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docs: update package READMEs (#33488)

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2025-10-15 10:49:35 -04:00
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9
README.md
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@@ -12,13 +12,16 @@
<p align="center"> <p align="center">
<a href="https://opensource.org/licenses/MIT" target="_blank"> <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>
<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"> <img src="https://img.shields.io/pepy/dt/langchain" alt="PyPI - Downloads">
</a> </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"> <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>
<a href="https://codespaces.new/langchain-ai/langchain" target="_blank"> <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"> <img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20">

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libs/cli/README.md
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# langchain-cli # langchain-cli
This package implements the official CLI for LangChain. Right now, it is most useful [![PyPI - Version](https://img.shields.io/pypi/v/langchain-cli?label=%20)](https://pypi.org/project/langchain-cli/#history)
for getting started with LangChain Templates! [![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) [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).

522
libs/cli/langchain_cli/integration_template/docs/chat.ipynb
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@@ -1,264 +1,264 @@
{ {
"cells": [ "cells": [
{ {
"cell_type": "raw", "cell_type": "raw",
"id": "afaf8039", "id": "afaf8039",
"metadata": {}, "metadata": {},
"source": [ "source": [
"---\n", "---\n",
"sidebar_label: __ModuleName__\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"
}
}, },
{ "nbformat": 4,
"cell_type": "markdown", "nbformat_minor": 5
"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
} }

470
libs/cli/langchain_cli/integration_template/docs/llms.ipynb
View File

@@ -1,238 +1,238 @@
{ {
"cells": [ "cells": [
{ {
"cell_type": "raw", "cell_type": "raw",
"id": "67db2992", "id": "67db2992",
"metadata": {}, "metadata": {},
"source": [ "source": [
"---\n", "---\n",
"sidebar_label: __ModuleName__\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"
}
}
}, },
{ "nbformat": 4,
"cell_type": "markdown", "nbformat_minor": 5
"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
} }

400
libs/cli/langchain_cli/integration_template/docs/stores.ipynb
View File

@@ -1,204 +1,204 @@
{ {
"cells": [ "cells": [
{ {
"cell_type": "raw", "cell_type": "raw",
"metadata": { "metadata": {
"vscode": { "vscode": {
"languageId": "raw" "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",
"---"
]
}, },
{ "nbformat": 4,
"cell_type": "markdown", "nbformat_minor": 2
"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
} }

536
libs/cli/langchain_cli/integration_template/docs/tools.ipynb
View File

@@ -1,271 +1,271 @@
{ {
"cells": [ "cells": [
{ {
"cell_type": "raw", "cell_type": "raw",
"id": "10238e62-3465-4973-9279-606cbb7ccf16", "id": "10238e62-3465-4973-9279-606cbb7ccf16",
"metadata": {}, "metadata": {},
"source": [ "source": [
"---\n", "---\n",
"sidebar_label: __ModuleName__\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"
}
}, },
{ "nbformat": 4,
"cell_type": "markdown", "nbformat_minor": 5
"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
} }

