forked from Archives/langchain
58 lines
3.0 KiB
Markdown
58 lines
3.0 KiB
Markdown
# 🦜️🔗 LangChain
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⚡ Building applications with LLMs through composability ⚡
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[](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [](https://opensource.org/licenses/MIT) [](https://twitter.com/langchainai) [](https://discord.gg/6adMQxSpJS)
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## Quick Install
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`pip install langchain`
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## 🤔 What is this?
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Large language models (LLMs) are emerging as a transformative technology, enabling
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developers to build applications that they previously could not.
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But using these LLMs in isolation is often not enough to
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create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
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This library is aimed at assisting in the development of those types of applications.
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## 📖 Documentation
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Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documentation on:
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- Getting started (installation, setting up the environment, simple examples)
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- How-To examples (demos, integrations, helper functions)
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- Reference (full API docs)
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Resources (high-level explanation of core concepts)
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## 🚀 What can this help with?
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There are four main areas that LangChain is designed to help with.
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These are, in increasing order of complexity:
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**📃 LLMs and Prompts:**
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This includes prompt management, prompt optimization, generic interface for all LLMs, and common utilities for working with LLMs.
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**🔗 Chains:**
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Chains go beyond just a single LLM call, and are 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.
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**🤖 Agents:**
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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.
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**🧠 Memory:**
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Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
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For more information on these concepts, please see our [full documentation](https://langchain.readthedocs.io/en/latest/?).
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## 💁 Contributing
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As an open source project in a rapidly developing field, we are extremely open
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to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
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For detailed information on how to contribute, see [here](CONTRIBUTING.md).
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