LangChainDoc.com - Backend RAG System 🦜🔗

The backend powering a RAG system for querying LangChain documentation

Overview

This project provides the backend for LangChainDoc.com, enabling querying of LangChain documentation. It uses:

• LangGraph for orchestrating the retrieval and response generation
• Vector database for storing and retrieving documentation content
• LLMs for generating responses with developer insights

Features

• Semantic Search: Find relevant documentation based on meaning
• Context-Aware Responses: Responses consider multiple documentation sources

Supported Providers

This project has been tested with:

• Vector Database: Pinecone
• LLM: OpenAI

The system is structured to work with other providers, but implementations for alternatives would need to be added.

Prerequisites

Before you begin, ensure you have the following API keys for:

• LangSmith
• OpenAI (for LLM access)
• Pinecone (for vector database access)

Getting Started

1. Copy .env.example to .env

cp .env.example .env 

2. Add your API keys and configuration to .env

3. Install the LangGraph CLI

pip install --upgrade "langgraph-cli[inmem]"

4. Launch LangGraph Server Start the LangGraph API server locally:

langgraph dev

Integration with Frontend

This backend system works with the LangChainDoc Client to provide a complete user experience.

About the Creator

This project is maintained by Luc Ebert, a LangChain developer.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License

Contact

For questions and support, please open an issue in the repository.