release(model-profiles): 0.0.2 (#33767)

styling in `base.py`, content updates in
`libs/core/langchain_core/tools/convert.py`

.github/scripts/check_diff.py

@@ -30,6 +30,7 @@ LANGCHAIN_DIRS = [
     "libs/text-splitters",
     "libs/langchain",
     "libs/langchain_v1",
+    "libs/model-profiles",
 ]
 
 # When set to True, we are ignoring core dependents
@@ -130,29 +131,20 @@ 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"}:
         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 +298,9 @@ 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

@@ -77,7 +77,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v5
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -149,8 +149,8 @@ jobs:
             fi
           fi
 
-          # if PREV_TAG is empty, let it be empty
-          if [ -z "$PREV_TAG" ]; then
+          # if PREV_TAG is empty or came out to 0.0.0, let it be empty
+          if [ -z "$PREV_TAG" ] || [ "$PREV_TAG" = "$PKG_NAME==0.0.0" ]; then
             echo "No previous tag found - first release"
           else
             # confirm prev-tag actually exists in git repo with git tag
@@ -179,8 +179,8 @@ jobs:
           PREV_TAG: ${{ steps.check-tags.outputs.prev-tag }}
         run: |
           PREAMBLE="Changes since $PREV_TAG"
-          # if PREV_TAG is empty, then we are releasing the first version
-          if [ -z "$PREV_TAG" ]; then
+          # if PREV_TAG is empty or 0.0.0, then we are releasing the first version
+          if [ -z "$PREV_TAG" ] || [ "$PREV_TAG" = "$PKG_NAME==0.0.0" ]; then
             PREAMBLE="Initial release"
             PREV_TAG=$(git rev-list --max-parents=0 HEAD)
           fi
@@ -208,7 +208,7 @@ jobs:
     steps:
       - uses: actions/checkout@v5
 
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v6
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -258,7 +258,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v6
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -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 }}
@@ -428,7 +428,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v6
         if: startsWith(inputs.working-directory, 'libs/core')
         with:
           name: dist
@@ -442,7 +442,7 @@ jobs:
             git ls-remote --tags origin "langchain-${{ matrix.partner }}*" \
             | awk '{print $2}' \
             | sed 's|refs/tags/||' \
-            | grep -E '[0-9]+\.[0-9]+\.[0-9]+([a-zA-Z]+[0-9]+)?$' \
+            | grep -E '[0-9]+\.[0-9]+\.[0-9]+$' \
             | sort -Vr \
             | head -n 1
           )"
@@ -497,7 +497,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v6
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -537,7 +537,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v6
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/

.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@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/pr_lint.yml

@@ -27,10 +27,10 @@
 #   * 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
+#   xai, infra, deps
 #
 # Rules:
 #   1. The 'Type' must start with a lowercase letter.
@@ -80,7 +80,8 @@ jobs:
             cli
             langchain
             langchain_v1
-            langchain_legacy
+            langchain-classic
+            model-profiles
             standard-tests
             text-splitters
             docs

.gitignore

@@ -1,6 +1,8 @@
 .vs/
 .claude/
 .idea/
+#Emacs backup
+*~
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

.mcp.json

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

AGENTS.md

@@ -163,9 +163,11 @@ def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
 **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

@@ -163,9 +163,11 @@ def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
 **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">
@@ -31,17 +34,19 @@
   </a>
 </p>
 
