You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
79 lines
3.0 KiB
Markdown
79 lines
3.0 KiB
Markdown
# LangChainHub
|
|
|
|
| 🌐 This repo is getting replaced by our hosted LangChain Hub Product! Visit it at [https://smith.langchain.com/hub](https://smith.langchain.com/hub) 🌐 |
|
|
| --- |
|
|
|
|
## Introduction
|
|
|
|
Taking inspiration from Hugging Face Hub, LangChainHub is collection of all artifacts useful for working with LangChain primitives such as prompts, chains and agents.
|
|
The goal of this repository is to be a central resource for sharing and discovering high quality prompts, chains and agents that combine together to form complex LLM applications.
|
|
|
|
We are starting off the hub with a collection of prompts, and we look forward to the LangChain community adding to this collection. We hope to expand to chains and agents shortly.
|
|
|
|
## Contributing
|
|
|
|
Since we are using GitHub to organize this Hub, adding artifacts can best be done in one of three ways:
|
|
|
|
1. Create a fork and then open a PR against the repo.
|
|
2. Create an issue on the repo with details of the artifact you would like to add.
|
|
3. Add an artifact with the appropriate Google form:
|
|
- [Prompts](https://forms.gle/aAhZ6nEUybdzVbYq6)
|
|
|
|
Each of the different types of artifacts (listed below) will have different instructions on how to upload them.
|
|
Please refer to the appropriate documentation to do so.
|
|
|
|
## 📖 Prompts
|
|
|
|
At a high level, prompts are organized by use case inside the `prompts` directory.
|
|
To load a prompt in LangChain, you should use the following code snippet:
|
|
|
|
```python
|
|
from langchain.prompts import load_prompt
|
|
|
|
prompt = load_prompt('lc://prompts/path/to/file.json')
|
|
```
|
|
|
|
In addition to prompt files themselves, each sub-directory also contains a README explaining how best to use that prompt in the appropriate LangChain chain.
|
|
|
|
For more detailed information on how prompts are organized in the Hub, and how best to upload one, please see the documentation [here](./prompts/README.md).
|
|
|
|
## 🔗 Chains
|
|
|
|
At a high level, chains are organized by use case inside the `chains` directory.
|
|
To load a chain in LangChain, you should use the following code snippet:
|
|
|
|
```python
|
|
from langchain.chains import load_chain
|
|
|
|
chain = load_chain('lc://chains/path/to/file.json')
|
|
```
|
|
|
|
In addition to chain files themselves, each sub-directory also contains a README explaining what that chain contains.
|
|
|
|
For more detailed information on how chains are organized in the Hub, and how best to upload one, please see the documentation [here](./chains/README.md).
|
|
|
|
|
|
## 🤖 Agents
|
|
|
|
At a high level, agents are organized by use case inside the `agents` directory.
|
|
To load an agent in LangChain, you should use the following code snippet:
|
|
|
|
```python
|
|
from langchain.agents import initialize_agent
|
|
|
|
llm = ...
|
|
tools = ...
|
|
|
|
agent = initialize_agent(tools, llm, agent="lc://agents/self-ask-with-search/agent.json")
|
|
```
|
|
|
|
In addition to agent files themselves, each sub-directory also contains a README explaining what that agent contains.
|
|
|
|
For more detailed information on how agents are organized in the Hub, and how best to upload one, please see the documentation [here](./agents/README.md).
|
|
|
|
|
|
|
|
## 👷 Agent Executors
|
|
|
|
Coming soon!
|