Data Objects

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of theService Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see thelaunch stage descriptions.

In Vector Search 2.0, Collections store data as individual JSON objectscalled Data Objects. This page provides information on how to createData Objects or import them from Cloud Storage buckets, and how to update anddelete them.

Creating a Data Object

The following example demonstrates adding a Data Object to a Collection with theIDCOLLECTION_ID.

REST

Before using any of the request data, make the following replacements:

  • DATA_OBJECT_ID: The ID of the data object.
  • COLLECTION_ID: The ID of the collection.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloudproject ID.

HTTP method and URL:

POST https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects?dataObjectId=DATA_OBJECT_ID

Request JSON body:

{  "data": {    "director": "Frank Darabont",    "genre": "Drama",    "title": "The Shawshank Redemption",    "year": 1994  },  "vectors":{    "genre_embedding": {      "dense": {        "values": [ 0.38638010860523064, 0.739343471733759, 0.16189056837017107, 0.5271366865924485 ]      }    },    "plot_embedding": {      "dense": {        "values": [ 0.4752082440607731, 0.09026746166854707, 0.8752307753619009 ]      }    },    "soundtrack_embedding": {      "dense": {        "values": [ 0.5920451749052875, 0.08301644173787519, 0.1264733498775969, 0.6196429624200321, 0.4925828581737443 ]      }    },    "sparse_embedding": {      "sparse": {        "indices": [ 4065, 13326, 17377, 25918, 28105, 32683, 42998 ],        "values": [ 1, 6, 3, 2, 8, 5, 2 ]      }    }  }}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects?dataObjectId=DATA_OBJECT_ID"

PowerShell (Windows)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects?dataObjectId=DATA_OBJECT_ID" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{  "name": "projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID",  "data": {    "director": "Frank Darabont",    "title": "The Shawshank Redemption",    "year": 1994,    "genre": "Drama"  },  "vectors": {    "genre_embedding": {      "dense": {        "values": [          0.3863801,          0.73934346,          0.16189057,          0.5271367        ]      }    },    "plot_embedding": {      "dense": {        "values": [          0.47520825,          0.090267465,          0.8752308        ]      }    },    "soundtrack_embedding": {      "dense": {        "values": [          0.5920452,          0.08301644,          0.12647335,          0.619643,          0.49258286        ]      }    },    "sparse_embedding": {      "sparse": {        "values": [          1,          6,          3,          2,          8,          5,          2        ],        "indices": [          4065,          13326,          17377,          25918,          28105,          32683,          42998        ]      }    }  }}

gcloud

Before using any of the command data below, make the following replacements:

  • DATA_FILE: The local path to the file containing data part of the data object.
  • VECTORS_FILE: The local path to the file containing the vectors part of the data object.
  • DATA_OBJECT_ID: The ID of the data object.
  • COLLECTION_ID: The ID of the collection.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloudproject ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloudbetavector-searchcollectionsdata-objectscreateDATA_OBJECT_ID\--data=DATA_FILE\--vectors=VECTORS_FILE\--collection=COLLECTION_ID\--location=LOCATION\--project=PROJECT_ID

Windows (PowerShell)

gcloudbetavector-searchcollectionsdata-objectscreateDATA_OBJECT_ID`--data=DATA_FILE`--vectors=VECTORS_FILE`--collection=COLLECTION_ID`--location=LOCATION`--project=PROJECT_ID

Windows (cmd.exe)

gcloudbetavector-searchcollectionsdata-objectscreateDATA_OBJECT_ID^--data=DATA_FILE^--vectors=VECTORS_FILE^--collection=COLLECTION_ID^--location=LOCATION^--project=PROJECT_ID

You should receive a response similar to the following:

Created dataObject [DATA_OBJECT_ID].

Python

fromgoogle.cloudimportvectorsearch_v1beta# Create the clientdata_object_service_client=vectorsearch_v1beta.DataObjectServiceClient()# Initialize requestdata_object=vectorsearch_v1beta.DataObject(data={"title":"The Shawshank Redemption","genre":"Drama","year":1994,"director":"Frank Darabont",},vectors={"plot_embedding":{"dense":{"values":[0.1,0.2,0.3]}},"genre_embedding":{"dense":{"values":[0.4,0.5,0.6,0.7]}},"soundtrack_embedding":{"dense":{"values":[0.8,0.9,1.0,1.1,1.2]}},"sparse_embedding":{"sparse":{"values":[1.0,2.0],"indices":[10,20]}},},)request=vectorsearch_v1beta.CreateDataObjectRequest(parent="projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID",data_object_id="DATA_OBJECT_ID",data_object=data_object,)# Make the requestresponse=data_object_service_client.create_data_object(request=request)# Handle the responseprint(response)

Embedding fields that have an auto-embedding specified in the Collection Schemaare automatically populated. You can also bring your own embeddings (BYOE)to set vector field values that are not automatically populated.

Note: Creating Data Objects is an atomic operation. All Data Objects are created,or the entire request fails, if there is an error.

Importing Data Objects

The following example demonstrates how to import Data Objects from Cloud Storageinto a Collection with the IDCOLLECTION_ID.

REST

Before using any of the request data, make the following replacements:

HTTP method and URL:

POST https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID:importDataObjects

Request JSON body:

{  "gcs_import": {    "contents_uri": "gs://your-bucket/path/to/your-data/",    "error_uri": "gs://your-bucket/path/to/import-errors/"  }}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID:importDataObjects"

PowerShell (Windows)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID:importDataObjects" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{  "name": "projects/PROJECT_ID/locations/LOCATION/operations/operation-1770039043815-649d75471f76e-08de3049-276a02be",  "metadata": {    "@type": "type.googleapis.com/google.cloud.vectorsearch.v1beta.ImportDataObjectsMetadata",    "createTime": "2026-02-02T13:30:43.874527852Z"  },  "done": false}

gcloud

Before using any of the command data below, make the following replacements:

  • COLLECTION_ID: The ID of the collection.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloudproject ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloudbetavector-searchcollectionsimport-data-objectsCOLLECTION_ID\--location=LOCATION\--project=PROJECT_ID\--gcs-import-contents-uri="gs://your-bucket/path/to/your-data/"\--gcs-import-error-uri="gs://your-bucket/path/to/import-errors/"\--async

Windows (PowerShell)

gcloudbetavector-searchcollectionsimport-data-objectsCOLLECTION_ID`--location=LOCATION`--project=PROJECT_ID`--gcs-import-contents-uri="gs://your-bucket/path/to/your-data/"`--gcs-import-error-uri="gs://your-bucket/path/to/import-errors/"`--async

Windows (cmd.exe)

gcloudbetavector-searchcollectionsimport-data-objectsCOLLECTION_ID^--location=LOCATION^--project=PROJECT_ID^--gcs-import-contents-uri="gs://your-bucket/path/to/your-data/"^--gcs-import-error-uri="gs://your-bucket/path/to/import-errors/"^--async

Python

fromgoogle.cloudimportvectorsearch_v1beta# Create the clientvector_search_service_client=vectorsearch_v1beta.VectorSearchServiceClient()# Initialize requestrequest=vectorsearch_v1beta.ImportDataObjectsRequest(name="projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID",gcs_import={"contents_uri":"gs://your-bucket/path/to/your-data/","error_uri":"gs://your-bucket/path/to/import-errors/",},)# Make the requestoperation=vector_search_service_client.import_data_objects(request=request)# Wait for the result (note this may take up to several minutes)operation.result()

Foldergs://your-bucket/path/to/your-data/ can contain one or more fileseach containing multiple Data Objects.Use this structure for large datasets that are spread across multiple files.The following file formats are supported in Vector Search 2.0:

  • JSONL, where each line is a JSON object having three top level properties:id,data, andvectors
  • AVRO
  • Vector Search JSON
  • Vector Search AVRO

The following provides an example of the JSONL with the required properties.

{"id":"movie-789","data":{"title":"The Shawshank Redemption","plot":"...","year":1994,"avg_rating":8.5,"movie_runtime_info":{"hours":2,"minutes":5},},"vectors":{"title_embedding":[-0.23,0.88,0.11,...],"sparse_embedding":{"values":[0.01,-0.93,0.27,...],"indices":[23,83,131,...]}}}
Note: "plot_embedding" is automatically populated and shouldn't be specified inthe JSONL file.