21
libs/core/README.md
View File

@@ -1,7 +1,14 @@
# 🦜🍎️ LangChain Core # 🦜🍎️ 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) [![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 ## Quick Install
@@ -9,7 +16,7 @@
pip install langchain-core pip install langchain-core
``` ```
## What is it? ## 🤔 What is this?
LangChain Core contains the base abstractions that power the LangChain ecosystem. LangChain Core contains the base abstractions that power the LangChain ecosystem.
@@ -17,8 +24,6 @@ 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. 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? ## ⛰️ Why build on top of LangChain Core?
The LangChain ecosystem is built on top of `langchain-core`. Some of the benefits: 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. - **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. - **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 ## 📕 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 ## 💁 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. 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).

68
libs/langchain/README.md
View File

@@ -1,9 +1,8 @@
# 🦜️🔗 LangChain # 🦜️🔗 LangChain Classic
⚡ Building applications with LLMs through composability ⚡ [![PyPI - Version](https://img.shields.io/pypi/v/langchain-classic?label=%20)](https://pypi.org/project/langchain-classic/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-classic)](https://opensource.org/licenses/MIT)
[![PyPI - License](https://img.shields.io/pypi/l/langchain?style=flat-square)](https://opensource.org/licenses/MIT) [![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-classic)](https://pypistats.org/packages/langchain-classic)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain)](https://pypistats.org/packages/langchain)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![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). Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
@@ -13,67 +12,26 @@ To help you ship LangChain apps to production faster, check out [LangSmith](http
## Quick Install ## Quick Install
`pip install langchain-classic` ```bash
pip install langchain-classic
```
## 🤔 What is this? ## 🤔 What is this?
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. Legacy chains, `langchain-community` re-exports, indexing API, deprecated functionality, and more.
This library aims to assist in the development of those types of applications. Common examples of these applications include: In most cases, you should be using the main [`langchain`](https://pypi.org/project/langchain/) package.
**❓ Question answering with RAG**
- [Documentation](https://python.langchain.com/docs/tutorials/rag/)
- End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
**🧱 Extracting structured output**
- [Documentation](https://python.langchain.com/docs/tutorials/extraction/)
- End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain-extract/)
**🤖 Chatbots**
- [Documentation](https://python.langchain.com/docs/tutorials/chatbot/)
- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
## 📖 Documentation ## 📖 Documentation
Please see [our full documentation](https://python.langchain.com) on: For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_classic).
- Getting started (installation, setting up the environment, simple examples) ## 📕 Releases & Versioning
- How-To examples (demos, integrations, helper functions)
- Reference (full API docs)
- Resources (high-level explanation of core concepts)
## 🚀 What can this help with? See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
There are five main areas that LangChain is designed to help with.
These are, in increasing order of complexity:
**🤖 Agents:**
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
**📚 Retrieval Augmented Generation:**
Retrieval Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
**🧐 Evaluation:**
Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
**📃 Models and Prompts:**
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with chat models and LLMs.
**🔗 Chains:**
Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
For more information on these concepts, please see our [full documentation](https://python.langchain.com).
## 💁 Contributing ## 💁 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. 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://python.langchain.com/docs/contributing/). For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).

