Welcome to the demonstration of a customized lightweight Trustworthy Retrieval-Augmented Generation (RAG) model. This application is designed for educational purposes to help you understand various aspects of crop growing and related technologies through the use of advanced transformer models and context-aware techniques.
The GENI Information Technology research team is dedicated to advancing the field of artificial intelligence through the development and application of transformer models and context-aware technologies. This project demonstrates how these technologies can be applied in agriculture to provide detailed crop growing guides.
This demo showcases:
- The capabilities of transformer models in generating detailed and contextually relevant crop growing guides.
- How context-aware systems can provide accurate and relevant information.
- The use of retrieval-augmented generation for educational purposes.
- https://generalragdemo.streamlit.app/
- Python 3.7 or higher
- Streamlit
- PyTorch
- Hugging Face Transformers
- SentenceTransformers
- Plotly