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Open Notebook

Open Notebook is an AI-powered knowledge management and question-answering system built with Streamlit. It allows users to create a personalized knowledge base from various sources and interact with it using natural language queries.

Features

  • AI-Powered Conversations: Utilizes OpenAI's GPT models for intelligent responses.
  • Custom Knowledge Base: Add content from websites, PDFs, and custom text inputs.
  • RAG (Retrieval-Augmented Generation): Enhances AI responses with relevant information from your knowledge base.
  • User-Friendly Interface: Clean, dark-themed UI with expandable sections for easy navigation.
  • Flexible Configuration: Customize AI model, retrieval parameters, and more.

Installation

  1. Clone the repository:

    https://github.com/mshojaei77/open-notebook.git

    cd open-notebook

  2. Install the required dependencies:

    pip install -r requirements.txt

  3. Set up your OpenAI API key:

    • Create a .env file in the project root.
    • Add your API key: OPENAI_API_KEY=your_api_key_here

Usage

  1. Run the Streamlit app:

    streamlit run app.py

  2. Open your web browser and navigate to the provided local URL (usually http://localhost:8501).

  3. Configure the app:

    • Enter your OpenAI API key if not set in the .env file.
    • Adjust advanced settings like AI model and chunk size if needed.

  4. Build your knowledge base:

    • Add websites by entering URLs.
    • Upload PDF documents.
    • Input custom text directly.

  5. Start asking questions! The AI will respond based on your knowledge base.

Configuration

  • AI Model: Choose between different GPT models.
  • Top K: Number of relevant documents to retrieve for each query.
  • Chunk Size: Size of text chunks for processing.
  • Chunk Overlap: Overlap between text chunks.

Managing Your Knowledge Base

  • View all items in your knowledge base.
  • Remove individual items or clear the entire knowledge base.
  • Refresh the app to see updates.

File Structure

  • app.py: Main application file.
  • crawler.py: Web scraping functionality.
  • system_prompt.txt: System prompt for the AI.
  • knowledge_base/: Directory for storing knowledge base files.
    • json/: JSON files of processed content.
    • faiss/: FAISS vector stores for efficient retrieval.

Dependencies

  • Streamlit
  • LangChain
  • OpenAI
  • FAISS
  • PyPDF2
  • python-dotenv

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

Acknowledgments

  • OpenAI for providing the GPT models.
  • Streamlit for the web app framework.
  • LangChain for RAG implementation.