager.
 
         Args:
-            handler (BaseCallbackHandler): The handler to add.
-            inherit (bool): Whether to inherit the handler. Default is True.
+            handler: The handler to add.
+            inherit: Whether to inherit the handler.
         """
         if handler not in self.handlers:
             self.handlers.append(handler)
@@ -1020,7 +1012,7 @@ class BaseCallbackManager(CallbackManagerMixin):
         """Remove a handler from the callback manager.
 
         Args:
-            handler (BaseCallbackHandler): The handler to remove.
+            handler: The handler to remove.
         """
         if handler in self.handlers:
             self.handlers.remove(handler)
@@ -1035,8 +1027,8 @@ class BaseCallbackManager(CallbackManagerMixin):
         """Set handlers as the only handlers on the callback manager.
 
         Args:
-            handlers (list[BaseCallbackHandler]): The handlers to set.
-            inherit (bool): Whether to inherit the handlers. Default is True.
+            handlers: The handlers to set.
+            inherit: Whether to inherit the handlers.
         """
         self.handlers = []
         self.inheritable_handlers = []
@@ -1051,8 +1043,8 @@ class BaseCallbackManager(CallbackManagerMixin):
         """Set handler as the only handler on the callback manager.
 
         Args:
-            handler (BaseCallbackHandler): The handler to set.
-            inherit (bool): Whether to inherit the handler. Default is True.
+            handler: The handler to set.
+            inherit: Whether to inherit the handler.
         """
         self.set_handlers([handler], inherit=inherit)
 
@@ -1064,8 +1056,8 @@ class BaseCallbackManager(CallbackManagerMixin):
         """Add tags to the callback manager.
 
         Args:
-            tags (list[str]): The tags to add.
-            inherit (bool): Whether to inherit the tags. Default is True.
+            tags: The tags to add.
+            inherit: Whether to inherit the tags.
         """
         for tag in tags:
             if tag in self.tags:
@@ -1078,7 +1070,7 @@ class BaseCallbackManager(CallbackManagerMixin):
         """Remove tags from the callback manager.
 
         Args:
-            tags (list[str]): The tags to remove.
+            tags: The tags to remove.
         """
         for tag in tags:
             if tag in self.tags:
@@ -1094,8 +1086,8 @@ class BaseCallbackManager(CallbackManagerMixin):
         """Add metadata to the callback manager.
 
         Args:
-            metadata (dict[str, Any]): The metadata to add.
-            inherit (bool): Whether to inherit the metadata. Default is True.
+            metadata: The metadata to add.
+            inherit: Whether to inherit the metadata.
         """
         self.metadata.update(metadata)
         if inherit:
@@ -1105,7 +1097,7 @@ class BaseCallbackManager(CallbackManagerMixin):
         """Remove metadata from the callback manager.
 
         Args:
-            keys (list[str]): The keys to remove.
+            keys: The keys to remove.
         """
         for key in keys:
             self.metadata.pop(key, None)

libs/core/langchain_core/callbacks/file.py

@@ -27,27 +27,27 @@ class FileCallbackHandler(BaseCallbackHandler):
     Examples:
         Using as a context manager (recommended):
 
-        .. code-block:: python
-
-            with FileCallbackHandler("output.txt") as handler:
-                # Use handler with your chain/agent
-                chain.invoke(inputs, config={"callbacks": [handler]})
+        ```python
+        with FileCallbackHandler("output.txt") as handler:
+            # Use handler with your chain/agent
+            chain.invoke(inputs, config={"callbacks": [handler]})
+        ```
 
         Direct instantiation (deprecated):
 
-        .. code-block:: python
-
-            handler = FileCallbackHandler("output.txt")
-            # File remains open until handler is garbage collected
-            try:
-                chain.invoke(inputs, config={"callbacks": [handler]})
-            finally:
-                handler.close()  # Explicit cleanup recommended
+        ```python
+        handler = FileCallbackHandler("output.txt")
+        # File remains open until handler is garbage collected
+        try:
+            chain.invoke(inputs, config={"callbacks": [handler]})
+        finally:
+            handler.close()  # Explicit cleanup recommended
+        ```
 
     Args:
         filename: The file path to write to.
         mode: The file open mode. Defaults to `'a'` (append).
-        color: Default color for text output. Defaults to `None`.
+        color: Default color for text output.
 
     !!! note
         When not used as a context manager, a deprecation warning will be issued
@@ -64,7 +64,7 @@ class FileCallbackHandler(BaseCallbackHandler):
         Args:
             filename: Path to the output file.
             mode: File open mode (e.g., `'w'`, `'a'`, `'x'`). Defaults to `'a'`.
-            color: Default text color for output. Defaults to `None`.
+            color: Default text color for output.
 
         """
         self.filename = filename
@@ -132,7 +132,7 @@ class FileCallbackHandler(BaseCallbackHandler):
         Args:
             text: The text to write to the file.
             color: Optional color for the text. Defaults to `self.color`.
-            end: String appended after the text. Defaults to `""`.
+            end: String appended after the text.
             file: Optional file to write to. Defaults to `self.file`.
 
         Raises:
@@ -239,7 +239,7 @@ class FileCallbackHandler(BaseCallbackHandler):
             text: The text to write.
             color: Color override for this specific output. If `None`, uses
                 `self.color`.
-            end: String appended after the text. Defaults to `""`.
+            end: String appended after the text.
             **kwargs: Additional keyword arguments.
 
         """

libs/core/langchain_core/callbacks/stdout.py

@@ -20,7 +20,7 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         """Initialize callback handler.
 
         Args:
-            color: The color to use for the text. Defaults to `None`.
+            color: The color to use for the text.
         """
         self.color = color
 
@@ -31,9 +31,9 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         """Print out that we are entering a chain.
 
         Args:
-            serialized (dict[str, Any]): The serialized chain.
-            inputs (dict[str, Any]): The inputs to the chain.
-            **kwargs (Any): Additional keyword arguments.
+            serialized: The serialized chain.
+            inputs: The inputs to the chain.
+            **kwargs: Additional keyword arguments.
         """
         if "name" in kwargs:
             name = kwargs["name"]
@@ -48,8 +48,8 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         """Print out that we finished a chain.
 
         Args:
-            outputs (dict[str, Any]): The outputs of the chain.
-            **kwargs (Any): Additional keyword arguments.
+            outputs: The outputs of the chain.
+            **kwargs: Additional keyword arguments.
         """
         print("\n\033[1m> Finished chain.\033[0m")  # noqa: T201
 
@@ -60,9 +60,9 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         """Run on agent action.
 
         Args:
-            action (AgentAction): The agent action.
-            color (str | None): The color to use for the text. Defaults to `None`.
-            **kwargs (Any): Additional keyword arguments.
+            action: The agent action.
+            color: The color to use for the text.
+            **kwargs: Additional keyword arguments.
         """
         print_text(action.log, color=color or self.color)
 
@@ -78,12 +78,11 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         """If not the final action, print out observation.
 
         Args:
-            output (Any): The output to print.
-            color (str | None): The color to use for the text. Defaults to `None`.
-            observation_prefix (str | None): The observation prefix.
-                Defaults to `None`.
-            llm_prefix (str | None): The LLM prefix. Defaults to `None`.
-            **kwargs (Any): Additional keyword arguments.
+            output: The output to print.
+            color: The color to use for the text.
+            observation_prefix: The observation prefix.
+            llm_prefix: The LLM prefix.
+            **kwargs: Additional keyword arguments.
         """
         output = str(output)
         if observation_prefix is not None:
@@ -103,10 +102,10 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         """Run when the agent ends.
 
         Args:
-            text (str): The text to print.
-            color (str | None): The color to use for the text. Defaults to `None`.
-            end (str): The end character to use. Defaults to "".
-            **kwargs (Any): Additional keyword arguments.
+            text: The text to print.
+            color: The color to use for the text.
+            end: The end character to use.
+            **kwargs: Additional keyword arguments.
         """
         print_text(text, color=color or self.color, end=end)
 
@@ -117,8 +116,8 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         """Run on the agent end.
 
         Args:
-            finish (AgentFinish): The agent finish.
-            color (str | None): The color to use for the text. Defaults to `None`.
-            **kwargs (Any): Additional keyword arguments.
+            finish: The agent finish.
+            color: The color to use for the text.
+            **kwargs: Additional keyword arguments.
         """
         print_text(finish.log, color=color or self.color, end="\n")

libs/core/langchain_core/callbacks/streaming_stdout.py

@@ -24,9 +24,9 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
         """Run when LLM starts running.
 
         Args:
-            serialized (dict[str, Any]): The serialized LLM.
-            prompts (list[str]): The prompts to run.
-            **kwargs (Any): Additional keyword arguments.
+            serialized: The serialized LLM.
+            prompts: The prompts to run.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_chat_model_start(
@@ -38,9 +38,9 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
         """Run when LLM starts running.
 
         Args:
-            serialized (dict[str, Any]): The serialized LLM.
-            messages (list[list[BaseMessage]]): The messages to run.
-            **kwargs (Any): Additional keyword arguments.
+            serialized: The serialized LLM.
+            messages: The messages to run.
+            **kwargs: Additional keyword arguments.
         """
 
     @override
@@ -48,8 +48,8 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
         """Run on new LLM token. Only available when streaming is enabled.
 
         Args:
-            token (str): The new token.
-            **kwargs (Any): Additional keyword arguments.
+            token: The new token.
+            **kwargs: Additional keyword arguments.
         """
         sys.stdout.write(token)
         sys.stdout.flush()
@@ -58,16 +58,16 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
         """Run when LLM ends running.
 
         Args:
-            response (LLMResult): The response from the LLM.
-            **kwargs (Any): Additional keyword arguments.
+            response: The response from the LLM.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
         """Run when LLM errors.
 
         Args:
-            error (BaseException): The error that occurred.
-            **kwargs (Any): Additional keyword arguments.
+            error: The error that occurred.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_chain_start(
@@ -76,25 +76,25 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
         """Run when a chain starts running.
 
         Args:
-            serialized (dict[str, Any]): The serialized chain.
-            inputs (dict[str, Any]): The inputs to the chain.
-            **kwargs (Any): Additional keyword arguments.
+            serialized: The serialized chain.
+            inputs: The inputs to the chain.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_chain_end(self, outputs: dict[str, Any], **kwargs: Any) -> None:
         """Run when a chain ends running.
 
         Args:
-            outputs (dict[str, Any]): The outputs of the chain.
-            **kwargs (Any): Additional keyword arguments.
+            outputs: The outputs of the chain.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_chain_error(self, error: BaseException, **kwargs: Any) -> None:
         """Run when chain errors.
 
         Args:
-            error (BaseException): The error that occurred.
-            **kwargs (Any): Additional keyword arguments.
+            error: The error that occurred.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_tool_start(
@@ -103,47 +103,47 @@ class StreamingStdOutCallbackHandler(BaseCallbackHandler):
         """Run when the tool starts running.
 
         Args:
-            serialized (dict[str, Any]): The serialized tool.
-            input_str (str): The input string.
-            **kwargs (Any): Additional keyword arguments.
+            serialized: The serialized tool.
+            input_str: The input string.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
         """Run on agent action.
 
         Args:
-            action (AgentAction): The agent action.
-            **kwargs (Any): Additional keyword arguments.
+            action: The agent action.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_tool_end(self, output: Any, **kwargs: Any) -> None:
         """Run when tool ends running.
 
         Args:
-            output (Any): The output of the tool.
-            **kwargs (Any): Additional keyword arguments.
+            output: The output of the tool.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_tool_error(self, error: BaseException, **kwargs: Any) -> None:
         """Run when tool errors.
 
         Args:
-            error (BaseException): The error that occurred.
-            **kwargs (Any): Additional keyword arguments.
+            error: The error that occurred.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_text(self, text: str, **kwargs: Any) -> None:
         """Run on an arbitrary text.
 
         Args:
-            text (str): The text to print.
-            **kwargs (Any): Additional keyword arguments.
+            text: The text to print.
+            **kwargs: Additional keyword arguments.
         """
 
     def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
         """Run on the agent end.
 
         Args:
-            finish (AgentFinish): The agent finish.
-            **kwargs (Any): Additional keyword arguments.
+            finish: The agent finish.
+            **kwargs: Additional keyword arguments.
         """

libs/core/langchain_core/callbacks/usage.py

@@ -19,30 +19,29 @@ class UsageMetadataCallbackHandler(BaseCallbackHandler):
     """Callback Handler that tracks AIMessage.usage_metadata.
 
     Example:
-        .. code-block:: python
+        ```python
+        from langchain.chat_models import init_chat_model
+        from langchain_core.callbacks import UsageMetadataCallbackHandler
 
-            from langchain.chat_models import init_chat_model
-            from langchain_core.callbacks import UsageMetadataCallbackHandler
+        llm_1 = init_chat_model(model="openai:gpt-4o-mini")
+        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-latest")
 
-            llm_1 = init_chat_model(model="openai:gpt-4o-mini")
-            llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-latest")
-
-            callback = UsageMetadataCallbackHandler()
-            result_1 = llm_1.invoke("Hello", config={"callbacks": [callback]})
-            result_2 = llm_2.invoke("Hello", config={"callbacks": [callback]})
-            callback.usage_metadata
-
-        .. code-block::
-
-            {'gpt-4o-mini-2024-07-18': {'input_tokens': 8,
-              'output_tokens': 10,
-              'total_tokens': 18,
-              'input_token_details': {'audio': 0, 'cache_read': 0},
-              'output_token_details': {'audio': 0, 'reasoning': 0}},
-             'claude-3-5-haiku-20241022': {'input_tokens': 8,
-              'output_tokens': 21,
-              'total_tokens': 29,
-              'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}
+        callback = UsageMetadataCallbackHandler()
+        result_1 = llm_1.invoke("Hello", config={"callbacks": [callback]})
+        result_2 = llm_2.invoke("Hello", config={"callbacks": [callback]})
+        callback.usage_metadata
+        ```
+        ```txt
+        {'gpt-4o-mini-2024-07-18': {'input_tokens': 8,
+          'output_tokens': 10,
+          'total_tokens': 18,
+          'input_token_details': {'audio': 0, 'cache_read': 0},
+          'output_token_details': {'audio': 0, 'reasoning': 0}},
+         'claude-3-5-haiku-20241022': {'input_tokens': 8,
+          'output_tokens': 21,
+          'total_tokens': 29,
+          'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}
+        ```
 
     !!! version-added "Added in version 0.3.49"
 
@@ -96,40 +95,44 @@ def get_usage_metadata_callback(
     """Get usage metadata callback.
 
     Get context manager for tracking usage metadata across chat model calls using
-    ``AIMessage.usage_metadata``.
+    `AIMessage.usage_metadata`.
 
     Args:
-        name (str): The name of the context variable. Defaults to
-            ``'usage_metadata_callback'``.
+        name: The name of the context variable.
 
     Yields:
         The usage metadata callback.
 
     Example:
-        .. code-block:: python
+        ```python
+        from langchain.chat_models import init_chat_model
+        from langchain_core.callbacks import get_usage_metadata_callback
 
-            from langchain.chat_models import init_chat_model
-            from langchain_core.callbacks import get_usage_metadata_callback
+        llm_1 = init_chat_model(model="openai:gpt-4o-mini")
+        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-latest")
 
-            llm_1 = init_chat_model(model="openai:gpt-4o-mini")
-            llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-latest")
-
-            with get_usage_metadata_callback() as cb:
-                llm_1.invoke("Hello")
-                llm_2.invoke("Hello")
-                print(cb.usage_metadata)
-
-        .. code-block::
-
-            {'gpt-4o-mini-2024-07-18': {'input_tokens': 8,
-              'output_tokens': 10,
-              'total_tokens': 18,
-              'input_token_details': {'audio': 0, 'cache_read': 0},
-              'output_token_details': {'audio': 0, 'reasoning': 0}},
-             'claude-3-5-haiku-20241022': {'input_tokens': 8,
-              'output_tokens': 21,
-              'total_tokens': 29,
-              'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}
+        with get_usage_metadata_callback() as cb:
+            llm_1.invoke("Hello")
+            llm_2.invoke("Hello")
+            print(cb.usage_metadata)
+        ```
+        ```txt
+        {
+            "gpt-4o-mini-2024-07-18": {
+                "input_tokens": 8,
+                "output_tokens": 10,
+                "total_tokens": 18,
+                "input_token_details": {"audio": 0, "cache_read": 0},
+                "output_token_details": {"audio": 0, "reasoning": 0},
+            },
+            "claude-3-5-haiku-20241022": {
+                "input_tokens": 8,
+                "output_tokens": 21,
+                "total_tokens": 29,
+                "input_token_details": {"cache_read": 0, "cache_creation": 0},
+            },
+        }
+        ```
 
     !!! version-added "Added in version 0.3.49"
 

libs/core/langchain_core/chat_history.py

@@ -1,18 +1,4 @@
-"""**Chat message history** stores a history of the message interactions in a chat.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseChatMessageHistory --> <name>ChatMessageHistory  # Examples: FileChatMessageHistory, PostgresChatMessageHistory
-
-**Main helpers:**
-
-.. code-block::
-
-    AIMessage, HumanMessage, BaseMessage
-
-"""  # noqa: E501
+"""**Chat message history** stores a history of the message interactions in a chat."""
 
 from __future__ import annotations
 
@@ -63,46 +49,45 @@ class BaseChatMessageHistory(ABC):
 
     Example: Shows a default implementation.
 
-        .. code-block:: python
-
-            import json
-            import os
-            from langchain_core.messages import messages_from_dict, message_to_dict
+        ```python
+        import json
+        import os
+        from langchain_core.messages import messages_from_dict, message_to_dict
 
 
-            class FileChatMessageHistory(BaseChatMessageHistory):
-                storage_path: str
-                session_id: str
+        class FileChatMessageHistory(BaseChatMessageHistory):
+            storage_path: str
+            session_id: str
 
-                @property
-                def messages(self) -> list[BaseMessage]:
-                    try:
-                        with open(
-                            os.path.join(self.storage_path, self.session_id),
-                            "r",
-                            encoding="utf-8",
-                        ) as f:
-                            messages_data = json.load(f)
-                        return messages_from_dict(messages_data)
-                    except FileNotFoundError:
-                        return []
+            @property
+            def messages(self) -> list[BaseMessage]:
+                try:
+                    with open(
+                        os.path.join(self.storage_path, self.session_id),
+                        "r",
+                        encoding="utf-8",
+                    ) as f:
+                        messages_data = json.load(f)
+                    return messages_from_dict(messages_data)
+                except FileNotFoundError:
+                    return []
 
-                def add_messages(self, messages: Sequence[BaseMessage]) -> None:
-                    all_messages = list(self.messages)  # Existing messages
-                    all_messages.extend(messages)  # Add new messages
+            def add_messages(self, messages: Sequence[BaseMessage]) -> None:
+                all_messages = list(self.messages)  # Existing messages
+                all_messages.extend(messages)  # Add new messages
 
-                    serialized = [message_to_dict(message) for message in all_messages]
-                    file_path = os.path.join(self.storage_path, self.session_id)
-                    os.makedirs(os.path.dirname(file_path), exist_ok=True)
-                    with open(file_path, "w", encoding="utf-8") as f:
-                        json.dump(serialized, f)
-
-                def clear(self) -> None:
-                    file_path = os.path.join(self.storage_path, self.session_id)
-                    os.makedirs(os.path.dirname(file_path), exist_ok=True)
-                    with open(file_path, "w", encoding="utf-8") as f:
-                        json.dump([], f)
+                serialized = [message_to_dict(message) for message in all_messages]
+                file_path = os.path.join(self.storage_path, self.session_id)
+                os.makedirs(os.path.dirname(file_path), exist_ok=True)
+                with open(file_path, "w", encoding="utf-8") as f:
+                    json.dump(serialized, f)
 
+            def clear(self) -> None:
+                file_path = os.path.join(self.storage_path, self.session_id)
+                os.makedirs(os.path.dirname(file_path), exist_ok=True)
+                with open(file_path, "w", encoding="utf-8") as f:
+                    json.dump([], f)
+        ```
     """
 
     messages: list[BaseMessage]
@@ -130,7 +115,7 @@ class BaseChatMessageHistory(ABC):
         """Convenience method for adding a human message string to the store.
 
         !!! note
-            This is a convenience method. Code should favor the bulk ``add_messages``
+            This is a convenience method. Code should favor the bulk `add_messages`
             interface instead to save on round-trips to the persistence layer.
 
         This method may be deprecated in a future release.
@@ -147,7 +132,7 @@ class BaseChatMessageHistory(ABC):
         """Convenience method for adding an AI message string to the store.
 
         !!! note
-            This is a convenience method. Code should favor the bulk ``add_messages``
+            This is a convenience method. Code should favor the bulk `add_messages`
             interface instead to save on round-trips to the persistence layer.
 
         This method may be deprecated in a future release.
@@ -168,7 +153,7 @@ class BaseChatMessageHistory(ABC):
 
         Raises:
             NotImplementedError: If the sub-class has not implemented an efficient
-                add_messages method.
+                `add_messages` method.
         """
         if type(self).add_messages != BaseChatMessageHistory.add_messages:
             # This means that the sub-class has implemented an efficient add_messages

libs/core/langchain_core/document_loaders/base.py

@@ -35,38 +35,38 @@ class BaseLoader(ABC):  # noqa: B024
     # Sub-classes should not implement this method directly. Instead, they
     # should implement the lazy load method.
     def load(self) -> list[Document]:
-        """Load data into Document objects.
+        """Load data into `Document` objects.
 
         Returns:
-            the documents.
+            The documents.
         """
         return list(self.lazy_load())
 
     async def aload(self) -> list[Document]:
-        """Load data into Document objects.
+        """Load data into `Document` objects.
 
         Returns:
-            the documents.
+            The documents.
         """
         return [document async for document in self.alazy_load()]
 
     def load_and_split(
         self, text_splitter: TextSplitter | None = None
     ) -> list[Document]:
-        """Load Documents and split into chunks. Chunks are returned as Documents.
+        """Load Documents and split into chunks. Chunks are returned as `Document`.
 
         Do not override this method. It should be considered to be deprecated!
 
         Args:
-            text_splitter: TextSplitter instance to use for splitting documents.
-                Defaults to RecursiveCharacterTextSplitter.
+            text_splitter: `TextSplitter` instance to use for splitting documents.
+                Defaults to `RecursiveCharacterTextSplitter`.
 
         Raises:
-            ImportError: If langchain-text-splitters is not installed
-                and no text_splitter is provided.
+            ImportError: If `langchain-text-splitters` is not installed
+                and no `text_splitter` is provided.
 
         Returns:
-            List of Documents.
+            List of `Document`.
         """
         if text_splitter is None:
             if not _HAS_TEXT_SPLITTERS:
@@ -86,10 +86,10 @@ class BaseLoader(ABC):  # noqa: B024
     # Attention: This method will be upgraded into an abstractmethod once it's
     #            implemented in all the existing subclasses.
     def lazy_load(self) -> Iterator[Document]:
-        """A lazy loader for Documents.
+        """A lazy loader for `Document`.
 
         Yields:
-            the documents.
+            The `Document` objects.
         """
         if type(self).load != BaseLoader.load:
             return iter(self.load())
@@ -97,10 +97,10 @@ class BaseLoader(ABC):  # noqa: B024
         raise NotImplementedError(msg)
 
     async def alazy_load(self) -> AsyncIterator[Document]:
-        """A lazy loader for Documents.
+        """A lazy loader for `Document`.
 
         Yields:
-            the documents.
+            The `Document` objects.
         """
         iterator = await run_in_executor(None, self.lazy_load)
         done = object()
@@ -115,7 +115,7 @@ class BaseBlobParser(ABC):
     """Abstract interface for blob parsers.
 
     A blob parser provides a way to parse raw data stored in a blob into one
-    or more documents.
+    or more `Document` objects.
 
     The parser can be composed with blob loaders, making it easy to reuse
     a parser independent of how the blob was originally loaded.
@@ -128,25 +128,25 @@ class BaseBlobParser(ABC):
         Subclasses are required to implement this method.
 
         Args:
-            blob: Blob instance
+            blob: `Blob` instance
 
         Returns:
-            Generator of documents
+            Generator of `Document` objects
         """
 
     def parse(self, blob: Blob) -> list[Document]:
-        """Eagerly parse the blob into a document or documents.
+        """Eagerly parse the blob into a `Document` or `Document` objects.
 
         This is a convenience method for interactive development environment.
 
-        Production applications should favor the lazy_parse method instead.
+        Production applications should favor the `lazy_parse` method instead.
 
         Subclasses should generally not over-ride this parse method.
 
         Args:
-            blob: Blob instance
+            blob: `Blob` instance
 
         Returns:
-            List of documents
+            List of `Document` objects
         """
         return list(self.lazy_parse(blob))

libs/core/langchain_core/document_loaders/langsmith.py

@@ -22,22 +22,22 @@ class LangSmithLoader(BaseLoader):
 
     ??? note "Lazy load"
 
-        .. code-block:: python
+        ```python
+        from langchain_core.document_loaders import LangSmithLoader
 
-            from langchain_core.document_loaders import LangSmithLoader
+        loader = LangSmithLoader(dataset_id="...", limit=100)
+        docs = []
+        for doc in loader.lazy_load():
+            docs.append(doc)
+        ```
 
-            loader = LangSmithLoader(dataset_id="...", limit=100)
-            docs = []
-            for doc in loader.lazy_load():
-                docs.append(doc)
-
-        .. code-block:: python
-
-            # -> [Document("...", metadata={"inputs": {...}, "outputs": {...}, ...}), ...]
+        ```python
+        # -> [Document("...", metadata={"inputs": {...}, "outputs": {...}, ...}), ...]
+        ```
 
     !!! version-added "Added in version 0.2.34"
 
-    """  # noqa: E501
+    """
 
     def __init__(
         self,
@@ -60,15 +60,15 @@ class LangSmithLoader(BaseLoader):
         """Create a LangSmith loader.
 
         Args:
-            dataset_id: The ID of the dataset to filter by. Defaults to `None`.
-            dataset_name: The name of the dataset to filter by. Defaults to `None`.
+            dataset_id: The ID of the dataset to filter by.
+            dataset_name: The name of the dataset to filter by.
             content_key: The inputs key to set as Document page content. `'.'` characters
                 are interpreted as nested keys. E.g. `content_key="first.second"` will
                 result in
                 `Document(page_content=format_content(example.inputs["first"]["second"]))`
             format_content: Function for converting the content extracted from the example
                 inputs into a string. Defaults to JSON-encoding the contents.
-            example_ids: The IDs of the examples to filter by. Defaults to `None`.
+            example_ids: The IDs of the examples to filter by.
             as_of: The dataset version tag OR
                 timestamp to retrieve the examples as of.
                 Response examples will only be those that were present at the time
@@ -76,10 +76,10 @@ class LangSmithLoader(BaseLoader):
             splits: A list of dataset splits, which are
                 divisions of your dataset such as 'train', 'test', or 'validation'.
                 Returns examples only from the specified splits.
-            inline_s3_urls: Whether to inline S3 URLs. Defaults to `True`.
-            offset: The offset to start from. Defaults to 0.
+            inline_s3_urls: Whether to inline S3 URLs.
+            offset: The offset to start from.
             limit: The maximum number of examples to return.
-            metadata: Metadata to filter by. Defaults to `None`.
+            metadata: Metadata to filter by.
             filter: A structured filter string to apply to the examples.
             client: LangSmith Client. If not provided will be initialized from below args.
             client_kwargs: Keyword args to pass to LangSmith client init. Should only be

libs/core/langchain_core/documents/__init__.py

@@ -2,7 +2,6 @@
 
 **Document** module is a collection of classes that handle documents
 and their transformations.
-
 """
 
 from typing import TYPE_CHECKING

libs/core/langchain_core/documents/base.py

@@ -57,52 +57,51 @@ class Blob(BaseMedia):
 
     Example: Initialize a blob from in-memory data
 
-        .. code-block:: python
+    ```python
+    from langchain_core.documents import Blob
 
-            from langchain_core.documents import Blob
+    blob = Blob.from_data("Hello, world!")
 
-            blob = Blob.from_data("Hello, world!")
+    # Read the blob as a string
+    print(blob.as_string())
 
-            # Read the blob as a string
-            print(blob.as_string())
+    # Read the blob as bytes
+    print(blob.as_bytes())
 
-            # Read the blob as bytes
-            print(blob.as_bytes())
-
-            # Read the blob as a byte stream
-            with blob.as_bytes_io() as f:
-                print(f.read())
+    # Read the blob as a byte stream
+    with blob.as_bytes_io() as f:
+        print(f.read())
+    ```
 
     Example: Load from memory and specify mime-type and metadata
 
-        .. code-block:: python
+    ```python
+    from langchain_core.documents import Blob
 
-            from langchain_core.documents import Blob
-
-            blob = Blob.from_data(
-                data="Hello, world!",
-                mime_type="text/plain",
-                metadata={"source": "https://example.com"},
-            )
+    blob = Blob.from_data(
+        data="Hello, world!",
+        mime_type="text/plain",
+        metadata={"source": "https://example.com"},
+    )
+    ```
 
     Example: Load the blob from a file
 
-        .. code-block:: python
+    ```python
+    from langchain_core.documents import Blob
 
-            from langchain_core.documents import Blob
+    blob = Blob.from_path("path/to/file.txt")
 
-            blob = Blob.from_path("path/to/file.txt")
+    # Read the blob as a string
+    print(blob.as_string())
 
-            # Read the blob as a string
-            print(blob.as_string())
-
-            # Read the blob as bytes
-            print(blob.as_bytes())
-
-            # Read the blob as a byte stream
-            with blob.as_bytes_io() as f:
-                print(f.read())
+    # Read the blob as bytes
+    print(blob.as_bytes())
 
+    # Read the blob as a byte stream
+    with blob.as_bytes_io() as f:
+        print(f.read())
+    ```
     """
 
     data: bytes | str | None = None
@@ -112,7 +111,7 @@ class Blob(BaseMedia):
     encoding: str = "utf-8"
     """Encoding to use if decoding the bytes into a string.
 
-    Use utf-8 as default encoding, if decoding to string.
+    Use `utf-8` as default encoding, if decoding to string.
     """
     path: PathLike | None = None
     """Location where the original content was found."""
@@ -128,7 +127,7 @@ class Blob(BaseMedia):
 
         If a path is associated with the blob, it will default to the path location.
 
-        Unless explicitly set via a metadata field called "source", in which
+        Unless explicitly set via a metadata field called `"source"`, in which
         case that value will be used instead.
         """
         if self.metadata and "source" in self.metadata:
@@ -212,11 +211,11 @@ class Blob(BaseMedia):
         """Load the blob from a path like object.
 
         Args:
-            path: path like object to file to be read
+            path: Path-like object to file to be read
             encoding: Encoding to use if decoding the bytes into a string
-            mime_type: if provided, will be set as the mime-type of the data
+            mime_type: If provided, will be set as the mime-type of the data
             guess_type: If `True`, the mimetype will be guessed from the file extension,
-                        if a mime-type was not provided
+                if a mime-type was not provided
             metadata: Metadata to associate with the blob
 
         Returns:
@@ -249,10 +248,10 @@ class Blob(BaseMedia):
         """Initialize the blob from in-memory data.
 
         Args:
-            data: the in-memory data associated with the blob
+            data: The in-memory data associated with the blob
             encoding: Encoding to use if decoding the bytes into a string
-            mime_type: if provided, will be set as the mime-type of the data
-            path: if provided, will be set as the source from which the data came
+            mime_type: If provided, will be set as the mime-type of the data
+            path: If provided, will be set as the source from which the data came
             metadata: Metadata to associate with the blob
 
         Returns:
@@ -278,15 +277,13 @@ class Document(BaseMedia):
     """Class for storing a piece of text and associated metadata.
 
     Example:
+        ```python
+        from langchain_core.documents import Document
 
-        .. code-block:: python
-
-            from langchain_core.documents import Document
-
-            document = Document(
-                page_content="Hello, world!", metadata={"source": "https://example.com"}
-            )
-
+        document = Document(
+            page_content="Hello, world!", metadata={"source": "https://example.com"}
+        )
+        ```
     """
 
     page_content: str
@@ -306,7 +303,7 @@ class Document(BaseMedia):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             ["langchain", "schema", "document"]

libs/core/langchain_core/documents/transformers.py

@@ -20,35 +20,34 @@ class BaseDocumentTransformer(ABC):
     sequence of transformed Documents.
 