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# 🦜️🔗 LangChain # 🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡ [![PyPI - Version](https://img.shields.io/pypi/v/langchain?label=%20)](https://pypi.org/project/langchain/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain)](https://opensource.org/licenses/MIT)
[![PyPI - License](https://img.shields.io/pypi/l/langchain?style=flat-square)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain)](https://pypistats.org/packages/langchain) [![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain)](https://pypistats.org/packages/langchain)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
@@ -13,67 +12,28 @@ To help you ship LangChain apps to production faster, check out [LangSmith](http
## Quick Install ## Quick Install
`pip install langchain` ```bash
pip install langchain
```
## 🤔 What is this? ## 🤔 What is this?
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. LangChain is the easiest way to start building agents and applications powered by LLMs. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and [more](https://docs.langchain.com/oss/python/integrations/providers/overview). LangChain provides a pre-built agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications.
This library aims to assist in the development of those types of applications. Common examples of these applications include: We recommend you use LangChain if you want to quickly build agents and autonomous applications. Use [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), our low-level agent orchestration framework and runtime, when you have more advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latency.
**❓ Question answering with RAG** LangChain [agents](https://docs.langchain.com/oss/python/langchain/agents) are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. (You do not need to know LangGraph for basic LangChain agent usage.)
- [Documentation](https://python.langchain.com/docs/tutorials/rag/)
- End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
**🧱 Extracting structured output**
- [Documentation](https://python.langchain.com/docs/tutorials/extraction/)
- End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain-extract/)
**🤖 Chatbots**
- [Documentation](https://python.langchain.com/docs/tutorials/chatbot/)
- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
## 📖 Documentation ## 📖 Documentation
Please see [our full documentation](https://python.langchain.com) on: For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_classic).
- Getting started (installation, setting up the environment, simple examples) ## 📕 Releases & Versioning
- How-To examples (demos, integrations, helper functions)
- Reference (full API docs)
- Resources (high-level explanation of core concepts)
## 🚀 What can this help with? See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
There are five main areas that LangChain is designed to help with.
These are, in increasing order of complexity:
**🤖 Agents:**
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
**📚 Retrieval Augmented Generation:**
Retrieval Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
**🧐 Evaluation:**
Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
**📃 Models and Prompts:**
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with chat models and LLMs.
**🔗 Chains:**
Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
For more information on these concepts, please see our [full documentation](https://python.langchain.com).
## 💁 Contributing ## 💁 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. 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://python.langchain.com/docs/contributing/). For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).

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@@ -1,3 +1,7 @@
# FAQ # FAQ
Looking for an integration not listed here? Check out the [integrations documentation](https://docs.langchain.com/oss/python/integrations/providers) and the [note](../README.md) in the `libs/` README about third-party maintained packages. Looking for an integration not listed here? Check out the [integrations documentation](https://docs.langchain.com/oss/python/integrations/providers) and the [note](../README.md) in the `libs/` README about third-party maintained packages.
## Integration docs
For full documentation, see the [primary](https://docs.langchain.com/oss/python/integrations/providers/overview) and [API reference](https://reference.langchain.com/python/integrations/) docs for integrations.

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# langchain-anthropic # langchain-anthropic
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-anthropic?label=%20)](https://pypi.org/project/langchain-anthropic/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-anthropic)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-anthropic)](https://pypistats.org/packages/langchain-anthropic)
[![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).
## Quick Install
```bash
pip install langchain-anthropic
```
## 🤔 What is this?
This package contains the LangChain integration for Anthropic's generative models. This package contains the LangChain integration for Anthropic's generative models.
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/anthropic) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/anthropic) for more details.

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# langchain-chroma # langchain-chroma
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-chroma?label=%20)](https://pypi.org/project/langchain-chroma/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-chroma)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-chroma)](https://pypistats.org/packages/langchain-chroma)
[![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).
## Quick Install
```bash
pip install langchain-chroma
```
## 🤔 What is this?
This package contains the LangChain integration with Chroma. This package contains the LangChain integration with Chroma.
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/chroma) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/chroma) for more details.

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# langchain-deepseek # langchain-deepseek
This package contains the LangChain integration with the DeepSeek API [![PyPI - Version](https://img.shields.io/pypi/v/langchain-deepseek?label=%20)](https://pypi.org/project/langchain-deepseek/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-deepseek)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-deepseek)](https://pypistats.org/packages/langchain-deepseek)
[![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).
## Quick Install
```bash
pip install langchain-deepseek
```
## 🤔 What is this?
This package contains the LangChain integration with DeepSeek.
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/deepseek) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/deepseek) for more details.

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# langchain-exa # langchain-exa
