typo

new urls

extended thinking isn't just 3.7 anymore

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.
 

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">

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/core/README.md

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

libs/core/langchain_core/agents.py

@@ -84,7 +84,7 @@ class AgentAction(Serializable):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "schema", "agent"]`
@@ -112,7 +112,7 @@ class AgentActionMessageLog(AgentAction):
     if (tool, tool_input) cannot be used to fully recreate the LLM
     prediction, and you need that LLM prediction (for future agent iteration).
     Compared to `log`, this is useful when the underlying LLM is a
-    ChatModel (and therefore returns messages rather than a string)."""
+    chat model (and therefore returns messages rather than a string)."""
     # Ignoring type because we're overriding the type from AgentAction.
     # And this is the correct thing to do in this case.
     # The type literal is used for serialization purposes.
@@ -161,7 +161,7 @@ class AgentFinish(Serializable):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "schema", "agent"]`

libs/core/langchain_core/caches.py

@@ -1,18 +1,15 @@
-"""Cache classes.
+"""`caches` provides an optional caching layer for language models.
 
 !!! warning
-    Beta Feature!
+    This is a beta feature! Please be wary of deploying experimental code to production
+    unless you've taken appropriate precautions.
 
-**Cache** provides an optional caching layer for LLMs.
+A cache is useful for two reasons:
 
-Cache is useful for two reasons:
-
-- It can save you money by reducing the number of API calls you make to the LLM
+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 +31,8 @@ class BaseCache(ABC):
 
     The cache interface consists of the following methods:
 
-    - lookup: Look up a value based on a prompt and llm_string.
-    - update: Update the cache based on a prompt and llm_string.
+    - lookup: Look up a value based on a prompt and `llm_string`.
+    - update: Update the cache based on a prompt and `llm_string`.
     - clear: Clear the cache.
 
     In addition, the cache interface provides an async version of each method.
@@ -47,14 +44,14 @@ class BaseCache(ABC):
 
     @abstractmethod
     def lookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Look up based on prompt and llm_string.
+        """Look up based on `prompt` and `llm_string`.
 
         A cache implementation is expected to generate a key from the 2-tuple
         of prompt and llm_string (e.g., by concatenating them with a delimiter).
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
                 This is used to capture the invocation parameters of the LLM
@@ -63,27 +60,27 @@ class BaseCache(ABC):
                 representation.
 
         Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
-            The cached value is a list of Generations (or subclasses).
+            On a cache miss, return `None`. On a cache hit, return the cached value.
+            The cached value is a list of `Generation` (or subclasses).
         """
 
     @abstractmethod
     def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
-        """Update cache based on prompt and llm_string.
+        """Update cache based on `prompt` and `llm_string`.
 
         The prompt and llm_string are used to generate a key for the cache.
         The key should match that of the lookup method.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
                 This is used to capture the invocation parameters of the LLM
                 (e.g., model name, temperature, stop tokens, max tokens, etc.).
                 These invocation parameters are serialized into a string
                 representation.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                 (or subclasses).
         """
 
@@ -92,14 +89,14 @@ class BaseCache(ABC):
         """Clear cache that can take additional keyword arguments."""
 
     async def alookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Async look up based on prompt and llm_string.
+        """Async look up based on `prompt` and `llm_string`.
 
         A cache implementation is expected to generate a key from the 2-tuple
         of prompt and llm_string (e.g., by concatenating them with a delimiter).
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
                 This is used to capture the invocation parameters of the LLM
@@ -108,29 +105,29 @@ class BaseCache(ABC):
                 representation.
 
         Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
-            The cached value is a list of Generations (or subclasses).
+            On a cache miss, return `None`. On a cache hit, return the cached value.
+            The cached value is a list of `Generation` (or subclasses).
         """
         return await run_in_executor(None, self.lookup, prompt, llm_string)
 
     async def aupdate(
         self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
     ) -> None:
-        """Async update cache based on prompt and llm_string.
+        """Async update cache based on `prompt` and `llm_string`.
 
         The prompt and llm_string are used to generate a key for the cache.
         The key should match that of the look up method.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
                 This is used to capture the invocation parameters of the LLM
                 (e.g., model name, temperature, stop tokens, max tokens, etc.).
                 These invocation parameters are serialized into a string
                 representation.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                 (or subclasses).
         """
         return await run_in_executor(None, self.update, prompt, llm_string, return_val)
@@ -150,10 +147,9 @@ class InMemoryCache(BaseCache):
             maxsize: The maximum number of items to store in the cache.
                 If `None`, the cache has no maximum size.
                 If the cache exceeds the maximum size, the oldest items are removed.
-                Default is None.
 
         Raises:
-            ValueError: If maxsize is less than or equal to 0.
+            ValueError: If `maxsize` is less than or equal to `0`.
         """
         self._cache: dict[tuple[str, str], RETURN_VAL_TYPE] = {}
         if maxsize is not None and maxsize <= 0:
@@ -162,28 +158,28 @@ class InMemoryCache(BaseCache):
         self._maxsize = maxsize
 
     def lookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Look up based on prompt and llm_string.
+        """Look up based on `prompt` and `llm_string`.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
 
         Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
+            On a cache miss, return `None`. On a cache hit, return the cached value.
         """
         return self._cache.get((prompt, llm_string), None)
 
     def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
-        """Update cache based on prompt and llm_string.
+        """Update cache based on `prompt` and `llm_string`.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                 (or subclasses).
         """
         if self._maxsize is not None and len(self._cache) == self._maxsize:
@@ -196,30 +192,30 @@ class InMemoryCache(BaseCache):
         self._cache = {}
 
     async def alookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Async look up based on prompt and llm_string.
+        """Async look up based on `prompt` and `llm_string`.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
 
         Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
+            On a cache miss, return `None`. On a cache hit, return the cached value.
         """
         return self.lookup(prompt, llm_string)
 
     async def aupdate(
         self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
     ) -> None:
-        """Async update cache based on prompt and llm_string.
+        """Async update cache based on `prompt` and `llm_string`.
 
         Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                 serialization of the prompt into the language model.
             llm_string: A string representation of the LLM configuration.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                 (or subclasses).
         """
         self.update(prompt, llm_string, return_val)

libs/core/langchain_core/callbacks/base.py

@@ -1001,7 +1001,7 @@ 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 +1028,7 @@ 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 +1044,7 @@ 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 +1057,7 @@ 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 +1087,7 @@ 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/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/chat_history.py

@@ -153,7 +153,7 @@ class BaseChatMessageHistory(ABC):
 
         Raises:
             NotImplementedError: If the sub-class has not implemented an efficient
-                add_messages method.
+                `add_messages` method.
         """
         if type(self).add_messages != BaseChatMessageHistory.add_messages:
             # This means that the sub-class has implemented an efficient add_messages

libs/core/langchain_core/document_loaders/base.py

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

libs/core/langchain_core/document_loaders/langsmith.py

@@ -76,8 +76,8 @@ class LangSmithLoader(BaseLoader):
             splits: A list of dataset splits, which are
                 divisions of your dataset such as 'train', 'test', or 'validation'.
                 Returns examples only from the specified splits.
-            inline_s3_urls: Whether to inline S3 URLs. Defaults to `True`.
-            offset: The offset to start from. Defaults to 0.
+            inline_s3_urls: Whether to inline S3 URLs.
+            offset: The offset to start from.
             limit: The maximum number of examples to return.
             metadata: Metadata to filter by.
             filter: A structured filter string to apply to the examples.

libs/core/langchain_core/documents/base.py

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

libs/core/langchain_core/embeddings/fake.py

@@ -18,7 +18,8 @@ class FakeEmbeddings(Embeddings, BaseModel):
 
     This embedding model creates embeddings by sampling from a normal distribution.
 
-    Do not use this outside of testing, as it is not a real embedding model.
+    !!! warning
+        Do not use this outside of testing, as it is not a real embedding model.
 
     Instantiate:
         ```python
@@ -72,7 +73,8 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
     This embedding model creates embeddings by sampling from a normal distribution
     with a seed based on the hash of the text.
 
-    Do not use this outside of testing, as it is not a real embedding model.
+    !!! warning
+        Do not use this outside of testing, as it is not a real embedding model.
 
     Instantiate:
         ```python

libs/core/langchain_core/example_selectors/semantic_similarity.py

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

libs/core/langchain_core/exceptions.py

@@ -16,7 +16,7 @@ class OutputParserException(ValueError, LangChainException):  # noqa: N818
     """Exception that output parsers should raise to signify a parsing error.
 
     This exists to differentiate parsing errors from other code or execution errors
-    that also may arise inside the output parser. OutputParserExceptions will be
+    that also may arise inside the output parser. `OutputParserException` will be
     available to catch and handle in ways to fix the parsing error, while other
     errors will be raised.
     """
@@ -28,7 +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

libs/core/langchain_core/indexing/api.py

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

libs/core/langchain_core/language_models/__init__.py

@@ -1,43 +1,29 @@
 """Language models.
 
-**Language Model** is a type of model that can generate text or complete
-text prompts.
+LangChain has two main classes to work with language models: chat models and
+"old-fashioned" LLMs.
 
-LangChain has two main classes to work with language models: **Chat Models**
-and "old-fashioned" **LLMs**.
-
-**Chat Models**
+**Chat models**
 
 Language models that use a sequence of messages as inputs and return chat messages
-as outputs (as opposed to using plain text). These are traditionally newer models (
-older models are generally LLMs, see below). Chat models support the assignment of
+as outputs (as opposed to using plain text). Chat models support the assignment of
 distinct roles to conversation messages, helping to distinguish messages from the AI,
 users, and instructions such as system messages.
 
 The key abstraction for chat models is `BaseChatModel`. Implementations
-should inherit from this class. Please see LangChain how-to guides with more
-information on how to implement a custom chat model.
+should inherit from this class.
 
-To implement a custom Chat Model, inherit from `BaseChatModel`. See
-the following guide for more information on how to implement a custom Chat Model:
-
-https://python.langchain.com/docs/how_to/custom_chat_model/
+See existing [chat model integrations](https://docs.langchain.com/oss/python/integrations/chat).
 
 **LLMs**
 
 Language models that takes a string as input and returns a string.
-These are traditionally older models (newer models generally are Chat Models,
-see below).
+These are traditionally older models (newer models generally are chat models).
 
