Hybrid search overview

Combine keyword and semantic retrieval in Pinecone with a text-match filter on a dense search, or by fusing separate searches with reciprocal rank fusion.

Hybrid search combines a keyword signal with a semantic signal so a single query benefits from both. Keyword retrieval (full-text BM25 or sparse vectors) matches specific tokens like product codes, error strings, and names. Semantic retrieval (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.

Combine signals

Pinecone gives you three ways to combine a keyword signal with a dense signal. Metadata filtering is a separate lever that composes with all of them: 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_vector` search ranks what remains, all in one request.	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. Works on either API.
Server-side combination (Vectors API)	Store a dense vector and a sparse vector on each record in a single vector index. Pinecone combines both in one query, and you set the dense/sparse balance client-side by scaling the query vectors before you send them (an `alpha` weighting).	Existing vector and records workloads on the Vectors API.

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 a dense_vector field and one or more full-text string fields in one schema, then either filter a dense search with a text-match filter or run a keyword search and a dense search and fuse them with RRF. See Full-text search and the multi-signal schema example.

For an existing vector or records workload, use the Vectors API.

Hybrid search on the Vectors API

The Vectors API supports two patterns:

Use a single index for dense and sparse vectors: Store both vectors per record and set the dense/sparse balance client-side by scaling the query vectors before the request (an alpha weighting). This is the simplest single-request architecture, though the unbounded sparse scores need normalizing.
Use separate indexes for dense and sparse vectors: Store dense and sparse in two indexes linked by ID, query each, and merge the results client-side. This is more flexible, but there is more to manage.