-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.
+LangChain is a framework for building agents and 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 langchain
 ```
 
+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.
+
 ---
 
 **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://docs.langchain.com/oss/python/langgraph/overview), our framework for building controllable agent workflows.
+**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
 
 > [!NOTE]
 > Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
@@ -55,20 +60,18 @@ Use LangChain for:
 - **Real-time data augmentation**. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
 - **Model interoperability**. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
 
-## LangChain’s ecosystem
+## LangChain ecosystem
 
 While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
 
 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://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).
+- [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.
+- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) - 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 [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
 
 ## Additional resources
 
-- [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.
+- [API Reference](https://reference.langchain.com/python): Detailed reference on navigating base packages and integrations for LangChain.
+- [Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview): List of LangChain integrations, including chat & embedding models, tools & toolkits, and more
+- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview): Learn how to contribute to LangChain and find good first issues.

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/docs/chat.ipynb

@@ -1,264 +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",
-    "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"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

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

@@ -1,238 +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",
-    "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"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

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": 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"
-  }
- },
- "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.
 

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/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" },
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     { 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 = [
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     { 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 = [
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-    { 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" },
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@@ -588,6 +610,62 @@ typing = [
     { name = "types-requests", specifier = ">=2.31.0.20240218,<3.0.0.0" },
 ]
 
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+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "langchain-core" },
+    { name = "langgraph-checkpoint" },
+    { name = "langgraph-prebuilt" },
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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

@@ -5,12 +5,10 @@
 
 !!! warning
     New agents should be built using the
-    [langgraph library](https://github.com/langchain-ai/langgraph), which provides a
+    [`langchain` library](https://pypi.org/project/langchain/), which provides a
     simpler and more flexible way to define agents.
 
-    Please see the
-    [migration guide](https://python.langchain.com/docs/how_to/migrate_agent/) for
-    information on how to migrate existing agents to modern langgraph agents.
+    See docs on [building agents](https://docs.langchain.com/oss/python/langchain/agents).
 
 Agents use language models to choose a sequence of actions to take.
 
@@ -84,7 +82,7 @@ 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 +110,7 @@ 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 +159,7 @@ 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.
 
-!!! warning
-    Beta Feature!
+Distinct from provider-based [prompt caching](https://docs.langchain.com/oss/python/langchain/models#prompt-caching).
 
-**Cache** provides an optional caching layer for LLMs.
+!!! warning "Beta feature"
+    This is a beta feature. Please be wary of deploying experimental code to production
+    unless you've taken appropriate precautions.
 
-Cache is useful for two reasons:
+A 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.
@@ -47,43 +46,46 @@ 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).
+        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
                 (e.g., model name, temperature, stop tokens, max tokens, etc.).
-                These invocation parameters are serialized into a string
-                representation.
+
+                These invocation parameters are serialized into a string 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).
         """
 
@@ -92,45 +94,49 @@ 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).
+        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
                 (e.g., model name, temperature, stop tokens, max tokens, etc.).
+
                 These invocation parameters are serialized into a string
                 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 +156,9 @@ 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 +167,28 @@ 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 +201,30 @@ 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

@@ -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

@@ -1566,9 +1566,6 @@ class CallbackManager(BaseCallbackManager):
 
         Raises:
             ValueError: If additional keyword arguments are passed.
-
-        !!! version-added "Added in version 0.2.14"
-
         """
         if not self.handlers:
             return
@@ -2042,8 +2039,6 @@ class AsyncCallbackManager(BaseCallbackManager):
 
         Raises:
             ValueError: If additional keyword arguments are passed.
-
-        !!! version-added "Added in version 0.2.14"
         """
         if not self.handlers:
             return
@@ -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

@@ -104,7 +104,7 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         Args:
             text: The text to print.
             color: The color to use for the text.
-            end: The end character to use. Defaults to "".
+            end: The end character to use.
             **kwargs: Additional keyword arguments.
         """
         print_text(text, color=color or self.color, end=end)

libs/core/langchain_core/callbacks/usage.py

@@ -24,7 +24,7 @@ class UsageMetadataCallbackHandler(BaseCallbackHandler):
         from langchain_core.callbacks import UsageMetadataCallbackHandler
 
         llm_1 = init_chat_model(model="openai:gpt-4o-mini")
-        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-latest")
+        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-20241022")
 
         callback = UsageMetadataCallbackHandler()
         result_1 = llm_1.invoke("Hello", config={"callbacks": [callback]})
@@ -109,7 +109,7 @@ def get_usage_metadata_callback(
         from langchain_core.callbacks import get_usage_metadata_callback
 
         llm_1 = init_chat_model(model="openai:gpt-4o-mini")
-        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-latest")
+        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-20241022")
 
         with get_usage_metadata_callback() as cb:
             llm_1.invoke("Hello")

libs/core/langchain_core/chat_history.py

@@ -121,7 +121,7 @@ class BaseChatMessageHistory(ABC):
         This method may be deprecated in a future release.
 
         Args:
-            message: The human message to add to the store.
+            message: The `HumanMessage` to add to the store.
         """
         if isinstance(message, HumanMessage):
             self.add_message(message)
@@ -129,7 +129,7 @@ class BaseChatMessageHistory(ABC):
             self.add_message(HumanMessage(content=message))
 
     def add_ai_message(self, message: AIMessage | str) -> None:
-        """Convenience method for adding an AI message string to the store.
+        """Convenience method for adding an `AIMessage` string to the store.
 
         !!! note
             This is a convenience method. Code should favor the bulk `add_messages`
@@ -138,7 +138,7 @@ class BaseChatMessageHistory(ABC):
         This method may be deprecated in a future release.
 
         Args:
-            message: The AI message to add.
+            message: The `AIMessage` to add.
         """
         if isinstance(message, AIMessage):
             self.add_message(message)
@@ -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
@@ -173,7 +173,7 @@ class BaseChatMessageHistory(ABC):
         in an efficient manner to avoid unnecessary round-trips to the underlying store.
 
         Args:
-            messages: A sequence of BaseMessage objects to store.
+            messages: A sequence of `BaseMessage` objects to store.
         """
         for message in messages:
             self.add_message(message)
@@ -182,7 +182,7 @@ class BaseChatMessageHistory(ABC):
         """Async add a list of messages.
 
         Args:
-            messages: A sequence of BaseMessage objects to store.
+            messages: A sequence of `BaseMessage` objects to store.
         """
         await run_in_executor(None, self.add_messages, messages)
 

libs/core/langchain_core/document_loaders/base.py

@@ -27,7 +27,7 @@ class BaseLoader(ABC):  # noqa: B024
     """Interface for Document Loader.
 
     Implementations should implement the lazy-loading method using generators
-    to avoid loading all Documents into memory at once.
+    to avoid loading all documents into memory at once.
 
     `load` is provided just for user convenience and should not be overridden.
     """
@@ -35,38 +35,40 @@ 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 `Document` and split into chunks. Chunks are returned as `Document`.
 
-        Do not override this method. It should be considered to be deprecated!
+        !!! danger
+
+            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 +88,10 @@ 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 +99,10 @@ 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 +117,7 @@ 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 +130,25 @@ 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 list of `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/blob_loaders.py

@@ -28,7 +28,7 @@ class BlobLoader(ABC):
     def yield_blobs(
         self,
     ) -> Iterable[Blob]:
-        """A lazy loader for raw data represented by LangChain's Blob object.
+        """A lazy loader for raw data represented by LangChain's `Blob` object.
 
         Returns:
             A generator over blobs

libs/core/langchain_core/document_loaders/langsmith.py

@@ -14,13 +14,13 @@ from langchain_core.documents import Document
 
 
 class LangSmithLoader(BaseLoader):
-    """Load LangSmith Dataset examples as Documents.
+    """Load LangSmith Dataset examples as `Document` objects.
 
-    Loads the example inputs as the Document page content and places the entire example
-    into the Document metadata. This allows you to easily create few-shot example
-    retrievers from the loaded documents.
+    Loads the example inputs as the `Document` page content and places the entire
+    example into the `Document` metadata. This allows you to easily create few-shot
+    example retrievers from the loaded documents.
 
-    ??? note "Lazy load"
+    ??? note "Lazy loading example"
 
         ```python
         from langchain_core.document_loaders import LangSmithLoader
@@ -34,9 +34,6 @@ class LangSmithLoader(BaseLoader):
         ```python
         # -> [Document("...", metadata={"inputs": {...}, "outputs": {...}, ...}), ...]
         ```
-
-    !!! version-added "Added in version 0.2.34"
-
     """
 
     def __init__(
@@ -69,15 +66,14 @@ class LangSmithLoader(BaseLoader):
             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.
-            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
-                of the tagged (or timestamped) version.
+            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 of
+                the tagged (or timestamped) version.
             splits: A list of dataset splits, which are
-                divisions of your dataset such as 'train', 'test', or 'validation'.
+                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.
             filter: A structured filter string to apply to the examples.

libs/core/langchain_core/documents/__init__.py

@@ -1,7 +1,28 @@
-"""Documents module.
+"""Documents module for data retrieval and processing workflows.
 
-**Document** module is a collection of classes that handle documents
-and their transformations.
+This module provides core abstractions for handling data in retrieval-augmented
+generation (RAG) pipelines, vector stores, and document processing workflows.
+
+!!! warning "Documents vs. message content"
+    This module is distinct from `langchain_core.messages.content`, which provides
+    multimodal content blocks for **LLM chat I/O** (text, images, audio, etc. within
+    messages).
+
+    **Key distinction:**
+
+    - **Documents** (this module): For **data retrieval and processing workflows**
+        - Vector stores, retrievers, RAG pipelines
+        - Text chunking, embedding, and semantic search
+        - Example: Chunks of a PDF stored in a vector database
+
+    - **Content Blocks** (`messages.content`): For **LLM conversational I/O**
+        - Multimodal message content sent to/from models
+        - Tool calls, reasoning, citations within chat
+        - Example: An image sent to a vision model in a chat message (via
+            [`ImageContentBlock`][langchain.messages.ImageContentBlock])
+
+    While both can represent similar data types (text, files), they serve different
+    architectural purposes in LangChain applications.
 """
 
 from typing import TYPE_CHECKING

libs/core/langchain_core/documents/base.py

@@ -1,4 +1,16 @@
-"""Base classes for media and documents."""
+"""Base classes for media and documents.
+
+This module contains core abstractions for **data retrieval and processing workflows**:
+
+- `BaseMedia`: Base class providing `id` and `metadata` fields
+- `Blob`: Raw data loading (files, binary data) - used by document loaders
+- `Document`: Text content for retrieval (RAG, vector stores, semantic search)
+
+!!! note "Not for LLM chat messages"
+    These classes are for data processing pipelines, not LLM I/O. For multimodal
+    content in chat messages (images, audio in conversations), see
+    `langchain.messages` content blocks instead.
+"""
 
 from __future__ import annotations
 
@@ -19,27 +31,23 @@ PathLike = str | PurePath
 
 
 class BaseMedia(Serializable):
-    """Use to represent media content.
+    """Base class for content used in retrieval and data processing workflows.
 
-    Media objects can be used to represent raw data, such as text or binary data.
+    Provides common fields for content that needs to be stored, indexed, or searched.
 
-    LangChain Media objects allow associating metadata and an optional identifier
-    with the content.
-
-    The presence of an ID and metadata make it easier to store, index, and search
-    over the content in a structured way.
+    !!! note
+        For multimodal content in **chat messages** (images, audio sent to/from LLMs),
+        use `langchain.messages` content blocks instead.
     """
 
     # The ID field is optional at the moment.
     # It will likely become required in a future major release after
-    # it has been adopted by enough vectorstore implementations.
+    # it has been adopted by enough VectorStore implementations.
     id: str | None = Field(default=None, coerce_numbers_to_str=True)
     """An optional identifier for the document.
 
     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)
@@ -47,15 +55,14 @@ class BaseMedia(Serializable):
 
 
 class Blob(BaseMedia):
-    """Blob represents raw data by either reference or value.
+    """Raw data abstraction for document loading and file processing.
 
-    Provides an interface to materialize the blob in different representations, and
-    help to decouple the development of data loaders from the downstream parsing of
-    the raw data.
+    Represents raw bytes or text, either in-memory or by file reference. Used
+    primarily by document loaders to decouple data loading from parsing.
 
-    Inspired by: https://developer.mozilla.org/en-US/docs/Web/API/Blob
+    Inspired by [Mozilla's `Blob`](https://developer.mozilla.org/en-US/docs/Web/API/Blob)
 
-    Example: Initialize a blob from in-memory data
+    ???+ example "Initialize a blob from in-memory data"
 
         ```python
         from langchain_core.documents import Blob
@@ -73,7 +80,7 @@ class Blob(BaseMedia):
             print(f.read())
         ```
 
-    Example: Load from memory and specify mime-type and metadata
+    ??? example "Load from memory and specify MIME type and metadata"
 
         ```python
         from langchain_core.documents import Blob
@@ -85,7 +92,7 @@ class Blob(BaseMedia):
         )
         ```
 
-    Example: Load the blob from a file
+    ??? example "Load the blob from a file"
 
         ```python
         from langchain_core.documents import Blob
@@ -105,13 +112,13 @@ class Blob(BaseMedia):
     """
 
     data: bytes | str | None = None