     Example:
-        .. code-block:: python
+        ```python
+        class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel):
+            embeddings: Embeddings
+            similarity_fn: Callable = cosine_similarity
+            similarity_threshold: float = 0.95
 
-            class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel):
-                embeddings: Embeddings
-                similarity_fn: Callable = cosine_similarity
-                similarity_threshold: float = 0.95
+            class Config:
+                arbitrary_types_allowed = True
 
-                class Config:
-                    arbitrary_types_allowed = True
-
-                def transform_documents(
-                    self, documents: Sequence[Document], **kwargs: Any
-                ) -> Sequence[Document]:
-                    stateful_documents = get_stateful_documents(documents)
-                    embedded_documents = _get_embeddings_from_stateful_docs(
-                        self.embeddings, stateful_documents
-                    )
-                    included_idxs = _filter_similar_embeddings(
-                        embedded_documents,
-                        self.similarity_fn,
-                        self.similarity_threshold,
-                    )
-                    return [stateful_documents[i] for i in sorted(included_idxs)]
-
-                async def atransform_documents(
-                    self, documents: Sequence[Document], **kwargs: Any
-                ) -> Sequence[Document]:
-                    raise NotImplementedError
+            def transform_documents(
+                self, documents: Sequence[Document], **kwargs: Any
+            ) -> Sequence[Document]:
+                stateful_documents = get_stateful_documents(documents)
+                embedded_documents = _get_embeddings_from_stateful_docs(
+                    self.embeddings, stateful_documents
+                )
+                included_idxs = _filter_similar_embeddings(
+                    embedded_documents,
+                    self.similarity_fn,
+                    self.similarity_threshold,
+                )
+                return [stateful_documents[i] for i in sorted(included_idxs)]
 
+            async def atransform_documents(
+                self, documents: Sequence[Document], **kwargs: Any
+            ) -> Sequence[Document]:
+                raise NotImplementedError
+        ```
     """
 
     @abstractmethod

libs/core/langchain_core/embeddings/fake.py

@@ -18,40 +18,38 @@ class FakeEmbeddings(Embeddings, BaseModel):
 
     This embedding model creates embeddings by sampling from a normal distribution.
 
-    Do not use this outside of testing, as it is not a real embedding model.
+    !!! warning
+        Do not use this outside of testing, as it is not a real embedding model.
 
     Instantiate:
-        .. code-block:: python
+        ```python
+        from langchain_core.embeddings import FakeEmbeddings
 
-            from langchain_core.embeddings import FakeEmbeddings
-
-            embed = FakeEmbeddings(size=100)
+        embed = FakeEmbeddings(size=100)
+        ```
 
     Embed single text:
-        .. code-block:: python
-
-            input_text = "The meaning of life is 42"
-            vector = embed.embed_query(input_text)
-            print(vector[:3])
-
-        .. code-block:: python
-
-            [-0.700234640213188, -0.581266257710429, -1.1328482266445354]
+        ```python
+        input_text = "The meaning of life is 42"
+        vector = embed.embed_query(input_text)
+        print(vector[:3])
+        ```
+        ```python
+        [-0.700234640213188, -0.581266257710429, -1.1328482266445354]
+        ```
 
     Embed multiple texts:
-        .. code-block:: python
-
-            input_texts = ["Document 1...", "Document 2..."]
-            vectors = embed.embed_documents(input_texts)
-            print(len(vectors))
-            # The first 3 coordinates for the first vector
-            print(vectors[0][:3])
-
-        .. code-block:: python
-
-            2
-            [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257]
-
+        ```python
+        input_texts = ["Document 1...", "Document 2..."]
+        vectors = embed.embed_documents(input_texts)
+        print(len(vectors))
+        # The first 3 coordinates for the first vector
+        print(vectors[0][:3])
+        ```
+        ```python
+        2
+        [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257]
+        ```
     """
 
     size: int
@@ -75,40 +73,38 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
     This embedding model creates embeddings by sampling from a normal distribution
     with a seed based on the hash of the text.
 
-    Do not use this outside of testing, as it is not a real embedding model.
+    !!! warning
+        Do not use this outside of testing, as it is not a real embedding model.
 
     Instantiate:
-        .. code-block:: python
+        ```python
+        from langchain_core.embeddings import DeterministicFakeEmbedding
 
-            from langchain_core.embeddings import DeterministicFakeEmbedding
-
-            embed = DeterministicFakeEmbedding(size=100)
+        embed = DeterministicFakeEmbedding(size=100)
+        ```
 
     Embed single text:
-        .. code-block:: python
-
-            input_text = "The meaning of life is 42"
-            vector = embed.embed_query(input_text)
-            print(vector[:3])
-
-        .. code-block:: python
-
-            [-0.700234640213188, -0.581266257710429, -1.1328482266445354]
+        ```python
+        input_text = "The meaning of life is 42"
+        vector = embed.embed_query(input_text)
+        print(vector[:3])
+        ```
+        ```python
+        [-0.700234640213188, -0.581266257710429, -1.1328482266445354]
+        ```
 
     Embed multiple texts:
-        .. code-block:: python
-
-            input_texts = ["Document 1...", "Document 2..."]
-            vectors = embed.embed_documents(input_texts)
-            print(len(vectors))
-            # The first 3 coordinates for the first vector
-            print(vectors[0][:3])
-
-        .. code-block:: python
-
-            2
-            [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257]
-
+        ```python
+        input_texts = ["Document 1...", "Document 2..."]
+        vectors = embed.embed_documents(input_texts)
+        print(len(vectors))
+        # The first 3 coordinates for the first vector
+        print(vectors[0][:3])
+        ```
+        ```python
+        2
+        [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257]
+        ```
     """
 
     size: int

libs/core/langchain_core/example_selectors/semantic_similarity.py

@@ -154,7 +154,7 @@ class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
             examples: List of examples to use in the prompt.
             embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
             vectorstore_cls: A vector store DB interface class, e.g. FAISS.
-            k: Number of examples to select. Default is 4.
+            k: Number of examples to select.
             input_keys: If provided, the search is based on the input variables
                 instead of all variables.
             example_keys: If provided, keys to filter examples to.
@@ -198,7 +198,7 @@ class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
             examples: List of examples to use in the prompt.
             embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
             vectorstore_cls: A vector store DB interface class, e.g. FAISS.
-            k: Number of examples to select. Default is 4.
+            k: Number of examples to select.
             input_keys: If provided, the search is based on the input variables
                 instead of all variables.
             example_keys: If provided, keys to filter examples to.
@@ -285,9 +285,8 @@ class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
             examples: List of examples to use in the prompt.
             embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
             vectorstore_cls: A vector store DB interface class, e.g. FAISS.
-            k: Number of examples to select. Default is 4.
+            k: Number of examples to select.
             fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-                Default is 20.
             input_keys: If provided, the search is based on the input variables
                 instead of all variables.
             example_keys: If provided, keys to filter examples to.
@@ -333,9 +332,8 @@ class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
             examples: List of examples to use in the prompt.
             embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
             vectorstore_cls: A vector store DB interface class, e.g. FAISS.
-            k: Number of examples to select. Default is 4.
+            k: Number of examples to select.
             fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-                Default is 20.
             input_keys: If provided, the search is based on the input variables
                 instead of all variables.
             example_keys: If provided, keys to filter examples to.

libs/core/langchain_core/exceptions.py

@@ -16,7 +16,7 @@ class OutputParserException(ValueError, LangChainException):  # noqa: N818
     """Exception that output parsers should raise to signify a parsing error.
 
     This exists to differentiate parsing errors from other code or execution errors
-    that also may arise inside the output parser. OutputParserExceptions will be
+    that also may arise inside the output parser. `OutputParserException` will be
     available to catch and handle in ways to fix the parsing error, while other
     errors will be raised.
     """
@@ -28,24 +28,23 @@ class OutputParserException(ValueError, LangChainException):  # noqa: N818
         llm_output: str | None = None,
         send_to_llm: bool = False,  # noqa: FBT001,FBT002
     ):
-        """Create an OutputParserException.
+        """Create an `OutputParserException`.
 
         Args:
             error: The error that's being re-raised or an error message.
             observation: String explanation of error which can be passed to a
-                model to try and remediate the issue. Defaults to `None`.
+                model to try and remediate the issue.
             llm_output: String model output which is error-ing.
-                Defaults to `None`.
+
             send_to_llm: Whether to send the observation and llm_output back to an Agent
-                after an OutputParserException has been raised.
+                after an `OutputParserException` has been raised.
                 This gives the underlying model driving the agent the context that the
                 previous output was improperly structured, in the hopes that it will
                 update the output to the correct format.
-                Defaults to `False`.
 
         Raises:
-            ValueError: If ``send_to_llm`` is True but either observation or
-                ``llm_output`` are not provided.
+            ValueError: If `send_to_llm` is True but either observation or
+                `llm_output` are not provided.
         """
         if isinstance(error, str):
             error = create_message(

libs/core/langchain_core/indexing/api.py

@@ -326,8 +326,8 @@ def index(
         record_manager: Timestamped set to keep track of which documents were
             updated.
         vector_store: VectorStore or DocumentIndex to index the documents into.
-        batch_size: Batch size to use when indexing. Default is 100.
-        cleanup: How to handle clean up of documents. Default is None.
+        batch_size: Batch size to use when indexing.
+        cleanup: How to handle clean up of documents.
 
             - incremental: Cleans up all documents that haven't been updated AND
               that are associated with source ids that were seen during indexing.
@@ -342,15 +342,12 @@ def index(
               source ids that were seen during indexing.
             - None: Do not delete any documents.
         source_id_key: Optional key that helps identify the original source
-            of the document. Default is None.
+            of the document.
         cleanup_batch_size: Batch size to use when cleaning up documents.
-            Default is 1_000.
         force_update: Force update documents even if they are present in the
             record manager. Useful if you are re-indexing with updated embeddings.
-            Default is False.
         key_encoder: Hashing algorithm to use for hashing the document content and
-            metadata. Default is "sha1".
-            Other options include "blake2b", "sha256", and "sha512".
+            metadata. Options include "blake2b", "sha256", and "sha512".
 
             !!! version-added "Added in version 0.3.66"
 
@@ -667,8 +664,8 @@ async def aindex(
         record_manager: Timestamped set to keep track of which documents were
             updated.
         vector_store: VectorStore or DocumentIndex to index the documents into.
-        batch_size: Batch size to use when indexing. Default is 100.
-        cleanup: How to handle clean up of documents. Default is None.
+        batch_size: Batch size to use when indexing.
+        cleanup: How to handle clean up of documents.
 
             - incremental: Cleans up all documents that haven't been updated AND
               that are associated with source ids that were seen during indexing.
@@ -683,15 +680,12 @@ async def aindex(
               source ids that were seen during indexing.
             - None: Do not delete any documents.
         source_id_key: Optional key that helps identify the original source
-            of the document. Default is None.
+            of the document.
         cleanup_batch_size: Batch size to use when cleaning up documents.
-            Default is 1_000.
         force_update: Force update documents even if they are present in the
             record manager. Useful if you are re-indexing with updated embeddings.
-            Default is False.
         key_encoder: Hashing algorithm to use for hashing the document content and
-            metadata. Default is "sha1".
-            Other options include "blake2b", "sha256", and "sha512".
+            metadata. Options include "blake2b", "sha256", and "sha512".
 
             !!! version-added "Added in version 0.3.66"
 

libs/core/langchain_core/indexing/base.py

@@ -61,7 +61,7 @@ class RecordManager(ABC):
         """Initialize the record manager.
 
         Args:
-            namespace (str): The namespace for the record manager.
+            namespace: The namespace for the record manager.
         """
         self.namespace = namespace
 
@@ -244,7 +244,7 @@ class InMemoryRecordManager(RecordManager):
         """Initialize the in-memory record manager.
 
         Args:
-            namespace (str): The namespace for the record manager.
+            namespace: The namespace for the record manager.
         """
         super().__init__(namespace)
         # Each key points to a dictionary
@@ -278,10 +278,10 @@ class InMemoryRecordManager(RecordManager):
         Args:
             keys: A list of record keys to upsert.
             group_ids: A list of group IDs corresponding to the keys.
-                Defaults to `None`.
+
             time_at_least: Optional timestamp. Implementation can use this
                 to optionally verify that the timestamp IS at least this time
-                in the system that stores. Defaults to `None`.
+                in the system that stores.
                 E.g., use to validate that the time in the postgres database
                 is equal to or larger than the given timestamp, if not
                 raise an error.
@@ -315,10 +315,10 @@ class InMemoryRecordManager(RecordManager):
         Args:
             keys: A list of record keys to upsert.
             group_ids: A list of group IDs corresponding to the keys.
-                Defaults to `None`.
+
             time_at_least: Optional timestamp. Implementation can use this
                 to optionally verify that the timestamp IS at least this time
-                in the system that stores. Defaults to `None`.
+                in the system that stores.
                 E.g., use to validate that the time in the postgres database
                 is equal to or larger than the given timestamp, if not
                 raise an error.
@@ -361,13 +361,13 @@ class InMemoryRecordManager(RecordManager):
 
         Args:
             before: Filter to list records updated before this time.
-                Defaults to `None`.
+
             after: Filter to list records updated after this time.
-                Defaults to `None`.
+
             group_ids: Filter to list records with specific group IDs.
-                Defaults to `None`.
+
             limit: optional limit on the number of records to return.
-                Defaults to `None`.
+
 
         Returns:
             A list of keys for the matching records.
@@ -397,13 +397,13 @@ class InMemoryRecordManager(RecordManager):
 
         Args:
             before: Filter to list records updated before this time.
-                Defaults to `None`.
+
             after: Filter to list records updated after this time.
-                Defaults to `None`.
+
             group_ids: Filter to list records with specific group IDs.
-                Defaults to `None`.
+
             limit: optional limit on the number of records to return.
-                Defaults to `None`.
+
 
         Returns:
             A list of keys for the matching records.
@@ -529,7 +529,7 @@ class DocumentIndex(BaseRetriever):
             **kwargs: Additional keyword arguments.
 
         Returns:
-            UpsertResponse: A response object that contains the list of IDs that were
+            A response object that contains the list of IDs that were
             successfully added or updated in the vectorstore and the list of IDs that
             failed to be added or updated.
         """
@@ -552,7 +552,7 @@ class DocumentIndex(BaseRetriever):
             **kwargs: Additional keyword arguments.
 
         Returns:
-            UpsertResponse: A response object that contains the list of IDs that were
+            A response object that contains the list of IDs that were
             successfully added or updated in the vectorstore and the list of IDs that
             failed to be added or updated.
         """
@@ -571,12 +571,12 @@ class DocumentIndex(BaseRetriever):
 
         Args:
             ids: List of ids to delete.
-            kwargs: Additional keyword arguments. This is up to the implementation.
+            **kwargs: Additional keyword arguments. This is up to the implementation.
                 For example, can include an option to delete the entire index,
                 or else issue a non-blocking delete etc.
 
         Returns:
-            DeleteResponse: A response object that contains the list of IDs that were
+            A response object that contains the list of IDs that were
             successfully deleted and the list of IDs that failed to be deleted.
         """
 
@@ -589,11 +589,11 @@ class DocumentIndex(BaseRetriever):
 
         Args:
             ids: List of ids to delete.
-            kwargs: Additional keyword arguments. This is up to the implementation.
+            **kwargs: Additional keyword arguments. This is up to the implementation.
                 For example, can include an option to delete the entire index.
 
         Returns:
-            DeleteResponse: A response object that contains the list of IDs that were
+            A response object that contains the list of IDs that were
             successfully deleted and the list of IDs that failed to be deleted.
         """
         return await run_in_executor(
@@ -624,10 +624,10 @@ class DocumentIndex(BaseRetriever):
 
         Args:
             ids: List of IDs to get.
-            kwargs: Additional keyword arguments. These are up to the implementation.
+            **kwargs: Additional keyword arguments. These are up to the implementation.
 
         Returns:
-            list[Document]: List of documents that were found.
+            List of documents that were found.
         """
 
     async def aget(
@@ -650,10 +650,10 @@ class DocumentIndex(BaseRetriever):
 
         Args:
             ids: List of IDs to get.
-            kwargs: Additional keyword arguments. These are up to the implementation.
+            **kwargs: Additional keyword arguments. These are up to the implementation.
 
         Returns:
-            list[Document]: List of documents that were found.
+            List of documents that were found.
         """
         return await run_in_executor(
             None,

libs/core/langchain_core/language_models/__init__.py

@@ -1,45 +1,29 @@
 """Language models.
 
-**Language Model** is a type of model that can generate text or complete
-text prompts.
+LangChain has two main classes to work with language models: chat models and
+"old-fashioned" LLMs.
 
-LangChain has two main classes to work with language models: **Chat Models**
-and "old-fashioned" **LLMs**.
-
-**Chat Models**
+**Chat models**
 
 Language models that use a sequence of messages as inputs and return chat messages
-as outputs (as opposed to using plain text). These are traditionally newer models (
-older models are generally LLMs, see below). Chat models support the assignment of
+as outputs (as opposed to using plain text). Chat models support the assignment of
 distinct roles to conversation messages, helping to distinguish messages from the AI,
 users, and instructions such as system messages.
 
 The key abstraction for chat models is `BaseChatModel`. Implementations
-should inherit from this class. Please see LangChain how-to guides with more
-information on how to implement a custom chat model.
+should inherit from this class.
 
-To implement a custom Chat Model, inherit from `BaseChatModel`. See
-the following guide for more information on how to implement a custom Chat Model:
-
-https://python.langchain.com/docs/how_to/custom_chat_model/
+See existing [chat model integrations](https://docs.langchain.com/oss/python/integrations/chat).
 
 **LLMs**
 
 Language models that takes a string as input and returns a string.
-These are traditionally older models (newer models generally are Chat Models,
-see below).
-
-Although the underlying models are string in, string out, the LangChain wrappers
-also allow these models to take messages as input. This gives them the same interface
-as Chat Models. When messages are passed in as input, they will be formatted into a
-string under the hood before being passed to the underlying model.
-
-To implement a custom LLM, inherit from `BaseLLM` or `LLM`.
-Please see the following guide for more information on how to implement a custom LLM:
-
-https://python.langchain.com/docs/how_to/custom_llm/
-
+These are traditionally older models (newer models generally are chat models).
 
+Although the underlying models are string in, string out, the LangChain wrappers also
+allow these models to take messages as input. This gives them the same interface as
+chat models. When messages are passed in as input, they will be formatted into a string
+under the hood before being passed to the underlying model.
 """
 
 from typing import TYPE_CHECKING

libs/core/langchain_core/language_models/_utils.py

@@ -35,7 +35,7 @@ def is_openai_data_block(
             different type, this function will return False.
 
     Returns:
-        True if the block is a valid OpenAI data block and matches the filter_
+        `True` if the block is a valid OpenAI data block and matches the filter_
         (if provided).
 
     """
@@ -89,21 +89,20 @@ class ParsedDataUri(TypedDict):
 def _parse_data_uri(uri: str) -> ParsedDataUri | None:
     """Parse a data URI into its components.
 
-    If parsing fails, return None. If either MIME type or data is missing, return None.
+    If parsing fails, return `None`. If either MIME type or data is missing, return
+    `None`.
 
     Example:
+        ```python
+        data_uri = "data:image/jpeg;base64,/9j/4AAQSkZJRg..."
+        parsed = _parse_data_uri(data_uri)
 
-        .. code-block:: python
-
-            data_uri = "data:image/jpeg;base64,/9j/4AAQSkZJRg..."
-            parsed = _parse_data_uri(data_uri)
-
-            assert parsed == {
-                "source_type": "base64",
-                "mime_type": "image/jpeg",
-                "data": "/9j/4AAQSkZJRg...",
-            }
-
+        assert parsed == {
+            "source_type": "base64",
+            "mime_type": "image/jpeg",
+            "data": "/9j/4AAQSkZJRg...",
+        }
+        ```
     """
     regex = r"^data:(?P<mime_type>[^;]+);base64,(?P<data>.+)$"
     match = re.match(regex, uri)
@@ -150,48 +149,48 @@ def _normalize_messages(
 
         `URLContentBlock`:
 
-        .. codeblock::
-
-            {
-                mime_type: NotRequired[str]
-                type: Literal['image', 'audio', 'file'],
-                source_type: Literal['url'],
-                url: str,
-            }
+        ```python
+        {
+            mime_type: NotRequired[str]
+            type: Literal['image', 'audio', 'file'],
+            source_type: Literal['url'],
+            url: str,
+        }
+        ```
 
         `Base64ContentBlock`:
 
-        .. codeblock::
-
-            {
-                mime_type: NotRequired[str]
-                type: Literal['image', 'audio', 'file'],
-                source_type: Literal['base64'],
-                data: str,
-            }
+        ```python
+        {
+            mime_type: NotRequired[str]
+            type: Literal['image', 'audio', 'file'],
+            source_type: Literal['base64'],
+            data: str,
+        }
+        ```
 
         `IDContentBlock`:
 
         (In practice, this was never used)
 
-        .. codeblock::
-
-            {
-                type: Literal['image', 'audio', 'file'],
-                source_type: Literal['id'],
-                id: str,
-            }
+        ```python
+        {
+            type: Literal["image", "audio", "file"],
+            source_type: Literal["id"],
+            id: str,
+        }
+        ```
 
         `PlainTextContentBlock`:
 
-        .. codeblock::
-
-            {
-                mime_type: NotRequired[str]
-                type: Literal['file'],
-                source_type: Literal['text'],
-                url: str,
-            }
+        ```python
+        {
+            mime_type: NotRequired[str]
+            type: Literal['file'],
+            source_type: Literal['text'],
+            url: str,
+        }
+        ```
 
     If a v1 message is passed in, it will be returned as-is, meaning it is safe to
     always pass in v1 messages to this function for assurance.

libs/core/langchain_core/language_models/base.py

@@ -96,9 +96,16 @@ def _get_token_ids_default_method(text: str) -> list[int]:
 
 
 LanguageModelInput = PromptValue | str | Sequence[MessageLikeRepresentation]
+"""Input to a language model."""
+
 LanguageModelOutput = BaseMessage | str
+"""Output from a language model."""
+
 LanguageModelLike = Runnable[LanguageModelInput, LanguageModelOutput]
+"""Input/output interface for a language model."""
+
 LanguageModelOutputVar = TypeVar("LanguageModelOutputVar", AIMessage, str)
+"""Type variable for the output of a language model."""
 
 
 def _get_verbosity() -> bool:
@@ -123,7 +130,6 @@ class BaseLanguageModel(
     * If instance of `BaseCache`, will use the provided cache.
 
     Caching is not currently supported for streaming methods of models.
-
     """
     verbose: bool = Field(default_factory=_get_verbosity, exclude=True, repr=False)
     """Whether to print out response text."""
@@ -146,7 +152,7 @@ class BaseLanguageModel(
     def set_verbose(cls, verbose: bool | None) -> bool:  # noqa: FBT001
         """If verbose is `None`, set it.
 
-        This allows users to pass in None as verbose to access the global setting.
+        This allows users to pass in `None` as verbose to access the global setting.
 
         Args:
             verbose: The verbosity setting to use.
@@ -186,12 +192,12 @@ class BaseLanguageModel(
         1. Take advantage of batched calls,
         2. Need more output from the model than just the top generated value,
         3. Are building chains that are agnostic to the underlying language model
-           type (e.g., pure text completion models vs chat models).
+            type (e.g., pure text completion models vs chat models).
 
         Args:
-            prompts: List of PromptValues. A PromptValue is an object that can be
-                converted to match the format of any language model (string for pure
-                text generation models and BaseMessages for chat models).
+            prompts: List of `PromptValue` objects. A `PromptValue` is an object that
+                can be converted to match the format of any language model (string for
+                pure text generation models and `BaseMessage` objects for chat models).
             stop: Stop words to use when generating. Model output is cut off at the
                 first occurrence of any of these substrings.
             callbacks: Callbacks to pass through. Used for executing additional
@@ -200,8 +206,8 @@ class BaseLanguageModel(
                 to the model provider API call.
 
         Returns:
-            An LLMResult, which contains a list of candidate Generations for each input
-            prompt and additional model provider-specific output.
+            An `LLMResult`, which contains a list of candidate `Generation` objects for
+            each input prompt and additional model provider-specific output.
 
         """
 
@@ -223,12 +229,12 @@ class BaseLanguageModel(
         1. Take advantage of batched calls,
         2. Need more output from the model than just the top generated value,
         3. Are building chains that are agnostic to the underlying language model
-           type (e.g., pure text completion models vs chat models).
+            type (e.g., pure text completion models vs chat models).
 
         Args:
-            prompts: List of PromptValues. A PromptValue is an object that can be
-                converted to match the format of any language model (string for pure
-                text generation models and BaseMessages for chat models).
+            prompts: List of `PromptValue` objects. A `PromptValue` is an object that
+                can be converted to match the format of any language model (string for
+                pure text generation models and `BaseMessage` objects for chat models).
             stop: Stop words to use when generating. Model output is cut off at the
                 first occurrence of any of these substrings.
             callbacks: Callbacks to pass through. Used for executing additional
@@ -237,8 +243,8 @@ class BaseLanguageModel(
                 to the model provider API call.
 
         Returns:
-            An `LLMResult`, which contains a list of candidate Generations for each
-            input prompt and additional model provider-specific output.
+            An `LLMResult`, which contains a list of candidate `Generation` objects for
+            each input prompt and additional model provider-specific output.
 
         """
 

libs/core/langchain_core/language_models/chat_models.py

@@ -108,7 +108,7 @@ def _generate_response_from_error(error: BaseException) -> list[ChatGeneration]:
 
 
 def _format_for_tracing(messages: list[BaseMessage]) -> list[BaseMessage]:
-    """Format messages for tracing in ``on_chat_model_start``.
+    """Format messages for tracing in `on_chat_model_start`.
 
     - Update image content blocks to OpenAI Chat Completions format (backward
     compatibility).
@@ -185,7 +185,7 @@ def generate_from_stream(stream: Iterator[ChatGenerationChunk]) -> ChatResult:
         ValueError: If no generations are found in the stream.
 
     Returns:
-        ChatResult: Chat result.
+        Chat result.
 
     """
     generation = next(stream, None)
@@ -213,7 +213,7 @@ async def agenerate_from_stream(
         stream: Iterator of `ChatGenerationChunk`.
 
     Returns:
-        ChatResult: Chat result.
+        Chat result.
 
     """
     chunks = [chunk async for chunk in stream]
@@ -240,79 +240,52 @@ def _format_ls_structured_output(ls_structured_output_format: dict | None) -> di
 
 
 class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
-    """Base class for chat models.
+    r"""Base class for chat models.
 
     Key imperative methods:
         Methods that actually call the underlying model.
 
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | Method                    | Input                                                          | Output                                                              | Description                                                                                      |
-        +===========================+================================================================+=====================================================================+==================================================================================================+
-        | `invoke`                  | str | list[dict | tuple | BaseMessage] | PromptValue           | BaseMessage                                                         | A single chat model call.                                                                        |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `ainvoke`                 | '''                                                            | BaseMessage                                                         | Defaults to running invoke in an async executor.                                                 |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `stream`                  | '''                                                            | Iterator[BaseMessageChunk]                                          | Defaults to yielding output of invoke.                                                           |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `astream`                 | '''                                                            | AsyncIterator[BaseMessageChunk]                                     | Defaults to yielding output of ainvoke.                                                          |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `astream_events`          | '''                                                            | AsyncIterator[StreamEvent]                                          | Event types: 'on_chat_model_start', 'on_chat_model_stream', 'on_chat_model_end'.                 |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `batch`                   | list[''']                                                      | list[BaseMessage]                                                   | Defaults to running invoke in concurrent threads.                                                |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `abatch`                  | list[''']                                                      | list[BaseMessage]                                                   | Defaults to running ainvoke in concurrent threads.                                               |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `batch_as_completed`      | list[''']                                                      | Iterator[tuple[int, Union[BaseMessage, Exception]]]                 | Defaults to running invoke in concurrent threads.                                                |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `abatch_as_completed`     | list[''']                                                      | AsyncIterator[tuple[int, Union[BaseMessage, Exception]]]            | Defaults to running ainvoke in concurrent threads.                                               |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
+        This table provides a brief overview of the main imperative methods. Please see the base `Runnable` reference for full documentation.
 
-        This table provides a brief overview of the main imperative methods. Please see the base Runnable reference for full documentation.
+        | Method                 | Input                                                        | Output                                                     | Description                                                                      |
+        | ---------------------- | ------------------------------------------------------------ | ---------------------------------------------------------- | -------------------------------------------------------------------------------- |
+        | `invoke`               | `str` \| `list[dict | tuple | BaseMessage]` \| `PromptValue` | `BaseMessage`                                              | A single chat model call.                                                        |
+        | `ainvoke`              | `'''`                                                        | `BaseMessage`                                              | Defaults to running `invoke` in an async executor.                               |
+        | `stream`               | `'''`                                                        | `Iterator[BaseMessageChunk]`                               | Defaults to yielding output of `invoke`.                                         |
+        | `astream`              | `'''`                                                        | `AsyncIterator[BaseMessageChunk]`                          | Defaults to yielding output of `ainvoke`.                                        |
+        | `astream_events`       | `'''`                                                        | `AsyncIterator[StreamEvent]`                               | Event types: `on_chat_model_start`, `on_chat_model_stream`, `on_chat_model_end`. |
+        | `batch`                | `list[''']`                                                  | `list[BaseMessage]`                                        | Defaults to running `invoke` in concurrent threads.                              |
+        | `abatch`               | `list[''']`                                                  | `list[BaseMessage]`                                        | Defaults to running `ainvoke` in concurrent threads.                             |
+        | `batch_as_completed`   | `list[''']`                                                  | `Iterator[tuple[int, Union[BaseMessage, Exception]]]`      | Defaults to running `invoke` in concurrent threads.                              |
+        | `abatch_as_completed`  | `list[''']`                                                  | `AsyncIterator[tuple[int, Union[BaseMessage, Exception]]]` | Defaults to running `ainvoke` in concurrent threads.                             |
 
     Key declarative methods:
-        Methods for creating another Runnable using the ChatModel.
-
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | Method                           | Description                                                                                               |
-        +==================================+===========================================================================================================+
-        | `bind_tools`                     | Create ChatModel that can call tools.                                                                     |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | `with_structured_output`         | Create wrapper that structures model output using schema.                                                 |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | `with_retry`                     | Create wrapper that retries model calls on failure.                                                       |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | `with_fallbacks`                 | Create wrapper that falls back to other models on failure.                                                |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | `configurable_fields`            | Specify init args of the model that can be configured at runtime via the RunnableConfig.                  |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | `configurable_alternatives`      | Specify alternative models which can be swapped in at runtime via the RunnableConfig.                     |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
+        Methods for creating another `Runnable` using the chat model.
 
         This table provides a brief overview of the main declarative methods. Please see the reference for each method for full documentation.
 
+        | Method                       | Description                                                                                |
+        | ---------------------------- | ------------------------------------------------------------------------------------------ |
+        | `bind_tools`                 | Create chat model that can call tools.                                                     |
+        | `with_structured_output`     | Create wrapper that structures model output using schema.                                  |
+        | `with_retry`                 | Create wrapper that retries model calls on failure.                                        |
+        | `with_fallbacks`             | Create wrapper that falls back to other models on failure.                                 |
+        | `configurable_fields`        | Specify init args of the model that can be configured at runtime via the `RunnableConfig`. |
+        | `configurable_alternatives`  | Specify alternative models which can be swapped in at runtime via the `RunnableConfig`.    |
+
     Creating custom chat model:
         Custom chat model implementations should inherit from this class.
         Please reference the table below for information about which
         methods and properties are required or optional for implementations.
 
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
-        | Method/Property                  | Description                                                        | Required/Optional |
-        +==================================+====================================================================+===================+
+        | Method/Property                  | Description                                                        | Required          |
+        | -------------------------------- | ------------------------------------------------------------------ | ----------------- |
         | `_generate`                      | Use to generate a chat result from a prompt                        | Required          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
         | `_llm_type` (property)           | Used to uniquely identify the type of the model. Used for logging. | Required          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
         | `_identifying_params` (property) | Represent model parameterization for tracing purposes.             | Optional          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
         | `_stream`                        | Use to implement streaming                                         | Optional          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
         | `_agenerate`                     | Use to implement a native async method                             | Optional          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
         | `_astream`                       | Use to implement async version of `_stream`                        | Optional          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
-
-        Follow the guide for more information on how to implement a custom Chat Model:
-        [Guide](https://python.langchain.com/docs/how_to/custom_chat_model/).
 