-Although the underlying models are string in, string out, the LangChain wrappers
-also allow these models to take messages as input. This gives them the same interface
-as Chat Models. When messages are passed in as input, they will be formatted into a
-string under the hood before being passed to the underlying model.
-
-To implement a custom LLM, inherit from `BaseLLM` or `LLM`.
-Please see the following guide for more information on how to implement a custom LLM:
-
-https://python.langchain.com/docs/how_to/custom_llm/
+Although the underlying models are string in, string out, the LangChain wrappers also
+allow these models to take messages as input. This gives them the same interface as
+chat models. When messages are passed in as input, they will be formatted into a string
+under the hood before being passed to the underlying model.
 """
 
 from typing import TYPE_CHECKING

libs/core/langchain_core/language_models/_utils.py

@@ -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:

libs/core/langchain_core/language_models/chat_models.py

@@ -264,21 +264,21 @@ 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 +287,6 @@ 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 +322,12 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
     Supported values:
 
     - `'v0'`: provider-specific format in content (can lazily-parse with
-        `.content_blocks`)
-    - `'v1'`: standardized format in content (consistent with `.content_blocks`)
+        `content_blocks`)
+    - `'v1'`: standardized format in content (consistent with `content_blocks`)
 
-    Partner packages (e.g., `langchain-openai`) can also use this field to roll out
-    new content formats in a backward-compatible way.
+    Partner packages (e.g.,
+    [`langchain-openai`](https://pypi.org/project/langchain-openai)) can also use this
+    field to roll out new content formats in a backward-compatible way.
 
     !!! version-added "Added in version 1.0"
 

libs/core/langchain_core/language_models/fake_chat_models.py

@@ -1,4 +1,4 @@
-"""Fake ChatModel for testing purposes."""
+"""Fake chat model for testing purposes."""
 
 import asyncio
 import re
@@ -19,7 +19,7 @@ from langchain_core.runnables import RunnableConfig
 
 
 class FakeMessagesListChatModel(BaseChatModel):
-    """Fake `ChatModel` for testing purposes."""
+    """Fake chat model for testing purposes."""
 
     responses: list[BaseMessage]
     """List of responses to **cycle** through in order."""
@@ -57,7 +57,7 @@ class FakeListChatModelError(Exception):
 
 
 class FakeListChatModel(SimpleChatModel):
-    """Fake ChatModel for testing purposes."""
+    """Fake chat model for testing purposes."""
 
     responses: list[str]
     """List of responses to **cycle** through in order."""

libs/core/langchain_core/language_models/llms.py

@@ -74,8 +74,8 @@ def create_base_retry_decorator(
 
     Args:
         error_types: List of error types to retry on.
-        max_retries: Number of retries. Default is 1.
-        run_manager: Callback manager for the run. Default is None.
+        max_retries: Number of retries.
+        run_manager: Callback manager for the run.
 
     Returns:
         A retry decorator.
@@ -153,7 +153,7 @@ def get_prompts(
     Args:
         params: Dictionary of parameters.
         prompts: List of prompts.
-        cache: Cache object. Default is None.
+        cache: Cache object.
 
     Returns:
         A tuple of existing prompts, llm_string, missing prompt indexes,
@@ -189,7 +189,7 @@ async def aget_prompts(
     Args:
         params: Dictionary of parameters.
         prompts: List of prompts.
-        cache: Cache object. Default is None.
+        cache: Cache object.
 
     Returns:
         A tuple of existing prompts, llm_string, missing prompt indexes,

libs/core/langchain_core/load/dump.py

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

libs/core/langchain_core/load/load.py

@@ -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

@@ -96,7 +96,7 @@ 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.
@@ -127,10 +127,10 @@ 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.

libs/core/langchain_core/messages/ai.py

@@ -148,27 +148,26 @@ class UsageMetadata(TypedDict):
 class AIMessage(BaseMessage):
     """Message from an AI.
 
-    AIMessage is returned from a chat model as a response to a prompt.
+    An `AIMessage` is returned from a chat model as a response to a prompt.
 
     This message represents the output of the model and consists of both
-    the raw output as returned by the model together standardized fields
+    the raw output as returned by the model and standardized fields
     (e.g., tool calls, usage metadata) added by the LangChain framework.
 
     """
 
     tool_calls: list[ToolCall] = []
-    """If provided, tool calls associated with the message."""
+    """If present, tool calls associated with the message."""
     invalid_tool_calls: list[InvalidToolCall] = []
-    """If provided, tool calls with parsing errors associated with the message."""
+    """If present, tool calls with parsing errors associated with the message."""
     usage_metadata: UsageMetadata | None = None
-    """If provided, usage metadata for a message, such as token counts.
+    """If present, usage metadata for a message, such as token counts.
 
     This is a standard representation of token usage that is consistent across models.
-
     """
 
     type: Literal["ai"] = "ai"
-    """The type of the message (used for deserialization). Defaults to "ai"."""
+    """The type of the message (used for deserialization)."""
 
     @overload
     def __init__(
@@ -191,7 +190,7 @@ class AIMessage(BaseMessage):
         content_blocks: list[types.ContentBlock] | None = None,
         **kwargs: Any,
     ) -> None:
-        """Initialize `AIMessage`.
+        """Initialize an `AIMessage`.
 
         Specify `content` as positional arg or `content_blocks` for typing.
 
@@ -217,7 +216,11 @@ class AIMessage(BaseMessage):
 
     @property
     def lc_attributes(self) -> dict:
-        """Attrs to be serialized even if they are derived from other init args."""
+        """Attributes to be serialized.
+
+        Includes all attributes, even if they are derived from other initialization
+        arguments.
+        """
         return {
             "tool_calls": self.tool_calls,
             "invalid_tool_calls": self.invalid_tool_calls,
@@ -225,7 +228,7 @@ class AIMessage(BaseMessage):
 
     @property
     def content_blocks(self) -> list[types.ContentBlock]:
-        """Return content blocks of the message.
+        """Return standard, typed `ContentBlock` dicts from the message.
 
         If the message has a known model provider, use the provider-specific translator
         first before falling back to best-effort parsing. For details, see the property
@@ -331,11 +334,10 @@ class AIMessage(BaseMessage):
 
     @override
     def pretty_repr(self, html: bool = False) -> str:
-        """Return a pretty representation of the message.
+        """Return a pretty representation of the message for display.
 
         Args:
             html: Whether to return an HTML-formatted string.
-                Defaults to `False`.
 
         Returns:
             A pretty representation of the message.
@@ -372,23 +374,19 @@ class AIMessage(BaseMessage):
 
 
 class AIMessageChunk(AIMessage, BaseMessageChunk):
-    """Message chunk from an AI."""
+    """Message chunk from an AI (yielded when streaming)."""
 
     # Ignoring mypy re-assignment here since we're overriding the value
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["AIMessageChunk"] = "AIMessageChunk"  # type: ignore[assignment]
-    """The type of the message (used for deserialization).
-
-    Defaults to `AIMessageChunk`.
-
-    """
+    """The type of the message (used for deserialization)."""
 
     tool_call_chunks: list[ToolCallChunk] = []
     """If provided, tool call chunks associated with the message."""
 
     chunk_position: Literal["last"] | None = None
-    """Optional span represented by an aggregated AIMessageChunk.
+    """Optional span represented by an aggregated `AIMessageChunk`.
 
     If a chunk with `chunk_position="last"` is aggregated into a stream,
     `tool_call_chunks` in message content will be parsed into `tool_calls`.
@@ -396,7 +394,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
 
     @property
     def lc_attributes(self) -> dict:
-        """Attrs to be serialized even if they are derived from other init args."""
+        """Attributes to be serialized, even if they are derived from other initialization args."""  # noqa: E501
         return {
             "tool_calls": self.tool_calls,
             "invalid_tool_calls": self.invalid_tool_calls,
@@ -404,7 +402,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
 
     @property
     def content_blocks(self) -> list[types.ContentBlock]:
-        """Return content blocks of the message."""
+        """Return standard, typed `ContentBlock` dicts from the message."""
         if self.response_metadata.get("output_version") == "v1":
             return cast("list[types.ContentBlock]", self.content)
 
@@ -545,12 +543,15 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
                     and call_id in id_to_tc
                 ):
                     self.content[idx] = cast("dict[str, Any]", id_to_tc[call_id])
+                    if "extras" in block:
+                        # mypy does not account for instance check for dict above
+                        self.content[idx]["extras"] = block["extras"]  # type: ignore[index]
 
         return self
 
     @model_validator(mode="after")
     def init_server_tool_calls(self) -> Self:
-        """Parse server_tool_call_chunks."""
+        """Parse `server_tool_call_chunks`."""
         if (
             self.chunk_position == "last"
             and self.response_metadata.get("output_version") == "v1"

libs/core/langchain_core/messages/base.py

@@ -92,11 +92,11 @@ class TextAccessor(str):
 class BaseMessage(Serializable):
     """Base abstract message class.
 
-    Messages are the inputs and outputs of a `ChatModel`.
+    Messages are the inputs and outputs of a chat model.
     """
 
     content: str | list[str | dict]
-    """The string contents of the message."""
+    """The contents of the message."""
 
     additional_kwargs: dict = Field(default_factory=dict)
     """Reserved for additional payload data associated with the message.
@@ -159,12 +159,12 @@ class BaseMessage(Serializable):
         content_blocks: list[types.ContentBlock] | None = None,
         **kwargs: Any,
     ) -> None:
-        """Initialize `BaseMessage`.
+        """Initialize a `BaseMessage`.
 
         Specify `content` as positional arg or `content_blocks` for typing.
 
         Args:
-            content: The string contents of the message.
+            content: The contents of the message.
             content_blocks: Typed standard content.
             **kwargs: Additional arguments to pass to the parent class.
         """
@@ -184,7 +184,7 @@ class BaseMessage(Serializable):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "schema", "messages"]`
@@ -262,7 +262,7 @@ class BaseMessage(Serializable):
         Can be used as both property (`message.text`) and method (`message.text()`).
 
         !!! deprecated
-            As of langchain-core 1.0.0, calling `.text()` as a method is deprecated.
+            As of `langchain-core` 1.0.0, calling `.text()` as a method is deprecated.
             Use `.text` as a property instead. This method will be removed in 2.0.0.
 
         Returns:
@@ -307,7 +307,7 @@ class BaseMessage(Serializable):
 
         Args:
             html: Whether to format the message as HTML. If `True`, the message will be
-                formatted with HTML tags. Default is False.
+                formatted with HTML tags.
 
         Returns:
             A pretty representation of the message.
@@ -464,7 +464,7 @@ def get_msg_title_repr(title: str, *, bold: bool = False) -> str:
 
     Args:
         title: The title.
-        bold: Whether to bold the title. Default is False.
+        bold: Whether to bold the title.
 
     Returns:
         The title representation.

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

@@ -28,7 +28,7 @@ dictionary with two keys:
 - `'translate_content'`: Function to translate `AIMessage` content.
 - `'translate_content_chunk'`: Function to translate `AIMessageChunk` content.
 
-When calling `.content_blocks` on an `AIMessage` or `AIMessageChunk`, if
+When calling `content_blocks` on an `AIMessage` or `AIMessageChunk`, if
 `model_provider` is set in `response_metadata`, the corresponding translator
 functions will be used to parse the content into blocks. Otherwise, best-effort parsing
 in `BaseMessage` will be used.

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

@@ -31,7 +31,7 @@ def _convert_to_v1_from_anthropic_input(
 ) -> list[types.ContentBlock]:
     """Convert Anthropic format blocks to v1 format.
 
