---
title: Vector Search
description: Vector Search from the SDK, to enable AI integration, semantic
  search, and use of RAG frameworks.
pubDate: 2026-08-17T09:53:44.266Z
antora:
  editUrl: https://github.com/couchbase/docs-sdk-rust/edit/release/1.0/modules/howtos/pages/vector-searching-with-sdk.adoc
  xref: xref:rust-sdk:howtos:vector-searching-with-sdk.adoc[]
---

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# Vector Search

> Vector Search from the SDK, to enable AI integration, semantic search, and use of RAG frameworks. 

Vector Search has been available in Couchbase Capella Operational and self-managed Server since version 7.6, using the Couchbase Search Service. Version 8.0 introduces vector query using Global Secondary Indexes (GSI), the Query Service index — using either a fast Hyperscale index, or a composite index to combine scalar queries with semantic search.

For fast and scalable vector queries, use one of the above two GSI choices — detailed in the next section. If you don't require the speed and scale of vector query with GSI, or need to combine vector, geo-spatial search, range search, and traditional fuzzy text searches, then consider [Vector Search With the Search Service](#vector-search-with-the-search-service).

## [](#vector-search-with-the-query-service-and-gsi)Vector Search With the Query Service and GSI

From the SDK, a vector query using GSI is the same as any other query. However, you will need to build one or more indexes.

### [](#prerequisites)Prerequisites

* Couchbase Server 8.0.0 or newer — or a recent Capella instance.
* Your chosen [vector index](../../../server/current/vector-index/use-vector-indexes.md) — hyperscale or composite.

You will need to refer to the [Use Vector Indexes for AI Applications](../../../server/current/vector-index/vectors-and-indexes-overview.md) pages for a full discussion of using Vector Indexes with Vector Queries. In particular, you will need to [create a Vector Index](../../../server/current/vector-index/hyperscale-filter.md#creating-a-hyperscale-vector-index-with-included-scalar-values).

### [](#examples)Examples

The [Use Vector Indexes for AI Applications](../../../server/current/vector-index/vectors-and-indexes-overview.md) pages contain examples using both hyperscale and compound indexes.

Here is the [Hyperscale Index](../../../server/current/vector-index/hyperscale-vector-index.md#query-example) example, wrapped inside the Rust SDK Query API.

Hyperscale Index Example

```rust
  let statement =
      "SELECT d.id, d.question, d.wanted_similar_color_from_search, \
       ARRAY_CONCAT( \
          d.couchbase_search_query.knn[0].vector[0:4], \
          ['...'] \
       ) AS vector \
       FROM `vector-sample`.`color`.`rgb-questions` AS d \
       WHERE d.id = '#87CEEB';";

  let mut result = cluster.query(statement, QueryOptions::new().metrics(true)).await?;

  let mut rows = result.rows();
  while let Some(row) = rows.next().await {
      let row: serde_json::Value = row?;
      println!("Row: {}", row);
  }
```

Parameterizing the query, as with [regular queries](sqlpp-queries-with-sdk.md#parameterized-queries), will allow the reuse of the [Query Plan](../../../server/current/n1ql/n1ql-intro/queriesandresults.md#prepare-stmts). This can be more efficient, unless you are doing a lot of optimization to your query.

Parameterized Vector Query

```rust
  let statement = "SELECT d.id, d.question, d.wanted_similar_color_from_search, \
                   ARRAY_CONCAT( \
                      d.couchbase_search_query.knn[0].vector[0:4], \
                      ['...'] \
                   ) AS vector \
                   FROM `vector-sample`.`color`.`rgb-questions` AS d \
                   WHERE d.id = $id;";

  let mut result = cluster
      .query(
          statement,
          QueryOptions::new().add_named_parameter("id", "#87CEEB")?,
      )
      .await?;
  let mut rows = result.rows();
  while let Some(row) = rows.next().await {
      let row: serde_json::Value = row?;
      println!("Row: {}", row);
  }
```

## [](#vector-search-with-the-search-service)Vector Search With the Search Service

Vector search is also implemented using [Search Indexes](full-text-searching-with-sdk.md), and can be combined with traditional full text search queries. Vector embeddings can be an array of floats or a [base64 encoded string](../../../server/current/vector-search/run-vector-search-ui.md#base64).

### [](#prerequisites-2)Prerequisites

Couchbase Server 7.6.0 (7.6.2 for base64-encoded vectors) — or recent Capella instance.

### [](#examples-2)Examples

#### [](#single-vector-query)Single Vector Query

In this first example we are performing a single vector query:

```rust
let request = SearchRequest::with_vector_search(VectorSearch::new(
    vec![VectorQuery::with_vector("vector_field", vector_query)],
    None,
));

let result = scope.search("vector-index", request, None).await?;
```

Let's break this down. We create a `SearchRequest`, which can contain a traditional Search query `SearchQuery` and/or the new `VectorSearch`. Here we are just using the latter.

