Constitution of India RAG System

A Retrieval-Augmented Generation (RAG) system for querying the Constitution of India using LangChain, Pinecone, HuggingFace embeddings, and GitHub Marketplace models.

Key Features

• 🔍 Semantic search through Constitution text
• 🤖 AI-powered answer generation using GitHub Marketplace models
• 📊 Source attribution with relevance scores
• ⚡ Fast vector-based retrieval using Pinecone
• 🎯 Context-aware responses
• 🔧 HuggingFace embeddings (no OpenAI API required)
• 📄 Support for both PDF and TXT file formats

Setup

Prerequisites

• Python 3.8 or higher
• Pinecone account and API key
• GitHub token with access to marketplace models

Installation

1. Clone the repository:

git clone <your-repo-url>
cd COI

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env
# Edit .env with your API keys

4. Add the Constitution document:

   • Place the Constitution of India file (PDF or TXT format) in the documents/ folder
   • Supported filenames:
     • documents/COI.pdf
     • documents/constitution_of_india.pdf
     • documents/constitution_of_india.txt

5. Run setup validation:

python setup.py

Running the Application

streamlit run src/app.py

Usage

1. The app will automatically process the Constitution document on first run
2. Enter your question in the text input
3. Click "Get Answer" to receive AI-generated responses with sources

Project Structure

COI/
├── src/
│   ├── app.py                 # Main Streamlit application
│   ├── config/
│   │   └── settings.py        # Configuration settings
│   ├── data/
│   │   └── constitution_processor.py  # Document processing
│   ├── embeddings/
│   │   └── embedding_service.py       # HuggingFace embedding generation
│   ├── vector_store/
│   │   └── pinecone_client.py         # Pinecone operations
│   ├── llm/
│   │   └── github_model_client.py     # GitHub model client
│   ├── retrieval/
│   │   └── rag_pipeline.py            # RAG pipeline
│   └── utils/
│       └── helpers.py                 # Utility functions
├── documents/
│   └── COI.pdf                        # Constitution document (PDF or TXT supported)
├── requirements.txt
├── .env.example
├── setup.py
└── README.md

Configuration

Edit src/config/settings.py to modify:

• Chunk size and overlap for document processing
• Number of retrieved documents
• HuggingFace model parameters

Environment Variables

Required environment variables in .env:

• PINECONE_API_KEY: Your Pinecone API key
• PINECONE_ENVIRONMENT: Your Pinecone environment (default: gcp-starter)
• GITHUB_TOKEN: Your GitHub token for marketplace models
• GITHUB_MODEL_ENDPOINT: Your GitHub marketplace model endpoint
• HUGGINGFACE_MODEL_NAME: HuggingFace model name (default: sentence-transformers/all-MiniLM-L6-v2)

Troubleshooting

1. Import errors: Make sure all dependencies are installed with pip install -r requirements.txt
2. Environment variables: Ensure all required variables are set in .env
3. Constitution file: Place the constitution file (PDF or TXT) in the documents/ folder with supported filename
4. Pinecone connection: Verify your Pinecone API key and environment

License

MIT License