     """  # noqa: E501
 
@@ -327,9 +300,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
     - If `True`, will always bypass streaming case.
     - If `'tool_calling'`, will bypass streaming case only when the model is called
-      with a `tools` keyword argument. In other words, LangChain will automatically
-      switch to non-streaming behavior (`invoke`) only when the tools argument is
-      provided. This offers the best of both worlds.
+        with a `tools` keyword argument. In other words, LangChain will automatically
+        switch to non-streaming behavior (`invoke`) only when the tools argument is
+        provided. This offers the best of both worlds.
     - If `False` (Default), will always use streaming case if available.
 
     The main reason for this flag is that code might be written using `stream` and
@@ -342,18 +315,19 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
     )
     """Version of `AIMessage` output format to store in message content.
 
-    `AIMessage.content_blocks` will lazily parse the contents of ``content`` into a
+    `AIMessage.content_blocks` will lazily parse the contents of `content` into a
     standard format. This flag can be used to additionally store the standard format
     in message content, e.g., for serialization purposes.
 
     Supported values:
 
     - `'v0'`: provider-specific format in content (can lazily-parse with
-      `.content_blocks`)
-    - `'v1'`: standardized format in content (consistent with `.content_blocks`)
+        `content_blocks`)
+    - `'v1'`: standardized format in content (consistent with `content_blocks`)
 
-    Partner packages (e.g., `langchain-openai`) can also use this field to roll out
-    new content formats in a backward-compatible way.
+    Partner packages (e.g.,
+    [`langchain-openai`](https://pypi.org/project/langchain-openai)) can also use this
+    field to roll out new content formats in a backward-compatible way.
 
     !!! version-added "Added in version 1.0"
 
@@ -1533,7 +1507,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                 - a `TypedDict` class,
                 - or a Pydantic class.
 
-                If ``schema`` is a Pydantic class then the model output will be a
+                If `schema` is a Pydantic class then the model output will be a
                 Pydantic instance of that class, and the model-generated fields will be
                 validated by the Pydantic class. Otherwise the model output will be a
                 dict and will not be validated. See `langchain_core.utils.function_calling.convert_to_openai_tool`
@@ -1543,110 +1517,111 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
             include_raw:
                 If `False` then only the parsed structured output is returned. If
                 an error occurs during model output parsing it will be raised. If `True`
-                then both the raw model response (a BaseMessage) and the parsed model
+                then both the raw model response (a `BaseMessage`) and the parsed model
                 response will be returned. If an error occurs during output parsing it
                 will be caught and returned as well. The final output is always a dict
-                with keys ``'raw'``, ``'parsed'``, and ``'parsing_error'``.
+                with keys `'raw'`, `'parsed'`, and `'parsing_error'`.
 
         Raises:
-            ValueError: If there are any unsupported ``kwargs``.
+            ValueError: If there are any unsupported `kwargs`.
             NotImplementedError: If the model does not implement
-                ``with_structured_output()``.
+                `with_structured_output()`.
 
         Returns:
             A Runnable that takes same inputs as a `langchain_core.language_models.chat.BaseChatModel`.
 
-            If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
-            an instance of ``schema`` (i.e., a Pydantic object).
+                If `include_raw` is False and `schema` is a Pydantic class, Runnable outputs
+                an instance of `schema` (i.e., a Pydantic object).
 
-            Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
+                Otherwise, if `include_raw` is False then Runnable outputs a dict.
 
-            If ``include_raw`` is True, then Runnable outputs a dict with keys:
+                If `include_raw` is True, then Runnable outputs a dict with keys:
 
-            - ``'raw'``: BaseMessage
-            - ``'parsed'``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
-            - ``'parsing_error'``: BaseException | None
+                - `'raw'`: BaseMessage
+                - `'parsed'`: None if there was a parsing error, otherwise the type
+                    depends on the `schema` as described above.
+                - `'parsing_error'`: BaseException | None
 
         Example: Pydantic schema (include_raw=False):
-            .. code-block:: python
-
-                from pydantic import BaseModel
+            ```python
+            from pydantic import BaseModel
 
 
-                class AnswerWithJustification(BaseModel):
-                    '''An answer to the user question along with justification for the answer.'''
+            class AnswerWithJustification(BaseModel):
+                '''An answer to the user question along with justification for the answer.'''
 
-                    answer: str
-                    justification: str
+                answer: str
+                justification: str
 
 
-                llm = ChatModel(model="model-name", temperature=0)
-                structured_llm = llm.with_structured_output(AnswerWithJustification)
+            model = ChatModel(model="model-name", temperature=0)
+            structured_model = model.with_structured_output(AnswerWithJustification)
 
-                structured_llm.invoke(
-                    "What weighs more a pound of bricks or a pound of feathers"
-                )
+            structured_model.invoke(
+                "What weighs more a pound of bricks or a pound of feathers"
+            )
 
-                # -> AnswerWithJustification(
-                #     answer='They weigh the same',
-                #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
-                # )
+            # -> AnswerWithJustification(
+            #     answer='They weigh the same',
+            #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
+            # )
+            ```
 
         Example: Pydantic schema (include_raw=True):
-            .. code-block:: python
-
-                from pydantic import BaseModel
+            ```python
+            from pydantic import BaseModel
 
 
-                class AnswerWithJustification(BaseModel):
-                    '''An answer to the user question along with justification for the answer.'''
+            class AnswerWithJustification(BaseModel):
+                '''An answer to the user question along with justification for the answer.'''
 
-                    answer: str
-                    justification: str
+                answer: str
+                justification: str
 
 
-                llm = ChatModel(model="model-name", temperature=0)
-                structured_llm = llm.with_structured_output(
-                    AnswerWithJustification, include_raw=True
-                )
+            model = ChatModel(model="model-name", temperature=0)
+            structured_model = model.with_structured_output(
+                AnswerWithJustification, include_raw=True
+            )
 
-                structured_llm.invoke(
-                    "What weighs more a pound of bricks or a pound of feathers"
-                )
-                # -> {
-                #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
-                #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
-                #     'parsing_error': None
-                # }
+            structured_model.invoke(
+                "What weighs more a pound of bricks or a pound of feathers"
+            )
+            # -> {
+            #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
+            #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
+            #     'parsing_error': None
+            # }
+            ```
 
         Example: Dict schema (include_raw=False):
-            .. code-block:: python
-
-                from pydantic import BaseModel
-                from langchain_core.utils.function_calling import convert_to_openai_tool
+            ```python
+            from pydantic import BaseModel
+            from langchain_core.utils.function_calling import convert_to_openai_tool
 
 
-                class AnswerWithJustification(BaseModel):
-                    '''An answer to the user question along with justification for the answer.'''
+            class AnswerWithJustification(BaseModel):
+                '''An answer to the user question along with justification for the answer.'''
 
-                    answer: str
-                    justification: str
+                answer: str
+                justification: str
 
 
-                dict_schema = convert_to_openai_tool(AnswerWithJustification)
-                llm = ChatModel(model="model-name", temperature=0)
-                structured_llm = llm.with_structured_output(dict_schema)
+            dict_schema = convert_to_openai_tool(AnswerWithJustification)
+            model = ChatModel(model="model-name", temperature=0)
+            structured_model = model.with_structured_output(dict_schema)
 
-                structured_llm.invoke(
-                    "What weighs more a pound of bricks or a pound of feathers"
-                )
-                # -> {
-                #     'answer': 'They weigh the same',
-                #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
-                # }
+            structured_model.invoke(
+                "What weighs more a pound of bricks or a pound of feathers"
+            )
+            # -> {
+            #     'answer': 'They weigh the same',
+            #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
+            # }
+            ```
 
         !!! warning "Behavior changed in 0.2.26"
-                Added support for TypedDict class.
+            Added support for TypedDict class.
 
         """  # noqa: E501
         _ = kwargs.pop("method", None)
@@ -1693,7 +1668,7 @@ class SimpleChatModel(BaseChatModel):
 
     !!! note
         This implementation is primarily here for backwards compatibility. For new
-        implementations, please use ``BaseChatModel`` directly.
+        implementations, please use `BaseChatModel` directly.
 
     """
 

libs/core/langchain_core/language_models/fake_chat_models.py

@@ -1,4 +1,4 @@
-"""Fake ChatModel for testing purposes."""
+"""Fake chat model for testing purposes."""
 
 import asyncio
 import re
@@ -19,7 +19,7 @@ from langchain_core.runnables import RunnableConfig
 
 
 class FakeMessagesListChatModel(BaseChatModel):
-    """Fake ``ChatModel`` for testing purposes."""
+    """Fake chat model for testing purposes."""
 
     responses: list[BaseMessage]
     """List of responses to **cycle** through in order."""
@@ -57,7 +57,7 @@ class FakeListChatModelError(Exception):
 
 
 class FakeListChatModel(SimpleChatModel):
-    """Fake ChatModel for testing purposes."""
+    """Fake chat model for testing purposes."""
 
     responses: list[str]
     """List of responses to **cycle** through in order."""
@@ -228,10 +228,10 @@ class GenericFakeChatModel(BaseChatModel):
     """Generic fake chat model that can be used to test the chat model interface.
 
     * Chat model should be usable in both sync and async tests
-    * Invokes ``on_llm_new_token`` to allow for testing of callback related code for new
-      tokens.
+    * Invokes `on_llm_new_token` to allow for testing of callback related code for new
+        tokens.
     * Includes logic to break messages into message chunk to facilitate testing of
-      streaming.
+        streaming.
 
     """
 
@@ -242,7 +242,7 @@ class GenericFakeChatModel(BaseChatModel):
     to make the interface more generic if needed.
 
     !!! note
-        if you want to pass a list, you can use ``iter`` to convert it to an iterator.
+        if you want to pass a list, you can use `iter` to convert it to an iterator.
 
     !!! warning
         Streaming is not implemented yet. We should try to implement it in the future by

libs/core/langchain_core/language_models/llms.py

@@ -74,8 +74,8 @@ def create_base_retry_decorator(
 
     Args:
         error_types: List of error types to retry on.
-        max_retries: Number of retries. Default is 1.
-        run_manager: Callback manager for the run. Default is None.
+        max_retries: Number of retries.
+        run_manager: Callback manager for the run.
 
     Returns:
         A retry decorator.
@@ -153,7 +153,7 @@ def get_prompts(
     Args:
         params: Dictionary of parameters.
         prompts: List of prompts.
-        cache: Cache object. Default is None.
+        cache: Cache object.
 
     Returns:
         A tuple of existing prompts, llm_string, missing prompt indexes,
@@ -189,7 +189,7 @@ async def aget_prompts(
     Args:
         params: Dictionary of parameters.
         prompts: List of prompts.
-        cache: Cache object. Default is None.
+        cache: Cache object.
 
     Returns:
         A tuple of existing prompts, llm_string, missing prompt indexes,
@@ -835,7 +835,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
         1. Take advantage of batched calls,
         2. Need more output from the model than just the top generated value,
         3. Are building chains that are agnostic to the underlying language model
-           type (e.g., pure text completion models vs chat models).
+            type (e.g., pure text completion models vs chat models).
 
         Args:
             prompts: List of string prompts.
@@ -857,8 +857,8 @@ class BaseLLM(BaseLanguageModel[str], ABC):
 
         Raises:
             ValueError: If prompts is not a list.
-            ValueError: If the length of ``callbacks``, ``tags``, ``metadata``, or
-                ``run_name`` (if provided) does not match the length of prompts.
+            ValueError: If the length of `callbacks`, `tags`, `metadata`, or
+                `run_name` (if provided) does not match the length of prompts.
 
         Returns:
             An LLMResult, which contains a list of candidate Generations for each input
@@ -1105,7 +1105,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
         1. Take advantage of batched calls,
         2. Need more output from the model than just the top generated value,
         3. Are building chains that are agnostic to the underlying language model
-           type (e.g., pure text completion models vs chat models).
+            type (e.g., pure text completion models vs chat models).
 
         Args:
             prompts: List of string prompts.
@@ -1126,8 +1126,8 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                 to the model provider API call.
 
         Raises:
-            ValueError: If the length of ``callbacks``, ``tags``, ``metadata``, or
-                ``run_name`` (if provided) does not match the length of prompts.
+            ValueError: If the length of `callbacks`, `tags`, `metadata`, or
+                `run_name` (if provided) does not match the length of prompts.
 
         Returns:
             An LLMResult, which contains a list of candidate Generations for each input
@@ -1340,11 +1340,9 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             ValueError: If the file path is not a string or Path object.
 
         Example:
-
-            .. code-block:: python
-
-                llm.save(file_path="path/llm.yaml")
-
+            ```python
+            llm.save(file_path="path/llm.yaml")
+            ```
         """
         # Convert file to Path object.
         save_path = Path(file_path)

libs/core/langchain_core/load/dump.py

@@ -42,10 +42,9 @@ def dumps(obj: Any, *, pretty: bool = False, **kwargs: Any) -> str:
 
     Args:
         obj: The object to dump.
-        pretty: Whether to pretty print the json. If true, the json will be
-            indented with 2 spaces (if no indent is provided as part of kwargs).
-            Default is False.
-        kwargs: Additional arguments to pass to json.dumps
+        pretty: Whether to pretty print the json. If `True`, the json will be
+            indented with 2 spaces (if no indent is provided as part of `kwargs`).
+        **kwargs: Additional arguments to pass to `json.dumps`
 
     Returns:
         A json string representation of the object.

libs/core/langchain_core/load/load.py

@@ -63,16 +63,13 @@ class Reviver:
         Args:
             secrets_map: A map of secrets to load. If a secret is not found in
                 the map, it will be loaded from the environment if `secrets_from_env`
-                is True. Defaults to `None`.
+                is True.
             valid_namespaces: A list of additional namespaces (modules)
-                to allow to be deserialized. Defaults to `None`.
+                to allow to be deserialized.
             secrets_from_env: Whether to load secrets from the environment.
-                Defaults to `True`.
             additional_import_mappings: A dictionary of additional namespace mappings
                 You can use this to override default mappings or add new mappings.
-                Defaults to `None`.
             ignore_unserializable_fields: Whether to ignore unserializable fields.
-                Defaults to `False`.
         """
         self.secrets_from_env = secrets_from_env
         self.secrets_map = secrets_map or {}
@@ -107,7 +104,7 @@ class Reviver:
             ValueError: If trying to deserialize something that cannot
                 be deserialized in the current version of langchain-core.
             NotImplementedError: If the object is not implemented and
-                ``ignore_unserializable_fields`` is False.
+                `ignore_unserializable_fields` is False.
         """
         if (
             value.get("lc") == 1
@@ -200,16 +197,13 @@ def loads(
         text: The string to load.
         secrets_map: A map of secrets to load. If a secret is not found in
             the map, it will be loaded from the environment if `secrets_from_env`
-            is True. Defaults to `None`.
+            is True.
         valid_namespaces: A list of additional namespaces (modules)
-            to allow to be deserialized. Defaults to `None`.
+            to allow to be deserialized.
         secrets_from_env: Whether to load secrets from the environment.
-            Defaults to `True`.
         additional_import_mappings: A dictionary of additional namespace mappings
             You can use this to override default mappings or add new mappings.
-            Defaults to `None`.
         ignore_unserializable_fields: Whether to ignore unserializable fields.
-            Defaults to `False`.
 
     Returns:
         Revived LangChain objects.
@@ -245,16 +239,13 @@ def load(
         obj: The object to load.
         secrets_map: A map of secrets to load. If a secret is not found in
             the map, it will be loaded from the environment if `secrets_from_env`
-            is True. Defaults to `None`.
+            is True.
         valid_namespaces: A list of additional namespaces (modules)
-            to allow to be deserialized. Defaults to `None`.
+            to allow to be deserialized.
         secrets_from_env: Whether to load secrets from the environment.
-            Defaults to `True`.
         additional_import_mappings: A dictionary of additional namespace mappings
             You can use this to override default mappings or add new mappings.
-            Defaults to `None`.
         ignore_unserializable_fields: Whether to ignore unserializable fields.
-            Defaults to `False`.
 
     Returns:
         Revived LangChain objects.

libs/core/langchain_core/load/serializable.py

@@ -25,16 +25,16 @@ class BaseSerialized(TypedDict):
     id: list[str]
     """The unique identifier of the object."""
     name: NotRequired[str]
-    """The name of the object. Optional."""
+    """The name of the object."""
     graph: NotRequired[dict[str, Any]]
-    """The graph of the object. Optional."""
+    """The graph of the object."""
 
 
 class SerializedConstructor(BaseSerialized):
     """Serialized constructor."""
 
     type: Literal["constructor"]
-    """The type of the object. Must be ``'constructor'``."""
+    """The type of the object. Must be `'constructor'`."""
     kwargs: dict[str, Any]
     """The constructor arguments."""
 
@@ -43,16 +43,16 @@ class SerializedSecret(BaseSerialized):
     """Serialized secret."""
 
     type: Literal["secret"]
-    """The type of the object. Must be ``'secret'``."""
+    """The type of the object. Must be `'secret'`."""
 
 
 class SerializedNotImplemented(BaseSerialized):
     """Serialized not implemented."""
 
     type: Literal["not_implemented"]
-    """The type of the object. Must be ``'not_implemented'``."""
+    """The type of the object. Must be `'not_implemented'`."""
     repr: str | None
-    """The representation of the object. Optional."""
+    """The representation of the object."""
 
 
 def try_neq_default(value: Any, key: str, model: BaseModel) -> bool:
@@ -61,7 +61,7 @@ def try_neq_default(value: Any, key: str, model: BaseModel) -> bool:
     Args:
         value: The value.
         key: The key.
-        model: The pydantic model.
+        model: The Pydantic model.
 
     Returns:
         Whether the value is different from the default.
@@ -93,18 +93,18 @@ class Serializable(BaseModel, ABC):
     It relies on the following methods and properties:
 
     - `is_lc_serializable`: Is this class serializable?
-      By design, even if a class inherits from Serializable, it is not serializable by
-      default. This is to prevent accidental serialization of objects that should not
-      be serialized.
-    - ``get_lc_namespace``: Get the namespace of the langchain object.
-      During deserialization, this namespace is used to identify
-      the correct class to instantiate.
-      Please see the ``Reviver`` class in ``langchain_core.load.load`` for more details.
-      During deserialization an additional mapping is handle
-      classes that have moved or been renamed across package versions.
-    - ``lc_secrets``: A map of constructor argument names to secret ids.
-    - ``lc_attributes``: List of additional attribute names that should be included
-      as part of the serialized representation.
+        By design, even if a class inherits from `Serializable`, it is not serializable
+        by default. This is to prevent accidental serialization of objects that should
+        not be serialized.
+    - `get_lc_namespace`: Get the namespace of the LangChain object.
+        During deserialization, this namespace is used to identify
+        the correct class to instantiate.
+        Please see the `Reviver` class in `langchain_core.load.load` for more details.
+        During deserialization an additional mapping is handle classes that have moved
+        or been renamed across package versions.
+    - `lc_secrets`: A map of constructor argument names to secret ids.
+    - `lc_attributes`: List of additional attribute names that should be included
+        as part of the serialized representation.
     """
 
     # Remove default BaseModel init docstring.
@@ -116,24 +116,24 @@ class Serializable(BaseModel, ABC):
     def is_lc_serializable(cls) -> bool:
         """Is this class serializable?
 
-        By design, even if a class inherits from Serializable, it is not serializable by
-        default. This is to prevent accidental serialization of objects that should not
-        be serialized.
+        By design, even if a class inherits from `Serializable`, it is not serializable
+        by default. This is to prevent accidental serialization of objects that should
+        not be serialized.
 
         Returns:
-            Whether the class is serializable. Default is False.
+            Whether the class is serializable. Default is `False`.
         """
         return False
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         For example, if the class is `langchain.llms.openai.OpenAI`, then the
-        namespace is ["langchain", "llms", "openai"]
+        namespace is `["langchain", "llms", "openai"]`
 
         Returns:
-            The namespace as a list of strings.
+            The namespace.
         """
         return cls.__module__.split(".")
 
@@ -141,8 +141,7 @@ class Serializable(BaseModel, ABC):
     def lc_secrets(self) -> dict[str, str]:
         """A map of constructor argument names to secret ids.
 
-        For example,
-            {"openai_api_key": "OPENAI_API_KEY"}
+        For example, `{"openai_api_key": "OPENAI_API_KEY"}`
         """
         return {}
 
@@ -151,6 +150,7 @@ class Serializable(BaseModel, ABC):
         """List of attribute names that should be included in the serialized kwargs.
 
         These attributes must be accepted by the constructor.
+
         Default is an empty dictionary.
         """
         return {}
@@ -194,7 +194,7 @@ class Serializable(BaseModel, ABC):
             ValueError: If the class has deprecated attributes.
 
         Returns:
-            A json serializable object or a SerializedNotImplemented object.
+            A json serializable object or a `SerializedNotImplemented` object.
         """
         if not self.is_lc_serializable():
             return self.to_json_not_implemented()
@@ -269,7 +269,7 @@ class Serializable(BaseModel, ABC):
         """Serialize a "not implemented" object.
 
         Returns:
-            SerializedNotImplemented.
+            `SerializedNotImplemented`.
         """
         return to_json_not_implemented(self)
 
@@ -284,8 +284,8 @@ def _is_field_useful(inst: Serializable, key: str, value: Any) -> bool:
 
     Returns:
         Whether the field is useful. If the field is required, it is useful.
-        If the field is not required, it is useful if the value is not None.
-        If the field is not required and the value is None, it is useful if the
+        If the field is not required, it is useful if the value is not `None`.
+        If the field is not required and the value is `None`, it is useful if the
         default value is different from the value.
     """
     field = type(inst).model_fields.get(key)
@@ -344,10 +344,10 @@ def to_json_not_implemented(obj: object) -> SerializedNotImplemented:
     """Serialize a "not implemented" object.
 
     Args:
-        obj: object to serialize.
+        obj: Object to serialize.
 
     Returns:
-        SerializedNotImplemented
+        `SerializedNotImplemented`
     """
     id_: list[str] = []
     try:

libs/core/langchain_core/messages/__init__.py

@@ -1,19 +1,4 @@
-"""**Messages** are objects used in prompts and chat conversations.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseMessage --> SystemMessage, AIMessage, HumanMessage, ChatMessage, FunctionMessage, ToolMessage
-                --> BaseMessageChunk --> SystemMessageChunk, AIMessageChunk, HumanMessageChunk, ChatMessageChunk, FunctionMessageChunk, ToolMessageChunk
-
-**Main helpers:**
-
-.. code-block::
-
-    ChatPromptTemplate
-
-"""  # noqa: E501
+"""**Messages** are objects used in prompts and chat conversations."""
 
 from typing import TYPE_CHECKING
 

libs/core/langchain_core/messages/ai.py

@@ -40,13 +40,13 @@ class InputTokenDetails(TypedDict, total=False):
     Does *not* need to sum to full input token count. Does *not* need to have all keys.
 
     Example:
-        .. code-block:: python
-
-            {
-                "audio": 10,
-                "cache_creation": 200,
-                "cache_read": 100,
-            }
+        ```python
+        {
+            "audio": 10,
+            "cache_creation": 200,
+            "cache_read": 100,
+        }
+        ```
 
     !!! version-added "Added in version 0.3.9"
 
@@ -76,12 +76,12 @@ class OutputTokenDetails(TypedDict, total=False):
     Does *not* need to sum to full output token count. Does *not* need to have all keys.
 
     Example:
-        .. code-block:: python
-
-            {
-                "audio": 10,
-                "reasoning": 200,
-            }
+        ```python
+        {
+            "audio": 10,
+            "reasoning": 200,
+        }
+        ```
 
     !!! version-added "Added in version 0.3.9"
 
@@ -104,22 +104,22 @@ class UsageMetadata(TypedDict):
     This is a standard representation of token usage that is consistent across models.
 
     Example:
-        .. code-block:: python
-
-            {
-                "input_tokens": 350,
-                "output_tokens": 240,
-                "total_tokens": 590,
-                "input_token_details": {
-                    "audio": 10,
-                    "cache_creation": 200,
-                    "cache_read": 100,
-                },
-                "output_token_details": {
-                    "audio": 10,
-                    "reasoning": 200,
-                },
-            }
+        ```python
+        {
+            "input_tokens": 350,
+            "output_tokens": 240,
+            "total_tokens": 590,
+            "input_token_details": {
+                "audio": 10,
+                "cache_creation": 200,
+                "cache_read": 100,
+            },
+            "output_token_details": {
+                "audio": 10,
+                "reasoning": 200,
+            },
+        }
+        ```
 
     !!! warning "Behavior changed in 0.3.9"
         Added `input_token_details` and `output_token_details`.
@@ -148,27 +148,26 @@ class UsageMetadata(TypedDict):
 class AIMessage(BaseMessage):
     """Message from an AI.
 
-    AIMessage is returned from a chat model as a response to a prompt.
+    An `AIMessage` is returned from a chat model as a response to a prompt.
 
     This message represents the output of the model and consists of both
-    the raw output as returned by the model together standardized fields
+    the raw output as returned by the model and standardized fields
     (e.g., tool calls, usage metadata) added by the LangChain framework.
 
     """
 
     tool_calls: list[ToolCall] = []
-    """If provided, tool calls associated with the message."""
+    """If present, tool calls associated with the message."""
     invalid_tool_calls: list[InvalidToolCall] = []
-    """If provided, tool calls with parsing errors associated with the message."""
+    """If present, tool calls with parsing errors associated with the message."""
     usage_metadata: UsageMetadata | None = None
-    """If provided, usage metadata for a message, such as token counts.
+    """If present, usage metadata for a message, such as token counts.
 
     This is a standard representation of token usage that is consistent across models.
-
     """
 
     type: Literal["ai"] = "ai"
-    """The type of the message (used for deserialization). Defaults to "ai"."""
+    """The type of the message (used for deserialization)."""
 
     @overload
     def __init__(
@@ -191,14 +190,14 @@ class AIMessage(BaseMessage):
         content_blocks: list[types.ContentBlock] | None = None,
         **kwargs: Any,
     ) -> None:
-        """Initialize `AIMessage`.
+        """Initialize an `AIMessage`.
 
-        Specify ``content`` as positional arg or ``content_blocks`` for typing.
+        Specify `content` as positional arg or `content_blocks` for typing.
 
         Args:
             content: The content of the message.
             content_blocks: Typed standard content.
-            kwargs: Additional arguments to pass to the parent class.
+            **kwargs: Additional arguments to pass to the parent class.
         """
         if content_blocks is not None:
             # If there are tool calls in content_blocks, but not in tool_calls, add them
@@ -217,7 +216,11 @@ class AIMessage(BaseMessage):
 
     @property
     def lc_attributes(self) -> dict:
-        """Attrs to be serialized even if they are derived from other init args."""
+        """Attributes to be serialized.
+
+        Includes all attributes, even if they are derived from other initialization
+        arguments.
+        """
         return {
             "tool_calls": self.tool_calls,
             "invalid_tool_calls": self.invalid_tool_calls,
@@ -225,7 +228,7 @@ class AIMessage(BaseMessage):
 
     @property
     def content_blocks(self) -> list[types.ContentBlock]:
-        """Return content blocks of the message.
+        """Return standard, typed `ContentBlock` dicts from the message.
 
         If the message has a known model provider, use the provider-specific translator
         first before falling back to best-effort parsing. For details, see the property
@@ -331,11 +334,10 @@ class AIMessage(BaseMessage):
 
     @override
     def pretty_repr(self, html: bool = False) -> str:
-        """Return a pretty representation of the message.
+        """Return a pretty representation of the message for display.
 
         Args:
             html: Whether to return an HTML-formatted string.
-                 Defaults to `False`.
 
         Returns:
             A pretty representation of the message.
@@ -372,31 +374,27 @@ class AIMessage(BaseMessage):
 
 
 class AIMessageChunk(AIMessage, BaseMessageChunk):
-    """Message chunk from an AI."""
+    """Message chunk from an AI (yielded when streaming)."""
 
     # Ignoring mypy re-assignment here since we're overriding the value
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["AIMessageChunk"] = "AIMessageChunk"  # type: ignore[assignment]
-    """The type of the message (used for deserialization).
-
-    Defaults to ``AIMessageChunk``.
-
-    """
+    """The type of the message (used for deserialization)."""
 
     tool_call_chunks: list[ToolCallChunk] = []
     """If provided, tool call chunks associated with the message."""
 
     chunk_position: Literal["last"] | None = None
-    """Optional span represented by an aggregated AIMessageChunk.
+    """Optional span represented by an aggregated `AIMessageChunk`.
 
-    If a chunk with ``chunk_position="last"`` is aggregated into a stream,
-    ``tool_call_chunks`` in message content will be parsed into `tool_calls`.
+    If a chunk with `chunk_position="last"` is aggregated into a stream,
+    `tool_call_chunks` in message content will be parsed into `tool_calls`.
     """
 
     @property
     def lc_attributes(self) -> dict:
-        """Attrs to be serialized even if they are derived from other init args."""
+        """Attributes to be serialized, even if they are derived from other initialization args."""  # noqa: E501
         return {
             "tool_calls": self.tool_calls,
             "invalid_tool_calls": self.invalid_tool_calls,
@@ -404,7 +402,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
 
     @property
     def content_blocks(self) -> list[types.ContentBlock]:
-        """Return content blocks of the message."""
+        """Return standard, typed `ContentBlock` dicts from the message."""
         if self.response_metadata.get("output_version") == "v1":
             return cast("list[types.ContentBlock]", self.content)
 
@@ -545,12 +543,15 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
                     and call_id in id_to_tc
                 ):
                     self.content[idx] = cast("dict[str, Any]", id_to_tc[call_id])
+                    if "extras" in block:
+                        # mypy does not account for instance check for dict above
+                        self.content[idx]["extras"] = block["extras"]  # type: ignore[index]
 
         return self
 
     @model_validator(mode="after")
     def init_server_tool_calls(self) -> Self:
-        """Parse server_tool_call_chunks."""
+        """Parse `server_tool_call_chunks`."""
         if (
             self.chunk_position == "last"
             and self.response_metadata.get("output_version") == "v1"
@@ -596,14 +597,14 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
 def add_ai_message_chunks(
     left: AIMessageChunk, *others: AIMessageChunk
 ) -> AIMessageChunk:
-    """Add multiple ``AIMessageChunk``s together.
+    """Add multiple `AIMessageChunk`s together.
 
     Args:
-        left: The first ``AIMessageChunk``.
-        *others: Other ``AIMessageChunk``s to add.
+        left: The first `AIMessageChunk`.
+        *others: Other `AIMessageChunk`s to add.
 
     Returns:
-        The resulting ``AIMessageChunk``.
+        The resulting `AIMessageChunk`.
 
     """
     content = merge_content(left.content, *(o.content for o in others))
@@ -681,43 +682,42 @@ def add_usage(left: UsageMetadata | None, right: UsageMetadata | None) -> UsageM
     """Recursively add two UsageMetadata objects.
 
     Example:
-        .. code-block:: python
+        ```python
+        from langchain_core.messages.ai import add_usage
 
-            from langchain_core.messages.ai import add_usage
+        left = UsageMetadata(
+            input_tokens=5,
+            output_tokens=0,
+            total_tokens=5,
+            input_token_details=InputTokenDetails(cache_read=3),
+        )
+        right = UsageMetadata(
+            input_tokens=0,
+            output_tokens=10,
+            total_tokens=10,
+            output_token_details=OutputTokenDetails(reasoning=4),
+        )
 
-            left = UsageMetadata(
-                input_tokens=5,
-                output_tokens=0,
-                total_tokens=5,
-                input_token_details=InputTokenDetails(cache_read=3),
-            )
-            right = UsageMetadata(
-                input_tokens=0,
-                output_tokens=10,
-                total_tokens=10,
-                output_token_details=OutputTokenDetails(reasoning=4),
-            )
-
-            add_usage(left, right)
+        add_usage(left, right)
+        ```
 
         results in
 
-        .. code-block:: python
-
-            UsageMetadata(
-                input_tokens=5,
-                output_tokens=10,
-                total_tokens=15,
-                input_token_details=InputTokenDetails(cache_read=3),
-                output_token_details=OutputTokenDetails(reasoning=4),
-            )
-
+        ```python
+        UsageMetadata(
+            input_tokens=5,
+            output_tokens=10,
+            total_tokens=15,
+            input_token_details=InputTokenDetails(cache_read=3),
+            output_token_details=OutputTokenDetails(reasoning=4),
+        )
+        ```
     Args:
-        left: The first ``UsageMetadata`` object.
-        right: The second ``UsageMetadata`` object.
+        left: The first `UsageMetadata` object.
+        right: The second `UsageMetadata` object.
 
     Returns:
-        The sum of the two ``UsageMetadata`` objects.
+        The sum of the two `UsageMetadata` objects.
 