-    """Raw data associated with the blob."""
+    """Raw data associated with the `Blob`."""
     mimetype: str | None = None
     """MimeType not to be confused with a file extension."""
     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."""
@@ -125,9 +132,9 @@ class Blob(BaseMedia):
     def source(self) -> str | None:
         """The source location of the blob as string if known otherwise none.
 
-        If a path is associated with the blob, it will default to the path location.
+        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:
@@ -211,15 +218,15 @@ 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
-            guess_type: If `True`, the mimetype will be guessed from the file extension,
-                        if a mime-type was not provided
-            metadata: Metadata to associate with the blob
+            mime_type: If provided, will be set as the MIME type of the data
+            guess_type: If `True`, the MIME type will be guessed from the file
+                extension, if a MIME type was not provided
+            metadata: Metadata to associate with the `Blob`
 
         Returns:
-            Blob instance
+            `Blob` instance
         """
         if mime_type is None and guess_type:
             mimetype = mimetypes.guess_type(path)[0] if guess_type else None
@@ -245,17 +252,17 @@ class Blob(BaseMedia):
         path: str | None = None,
         metadata: dict | None = None,
     ) -> Blob:
-        """Initialize the blob from in-memory data.
+        """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
-            metadata: Metadata to associate with the blob
+            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:
-            Blob instance
+            `Blob` instance
         """
         return cls(
             data=data,
@@ -276,6 +283,10 @@ class Blob(BaseMedia):
 class Document(BaseMedia):
     """Class for storing a piece of text and associated metadata.
 
+    !!! note
+        `Document` is for **retrieval workflows**, not chat I/O. For sending text
+        to an LLM in a conversation, use message types from `langchain.messages`.
+
     Example:
         ```python
         from langchain_core.documents import Document
@@ -303,7 +314,7 @@ 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/compressor.py

@@ -21,14 +21,14 @@ class BaseDocumentCompressor(BaseModel, ABC):
 
     This abstraction is primarily used for post-processing of retrieved documents.
 
-    Documents matching a given query are first retrieved.
+    `Document` objects matching a given query are first retrieved.
 
     Then the list of documents can be further processed.
 
     For example, one could re-rank the retrieved documents using an LLM.
 
     !!! note
-        Users should favor using a RunnableLambda instead of sub-classing from this
+        Users should favor using a `RunnableLambda` instead of sub-classing from this
         interface.
 
     """
@@ -43,9 +43,9 @@ class BaseDocumentCompressor(BaseModel, ABC):
         """Compress retrieved documents given the query context.
 
         Args:
-            documents: The retrieved documents.
+            documents: The retrieved `Document` objects.
             query: The query context.
-            callbacks: Optional callbacks to run during compression.
+            callbacks: Optional `Callbacks` to run during compression.
 
         Returns:
             The compressed documents.
@@ -61,9 +61,9 @@ class BaseDocumentCompressor(BaseModel, ABC):
         """Async compress retrieved documents given the query context.
 
         Args:
-            documents: The retrieved documents.
+            documents: The retrieved `Document` objects.
             query: The query context.
-            callbacks: Optional callbacks to run during compression.
+            callbacks: Optional `Callbacks` to run during compression.
 
         Returns:
             The compressed documents.

libs/core/langchain_core/documents/transformers.py

@@ -16,8 +16,8 @@ if TYPE_CHECKING:
 class BaseDocumentTransformer(ABC):
     """Abstract base class for document transformation.
 
-    A document transformation takes a sequence of Documents and returns a
-    sequence of transformed Documents.
+    A document transformation takes a sequence of `Document` objects and returns a
+    sequence of transformed `Document` objects.
 
     Example:
         ```python
@@ -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.
+    !!! danger "Toy model"
+        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.
+    !!! danger "Toy model"
+        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

@@ -41,7 +41,7 @@ class _VectorStoreExampleSelector(BaseExampleSelector, BaseModel, ABC):
     """Optional keys to filter input to. If provided, the search is based on
     the input variables instead of all variables."""
     vectorstore_kwargs: dict[str, Any] | None = None
-    """Extra arguments passed to similarity_search function of the vectorstore."""
+    """Extra arguments passed to similarity_search function of the `VectorStore`."""
 
     model_config = ConfigDict(
         arbitrary_types_allowed=True,
@@ -154,12 +154,12 @@ 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.
             vectorstore_kwargs: Extra arguments passed to similarity_search function
-                of the vectorstore.
+                of the `VectorStore`.
             vectorstore_cls_kwargs: optional kwargs containing url for vector store
 
         Returns:
@@ -198,12 +198,12 @@ 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.
             vectorstore_kwargs: Extra arguments passed to similarity_search function
-                of the vectorstore.
+                of the `VectorStore`.
             vectorstore_cls_kwargs: optional kwargs containing url for vector store
 
         Returns:
@@ -285,14 +285,13 @@ 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.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-                Default is 20.
+            k: Number of examples to select.
+            fetch_k: Number of `Document` objects to fetch to pass to MMR algorithm.
             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.
             vectorstore_kwargs: Extra arguments passed to similarity_search function
-                of the vectorstore.
+                of the `VectorStore`.
             vectorstore_cls_kwargs: optional kwargs containing url for vector store
 
         Returns:
@@ -333,14 +332,13 @@ 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.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-                Default is 20.
+            k: Number of examples to select.
+            fetch_k: Number of `Document` objects to fetch to pass to MMR algorithm.
             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.
             vectorstore_kwargs: Extra arguments passed to similarity_search function
-                of the vectorstore.
+                of the `VectorStore`.
             vectorstore_cls_kwargs: optional kwargs containing url for vector store
 
         Returns:

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.
     """
@@ -28,7 +28,7 @@ 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.
@@ -37,11 +37,10 @@ class OutputParserException(ValueError, LangChainException):  # noqa: N818
             llm_output: String model output which is error-ing.
 
             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
@@ -87,6 +86,6 @@ def create_message(*, message: str, error_code: ErrorCode) -> str:
     """
     return (
         f"{message}\n"
-        "For troubleshooting, visit: https://python.langchain.com/docs/"
-        f"troubleshooting/errors/{error_code.value} "
+        "For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain"
+        f"/errors/{error_code.value} "
     )

libs/core/langchain_core/indexing/__init__.py

@@ -1,7 +1,7 @@
 """Code to help indexing data into a vectorstore.
 
 This package contains helper logic to help deal with indexing data into
-a vectorstore while avoiding duplicated content and over-writing content
+a `VectorStore` while avoiding duplicated content and over-writing content
 if it's unchanged.
 """
 

libs/core/langchain_core/indexing/api.py

@@ -304,53 +304,50 @@ def index(
     !!! warning
 
         * In full mode, the loader should be returning
-          the entire dataset, and not just a subset of the dataset.
-          Otherwise, the auto_cleanup will remove documents that it is not
-          supposed to.
+            the entire dataset, and not just a subset of the dataset.
+            Otherwise, the auto_cleanup will remove documents that it is not
+            supposed to.
         * In incremental mode, if documents associated with a particular
-          source id appear across different batches, the indexing API
-          will do some redundant work. This will still result in the
-          correct end state of the index, but will unfortunately not be
-          100% efficient. For example, if a given document is split into 15
-          chunks, and we index them using a batch size of 5, we'll have 3 batches
-          all with the same source id. In general, to avoid doing too much
-          redundant work select as big a batch size as possible.
+            source id appear across different batches, the indexing API
+            will do some redundant work. This will still result in the
+            correct end state of the index, but will unfortunately not be
+            100% efficient. For example, if a given document is split into 15
+            chunks, and we index them using a batch size of 5, we'll have 3 batches
+            all with the same source id. In general, to avoid doing too much
+            redundant work select as big a batch size as possible.
         * The `scoped_full` mode is suitable if determining an appropriate batch size
-          is challenging or if your data loader cannot return the entire dataset at
-          once. This mode keeps track of source IDs in memory, which should be fine
-          for most use cases. If your dataset is large (10M+ docs), you will likely
-          need to parallelize the indexing process regardless.
+            is challenging or if your data loader cannot return the entire dataset at
+            once. This mode keeps track of source IDs in memory, which should be fine
+            for most use cases. If your dataset is large (10M+ docs), you will likely
+            need to parallelize the indexing process regardless.
 
     Args:
         docs_source: Data loader or iterable of documents to 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.
+        vector_store: `VectorStore` or DocumentIndex to index the documents into.
+        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.
-              Clean up is done continuously during indexing helping to minimize the
-              probability of users seeing duplicated content.
+                that are associated with source IDs that were seen during indexing.
+                Clean up is done continuously during indexing helping to minimize the
+                probability of users seeing duplicated content.
             - full: Delete all documents that have not been returned by the loader
-              during this run of indexing.
-              Clean up runs after all documents have been indexed.
-              This means that users may see duplicated content during indexing.
+                during this run of indexing.
+                Clean up runs after all documents have been indexed.
+                This means that users may see duplicated content during indexing.
             - scoped_full: Similar to Full, but only deletes all documents
-              that haven't been updated AND that are associated with
-              source ids that were seen during indexing.
+                that haven't been updated AND that are associated with
+                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"
 
@@ -366,7 +363,7 @@ def index(
             When changing the key encoder, you must change the
             index as well to avoid duplicated documents in the cache.
         upsert_kwargs: Additional keyword arguments to pass to the add_documents
-            method of the VectorStore or the upsert method of the DocumentIndex.
+            method of the `VectorStore` or the upsert method of the DocumentIndex.
             For example, you can use this to specify a custom vector_field:
             upsert_kwargs={"vector_field": "embedding"}
             !!! version-added "Added in version 0.3.10"
@@ -378,10 +375,10 @@ def index(
     Raises:
         ValueError: If cleanup mode is not one of 'incremental', 'full' or None
         ValueError: If cleanup mode is incremental and source_id_key is None.
-        ValueError: If vectorstore does not have
+        ValueError: If `VectorStore` does not have
             "delete" and "add_documents" required methods.
         ValueError: If source_id_key is not None, but is not a string or callable.
-        TypeError: If `vectorstore` is not a VectorStore or a DocumentIndex.
+        TypeError: If `vectorstore` is not a `VectorStore` or a DocumentIndex.
         AssertionError: If `source_id` is None when cleanup mode is incremental.
             (should be unreachable code).
     """
@@ -418,7 +415,7 @@ def index(
                 raise ValueError(msg)
 
         if type(destination).delete == VectorStore.delete:
-            # Checking if the vectorstore has overridden the default delete method
+            # Checking if the VectorStore has overridden the default delete method
             # implementation which just raises a NotImplementedError
             msg = "Vectorstore has not implemented the delete method"
             raise ValueError(msg)
@@ -469,11 +466,11 @@ def index(
         ]
 
         if cleanup in {"incremental", "scoped_full"}:
-            # source ids are required.
+            # Source IDs are required.
             for source_id, hashed_doc in zip(source_ids, hashed_docs, strict=False):
                 if source_id is None:
                     msg = (
-                        f"Source ids are required when cleanup mode is "
+                        f"Source IDs are required when cleanup mode is "
                         f"incremental or scoped_full. "
                         f"Document that starts with "
                         f"content: {hashed_doc.page_content[:100]} "
@@ -482,7 +479,7 @@ def index(
                     raise ValueError(msg)
                 if cleanup == "scoped_full":
                     scoped_full_cleanup_source_ids.add(source_id)
-            # source ids cannot be None after for loop above.
+            # Source IDs cannot be None after for loop above.
             source_ids = cast("Sequence[str]", source_ids)
 
         exists_batch = record_manager.exists(
@@ -541,7 +538,7 @@ def index(
         # If source IDs are provided, we can do the deletion incrementally!
         if cleanup == "incremental":
             # Get the uids of the documents that were not returned by the loader.
-            # mypy isn't good enough to determine that source ids cannot be None
+            # mypy isn't good enough to determine that source IDs cannot be None
             # here due to a check that's happening above, so we check again.
             for source_id in source_ids:
                 if source_id is None:
@@ -645,53 +642,50 @@ async def aindex(
     !!! warning
 
         * In full mode, the loader should be returning
-          the entire dataset, and not just a subset of the dataset.
-          Otherwise, the auto_cleanup will remove documents that it is not
-          supposed to.
+            the entire dataset, and not just a subset of the dataset.
+            Otherwise, the auto_cleanup will remove documents that it is not
+            supposed to.
         * In incremental mode, if documents associated with a particular
-          source id appear across different batches, the indexing API
-          will do some redundant work. This will still result in the
-          correct end state of the index, but will unfortunately not be
-          100% efficient. For example, if a given document is split into 15
-          chunks, and we index them using a batch size of 5, we'll have 3 batches
-          all with the same source id. In general, to avoid doing too much
-          redundant work select as big a batch size as possible.
+            source id appear across different batches, the indexing API
+            will do some redundant work. This will still result in the
+            correct end state of the index, but will unfortunately not be
+            100% efficient. For example, if a given document is split into 15
+            chunks, and we index them using a batch size of 5, we'll have 3 batches
+            all with the same source id. In general, to avoid doing too much
+            redundant work select as big a batch size as possible.
         * The `scoped_full` mode is suitable if determining an appropriate batch size
-          is challenging or if your data loader cannot return the entire dataset at
-          once. This mode keeps track of source IDs in memory, which should be fine
-          for most use cases. If your dataset is large (10M+ docs), you will likely
-          need to parallelize the indexing process regardless.
+            is challenging or if your data loader cannot return the entire dataset at
+            once. This mode keeps track of source IDs in memory, which should be fine
