This post explores the basics of AI-powered search engines, including the components of search engines, lexical retrieval, and the limitations of BERT embeddings for semantic search. It introduces sBERT as a solution to improve the semantic meaningfulness of BERT embeddings and provides information on siamese and triplet networks. The post also discusses the Sentence Transformers library and how it can be used for vector search and ranking. It concludes with information on making sBERT multilingual and the BEIR benchmark for information retrieval.
Table of contents
Basic Components of a Search EngineUsing BERT for SearchBetter Bi-Encoders for Vector SearchFinal RemarksSort: