chore(infra): remove openai from langchain-core release test matrix (#33661)

Release 1.0.2

.github/scripts/check_diff.py

@@ -130,29 +130,20 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
         return _get_pydantic_test_configs(dir_)
 
     if job == "codspeed":
-        py_versions = ["3.12"]  # 3.13 is not yet supported
+        py_versions = ["3.13"]
     elif dir_ == "libs/core":
-        py_versions = ["3.10", "3.11", "3.12", "3.13"]
+        py_versions = ["3.10", "3.11", "3.12", "3.13", "3.14"]
     # custom logic for specific directories
-
-    elif dir_ == "libs/langchain" and job == "extended-tests":
+    elif dir_ in {"libs/partners/chroma", "libs/partners/nomic"}:
         py_versions = ["3.10", "3.13"]
-    elif dir_ == "libs/langchain_v1":
-        py_versions = ["3.10", "3.13"]
-    elif dir_ in {"libs/cli"}:
-        py_versions = ["3.10", "3.13"]
-
-    elif dir_ == ".":
-        # unable to install with 3.13 because tokenizers doesn't support 3.13 yet
-        py_versions = ["3.10", "3.12"]
     else:
-        py_versions = ["3.10", "3.13"]
+        py_versions = ["3.10", "3.14"]
 
     return [{"working-directory": dir_, "python-version": py_v} for py_v in py_versions]
 
 
 def _get_pydantic_test_configs(
-    dir_: str, *, python_version: str = "3.11"
+    dir_: str, *, python_version: str = "3.12"
 ) -> List[Dict[str, str]]:
     with open("./libs/core/uv.lock", "rb") as f:
         core_uv_lock_data = tomllib.load(f)
@@ -306,7 +297,9 @@ if __name__ == "__main__":
                 if not filename.startswith(".")
             ] != ["README.md"]:
                 dirs_to_run["test"].add(f"libs/partners/{partner_dir}")
-                dirs_to_run["codspeed"].add(f"libs/partners/{partner_dir}")
+                # Skip codspeed for partners without benchmarks or in IGNORED_PARTNERS
+                if partner_dir not in IGNORED_PARTNERS:
+                    dirs_to_run["codspeed"].add(f"libs/partners/{partner_dir}")
             # Skip if the directory was deleted or is just a tombstone readme
         elif file.startswith("libs/"):
             # Check if this is a root-level file in libs/ (e.g., libs/README.md)

.github/workflows/_release.yml

@@ -395,7 +395,7 @@ jobs:
       contents: read
     strategy:
       matrix:
-        partner: [openai, anthropic]
+        partner: [anthropic]
       fail-fast: false # Continue testing other partners if one fails
     env:
       ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}

.github/workflows/_test_pydantic.yml

@@ -13,7 +13,7 @@ on:
         required: false
         type: string
         description: "Python version to use"
-        default: "3.11"
+        default: "3.12"
       pydantic-version:
         required: true
         type: string
@@ -51,7 +51,9 @@ jobs:
 
       - name: "🔄 Install Specific Pydantic Version"
         shell: bash
-        run: VIRTUAL_ENV=.venv uv pip install pydantic~=${{ inputs.pydantic-version }}
+        env:
+          PYDANTIC_VERSION: ${{ inputs.pydantic-version }}
+        run: VIRTUAL_ENV=.venv uv pip install "pydantic~=$PYDANTIC_VERSION"
 
       - name: "🧪 Run Core Tests"
         shell: bash

.github/workflows/check_diffs.yml

@@ -184,15 +184,14 @@ jobs:
     steps:
       - uses: actions/checkout@v5
 
-      # 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"
+          python-version: "3.13"
 
       - uses: actions/setup-python@v6
         with:
-          python-version: "3.12"
+          python-version: "3.13"
 
       - name: "📦 Install Test Dependencies"
         run: uv sync --group test

.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 }}

.github/workflows/pr_lint.yml

@@ -27,7 +27,7 @@
 #   * release    — prepare a new release
 #
 # Allowed Scopes (optional):
-#   core, cli, langchain, langchain_v1, langchain_legacy, standard-tests,
+#   core, cli, langchain, langchain_v1, langchain-classic, standard-tests,
 #   text-splitters, docs, anthropic, chroma, deepseek, exa, fireworks, groq,
 #   huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant,
 #   xai, infra
@@ -80,7 +80,7 @@ jobs:
             cli
             langchain
             langchain_v1
-            langchain_legacy
+            langchain-classic
             standard-tests
             text-splitters
             docs

.mcp.json

@@ -0,0 +1,8 @@
+{
+  "mcpServers": {
+    "docs-langchain": {
+      "type": "http",
+      "url": "https://docs.langchain.com/mcp"
+    }
+  }
+}

AGENTS.md

@@ -152,20 +152,22 @@ def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
         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

@@ -152,20 +152,22 @@ def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
         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.
 

MIGRATE.md

@@ -2,6 +2,7 @@
 
 Please see the following guides for migrating LangChain code:
 
+* Migrate to [LangChain v1.0](https://docs.langchain.com/oss/python/migrate/langchain-v1)
 * Migrate to [LangChain v0.3](https://python.langchain.com/docs/versions/v0_3/)
 * Migrate to [LangChain v0.2](https://python.langchain.com/docs/versions/v0_2/)
 * Migrating from [LangChain 0.0.x Chains](https://python.langchain.com/docs/versions/migrating_chains/)

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 +65,14 @@ 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.
+- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview): Learn how to contribute to LangChain and find good first issues.
+- [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

@@ -36,20 +36,20 @@ class Chat__ModuleName__(BaseChatModel):
 
     # TODO: Populate with relevant params.
     Key init args — completion params:
-        model: str
+        model:
             Name of __ModuleName__ model to use.
-        temperature: float
+        temperature:
             Sampling temperature.
-        max_tokens: int | None
+        max_tokens:
             Max number of tokens to generate.
 
     # TODO: Populate with relevant params.
     Key init args — client params:
-        timeout: float | None
+        timeout:
             Timeout for requests.
-        max_retries: int
+        max_retries:
             Max number of retries.
-        api_key: str | None
+        api_key:
             __ModuleName__ API key. If not passed in will be read from env var
             __MODULE_NAME___API_KEY.
 
@@ -60,7 +60,7 @@ class Chat__ModuleName__(BaseChatModel):
         ```python
         from __module_name__ import Chat__ModuleName__
 
-        llm = Chat__ModuleName__(
+        model = Chat__ModuleName__(
             model="...",
             temperature=0,
             max_tokens=None,
@@ -77,7 +77,7 @@ class Chat__ModuleName__(BaseChatModel):
             ("system", "You are a helpful translator. Translate the user sentence to French."),
             ("human", "I love programming."),
         ]
-        llm.invoke(messages)
+        model.invoke(messages)
         ```
 
         ```python
@@ -87,7 +87,7 @@ class Chat__ModuleName__(BaseChatModel):
     # TODO: Delete if token-level streaming isn't supported.
     Stream:
         ```python
-        for chunk in llm.stream(messages):
+        for chunk in model.stream(messages):
             print(chunk.text, end="")
         ```
 
@@ -96,7 +96,7 @@ class Chat__ModuleName__(BaseChatModel):
         ```
 
         ```python
-        stream = llm.stream(messages)
+        stream = model.stream(messages)
         full = next(stream)
         for chunk in stream:
             full += chunk
@@ -110,13 +110,13 @@ class Chat__ModuleName__(BaseChatModel):
     # TODO: Delete if native async isn't supported.
     Async:
         ```python
-        await llm.ainvoke(messages)
+        await model.ainvoke(messages)
 
         # stream:
-        # async for chunk in (await llm.astream(messages))
+        # async for chunk in (await model.astream(messages))
 
         # batch:
-        # await llm.abatch([messages])
+        # await model.abatch([messages])
         ```
 
         ```python
@@ -137,8 +137,8 @@ class Chat__ModuleName__(BaseChatModel):
 
             location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
 
-        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?")
+        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
         ```
 
@@ -162,8 +162,8 @@ class Chat__ModuleName__(BaseChatModel):
             punchline: str = Field(description="The punchline to the joke")
             rating: int | None = Field(description="How funny the joke is, from 1 to 10")
 
-        structured_llm = llm.with_structured_output(Joke)
-        structured_llm.invoke("Tell me a joke about cats")
+        structured_model = model.with_structured_output(Joke)
+        structured_model.invoke("Tell me a joke about cats")
         ```
 
         ```python
@@ -176,8 +176,8 @@ class Chat__ModuleName__(BaseChatModel):
     JSON mode:
         ```python
         # 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]")
+        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
         ```
 
@@ -204,7 +204,7 @@ class Chat__ModuleName__(BaseChatModel):
                 },
             ],
         )
-        ai_msg = llm.invoke([message])
+        ai_msg = model.invoke([message])
         ai_msg.content
         ```
 
@@ -235,7 +235,7 @@ class Chat__ModuleName__(BaseChatModel):
     # TODO: Delete if token usage metadata isn't supported.
     Token usage:
         ```python
-        ai_msg = llm.invoke(messages)
+        ai_msg = model.invoke(messages)
         ai_msg.usage_metadata
         ```
 
@@ -247,8 +247,8 @@ class Chat__ModuleName__(BaseChatModel):
     Logprobs:
         ```python
         # TODO: Replace with appropriate bind arg.
-        logprobs_llm = llm.bind(logprobs=True)
-        ai_msg = logprobs_llm.invoke(messages)
+        logprobs_model = model.bind(logprobs=True)
+        ai_msg = logprobs_model.invoke(messages)
         ai_msg.response_metadata["logprobs"]
         ```
 
@@ -257,7 +257,7 @@ class Chat__ModuleName__(BaseChatModel):
         ```
     Response metadata
         ```python
-        ai_msg = llm.invoke(messages)
+        ai_msg = model.invoke(messages)
         ai_msg.response_metadata
         ```
 

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

@@ -65,7 +65,7 @@ class __ModuleName__Retriever(BaseRetriever):
         Question: {question}\"\"\"
         )
 
-        llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
+        model = ChatOpenAI(model="gpt-3.5-turbo-0125")
 
         def format_docs(docs):
             return "\\n\\n".join(doc.page_content for doc in docs)
@@ -73,7 +73,7 @@ class __ModuleName__Retriever(BaseRetriever):
         chain = (
             {"context": retriever | format_docs, "question": RunnablePassthrough()}
             | prompt
-            | llm
+            | model
             | StrOutputParser()
         )
 

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

@@ -37,16 +37,16 @@ class __ModuleName__VectorStore(VectorStore):
 
     # TODO: Populate with relevant params.
     Key init args — indexing params:
-        collection_name: str
+        collection_name:
             Name of the collection.
-        embedding_function: Embeddings
+        embedding_function:
             Embedding function to use.
 
     # TODO: Populate with relevant params.
     Key init args — client params:
-        client: Client | None
+        client:
             Client to use.
-        connection_args: dict | None
+        connection_args:
             Connection arguments.
 
     # TODO: Replace with relevant init params.

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/cli/langchain_cli/utils/git.py

@@ -182,7 +182,7 @@ def parse_dependencies(
     inner_branches = _list_arg_to_length(branch, num_deps)
 
     return list(
-        map(  # type: ignore[call-overload]
+        map(  # type: ignore[call-overload, unused-ignore]
             parse_dependency_string,
             inner_deps,
             inner_repos,

libs/cli/pyproject.toml

@@ -20,12 +20,13 @@ description = "CLI for interacting with LangChain"
 readme = "README.md"
 
 [project.urls]
-homepage = "https://docs.langchain.com/"
-repository = "https://github.com/langchain-ai/langchain/tree/master/libs/cli"
-changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-cli%3D%3D1%22"
-twitter = "https://x.com/LangChainAI"
-slack = "https://www.langchain.com/join-community"
-reddit = "https://www.reddit.com/r/LangChain/"
+Homepage = "https://docs.langchain.com/"
+Documentation = "https://docs.langchain.com/"
+Source = "https://github.com/langchain-ai/langchain/tree/master/libs/cli"
+Changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-cli%3D%3D1%22"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [project.scripts]
 langchain = "langchain_cli.cli:app"
@@ -42,14 +43,14 @@ lint = [
 ]
 test = [
     "langchain-core",
-    "langchain"
+    "langchain-classic"
 ]
-typing = ["langchain"]
+typing = ["langchain-classic"]
 test_integration = []
 
 [tool.uv.sources]
 langchain-core = { path = "../core", editable = true }
-langchain = { path = "../langchain", editable = true }
+langchain-classic = { path = "../langchain", editable = true }
 
 [tool.ruff.format]
 docstring-code-format = true

libs/cli/tests/unit_tests/migrate/generate/test_langchain_migration.py

@@ -1,5 +1,5 @@
 import pytest
-from langchain._api import suppress_langchain_deprecation_warning as sup2
+from langchain_classic._api import suppress_langchain_deprecation_warning as sup2
 from langchain_core._api import suppress_langchain_deprecation_warning as sup1
 
 from langchain_cli.namespaces.migrate.generate.generic import (

libs/cli/uv.lock

@@ -327,7 +327,21 @@ wheels = [
 
 [[package]]
 name = "langchain"
-version = "0.3.27"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "langchain-core" },
+    { name = "langgraph" },
+    { name = "pydantic" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7d/b8/36078257ba52351608129ee983079a4d77ee69eb1470ee248cd8f5728a31/langchain-1.0.0.tar.gz", hash = "sha256:56bf90d935ac1dda864519372d195ca58757b755dd4c44b87840b67d069085b7", size = 466932, upload-time = "2025-10-17T20:53:20.319Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c4/4d/2758a16ad01716c0fb3fe9ec205fd530eae4528b35a27ff44837c399e032/langchain-1.0.0-py3-none-any.whl", hash = "sha256:8c95e41250fc86d09a978fbdf999f86c18d50a28a2addc5da88546af00a1ad15", size = 106202, upload-time = "2025-10-17T20:53:18.685Z" },
+]
+
+[[package]]
+name = "langchain-classic"
+version = "1.0.0"
 source = { editable = "../langchain" }
 dependencies = [
     { name = "async-timeout", marker = "python_full_version < '3.11'" },
@@ -344,20 +358,28 @@ dependencies = [
 requires-dist = [
     { name = "async-timeout", marker = "python_full_version < '3.11'", specifier = ">=4.0.0,<5.0.0" },
     { name = "langchain-anthropic", marker = "extra == 'anthropic'" },
-    { name = "langchain-community", marker = "extra == 'community'" },
+    { name = "langchain-aws", marker = "extra == 'aws'" },
     { name = "langchain-core", editable = "../core" },
+    { name = "langchain-deepseek", marker = "extra == 'deepseek'" },
+    { name = "langchain-fireworks", marker = "extra == 'fireworks'" },
     { name = "langchain-google-genai", marker = "extra == 'google-genai'" },
     { name = "langchain-google-vertexai", marker = "extra == 'google-vertexai'" },
+    { name = "langchain-groq", marker = "extra == 'groq'" },
+    { name = "langchain-huggingface", marker = "extra == 'huggingface'" },
+    { name = "langchain-mistralai", marker = "extra == 'mistralai'" },
+    { name = "langchain-ollama", marker = "extra == 'ollama'" },
     { name = "langchain-openai", marker = "extra == 'openai'", editable = "../partners/openai" },
+    { name = "langchain-perplexity", marker = "extra == 'perplexity'" },
     { name = "langchain-text-splitters", editable = "../text-splitters" },
     { name = "langchain-together", marker = "extra == 'together'" },
+    { name = "langchain-xai", marker = "extra == 'xai'" },
     { name = "langsmith", specifier = ">=0.1.17,<1.0.0" },
     { name = "pydantic", specifier = ">=2.7.4,<3.0.0" },
     { name = "pyyaml", specifier = ">=5.3.0,<7.0.0" },
     { name = "requests", specifier = ">=2.0.0,<3.0.0" },
     { name = "sqlalchemy", specifier = ">=1.4.0,<3.0.0" },
 ]
-provides-extras = ["community", "anthropic", "openai", "google-vertexai", "google-genai", "together"]
+provides-extras = ["anthropic", "openai", "google-vertexai", "google-genai", "fireworks", "ollama", "together", "mistralai", "huggingface", "groq", "aws", "deepseek", "xai", "perplexity"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -376,7 +398,6 @@ test = [
     { name = "blockbuster", specifier = ">=1.5.18,<1.6.0" },
     { name = "cffi", marker = "python_full_version < '3.10'", specifier = "<1.17.1" },
     { name = "cffi", marker = "python_full_version >= '3.10'" },
-    { name = "duckdb-engine", specifier = ">=0.9.2,<1.0.0" },
     { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
     { name = "langchain-core", editable = "../core" },
     { name = "langchain-openai", editable = "../partners/openai" },
@@ -411,9 +432,10 @@ test-integration = [
     { name = "wrapt", specifier = ">=1.15.0,<2.0.0" },
 ]
 typing = [
+    { name = "fastapi", specifier = ">=0.116.1,<1.0.0" },
     { name = "langchain-core", editable = "../core" },
     { name = "langchain-text-splitters", editable = "../text-splitters" },
-    { name = "mypy", specifier = ">=1.15.0,<1.16.0" },
+    { name = "mypy", specifier = ">=1.18.2,<1.19.0" },
     { name = "mypy-protobuf", specifier = ">=3.0.0,<4.0.0" },
     { name = "numpy", marker = "python_full_version < '3.13'", specifier = ">=1.26.4" },
     { name = "numpy", marker = "python_full_version >= '3.13'", specifier = ">=2.1.0" },
@@ -448,11 +470,11 @@ lint = [
     { name = "ruff" },
 ]
 test = [
-    { name = "langchain" },
+    { name = "langchain-classic" },
     { name = "langchain-core" },
 ]
 typing = [
-    { name = "langchain" },
+    { name = "langchain-classic" },
 ]
 
 [package.metadata]
@@ -475,15 +497,15 @@ lint = [
     { name = "ruff", specifier = ">=0.13.1,<0.14" },
 ]
 test = [
-    { name = "langchain", editable = "../langchain" },
+    { name = "langchain-classic", editable = "../langchain" },
     { name = "langchain-core", editable = "../core" },
 ]
 test-integration = []
-typing = [{ name = "langchain", editable = "../langchain" }]
+typing = [{ name = "langchain-classic", editable = "../langchain" }]
 
 [[package]]
 name = "langchain-core"
-version = "1.0.0a6"
+version = "1.0.0"
 source = { editable = "../core" }
 dependencies = [
     { name = "jsonpatch" },
@@ -541,7 +563,7 @@ typing = [
 
 [[package]]
 name = "langchain-text-splitters"
-version = "1.0.0a1"
+version = "1.0.0"
 source = { editable = "../text-splitters" }
 dependencies = [
     { name = "langchain-core" },
@@ -574,8 +596,8 @@ test-integration = [
     { name = "nltk", specifier = ">=3.9.1,<4.0.0" },
     { name = "scipy", marker = "python_full_version == '3.12.*'", specifier = ">=1.7.0,<2.0.0" },
     { name = "scipy", marker = "python_full_version >= '3.13'", specifier = ">=1.14.1,<2.0.0" },
-    { name = "sentence-transformers", specifier = ">=3.0.1,<4.0.0" },
-    { name = "spacy", specifier = ">=3.8.7,<4.0.0" },
+    { name = "sentence-transformers", marker = "python_full_version < '3.14'", specifier = ">=3.0.1,<4.0.0" },
+    { name = "spacy", marker = "python_full_version < '3.14'", specifier = ">=3.8.7,<4.0.0" },
     { name = "thinc", specifier = ">=8.3.6,<9.0.0" },
     { name = "tiktoken", specifier = ">=0.8.0,<1.0.0" },
     { name = "transformers", specifier = ">=4.51.3,<5.0.0" },
@@ -588,6 +610,62 @@ typing = [
     { name = "types-requests", specifier = ">=2.31.0.20240218,<3.0.0.0" },
 ]
 
+[[package]]
+name = "langgraph"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "langchain-core" },
+    { name = "langgraph-checkpoint" },
+    { name = "langgraph-prebuilt" },
+    { name = "langgraph-sdk" },
+    { name = "pydantic" },
+    { name = "xxhash" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/57/f7/7ae10f1832ab1a6a402f451e54d6dab277e28e7d4e4204e070c7897ca71c/langgraph-1.0.0.tar.gz", hash = "sha256:5f83ed0e9bbcc37635bc49cbc9b3d9306605fa07504f955b7a871ed715f9964c", size = 472835, upload-time = "2025-10-17T20:23:38.263Z" }
+wheels = [
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+]
+
+[[package]]
+name = "langgraph-checkpoint"
+version = "2.1.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "langchain-core" },
+    { name = "ormsgpack" },
+]
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+]
+
+[[package]]
+name = "langgraph-prebuilt"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "langchain-core" },
+    { name = "langgraph-checkpoint" },
+]
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+]
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+[[package]]
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+version = "0.2.9"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "httpx" },
+    { name = "orjson" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/23/d8/40e01190a73c564a4744e29a6c902f78d34d43dad9b652a363a92a67059c/langgraph_sdk-0.2.9.tar.gz", hash = "sha256:b3bd04c6be4fa382996cd2be8fbc1e7cc94857d2bc6b6f4599a7f2a245975303", size = 99802, upload-time = "2025-09-20T18:49:14.734Z" }
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 name = "langserve"
 version = "0.0.51"
@@ -780,6 +858,61 @@ wheels = [
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 ]
 
+[[package]]
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+version = "1.11.0"
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+
 [[package]]
 name = "zstandard"
 version = "0.25.0"

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/agents.py

@@ -84,7 +84,7 @@ 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"]`
@@ -112,7 +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.
@@ -161,7 +161,7 @@ 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"]`

libs/core/langchain_core/caches.py

@@ -1,18 +1,17 @@
-"""Cache classes.
+"""Optional caching layer for language models.
+
+Distinct from provider-based [prompt caching](https://docs.langchain.com/oss/python/langchain/models#prompt-caching).
 
 !!! 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
+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.
-- 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.
+2. It can speed up your application by reducing the number of API calls you make to the
+    LLM provider.
 """
 
 from __future__ import annotations
@@ -34,8 +33,8 @@ 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.
@@ -47,14 +46,14 @@ 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
@@ -63,27 +62,27 @@ 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).
         """
 
@@ -92,14 +91,14 @@ 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
@@ -108,29 +107,29 @@ 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)
@@ -150,10 +149,9 @@ 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:
@@ -162,28 +160,28 @@ 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:
@@ -196,30 +194,30 @@ 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/base.py

@@ -420,8 +420,6 @@ class RunManagerMixin:
                 (includes inherited tags).
             metadata: The metadata associated with the custom event
                 (includes inherited metadata).
-
-        !!! version-added "Added in version 0.2.15"
         """
 
 
@@ -882,8 +880,6 @@ class AsyncCallbackHandler(BaseCallbackHandler):
                 (includes inherited tags).
             metadata: The metadata associated with the custom event
                 (includes inherited metadata).
-
-        !!! version-added "Added in version 0.2.15"
         """
 
 
@@ -1001,7 +997,7 @@ class BaseCallbackManager(CallbackManagerMixin):
 
         Args:
             handler: The handler to add.
-            inherit: Whether to inherit the handler. Default is True.
+            inherit: Whether to inherit the handler.
         """
         if handler not in self.handlers:
             self.handlers.append(handler)
@@ -1028,7 +1024,7 @@ class BaseCallbackManager(CallbackManagerMixin):
 
         Args:
             handlers: The handlers to set.
-            inherit: Whether to inherit the handlers. Default is True.
+            inherit: Whether to inherit the handlers.
         """
         self.handlers = []
         self.inheritable_handlers = []
@@ -1044,7 +1040,7 @@ class BaseCallbackManager(CallbackManagerMixin):
 
         Args:
             handler: The handler to set.
-            inherit: Whether to inherit the handler. Default is True.
+            inherit: Whether to inherit the handler.
         """
         self.set_handlers([handler], inherit=inherit)
 
@@ -1057,7 +1053,7 @@ class BaseCallbackManager(CallbackManagerMixin):
 
         Args:
             tags: The tags to add.
-            inherit: Whether to inherit the tags. Default is True.
+            inherit: Whether to inherit the tags.
         """
         for tag in tags:
             if tag in self.tags:
@@ -1087,7 +1083,7 @@ class BaseCallbackManager(CallbackManagerMixin):
 
         Args:
             metadata: The metadata to add.
-            inherit: Whether to inherit the metadata. Default is True.
+            inherit: Whether to inherit the metadata.
         """
         self.metadata.update(metadata)
         if inherit:

libs/core/langchain_core/callbacks/file.py

@@ -47,7 +47,7 @@ class FileCallbackHandler(BaseCallbackHandler):
     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/manager.py

@@ -79,13 +79,13 @@ def trace_as_chain_group(
 
     Args:
         group_name: The name of the chain group.
-        callback_manager: The callback manager to use. Defaults to `None`.
-        inputs: The inputs to the chain group. Defaults to `None`.
-        project_name: The name of the project. Defaults to `None`.
-        example_id: The ID of the example. Defaults to `None`.
+        callback_manager: The callback manager to use.
+        inputs: The inputs to the chain group.
+        project_name: The name of the project.
+        example_id: The ID of the example.
         run_id: The ID of the run.
-        tags: The inheritable tags to apply to all runs. Defaults to `None`.
-        metadata: The metadata to apply to all runs. Defaults to `None`.
+        tags: The inheritable tags to apply to all runs.
+        metadata: The metadata to apply to all runs.
 
     !!! note
         Must have `LANGCHAIN_TRACING_V2` env var set to true to see the trace in
@@ -155,13 +155,13 @@ async def atrace_as_chain_group(
     Args:
         group_name: The name of the chain group.
         callback_manager: The async callback manager to use,
-            which manages tracing and other callback behavior. Defaults to `None`.
-        inputs: The inputs to the chain group. Defaults to `None`.
-        project_name: The name of the project. Defaults to `None`.
-        example_id: The ID of the example. Defaults to `None`.
+            which manages tracing and other callback behavior.
+        inputs: The inputs to the chain group.
+        project_name: The name of the project.
+        example_id: The ID of the example.
         run_id: The ID of the run.
-        tags: The inheritable tags to apply to all runs. Defaults to `None`.
-        metadata: The metadata to apply to all runs. Defaults to `None`.
+        tags: The inheritable tags to apply to all runs.
+        metadata: The metadata to apply to all runs.
 
     Yields:
         The async callback manager for the chain group.
@@ -462,11 +462,11 @@ class BaseRunManager(RunManagerMixin):
             run_id: The ID of the run.
             handlers: The list of handlers.
             inheritable_handlers: The list of inheritable handlers.
-            parent_run_id: The ID of the parent run. Defaults to `None`.
-            tags: The list of tags. Defaults to `None`.
-            inheritable_tags: The list of inheritable tags. Defaults to `None`.
-            metadata: The metadata. Defaults to `None`.
-            inheritable_metadata: The inheritable metadata. Defaults to `None`.
+            parent_run_id: The ID of the parent run.
+            tags: The list of tags.
+            inheritable_tags: The list of inheritable tags.
+            metadata: The metadata.
+            inheritable_metadata: The inheritable metadata.
 
         """
         self.run_id = run_id
@@ -557,7 +557,7 @@ class ParentRunManager(RunManager):
         """Get a child callback manager.
 
         Args:
-            tag: The tag for the child callback manager. Defaults to `None`.
+            tag: The tag for the child callback manager.
 
         Returns:
             The child callback manager.
@@ -641,7 +641,7 @@ class AsyncParentRunManager(AsyncRunManager):
         """Get a child callback manager.
 
         Args:
-            tag: The tag for the child callback manager. Defaults to `None`.
+            tag: The tag for the child callback manager.
 