     """
     if not (left or right):
@@ -740,48 +740,47 @@ def add_usage(left: UsageMetadata | None, right: UsageMetadata | None) -> UsageM
 def subtract_usage(
     left: UsageMetadata | None, right: UsageMetadata | None
 ) -> UsageMetadata:
-    """Recursively subtract two ``UsageMetadata`` objects.
+    """Recursively subtract two `UsageMetadata` objects.
 
-    Token counts cannot be negative so the actual operation is ``max(left - right, 0)``.
+    Token counts cannot be negative so the actual operation is `max(left - right, 0)`.
 
     Example:
-        .. code-block:: python
+        ```python
+        from langchain_core.messages.ai import subtract_usage
 
-            from langchain_core.messages.ai import subtract_usage
+        left = UsageMetadata(
+            input_tokens=5,
+            output_tokens=10,
+            total_tokens=15,
+            input_token_details=InputTokenDetails(cache_read=4),
+        )
+        right = UsageMetadata(
+            input_tokens=3,
+            output_tokens=8,
+            total_tokens=11,
+            output_token_details=OutputTokenDetails(reasoning=4),
+        )
 
-            left = UsageMetadata(
-                input_tokens=5,
-                output_tokens=10,
-                total_tokens=15,
-                input_token_details=InputTokenDetails(cache_read=4),
-            )
-            right = UsageMetadata(
-                input_tokens=3,
-                output_tokens=8,
-                total_tokens=11,
-                output_token_details=OutputTokenDetails(reasoning=4),
-            )
-
-            subtract_usage(left, right)
+        subtract_usage(left, right)
+        ```
 
         results in
 
-        .. code-block:: python
-
-            UsageMetadata(
-                input_tokens=2,
-                output_tokens=2,
-                total_tokens=4,
-                input_token_details=InputTokenDetails(cache_read=4),
-                output_token_details=OutputTokenDetails(reasoning=0),
-            )
-
+        ```python
+        UsageMetadata(
+            input_tokens=2,
+            output_tokens=2,
+            total_tokens=4,
+            input_token_details=InputTokenDetails(cache_read=4),
+            output_token_details=OutputTokenDetails(reasoning=0),
+        )
+        ```
     Args:
-        left: The first ``UsageMetadata`` object.
-        right: The second ``UsageMetadata`` object.
+        left: The first `UsageMetadata` object.
+        right: The second `UsageMetadata` object.
 
     Returns:
-        The resulting ``UsageMetadata`` after subtraction.
+        The resulting `UsageMetadata` after subtraction.
 
     """
     if not (left or right):

libs/core/langchain_core/messages/base.py

@@ -48,13 +48,13 @@ class TextAccessor(str):
 
     Exists to maintain backward compatibility while transitioning from method-based to
     property-based text access in message objects. In LangChain <v1.0, message text was
-    accessed via ``.text()`` method calls. In v1.0=<, the preferred pattern is property
-    access via ``.text``.
+    accessed via `.text()` method calls. In v1.0=<, the preferred pattern is property
+    access via `.text`.
 
-    Rather than breaking existing code immediately, ``TextAccessor`` allows both
+    Rather than breaking existing code immediately, `TextAccessor` allows both
     patterns:
-    - Modern property access: ``message.text`` (returns string directly)
-    - Legacy method access: ``message.text()`` (callable, emits deprecation warning)
+    - Modern property access: `message.text` (returns string directly)
+    - Legacy method access: `message.text()` (callable, emits deprecation warning)
 
     """
 
@@ -67,12 +67,12 @@ class TextAccessor(str):
     def __call__(self) -> str:
         """Enable method-style text access for backward compatibility.
 
-        This method exists solely to support legacy code that calls ``.text()``
-        as a method. New code should use property access (``.text``) instead.
+        This method exists solely to support legacy code that calls `.text()`
+        as a method. New code should use property access (`.text`) instead.
 
         !!! deprecated
-            As of `langchain-core` 1.0.0, calling ``.text()`` as a method is deprecated.
-            Use ``.text`` as a property instead. This method will be removed in 2.0.0.
+            As of `langchain-core` 1.0.0, calling `.text()` as a method is deprecated.
+            Use `.text` as a property instead. This method will be removed in 2.0.0.
 
         Returns:
             The string content, identical to property access.
@@ -92,11 +92,11 @@ class TextAccessor(str):
 class BaseMessage(Serializable):
     """Base abstract message class.
 
-    Messages are the inputs and outputs of a ``ChatModel``.
+    Messages are the inputs and outputs of a chat model.
     """
 
     content: str | list[str | dict]
-    """The string contents of the message."""
+    """The contents of the message."""
 
     additional_kwargs: dict = Field(default_factory=dict)
     """Reserved for additional payload data associated with the message.
@@ -159,14 +159,14 @@ class BaseMessage(Serializable):
         content_blocks: list[types.ContentBlock] | None = None,
         **kwargs: Any,
     ) -> None:
-        """Initialize `BaseMessage`.
+        """Initialize a `BaseMessage`.
 
-        Specify ``content`` as positional arg or ``content_blocks`` for typing.
+        Specify `content` as positional arg or `content_blocks` for typing.
 
         Args:
-            content: The string contents of the message.
+            content: The contents of the message.
             content_blocks: Typed standard content.
-            kwargs: Additional arguments to pass to the parent class.
+            **kwargs: Additional arguments to pass to the parent class.
         """
         if content_blocks is not None:
             super().__init__(content=content_blocks, **kwargs)
@@ -184,10 +184,10 @@ class BaseMessage(Serializable):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "schema", "messages"]``
+            `["langchain", "schema", "messages"]`
         """
         return ["langchain", "schema", "messages"]
 
@@ -259,11 +259,11 @@ class BaseMessage(Serializable):
     def text(self) -> TextAccessor:
         """Get the text content of the message as a string.
 
-        Can be used as both property (``message.text``) and method (``message.text()``).
+        Can be used as both property (`message.text`) and method (`message.text()`).
 
         !!! deprecated
-            As of langchain-core 1.0.0, calling ``.text()`` as a method is deprecated.
-            Use ``.text`` as a property instead. This method will be removed in 2.0.0.
+            As of `langchain-core` 1.0.0, calling `.text()` as a method is deprecated.
+            Use `.text` as a property instead. This method will be removed in 2.0.0.
 
         Returns:
             The text content of the message.
@@ -307,7 +307,7 @@ class BaseMessage(Serializable):
 
         Args:
             html: Whether to format the message as HTML. If `True`, the message will be
-                formatted with HTML tags. Default is False.
+                formatted with HTML tags.
 
         Returns:
             A pretty representation of the message.
@@ -331,8 +331,8 @@ def merge_content(
     """Merge multiple message contents.
 
     Args:
-        first_content: The first ``content``. Can be a string or a list.
-        contents: The other ``content``s. Can be a string or a list.
+        first_content: The first `content`. Can be a string or a list.
+        contents: The other `content`s. Can be a string or a list.
 
     Returns:
         The merged content.
@@ -388,9 +388,9 @@ class BaseMessageChunk(BaseMessage):
 
         For example,
 
-        ``AIMessageChunk(content="Hello") + AIMessageChunk(content=" World")``
+        `AIMessageChunk(content="Hello") + AIMessageChunk(content=" World")`
 
-        will give ``AIMessageChunk(content="Hello World")``
+        will give `AIMessageChunk(content="Hello World")`
 
         """
         if isinstance(other, BaseMessageChunk):
@@ -440,7 +440,7 @@ def message_to_dict(message: BaseMessage) -> dict:
 
     Returns:
         Message as a dict. The dict will have a `type` key with the message type
-        and a ``data`` key with the message data as a dict.
+        and a `data` key with the message data as a dict.
 
     """
     return {"type": message.type, "data": message.model_dump()}
@@ -464,7 +464,7 @@ def get_msg_title_repr(title: str, *, bold: bool = False) -> str:
 
     Args:
         title: The title.
-        bold: Whether to bold the title. Default is False.
+        bold: Whether to bold the title.
 
     Returns:
         The title representation.

libs/core/langchain_core/messages/block_translators/__init__.py

@@ -1,7 +1,7 @@
 """Derivations of standard content blocks from provider content.
 
 `AIMessage` will first attempt to use a provider-specific translator if
-``model_provider`` is set in `response_metadata` on the message. Consequently, each
+`model_provider` is set in `response_metadata` on the message. Consequently, each
 provider translator must handle all possible content response types from the provider,
 including text.
 
@@ -23,13 +23,13 @@ if TYPE_CHECKING:
 PROVIDER_TRANSLATORS: dict[str, dict[str, Callable[..., list[types.ContentBlock]]]] = {}
 """Map model provider names to translator functions.
 
-The dictionary maps provider names (e.g. ``'openai'``, ``'anthropic'``) to another
+The dictionary maps provider names (e.g. `'openai'`, `'anthropic'`) to another
 dictionary with two keys:
-- ``'translate_content'``: Function to translate `AIMessage` content.
-- ``'translate_content_chunk'``: Function to translate ``AIMessageChunk`` content.
+- `'translate_content'`: Function to translate `AIMessage` content.
+- `'translate_content_chunk'`: Function to translate `AIMessageChunk` content.
 
-When calling `.content_blocks` on an `AIMessage` or ``AIMessageChunk``, if
-``model_provider`` is set in `response_metadata`, the corresponding translator
+When calling `content_blocks` on an `AIMessage` or `AIMessageChunk`, if
+`model_provider` is set in `response_metadata`, the corresponding translator
 functions will be used to parse the content into blocks. Otherwise, best-effort parsing
 in `BaseMessage` will be used.
 """
@@ -43,9 +43,9 @@ def register_translator(
     """Register content translators for a provider in `PROVIDER_TRANSLATORS`.
 
     Args:
-        provider: The model provider name (e.g. ``'openai'``, ``'anthropic'``).
+        provider: The model provider name (e.g. `'openai'`, `'anthropic'`).
         translate_content: Function to translate `AIMessage` content.
-        translate_content_chunk: Function to translate ``AIMessageChunk`` content.
+        translate_content_chunk: Function to translate `AIMessageChunk` content.
     """
     PROVIDER_TRANSLATORS[provider] = {
         "translate_content": translate_content,
@@ -62,7 +62,7 @@ def get_translator(
         provider: The model provider name.
 
     Returns:
-        Dictionary with ``'translate_content'`` and ``'translate_content_chunk'``
+        Dictionary with `'translate_content'` and `'translate_content_chunk'`
         functions, or None if no translator is registered for the provider. In such
         case, best-effort parsing in `BaseMessage` will be used.
     """
@@ -72,10 +72,10 @@ def get_translator(
 def _register_translators() -> None:
     """Register all translators in langchain-core.
 
-    A unit test ensures all modules in ``block_translators`` are represented here.
+    A unit test ensures all modules in `block_translators` are represented here.
 
     For translators implemented outside langchain-core, they can be registered by
-    calling ``register_translator`` from within the integration package.
+    calling `register_translator` from within the integration package.
     """
     from langchain_core.messages.block_translators.anthropic import (  # noqa: PLC0415
         _register_anthropic_translator,

libs/core/langchain_core/messages/block_translators/anthropic.py

@@ -31,12 +31,12 @@ def _convert_to_v1_from_anthropic_input(
 ) -> list[types.ContentBlock]:
     """Convert Anthropic format blocks to v1 format.
 
-    During the `.content_blocks` parsing process, we wrap blocks not recognized as a v1
-    block as a ``'non_standard'`` block with the original block stored in the ``value``
+    During the `content_blocks` parsing process, we wrap blocks not recognized as a v1
+    block as a `'non_standard'` block with the original block stored in the `value`
     field. This function attempts to unpack those blocks and convert any blocks that
     might be Anthropic format to v1 ContentBlocks.
 
-    If conversion fails, the block is left as a ``'non_standard'`` block.
+    If conversion fails, the block is left as a `'non_standard'` block.
 
     Args:
         content: List of content blocks to process.

libs/core/langchain_core/messages/block_translators/bedrock_converse.py

@@ -35,12 +35,12 @@ def _convert_to_v1_from_converse_input(
 ) -> list[types.ContentBlock]:
     """Convert Bedrock Converse format blocks to v1 format.
 
-    During the `.content_blocks` parsing process, we wrap blocks not recognized as a v1
-    block as a ``'non_standard'`` block with the original block stored in the ``value``
+    During the `content_blocks` parsing process, we wrap blocks not recognized as a v1
+    block as a `'non_standard'` block with the original block stored in the `value`
     field. This function attempts to unpack those blocks and convert any blocks that
     might be Converse format to v1 ContentBlocks.
 
-    If conversion fails, the block is left as a ``'non_standard'`` block.
+    If conversion fails, the block is left as a `'non_standard'` block.
 
     Args:
         content: List of content blocks to process.

libs/core/langchain_core/messages/block_translators/google_genai.py

@@ -105,12 +105,12 @@ def _convert_to_v1_from_genai_input(
     Called when message isn't an `AIMessage` or `model_provider` isn't set on
     `response_metadata`.
 
-    During the `.content_blocks` parsing process, we wrap blocks not recognized as a v1
-    block as a ``'non_standard'`` block with the original block stored in the ``value``
+    During the `content_blocks` parsing process, we wrap blocks not recognized as a v1
+    block as a `'non_standard'` block with the original block stored in the `value`
     field. This function attempts to unpack those blocks and convert any blocks that
     might be GenAI format to v1 ContentBlocks.
 
-    If conversion fails, the block is left as a ``'non_standard'`` block.
+    If conversion fails, the block is left as a `'non_standard'` block.
 
     Args:
         content: List of content blocks to process.
@@ -453,9 +453,10 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                     "status": status,  # type: ignore[typeddict-item]
                     "output": item.get("code_execution_result", ""),
                 }
+                server_tool_result_block["extras"] = {"block_type": item_type}
                 # Preserve original outcome in extras
                 if outcome is not None:
-                    server_tool_result_block["extras"] = {"outcome": outcome}
+                    server_tool_result_block["extras"]["outcome"] = outcome
                 converted_blocks.append(server_tool_result_block)
             else:
                 # Unknown type, preserve as non-standard

libs/core/langchain_core/messages/block_translators/google_vertexai.py

@@ -1,37 +1,9 @@
 """Derivations of standard content blocks from Google (VertexAI) content."""
 
-import warnings
-
-from langchain_core.messages import AIMessage, AIMessageChunk
-from langchain_core.messages import content as types
-
-WARNED = False
-
-
-def translate_content(message: AIMessage) -> list[types.ContentBlock]:  # noqa: ARG001
-    """Derive standard content blocks from a message with Google (VertexAI) content."""
-    global WARNED  # noqa: PLW0603
-    if not WARNED:
-        warning_message = (
-            "Content block standardization is not yet fully supported for Google "
-            "VertexAI."
-        )
-        warnings.warn(warning_message, stacklevel=2)
-        WARNED = True
-    raise NotImplementedError
-
-
-def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:  # noqa: ARG001
-    """Derive standard content blocks from a chunk with Google (VertexAI) content."""
-    global WARNED  # noqa: PLW0603
-    if not WARNED:
-        warning_message = (
-            "Content block standardization is not yet fully supported for Google "
-            "VertexAI."
-        )
-        warnings.warn(warning_message, stacklevel=2)
-        WARNED = True
-    raise NotImplementedError
+from langchain_core.messages.block_translators.google_genai import (
+    translate_content,
+    translate_content_chunk,
+)
 
 
 def _register_google_vertexai_translator() -> None:

libs/core/langchain_core/messages/block_translators/groq.py

@@ -1,39 +1,135 @@
 """Derivations of standard content blocks from Groq content."""
 
-import warnings
+import json
+import re
+from typing import Any
 
 from langchain_core.messages import AIMessage, AIMessageChunk
 from langchain_core.messages import content as types
-
-WARNED = False
+from langchain_core.messages.base import _extract_reasoning_from_additional_kwargs
 
 
-def translate_content(message: AIMessage) -> list[types.ContentBlock]:  # noqa: ARG001
-    """Derive standard content blocks from a message with Groq content."""
-    global WARNED  # noqa: PLW0603
-    if not WARNED:
-        warning_message = (
-            "Content block standardization is not yet fully supported for Groq."
+def _populate_extras(
+    standard_block: types.ContentBlock, block: dict[str, Any], known_fields: set[str]
+) -> types.ContentBlock:
+    """Mutate a block, populating extras."""
+    if standard_block.get("type") == "non_standard":
+        return standard_block
+
+    for key, value in block.items():
+        if key not in known_fields:
+            if "extras" not in standard_block:
+                # Below type-ignores are because mypy thinks a non-standard block can
+                # get here, although we exclude them above.
+                standard_block["extras"] = {}  # type: ignore[typeddict-unknown-key]
+            standard_block["extras"][key] = value  # type: ignore[typeddict-item]
+
+    return standard_block
+
+
+def _parse_code_json(s: str) -> dict:
+    """Extract Python code from Groq built-in tool content.
+
+    Extracts the value of the 'code' field from a string of the form:
+    {"code": some_arbitrary_text_with_unescaped_quotes}
+
+    As Groq may not escape quotes in the executed tools, e.g.:
+    ```
+    '{"code": "import math; print("The square root of 101 is: "); print(math.sqrt(101))"}'
+    ```
+    """  # noqa: E501
+    m = re.fullmatch(r'\s*\{\s*"code"\s*:\s*"(.*)"\s*\}\s*', s, flags=re.DOTALL)
+    if not m:
+        msg = (
+            "Could not extract Python code from Groq tool arguments. "
+            "Expected a JSON object with a 'code' field."
         )
-        warnings.warn(warning_message, stacklevel=2)
-        WARNED = True
-    raise NotImplementedError
+        raise ValueError(msg)
+    return {"code": m.group(1)}
 
 
-def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:  # noqa: ARG001
-    """Derive standard content blocks from a message chunk with Groq content."""
-    global WARNED  # noqa: PLW0603
-    if not WARNED:
-        warning_message = (
-            "Content block standardization is not yet fully supported for Groq."
+def _convert_to_v1_from_groq(message: AIMessage) -> list[types.ContentBlock]:
+    """Convert groq message content to v1 format."""
+    content_blocks: list[types.ContentBlock] = []
+
+    if reasoning_block := _extract_reasoning_from_additional_kwargs(message):
+        content_blocks.append(reasoning_block)
+
+    if executed_tools := message.additional_kwargs.get("executed_tools"):
+        for idx, executed_tool in enumerate(executed_tools):
+            args: dict[str, Any] | None = None
+            if arguments := executed_tool.get("arguments"):
+                try:
+                    args = json.loads(arguments)
+                except json.JSONDecodeError:
+                    if executed_tool.get("type") == "python":
+                        try:
+                            args = _parse_code_json(arguments)
+                        except ValueError:
+                            continue
+                    elif (
+                        executed_tool.get("type") == "function"
+                        and executed_tool.get("name") == "python"
+                    ):
+                        # GPT-OSS
+                        args = {"code": arguments}
+                    else:
+                        continue
+            if isinstance(args, dict):
+                name = ""
+                if executed_tool.get("type") == "search":
+                    name = "web_search"
+                elif executed_tool.get("type") == "python" or (
+                    executed_tool.get("type") == "function"
+                    and executed_tool.get("name") == "python"
+                ):
+                    name = "code_interpreter"
+                server_tool_call: types.ServerToolCall = {
+                    "type": "server_tool_call",
+                    "name": name,
+                    "id": str(idx),
+                    "args": args,
+                }
+                content_blocks.append(server_tool_call)
+            if tool_output := executed_tool.get("output"):
+                tool_result: types.ServerToolResult = {
+                    "type": "server_tool_result",
+                    "tool_call_id": str(idx),
+                    "output": tool_output,
+                    "status": "success",
+                }
+                known_fields = {"type", "arguments", "index", "output"}
+                _populate_extras(tool_result, executed_tool, known_fields)
+                content_blocks.append(tool_result)
+
+    if isinstance(message.content, str) and message.content:
+        content_blocks.append({"type": "text", "text": message.content})
+
+    for tool_call in message.tool_calls:
+        content_blocks.append(  # noqa: PERF401
+            {
+                "type": "tool_call",
+                "name": tool_call["name"],
+                "args": tool_call["args"],
+                "id": tool_call.get("id"),
+            }
         )
-        warnings.warn(warning_message, stacklevel=2)
-        WARNED = True
-    raise NotImplementedError
+
+    return content_blocks
+
+
+def translate_content(message: AIMessage) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message with groq content."""
+    return _convert_to_v1_from_groq(message)
+
+
+def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
+    """Derive standard content blocks from a message chunk with groq content."""
+    return _convert_to_v1_from_groq(message)
 
 
 def _register_groq_translator() -> None:
-    """Register the Groq translator with the central registry.
+    """Register the groq translator with the central registry.
 
     Run automatically when the module is imported.
     """

libs/core/langchain_core/messages/block_translators/langchain_v0.py

@@ -10,12 +10,12 @@ def _convert_v0_multimodal_input_to_v1(
 ) -> list[types.ContentBlock]:
     """Convert v0 multimodal blocks to v1 format.
 
-    During the `.content_blocks` parsing process, we wrap blocks not recognized as a v1
-    block as a ``'non_standard'`` block with the original block stored in the ``value``
+    During the `content_blocks` parsing process, we wrap blocks not recognized as a v1
+    block as a `'non_standard'` block with the original block stored in the `value`
     field. This function attempts to unpack those blocks and convert any v0 format
     blocks to v1 format.
 
-    If conversion fails, the block is left as a ``'non_standard'`` block.
+    If conversion fails, the block is left as a `'non_standard'` block.
 
     Args:
         content: List of content blocks to process.

libs/core/langchain_core/messages/block_translators/openai.py

@@ -18,7 +18,7 @@ if TYPE_CHECKING:
 
 
 def convert_to_openai_image_block(block: dict[str, Any]) -> dict:
-    """Convert ``ImageContentBlock`` to format expected by OpenAI Chat Completions."""
+    """Convert `ImageContentBlock` to format expected by OpenAI Chat Completions."""
     if "url" in block:
         return {
             "type": "image_url",
@@ -155,12 +155,12 @@ def _convert_to_v1_from_chat_completions_input(
 ) -> list[types.ContentBlock]:
     """Convert OpenAI Chat Completions format blocks to v1 format.
 
-    During the `.content_blocks` parsing process, we wrap blocks not recognized as a v1
-    block as a ``'non_standard'`` block with the original block stored in the ``value``
+    During the `content_blocks` parsing process, we wrap blocks not recognized as a v1
+    block as a `'non_standard'` block with the original block stored in the `value`
     field. This function attempts to unpack those blocks and convert any blocks that
     might be OpenAI format to v1 ContentBlocks.
 
-    If conversion fails, the block is left as a ``'non_standard'`` block.
+    If conversion fails, the block is left as a `'non_standard'` block.
 
     Args:
         content: List of content blocks to process.
@@ -263,7 +263,7 @@ _FUNCTION_CALL_IDS_MAP_KEY = "__openai_function_call_ids__"
 
 
 def _convert_from_v03_ai_message(message: AIMessage) -> AIMessage:
-    """Convert v0 AIMessage into ``output_version="responses/v1"`` format."""
+    """Convert v0 AIMessage into `output_version="responses/v1"` format."""
     from langchain_core.messages import AIMessageChunk  # noqa: PLC0415
 
     # Only update ChatOpenAI v0.3 AIMessages

libs/core/langchain_core/messages/chat.py

@@ -19,7 +19,7 @@ class ChatMessage(BaseMessage):
     """The speaker / role of the Message."""
 
     type: Literal["chat"] = "chat"
-    """The type of the message (used during serialization). Defaults to "chat"."""
+    """The type of the message (used during serialization)."""
 
 
 class ChatMessageChunk(ChatMessage, BaseMessageChunk):
@@ -29,11 +29,7 @@ class ChatMessageChunk(ChatMessage, BaseMessageChunk):
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["ChatMessageChunk"] = "ChatMessageChunk"  # type: ignore[assignment]
-    """The type of the message (used during serialization).
-
-    Defaults to ``'ChatMessageChunk'``.
-
-    """
+    """The type of the message (used during serialization)."""
 
     @override
     def __add__(self, other: Any) -> BaseMessageChunk:  # type: ignore[override]

libs/core/langchain_core/messages/content.py

@@ -5,7 +5,7 @@
     change in future releases.
 
 This module provides standardized data structures for representing inputs to and
-outputs from LLMs. The core abstraction is the **Content Block**, a ``TypedDict``.
+outputs from LLMs. The core abstraction is the **Content Block**, a `TypedDict`.
 
 **Rationale**
 
@@ -20,59 +20,59 @@ blocks into the format required by its API.
 **Extensibility**
 
 Data **not yet mapped** to a standard block may be represented using the
-``NonStandardContentBlock``, which allows for provider-specific data to be included
+`NonStandardContentBlock`, which allows for provider-specific data to be included
 without losing the benefits of type checking and validation.
 
 Furthermore, provider-specific fields **within** a standard block are fully supported
-by default in the ``extras`` field of each block. This allows for additional metadata
+by default in the `extras` field of each block. This allows for additional metadata
 to be included without breaking the standard structure.
 
 !!! warning
-    Do not heavily rely on the ``extras`` field for provider-specific data! This field
+    Do not heavily rely on the `extras` field for provider-specific data! This field
     is subject to deprecation in future releases as we move towards PEP 728.
 
 !!! note
-    Following widespread adoption of `PEP 728 <https://peps.python.org/pep-0728/>`__, we
-    will add ``extra_items=Any`` as a param to Content Blocks. This will signify to type
+    Following widespread adoption of [PEP 728](https://peps.python.org/pep-0728/), we
+    will add `extra_items=Any` as a param to Content Blocks. This will signify to type
     checkers that additional provider-specific fields are allowed outside of the
-    ``extras`` field, and that will become the new standard approach to adding
+    `extras` field, and that will become the new standard approach to adding
     provider-specific metadata.
 
     ??? note
 
         **Example with PEP 728 provider-specific fields:**
 
-        .. code-block:: python
+        ```python
+        # Content block definition
+        # NOTE: `extra_items=Any`
+        class TextContentBlock(TypedDict, extra_items=Any):
+            type: Literal["text"]
+            id: NotRequired[str]
+            text: str
+            annotations: NotRequired[list[Annotation]]
+            index: NotRequired[int]
+        ```
 
-            # Content block definition
-            # NOTE: `extra_items=Any`
-            class TextContentBlock(TypedDict, extra_items=Any):
-                type: Literal["text"]
-                id: NotRequired[str]
-                text: str
-                annotations: NotRequired[list[Annotation]]
-                index: NotRequired[int]
+        ```python
+        from langchain_core.messages.content import TextContentBlock
 
-        .. code-block:: python
+        # Create a text content block with provider-specific fields
+        my_block: TextContentBlock = {
+            # Add required fields
+            "type": "text",
+            "text": "Hello, world!",
+            # Additional fields not specified in the TypedDict
+            # These are valid with PEP 728 and are typed as Any
+            "openai_metadata": {"model": "gpt-4", "temperature": 0.7},
+            "anthropic_usage": {"input_tokens": 10, "output_tokens": 20},
+            "custom_field": "any value",
+        }
 
-            from langchain_core.messages.content import TextContentBlock
+        # Mutating an existing block to add provider-specific fields
+        openai_data = my_block["openai_metadata"]  # Type: Any
+        ```
 
-            # Create a text content block with provider-specific fields
-            my_block: TextContentBlock = {
-                # Add required fields
-                "type": "text",
-                "text": "Hello, world!",
-                # Additional fields not specified in the TypedDict
-                # These are valid with PEP 728 and are typed as Any
-                "openai_metadata": {"model": "gpt-4", "temperature": 0.7},
-                "anthropic_usage": {"input_tokens": 10, "output_tokens": 20},
-                "custom_field": "any value",
-            }
-
-            # Mutating an existing block to add provider-specific fields
-            openai_data = my_block["openai_metadata"]  # Type: Any
-
-        PEP 728 is enabled with ``# type: ignore[call-arg]`` comments to suppress
+        PEP 728 is enabled with `# type: ignore[call-arg]` comments to suppress
         warnings from type checkers that don't yet support it. The functionality works
         correctly in Python 3.13+ and will be fully supported as the ecosystem catches
         up.
@@ -81,52 +81,51 @@ to be included without breaking the standard structure.
 
 The module defines several types of content blocks, including:
 
-- ``TextContentBlock``: Standard text output.
-- ``Citation``: For annotations that link text output to a source document.
-- ``ToolCall``: For function calling.
-- ``ReasoningContentBlock``: To capture a model's thought process.
+- `TextContentBlock`: Standard text output.
+- `Citation`: For annotations that link text output to a source document.
+- `ToolCall`: For function calling.
+- `ReasoningContentBlock`: To capture a model's thought process.
 - Multimodal data:
-    - ``ImageContentBlock``
-    - ``AudioContentBlock``
-    - ``VideoContentBlock``
-    - ``PlainTextContentBlock`` (e.g. .txt or .md files)
-    - ``FileContentBlock`` (e.g. PDFs, etc.)
+    - `ImageContentBlock`
+    - `AudioContentBlock`
+    - `VideoContentBlock`
+    - `PlainTextContentBlock` (e.g. .txt or .md files)
+    - `FileContentBlock` (e.g. PDFs, etc.)
 
 **Example Usage**
 
-.. code-block:: python
+```python
+# Direct construction:
+from langchain_core.messages.content import TextContentBlock, ImageContentBlock
 
-    # Direct construction:
-    from langchain_core.messages.content import TextContentBlock, ImageContentBlock
+multimodal_message: AIMessage(
+    content_blocks=[
+        TextContentBlock(type="text", text="What is shown in this image?"),
+        ImageContentBlock(
+            type="image",
+            url="https://www.langchain.com/images/brand/langchain_logo_text_w_white.png",
+            mime_type="image/png",
+        ),
+    ]
+)
 
-    multimodal_message: AIMessage(
-        content_blocks=[
-            TextContentBlock(type="text", text="What is shown in this image?"),
-            ImageContentBlock(
-                type="image",
-                url="https://www.langchain.com/images/brand/langchain_logo_text_w_white.png",
-                mime_type="image/png",
-            ),
-        ]
-    )
+# Using factories:
+from langchain_core.messages.content import create_text_block, create_image_block
 
-    # Using factories:
-    from langchain_core.messages.content import create_text_block, create_image_block
-
-    multimodal_message: AIMessage(
-        content=[
-            create_text_block("What is shown in this image?"),
-            create_image_block(
-                url="https://www.langchain.com/images/brand/langchain_logo_text_w_white.png",
-                mime_type="image/png",
-            ),
-        ]
-    )
+multimodal_message: AIMessage(
+    content=[
+        create_text_block("What is shown in this image?"),
+        create_image_block(
+            url="https://www.langchain.com/images/brand/langchain_logo_text_w_white.png",
+            mime_type="image/png",
+        ),
+    ]
+)
+```
 
 Factory functions offer benefits such as:
 - Automatic ID generation (when not provided)
 - No need to manually specify the `type` field
-
 """
 
 from typing import Any, Literal, get_args, get_type_hints
@@ -140,12 +139,12 @@ class Citation(TypedDict):
     """Annotation for citing data from a document.
 
     !!! note
-        ``start``/``end`` indices refer to the **response text**,
+        `start`/`end` indices refer to the **response text**,
         not the source text. This means that the indices are relative to the model's
-        response, not the original document (as specified in the ``url``).
+        response, not the original document (as specified in the `url`).
 
-    !!! note
-        ``create_citation`` may also be used as a factory to create a ``Citation``.
+    !!! note "Factory function"
+        `create_citation` may also be used as a factory to create a `Citation`.
         Benefits include:
 
         * Automatic ID generation (when not provided)
@@ -157,10 +156,12 @@ class Citation(TypedDict):
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier. Either:
+    """Content block identifier.
+
+    Either:
 
     - Generated by the provider (e.g., OpenAI's file ID)
-    - Generated by LangChain upon creation (``UUID4`` prefixed with ``'lc_'``))
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
     """
 
@@ -174,10 +175,10 @@ class Citation(TypedDict):
     """
 
     start_index: NotRequired[int]
-    """Start index of the **response text** (``TextContentBlock.text``)."""
+    """Start index of the **response text** (`TextContentBlock.text`)."""
 
     end_index: NotRequired[int]
-    """End index of the **response text** (``TextContentBlock.text``)"""
+    """End index of the **response text** (`TextContentBlock.text`)"""
 
     cited_text: NotRequired[str]
     """Excerpt of source text being cited."""
@@ -202,8 +203,9 @@ class NonStandardAnnotation(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
-    - Generated by LangChain upon creation (``UUID4`` prefixed with ``'lc_'``))
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
     """
 
@@ -212,6 +214,7 @@ class NonStandardAnnotation(TypedDict):
 
 
 Annotation = Citation | NonStandardAnnotation
+"""A union of all defined `Annotation` types."""
 
 
 class TextContentBlock(TypedDict):
@@ -220,9 +223,9 @@ class TextContentBlock(TypedDict):
     This typically represents the main text content of a message, such as the response
     from a language model or the text of a user message.
 
