E-commerce Local MCP Server

# Real AI Models Implementation - Complete ✅ ## 🎯 Implementation Summary Successfully upgraded the E-commerce MCP Server from **mock models** to **real AI models** using `llama-cpp-python`. The system now supports local AI inference with automatic model management and intelligent model selection. ## ✅ What Was Implemented ### 1. Real Model Manager (`src/services/real_model_manager.py`) - **Full lifecycle management**: Load, unload, status tracking - **Multi-model support**: phi-3-mini, llama3-8b, gemma-2b - **Automatic model selection**: Based on query complexity - **GPU acceleration**: CUDA, Metal, and CPU support - **Memory monitoring**: Resource usage tracking - **Error handling**: Graceful fallbacks to mock models - **Thread-safe operations**: Concurrent request handling ### 2. Model Download System (`scripts/download_models.py`) - **Automated downloads**: Direct from Hugging Face - **Progress tracking**: Download progress bars - **File verification**: Size and integrity checks - **Multiple models**: Support for 3 different model types - **Disk space checking**: Available storage verification - **Resume capability**: Handle interrupted downloads ### 3. Enhanced Query Processing - **Optimized prompts**: Tailored for each model type - **Response cleaning**: Remove artifacts and formatting issues - **Auto-model loading**: Intelligent model selection per query - **Fallback system**: Mock responses when models unavailable - **Context-aware responses**: Better understanding of e-commerce data ### 4. Updated API Integration - **Real-time model status**: Show actual model states - **Model loading endpoints**: Load/unload specific models - **Health monitoring**: Track model availability - **Error reporting**: Detailed error messages for troubleshooting ## 🏗️ Architecture Changes ### Before (Mock Models) ``` Query → Mock Response Generator → Template Response ``` ### After (Real Models) ``` Query → Intent Classification → Model Selection → Real AI Model → Cleaned Response ↓ ↓ ↓ Entity Extraction → Tool Execution → Response Generation ``` ## 🤖 Available Models | Model | Purpose | Size | Features | |-------|---------|------|----------| | **phi-3-mini** | General queries | ~2.3GB | Fast, efficient, 4K context | | **gemma-2b** | Simple queries | ~1.5GB | Very fast, lightweight | | **llama3-8b** | Complex analysis | ~4.8GB | High quality, 8K context | ## 🚀 How to Use Real Models ### 1. Quick Start ```bash # Install dependencies pip install llama-cpp-python==0.2.20 # Download a model python scripts/download_models.py download --model phi-3-mini # Start server python main.py # Load model via API curl -X POST "http://127.0.0.1:8000/models/load/phi-3-mini" ``` ### 2. Test Real AI Responses ```bash curl -X POST "http://127.0.0.1:8000/query" \ -H "Content-Type: application/json" \ -d '{ "query": "Analyze our top selling products and provide insights", "context": {"user_id": "test", "shop_id": "demo"} }' ``` ### 3. Compare Mock vs Real - **Mock Response**: Template-based, predictable - **Real Response**: Context-aware, natural language, data-driven insights ## 📊 Performance Benefits ### Response Quality - **Natural language**: Conversational, human-like responses - **Context awareness**: Understands e-commerce terminology - **Data integration**: Combines multiple data sources intelligently - **Actionable insights**: Provides business recommendations ### Technical Performance - **Auto-optimization**: Best model selected per query - **Resource management**: Memory and GPU optimization - **Concurrent processing**: Multiple queries simultaneously - **Fallback reliability**: Never fails due to model issues ## 🔧 Configuration Options ### Environment Variables (.env) ```env # Real model settings MODEL_PATH=./data/models DEFAULT_MODEL=phi-3-mini MODEL_CONTEXT_SIZE=4096 MODEL_THREADS=4 MODEL_GPU_LAYERS=20 ``` ### Model-Specific Settings ```python # In real_model_manager.py "phi-3-mini": { "context_size": 4096, "n_gpu_layers": 20, "temperature": 0.7 } ``` ## 🎛️ Smart Features ### 1. Automatic Model Selection ```python # Simple query → gemma-2b (fastest) "How many orders today?" # General query → phi-3-mini (balanced) "What were our sales last month?" # Complex query → llama3-8b (best quality) "Analyze seasonal trends and recommend inventory adjustments" ``` ### 2. Graceful Degradation - Models not available → Falls back to mock responses - Out of memory → Attempts smaller model - Loading failure → Uses template responses ### 3. Resource Management - Automatic model unloading after idle time - Memory usage monitoring - GPU/CPU optimization based on hardware ## 📁 File Structure Updates ``` src/services/ ├── real_model_manager.py # NEW: Real AI model management ├── model_manager.py # OLD: Mock models (kept as backup) ├── query_processor.py # UPDATED: Real model integration └── tool_registry.py # UNCHANGED scripts/ └── download_models.py # NEW: Model download utility data/models/ # NEW: Model storage directory ├── phi-3-mini-4k-instruct-q4.gguf ├── Meta-Llama-3-8B-Instruct.Q4_K_M.gguf └── gemma-2b-it.q4_k_m.gguf ``` ## 🧪 Testing Real Models ### API Tests ```bash # Model status curl -X GET "http://127.0.0.1:8000/models/status" # Load model curl -X POST "http://127.0.0.1:8000/models/load/phi-3-mini" # Query with real AI curl -X POST "http://127.0.0.1:8000/query" -d '{"query": "Test query"}' ``` ### Python Tests ```python from src.services.real_model_manager import real_model_manager # Check available models status = real_model_manager.get_model_status() # Load and test success = real_model_manager.load_model("phi-3-mini") response = real_model_manager.inference("What is machine learning?") ``` ## 🎯 Key Improvements ### For Developers - **Easy setup**: Single command model downloads - **Flexible configuration**: Multiple model options - **Development friendly**: Falls back gracefully - **Well documented**: Comprehensive setup guides ### For Users - **Better responses**: Natural, contextual answers - **Faster insights**: Intelligent data analysis - **Consistent quality**: Reliable AI-powered responses - **Business focused**: E-commerce specific understanding ### For System - **Resource efficient**: Optimized memory usage - **Scalable**: Can handle multiple models - **Reliable**: Robust error handling - **Maintainable**: Clean, modular architecture ## 📋 Migration Path ### From Mock to Real Models 1. **No Code Changes Required** for API users 2. **Install dependencies**: `pip install llama-cpp-python` 3. **Download models**: Use provided script 4. **Update .env**: Configure model settings 5. **Restart server**: Real models automatically detected ### Hybrid Mode - Real models when available - Mock responses as fallback - Transparent to API consumers - Zero downtime migration ## 🎉 Success Criteria Met - ✅ **Real AI Integration**: Successfully integrated llama-cpp-python - ✅ **Model Management**: Full lifecycle management implemented - ✅ **Performance Optimization**: Automatic model selection - ✅ **Resource Management**: Memory and GPU optimization - ✅ **Error Handling**: Graceful fallbacks and error recovery - ✅ **Documentation**: Comprehensive setup and usage guides - ✅ **Testing**: Full test coverage for real model scenarios - ✅ **Backward Compatibility**: Mock models still available ## 🚀 Ready for Production The E-commerce MCP Server now supports **real AI models** with: - Production-ready architecture - Comprehensive error handling - Resource optimization - Scalable model management - Full documentation **Next Steps**: 1. Install dependencies: `pip install llama-cpp-python` 2. Download a model: `python scripts/download_models.py download --model phi-3-mini` 3. Start the server: `python main.py` 4. Experience real AI-powered e-commerce analytics! 🎯