Hybrid search

Hybrid search runs two ways of searching in parallel and merges their results. The first is semantic search, which compares the meaning of the query and the passages through their vector representations in a vector database — it handles paraphrases and synonyms. The second is classic keyword matching, which looks for exactly the same character strings. Results from both channels are merged into a shared ranking, most often by weighting each one's contribution with a single relevance score.

The reason for combining them is that each method fails where the other is strong. Semantic search on its own can miss an exact catalog number, a proper name, an abbreviation, or a snippet of code, because the vector "understands" them only roughly. Keyword matching on its own, in turn, will not find a document that talks about the same thing in different words. The hybrid covers both cases at once, which lifts relevance especially in technical, legal, and product knowledge bases, where both meaning and literal terms count.

In deployment practice, hybrid search is the standard first stage in a RAG architecture. It returns a broader and more reliable pool of candidates than a single method, after which it is worth ordering that pool with reranking, which arranges the passages by actual relevance before they reach the model. This setup — hybrid plus reranking — is a common way to improve answers without rebuilding the whole system.