# Hybrid search overview

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.

:::callout{intent="note"}
"Hybrid" isn't one fixed method. Qualify what you are combining: full-text (BM25) plus dense, sparse plus dense, or a keyword filter plus dense. This page uses those qualifiers throughout.
:::

## Combine signals

Pinecone gives you three ways to combine a keyword signal with a dense signal. [Metadata filtering](/guides/index-data-search-filter-by-metadata) 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](/guides/index-data-search-filter-by-metadata#text-match-filters) (`$match_phrase`, `$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)](/guides/index-data-search-reciprocal-rank-fusion).                                                                             | 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](/guides/index-data-search-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](/guides/index-data-search-full-text-search) and the [multi-signal schema example](/guides/index-data-data-modeling#schema-patterns).

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](/guides/index-data-search-hybrid-search-single-index): 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](/guides/index-data-search-hybrid-search-separate-indexes): 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.

## Related pages

- [Run hybrid search on a single index](./index-data-search-hybrid-search-single-index.md)
- [Run hybrid search on separate indexes](./index-data-search-hybrid-search-separate-indexes.md)
- [Reciprocal rank fusion](./index-data-search-reciprocal-rank-fusion.md)

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