-    !!! note
-        ``create_text_block`` may also be used as a factory to create a
-        ``TextContentBlock``. Benefits include:
+    !!! note "Factory function"
+        `create_text_block` may also be used as a factory to create a
+        `TextContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
@@ -236,8 +239,9 @@ class TextContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
-    - Generated by LangChain upon creation (``UUID4`` prefixed with ``'lc_'``))
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
     """
 
@@ -245,7 +249,7 @@ class TextContentBlock(TypedDict):
     """Block text."""
 
     annotations: NotRequired[list[Annotation]]
-    """``Citation``s and other annotations."""
+    """`Citation`s and other annotations."""
 
     index: NotRequired[int | str]
     """Index of block in aggregate response. Used during streaming."""
@@ -255,20 +259,19 @@ class TextContentBlock(TypedDict):
 
 
 class ToolCall(TypedDict):
-    """Represents a request to call a tool.
+    """Represents an AI's request to call a tool.
 
     Example:
-
-        .. code-block:: python
-
-            {"name": "foo", "args": {"a": 1}, "id": "123"}
+        ```python
+        {"name": "foo", "args": {"a": 1}, "id": "123"}
+        ```
 
         This represents a request to call the tool named "foo" with arguments {"a": 1}
         and an identifier of "123".
 
-    !!! note
-        ``create_tool_call`` may also be used as a factory to create a
-        ``ToolCall``. Benefits include:
+    !!! note "Factory function"
+        `create_tool_call` may also be used as a factory to create a
+        `ToolCall`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
@@ -301,24 +304,22 @@ class ToolCall(TypedDict):
 
 
 class ToolCallChunk(TypedDict):
-    """A chunk of a tool call (e.g., as part of a stream).
+    """A chunk of a tool call (yielded when streaming).
 
-    When merging ``ToolCallChunks`` (e.g., via ``AIMessageChunk.__add__``),
+    When merging `ToolCallChunks` (e.g., via `AIMessageChunk.__add__`),
     all string attributes are concatenated. Chunks are only merged if their
-    values of ``index`` are equal and not `None`.
+    values of `index` are equal and not `None`.
 
     Example:
+    ```python
+    left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)]
+    right_chunks = [ToolCallChunk(name=None, args="1}", index=0)]
 
-    .. code-block:: python
-
-        left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)]
-        right_chunks = [ToolCallChunk(name=None, args="1}", index=0)]
-
-        (
-            AIMessageChunk(content="", tool_call_chunks=left_chunks)
-            + AIMessageChunk(content="", tool_call_chunks=right_chunks)
-        ).tool_call_chunks == [ToolCallChunk(name="foo", args='{"a":1}', index=0)]
-
+    (
+        AIMessageChunk(content="", tool_call_chunks=left_chunks)
+        + AIMessageChunk(content="", tool_call_chunks=right_chunks)
+    ).tool_call_chunks == [ToolCallChunk(name="foo", args='{"a":1}', index=0)]
+    ```
     """
 
     # TODO: Consider making fields NotRequired[str] in the future.
@@ -385,7 +386,10 @@ class InvalidToolCall(TypedDict):
 
 
 class ServerToolCall(TypedDict):
-    """Tool call that is executed server-side."""
+    """Tool call that is executed server-side.
+
+    For example: code execution, web search, etc.
+    """
 
     type: Literal["server_tool_call"]
     """Used for discrimination."""
@@ -407,7 +411,7 @@ class ServerToolCall(TypedDict):
 
 
 class ServerToolCallChunk(TypedDict):
-    """A chunk of a tool call (as part of a stream)."""
+    """A chunk of a server-side tool call (yielded when streaming)."""
 
     type: Literal["server_tool_call_chunk"]
     """Used for discrimination."""
@@ -456,9 +460,9 @@ class ServerToolResult(TypedDict):
 class ReasoningContentBlock(TypedDict):
     """Reasoning output from a LLM.
 
-    !!! note
-        ``create_reasoning_block`` may also be used as a factory to create a
-        ``ReasoningContentBlock``. Benefits include:
+    !!! note "Factory function"
+        `create_reasoning_block` may also be used as a factory to create a
+        `ReasoningContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
@@ -472,8 +476,9 @@ class ReasoningContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
-    - Generated by LangChain upon creation (``UUID4`` prefixed with ``'lc_'``))
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
     """
 
@@ -481,7 +486,7 @@ class ReasoningContentBlock(TypedDict):
     """Reasoning text.
 
     Either the thought summary or the raw reasoning text itself. This is often parsed
-    from ``<think>`` tags in the model's response.
+    from `<think>` tags in the model's response.
 
     """
 
@@ -498,9 +503,9 @@ class ReasoningContentBlock(TypedDict):
 class ImageContentBlock(TypedDict):
     """Image data.
 
-    !!! note
-        ``create_image_block`` may also be used as a factory to create a
-        ``ImageContentBlock``. Benefits include:
+    !!! note "Factory function"
+        `create_image_block` may also be used as a factory to create a
+        `ImageContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
@@ -514,8 +519,9 @@ class ImageContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
-    - Generated by LangChain upon creation (``UUID4`` prefixed with ``'lc_'``))
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
     """
 
@@ -525,7 +531,7 @@ class ImageContentBlock(TypedDict):
     mime_type: NotRequired[str]
     """MIME type of the image. Required for base64.
 
-    `Examples from IANA <https://www.iana.org/assignments/media-types/media-types.xhtml#image>`__
+    [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml#image)
 
     """
 
@@ -545,9 +551,9 @@ class ImageContentBlock(TypedDict):
 class VideoContentBlock(TypedDict):
     """Video data.
 
-    !!! note
-        ``create_video_block`` may also be used as a factory to create a
-        ``VideoContentBlock``. Benefits include:
+    !!! note "Factory function"
+        `create_video_block` may also be used as a factory to create a
+        `VideoContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
@@ -561,8 +567,9 @@ class VideoContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
-    - Generated by LangChain upon creation (``UUID4`` prefixed with ``'lc_'``))
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
     """
 
@@ -572,7 +579,7 @@ class VideoContentBlock(TypedDict):
     mime_type: NotRequired[str]
     """MIME type of the video. Required for base64.
 
-    `Examples from IANA <https://www.iana.org/assignments/media-types/media-types.xhtml#video>`__
+    [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml#video)
 
     """
 
@@ -592,9 +599,9 @@ class VideoContentBlock(TypedDict):
 class AudioContentBlock(TypedDict):
     """Audio data.
 
-    !!! note
-        ``create_audio_block`` may also be used as a factory to create an
-        ``AudioContentBlock``. Benefits include:
+    !!! note "Factory function"
+        `create_audio_block` may also be used as a factory to create an
+        `AudioContentBlock`. Benefits include:
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
 
@@ -607,8 +614,9 @@ class AudioContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
-    - Generated by LangChain upon creation (``UUID4`` prefixed with ``'lc_'``))
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
     """
 
@@ -618,7 +626,7 @@ class AudioContentBlock(TypedDict):
     mime_type: NotRequired[str]
     """MIME type of the audio. Required for base64.
 
-    `Examples from IANA <https://www.iana.org/assignments/media-types/media-types.xhtml#audio>`__
+    [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml#audio)
 
     """
 
@@ -639,18 +647,18 @@ class PlainTextContentBlock(TypedDict):
     """Plaintext data (e.g., from a document).
 
     !!! note
-        A ``PlainTextContentBlock`` existed in ``langchain-core<1.0.0``. Although the
+        A `PlainTextContentBlock` existed in `langchain-core<1.0.0`. Although the
         name has carried over, the structure has changed significantly. The only shared
-        keys between the old and new versions are `type` and ``text``, though the
-        `type` value has changed from ``'text'`` to ``'text-plain'``.
+        keys between the old and new versions are `type` and `text`, though the
+        `type` value has changed from `'text'` to `'text-plain'`.
 
     !!! note
         Title and context are optional fields that may be passed to the model. See
-        Anthropic `example <https://docs.anthropic.com/en/docs/build-with-claude/citations#citable-vs-non-citable-content>`__.
+        Anthropic [example](https://docs.claude.com/en/docs/build-with-claude/citations#citable-vs-non-citable-content).
 
-    !!! note
-        ``create_plaintext_block`` may also be used as a factory to create a
-        ``PlainTextContentBlock``. Benefits include:
+    !!! note "Factory function"
+        `create_plaintext_block` may also be used as a factory to create a
+        `PlainTextContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
@@ -664,8 +672,9 @@ class PlainTextContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
-    - Generated by LangChain upon creation (``UUID4`` prefixed with ``'lc_'``))
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
     """
 
@@ -698,18 +707,18 @@ class PlainTextContentBlock(TypedDict):
 
 
 class FileContentBlock(TypedDict):
-    """File data that doesn't fit into other multimodal blocks.
+    """File data that doesn't fit into other multimodal block types.
 
     This block is intended for files that are not images, audio, or plaintext. For
     example, it can be used for PDFs, Word documents, etc.
 
     If the file is an image, audio, or plaintext, you should use the corresponding
-    content block type (e.g., ``ImageContentBlock``, ``AudioContentBlock``,
-    ``PlainTextContentBlock``).
+    content block type (e.g., `ImageContentBlock`, `AudioContentBlock`,
+    `PlainTextContentBlock`).
 
-    !!! note
-        ``create_file_block`` may also be used as a factory to create a
-        ``FileContentBlock``. Benefits include:
+    !!! note "Factory function"
+        `create_file_block` may also be used as a factory to create a
+        `FileContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
@@ -723,8 +732,9 @@ class FileContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
-    - Generated by LangChain upon creation (``UUID4`` prefixed with ``'lc_'``))
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
     """
 
@@ -734,7 +744,7 @@ class FileContentBlock(TypedDict):
     mime_type: NotRequired[str]
     """MIME type of the file. Required for base64.
 
-    `Examples from IANA <https://www.iana.org/assignments/media-types/media-types.xhtml>`__
+    [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml)
 
     """
 
@@ -764,14 +774,14 @@ class NonStandardContentBlock(TypedDict):
     The purpose of this block should be to simply hold a provider-specific payload.
     If a provider's non-standard output includes reasoning and tool calls, it should be
     the adapter's job to parse that payload and emit the corresponding standard
-    ``ReasoningContentBlock`` and ``ToolCalls``.
+    `ReasoningContentBlock` and `ToolCalls`.
 
-    Has no ``extras`` field, as provider-specific data should be included in the
-    ``value`` field.
+    Has no `extras` field, as provider-specific data should be included in the
+    `value` field.
 
-    !!! note
-        ``create_non_standard_block`` may also be used as a factory to create a
-        ``NonStandardContentBlock``. Benefits include:
+    !!! note "Factory function"
+        `create_non_standard_block` may also be used as a factory to create a
+        `NonStandardContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
@@ -785,8 +795,9 @@ class NonStandardContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
-    - Generated by LangChain upon creation (``UUID4`` prefixed with ``'lc_'``))
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
     """
 
@@ -805,6 +816,7 @@ DataContentBlock = (
     | PlainTextContentBlock
     | FileContentBlock
 )
+"""A union of all defined multimodal data `ContentBlock` types."""
 
 ToolContentBlock = (
     ToolCall | ToolCallChunk | ServerToolCall | ServerToolCallChunk | ServerToolResult
@@ -818,6 +830,7 @@ ContentBlock = (
     | DataContentBlock
     | ToolContentBlock
 )
+"""A union of all defined `ContentBlock` types and aliases."""
 
 
 KNOWN_BLOCK_TYPES = {
@@ -842,7 +855,7 @@ KNOWN_BLOCK_TYPES = {
     "non_standard",
     # citation and non_standard_annotation intentionally omitted
 }
-"""These are block types known to ``langchain-core>=1.0.0``.
+"""These are block types known to `langchain-core>=1.0.0`.
 
 If a block has a type not in this set, it is considered to be provider-specific.
 """
@@ -881,7 +894,7 @@ def is_data_content_block(block: dict) -> bool:
         block: The content block to check.
 
     Returns:
-        True if the content block is a data content block, False otherwise.
+        `True` if the content block is a data content block, `False` otherwise.
 
     """
     if block.get("type") not in _get_data_content_block_types():
@@ -923,20 +936,20 @@ def create_text_block(
     index: int | str | None = None,
     **kwargs: Any,
 ) -> TextContentBlock:
-    """Create a ``TextContentBlock``.
+    """Create a `TextContentBlock`.
 
     Args:
         text: The text content of the block.
         id: Content block identifier. Generated automatically if not provided.
-        annotations: ``Citation``s and other annotations for the text.
+        annotations: `Citation`s and other annotations for the text.
         index: Index of block in aggregate response. Used during streaming.
 
     Returns:
-        A properly formatted ``TextContentBlock``.
+        A properly formatted `TextContentBlock`.
 
     !!! note
         The `id` is generated automatically if not provided, using a UUID4 format
-        prefixed with ``'lc_'`` to indicate it is a LangChain-generated ID.
+        prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
 
     """
     block = TextContentBlock(
@@ -966,7 +979,7 @@ def create_image_block(
     index: int | str | None = None,
     **kwargs: Any,
 ) -> ImageContentBlock:
-    """Create an ``ImageContentBlock``.
+    """Create an `ImageContentBlock`.
 
     Args:
         url: URL of the image.
@@ -977,15 +990,15 @@ def create_image_block(
         index: Index of block in aggregate response. Used during streaming.
 
     Returns:
-        A properly formatted ``ImageContentBlock``.
+        A properly formatted `ImageContentBlock`.
 
     Raises:
-        ValueError: If no image source is provided or if ``base64`` is used without
-            ``mime_type``.
+        ValueError: If no image source is provided or if `base64` is used without
+            `mime_type`.
 
     !!! note
         The `id` is generated automatically if not provided, using a UUID4 format
-        prefixed with ``'lc_'`` to indicate it is a LangChain-generated ID.
+        prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
 
     """
     if not any([url, base64, file_id]):
@@ -1022,7 +1035,7 @@ def create_video_block(
     index: int | str | None = None,
     **kwargs: Any,
 ) -> VideoContentBlock:
-    """Create a ``VideoContentBlock``.
+    """Create a `VideoContentBlock`.
 
     Args:
         url: URL of the video.
@@ -1033,15 +1046,15 @@ def create_video_block(
         index: Index of block in aggregate response. Used during streaming.
 
     Returns:
-        A properly formatted ``VideoContentBlock``.
+        A properly formatted `VideoContentBlock`.
 
     Raises:
-        ValueError: If no video source is provided or if ``base64`` is used without
-            ``mime_type``.
+        ValueError: If no video source is provided or if `base64` is used without
+            `mime_type`.
 
     !!! note
         The `id` is generated automatically if not provided, using a UUID4 format
-        prefixed with ``'lc_'`` to indicate it is a LangChain-generated ID.
+        prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
 
     """
     if not any([url, base64, file_id]):
@@ -1082,7 +1095,7 @@ def create_audio_block(
     index: int | str | None = None,
     **kwargs: Any,
 ) -> AudioContentBlock:
-    """Create an ``AudioContentBlock``.
+    """Create an `AudioContentBlock`.
 
     Args:
         url: URL of the audio.
@@ -1093,15 +1106,15 @@ def create_audio_block(
         index: Index of block in aggregate response. Used during streaming.
 
     Returns:
-        A properly formatted ``AudioContentBlock``.
+        A properly formatted `AudioContentBlock`.
 
     Raises:
-        ValueError: If no audio source is provided or if ``base64`` is used without
-            ``mime_type``.
+        ValueError: If no audio source is provided or if `base64` is used without
+            `mime_type`.
 
     !!! note
         The `id` is generated automatically if not provided, using a UUID4 format
-        prefixed with ``'lc_'`` to indicate it is a LangChain-generated ID.
+        prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
 
     """
     if not any([url, base64, file_id]):
@@ -1142,7 +1155,7 @@ def create_file_block(
     index: int | str | None = None,
     **kwargs: Any,
 ) -> FileContentBlock:
-    """Create a ``FileContentBlock``.
+    """Create a `FileContentBlock`.
 
     Args:
         url: URL of the file.
@@ -1153,15 +1166,15 @@ def create_file_block(
         index: Index of block in aggregate response. Used during streaming.
 
     Returns:
-        A properly formatted ``FileContentBlock``.
+        A properly formatted `FileContentBlock`.
 
     Raises:
-        ValueError: If no file source is provided or if ``base64`` is used without
-            ``mime_type``.
+        ValueError: If no file source is provided or if `base64` is used without
+            `mime_type`.
 
     !!! note
         The `id` is generated automatically if not provided, using a UUID4 format
-        prefixed with ``'lc_'`` to indicate it is a LangChain-generated ID.
+        prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
 
     """
     if not any([url, base64, file_id]):
@@ -1203,7 +1216,7 @@ def create_plaintext_block(
     index: int | str | None = None,
     **kwargs: Any,
 ) -> PlainTextContentBlock:
-    """Create a ``PlainTextContentBlock``.
+    """Create a `PlainTextContentBlock`.
 
     Args:
         text: The plaintext content.
@@ -1216,11 +1229,11 @@ def create_plaintext_block(
         index: Index of block in aggregate response. Used during streaming.
 
     Returns:
-        A properly formatted ``PlainTextContentBlock``.
+        A properly formatted `PlainTextContentBlock`.
 
     !!! note
         The `id` is generated automatically if not provided, using a UUID4 format
-        prefixed with ``'lc_'`` to indicate it is a LangChain-generated ID.
+        prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
 
     """
     block = PlainTextContentBlock(
@@ -1259,7 +1272,7 @@ def create_tool_call(
     index: int | str | None = None,
     **kwargs: Any,
 ) -> ToolCall:
-    """Create a ``ToolCall``.
+    """Create a `ToolCall`.
 
     Args:
         name: The name of the tool to be called.
@@ -1268,11 +1281,11 @@ def create_tool_call(
         index: Index of block in aggregate response. Used during streaming.
 
     Returns:
-        A properly formatted ``ToolCall``.
+        A properly formatted `ToolCall`.
 
     !!! note
         The `id` is generated automatically if not provided, using a UUID4 format
-        prefixed with ``'lc_'`` to indicate it is a LangChain-generated ID.
+        prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
 
     """
     block = ToolCall(
@@ -1298,7 +1311,7 @@ def create_reasoning_block(
     index: int | str | None = None,
     **kwargs: Any,
 ) -> ReasoningContentBlock:
-    """Create a ``ReasoningContentBlock``.
+    """Create a `ReasoningContentBlock`.
 
     Args:
         reasoning: The reasoning text or thought summary.
@@ -1306,11 +1319,11 @@ def create_reasoning_block(
         index: Index of block in aggregate response. Used during streaming.
 
     Returns:
-        A properly formatted ``ReasoningContentBlock``.
+        A properly formatted `ReasoningContentBlock`.
 
     !!! note
         The `id` is generated automatically if not provided, using a UUID4 format
-        prefixed with ``'lc_'`` to indicate it is a LangChain-generated ID.
+        prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
 
     """
     block = ReasoningContentBlock(
@@ -1339,7 +1352,7 @@ def create_citation(
     id: str | None = None,
     **kwargs: Any,
 ) -> Citation:
-    """Create a ``Citation``.
+    """Create a `Citation`.
 
     Args:
         url: URL of the document source.
@@ -1350,11 +1363,11 @@ def create_citation(
         id: Content block identifier. Generated automatically if not provided.
 
     Returns:
-        A properly formatted ``Citation``.
+        A properly formatted `Citation`.
 
     !!! note
         The `id` is generated automatically if not provided, using a UUID4 format
-        prefixed with ``'lc_'`` to indicate it is a LangChain-generated ID.
+        prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
 
     """
     block = Citation(type="citation", id=ensure_id(id))
@@ -1383,7 +1396,7 @@ def create_non_standard_block(
     id: str | None = None,
     index: int | str | None = None,
 ) -> NonStandardContentBlock:
-    """Create a ``NonStandardContentBlock``.
+    """Create a `NonStandardContentBlock`.
 
     Args:
         value: Provider-specific data.
@@ -1391,11 +1404,11 @@ def create_non_standard_block(
         index: Index of block in aggregate response. Used during streaming.
 
     Returns:
-        A properly formatted ``NonStandardContentBlock``.
+        A properly formatted `NonStandardContentBlock`.
 
     !!! note
         The `id` is generated automatically if not provided, using a UUID4 format
-        prefixed with ``'lc_'`` to indicate it is a LangChain-generated ID.
+        prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
 
     """
     block = NonStandardContentBlock(

libs/core/langchain_core/messages/function.py

@@ -15,11 +15,11 @@ from langchain_core.utils._merge import merge_dicts
 class FunctionMessage(BaseMessage):
     """Message for passing the result of executing a tool back to a model.
 
-    ``FunctionMessage`` are an older version of the `ToolMessage` schema, and
+    `FunctionMessage` are an older version of the `ToolMessage` schema, and
     do not contain the `tool_call_id` field.
 
     The `tool_call_id` field is used to associate the tool call request with the
-    tool call response. This is useful in situations where a chat model is able
+    tool call response. Useful in situations where a chat model is able
     to request multiple tool calls in parallel.
 
     """
@@ -28,7 +28,7 @@ class FunctionMessage(BaseMessage):
     """The name of the function that was executed."""
 
     type: Literal["function"] = "function"
-    """The type of the message (used for serialization). Defaults to ``'function'``."""
+    """The type of the message (used for serialization)."""
 
 
 class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
@@ -38,11 +38,7 @@ class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["FunctionMessageChunk"] = "FunctionMessageChunk"  # type: ignore[assignment]
-    """The type of the message (used for serialization).
-
-    Defaults to ``'FunctionMessageChunk'``.
-
-    """
+    """The type of the message (used for serialization)."""
 
     @override
     def __add__(self, other: Any) -> BaseMessageChunk:  # type: ignore[override]

libs/core/langchain_core/messages/human.py

@@ -7,33 +7,27 @@ from langchain_core.messages.base import BaseMessage, BaseMessageChunk
 
 
 class HumanMessage(BaseMessage):
-    """Message from a human.
+    """Message from the user.
 
-    `HumanMessage`s are messages that are passed in from a human to the model.
+    A `HumanMessage` is a message that is passed in from a user to the model.
 
     Example:
+        ```python
+        from langchain_core.messages import HumanMessage, SystemMessage
 
-        .. code-block:: python
-
-            from langchain_core.messages import HumanMessage, SystemMessage
-
-            messages = [
-                SystemMessage(content="You are a helpful assistant! Your name is Bob."),
-                HumanMessage(content="What is your name?"),
-            ]
-
-            # Instantiate a chat model and invoke it with the messages
-            model = ...
-            print(model.invoke(messages))
+        messages = [
+            SystemMessage(content="You are a helpful assistant! Your name is Bob."),
+            HumanMessage(content="What is your name?"),
+        ]
 
+        # Instantiate a chat model and invoke it with the messages
+        model = ...
+        print(model.invoke(messages))
+        ```
     """
 
     type: Literal["human"] = "human"
-    """The type of the message (used for serialization).
-
-    Defaults to ``'human'``.
-
-    """
+    """The type of the message (used for serialization)."""
 
     @overload
     def __init__(
@@ -56,7 +50,7 @@ class HumanMessage(BaseMessage):
         content_blocks: list[types.ContentBlock] | None = None,
         **kwargs: Any,
     ) -> None:
-        """Specify ``content`` as positional arg or ``content_blocks`` for typing."""
+        """Specify `content` as positional arg or `content_blocks` for typing."""
         if content_blocks is not None:
             super().__init__(
                 content=cast("str | list[str | dict]", content_blocks),
@@ -73,5 +67,4 @@ class HumanMessageChunk(HumanMessage, BaseMessageChunk):
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["HumanMessageChunk"] = "HumanMessageChunk"  # type: ignore[assignment]
-    """The type of the message (used for serialization).
-    Defaults to "HumanMessageChunk"."""
+    """The type of the message (used for serialization)."""

libs/core/langchain_core/messages/modifier.py

@@ -9,7 +9,7 @@ class RemoveMessage(BaseMessage):
     """Message responsible for deleting other messages."""
 
     type: Literal["remove"] = "remove"
-    """The type of the message (used for serialization). Defaults to "remove"."""
+    """The type of the message (used for serialization)."""
 
     def __init__(
         self,
@@ -20,7 +20,7 @@ class RemoveMessage(BaseMessage):
 
         Args:
             id: The ID of the message to remove.
-            kwargs: Additional fields to pass to the message.
+            **kwargs: Additional fields to pass to the message.
 
         Raises:
             ValueError: If the 'content' field is passed in kwargs.

libs/core/langchain_core/messages/system.py

@@ -13,27 +13,21 @@ class SystemMessage(BaseMessage):
     of input messages.
 
     Example:
+        ```python
+        from langchain_core.messages import HumanMessage, SystemMessage
 
-        .. code-block:: python
-
-            from langchain_core.messages import HumanMessage, SystemMessage
-
-            messages = [
-                SystemMessage(content="You are a helpful assistant! Your name is Bob."),
-                HumanMessage(content="What is your name?"),
-            ]
-
-            # Define a chat model and invoke it with the messages
-            print(model.invoke(messages))
+        messages = [
+            SystemMessage(content="You are a helpful assistant! Your name is Bob."),
+            HumanMessage(content="What is your name?"),
+        ]
 
+        # Define a chat model and invoke it with the messages
+        print(model.invoke(messages))
+        ```
     """
 
     type: Literal["system"] = "system"
-    """The type of the message (used for serialization).
-
-    Defaults to ``'system'``.
-
-    """
+    """The type of the message (used for serialization)."""
 
     @overload
     def __init__(
@@ -56,7 +50,7 @@ class SystemMessage(BaseMessage):
         content_blocks: list[types.ContentBlock] | None = None,
         **kwargs: Any,
     ) -> None:
-        """Specify ``content`` as positional arg or ``content_blocks`` for typing."""
+        """Specify `content` as positional arg or `content_blocks` for typing."""
         if content_blocks is not None:
             super().__init__(
                 content=cast("str | list[str | dict]", content_blocks),
@@ -73,8 +67,4 @@ class SystemMessageChunk(SystemMessage, BaseMessageChunk):
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["SystemMessageChunk"] = "SystemMessageChunk"  # type: ignore[assignment]
-    """The type of the message (used for serialization).
-
-    Defaults to ``'SystemMessageChunk'``.
-
-    """
+    """The type of the message (used for serialization)."""

libs/core/langchain_core/messages/tool.py

@@ -16,8 +16,8 @@ from langchain_core.utils._merge import merge_dicts, merge_obj
 class ToolOutputMixin:
     """Mixin for objects that tools can return directly.
 
-    If a custom BaseTool is invoked with a ``ToolCall`` and the output of custom code is
-    not an instance of ``ToolOutputMixin``, the output will automatically be coerced to
+    If a custom BaseTool is invoked with a `ToolCall` and the output of custom code is
+    not an instance of `ToolOutputMixin`, the output will automatically be coerced to
     a string and wrapped in a `ToolMessage`.
 
     """
@@ -27,41 +27,38 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
     """Message for passing the result of executing a tool back to a model.
 
     `ToolMessage` objects contain the result of a tool invocation. Typically, the result
-    is encoded inside the ``content`` field.
+    is encoded inside the `content` field.
 
-    Example: A `ToolMessage` representing a result of ``42`` from a tool call with id
+    Example: A `ToolMessage` representing a result of `42` from a tool call with id
 
-        .. code-block:: python
-
-            from langchain_core.messages import ToolMessage
-
-            ToolMessage(content="42", tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL")
+    ```python
+    from langchain_core.messages import ToolMessage
 
+    ToolMessage(content="42", tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL")
+    ```
 
     Example: A `ToolMessage` where only part of the tool output is sent to the model
-        and the full output is passed in to artifact.
+    and the full output is passed in to artifact.
 
-        !!! version-added "Added in version 0.2.17"
+    ```python
+    from langchain_core.messages import ToolMessage
 
-        .. code-block:: python
+    tool_output = {
+        "stdout": "From the graph we can see that the correlation between "
+        "x and y is ...",
+        "stderr": None,
+        "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."},
+    }
 
-            from langchain_core.messages import ToolMessage
-
-            tool_output = {
-                "stdout": "From the graph we can see that the correlation between "
-                "x and y is ...",
-                "stderr": None,
-                "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."},
-            }
-
-            ToolMessage(
-                content=tool_output["stdout"],
-                artifact=tool_output,
-                tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL",
-            )
+    ToolMessage(
+        content=tool_output["stdout"],
+        artifact=tool_output,
+        tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL",
+    )
+    ```
 
     The `tool_call_id` field is used to associate the tool call request with the
-    tool call response. This is useful in situations where a chat model is able
+    tool call response. Useful in situations where a chat model is able
     to request multiple tool calls in parallel.
 
     """
@@ -70,11 +67,7 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
     """Tool call that this message is responding to."""
 
     type: Literal["tool"] = "tool"
-    """The type of the message (used for serialization).
-
-    Defaults to ``'tool'``.
-
-    """
+    """The type of the message (used for serialization)."""
 
     artifact: Any = None
     """Artifact of the Tool execution which is not meant to be sent to the model.
@@ -83,21 +76,15 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
     a subset of the full tool output is being passed as message content but the full
     output is needed in other parts of the code.
 
-    !!! version-added "Added in version 0.2.17"
-
     """
 
     status: Literal["success", "error"] = "success"
-    """Status of the tool invocation.
-
-    !!! version-added "Added in version 0.2.24"
-
-    """
+    """Status of the tool invocation."""
 
     additional_kwargs: dict = Field(default_factory=dict, repr=False)
-    """Currently inherited from BaseMessage, but not used."""
+    """Currently inherited from `BaseMessage`, but not used."""
     response_metadata: dict = Field(default_factory=dict, repr=False)
-    """Currently inherited from BaseMessage, but not used."""
+    """Currently inherited from `BaseMessage`, but not used."""
 
     @model_validator(mode="before")
     @classmethod
@@ -165,12 +152,12 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
         content_blocks: list[types.ContentBlock] | None = None,
         **kwargs: Any,
     ) -> None:
-        """Initialize `ToolMessage`.
+        """Initialize a `ToolMessage`.
 
-        Specify ``content`` as positional arg or ``content_blocks`` for typing.
+        Specify `content` as positional arg or `content_blocks` for typing.
 
         Args:
-            content: The string contents of the message.
+            content: The contents of the message.
             content_blocks: Typed standard content.
             **kwargs: Additional fields.
         """
@@ -216,16 +203,15 @@ class ToolMessageChunk(ToolMessage, BaseMessageChunk):
 
 
 class ToolCall(TypedDict):
-    """Represents a request to call a tool.
+    """Represents an AI's request to call a tool.
 
     Example:
+        ```python
+        {"name": "foo", "args": {"a": 1}, "id": "123"}
+        ```
 
-        .. code-block:: python
-
-            {"name": "foo", "args": {"a": 1}, "id": "123"}
-
-        This represents a request to call the tool named ``'foo'`` with arguments
-        ``{"a": 1}`` and an identifier of ``'123'``.
+        This represents a request to call the tool named `'foo'` with arguments
+        `{"a": 1}` and an identifier of `'123'`.
 
     """
 
@@ -263,24 +249,22 @@ def tool_call(
 
 
 class ToolCallChunk(TypedDict):
-    """A chunk of a tool call (e.g., as part of a stream).
+    """A chunk of a tool call (yielded when streaming).
 
-    When merging ``ToolCallChunk``s (e.g., via ``AIMessageChunk.__add__``),
+    When merging `ToolCallChunk`s (e.g., via `AIMessageChunk.__add__`),
     all string attributes are concatenated. Chunks are only merged if their
-    values of ``index`` are equal and not None.
+    values of `index` are equal and not None.
 