         Returns:
             The child callback manager.
@@ -1303,7 +1303,7 @@ class CallbackManager(BaseCallbackManager):
         Args:
             serialized: The serialized LLM.
             prompts: The list of prompts.
-            run_id: The ID of the run. Defaults to `None`.
+            run_id: The ID of the run.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -1354,7 +1354,7 @@ class CallbackManager(BaseCallbackManager):
         Args:
             serialized: The serialized LLM.
             messages: The list of messages.
-            run_id: The ID of the run. Defaults to `None`.
+            run_id: The ID of the run.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -1408,7 +1408,7 @@ class CallbackManager(BaseCallbackManager):
         Args:
             serialized: The serialized chain.
             inputs: The inputs to the chain.
-            run_id: The ID of the run. Defaults to `None`.
+            run_id: The ID of the run.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -1457,8 +1457,8 @@ class CallbackManager(BaseCallbackManager):
             serialized: Serialized representation of the tool.
             input_str: The  input to the tool as a string.
                 Non-string inputs are cast to strings.
-            run_id: ID for the run. Defaults to `None`.
-            parent_run_id: The ID of the parent run. Defaults to `None`.
+            run_id: ID for the run.
+            parent_run_id: The ID of the parent run.
             inputs: The original input to the tool if provided.
                 Recommended for usage instead of input_str when the original
                 input is needed.
@@ -1512,8 +1512,8 @@ class CallbackManager(BaseCallbackManager):
         Args:
             serialized: The serialized retriever.
             query: The query.
-            run_id: The ID of the run. Defaults to `None`.
-            parent_run_id: The ID of the parent run. Defaults to `None`.
+            run_id: The ID of the run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -1562,13 +1562,10 @@ class CallbackManager(BaseCallbackManager):
         Args:
             name: The name of the adhoc event.
             data: The data for the adhoc event.
-            run_id: The ID of the run. Defaults to `None`.
+            run_id: The ID of the run.
 
         Raises:
             ValueError: If additional keyword arguments are passed.
-
-        !!! version-added "Added in version 0.2.14"
-
         """
         if not self.handlers:
             return
@@ -1782,7 +1779,7 @@ class AsyncCallbackManager(BaseCallbackManager):
         Args:
             serialized: The serialized LLM.
             prompts: The list of prompts.
-            run_id: The ID of the run. Defaults to `None`.
+            run_id: The ID of the run.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -1870,7 +1867,7 @@ class AsyncCallbackManager(BaseCallbackManager):
         Args:
             serialized: The serialized LLM.
             messages: The list of messages.
-            run_id: The ID of the run. Defaults to `None`.
+            run_id: The ID of the run.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -1941,7 +1938,7 @@ class AsyncCallbackManager(BaseCallbackManager):
         Args:
             serialized: The serialized chain.
             inputs: The inputs to the chain.
-            run_id: The ID of the run. Defaults to `None`.
+            run_id: The ID of the run.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -1988,8 +1985,8 @@ class AsyncCallbackManager(BaseCallbackManager):
         Args:
             serialized: The serialized tool.
             input_str: The input to the tool.
-            run_id: The ID of the run. Defaults to `None`.
-            parent_run_id: The ID of the parent run. Defaults to `None`.
+            run_id: The ID of the run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -2038,12 +2035,10 @@ class AsyncCallbackManager(BaseCallbackManager):
         Args:
             name: The name of the adhoc event.
             data: The data for the adhoc event.
-            run_id: The ID of the run. Defaults to `None`.
+            run_id: The ID of the run.
 
         Raises:
             ValueError: If additional keyword arguments are passed.
-
-        !!! version-added "Added in version 0.2.14"
         """
         if not self.handlers:
             return
@@ -2082,8 +2077,8 @@ class AsyncCallbackManager(BaseCallbackManager):
         Args:
             serialized: The serialized retriever.
             query: The query.
-            run_id: The ID of the run. Defaults to `None`.
-            parent_run_id: The ID of the parent run. Defaults to `None`.
+            run_id: The ID of the run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -2555,9 +2550,6 @@ async def adispatch_custom_event(
         This is due to a limitation in asyncio for python <= 3.10 that prevents
         LangChain from automatically propagating the config object on the user's
         behalf.
-
-    !!! version-added "Added in version 0.2.15"
-
     """
     # Import locally to prevent circular imports.
     from langchain_core.runnables.config import (  # noqa: PLC0415
@@ -2630,9 +2622,6 @@ def dispatch_custom_event(
         foo_ = RunnableLambda(foo)
         foo_.invoke({"a": "1"}, {"callbacks": [CustomCallbackManager()]})
         ```
-
-    !!! version-added "Added in version 0.2.15"
-
     """
     # Import locally to prevent circular imports.
     from langchain_core.runnables.config import (  # noqa: PLC0415

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
 
@@ -61,7 +61,7 @@ class StdOutCallbackHandler(BaseCallbackHandler):
 
         Args:
             action: The agent action.
-            color: The color to use for the text. Defaults to `None`.
+            color: The color to use for the text.
             **kwargs: Additional keyword arguments.
         """
         print_text(action.log, color=color or self.color)
@@ -79,9 +79,9 @@ class StdOutCallbackHandler(BaseCallbackHandler):
 
         Args:
             output: The output to print.
-            color: The color to use for the text. Defaults to `None`.
-            observation_prefix: The observation prefix. Defaults to `None`.
-            llm_prefix: The LLM prefix. Defaults to `None`.
+            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)
@@ -103,8 +103,8 @@ class StdOutCallbackHandler(BaseCallbackHandler):
 
         Args:
             text: The text to print.
-            color: The color to use for the text. Defaults to `None`.
-            end: The end character to use. Defaults to "".
+            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)
@@ -117,7 +117,7 @@ class StdOutCallbackHandler(BaseCallbackHandler):
 
         Args:
             finish: The agent finish.
-            color: The color to use for the text. Defaults to `None`.
+            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/chat_history.py

@@ -153,7 +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

@@ -34,9 +34,6 @@ class LangSmithLoader(BaseLoader):
         ```python
         # -> [Document("...", metadata={"inputs": {...}, "outputs": {...}, ...}), ...]
         ```
-
-    !!! version-added "Added in version 0.2.34"
-
     """
 
     def __init__(
@@ -60,15 +57,15 @@ 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 +73,10 @@ 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/base.py

@@ -38,8 +38,6 @@ class BaseMedia(Serializable):
 
     Ideally this should be unique across the document collection and formatted
     as a UUID, but this will not be enforced.
-
-    !!! version-added "Added in version 0.2.11"
     """
 
     metadata: dict = Field(default_factory=dict)
@@ -57,51 +55,51 @@ class Blob(BaseMedia):
 
     Example: Initialize a blob from in-memory data
 
-        ```python
-        from langchain_core.documents import Blob
+    ```python
+    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
 
-        ```python
-        from langchain_core.documents import Blob
+    ```python
+    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
 
-        ```python
-        from langchain_core.documents import Blob
+    ```python
+    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 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())
+    ```
     """
 
     data: bytes | str | None = None
@@ -111,7 +109,7 @@ 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."""
@@ -127,7 +125,7 @@ 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:
@@ -211,11 +209,11 @@ 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:
@@ -248,10 +246,10 @@ 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:
@@ -303,7 +301,7 @@ 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

@@ -57,10 +57,10 @@ class BaseDocumentTransformer(ABC):
         """Transform a list of documents.
 
         Args:
-            documents: A sequence of Documents to be transformed.
+            documents: A sequence of `Document` objects to be transformed.
 
         Returns:
-            A sequence of transformed Documents.
+            A sequence of transformed `Document` objects.
         """
 
     async def atransform_documents(
@@ -69,10 +69,10 @@ class BaseDocumentTransformer(ABC):
         """Asynchronously transform a list of documents.
 
         Args:
-            documents: A sequence of Documents to be transformed.
+            documents: A sequence of `Document` objects to be transformed.
 
         Returns:
-            A sequence of transformed Documents.
+            A sequence of transformed `Document` objects.
         """
         return await run_in_executor(
             None, self.transform_documents, documents, **kwargs

libs/core/langchain_core/embeddings/fake.py

@@ -18,7 +18,8 @@ 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:
         ```python
@@ -72,7 +73,8 @@ 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:
         ```python

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.
     """
@@ -28,20 +28,19 @@ 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

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

@@ -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.
@@ -508,8 +508,6 @@ class DocumentIndex(BaseRetriever):
     1. Storing document in the index.
     2. Fetching document by ID.
     3. Searching for document using a query.
-
-    !!! version-added "Added in version 0.2.29"
     """
 
     @abc.abstractmethod
@@ -522,10 +520,10 @@ class DocumentIndex(BaseRetriever):
 
         When an ID is specified and the content already exists in the vectorstore,
         the upsert method should update the content with the new data. If the content
-        does not exist, the upsert method should add the item to the vectorstore.
+        does not exist, the upsert method should add the item to the `VectorStore`.
 
         Args:
-            items: Sequence of documents to add to the vectorstore.
+            items: Sequence of documents to add to the `VectorStore`.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -545,10 +543,10 @@ class DocumentIndex(BaseRetriever):
 
         When an ID is specified and the item already exists in the vectorstore,
         the upsert method should update the item with the new data. If the item
-        does not exist, the upsert method should add the item to the vectorstore.
+        does not exist, the upsert method should add the item to the `VectorStore`.
 
         Args:
-            items: Sequence of documents to add to the vectorstore.
+            items: Sequence of documents to add to the `VectorStore`.
             **kwargs: Additional keyword arguments.
 
         Returns:

libs/core/langchain_core/indexing/in_memory.py

@@ -23,8 +23,6 @@ class InMemoryDocumentIndex(DocumentIndex):
 
     It provides a simple search API that returns documents by the number of
     counts the given query appears in the document.
-
-    !!! version-added "Added in version 0.2.29"
     """
 
     store: dict[str, Document] = Field(default_factory=dict)

libs/core/langchain_core/language_models/__init__.py

@@ -1,43 +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).
+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.
-
-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/
+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).
 
     """
@@ -89,7 +89,8 @@ 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

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.
@@ -186,22 +192,22 @@ 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
+            callbacks: `Callbacks` to pass through. Used for executing additional
                 functionality, such as logging or streaming, throughout generation.
             **kwargs: Arbitrary additional keyword arguments. These are usually passed
                 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.
 
         """
 
@@ -223,22 +229,22 @@ 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
+            callbacks: `Callbacks` to pass through. Used for executing additional
                 functionality, such as logging or streaming, throughout generation.
             **kwargs: Arbitrary additional keyword arguments. These are usually passed
                 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.
 
         """
 
@@ -263,8 +269,7 @@ class BaseLanguageModel(
 
         Returns:
             A list of ids corresponding to the tokens in the text, in order they occur
-            in the text.
-
+                in the text.
         """
         if self.custom_get_token_ids is not None:
             return self.custom_get_token_ids(text)

libs/core/langchain_core/language_models/chat_models.py

@@ -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
@@ -349,11 +322,12 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
     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"
 
@@ -864,13 +838,13 @@ class BaseChatModel(BaseLanguageModel[AIMessage], 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:
             messages: List of list of messages.
             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
+            callbacks: `Callbacks` to pass through. Used for executing additional
                 functionality, such as logging or streaming, throughout generation.
             tags: The tags to apply.
             metadata: The metadata to apply.
@@ -880,8 +854,8 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                 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 `Generations` for each
+                input prompt and additional model provider-specific output.
 
         """
         ls_structured_output_format = kwargs.pop(
@@ -982,13 +956,13 @@ class BaseChatModel(BaseLanguageModel[AIMessage], 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:
             messages: List of list of messages.
             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
+            callbacks: `Callbacks` to pass through. Used for executing additional
                 functionality, such as logging or streaming, throughout generation.
             tags: The tags to apply.
             metadata: The metadata to apply.
@@ -998,8 +972,8 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                 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 `Generations` for each
+                input prompt and additional model provider-specific output.
 
         """
         ls_structured_output_format = kwargs.pop(
@@ -1536,17 +1510,21 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                 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`
-                for more on how to properly specify types and descriptions of
-                schema fields when specifying a Pydantic or `TypedDict` class.
+                dict and will not be validated.
+
+                See `langchain_core.utils.function_calling.convert_to_openai_tool` for
+                more on how to properly specify types and descriptions of schema fields
+                when specifying a Pydantic or `TypedDict` class.
 
             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'`.
+                will be caught and returned as well.
+
+                The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
+                `'parsing_error'`.
 
         Raises:
             ValueError: If there are any unsupported `kwargs`.
@@ -1554,99 +1532,102 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                 `with_structured_output()`.
 
         Returns:
-            A Runnable that takes same inputs as a `langchain_core.language_models.chat.BaseChatModel`.
+            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). Otherwise, if `include_raw` is
+                `False` then `Runnable` outputs a `dict`.
 
-            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 `True`, then `Runnable` outputs a `dict` with keys:
 
-            Otherwise, if `include_raw` is False then Runnable outputs a dict.
+                - `'raw'`: `BaseMessage`
+                - `'parsed'`: `None` if there was a parsing error, otherwise the type
+                    depends on the `schema` as described above.
+                - `'parsing_error'`: `BaseException | None`
 
-            If `include_raw` is True, then Runnable outputs a dict with keys:
+        Example: Pydantic schema (`include_raw=False`):
 
-            - `'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):
-            ```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):
-            ```python
-            from pydantic import BaseModel
+        Example: Pydantic schema (`include_raw=True`):
+
+        ```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):
-            ```python
-            from pydantic import BaseModel
-            from langchain_core.utils.function_calling import convert_to_openai_tool
+        Example: `dict` schema (`include_raw=False`):
+
+        ```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)

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."""

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.
@@ -91,13 +91,17 @@ def create_base_retry_decorator(
             if isinstance(run_manager, AsyncCallbackManagerForLLMRun):
                 coro = run_manager.on_retry(retry_state)
                 try:
-                    loop = asyncio.get_event_loop()
-                    if loop.is_running():
-                        # TODO: Fix RUF006 - this task should have a reference
-                        #  and be awaited somewhere
-                        loop.create_task(coro)  # noqa: RUF006
-                    else:
+                    try:
+                        loop = asyncio.get_event_loop()
+                    except RuntimeError:
                         asyncio.run(coro)
+                    else:
+                        if loop.is_running():
+                            # TODO: Fix RUF006 - this task should have a reference
+                            #  and be awaited somewhere
+                            loop.create_task(coro)  # noqa: RUF006
+                        else:
+                            asyncio.run(coro)
                 except Exception as e:
                     _log_error_once(f"Error in on_retry: {e}")
             else:
@@ -153,7 +157,7 @@ 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 +193,7 @@ 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,
@@ -841,7 +845,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             prompts: List of string prompts.
             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
+            callbacks: `Callbacks` to pass through. Used for executing additional
                 functionality, such as logging or streaming, throughout generation.
             tags: List of tags to associate with each prompt. If provided, the length
                 of the list must match the length of the prompts list.
@@ -861,8 +865,8 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                 `run_name` (if provided) does not match the length of prompts.
 
         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 `Generations` for each
+                input prompt and additional model provider-specific output.
         """
         if not isinstance(prompts, list):
             msg = (
@@ -1111,7 +1115,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             prompts: List of string prompts.
             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
+            callbacks: `Callbacks` to pass through. Used for executing additional
                 functionality, such as logging or streaming, throughout generation.
             tags: List of tags to associate with each prompt. If provided, the length
                 of the list must match the length of the prompts list.
@@ -1130,8 +1134,8 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                 `run_name` (if provided) does not match the length of prompts.
 
         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 `Generations` for each
+                input prompt and additional model provider-specific output.
         """
         if isinstance(metadata, list):
             metadata = [

libs/core/langchain_core/load/dump.py

@@ -38,17 +38,16 @@ def _dump_pydantic_models(obj: Any) -> Any:
 
 
 def dumps(obj: Any, *, pretty: bool = False, **kwargs: Any) -> str:
-    """Return a json string representation of an object.
+    """Return a JSON string representation of an object.
 
     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.
+        A JSON string representation of the object.
 
     Raises:
         ValueError: If `default` is passed as a kwarg.
@@ -72,14 +71,12 @@ def dumps(obj: Any, *, pretty: bool = False, **kwargs: Any) -> str:
 def dumpd(obj: Any) -> Any:
     """Return a dict representation of an object.
 
-    !!! note
-        Unfortunately this function is not as efficient as it could be because it first
-        dumps the object to a json string and then loads it back into a dictionary.
-
     Args:
         obj: The object to dump.
 
     Returns:
-        dictionary that can be serialized to json using json.dumps
+        Dictionary that can be serialized to json using `json.dumps`.
     """
+    # Unfortunately this function is not as efficient as it could be because it first
+    # dumps the object to a json string and then loads it back into a dictionary.
     return json.loads(dumps(obj))

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 {}
@@ -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

@@ -25,9 +25,9 @@ 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):
@@ -52,7 +52,7 @@ class SerializedNotImplemented(BaseSerialized):
     type: Literal["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.
+        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.
+        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.
+        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/ai.py

@@ -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__(
@@ -191,7 +190,7 @@ 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.
 
@@ -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.
@@ -372,23 +374,19 @@ 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`.
@@ -396,7 +394,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
 
     @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"

libs/core/langchain_core/messages/base.py

@@ -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.
@@ -159,12 +159,12 @@ 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.
 
         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.
         """
@@ -184,7 +184,7 @@ 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"]`
@@ -262,7 +262,7 @@ class BaseMessage(Serializable):
         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.
+            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:
@@ -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.
@@ -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

@@ -28,7 +28,7 @@ dictionary with two keys:
 - `'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
+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.

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

@@ -31,7 +31,7 @@ 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
+    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.

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

@@ -35,7 +35,7 @@ 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
+    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.

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

@@ -105,7 +105,7 @@ 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
+    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.
@@ -282,7 +282,7 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
     standard content blocks for returning.
 
     Args:
-        message: The AIMessage or AIMessageChunk to convert.
+        message: The `AIMessage` or `AIMessageChunk` to convert.
 
     Returns:
         List of standard content blocks derived from the message content.
@@ -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

@@ -10,7 +10,7 @@ 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
+    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.

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

@@ -155,7 +155,7 @@ 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
+    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.

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

@@ -143,7 +143,7 @@ class Citation(TypedDict):
         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`).
 
-    !!! note
+    !!! note "Factory function"
         `create_citation` may also be used as a factory to create a `Citation`.
         Benefits include:
 
@@ -156,7 +156,9 @@ 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_'`))
@@ -201,6 +203,7 @@ 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_'`))
 
@@ -211,6 +214,7 @@ class NonStandardAnnotation(TypedDict):
 
 
 Annotation = Citation | NonStandardAnnotation
+"""A union of all defined `Annotation` types."""
 
 
 class TextContentBlock(TypedDict):
@@ -219,7 +223,7 @@ 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
+    !!! note "Factory function"
         `create_text_block` may also be used as a factory to create a
         `TextContentBlock`. Benefits include:
 
@@ -235,6 +239,7 @@ 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_'`))
 
@@ -254,7 +259,7 @@ class TextContentBlock(TypedDict):
 
 
 class ToolCall(TypedDict):
-    """Represents a request to call a tool.
+    """Represents an AI's request to call a tool.
 
     Example:
         ```python
@@ -264,7 +269,7 @@ class ToolCall(TypedDict):
         This represents a request to call the tool named "foo" with arguments {"a": 1}
         and an identifier of "123".
 
-    !!! note
+    !!! note "Factory function"
         `create_tool_call` may also be used as a factory to create a
         `ToolCall`. Benefits include:
 
@@ -299,7 +304,7 @@ 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__`),
     all string attributes are concatenated. Chunks are only merged if their
@@ -381,7 +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."""
@@ -403,7 +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."""
@@ -452,7 +460,7 @@ class ServerToolResult(TypedDict):
 class ReasoningContentBlock(TypedDict):
     """Reasoning output from a LLM.
 
-    !!! note
+    !!! note "Factory function"
         `create_reasoning_block` may also be used as a factory to create a
         `ReasoningContentBlock`. Benefits include:
 
@@ -468,6 +476,7 @@ 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_'`))
 
@@ -494,7 +503,7 @@ class ReasoningContentBlock(TypedDict):
 class ImageContentBlock(TypedDict):
     """Image data.
 
-    !!! note
+    !!! note "Factory function"
         `create_image_block` may also be used as a factory to create a
         `ImageContentBlock`. Benefits include:
 
@@ -510,6 +519,7 @@ 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_'`))
 
@@ -541,7 +551,7 @@ class ImageContentBlock(TypedDict):
 class VideoContentBlock(TypedDict):
     """Video data.
 
-    !!! note
+    !!! note "Factory function"
         `create_video_block` may also be used as a factory to create a
         `VideoContentBlock`. Benefits include:
 
@@ -557,6 +567,7 @@ 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_'`))
 
@@ -588,7 +599,7 @@ class VideoContentBlock(TypedDict):
 class AudioContentBlock(TypedDict):
     """Audio data.
 
-    !!! note
+    !!! 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)
@@ -603,6 +614,7 @@ 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_'`))
 
@@ -642,9 +654,9 @@ class PlainTextContentBlock(TypedDict):
 
     !!! 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
+    !!! note "Factory function"
         `create_plaintext_block` may also be used as a factory to create a
         `PlainTextContentBlock`. Benefits include:
 
@@ -660,6 +672,7 @@ 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_'`))
 
@@ -694,7 +707,7 @@ 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.
@@ -703,7 +716,7 @@ class FileContentBlock(TypedDict):
     content block type (e.g., `ImageContentBlock`, `AudioContentBlock`,
     `PlainTextContentBlock`).
 
-    !!! note
+    !!! note "Factory function"
         `create_file_block` may also be used as a factory to create a
         `FileContentBlock`. Benefits include:
 
@@ -719,6 +732,7 @@ 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_'`))
 
@@ -765,7 +779,7 @@ class NonStandardContentBlock(TypedDict):
     Has no `extras` field, as provider-specific data should be included in the
     `value` field.
 
-    !!! note
+    !!! note "Factory function"
         `create_non_standard_block` may also be used as a factory to create a
         `NonStandardContentBlock`. Benefits include:
 
@@ -781,6 +795,7 @@ 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_'`))
 
@@ -801,6 +816,7 @@ DataContentBlock = (
     | PlainTextContentBlock
     | FileContentBlock
 )
+"""A union of all defined multimodal data `ContentBlock` types."""
 
 ToolContentBlock = (
     ToolCall | ToolCallChunk | ServerToolCall | ServerToolCallChunk | ServerToolResult
@@ -814,6 +830,7 @@ ContentBlock = (
     | DataContentBlock
     | ToolContentBlock
 )
+"""A union of all defined `ContentBlock` types and aliases."""
 
 
 KNOWN_BLOCK_TYPES = {
@@ -877,7 +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():

libs/core/langchain_core/messages/function.py

@@ -19,7 +19,7 @@ class FunctionMessage(BaseMessage):
     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

@@ -7,9 +7,9 @@ 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
@@ -27,11 +27,7 @@ class HumanMessage(BaseMessage):
     """
 
     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__(
@@ -71,5 +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,

libs/core/langchain_core/messages/system.py

@@ -27,11 +27,7 @@ class SystemMessage(BaseMessage):
     """
 
     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__(
@@ -71,8 +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

@@ -31,36 +31,34 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
 
     Example: A `ToolMessage` representing a result of `42` from a tool call with id
 
-        ```python
-        from langchain_core.messages import ToolMessage
+    ```python
+    from langchain_core.messages import ToolMessage
 
-        ToolMessage(content="42", tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL")
-        ```
+    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
 
-        ```python
-        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..."},
+    }
 
-        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.
 
     """
@@ -69,11 +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.
@@ -82,21 +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
@@ -164,12 +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.
 
         Args:
-            content: The string contents of the message.
+            content: The contents of the message.
             content_blocks: Typed standard content.
             **kwargs: Additional fields.
         """
@@ -215,7 +203,7 @@ 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
@@ -261,7 +249,7 @@ 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__`),
     all string attributes are concatenated. Chunks are only merged if their

libs/core/langchain_core/messages/utils.py

@@ -86,6 +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(
@@ -96,9 +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.
@@ -211,6 +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(
@@ -227,10 +227,10 @@ def _create_message_from_message_type(
     Args:
         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:
@@ -319,7 +319,7 @@ def _convert_to_message(message: MessageLikeRepresentation) -> BaseMessage:
         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.
@@ -425,22 +425,22 @@ 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`
             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`
             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.
+            - `True`: All `AIMessage` objects with tool calls and all `ToolMessage`
+                objects will be excluded.
             - a sequence of tool call IDs to exclude:
                 - `ToolMessage` objects with the corresponding tool call ID will be
                     excluded.
@@ -568,7 +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
@@ -703,7 +702,7 @@ def trim_messages(
     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.
+    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
@@ -714,8 +713,6 @@ def trim_messages(
         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
     2. It includes recent messages and drops old messages in the chat history.
         To achieve this set the `strategy='last'`.
     3. Usually, the new chat history should include the `SystemMessage` if it
@@ -745,12 +742,10 @@ def trim_messages(
         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'`.
         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
             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
@@ -759,7 +754,7 @@ def trim_messages(
             `'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
             the first occurrence of this type is ignored. This is done after we trim
@@ -768,10 +763,9 @@ def trim_messages(
             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.
+
+        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
@@ -782,7 +776,7 @@ def trim_messages(
             newlines.
 
     Returns:
-        list of trimmed `BaseMessage`.
+        List of trimmed `BaseMessage`.
 
     Raises:
         ValueError: if two incompatible arguments are specified or an unrecognized
@@ -1683,12 +1677,12 @@ 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).
-            You can also specify float values for more fine-grained control.
+            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).
-            You can also specify float values for more fine-grained control.
+        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).
         count_name: Whether to include message names in the count.
             Enabled by default.

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)
 
 
@@ -172,7 +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__"):
@@ -234,16 +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.
@@ -264,20 +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:
@@ -299,13 +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

@@ -146,7 +146,7 @@ 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"]`

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.
@@ -177,16 +177,15 @@ 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:
         ```python
@@ -221,7 +220,7 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
     """
 
     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.
@@ -230,7 +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.
@@ -239,7 +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:
@@ -262,10 +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.
@@ -288,13 +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."""
@@ -305,7 +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

@@ -19,7 +19,7 @@ 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"]`

libs/core/langchain_core/outputs/generation.py

@@ -44,7 +44,7 @@ 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"]`

libs/core/langchain_core/outputs/llm_result.py

@@ -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
@@ -35,7 +35,7 @@ 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.
 
@@ -62,7 +62,7 @@ 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.
 
@@ -99,7 +99,7 @@ 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.
 
@@ -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/base.py

@@ -96,7 +96,7 @@ 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"]`

libs/core/langchain_core/prompts/chat.py

@@ -135,7 +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
@@ -147,7 +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.
@@ -195,7 +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.
@@ -235,13 +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`.
+
             **kwargs: keyword arguments to pass to the constructor.
 
         Returns:
@@ -330,7 +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.
@@ -412,9 +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:
@@ -637,7 +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.
@@ -750,7 +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.
@@ -905,7 +904,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
                 (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".
+            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
@@ -971,7 +970,7 @@ 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"]`
@@ -1128,7 +1127,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
                 (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".
+            template_format: format of the template.
 
         Returns:
             a chat prompt template.
@@ -1287,7 +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.
@@ -1306,7 +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.
@@ -1383,7 +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

@@ -74,7 +74,7 @@ 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"]`
@@ -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_with_templates.py

@@ -46,7 +46,7 @@ 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"]`

libs/core/langchain_core/prompts/image.py

@@ -49,7 +49,7 @@ 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"]`

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

@@ -23,7 +23,7 @@ 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"]`
@@ -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

@@ -66,7 +66,7 @@ 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"]`
@@ -220,7 +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.
@@ -275,13 +275,12 @@ 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`.
+                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:

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
 
@@ -122,13 +122,16 @@ def mustache_formatter(template: str, /, **kwargs: Any) -> str:
 def mustache_template_vars(
     template: str,
 ) -> set[str]:
-    """Get the variables from a mustache template.
+    """Get the top-level variables from a mustache template.
+
+    For nested variables like `{{person.name}}`, only the top-level
+    key (`person`) is returned.
 
     Args:
         template: The template string.
 
     Returns:
-        The variables from the template.
+       The top-level variables from the template.
     """
     variables: set[str] = set()
     section_depth = 0
@@ -149,9 +152,7 @@ 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 +176,11 @@ 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()
@@ -273,7 +279,7 @@ 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"]`
@@ -327,3 +333,12 @@ 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)))

libs/core/langchain_core/prompts/structured.py

@@ -63,13 +63,13 @@ class StructuredPrompt(ChatPromptTemplate):
 
     @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"]`
 
         Returns:
-            The namespace of the langchain object.
+            The namespace of the LangChain object.
         """
         return cls.__module__.split(".")
 
