TP-Link Product Assistant v2.0

🤖 AI-powered TP-Link product recommendation and comparison system with RAG (Retrieval-Augmented Generation) technology.

✨ Features

• 🎯 Smart Product Recommendations: AI-powered suggestions based on your specific needs
• 📊 Intelligent Product Comparison: Side-by-side comparison of technical specifications
• 📋 Detailed Specifications: Comprehensive product information and technical specs
• 🔧 Feature Analysis: In-depth feature exploration and capability assessment
• 💡 Next Question Suggestions: Intelligent follow-up question recommendations
• 📱 Responsive Design: Modern light theme optimized for desktop and mobile
• 🌐 Multi-language Support: English and Chinese language support
• 🔍 Advanced RAG System: Enhanced product search and retrieval

🚀 Quick Start

Prerequisites

• Node.js 16+
• OpenAI API Key
• npm or yarn

Installation

1. Clone the repository

   git clone <repository-url>
   cd WebAI

2. Install dependencies

   npm install

3. Set up environment variables

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

4. Start the application

   npm start

5. Access the application

   • Web Interface: http://localhost:3001
   • API Health Check: http://localhost:3001/api/health

📦 Product Database

The system includes comprehensive data for 6 product categories:

• Deco Mesh Systems: BE65, BE95, X50
• WiFi Routers: AX73, AX55, BE800
• Range Extenders: RE705X, RE200
• Access Points: EAP660 HD
• Network Switches: TL-SG1005P
• Powerline Adapters: TL-PA7017

🛠️ Deployment Options

1. Local Development

npm run dev

2. Production Server

NODE_ENV=production npm start

3. PM2 Process Manager

# Single instance
npm run pm2:start

# Cluster mode
pm2 start ecosystem.config.js --only tp-link-assistant

4. Docker

# Build and run
npm run docker:build
npm run docker:run

# Or use Docker Compose
docker-compose up -d

5. Automated Deployment

chmod +x deploy.sh
./deploy.sh

📖 API Documentation

Core Endpoints

Product Recommendations

POST /api/recommend
Content-Type: application/json

{
  "query": "I need a router for gaming"
}

Product Comparison

POST /api/compare
Content-Type: application/json

{
  "products": ["BE65", "BE95"]
}

Specifications Query

POST /api/spec
Content-Type: application/json

{
  "model": "Deco BE65",
  "spec": "speed"
}

General Query

POST /api/query
Content-Type: application/json

{
  "message": "What's the best mesh system for large homes?"
}

Response Format

{
  "success": true,
  "data": {
    "intent": "product_recommendation",
    "result": [...],
    "nextQuestions": [...]
  },
  "timestamp": "2024-01-01T00:00:00.000Z"
}

🏗️ Architecture

Core Components

• RAG Agent (rag-agent.js): Advanced retrieval and generation system
• Product Index: Semantic search capabilities for product matching
• Intent Classification: Smart query understanding and routing
• Response Generation: Context-aware AI responses with OpenAI GPT-4
• UI Framework: Modern responsive design with CSS custom properties

Data Flow

1. User query → Intent classification
2. RAG retrieval → Relevant product matching
3. OpenAI processing → Personalized recommendations
4. Response formatting → UI display
5. Next questions → Intelligent suggestions

🔧 Configuration

Environment Variables

# Required
OPENAI_API_KEY=your_openai_api_key

# Optional
PORT=3001
NODE_ENV=development
MAX_REQUESTS_PER_MINUTE=60

PM2 Configuration

Edit ecosystem.config.js for production deployment settings:

module.exports = {
  apps: [{
    name: 'tp-link-assistant',
    script: 'server.js',
    instances: 'max',
    env_production: {
      NODE_ENV: 'production',
      PORT: 8080
    }
  }]
};

🧪 Testing

# Run tests
npm test

# Test API endpoints
curl http://localhost:3001/api/health

📊 Monitoring

Logs

• Development: Console output
• Production: ./logs/ directory
• PM2: pm2 logs
• Docker: docker logs tp-link-assistant

Health Check

curl http://localhost:3001/api/health

🔒 Security Features

• Environment variable protection for API keys
• CORS enabled for cross-origin requests
• Input validation and sanitization
• Rate limiting support
• Non-root Docker container execution

🚀 Performance

• RAG System: Semantic product matching and retrieval
• Caching: Intelligent response caching
• Optimization: Compressed assets and efficient queries
• Scalability: PM2 cluster mode and Docker support

📱 UI Features

• Modern Design: Light theme with CSS custom properties
• Responsive Layout: Mobile-first responsive design
• Real-time Suggestions: Dynamic next question recommendations
• Interactive Elements: Hover effects and smooth animations
• Accessibility: Semantic HTML and keyboard navigation

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests
5. Submit a pull request

📄 License

ISC License - see LICENSE file for details

🆘 Support

• Documentation: Check this README and inline code comments
• Issues: Report bugs and feature requests
• API Questions: Test endpoints with provided examples

---

TP-Link Product Assistant v2.0 - Powered by OpenAI GPT-4 and RAG Technology 🚀