+            for most use cases. If your dataset is large (10M+ docs), you will likely
+            need to parallelize the indexing process regardless.
 
     Args:
         docs_source: Data loader or iterable of documents to 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.
+        vector_store: `VectorStore` or DocumentIndex to index the documents into.
+        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.
-              Clean up is done continuously during indexing helping to minimize the
-              probability of users seeing duplicated content.
+                that are associated with source IDs that were seen during indexing.
+                Clean up is done continuously during indexing helping to minimize the
+                probability of users seeing duplicated content.
             - full: Delete all documents that have not been returned by the loader
-              during this run of indexing.
-              Clean up runs after all documents have been indexed.
-              This means that users may see duplicated content during indexing.
+                during this run of indexing.
+                Clean up runs after all documents have been indexed.
+                This means that users may see duplicated content during indexing.
             - scoped_full: Similar to Full, but only deletes all documents
-              that haven't been updated AND that are associated with
-              source ids that were seen during indexing.
+                that haven't been updated AND that are associated with
+                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"
 
@@ -707,7 +701,7 @@ async def aindex(
             When changing the key encoder, you must change the
             index as well to avoid duplicated documents in the cache.
         upsert_kwargs: Additional keyword arguments to pass to the add_documents
-            method of the VectorStore or the upsert method of the DocumentIndex.
+            method of the `VectorStore` or the upsert method of the DocumentIndex.
             For example, you can use this to specify a custom vector_field:
             upsert_kwargs={"vector_field": "embedding"}
             !!! version-added "Added in version 0.3.10"
@@ -719,10 +713,10 @@ async def aindex(
     Raises:
         ValueError: If cleanup mode is not one of 'incremental', 'full' or None
         ValueError: If cleanup mode is incremental and source_id_key is None.
-        ValueError: If vectorstore does not have
+        ValueError: If `VectorStore` does not have
             "adelete" and "aadd_documents" required methods.
         ValueError: If source_id_key is not None, but is not a string or callable.
-        TypeError: If `vector_store` is not a VectorStore or DocumentIndex.
+        TypeError: If `vector_store` is not a `VectorStore` or DocumentIndex.
         AssertionError: If `source_id_key` is None when cleanup mode is
             incremental or `scoped_full` (should be unreachable).
     """
@@ -763,7 +757,7 @@ async def aindex(
             type(destination).adelete == VectorStore.adelete
             and type(destination).delete == VectorStore.delete
         ):
-            # Checking if the vectorstore has overridden the default adelete or delete
+            # Checking if the VectorStore has overridden the default adelete or delete
             # methods implementation which just raises a NotImplementedError
             msg = "Vectorstore has not implemented the adelete or delete method"
             raise ValueError(msg)
@@ -821,11 +815,11 @@ async def aindex(
         ]
 
         if cleanup in {"incremental", "scoped_full"}:
-            # If the cleanup mode is incremental, source ids are required.
+            # If the cleanup mode is incremental, source IDs are required.
             for source_id, hashed_doc in zip(source_ids, hashed_docs, strict=False):
                 if source_id is None:
                     msg = (
-                        f"Source ids are required when cleanup mode is "
+                        f"Source IDs are required when cleanup mode is "
                         f"incremental or scoped_full. "
                         f"Document that starts with "
                         f"content: {hashed_doc.page_content[:100]} "
@@ -834,7 +828,7 @@ async def aindex(
                     raise ValueError(msg)
                 if cleanup == "scoped_full":
                     scoped_full_cleanup_source_ids.add(source_id)
-            # source ids cannot be None after for loop above.
+            # Source IDs cannot be None after for loop above.
             source_ids = cast("Sequence[str]", source_ids)
 
         exists_batch = await record_manager.aexists(
@@ -894,7 +888,7 @@ async def aindex(
         if cleanup == "incremental":
             # Get the uids of the documents that were not returned by the loader.
 
-            # mypy isn't good enough to determine that source ids cannot be None
+            # mypy isn't good enough to determine that source IDs cannot be None
             # here due to a check that's happening above, so we check again.
             for source_id in source_ids:
                 if source_id is None:

libs/core/langchain_core/indexing/base.py

@@ -25,7 +25,7 @@ class RecordManager(ABC):
     The record manager abstraction is used by the langchain indexing API.
 
     The record manager keeps track of which documents have been
-    written into a vectorstore and when they were written.
+    written into a `VectorStore` and when they were written.
 
     The indexing API computes hashes for each document and stores the hash
     together with the write time and the source id in the record manager.
@@ -37,7 +37,7 @@ class RecordManager(ABC):
     already been indexed, and to only index new documents.
 
     The main benefit of this abstraction is that it works across many vectorstores.
-    To be supported, a vectorstore needs to only support the ability to add and
+    To be supported, a `VectorStore` needs to only support the ability to add and
     delete documents by ID. Using the record manager, the indexing API will
     be able to delete outdated documents and avoid redundant indexing of documents
     that have already been indexed.
@@ -45,13 +45,13 @@ class RecordManager(ABC):
     The main constraints of this abstraction are:
 
     1. It relies on the time-stamps to determine which documents have been
-       indexed and which have not. This means that the time-stamps must be
-       monotonically increasing. The timestamp should be the timestamp
-       as measured by the server to minimize issues.
+        indexed and which have not. This means that the time-stamps must be
+        monotonically increasing. The timestamp should be the timestamp
+        as measured by the server to minimize issues.
     2. The record manager is currently implemented separately from the
-       vectorstore, which means that the overall system becomes distributed
-       and may create issues with consistency. For example, writing to
-       record manager succeeds, but corresponding writing to vectorstore fails.
+        vectorstore, which means that the overall system becomes distributed
+        and may create issues with consistency. For example, writing to
+        record manager succeeds, but corresponding writing to `VectorStore` fails.
     """
 
     def __init__(
@@ -460,7 +460,7 @@ class UpsertResponse(TypedDict):
 class DeleteResponse(TypedDict, total=False):
     """A generic response for delete operation.
 
-    The fields in this response are optional and whether the vectorstore
+    The fields in this response are optional and whether the `VectorStore`
     returns them or not is up to the implementation.
     """
 
@@ -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
@@ -520,40 +518,40 @@ class DocumentIndex(BaseRetriever):
         if it is provided. If the ID is not provided, the upsert method is free
         to generate an ID for the content.
 
-        When an ID is specified and the content already exists in the vectorstore,
+        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:
             A response object that contains the list of IDs that were
-            successfully added or updated in the vectorstore and the list of IDs that
+            successfully added or updated in the `VectorStore` and the list of IDs that
             failed to be added or updated.
         """
 
     async def aupsert(
         self, items: Sequence[Document], /, **kwargs: Any
     ) -> UpsertResponse:
-        """Add or update documents in the vectorstore. Async version of upsert.
+        """Add or update documents in the `VectorStore`. Async version of `upsert`.
 
         The upsert functionality should utilize the ID field of the item
         if it is provided. If the ID is not provided, the upsert method is free
         to generate an ID for the item.
 
-        When an ID is specified and the item already exists in the vectorstore,
+        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:
             A response object that contains the list of IDs that were
-            successfully added or updated in the vectorstore and the list of IDs that
+            successfully added or updated in the `VectorStore` and the list of IDs that
             failed to be added or updated.
         """
         return await run_in_executor(
@@ -570,7 +568,7 @@ class DocumentIndex(BaseRetriever):
         Calling delete without any input parameters should raise a ValueError!
 
         Args:
-            ids: List of ids to delete.
+            ids: List of IDs to delete.
             **kwargs: Additional keyword arguments. This is up to the implementation.
                 For example, can include an option to delete the entire index,
                 or else issue a non-blocking delete etc.
@@ -588,7 +586,7 @@ class DocumentIndex(BaseRetriever):
         Calling adelete without any input parameters should raise a ValueError!
 
         Args:
-            ids: List of ids to delete.
+            ids: List of IDs to delete.
             **kwargs: Additional keyword arguments. This is up to the implementation.
                 For example, can include an option to delete the entire index.
 

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)
@@ -64,10 +62,10 @@ class InMemoryDocumentIndex(DocumentIndex):
         """Delete by IDs.
 
         Args:
-            ids: List of ids to delete.
+            ids: List of IDs to delete.
 
         Raises:
-            ValueError: If ids is None.
+            ValueError: If IDs is None.
 
         Returns:
             A response object that contains the list of IDs that were successfully

libs/core/langchain_core/language_models/__init__.py

@@ -1,43 +1,30 @@
 """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
-distinct roles to conversation messages, helping to distinguish messages from the AI,
-users, and instructions such as system messages.
+as outputs (as opposed to using plain text).
 
-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.
+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.
 
-To implement a custom Chat Model, inherit from `BaseChatModel`. See
-the following guide for more information on how to implement a custom Chat Model:
+The key abstraction for chat models is `BaseChatModel`. Implementations should inherit
+from this class.
 
-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

@@ -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:
@@ -193,14 +200,14 @@ class BaseLanguageModel(
                 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 `Generation` objects for
-            each input prompt and additional model provider-specific output.
+                each input prompt and additional model provider-specific output.
 
         """
 
@@ -230,14 +237,14 @@ class BaseLanguageModel(
                 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 `Generation` objects for
-            each input prompt and additional model provider-specific output.
+                each input prompt and additional model provider-specific output.
 
         """
 
@@ -255,15 +262,14 @@ class BaseLanguageModel(
         return self.lc_attributes
 
     def get_token_ids(self, text: str) -> list[int]:
-        """Return the ordered ids of the tokens in a text.
+        """Return the ordered IDs of the tokens in a text.
 
         Args:
             text: The string input to tokenize.
 
         Returns:
-            A list of ids corresponding to the tokens in the text, in order they occur
-            in the text.
-
+            A list of IDs corresponding to the tokens in the text, in order they occur
+                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

@@ -15,6 +15,7 @@ from typing import TYPE_CHECKING, Any, Literal, cast
 from pydantic import BaseModel, ConfigDict, Field
 from typing_extensions import override
 
+from langchain_core._api.beta_decorator import beta
 from langchain_core.caches import BaseCache
 from langchain_core.callbacks import (
     AsyncCallbackManager,
@@ -75,6 +76,8 @@ from langchain_core.utils.utils import LC_ID_PREFIX, from_env
 if TYPE_CHECKING:
     import uuid
 
+    from langchain_model_profiles import ModelProfile  # type: ignore[import-not-found]
+
     from langchain_core.output_parsers.base import OutputParserLike
     from langchain_core.runnables import Runnable, RunnableConfig
     from langchain_core.tools import BaseTool
@@ -264,21 +267,21 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
         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`.        |
+        | 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          |
@@ -287,9 +290,6 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         | `_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
 
     rate_limiter: BaseRateLimiter | None = Field(default=None, exclude=True)
@@ -325,11 +325,12 @@ 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"
 
@@ -840,13 +841,13 @@ 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.
@@ -856,8 +857,8 @@ 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(
@@ -958,13 +959,13 @@ 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.
@@ -974,8 +975,8 @@ 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(
@@ -1512,17 +1513,21 @@ 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`.
@@ -1530,99 +1535,102 @@ 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
 
 
-            model = ChatModel(model="model-name", temperature=0)
-            structured_model = model.with_structured_output(AnswerWithJustification)
+        model = ChatModel(model="model-name", temperature=0)
+        structured_model = model.with_structured_output(AnswerWithJustification)
 
-            structured_model.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
 
 
-            model = ChatModel(model="model-name", temperature=0)
-            structured_model = model.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_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
-            # }
-            ```
+        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)
-            model = ChatModel(model="model-name", temperature=0)
-            structured_model = model.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_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.'
-            # }
-            ```
+        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)
@@ -1663,6 +1671,41 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
             return RunnableMap(raw=llm) | parser_with_fallback
         return llm | output_parser
 
+    @property
+    @beta()
+    def profile(self) -> ModelProfile:
+        """Return profiling information for the model.
+
+        This property will relies on the `langchain-model-profiles` package to
+        retrieve chat model capabilities, such as context window sizes and supported
+        features.
+
+        Raises:
+            ImportError: If `langchain-model-profiles` is not installed.
+
+        Returns:
+            A `ModelProfile` object containing profiling information for the model.
+        """
+        try:
+            from langchain_model_profiles import get_model_profile  # noqa: PLC0415
+        except ImportError as err:
+            informative_error_message = (
+                "To access model profiling information, please install the "
+                "`langchain-model-profiles` package: "
+                "`pip install langchain-model-profiles`."
+            )
+            raise ImportError(informative_error_message) from err
+
+        provider_id = self._llm_type
+        model_name = (
+            # Model name is not standardized across integrations. New integrations
+            # should prefer `model`.
+            getattr(self, "model", None)
+            or getattr(self, "model_name", None)
+            or getattr(self, "model_id", "")
+        )
+        return get_model_profile(provider_id, model_name) or {}
+
 
 class SimpleChatModel(BaseChatModel):
     """Simplified implementation for a chat model to inherit from.

libs/core/langchain_core/language_models/fake_chat_models.py

@@ -1,4 +1,4 @@
-"""Fake ChatModel for testing purposes."""
+"""Fake chat models 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

@@ -1,4 +1,7 @@
-"""Base interface for large language models to expose."""
+"""Base interface for traditional large language models (LLMs) to expose.
+
+These are traditionally older models (newer models generally are chat models).
+"""
 
 from __future__ import annotations
 
