Skip to content

Latest commit

 

History

History
129 lines (95 loc) · 3 KB

README.md

File metadata and controls

129 lines (95 loc) · 3 KB
Raw

Scientia AI 🧠

Scientia is an AI-powered knowledge exploration and management system that combines a powerful language model with a local knowledge base to provide intelligent responses and insights.

Features

  • 🤖 Advanced AI Chat Interface
  • 📚 Local Knowledge Base Management
  • 🔍 Intelligent Document Processing
  • 📊 Topic Analysis and Exploration
  • 🔄 RAG (Retrieval-Augmented Generation)
  • 📝 Multi-format Document Support (PDF, DOCX, Images)
  • 🔒 Privacy-focused (all data stays local)

Installation

Using Homebrew (recommended)

  1. Install Homebrew if you haven't already:

    /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

  2. Tap the Scientia repository:

    brew tap scientia-ai/scientia

  3. Install Scientia:

    brew install scientia

From Source

  1. Clone the repository:

    git clone https://github.com/scientia-ai/scientia.git
cd scientia

  2. Create a virtual environment and activate it:

    python3 -m venv venv
source venv/bin/activate

  3. Install uv:

    pip install uv

  4. Install dependencies using uv:

    uv sync

  5. Set up the configuration:

    cp config.example.yml config.yml

    Edit config.yml with your preferred settings.

  6. Run the application:

    python src/main.py

Web Interface

The web interface provides several features:

  1. Chat Interface

    • Interactive conversations with AI
    • Knowledge base integration
    • Suggested follow-up questions
    • Topic analysis mode

  2. Knowledge Base

    • Add text or documents
    • Search existing knowledge
    • Manage privacy levels
    • Tag and organize information

  3. Advanced Tools

    • Knowledge visualization (coming soon)
    • Concept mapping (coming soon)
    • Source analysis (coming soon)

System Requirements

  • Python 3.10+
  • 8GB RAM (16GB recommended)
  • Local storage for knowledge base
  • Optional: NVIDIA GPU for faster processing

Core Dependencies

  • PyTorch: Machine learning framework
  • Transformers: Language model support
  • ChromaDB: Vector database for knowledge storage
  • Streamlit: Web interface
  • Tesseract: OCR support

Development Setup

  1. Set up your environment:

Contributing

  1. Fork the repository from https://github.com/dirvine/scientia
  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

License

MIT License - see LICENSE

Support

Built with ❤️ using Streamlit, Hugging Face, and ChromaDB