     Example:
+    ```python
+    left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)]
+    right_chunks = [ToolCallChunk(name=None, args="1}", index=0)]
 
-    .. code-block:: python
-
-        left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)]
-        right_chunks = [ToolCallChunk(name=None, args="1}", index=0)]
-
-        (
-            AIMessageChunk(content="", tool_call_chunks=left_chunks)
-            + AIMessageChunk(content="", tool_call_chunks=right_chunks)
-        ).tool_call_chunks == [ToolCallChunk(name="foo", args='{"a":1}', index=0)]
-
+    (
+        AIMessageChunk(content="", tool_call_chunks=left_chunks)
+        + AIMessageChunk(content="", tool_call_chunks=right_chunks)
+    ).tool_call_chunks == [ToolCallChunk(name="foo", args='{"a":1}', index=0)]
+    ```
     """
 
     name: str | None

libs/core/langchain_core/messages/utils.py

@@ -5,7 +5,6 @@ Some examples of what you can do with these functions include:
 * Convert messages to strings (serialization)
 * Convert messages from dicts to Message objects (deserialization)
 * Filter messages from a list of messages based on name, type or id etc.
-
 """
 
 from __future__ import annotations
@@ -87,6 +86,7 @@ AnyMessage = Annotated[
     | Annotated[ToolMessageChunk, Tag(tag="ToolMessageChunk")],
     Field(discriminator=Discriminator(_get_type)),
 ]
+""""A type representing any defined `Message` or `MessageChunk` type."""
 
 
 def get_buffer_string(
@@ -97,9 +97,7 @@ def get_buffer_string(
     Args:
         messages: Messages to be converted to strings.
         human_prefix: The prefix to prepend to contents of `HumanMessage`s.
-            Default is ``'Human'``.
-        ai_prefix: The prefix to prepend to contents of `AIMessage`. Default is
-            ``'AI'``.
+        ai_prefix: The prefix to prepend to contents of `AIMessage`.
 
     Returns:
         A single string concatenation of all input messages.
@@ -108,17 +106,16 @@ def get_buffer_string(
         ValueError: If an unsupported message type is encountered.
 
     Example:
-        .. code-block:: python
-
-            from langchain_core import AIMessage, HumanMessage
-
-            messages = [
-                HumanMessage(content="Hi, how are you?"),
-                AIMessage(content="Good, how are you?"),
-            ]
-            get_buffer_string(messages)
-            # -> "Human: Hi, how are you?\nAI: Good, how are you?"
+        ```python
+        from langchain_core import AIMessage, HumanMessage
 
+        messages = [
+            HumanMessage(content="Hi, how are you?"),
+            AIMessage(content="Good, how are you?"),
+        ]
+        get_buffer_string(messages)
+        # -> "Human: Hi, how are you?\nAI: Good, how are you?"
+        ```
     """
     string_messages = []
     for m in messages:
@@ -178,7 +175,7 @@ def _message_from_dict(message: dict) -> BaseMessage:
 
 
 def messages_from_dict(messages: Sequence[dict]) -> list[BaseMessage]:
-    """Convert a sequence of messages from dicts to ``Message`` objects.
+    """Convert a sequence of messages from dicts to `Message` objects.
 
     Args:
         messages: Sequence of messages (as dicts) to convert.
@@ -191,7 +188,7 @@ def messages_from_dict(messages: Sequence[dict]) -> list[BaseMessage]:
 
 
 def message_chunk_to_message(chunk: BaseMessage) -> BaseMessage:
-    """Convert a message chunk to a ``Message``.
+    """Convert a message chunk to a `Message`.
 
     Args:
         chunk: Message chunk to convert.
@@ -213,6 +210,7 @@ def message_chunk_to_message(chunk: BaseMessage) -> BaseMessage:
 MessageLikeRepresentation = (
     BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]
 )
+"""A type representing the various ways a message can be represented."""
 
 
 def _create_message_from_message_type(
@@ -224,24 +222,24 @@ def _create_message_from_message_type(
     id: str | None = None,
     **additional_kwargs: Any,
 ) -> BaseMessage:
-    """Create a message from a ``Message`` type and content string.
+    """Create a message from a `Message` type and content string.
 
     Args:
-        message_type: (str) the type of the message (e.g., ``'human'``, ``'ai'``, etc.).
+        message_type: (str) the type of the message (e.g., `'human'`, `'ai'`, etc.).
         content: (str) the content string.
-        name: (str) the name of the message. Default is None.
-        tool_call_id: (str) the tool call id. Default is None.
-        tool_calls: (list[dict[str, Any]]) the tool calls. Default is None.
-        id: (str) the id of the message. Default is None.
+        name: (str) the name of the message.
+        tool_call_id: (str) the tool call id.
+        tool_calls: (list[dict[str, Any]]) the tool calls.
+        id: (str) the id of the message.
         additional_kwargs: (dict[str, Any]) additional keyword arguments.
 
     Returns:
         a message of the appropriate type.
 
     Raises:
-        ValueError: if the message type is not one of ``'human'``, ``'user'``, ``'ai'``,
-            ``'assistant'``, ``'function'``, ``'tool'``, ``'system'``, or
-            ``'developer'``.
+        ValueError: if the message type is not one of `'human'`, `'user'`, `'ai'`,
+            `'assistant'`, `'function'`, `'tool'`, `'system'`, or
+            `'developer'`.
     """
     kwargs: dict[str, Any] = {}
     if name is not None:
@@ -307,21 +305,21 @@ def _create_message_from_message_type(
 
 
 def _convert_to_message(message: MessageLikeRepresentation) -> BaseMessage:
-    """Instantiate a ``Message`` from a variety of message formats.
+    """Instantiate a `Message` from a variety of message formats.
 
     The message format can be one of the following:
 
-    - ``BaseMessagePromptTemplate``
+    - `BaseMessagePromptTemplate`
     - `BaseMessage`
-    - 2-tuple of (role string, template); e.g., (``'human'``, ``'{user_input}'``)
+    - 2-tuple of (role string, template); e.g., (`'human'`, `'{user_input}'`)
     - dict: a message dict with role and content keys
-    - string: shorthand for (``'human'``, template); e.g., ``'{user_input}'``
+    - string: shorthand for (`'human'`, template); e.g., `'{user_input}'`
 
     Args:
         message: a representation of a message in one of the supported formats.
 
     Returns:
-        an instance of a message or a message template.
+        An instance of a message or a message template.
 
     Raises:
         NotImplementedError: if the message type is not supported.
@@ -427,75 +425,74 @@ def filter_messages(
 
     Args:
         messages: Sequence Message-like objects to filter.
-        include_names: Message names to include. Default is None.
-        exclude_names: Messages names to exclude. Default is None.
+        include_names: Message names to include.
+        exclude_names: Messages names to exclude.
         include_types: Message types to include. Can be specified as string names
-            (e.g. ``'system'``, ``'human'``, ``'ai'``, ...) or as `BaseMessage`
+            (e.g. `'system'`, `'human'`, `'ai'`, ...) or as `BaseMessage`
             classes (e.g. `SystemMessage`, `HumanMessage`, `AIMessage`, ...).
-            Default is None.
+
         exclude_types: Message types to exclude. Can be specified as string names
-            (e.g. ``'system'``, ``'human'``, ``'ai'``, ...) or as `BaseMessage`
+            (e.g. `'system'`, `'human'`, `'ai'`, ...) or as `BaseMessage`
             classes (e.g. `SystemMessage`, `HumanMessage`, `AIMessage`, ...).
-            Default is None.
-        include_ids: Message IDs to include. Default is None.
-        exclude_ids: Message IDs to exclude. Default is None.
-        exclude_tool_calls: Tool call IDs to exclude. Default is None.
+
+        include_ids: Message IDs to include.
+        exclude_ids: Message IDs to exclude.
+        exclude_tool_calls: Tool call IDs to exclude.
             Can be one of the following:
             - `True`: all `AIMessage`s with tool calls and all
-              `ToolMessage` objects will be excluded.
+                `ToolMessage` objects will be excluded.
             - a sequence of tool call IDs to exclude:
-              - `ToolMessage` objects with the corresponding tool call ID will be
-                excluded.
-              - The `tool_calls` in the AIMessage will be updated to exclude
-                matching tool calls. If all `tool_calls` are filtered from an
-                AIMessage, the whole message is excluded.
+                - `ToolMessage` objects with the corresponding tool call ID will be
+                    excluded.
+                - The `tool_calls` in the AIMessage will be updated to exclude
+                    matching tool calls. If all `tool_calls` are filtered from an
+                    AIMessage, the whole message is excluded.
 
     Returns:
-        A list of Messages that meets at least one of the ``incl_*`` conditions and none
-        of the ``excl_*`` conditions. If not ``incl_*`` conditions are specified then
+        A list of Messages that meets at least one of the `incl_*` conditions and none
+        of the `excl_*` conditions. If not `incl_*` conditions are specified then
         anything that is not explicitly excluded will be included.
 
     Raises:
         ValueError: If two incompatible arguments are provided.
 
     Example:
-        .. code-block:: python
+        ```python
+        from langchain_core.messages import (
+            filter_messages,
+            AIMessage,
+            HumanMessage,
+            SystemMessage,
+        )
 
-            from langchain_core.messages import (
-                filter_messages,
-                AIMessage,
-                HumanMessage,
-                SystemMessage,
-            )
+        messages = [
+            SystemMessage("you're a good assistant."),
+            HumanMessage("what's your name", id="foo", name="example_user"),
+            AIMessage("steve-o", id="bar", name="example_assistant"),
+            HumanMessage(
+                "what's your favorite color",
+                id="baz",
+            ),
+            AIMessage(
+                "silicon blue",
+                id="blah",
+            ),
+        ]
 
-            messages = [
-                SystemMessage("you're a good assistant."),
-                HumanMessage("what's your name", id="foo", name="example_user"),
-                AIMessage("steve-o", id="bar", name="example_assistant"),
-                HumanMessage(
-                    "what's your favorite color",
-                    id="baz",
-                ),
-                AIMessage(
-                    "silicon blue",
-                    id="blah",
-                ),
-            ]
-
-            filter_messages(
-                messages,
-                incl_names=("example_user", "example_assistant"),
-                incl_types=("system",),
-                excl_ids=("bar",),
-            )
-
-        .. code-block:: python
-
-            [
-                SystemMessage("you're a good assistant."),
-                HumanMessage("what's your name", id="foo", name="example_user"),
-            ]
+        filter_messages(
+            messages,
+            incl_names=("example_user", "example_assistant"),
+            incl_types=("system",),
+            excl_ids=("bar",),
+        )
+        ```
 
+        ```python
+        [
+            SystemMessage("you're a good assistant."),
+            HumanMessage("what's your name", id="foo", name="example_user"),
+        ]
+        ```
     """
     messages = convert_to_messages(messages)
     filtered: list[BaseMessage] = []
@@ -571,7 +568,6 @@ def merge_message_runs(
     Args:
         messages: Sequence Message-like objects to merge.
         chunk_separator: Specify the string to be inserted between message chunks.
-            Defaults to ``'\n'``.
 
     Returns:
         list of BaseMessages with consecutive runs of message types merged into single
@@ -579,87 +575,86 @@ def merge_message_runs(
         the merged content is a concatenation of the two strings with a new-line
         separator.
         The separator inserted between message chunks can be controlled by specifying
-        any string with ``chunk_separator``. If at least one of the messages has a list
+        any string with `chunk_separator`. If at least one of the messages has a list
         of content blocks, the merged content is a list of content blocks.
 
     Example:
+        ```python
+        from langchain_core.messages import (
+            merge_message_runs,
+            AIMessage,
+            HumanMessage,
+            SystemMessage,
+            ToolCall,
+        )
 
-        .. code-block:: python
+        messages = [
+            SystemMessage("you're a good assistant."),
+            HumanMessage(
+                "what's your favorite color",
+                id="foo",
+            ),
+            HumanMessage(
+                "wait your favorite food",
+                id="bar",
+            ),
+            AIMessage(
+                "my favorite colo",
+                tool_calls=[
+                    ToolCall(
+                        name="blah_tool", args={"x": 2}, id="123", type="tool_call"
+                    )
+                ],
+                id="baz",
+            ),
+            AIMessage(
+                [{"type": "text", "text": "my favorite dish is lasagna"}],
+                tool_calls=[
+                    ToolCall(
+                        name="blah_tool",
+                        args={"x": -10},
+                        id="456",
+                        type="tool_call",
+                    )
+                ],
+                id="blur",
+            ),
+        ]
 
-            from langchain_core.messages import (
-                merge_message_runs,
-                AIMessage,
-                HumanMessage,
-                SystemMessage,
-                ToolCall,
-            )
+        merge_message_runs(messages)
+        ```
 
-            messages = [
-                SystemMessage("you're a good assistant."),
-                HumanMessage(
-                    "what's your favorite color",
-                    id="foo",
-                ),
-                HumanMessage(
-                    "wait your favorite food",
-                    id="bar",
-                ),
-                AIMessage(
+        ```python
+        [
+            SystemMessage("you're a good assistant."),
+            HumanMessage(
+                "what's your favorite color\\n"
+                "wait your favorite food", id="foo",
+            ),
+            AIMessage(
+                [
                     "my favorite colo",
-                    tool_calls=[
-                        ToolCall(
-                            name="blah_tool", args={"x": 2}, id="123", type="tool_call"
-                        )
-                    ],
-                    id="baz",
-                ),
-                AIMessage(
-                    [{"type": "text", "text": "my favorite dish is lasagna"}],
-                    tool_calls=[
-                        ToolCall(
-                            name="blah_tool",
-                            args={"x": -10},
-                            id="456",
-                            type="tool_call",
-                        )
-                    ],
-                    id="blur",
-                ),
-            ]
-
-            merge_message_runs(messages)
-
-        .. code-block:: python
-
-            [
-                SystemMessage("you're a good assistant."),
-                HumanMessage(
-                    "what's your favorite color\\n"
-                    "wait your favorite food", id="foo",
-                ),
-                AIMessage(
-                    [
-                        "my favorite colo",
-                        {"type": "text", "text": "my favorite dish is lasagna"}
-                    ],
-                    tool_calls=[
-                        ToolCall({
-                            "name": "blah_tool",
-                            "args": {"x": 2},
-                            "id": "123",
-                            "type": "tool_call"
-                        }),
-                        ToolCall({
-                            "name": "blah_tool",
-                            "args": {"x": -10},
-                            "id": "456",
-                            "type": "tool_call"
-                        })
-                    ]
-                    id="baz"
-                ),
-            ]
+                    {"type": "text", "text": "my favorite dish is lasagna"}
+                ],
+                tool_calls=[
+                    ToolCall({
+                        "name": "blah_tool",
+                        "args": {"x": 2},
+                        "id": "123",
+                        "type": "tool_call"
+                    }),
+                    ToolCall({
+                        "name": "blah_tool",
+                        "args": {"x": -10},
+                        "id": "456",
+                        "type": "tool_call"
+                    })
+                ]
+                id="baz"
+            ),
+        ]
 
+        ```
     """
     if not messages:
         return []
@@ -706,30 +701,28 @@ def trim_messages(
 ) -> list[BaseMessage]:
     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.
+    `trim_messages` can be used to reduce the size of a chat history to a specified
+    token or message count.
 
     In either case, if passing the trimmed chat history back into a chat model
     directly, the resulting chat history should usually satisfy the following
     properties:
 
     1. The resulting chat history should be valid. Most chat models expect that chat
-       history starts with either (1) a `HumanMessage` or (2) a `SystemMessage`
-       followed by a `HumanMessage`. To achieve this, set ``start_on='human'``.
-       In addition, generally a `ToolMessage` can only appear after an `AIMessage`
-       that involved a tool call.
-       Please see the following link for more information about messages:
-       https://python.langchain.com/docs/concepts/#messages
+        history starts with either (1) a `HumanMessage` or (2) a `SystemMessage`
+        followed by a `HumanMessage`. To achieve this, set `start_on='human'`.
+        In addition, generally a `ToolMessage` can only appear after an `AIMessage`
+        that involved a tool call.
     2. It includes recent messages and drops old messages in the chat history.
-       To achieve this set the ``strategy='last'``.
+        To achieve this set the `strategy='last'`.
     3. Usually, the new chat history should include the `SystemMessage` if it
-       was present in the original chat history since the `SystemMessage` includes
-       special instructions to the chat model. The `SystemMessage` is almost always
-       the first message in the history if present. To achieve this set the
-       ``include_system=True``.
+        was present in the original chat history since the `SystemMessage` includes
+        special instructions to the chat model. The `SystemMessage` is almost always
+        the first message in the history if present. To achieve this set the
+        `include_system=True`.
 
     !!! note
-        The examples below show how to configure ``trim_messages`` to achieve a behavior
+        The examples below show how to configure `trim_messages` to achieve a behavior
         consistent with the above properties.
 
     Args:
@@ -737,118 +730,113 @@ def trim_messages(
         max_tokens: Max token count of trimmed messages.
         token_counter: Function or llm for counting tokens in a `BaseMessage` or a
             list of `BaseMessage`. If a `BaseLanguageModel` is passed in then
-            ``BaseLanguageModel.get_num_tokens_from_messages()`` will be used.
-            Set to ``len`` to count the number of **messages** in the chat history.
+            `BaseLanguageModel.get_num_tokens_from_messages()` will be used.
+            Set to `len` to count the number of **messages** in the chat history.
 
             !!! note
-                Use ``count_tokens_approximately`` to get fast, approximate token
+                Use `count_tokens_approximately` to get fast, approximate token
                 counts.
-                This is recommended for using ``trim_messages`` on the hot path, where
+                This is recommended for using `trim_messages` on the hot path, where
                 exact token counting is not necessary.
 
         strategy: Strategy for trimming.
-            - ``'first'``: Keep the first ``<= n_count`` tokens of the messages.
-            - ``'last'``: Keep the last ``<= n_count`` tokens of the messages.
-            Default is ``'last'``.
+            - `'first'`: Keep the first `<= n_count` tokens of the messages.
+            - `'last'`: Keep the last `<= n_count` tokens of the messages.
         allow_partial: Whether to split a message if only part of the message can be
-            included. If ``strategy='last'`` then the last partial contents of a message
-            are included. If ``strategy='first'`` then the first partial contents of a
+            included. If `strategy='last'` then the last partial contents of a message
+            are included. If `strategy='first'` then the first partial contents of a
             message are included.
-            Default is False.
         end_on: The message type to end on. If specified then every message after the
-            last occurrence of this type is ignored. If ``strategy='last'`` then this
-            is done before we attempt to get the last ``max_tokens``. If
-            ``strategy='first'`` then this is done after we get the first
-            ``max_tokens``. Can be specified as string names (e.g. ``'system'``,
-            ``'human'``, ``'ai'``, ...) or as `BaseMessage` classes (e.g.
+            last occurrence of this type is ignored. If `strategy='last'` then this
+            is done before we attempt to get the last `max_tokens`. If
+            `strategy='first'` then this is done after we get the first
+            `max_tokens`. Can be specified as string names (e.g. `'system'`,
+            `'human'`, `'ai'`, ...) or as `BaseMessage` classes (e.g.
             `SystemMessage`, `HumanMessage`, `AIMessage`, ...). Can be a single
             type or a list of types.
-            Default is None.
+
         start_on: The message type to start on. Should only be specified if
-            ``strategy='last'``. If specified then every message before
+            `strategy='last'`. If specified then every message before
             the first occurrence of this type is ignored. This is done after we trim
-            the initial messages to the last ``max_tokens``. Does not
-            apply to a `SystemMessage` at index 0 if ``include_system=True``. Can be
-            specified as string names (e.g. ``'system'``, ``'human'``, ``'ai'``, ...) or
+            the initial messages to the last `max_tokens`. Does not
+            apply to a `SystemMessage` at index 0 if `include_system=True`. Can be
+            specified as string names (e.g. `'system'`, `'human'`, `'ai'`, ...) or
             as `BaseMessage` classes (e.g. `SystemMessage`, `HumanMessage`,
             `AIMessage`, ...). Can be a single type or a list of types.
-            Default is None.
-        include_system: Whether to keep the SystemMessage if there is one at index 0.
-            Should only be specified if ``strategy="last"``.
-            Default is False.
-        text_splitter: Function or ``langchain_text_splitters.TextSplitter`` for
+
+        include_system: Whether to keep the `SystemMessage` if there is one at index
+            `0`. Should only be specified if `strategy="last"`.
+        text_splitter: Function or `langchain_text_splitters.TextSplitter` for
             splitting the string contents of a message. Only used if
-            ``allow_partial=True``. If ``strategy='last'`` then the last split tokens
-            from a partial message will be included. if ``strategy='first'`` then the
+            `allow_partial=True`. If `strategy='last'` then the last split tokens
+            from a partial message will be included. if `strategy='first'` then the
             first split tokens from a partial message will be included. Token splitter
             assumes that separators are kept, so that split contents can be directly
             concatenated to recreate the original text. Defaults to splitting on
             newlines.
 
     Returns:
-        list of trimmed `BaseMessage`.
+        List of trimmed `BaseMessage`.
 
     Raises:
         ValueError: if two incompatible arguments are specified or an unrecognized
-            ``strategy`` is specified.
+            `strategy` is specified.
 
     Example:
         Trim chat history based on token count, keeping the `SystemMessage` if
         present, and ensuring that the chat history starts with a `HumanMessage` (
         or a `SystemMessage` followed by a `HumanMessage`).
 
-        .. code-block:: python
+        ```python
+        from langchain_core.messages import (
+            AIMessage,
+            HumanMessage,
+            BaseMessage,
+            SystemMessage,
+            trim_messages,
+        )
 
-            from langchain_core.messages import (
-                AIMessage,
-                HumanMessage,
-                BaseMessage,
-                SystemMessage,
-                trim_messages,
-            )
-
-            messages = [
-                SystemMessage(
-                    "you're a good assistant, you always respond with a joke."
-                ),
-                HumanMessage("i wonder why it's called langchain"),
-                AIMessage(
-                    'Well, I guess they thought "WordRope" and "SentenceString" just '
-                    "didn't have the same ring to it!"
-                ),
-                HumanMessage("and who is harrison chasing anyways"),
-                AIMessage(
-                    "Hmmm let me think.\n\nWhy, he's probably chasing after the last "
-                    "cup of coffee in the office!"
-                ),
-                HumanMessage("what do you call a speechless parrot"),
-            ]
+        messages = [
+            SystemMessage("you're a good assistant, you always respond with a joke."),
+            HumanMessage("i wonder why it's called langchain"),
+            AIMessage(
+                'Well, I guess they thought "WordRope" and "SentenceString" just '
+                "didn't have the same ring to it!"
+            ),
+            HumanMessage("and who is harrison chasing anyways"),
+            AIMessage(
+                "Hmmm let me think.\n\nWhy, he's probably chasing after the last "
+                "cup of coffee in the office!"
+            ),
+            HumanMessage("what do you call a speechless parrot"),
+        ]
 
 
-            trim_messages(
-                messages,
-                max_tokens=45,
-                strategy="last",
-                token_counter=ChatOpenAI(model="gpt-4o"),
-                # Most chat models expect that chat history starts with either:
-                # (1) a HumanMessage or
-                # (2) a SystemMessage followed by a HumanMessage
-                start_on="human",
-                # Usually, we want to keep the SystemMessage
-                # if it's present in the original history.
-                # The SystemMessage has special instructions for the model.
-                include_system=True,
-                allow_partial=False,
-            )
+        trim_messages(
+            messages,
+            max_tokens=45,
+            strategy="last",
+            token_counter=ChatOpenAI(model="gpt-4o"),
+            # Most chat models expect that chat history starts with either:
+            # (1) a HumanMessage or
+            # (2) a SystemMessage followed by a HumanMessage
+            start_on="human",
+            # Usually, we want to keep the SystemMessage
+            # if it's present in the original history.
+            # The SystemMessage has special instructions for the model.
+            include_system=True,
+            allow_partial=False,
+        )
+        ```
 
-        .. code-block:: python
-
-            [
-                SystemMessage(
-                    content="you're a good assistant, you always respond with a joke."
-                ),
-                HumanMessage(content="what do you call a speechless parrot"),
-            ]
+        ```python
+        [
+            SystemMessage(
+                content="you're a good assistant, you always respond with a joke."
+            ),
+            HumanMessage(content="what do you call a speechless parrot"),
+        ]
+        ```
 
         Trim chat history based on the message count, keeping the `SystemMessage` if
         present, and ensuring that the chat history starts with a `HumanMessage` (
@@ -874,100 +862,95 @@ def trim_messages(
                 allow_partial=False,
             )
 
-        .. code-block:: python
-
-            [
-                SystemMessage(
-                    content="you're a good assistant, you always respond with a joke."
-                ),
-                HumanMessage(content="and who is harrison chasing anyways"),
-                AIMessage(
-                    content="Hmmm let me think.\n\nWhy, he's probably chasing after "
-                    "the last cup of coffee in the office!"
-                ),
-                HumanMessage(content="what do you call a speechless parrot"),
-            ]
-
-
+        ```python
+        [
+            SystemMessage(
+                content="you're a good assistant, you always respond with a joke."
+            ),
+            HumanMessage(content="and who is harrison chasing anyways"),
+            AIMessage(
+                content="Hmmm let me think.\n\nWhy, he's probably chasing after "
+                "the last cup of coffee in the office!"
+            ),
+            HumanMessage(content="what do you call a speechless parrot"),
+        ]
+        ```
         Trim chat history using a custom token counter function that counts the
         number of tokens in each message.
 
-        .. code-block:: python
-
-            messages = [
-                SystemMessage("This is a 4 token text. The full message is 10 tokens."),
-                HumanMessage(
-                    "This is a 4 token text. The full message is 10 tokens.", id="first"
-                ),
-                AIMessage(
-                    [
-                        {"type": "text", "text": "This is the FIRST 4 token block."},
-                        {"type": "text", "text": "This is the SECOND 4 token block."},
-                    ],
-                    id="second",
-                ),
-                HumanMessage(
-                    "This is a 4 token text. The full message is 10 tokens.", id="third"
-                ),
-                AIMessage(
-                    "This is a 4 token text. The full message is 10 tokens.",
-                    id="fourth",
-                ),
-            ]
+        ```python
+        messages = [
+            SystemMessage("This is a 4 token text. The full message is 10 tokens."),
+            HumanMessage(
+                "This is a 4 token text. The full message is 10 tokens.", id="first"
+            ),
+            AIMessage(
+                [
+                    {"type": "text", "text": "This is the FIRST 4 token block."},
+                    {"type": "text", "text": "This is the SECOND 4 token block."},
+                ],
+                id="second",
+            ),
+            HumanMessage(
+                "This is a 4 token text. The full message is 10 tokens.", id="third"
+            ),
+            AIMessage(
+                "This is a 4 token text. The full message is 10 tokens.",
+                id="fourth",
+            ),
+        ]
 
 
-            def dummy_token_counter(messages: list[BaseMessage]) -> int:
-                # treat each message like it adds 3 default tokens at the beginning
-                # of the message and at the end of the message. 3 + 4 + 3 = 10 tokens
-                # per message.
+        def dummy_token_counter(messages: list[BaseMessage]) -> int:
+            # treat each message like it adds 3 default tokens at the beginning
+            # of the message and at the end of the message. 3 + 4 + 3 = 10 tokens
+            # per message.
 
-                default_content_len = 4
-                default_msg_prefix_len = 3
-                default_msg_suffix_len = 3
+            default_content_len = 4
+            default_msg_prefix_len = 3
+            default_msg_suffix_len = 3
 
-                count = 0
-                for msg in messages:
-                    if isinstance(msg.content, str):
-                        count += (
-                            default_msg_prefix_len
-                            + default_content_len
-                            + default_msg_suffix_len
-                        )
-                    if isinstance(msg.content, list):
-                        count += (
-                            default_msg_prefix_len
-                            + len(msg.content) * default_content_len
-                            + default_msg_suffix_len
-                        )
-                return count
+            count = 0
+            for msg in messages:
+                if isinstance(msg.content, str):
+                    count += (
+                        default_msg_prefix_len
+                        + default_content_len
+                        + default_msg_suffix_len
+                    )
+                if isinstance(msg.content, list):
+                    count += (
+                        default_msg_prefix_len
+                        + len(msg.content) * default_content_len
+                        + default_msg_suffix_len
+                    )
+            return count
+        ```
 
         First 30 tokens, allowing partial messages:
-            .. code-block:: python
-
-                trim_messages(
-                    messages,
-                    max_tokens=30,
-                    token_counter=dummy_token_counter,
-                    strategy="first",
-                    allow_partial=True,
-                )
-
-            .. code-block:: python
-
-                [
-                    SystemMessage(
-                        "This is a 4 token text. The full message is 10 tokens."
-                    ),
-                    HumanMessage(
-                        "This is a 4 token text. The full message is 10 tokens.",
-                        id="first",
-                    ),
-                    AIMessage(
-                        [{"type": "text", "text": "This is the FIRST 4 token block."}],
-                        id="second",
-                    ),
-                ]
+        ```python
+        trim_messages(
+            messages,
+            max_tokens=30,
+            token_counter=dummy_token_counter,
+            strategy="first",
+            allow_partial=True,
+        )
+        ```
 
+        ```python
+        [
+            SystemMessage("This is a 4 token text. The full message is 10 tokens."),
+            HumanMessage(
+                "This is a 4 token text. The full message is 10 tokens.",
+                id="first",
+            ),
+            AIMessage(
+                [{"type": "text", "text": "This is the FIRST 4 token block."}],
+                id="second",
+            ),
+        ]
+        ```
     """
     # Validate arguments
     if start_on and strategy == "first":
@@ -1042,21 +1025,21 @@ def convert_to_openai_messages(
         messages: Message-like object or iterable of objects whose contents are
             in OpenAI, Anthropic, Bedrock Converse, or VertexAI formats.
         text_format: How to format string or text block contents:
-                - ``'string'``:
+                - `'string'`:
                     If a message has a string content, this is left as a string. If
-                    a message has content blocks that are all of type ``'text'``, these
+                    a message has content blocks that are all of type `'text'`, these
                     are joined with a newline to make a single string. If a message has
-                    content blocks and at least one isn't of type ``'text'``, then
+                    content blocks and at least one isn't of type `'text'`, then
                     all blocks are left as dicts.
-                - ``'block'``:
+                - `'block'`:
                     If a message has a string content, this is turned into a list
-                    with a single content block of type ``'text'``. If a message has
+                    with a single content block of type `'text'`. If a message has
                     content blocks these are left as is.
         include_id: Whether to include message ids in the openai messages, if they
                     are present in the source messages.
 
     Raises:
-        ValueError: if an unrecognized ``text_format`` is specified, or if a message
+        ValueError: if an unrecognized `text_format` is specified, or if a message
             content block is missing expected keys.
 
     Returns:
@@ -1070,50 +1053,49 @@ def convert_to_openai_messages(
             message dicts is returned.
 
