Recommendation Module Based on Web Scraping and LLM Models with the RAG Technique

In this research, we investigate the implementation of Retrieval-Augmented Generation (RAG) technology to recommend suitable engineering internships to individuals who are looking for an internship based on their CVs by improving the search for correspondence between candidates and employers, ensuring that both parties find the best adequacy. This innovative approach leverages web scraping, data preprocessing and advanced machine learning techniques including vector databases designed to handle high-dimentional vector data allowing for searches based on the proximity or similarity of vectors, and embedding methods used to convert high-dimentional data into vector representations that can be stored in these vector databases. By analyzing and understanding the content of each CV and internship offer, our methodology aims to deliver personalized and relevant internship opportunities, enhancing candidate-employer matching.