📚 NCERT Q&A Web Application

A production-ready, full-stack web application for AI-powered question-answering based on NCERT textbooks using RAG (Retrieval-Augmented Generation).

🎯 Overview

This application transforms NCERT textbooks into an intelligent Q&A system. Ask questions in natural language and get accurate answers with source citations.

🏗️ Architecture

• Backend: FastAPI (REST API)
• Frontend: Flask (Web Interface)
• Vector Database: ChromaDB
• LLM: Groq (Llama3)
• Embeddings: Ollama (nomic-embed-text)

Project Structure

├── backend/
│   ├── api/
│   │   ├── models.py          # Pydantic models
│   │   └── routes.py          # API endpoints
│   ├── services/
│   │   ├── vector_db_service.py   # Vector DB operations
│   │   └── qa_service.py          # Q&A logic
│   ├── config/
│   │   └── settings.py        # Configuration
│   └── main.py                # FastAPI app
├── frontend/
│   ├── static/
│   │   ├── css/
│   │   │   └── style.css      # Styles
│   │   └── js/
│   │       └── app.js         # Frontend logic
│   ├── templates/
│   │   └── index.html         # Main page
│   └── app.py                 # Flask app
├── utils/
│   └── vector_db_maker.py     # Vector DB creation
└── requirements.txt

Prerequisites

1. Python 3.8+
2. Ollama - Install from ollama.ai
3. Groq API Key - Get from console.groq.com

Installation

1. Clone the repository and install dependencies:

pip install -r requirements.txt

2. Install Ollama and pull the embedding model:

ollama pull nomic-embed-text

3. Create .env file from example:

cp .env.example .env

4. Edit .env and add your Groq API key:

GROQ_API_KEY=your_actual_api_key_here

Setup Vector Database

Before running the application, create the vector database from PDFs:

python utils/vector_db_maker.py

This will process the NCERT PDF files and create a vector database in the vector_db directory.

Running the Application

Option 1: Run Both Servers Separately

Terminal 1 - Start Backend (FastAPI):

python backend/main.py

Backend will run on http://localhost:8000

Terminal 2 - Start Frontend (Flask):

python frontend/app.py

Frontend will run on http://localhost:5000

Option 2: Using Uvicorn and Flask CLI

Backend:

uvicorn backend.main:app --reload --host 0.0.0.0 --port 8000

Frontend:

flask --app frontend/app run --host 0.0.0.0 --port 5000

Usage

1. Open your browser and navigate to http://localhost:5000
2. Enter your question in the text box
3. Click "Ask Question"
4. View the answer and retrieved source documents

API Documentation

Once the backend is running, visit:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

API Endpoints

POST /api/v1/ask

{
  "question": "What is Newton's first law?"
}

Response:

{
  "answer": "...",
  "retrieved_documents": [
    {
      "page": "42",
      "link": "NCERT-Class-12-Physics-Part-1.pdf",
      "snippet": "..."
    }
  ]
}

GET /api/v1/health

{
  "status": "healthy",
  "message": "Service is running",
  "vector_db_loaded": true
}

Configuration

Edit backend/config/settings.py or use environment variables to configure:

• GROQ_API_KEY: Your Groq API key
• VECTOR_DB_PATH: Path to vector database
• EMBEDDING_MODEL: Ollama embedding model
• LLM_MODEL: Groq LLM model
• RETRIEVAL_K: Number of documents to retrieve
• CHUNK_SIZE: Text chunk size for processing
• CHUNK_OVERLAP: Overlap between chunks

Features

• ✅ Modular architecture with separation of concerns
• ✅ RESTful API with FastAPI
• ✅ Modern web interface with Flask
• ✅ RAG-based question answering
• ✅ Document retrieval with re-ranking
• ✅ Source document display
• ✅ Health check endpoints
• ✅ Error handling
• ✅ Responsive design
• ✅ Example questions
• ✅ Status indicator

Troubleshooting

Vector database not found:

• Run python utils/vector_db_maker.py to create it

Ollama connection error:

• Make sure Ollama is running: ollama serve
• Check if model is installed: ollama list

Backend connection error:

• Ensure backend is running on port 8000
• Check BACKEND_URL in frontend configuration

Contributor Guide

You may follow these steps to contribute:

Fork the official repository. This will create a copy of the official repository in your own account. Sync the branches. This will ensure that your copy of the repository is up-to-date with the latest changes from the official repository. Work on your forked repository's feature branch. This is where you will make your changes to the code. Commit your updates on your forked repository's feature branch. This will save your changes to your copy of the repository. Submit a pull request to the official repository's main branch. This will request that your changes be merged into the official repository. Resolve any linting errors. This will ensure that your changes are formatted correctly. Here are some additional things to keep in mind during the process:

Test your changes. Before you submit a pull request, make sure that your changes work as expected. Be patient. It may take some time for your pull request to be reviewed and merged.