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| title: IoT Sensor Data RAG for Smart Buildings | |
| emoji: 🏢 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: streamlit | |
| sdk_version: "1.42.1" | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # IoT Sensor Data RAG for Smart Buildings | |
| ## 🏢 Problem Statement | |
| Create a RAG system that processes IoT sensor data, maintenance manuals, and building specifications to provide predictive maintenance insights and operational optimization. | |
| ## 🎯 Key Requirements | |
| - ✅ **IoT sensor data ingestion and real-time processing** | |
| - ✅ **Maintenance manual and building specification integration** | |
| - ✅ **Predictive maintenance algorithm implementation** | |
| - ✅ **Operational efficiency optimization recommendations** | |
| - ✅ **Anomaly detection and alert systems** | |
| ## 🚀 Technical Challenges Solved | |
| - ✅ **Real-time sensor data streaming and processing** | |
| - ✅ **Multi-sensor data fusion and correlation** | |
| - ✅ **Predictive modeling for equipment failure** | |
| - ✅ **Building system integration and compatibility** | |
| - ✅ **Energy efficiency optimization algorithms** | |
| ## 🏗️ System Architecture | |
| ### Core Components | |
| - **RAG Engine**: Vector database (ChromaDB) with Sentence-Transformers embeddings | |
| - **IoT Data Processor**: Real-time sensor data streaming and anomaly detection | |
| - **Predictive Analytics**: Equipment failure prediction and maintenance recommendations | |
| - **Document Intelligence**: PDF/TXT processing with smart chunking strategies | |
| - **Web Interface**: Modern Streamlit dashboard with Material design theme | |
| ### Technology Stack | |
| - **Backend**: Python, Streamlit, ChromaDB | |
| - **Embeddings**: Sentence-Transformers (all-MiniLM-L6-v2) | |
| - **Vector Database**: ChromaDB with cosine similarity | |
| - **LLM Integration**: Local Transformers + OpenAI API (optional) | |
| - **Data Processing**: Pandas, NumPy, Scikit-learn | |
| - **Visualization**: Plotly for real-time sensor monitoring | |
| ## 📊 Features | |
| ### 1. Real-Time IoT Monitoring | |
| - Live sensor data streaming simulation | |
| - Multi-sensor data fusion (temperature, humidity, power consumption) | |
| - Real-time anomaly detection using rolling z-score analysis | |
| - Interactive time-series visualizations | |
| ### 2. Intelligent Document RAG | |
| - PDF and TXT document ingestion | |
| - Smart text chunking (500 tokens with 50 token overlap) | |
| - Context-aware retrieval using vector similarity | |
| - Source attribution and relevance scoring | |
| ### 3. Predictive Maintenance | |
| - Equipment failure prediction algorithms | |
| - Maintenance schedule optimization | |
| - Energy efficiency recommendations | |
| - Anomaly-based alert systems | |
| ### 4. Evaluation & Analytics | |
| - Retrieval accuracy metrics | |
| - Response latency measurement | |
| - Document relevance scoring | |
| - System performance monitoring | |
| ## 🚀 Quick Start | |
| ### Prerequisites | |
| - Python 3.8+ | |
| - 8GB+ RAM (for local LLM models) | |
| - Internet connection (for initial model downloads) | |
| ### Installation | |
| ```bash | |
| # Clone the repository | |
| git clone https://github.com/itsnewcoder/iot-smart-building-rag.git | |
| cd iot-smart-building-rag | |
| # Create virtual environment | |
| python -m venv .venv | |
| .venv\Scripts\activate # Windows | |
| # source .venv/bin/activate # Linux/Mac | |
| # Install dependencies | |
| pip install -r requirements.txt | |
| ``` | |
| ### Configuration | |
| Create a `.env` file in the root directory (optional): | |
| ```env | |
| OPENAI_API_KEY=your_openai_api_key_here | |
| ``` | |
| ### Run Locally | |
| ```bash | |
| streamlit run app.py | |
| ``` | |
| **Access your app at:** `http://localhost:8501` | |
| ## 📁 Project Structure | |
| ``` | |
| iot-smart-building-rag/ | |
| ├── app.py # Main Streamlit application | |
| ├── requirements.txt # Python dependencies | |
| ├── README.md # This file | |
| ├── .streamlit/ | |
| │ └── config.toml # Streamlit theme configuration | |
| ├── rag/ # RAG system core | |
| │ ├── __init__.py | |
| │ ├── ingest.py # Document ingestion & vector store | |
| │ ├── retrieval.py # Context retrieval engine | |
| │ ├── generate.py # LLM response generation | |
| │ └── evaluate.py # System evaluation metrics | |
| ├── models/ # Predictive models | |
| │ ├── __init__.py | |
| │ └── predictive.py # Anomaly detection & maintenance | |
| ├── data/ # Sample data | |
| │ ├── manuals/ # Maintenance manuals (PDF/TXT) | |
| │ ├── specs/ # Building specifications | |
| │ └── sensors/ # IoT sensor data (CSV) | |
| └── .chroma/ # Vector database storage | |
| ``` | |
| ## 🔧 Usage Guide | |
| ### 1. Dashboard Tab | |
| - **Start Stream**: Begin real-time sensor data simulation | |
| - **Live Monitoring**: View real-time sensor readings and trends | |
| - **Anomaly Detection**: See detected anomalies with z-score analysis | |
| - **Maintenance Tips**: Get AI-powered maintenance recommendations | |
| ### 2. RAG QA Tab | |
| - **Ask Questions**: Query maintenance procedures and building specs | |
