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Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
137 lines
9.4 KiB
Markdown
137 lines
9.4 KiB
Markdown
# 🦜️🔗 LangChain
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⚡ Build context-aware reasoning applications ⚡
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[](https://github.com/langchain-ai/langchain/releases)
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[](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml)
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[](https://pepy.tech/project/langchain)
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[](https://opensource.org/licenses/MIT)
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[](https://twitter.com/langchainai)
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[](https://discord.gg/6adMQxSpJS)
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[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
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[](https://codespaces.new/langchain-ai/langchain)
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[](https://star-history.com/#langchain-ai/langchain)
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[](https://libraries.io/github/langchain-ai/langchain)
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[](https://github.com/langchain-ai/langchain/issues)
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Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
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[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.
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Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team.
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## Quick Install
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With pip:
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```bash
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pip install langchain
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```
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With conda:
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```bash
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conda install langchain -c conda-forge
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```
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## 🤔 What is LangChain?
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**LangChain** is a framework for developing applications powered by large language models (LLMs).
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For these applications, LangChain simplifies the entire application lifecycle:
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- **Open-source libraries**: Build your applications using LangChain's [modular building blocks](https://python.langchain.com/docs/expression_language/) and [components](https://python.langchain.com/docs/modules/). Integrate with hundreds of [third-party providers](https://python.langchain.com/docs/integrations/platforms/).
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- **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://python.langchain.com/docs/langsmith/) so that you can constantly optimize and deploy with confidence.
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- **Deployment**: Turn any chain into a REST API with [LangServe](https://python.langchain.com/docs/langserve).
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### Open-source libraries
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- **`langchain-core`**: Base abstractions and LangChain Expression Language.
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- **`langchain-community`**: Third party integrations.
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- Some integrations have been further split into **partner packages** that only rely on **`langchain-core`**. Examples include **`langchain_openai`** and **`langchain_anthropic`**.
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- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
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- **[LangGraph](https://python.langchain.com/docs/langgraph)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
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### Productionization:
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- **[LangSmith](https://python.langchain.com/docs/langsmith)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
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### Deployment:
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- **[LangServe](https://python.langchain.com/docs/langserve)**: A library for deploying LangChain chains as REST APIs.
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## 🧱 What can you build with LangChain?
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**❓ Question answering with RAG**
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- [Documentation](https://python.langchain.com/docs/use_cases/question_answering/)
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- End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
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**🧱 Extracting structured output**
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- [Documentation](https://python.langchain.com/docs/use_cases/extraction/)
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- End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain-extract/)
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**🤖 Chatbots**
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- [Documentation](https://python.langchain.com/docs/use_cases/chatbots)
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- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
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And much more! Head to the [Use cases](https://python.langchain.com/docs/use_cases/) section of the docs for more.
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## 🚀 How does LangChain help?
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The main value props of the LangChain libraries are:
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1. **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
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2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks
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Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
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## LangChain Expression Language (LCEL)
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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.
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- **[Overview](https://python.langchain.com/docs/expression_language/)**: LCEL and its benefits
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- **[Interface](https://python.langchain.com/docs/expression_language/interface)**: The standard interface for LCEL objects
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- **[Primitives](https://python.langchain.com/docs/expression_language/primitives)**: More on the primitives LCEL includes
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## Components
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Components fall into the following **modules**:
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**📃 Model I/O:**
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This includes [prompt management](https://python.langchain.com/docs/modules/model_io/prompts/), [prompt optimization](https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/), a generic interface for [chat models](https://python.langchain.com/docs/modules/model_io/chat/) and [LLMs](https://python.langchain.com/docs/modules/model_io/llms/), and common utilities for working with [model outputs](https://python.langchain.com/docs/modules/model_io/output_parsers/).
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**📚 Retrieval:**
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Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/modules/data_connection/document_loaders/) from a variety of sources, [preparing it](https://python.langchain.com/docs/modules/data_connection/document_loaders/), [then retrieving it](https://python.langchain.com/docs/modules/data_connection/retrievers/) for use in the generation step.
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**🤖 Agents:**
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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](https://python.langchain.com/docs/modules/agents/), a [selection of agents](https://python.langchain.com/docs/modules/agents/agent_types/) to choose from, and examples of end-to-end agents.
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## 📖 Documentation
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Please see [here](https://python.langchain.com) for full documentation, which includes:
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- [Getting started](https://python.langchain.com/docs/get_started/introduction): installation, setting up the environment, simple examples
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- [Use case](https://python.langchain.com/docs/use_cases/) walkthroughs and best practice [guides](https://python.langchain.com/docs/guides/)
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- Overviews of the [interfaces](https://python.langchain.com/docs/expression_language/), [components](https://python.langchain.com/docs/modules/), and [integrations](https://python.langchain.com/docs/integrations/providers)
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You can also check out the full [API Reference docs](https://api.python.langchain.com).
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## 🌐 Ecosystem
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- [🦜🛠️ LangSmith](https://python.langchain.com/docs/langsmith/): Tracing and evaluating your language model applications and intelligent agents to help you move from prototype to production.
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- [🦜🕸️ LangGraph](https://python.langchain.com/docs/langgraph): Creating stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.
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- [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploying LangChain runnables and chains as REST APIs.
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- [LangChain Templates](https://python.langchain.com/docs/templates/): Example applications hosted with LangServe.
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## 💁 Contributing
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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.
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For detailed information on how to contribute, see [here](https://python.langchain.com/docs/contributing/).
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## 🌟 Contributors
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[](https://github.com/langchain-ai/langchain/graphs/contributors)
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