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80 lines
4.2 KiB
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80 lines
4.2 KiB
Plaintext
---
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sidebar_position: 0
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---
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# Introduction
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**LangChain** is a framework for developing applications powered by language models. It enables applications that:
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- **Are context-aware**: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
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- **Reason**: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)
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This framework consists of several parts.
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You can see how the parts interact with each other below:
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These parts include:
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- **[LangChain Packages]**: The Python and JavaScript packages. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
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- **[LangChain Templates](https://github.com/langchain-ai/langchain/tree/master/templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.
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- **[LangServe](https://github.com/langchain-ai/langserve)**: A library for deploying LangChain chains as a REST API.
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- **[LangSmith](https://smith.langchain.com/)**: 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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Together, these products simplify the entire application lifecycle:
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- **Develop**: Write your applications in LangChain/LangChain.js. Hit the ground running using Templates for reference.
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- **Productionize**: Use LangSmith to inspect, test and monitor your chains, so that you can constantly improve and deploy with confidence.
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- **Deploy**: Turn any chain into an API with LangServe.
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## LangChain Packages
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The main value props of the LangChain packages are:
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1. **Components**: composable 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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## Get started
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[Here’s](/docs/get_started/installation) how to install LangChain, set up your environment, and start building.
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We recommend following our [Quickstart](/docs/get_started/quickstart) guide to familiarize yourself with the framework by building your first LangChain application.
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## Modules
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LangChain provides standard, extendable interfaces and integrations for the following modules, listed from least to most complex:
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#### [Model I/O](/docs/modules/model_io/)
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Interface with language models
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#### [Retrieval](/docs/modules/data_connection/)
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Interface with application-specific data
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#### [Chains](/docs/modules/chains/)
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Construct sequences of calls
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#### [Agents](/docs/modules/agents/)
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Let chains choose which tools to use given high-level directives
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#### [Memory](/docs/modules/memory/)
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Persist application state between runs of a chain
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#### [Callbacks](/docs/modules/callbacks/)
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Log and stream intermediate steps of any chain
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## Examples, ecosystem, and resources
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### [Use cases](/docs/use_cases/question_answering/)
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Walkthroughs and techniques for common end-to-end use cases, like:
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- [Document question answering](/docs/use_cases/question_answering/)
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- [Chatbots](/docs/use_cases/chatbots/)
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- [Analyzing structured data](/docs/use_cases/qa_structured/sql/)
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- and much more...
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### [Guides](/docs/guides/adapters/openai)
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Best practices for developing with LangChain.
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### [Ecosystem](/docs/integrations/providers/)
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LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/providers/) and [dependent repos](/docs/additional_resources/dependents).
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### [Community](/docs/community)
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Head to the [Community navigator](/docs/community) to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s.
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## API reference
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Head to the [reference](https://api.python.langchain.com) section for full documentation of all classes and methods in the LangChain Python package.
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