HCT Chatbot – Reflective Reasoning Engine

A Streamlit-based chatbot that demonstrates multi-pass reflective reasoning before generating a final answer.
The system explicitly structures intermediate reasoning steps and streams a concise final response grounded in those steps.

🚀 Features

• Multi-pass reflective reasoning with confidence-based stopping
• Structured reasoning steps using Pydantic schemas
• Streaming final answers for improved user experience
• Clean separation of UI and reasoning engine
• Built with Streamlit, LangChain, and OpenAI

🧠 How It Works

1. User enters a question in the Streamlit interface
2. The model generates reasoning step-by-step
3. Each reasoning step contains:
   • A short heading
   • A detailed thought explanation
   • A confidence score
4. Reasoning automatically stops once the confidence threshold is met
5. A final concise answer is streamed to the UI

📁 Project Structure

├── app.py # Streamlit UI
├── reasoning_engine.py # Reflective reasoning logic
├── .env # Environment variables
├── requirements.txt
└── README.md

🛠 Tech Stack

    Python

    Streamlit

    LangChain

    OpenAI (ChatOpenAI)

    Pydantic

    python-dotenv

🎯 Design Philosophy

UI never directly initializes language models

Reasoning is explicit, structured, and inspectable

Confidence-driven stopping instead of fixed depth

Final answers are concise and grounded in reasoning

📌 Future Improvements

    Toggle reasoning visibility from UI

    Export reasoning steps as JSON or Markdown

    Support multiple reasoning strategies

    Add model selection from the interface

📄 License

MIT License
Free to use, modify, and distribute.

🙌 Acknowledgements

Built as an exploration of transparent reasoning systems and explainable LLM application design.