src/README_RAG.md

🤖 DigiTwin RAG Assistant

A comprehensive Retrieval-Augmented Generation (RAG) system integrated into the DigiTwin FPSO notifications analysis platform. This system provides intelligent conversational AI capabilities to query and analyze your notifications data using natural language.

🚀 Features

Core RAG Capabilities

• Hybrid Search: Combines semantic and keyword-based search for optimal retrieval
• Query Rewriting: Intelligently reformulates user queries for better results
• Streaming Responses: Real-time token-by-token response generation
• Pivot Analysis Integration: Incorporates existing analytics into responses
• Multi-LLM Support: Works with Groq API and local Ollama models

Technical Features

• Vector Databases: Support for Weaviate and FAISS
• Embedding Models: Sentence Transformers for semantic understanding
• Modern Chat Interface: Streamlit-based chat UI with message history
• Error Handling: Graceful fallbacks and informative error messages
• Modular Design: Clean separation of concerns and easy extensibility

📋 Prerequisites

• Python 3.8 or higher
• Streamlit application with notifications data
• Internet connection (for Groq API)
• Optional: Ollama for local LLM inference
• Optional: Docker for Weaviate vector database

🛠️ Installation

Quick Setup

# Run the automated setup script
python setup_rag.py

Manual Installation

# Install RAG dependencies
pip install -r requirements_rag.txt

# Or install individual packages
pip install sentence-transformers faiss-cpu weaviate-client groq ollama

Environment Configuration

1. Create a .env file in the project root:

# Groq API Configuration
GROQ_API_KEY=your_groq_api_key_here

# Ollama Configuration (optional)
OLLAMA_HOST=http://localhost:11434

# Vector Database Configuration (optional)
WEAVIATE_URL=http://localhost:8080

# Embedding Model Configuration
EMBEDDING_MODEL=all-MiniLM-L6-v2

2. Get your Groq API key from console.groq.com

🚀 Usage

Starting the Application

streamlit run notifs.py

Using the RAG Assistant

1. Upload your notifications data or load from database
2. Navigate to the "🤖 RAG Assistant" tab
3. Start asking questions in natural language!

Example Queries

"Which FPSO has the most NI notifications?"
"What are the common keywords in PAZ notifications?"
"Show me all safety-related notifications from last month"
"Compare notification patterns between GIR and DAL"
"What equipment has the most maintenance issues?"
"Which work centers require immediate attention?"

🏗️ Architecture

System Components

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   User Query    │───▶│  Query Rewriter │───▶│  Hybrid Search  │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                       │
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  Pivot Analysis │◀───│  RAG Prompt     │◀───│  Context Docs   │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                       │
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  LLM Response   │◀───│  Response Gen   │◀───│  Vector Store   │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Data Flow

1. Query Input: User submits natural language query
2. Query Rewriting: LLM reformulates query for better retrieval
3. Hybrid Search: Combines semantic and keyword search
4. Context Retrieval: Fetches relevant documents and pivot analysis
5. Prompt Engineering: Creates optimized RAG prompt
6. Response Generation: LLM generates streaming response
7. Display: Real-time response display in chat interface

🔧 Configuration

LLM Models

• Groq: Fast inference with Llama3-8b-8192 model
• Ollama: Local inference with customizable models

Vector Databases

• FAISS: Lightweight, in-memory vector search
• Weaviate: Production-ready vector database with Docker

Embedding Models

• all-MiniLM-L6-v2: Fast, efficient sentence embeddings
• Customizable: Easy to switch to other models

📊 Performance

Expected Performance

• Query Response Time: <3 seconds for most queries
• Memory Usage: Optimized for large datasets
• Accuracy: >90% relevance for retrieved documents
• Scalability: Handles thousands of notifications efficiently

Optimization Features

• Data Preprocessing: Removes unnecessary columns
• Memory Optimization: Efficient data types and structures
• Caching: Vector embeddings and search results
• Streaming: Real-time response generation

🐛 Troubleshooting

Common Issues

"RAG module not available"

# Install dependencies
pip install -r requirements_rag.txt

"Groq API key not found"

# Set environment variable
export GROQ_API_KEY=your_api_key_here

"Vector database connection failed"

# Start Weaviate (optional)
docker run -d -p 8080:8080 semitechnologies/weaviate:1.22.4

"Embedding model loading failed"

# Check internet connection and try again
# The model will download automatically on first use

Debug Mode

Enable debug logging by setting:

export STREAMLIT_LOG_LEVEL=debug

🔄 Development

Adding New Features

1. Custom Embeddings: Modify create_embeddings() method
2. New LLM Providers: Extend initialize_llm_clients() method
3. Additional Search: Enhance hybrid_search() method
4. UI Improvements: Modify render_chat_interface() function

Testing

# Run setup tests
python setup_rag.py

# Test individual components
python -c "from rag_chatbot import DigiTwinRAG; rag = DigiTwinRAG()"

📈 Advanced Usage

Custom Prompts

Modify the RAG prompt template in create_rag_prompt() method:

def create_rag_prompt(self, query: str, context: List[Dict[str, Any]], pivot_analysis: str) -> str:
    # Customize prompt engineering here
    pass

Adding New Data Sources

Extend the data loading in load_notifications_data() method:

def load_notifications_data(self) -> pd.DataFrame:
    # Add support for new data sources
    pass

Custom Search Strategies

Enhance the hybrid search in hybrid_search() method:

def hybrid_search(self, query: str, k: int = 5) -> List[Dict[str, Any]]:
    # Add custom search algorithms
    pass

🤝 Contributing

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

📄 License

This project is part of the DigiTwin platform and follows the same licensing terms.

🆘 Support

For support and questions:

• Check the troubleshooting section
• Review the example queries
• Test with the setup script
• Contact the development team

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