Spaces:
Running
Running
| # HuggingFace Spaces Deployment Guide | |
| ## Enhanced RISC-V RAG System | |
| ### 🚀 Quick Deployment Steps | |
| 1. **Create HuggingFace Space** | |
| - Go to [HuggingFace Spaces](https://huggingface.co/spaces) | |
| - Click "Create new Space" | |
| - Choose **Streamlit** as SDK | |
| - Set hardware to **CPU Basic** (2 cores, 16GB RAM) | |
| 2. **Upload Files** | |
| Upload all files from this directory to your space: | |
| ``` | |
| app.py # Main entry point | |
| streamlit_epic2_demo.py # Enhanced RAG demo | |
| requirements.txt # Dependencies | |
| config/ # Configuration files | |
| src/ # Core system | |
| data/ # Sample documents | |
| demo/ # Demo utilities | |
| ``` | |
| 3. **Set Environment Variables** (Optional) | |
| In your Space settings, add: | |
| ``` | |
| HF_TOKEN=your_huggingface_token_here | |
| ``` | |
| **Note**: The system works without HF_TOKEN but provides enhanced capabilities with it. | |
| 4. **Build & Deploy** | |
| - HuggingFace Spaces will automatically build your app | |
| - Monitor build logs for any issues | |
| - App will be available at: `https://huggingface.co/spaces/your-username/your-space-name` | |
| ### 🔧 System Capabilities | |
| #### **With HF_TOKEN (Recommended)** | |
| - ✅ Full advanced RAG capabilities | |
| - ✅ Neural reranking with cross-encoder models | |
| - ✅ Graph enhancement for document relationships | |
| - ✅ Real-time analytics and performance monitoring | |
| - ✅ API-based LLM integration (memory efficient) | |
| #### **Without HF_TOKEN (Demo Mode)** | |
| - ✅ System architecture demonstration | |
| - ✅ Performance metrics display | |
| - ✅ Technical documentation showcase | |
| - ℹ️ Limited live query functionality | |
| ### 📊 Performance Expectations | |
| **Memory Usage**: < 16GB (HF Spaces compatible) | |
| **Startup Time**: 30-60 seconds (model loading) | |
| **Query Response**: 1-3 seconds per query | |
| **Concurrent Users**: Supports multiple simultaneous users | |
| ### 🔍 Monitoring & Troubleshooting | |
| #### **Common Issues** | |
| 1. **Build Fails** | |
| - Check `requirements.txt` compatibility | |
| - Ensure all files are uploaded | |
| - Monitor build logs for specific errors | |
| 2. **High Memory Usage** | |
| - System is optimized for <16GB usage | |
| - Models load efficiently with lazy loading | |
| - Consider upgrading to CPU Persistent if needed | |
| 3. **Slow Response Times** | |
| - First query may be slower (model loading) | |
| - Subsequent queries should be <3 seconds | |
| - Check HF_TOKEN configuration for API access | |
| #### **Health Check Endpoints** | |
| The system provides built-in health monitoring: | |
| - Automatic environment detection | |
| - Configuration validation | |
| - Component status reporting | |
| ### 💡 Tips for Best Performance | |
| 1. **Use HF_TOKEN**: Enables full capabilities and better performance | |
| 2. **Monitor Logs**: Check for initialization and query processing | |
| 3. **Sample Queries**: Use provided RISC-V technical queries for demo | |
| 4. **Configuration**: System auto-selects optimal configuration based on environment | |
| ### 📈 Expected Demo Results | |
| With proper setup, your demo will showcase: | |
| - **Neural reranking** with cross-encoder models | |
| - **Graph enhancement** for document relationships | |
| - **Hybrid search** combining semantic and keyword matching | |
| - **Real-time analytics** with performance metrics | |
| - **Professional UI** with technical feature focus | |
| ### 🎯 Portfolio Impact | |
| This deployment demonstrates: | |
| - Advanced RAG system implementation | |
| - Modular 6-component architecture | |
| - Neural reranking and graph enhancement techniques | |
| - Modern ML engineering practices | |
| Showcases technical RAG implementation skills with focus on advanced features. |