This package contains the LangChain integrations for Exa Cloud generative models. [![PyPI - Version](https://img.shields.io/pypi/v/langchain-exa?label=%20)](https://pypi.org/project/langchain-exa/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-exa)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-exa)](https://pypistats.org/packages/langchain-exa)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/exa_search) for more details. Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
## Quick Install
```bash
pip install langchain-exa
```
## 🤔 What is this?
This package contains the LangChain integration with Exa.
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/exa) for more details.

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# LangChain-Fireworks # langchain-fireworks
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-fireworks?label=%20)](https://pypi.org/project/langchain-fireworks/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-fireworks)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-fireworks)](https://pypistats.org/packages/langchain-fireworks)
[![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).
## Quick Install
```bash
pip install langchain-fireworks
```
## 🤔 What is this?
This is the partner package for tying Fireworks.ai and LangChain. Fireworks really strive to provide good support for LangChain use cases, so if you run into any issues please let us know. You can reach out to us [in our Discord channel](https://discord.com/channels/1137072072808472616/) This is the partner package for tying Fireworks.ai and LangChain. Fireworks really strive to provide good support for LangChain use cases, so if you run into any issues please let us know. You can reach out to us [in our Discord channel](https://discord.com/channels/1137072072808472616/)
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/fireworks) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/fireworks) for more details.

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@@ -1,3 +1,18 @@
# langchain-groq # langchain-groq
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-groq?label=%20)](https://pypi.org/project/langchain-groq/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-groq)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-groq)](https://pypistats.org/packages/langchain-groq)
[![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).
## Quick Install
```bash
pip install langchain-groq
```
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/groq) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/groq) for more details.

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# langchain-huggingface # langchain-huggingface
This package contains the LangChain integrations for huggingface related classes. [![PyPI - Version](https://img.shields.io/pypi/v/langchain-huggingface?label=%20)](https://pypi.org/project/langchain-huggingface/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-huggingface)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-huggingface)](https://pypistats.org/packages/langchain-huggingface)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/groq) for more details. Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
## Quick Install
```bash
pip install langchain-huggingface
```
## 🤔 What is this?
This package contains the LangChain integrations for Hugging Face related classes.
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/huggingface) for more details.

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# langchain-mistralai # langchain-mistralai
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-mistralai?label=%20)](https://pypi.org/project/langchain-mistralai/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-mistralai)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-mistralai)](https://pypistats.org/packages/langchain-mistralai)
[![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).
## Quick Install
```bash
pip install langchain-mistralai
```
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/mistralai) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/mistralai) for more details.

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# langchain-nomic # langchain-nomic
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-nomic?label=%20)](https://pypi.org/project/langchain-nomic/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-nomic)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-nomic)](https://pypistats.org/packages/langchain-nomic)
[![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).
## Quick Install
```bash
pip install langchain-nomic
```
## 🤔 What is this?
This package contains the LangChain integration with Nomic This package contains the LangChain integration with Nomic
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/nomic) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/nomic) for more details.

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# langchain-ollama # langchain-ollama
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-ollama?label=%20)](https://pypi.org/project/langchain-ollama/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-ollama)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-ollama)](https://pypistats.org/packages/langchain-ollama)
[![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).
## Quick Install
```bash
pip install langchain-ollama
```
## 🤔 What is this?
This package contains the LangChain integration with Ollama This package contains the LangChain integration with Ollama
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/ollama) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/ollama) for more details.
## Development ## Development

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# langchain-openai # langchain-openai
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?label=%20)](https://pypi.org/project/langchain-openai/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-openai)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-openai)](https://pypistats.org/packages/langchain-openai)
[![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).
## Quick Install
```bash
pip install langchain-openai
```
## 🤔 What is this?
This package contains the LangChain integrations for OpenAI through their `openai` SDK. This package contains the LangChain integrations for OpenAI through their `openai` SDK.
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/openai) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/openai) for more details.

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# langchain-perplexity # langchain-perplexity
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-perplexity?label=%20)](https://pypi.org/project/langchain-perplexity/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-perplexity)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-perplexity)](https://pypistats.org/packages/langchain-perplexity)
[![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).
## Quick Install
```bash
pip install langchain-perplexity
```
## 🤔 What is this?
This package contains the LangChain integration with Perplexity. This package contains the LangChain integration with Perplexity.
