---
title: Run a Vector Search with the Capella UI
description: Run a Vector Search query from the Couchbase Capella UI to preview
  and test the search results from a Search Vector Index.
pubDate: 2026-08-17T09:53:44.266Z
antora:
  editUrl: https://github.com/couchbaselabs/docs-devex/edit/capella/modules/vector-search/pages/run-vector-search-ui.adoc
  xref: xref:cloud:vector-search:run-vector-search-ui.adoc[]
---

[Consult the llms.txt file for a full list of contents](/llms.txt)
[View original HTML](/cloud/vector-search/run-vector-search-ui.html)

# Run a Vector Search with the Capella UI

> Run a Vector Search query from the Couchbase Capella UI to preview and test the search results from a Search Vector Index. 

For more information about how the Search Service scores documents in search results, see [Scoring for Search Queries](#run-searches.adoc#scoring).

## [](#prerequisites)Prerequisites

* You have the Search Service enabled on a node in your operational cluster. For more information about how to change Services on your operational cluster, see [Modify a Paid Cluster](../clusters/modify-database.md).
* You have created a Search Vector Index.  
For more information about how to create a Search Vector Index, see [Create a Search Vector Index in Quick Mode](create-vector-search-index-ui.md).  
> [!TIP]  
> You can import a sample dataset to use with the procedure or examples on this page.  
>  
> Go to **Data Tools** **Import** from your cluster and [import the color-vector-sample](../clusters/data-service/import-data-documents.md#import-sample-data) sample data.  
>  
> For the best results, consider using the sample Search Vector Index from [Create a Search Vector Index in Quick Mode](create-vector-search-index-ui.md#example).
* You have logged in to the Couchbase Capella UI.

## [](#procedure)Procedure

To run a Vector Search with the Capella UI:

1. In the navigation breadcrumbs in the Capella UI, do 1 of the following:

  1. Click your organization name and go to **Operational**.
  2. Click your current project name or search for a project and go to **Operational**.
  3. Expand the cluster breadcrumb and search for a cluster.
2. Select the cluster where you created your Search Vector Index.
3. Go to **Data Tools** **Search**.
4. Next to your Search Vector Index, click **Search**.
5. In the **Search** field, enter a search query.
6. Press Enter or click **Search**.
7. (Optional) To view a document and its source collection, click a document name in the search results list.

### [](#similarity)Example: Running a Simple Vector Similarity Query

For example, the following query searches for the top 2 vectors similar to the vector `[ 0, 0, 128 ]` in the `colorvect_l2` field:

```json
{
    "fields": ["*"], 
    "knn": [
      {
        "k": 2, 
        "field": "colorvect_l2", 
        "vector": [ 0, 0, 128 ]
      }
    ]
}
```

The Search query is only a Vector Search query. It only returns the `k` number of similar vectors.

The Search Service combines the Vector search results from a `knn` object with the traditional `query` object by using an `OR` function. If the same documents match the `knn` and `query` objects, the Search Service ranks those documents higher in search results.

The document for the color `navy` should be the first result, followed by a similar color.

### [](#hybrid)Example: Running a Simple Hybrid Search Query

The following hybrid Search query searches for the top vector similar to the vector `[ 0, 0, 128 ]` in the `colorvect_l2` field. It also runs a [Numeric Range Query](../search/search-request-params.md#numeric-range-queries) on the `brightness` field to only return colors that have a brightness value between `70` and `80`:

```json
{
    "fields": ["*"], 
    "query": { 
      "min": 70,
      "max": 80,
      "inclusive_min": false,
      "inclusive_max": true,
      "field": "brightness"
    }, 
    "knn": [
      {
        "k": 1, 
        "field": "colorvect_l2", 
        "vector": [ 0, 0, 128 ]
      }
    ]
}
```

The Search Service combines the Vector search results from a `knn` object with the traditional `query` object by using an `OR` function. If the same documents match the `knn` and `query` objects, the Search Service ranks those documents higher in search results.

The document for the color `navy` should be the first result, followed by colors that are similar and match the `brightness` field query.

> [!TIP]
> If you want to run a hybrid Search query on a large, partitioned Search index and your cluster is on Couchbase Server version 8.0 or later, use the `bm25` scoring model for your Search index. For more information, see [Configure Additional Search Index Settings](../search/create-search-index-ui.md#configure-settings), [Scoring for Search Queries](#run-searches.adoc#scoring), or [Scoring Model](#customize-index.adoc#scoring-model).

### [](#large)Example: Running a Semantic Search Query with a Large Embedding Vector

The following query searches for matches to a large embedding vector, generated by the [OpenAI embedding model](https://platform.openai.com/docs/guides/embeddings), `text-embedding-ada-002-v2`.

