/RAG-nificent

Production-ready Chainlit RAG application with Pinecone pipeline offering all Groq and OpenAI Models, to chat with your documents.

Primary LanguagePythonOtherNOASSERTION

RAG-nificent: An AI Chatbot Powered by LLMs for Citation of Custom PDFs, Reports, and Guidelines

Now Supports Llama-3.3 by Meta AI and GPT-4o by OpenAI

Python License: CC BY-NC 4.0 Docker LangChain Chainlit Pinecone

RAG-nificent Logo

RAG-nificent is a state-of-the-art repository that leverages the power of Retrieval-Augmented Generation (RAG) to provide instant answers and references from a curated directory of PDFs containing information on any given topic such as WHO recommendations documents. This system is designed to aid researchers, policy makers, and the public in quickly finding specific information within extensive documents.

Features

  • Conversational Interface: Engage with the system using natural language queries to receive responses directly sourced from the PDFs.
  • Direct Citation: Every response from the system includes a direct link to the source PDF page, ensuring traceability and verification.
  • PDF Directory: A predefined set of key PDF documents, currently including WHO recommendations on major health topics such as schistosomiasis and malaria.

Available Models

OpenAI Models

  • 📘 gpt-4o-mini: Efficient and cost-effective model for most general-purpose tasks
  • 📘 gpt-4o: Advanced model with strong reasoning and instruction-following capabilities
  • 📘 gpt-4-turbo: High-performance model optimized for complex reasoning tasks
  • 📘 gpt-3.5-turbo: Fast and efficient model for general-purpose conversational AI

Groq Models

  • 🦙 llama-3.1-8b-instant: Fast and efficient open-source model for quick interactions
  • 🦙 llama-3.1-70b-versatile: Powerful large language model for complex reasoning tasks
  • 🦙 llama-3.3-70b-versatile: Latest version of Llama with enhanced capabilities
  • 🦙 llama3-70b-8192: Large context window model for processing extensive documents
  • 🦙 llama3-8b-8192: Efficient model with extended context window capabilities
  • 🌟 mixtral-8x7b-32768: Mixture-of-experts model with very large context window
  • 💎 gemma2-9b-it: Google's efficient instruction-tuned language model

Demo

demo.mov

How It Works

The application utilizes a combination of OpenAI embeddings, Pinecone vector search, and a conversational interface to provide a seamless retrieval experience. When a query is made, the system:

  1. Converts the query into embeddings.
  2. Searches for the most relevant document sections using Pinecone's vector search.
  3. Returns the answer along with citations and links to the source documents.

Setup

Option 1: Standard Setup

  1. Clone the repository:

    git clone https://github.com/yourusername/RAG-nificent.git

  2. Install dependencies:

    pip install -r requirements.txt

  3. Set environment variables in a .env (also see .env.examplefile:

    • PINECONE_INDEX_NAME
    • PINECONE_NAME_SPACE
    • OPENAI_API_KEY
    • PINECONE_API_KEY
    • GROQ_API_KEY

  4. Create a Pinecone index with the same name as PINECONE_INDEX_NAME. Set it up with dimensions=1536 and metric=cosine.

  5. Place your PDFs in the pdf_data directory and run data_ingestion.py

  6. Run the application:

    chainlit run src/app.py

Option 2: Docker Setup

  1. Clone the repository:

    git clone https://github.com/yourusername/RAG-nificent.git

  2. Create a .env file with the required environment variables (see .env.example):

    • PINECONE_INDEX_NAME
    • PINECONE_NAME_SPACE
    • OPENAI_API_KEY
    • PINECONE_API_KEY
    • GROQ_API_KEY

  3. Build and run using Docker Compose:

    docker-compose up -d

  4. Access the application at http://localhost:8000

  5. To ingest PDFs with Docker:

    # Place your PDFs in the pdf_data directory first
docker-compose exec ragnificent python data_ingestion.py

  6. To stop the application:

    docker-compose down

Development with Docker

For development purposes, you can use the following commands:

# Build the Docker image
docker-compose build

# Run the application in development mode (with live reloading)
docker-compose up

# View logs
docker-compose logs -f

# Run a specific command inside the container
docker-compose exec ragnificent <command>

Source Documents

The system currently includes guidelines from the following PDFs with direct links to the documents: