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Hybrid search
Whereas semantic search utilizing vector embeddings performs nicely for capturing rephrased or paraphrased meanings, it may not do nicely on searches that contain uncommon phrases or jargon. In these instances, combining semantic search with the extra conventional sparse retrieval strategies (BM25 or TF-IDF), which incorporate points like key phrase frequency, typically helps enhance the retrieval course of. As a way to incorporate each of these kind of 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
Upon getting run the preliminary search to retrieve related chunks, performing a last step of rating these outcomes helps to make sure that essentially the most helpful data is offered to the person. The rationale for that is that though the chunks may technically be comparable, they may not be essentially the most useful reply to the person’s question.
There are a couple of other ways wherein reranking is finished 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 advantage of this method is that it’s often computationally cheap and quick.