-    During the `.content_blocks` parsing process, we wrap blocks not recognized as a v1
+    During the `content_blocks` parsing process, we wrap blocks not recognized as a v1
     block as a `'non_standard'` block with the original block stored in the `value`
     field. This function attempts to unpack those blocks and convert any blocks that
     might be Anthropic format to v1 ContentBlocks.

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

@@ -35,7 +35,7 @@ def _convert_to_v1_from_converse_input(
 ) -> list[types.ContentBlock]:
     """Convert Bedrock Converse format blocks to v1 format.
 
-    During the `.content_blocks` parsing process, we wrap blocks not recognized as a v1
+    During the `content_blocks` parsing process, we wrap blocks not recognized as a v1
     block as a `'non_standard'` block with the original block stored in the `value`
     field. This function attempts to unpack those blocks and convert any blocks that
     might be Converse format to v1 ContentBlocks.

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

@@ -105,7 +105,7 @@ def _convert_to_v1_from_genai_input(
     Called when message isn't an `AIMessage` or `model_provider` isn't set on
     `response_metadata`.
 
-    During the `.content_blocks` parsing process, we wrap blocks not recognized as a v1
+    During the `content_blocks` parsing process, we wrap blocks not recognized as a v1
     block as a `'non_standard'` block with the original block stored in the `value`
     field. This function attempts to unpack those blocks and convert any blocks that
     might be GenAI format to v1 ContentBlocks.
@@ -453,9 +453,10 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                     "status": status,  # type: ignore[typeddict-item]
                     "output": item.get("code_execution_result", ""),
                 }
+                server_tool_result_block["extras"] = {"block_type": item_type}
                 # Preserve original outcome in extras
                 if outcome is not None:
-                    server_tool_result_block["extras"] = {"outcome": outcome}
+                    server_tool_result_block["extras"]["outcome"] = outcome
                 converted_blocks.append(server_tool_result_block)
             else:
                 # Unknown type, preserve as non-standard

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

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

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

@@ -10,7 +10,7 @@ def _convert_v0_multimodal_input_to_v1(
 ) -> list[types.ContentBlock]:
     """Convert v0 multimodal blocks to v1 format.
 
-    During the `.content_blocks` parsing process, we wrap blocks not recognized as a v1
+    During the `content_blocks` parsing process, we wrap blocks not recognized as a v1
     block as a `'non_standard'` block with the original block stored in the `value`
     field. This function attempts to unpack those blocks and convert any v0 format
     blocks to v1 format.

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

@@ -155,7 +155,7 @@ def _convert_to_v1_from_chat_completions_input(
 ) -> list[types.ContentBlock]:
     """Convert OpenAI Chat Completions format blocks to v1 format.
 
-    During the `.content_blocks` parsing process, we wrap blocks not recognized as a v1
+    During the `content_blocks` parsing process, we wrap blocks not recognized as a v1
     block as a `'non_standard'` block with the original block stored in the `value`
     field. This function attempts to unpack those blocks and convert any blocks that
     might be OpenAI format to v1 ContentBlocks.

libs/core/langchain_core/messages/chat.py

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

libs/core/langchain_core/messages/content.py

@@ -143,7 +143,7 @@ class Citation(TypedDict):
         not the source text. This means that the indices are relative to the model's
         response, not the original document (as specified in the `url`).
 
-    !!! note
+    !!! note "Factory function"
         `create_citation` may also be used as a factory to create a `Citation`.
         Benefits include:
 
@@ -156,7 +156,9 @@ class Citation(TypedDict):
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier. Either:
+    """Content block identifier.
+
+    Either:
 
     - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
@@ -201,6 +203,7 @@ class NonStandardAnnotation(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
@@ -211,6 +214,7 @@ class NonStandardAnnotation(TypedDict):
 
 
 Annotation = Citation | NonStandardAnnotation
+"""A union of all defined `Annotation` types."""
 
 
 class TextContentBlock(TypedDict):
@@ -219,7 +223,7 @@ class TextContentBlock(TypedDict):
     This typically represents the main text content of a message, such as the response
     from a language model or the text of a user message.
 
-    !!! note
+    !!! note "Factory function"
         `create_text_block` may also be used as a factory to create a
         `TextContentBlock`. Benefits include:
 
@@ -235,6 +239,7 @@ class TextContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
@@ -254,7 +259,7 @@ class TextContentBlock(TypedDict):
 
 
 class ToolCall(TypedDict):
-    """Represents a request to call a tool.
+    """Represents an AI's request to call a tool.
 
     Example:
         ```python
@@ -264,7 +269,7 @@ class ToolCall(TypedDict):
         This represents a request to call the tool named "foo" with arguments {"a": 1}
         and an identifier of "123".
 
-    !!! note
+    !!! note "Factory function"
         `create_tool_call` may also be used as a factory to create a
         `ToolCall`. Benefits include:
 
@@ -299,7 +304,7 @@ class ToolCall(TypedDict):
 
 
 class ToolCallChunk(TypedDict):
-    """A chunk of a tool call (e.g., as part of a stream).
+    """A chunk of a tool call (yielded when streaming).
 
     When merging `ToolCallChunks` (e.g., via `AIMessageChunk.__add__`),
     all string attributes are concatenated. Chunks are only merged if their
@@ -381,7 +386,10 @@ class InvalidToolCall(TypedDict):
 
 
 class ServerToolCall(TypedDict):
-    """Tool call that is executed server-side."""
+    """Tool call that is executed server-side.
+
+    For example: code execution, web search, etc.
+    """
 
     type: Literal["server_tool_call"]
     """Used for discrimination."""
@@ -403,7 +411,7 @@ class ServerToolCall(TypedDict):
 
 
 class ServerToolCallChunk(TypedDict):
-    """A chunk of a tool call (as part of a stream)."""
+    """A chunk of a server-side tool call (yielded when streaming)."""
 
     type: Literal["server_tool_call_chunk"]
     """Used for discrimination."""
@@ -452,7 +460,7 @@ class ServerToolResult(TypedDict):
 class ReasoningContentBlock(TypedDict):
     """Reasoning output from a LLM.
 
-    !!! note
+    !!! note "Factory function"
         `create_reasoning_block` may also be used as a factory to create a
         `ReasoningContentBlock`. Benefits include:
 
@@ -468,6 +476,7 @@ class ReasoningContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
@@ -494,7 +503,7 @@ class ReasoningContentBlock(TypedDict):
 class ImageContentBlock(TypedDict):
     """Image data.
 
-    !!! note
+    !!! note "Factory function"
         `create_image_block` may also be used as a factory to create a
         `ImageContentBlock`. Benefits include:
 
@@ -510,6 +519,7 @@ class ImageContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
@@ -541,7 +551,7 @@ class ImageContentBlock(TypedDict):
 class VideoContentBlock(TypedDict):
     """Video data.
 
-    !!! note
+    !!! note "Factory function"
         `create_video_block` may also be used as a factory to create a
         `VideoContentBlock`. Benefits include:
 
@@ -557,6 +567,7 @@ class VideoContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
@@ -588,7 +599,7 @@ class VideoContentBlock(TypedDict):
 class AudioContentBlock(TypedDict):
     """Audio data.
 
-    !!! note
+    !!! note "Factory function"
         `create_audio_block` may also be used as a factory to create an
         `AudioContentBlock`. Benefits include:
         * Automatic ID generation (when not provided)
@@ -603,6 +614,7 @@ class AudioContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
@@ -642,9 +654,9 @@ class PlainTextContentBlock(TypedDict):
 
     !!! note
         Title and context are optional fields that may be passed to the model. See
-        Anthropic [example](https://docs.anthropic.com/en/docs/build-with-claude/citations#citable-vs-non-citable-content).
+        Anthropic [example](https://docs.claude.com/en/docs/build-with-claude/citations#citable-vs-non-citable-content).
 
-    !!! note
+    !!! note "Factory function"
         `create_plaintext_block` may also be used as a factory to create a
         `PlainTextContentBlock`. Benefits include:
 
@@ -660,6 +672,7 @@ class PlainTextContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
@@ -694,7 +707,7 @@ class PlainTextContentBlock(TypedDict):
 
 
 class FileContentBlock(TypedDict):
-    """File data that doesn't fit into other multimodal blocks.
+    """File data that doesn't fit into other multimodal block types.
 
     This block is intended for files that are not images, audio, or plaintext. For
     example, it can be used for PDFs, Word documents, etc.
@@ -703,7 +716,7 @@ class FileContentBlock(TypedDict):
     content block type (e.g., `ImageContentBlock`, `AudioContentBlock`,
     `PlainTextContentBlock`).
 
-    !!! note
+    !!! note "Factory function"
         `create_file_block` may also be used as a factory to create a
         `FileContentBlock`. Benefits include:
 
@@ -719,6 +732,7 @@ class FileContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
@@ -765,7 +779,7 @@ class NonStandardContentBlock(TypedDict):
     Has no `extras` field, as provider-specific data should be included in the
     `value` field.
 
-    !!! note
+    !!! note "Factory function"
         `create_non_standard_block` may also be used as a factory to create a
         `NonStandardContentBlock`. Benefits include:
 
@@ -781,6 +795,7 @@ class NonStandardContentBlock(TypedDict):
     """Content block identifier.
 
     Either:
+
     - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
 
@@ -801,6 +816,7 @@ DataContentBlock = (
     | PlainTextContentBlock
     | FileContentBlock
 )
+"""A union of all defined multimodal data `ContentBlock` types."""
 
 ToolContentBlock = (
     ToolCall | ToolCallChunk | ServerToolCall | ServerToolCallChunk | ServerToolResult
@@ -814,6 +830,7 @@ ContentBlock = (
     | DataContentBlock
     | ToolContentBlock
 )
+"""A union of all defined `ContentBlock` types and aliases."""
 
 
 KNOWN_BLOCK_TYPES = {

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.
@@ -425,19 +425,19 @@ 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.
@@ -568,7 +568,6 @@ def merge_message_runs(
     Args:
         messages: Sequence Message-like objects to merge.
         chunk_separator: Specify the string to be inserted between message chunks.
-            Defaults to `'\n'`.
 
     Returns:
         list of BaseMessages with consecutive runs of message types merged into single
@@ -703,7 +702,7 @@ def trim_messages(
     r"""Trim messages to be below a token count.
 
     `trim_messages` can be used to reduce the size of a chat history to a specified
-    token count or specified message count.
+    token or message count.
 
