From Atomic Tags to Infinite Books. Bi-Directional Knowledge Transformation.

SUM is the world's first bi-directional text engine. It doesn't just summarize; it can also extrapolate. It allows you to move fluidly across the spectrum of knowledge density, from the atomic level (Tags) to the universal level (Books).

Turn massive documents into their essence.

• Tags: Extract the atomic concepts.
• SUM: Generate a minimal "elevator pitch" summary.
• Thought: Create coherent paragraphs that capture the core message.
• Context: Produce article-length summaries that preserve nuance.

Turn simple seeds into comprehensive content.

• Deepen: Expand a sentence into a full essay.
• Create: Turn a single concept tag into a 5-chapter book.
• Architect: Automatically generate Table of Contents and fill them with content.

Our revolutionary UI features a single slider that controls knowledge density.

• Left Side (0-2): Contraction. Reduces information entropy.
• Right Side (3-5): Expansion. Increases information entropy.

• Recursive Book Generation: Automatically architects a book structure and writes it chapter by chapter using a two-phase "Blueprint -> Draft" process.
• Streaming Intelligence: Watch as the system "thinks" and generates content in real-time with transparent system logs.
• Unlimited Context: Process files from 1KB to 1TB using intelligent memory mapping (mmap) and streaming chunkers.
• Legendary Intelligence: Includes GraphRAG and RAPTOR implementations with robust "Light Mode" fallbacks for environments without heavy ML libraries.
• Smart Caching: Instant results for previously processed concepts.
• Live Markdown: Beautifully formatted output with headers, bolding, and structure.

# Clone the repository
git clone https://github.com/OtotaO/SUM.git
cd SUM

# Install dependencies
pip install -r requirements.txt

# Run the Universal Engine
python main.py

1. Open http://localhost:5001
2. Choose your input: Paste text or upload a file.
3. Slide the Universal Spectrum Slider:
   • Slide Left to distill down to tags.
   • Slide Right to expand into a book.
4. Watch the transformation happen in real-time.

• Backend: Python, Flask, Universal LLM Backend (OpenAI/Ollama/Local)
• Engine: Hierarchical Densification Engine (for Summarization) & Recursive Extrapolation Engine (for Expansion)
• Frontend: SSE (Server-Sent Events) for real-time streaming, dynamic CSS variables.
• Intelligence Architecture:
  • Core: Extractive & Abstractive summarization via NLTK & LLMs.
  • Advanced: GraphRAG (Graph-based retrieval) & RAPTOR (Recursive tree summarization).
  • Multi-Agent: Prototype orchestration system simulating 10+ specialized roles.

This is a server-side Python application using Flask. It requires a runtime environment and cannot be deployed to static hosting (like Firebase Hosting or GitHub Pages).

Recommended Deployment Options:

• Google Cloud Run: Ideal for containerized Python apps.
• AWS App Runner: Fully managed container application service.
• Heroku / Railway / Render: Simple PaaS deployment.
• VPS (DigitalOcean/Linode): Run with gunicorn or systemd.

Set your API keys in .env or environment variables:

OPENAI_API_KEY=sk-...
# OR
ANTHROPIC_API_KEY=sk-ant-...
# OR use local models (Ollama) automatically if installed!

We welcome visionaries who want to help map the entire spectrum of human knowledge.

1. Fork it
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

Apache 2.0 - Built for the future of Man and Machine.

SUM - Distill the Universe. Expand the Atom.