@@ -144,7 +144,7 @@ class StructuredPrompt(ChatPromptTemplate):
 
         Args:
             others: The language model to pipe the structured prompt to.
-            name: The name of the pipeline. Defaults to `None`.
+            name: The name of the pipeline.
 
         Returns:
             A RunnableSequence object.

libs/core/langchain_core/pydantic_v1/__init__.py

@@ -1,30 +0,0 @@
-"""Pydantic v1 compatibility shim."""
-
-from importlib import metadata
-
-from pydantic.v1 import *  # noqa: F403
-
-from langchain_core._api.deprecation import warn_deprecated
-
-try:
-    _PYDANTIC_MAJOR_VERSION: int = int(metadata.version("pydantic").split(".")[0])
-except metadata.PackageNotFoundError:
-    _PYDANTIC_MAJOR_VERSION = 0
-
-warn_deprecated(
-    "0.3.0",
-    removal="1.0.0",
-    alternative="pydantic.v1 or pydantic",
-    message=(
-        "As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. "
-        "The langchain_core.pydantic_v1 module was a "
-        "compatibility shim for pydantic v1, and should no longer be used. "
-        "Please update the code to import from Pydantic directly.\n\n"
-        "For example, replace imports like: "
-        "`from langchain_core.pydantic_v1 import BaseModel`\n"
-        "with: `from pydantic import BaseModel`\n"
-        "or the v1 compatibility namespace if you are working in a code base "
-        "that has not been fully upgraded to pydantic 2 yet. "
-        "\tfrom pydantic.v1 import BaseModel\n"
-    ),
-)

libs/core/langchain_core/pydantic_v1/dataclasses.py

@@ -1,23 +0,0 @@
-"""Pydantic v1 compatibility shim."""
-
-from pydantic.v1.dataclasses import *  # noqa: F403
-
-from langchain_core._api import warn_deprecated
-
-warn_deprecated(
-    "0.3.0",
-    removal="1.0.0",
-    alternative="pydantic.v1 or pydantic",
-    message=(
-        "As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. "
-        "The langchain_core.pydantic_v1 module was a "
-        "compatibility shim for pydantic v1, and should no longer be used. "
-        "Please update the code to import from Pydantic directly.\n\n"
-        "For example, replace imports like: "
-        "`from langchain_core.pydantic_v1 import BaseModel`\n"
-        "with: `from pydantic import BaseModel`\n"
-        "or the v1 compatibility namespace if you are working in a code base "
-        "that has not been fully upgraded to pydantic 2 yet. "
-        "\tfrom pydantic.v1 import BaseModel\n"
-    ),
-)

libs/core/langchain_core/pydantic_v1/main.py

@@ -1,23 +0,0 @@
-"""Pydantic v1 compatibility shim."""
-
-from pydantic.v1.main import *  # noqa: F403
-
-from langchain_core._api import warn_deprecated
-
-warn_deprecated(
-    "0.3.0",
-    removal="1.0.0",
-    alternative="pydantic.v1 or pydantic",
-    message=(
-        "As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. "
-        "The langchain_core.pydantic_v1 module was a "
-        "compatibility shim for pydantic v1, and should no longer be used. "
-        "Please update the code to import from Pydantic directly.\n\n"
-        "For example, replace imports like: "
-        "`from langchain_core.pydantic_v1 import BaseModel`\n"
-        "with: `from pydantic import BaseModel`\n"
-        "or the v1 compatibility namespace if you are working in a code base "
-        "that has not been fully upgraded to pydantic 2 yet. "
-        "\tfrom pydantic.v1 import BaseModel\n"
-    ),
-)

libs/core/langchain_core/rate_limiters.py

@@ -21,11 +21,8 @@ class BaseRateLimiter(abc.ABC):
     Current limitations:
 
     - Rate limiting information is not surfaced in tracing or callbacks. This means
-      that the total time it takes to invoke a chat model will encompass both
-      the time spent waiting for tokens and the time spent making the request.
-
-
-    !!! version-added "Added in version 0.2.24"
+        that the total time it takes to invoke a chat model will encompass both
+        the time spent waiting for tokens and the time spent making the request.
     """
 
     @abc.abstractmethod
@@ -33,18 +30,18 @@ class BaseRateLimiter(abc.ABC):
         """Attempt to acquire the necessary tokens for the rate limiter.
 
         This method blocks until the required tokens are available if `blocking`
-        is set to True.
+        is set to `True`.
 
-        If `blocking` is set to False, the method will immediately return the result
+        If `blocking` is set to `False`, the method will immediately return the result
         of the attempt to acquire the tokens.
 
         Args:
             blocking: If `True`, the method will block until the tokens are available.
                 If `False`, the method will return immediately with the result of
-                the attempt. Defaults to `True`.
+                the attempt.
 
         Returns:
-           True if the tokens were successfully acquired, False otherwise.
+            `True` if the tokens were successfully acquired, `False` otherwise.
         """
 
     @abc.abstractmethod
@@ -52,18 +49,18 @@ class BaseRateLimiter(abc.ABC):
         """Attempt to acquire the necessary tokens for the rate limiter.
 
         This method blocks until the required tokens are available if `blocking`
-        is set to True.
+        is set to `True`.
 
-        If `blocking` is set to False, the method will immediately return the result
+        If `blocking` is set to `False`, the method will immediately return the result
         of the attempt to acquire the tokens.
 
         Args:
             blocking: If `True`, the method will block until the tokens are available.
                 If `False`, the method will return immediately with the result of
-                the attempt. Defaults to `True`.
+                the attempt.
 
         Returns:
-           True if the tokens were successfully acquired, False otherwise.
+            `True` if the tokens were successfully acquired, `False` otherwise.
         """
 
 
@@ -84,7 +81,7 @@ class InMemoryRateLimiter(BaseRateLimiter):
     not enough tokens in the bucket, the request is blocked until there are
     enough tokens.
 
-    These *tokens* have NOTHING to do with LLM tokens. They are just
+    These tokens have nothing to do with LLM tokens. They are just
     a way to keep track of how many requests can be made at a given time.
 
     Current limitations:
@@ -108,9 +105,7 @@ class InMemoryRateLimiter(BaseRateLimiter):
 
         from langchain_anthropic import ChatAnthropic
 
-        model = ChatAnthropic(
-            model_name="claude-3-opus-20240229", rate_limiter=rate_limiter
-        )
+        model = ChatAnthropic(model_name="claude-sonnet-4-5", rate_limiter=rate_limiter)
 
         for _ in range(5):
             tic = time.time()
@@ -118,9 +113,6 @@ class InMemoryRateLimiter(BaseRateLimiter):
             toc = time.time()
             print(toc - tic)
         ```
-
-    !!! version-added "Added in version 0.2.24"
-
     """  # noqa: E501
 
     def __init__(
@@ -132,7 +124,7 @@ class InMemoryRateLimiter(BaseRateLimiter):
     ) -> None:
         """A rate limiter based on a token bucket.
 
-        These *tokens* have NOTHING to do with LLM tokens. They are just
+        These tokens have nothing to do with LLM tokens. They are just
         a way to keep track of how many requests can be made at a given time.
 
         This rate limiter is designed to work in a threaded environment.
@@ -145,11 +137,11 @@ class InMemoryRateLimiter(BaseRateLimiter):
         Args:
             requests_per_second: The number of tokens to add per second to the bucket.
                 The tokens represent "credit" that can be used to make requests.
-            check_every_n_seconds: check whether the tokens are available
+            check_every_n_seconds: Check whether the tokens are available
                 every this many seconds. Can be a float to represent
                 fractions of a second.
             max_bucket_size: The maximum number of tokens that can be in the bucket.
-                Must be at least 1. Used to prevent bursts of requests.
+                Must be at least `1`. Used to prevent bursts of requests.
         """
         # Number of requests that we can make per second.
         self.requests_per_second = requests_per_second
@@ -199,18 +191,18 @@ class InMemoryRateLimiter(BaseRateLimiter):
         """Attempt to acquire a token from the rate limiter.
 
         This method blocks until the required tokens are available if `blocking`
-        is set to True.
+        is set to `True`.
 
-        If `blocking` is set to False, the method will immediately return the result
+        If `blocking` is set to `False`, the method will immediately return the result
         of the attempt to acquire the tokens.
 
         Args:
             blocking: If `True`, the method will block until the tokens are available.
                 If `False`, the method will return immediately with the result of
-                the attempt. Defaults to `True`.
+                the attempt.
 
         Returns:
-           True if the tokens were successfully acquired, False otherwise.
+            `True` if the tokens were successfully acquired, `False` otherwise.
         """
         if not blocking:
             return self._consume()
@@ -223,18 +215,18 @@ class InMemoryRateLimiter(BaseRateLimiter):
         """Attempt to acquire a token from the rate limiter. Async version.
 
         This method blocks until the required tokens are available if `blocking`
-        is set to True.
+        is set to `True`.
 
-        If `blocking` is set to False, the method will immediately return the result
+        If `blocking` is set to `False`, the method will immediately return the result
         of the attempt to acquire the tokens.
 
         Args:
             blocking: If `True`, the method will block until the tokens are available.
                 If `False`, the method will return immediately with the result of
-                the attempt. Defaults to `True`.
+                the attempt.
 
         Returns:
-           True if the tokens were successfully acquired, False otherwise.
+            `True` if the tokens were successfully acquired, `False` otherwise.
         """
         if not blocking:
             return self._consume()

libs/core/langchain_core/retrievers.py

@@ -7,7 +7,6 @@ the backbone of a retriever, but there are other types of retrievers as well.
 
 from __future__ import annotations
 
-import warnings
 from abc import ABC, abstractmethod
 from inspect import signature
 from typing import TYPE_CHECKING, Any
@@ -15,8 +14,6 @@ from typing import TYPE_CHECKING, Any
 from pydantic import ConfigDict
 from typing_extensions import Self, TypedDict, override
 
-from langchain_core._api import deprecated
-from langchain_core.callbacks import Callbacks
 from langchain_core.callbacks.manager import AsyncCallbackManager, CallbackManager
 from langchain_core.documents import Document
 from langchain_core.runnables import (
@@ -73,45 +70,45 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
 
     Example: A retriever that returns the first 5 documents from a list of documents
 
-        ```python
-        from langchain_core.documents import Document
-        from langchain_core.retrievers import BaseRetriever
+    ```python
+    from langchain_core.documents import Document
+    from langchain_core.retrievers import BaseRetriever
 
-        class SimpleRetriever(BaseRetriever):
-            docs: list[Document]
-            k: int = 5
+    class SimpleRetriever(BaseRetriever):
+        docs: list[Document]
+        k: int = 5
 
-            def _get_relevant_documents(self, query: str) -> list[Document]:
-                \"\"\"Return the first k documents from the list of documents\"\"\"
-                return self.docs[:self.k]
+        def _get_relevant_documents(self, query: str) -> list[Document]:
+            \"\"\"Return the first k documents from the list of documents\"\"\"
+            return self.docs[:self.k]
 
-            async def _aget_relevant_documents(self, query: str) -> list[Document]:
-                \"\"\"(Optional) async native implementation.\"\"\"
-                return self.docs[:self.k]
-        ```
+        async def _aget_relevant_documents(self, query: str) -> list[Document]:
+            \"\"\"(Optional) async native implementation.\"\"\"
+            return self.docs[:self.k]
+    ```
 
     Example: A simple retriever based on a scikit-learn vectorizer
 
-        ```python
-        from sklearn.metrics.pairwise import cosine_similarity
+    ```python
+    from sklearn.metrics.pairwise import cosine_similarity
 
 
-        class TFIDFRetriever(BaseRetriever, BaseModel):
-            vectorizer: Any
-            docs: list[Document]
-            tfidf_array: Any
-            k: int = 4
+    class TFIDFRetriever(BaseRetriever, BaseModel):
+        vectorizer: Any
+        docs: list[Document]
+        tfidf_array: Any
+        k: int = 4
 
-            class Config:
-                arbitrary_types_allowed = True
+        class Config:
+            arbitrary_types_allowed = True
 
-            def _get_relevant_documents(self, query: str) -> list[Document]:
-                # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)
-                query_vec = self.vectorizer.transform([query])
-                # Op -- (n_docs,1) -- Cosine Sim with each doc
-                results = cosine_similarity(self.tfidf_array, query_vec).reshape((-1,))
-                return [self.docs[i] for i in results.argsort()[-self.k :][::-1]]
-        ```
+        def _get_relevant_documents(self, query: str) -> list[Document]:
+            # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)
+            query_vec = self.vectorizer.transform([query])
+            # Op -- (n_docs,1) -- Cosine Sim with each doc
+            results = cosine_similarity(self.tfidf_array, query_vec).reshape((-1,))
+            return [self.docs[i] for i in results.argsort()[-self.k :][::-1]]
+    ```
     """
 
     model_config = ConfigDict(
@@ -121,14 +118,14 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
     _new_arg_supported: bool = False
     _expects_other_args: bool = False
     tags: list[str] | None = None
-    """Optional list of tags associated with the retriever. Defaults to `None`.
+    """Optional list of tags associated with the retriever.
     These tags will be associated with each call to this retriever,
     and passed as arguments to the handlers defined in `callbacks`.
     You can use these to eg identify a specific instance of a retriever with its
     use case.
     """
     metadata: dict[str, Any] | None = None
-    """Optional metadata associated with the retriever. Defaults to `None`.
+    """Optional metadata associated with the retriever.
     This metadata will be associated with each call to this retriever,
     and passed as arguments to the handlers defined in `callbacks`.
     You can use these to eg identify a specific instance of a retriever with its
@@ -138,35 +135,6 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
     @override
     def __init_subclass__(cls, **kwargs: Any) -> None:
         super().__init_subclass__(**kwargs)
-        # Version upgrade for old retrievers that implemented the public
-        # methods directly.
-        if cls.get_relevant_documents != BaseRetriever.get_relevant_documents:
-            warnings.warn(
-                "Retrievers must implement abstract `_get_relevant_documents` method"
-                " instead of `get_relevant_documents`",
-                DeprecationWarning,
-                stacklevel=4,
-            )
-            swap = cls.get_relevant_documents
-            cls.get_relevant_documents = (  # type: ignore[method-assign]
-                BaseRetriever.get_relevant_documents
-            )
-            cls._get_relevant_documents = swap  # type: ignore[method-assign]
-        if (
-            hasattr(cls, "aget_relevant_documents")
-            and cls.aget_relevant_documents != BaseRetriever.aget_relevant_documents
-        ):
-            warnings.warn(
-                "Retrievers must implement abstract `_aget_relevant_documents` method"
-                " instead of `aget_relevant_documents`",
-                DeprecationWarning,
-                stacklevel=4,
-            )
-            aswap = cls.aget_relevant_documents
-            cls.aget_relevant_documents = (  # type: ignore[method-assign]
-                BaseRetriever.aget_relevant_documents
-            )
-            cls._aget_relevant_documents = aswap  # type: ignore[method-assign]
         parameters = signature(cls._get_relevant_documents).parameters
         cls._new_arg_supported = parameters.get("run_manager") is not None
         if (
@@ -207,7 +175,7 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
 
         Args:
             input: The query string.
-            config: Configuration for the retriever. Defaults to `None`.
+            config: Configuration for the retriever.
             **kwargs: Additional arguments to pass to the retriever.
 