@@ -74,8 +77,8 @@ 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 +94,17 @@ 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 +160,7 @@ 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 +196,7 @@ 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 +848,7 @@ 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 +868,8 @@ 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 +1118,7 @@ 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 +1137,8 @@ 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 = [
@@ -1387,11 +1394,6 @@ class LLM(BaseLLM):
         `astream` will use `_astream` if provided, otherwise it will implement
         a fallback behavior that will use `_stream` if `_stream` is implemented,
         and use `_acall` if `_stream` is not implemented.
-
-    Please see the following guide for more information on how to
-    implement a custom LLM:
-
-    https://python.langchain.com/docs/how_to/custom_llm/
     """
 
     @abstractmethod

libs/core/langchain_core/load/dump.py

@@ -17,7 +17,7 @@ def default(obj: Any) -> Any:
         obj: The object to serialize to json if it is a Serializable object.
 
     Returns:
-        A json serializable object or a SerializedNotImplemented object.
+        A JSON serializable object or a SerializedNotImplemented object.
     """
     if isinstance(obj, Serializable):
         return obj.to_json()
@@ -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

@@ -67,12 +67,9 @@ class Reviver:
             valid_namespaces: A list of additional namespaces (modules)
                 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.
-
             ignore_unserializable_fields: Whether to ignore unserializable fields.
-                Defaults to `False`.
         """
         self.secrets_from_env = secrets_from_env
         self.secrets_map = secrets_map or {}
@@ -204,12 +201,9 @@ def loads(
         valid_namespaces: A list of additional namespaces (modules)
             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.
-
         ignore_unserializable_fields: Whether to ignore unserializable fields.
-            Defaults to `False`.
 
     Returns:
         Revived LangChain objects.
@@ -249,12 +243,9 @@ def load(
         valid_namespaces: A list of additional namespaces (modules)
             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.
-
         ignore_unserializable_fields: Whether to ignore unserializable fields.
-            Defaults to `False`.
 
     Returns:
         Revived LangChain objects.

libs/core/langchain_core/load/serializable.py

@@ -96,12 +96,15 @@ class Serializable(BaseModel, ABC):
         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.
+    - `get_lc_namespace`: Get the namespace of the LangChain object.
+
         During deserialization, this namespace is used to identify
         the correct class to instantiate.
+
         Please see the `Reviver` class in `langchain_core.load.load` for more details.
         During deserialization an additional mapping is handle classes that have moved
         or been renamed across package versions.
+
     - `lc_secrets`: A map of constructor argument names to secret ids.
     - `lc_attributes`: List of additional attribute names that should be included
         as part of the serialized representation.
@@ -127,10 +130,10 @@ class Serializable(BaseModel, ABC):
 
     @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.
@@ -194,7 +197,7 @@ 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()

libs/core/langchain_core/messages/__init__.py

@@ -9,6 +9,9 @@ if TYPE_CHECKING:
     from langchain_core.messages.ai import (
         AIMessage,
         AIMessageChunk,
+        InputTokenDetails,
+        OutputTokenDetails,
+        UsageMetadata,
     )
     from langchain_core.messages.base import (
         BaseMessage,
@@ -87,10 +90,12 @@ __all__ = (
     "HumanMessage",
     "HumanMessageChunk",
     "ImageContentBlock",
+    "InputTokenDetails",
     "InvalidToolCall",
     "MessageLikeRepresentation",
     "NonStandardAnnotation",
     "NonStandardContentBlock",
+    "OutputTokenDetails",
     "PlainTextContentBlock",
     "ReasoningContentBlock",
     "RemoveMessage",
@@ -104,6 +109,7 @@ __all__ = (
     "ToolCallChunk",
     "ToolMessage",
     "ToolMessageChunk",
+    "UsageMetadata",
     "VideoContentBlock",
     "_message_from_dict",
     "convert_to_messages",
@@ -145,6 +151,7 @@ _dynamic_imports = {
     "HumanMessageChunk": "human",
     "NonStandardAnnotation": "content",
     "NonStandardContentBlock": "content",
+    "OutputTokenDetails": "ai",
     "PlainTextContentBlock": "content",
     "ReasoningContentBlock": "content",
     "RemoveMessage": "modifier",
@@ -154,12 +161,14 @@ _dynamic_imports = {
     "SystemMessage": "system",
     "SystemMessageChunk": "system",
     "ImageContentBlock": "content",
+    "InputTokenDetails": "ai",
     "InvalidToolCall": "tool",
     "TextContentBlock": "content",
     "ToolCall": "tool",
     "ToolCallChunk": "tool",
     "ToolMessage": "tool",
     "ToolMessageChunk": "tool",
+    "UsageMetadata": "ai",
     "VideoContentBlock": "content",
     "AnyMessage": "utils",
     "MessageLikeRepresentation": "utils",

libs/core/langchain_core/messages/ai.py

@@ -48,10 +48,10 @@ class InputTokenDetails(TypedDict, total=False):
         }
         ```
 
-    !!! version-added "Added in version 0.3.9"
-
     May also hold extra provider-specific keys.
 
+    !!! version-added "Added in version 0.3.9"
+
     """
 
     audio: int
@@ -83,6 +83,8 @@ class OutputTokenDetails(TypedDict, total=False):
         }
         ```
 
+    May also hold extra provider-specific keys.
+
     !!! version-added "Added in version 0.3.9"
 
     """
@@ -124,6 +126,10 @@ class UsageMetadata(TypedDict):
     !!! warning "Behavior changed in 0.3.9"
         Added `input_token_details` and `output_token_details`.
 
+    !!! note "LangSmith SDK"
+        The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
+        LangSmith's `UsageMetadata` has additional fields to capture cost information
+        used by the LangSmith platform.
     """
 
     input_tokens: int
@@ -131,7 +137,7 @@ class UsageMetadata(TypedDict):
     output_tokens: int
     """Count of output (or completion) tokens. Sum of all output token types."""
     total_tokens: int
-    """Total token count. Sum of input_tokens + output_tokens."""
+    """Total token count. Sum of `input_tokens` + `output_tokens`."""
     input_token_details: NotRequired[InputTokenDetails]
     """Breakdown of input token counts.
 
@@ -141,34 +147,31 @@ class UsageMetadata(TypedDict):
     """Breakdown of output token counts.
 
     Does *not* need to sum to full output token count. Does *not* need to have all keys.
-
     """
 
 
 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 +194,7 @@ 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 +220,11 @@ 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 +232,7 @@ 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 +338,10 @@ 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 +378,19 @@ 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 +398,7 @@ 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 +406,7 @@ 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 +547,15 @@ 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"
@@ -650,13 +655,13 @@ def add_ai_message_chunks(
             chunk_id = id_
             break
     else:
-        # second pass: prefer lc_run-* ids over lc_* ids
+        # second pass: prefer lc_run-* IDs over lc_* IDs
         for id_ in candidates:
             if id_ and id_.startswith(LC_ID_PREFIX):
                 chunk_id = id_
                 break
         else:
-            # third pass: take any remaining id (auto-generated lc_* ids)
+            # third pass: take any remaining ID (auto-generated lc_* IDs)
             for id_ in candidates:
                 if id_:
                     chunk_id = id_

libs/core/langchain_core/messages/base.py

@@ -92,11 +92,15 @@ 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.
+
+    Examples include [`HumanMessage`][langchain.messages.HumanMessage],
+    [`AIMessage`][langchain.messages.AIMessage], and
+    [`SystemMessage`][langchain.messages.SystemMessage].
     """
 
     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 +163,12 @@ 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 +188,7 @@ 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 +266,7 @@ 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 +311,7 @@ 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 +468,7 @@ 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.
@@ -368,7 +368,7 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                     else:
                         # Assume it's raw base64 without data URI
                         try:
-                            # Validate base64 and decode for mime type detection
+                            # Validate base64 and decode for MIME type detection
                             decoded_bytes = base64.b64decode(url, validate=True)
 
                             image_url_b64_block = {
@@ -379,7 +379,7 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                             try:
                                 import filetype  # type: ignore[import-not-found] # noqa: PLC0415
 
-                                # Guess mime type based on file bytes
+                                # Guess MIME type based on file bytes
                                 mime_type = None
                                 kind = filetype.guess(decoded_bytes)
                                 if kind:
@@ -453,10 +453,13 @@ 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)
+            elif item_type == "text":
+                converted_blocks.append(cast("types.TextContentBlock", item))
             else:
                 # Unknown type, preserve as non-standard
                 converted_blocks.append({"type": "non_standard", "value": item})

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/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_'`))
 
@@ -632,7 +644,7 @@ class AudioContentBlock(TypedDict):
 
 
 class PlainTextContentBlock(TypedDict):
-    """Plaintext data (e.g., from a document).
+    """Plaintext data (e.g., from a `.txt` or `.md` document).
 
     !!! note
         A `PlainTextContentBlock` existed in `langchain-core<1.0.0`. Although the
@@ -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_'`))
 
@@ -753,7 +767,7 @@ class FileContentBlock(TypedDict):
 
 
 class NonStandardContentBlock(TypedDict):
-    """Provider-specific data.
+    """Provider-specific content data.
 
     This block contains data for which there is not yet a standard type.
 
@@ -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:
 
@@ -781,13 +795,14 @@ 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_'`))
 
     """
 
     value: dict[str, Any]
-    """Provider-specific data."""
+    """Provider-specific content data."""
 
     index: NotRequired[int | str]
     """Index of block in aggregate response. Used during streaming."""
@@ -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 = {
@@ -1382,7 +1399,7 @@ def create_non_standard_block(
     """Create a `NonStandardContentBlock`.
 
     Args:
-        value: Provider-specific data.
+        value: Provider-specific content data.
         id: Content block identifier. Generated automatically if not provided.
         index: Index of block in aggregate response. Used during streaming.
 

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
@@ -1031,18 +1025,18 @@ def convert_to_openai_messages(
         messages: Message-like object or iterable of objects whose contents are
             in OpenAI, Anthropic, Bedrock Converse, or VertexAI formats.
         text_format: How to format string or text block contents:
-                - `'string'`:
-                    If a message has a string content, this is left as a string. If
-                    a message has content blocks that are all of type `'text'`, these
-                    are joined with a newline to make a single string. If a message has
-                    content blocks and at least one isn't of type `'text'`, then
-                    all blocks are left as dicts.
-                - `'block'`:
-                    If a message has a string content, this is turned into a list
-                    with a single content block of type `'text'`. If a message has
-                    content blocks these are left as is.
-        include_id: Whether to include message ids in the openai messages, if they
-                    are present in the source messages.
+            - `'string'`:
+                If a message has a string content, this is left as a string. If
+                a message has content blocks that are all of type `'text'`, these
+                are joined with a newline to make a single string. If a message has
+                content blocks and at least one isn't of type `'text'`, then
+                all blocks are left as dicts.
+            - `'block'`:
+                If a message has a string content, this is turned into a list
+                with a single content block of type `'text'`. If a message has
+                content blocks these are left as is.
+        include_id: Whether to include message IDs in the openai messages, if they
+            are present in the source messages.
 
     Raises:
         ValueError: if an unrecognized `text_format` is specified, or if a message
@@ -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/__init__.py

@@ -1,4 +1,20 @@
-"""**OutputParser** classes parse the output of an LLM call."""
+"""`OutputParser` classes parse the output of an LLM call into structured data.
+
+!!! tip "Structured output"
+
+    Output parsers emerged as an early solution to the challenge of obtaining structured
+    output from LLMs.
+
+    Today, most LLMs support [structured output](https://docs.langchain.com/oss/python/langchain/models#structured-outputs)
+    natively. In such cases, using output parsers may be unnecessary, and you should
+    leverage the model's built-in capabilities for structured output. Refer to the
+    [documentation of your chosen model](https://docs.langchain.com/oss/python/integrations/providers/overview)
+    for guidance on how to achieve structured output directly.
+
+    Output parsers remain valuable when working with models that do not support
+    structured output natively, or when you require additional processing or validation
+    of the model's output beyond its inherent capabilities.
+"""
 
 from typing import TYPE_CHECKING
 

libs/core/langchain_core/output_parsers/base.py

@@ -31,13 +31,13 @@ class BaseLLMOutputParser(ABC, Generic[T]):
 
     @abstractmethod
     def parse_result(self, result: list[Generation], *, partial: bool = False) -> T:
-        """Parse a list of candidate model Generations into a specific format.
+        """Parse a list of candidate model `Generation` objects into a specific format.
 
         Args:
-            result: A list of Generations to be parsed. The Generations are assumed
-                to be different candidate outputs for a single model input.
+            result: A list of `Generation` to be parsed. The `Generation` objects are
+                assumed to be different candidate outputs for a single model input.
             partial: Whether to parse the output as a partial result. This is useful
-                for parsers that can parse partial results. Default is False.
+                for parsers that can parse partial results.
 
         Returns:
             Structured output.
@@ -46,17 +46,17 @@ class BaseLLMOutputParser(ABC, Generic[T]):
     async def aparse_result(
         self, result: list[Generation], *, partial: bool = False
     ) -> T:
-        """Async parse a list of candidate model Generations into a specific format.
+        """Async parse a list of candidate model `Generation` objects into a specific format.
 
         Args:
-            result: A list of Generations to be parsed. The Generations are assumed
+            result: A list of `Generation` to be parsed. The Generations are assumed
                 to be different candidate outputs for a single model input.
             partial: Whether to parse the output as a partial result. This is useful
-                for parsers that can parse partial results. Default is False.
+                for parsers that can parse partial results.
 
         Returns:
             Structured output.
-        """
+        """  # noqa: E501
         return await run_in_executor(None, self.parse_result, result, partial=partial)
 
 
@@ -135,6 +135,9 @@ class BaseOutputParser(
 
     Example:
         ```python
+        # Implement a simple boolean output parser
+
+
         class BooleanOutputParser(BaseOutputParser[bool]):
             true_val: str = "YES"
             false_val: str = "NO"
@@ -172,7 +175,7 @@ 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 +237,16 @@ 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 +267,20 @@ 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 +302,13 @@ 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/format_instructions.py

@@ -1,11 +1,16 @@
 """Format instructions."""
 
-JSON_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
+JSON_FORMAT_INSTRUCTIONS = """STRICT OUTPUT FORMAT:
+- Return only the JSON value that conforms to the schema. Do not include any additional text, explanations, headings, or separators.
+- Do not wrap the JSON in Markdown or code fences (no ``` or ```json).
+- Do not prepend or append any text (e.g., do not write "Here is the JSON:").
+- The response must be a single top-level JSON value exactly as required by the schema (object/array/etc.), with no trailing commas or comments.
 