     In either case, if passing the trimmed chat history back into a chat model
     directly, the resulting chat history should usually satisfy the following
@@ -714,8 +713,6 @@ def trim_messages(
         followed by a `HumanMessage`. To achieve this, set `start_on='human'`.
         In addition, generally a `ToolMessage` can only appear after an `AIMessage`
         that involved a tool call.
-        Please see the following link for more information about messages:
-        https://python.langchain.com/docs/concepts/#messages
     2. It includes recent messages and drops old messages in the chat history.
         To achieve this set the `strategy='last'`.
     3. Usually, the new chat history should include the `SystemMessage` if it
@@ -745,12 +742,10 @@ def trim_messages(
         strategy: Strategy for trimming.
             - `'first'`: Keep the first `<= n_count` tokens of the messages.
             - `'last'`: Keep the last `<= n_count` tokens of the messages.
-            Default is `'last'`.
         allow_partial: Whether to split a message if only part of the message can be
             included. If `strategy='last'` then the last partial contents of a message
             are included. If `strategy='first'` then the first partial contents of a
             message are included.
-            Default is False.
         end_on: The message type to end on. If specified then every message after the
             last occurrence of this type is ignored. If `strategy='last'` then this
             is done before we attempt to get the last `max_tokens`. If
@@ -759,7 +754,7 @@ def trim_messages(
             `'human'`, `'ai'`, ...) or as `BaseMessage` classes (e.g.
             `SystemMessage`, `HumanMessage`, `AIMessage`, ...). Can be a single
             type or a list of types.
-            Default is None.
+
         start_on: The message type to start on. Should only be specified if
             `strategy='last'`. If specified then every message before
             the first occurrence of this type is ignored. This is done after we trim
@@ -768,10 +763,9 @@ def trim_messages(
             specified as string names (e.g. `'system'`, `'human'`, `'ai'`, ...) or
             as `BaseMessage` classes (e.g. `SystemMessage`, `HumanMessage`,
             `AIMessage`, ...). Can be a single type or a list of types.
-            Default is None.
-        include_system: Whether to keep the SystemMessage if there is one at index 0.
-            Should only be specified if `strategy="last"`.
-            Default is False.
+
+        include_system: Whether to keep the `SystemMessage` if there is one at index
+            `0`. Should only be specified if `strategy="last"`.
         text_splitter: Function or `langchain_text_splitters.TextSplitter` for
             splitting the string contents of a message. Only used if
             `allow_partial=True`. If `strategy='last'` then the last split tokens
@@ -782,7 +776,7 @@ def trim_messages(
             newlines.
 
     Returns:
-        list of trimmed `BaseMessage`.
+        List of trimmed `BaseMessage`.
 
     Raises:
         ValueError: if two incompatible arguments are specified or an unrecognized
@@ -1683,11 +1677,11 @@ def count_tokens_approximately(
     Args:
         messages: List of messages to count tokens for.
         chars_per_token: Number of characters per token to use for the approximation.
-            Default is 4 (one token corresponds to ~4 chars for common English text).
+            One token corresponds to ~4 chars for common English text.
             You can also specify float values for more fine-grained control.
             [See more here](https://platform.openai.com/tokenizer).
-        extra_tokens_per_message: Number of extra tokens to add per message.
-            Default is 3 (special tokens, including beginning/end of message).
+        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.

libs/core/langchain_core/output_parsers/base.py

@@ -31,13 +31,13 @@ class BaseLLMOutputParser(ABC, Generic[T]):
 
     @abstractmethod
     def parse_result(self, result: list[Generation], *, partial: bool = False) -> T:
-        """Parse a list of candidate model Generations into a specific format.
+        """Parse a list of candidate model `Generation` objects into a specific format.
 
         Args:
-            result: A list of Generations to be parsed. The Generations are assumed
-                to be different candidate outputs for a single model input.
+            result: A list of `Generation` to be parsed. The `Generation` objects are
+                assumed to be different candidate outputs for a single model input.
             partial: Whether to parse the output as a partial result. This is useful
-                for parsers that can parse partial results. Default is False.
+                for parsers that can parse partial results.
 
         Returns:
             Structured output.
@@ -46,17 +46,17 @@ class BaseLLMOutputParser(ABC, Generic[T]):
     async def aparse_result(
         self, result: list[Generation], *, partial: bool = False
     ) -> T:
-        """Async parse a list of candidate model Generations into a specific format.
+        """Async parse a list of candidate model `Generation` objects into a specific format.
 
         Args:
-            result: A list of Generations to be parsed. The Generations are assumed
+            result: A list of `Generation` to be parsed. The Generations are assumed
                 to be different candidate outputs for a single model input.
             partial: Whether to parse the output as a partial result. This is useful
-                for parsers that can parse partial results. Default is False.
+                for parsers that can parse partial results.
 
         Returns:
             Structured output.
-        """
+        """  # noqa: E501
         return await run_in_executor(None, self.parse_result, result, partial=partial)
 
 
@@ -172,7 +172,7 @@ class BaseOutputParser(
         This property is inferred from the first type argument of the class.
 
         Raises:
-            TypeError: If the class doesn't have an inferable OutputType.
+            TypeError: If the class doesn't have an inferable `OutputType`.
         """
         for base in self.__class__.mro():
             if hasattr(base, "__pydantic_generic_metadata__"):
@@ -234,16 +234,16 @@ class BaseOutputParser(
 
     @override
     def parse_result(self, result: list[Generation], *, partial: bool = False) -> T:
-        """Parse a list of candidate model Generations into a specific format.
+        """Parse a list of candidate model `Generation` objects into a specific format.
 
-        The return value is parsed from only the first Generation in the result, which
-            is assumed to be the highest-likelihood Generation.
+        The return value is parsed from only the first `Generation` in the result, which
+            is assumed to be the highest-likelihood `Generation`.
 
         Args:
-            result: A list of Generations to be parsed. The Generations are assumed
-                to be different candidate outputs for a single model input.
+            result: A list of `Generation` to be parsed. The `Generation` objects are
+                assumed to be different candidate outputs for a single model input.
             partial: Whether to parse the output as a partial result. This is useful
-                for parsers that can parse partial results. Default is False.
+                for parsers that can parse partial results.
 
         Returns:
             Structured output.
@@ -264,20 +264,20 @@ class BaseOutputParser(
     async def aparse_result(
         self, result: list[Generation], *, partial: bool = False
     ) -> T:
-        """Async parse a list of candidate model Generations into a specific format.
+        """Async parse a list of candidate model `Generation` objects into a specific format.
 
-        The return value is parsed from only the first Generation in the result, which
-            is assumed to be the highest-likelihood Generation.
+        The return value is parsed from only the first `Generation` in the result, which
+            is assumed to be the highest-likelihood `Generation`.
 
         Args:
-            result: A list of Generations to be parsed. The Generations are assumed
-                to be different candidate outputs for a single model input.
+            result: A list of `Generation` to be parsed. The `Generation` objects are
+                assumed to be different candidate outputs for a single model input.
             partial: Whether to parse the output as a partial result. This is useful
-                for parsers that can parse partial results. Default is False.
+                for parsers that can parse partial results.
 
         Returns:
             Structured output.
-        """
+        """  # noqa: E501
         return await run_in_executor(None, self.parse_result, result, partial=partial)
 
     async def aparse(self, text: str) -> T:
@@ -299,13 +299,13 @@ class BaseOutputParser(
     ) -> Any:
         """Parse the output of an LLM call with the input prompt for context.
 
-        The prompt is largely provided in the event the OutputParser wants
+        The prompt is largely provided in the event the `OutputParser` wants
         to retry or fix the output in some way, and needs information from
         the prompt to do so.
 
         Args:
             completion: String output of a language model.
-            prompt: Input PromptValue.
+            prompt: Input `PromptValue`.
 
         Returns:
             Structured output.

libs/core/langchain_core/output_parsers/json.py

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

libs/core/langchain_core/output_parsers/list.py

@@ -146,7 +146,7 @@ class CommaSeparatedListOutputParser(ListOutputParser):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "output_parsers", "list"]`

libs/core/langchain_core/output_parsers/openai_functions.py

@@ -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.
@@ -229,7 +226,6 @@ class JsonOutputKeyToolsParser(JsonOutputToolsParser):
                 If `True`, the output will be a JSON object containing
                 all the keys that have been returned so far.
                 If `False`, the output will be the full JSON object.
-                Default is False.
 
         Raises:
             OutputParserException: If the generation is not a chat generation.
@@ -313,7 +309,6 @@ class PydanticToolsParser(JsonOutputToolsParser):
                 If `True`, the output will be a JSON object containing
                 all the keys that have been returned so far.
                 If `False`, the output will be the full JSON object.
-                Default is False.
 
         Returns:
             The parsed Pydantic objects.

libs/core/langchain_core/output_parsers/string.py

@@ -19,7 +19,7 @@ class StrOutputParser(BaseTransformOutputParser[str]):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "schema", "output_parser"]`

libs/core/langchain_core/outputs/generation.py

@@ -44,7 +44,7 @@ class Generation(Serializable):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "schema", "output"]`

libs/core/langchain_core/prompt_values.py

@@ -24,8 +24,8 @@ from langchain_core.messages import (
 class PromptValue(Serializable, ABC):
     """Base abstract class for inputs to any language model.
 
-    PromptValues can be converted to both LLM (pure text-generation) inputs and
-    ChatModel inputs.
+    `PromptValues` can be converted to both LLM (pure text-generation) inputs and
+    chat model inputs.
     """
 
     @classmethod
@@ -35,7 +35,7 @@ class PromptValue(Serializable, ABC):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         This is used to determine the namespace of the object when serializing.
 
@@ -62,7 +62,7 @@ class StringPromptValue(PromptValue):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         This is used to determine the namespace of the object when serializing.
 
@@ -99,7 +99,7 @@ class ChatPromptValue(PromptValue):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         This is used to determine the namespace of the object when serializing.
 
@@ -113,11 +113,11 @@ class ImageURL(TypedDict, total=False):
     """Image URL."""
 
     detail: Literal["auto", "low", "high"]
-    """Specifies the detail level of the image. Defaults to `'auto'`.
+    """Specifies the detail level of the image.
+
     Can be `'auto'`, `'low'`, or `'high'`.
 
     This follows OpenAI's Chat Completion API's image URL format.
-
     """
 
     url: str

libs/core/langchain_core/prompts/base.py

@@ -96,7 +96,7 @@ class BasePromptTemplate(
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "schema", "prompt_template"]`

libs/core/langchain_core/prompts/chat.py

@@ -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.
@@ -905,7 +904,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
                 (message type, template); e.g., ("human", "{user_input}"),
                 (4) 2-tuple of (message class, template), (5) a string which is
                 shorthand for ("human", template); e.g., "{user_input}".
-            template_format: format of the template. Defaults to "f-string".
+            template_format: format of the template.
             input_variables: A list of the names of the variables whose values are
                 required as inputs to the prompt.
             optional_variables: A list of the names of the variables for placeholder
@@ -971,7 +970,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "prompts", "chat"]`
@@ -1128,7 +1127,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
                 (message type, template); e.g., ("human", "{user_input}"),
                 (4) 2-tuple of (message class, template), (5) a string which is
                 shorthand for ("human", template); e.g., "{user_input}".
-            template_format: format of the template. Defaults to "f-string".
+            template_format: format of the template.
 
         Returns:
             a chat prompt template.
@@ -1287,7 +1286,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         """Human-readable representation.
 
         Args:
-            html: Whether to format as HTML. Defaults to `False`.
+            html: Whether to format as HTML.
 
