Data Objects

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

Creating a Data Object

The following example demonstrates adding a Data Object to a Collection with the ID COLLECTION_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 Cloud project 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 the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell , which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.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 the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.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 Cloud project ID .

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud  
beta  
vector-search  
collections  
data-objects  
create  
 DATA_OBJECT_ID 
  
 \ 
  
--data = 
 DATA_FILE 
  
 \ 
  
--vectors = 
 VECTORS_FILE 
  
 \ 
  
--collection = 
 COLLECTION_ID 
  
 \ 
  
--location = 
 LOCATION 
  
 \ 
  
--project = 
 PROJECT_ID

Windows (PowerShell)

gcloud  
beta  
vector-search  
collections  
data-objects  
create  
 DATA_OBJECT_ID 
  
 ` 
  
--data = 
 DATA_FILE 
  
 ` 
  
--vectors = 
 VECTORS_FILE 
  
 ` 
  
--collection = 
 COLLECTION_ID 
  
 ` 
  
--location = 
 LOCATION 
  
 ` 
  
--project = 
 PROJECT_ID

Windows (cmd.exe)

gcloud  
beta  
vector-search  
collections  
data-objects  
create  
 DATA_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

  from 
  
 google.cloud 
  
 import 
  vectorsearch_v1beta 
 
 # Create the client 
 data_object_service_client 
 = 
  vectorsearch_v1beta 
 
 . 
  DataObjectServiceClient 
 
 () 
 # Initialize request 
 data_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 request 
 response 
 = 
 data_object_service_client 
 . 
  create_data_object 
 
 ( 
 request 
 = 
 request 
 ) 
 # Handle the response 
 print 
 ( 
 response 
 )

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

Importing Data Objects

The following example demonstrates how to import Data Objects from Cloud Storage into a Collection with the ID COLLECTION_ID .

REST

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

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

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)

Save the request body in a file named request.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)

Save the request body in a file named request.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 Cloud project ID .

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud  
beta  
vector-search  
collections  
import-data-objects  
 COLLECTION_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)

gcloud  
beta  
vector-search  
collections  
import-data-objects  
 COLLECTION_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)

gcloud  
beta  
vector-search  
collections  
import-data-objects  
 COLLECTION_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

  from 
  
 google.cloud 
  
 import 
  vectorsearch_v1beta 
 
 # Create the client 
 vector_search_service_client 
 = 
  vectorsearch_v1beta 
 
 . 
  VectorSearchServiceClient 
 
 () 
 # Initialize request 
 request 
 = 
  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 request 
 operation 
 = 
 vector_search_service_client 
 . 
  import_data_objects 
 
 ( 
 request 
 = 
 request 
 ) 
 # Wait for the result (note this may take up to several minutes) 
 operation 
 . 
 result 
 ()

Folder gs://your-bucket/path/to/your-data/ can contain one or more files each 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 , and vectors
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 
 , 
  
 ... 
 ] 
  
 } 
  
 } 
 }

Get a Data Object

The following example demonstrates how to get a Data Object with the ID DATA_OBJECT_ID from a Collection with the ID COLLECTION_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 Cloud project ID .

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)

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)

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 Cloud project ID .

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud  
beta  
vector-search  
collections  
data-objects  
describe  
 DATA_OBJECT_ID 
  
 \ 
  
--collection = 
 COLLECTION_ID 
  
 \ 
  
--location = 
 LOCATION 
  
 \ 
  
--project = 
 PROJECT_ID

Windows (PowerShell)

gcloud  
beta  
vector-search  
collections  
data-objects  
describe  
 DATA_OBJECT_ID 
  
 ` 
  
--collection = 
 COLLECTION_ID 
  
 ` 
  
--location = 
 LOCATION 
  
 ` 
  
--project = 
 PROJECT_ID

Windows (cmd.exe)

gcloud  
beta  
vector-search  
collections  
data-objects  
describe  
 DATA_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_ID 
data:
  director: Frank Darabont
  genre: Drama
  title: The Shawshank Redemption
  year: 1994
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:
      indices:
      - 4065
      - 13326
      - 17377
      - 25918
      - 28105
      - 32683
      - 42998
      values:
      - 1.0
      - 6.0
      - 3.0
      - 2.0
      - 8.0
      - 5.0
      - 2.0

