A modern, enterprise-grade chatbot application featuring an intelligent AI assistant powered by cutting-edge technology. Built with a React frontend and a comprehensive microservices backend architecture.
- Intelligent Conversations: AI-powered chatbot using GitHub's GPT-4.1 model
- Real-time Messaging: WebSocket support with streaming responses
- Advanced Authentication: JWT-based auth with Redis caching and session management
- Vector Search: Semantic similarity search using Pinecone vector database
- Chat History: Persistent conversation history with PostgreSQL storage
- Responsive Design: Modern UI with Tailwind CSS and smooth animations
- Microservices Architecture: Scalable backend with Docker containerization
- Real-time Monitoring: RabbitMQ message queuing and monitoring
- Performance Optimized: Redis caching, connection pooling, and efficient data handling
- React 18.3 with modern hooks and function components
- Vite for fast development and optimized builds
- Tailwind CSS for responsive, utility-first styling
- Framer Motion for smooth animations and transitions
- React Router for client-side navigation
- Axios with request/response interceptors for API communication
- Real-time monitoring with Datadog RUM integration
- Django REST Framework with JWT authentication
- Redis caching for optimized user session management
- Custom user model with email-based authentication
- Thread pool executor for async token processing
- Password reset functionality with email support
- PostgreSQL for user data persistence
- FastAPI for high-performance async API
- GitHub AI Models integration (GPT-4.1)
- LangChain for AI conversation management
- Redis for chat history caching
- RabbitMQ for async message processing
- Vector similarity search integration
- FastAPI for conversation persistence
- PostgreSQL for chat history storage
- RabbitMQ consumer for async history logging
- Vector service integration for chat upserting
- FastAPI with Pinecone vector database
- Sentence Transformers for text embeddings
- Semantic similarity search capabilities
- Text preprocessing with spaCy NLP
- Context-aware search results
- PostgreSQL 17 - Primary database
- Redis Stack - Caching and session management
- RabbitMQ - Message queuing and async processing
- Docker & Docker Compose - Containerization
- Supervisor - Process management
- Nginx - Reverse proxy (production)
- Node.js 20+ (for frontend)
- Python 3.12+ (for backend)
- Docker & Docker Compose (recommended)
- Git for version control
git clone <repository-url>
cd chatbot# Navigate to backend directory
cd backend
# Copy environment file and configure
cp .env.example .env
# Edit .env with your configuration
# Start all backend services
docker-compose up --buildServices will be available at:
- Authentication: http://localhost:8000
- Chatbot API: http://localhost:8001
- History Service: http://localhost:8003
- Vector Service: http://localhost:8002
- PostgreSQL: localhost:5432
- Redis: localhost:6379
# Navigate to frontend directory
cd chatbot-frontend
# Install dependencies
npm install
# Start development server
npm run devFrontend will be available at: http://localhost:5173
If you prefer running services individually:
# For each service directory (Auth/, chatbot/, chatbot_history/, vector_services/)
cd backend/[service-name]
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Run the service
# For Django (Auth service):
python manage.py migrate
python manage.py runserver
# For FastAPI services:
uvicorn main:app --reload --port [service-port]- Access the Application: Open http://localhost:5173
- Register/Login: Create an account or sign in
- Start Chatting: Begin conversations with the AI assistant
- Features Available:
- Real-time messaging with streaming responses
- Message editing and copying
- Chat history persistence
- Responsive mobile-friendly interface
- Dark/light theme support
cd chatbot-frontend
# Development with hot reload
npm run dev
# Build for production
npm run build
# Preview production build
npm run preview
# Lint code
npm run lint# Run with auto-reload
uvicorn main:app --reload --port 8001
# Run tests
python -m pytest
# Database migrations (Django)
python manage.py makemigrations
python manage.py migrateCreate .env files in respective directories:
Backend (.env):
# Database
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_USER=your_user
POSTGRES_PASSWORD=your_password
POSTGRES_DB=chatbot_db
# Redis
REDIS_URL=redis://localhost:6379
# API Keys
GITHUB_TOKEN=your_github_token
PINECONE_API_KEY=your_pinecone_key
PINECONE_ENV=your_pinecone_environment
# RabbitMQ
RABBITMQ_URL=amqp://guest:guest@localhost:5672/| Technology | Purpose | Version |
|---|---|---|
| React | UI Framework | 18.3.1 |
| Vite | Build Tool | 6.1.0 |
| Tailwind CSS | Styling | 3.4.17 |
| React Router | Navigation | 7.1.5 |
| Framer Motion | Animations | 12.4.2 |
| Axios | HTTP Client | 1.7.9 |
| Lucide React | Icons | 0.475.0 |
| Technology | Purpose | Version |
|---|---|---|
| Django | Auth Framework | 5.2 |
| FastAPI | API Framework | 0.110.2 |
| PostgreSQL | Database | 17 |
| Redis | Caching | Latest |
| RabbitMQ | Message Queue | 3-management |
| LangChain | AI Framework | Latest |
| Pinecone | Vector DB | 6.0.0+ |
- GitHub AI Models (GPT-4.1)
- Sentence Transformers for embeddings
- spaCy for NLP preprocessing
- LangChain for conversation management
POST /auth/register/- User registrationPOST /auth/login/- User loginGET /auth/status/- Check auth statusPOST /auth/logout/- User logoutPOST /auth/token/refresh/- Refresh JWT token
POST /api/chat/- Send message to chatbotGET /api/chat/history/- Get chat history
POST /similarity-search/- Semantic similarity searchPOST /upsert-history/- Store chat for vector search
# Build all services
docker-compose -f docker-compose.prod.yml up --build
# Scale services
docker-compose up --scale chatbot-service=3- Configure environment variables for production
- Set up SSL certificates
- Configure Nginx reverse proxy
- Set up monitoring and logging
- Configure backup strategies for PostgreSQL
Current Version: 1.3.6
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit changes (
git commit -m 'Add amazing feature') - Push to branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
Built with ❤️ by the DeepSource Team