         Returns:
@@ -268,7 +236,7 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
 
         Args:
             input: The query string.
-            config: Configuration for the retriever. Defaults to `None`.
+            config: Configuration for the retriever.
             **kwargs: Additional arguments to pass to the retriever.
 
         Returns:
@@ -348,91 +316,3 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
             query,
             run_manager=run_manager.get_sync(),
         )
-
-    @deprecated(since="0.1.46", alternative="invoke", removal="1.0")
-    def get_relevant_documents(
-        self,
-        query: str,
-        *,
-        callbacks: Callbacks = None,
-        tags: list[str] | None = None,
-        metadata: dict[str, Any] | None = None,
-        run_name: str | None = None,
-        **kwargs: Any,
-    ) -> list[Document]:
-        """Retrieve documents relevant to a query.
-
-        Users should favor using `.invoke` or `.batch` rather than
-        `get_relevant_documents directly`.
-
-        Args:
-            query: string to find relevant documents for.
-            callbacks: Callback manager or list of callbacks. Defaults to `None`.
-            tags: Optional list of tags associated with the retriever.
-                These tags will be associated with each call to this retriever,
-                and passed as arguments to the handlers defined in `callbacks`.
-                Defaults to `None`.
-            metadata: Optional metadata associated with the retriever.
-                This metadata will be associated with each call to this retriever,
-                and passed as arguments to the handlers defined in `callbacks`.
-                Defaults to `None`.
-            run_name: Optional name for the run. Defaults to `None`.
-            **kwargs: Additional arguments to pass to the retriever.
-
-        Returns:
-            List of relevant documents.
-        """
-        config: RunnableConfig = {}
-        if callbacks:
-            config["callbacks"] = callbacks
-        if tags:
-            config["tags"] = tags
-        if metadata:
-            config["metadata"] = metadata
-        if run_name:
-            config["run_name"] = run_name
-        return self.invoke(query, config, **kwargs)
-
-    @deprecated(since="0.1.46", alternative="ainvoke", removal="1.0")
-    async def aget_relevant_documents(
-        self,
-        query: str,
-        *,
-        callbacks: Callbacks = None,
-        tags: list[str] | None = None,
-        metadata: dict[str, Any] | None = None,
-        run_name: str | None = None,
-        **kwargs: Any,
-    ) -> list[Document]:
-        """Asynchronously get documents relevant to a query.
-
-        Users should favor using `.ainvoke` or `.abatch` rather than
-        `aget_relevant_documents directly`.
-
-        Args:
-            query: string to find relevant documents for.
-            callbacks: Callback manager or list of callbacks.
-            tags: Optional list of tags associated with the retriever.
-                These tags will be associated with each call to this retriever,
-                and passed as arguments to the handlers defined in `callbacks`.
-                Defaults to `None`.
-            metadata: Optional metadata associated with the retriever.
-                This metadata will be associated with each call to this retriever,
-                and passed as arguments to the handlers defined in `callbacks`.
-                Defaults to `None`.
-            run_name: Optional name for the run. Defaults to `None`.
-            **kwargs: Additional arguments to pass to the retriever.
-
-        Returns:
-            List of relevant documents.
-        """
-        config: RunnableConfig = {}
-        if callbacks:
-            config["callbacks"] = callbacks
-        if tags:
-            config["tags"] = tags
-        if metadata:
-            config["metadata"] = metadata
-        if run_name:
-            config["run_name"] = run_name
-        return await self.ainvoke(query, config, **kwargs)

libs/core/langchain_core/runnables/base.py

@@ -304,7 +304,7 @@ class Runnable(ABC, Generic[Input, Output]):
             TypeError: If the input type cannot be inferred.
         """
         # First loop through all parent classes and if any of them is
-        # a pydantic model, we will pick up the generic parameterization
+        # a Pydantic model, we will pick up the generic parameterization
         # from that model via the __pydantic_generic_metadata__ attribute.
         for base in self.__class__.mro():
             if hasattr(base, "__pydantic_generic_metadata__"):
@@ -312,7 +312,7 @@ class Runnable(ABC, Generic[Input, Output]):
                 if "args" in metadata and len(metadata["args"]) == 2:
                     return metadata["args"][0]
 
-        # If we didn't find a pydantic model in the parent classes,
+        # If we didn't find a Pydantic model in the parent classes,
         # then loop through __orig_bases__. This corresponds to
         # Runnables that are not pydantic models.
         for cls in self.__class__.__orig_bases__:  # type: ignore[attr-defined]
@@ -390,7 +390,7 @@ class Runnable(ABC, Generic[Input, Output]):
             self.get_name("Input"),
             root=root_type,
             # create model needs access to appropriate type annotations to be
-            # able to construct the pydantic model.
+            # able to construct the Pydantic model.
             # When we create the model, we pass information about the namespace
             # where the model is being created, so the type annotations can
             # be resolved correctly as well.
@@ -433,7 +433,7 @@ class Runnable(ABC, Generic[Input, Output]):
     def output_schema(self) -> type[BaseModel]:
         """Output schema.
 
-        The type of output this `Runnable` produces specified as a pydantic model.
+        The type of output this `Runnable` produces specified as a Pydantic model.
         """
         return self.get_output_schema()
 
@@ -468,7 +468,7 @@ class Runnable(ABC, Generic[Input, Output]):
             self.get_name("Output"),
             root=root_type,
             # create model needs access to appropriate type annotations to be
-            # able to construct the pydantic model.
+            # able to construct the Pydantic model.
             # When we create the model, we pass information about the namespace
             # where the model is being created, so the type annotations can
             # be resolved correctly as well.
@@ -776,11 +776,11 @@ class Runnable(ABC, Generic[Input, Output]):
             SystemMessagePromptTemplate.from_template("You are a nice assistant.")
             + "{question}"
         )
-        llm = FakeStreamingListLLM(responses=["foo-lish"])
+        model = FakeStreamingListLLM(responses=["foo-lish"])
 
-        chain: Runnable = prompt | llm | {"str": StrOutputParser()}
+        chain: Runnable = prompt | model | {"str": StrOutputParser()}
 
-        chain_with_assign = chain.assign(hello=itemgetter("str") | llm)
+        chain_with_assign = chain.assign(hello=itemgetter("str") | model)
 
         print(chain_with_assign.input_schema.model_json_schema())
         # {'title': 'PromptInput', 'type': 'object', 'properties':
@@ -821,7 +821,7 @@ class Runnable(ABC, Generic[Input, Output]):
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
                 do in parallel, and other keys. Please refer to the `RunnableConfig`
-                for more details. Defaults to `None`.
+                for more details.
 
         Returns:
             The output of the `Runnable`.
@@ -841,7 +841,7 @@ class Runnable(ABC, Generic[Input, Output]):
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
                 do in parallel, and other keys. Please refer to the `RunnableConfig`
-                for more details. Defaults to `None`.
+                for more details.
 
         Returns:
             The output of the `Runnable`.
@@ -860,7 +860,7 @@ class Runnable(ABC, Generic[Input, Output]):
 
         The default implementation of batch works well for IO bound runnables.
 
-        Subclasses should override this method if they can batch more efficiently;
+        Subclasses must override this method if they can batch more efficiently;
         e.g., if the underlying `Runnable` uses an API which supports a batch mode.
 
         Args:
@@ -869,9 +869,8 @@ class Runnable(ABC, Generic[Input, Output]):
                 standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work
                 to do in parallel, and other keys. Please refer to the
-                `RunnableConfig` for more details. Defaults to `None`.
+                `RunnableConfig` for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
-                Defaults to `False`.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
         Returns:
@@ -936,9 +935,8 @@ class Runnable(ABC, Generic[Input, Output]):
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
                 do in parallel, and other keys. Please refer to the `RunnableConfig`
-                for more details. Defaults to `None`.
+                for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
-                Defaults to `False`.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
         Yields:
@@ -994,7 +992,7 @@ class Runnable(ABC, Generic[Input, Output]):
 
         The default implementation of `batch` works well for IO bound runnables.
 
-        Subclasses should override this method if they can batch more efficiently;
+        Subclasses must override this method if they can batch more efficiently;
         e.g., if the underlying `Runnable` uses an API which supports a batch mode.
 
         Args:
@@ -1003,9 +1001,8 @@ class Runnable(ABC, Generic[Input, Output]):
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
                 do in parallel, and other keys. Please refer to the `RunnableConfig`
-                for more details. Defaults to `None`.
+                for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
-                Defaults to `False`.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
         Returns:
@@ -1067,9 +1064,8 @@ class Runnable(ABC, Generic[Input, Output]):
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
                 do in parallel, and other keys. Please refer to the `RunnableConfig`
-                for more details. Defaults to `None`.
+                for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
-                Defaults to `False`.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
         Yields:
@@ -1116,11 +1112,11 @@ class Runnable(ABC, Generic[Input, Output]):
     ) -> Iterator[Output]:
         """Default implementation of `stream`, which calls `invoke`.
 
-        Subclasses should override this method if they support streaming output.
+        Subclasses must override this method if they support streaming output.
 
         Args:
             input: The input to the `Runnable`.
-            config: The config to use for the `Runnable`. Defaults to `None`.
+            config: The config to use for the `Runnable`.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
         Yields:
@@ -1137,11 +1133,11 @@ class Runnable(ABC, Generic[Input, Output]):
     ) -> AsyncIterator[Output]:
         """Default implementation of `astream`, which calls `ainvoke`.
 
-        Subclasses should override this method if they support streaming output.
+        Subclasses must override this method if they support streaming output.
 
         Args:
             input: The input to the `Runnable`.
-            config: The config to use for the `Runnable`. Defaults to `None`.
+            config: The config to use for the `Runnable`.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
         Yields:
@@ -1273,22 +1269,20 @@ class Runnable(ABC, Generic[Input, Output]):
 
         A `StreamEvent` is a dictionary with the following schema:
 
-        - `event`: **str** - Event names are of the format:
+        - `event`: Event names are of the format:
             `on_[runnable_type]_(start|stream|end)`.
-        - `name`: **str** - The name of the `Runnable` that generated the event.
-        - `run_id`: **str** - randomly generated ID associated with the given
-            execution of the `Runnable` that emitted the event. A child `Runnable` that gets
-            invoked as part of the execution of a parent `Runnable` is assigned its own
-            unique ID.
-        - `parent_ids`: **list[str]** - The IDs of the parent runnables that generated
-            the event. The root `Runnable` will have an empty list. The order of the parent
-            IDs is from the root to the immediate parent. Only available for v2 version of
-            the API. The v1 version of the API will return an empty list.
-        - `tags`: **list[str] | None** - The tags of the `Runnable` that generated
-            the event.
-        - `metadata`: **dict[str, Any] | None** - The metadata of the `Runnable` that
-            generated the event.
-        - `data`: **dict[str, Any]**
+        - `name`: The name of the `Runnable` that generated the event.
+        - `run_id`: Randomly generated ID associated with the given execution of the
+            `Runnable` that emitted the event. A child `Runnable` that gets invoked as
+            part of the execution of a parent `Runnable` is assigned its own unique ID.
+        - `parent_ids`: The IDs of the parent runnables that generated the event. The
+            root `Runnable` will have an empty list. The order of the parent IDs is from
+            the root to the immediate parent. Only available for v2 version of the API.
+            The v1 version of the API will return an empty list.
+        - `tags`: The tags of the `Runnable` that generated the event.
+        - `metadata`: The metadata of the `Runnable` that generated the event.
+        - `data`: The data associated with the event. The contents of this field
+            depend on the type of event. See the table below for more details.
 
         Below is a table that illustrates some events that might be emitted by various
         chains. Metadata fields have been omitted from the table for brevity.
@@ -1297,39 +1291,23 @@ class Runnable(ABC, Generic[Input, Output]):
         !!! note
             This reference table is for the v2 version of the schema.
 