         Returns:
             Human-readable representation.
@@ -1306,7 +1305,7 @@ def _create_template_from_message_type(
     Args:
         message_type: str the type of the message template (e.g., "human", "ai", etc.)
         template: str the template string.
-        template_format: format of the template. Defaults to "f-string".
+        template_format: format of the template.
 
     Returns:
         a message prompt template of the appropriate type.
@@ -1383,7 +1382,7 @@ def _convert_to_message_template(
 
     Args:
         message: a representation of a message in one of the supported formats.
-        template_format: format of the template. Defaults to "f-string".
+        template_format: format of the template.
 
     Returns:
         an instance of a message or a message template.

libs/core/langchain_core/prompts/dict.py

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

libs/core/langchain_core/prompts/few_shot_with_templates.py

@@ -46,7 +46,7 @@ class FewShotPromptWithTemplates(StringPromptTemplate):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "prompts", "few_shot_with_templates"]`

libs/core/langchain_core/prompts/image.py

@@ -49,7 +49,7 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "prompts", "image"]`

libs/core/langchain_core/prompts/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

@@ -276,7 +276,7 @@ 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(".")
 

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

@@ -70,45 +70,45 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
 
     Example: A retriever that returns the first 5 documents from a list of documents
 
-        ```python
-        from langchain_core.documents import Document
-        from langchain_core.retrievers import BaseRetriever
+    ```python
+    from langchain_core.documents import Document
+    from langchain_core.retrievers import BaseRetriever
 
-        class SimpleRetriever(BaseRetriever):
-            docs: list[Document]
-            k: int = 5
+    class SimpleRetriever(BaseRetriever):
+        docs: list[Document]
+        k: int = 5
 
-            def _get_relevant_documents(self, query: str) -> list[Document]:
-                \"\"\"Return the first k documents from the list of documents\"\"\"
-                return self.docs[:self.k]
+        def _get_relevant_documents(self, query: str) -> list[Document]:
+            \"\"\"Return the first k documents from the list of documents\"\"\"
+            return self.docs[:self.k]
 
-            async def _aget_relevant_documents(self, query: str) -> list[Document]:
-                \"\"\"(Optional) async native implementation.\"\"\"
-                return self.docs[:self.k]
-        ```
+        async def _aget_relevant_documents(self, query: str) -> list[Document]:
+            \"\"\"(Optional) async native implementation.\"\"\"
+            return self.docs[:self.k]
+    ```
 
     Example: A simple retriever based on a scikit-learn vectorizer
 
-        ```python
-        from sklearn.metrics.pairwise import cosine_similarity
+    ```python
+    from sklearn.metrics.pairwise import cosine_similarity
 
 
-        class TFIDFRetriever(BaseRetriever, BaseModel):
-            vectorizer: Any
-            docs: list[Document]
-            tfidf_array: Any
-            k: int = 4
+    class TFIDFRetriever(BaseRetriever, BaseModel):
+        vectorizer: Any
+        docs: list[Document]
+        tfidf_array: Any
+        k: int = 4
 
-            class Config:
-                arbitrary_types_allowed = True
+        class Config:
+            arbitrary_types_allowed = True
 
-            def _get_relevant_documents(self, query: str) -> list[Document]:
-                # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)
-                query_vec = self.vectorizer.transform([query])
-                # Op -- (n_docs,1) -- Cosine Sim with each doc
-                results = cosine_similarity(self.tfidf_array, query_vec).reshape((-1,))
-                return [self.docs[i] for i in results.argsort()[-self.k :][::-1]]
-        ```
+        def _get_relevant_documents(self, query: str) -> list[Document]:
+            # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)
+            query_vec = self.vectorizer.transform([query])
+            # Op -- (n_docs,1) -- Cosine Sim with each doc
+            results = cosine_similarity(self.tfidf_array, query_vec).reshape((-1,))
+            return [self.docs[i] for i in results.argsort()[-self.k :][::-1]]
+    ```
     """
 
     model_config = ConfigDict(

libs/core/langchain_core/runnables/base.py

@@ -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:
@@ -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:
@@ -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
 
@@ -1837,11 +1834,10 @@ 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).
@@ -1929,7 +1925,6 @@ 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.
                 If `None`, exceptions will not be passed to fallbacks.
@@ -2633,9 +2628,7 @@ 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.
 
@@ -2896,7 +2889,7 @@ 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"]`
@@ -3627,7 +3620,7 @@ 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 +5149,7 @@ 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 +5472,7 @@ 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 +5754,7 @@ 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

@@ -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"]`

libs/core/langchain_core/runnables/config.py

@@ -536,7 +536,7 @@ class ContextThreadPoolExecutor(ThreadPoolExecutor):
             fn: The function to map.
             *iterables: The iterables to map over.
             timeout: The timeout for the map.
-            chunksize: The chunksize for the map. Defaults to 1.
+            chunksize: The chunksize for the map.
 
         Returns:
             The iterator for the mapped function.

libs/core/langchain_core/runnables/configurable.py

@@ -72,7 +72,7 @@ class DynamicRunnable(RunnableSerializable[Input, Output]):
     @classmethod
     @override
     def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.
 
         Returns:
             `["langchain", "schema", "runnable"]`
@@ -540,7 +540,7 @@ class RunnableConfigurableAlternatives(DynamicRunnable[Input, Output]):
     """The alternatives to choose from."""
 
     default_key: str = "default"
-    """The enum value to use for the default option. Defaults to `'default'`."""
+    """The enum value to use for the default option."""
 
     prefix_keys: bool
     """Whether to prefix configurable fields of each alternative with a namespace

libs/core/langchain_core/runnables/fallbacks.py

@@ -143,7 +143,7 @@ class RunnableWithFallbacks(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"]`

libs/core/langchain_core/runnables/graph.py

@@ -71,7 +71,7 @@ class Edge(NamedTuple):
     data: Stringifiable | None = None
     """Optional data associated with the edge. """
     conditional: bool = False
-    """Whether the edge is conditional. Defaults to `False`."""
+    """Whether the edge is conditional."""
 
     def copy(self, *, source: str | None = None, target: str | None = None) -> Edge:
         """Return a copy of the edge with optional new source and target nodes.
@@ -157,9 +157,9 @@ class NodeStyles:
     """Schema for Hexadecimal color codes for different node types.
 
     Args:
-        default: The default color code. Defaults to "fill:#f2f0ff,line-height:1.2".
-        first: The color code for the first node. Defaults to "fill-opacity:0".
-        last: The color code for the last node. Defaults to "fill:#bfb6fc".
+        default: The default color code.
+        first: The color code for the first node.
+        last: The color code for the last node.
     """
 
     default: str = "fill:#f2f0ff,line-height:1.2"
@@ -201,9 +201,9 @@ def node_data_json(
     """Convert the data of a node to a JSON-serializable format.
 
     Args:
-        node: The node to convert.
-        with_schemas: Whether to include the schema of the data if
-            it is a Pydantic model. Defaults to `False`.
+        node: The `Node` to convert.
+        with_schemas: Whether to include the schema of the data if it is a Pydantic
+            model.
 
     Returns:
         A dictionary with the type of the data and the data itself.
@@ -267,7 +267,7 @@ class Graph:
 
         Args:
             with_schemas: Whether to include the schemas of the nodes if they are
-                Pydantic models. Defaults to `False`.
+                Pydantic models.
 
         Returns:
             A dictionary with the nodes and edges of the graph.
@@ -362,7 +362,7 @@ class Graph:
             source: The source node of the edge.
             target: The target node of the edge.
             data: Optional data associated with the edge.
-            conditional: Whether the edge is conditional. Defaults to `False`.
+            conditional: Whether the edge is conditional.
 
         Returns:
             The edge that was added to the graph.
@@ -391,7 +391,7 @@ class Graph:
 
         Args:
             graph: The graph to add.
-            prefix: The prefix to add to the node ids. Defaults to "".
+            prefix: The prefix to add to the node ids.
 
         Returns:
             A tuple of the first and last nodes of the subgraph.
@@ -458,7 +458,7 @@ class Graph:
     def first_node(self) -> Node | None:
         """Find the single node that is not a target of any edge.
 
-        If there is no such node, or there are multiple, return None.
+        If there is no such node, or there are multiple, return `None`.
         When drawing the graph, this node would be the origin.
 
         Returns:
@@ -470,7 +470,7 @@ class Graph:
     def last_node(self) -> Node | None:
         """Find the single node that is not a source of any edge.
 
-        If there is no such node, or there are multiple, return None.
+        If there is no such node, or there are multiple, return `None`.
         When drawing the graph, this node would be the destination.
 
         Returns:
@@ -585,11 +585,10 @@ class Graph:
         """Draw the graph as a Mermaid syntax string.
 
         Args:
-            with_styles: Whether to include styles in the syntax. Defaults to `True`.
-            curve_style: The style of the edges. Defaults to CurveStyle.LINEAR.
-            node_colors: The colors of the nodes. Defaults to NodeStyles().
+            with_styles: Whether to include styles in the syntax.
+            curve_style: The style of the edges.
+            node_colors: The colors of the nodes.
             wrap_label_n_words: The number of words to wrap the node labels at.
-                Defaults to 9.
             frontmatter_config: Mermaid frontmatter config.
                 Can be used to customize theme and styles. Will be converted to YAML and
                 added to the beginning of the mermaid graph.
@@ -647,20 +646,16 @@ class Graph:
         """Draw the graph as a PNG image using Mermaid.
 
         Args:
-            curve_style: The style of the edges. Defaults to CurveStyle.LINEAR.
-            node_colors: The colors of the nodes. Defaults to NodeStyles().
+            curve_style: The style of the edges.
+            node_colors: The colors of the nodes.
             wrap_label_n_words: The number of words to wrap the node labels at.
-                Defaults to 9.
             output_file_path: The path to save the image to. If `None`, the image
                 is not saved.
             draw_method: The method to use to draw the graph.
-                Defaults to MermaidDrawMethod.API.
-            background_color: The color of the background. Defaults to "white".
-            padding: The padding around the graph. Defaults to 10.
-            max_retries: The maximum number of retries (MermaidDrawMethod.API).
-                Defaults to 1.
-            retry_delay: The delay between retries (MermaidDrawMethod.API).
-                Defaults to 1.0.
+            background_color: The color of the background.
+            padding: The padding around the graph.
+            max_retries: The maximum number of retries (`MermaidDrawMethod.API`).
+            retry_delay: The delay between retries (`MermaidDrawMethod.API`).
             frontmatter_config: Mermaid frontmatter config.
                 Can be used to customize theme and styles. Will be converted to YAML and
                 added to the beginning of the mermaid graph.
@@ -712,7 +707,7 @@ def _first_node(graph: Graph, exclude: Sequence[str] = ()) -> Node | None:
     """Find the single node that is not a target of any edge.
 
     Exclude nodes/sources with ids in the exclude list.
-    If there is no such node, or there are multiple, return None.
+    If there is no such node, or there are multiple, return `None`.
     When drawing the graph, this node would be the origin.
     """
     targets = {edge.target for edge in graph.edges if edge.source not in exclude}
@@ -728,7 +723,7 @@ def _last_node(graph: Graph, exclude: Sequence[str] = ()) -> Node | None:
     """Find the single node that is not a source of any edge.
 