Get a Data Object

The following example demonstrates how to get a Data Object with the IDDATA_OBJECT_ID from a Collection with the IDCOLLECTION_ID.

REST

Before using any of the request data, make the following replacements:

HTTP method and URL:

GET https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Execute the following command:

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID"

PowerShell (Windows)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{  "name": "projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID",  "createTime": "2026-01-31T20:05:06Z",  "updateTime": "2026-01-31T20:05:06Z",  "data": {    "title": "The Shawshank Redemption",    "director": "Frank Darabont",    "year": 1994,    "genre": "Drama"  },  "vectors": {    "sparse_embedding": {      "sparse": {        "values": [          1,          6,          3,          2,          8,          5,          2        ],        "indices": [          4065,          13326,          17377,          25918,          28105,          32683,          42998        ]      }    },    "genre_embedding": {      "dense": {        "values": [          0.3863801,          0.73934346,          0.16189057,          0.5271367        ]      }    },    "plot_embedding": {      "dense": {        "values": [          0.47520825,          0.090267465,          0.8752308        ]      }    },    "soundtrack_embedding": {      "dense": {        "values": [          0.5920452,          0.08301644,          0.12647335,          0.619643,          0.49258286        ]      }    }  }}

gcloud

Before using any of the command data below, make the following replacements:

  • DATA_OBJECT_ID: The ID of the data object.
  • COLLECTION_ID: The ID of the collection.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloudproject ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloudbetavector-searchcollectionsdata-objectsdescribeDATA_OBJECT_ID\--collection=COLLECTION_ID\--location=LOCATION\--project=PROJECT_ID

Windows (PowerShell)

gcloudbetavector-searchcollectionsdata-objectsdescribeDATA_OBJECT_ID`--collection=COLLECTION_ID`--location=LOCATION`--project=PROJECT_ID

Windows (cmd.exe)

gcloudbetavector-searchcollectionsdata-objectsdescribeDATA_OBJECT_ID^--collection=COLLECTION_ID^--location=LOCATION^--project=PROJECT_ID

You should receive a response similar to the following:

name: projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_IDdata:  director: Frank Darabont  genre: Drama  title: The Shawshank Redemption  year: 1994vectors:  genre_embedding:    dense:      values:      - 0.3863801      - 0.73934346      - 0.16189057      - 0.5271367  plot_embedding:    dense:      values:      - 0.47520825      - 0.090267465      - 0.8752308  soundtrack_embedding:    dense:      values:      - 0.5920452      - 0.08301644      - 0.12647335      - 0.619643      - 0.49258286  sparse_embedding:    sparse:      indices:      - 4065      - 13326      - 17377      - 25918      - 28105      - 32683      - 42998      values:      - 1.0      - 6.0      - 3.0      - 2.0      - 8.0      - 5.0      - 2.0

Python

fromgoogle.cloudimportvectorsearch_v1beta# Create the clientdata_object_service_client=vectorsearch_v1beta.DataObjectServiceClient()# Initialize requestrequest=vectorsearch_v1beta.GetDataObjectRequest(name="projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID",)# Make the requestresponse=data_object_service_client.get_data_object(request=request)# Handle the responseprint(response)

Updating a Data Object

The following example demonstrates how to update thetitle data fieldandplot_embedding vector values in the Data Object with the IDDATA_OBJECT_IDin a Collection with the IDCOLLECTION_ID.

REST

Before using any of the request data, make the following replacements:

  • DATA_OBJECT_ID: The ID of the data object.
  • COLLECTION_ID: The ID of the collection.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloudproject ID.

HTTP method and URL:

PATCH https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID

Request JSON body:

{  "data": {    "title": "The Shawshank Redemption (updated)"  },  "vectors": {    "plot_embedding": {      "dense": {        "values": [          1.0,          1.0,          1.0        ]      }    }  }}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

curl -X PATCH \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID"

PowerShell (Windows)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method PATCH `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{  "name": "projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID",  "data": {    "title": "The Shawshank Redemption (updated)"  },  "vectors": {    "plot_embedding": {      "dense": {        "values": [          1,          1,          1        ]      }    }  }}

gcloud

Before using any of the command data below, make the following replacements:

  • DATA_OBJECT_ID: The ID of the data object.
  • COLLECTION_ID: The ID of the collection.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloudproject ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloudbetavector-searchcollectionsdata-objectsupdateDATA_OBJECT_ID\--collection=COLLECTION_ID\--location=LOCATION\--project=PROJECT_ID\--data='{"title": "The Shawshank Redemption (updated)"}'\--update-vectors='{"plot_embedding": {"dense": {"values": [1.0, 1.0, 1.0]}}}'

Windows (PowerShell)

gcloudbetavector-searchcollectionsdata-objectsupdateDATA_OBJECT_ID`--collection=COLLECTION_ID`--location=LOCATION`--project=PROJECT_ID`--data='{"title": "The Shawshank Redemption (updated)"}'`--update-vectors='{"plot_embedding": {"dense": {"values": [1.0, 1.0, 1.0]}}}'

Windows (cmd.exe)

Note: If this command uses' for quoting content, replace these single quotes with double quotes. If quoting is nested, use\" to escape the inner quotes.
gcloudbetavector-searchcollectionsdata-objectsupdateDATA_OBJECT_ID^--collection=COLLECTION_ID^--location=LOCATION^--project=PROJECT_ID^--data='{"title": "The Shawshank Redemption (updated)"}'^--update-vectors='{"plot_embedding": {"dense": {"values": [1.0, 1.0, 1.0]}}}'

You should receive a response similar to the following:

Updated dataObject [DATA_OBJECT_ID].

Python

fromgoogle.cloudimportvectorsearch_v1beta# Create the clientdata_object_service_client=vectorsearch_v1beta.DataObjectServiceClient()# Initialize requestdata_object=vectorsearch_v1beta.DataObject(name="projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID",data={"title":"The Shawshank Redemption (updated)"},vectors={"plot_embedding":{"dense":{"values":[1.,1.,1.]}},},)request=vectorsearch_v1beta.UpdateDataObjectRequest(data_object=data_object,)# Make the requestresponse=data_object_service_client.update_data_object(request=request)# Handle the responseprint(response)

Delete Data Objects

You can delete individual Data Objects by name or batch delete Data Objects.

The following shows how to delete the Data ObjectDATA_OBJECT_IDfrom a Collection with the IDCOLLECTION_ID.

REST

Before using any of the request data, make the following replacements:

  • DATA_OBJECT_ID: The ID of the data object.
  • COLLECTION_ID: The ID of the collection.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloudproject ID.

HTTP method and URL:

DELETE https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Execute the following command:

curl -X DELETE \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID"

PowerShell (Windows)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://vectorsearch.googleapis.com/v1beta/projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{  "name": "projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID",  "data": {    "title": "The Shawshank Redemption (updated)"  },  "vectors": {    "plot_embedding": {      "dense": {        "values": [          1,          1,          1        ]      }    }  }}

gcloud

Before using any of the command data below, make the following replacements:

  • DATA_OBJECT_ID: The ID of the data object.
  • COLLECTION_ID: The ID of the collection.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloudproject ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloudbetavector-searchcollectionsdata-objectsdeleteDATA_OBJECT_ID\--collection=COLLECTION_ID\--location=LOCATION\--project=PROJECT_ID

Windows (PowerShell)

gcloudbetavector-searchcollectionsdata-objectsdeleteDATA_OBJECT_ID`--collection=COLLECTION_ID`--location=LOCATION`--project=PROJECT_ID

Windows (cmd.exe)

gcloudbetavector-searchcollectionsdata-objectsdeleteDATA_OBJECT_ID^--collection=COLLECTION_ID^--location=LOCATION^--project=PROJECT_ID

You should receive a response similar to the following:

Deleted dataObject [DATA_OBJECT_ID].

Python

fromgoogle.cloudimportvectorsearch_v1beta# Create the clientdata_object_service_client=vectorsearch_v1beta.DataObjectServiceClient()# Initialize requestrequest=vectorsearch_v1beta.DeleteDataObjectRequest(name="projects/PROJECT_ID/locations/LOCATION/collections/COLLECTION_ID/dataObjects/DATA_OBJECT_ID",)# Make the requestdata_object_service_client.delete_data_object(request=request)

What's next?

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Last updated 2026-02-19 UTC.