What is Hybrid search?
Verified 5 August 2026
Lexical retrieval matches words and fails when the user and the document use different vocabulary. Vector retrieval matches meaning and fails on exact identifiers, which have no useful neighbourhood in embedding space. Hybrid search runs both and combines them, so a result strong in either space surfaces.
The fusion step matters more than it appears. BM25 scores and cosine similarities are not comparable numbers, so weighting them against each other requires tuning that does not transfer between corpora. Reciprocal rank fusion sidesteps this by combining positions rather than scores — each result is scored by the reciprocal of its rank in each list, which needs no tuning at all.
unlob runs hybrid by default, fusing by reciprocal rank. It costs about 2.5 ms more than keyword-only at the median, which for most workloads is not a consideration.
How unlob handles this: Search modes
Common questions
What is Hybrid search?
Hybrid search runs a lexical query and a vector query over the same corpus and fuses the two rankings into one result set.
How does Hybrid search work in practice?
The fusion step matters more than it appears. BM25 scores and cosine similarities are not comparable numbers, so weighting them against each other requires tuning that does not transfer between corpora. Reciprocal rank fusion sidesteps this by combining positions rather than scores — each result is scored by the reciprocal of its rank in each list, which needs no tuning at all.
See it working
The free tier is 10,000 requests a month with no card — enough to test any of this against your own queries.