Manage entity types

Learn how to create, list, and delete entity types.

Create an entity type

Create an entity type so that you can create its related features.

Web UI

In the Vertex AI section of the Google Cloud console, go to the Features page.
Go to the Features page
From the action bar, click Create entity typeto open the Create entity typepane.
Select a region from the Regiondrop-down list that includes the featurestore where you want to create an entity type.
Select a featurestore.
Specify a name for the entity type.
If you want to include a description for the entity type, enter a description.
To enable feature value monitoring ( Preview ), set monitoring to Enabledand then specify the snapshot interval in days. This monitoring configuration applies to all features under this entity type. For more information, see Feature value monitoring .
Click Create.

Terraform

The following sample creates a new featurestore and then uses the google_vertex_ai_featurestore_entitytype Terraform resource to create an entity type named featurestore_entitytype within that feature store.

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands .

  # Featurestore name must be unique for the project 
 resource 
  
 "random_id" 
  
 "featurestore_name_suffix" 
  
 { 
  
 byte_length 
  
 = 
  
 8 
 } 
 resource 
  
 "google_vertex_ai_featurestore" 
  
 "featurestore" 
  
 { 
  
 name 
  
 = 
  
 "featurestore_${random_id.featurestore_name_suffix.hex}" 
  
 region 
  
 = 
  
 "us-central1" 
  
 labels 
  
 = 
  
 { 
  
 environment 
  
 = 
  
 "testing" 
  
 } 
  
 online_serving_config 
  
 { 
  
 fixed_node_count 
  
 = 
  
 1 
  
 } 
  
 force_destroy 
  
 = 
  
 true 
 } 
 output 
  
 "featurestore_id" 
  
 { 
  
 value 
  
 = 
  
 google_vertex_ai_featurestore.featurestore.id 
 } 
 resource 
  
 "google_vertex_ai_featurestore_entitytype" 
  
 "entity" 
  
 { 
  
 name 
  
 = 
  
 "featurestore_entitytype" 
  
 labels 
  
 = 
  
 { 
  
 environment 
  
 = 
  
 "testing" 
  
 } 
  
 featurestore 
  
 = 
  
 google_vertex_ai_featurestore.featurestore.id 
  
 monitoring_config 
  
 { 
  
 snapshot_analysis 
  
 { 
  
 disabled 
  
 = 
  
 false 
  
 } 
  
 } 
  
 depends_on 
  
 = 
  
 [ 
 google_vertex_ai_featurestore.featurestore 
 ] 
 }

REST

To create an entity type, send a POST request by using the featurestores.entityTypes.create method.

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

LOCATION_ID : Region where the featurestore is located, such as us-central1 .
PROJECT_ID : Your project ID .
FEATURESTORE_ID : ID of the featurestore.
ENTITY_TYPE_ID : ID of the entity type.
DESCRIPTION : Description of the entity type.

HTTP method and URL:

POST https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes?entityTypeId= ENTITY_TYPE_ID

Request JSON body:

{
  "description": " DESCRIPTION 
"
}

To send your request, choose one of these options:

curl

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:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes?entityTypeId= ENTITY_TYPE_ID 
"

PowerShell

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:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes?entityTypeId= ENTITY_TYPE_ID 
" | Select-Object -Expand Content

You should see output similar to the following. You can use the OPERATION_ID in the response to get the status of the operation.

{
  "name": "projects/ PROJECT_NUMBER 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/bikes/operations/ OPERATION_ID 
",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateEntityTypeOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-03-02T00:04:13.039166Z",
      "updateTime": "2021-03-02T00:04:13.039166Z"
    }
  }
}

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

  from 
  
 google.cloud 
  
 import 
 aiplatform 
 def 
  
 create_entity_type_sample 
 ( 
 project 
 : 
 str 
 , 
 location 
 : 
 str 
 , 
 entity_type_id 
 : 
 str 
 , 
 featurestore_name 
 : 
 str 
 , 
 ): 
 aiplatform 
 . 
 init 
 ( 
 project 
 = 
 project 
 , 
 location 
 = 
 location 
 ) 
 my_entity_type 
 = 
 aiplatform 
 . 
 EntityType 
 . 
 create 
 ( 
 entity_type_id 
 = 
 entity_type_id 
 , 
 featurestore_name 
 = 
 featurestore_name 
 ) 
 my_entity_type 
 . 
 wait 
 () 
 return 
 my_entity_type

