metadata

title: AI Notebook Tutor
emoji: 📖
colorFrom: pink
colorTo: yellow
sdk: docker
pinned: false

AI-Notebook-Tutor

RAG Application for QA in Jupyter Notebook

AI-Notebook-Tutor is designed to provide question-answering capabilities in a Jupyter Notebook using the Retrieval Augmented Generation (RAG) model. It's built on top of the LangChain and Chainlit platforms, and it uses the OpenAI API for the chat model.

Features

Document processing: Load a Jupyter notebook and split it into chunks for processing.
Query answering: Use the RAG model to answer queries based on the processed document.
User interaction: Interact with the application through a chat interface.

Setup

Clone the repository.
Install the required dependencies using pip:

pip install -r requirements.txt

Create a .env file and add you environment variables. You'll need to provide your OpenAI API key in the format:

OPENAI_API_KEY=your-key-here

Run the application using the following command:

chainlit run notebook_tutor/app.py

Usage

Start a chat session and upload a Jupyter notebook file. The application will process the document and you can then ask questions related to the content of the notebook. It might take some time to answer some question (should be less than 1 min), so please be patient.

Acknowledgements

This project uses technologies including LangChain, OpenAI's GPT models, Qdrant for vector storage and ChainLit. Thanks to all open-source contributors and organizations that make these tools available.