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | event                    | name             | chunk                               | input                                             | output                                              |
-        +==========================+==================+=====================================+===================================================+=====================================================+
-        | `on_chat_model_start`  | [model name]     |                                     | `{"messages": [[SystemMessage, HumanMessage]]}` |                                                     |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_chat_model_stream` | [model name]     | `AIMessageChunk(content="hello")` |                                                   |                                                     |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_chat_model_end`    | [model name]     |                                     | `{"messages": [[SystemMessage, HumanMessage]]}` | `AIMessageChunk(content="hello world")`           |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_llm_start`         | [model name]     |                                     | `{'input': 'hello'}`                            |                                                     |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_llm_stream`        | [model name]     | `'Hello' `                        |                                                   |                                                     |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_llm_end`           | [model name]     |                                     | `'Hello human!'`                                |                                                     |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_chain_start`       | format_docs      |                                     |                                                   |                                                     |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_chain_stream`      | format_docs      | `'hello world!, goodbye world!'`  |                                                   |                                                     |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_chain_end`         | format_docs      |                                     | `[Document(...)]`                               | `'hello world!, goodbye world!'`                  |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_tool_start`        | some_tool        |                                     | `{"x": 1, "y": "2"}`                            |                                                     |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_tool_end`          | some_tool        |                                     |                                                   | `{"x": 1, "y": "2"}`                              |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_retriever_start`   | [retriever name] |                                     | `{"query": "hello"}`                            |                                                     |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_retriever_end`     | [retriever name] |                                     | `{"query": "hello"}`                            | `[Document(...), ..]`                             |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_prompt_start`      | [template_name]  |                                     | `{"question": "hello"}`                         |                                                     |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
-        | `on_prompt_end`        | [template_name]  |                                     | `{"question": "hello"}`                         | `ChatPromptValue(messages: [SystemMessage, ...])` |
-        +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+
+        | event                  | name                 | chunk                               | input                                             | output                                              |
+        | ---------------------- | -------------------- | ----------------------------------- | ------------------------------------------------- | --------------------------------------------------- |
+        | `on_chat_model_start`  | `'[model name]'`     |                                     | `{"messages": [[SystemMessage, HumanMessage]]}`   |                                                     |
+        | `on_chat_model_stream` | `'[model name]'`     | `AIMessageChunk(content="hello")`   |                                                   |                                                     |
+        | `on_chat_model_end`    | `'[model name]'`     |                                     | `{"messages": [[SystemMessage, HumanMessage]]}`   | `AIMessageChunk(content="hello world")`             |
+        | `on_llm_start`         | `'[model name]'`     |                                     | `{'input': 'hello'}`                              |                                                     |
+        | `on_llm_stream`        | `'[model name]'`     | `'Hello' `                          |                                                   |                                                     |
+        | `on_llm_end`           | `'[model name]'`     |                                     | `'Hello human!'`                                  |                                                     |
+        | `on_chain_start`       | `'format_docs'`      |                                     |                                                   |                                                     |
+        | `on_chain_stream`      | `'format_docs'`      | `'hello world!, goodbye world!'`    |                                                   |                                                     |
+        | `on_chain_end`         | `'format_docs'`      |                                     | `[Document(...)]`                                 | `'hello world!, goodbye world!'`                    |
+        | `on_tool_start`        | `'some_tool'`        |                                     | `{"x": 1, "y": "2"}`                              |                                                     |
+        | `on_tool_end`          | `'some_tool'`        |                                     |                                                   | `{"x": 1, "y": "2"}`                                |
+        | `on_retriever_start`   | `'[retriever name]'` |                                     | `{"query": "hello"}`                              |                                                     |
+        | `on_retriever_end`     | `'[retriever name]'` |                                     | `{"query": "hello"}`                              | `[Document(...), ..]`                               |
+        | `on_prompt_start`      | `'[template_name]'`  |                                     | `{"question": "hello"}`                           |                                                     |
+        | `on_prompt_end`        | `'[template_name]'`  |                                     | `{"question": "hello"}`                           | `ChatPromptValue(messages: [SystemMessage, ...])`   |
 
         In addition to the standard events, users can also dispatch custom events (see example below).
 
@@ -1337,13 +1315,10 @@ class Runnable(ABC, Generic[Input, Output]):
 
         A custom event has following format:
 
-        +-----------+------+-----------------------------------------------------------------------------------------------------------+
-        | Attribute | Type | Description                                                                                               |
-        +===========+======+===========================================================================================================+
-        | name      | str  | A user defined name for the event.                                                                        |
-        +-----------+------+-----------------------------------------------------------------------------------------------------------+
-        | data      | Any  | The data associated with the event. This can be anything, though we suggest making it JSON serializable.  |
-        +-----------+------+-----------------------------------------------------------------------------------------------------------+
+        | Attribute   | Type   | Description                                                                                               |
+        | ----------- | ------ | --------------------------------------------------------------------------------------------------------- |
+        | `name`      | `str`  | A user defined name for the event.                                                                        |
+        | `data`      | `Any`  | The data associated with the event. This can be anything, though we suggest making it JSON serializable.  |
 
         Here are declarations associated with the standard events shown above:
 
@@ -1378,7 +1353,8 @@ class Runnable(ABC, Generic[Input, Output]):
         ).with_config({"run_name": "my_template", "tags": ["my_template"]})
         ```
 
-        Example:
+        For instance:
+
         ```python
         from langchain_core.runnables import RunnableLambda
 
@@ -1391,8 +1367,8 @@ class Runnable(ABC, Generic[Input, Output]):
 
         events = [event async for event in chain.astream_events("hello", version="v2")]
 
-        # will produce the following events (run_id, and parent_ids
-        # has been omitted for brevity):
+        # Will produce the following events
+        # (run_id, and parent_ids has been omitted for brevity):
         [
             {
                 "data": {"input": "hello"},
@@ -1447,7 +1423,7 @@ class Runnable(ABC, Generic[Input, Output]):
 
         async for event in slow_thing.astream_events("some_input", version="v2"):
             print(event)
-        ``
+        ```
 
         Args:
             input: The input to the `Runnable`.
@@ -1521,12 +1497,12 @@ class Runnable(ABC, Generic[Input, Output]):
 
         Default implementation of transform, which buffers input and calls `astream`.
 
-        Subclasses should override this method if they can start producing output while
+        Subclasses must override this method if they can start producing output while
         input is still being generated.
 
         Args:
             input: An iterator of inputs to the `Runnable`.
-            config: The config to use for the `Runnable`. Defaults to `None`.
+            config: The config to use for the `Runnable`.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
         Yields:
@@ -1566,12 +1542,12 @@ class Runnable(ABC, Generic[Input, Output]):
 
         Default implementation of atransform, which buffers input and calls `astream`.
 
-        Subclasses should override this method if they can start producing output while
+        Subclasses must override this method if they can start producing output while
         input is still being generated.
 
         Args:
             input: An async iterator of inputs to the `Runnable`.
-            config: The config to use for the `Runnable`. Defaults to `None`.
+            config: The config to use for the `Runnable`.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
         Yields:
@@ -1619,16 +1595,16 @@ class Runnable(ABC, Generic[Input, Output]):
             from langchain_ollama import ChatOllama
             from langchain_core.output_parsers import StrOutputParser
 
-            llm = ChatOllama(model="llama3.1")
+            model = ChatOllama(model="llama3.1")
 
             # Without bind
-            chain = llm | StrOutputParser()
+            chain = model | StrOutputParser()
 
             chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
             # Output is 'One two three four five.'
 
             # With bind
-            chain = llm.bind(stop=["three"]) | StrOutputParser()
+            chain = model.bind(stop=["three"]) | StrOutputParser()
 
             chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
             # Output is 'One two'
@@ -1682,11 +1658,11 @@ class Runnable(ABC, Generic[Input, Output]):
 
         Args:
             on_start: Called before the `Runnable` starts running, with the `Run`
-                object. Defaults to `None`.
+                object.
             on_end: Called after the `Runnable` finishes running, with the `Run`
-                object. Defaults to `None`.
+                object.
             on_error: Called if the `Runnable` throws an error, with the `Run`
-                object. Defaults to `None`.
+                object.
 
         Returns:
             A new `Runnable` with the listeners bound.
@@ -1750,11 +1726,11 @@ class Runnable(ABC, Generic[Input, Output]):
 
         Args:
             on_start: Called asynchronously before the `Runnable` starts running,
-                with the `Run` object. Defaults to `None`.
+                with the `Run` object.
             on_end: Called asynchronously after the `Runnable` finishes running,
-                with the `Run` object. Defaults to `None`.
+                with the `Run` object.
             on_error: Called asynchronously if the `Runnable` throws an error,
-                with the `Run` object. Defaults to `None`.
+                with the `Run` object.
 
         Returns:
             A new `Runnable` with the listeners bound.
@@ -1833,11 +1809,11 @@ class Runnable(ABC, Generic[Input, Output]):
         """Bind input and output types to a `Runnable`, returning a new `Runnable`.
 
         Args:
-            input_type: The input type to bind to the `Runnable`. Defaults to `None`.
-            output_type: The output type to bind to the `Runnable`. Defaults to `None`.
+            input_type: The input type to bind to the `Runnable`.
+            output_type: The output type to bind to the `Runnable`.
 
         Returns:
-            A new Runnable with the types bound.
+            A new `Runnable` with the types bound.
         """
         return RunnableBinding(
             bound=self,
@@ -1858,14 +1834,13 @@ class Runnable(ABC, Generic[Input, Output]):
 
         Args:
             retry_if_exception_type: A tuple of exception types to retry on.
-                Defaults to (Exception,).
             wait_exponential_jitter: Whether to add jitter to the wait
-                time between retries. Defaults to `True`.
+                time between retries.
             stop_after_attempt: The maximum number of attempts to make before
-                giving up. Defaults to 3.
+                giving up.
             exponential_jitter_params: Parameters for
                 `tenacity.wait_exponential_jitter`. Namely: `initial`, `max`,
-                `exp_base`, and `jitter` (all float values).
+                `exp_base`, and `jitter` (all `float` values).
 
         Returns:
             A new Runnable that retries the original Runnable on exceptions.
@@ -1950,16 +1925,15 @@ class Runnable(ABC, Generic[Input, Output]):
             fallbacks: A sequence of runnables to try if the original `Runnable`
                 fails.
             exceptions_to_handle: A tuple of exception types to handle.
-                Defaults to `(Exception,)`.
-            exception_key: If string is specified then handled exceptions will be passed
-                to fallbacks as part of the input under the specified key.
+            exception_key: If `string` is specified then handled exceptions will be
+                passed to fallbacks as part of the input under the specified key.
                 If `None`, exceptions will not be passed to fallbacks.
                 If used, the base `Runnable` and its fallbacks must accept a
-                dictionary as input. Defaults to `None`.
+                dictionary as input.
 
         Returns:
             A new `Runnable` that will try the original `Runnable`, and then each
-            Fallback in order, upon failures.
+                Fallback in order, upon failures.
 
         Example:
             ```python
@@ -1987,16 +1961,15 @@ class Runnable(ABC, Generic[Input, Output]):
             fallbacks: A sequence of runnables to try if the original `Runnable`
                 fails.
             exceptions_to_handle: A tuple of exception types to handle.
-            exception_key: If string is specified then handled exceptions will be passed
-                to fallbacks as part of the input under the specified key.
+            exception_key: If `string` is specified then handled exceptions will be
+                passed to fallbacks as part of the input under the specified key.
                 If `None`, exceptions will not be passed to fallbacks.
                 If used, the base `Runnable` and its fallbacks must accept a
                 dictionary as input.
 
         Returns:
             A new `Runnable` that will try the original `Runnable`, and then each
-            Fallback in order, upon failures.
-
+                Fallback in order, upon failures.
         """
         # Import locally to prevent circular import
         from langchain_core.runnables.fallbacks import (  # noqa: PLC0415
@@ -2462,10 +2435,10 @@ class Runnable(ABC, Generic[Input, Output]):
         pass `arg_types` to just specify the required arguments and their types.
 
         Args:
-            args_schema: The schema for the tool. Defaults to `None`.
-            name: The name of the tool. Defaults to `None`.
-            description: The description of the tool. Defaults to `None`.
-            arg_types: A dictionary of argument names to types. Defaults to `None`.
+            args_schema: The schema for the tool.
+            name: The name of the tool.
+            description: The description of the tool.
+            arg_types: A dictionary of argument names to types.
 
         Returns:
             A `BaseTool` instance.
@@ -2546,9 +2519,6 @@ class Runnable(ABC, Generic[Input, Output]):
         as_tool = runnable.as_tool()
         as_tool.invoke("b")
         ```
-
-        !!! version-added "Added in version 0.2.14"
-
         """
         # Avoid circular import
         from langchain_core.tools import convert_runnable_to_tool  # noqa: PLC0415
@@ -2654,9 +2624,7 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]):
             which: The `ConfigurableField` instance that will be used to select the
                 alternative.
             default_key: The default key to use if no alternative is selected.
-                Defaults to `'default'`.
             prefix_keys: Whether to prefix the keys with the `ConfigurableField` id.
-                Defaults to `False`.
             **kwargs: A dictionary of keys to `Runnable` instances or callables that
                 return `Runnable` instances.
 
@@ -2888,10 +2856,10 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
 
         Args:
             steps: The steps to include in the sequence.
-            name: The name of the `Runnable`. Defaults to `None`.
-            first: The first `Runnable` in the sequence. Defaults to `None`.
-            middle: The middle `Runnable` objects in the sequence. Defaults to `None`.
-            last: The last Runnable in the sequence. Defaults to `None`.
+            name: The name of the `Runnable`.
+            first: The first `Runnable` in the sequence.
+            middle: The middle `Runnable` objects in the sequence.
+            last: The last Runnable in the sequence.
 
         Raises:
             ValueError: If the sequence has less than 2 steps.
@@ -2917,7 +2885,7 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
     @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", "schema", "runnable"]`
@@ -2960,7 +2928,7 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
         """Get the input schema of the `Runnable`.
 
         Args:
-            config: The config to use. Defaults to `None`.
+            config: The config to use.
 
         Returns:
             The input schema of the `Runnable`.
@@ -2975,7 +2943,7 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
         """Get the output schema of the `Runnable`.
 
         Args:
-            config: The config to use. Defaults to `None`.
+            config: The config to use.
 
         Returns:
             The output schema of the `Runnable`.
@@ -3002,7 +2970,7 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
         """Get the graph representation of the `Runnable`.
 
         Args:
-            config: The config to use. Defaults to `None`.
+            config: The config to use.
 
         Returns:
             The graph representation of the `Runnable`.
@@ -3629,7 +3597,7 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
         """Create a `RunnableParallel`.
 
         Args:
-            steps__: The steps to include. Defaults to `None`.
+            steps__: The steps to include.
             **kwargs: Additional steps to include.
 
         """
@@ -3648,7 +3616,7 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
     @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", "schema", "runnable"]`
@@ -3664,8 +3632,8 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
         """Get the name of the `Runnable`.
 
         Args:
-            suffix: The suffix to use. Defaults to `None`.
-            name: The name to use. Defaults to `None`.
+            suffix: The suffix to use.
+            name: The name to use.
 
         Returns:
             The name of the `Runnable`.
@@ -3689,7 +3657,7 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
         """Get the input schema of the `Runnable`.
 
         Args:
-            config: The config to use. Defaults to `None`.
+            config: The config to use.
 
         Returns:
             The input schema of the `Runnable`.
@@ -3720,7 +3688,7 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
         """Get the output schema of the `Runnable`.
 