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  import 
  
 com.google.api.gax.longrunning. OperationFuture 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. CreateEntityTypeOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. CreateEntityTypeRequest 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EntityType 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreServiceSettings 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 import 
  
 java.util.concurrent.TimeoutException 
 ; 
 public 
  
 class 
 CreateEntityTypeSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 featurestoreId 
  
 = 
  
 "YOUR_FEATURESTORE_ID" 
 ; 
  
 String 
  
 entityTypeId 
  
 = 
  
 "YOUR_ENTITY_TYPE_ID" 
 ; 
  
 String 
  
 description 
  
 = 
  
 "YOUR_ENTITY_TYPE_DESCRIPTION" 
 ; 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
 String 
  
 endpoint 
  
 = 
  
 "us-central1-aiplatform.googleapis.com:443" 
 ; 
  
 int 
  
 timeout 
  
 = 
  
 300 
 ; 
  
 createEntityTypeSample 
 ( 
  
 project 
 , 
  
 featurestoreId 
 , 
  
 entityTypeId 
 , 
  
 description 
 , 
  
 location 
 , 
  
 endpoint 
 , 
  
 timeout 
 ); 
  
 } 
  
 static 
  
 void 
  
 createEntityTypeSample 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 featurestoreId 
 , 
  
 String 
  
 entityTypeId 
 , 
  
 String 
  
 description 
 , 
  
 String 
  
 location 
 , 
  
 String 
  
 endpoint 
 , 
  
 int 
  
 timeout 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
  FeaturestoreServiceSettings 
 
  
 featurestoreServiceSettings 
  
 = 
  
  FeaturestoreServiceSettings 
 
 . 
 newBuilder 
 (). 
 setEndpoint 
 ( 
 endpoint 
 ). 
 build 
 (); 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. After completing all of your requests, call 
  
 // the "close" method on the client to safely clean up any remaining background resources. 
  
 try 
  
 ( 
  FeaturestoreServiceClient 
 
  
 featurestoreServiceClient 
  
 = 
  
  FeaturestoreServiceClient 
 
 . 
 create 
 ( 
 featurestoreServiceSettings 
 )) 
  
 { 
  
  EntityType 
 
  
 entityType 
  
 = 
  
  EntityType 
 
 . 
 newBuilder 
 (). 
 setDescription 
 ( 
 description 
 ). 
 build 
 (); 
  
  CreateEntityTypeRequest 
 
  
 createEntityTypeRequest 
  
 = 
  
  CreateEntityTypeRequest 
 
 . 
 newBuilder 
 () 
  
 . 
 setParent 
 ( 
  FeaturestoreName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 , 
  
 featurestoreId 
 ). 
 toString 
 ()) 
  
 . 
 setEntityType 
 ( 
 entityType 
 ) 
  
 . 
 setEntityTypeId 
 ( 
 entityTypeId 
 ) 
  
 . 
 build 
 (); 
  
 OperationFuture<EntityType 
 , 
  
 CreateEntityTypeOperationMetadata 
>  
 entityTypeFuture 
  
 = 
  
 featurestoreServiceClient 
 . 
  createEntityTypeAsync 
 
 ( 
 createEntityTypeRequest 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Operation name: %s%n" 
 , 
  
 entityTypeFuture 
 . 
 getInitialFuture 
 (). 
  get 
 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Waiting for operation to finish..." 
 ); 
  
  EntityType 
 
  
 entityTypeResponse 
  
 = 
  
 entityTypeFuture 
 . 
  get 
 
 ( 
 timeout 
 , 
  
 TimeUnit 
 . 
 SECONDS 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Create Entity Type Response" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Name: %s%n" 
 , 
  
 entityTypeResponse 
 . 
  getName 
 
 ()); 
  
