Parliamentary Meeting Minutes Analysis with GraphRAG

This system provides AI-powered analysis of parliamentary meeting minutes using a combination of knowledge graph and vector storage technologies (GraphRAG).

📋 Table of Contents

• Parliamentary Meeting Minutes Analysis with GraphRAG
  • 📋 Table of Contents
  • ✨ Features
  • 🏗️ Architecture
  • 🛠️ Setup
    • Prerequisites
    • Virtual Environment Setup
    • Ollama Setup
    • Configuration
  • 🚀 Running the Application
    • Demo Script
    • Web Application
  • 📁 Project Structure
  • ❓ Troubleshooting
    • Ollama Service Issues
    • Embedding Dimensions
    • Streamlit Errors
  • 📊 Data Requirements
  • 🤝 Contributing
  • 📄 License

✨ Features

• 📊 Parliamentary data loading and preprocessing
• 🔍 Named Entity Recognition using GLiNER
• 🕸️ Knowledge graph construction and visualization
• 🔠 Vector embeddings for semantic search
• 🧠 GraphRAG query processing combining graph and vector search
• 🖥️ Interactive web interface with Streamlit
• 🔄 Flexible vector storage with Qdrant and ChromaDB fallback

🏗️ Architecture

GraphRAG combines the power of knowledge graphs with vector similarity search to provide more accurate answers:

1. Knowledge Graph: Captures relationships between entities in parliamentary data
2. Vector Storage: Enables semantic similarity search across meeting content
3. Hybrid Search: Combines graph traversal with vector similarity for enhanced retrieval
4. LLM Integration: Uses Ollama for context-aware response generation

🛠️ Setup

Prerequisites

• Python 3.10+
• Conda (for virtual environment management)
• Ollama (for local LLM support)

Virtual Environment Setup

This project uses a conda virtual environment. To set up and activate the environment:

# Create the environment
conda create -n mentor360 python=3.10

# Activate the environment
conda activate mentor360

# Install dependencies
pip install -r requirements.txt

Ollama Setup

1. Install Ollama by following the instructions at https://ollama.ai/
2. Pull the required model:

   ollama pull llama3

3. Ensure the Ollama service is running before starting the application:

   # On Windows
   ollama serve
   
   # On macOS/Linux
   sudo systemctl start ollama

Configuration

The application uses a configuration system that can be customized:

1. Default configuration is loaded from src/utils/config.py
2. You can override settings by creating a config.json file in the project root
3. Environment variables can also override configuration settings

Example config.json:

{
  "ollama": {
    "base_url": "http://localhost:11434",
    "model_name": "qwq",
    "embedding_dim": 4096
  },
  "vector_store": {
    "primary": "qdrant",
    "fallback": "chroma"
  }
}

🚀 Running the Application

Demo Script

To run the GraphRAG demo script that shows the core functionality:

conda activate mentor360
python src/demo/graphrag_demo.py

This will demonstrate:

• Loading parliamentary data
• Extracting entities
• Building a knowledge graph
• Performing queries using graph mode, vector mode, and hybrid mode

Web Application

To run the Streamlit web interface:

conda activate mentor360
streamlit run src/web/app.py

Then open your browser to the URL displayed in the console (typically http://localhost:8501).

📁 Project Structure

• src/data/: Data loading and preprocessing
• src/models/: Core models including NER, knowledge graph, and GraphRAG
• src/services/: External service integrations (e.g., Ollama)
• src/storage/: Vector storage implementation
• src/utils/: Utility functions for logging, configuration, etc.
• src/web/: Streamlit web application
• src/demo/: Demo scripts
• tests/: Unit and integration tests
• data/: Sample and processed data files
• config/: Configuration files

❓ Troubleshooting

Ollama Service Issues

If you encounter errors related to the Ollama service:

1. Ensure Ollama is installed and running
2. Check that you have pulled the required model (ollama pull qwq)
3. The application will attempt to initialize the Ollama service automatically if not provided

Embedding Dimensions

If you encounter an error about embedding_dimensions, ensure your Ollama configuration in config.json uses embedding_dim instead:

{
  "ollama": {
    "base_url": "http://localhost:11434",
    "model_name": "llama3",
    "embedding_dim": 4096
  }
}

Streamlit Errors

If you encounter errors with the Streamlit application:

1. Ensure you have the correct version of Streamlit installed (specified in requirements.txt)
2. Try clearing the Streamlit cache: streamlit cache clear
3. Check your Python version (3.9 recommended)

📊 Data Requirements

Parliamentary meeting minutes should be in a CSV format with columns for date, speaker, and content.

Sample data format:

date,speaker,content
2023-01-15,John Smith,"Mr. Speaker, I rise today to discuss the importance of..."
2023-01-15,Jane Doe,"I would like to respond to the honorable member's point about..."

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the terms of the MIT license.