Mono Blog Testing Embedding Models for RAG

Retrieval-Augmented Generation (RAG) is a powerful approach that combines traditional information retrieval techniques with advanced generative models to enhance the quality and relevance of generated responses. The primary objective of this project was to evaluate the performance of various embedding models in RAG and to assess their processing speed in executing this task. The Dataset Before testing different models, it was imperative to identify a suitable dataset. Specifically, we sought a dataset rich in text chunks that could serve as answers, accompanied by a substantial collection of questions relevant to those text chunks.