News Chatbot with RAG

A full-stack chatbot application that answers queries using a Retrieval-Augmented Generation (RAG) pipeline over a news corpus.

Features

• RAG Pipeline: Ingest news articles, generate embeddings, and retrieve relevant context
• Session Management: Each user gets a unique session with chat history
• Persistent Storage: Redis for session management and chat history
• Modern UI: Built with React and Tailwind CSS

Tech Stack

• Backend: FastAPI (Python 3.11+)
• Frontend: React + Vite + Tailwind CSS
• Vector Database: ChromaDB
• Embeddings: Jina AI
• LLM: Google Gemini
• Caching & Sessions: Redis

Prerequisites

• Python 3.11+
• Node.js 18+
• Redis
• Google Gemini API Key
• Jina AI API Key

Demo

Setup

Backend

1. Clone the repository
2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\activate

3. Install dependencies:

   cd backend
   pip install -r requirements.txt

4. Set up environment variables:
   • Copy .env.example to .env
   • Update the values in .env with your API keys

Frontend

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Create a .env file with:

   VITE_API_URL=http://localhost:8000
   

Running the Application

Start Redis

Make sure Redis is running locally or update the Redis configuration in .env.

Start Backend

cd backend
uvicorn app.main:app --reload

The API will be available at http://localhost:8000

Start Frontend

cd frontend
npm run dev

The frontend will be available at http://localhost:5173

API Endpoints

• POST /api/chat - Send a chat message
• GET /api/chat/{session_id} - Get chat history for a session
• DELETE /api/chat/{session_id} - Delete a chat session

Configuration

Redis TTL

Session data in Redis is set to expire after 24 hours by default. You can adjust this in app/db/redis_client.py by modifying the ttl attribute in the RedisManager class.

Caching

• Chat History: Stored in Redis with TTL
• Embeddings: Cached in ChromaDB

Deployment

Backend

1. Set ENVIRONMENT=production in .env
2. Use a production ASGI server like Uvicorn with Gunicorn:

   gunicorn -w 4 -k uvicorn.workers.UvicornWorker app.main:app

Frontend

Build for production:

cd frontend
npm run build

License

MIT