mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
58 lines
3.0 KiB
Markdown
58 lines
3.0 KiB
Markdown
# 🦜️🔗 LangChain
|
|
|
|
⚡ Building applications with LLMs through composability ⚡
|
|
|
|
[](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)
|
|
|
|
## Quick Install
|
|
|
|
`pip install langchain`
|
|
|
|
## 🤔 What is this?
|
|
|
|
Large language models (LLMs) are emerging as a transformative technology, enabling
|
|
developers to build applications that they previously could not.
|
|
But using these LLMs in isolation is often not enough to
|
|
create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
|
|
|
|
This library is aimed at assisting in the development of those types of applications.
|
|
|
|
## 📖 Documentation
|
|
|
|
Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documentation on:
|
|
|
|
- Getting started (installation, setting up the environment, simple examples)
|
|
- How-To examples (demos, integrations, helper functions)
|
|
- Reference (full API docs)
|
|
Resources (high-level explanation of core concepts)
|
|
|
|
## 🚀 What can this help with?
|
|
|
|
There are four main areas that LangChain is designed to help with.
|
|
These are, in increasing order of complexity:
|
|
|
|
**📃 LLMs and Prompts:**
|
|
|
|
This includes prompt management, prompt optimization, generic interface for all LLMs, and common utilities for working with LLMs.
|
|
|
|
**🔗 Chains:**
|
|
|
|
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.
|
|
|
|
**🤖 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.
|
|
|
|
**🧠 Memory:**
|
|
|
|
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.
|
|
|
|
For more information on these concepts, please see our [full documentation](https://langchain.readthedocs.io/en/latest/?).
|
|
|
|
## 💁 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 infra, or better documentation.
|
|
|
|
For detailed information on how to contribute, see [here](CONTRIBUTING.md).
|