 } 
  
 } 
 }

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  /** 
 * TODO(developer): Uncomment these variables before running the sample.\ 
 * (Not necessary if passing values as arguments) 
 */ 
 // const project = 'YOUR_PROJECT_ID'; 
 // const featurestoreId = 'YOUR_FEATURESTORE_ID'; 
 // const entityTypeId = 'YOUR_ENTITY_TYPE_ID'; 
 // const description = 'YOUR_ENTITY_TYPE_DESCRIPTION'; 
 // const location = 'YOUR_PROJECT_LOCATION'; 
 // const apiEndpoint = 'YOUR_API_ENDPOINT'; 
 // const timeout = <TIMEOUT_IN_MILLI_SECONDS>; 
 // Imports the Google Cloud Featurestore Service Client library 
 const 
  
 { 
 FeaturestoreServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ). 
 v1 
 ; 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 apiEndpoint 
 , 
 }; 
 // Instantiates a client 
 const 
  
 featurestoreServiceClient 
  
 = 
  
 new 
  
  FeaturestoreServiceClient 
 
 ( 
  
 clientOptions 
 ); 
 async 
  
 function 
  
 createEntityType 
 () 
  
 { 
  
 // Configure the parent resource 
  
 const 
  
 parent 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 /featurestores/ 
 ${ 
 featurestoreId 
 } 
 ` 
 ; 
  
 const 
  
 entityType 
  
 = 
  
 { 
  
 description 
 : 
  
 description 
 , 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 parent 
 : 
  
 parent 
 , 
  
 entityTypeId 
 : 
  
 entityTypeId 
 , 
  
 entityType 
 : 
  
 entityType 
 , 
  
 }; 
  
 // Create EntityType request 
  
 const 
  
 [ 
 operation 
 ] 
  
 = 
  
 await 
  
 featurestoreServiceClient 
 . 
 createEntityType 
 ( 
  
 request 
 , 
  
 { 
 timeout 
 : 
  
 Number 
 ( 
 timeout 
 )} 
  
 ); 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 operation 
 . 
 promise 
 (); 
  
 console 
 . 
 log 
 ( 
 'Create entity type response' 
 ); 
  
 console 
 . 
 log 
 ( 
 `Name : 
 ${ 
 response 
 . 
 name 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 'Raw response:' 
 ); 
  
 console 
 . 
 log 
 ( 
 JSON 
 . 
 stringify 
 ( 
 response 
 , 
  
 null 
 , 
  
 2 
 )); 
 } 
 createEntityType 
 ();

List entity types

List all entity types in a featurestore.

Web UI

In the Vertex AI section of the Google Cloud console, go to the Features page.
Go to the Features page
Select a region from the Regiondrop-down list.
In the features table, view the Entity typecolumn to see the entity types in your project for the selected region.

REST

To list entity types, send a GET request by using the featurestores.entityTypes.list method.

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

LOCATION_ID : Region where the featurestore is located, such as us-central1 .
PROJECT_ID : .
FEATURESTORE_ID : ID of the featurestore.

HTTP method and URL:

GET https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes

To send your request, choose one of these options:

curl

Execute the following command:

curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes"

PowerShell

Execute the following command:

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

Invoke-WebRequest `
-Method GET `
-Headers $headers `
-Uri "https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "entityTypes": [
    {
      "name": "projects/ PROJECT_NUMBER 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID_1 
",
      "description": " ENTITY_TYPE_DESCRIPTION 
",
      "createTime": "2021-02-25T01:20:43.082628Z",
      "updateTime": "2021-02-25T01:20:43.082628Z",
      "etag": "AMEw9yOBqKIdbBGZcxdKLrlZJAf9eTO2DEzcE81YDKA2LymDMFB8ucRbmKwKo2KnvOg="
    },
    {
      "name": "projects/ PROJECT_NUMBER 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID_2 
",
      "description": " ENTITY_TYPE_DESCRIPTION 
",
      "createTime": "2021-02-25T01:34:26.198628Z",
      "updateTime": "2021-02-25T01:34:26.198628Z",
      "etag": "AMEw9yNuv-ILYG8VLLm1lgIKc7asGIAVFErjvH2Cyc_wIQm7d6DL4ZGv59cwZmxTumU="
    }
  ]
}