The `VectorSearch` allows us to perform one or more `VectorQuery` s.

The `VectorQuery` itself takes the name of the document field that contains embedded vectors ("vector\_field" here), plus actual vector query in the form of a `float[]`.

(Note that Couchbase itself is not involved in generating the vectors, and these will come from an external source such as an embeddings API.)

Finally we execute the `SearchRequest` against the Search index "travel-sample-index", which has previously been setup to vector index the "vector\_field" field.

This happens to be a scoped index so we are using `scope.search()`. If it was a global index we would use `cluster.search()` instead - see [\[Scoped vs Global Indexes\]](#Scoped vs Global Indexes).

It returns the same `SearchResult` detailed earlier.

#### [](#pre-filters)Pre-Filters

From Couchbase Server 7.6.4 — and in Capella Operational clusters — [pre-filtering with similarity search](../../../server/current/vector-search/pre-filtering-vector-search.md#about-pre-filtering) is available. This is a non-vector query that the server executes first to get an intermediate result. Then it executes the vector query on the intermediate result to get the final result.

```rust
impl VectorQuery {
    pub fn prefilter(mut self, prefilter: Query) -> Self;
}
```

If no prefilter is specified, the server executes the vector query on all indexed documents.

```rust
    VectorQuery::with_vector("vector_field", vector_query)
        .num_candidates(num_candidates)
        .prefilter(
            Query::Match(
                MatchQuery::new("primary")
                    .field("color_wheel_pos"),
            ),
        );
```

Note that `num_candidates` sets how many similar vectors are returned. If it is not set, then the Cluster's default of `3` will be used — this corresponds with `k` on the Server side, for K-Nearest Neighbors.

The prefilter can be any Search Query — from a simple match, as above, to a string query:

```rust
    .prefilter(Query::QueryString(QueryStringQuery::new("+description:sea -color_hex:fff5ee")))
```

See the [API reference](https://docs.couchbase.com/sdk-api/couchbase-java-client/com/couchbase/client/java/search/vector/VectorQuery.html#prefilter%28com.couchbase.client.java.search.SearchQuery%29).

#### [](#multiple-vector-queries)Multiple Vector Queries

You can run multiple vector queries together:

```rust
let request = SearchRequest::with_vector_search(VectorSearch::new(
    vec![
        VectorQuery::with_vector("vector_field", vector_query1)
            .num_candidates(2)
            .boost(0.3),
        VectorQuery::with_vector("vector_field", vector_query2)
            .num_candidates(5)
            .boost(0.7),
    ],
    None,
));

let result = scope.search("vector-index", request, None).await?;
```

How the results are combined (ANDed or ORed) can be controlled with `VectorSearchOptions().query_combination()`.

### [](#combining-search-and-vector-queries)Combining Search and Vector Queries

You can combine a traditional Search query with vector queries:

```rust
let vector_search = VectorSearch::new(
    vec![VectorQuery::with_vector("vector_field", vector_query)],
    None,
);
let fts_search = Query::MatchAll(MatchAllQuery::new());

let request = SearchRequest::with_search_query(fts_search).vector_search(vector_search)?;

let result = scope.search("vector-index", request, None).await?;
```

How the results are combined (ANDed or ORed) can be controlled with `VectorSearchOptions().query_combination()`.

Scoring for these hybrid search queries combines the boost multipliers to get to the final score.

hit_score = (query_1_boost * query_1_hit_score) + (query_2_boost * query_2_hit_score)

### [](#query-methods)Query Methods

As part of the Search Service, you can use the same [Search query methods](full-text-searching-with-sdk.md#search-queries) as regular Searches. See a fuller list, with Vector properties, in the [Capella docs](../../../cloud/search/search-request-params.md).

## [](#further-reading)Further Reading

### [](#vector-query)Vector Query

* [Vector Query for AI Apps docs (self-managed Couchbase Server)](../../../server/current/vector-index/vectors-and-indexes-overview.md).
* [Vector Query for AI Apps docs (Capella DBaaS)](#cloud::vector-index/vectors-and-indexes-overview.adoc)

### [](#vector-search)Vector Search

* [Vector Search for AI Apps docs (self-managed Couchbase Server)](../../../server/current/vector-search/vector-search.md)
* [Vector Search for AI Apps docs (Capella DBaaS)](#cloud::vector-search:vector-search.adoc)
* Vector Search in the [Rust API reference](https://docs.rs/couchbase/latest/couchbase/search/request/struct.SearchRequest.html#method.vector%5Fsearch).