Python

  from 
  
 google.cloud 
  
 import 
  vectorsearch_v1beta 
 
 # Create the client 
 data_object_service_client 
 = 
  vectorsearch_v1beta 
 
 . 
  DataObjectServiceClient 
 
 () 
 # Initialize request 
 request 
 = 
  vectorsearch_v1beta 
 
 . 
  GetDataObjectRequest 
 
 ( 
 name 
 = 
 "projects/ PROJECT_ID 
/locations/ LOCATION 
/collections/ COLLECTION_ID 
/dataObjects/ DATA_OBJECT_ID 
" 
 , 
 ) 
 # Make the request 
 response 
 = 
 data_object_service_client 
 . 
  get_data_object 
 
 ( 
 request 
 = 
 request 
 ) 
 # Handle the response 
 print 
 ( 
 response 
 )

Updating a Data Object

The following example demonstrates how to update the title data field and plot_embedding vector values in the Data Object with the ID DATA_OBJECT_ID in a Collection with the ID COLLECTION_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 Cloud project 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)

Save the request body in a file named request.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)

Save the request body in a file named request.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 Cloud project ID .

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud  
beta  
vector-search  
collections  
data-objects  
update  
 DATA_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)

gcloud  
beta  
vector-search  
collections  
data-objects  
update  
 DATA_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.

gcloud  
beta  
vector-search  
collections  
data-objects  
update  
 DATA_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

  from 
  
 google.cloud 
  
 import 
  vectorsearch_v1beta 
 
 # Create the client 
 data_object_service_client 
 = 
  vectorsearch_v1beta 
 
 . 
  DataObjectServiceClient 
 
 () 
 # Initialize request 
 data_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 request 
 response 
 = 
 data_object_service_client 
 . 
  update_data_object 
 
 ( 
 request 
 = 
 request 
 ) 
 # Handle the response 
 print 
 ( 
 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 Object DATA_OBJECT_ID from a Collection with the ID COLLECTION_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 Cloud project 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)

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)

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 Cloud project ID .

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud  
beta  
vector-search  
collections  
data-objects  
delete  
 DATA_OBJECT_ID 
  
 \ 
  
--collection = 
 COLLECTION_ID 
  
 \ 
  
--location = 
 LOCATION 
  
 \ 
  
--project = 
 PROJECT_ID

Windows (PowerShell)

gcloud  
beta  
vector-search  
collections  
data-objects  
delete  
 DATA_OBJECT_ID 
  
 ` 
  
--collection = 
 COLLECTION_ID 
  
 ` 
  
--location = 
 LOCATION 
  
 ` 
  
--project = 
 PROJECT_ID

Windows (cmd.exe)

gcloud  
beta  
vector-search  
collections  
data-objects  
delete  
 DATA_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

  from 
  
 google.cloud 
  
 import 
  vectorsearch_v1beta 
 
 # Create the client 
 data_object_service_client 
 = 
  vectorsearch_v1beta 
 
 . 
  DataObjectServiceClient 
 
 () 
 # Initialize request 
 request 
 = 
  vectorsearch_v1beta 
 
 . 
  DeleteDataObjectRequest 
 
 ( 
 name 
 = 
 "projects/ PROJECT_ID 
/locations/ LOCATION 
/collections/ COLLECTION_ID 
/dataObjects/ DATA_OBJECT_ID 
" 
 , 
 ) 
 # Make the request 
 data_object_service_client 
 . 
  delete_data_object 
 
 ( 
 request 
 = 
 request 
 )

What's next?

Learn about Collection Indexes .
See how to query and search for Data Objects.

Data Objects Stay organized with collections Save and categorize content based on your preferences.

Creating a Data Object

REST

curl (Linux, macOS, or Cloud Shell)

PowerShell (Windows)

gcloud

Linux, macOS, or Cloud Shell

Windows (PowerShell)

Windows (cmd.exe)

Python

Importing Data Objects

REST

curl (Linux, macOS, or Cloud Shell)

PowerShell (Windows)

gcloud

Linux, macOS, or Cloud Shell

Windows (PowerShell)

Windows (cmd.exe)

Python

Get a Data Object

REST

curl (Linux, macOS, or Cloud Shell)

PowerShell (Windows)

gcloud

Linux, macOS, or Cloud Shell

Windows (PowerShell)

Windows (cmd.exe)

Python

Updating a Data Object

REST

curl (Linux, macOS, or Cloud Shell)

PowerShell (Windows)

gcloud

Linux, macOS, or Cloud Shell

Windows (PowerShell)

Windows (cmd.exe)

Python

Delete Data Objects

REST

curl (Linux, macOS, or Cloud Shell)

PowerShell (Windows)

gcloud

Linux, macOS, or Cloud Shell

Windows (PowerShell)

Windows (cmd.exe)

Python

What's next?

Data Objects