     Example:
+        ```python
+        from langchain_core.messages import (
+            convert_to_openai_messages,
+            AIMessage,
+            SystemMessage,
+            ToolMessage,
+        )
 
-        .. code-block:: python
-
-            from langchain_core.messages import (
-                convert_to_openai_messages,
-                AIMessage,
-                SystemMessage,
-                ToolMessage,
-            )
-
-            messages = [
-                SystemMessage([{"type": "text", "text": "foo"}]),
-                {
-                    "role": "user",
-                    "content": [
-                        {"type": "text", "text": "whats in this"},
-                        {
-                            "type": "image_url",
-                            "image_url": {"url": "data:image/png;base64,'/9j/4AAQSk'"},
-                        },
-                    ],
-                },
-                AIMessage(
-                    "",
-                    tool_calls=[
-                        {
-                            "name": "analyze",
-                            "args": {"baz": "buz"},
-                            "id": "1",
-                            "type": "tool_call",
-                        }
-                    ],
-                ),
-                ToolMessage("foobar", tool_call_id="1", name="bar"),
-                {"role": "assistant", "content": "thats nice"},
-            ]
-            oai_messages = convert_to_openai_messages(messages)
-            # -> [
-            #   {'role': 'system', 'content': 'foo'},
-            #   {'role': 'user', 'content': [{'type': 'text', 'text': 'whats in this'}, {'type': 'image_url', 'image_url': {'url': "data:image/png;base64,'/9j/4AAQSk'"}}]},
-            #   {'role': 'assistant', 'tool_calls': [{'type': 'function', 'id': '1','function': {'name': 'analyze', 'arguments': '{"baz": "buz"}'}}], 'content': ''},
-            #   {'role': 'tool', 'name': 'bar', 'content': 'foobar'},
-            #   {'role': 'assistant', 'content': 'thats nice'}
-            # ]
+        messages = [
+            SystemMessage([{"type": "text", "text": "foo"}]),
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "whats in this"},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": "data:image/png;base64,'/9j/4AAQSk'"},
+                    },
+                ],
+            },
+            AIMessage(
+                "",
+                tool_calls=[
+                    {
+                        "name": "analyze",
+                        "args": {"baz": "buz"},
+                        "id": "1",
+                        "type": "tool_call",
+                    }
+                ],
+            ),
+            ToolMessage("foobar", tool_call_id="1", name="bar"),
+            {"role": "assistant", "content": "thats nice"},
+        ]
+        oai_messages = convert_to_openai_messages(messages)
+        # -> [
+        #   {'role': 'system', 'content': 'foo'},
+        #   {'role': 'user', 'content': [{'type': 'text', 'text': 'whats in this'}, {'type': 'image_url', 'image_url': {'url': "data:image/png;base64,'/9j/4AAQSk'"}}]},
+        #   {'role': 'assistant', 'tool_calls': [{'type': 'function', 'id': '1','function': {'name': 'analyze', 'arguments': '{"baz": "buz"}'}}], 'content': ''},
+        #   {'role': 'tool', 'name': 'bar', 'content': 'foobar'},
+        #   {'role': 'assistant', 'content': 'thats nice'}
+        # ]
+        ```
 
     !!! version-added "Added in version 0.3.11"
 
@@ -1695,13 +1677,13 @@ def count_tokens_approximately(
     Args:
         messages: List of messages to count tokens for.
         chars_per_token: Number of characters per token to use for the approximation.
-            Default is 4 (one token corresponds to ~4 chars for common English text).
+            One token corresponds to ~4 chars for common English text.
             You can also specify float values for more fine-grained control.
-            `See more here. <https://platform.openai.com/tokenizer>`__
-        extra_tokens_per_message: Number of extra tokens to add per message.
-            Default is 3 (special tokens, including beginning/end of message).
+            [See more here](https://platform.openai.com/tokenizer).
+        extra_tokens_per_message: Number of extra tokens to add per message, e.g.
+            special tokens, including beginning/end of message.
             You can also specify float values for more fine-grained control.
-            `See more here. <https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb>`__
+            [See more here](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb).
         count_name: Whether to include message names in the count.
             Enabled by default.
 

libs/core/langchain_core/output_parsers/__init__.py

@@ -1,17 +1,4 @@
-"""**OutputParser** classes parse the output of an LLM call.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseLLMOutputParser --> BaseOutputParser --> <name>OutputParser  # ListOutputParser, PydanticOutputParser
-
-**Main helpers:**
-
-.. code-block::
-
-    Serializable, Generation, PromptValue
-"""  # noqa: E501
+"""**OutputParser** classes parse the output of an LLM call."""
 
 from typing import TYPE_CHECKING
 

libs/core/langchain_core/output_parsers/base.py

@@ -31,13 +31,13 @@ class BaseLLMOutputParser(ABC, Generic[T]):
 
     @abstractmethod
     def parse_result(self, result: list[Generation], *, partial: bool = False) -> T:
-        """Parse a list of candidate model Generations into a specific format.
+        """Parse a list of candidate model `Generation` objects into a specific format.
 
         Args:
-            result: A list of Generations to be parsed. The Generations are assumed
-                to be different candidate outputs for a single model input.
+            result: A list of `Generation` to be parsed. The `Generation` objects are
+                assumed to be different candidate outputs for a single model input.
             partial: Whether to parse the output as a partial result. This is useful
-                for parsers that can parse partial results. Default is False.
+                for parsers that can parse partial results.
 
         Returns:
             Structured output.
@@ -46,17 +46,17 @@ class BaseLLMOutputParser(ABC, Generic[T]):
     async def aparse_result(
         self, result: list[Generation], *, partial: bool = False
     ) -> T:
-        """Async parse a list of candidate model Generations into a specific format.
+        """Async parse a list of candidate model `Generation` objects into a specific format.
 
         Args:
-            result: A list of Generations to be parsed. The Generations are assumed
+            result: A list of `Generation` to be parsed. The Generations are assumed
                 to be different candidate outputs for a single model input.
             partial: Whether to parse the output as a partial result. This is useful
-                for parsers that can parse partial results. Default is False.
+                for parsers that can parse partial results.
 
         Returns:
             Structured output.
-        """
+        """  # noqa: E501
         return await run_in_executor(None, self.parse_result, result, partial=partial)
 
 
@@ -134,29 +134,28 @@ class BaseOutputParser(
     Output parsers help structure language model responses.
 
     Example:
-        .. code-block:: python
+        ```python
+        class BooleanOutputParser(BaseOutputParser[bool]):
+            true_val: str = "YES"
+            false_val: str = "NO"
 
-            class BooleanOutputParser(BaseOutputParser[bool]):
-                true_val: str = "YES"
-                false_val: str = "NO"
-
-                def parse(self, text: str) -> bool:
-                    cleaned_text = text.strip().upper()
-                    if cleaned_text not in (
-                        self.true_val.upper(),
-                        self.false_val.upper(),
-                    ):
-                        raise OutputParserException(
-                            f"BooleanOutputParser expected output value to either be "
-                            f"{self.true_val} or {self.false_val} (case-insensitive). "
-                            f"Received {cleaned_text}."
-                        )
-                    return cleaned_text == self.true_val.upper()
-
-                @property
-                def _type(self) -> str:
-                    return "boolean_output_parser"
+            def parse(self, text: str) -> bool:
+                cleaned_text = text.strip().upper()
+                if cleaned_text not in (
+                    self.true_val.upper(),
+                    self.false_val.upper(),
+                ):
+                    raise OutputParserException(
+                        f"BooleanOutputParser expected output value to either be "
+                        f"{self.true_val} or {self.false_val} (case-insensitive). "
+                        f"Received {cleaned_text}."
+                    )
+                return cleaned_text == self.true_val.upper()
 
+            @property
+            def _type(self) -> str:
+                return "boolean_output_parser"
+        ```
     """
 
     @property
@@ -173,7 +172,7 @@ class BaseOutputParser(
         This property is inferred from the first type argument of the class.
 
         Raises:
-            TypeError: If the class doesn't have an inferable OutputType.
+            TypeError: If the class doesn't have an inferable `OutputType`.
         """
         for base in self.__class__.mro():
             if hasattr(base, "__pydantic_generic_metadata__"):
@@ -235,16 +234,16 @@ class BaseOutputParser(
 
     @override
     def parse_result(self, result: list[Generation], *, partial: bool = False) -> T:
-        """Parse a list of candidate model Generations into a specific format.
+        """Parse a list of candidate model `Generation` objects into a specific format.
 
-        The return value is parsed from only the first Generation in the result, which
-            is assumed to be the highest-likelihood Generation.
+        The return value is parsed from only the first `Generation` in the result, which
+            is assumed to be the highest-likelihood `Generation`.
 
         Args:
-            result: A list of Generations to be parsed. The Generations are assumed
-                to be different candidate outputs for a single model input.
+            result: A list of `Generation` to be parsed. The `Generation` objects are
+                assumed to be different candidate outputs for a single model input.
             partial: Whether to parse the output as a partial result. This is useful
-                for parsers that can parse partial results. Default is False.
+                for parsers that can parse partial results.
 
         Returns:
             Structured output.
@@ -265,20 +264,20 @@ class BaseOutputParser(
     async def aparse_result(
         self, result: list[Generation], *, partial: bool = False
     ) -> T:
-        """Async parse a list of candidate model Generations into a specific format.
+        """Async parse a list of candidate model `Generation` objects into a specific format.
 
-        The return value is parsed from only the first Generation in the result, which
-            is assumed to be the highest-likelihood Generation.
+        The return value is parsed from only the first `Generation` in the result, which
+            is assumed to be the highest-likelihood `Generation`.
 
         Args:
-            result: A list of Generations to be parsed. The Generations are assumed
-                to be different candidate outputs for a single model input.
+            result: A list of `Generation` to be parsed. The `Generation` objects are
+                assumed to be different candidate outputs for a single model input.
             partial: Whether to parse the output as a partial result. This is useful
-                for parsers that can parse partial results. Default is False.
+                for parsers that can parse partial results.
 
         Returns:
             Structured output.
-        """
+        """  # noqa: E501
         return await run_in_executor(None, self.parse_result, result, partial=partial)
 
     async def aparse(self, text: str) -> T:
@@ -300,13 +299,13 @@ class BaseOutputParser(
     ) -> Any:
         """Parse the output of an LLM call with the input prompt for context.
 
-        The prompt is largely provided in the event the OutputParser wants
+        The prompt is largely provided in the event the `OutputParser` wants
         to retry or fix the output in some way, and needs information from
         the prompt to do so.
 
         Args:
             completion: String output of a language model.
-            prompt: Input PromptValue.
+            prompt: Input `PromptValue`.
 
         Returns:
             Structured output.

libs/core/langchain_core/output_parsers/json.py

@@ -62,7 +62,6 @@ class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]):
                 If `True`, the output will be a JSON object containing
                 all the keys that have been returned so far.
                 If `False`, the output will be the full JSON object.
-                Default is False.
 
         Returns:
             The parsed JSON object.

libs/core/langchain_core/output_parsers/list.py

@@ -146,10 +146,10 @@ class CommaSeparatedListOutputParser(ListOutputParser):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "output_parsers", "list"]``
+            `["langchain", "output_parsers", "list"]`
         """
         return ["langchain", "output_parsers", "list"]
 

libs/core/langchain_core/output_parsers/openai_functions.py

@@ -31,13 +31,13 @@ class OutputFunctionsParser(BaseGenerationOutputParser[Any]):
 
         Args:
             result: The result of the LLM call.
-            partial: Whether to parse partial JSON objects. Default is False.
+            partial: Whether to parse partial JSON objects.
 
         Returns:
             The parsed JSON object.
 
         Raises:
-            OutputParserException: If the output is not valid JSON.
+            `OutputParserException`: If the output is not valid JSON.
         """
         generation = result[0]
         if not isinstance(generation, ChatGeneration):
@@ -56,7 +56,7 @@ class OutputFunctionsParser(BaseGenerationOutputParser[Any]):
 
 
 class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
-    """Parse an output as the Json object."""
+    """Parse an output as the JSON object."""
 
     strict: bool = False
     """Whether to allow non-JSON-compliant strings.
@@ -82,13 +82,13 @@ class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
 
         Args:
             result: The result of the LLM call.
-            partial: Whether to parse partial JSON objects. Default is False.
+            partial: Whether to parse partial JSON objects.
 
         Returns:
             The parsed JSON object.
 
         Raises:
-            OutputParserException: If the output is not valid JSON.
+            OutputParserExcept`ion: If the output is not valid JSON.
         """
         if len(result) != 1:
             msg = f"Expected exactly one result, but got {len(result)}"
@@ -155,7 +155,7 @@ class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
 
 
 class JsonKeyOutputFunctionsParser(JsonOutputFunctionsParser):
-    """Parse an output as the element of the Json object."""
+    """Parse an output as the element of the JSON object."""
 
     key_name: str
     """The name of the key to return."""
@@ -165,7 +165,7 @@ class JsonKeyOutputFunctionsParser(JsonOutputFunctionsParser):
 
         Args:
             result: The result of the LLM call.
-            partial: Whether to parse partial JSON objects. Default is False.
+            partial: Whether to parse partial JSON objects.
 
         Returns:
             The parsed JSON object.
@@ -177,48 +177,50 @@ class JsonKeyOutputFunctionsParser(JsonOutputFunctionsParser):
 
 
 class PydanticOutputFunctionsParser(OutputFunctionsParser):
-    """Parse an output as a pydantic object.
+    """Parse an output as a Pydantic object.
 
-    This parser is used to parse the output of a ChatModel that uses
-    OpenAI function format to invoke functions.
+    This parser is used to parse the output of a chat model that uses OpenAI function
+    format to invoke functions.
 
-    The parser extracts the function call invocation and matches
-    them to the pydantic schema provided.
+    The parser extracts the function call invocation and matches them to the Pydantic
+    schema provided.
 
-    An exception will be raised if the function call does not match
-    the provided schema.
+    An exception will be raised if the function call does not match the provided schema.
 
     Example:
-        ... code-block:: python
+        ```python
+        message = AIMessage(
+            content="This is a test message",
+            additional_kwargs={
+                "function_call": {
+                    "name": "cookie",
+                    "arguments": json.dumps({"name": "value", "age": 10}),
+                }
+            },
+        )
+        chat_generation = ChatGeneration(message=message)
 
-            message = AIMessage(
-                content="This is a test message",
-                additional_kwargs={
-                    "function_call": {
-                        "name": "cookie",
-                        "arguments": json.dumps({"name": "value", "age": 10}),
-                    }
-                },
-            )
-            chat_generation = ChatGeneration(message=message)
 
-            class Cookie(BaseModel):
-                name: str
-                age: int
+        class Cookie(BaseModel):
+            name: str
+            age: int
 
-            class Dog(BaseModel):
-                species: str
 
-            # Full output
-            parser = PydanticOutputFunctionsParser(
-                pydantic_schema={"cookie": Cookie, "dog": Dog}
-            )
-            result = parser.parse_result([chat_generation])
+        class Dog(BaseModel):
+            species: str
+
+
+        # Full output
+        parser = PydanticOutputFunctionsParser(
+            pydantic_schema={"cookie": Cookie, "dog": Dog}
+        )
+        result = parser.parse_result([chat_generation])
+        ```
 
     """
 
     pydantic_schema: type[BaseModel] | dict[str, type[BaseModel]]
-    """The pydantic schema to parse the output with.
+    """The Pydantic schema to parse the output with.
 
     If multiple schemas are provided, then the function name will be used to
     determine which schema to use.
@@ -227,7 +229,7 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
     @model_validator(mode="before")
     @classmethod
     def validate_schema(cls, values: dict) -> Any:
-        """Validate the pydantic schema.
+        """Validate the Pydantic schema.
 
         Args:
             values: The values to validate.
@@ -236,7 +238,7 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
             The validated values.
 
         Raises:
-            ValueError: If the schema is not a pydantic schema.
+            ValueError: If the schema is not a Pydantic schema.
         """
         schema = values["pydantic_schema"]
         if "args_only" not in values:
@@ -259,10 +261,10 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
 
         Args:
             result: The result of the LLM call.
-            partial: Whether to parse partial JSON objects. Default is False.
+            partial: Whether to parse partial JSON objects.
 
         Raises:
-            ValueError: If the pydantic schema is not valid.
+            ValueError: If the Pydantic schema is not valid.
 
         Returns:
             The parsed JSON object.
@@ -285,13 +287,13 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
             elif issubclass(pydantic_schema, BaseModelV1):
                 pydantic_args = pydantic_schema.parse_raw(args)
             else:
-                msg = f"Unsupported pydantic schema: {pydantic_schema}"
+                msg = f"Unsupported Pydantic schema: {pydantic_schema}"
                 raise ValueError(msg)
         return pydantic_args
 
 
 class PydanticAttrOutputFunctionsParser(PydanticOutputFunctionsParser):
-    """Parse an output as an attribute of a pydantic object."""
+    """Parse an output as an attribute of a Pydantic object."""
 
     attr_name: str
     """The name of the attribute to return."""
@@ -302,7 +304,7 @@ class PydanticAttrOutputFunctionsParser(PydanticOutputFunctionsParser):
 
         Args:
             result: The result of the LLM call.
-            partial: Whether to parse partial JSON objects. Default is False.
+            partial: Whether to parse partial JSON objects.
 
         Returns:
             The parsed JSON object.

libs/core/langchain_core/output_parsers/openai_tools.py

@@ -31,10 +31,9 @@ def parse_tool_call(
 
     Args:
         raw_tool_call: The raw tool call to parse.
-        partial: Whether to parse partial JSON. Default is False.
+        partial: Whether to parse partial JSON.
         strict: Whether to allow non-JSON-compliant strings.
-            Default is False.
-        return_id: Whether to return the tool call id. Default is True.
+        return_id: Whether to return the tool call id.
 
     Returns:
         The parsed tool call.
@@ -105,10 +104,9 @@ def parse_tool_calls(
 
     Args:
         raw_tool_calls: The raw tool calls to parse.
-        partial: Whether to parse partial JSON. Default is False.
+        partial: Whether to parse partial JSON.
         strict: Whether to allow non-JSON-compliant strings.
-            Default is False.
-        return_id: Whether to return the tool call id. Default is True.
+        return_id: Whether to return the tool call id.
 
     Returns:
         The parsed tool calls.
@@ -165,7 +163,6 @@ class JsonOutputToolsParser(BaseCumulativeTransformOutputParser[Any]):
                 If `True`, the output will be a JSON object containing
                 all the keys that have been returned so far.
                 If `False`, the output will be the full JSON object.
-                Default is False.
 
         Returns:
             The parsed tool calls.
@@ -229,7 +226,6 @@ class JsonOutputKeyToolsParser(JsonOutputToolsParser):
                 If `True`, the output will be a JSON object containing
                 all the keys that have been returned so far.
                 If `False`, the output will be the full JSON object.
-                Default is False.
 
         Raises:
             OutputParserException: If the generation is not a chat generation.
@@ -313,7 +309,6 @@ class PydanticToolsParser(JsonOutputToolsParser):
                 If `True`, the output will be a JSON object containing
                 all the keys that have been returned so far.
                 If `False`, the output will be the full JSON object.
-                Default is False.
 
         Returns:
             The parsed Pydantic objects.

libs/core/langchain_core/output_parsers/pydantic.py

@@ -17,10 +17,10 @@ from langchain_core.utils.pydantic import (
 
 
 class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
-    """Parse an output using a pydantic model."""
+    """Parse an output using a Pydantic model."""
 
     pydantic_object: Annotated[type[TBaseModel], SkipValidation()]
-    """The pydantic model to parse."""
+    """The Pydantic model to parse."""
 
     def _parse_obj(self, obj: dict) -> TBaseModel:
         try:
@@ -45,21 +45,20 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
     def parse_result(
         self, result: list[Generation], *, partial: bool = False
     ) -> TBaseModel | None:
-        """Parse the result of an LLM call to a pydantic object.
+        """Parse the result of an LLM call to a Pydantic object.
 
         Args:
             result: The result of the LLM call.
             partial: Whether to parse partial JSON objects.
                 If `True`, the output will be a JSON object containing
                 all the keys that have been returned so far.
-                Defaults to `False`.
 
         Raises:
-            OutputParserException: If the result is not valid JSON
-                or does not conform to the pydantic model.
+            `OutputParserException`: If the result is not valid JSON
+                or does not conform to the Pydantic model.
 
         Returns:
-            The parsed pydantic object.
+            The parsed Pydantic object.
         """
         try:
             json_object = super().parse_result(result)
@@ -70,13 +69,13 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
             raise
 
     def parse(self, text: str) -> TBaseModel:
-        """Parse the output of an LLM call to a pydantic object.
+        """Parse the output of an LLM call to a Pydantic object.
 
         Args:
             text: The output of the LLM call.
 
         Returns:
-            The parsed pydantic object.
+            The parsed Pydantic object.
         """
         return super().parse(text)
 
@@ -107,7 +106,7 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
     @property
     @override
     def OutputType(self) -> type[TBaseModel]:
-        """Return the pydantic model."""
+        """Return the Pydantic model."""
         return self.pydantic_object
 
 

libs/core/langchain_core/output_parsers/string.py

@@ -19,10 +19,10 @@ class StrOutputParser(BaseTransformOutputParser[str]):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "schema", "output_parser"]``
+            `["langchain", "schema", "output_parser"]`
         """
         return ["langchain", "schema", "output_parser"]
 

libs/core/langchain_core/output_parsers/transform.py

@@ -64,7 +64,7 @@ class BaseTransformOutputParser(BaseOutputParser[T]):
         Args:
             input: The input to transform.
             config: The configuration to use for the transformation.
-            kwargs: Additional keyword arguments.
+            **kwargs: Additional keyword arguments.
 
         Yields:
             The transformed output.
@@ -85,7 +85,7 @@ class BaseTransformOutputParser(BaseOutputParser[T]):
         Args:
             input: The input to transform.
             config: The configuration to use for the transformation.
-            kwargs: Additional keyword arguments.
+            **kwargs: Additional keyword arguments.
 
         Yields:
             The transformed output.

libs/core/langchain_core/output_parsers/xml.py

@@ -82,7 +82,7 @@ class _StreamingParser:
             chunk: A chunk of text to parse. This can be a string or a BaseMessage.
 
         Yields:
-            AddableDict: A dictionary representing the parsed XML element.
+            A dictionary representing the parsed XML element.
 
         Raises:
             xml.etree.ElementTree.ParseError: If the XML is not well-formed.

libs/core/langchain_core/outputs/__init__.py

@@ -12,7 +12,7 @@ When invoking models via the standard runnable methods (e.g. invoke, batch, etc.
 - LLMs will return regular text strings.
 
 In addition, users can access the raw output of either LLMs or chat models via
-callbacks. The ``on_chat_model_end`` and ``on_llm_end`` callbacks will return an
+callbacks. The `on_chat_model_end` and `on_llm_end` callbacks will return an
 LLMResult object containing the generated outputs and any additional information
 returned by the model provider.
 

libs/core/langchain_core/outputs/chat_generation.py

@@ -15,10 +15,10 @@ from langchain_core.utils._merge import merge_dicts
 class ChatGeneration(Generation):
     """A single chat generation output.
 
-    A subclass of ``Generation`` that represents the response from a chat model
+    A subclass of `Generation` that represents the response from a chat model
     that generates chat messages.
 
-    The ``message`` attribute is a structured representation of the chat message.
+    The `message` attribute is a structured representation of the chat message.
     Most of the time, the message will be of type `AIMessage`.
 
     Users working with chat models will usually access information via either
@@ -70,9 +70,9 @@ class ChatGeneration(Generation):
 
 
 class ChatGenerationChunk(ChatGeneration):
-    """``ChatGeneration`` chunk.
+    """`ChatGeneration` chunk.
 
-    ``ChatGeneration`` chunks can be concatenated with other ``ChatGeneration`` chunks.
+    `ChatGeneration` chunks can be concatenated with other `ChatGeneration` chunks.
     """
 
     message: BaseMessageChunk

libs/core/langchain_core/outputs/generation.py

@@ -44,10 +44,10 @@ class Generation(Serializable):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "schema", "output"]``
+            `["langchain", "schema", "output"]`
         """
         return ["langchain", "schema", "output"]
 
@@ -56,16 +56,16 @@ class GenerationChunk(Generation):
     """Generation chunk, which can be concatenated with other Generation chunks."""
 
     def __add__(self, other: GenerationChunk) -> GenerationChunk:
-        """Concatenate two ``GenerationChunk``s.
+        """Concatenate two `GenerationChunk`s.
 
         Args:
-            other: Another ``GenerationChunk`` to concatenate with.
+            other: Another `GenerationChunk` to concatenate with.
 
         Raises:
-            TypeError: If other is not a ``GenerationChunk``.
+            TypeError: If other is not a `GenerationChunk`.
 
         Returns:
-            A new ``GenerationChunk`` concatenated from self and other.
+            A new `GenerationChunk` concatenated from self and other.
         """
         if isinstance(other, GenerationChunk):
             generation_info = merge_dicts(

libs/core/langchain_core/outputs/llm_result.py

@@ -30,8 +30,8 @@ class LLMResult(BaseModel):
     The second dimension of the list represents different candidate generations for a
     given prompt.
 
-    - When returned from **an LLM**, the type is ``list[list[Generation]]``.
-    - When returned from a **chat model**, the type is ``list[list[ChatGeneration]]``.
+    - When returned from **an LLM**, the type is `list[list[Generation]]`.
+    - When returned from a **chat model**, the type is `list[list[ChatGeneration]]`.
 
     ChatGeneration is a subclass of Generation that has a field for a structured chat
     message.
@@ -97,7 +97,7 @@ class LLMResult(BaseModel):
             other: Another `LLMResult` object to compare against.
 
         Returns:
-            True if the generations and ``llm_output`` are equal, False otherwise.
+            `True` if the generations and `llm_output` are equal, `False` otherwise.
         """
         if not isinstance(other, LLMResult):
             return NotImplemented

libs/core/langchain_core/prompt_values.py

@@ -24,8 +24,8 @@ from langchain_core.messages import (
 class PromptValue(Serializable, ABC):
     """Base abstract class for inputs to any language model.
 
-    PromptValues can be converted to both LLM (pure text-generation) inputs and
-    ChatModel inputs.
+    `PromptValues` can be converted to both LLM (pure text-generation) inputs and
+    chat model inputs.
     """
 
     @classmethod
@@ -35,12 +35,12 @@ class PromptValue(Serializable, ABC):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         This is used to determine the namespace of the object when serializing.
 
         Returns:
-            ``["langchain", "schema", "prompt"]``
+            `["langchain", "schema", "prompt"]`
         """
         return ["langchain", "schema", "prompt"]
 
@@ -62,12 +62,12 @@ class StringPromptValue(PromptValue):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         This is used to determine the namespace of the object when serializing.
 
         Returns:
-            ``["langchain", "prompts", "base"]``
+            `["langchain", "prompts", "base"]`
         """
         return ["langchain", "prompts", "base"]
 
@@ -99,12 +99,12 @@ class ChatPromptValue(PromptValue):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         This is used to determine the namespace of the object when serializing.
 
         Returns:
-            ``["langchain", "prompts", "chat"]``
+            `["langchain", "prompts", "chat"]`
         """
         return ["langchain", "prompts", "chat"]
 
@@ -113,11 +113,11 @@ class ImageURL(TypedDict, total=False):
     """Image URL."""
 
     detail: Literal["auto", "low", "high"]
-    """Specifies the detail level of the image. Defaults to `'auto'`.
+    """Specifies the detail level of the image.
+
     Can be `'auto'`, `'low'`, or `'high'`.
 
     This follows OpenAI's Chat Completion API's image URL format.
-
     """
 
     url: str

libs/core/langchain_core/prompts/__init__.py

@@ -1,28 +1,8 @@
 """**Prompt** is the input to the model.
 
-Prompt is often constructed
-from multiple components and prompt values. Prompt classes and functions make constructing
- and working with prompts easy.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BasePromptTemplate --> StringPromptTemplate --> PromptTemplate
-                                                    FewShotPromptTemplate
-                                                    FewShotPromptWithTemplates
-                           BaseChatPromptTemplate --> AutoGPTPrompt
-                                                      ChatPromptTemplate --> AgentScratchPadChatPromptTemplate
-
-
-
-    BaseMessagePromptTemplate --> MessagesPlaceholder
-                                  BaseStringMessagePromptTemplate --> ChatMessagePromptTemplate
-                                                                      HumanMessagePromptTemplate
-                                                                      AIMessagePromptTemplate
-                                                                      SystemMessagePromptTemplate
-
-"""  # noqa: E501
+Prompt is often constructed from multiple components and prompt values. Prompt classes
+and functions make constructing and working with prompts easy.
+"""
 
 from typing import TYPE_CHECKING
 

libs/core/langchain_core/prompts/base.py

@@ -96,10 +96,10 @@ class BasePromptTemplate(
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "schema", "prompt_template"]``
+            `["langchain", "schema", "prompt_template"]`
         """
         return ["langchain", "schema", "prompt_template"]
 
@@ -127,10 +127,10 @@ class BasePromptTemplate(
         """Get the input schema for the prompt.
 
         Args:
-            config: RunnableConfig, configuration for the prompt.
+            config: configuration for the prompt.
 
         Returns:
-            Type[BaseModel]: The input schema for the prompt.
+            The input schema for the prompt.
         """
         # This is correct, but pydantic typings/mypy don't think so.
         required_input_variables = {
@@ -199,7 +199,7 @@ class BasePromptTemplate(
             config: RunnableConfig, configuration for the prompt.
 
         Returns:
-            PromptValue: The output of the prompt.
+            The output of the prompt.
         """
         config = ensure_config(config)
         if self.metadata:
@@ -225,7 +225,7 @@ class BasePromptTemplate(
             config: RunnableConfig, configuration for the prompt.
 
         Returns:
-            PromptValue: The output of the prompt.
+            The output of the prompt.
         """
         config = ensure_config(config)
         if self.metadata:
@@ -245,20 +245,20 @@ class BasePromptTemplate(
         """Create Prompt Value.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
-            PromptValue: The output of the prompt.
+            The output of the prompt.
         """
 
     async def aformat_prompt(self, **kwargs: Any) -> PromptValue:
         """Async create Prompt Value.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
-            PromptValue: The output of the prompt.
+            The output of the prompt.
         """
         return self.format_prompt(**kwargs)
 
@@ -266,10 +266,10 @@ class BasePromptTemplate(
         """Return a partial of the prompt template.
 
         Args:
-            kwargs: Union[str, Callable[[], str]], partial variables to set.
+            **kwargs: partial variables to set.
 
         Returns:
-            BasePromptTemplate: A partial of the prompt template.
+            A partial of the prompt template.
         """
         prompt_dict = self.__dict__.copy()
         prompt_dict["input_variables"] = list(
@@ -290,34 +290,30 @@ class BasePromptTemplate(
         """Format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.
 
         Example:
-
-        .. code-block:: python
-
-            prompt.format(variable1="foo")
-
+        ```python
+        prompt.format(variable1="foo")
+        ```
         """
 
     async def aformat(self, **kwargs: Any) -> FormatOutputType:
         """Async format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.
 
         Example:
-
-        .. code-block:: python
-
-            await prompt.aformat(variable1="foo")
-
+        ```python
+        await prompt.aformat(variable1="foo")
+        ```
         """
         return self.format(**kwargs)
 
@@ -330,10 +326,10 @@ class BasePromptTemplate(
         """Return dictionary representation of prompt.
 
         Args:
-            kwargs: Any additional arguments to pass to the dictionary.
+            **kwargs: Any additional arguments to pass to the dictionary.
 
         Returns:
-            Dict: Dictionary representation of the prompt.
+            Dictionary representation of the prompt.
         """
         prompt_dict = super().model_dump(**kwargs)
         with contextlib.suppress(NotImplementedError):
@@ -352,10 +348,9 @@ class BasePromptTemplate(
             NotImplementedError: If the prompt type is not implemented.
 
         Example:
-        .. code-block:: python
-
-            prompt.save(file_path="path/prompt.yaml")
-
+        ```python
+        prompt.save(file_path="path/prompt.yaml")
+        ```
         """
         if self.partial_variables:
             msg = "Cannot save prompt with partial variables."
@@ -426,16 +421,16 @@ def format_document(doc: Document, prompt: BasePromptTemplate[str]) -> str:
         string of the document formatted.
 
     Example:
-        .. code-block:: python
+        ```python
+        from langchain_core.documents import Document
+        from langchain_core.prompts import PromptTemplate
 
-            from langchain_core.documents import Document
-            from langchain_core.prompts import PromptTemplate
-
-            doc = Document(page_content="This is a joke", metadata={"page": "1"})
-            prompt = PromptTemplate.from_template("Page {page}: {page_content}")
-            format_document(doc, prompt)
-            >>> "Page 1: This is a joke"
+        doc = Document(page_content="This is a joke", metadata={"page": "1"})
+        prompt = PromptTemplate.from_template("Page {page}: {page_content}")
+        format_document(doc, prompt)
+        >>> "Page 1: This is a joke"
 
+        ```
     """
     return prompt.format(**_get_document_info(doc, prompt))
 

libs/core/langchain_core/prompts/chat.py

@@ -59,71 +59,70 @@ class MessagesPlaceholder(BaseMessagePromptTemplate):
 
     Direct usage:
 
-        .. code-block:: python
+        ```python
+        from langchain_core.prompts import MessagesPlaceholder
 
-            from langchain_core.prompts import MessagesPlaceholder
+        prompt = MessagesPlaceholder("history")
+        prompt.format_messages()  # raises KeyError
 
-            prompt = MessagesPlaceholder("history")
-            prompt.format_messages()  # raises KeyError
+        prompt = MessagesPlaceholder("history", optional=True)
+        prompt.format_messages()  # returns empty list []
 
-            prompt = MessagesPlaceholder("history", optional=True)
-            prompt.format_messages()  # returns empty list []
-
-            prompt.format_messages(
-                history=[
-                    ("system", "You are an AI assistant."),
-                    ("human", "Hello!"),
-                ]
-            )
-            # -> [
-            #     SystemMessage(content="You are an AI assistant."),
-            #     HumanMessage(content="Hello!"),
-            # ]
+        prompt.format_messages(
+            history=[
+                ("system", "You are an AI assistant."),
+                ("human", "Hello!"),
+            ]
+        )
+        # -> [
+        #     SystemMessage(content="You are an AI assistant."),
+        #     HumanMessage(content="Hello!"),
+        # ]
+        ```
 
     Building a prompt with chat history:
 
-        .. code-block:: python
+        ```python
+        from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 
-            from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-
-            prompt = ChatPromptTemplate.from_messages(
-                [
-                    ("system", "You are a helpful assistant."),
-                    MessagesPlaceholder("history"),
-                    ("human", "{question}"),
-                ]
-            )
-            prompt.invoke(
-                {
-                    "history": [("human", "what's 5 + 2"), ("ai", "5 + 2 is 7")],
-                    "question": "now multiply that by 4",
-                }
-            )
-            # -> ChatPromptValue(messages=[
-            #     SystemMessage(content="You are a helpful assistant."),
-            #     HumanMessage(content="what's 5 + 2"),
-            #     AIMessage(content="5 + 2 is 7"),
-            #     HumanMessage(content="now multiply that by 4"),
-            # ])
+        prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", "You are a helpful assistant."),
+                MessagesPlaceholder("history"),
+                ("human", "{question}"),
+            ]
+        )
+        prompt.invoke(
+            {
+                "history": [("human", "what's 5 + 2"), ("ai", "5 + 2 is 7")],
+                "question": "now multiply that by 4",
+            }
+        )
+        # -> ChatPromptValue(messages=[
+        #     SystemMessage(content="You are a helpful assistant."),
+        #     HumanMessage(content="what's 5 + 2"),
+        #     AIMessage(content="5 + 2 is 7"),
+        #     HumanMessage(content="now multiply that by 4"),
+        # ])
+        ```
 
     Limiting the number of messages:
 
-        .. code-block:: python
+        ```python
+        from langchain_core.prompts import MessagesPlaceholder
 
-            from langchain_core.prompts import MessagesPlaceholder
-
-            prompt = MessagesPlaceholder("history", n_messages=1)
-
-            prompt.format_messages(
-                history=[
-                    ("system", "You are an AI assistant."),
-                    ("human", "Hello!"),
-                ]
-            )
-            # -> [
-            #     HumanMessage(content="Hello!"),
-            # ]
+        prompt = MessagesPlaceholder("history", n_messages=1)
 
+        prompt.format_messages(
+            history=[
+                ("system", "You are an AI assistant."),
+                ("human", "Hello!"),
+            ]
+        )
+        # -> [
+        #     HumanMessage(content="Hello!"),
+        # ]
+        ```
     """
 
     variable_name: str
@@ -136,7 +135,7 @@ class MessagesPlaceholder(BaseMessagePromptTemplate):
 
     n_messages: PositiveInt | None = None
     """Maximum number of messages to include. If `None`, then will include all.
-    Defaults to `None`."""
+    """
 
     def __init__(
         self, variable_name: str, *, optional: bool = False, **kwargs: Any
@@ -148,7 +147,6 @@ class MessagesPlaceholder(BaseMessagePromptTemplate):
             optional: If `True` format_messages can be called with no arguments and will
                 return an empty list. If `False` then a named argument with name
                 `variable_name` must be passed in, even if the value is an empty list.
-                Defaults to `False`.]
         """
         # mypy can't detect the init which is defined in the parent class
         # b/c these are BaseModel classes.
@@ -196,7 +194,7 @@ class MessagesPlaceholder(BaseMessagePromptTemplate):
         """Human-readable representation.
 
