RAG Enhanced NCERT Tutor

NCERT Books RAG System

This project implements a Retrieval-Augmented Generation (RAG) system for NCERT books using Ollama for text embedding and vector database, and Groq API for the language model response.

Features

• Uses Nomic text embedding model via Ollama for creating vector embeddings
• Stores embeddings in ChromaDB
• Utilizes Groq API with LLaMA 3 8B model for generating responses
• Provides a FastAPI backend and Streamlit frontend for user interaction

Streamlit Interface

Below is a screenshot of the Streamlit interface for our NCERT Books RAG system:

System Architecture

Here's an overview of the NCERT Books RAG system architecture:

The system architecture consists of the following components:

1. Data Ingestion: NCERT books are processed and prepared for embedding.
2. Embedding Generation: Ollama with the Nomic text embedding model creates vector embeddings for the processed text.
3. Vector Storage: ChromaDB stores the generated embeddings for efficient retrieval.
4. Query Processing: User queries are processed and relevant embeddings are retrieved from ChromaDB.
5. Language Model: Groq API with LLaMA 3 8B model generates responses based on the retrieved context and user query.
6. Backend: FastAPI handles the communication between the frontend and the various system components.
7. Frontend: Streamlit provides an interactive user interface for querying the system and displaying results.

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.7+
• Ollama installed and set up
• Groq API account and API key

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/ncert-rag-system.git
   cd ncert-rag-system
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Download and set up Ollama:

   • Follow the instructions at Ollama's official website to install Ollama
   • Download the Nomic text embedding model:

     ollama pull nomic-embed-text
     

4. Set up your Groq API key:

   • Create a .env file in the project root
   • Add your Groq API key:

     GROQ_API_KEY=your_api_key_here
     

Usage

1. Start the FastAPI backend:

   uvicorn main:app --reload
   

2. Launch the Streamlit UI:

   streamlit run streamlit_app.py
   

3. Open your web browser and navigate to the Streamlit app URL (typically http://localhost:8501)

4. Use the interface to interact with the NCERT books RAG system

RAG Evaluation

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

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

Acknowledgements

• Ollama for providing the embedding model
• Groq for their LLM API
• FastAPI and Streamlit for the backend and frontend frameworks