-As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}}
-the object {{"foo": ["bar", "baz"]}} is a well-formatted instance of the schema. The object {{"properties": {{"foo": ["bar", "baz"]}}}} is not well-formatted.
+The output should be formatted as a JSON instance that conforms to the JSON schema below.
 
-Here is the output schema:
+As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}} the object {{"foo": ["bar", "baz"]}} is a well-formatted instance of the schema. The object {{"properties": {{"foo": ["bar", "baz"]}}}} is not well-formatted.
+
+Here is the output schema (shown in a code block for readability only — do not include any backticks or Markdown in your output):
 ```
 {schema}
 ```"""  # noqa: E501

libs/core/langchain_core/output_parsers/json.py

@@ -31,11 +31,14 @@ TBaseModel = TypeVar("TBaseModel", bound=PydanticBaseModel)
 class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]):
     """Parse the output of an LLM call to a JSON object.
 
+    Probably the most reliable output parser for getting structured data that does *not*
+    use function calling.
+
     When used in streaming mode, it will yield partial JSON objects containing
     all the keys that have been returned so far.
 
-    In streaming, if `diff` is set to `True`, yields JSONPatch operations
-    describing the difference between the previous and the current object.
+    In streaming, if `diff` is set to `True`, yields JSONPatch operations describing the
+    difference between the previous and the current object.
     """
 
     pydantic_object: Annotated[type[TBaseModel] | None, SkipValidation()] = None  # type: ignore[valid-type]
@@ -62,7 +65,6 @@ 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

@@ -41,7 +41,7 @@ def droplastn(
 
 
 class ListOutputParser(BaseTransformOutputParser[list[str]]):
-    """Parse the output of an LLM call to a list."""
+    """Parse the output of a model to a list."""
 
     @property
     def _type(self) -> str:
@@ -74,30 +74,30 @@ class ListOutputParser(BaseTransformOutputParser[list[str]]):
         buffer = ""
         for chunk in input:
             if isinstance(chunk, BaseMessage):
-                # extract text
+                # Extract text
                 chunk_content = chunk.content
                 if not isinstance(chunk_content, str):
                     continue
                 buffer += chunk_content
             else:
-                # add current chunk to buffer
+                # Add current chunk to buffer
                 buffer += chunk
-            # parse buffer into a list of parts
+            # Parse buffer into a list of parts
             try:
                 done_idx = 0
-                # yield only complete parts
+                # Yield only complete parts
                 for m in droplastn(self.parse_iter(buffer), 1):
                     done_idx = m.end()
                     yield [m.group(1)]
                 buffer = buffer[done_idx:]
             except NotImplementedError:
                 parts = self.parse(buffer)
-                # yield only complete parts
+                # Yield only complete parts
                 if len(parts) > 1:
                     for part in parts[:-1]:
                         yield [part]
                     buffer = parts[-1]
-        # yield the last part
+        # Yield the last part
         for part in self.parse(buffer):
             yield [part]
 
@@ -108,45 +108,45 @@ class ListOutputParser(BaseTransformOutputParser[list[str]]):
         buffer = ""
         async for chunk in input:
             if isinstance(chunk, BaseMessage):
-                # extract text
+                # Extract text
                 chunk_content = chunk.content
                 if not isinstance(chunk_content, str):
                     continue
                 buffer += chunk_content
             else:
-                # add current chunk to buffer
+                # Add current chunk to buffer
                 buffer += chunk
-            # parse buffer into a list of parts
+            # Parse buffer into a list of parts
             try:
                 done_idx = 0
-                # yield only complete parts
+                # Yield only complete parts
                 for m in droplastn(self.parse_iter(buffer), 1):
                     done_idx = m.end()
                     yield [m.group(1)]
                 buffer = buffer[done_idx:]
             except NotImplementedError:
                 parts = self.parse(buffer)
-                # yield only complete parts
+                # Yield only complete parts
                 if len(parts) > 1:
                     for part in parts[:-1]:
                         yield [part]
                     buffer = parts[-1]
-        # yield the last part
+        # Yield the last part
         for part in self.parse(buffer):
             yield [part]
 
 
 class CommaSeparatedListOutputParser(ListOutputParser):
-    """Parse the output of an LLM call to a comma-separated list."""
+    """Parse the output of a model to a comma-separated list."""
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        """Return True as this class is serializable."""
+        """Return `True` as this class is serializable."""
         return True
 
     @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"]`
@@ -177,7 +177,7 @@ class CommaSeparatedListOutputParser(ListOutputParser):
             )
             return [item for sublist in reader for item in sublist]
         except csv.Error:
-            # keep old logic for backup
+            # Keep old logic for backup
             return [part.strip() for part in text.split(",")]
 
     @property

libs/core/langchain_core/output_parsers/openai_functions.py

@@ -238,7 +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:
@@ -264,7 +264,7 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
             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.

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.
@@ -227,9 +224,8 @@ class JsonOutputKeyToolsParser(JsonOutputToolsParser):
             result: The result of the LLM call.
             partial: Whether to parse partial JSON.
                 If `True`, the output will be a JSON object containing
-                all the keys that have been returned so far.
+                    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.
@@ -311,9 +307,8 @@ class PydanticToolsParser(JsonOutputToolsParser):
             result: The result of the LLM call.
             partial: Whether to parse partial JSON.
                 If `True`, the output will be a JSON object containing
-                all the keys that have been returned so far.
+                    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

@@ -86,7 +86,7 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
             The format instructions for the JSON output.
         """
         # Copy schema to avoid altering original Pydantic schema.
-        schema = dict(self.pydantic_object.model_json_schema().items())
+        schema = dict(self._get_schema(self.pydantic_object).items())
 
         # Remove extraneous fields.
         reduced_schema = schema

libs/core/langchain_core/output_parsers/string.py

@@ -6,20 +6,20 @@ from langchain_core.output_parsers.transform import BaseTransformOutputParser
 
 
 class StrOutputParser(BaseTransformOutputParser[str]):
-    """OutputParser that parses LLMResult into the top likely string."""
+    """OutputParser that parses `LLMResult` into the top likely string."""
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        """StrOutputParser is serializable.
+        """`StrOutputParser` is serializable.
 
         Returns:
-            True
+            `True`
         """
         return True
 
     @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/output_parsers/xml.py

@@ -43,19 +43,19 @@ class _StreamingParser:
     """Streaming parser for XML.
 
     This implementation is pulled into a class to avoid implementation
-    drift between transform and atransform of the XMLOutputParser.
+    drift between transform and atransform of the `XMLOutputParser`.
     """
 
     def __init__(self, parser: Literal["defusedxml", "xml"]) -> None:
         """Initialize the streaming parser.
 
         Args:
-            parser: Parser to use for XML parsing. Can be either 'defusedxml' or 'xml'.
-              See documentation in XMLOutputParser for more information.
+            parser: Parser to use for XML parsing. Can be either `'defusedxml'` or
+                `'xml'`. See documentation in `XMLOutputParser` for more information.
 
         Raises:
-            ImportError: If defusedxml is not installed and the defusedxml
-                parser is requested.
+            ImportError: If `defusedxml` is not installed and the `defusedxml` parser is
+                requested.
         """
         if parser == "defusedxml":
             if not _HAS_DEFUSEDXML:
@@ -79,10 +79,10 @@ class _StreamingParser:
         """Parse a chunk of text.
 
         Args:
-            chunk: A chunk of text to parse. This can be a string or a BaseMessage.
+            chunk: A chunk of text to parse. This can be a `str` or a `BaseMessage`.
 
         Yields:
-            A dictionary representing the parsed XML element.
+            A `dict` representing the parsed XML element.
 
         Raises:
             xml.etree.ElementTree.ParseError: If the XML is not well-formed.
@@ -147,46 +147,49 @@ class _StreamingParser:
 
 
 class XMLOutputParser(BaseTransformOutputParser):
-    """Parse an output using xml format."""
+    """Parse an output using xml format.
+
+    Returns a dictionary of tags.
+    """
 
     tags: list[str] | None = None
     """Tags to tell the LLM to expect in the XML output.
 
     Note this may not be perfect depending on the LLM implementation.
 
-    For example, with tags=["foo", "bar", "baz"]:
+    For example, with `tags=["foo", "bar", "baz"]`:
 
     1. A well-formatted XML instance:
-       "<foo>\n   <bar>\n      <baz></baz>\n   </bar>\n</foo>"
+        `"<foo>\n   <bar>\n      <baz></baz>\n   </bar>\n</foo>"`
 
     2. A badly-formatted XML instance (missing closing tag for 'bar'):
-       "<foo>\n   <bar>\n   </foo>"
+        `"<foo>\n   <bar>\n   </foo>"`
 
     3. A badly-formatted XML instance (unexpected 'tag' element):
-       "<foo>\n   <tag>\n   </tag>\n</foo>"
+        `"<foo>\n   <tag>\n   </tag>\n</foo>"`
     """
     encoding_matcher: re.Pattern = re.compile(
         r"<([^>]*encoding[^>]*)>\n(.*)", re.MULTILINE | re.DOTALL
     )
     parser: Literal["defusedxml", "xml"] = "defusedxml"
-    """Parser to use for XML parsing. Can be either 'defusedxml' or 'xml'.
+    """Parser to use for XML parsing. Can be either `'defusedxml'` or `'xml'`.
 
-    * 'defusedxml' is the default parser and is used to prevent XML vulnerabilities
-       present in some distributions of Python's standard library xml.
-       `defusedxml` is a wrapper around the standard library parser that
-       sets up the parser with secure defaults.
-    * 'xml' is the standard library parser.
+    * `'defusedxml'` is the default parser and is used to prevent XML vulnerabilities
+        present in some distributions of Python's standard library xml.
+        `defusedxml` is a wrapper around the standard library parser that
+        sets up the parser with secure defaults.
+    * `'xml'` is the standard library parser.
 
-    Use `xml` only if you are sure that your distribution of the standard library
-    is not vulnerable to XML vulnerabilities.
+    Use `xml` only if you are sure that your distribution of the standard library is not
+    vulnerable to XML vulnerabilities.
 
     Please review the following resources for more information:
 
     * https://docs.python.org/3/library/xml.html#xml-vulnerabilities
     * https://github.com/tiran/defusedxml
 
-    The standard library relies on libexpat for parsing XML:
-    https://github.com/libexpat/libexpat
+    The standard library relies on [`libexpat`](https://github.com/libexpat/libexpat)
+    for parsing XML.
     """
 
     def get_format_instructions(self) -> str:
@@ -200,12 +203,12 @@ class XMLOutputParser(BaseTransformOutputParser):
             text: The output of an LLM call.
 
         Returns:
-            A dictionary representing the parsed XML.
+            A `dict` representing the parsed XML.
 
         Raises:
             OutputParserException: If the XML is not well-formed.
-            ImportError: If defusedxml is not installed and the defusedxml
-                parser is requested.
+            ImportError: If defus`edxml is not installed and the `defusedxml` parser is
+                requested.
         """
         # Try to find XML string within triple backticks
         # Imports are temporarily placed here to avoid issue with caching on CI

libs/core/langchain_core/outputs/generation.py

@@ -11,9 +11,8 @@ from langchain_core.utils._merge import merge_dicts
 class Generation(Serializable):
     """A single text generation output.
 
-    Generation represents the response from an
-    `"old-fashioned" LLM <https://python.langchain.com/docs/concepts/text_llms/>__` that
-    generates regular text (not chat messages).
+    Generation represents the response from an "old-fashioned" LLM (string-in,
+    string-out) that generates regular text (not chat messages).
 
     This model is used internally by chat model and will eventually
     be mapped to a more general `LLMResult` object, and then projected into
@@ -44,7 +43,7 @@ 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/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
@@ -35,9 +35,7 @@ class PromptValue(Serializable, ABC):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
-
-        This is used to determine the namespace of the object when serializing.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "schema", "prompt"]`
@@ -62,9 +60,7 @@ class StringPromptValue(PromptValue):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
-
-        This is used to determine the namespace of the object when serializing.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "prompts", "base"]`
@@ -99,9 +95,7 @@ class ChatPromptValue(PromptValue):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
-
-        This is used to determine the namespace of the object when serializing.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "prompts", "chat"]`
@@ -113,11 +107,11 @@ 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

@@ -48,11 +48,13 @@ class BasePromptTemplate(
     """A list of the names of the variables whose values are required as inputs to the
     prompt."""
     optional_variables: list[str] = Field(default=[])
-    """optional_variables: A list of the names of the variables for placeholder
-       or MessagePlaceholder that are optional. These variables are auto inferred
-       from the prompt and user need not provide them."""
+    """A list of the names of the variables for placeholder or `MessagePlaceholder` that
+    are optional.
+
+    These variables are auto inferred from the prompt and user need not provide them."""
     input_types: typing.Dict[str, Any] = Field(default_factory=dict, exclude=True)  # noqa: UP006
     """A dictionary of the types of the variables the prompt template expects.
+
     If not provided, all variables are assumed to be strings."""
     output_parser: BaseOutputParser | None = None
     """How to parse the output of calling an LLM on this formatted prompt."""
@@ -96,7 +98,7 @@ 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

@@ -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.
@@ -235,7 +234,7 @@ 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
@@ -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.
@@ -412,7 +411,7 @@ 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.
 
             **kwargs: keyword arguments to pass to the constructor.
@@ -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.
@@ -777,42 +776,36 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
     Use to create flexible templated prompts for chat models.
 
-    Examples:
-        !!! warning "Behavior changed in 0.2.24"
-            You can pass any Message-like formats supported by
-            `ChatPromptTemplate.from_messages()` directly to `ChatPromptTemplate()`
-            init.
+    ```python
+    from langchain_core.prompts import ChatPromptTemplate
 
-        ```python
-        from langchain_core.prompts import ChatPromptTemplate
+    template = ChatPromptTemplate(
+        [
+            ("system", "You are a helpful AI bot. Your name is {name}."),
+            ("human", "Hello, how are you doing?"),
+            ("ai", "I'm doing well, thanks!"),
+            ("human", "{user_input}"),
+        ]
+    )
 
-        template = ChatPromptTemplate(
-            [
-                ("system", "You are a helpful AI bot. Your name is {name}."),
-                ("human", "Hello, how are you doing?"),
-                ("ai", "I'm doing well, thanks!"),
-                ("human", "{user_input}"),
-            ]
-        )
+    prompt_value = template.invoke(
+        {
+            "name": "Bob",
+            "user_input": "What is your name?",
+        }
+    )
+    # Output:
+    # ChatPromptValue(
+    #    messages=[
+    #        SystemMessage(content='You are a helpful AI bot. Your name is Bob.'),
+    #        HumanMessage(content='Hello, how are you doing?'),
+    #        AIMessage(content="I'm doing well, thanks!"),
+    #        HumanMessage(content='What is your name?')
+    #    ]
+    # )
+    ```
 
-        prompt_value = template.invoke(
-            {
-                "name": "Bob",
-                "user_input": "What is your name?",
-            }
-        )
-        # Output:
-        # ChatPromptValue(
-        #    messages=[
-        #        SystemMessage(content='You are a helpful AI bot. Your name is Bob.'),
-        #        HumanMessage(content='Hello, how are you doing?'),
-        #        AIMessage(content="I'm doing well, thanks!"),
-        #        HumanMessage(content='What is your name?')
-        #    ]
-        # )
-        ```
-
-    Messages Placeholder:
+    !!! note "Messages Placeholder"
 
         ```python
         # In addition to Human/AI/Tool/Function messages,
@@ -853,13 +846,12 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         # )
         ```
 
-    Single-variable template:
+    !!! note "Single-variable template"
 
         If your prompt has only a single input variable (i.e., 1 instance of "{variable_nams}"),
         and you invoke the template with a non-dict object, the prompt template will
         inject the provided argument into that variable location.
 
-
         ```python
         from langchain_core.prompts import ChatPromptTemplate
 
@@ -899,25 +891,35 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         """Create a chat prompt template from a variety of message formats.
 
         Args:
-            messages: sequence of message representations.
+            messages: Sequence of message representations.
+
                 A message can be represented using the following formats:
-                (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of
-                (message type, template); e.g., ("human", "{user_input}"),
-                (4) 2-tuple of (message class, template), (5) a string which is
-                shorthand for ("human", template); e.g., "{user_input}".
-            template_format: format of the template. Defaults to "f-string".
+
+                1. `BaseMessagePromptTemplate`
+                2. `BaseMessage`
+                3. 2-tuple of `(message type, template)`; e.g.,
+                    `("human", "{user_input}")`
+                4. 2-tuple of `(message class, template)`
+                5. A string which is shorthand for `("human", template)`; e.g.,
+                    `"{user_input}"`
+            template_format: Format of the template.
             input_variables: A list of the names of the variables whose values are
                 required as inputs to the prompt.
             optional_variables: A list of the names of the variables for placeholder
                 or MessagePlaceholder that are optional.
+
                 These variables are auto inferred from the prompt and user need not
                 provide them.
             partial_variables: A dictionary of the partial variables the prompt
-                template carries. Partial variables populate the template so that you
-                don't need to pass them in every time you call the prompt.
+                template carries.
+
+                Partial variables populate the template so that you don't need to pass
+                them in every time you call the prompt.
             validate_template: Whether to validate the template.
             input_types: A dictionary of the types of the variables the prompt template
-                expects. If not provided, all variables are assumed to be strings.
+                expects.
+
+                If not provided, all variables are assumed to be strings.
 