         Args:
-            html: Whether to format as HTML. Defaults to `False`.
+            html: Whether to format as HTML.
 
         Returns:
             Human-readable representation.
@@ -236,13 +234,13 @@ class BaseStringMessagePromptTemplate(BaseMessagePromptTemplate, ABC):
 
         Args:
             template: a template.
-            template_format: format of the template. Defaults to "f-string".
+            template_format: format of the template.
             partial_variables: A dictionary of variables that can be used to partially
-                               fill in the template. For example, if the template is
-                              `"{variable1} {variable2}"`, and `partial_variables` is
-                              `{"variable1": "foo"}`, then the final prompt will be
-                              `"foo {variable2}"`.
-                              Defaults to `None`.
+                fill in the template. For example, if the template is
+                `"{variable1} {variable2}"`, and `partial_variables` is
+                `{"variable1": "foo"}`, then the final prompt will be
+                `"foo {variable2}"`.
+
             **kwargs: keyword arguments to pass to the constructor.
 
         Returns:
@@ -331,7 +329,7 @@ class BaseStringMessagePromptTemplate(BaseMessagePromptTemplate, ABC):
         """Human-readable representation.
 
         Args:
-            html: Whether to format as HTML. Defaults to `False`.
+            html: Whether to format as HTML.
 
         Returns:
             Human-readable representation.
@@ -413,9 +411,9 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate):
         Args:
             template: a template.
             template_format: format of the template.
-                Options are: 'f-string', 'mustache', 'jinja2'. Defaults to "f-string".
+                Options are: 'f-string', 'mustache', 'jinja2'.
             partial_variables: A dictionary of variables that can be used too partially.
-                Defaults to `None`.
+
             **kwargs: keyword arguments to pass to the constructor.
 
         Returns:
@@ -638,7 +636,7 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate):
         """Human-readable representation.
 
         Args:
-            html: Whether to format as HTML. Defaults to `False`.
+            html: Whether to format as HTML.
 
         Returns:
             Human-readable representation.
@@ -685,7 +683,7 @@ class BaseChatPromptTemplate(BasePromptTemplate, ABC):
 
         Args:
             **kwargs: keyword arguments to use for filling in template variables
-                      in all the template messages in this chat template.
+                in all the template messages in this chat template.
 
         Returns:
             formatted string.
@@ -697,7 +695,7 @@ class BaseChatPromptTemplate(BasePromptTemplate, ABC):
 
         Args:
             **kwargs: keyword arguments to use for filling in template variables
-                      in all the template messages in this chat template.
+                in all the template messages in this chat template.
 
         Returns:
             formatted string.
@@ -751,7 +749,7 @@ class BaseChatPromptTemplate(BasePromptTemplate, ABC):
         """Human-readable representation.
 
         Args:
-            html: Whether to format as HTML. Defaults to `False`.
+            html: Whether to format as HTML.
 
         Returns:
             Human-readable representation.
@@ -781,78 +779,78 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
     Examples:
         !!! warning "Behavior changed in 0.2.24"
             You can pass any Message-like formats supported by
-            ``ChatPromptTemplate.from_messages()`` directly to ``ChatPromptTemplate()``
+            `ChatPromptTemplate.from_messages()` directly to `ChatPromptTemplate()`
             init.
 
-        .. code-block:: python
+        ```python
+        from langchain_core.prompts import ChatPromptTemplate
 
-            from langchain_core.prompts import ChatPromptTemplate
+        template = ChatPromptTemplate(
+            [
+                ("system", "You are a helpful AI bot. Your name is {name}."),
+                ("human", "Hello, how are you doing?"),
+                ("ai", "I'm doing well, thanks!"),
+                ("human", "{user_input}"),
+            ]
+        )
 
-            template = ChatPromptTemplate(
-                [
-                    ("system", "You are a helpful AI bot. Your name is {name}."),
-                    ("human", "Hello, how are you doing?"),
-                    ("ai", "I'm doing well, thanks!"),
-                    ("human", "{user_input}"),
-                ]
-            )
-
-            prompt_value = template.invoke(
-                {
-                    "name": "Bob",
-                    "user_input": "What is your name?",
-                }
-            )
-            # Output:
-            # ChatPromptValue(
-            #    messages=[
-            #        SystemMessage(content='You are a helpful AI bot. Your name is Bob.'),
-            #        HumanMessage(content='Hello, how are you doing?'),
-            #        AIMessage(content="I'm doing well, thanks!"),
-            #        HumanMessage(content='What is your name?')
-            #    ]
-            # )
+        prompt_value = template.invoke(
+            {
+                "name": "Bob",
+                "user_input": "What is your name?",
+            }
+        )
+        # Output:
+        # ChatPromptValue(
+        #    messages=[
+        #        SystemMessage(content='You are a helpful AI bot. Your name is Bob.'),
+        #        HumanMessage(content='Hello, how are you doing?'),
+        #        AIMessage(content="I'm doing well, thanks!"),
+        #        HumanMessage(content='What is your name?')
+        #    ]
+        # )
+        ```
 
     Messages Placeholder:
 
-        .. code-block:: python
+        ```python
+        # In addition to Human/AI/Tool/Function messages,
+        # you can initialize the template with a MessagesPlaceholder
+        # either using the class directly or with the shorthand tuple syntax:
 
-            # In addition to Human/AI/Tool/Function messages,
-            # you can initialize the template with a MessagesPlaceholder
-            # either using the class directly or with the shorthand tuple syntax:
+        template = ChatPromptTemplate(
+            [
+                ("system", "You are a helpful AI bot."),
+                # Means the template will receive an optional list of messages under
+                # the "conversation" key
+                ("placeholder", "{conversation}"),
+                # Equivalently:
+                # MessagesPlaceholder(variable_name="conversation", optional=True)
+            ]
+        )
 
-            template = ChatPromptTemplate(
-                [
-                    ("system", "You are a helpful AI bot."),
-                    # Means the template will receive an optional list of messages under
-                    # the "conversation" key
-                    ("placeholder", "{conversation}"),
-                    # Equivalently:
-                    # MessagesPlaceholder(variable_name="conversation", optional=True)
+        prompt_value = template.invoke(
+            {
+                "conversation": [
+                    ("human", "Hi!"),
+                    ("ai", "How can I assist you today?"),
+                    ("human", "Can you make me an ice cream sundae?"),
+                    ("ai", "No."),
                 ]
-            )
+            }
+        )
 
-            prompt_value = template.invoke(
-                {
-                    "conversation": [
-                        ("human", "Hi!"),
-                        ("ai", "How can I assist you today?"),
-                        ("human", "Can you make me an ice cream sundae?"),
-                        ("ai", "No."),
-                    ]
-                }
-            )
-
-            # Output:
-            # ChatPromptValue(
-            #    messages=[
-            #        SystemMessage(content='You are a helpful AI bot.'),
-            #        HumanMessage(content='Hi!'),
-            #        AIMessage(content='How can I assist you today?'),
-            #        HumanMessage(content='Can you make me an ice cream sundae?'),
-            #        AIMessage(content='No.'),
-            #    ]
-            # )
+        # Output:
+        # ChatPromptValue(
+        #    messages=[
+        #        SystemMessage(content='You are a helpful AI bot.'),
+        #        HumanMessage(content='Hi!'),
+        #        AIMessage(content='How can I assist you today?'),
+        #        HumanMessage(content='Can you make me an ice cream sundae?'),
+        #        AIMessage(content='No.'),
+        #    ]
+        # )
+        ```
 
     Single-variable template:
 
@@ -861,29 +859,28 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         inject the provided argument into that variable location.
 
 
-        .. code-block:: python
+        ```python
+        from langchain_core.prompts import ChatPromptTemplate
 
-            from langchain_core.prompts import ChatPromptTemplate
+        template = ChatPromptTemplate(
+            [
+                ("system", "You are a helpful AI bot. Your name is Carl."),
+                ("human", "{user_input}"),
+            ]
+        )
 
-            template = ChatPromptTemplate(
-                [
-                    ("system", "You are a helpful AI bot. Your name is Carl."),
-                    ("human", "{user_input}"),
-                ]
-            )
-
-            prompt_value = template.invoke("Hello, there!")
-            # Equivalent to
-            # prompt_value = template.invoke({"user_input": "Hello, there!"})
-
-            # Output:
-            #  ChatPromptValue(
-            #     messages=[
-            #         SystemMessage(content='You are a helpful AI bot. Your name is Carl.'),
-            #         HumanMessage(content='Hello, there!'),
-            #     ]
-            # )
+        prompt_value = template.invoke("Hello, there!")
+        # Equivalent to
+        # prompt_value = template.invoke({"user_input": "Hello, there!"})
 
+        # Output:
+        #  ChatPromptValue(
+        #     messages=[
+        #         SystemMessage(content='You are a helpful AI bot. Your name is Carl.'),
+        #         HumanMessage(content='Hello, there!'),
+        #     ]
+        # )
+        ```
     """  # noqa: E501
 
     messages: Annotated[list[MessageLike], SkipValidation()]
@@ -902,12 +899,12 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
         Args:
             messages: sequence of message representations.
-                  A message can be represented using the following formats:
-                  (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of
-                  (message type, template); e.g., ("human", "{user_input}"),
-                  (4) 2-tuple of (message class, template), (5) a string which is
-                  shorthand for ("human", template); e.g., "{user_input}".
-            template_format: format of the template. Defaults to "f-string".
+                A message can be represented using the following formats:
+                (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of
+                (message type, template); e.g., ("human", "{user_input}"),
+                (4) 2-tuple of (message class, template), (5) a string which is
+                shorthand for ("human", template); e.g., "{user_input}".
+            template_format: format of the template.
             input_variables: A list of the names of the variables whose values are
                 required as inputs to the prompt.
             optional_variables: A list of the names of the variables for placeholder
@@ -924,27 +921,26 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         Examples:
             Instantiation from a list of message templates:
 
-            .. code-block:: python
-
-                template = ChatPromptTemplate(
-                    [
-                        ("human", "Hello, how are you?"),
-                        ("ai", "I'm doing well, thanks!"),
-                        ("human", "That's good to hear."),
-                    ]
-                )
+            ```python
+            template = ChatPromptTemplate(
+                [
+                    ("human", "Hello, how are you?"),
+                    ("ai", "I'm doing well, thanks!"),
+                    ("human", "That's good to hear."),
+                ]
+            )
+            ```
 
             Instantiation from mixed message formats:
 
-            .. code-block:: python
-
-                template = ChatPromptTemplate(
-                    [
-                        SystemMessage(content="hello"),
-                        ("human", "Hello, how are you?"),
-                    ]
-                )
-
+            ```python
+            template = ChatPromptTemplate(
+                [
+                    SystemMessage(content="hello"),
+                    ("human", "Hello, how are you?"),
+                ]
+            )
+            ```
         """
         messages_ = [
             _convert_to_message_template(message, template_format)
@@ -974,10 +970,10 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "prompts", "chat"]``
+            `["langchain", "prompts", "chat"]`
         """
         return ["langchain", "prompts", "chat"]
 
@@ -1104,35 +1100,34 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         Examples:
             Instantiation from a list of message templates:
 
-            .. code-block:: python
-
-                template = ChatPromptTemplate.from_messages(
-                    [
-                        ("human", "Hello, how are you?"),
-                        ("ai", "I'm doing well, thanks!"),
-                        ("human", "That's good to hear."),
-                    ]
-                )
+            ```python
+            template = ChatPromptTemplate.from_messages(
+                [
+                    ("human", "Hello, how are you?"),
+                    ("ai", "I'm doing well, thanks!"),
+                    ("human", "That's good to hear."),
+                ]
+            )
+            ```
 
             Instantiation from mixed message formats:
 
-            .. code-block:: python
-
-                template = ChatPromptTemplate.from_messages(
-                    [
-                        SystemMessage(content="hello"),
-                        ("human", "Hello, how are you?"),
-                    ]
-                )
-
+            ```python
+            template = ChatPromptTemplate.from_messages(
+                [
+                    SystemMessage(content="hello"),
+                    ("human", "Hello, how are you?"),
+                ]
+            )
+            ```
         Args:
             messages: sequence of message representations.
-                  A message can be represented using the following formats:
-                  (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of
-                  (message type, template); e.g., ("human", "{user_input}"),
-                  (4) 2-tuple of (message class, template), (5) a string which is
-                  shorthand for ("human", template); e.g., "{user_input}".
-            template_format: format of the template. Defaults to "f-string".
+                A message can be represented using the following formats:
+                (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of
+                (message type, template); e.g., ("human", "{user_input}"),
+                (4) 2-tuple of (message class, template), (5) a string which is
+                shorthand for ("human", template); e.g., "{user_input}".
+            template_format: format of the template.
 
         Returns:
             a chat prompt template.
@@ -1145,7 +1140,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
         Args:
             **kwargs: keyword arguments to use for filling in template variables
-                      in all the template messages in this chat template.
+                in all the template messages in this chat template.
 
         Raises:
             ValueError: if messages are of unexpected types.
@@ -1173,7 +1168,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
         Args:
             **kwargs: keyword arguments to use for filling in template variables
-                      in all the template messages in this chat template.
+                in all the template messages in this chat template.
 
         Returns:
             list of formatted messages.
@@ -1208,23 +1203,21 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
 
         Example:
+            ```python
+            from langchain_core.prompts import ChatPromptTemplate
 
-            .. code-block:: python
-
-                from langchain_core.prompts import ChatPromptTemplate
-
-                template = ChatPromptTemplate.from_messages(
-                    [
-                        ("system", "You are an AI assistant named {name}."),
-                        ("human", "Hi I'm {user}"),
-                        ("ai", "Hi there, {user}, I'm {name}."),
-                        ("human", "{input}"),
-                    ]
-                )
-                template2 = template.partial(user="Lucy", name="R2D2")
-
-                template2.format_messages(input="hello")
+            template = ChatPromptTemplate.from_messages(
+                [
+                    ("system", "You are an AI assistant named {name}."),
+                    ("human", "Hi I'm {user}"),
+                    ("ai", "Hi there, {user}, I'm {name}."),
+                    ("human", "{input}"),
+                ]
+            )
+            template2 = template.partial(user="Lucy", name="R2D2")
 
+            template2.format_messages(input="hello")
+            ```
         """
         prompt_dict = self.__dict__.copy()
         prompt_dict["input_variables"] = list(
@@ -1262,7 +1255,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
         Returns:
             If index is an int, returns the message at that index.
-            If index is a slice, returns a new ``ChatPromptTemplate``
+            If index is a slice, returns a new `ChatPromptTemplate`
             containing the messages in that slice.
         """
         if isinstance(index, slice):
@@ -1293,7 +1286,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         """Human-readable representation.
 
         Args:
-            html: Whether to format as HTML. Defaults to `False`.
+            html: Whether to format as HTML.
 
         Returns:
             Human-readable representation.
@@ -1312,7 +1305,7 @@ def _create_template_from_message_type(
     Args:
         message_type: str the type of the message template (e.g., "human", "ai", etc.)
         template: str the template string.
-        template_format: format of the template. Defaults to "f-string".
+        template_format: format of the template.
 
     Returns:
         a message prompt template of the appropriate type.
@@ -1389,7 +1382,7 @@ def _convert_to_message_template(
 
     Args:
         message: a representation of a message in one of the supported formats.
-        template_format: format of the template. Defaults to "f-string".
+        template_format: format of the template.
 
     Returns:
         an instance of a message or a message template.

libs/core/langchain_core/prompts/dict.py

@@ -74,10 +74,10 @@ class DictPromptTemplate(RunnableSerializable[dict, dict]):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain_core", "prompts", "dict"]``
+            `["langchain_core", "prompts", "dict"]`
         """
         return ["langchain_core", "prompts", "dict"]
 
@@ -85,7 +85,7 @@ class DictPromptTemplate(RunnableSerializable[dict, dict]):
         """Human-readable representation.
 
         Args:
-            html: Whether to format as HTML. Defaults to `False`.
+            html: Whether to format as HTML.
 
         Returns:
             Human-readable representation.

libs/core/langchain_core/prompts/few_shot.py

@@ -268,97 +268,90 @@ class FewShotChatMessagePromptTemplate(
         Prompt template with a fixed list of examples (matching the sample
         conversation above):
 
-        .. code-block:: python
+        ```python
+        from langchain_core.prompts import (
+            FewShotChatMessagePromptTemplate,
+            ChatPromptTemplate,
+        )
 
-            from langchain_core.prompts import (
-                FewShotChatMessagePromptTemplate,
-                ChatPromptTemplate,
-            )
+        examples = [
+            {"input": "2+2", "output": "4"},
+            {"input": "2+3", "output": "5"},
+        ]
 
-            examples = [
-                {"input": "2+2", "output": "4"},
-                {"input": "2+3", "output": "5"},
+        example_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("human", "What is {input}?"),
+                ("ai", "{output}"),
             ]
+        )
 
-            example_prompt = ChatPromptTemplate.from_messages(
-                [
-                    ("human", "What is {input}?"),
-                    ("ai", "{output}"),
-                ]
-            )
+        few_shot_prompt = FewShotChatMessagePromptTemplate(
+            examples=examples,
+            # This is a prompt template used to format each individual example.
+            example_prompt=example_prompt,
+        )
 
-            few_shot_prompt = FewShotChatMessagePromptTemplate(
-                examples=examples,
-                # This is a prompt template used to format each individual example.
-                example_prompt=example_prompt,
-            )
-
-            final_prompt = ChatPromptTemplate.from_messages(
-                [
-                    ("system", "You are a helpful AI Assistant"),
-                    few_shot_prompt,
-                    ("human", "{input}"),
-                ]
-            )
-            final_prompt.format(input="What is 4+4?")
+        final_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", "You are a helpful AI Assistant"),
+                few_shot_prompt,
+                ("human", "{input}"),
+            ]
+        )
+        final_prompt.format(input="What is 4+4?")
+        ```
 
         Prompt template with dynamically selected examples:
 
-        .. code-block:: python
+        ```python
+        from langchain_core.prompts import SemanticSimilarityExampleSelector
+        from langchain_core.embeddings import OpenAIEmbeddings
+        from langchain_core.vectorstores import Chroma
 
-            from langchain_core.prompts import SemanticSimilarityExampleSelector
-            from langchain_core.embeddings import OpenAIEmbeddings
-            from langchain_core.vectorstores import Chroma
+        examples = [
+            {"input": "2+2", "output": "4"},
+            {"input": "2+3", "output": "5"},
+            {"input": "2+4", "output": "6"},
+            # ...
+        ]
 
-            examples = [
-                {"input": "2+2", "output": "4"},
-                {"input": "2+3", "output": "5"},
-                {"input": "2+4", "output": "6"},
-                # ...
-            ]
+        to_vectorize = [" ".join(example.values()) for example in examples]
+        embeddings = OpenAIEmbeddings()
+        vectorstore = Chroma.from_texts(to_vectorize, embeddings, metadatas=examples)
+        example_selector = SemanticSimilarityExampleSelector(vectorstore=vectorstore)
 
-            to_vectorize = [" ".join(example.values()) for example in examples]
-            embeddings = OpenAIEmbeddings()
-            vectorstore = Chroma.from_texts(
-                to_vectorize, embeddings, metadatas=examples
-            )
-            example_selector = SemanticSimilarityExampleSelector(
-                vectorstore=vectorstore
-            )
+        from langchain_core import SystemMessage
+        from langchain_core.prompts import HumanMessagePromptTemplate
+        from langchain_core.prompts.few_shot import FewShotChatMessagePromptTemplate
 
-            from langchain_core import SystemMessage
-            from langchain_core.prompts import HumanMessagePromptTemplate
-            from langchain_core.prompts.few_shot import FewShotChatMessagePromptTemplate
+        few_shot_prompt = FewShotChatMessagePromptTemplate(
+            # Which variable(s) will be passed to the example selector.
+            input_variables=["input"],
+            example_selector=example_selector,
+            # Define how each example will be formatted.
+            # In this case, each example will become 2 messages:
+            # 1 human, and 1 AI
+            example_prompt=(
+                HumanMessagePromptTemplate.from_template("{input}")
+                + AIMessagePromptTemplate.from_template("{output}")
+            ),
+        )
+        # Define the overall prompt.
+        final_prompt = (
+            SystemMessagePromptTemplate.from_template("You are a helpful AI Assistant")
+            + few_shot_prompt
+            + HumanMessagePromptTemplate.from_template("{input}")
+        )
+        # Show the prompt
+        print(final_prompt.format_messages(input="What's 3+3?"))  # noqa: T201
 
-            few_shot_prompt = FewShotChatMessagePromptTemplate(
-                # Which variable(s) will be passed to the example selector.
-                input_variables=["input"],
-                example_selector=example_selector,
-                # Define how each example will be formatted.
-                # In this case, each example will become 2 messages:
-                # 1 human, and 1 AI
-                example_prompt=(
-                    HumanMessagePromptTemplate.from_template("{input}")
-                    + AIMessagePromptTemplate.from_template("{output}")
-                ),
-            )
-            # Define the overall prompt.
-            final_prompt = (
-                SystemMessagePromptTemplate.from_template(
-                    "You are a helpful AI Assistant"
-                )
-                + few_shot_prompt
-                + HumanMessagePromptTemplate.from_template("{input}")
-            )
-            # Show the prompt
-            print(final_prompt.format_messages(input="What's 3+3?"))  # noqa: T201
-
-            # Use within an LLM
-            from langchain_core.chat_models import ChatAnthropic
-
-            chain = final_prompt | ChatAnthropic(model="claude-3-haiku-20240307")
-            chain.invoke({"input": "What's 3+3?"})
+        # Use within an LLM
+        from langchain_core.chat_models import ChatAnthropic
 
+        chain = final_prompt | ChatAnthropic(model="claude-3-haiku-20240307")
+        chain.invoke({"input": "What's 3+3?"})
+        ```
     """
 
     input_variables: list[str] = Field(default_factory=list)

libs/core/langchain_core/prompts/few_shot_with_templates.py

@@ -46,10 +46,10 @@ class FewShotPromptWithTemplates(StringPromptTemplate):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "prompts", "few_shot_with_templates"]``
+            `["langchain", "prompts", "few_shot_with_templates"]`
         """
         return ["langchain", "prompts", "few_shot_with_templates"]
 
@@ -116,17 +116,15 @@ class FewShotPromptWithTemplates(StringPromptTemplate):
         """Format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.
 
         Example:
-
-        .. code-block:: python
-
-            prompt.format(variable1="foo")
-
+        ```python
+        prompt.format(variable1="foo")
+        ```
         """
         kwargs = self._merge_partial_and_user_variables(**kwargs)
         # Get the examples to use.
@@ -165,7 +163,7 @@ class FewShotPromptWithTemplates(StringPromptTemplate):
         """Async format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.

libs/core/langchain_core/prompts/image.py

@@ -26,8 +26,8 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
         """Create an image prompt template.
 
         Raises:
-            ValueError: If the input variables contain ``'url'``, ``'path'``, or
-                ``'detail'``.
+            ValueError: If the input variables contain `'url'`, `'path'`, or
+                `'detail'`.
         """
         if "input_variables" not in kwargs:
             kwargs["input_variables"] = []
@@ -49,10 +49,10 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "prompts", "image"]``
+            `["langchain", "prompts", "image"]`
         """
         return ["langchain", "prompts", "image"]
 
@@ -60,7 +60,7 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
         """Format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.
@@ -71,7 +71,7 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
         """Async format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.
@@ -85,7 +85,7 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
         """Format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.
@@ -93,14 +93,12 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
         Raises:
             ValueError: If the url is not provided.
             ValueError: If the url is not a string.
-            ValueError: If ``'path'`` is provided in the template or kwargs.
+            ValueError: If `'path'` is provided in the template or kwargs.
 
         Example:
-
-            .. code-block:: python
-
-                prompt.format(variable1="foo")
-
+            ```python
+            prompt.format(variable1="foo")
+            ```
         """
         formatted = {}
         for k, v in self.template.items():
@@ -134,7 +132,7 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
         """Async format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.

libs/core/langchain_core/prompts/loading.py

@@ -139,7 +139,7 @@ def load_prompt(path: str | Path, encoding: str | None = None) -> BasePromptTemp
 
     Args:
         path: Path to the prompt file.
-        encoding: Encoding of the file. Defaults to `None`.
+        encoding: Encoding of the file.
 
     Returns:
         A PromptTemplate object.

libs/core/langchain_core/prompts/message.py

@@ -23,10 +23,10 @@ class BaseMessagePromptTemplate(Serializable, ABC):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "prompts", "chat"]``
+            `["langchain", "prompts", "chat"]`
         """
         return ["langchain", "prompts", "chat"]
 
@@ -68,7 +68,7 @@ class BaseMessagePromptTemplate(Serializable, ABC):
         """Human-readable representation.
 
         Args:
-            html: Whether to format as HTML. Defaults to `False`.
+            html: Whether to format as HTML.
 
         Returns:
             Human-readable representation.

libs/core/langchain_core/prompts/prompt.py

@@ -44,18 +44,16 @@ class PromptTemplate(StringPromptTemplate):
         from untrusted sources.
 
     Example:
+        ```python
+        from langchain_core.prompts import PromptTemplate
 
-        .. code-block:: python
-
-            from langchain_core.prompts import PromptTemplate
-
-            # Instantiation using from_template (recommended)
-            prompt = PromptTemplate.from_template("Say {foo}")
-            prompt.format(foo="bar")
-
-            # Instantiation using initializer
-            prompt = PromptTemplate(template="Say {foo}")
+        # Instantiation using from_template (recommended)
+        prompt = PromptTemplate.from_template("Say {foo}")
+        prompt.format(foo="bar")
 
+        # Instantiation using initializer
+        prompt = PromptTemplate(template="Say {foo}")
+        ```
     """
 
     @property
@@ -68,10 +66,10 @@ class PromptTemplate(StringPromptTemplate):
     @classmethod
     @override
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "prompts", "prompt"]``
+            `["langchain", "prompts", "prompt"]`
         """
         return ["langchain", "prompts", "prompt"]
 
@@ -144,10 +142,10 @@ class PromptTemplate(StringPromptTemplate):
         Raises:
             ValueError: If the template formats are not f-string or if there are
                 conflicting partial variables.
-            NotImplementedError: If the other object is not a ``PromptTemplate`` or str.
+            NotImplementedError: If the other object is not a `PromptTemplate` or str.
 
         Returns:
-            A new ``PromptTemplate`` that is the combination of the two.
+            A new `PromptTemplate` that is the combination of the two.
         """
         # Allow for easy combining
         if isinstance(other, PromptTemplate):
@@ -191,7 +189,7 @@ class PromptTemplate(StringPromptTemplate):
         """Format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.
@@ -222,7 +220,7 @@ class PromptTemplate(StringPromptTemplate):
             example_separator: The separator to use in between examples. Defaults
                 to two new line characters.
             prefix: String that should go before any examples. Generally includes
-                examples. Default to an empty string.
+                examples.
 
         Returns:
             The final prompt generated.
@@ -277,14 +275,13 @@ class PromptTemplate(StringPromptTemplate):
         Args:
             template: The template to load.
             template_format: The format of the template. Use `jinja2` for jinja2,
-                             `mustache` for mustache, and `f-string` for f-strings.
-                             Defaults to `f-string`.
+                `mustache` for mustache, and `f-string` for f-strings.
             partial_variables: A dictionary of variables that can be used to partially
-                               fill in the template. For example, if the template is
-                              `"{variable1} {variable2}"`, and `partial_variables` is
-                              `{"variable1": "foo"}`, then the final prompt will be
-                              `"foo {variable2}"`. Defaults to `None`.
-            kwargs: Any other arguments to pass to the prompt template.
+                fill in the template. For example, if the template is
+                `"{variable1} {variable2}"`, and `partial_variables` is
+                `{"variable1": "foo"}`, then the final prompt will be
+                `"foo {variable2}"`.
+            **kwargs: Any other arguments to pass to the prompt template.
 
         Returns:
             The prompt template loaded from the template.

libs/core/langchain_core/prompts/string.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 import warnings
 from abc import ABC
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
 from string import Formatter
 from typing import Any, Literal
 
@@ -149,9 +149,7 @@ def mustache_template_vars(
 Defs = dict[str, "Defs"]
 
 
-def mustache_schema(
-    template: str,
-) -> type[BaseModel]:
+def mustache_schema(template: str) -> type[BaseModel]:
     """Get the variables from a mustache template.
 
     Args:
@@ -175,6 +173,11 @@ def mustache_schema(
             fields[prefix] = False
         elif type_ in {"variable", "no escape"}:
             fields[prefix + tuple(key.split("."))] = True
+
+    for fkey, fval in fields.items():
+        fields[fkey] = fval and not any(
+            is_subsequence(fkey, k) for k in fields if k != fkey
+        )
     defs: Defs = {}  # None means leaf node
     while fields:
         field, is_leaf = fields.popitem()
@@ -273,10 +276,10 @@ class StringPromptTemplate(BasePromptTemplate, ABC):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
-            ``["langchain", "prompts", "base"]``
+            `["langchain", "prompts", "base"]`
         """
         return ["langchain", "prompts", "base"]
 
@@ -284,7 +287,7 @@ class StringPromptTemplate(BasePromptTemplate, ABC):
         """Format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.
@@ -295,7 +298,7 @@ class StringPromptTemplate(BasePromptTemplate, ABC):
         """Async format the prompt with the inputs.
 
         Args:
-            kwargs: Any arguments to be passed to the prompt template.
+            **kwargs: Any arguments to be passed to the prompt template.
 
         Returns:
             A formatted string.
@@ -327,3 +330,12 @@ class StringPromptTemplate(BasePromptTemplate, ABC):
     def pretty_print(self) -> None:
         """Print a pretty representation of the prompt."""
         print(self.pretty_repr(html=is_interactive_env()))  # noqa: T201
+
+
+def is_subsequence(child: Sequence, parent: Sequence) -> bool:
+    """Return True if child is subsequence of parent."""
+    if len(child) == 0 or len(parent) == 0:
+        return False
+    if len(parent) < len(child):
+        return False
+    return all(child[i] == parent[i] for i in range(len(child)))