🤖 Chatbox - AI Assistant

A modern chatbot application built with LlamaIndex, FastAPI, MongoDB, and OpenAI. Features a beautiful web interface with real-time streaming chat capabilities.

✨ Features

• 🚀 Fast & Modern: Built with FastAPI for high performance
• 🧠 AI-Powered: Uses OpenAI GPT models via LlamaIndex
• 💬 Real-time Chat: Streaming responses for instant interaction
• 📚 Memory: Persistent chat history with MongoDB
• 🎨 Beautiful UI: Modern, responsive web interface
• 🐳 Docker Ready: Easy deployment with Docker Compose
• ⚡ Session Management: Multiple concurrent chat sessions
• 🔍 Health Monitoring: Built-in health checks and status monitoring

🛠️ Tech Stack

• Backend: FastAPI, Python 3.11
• AI/LLM: LlamaIndex, OpenAI GPT-4o-mini
• Database: MongoDB
• Frontend: HTML5, CSS3, JavaScript (Vanilla)
• Deployment: Docker, Docker Compose

🚀 Quick Start

Prerequisites

• Docker and Docker Compose installed
• OpenAI API key

Setup

1. Clone and navigate to the project:

   cd /path/to/chatbox

2. Set up environment variables:

   cp .env.example .env
   # Edit .env and add your OpenAI API key

3. Start the application:

   docker-compose up -d

4. Access the application:

   • Chatbox: http://localhost:8000
   • MongoDB Express (DB Admin): http://localhost:8081
   • API Docs: http://localhost:8000/docs

Environment Variables

Create a .env file with:

OPENAI_API_KEY=your_openai_api_key_here

📡 API Endpoints

Chat Endpoints

• POST /api/chat - Send a message (non-streaming)
• POST /api/chat/stream - Send a message (streaming)
• GET /api/sessions/{session_id} - Get chat history
• DELETE /api/sessions/{session_id} - Delete chat session

Utility Endpoints

• GET / - Web interface
• GET /health - Health check
• GET /docs - API documentation

🐳 Docker Commands

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f chatbox

# Stop all services
docker-compose down

# Rebuild and restart
docker-compose up --build -d

# Remove everything (including data)
docker-compose down -v

🔧 Development

Local Development Setup

1. Install Python dependencies:

   pip install -r requirements.txt

2. Start MongoDB:

   docker-compose up -d mongodb

3. Set environment variables:

   export OPENAI_API_KEY="your_key_here"
   export MONGODB_URL="mongodb://admin:password123@localhost:27017/chatbox?authSource=admin"

4. Run the application:

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

Project Structure

chatbox/
├── main.py                 # FastAPI application
├── requirements.txt        # Python dependencies
├── Dockerfile             # Container configuration
├── docker-compose.yml     # Multi-service setup
├── init-mongo.js          # MongoDB initialization
├── .env.example           # Environment template
├── templates/
│   └── index.html         # Web interface
└── README.md             # This file

🔍 Monitoring

Health Check

Visit /health to check service status:

{
  "status": "healthy",
  "mongodb": "connected",
  "openai": "connected",
  "version": "1.0.0"
}

MongoDB Administration

Access MongoDB Express at http://localhost:8081:

• Username: admin
• Password: admin123

🎨 Features Overview

Web Interface

• Modern Design: Beautiful gradient UI with smooth animations
• Responsive: Works on desktop and mobile devices
• Real-time: Streaming chat with typing indicators
• Session Persistence: Chat history saved automatically
• Status Indicators: Connection status and health monitoring

Backend Features

• Streaming Chat: Real-time response streaming
• Session Management: Unique session IDs for each conversation
• Error Handling: Comprehensive error handling and logging
• Health Monitoring: Built-in health checks
• API Documentation: Auto-generated OpenAPI docs

Database Features

• Persistent Storage: Chat history stored in MongoDB
• Indexing: Optimized queries with proper indexing
• Session Tracking: Efficient session management
• Scalable: Ready for horizontal scaling

🛡️ Security Considerations

• Environment variables for sensitive data
• Input validation and sanitization
• Rate limiting ready (can be added)
• CORS configuration available
• Database authentication enabled

🚀 Production Deployment

For production deployment:

1. Use environment-specific configurations
2. Set up proper logging and monitoring
3. Configure SSL/TLS certificates
4. Set up database backups
5. Use production-grade web server (nginx)
6. Implement rate limiting
7. Set up health checks and alerts

📄 License

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

🤝 Contributing

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

📞 Support

If you have any questions or issues, please open an issue on the repository.

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Built with ❤️ using LlamaIndex, FastAPI, and OpenAI