  The domain of natural language processing (NLP), which has greatly evolved
over the last years, has highly benefited from the recent developments in word
and sentence embeddings. Such embeddings enable the transformation of complex
NLP tasks, like semantic similarity or Question and Answering (Q&A), into much
simpler to perform vector comparisons. However, such a problem transformation
raises new challenges like the efficient comparison of embeddings and their
manipulation. In this work, we will discuss about various word and sentence
embeddings algorithms, we will select a sentence embedding algorithm, BERT, as
our algorithm of choice and we will evaluate the performance of two vector
comparison approaches, FAISS and Elasticsearch, in the specific problem of
sentence embeddings. According to the results, FAISS outperforms Elasticsearch
when used in a centralized environment with only one node, especially when big
datasets are included.
