This project demonstrates a real-time AI chatbot application using Server-Sent Events (SSE) for streaming LLM responses. The application features a React frontend and an Egg.js-powered Node.js backend, enabling real-time, synchronous rendering of AI responses as they are generated.

• Real-time streaming of LLM responses using Server-Sent Events (SSE)
• Token-by-token rendering of AI responses
• Built with Egg.js framework for robust backend services
• React-based interactive chat interface

.
├── src/ # Frontend React application with SSE client implementation
├── server/ # Egg.js backend server with SSE controller
├── public/ # Static files
└── package.json # Frontend dependencies

• Node.js (v14 or higher)
• npm (v6 or higher)
• An LLM API key (if using external AI services)

1. Install frontend dependencies:

npm install

1. Navigate to the server directory:

cd server

2. Install backend dependencies:

npm install

3. Configure your environment variables:
   • Create a .env file in the server directory
   • Add your LLM API credentials (if required)

1. From the server directory:

cd server
npm start

The Egg.js server will start and listen for incoming connections, handling SSE streams for AI responses.

1. In a new terminal, from the project root:

npm start

The frontend development server will start and automatically open your default browser to http://localhost:3000.

• Frontend runs on: http://localhost:3000
• Egg.js backend API runs on: http://localhost:7001
• SSE endpoint: http://localhost:7001/api/chat/stream

Frontend:

• npm start - Starts the development server
• npm test - Runs the test suite
• npm run build - Creates a production build
• npm run eject - Ejects from create-react-app

Backend (Egg.js):

• npm start - Starts the Egg.js server
• npm test - Runs the backend tests
• npm run dev - Starts the server in development mode with hot reload

The application uses SSE to establish a one-way communication channel from the server to the client, enabling real-time streaming of LLM responses. This provides several benefits:

• Efficient streaming of AI responses
• Real-time token-by-token display
• Reduced latency compared to traditional polling
• Native browser support without WebSocket complexity

The backend is built with Egg.js, a robust Node.js framework that provides:

• Built-in security features
• Efficient middleware system
• Easy-to-use plugin system
• Production-ready architecture

This project is licensed under the MIT License - see the LICENSE file for details.