     Exclude nodes/targets with ids in the exclude list.
-    If there is no such node, or there are multiple, return None.
+    If there is no such node, or there are multiple, return `None`.
     When drawing the graph, this node would be the destination.
     """
     sources = {edge.source for edge in graph.edges if edge.target not in exclude}

libs/core/langchain_core/runnables/graph_mermaid.py

@@ -60,10 +60,10 @@ def draw_mermaid(
         edges: List of edges, object with a source, target and data.
         first_node: Id of the first node.
         last_node: Id of the last node.
-        with_styles: Whether to include styles in the graph. Defaults to `True`.
-        curve_style: Curve style for the edges. Defaults to CurveStyle.LINEAR.
-        node_styles: Node colors for different types. Defaults to NodeStyles().
-        wrap_label_n_words: Words to wrap the edge labels. Defaults to 9.
+        with_styles: Whether to include styles in the graph.
+        curve_style: Curve style for the edges.
+        node_styles: Node colors for different types.
+        wrap_label_n_words: Words to wrap the edge labels.
         frontmatter_config: Mermaid frontmatter config.
             Can be used to customize theme and styles. Will be converted to YAML and
             added to the beginning of the mermaid graph.
@@ -287,11 +287,11 @@ def draw_mermaid_png(
     Args:
         mermaid_syntax: Mermaid graph syntax.
         output_file_path: Path to save the PNG image.
-        draw_method: Method to draw the graph. Defaults to MermaidDrawMethod.API.
-        background_color: Background color of the image. Defaults to "white".
-        padding: Padding around the image. Defaults to 10.
-        max_retries: Maximum number of retries (MermaidDrawMethod.API). Defaults to 1.
-        retry_delay: Delay between retries (MermaidDrawMethod.API). Defaults to 1.0.
+        draw_method: Method to draw the graph.
+        background_color: Background color of the image.
+        padding: Padding around the image.
+        max_retries: Maximum number of retries (MermaidDrawMethod.API).
+        retry_delay: Delay between retries (MermaidDrawMethod.API).
         base_url: Base URL for the Mermaid.ink API.
 
     Returns:

libs/core/langchain_core/runnables/graph_png.py

@@ -105,7 +105,7 @@ class PngDrawer:
             source: The source node.
             target: The target node.
             label: The label for the edge.
-            conditional: Whether the edge is conditional. Defaults to `False`.
+            conditional: Whether the edge is conditional.
         """
         viz.add_edge(
             source,

libs/core/langchain_core/runnables/history.py

@@ -296,9 +296,9 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
                 ```
 
             input_messages_key: Must be specified if the base runnable accepts a dict
-                as input. Default is None.
+                as input.
             output_messages_key: Must be specified if the base runnable returns a dict
-                as output. Default is None.
+                as output.
             history_messages_key: Must be specified if the base runnable accepts a dict
                 as input and expects a separate key for historical messages.
             history_factory_config: Configure fields that should be passed to the

libs/core/langchain_core/runnables/passthrough.py

@@ -185,7 +185,7 @@ class RunnablePassthrough(RunnableSerializable[Other, Other]):
 
     @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", "runnable"]`
@@ -409,7 +409,7 @@ class RunnableAssign(RunnableSerializable[dict[str, Any], 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"]`
@@ -714,7 +714,7 @@ class RunnablePick(RunnableSerializable[dict[str, Any], 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"]`

libs/core/langchain_core/runnables/router.py

@@ -96,7 +96,7 @@ class RouterRunnable(RunnableSerializable[RouterInput, 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"]`

libs/core/langchain_core/runnables/utils.py

@@ -136,7 +136,7 @@ def coro_with_context(
     Args:
         coro: The coroutine to await.
         context: The context to use.
-        create_task: Whether to create a task. Defaults to `False`.
+        create_task: Whether to create a task.
 
     Returns:
         The coroutine with the context.
@@ -558,7 +558,7 @@ class ConfigurableField(NamedTuple):
     annotation: Any | None = None
     """The annotation of the field. """
     is_shared: bool = False
-    """Whether the field is shared. Defaults to `False`."""
+    """Whether the field is shared."""
 
     @override
     def __hash__(self) -> int:
@@ -579,7 +579,7 @@ class ConfigurableFieldSingleOption(NamedTuple):
     description: str | None = None
     """The description of the field. """
     is_shared: bool = False
-    """Whether the field is shared. Defaults to `False`."""
+    """Whether the field is shared."""
 
     @override
     def __hash__(self) -> int:
@@ -600,7 +600,7 @@ class ConfigurableFieldMultiOption(NamedTuple):
     description: str | None = None
     """The description of the field. """
     is_shared: bool = False
-    """Whether the field is shared. Defaults to `False`."""
+    """Whether the field is shared."""
 
     @override
     def __hash__(self) -> int:
@@ -626,7 +626,7 @@ class ConfigurableFieldSpec(NamedTuple):
     default: Any = None
     """The default value for the field. """
     is_shared: bool = False
-    """Whether the field is shared. Defaults to `False`."""
+    """Whether the field is shared."""
     dependencies: list[str] | None = None
     """The dependencies of the field. """
 

libs/core/langchain_core/tools/base.py

@@ -293,10 +293,9 @@ def create_schema_from_function(
         filter_args: Optional list of arguments to exclude from the schema.
             Defaults to `FILTERED_ARGS`.
         parse_docstring: Whether to parse the function's docstring for descriptions
-            for each argument. Defaults to `False`.
+            for each argument.
         error_on_invalid_docstring: if `parse_docstring` is provided, configure
             whether to raise `ValueError` on invalid Google Style docstrings.
-            Defaults to `False`.
         include_injected: Whether to include injected arguments in the schema.
             Defaults to `True`, since we want to include them in the schema
             when *validating* tool inputs.
@@ -481,11 +480,11 @@ class ChildTool(BaseTool):
     """Handle the content of the ValidationError thrown."""
 
     response_format: Literal["content", "content_and_artifact"] = "content"
-    """The tool response format. Defaults to 'content'.
+    """The tool response format.
 
-    If "content" then the output of the tool is interpreted as the contents of a
-    ToolMessage. If "content_and_artifact" then the output is expected to be a
-    two-tuple corresponding to the (content, artifact) of a ToolMessage.
+    If `"content"` then the output of the tool is interpreted as the contents of a
+    ToolMessage. If `"content_and_artifact"` then the output is expected to be a
+    two-tuple corresponding to the (content, artifact) of a `ToolMessage`.
     """
 
     def __init__(self, **kwargs: Any) -> None:
@@ -768,8 +767,8 @@ class ChildTool(BaseTool):
         Args:
             tool_input: The input to the tool.
             verbose: Whether to log the tool's progress.
-            start_color: The color to use when starting the tool. Defaults to 'green'.
-            color: The color to use when ending the tool. Defaults to 'green'.
+            start_color: The color to use when starting the tool.
+            color: The color to use when ending the tool.
             callbacks: Callbacks to be called during tool execution.
             tags: Optional list of tags associated with the tool.
             metadata: Optional metadata associated with the tool.
@@ -880,8 +879,8 @@ class ChildTool(BaseTool):
         Args:
             tool_input: The input to the tool.
             verbose: Whether to log the tool's progress.
-            start_color: The color to use when starting the tool. Defaults to 'green'.
-            color: The color to use when ending the tool. Defaults to 'green'.
+            start_color: The color to use when starting the tool.
+            color: The color to use when ending the tool.
             callbacks: Callbacks to be called during tool execution.
             tags: Optional list of tags associated with the tool.
             metadata: Optional metadata associated with the tool.

libs/core/langchain_core/tools/convert.py

@@ -81,7 +81,7 @@ def tool(
     parse_docstring: bool = False,
     error_on_invalid_docstring: bool = True,
 ) -> BaseTool | Callable[[Callable | Runnable], BaseTool]:
-    """Make tools out of functions, can be used with or without arguments.
+    """Make tools out of Python functions, can be used with or without arguments.
 
     Args:
         name_or_callable: Optional name of the tool or the callable to be
@@ -93,30 +93,26 @@ def tool(
 
             - `description` argument
                 (used even if docstring and/or `args_schema` are provided)
-            - tool function docstring
+            - Tool function docstring
                 (used even if `args_schema` is provided)
             - `args_schema` description
                 (used only if `description` / docstring are not provided)
         *args: Extra positional arguments. Must be empty.
         return_direct: Whether to return directly from the tool rather
-            than continuing the agent loop. Defaults to `False`.
-        args_schema: optional argument schema for user to specify.
+            than continuing the agent loop.
+        args_schema: Optional argument schema for user to specify.
 
         infer_schema: Whether to infer the schema of the arguments from
             the function's signature. This also makes the resultant tool
             accept a dictionary input to its `run()` function.
-            Defaults to `True`.
-        response_format: The tool response format. If "content" then the output of
-            the tool is interpreted as the contents of a ToolMessage. If
-            "content_and_artifact" then the output is expected to be a two-tuple
-            corresponding to the (content, artifact) of a ToolMessage.
-            Defaults to "content".
+        response_format: The tool response format. If `"content"` then the output of
+            the tool is interpreted as the contents of a `ToolMessage`. If
+            `"content_and_artifact"` then the output is expected to be a two-tuple
+            corresponding to the `(content, artifact)` of a `ToolMessage`.
         parse_docstring: if `infer_schema` and `parse_docstring`, will attempt to
             parse parameter descriptions from Google Style function docstrings.
-            Defaults to `False`.
         error_on_invalid_docstring: if `parse_docstring` is provided, configure
-            whether to raise ValueError on invalid Google Style docstrings.
-            Defaults to `True`.
+            whether to raise `ValueError` on invalid Google Style docstrings.
 
     Raises:
         ValueError: If too many positional arguments are provided.
@@ -124,8 +120,8 @@ def tool(
         ValueError: If the first argument is not a string or callable with
             a `__name__` attribute.
         ValueError: If the function does not have a docstring and description
-            is not provided and `infer_schema` is False.
-        ValueError: If `parse_docstring` is True and the function has an invalid
+            is not provided and `infer_schema` is `False`.
+        ValueError: If `parse_docstring` is `True` and the function has an invalid
             Google-style docstring and `error_on_invalid_docstring` is True.
         ValueError: If a Runnable is provided that does not have an object schema.
 
@@ -133,7 +129,7 @@ def tool(
         The tool.
 
     Requires:
-        - Function must be of type (str) -> str
+        - Function must be of type `(str) -> str`
         - Function must have a docstring
 
     Examples:
@@ -197,7 +193,7 @@ def tool(
         Note that parsing by default will raise `ValueError` if the docstring
         is considered invalid. A docstring is considered invalid if it contains
         arguments not in the function signature, or is unable to be parsed into
-        a summary and "Args:" blocks. Examples below:
+        a summary and `"Args:"` blocks. Examples below:
 
         ```python
         # No args section

libs/core/langchain_core/tools/retriever.py

@@ -82,12 +82,12 @@ def create_retriever_tool(
         description: The description for the tool. This will be passed to the language
             model, so should be descriptive.
         document_prompt: The prompt to use for the document.
-        document_separator: The separator to use between documents. Defaults to "\n\n".
-        response_format: The tool response format. If "content" then the output of
-            the tool is interpreted as the contents of a ToolMessage. If
-            "content_and_artifact" then the output is expected to be a two-tuple
-            corresponding to the (content, artifact) of a ToolMessage (artifact
-            being a list of documents in this case). Defaults to "content".
+        document_separator: The separator to use between documents.
+        response_format: The tool response format. If `"content"` then the output of
+            the tool is interpreted as the contents of a `ToolMessage`. If
+            `"content_and_artifact"` then the output is expected to be a two-tuple
+            corresponding to the `(content, artifact)` of a `ToolMessage` (artifact
+            being a list of documents in this case).
 
     Returns:
         Tool class to pass to an agent.

libs/core/langchain_core/tools/simple.py

@@ -176,7 +176,7 @@ class Tool(BaseTool):
             func: The function to create the tool from.
             name: The name of the tool.
             description: The description of the tool.
-            return_direct: Whether to return the output directly. Defaults to `False`.
+            return_direct: Whether to return the output directly.
             args_schema: The schema of the tool's input arguments.
             coroutine: The asynchronous version of the function.
             **kwargs: Additional arguments to pass to the tool.

libs/core/langchain_core/tools/structured.py

@@ -149,21 +149,16 @@ class StructuredTool(BaseTool):
             description: The description of the tool.
                 Defaults to the function docstring.
             return_direct: Whether to return the result directly or as a callback.
-                Defaults to `False`.
             args_schema: The schema of the tool's input arguments.
             infer_schema: Whether to infer the schema from the function's signature.
-                Defaults to `True`.
-            response_format: The tool response format. If "content" then the output of
-                the tool is interpreted as the contents of a ToolMessage. If
-                "content_and_artifact" then the output is expected to be a two-tuple
-                corresponding to the (content, artifact) of a ToolMessage.
-                Defaults to "content".
+            response_format: The tool response format. If `"content"` then the output of
+                the tool is interpreted as the contents of a `ToolMessage`. If
+                `"content_and_artifact"` then the output is expected to be a two-tuple
+                corresponding to the `(content, artifact)` of a `ToolMessage`.
             parse_docstring: if `infer_schema` and `parse_docstring`, will attempt
                 to parse parameter descriptions from Google Style function docstrings.
-                Defaults to `False`.
             error_on_invalid_docstring: if `parse_docstring` is provided, configure
-                whether to raise ValueError on invalid Google Style docstrings.
-                Defaults to `False`.
+                whether to raise `ValueError` on invalid Google Style docstrings.
             **kwargs: Additional arguments to pass to the tool
 
         Returns:

libs/core/langchain_core/tracers/memory_stream.py

@@ -5,7 +5,7 @@ channel. The writer and reader can be in the same event loop or in different eve
 loops. When they're in different event loops, they will also be in different
 threads.
 
-This is useful in situations when there's a mix of synchronous and asynchronous
+Useful in situations when there's a mix of synchronous and asynchronous
 used in the code.
 """
 

libs/core/langchain_core/tracers/root_listeners.py

@@ -24,7 +24,7 @@ class RootListenersTracer(BaseTracer):
     """Tracer that calls listeners on run start, end, and error."""
 
     log_missing_parent = False
-    """Whether to log a warning if the parent is missing. Default is False."""
+    """Whether to log a warning if the parent is missing."""
 
     def __init__(
         self,
@@ -79,7 +79,7 @@ class AsyncRootListenersTracer(AsyncBaseTracer):
     """Async Tracer that calls listeners on run start, end, and error."""
 
     log_missing_parent = False
-    """Whether to log a warning if the parent is missing. Default is False."""
+    """Whether to log a warning if the parent is missing."""
 
     def __init__(
         self,

libs/core/langchain_core/tracers/schemas.py

@@ -2,140 +2,13 @@
 
 from __future__ import annotations
 
-import warnings
-from datetime import datetime, timezone
-from typing import Any
-from uuid import UUID
-
 from langsmith import RunTree
-from langsmith.schemas import RunTypeEnum as RunTypeEnumDep
-from pydantic import PydanticDeprecationWarning
-from pydantic.v1 import BaseModel as BaseModelV1
-from pydantic.v1 import Field as FieldV1
-
-from langchain_core._api import deprecated
-
-
-@deprecated("0.1.0", alternative="Use string instead.", removal="1.0")
-def RunTypeEnum() -> type[RunTypeEnumDep]:  # noqa: N802
-    """`RunTypeEnum`.
-
-    Returns:
-        The `RunTypeEnum` class.
-    """
-    warnings.warn(
-        "RunTypeEnum is deprecated. Please directly use a string instead"
-        " (e.g. 'llm', 'chain', 'tool').",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    return RunTypeEnumDep
-
-
-@deprecated("0.1.0", removal="1.0")
-class TracerSessionV1Base(BaseModelV1):
-    """Base class for TracerSessionV1."""
-
-    start_time: datetime = FieldV1(default_factory=lambda: datetime.now(timezone.utc))
-    name: str | None = None
-    extra: dict[str, Any] | None = None
-
-
-@deprecated("0.1.0", removal="1.0")
-class TracerSessionV1Create(TracerSessionV1Base):
-    """Create class for TracerSessionV1."""
-
-
-@deprecated("0.1.0", removal="1.0")
-class TracerSessionV1(TracerSessionV1Base):
-    """TracerSessionV1 schema."""
-
-    id: int
-
-
-@deprecated("0.1.0", removal="1.0")
-class TracerSessionBase(TracerSessionV1Base):
-    """Base class for TracerSession."""
-
-    tenant_id: UUID
-
-
-@deprecated("0.1.0", removal="1.0")
-class TracerSession(TracerSessionBase):
-    """TracerSessionV1 schema for the V2 API."""
-
-    id: UUID
-
-
-@deprecated("0.1.0", alternative="Run", removal="1.0")
-class BaseRun(BaseModelV1):
-    """Base class for Run."""
-
-    uuid: str
-    parent_uuid: str | None = None
-    start_time: datetime = FieldV1(default_factory=lambda: datetime.now(timezone.utc))
-    end_time: datetime = FieldV1(default_factory=lambda: datetime.now(timezone.utc))
-    extra: dict[str, Any] | None = None
-    execution_order: int
-    child_execution_order: int
-    serialized: dict[str, Any]
-    session_id: int
-    error: str | None = None
-
-
-@deprecated("0.1.0", alternative="Run", removal="1.0")
-class LLMRun(BaseRun):
-    """Class for LLMRun."""
-
-    prompts: list[str]
-
-
-@deprecated("0.1.0", alternative="Run", removal="1.0")
-class ChainRun(BaseRun):
-    """Class for ChainRun."""
-
-    inputs: dict[str, Any]
-    outputs: dict[str, Any] | None = None
-    child_llm_runs: list[LLMRun] = FieldV1(default_factory=list)
-    child_chain_runs: list[ChainRun] = FieldV1(default_factory=list)
-    child_tool_runs: list[ToolRun] = FieldV1(default_factory=list)
-
-
-@deprecated("0.1.0", alternative="Run", removal="1.0")
-class ToolRun(BaseRun):
-    """Class for ToolRun."""
-
-    tool_input: str
-    output: str | None = None
-    action: str
-    child_llm_runs: list[LLMRun] = FieldV1(default_factory=list)
-    child_chain_runs: list[ChainRun] = FieldV1(default_factory=list)
-    child_tool_runs: list[ToolRun] = FieldV1(default_factory=list)
-
 
 # Begin V2 API Schemas
 
 
 Run = RunTree  # For backwards compatibility
 
-# TODO: Update once langsmith moves to Pydantic V2 and we can swap Run.model_rebuild
-# for Run.update_forward_refs
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore", category=PydanticDeprecationWarning)
-
-    ChainRun.update_forward_refs()
-    ToolRun.update_forward_refs()
-
 __all__ = [
-    "BaseRun",
-    "ChainRun",
-    "LLMRun",
     "Run",
-    "RunTypeEnum",
-    "ToolRun",
-    "TracerSession",
-    "TracerSessionBase",
-    "TracerSessionV1",
-    "TracerSessionV1Base",
-    "TracerSessionV1Create",
 ]

libs/core/langchain_core/tracers/stdout.py

@@ -49,8 +49,7 @@ class FunctionCallbackHandler(BaseTracer):
     """Tracer that calls a function with a single str parameter."""
 
     name: str = "function_callback_handler"
-    """The name of the tracer. This is used to identify the tracer in the logs.
-    Default is "function_callback_handler"."""
+    """The name of the tracer. This is used to identify the tracer in the logs."""
 
     def __init__(self, function: Callable[[str], None], **kwargs: Any) -> None:
         """Create a FunctionCallbackHandler.

libs/core/langchain_core/utils/__init__.py

@@ -1,4 +1,4 @@
-"""**Utility functions** for LangChain.
+"""Utility functions for LangChain.
 
 These functions do not depend on any other LangChain module.
 """

libs/core/langchain_core/utils/aiter.py

@@ -201,7 +201,7 @@ class Tee(Generic[T]):
 
         Args:
             iterable: The iterable to split.
-            n: The number of iterators to create. Defaults to 2.
+            n: The number of iterators to create.
             lock: The lock to synchronise access to the shared buffers.
 
         """

libs/core/langchain_core/utils/function_calling.py

@@ -114,7 +114,7 @@ def _convert_json_schema_to_openai_function(
             used.
         description: The description of the function. If not provided, the description
             of the schema will be used.
-        rm_titles: Whether to remove titles from the schema. Defaults to `True`.
+        rm_titles: Whether to remove titles from the schema.
 
     Returns:
         The function description.
@@ -148,7 +148,7 @@ def _convert_pydantic_to_openai_function(
             used.
         description: The description of the function. If not provided, the description
             of the schema will be used.
-        rm_titles: Whether to remove titles from the schema. Defaults to `True`.
+        rm_titles: Whether to remove titles from the schema.
 
     Raises:
         TypeError: If the model is not a Pydantic model.
@@ -334,11 +334,11 @@ def convert_to_openai_function(
 
     Args:
         function:
-            A dictionary, Pydantic BaseModel class, TypedDict class, a LangChain
-            Tool object, or a Python function. If a dictionary is passed in, it is
+            A dictionary, Pydantic `BaseModel` class, `TypedDict` class, a LangChain
+            `Tool` object, or a Python function. If a dictionary is passed in, it is
             assumed to already be a valid OpenAI function, a JSON schema with
-            top-level 'title' key specified, an Anthropic format
-            tool, or an Amazon Bedrock Converse format tool.
+            top-level `title` key specified, an Anthropic format tool, or an Amazon
+            Bedrock Converse format tool.
         strict:
             If `True`, model output is guaranteed to exactly match the JSON Schema
             provided in the function definition. If `None`, `strict` argument will not
@@ -351,17 +351,8 @@ def convert_to_openai_function(
     Raises:
         ValueError: If function is not in a supported format.
 
