edubotics-core 0.1.1

Core modules for edubotics-based LLM AI chatbots

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title: AI Class Tutor -- Dev description: An LLM based AI class tutor with RAG on DL4DS course emoji: 🐶 colorFrom: red colorTo: green sdk: docker app_port: 7860

DL4DS Tutor 🏃

Check out the configuration reference at Hugging Face Spaces Config Reference.

You can find a "production" implementation of the Tutor running live at DL4DS Tutor from the Hugging Face Space. It is pushed automatically from the main branch of this repo by this Actions Workflow upon a push to main.

A "development" version of the Tutor is running live at DL4DS Tutor -- Dev from this Hugging Face Space. It is pushed automatically from the dev_branch branch of this repo by this Actions Workflow upon a push to dev_branch.

Setup

Please visit setup for more information on setting up the project.

Running Locally

1. Clone the Repository

   git clone https://github.com/edubotics-ai/edubot-core
   

2. Create your app in the apps folder. (An example is the apps/ai_tutor app)

   cd apps
   mkdir your_app
   

3. Put your data under the apps/your_app/storage/data directory

   • Add URLs in the urls.txt file.
   • Add other PDF files in the apps/your_app/storage/data directory.

4. To test Data Loading (Optional)

   cd apps/your_app
   python -m edubotics_core.dataloader.data_loader --links "your_pdf_link" --config_file config/config.yml --project_config_file config/project_config.yml
   

5. Create the Vector Database

   cd apps/your_app
   python -m edubotics_core.vectorstore.store_manager --config_file config/config.yml --project_config_file config/project_config.yml
   

6. Run the FastAPI App

   cd apps/your_app
   uvicorn app:app --port 7860 
   

Documentation

Please visit the docs for more information.

Docker

The HuggingFace Space is built using the Dockerfile in the repository. To run it locally, use the Dockerfile.dev file.

docker build --tag dev  -f Dockerfile.dev .
docker run -it --rm -p 7860:7860 dev

Contributing

Please create an issue if you have any suggestions or improvements, and start working on it by creating a branch and by making a pull request to the dev_branch.

Please visit contribute for more information on contributing.

Future Work

For more information on future work, please visit roadmap.