Advanced RAG with LangGraph

A sophisticated Retrieval-Augmented Generation (RAG) application that enhances LLM responses with contextual document retrieval and multi-stage verification.

Getting Started

This guide will help you set up and run the Advanced RAG application on your local machine.

Prerequisites

Before you begin, make sure you have the following installed:

• Python 3.12 or higher
• Git

Installation

1. Clone the Repository

   Open your terminal and run:

   git clone https://github.com/segave/Advanced-RAG---LangGraph
   cd Advanced-RAG-LangGraph

2. Install Poetry

   # Install Poetry using pip
   pip install -r requirements.txt

3. Install Project Dependencies

   # Install all project dependencies
   poetry install
   
   # Activate the virtual environment
   poetry shell

4. Run the Application

   Once you have activated the virtual environment with poetry shell, you can run the application:

   # Make sure you are in the project directory
   streamlit run app.py

   The application will start and automatically open in your default web browser. If it doesn't open automatically, you can access it at:

   • Local URL: http://localhost:8501
   • Network URL: http://192.168.X.X:8501 (for accessing from other devices on your network)

Environment Configuration

1. Create Environment File

   Copy the example environment file and add your API keys:

   cp .env.example .env

   Then edit the .env file and add your API keys:

   # Required
   OPENAI_API_KEY=your_openai_api_key
   TAVILY_API_KEY=your_tavily_api_key

2. Optional Environment Variables

   You can customize these settings in your .env file:

    LANGCHAIN_API_KEY=your_langchain_api_key_here
    LANGCHAIN_PROJECT=your_project_name
    LANGCHAIN_TRACING_V2=true
    PYTHONPATH=your_project_path 

Features and Functionality

1. Document Processing

• Upload and process multiple document formats:
  • PDF documents (.pdf)
  • Word documents (.docx)
  • Text files (.txt)
• Automatic text chunking and vectorization
• Persistent vector storage using ChromaDB

2. Advanced RAG Pipeline

• Multi-stage document retrieval and verification:
  • Entry classification for query routing
  • Contextual document retrieval
  • Relevance grading of retrieved documents
  • Response generation with context
  • Hallucination detection
  • Answer quality assessment
  • Web search if needed

3. LLM Model Selection

• Choose between different OpenAI models:
  • GPT-4o
  • GPT-4o-mini
• Dynamic model switching without application restart

4. Document Management

• Upload interface for multiple documents
• Document database cleanup functionality
• Vector store persistence across sessions
• Efficient document chunking and retrieval

5. Chat Interface

• Interactive chat with context-aware responses
• Chat history maintenance
• Clear conversation option
• Real-time response generation

6. Quality Control

• Automatic verification of responses against sources
• Detection of potential hallucinations
• Relevance scoring of retrieved documents
• Answer quality assessment

Docker Installation

If you prefer to use Docker, you can run the application using our Docker image (available on Docker Hub):

Option 1: Using Docker Run

1. Pull the Docker image:

   docker pull sergiogaliana/advanced-rag-langgraph:latest

2. Run the container with environment variables:

   docker run -p 8501:8501 \
     -e OPENAI_API_KEY=your_key \
     -e TAVILY_API_KEY=your_key \
     sergiogaliana/advanced-rag-langgraph:latest

Option 2: Using Docker Compose (Recommended)

1. Create a docker-compose.yml file:

   cp docker-compose.yml.example docker-compose.yml

2. Add your environment variables: Either edit the docker-compose.yml directly or create a .env file with your variables:

   OPENAI_API_KEY=your_key
   TAVILY_API_KEY=your_key

3. Run the container:

   docker-compose up

The application will be available at:

• Local URL: http://localhost:8501

More sections about configuration and usage will be added soon...