In this project two word embedding models (Word2Vec and GloVe) are used to expand a series of queries indexed on a ElasticSearch server. The results obtained are compared and a simple performance test is also done. In the Documentacion folder you can find the notebook where the query expansion is performed and documented. The notebook was originally written in Spanish, and there is an English version also available. This version was automatically generated by nbTranslate, so there may be some errors in it. In the src folder all the source files used by the notebook are stored, as well as an index.py script used to index the collection in ElasticSearch.