GraphFleet

A powerful graph-based knowledge management and query system designed for large-scale data processing and analysis. GraphFleet combines semantic search, knowledge graphs, and advanced analytics to provide deep insights into your data.

🚀 Key Features

• Advanced Document Processing

  • Semantic indexing and search
  • Automatic knowledge graph construction
  • Multi-format document support (PDF, Markdown, Code, etc.)

• Graph Analytics

  • Custom query pipelines
  • Concept drift analysis
  • Community detection
  • Path analysis and recommendations

• Performance & Scale

  • Distributed processing support
  • Native extensions for performance-critical operations
  • Efficient batch processing
  • Real-time query capabilities

• Developer Experience

  • Modern React frontend
  • RESTful and GraphQL APIs
  • Comprehensive documentation
  • Docker-based development environment

🏗️ Architecture

GraphFleet consists of three main components:

1. Core Engine (graphfleet)

   • Document processing and indexing
   • Knowledge graph management
   • Query processing

2. Graph Processing (graspologic)

   • Graph algorithms implementation
   • Analytics and metrics
   • Visualization utilities

3. Native Extensions (graspologic-native)

   • Performance-critical operations in Rust
   • SIMD optimizations
   • Custom memory management

🛠️ Installation

Using uv

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install graphfleet
uv pip install graphfleet[all]

Development Setup

1. Clone the repository:

git clone https://github.com/qredence/graphfleet.git
cd graphfleet

2. Set up development environment:

# Using Docker (recommended)
docker compose up -d

# Or manual setup
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies with uv
uv pip install --upgrade uv  # Ensure latest uv version
uv pip install build hatchling  # Install build tools
uv sync  # Install all dependencies from pyproject.toml
uv pip install -e ".[dev]"  # Install package in editable mode

3. Configure environment:

cp .env.example .env
# Edit .env with your settings

📚 Documentation

• Getting Started Guide
• API Reference
• Architecture Overview
• Contributing Guidelines

🧪 Examples

The examples/ directory contains various use cases and tutorials:

• Basic document indexing and search
• Custom query pipeline creation
• Knowledge graph visualization
• Performance optimization techniques

🛣️ Project Structure

graphfleet/
├── backend/                    # Backend service
│   ├── app/                   # Main application code
│   │   ├── api/              # API endpoints
│   │   ├── core/             # Core business logic
│   │   ├── models/           # Data models
│   │   └── utils/            # Utility functions
│   ├── graphfleet/           # GraphFleet core library
│   └── tests/                # Backend tests
├── graspologic/               # Graph processing library
│   ├── src/                  # Source code
│   ├── tests/                # Tests
│   └─�� docs/                 # Library documentation
├── graspologic-native/        # Native extensions
│   ├── src/                  # Rust/C++ source
│   └── python/               # Python bindings
├── frontend/                  # Frontend application
└── docs/                     # Project documentation

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Process

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

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🔒 Security

For security concerns, please refer to our Security Policy.

🌟 Acknowledgments

GraphFleet is built on top of several amazing open-source projects:

• NetworkX
• FastAPI
• React
• sentence-transformers

✨ Core Team

• Zachary BENSALEM (@zbensalem) - Project Lead