LangChain Query System (langchain-qs)

A Multi-Agent Retrieval Augmented Generation (RAG) system built with LangChain and LangGraph.

Architecture

This repository implements a graph-based multi-agent RAG system with the following components:

• Document Processing Pipeline: Handles document ingestion, chunking, and indexing
• Agent-based Graph System: Utilizes LangGraph for orchestrating multiple agents
• Streamlit Web Interface: Provides an interactive UI for querying the system
• Integration with LLMs: Uses various large language models to power the agents

Prerequisites

• Python 3.9+
• Git
• Access to LLM APIs (Fireworks, OpenAI, etc.)
• Sufficient disk space for document storage

Installation

1. Clone the repository:

git clone https://github.com/yourusername/langchain-qs.git
cd langchain-qs

2. Use the Makefile to set up your environment and install dependencies:

# Create virtual environment and install dependencies
make install

# Create .env file with placeholder values
make env

3. Configure your .env file with your API keys:

TAVILY_API_KEY=
FIREWORKS_API_KEY=
API_PUBLIC_KEY=
API_PRIVATE_KEY=
GROUP_ID=
# Add other API keys as needed

Running the Application

Start the Streamlit app:

make all

The application will be available at http://localhost:8501 by default.

Docker Support

You can also run the application using Docker:

# Build Docker image
make docker-build

# Run Docker container
make docker-run

Usage

1. Open the application in your web browser
2. Select your preferred LLM model from the sidebar
3. Enter your query in the input field
4. The system will:
   • Process your query
   • Retrieve relevant information
   • Generate a response using the Multi-Agent framework

Features

• Multiple LLM Support: Choose between different language models
• Graph-based Agent Workflow: Agents collaborate to improve response quality
• Document Retrieval: Automatically fetches relevant information from indexed documents
• Query Refinement: Rewrite queries to improve search results when needed
• One-Click Deployments: Deploy easily to various cloud platforms
• GitHub Integration: Seamless version control and collaboration support
• Expanded LLM Providers: Support for newer models from various providers
• Enhanced Caching: Improved response times through optimized caching

Makefile Commands

For convenience, the following make commands are available:

make help        # Show all available commands

Contributing

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

License

MIT License