c010ec8b71
- `.get_relevant_documents(query)` -> `.invoke(query)`
- `.get_relevant_documents(query=query)` -> `.invoke(query)`
- `.get_relevant_documents(query, callbacks=callbacks)` ->
`.invoke(query, config={"callbacks": callbacks})`
- `.get_relevant_documents(query, **kwargs)` -> `.invoke(query,
**kwargs)`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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| docs | ||
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| templates | ||
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| .gitignore | ||
| .readthedocs.yaml | ||
| CITATION.cff | ||
| LICENSE | ||
| Makefile | ||
| MIGRATE.md | ||
| poetry.lock | ||
| poetry.toml | ||
| pyproject.toml | ||
| README.md | ||
| SECURITY.md | ||
🦜️🔗 LangChain
⚡ Build context-aware reasoning applications ⚡
Looking for the JS/TS library? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. Fill out this form to speak with our sales team.
Quick Install
With pip:
pip install langchain
With conda:
conda install langchain -c conda-forge
🤔 What is LangChain?
LangChain is a framework for developing applications powered by large language models (LLMs).
For these applications, LangChain simplifies the entire application lifecycle:
- Open-source libraries: Build your applications using LangChain's modular building blocks and components. Integrate with hundreds of third-party providers.
- Productionization: Inspect, monitor, and evaluate your apps with LangSmith so that you can constantly optimize and deploy with confidence.
- Deployment: Turn any chain into a REST API with LangServe.
Open-source libraries
langchain-core: Base abstractions and LangChain Expression Language.langchain-community: Third party integrations.- Some integrations have been further split into partner packages that only rely on
langchain-core. Examples includelangchain_openaiandlangchain_anthropic.
- Some integrations have been further split into partner packages that only rely on
langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.- LangGraph: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
Productionization:
- LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
Deployment:
- LangServe: A library for deploying LangChain chains as REST APIs.
🧱 What can you build with LangChain?
❓ Question answering with RAG
- Documentation
- End-to-end Example: Chat LangChain and repo
🧱 Extracting structured output
- Documentation
- End-to-end Example: SQL Llama2 Template
🤖 Chatbots
- Documentation
- End-to-end Example: Web LangChain (web researcher chatbot) and repo
And much more! Head to the Use cases section of the docs for more.
🚀 How does LangChain help?
The main value props of the LangChain libraries are:
- Components: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
- Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks
Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
LangChain Expression Language (LCEL)
LCEL is the foundation of many of LangChain's components, and is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains.
- Overview: LCEL and its benefits
- Interface: The standard interface for LCEL objects
- Primitives: More on the primitives LCEL includes
Components
Components fall into the following modules:
📃 Model I/O:
This includes prompt management, prompt optimization, a generic interface for chat models and LLMs, and common utilities for working with model outputs.
📚 Retrieval:
Retrieval Augmented Generation involves loading data from a variety of sources, preparing it, then retrieving it for use in the generation step.
🤖 Agents:
Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
📖 Documentation
Please see here for full documentation, which includes:
- Getting started: installation, setting up the environment, simple examples
- Use case walkthroughs and best practice guides
- Overviews of the interfaces, components, and integrations
You can also check out the full API Reference docs.
🌐 Ecosystem
- 🦜🛠️ LangSmith: Tracing and evaluating your language model applications and intelligent agents to help you move from prototype to production.
- 🦜🕸️ LangGraph: Creating stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.
- 🦜🏓 LangServe: Deploying LangChain runnables and chains as REST APIs.
- LangChain Templates: Example applications hosted with LangServe.
💁 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 here.