Hybrid Search: Definition and Examples

Hybrid Search is an approach that merges two fundamental paradigms of information retrieval: traditional lexical search (such as BM25 or TF-IDF) and semantic search based on vector embeddings. The goal is to get the best of both worlds to maximize the relevance of results.

Lexical search excels at finding exact keyword matches, proper names, identifiers, or specific technical terms. However, it fails when the user formulates their query differently from the target document. Semantic search, on the other hand, understands the meaning and intent behind a query thanks to embedding models, but may miss important exact matches.

By combining both approaches, Hybrid Search typically uses a score fusion mechanism (such as Reciprocal Rank Fusion or linear weighting) to produce a unified final ranking. Each method generates its own results and scores, then an algorithm combines them by assigning a relative weight to each source.

This technique has become essential in RAG (Retrieval-Augmented Generation) systems where the quality of retrieval directly impacts the relevance of LLM-generated responses. Databases like Weaviate, Pinecone, Qdrant, or Elasticsearch now offer Hybrid Search natively, making its adoption accessible to most developers.