🧠 GraphMind: AI-Powered Knowledge Graph Analysis

GraphMind is an intelligent knowledge graph visualization and analysis platform that leverages AI to extract, analyze, and explore complex relationships from unstructured documents.

🎯 Features

• 📊 Interactive 3D Graph Visualization – Explore knowledge graphs in an immersive 3D environment powered by Three.js
• 🤖 AI-Powered Entity Extraction – Automatically extract entities and relationships from PDF documents using NVIDIA Nemotron LLM
• 💬 Intelligent Chat Interface – Ask questions about your graph with context-aware AI responses
• 🔍 Advanced Query System – Analyze node relationships and connections with natural language queries
• 🧠 Reasoning Display – View AI's internal reasoning process for transparency and debugging
• ⚡ Real-time Updates – Dynamic graph updates as new data is processed

🏗️ Architecture

GraphMind/
├── backend/          # FastAPI server & graph processing
│   ├── main.py      # API endpoints and AI integration
│   ├── generate_data.py   # Sample data generation
│   └── data.json    # Knowledge graph data store
├── frontend/        # React + Vite + Three.js
│   ├── src/
│   │   ├── App.jsx  # Main UI component
│   │   └── main.jsx # Entry point
│   └── package.json # Frontend dependencies
└── README.md

🚀 Quick Start

Prerequisites

• Python 3.9+
• Node.js 18+
• NVIDIA API Key (for Nemotron LLM access)

Backend Setup

cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env and add your NVIDIA_API_KEY

# Generate sample data (optional)
python generate_data.py

# Run the server
uvicorn main:app --reload

Backend runs on: http://127.0.0.1:8000

API Documentation: http://127.0.0.1:8000/docs

Frontend Setup

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

Frontend runs on: http://localhost:5173

📡 API Endpoints

GET /graph-data

Returns the current knowledge graph data.

{
  "nodes": [
    {"id": "Entity1", "group": "Category", "type": "Type"}
  ],
  "links": [
    {"source": "Entity1", "target": "Entity2", "value": "relationship"}
  ]
}

POST /chat

Send a query about the graph with optional node context.

Request:

{
  "message": "What is connected to this node?",
  "node_id": "Entity1"
}

Response:

{
  "reply": "Entity1 is connected to Entity2 through a relationship..."
}

POST /upload-pdf

Extract entities and relationships from a PDF document.

Request: Multipart form with file field containing PDF

Response:

{
  "status": "success",
  "new_nodes": 15
}

⚙️ Configuration

Environment Variables

Create a .env file in the backend/ directory:

NVIDIA_API_KEY=your_nvidia_api_key_here

See .env.example for all available options.

🛠️ Development

Running in Development Mode

Terminal 1 (Backend):

cd backend
source venv/bin/activate  # or venv\Scripts\activate on Windows
uvicorn main:app --reload

Terminal 2 (Frontend):

cd frontend
npm run dev

Build for Production

Backend:

# No build step needed - deploy uvicorn server directly

Frontend:

cd frontend
npm run build
# Output: frontend/dist/

Linting & Quality

cd frontend
npm run lint

📚 How It Works

1. PDF Upload → Extract text from documents
2. AI Processing → Use Nemotron LLM to identify entities and relationships
3. Graph Construction → Build NetworkX graph from extracted data
4. Visualization → Render interactive 3D graph in React
5. Query & Analysis → Chat interface for graph exploration with AI assistance

🔐 Security Considerations

• API CORS is set to allow all origins (*) in development. Restrict this in production.
• API keys should never be committed to version control. Use .env files.
• Validate and sanitize all user inputs before processing.
• Consider rate limiting for production deployments.

📦 Dependencies

Backend

• FastAPI – Modern Python web framework
• NetworkX – Graph data structures and algorithms
• OpenAI SDK – Integration with Nemotron LLM
• pypdf – PDF text extraction
• python-dotenv – Environment variable management

Frontend

• React 19 – UI library
• Vite – Build tool
• Three.js (via react-force-graph-3d) – 3D visualization
• react-markdown – Markdown rendering

🐛 Troubleshooting

PDF Upload Returns "Expecting value: line 1 column 1"

The AI response may contain non-JSON content like <think> tags. The backend now strips these automatically. If the issue persists, check:

• NVIDIA API key is valid
• PDF has extractable text (not scanned image-only)
• Check backend logs for detailed error messages

Graph Data Not Loading

• Ensure backend is running (http://127.0.0.1:8000/docs)
• Check browser console for network errors
• Verify CORS settings are correct

3D Graph Not Rendering

• Ensure WebGL is supported in your browser
• Try a different browser (Chrome, Firefox, Edge)
• Clear browser cache and restart dev server

🤝 Contributing

Contributions are welcome! Please:

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

See CONTRIBUTING.md for detailed guidelines.

📄 License

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

🔗 References

• FastAPI Documentation
• React Documentation
• Three.js Documentation
• NetworkX Documentation
• NVIDIA API Documentation

📞 Support

For issues, questions, or suggestions:

• Open an issue on GitHub
• Check existing issues for solutions
• Review the troubleshooting section above

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Made with ❤️ using React, FastAPI, and AI