Hybrid Search

Hybrid Search — explained

Pure vector search struggles with rare proper nouns, product codes, or typos — terms that barely appear in the training corpus have no meaningful semantic neighborhood. Pure full-text search, on the other hand, fails on synonyms and paraphrases. Hybrid search runs both methods in parallel and fuses the result lists — usually via Reciprocal Rank Fusion (RRF) or a linear score combination. The end result is noticeably more robust than either retriever alone.

Example / Practical context

A support bot needs to answer both “How do I reset my password?” and “Error message E_AUTH_402” with good hits. Vector search dominates the first (natural language question), BM25 the second (exact code). Hybrid search produces usable top results for both. Vector DBs like Qdrant, Weaviate, Pinecone, and Postgres with pgvector + tsvector support hybrid setups out of the box.

Distinction from related terms

Hybrid search is a retrieval strategy, not its own algorithm — the building blocks are still classic BM25 and vector indexes. Reranking is the step that often follows: a cross-encoder reorders the fused top-N more precisely. Multi-query and query expansion modify the query itself, instead of combining two retrievers.

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