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  import 
  
 com.google.cloud.aiplatform.v1. EntityType 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreServiceSettings 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ListEntityTypesRequest 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 public 
  
 class 
 ListEntityTypesSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 featurestoreId 
  
 = 
  
 "YOUR_FEATURESTORE_ID" 
 ; 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
 String 
  
 endpoint 
  
 = 
  
 "us-central1-aiplatform.googleapis.com:443" 
 ; 
  
 listEntityTypesSample 
 ( 
 project 
 , 
  
 featurestoreId 
 , 
  
 location 
 , 
  
 endpoint 
 ); 
  
 } 
  
 static 
  
 void 
  
 listEntityTypesSample 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 featurestoreId 
 , 
  
 String 
  
 location 
 , 
  
 String 
  
 endpoint 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
  FeaturestoreServiceSettings 
 
  
 featurestoreServiceSettings 
  
 = 
  
  FeaturestoreServiceSettings 
 
 . 
 newBuilder 
 (). 
 setEndpoint 
 ( 
 endpoint 
 ). 
 build 
 (); 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. After completing all of your requests, call 
  
 // the "close" method on the client to safely clean up any remaining background resources. 
  
 try 
  
 ( 
  FeaturestoreServiceClient 
 
  
 featurestoreServiceClient 
  
 = 
  
  FeaturestoreServiceClient 
 
 . 
 create 
 ( 
 featurestoreServiceSettings 
 )) 
  
 { 
  
  ListEntityTypesRequest 
 
  
 listEntityTypeRequest 
  
 = 
  
  ListEntityTypesRequest 
 
 . 
 newBuilder 
 () 
  
 . 
 setParent 
 ( 
  FeaturestoreName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 , 
  
 featurestoreId 
 ). 
 toString 
 ()) 
  
 . 
 build 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "List Entity Types Response" 
 ); 
  
 for 
  
 ( 
  EntityType 
 
  
 element 
  
 : 
  
 featurestoreServiceClient 
 . 
 listEntityTypes 
 ( 
 listEntityTypeRequest 
 ). 
 iterateAll 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 element 
 ); 
  
 } 
  
 } 
  
 } 
 }

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  /** 
 * TODO(developer): Uncomment these variables before running the sample.\ 
 * (Not necessary if passing values as arguments) 
 */ 
 // const project = 'YOUR_PROJECT_ID'; 
 // const featurestoreId = 'YOUR_FEATURESTORE_ID'; 
 // const location = 'YOUR_PROJECT_LOCATION'; 
 // const apiEndpoint = 'YOUR_API_ENDPOINT'; 
 // const timeout = <TIMEOUT_IN_MILLI_SECONDS>; 
 // Imports the Google Cloud Featurestore Service Client library 
 const 
  
 { 
 FeaturestoreServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ). 
 v1 
 ; 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 apiEndpoint 
 , 
 }; 
 // Instantiates a client 
 const 
  
 featurestoreServiceClient 
  
 = 
  
 new 
  
  FeaturestoreServiceClient 
 
 ( 
  
 clientOptions 
 ); 
 async 
  
 function 
  
 listEntityTypes 
 () 
  
 { 
  
 // Configure the parent resource 
  
 const 
  
 parent 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 /featurestores/ 
 ${ 
 featurestoreId 
 } 
 ` 
 ; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 parent 
 : 
  
 parent 
 , 
  
 }; 
  
 // List EntityTypes request 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 featurestoreServiceClient 
 . 
 listEntityTypes 
 ( 
  
 request 
 , 
  
 { 
 timeout 
 : 
  
 Number 
 ( 
 timeout 
 )} 
  
 ); 
  
 console 
 . 
 log 
 ( 
 'List entity types response' 
 ); 
  
 console 
 . 
 log 
 ( 
 'Raw response:' 
 ); 
  
 console 
 . 
 log 
 ( 
 JSON 
 . 
 stringify 
 ( 
 response 
 , 
  
 null 
 , 
  
 2 
 )); 
 } 
 listEntityTypes 
 ();

Additional languages

To learn how to install and use the Vertex AI SDK for Python, see Use the Vertex AI SDK for Python . For more information, see the Vertex AI SDK for Python API reference documentation.

