🤖 Simple RAG Chat

A beautiful React-based chat interface for your RAG (Retrieval-Augmented Generation) system powered by ChromaDB, Gemini embeddings, and FastAPI.

Demo Chat

✨ Features

• Beautiful Chat UI: Modern, responsive React interface with chat history
• RAG Integration: Uses ChromaDB for vector storage and Gemini for embeddings
• Real-time Chat: Ask questions and get AI-powered answers from your documents
• Document Indexing: Automatically processes .txt files from my_data/ folder
• Error Handling: Graceful error handling and loading states
• Mobile Responsive: Works great on desktop and mobile devices

🚀 Quick Start

Prerequisites

• Python 3.9+
• Node.js 18+ (for React development)
• A Gemini API key

1. Setup Environment

# Clone or navigate to the project directory
cd "simple rag"

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install Python dependencies
pip install -r requirements.txt

# Set up your Gemini API key
echo "GEMINI_API_KEY=your_actual_api_key_here" > .env

2. Add Your Documents

Place your .txt files in the my_data/ folder. The system will automatically index them.

3. Start the Application

Option A: Use the startup script (recommended)

./start.sh

Option B: Manual startup

Terminal 1 (Backend):

source .venv/bin/activate
uvicorn api:app --host 127.0.0.1 --port 8000

Terminal 2 (Frontend):

cd ui
npm install
npm run dev -- --host 127.0.0.1 --port 5173

4. Open the App

Visit http://127.0.0.1:5173 in your browser.

📁 Project Structure

simple rag/
├── api.py                 # FastAPI server with /ask endpoint
├── rag_core.py           # Core RAG logic and ChromaDB integration
├── rag.py                # CLI interface (original script)
├── requirements.txt      # Python dependencies
├── start.sh              # Startup script for both servers
├── .env                  # Environment variables (create this)
├── my_data/              # Your documents go here
│   ├── doc.txt
│   └── mamun.txt
└── ui/                   # React frontend
    ├── src/
    │   ├── App.jsx       # Main chat component
    │   ├── App.css       # Beautiful styling
    │   └── index.css     # Base styles
    └── package.json

🔧 API Endpoints

• POST /ask - Ask a question and get an AI-generated answer

  {
    "question": "Tell me about Samrat",
    "top_k": 5
  }

• GET /docs - Interactive API documentation

🎨 UI Features

• Chat History: See all your previous questions and answers
• Loading States: Visual feedback while processing
• Error Handling: Clear error messages if something goes wrong
• Responsive Design: Works on all screen sizes
• Clear History: Reset the conversation anytime

🛠️ Development

Backend Development

source .venv/bin/activate
uvicorn api:app --host 127.0.0.1 --port 8000 --reload

Frontend Development

cd ui
npm run dev

Adding New Documents

Just add .txt files to the my_data/ folder and restart the backend. The system will automatically re-index if the collection is empty.

🔍 How It Works

1. Document Processing: Text files are chunked and embedded using Gemini's text-embedding-004 model
2. Vector Storage: Embeddings are stored in ChromaDB for fast similarity search
3. Query Processing: User questions are embedded and matched against stored chunks
4. Answer Generation: Relevant context is sent to Gemini 1.5 Flash for answer generation
5. Response Display: Answers are displayed in the beautiful chat interface

🐛 Troubleshooting

Node.js Version Issues

If you get Node.js version errors, the project uses Vite 4.5.3 which is compatible with Node 18+.

API Connection Issues

Make sure both servers are running:

• Backend: http://127.0.0.1:8000
• Frontend: http://127.0.0.1:5173

Missing Dependencies

# Backend
pip install -r requirements.txt

# Frontend
cd ui && npm install

📝 License

This project is open source and available under the MIT License.

🤝 Contributing

Feel free to submit issues and enhancement requests!