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/perplexity) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/perplexity) for more details.

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# langchain-prompty # langchain-prompty
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-prompty?label=%20)](https://pypi.org/project/langchain-prompty/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-prompty)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-prompty)](https://pypistats.org/packages/langchain-prompty)
[![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).
## Quick Install
```bash
pip install langchain-prompty
```
## 🤔 What is this?
This package contains the LangChain integration with Microsoft Prompty. This package contains the LangChain integration with Microsoft Prompty.
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/microsoft) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/microsoft) for more details.
## Installation
```bash
pip install -U langchain-prompty
```
## Usage ## Usage
Use the `create_chat_prompt` function to load `prompty` file as prompt. Use the `create_chat_prompt` function to load `prompty` file as prompt.

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# langchain-qdrant # langchain-qdrant
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-qdrant?label=%20)](https://pypi.org/project/langchain-qdrant/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-qdrant)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-qdrant)](https://pypistats.org/packages/langchain-qdrant)
[![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).
## Quick Install
```bash
pip install langchain-qdrant
```
## 🤔 What is this?
This package contains the LangChain integration with [Qdrant](https://qdrant.tech/). This package contains the LangChain integration with [Qdrant](https://qdrant.tech/).
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/qdrant) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/qdrant) for more details.

17
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@@ -1,5 +1,22 @@
# langchain-xai # langchain-xai
[![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?label=%20)](https://pypi.org/project/langchain-xai/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-xai)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-xai)](https://pypistats.org/packages/langchain-xai)
[![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).
## Quick Install
```bash
pip install langchain-xai
```
## 🤔 What is this?
This package contains the LangChain integrations for [xAI](https://x.ai/) through their [APIs](https://console.x.ai). This package contains the LangChain integrations for [xAI](https://x.ai/) through their [APIs](https://console.x.ai).
## 📖 Documentation
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/xai) for more details. View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/xai) for more details.

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@@ -1,28 +1,39 @@
# langchain-tests # 🦜️🔗 langchain-tests
This is a testing library for LangChain integrations. It contains the base classes for [![PyPI - Version](https://img.shields.io/pypi/v/langchain-tests?label=%20)](https://pypi.org/project/langchain-tests/#history)
a standard set of tests. [![PyPI - License](https://img.shields.io/pypi/l/langchain-tests)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-tests)](https://pypistats.org/packages/langchain-tests)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
## Installation Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
We encourage pinning your version to a specific version in order to avoid breaking ## Quick Install
your CI when we publish new tests. We recommend upgrading to the latest version
periodically to make sure you have the latest tests.
Not pinning your version will ensure you always have the latest tests, but it may
also break your CI if we introduce tests that your integration doesn't pass.
Pip:
```bash ```bash
pip install -U langchain-tests pip install langchain-tests
``` ```
uv: ## 🤔 What is this?
```bash This is a testing library for LangChain integrations. It contains the base classes for a standard set of tests.
uv add langchain-tests
``` ## 📖 Documentation
For full documentation, see the [API reference](https://reference.langchain.com/python/langchain).
## 📕 Releases & Versioning
See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
We encourage pinning your version to a specific version in order to avoid breaking your CI when we publish new tests. We recommend upgrading to the latest version periodically to make sure you have the latest tests.
Not pinning your version will ensure you always have the latest tests, but it may also break your CI if we introduce tests that your integration doesn't pass.
## 💁 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).
## Usage ## Usage

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@@ -1,7 +1,11 @@
# 🦜✂️ LangChain Text Splitters # 🦜✂️ LangChain Text Splitters
[![PyPI - License](https://img.shields.io/pypi/l/langchain-text-splitters?style=flat-square)](https://opensource.org/licenses/MIT) [![PyPI - Version](https://img.shields.io/pypi/v/langchain-text-splitters?label=%20)](https://pypi.org/project/langchain-text-splitters/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-text-splitters)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-text-splitters)](https://pypistats.org/packages/langchain-text-splitters) [![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-text-splitters)](https://pypistats.org/packages/langchain-text-splitters)
[![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).
## Quick Install ## Quick Install
@@ -9,28 +13,24 @@
pip install langchain-text-splitters pip install langchain-text-splitters
``` ```
## What is it? ## 🤔 What is this?
LangChain Text Splitters contains utilities for splitting into chunks a wide variety of text documents. LangChain Text Splitters contains utilities for splitting into chunks a wide variety of text documents.
For full documentation see the [API reference](https://python.langchain.com/api_reference/text_splitters/index.html) ## 📖 Documentation
and the [Text Splitters](https://python.langchain.com/docs/how_to/#text-splitters) module in the main docs.
For full documentation, see the [API reference](https://reference.langchain.com/python/langchain).
## 📕 Releases & Versioning ## 📕 Releases & Versioning
Minor version increases will occur for: See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
- Breaking changes for any public interfaces NOT marked `beta` We encourage pinning your version to a specific version in order to avoid breaking your CI when we publish new tests. We recommend upgrading to the latest version periodically to make sure you have the latest tests.
Patch version increases will occur for: Not pinning your version will ensure you always have the latest tests, but it may also break your CI if we introduce tests that your integration doesn't pass.
- Bug fixes
- New features
- Any changes to private interfaces
- Any changes to `beta` features
## 💁 Contributing ## 💁 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. 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).