Implementación de redes tipo transformer en la selección estratégica de perfiles laborales a nivel empresarial

Abstract

In this project, a RAG (Retrieval-Augmented Generation) model is developed for application in the context of recruitment and personnel selection (limited to areas related to Electronic Engineering). The starting point is the creation of a document database (composed of PDF files), followed by a preprocessing phase based on text cleaning and tokenization, which is then converted into a vectorized database. The data is prepared for model training through chunking and indexing operations, enabling the inclusion of a Large Language Model (LLM) based on a transformer model. This model, along with vector search mechanisms and similarity learning, allows for language generation and information retrieval, respectively. By integrating each of these components, the RAG model is constructed. The aim is to find the best parameters according to the given conditions, evaluating the performance obtained in each case to achieve the best result.