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Hybrid search
While semantic search utilizing vector embeddings performs nicely for capturing rephrased or paraphrased meanings, it won’t do nicely on searches that contain uncommon phrases or jargon. In these circumstances, combining semantic search with the extra conventional sparse retrieval methods (BM25 or TF-IDF), which incorporate features like key phrase frequency, typically helps enhance the retrieval course of. In order to include each of a lot of these retrieval mechanisms, you possibly can have chunks be assigned each scores, with the ultimate rating being a weighted mixture of the 2, or you possibly can use sparse retrieval as a first-pass filter adopted by semantic search.
Reranking – the ultimate step
Once you have got run the preliminary search to retrieve related chunks, performing a remaining step of rating these outcomes helps to make sure that probably the most helpful info is introduced to the consumer. The motive for that is that though the chunks would possibly technically be related, they may not be probably the most useful reply to the consumer’s question.
There are a number of other ways wherein reranking is completed in follow. One method is to make use of heuristics on sure metadata of the chunks, such because the writer, date, supply reliability, and so on. A good thing about this method is that it’s normally computationally cheap and quick.