README.md

GraphRAG Explorer 🔍

A powerful Streamlit-based application for exploring and visualizing Graph-based Retrieval-Augmented Generation (GraphRAG). This tool helps researchers and developers understand and analyze their RAG implementations through interactive graph visualizations.

🌟 Features

Data Processing

• Support for multiple file formats:
  • CSV (structured data)
  • TXT (raw text)
  • PDF (coming soon)
• Real-time data preview
• Error handling and validation

Embedding Services

• OpenAI embeddings
• Sentence Transformers
• Ollama models

LLM Integration

• OpenAI GPT models
• Ollaam models
• Adjustable parameters (temperature, etc.)

Graph Analysis

• Multiple analysis methods:
  • Node Importance Analysis
  • Path Analysis
  • Subgraph Extraction
• Centrality measures:
  • Degree Centrality
  • Betweenness Centrality
  • PageRank

Visualization

• Interactive graph visualization
• Real-time graph statistics
• Property analysis
• Customizable graph layouts

🚀 Quick Start

Prerequisites

• Python 3.10+
• pip package manager

Installation

1. Clone the repository:

git clone <repository-url>
cd graphrag_explorer

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Running the Application

1. Start the Streamlit app:

streamlit run app.py

2. Open your browser and navigate to http://localhost:8501

3. Demo

📊 Usage Guide

1. Data Upload

   • Upload your document or dataset using the file upload section
   • Supported formats: CSV, TXT (PDF coming soon)
   • Preview your data before processing

2. Configure Services

   • Select your preferred embedding service
   • Choose an LLM model
   • Adjust model parameters as needed

3. Select Analysis Method

   • Choose your preferred graph analysis method
   • Configure analysis parameters
   • View results in real-time

4. Explore Visualization

   • Interact with the graph visualization
   • Analyze node and edge statistics
   • Export results if needed

🛠️ Configuration

Supported Embedding Models

• OpenAI: OpenAI-like services
• Ollama: Various models
• Sentence Transformers: all-MiniLM-L6-v2, all-mpnet-base-v2

Supported LLM Models

• OpenAI: OpenAI-like services
• Ollama: Various models

📝 Contributing

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

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Support

If you encounter any issues or have questions:

• Open an issue in the GitHub repository

🙏 Acknowledgments

• Streamlit for the amazing framework
• NetworkX for graph processing
• The RAG and LLM communities for inspiration