This project utilizes pretrained language models for retrieval-augmented generation (RAG) tasks, specifically focusing on text chunk embedding and query-based generation. It incorporates efficient data loading, model setup, and real-time interaction for generating responses.
This project aims to learn how Retrieval-Augmented Generation (RAG) models work and how they can be used for understanding and generating natural language.