Hybrid search overview

Hybrid search combines a keyword signal with a semantic signal so your results benefit from both. Full-text search (BM25) and sparse-vector search match specific terms like product codes, error strings, and names. Semantic search (dense vectors) matches on meaning, so a query still finds an answer phrased with different words. Each method misses what the other catches, and hybrid search closes that gap.

“Hybrid” covers several combinations: full-text (BM25) plus dense, sparse plus dense, or a text-match filter plus dense.

Combine signals

Pinecone gives you two ways to combine a keyword signal with a dense signal. Metadata filtering is a separate lever that composes with both: it narrows the candidate pool before ranking, so an out-of-scope document can’t compete for a result slot.

Pattern	How it works	When to use it
Filter, then rank	A text-match filter (`$match_phrase`, `$match_all`, `$match_any`) on a full-text field narrows the candidates, then a dense search ranks what remains, all in one request. The dense search can score the same `string` field with `embed`, or score a `dense_vector` field.	Document and text workloads on the Documents API. One index, one call, and no score normalization.
Client-side fusion	Run a keyword search and a dense search separately, then merge the two ranked lists with reciprocal rank fusion (RRF).	When you want both signals to contribute to the ranking, not just to filter.

RRF is a fusion method, not a synonym for hybrid search. It’s one way to merge ranked lists, while “hybrid search” is the broader approach of combining signals. See Reciprocal rank fusion.

Choose an approach

For a new document or text workload, use the Documents API. Declare one string field with both full_text_search and embed, so Pinecone indexes its text for full-text search and embeds it for semantic search. Then either filter an embed search with a text-match filter on that field, or run a text search and an embed search and fuse them with RRF. See Integrated embedding. If you generate your own embeddings, declare a dense_vector field alongside one or more full-text string fields instead. See the full-text search example schemas. For hybrid search on a legacy vector index, see Hybrid search on a single index and Hybrid search on separate indexes.

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Combine signals

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​Combine signals

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Combine signals

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