SynaFlow

A scientific question answering system built with SynaLinks, leveraging language models to provide accurate scientific answers with citations.

Features

• Scientific question answering with domain-specific knowledge
• Citation and reference tracking for answers
• API for integration with other applications
• Customizable examples for improved responses
• Docker support for easy deployment
• Responsive GUI for desktop, tablet, and mobile devices

Getting Started

Prerequisites

• Python 3.10+
• Docker and Docker Compose (for containerized deployment)
• OpenAI API key

Installation

1. Clone the repository

   git clone https://github.com/jmanhype/synaflow.git
   cd synaflow
   

2. Install dependencies

   pip install -r requirements.txt
   

3. Set up environment variables

   cp .env.example .env
   # Edit .env with your API keys
   

Running Locally

1. Train the model (optional, use pre-trained model if available)

   python main.py --train
   

2. Run the interactive demo

   python examples/simplified_demo.py
   

3. Start the API server with GUI

   python api/app.py
   

   Then open your browser and navigate to http://localhost:8000

4. Or start the API server without GUI (legacy)

   python api/app_direct.py
   

Docker Deployment

1. Build and start the containers

   docker-compose up -d
   

2. Check the API is running

   curl http://localhost:8000/health
   

Using the Responsive GUI

The SynaFlow application now includes a responsive GUI that works on desktop, tablet, and mobile devices:

1. Ask Questions: Enter your scientific question, optionally select a domain, and provide additional context if needed.

2. View Structured Answers: Receive comprehensive answers with background information, reasoning, confidence level, citations, and further reading recommendations.

3. Question History: Access your previous questions and answers from the sidebar.

4. Mobile-Friendly: The interface adapts to different screen sizes for a seamless experience on any device.

API Usage

Generate a scientific answer

POST /query

{
  "question": "What is quantum entanglement?",
  "domain": "physics",
  "context": "I'm a university student"
}

Response:

{
  "request_id": "550e8400-e29b-41d4-a716-446655440000",
  "timestamp": "2023-11-18T12:34:56.789Z",
  "result": {
    "background": "Quantum entanglement is a fundamental concept in quantum physics...",
    "reasoning": "When we consider the mathematical framework of quantum mechanics...",
    "answer": "Quantum entanglement occurs when pairs or groups of particles interact...",
    "confidence": 0.95,
    "citations": [
      {
        "title": "Quantum entanglement and information",
        "authors": ["Einstein, A.", "Podolsky, B.", "Rosen, N."],
        "year": 1935,
        "source": "Physical Review",
        "url": "https://example.com/paper"
      }
    ],
    "further_reading": [
      "Quantum Computation and Quantum Information by Nielsen and Chuang"
    ]
  }
}

Project Structure

synaflow/
├── api/                 # API implementation
│   ├── app.py           # FastAPI application with GUI
│   └── app_direct.py    # FastAPI application (legacy)
├── examples/            # Example scripts and demos
│   ├── simplified_demo.py
│   ├── comparison_test.py
│   └── interactive_demo.py
├── models/              # Trained models
│   └── trained_qa_program.json
├── src/                 # Source code
│   ├── config.py        # Configuration settings
│   ├── data_models.py   # Data models and schemas
│   ├── language_models.py # LLM integration
│   ├── programs.py      # Program implementations
│   ├── simplified_program.py # Simplified implementation
│   ├── training.py      # Training utilities
│   └── utils.py         # Utility functions
├── static/              # Static files for GUI
│   ├── css/             # CSS stylesheets
│   ├── js/              # JavaScript files
│   └── index.html       # Main HTML file
├── tests/               # Test suite
├── documentation.md     # Detailed documentation
├── .env.example         # Example environment variables
├── docker-compose.yml   # Docker Compose configuration
├── Dockerfile           # Docker build configuration
├── main.py              # Main entry point
└── requirements.txt     # Python dependencies

License

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

Acknowledgments

• SynaLinks for the underlying framework