-    !!! warning "Behavior changed in 0.2.29"
-        `strict` arg added.
-
-    !!! warning "Behavior changed in 0.3.13"
-        Support for Anthropic format tools added.
-
-    !!! warning "Behavior changed in 0.3.14"
-        Support for Amazon Bedrock Converse format tools added.
-
     !!! warning "Behavior changed in 0.3.16"
-        'description' and 'parameters' keys are now optional. Only 'name' is
+        `description` and `parameters` keys are now optional. Only `name` is
         required and guaranteed to be part of the output.
     """
     # an Anthropic format tool
@@ -459,16 +450,14 @@ def convert_to_openai_tool(
 ) -> dict[str, Any]:
     """Convert a tool-like object to an OpenAI tool schema.
 
-    OpenAI tool schema reference:
-    https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools
+    [OpenAI tool schema reference](https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools)
 
     Args:
         tool:
-            Either a dictionary, a pydantic.BaseModel class, Python function, or
-            BaseTool. If a dictionary is passed in, it is
-            assumed to already be a valid OpenAI function, a JSON schema with
-            top-level 'title' key specified, an Anthropic format
-            tool, or an Amazon Bedrock Converse format tool.
+            Either a dictionary, a `pydantic.BaseModel` class, Python function, or
+            `BaseTool`. If a dictionary is passed in, it is assumed to already be a
+            valid OpenAI function, a JSON schema with top-level `title` key specified,
+            an Anthropic format tool, or an Amazon Bedrock Converse format tool.
         strict:
             If `True`, model output is guaranteed to exactly match the JSON Schema
             provided in the function definition. If `None`, `strict` argument will not
@@ -478,26 +467,14 @@ def convert_to_openai_tool(
         A dict version of the passed in tool which is compatible with the
         OpenAI tool-calling API.
 
-    !!! warning "Behavior changed in 0.2.29"
-        `strict` arg added.
-
-    !!! warning "Behavior changed in 0.3.13"
-        Support for Anthropic format tools added.
-
-    !!! warning "Behavior changed in 0.3.14"
-        Support for Amazon Bedrock Converse format tools added.
-
     !!! warning "Behavior changed in 0.3.16"
-        'description' and 'parameters' keys are now optional. Only 'name' is
+        `description` and `parameters` keys are now optional. Only `name` is
         required and guaranteed to be part of the output.
 
     !!! warning "Behavior changed in 0.3.44"
         Return OpenAI Responses API-style tools unchanged. This includes
-        any dict with "type" in "file_search", "function", "computer_use_preview",
-        "web_search_preview".
-
-    !!! warning "Behavior changed in 0.3.61"
-        Added support for OpenAI's built-in code interpreter and remote MCP tools.
+        any dict with `"type"` in `"file_search"`, `"function"`,
+        `"computer_use_preview"`, `"web_search_preview"`.
 
     !!! warning "Behavior changed in 0.3.63"
         Added support for OpenAI's image generation built-in tool.

libs/core/langchain_core/utils/input.py

@@ -66,7 +66,7 @@ def print_text(
     Args:
         text: The text to print.
         color: The color to use.
-        end: The end character to use. Defaults to "".
+        end: The end character to use.
         file: The file to write to.
     """
     text_to_print = get_colored_text(text, color) if color else text

libs/core/langchain_core/utils/iter.py

@@ -137,7 +137,7 @@ class Tee(Generic[T]):
 
         Args:
             iterable: The iterable to split.
-            n: The number of iterators to create. Defaults to 2.
+            n: The number of iterators to create.
             lock: The lock to synchronise access to the shared buffers.
 
         """

libs/core/langchain_core/utils/json.py

@@ -51,7 +51,7 @@ def parse_partial_json(s: str, *, strict: bool = False) -> Any:
 
     Args:
         s: The JSON string to parse.
-        strict: Whether to use strict parsing. Defaults to `False`.
+        strict: Whether to use strict parsing.
 
     Returns:
         The parsed JSON object as a Python dictionary.

libs/core/langchain_core/utils/strings.py

@@ -57,7 +57,7 @@ def sanitize_for_postgres(text: str, replacement: str = "") -> str:
 
     Args:
         text: The text to sanitize.
-        replacement: String to replace NUL bytes with. Defaults to empty string.
+        replacement: String to replace NUL bytes with.
 
     Returns:
         The sanitized text with NUL bytes removed or replaced.

libs/core/langchain_core/vectorstores/base.py

@@ -109,7 +109,7 @@ class VectorStore(ABC):
         """Delete by vector ID or other criteria.
 
         Args:
-            ids: List of ids to delete. If `None`, delete all. Default is None.
+            ids: List of ids to delete. If `None`, delete all.
             **kwargs: Other keyword arguments that subclasses might use.
 
         Returns:
@@ -176,7 +176,7 @@ class VectorStore(ABC):
         """Async delete by vector ID or other criteria.
 
         Args:
-            ids: List of ids to delete. If `None`, delete all. Default is None.
+            ids: List of ids to delete. If `None`, delete all.
             **kwargs: Other keyword arguments that subclasses might use.
 
         Returns:
@@ -197,7 +197,6 @@ class VectorStore(ABC):
         Args:
             texts: Iterable of strings to add to the vectorstore.
             metadatas: Optional list of metadatas associated with the texts.
-                Default is None.
             ids: Optional list
             **kwargs: vectorstore specific parameters.
 
@@ -365,7 +364,7 @@ class VectorStore(ABC):
 
         Args:
             query: Input text.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             **kwargs: Arguments to pass to the search method.
 
         Returns:
@@ -462,7 +461,7 @@ class VectorStore(ABC):
 
         Args:
             query: Input text.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             **kwargs: kwargs to be passed to similarity search. Should include:
                 score_threshold: Optional, a floating point value between 0 to 1 to
                     filter the resulting set of retrieved docs
@@ -489,7 +488,7 @@ class VectorStore(ABC):
 
         Args:
             query: Input text.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             **kwargs: kwargs to be passed to similarity search. Should include:
                 score_threshold: Optional, a floating point value between 0 to 1 to
                     filter the resulting set of retrieved docs
@@ -513,7 +512,7 @@ class VectorStore(ABC):
 
         Args:
             query: Input text.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             **kwargs: kwargs to be passed to similarity search. Should include:
                 score_threshold: Optional, a floating point value between 0 to 1 to
                     filter the resulting set of retrieved docs.
@@ -562,7 +561,7 @@ class VectorStore(ABC):
 
         Args:
             query: Input text.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             **kwargs: kwargs to be passed to similarity search. Should include:
                 score_threshold: Optional, a floating point value between 0 to 1 to
                     filter the resulting set of retrieved docs
@@ -606,7 +605,7 @@ class VectorStore(ABC):
 
         Args:
             query: Input text.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             **kwargs: Arguments to pass to the search method.
 
         Returns:
@@ -624,7 +623,7 @@ class VectorStore(ABC):
 
         Args:
             embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             **kwargs: Arguments to pass to the search method.
 
         Returns:
@@ -639,7 +638,7 @@ class VectorStore(ABC):
 
         Args:
             embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             **kwargs: Arguments to pass to the search method.
 
         Returns:
@@ -667,13 +666,11 @@ class VectorStore(ABC):
 
         Args:
             query: Text to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-                Default is 20.
             lambda_mult: Number between 0 and 1 that determines the degree
                 of diversity among the results with 0 corresponding
                 to maximum diversity and 1 to minimum diversity.
-                Defaults to 0.5.
             **kwargs: Arguments to pass to the search method.
 
         Returns:
@@ -696,13 +693,11 @@ class VectorStore(ABC):
 
         Args:
             query: Text to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-                Default is 20.
             lambda_mult: Number between 0 and 1 that determines the degree
                 of diversity among the results with 0 corresponding
                 to maximum diversity and 1 to minimum diversity.
-                Defaults to 0.5.
             **kwargs: Arguments to pass to the search method.
 
         Returns:
@@ -736,13 +731,11 @@ class VectorStore(ABC):
 
         Args:
             embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-                Default is 20.
             lambda_mult: Number between 0 and 1 that determines the degree
                 of diversity among the results with 0 corresponding
                 to maximum diversity and 1 to minimum diversity.
-                Defaults to 0.5.
             **kwargs: Arguments to pass to the search method.
 
         Returns:
@@ -765,13 +758,11 @@ class VectorStore(ABC):
 
         Args:
             embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
+            k: Number of Documents to return.
             fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-                Default is 20.
             lambda_mult: Number between 0 and 1 that determines the degree
                 of diversity among the results with 0 corresponding
                 to maximum diversity and 1 to minimum diversity.
-                Defaults to 0.5.
             **kwargs: Arguments to pass to the search method.
 
         Returns:
@@ -864,7 +855,6 @@ class VectorStore(ABC):
             texts: Texts to add to the vectorstore.
             embedding: Embedding function to use.
             metadatas: Optional list of metadatas associated with the texts.
-                Default is None.
             ids: Optional list of IDs associated with the texts.
             **kwargs: Additional keyword arguments.
 
@@ -888,7 +878,6 @@ class VectorStore(ABC):
             texts: Texts to add to the vectorstore.
             embedding: Embedding function to use.
             metadatas: Optional list of metadatas associated with the texts.
-                Default is None.
             ids: Optional list of IDs associated with the texts.
             **kwargs: Additional keyword arguments.
 

libs/core/langchain_core/vectorstores/utils.py

@@ -112,8 +112,8 @@ def maximal_marginal_relevance(
     Args:
         query_embedding: The query embedding.
         embedding_list: A list of embeddings.
-        lambda_mult: The lambda parameter for MMR. Default is 0.5.
-        k: The number of embeddings to return. Default is 4.
+        lambda_mult: The lambda parameter for MMR.
+        k: The number of embeddings to return.
 
     Returns:
         A list of indices of the embeddings to return.

libs/core/langchain_core/version.py

@@ -1,3 +1,3 @@
 """langchain-core version information and utilities."""
 
-VERSION = "1.0.0a8"
+VERSION = "1.0.0rc1"

libs/core/pyproject.toml

@@ -16,7 +16,7 @@ dependencies = [
     "pydantic>=2.7.4,<3.0.0",
 ]
 name = "langchain-core"
-version = "1.0.0a8"
+version = "1.0.0rc1"
 description = "Building applications with LLMs through composability"
 readme = "README.md"
 

libs/core/tests/unit_tests/language_models/chat_models/test_cache.py

@@ -26,11 +26,11 @@ class InMemoryCache(BaseCache):
         self._cache: dict[tuple[str, str], RETURN_VAL_TYPE] = {}
 
     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`."""
         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`."""
         self._cache[prompt, llm_string] = return_val
 
     @override