The RAG project is a software tool developed to generate didactic mini-scenarios for educational games. It employs recurrent autoregressive models to create context-specific scenarios based on provided didactic mechanics. This tool aims to assist educators and game developers in designing engaging and educational gameplay experiences.
- Mini-scenario Generation: Utilizes recurrent autoregressive models to generate context-specific mini-scenarios.
- Contextual Relevance: Ensures that generated mini-scenarios are directly related to the provided didactic mechanics and context.
- Customization: Allows users to specify the number of mini-scenarios to generate and the language of the output.
- Example-Based Learning: Provides examples of well-formulated mini-scenarios to guide users in creating contextually relevant scenarios.
To generate didactic mini-scenarios, follow these steps:
- Clone this repository to your local machine.
- Install the required dependencies specified in the
requirements.txtfile. - deployment app on docker
git clone https://github.com/Yema7D/RAG-finetuning.git
cd RAG-finetuning
- Create a virtual environment for this project and install dependencies
python3 -m venv .env
- Activate the virtual environment
source .env/bin/activate
- Install the dependencies
pip install -r requirements.txt
- Run the application
uvicorn main:app- install images and run containers on docker
docker-compose up