| - **Context Retrieval**: View relevant document chunks and sources | |
| - **AI Responses**: Get context-aware answers from local or OpenAI models | |
| ### 3. Evaluation Tab | |
| - **Retrieval Testing**: Test system with custom queries | |
| - **Performance Metrics**: View latency and relevance scores | |
| - **Quality Assessment**: Evaluate RAG system effectiveness | |
| ### 4. Data Manager Tab | |
| - **Document Index**: View indexed documents and sources | |
| - **File Upload**: Add new PDFs/TXTs to the knowledge base | |
| - **Vector Store**: Manage document embeddings and storage | |
| ## 📈 Sample Queries | |
| Try these example questions in the RAG QA tab: | |
| - "How to reset chiller pump?" | |
| - "What are the fault codes for HVAC systems?" | |
| - "How to maintain building temperature sensors?" | |
| - "What are the power consumption optimization tips?" | |
| - "How to troubleshoot humidity sensor issues?" | |
| ## 🎯 Evaluation Metrics | |
| ### Retrieval Quality | |
| - **Relevance Scoring**: Cosine similarity-based ranking | |
| - **Source Attribution**: Document source tracking | |
| - **Context Retrieval**: Top-k document retrieval | |
| ### Performance Metrics | |
| - **Response Latency**: End-to-end query processing time | |
| - **Throughput**: Queries processed per second | |
| - **Memory Usage**: Vector database storage efficiency | |
| ### RAG Effectiveness | |
| - **Context Relevance**: Retrieved document quality | |
| - **Answer Accuracy**: Response relevance to queries | |
| - **Source Diversity**: Multiple document source utilization | |
| ## 🌐 Deployment | |
| ### HuggingFace Spaces (Recommended) | |
| 1. Create new Space at [huggingface.co/spaces](https://huggingface.co/spaces) | |
| 2. Choose **Streamlit** as SDK | |
| 3. Upload project files | |
| 4. Set environment variables in Space settings | |
| ### Streamlit Cloud | |
| 1. Push code to GitHub | |
| 2. Connect repository at [share.streamlit.io](https://share.streamlit.io) | |
| 3. Deploy automatically | |
| ### Local Deployment | |
| ```bash | |
| # Production server | |
| streamlit run app.py --server.port 8501 --server.address 0.0.0.0 | |
| ``` | |
| ## 🔍 Technical Implementation Details | |
| ### Embedding Strategy | |
| - **Model**: `sentence-transformers/all-MiniLM-L6-v2` | |
| - **Dimensions**: 384 | |
| - **Normalization**: L2 normalization for cosine similarity | |
| - **Chunking**: 500 tokens with 50 token overlap | |
| ### Vector Database | |
| - **Database**: ChromaDB | |
| - **Similarity**: Cosine distance | |
| - **Persistence**: Local file storage (.chroma directory) | |
| - **Indexing**: HNSW algorithm for fast retrieval | |
| ### Anomaly Detection | |
| - **Method**: Rolling z-score analysis | |
| - **Window Size**: 50 data points | |
| - **Threshold**: Z-score > 3.0 | |
| - **Metrics**: Temperature, humidity, power consumption | |
| ### Predictive Maintenance | |
| - **Algorithm**: Rule-based heuristics + statistical analysis | |
| - **Input**: Sensor data + anomaly patterns | |
| - **Output**: Maintenance recommendations + efficiency tips | |
| - **Real-time**: Continuous monitoring and updates | |
| ## 🧪 Testing | |
| ### Local Testing | |
| ```bash | |
| # Test RAG modules | |
| python -c "from rag.ingest import ensure_vector_store; print('✅ RAG Ready')" | |
| # Test predictive models | |
| python -c "from models.predictive import detect_anomalies; print('✅ Models Ready')" | |
| # Test full application | |
| streamlit run app.py | |
| ``` | |
| ### Sample Data | |
| The system includes sample data for testing: | |
| - **HVAC Sensor Data**: Temperature, humidity, power readings | |
| - **Chiller Manual**: Maintenance procedures and fault codes | |
| - **Building Specs**: System specifications and requirements | |
| ## 🤝 Contributing | |
| 1. Fork the repository | |
| 2. Create a feature branch | |
| 3. Make your changes | |
| 4. Test thoroughly | |
| 5. Submit a pull request | |
| ## 📄 License | |
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. | |
| ## 🎓 Academic Use | |
| This project was developed as part of an academic RAG system implementation course. It demonstrates: | |
| - **RAG Architecture**: Complete retrieval-augmented generation system | |
| - **IoT Integration**: Real-time sensor data processing | |
| - **Predictive Analytics**: Machine learning for maintenance | |
| - **Vector Databases**: ChromaDB implementation | |
| - **Modern Web UI**: Streamlit-based dashboard | |
| ## 📞 Support | |
| For questions or issues: | |
| - **GitHub Issues**: [Create an issue](https://github.com/itsnewcoder/iot-smart-building-rag/issues) | |
| - **Documentation**: Check this README and code comments | |
| - **Community**: Streamlit and HuggingFace communities | |
| ## 🚀 Future Enhancements | |
| - [ ] Real-time IoT device integration | |
| - [ ] Advanced ML models for failure prediction | |
| - [ ] Multi-modal document support (images, audio) | |
| - [ ] API endpoints for external systems | |
| - [ ] Mobile-responsive interface | |
| - [ ] Advanced analytics dashboard | |
| - [ ] Integration with building management systems | |
| --- | |
| **Built with ❤️ for Smart Building Intelligence** | |
| *Last updated: January 2025* | |