Sequential Thinking Paper

This repository contains the implementation of a Sequential Thinking Server for AI-powered article illustration. The server analyzes article content using a step-by-step approach to extract key information and generate appropriate visual suggestions.

Features

• Content Analysis: Breaks down articles into meaningful segments
• Keyword Extraction: Identifies key themes and topics
• Emotion Analysis: Determines the emotional tone of content
• Visualization Suggestions: Recommends appropriate visualization types
• Image Prompt Generation: Creates prompts for AI image generation

Architecture

The system is built using:

• FastAPI for the web server
• OpenAI's GPT-4 for content analysis
• Pydantic for data validation
• pytest for testing

Installation

1. Clone the repository:

git clone https://github.com/your-username/sequential-thinking-paper.git
cd sequential-thinking-paper

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

cp .env.example .env
# Edit .env with your OpenAI API key

Usage

1. Start the server:

python run_servers.py

2. Send a POST request to analyze content:

curl -X POST "http://localhost:8000/analyze" \
     -H "Content-Type: application/json" \
     -d '{"content": "Your article text here", "max_segments": 1, "analysis_type": "article"}'

Testing

Run tests with:

python -m pytest tests/ -v

Contributing

1. Fork the repository
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

License

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

Citation

If you use this work in your research, please cite:

@article{sequential-thinking,
  title={Sequential Thinking: A Step-by-Step Approach to AI Content Analysis},
  author={Your Name},
  year={2025},
  journal={Preprint}
}