> [!TIP]
> You can find generated embedding vectors for each color's `description` field in `rgb.json`.

This query should return the document for the color `navy`, based on a generated embedding vector for:

What is a classic, refined hue that exudes elegance and is often linked to power and stability?

The following shows part of the sample Search query:

```json
{
    "fields": ["*"],
    "knn": [
      {
        "field": "embedding_vector_dot",
        "k": 3,
        "vector": [
          0.024032991379499435,
          -0.009131478145718575,
          0.013961897231638432,
          -0.024734394624829292,
          -0.020605377852916718,
          0.006739427801221609,
          -0.012539239600300789,
          0.0063192471861839294,
          0.000004374724539957242,
          -0.030252983793616295,
          -0.010944539681077003,
          -0.0012845275923609734,
          0.0059850881807506084,
          -0.006388725712895393,
          -0.016304319724440575,
          0.03046472743153572,
          0.029988301917910576,
          -0.013121536932885647,
          0.01815708354115486,
          -0.011096730828285217,
          -0.0423753522336483,
          -0.0023523480631411076,
          -0.00022332418302539736,
          -0.0024681459181010723,
          -0.02911485731601715,
```

> [!NOTE]
> Due to the size of the embedding vector, only part of the full query is being displayed in the documentation.
> 
> Click **View** to view and copy the entire Vector Search query payload. Make sure you remove the lines for `// tag::partial[]` and `// end::partial[]`.

### [](#base64)Example: Running a Semantic Search Query with a base64 Encoded String

If your operational cluster is running Couchbase Server version 7.6.2 or later, you can use vectors encoded as base64 strings with Vector Search. For example, the following document describes the color `navy`, with base64 encoded strings in the `embedding_vector_dot` and `colorvect_l2` fields instead of arrays:

```json
{
    "id": "#000080",
    "color": "navy",
    "brightness": 14.592,
    "colorvect_l2": "AACA",
    "wheel_pos": "other",
    "verbs": [
        "deep",
        "rich",
        "sophisticated"
    ],
    "description": "Navy is a deep, rich color that exudes sophistication. It is a dark shade of blue that is often associated with authority, stability, and elegance. Navy is a versatile color that can be both bold and understated, making it a popular choice in fashion and interior design. It is a timeless color that never goes out of style and adds a touch of sophistication to any look or space.",
    "embedding_model": "text-embedding-ada-002-v2",
    "embedding_vector_dot": "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"
},
```

The following query uses a base64 encoded string for the same query as [Running a Semantic Search Query with a Large Embedding Vector](#large) to return the document for `navy`:

```json
{
    "fields": ["*"],
    "knn": [
      {
        "field": "embedding_vector_dot",
        "k": 3,
        "vector_base64": "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"
      }
    ]
}
```

> [!NOTE]
> You can only use base64 encoded strings in your Vector Search queries if your documents use base64 encoded strings, indexed with the **vector\_base64** field data type. You cannot search for and return vectors you indexed as arrays with the **vector** field data type by using a Search query with a base64 encoded string.

### [](#example-use-global%5Fscoring-with-a-bm25-search-index)Example: Use global\_scoring With a bm25 Search Index

In the following example, the Search query uses both a `query` and `knn` object to run both a Vector Search and traditional Search query on an index named `products-index`.

The query searches for a specific embedding vector generated from an ecommerce website's product description. The Search vector is generated from the phrase `long battery life wireless earbuds`. The `query` object specifically searches for documents that have `Electronics` as their category, with a price between `100.00` and `300.00`. The query returns the `description`, `price`, and `product_name` fields in results. Since the query is on a large, partitioned index and uses the `bm25` scoring algorithm, the query also uses `global_scoring` to keep document scores consistent across the Search index's partitions:

```json
{
    "fields": ["description", "price", "product_name"],
    "query": {
      "conjuncts": [
        {
          "term": "Electronics",
          "field": "category"
        },
        {
          "field": "price",
          "min": 100.00,
          "max": 300.00,
          "inclusive_max": true
        }
      ]
    },
    "knn": [
      {
        "k": 5,
        "field": "embedding",
        "vector": [0.23, -0.75, 0.61, ...]
      }
    ],
    "ctl": {
      "global_scoring": true
    }
}
```

> [!NOTE]
> The vector embedding has been truncated for this example. The vector embedding in your Search query must match the configured dimension and similarity metric for your Search index.

For more information about the `bm25` scoring algorithm, see [Scoring for Search Queries](../search/run-searches.md#scoring).

## [](#next-steps)Next Steps

If you do not get the search results you were expecting, you can change the [JSON payload for your Search query](../search/search-request-params.md).

You can also [add additional features to your Search index](../search/customize-index.md).