         Args:
-            config: The config to use. Defaults to `None`.
+            config: The config to use.
 
         Returns:
             The output schema of the `Runnable`.
@@ -3747,7 +3715,7 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
         """Get the graph representation of the `Runnable`.
 
         Args:
-            config: The config to use. Defaults to `None`.
+            config: The config to use.
 
         Returns:
             The graph representation of the `Runnable`.
@@ -4157,8 +4125,8 @@ class RunnableGenerator(Runnable[Input, Output]):
 
         Args:
             transform: The transform function.
-            atransform: The async transform function. Defaults to `None`.
-            name: The name of the `Runnable`. Defaults to `None`.
+            atransform: The async transform function.
+            name: The name of the `Runnable`.
 
         Raises:
             TypeError: If the transform is not a generator function.
@@ -4435,8 +4403,8 @@ class RunnableLambda(Runnable[Input, Output]):
         Args:
             func: Either sync or async callable
             afunc: An async callable that takes an input and returns an output.
-                Defaults to `None`.
-            name: The name of the `Runnable`. Defaults to `None`.
+
+            name: The name of the `Runnable`.
 
         Raises:
             TypeError: If the `func` is not a callable type.
@@ -4493,10 +4461,10 @@ class RunnableLambda(Runnable[Input, Output]):
 
     @override
     def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
-        """The pydantic schema for the input to this `Runnable`.
+        """The Pydantic schema for the input to this `Runnable`.
 
         Args:
-            config: The config to use. Defaults to `None`.
+            config: The config to use.
 
         Returns:
             The input schema for this `Runnable`.
@@ -4830,7 +4798,7 @@ class RunnableLambda(Runnable[Input, Output]):
 
         Args:
             input: The input to this `Runnable`.
-            config: The config to use. Defaults to `None`.
+            config: The config to use.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -4861,7 +4829,7 @@ class RunnableLambda(Runnable[Input, Output]):
 
         Args:
             input: The input to this `Runnable`.
-            config: The config to use. Defaults to `None`.
+            config: The config to use.
             **kwargs: Additional keyword arguments.
 
         Returns:
@@ -5127,7 +5095,7 @@ class RunnableEachBase(RunnableSerializable[list[Input], list[Output]]):
                 None,
             ),
             # create model needs access to appropriate type annotations to be
-            # able to construct the pydantic model.
+            # able to construct the Pydantic model.
             # When we create the model, we pass information about the namespace
             # where the model is being created, so the type annotations can
             # be resolved correctly as well.
@@ -5150,7 +5118,7 @@ class RunnableEachBase(RunnableSerializable[list[Input], list[Output]]):
             self.get_name("Output"),
             root=list[schema],  # type: ignore[valid-type]
             # create model needs access to appropriate type annotations to be
-            # able to construct the pydantic model.
+            # able to construct the Pydantic model.
             # When we create the model, we pass information about the namespace
             # where the model is being created, so the type annotations can
             # be resolved correctly as well.
@@ -5177,7 +5145,7 @@ class RunnableEachBase(RunnableSerializable[list[Input], list[Output]]):
     @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", "schema", "runnable"]`
@@ -5303,11 +5271,11 @@ class RunnableEach(RunnableEachBase[Input, Output]):
 
         Args:
             on_start: Called before the `Runnable` starts running, with the `Run`
-                object. Defaults to `None`.
+                object.
             on_end: Called after the `Runnable` finishes running, with the `Run`
-                object. Defaults to `None`.
+                object.
             on_error: Called if the `Runnable` throws an error, with the `Run`
-                object. Defaults to `None`.
+                object.
 
         Returns:
             A new `Runnable` with the listeners bound.
@@ -5336,11 +5304,11 @@ class RunnableEach(RunnableEachBase[Input, Output]):
 
         Args:
             on_start: Called asynchronously before the `Runnable` starts running,
-                with the `Run` object. Defaults to `None`.
+                with the `Run` object.
             on_end: Called asynchronously after the `Runnable` finishes running,
-                with the `Run` object. Defaults to `None`.
+                with the `Run` object.
             on_error: Called asynchronously if the `Runnable` throws an error,
-                with the `Run` object. Defaults to `None`.
+                with the `Run` object.
 
         Returns:
             A new `Runnable` with the listeners bound.
@@ -5387,13 +5355,13 @@ class RunnableBindingBase(RunnableSerializable[Input, Output]):  # type: ignore[
     custom_input_type: Any | None = None
     """Override the input type of the underlying `Runnable` with a custom type.
 
-    The type can be a pydantic model, or a type annotation (e.g., `list[str]`).
+    The type can be a Pydantic model, or a type annotation (e.g., `list[str]`).
     """
     # Union[Type[Output], BaseModel] + things like list[str]
     custom_output_type: Any | None = None
     """Override the output type of the underlying `Runnable` with a custom type.
 
-    The type can be a pydantic model, or a type annotation (e.g., `list[str]`).
+    The type can be a Pydantic model, or a type annotation (e.g., `list[str]`).
     """
 
     model_config = ConfigDict(
@@ -5420,16 +5388,16 @@ class RunnableBindingBase(RunnableSerializable[Input, Output]):  # type: ignore[
             kwargs: optional kwargs to pass to the underlying `Runnable`, when running
                 the underlying `Runnable` (e.g., via `invoke`, `batch`,
                 `transform`, or `stream` or async variants)
-                Defaults to `None`.
+
             config: optional config to bind to the underlying `Runnable`.
-                Defaults to `None`.
+
             config_factories: optional list of config factories to apply to the
                 config before binding to the underlying `Runnable`.
-                Defaults to `None`.
+
             custom_input_type: Specify to override the input type of the underlying
-                `Runnable` with a custom type. Defaults to `None`.
+                `Runnable` with a custom type.
             custom_output_type: Specify to override the output type of the underlying
-                `Runnable` with a custom type. Defaults to `None`.
+                `Runnable` with a custom type.
             **other_kwargs: Unpacked into the base class.
         """
         super().__init__(
@@ -5500,7 +5468,7 @@ class RunnableBindingBase(RunnableSerializable[Input, Output]):  # type: ignore[
     @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", "schema", "runnable"]`
@@ -5782,7 +5750,7 @@ class RunnableBinding(RunnableBindingBase[Input, Output]):  # type: ignore[no-re
     `bind`: Bind kwargs to pass to the underlying `Runnable` when running it.
 
         ```python
-        # Create a Runnable binding that invokes the ChatModel with the
+        # Create a Runnable binding that invokes the chat model with the
         # additional kwarg `stop=['-']` when running it.
         from langchain_community.chat_models import ChatOpenAI
 
@@ -5866,11 +5834,11 @@ class RunnableBinding(RunnableBindingBase[Input, Output]):  # type: ignore[no-re
 
         Args:
             on_start: Called before the `Runnable` starts running, with the `Run`
-                object. Defaults to `None`.
+                object.
             on_end: Called after the `Runnable` finishes running, with the `Run`
-                object. Defaults to `None`.
+                object.
             on_error: Called if the `Runnable` throws an error, with the `Run`
-                object. Defaults to `None`.
+                object.
 
         Returns:
             A new `Runnable` with the listeners bound.
@@ -6077,10 +6045,10 @@ def chain(
         @chain
         def my_func(fields):
             prompt = PromptTemplate("Hello, {name}!")
-            llm = OpenAI()
+            model = OpenAI()
             formatted = prompt.invoke(**fields)
 
-            for chunk in llm.stream(formatted):
+            for chunk in model.stream(formatted):
                 yield chunk
         ```
     """

libs/core/langchain_core/runnables/branch.py

@@ -40,13 +40,13 @@ from langchain_core.runnables.utils import (
 class RunnableBranch(RunnableSerializable[Input, Output]):
     """Runnable that selects which branch to run based on a condition.
 
-    The Runnable is initialized with a list of (condition, Runnable) pairs and
+    The Runnable is initialized with a list of `(condition, Runnable)` pairs and
     a default branch.
 
     When operating on an input, the first condition that evaluates to True is
-    selected, and the corresponding Runnable is run on the input.
+    selected, and the corresponding `Runnable` is run on the input.
 
-    If no condition evaluates to True, the default branch is run on the input.
+    If no condition evaluates to `True`, the default branch is run on the input.
 
     Examples:
         ```python
@@ -65,9 +65,9 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
     """
 
     branches: Sequence[tuple[Runnable[Input, bool], Runnable[Input, Output]]]
-    """A list of (condition, Runnable) pairs."""
+    """A list of `(condition, Runnable)` pairs."""
     default: Runnable[Input, Output]
-    """A Runnable to run if no condition is met."""
+    """A `Runnable` to run if no condition is met."""
 
     def __init__(
         self,
@@ -79,15 +79,15 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
         ]
         | RunnableLike,
     ) -> None:
-        """A Runnable that runs one of two branches based on a condition.
+        """A `Runnable` that runs one of two branches based on a condition.
 
         Args:
-            *branches: A list of (condition, Runnable) pairs.
-                Defaults a Runnable to run if no condition is met.
+            *branches: A list of `(condition, Runnable)` pairs.
+                Defaults a `Runnable` to run if no condition is met.
 
         Raises:
             ValueError: If the number of branches is less than 2.
-            TypeError: If the default branch is not Runnable, Callable or Mapping.
+            TypeError: If the default branch is not `Runnable`, `Callable` or `Mapping`.
             TypeError: If a branch is not a tuple or list.
             ValueError: If a branch is not of length 2.
         """
@@ -146,7 +146,7 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
     @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", "schema", "runnable"]`
@@ -191,7 +191,7 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
 
         Args:
             input: The input to the Runnable.
-            config: The configuration for the Runnable. Defaults to `None`.
+            config: The configuration for the Runnable.
             **kwargs: Additional keyword arguments to pass to the Runnable.
 
         Returns:
@@ -301,7 +301,7 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
 
         Args:
             input: The input to the Runnable.
-            config: The configuration for the Runnable. Defaults to `None`.
+            config: The configuration for the Runnable.
             **kwargs: Additional keyword arguments to pass to the Runnable.
 
         Yields:
@@ -385,7 +385,7 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
 
         Args:
             input: The input to the Runnable.
-            config: The configuration for the Runnable. Defaults to `None`.
+            config: The configuration for the Runnable.
             **kwargs: Additional keyword arguments to pass to the Runnable.
 
         Yields:

libs/core/langchain_core/runnables/config.py

@@ -75,26 +75,26 @@ class RunnableConfig(TypedDict, total=False):
     max_concurrency: int | None
     """
     Maximum number of parallel calls to make. If not provided, defaults to
-    ThreadPoolExecutor's default.
+    `ThreadPoolExecutor`'s default.
     """
 
     recursion_limit: int
     """
-    Maximum number of times a call can recurse. If not provided, defaults to 25.
+    Maximum number of times a call can recurse. If not provided, defaults to `25`.
     """
 
     configurable: dict[str, Any]
     """
-    Runtime values for attributes previously made configurable on this Runnable,
-    or sub-Runnables, through .configurable_fields() or .configurable_alternatives().
-    Check .output_schema() for a description of the attributes that have been made
+    Runtime values for attributes previously made configurable on this `Runnable`,
+    or sub-Runnables, through `configurable_fields` or `configurable_alternatives`.
+    Check `output_schema` for a description of the attributes that have been made
     configurable.
     """
 
     run_id: uuid.UUID | None
     """
     Unique identifier for the tracer run for this call. If not provided, a new UUID
-        will be generated.
+    will be generated.
     """
 
 
@@ -193,7 +193,7 @@ def ensure_config(config: RunnableConfig | None = None) -> RunnableConfig:
     """Ensure that a config is a dict with all keys present.
 
     Args:
-        config: The config to ensure. Defaults to `None`.
+        config: The config to ensure.
 
     Returns:
         The ensured config.
@@ -412,7 +412,7 @@ def call_func_with_variable_args(
         func: The function to call.
         input: The input to the function.
         config: The config to pass to the function.
-        run_manager: The run manager to pass to the function. Defaults to `None`.
+        run_manager: The run manager to pass to the function.
         **kwargs: The keyword arguments to pass to the function.
 
     Returns:
@@ -446,7 +446,7 @@ def acall_func_with_variable_args(
         func: The function to call.
         input: The input to the function.
         config: The config to pass to the function.
-        run_manager: The run manager to pass to the function. Defaults to `None`.
+        run_manager: The run manager to pass to the function.
         **kwargs: The keyword arguments to pass to the function.
 
     Returns:
@@ -527,16 +527,15 @@ class ContextThreadPoolExecutor(ThreadPoolExecutor):
         self,
         fn: Callable[..., T],
         *iterables: Iterable[Any],
-        timeout: float | None = None,
-        chunksize: int = 1,
+        **kwargs: Any,
     ) -> Iterator[T]:
         """Map a function to multiple iterables.
 
         Args:
             fn: The function to map.
             *iterables: The iterables to map over.
-            timeout: The timeout for the map. Defaults to `None`.
-            chunksize: The chunksize for the map. Defaults to 1.
+            timeout: The timeout for the map.
+            chunksize: The chunksize for the map.
 
         Returns:
             The iterator for the mapped function.
@@ -549,8 +548,7 @@ class ContextThreadPoolExecutor(ThreadPoolExecutor):
         return super().map(
             _wrapped_fn,
             *iterables,
-            timeout=timeout,
-            chunksize=chunksize,
+            **kwargs,
         )
 
 

libs/core/langchain_core/runnables/configurable.py

@@ -72,7 +72,7 @@ class DynamicRunnable(RunnableSerializable[Input, Output]):
     @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", "schema", "runnable"]`
@@ -123,7 +123,7 @@ class DynamicRunnable(RunnableSerializable[Input, Output]):
         """Prepare the Runnable for invocation.
 
         Args:
-            config: The configuration to use. Defaults to `None`.
+            config: The configuration to use.
 
         Returns:
             The prepared Runnable and configuration.
@@ -540,7 +540,7 @@ class RunnableConfigurableAlternatives(DynamicRunnable[Input, Output]):
     """The alternatives to choose from."""
 
     default_key: str = "default"
-    """The enum value to use for the default option. Defaults to `'default'`."""
+    """The enum value to use for the default option."""
 
     prefix_keys: bool
     """Whether to prefix configurable fields of each alternative with a namespace