         Examples:
             Instantiation from a list of message templates:
@@ -971,7 +973,7 @@ 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"]`
@@ -1122,13 +1124,18 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
             )
             ```
         Args:
-            messages: sequence of message representations.
+            messages: Sequence of message representations.
+
                 A message can be represented using the following formats:
-                (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of
-                (message type, template); e.g., ("human", "{user_input}"),
-                (4) 2-tuple of (message class, template), (5) a string which is
-                shorthand for ("human", template); e.g., "{user_input}".
-            template_format: format of the template. Defaults to "f-string".
+
+                1. `BaseMessagePromptTemplate`
+                2. `BaseMessage`
+                3. 2-tuple of `(message type, template)`; e.g.,
+                    `("human", "{user_input}")`
+                4. 2-tuple of `(message class, template)`
+                5. A string which is shorthand for `("human", template)`; e.g.,
+                    `"{user_input}"`
+            template_format: format of the template.
 
         Returns:
             a chat prompt template.
@@ -1239,7 +1246,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         """Extend the chat template with a sequence of messages.
 
         Args:
-            messages: sequence of message representations to append.
+            messages: Sequence of message representations to append.
         """
         self.messages.extend(
             [_convert_to_message_template(message) for message in messages]
@@ -1287,7 +1294,7 @@ 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 +1313,7 @@ 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 +1390,7 @@ 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/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}"`.
+                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

@@ -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
@@ -276,7 +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"]`

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(".")
 
@@ -104,19 +104,23 @@ class StructuredPrompt(ChatPromptTemplate):
             )
             ```
         Args:
-            messages: sequence of message representations.
+            messages: Sequence of message representations.
+
                 A message can be represented using the following formats:
-                (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of
-                (message type, template); e.g., ("human", "{user_input}"),
-                (4) 2-tuple of (message class, template), (5) a string which is
-                shorthand for ("human", template); e.g., "{user_input}"
-            schema: a dictionary representation of function call, or a Pydantic model.
+
+                1. `BaseMessagePromptTemplate`
+                2. `BaseMessage`
+                3. 2-tuple of `(message type, template)`; e.g.,
+                    `("human", "{user_input}")`
+                4. 2-tuple of `(message class, template)`
+                5. A string which is shorthand for `("human", template)`; e.g.,
+                    `"{user_input}"`
+            schema: A dictionary representation of function call, or a Pydantic model.
             **kwargs: Any additional kwargs to pass through to
                 `ChatModel.with_structured_output(schema, **kwargs)`.
 
         Returns:
-            a structured prompt template
-
+            A structured prompt template
         """
         return cls(messages, schema, **kwargs)
 

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:
@@ -109,7 +106,7 @@ class InMemoryRateLimiter(BaseRateLimiter):
         from langchain_anthropic import ChatAnthropic
 
         model = ChatAnthropic(
-            model_name="claude-3-opus-20240229", rate_limiter=rate_limiter
+            model_name="claude-sonnet-4-5-20250929", rate_limiter=rate_limiter
         )
 
         for _ in range(5):
@@ -118,9 +115,6 @@ class InMemoryRateLimiter(BaseRateLimiter):
             toc = time.time()
             print(toc - tic)
         ```
-
-    !!! version-added "Added in version 0.2.24"
-
     """  # noqa: E501
 
     def __init__(
@@ -132,7 +126,7 @@ 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 +139,11 @@ 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 +193,18 @@ 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 +217,18 @@ 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

@@ -50,25 +50,25 @@ class LangSmithRetrieverParams(TypedDict, total=False):
 
 
 class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
-    """Abstract base class for a Document retrieval system.
+    """Abstract base class for a document retrieval system.
 
     A retrieval system is defined as something that can take string queries and return
-    the most 'relevant' Documents from some source.
+    the most 'relevant' documents from some source.
 
     Usage:
 
-    A retriever follows the standard Runnable interface, and should be used
-    via the standard Runnable methods of `invoke`, `ainvoke`, `batch`, `abatch`.
+    A retriever follows the standard `Runnable` interface, and should be used via the
+    standard `Runnable` methods of `invoke`, `ainvoke`, `batch`, `abatch`.
 
     Implementation:
 
-    When implementing a custom retriever, the class should implement
-    the `_get_relevant_documents` method to define the logic for retrieving documents.
+    When implementing a custom retriever, the class should implement the
+    `_get_relevant_documents` method to define the logic for retrieving documents.
 
     Optionally, an async native implementations can be provided by overriding the
     `_aget_relevant_documents` method.
 
-    Example: A retriever that returns the first 5 documents from a list of documents
+    !!! example "Retriever that returns the first 5 documents from a list of documents"
 
         ```python
         from langchain_core.documents import Document
@@ -87,7 +87,7 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
                 return self.docs[:self.k]
         ```
 
-    Example: A simple retriever based on a scikit-learn vectorizer
+    !!! example "Simple retriever based on a scikit-learn vectorizer"
 
         ```python
         from sklearn.metrics.pairwise import cosine_similarity
@@ -119,15 +119,19 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
     _expects_other_args: bool = False
     tags: list[str] | None = 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.
+
     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
     use case.
     """

libs/core/langchain_core/runnables/base.py

@@ -147,11 +147,11 @@ class Runnable(ABC, Generic[Input, Output]):
     the `input_schema` property, the `output_schema` property and `config_schema`
     method.
 
-    LCEL and Composition
-    ====================
+    Composition
+    ===========
+
+    Runnable objects can be composed together to create chains in a declarative way.
 
-    The LangChain Expression Language (LCEL) is a declarative way to compose
-    `Runnable` objectsinto chains.
     Any chain constructed this way will automatically have sync, async, batch, and
     streaming support.
 
@@ -235,21 +235,21 @@ class Runnable(ABC, Generic[Input, Output]):
 
     You can set the global debug flag to True to enable debug output for all chains:
 
-        ```python
-        from langchain_core.globals import set_debug
+    ```python
+    from langchain_core.globals import set_debug
 
-        set_debug(True)
-        ```
+    set_debug(True)
+    ```
 
     Alternatively, you can pass existing or custom callbacks to any given chain:
 
-        ```python
-        from langchain_core.tracers import ConsoleCallbackHandler
+    ```python
+    from langchain_core.tracers import ConsoleCallbackHandler
 
-        chain.invoke(..., config={"callbacks": [ConsoleCallbackHandler()]})
-        ```
+    chain.invoke(..., config={"callbacks": [ConsoleCallbackHandler()]})
+    ```
 
-    For a UI (and much more) checkout [LangSmith](https://docs.smith.langchain.com/).
+    For a UI (and much more) checkout [LangSmith](https://docs.langchain.com/langsmith/home).
 
     """
 
@@ -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:
@@ -871,7 +871,6 @@ class Runnable(ABC, Generic[Input, Output]):
                 to do in parallel, and other keys. Please refer to the
                 `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:
@@ -938,7 +937,6 @@ class Runnable(ABC, Generic[Input, Output]):
                 do in parallel, and other keys. Please refer to the `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`.
 
         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:
@@ -1005,7 +1003,6 @@ class Runnable(ABC, Generic[Input, Output]):
                 do in parallel, and other keys. Please refer to the `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:
@@ -1069,7 +1066,6 @@ class Runnable(ABC, Generic[Input, Output]):
                 do in parallel, and other keys. Please refer to the `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`.
 
         Yields:
@@ -1116,7 +1112,7 @@ 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`.
@@ -1137,7 +1133,7 @@ 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`.
@@ -1357,7 +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
 
@@ -1370,8 +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"},
@@ -1426,7 +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`.
@@ -1500,7 +1497,7 @@ 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:
@@ -1545,7 +1542,7 @@ 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:
@@ -1816,7 +1813,7 @@ class Runnable(ABC, Generic[Input, Output]):
             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,
@@ -1837,14 +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.
@@ -1929,16 +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.
 
         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
@@ -1966,16 +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
@@ -2525,9 +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
@@ -2633,9 +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.
 
@@ -2648,7 +2637,7 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]):
         from langchain_openai import ChatOpenAI
 
         model = ChatAnthropic(
-            model_name="claude-3-7-sonnet-20250219"
+            model_name="claude-sonnet-4-5-20250929"
         ).configurable_alternatives(
             ConfigurableField(id="llm"),
             default_key="anthropic",
@@ -2896,7 +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"]`
@@ -3511,7 +3500,7 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
 
     Returns a mapping of their outputs.
 
-    `RunnableParallel` is one of the two main composition primitives for the LCEL,
+    `RunnableParallel` is one of the two main composition primitives,
     alongside `RunnableSequence`. It invokes `Runnable`s concurrently, providing the
     same input to each.
 
@@ -3627,7 +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"]`
@@ -5156,7 +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"]`
@@ -5479,7 +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"]`
@@ -5761,7 +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
 

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"]`