Delete an entity type

Delete an entity type. If you use the Google Cloud console, Vertex AI Feature Store (Legacy) deletes the entity type and all of its contents. If you use the API, enable the force query parameter to delete the entity type and all of its contents.

Web UI

In the Vertex AI section of the Google Cloud console, go to the Features page.
Go to the Features page
Select a region from the Regiondrop-down list.
In the features table, view the Entity typecolumn and find the entity type to delete.
Click the name of the entity type.
From the action bar, click Delete.
Click Confirmto delete the entity type.

REST

To delete an entity type, send a DELETE request by using the featurestores.entityTypes.delete method.

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

LOCATION_ID : Region where the featurestore is located, such as us-central1 .
PROJECT_ID : .
FEATURESTORE_ID : ID of the featurestore.
ENTITY_TYPE_ID : ID of the entity type.
BOOLEAN : Whether to delete the entity type even if it contains features. The force query parameter is optional and is false by default.

HTTP method and URL:

DELETE https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
?force= BOOLEAN

To send your request, choose one of these options:

curl

Execute the following command:

curl -X DELETE \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
?force= BOOLEAN 
"

PowerShell

Execute the following command:

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

Invoke-WebRequest `
-Method DELETE `
-Headers $headers `
-Uri "https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/entityTypes/ ENTITY_TYPE_ID 
?force= BOOLEAN 
" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/ PROJECT_NUMBER 
/locations/ LOCATION_ID 
/featurestores/ FEATURESTORE_ID 
/operations/ OPERATION_ID 
",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeleteOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-02-26T17:32:56.008325Z",
      "updateTime": "2021-02-26T17:32:56.008325Z"
    }
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  import 
  
 com.google.api.gax.longrunning. OperationFuture 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DeleteEntityTypeRequest 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DeleteOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EntityTypeName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. FeaturestoreServiceSettings 
 
 ; 
 import 
  
 com.google.protobuf. Empty 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 import 
  
 java.util.concurrent.TimeoutException 
 ; 
 public 
  
 class 
 DeleteEntityTypeSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 featurestoreId 
  
 = 
  
 "YOUR_FEATURESTORE_ID" 
 ; 
  
 String 
  
 entityTypeId 
  
 = 
  
 "YOUR_ENTITY_TYPE_ID" 
 ; 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
 String 
  
 endpoint 
  
 = 
  
 "us-central1-aiplatform.googleapis.com:443" 
 ; 
  
 int 
  
 timeout 
  
 = 
  
 300 
 ; 
  
 deleteEntityTypeSample 
 ( 
 project 
 , 
  
 featurestoreId 
 , 
  
 entityTypeId 
 , 
  
 location 
 , 
  
 endpoint 
 , 
  
 timeout 
 ); 
  
 } 
  
 static 
  
 void 
  
 deleteEntityTypeSample 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 featurestoreId 
 , 
  
 String 
  
 entityTypeId 
 , 
  
 String 
  
 location 
 , 
  
 String 
  
 endpoint 
 , 
  
 int 
  
 timeout 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
  FeaturestoreServiceSettings 
 
  
 featurestoreServiceSettings 
  
 = 
  
  FeaturestoreServiceSettings 
 
 . 
 newBuilder 
 (). 
 setEndpoint 
 ( 
 endpoint 
 ). 
 build 
 (); 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. After completing all of your requests, call 
  
 // the "close" method on the client to safely clean up any remaining background resources. 
  
 try 
  
 ( 
  FeaturestoreServiceClient 
 
  
 featurestoreServiceClient 
  
 = 
  
  FeaturestoreServiceClient 
 
 . 
 create 
 ( 
 featurestoreServiceSettings 
 )) 
  
 { 
  
  DeleteEntityTypeRequest 
 
  
 deleteEntityTypeRequest 
  
 = 
  
  DeleteEntityTypeRequest 
 
 . 
 newBuilder 
 () 
  
 . 
 setName 
 ( 
  
  EntityTypeName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 , 
  
 featurestoreId 
 , 
  
 entityTypeId 
 ). 
 toString 
 ()) 
  
 . 
 setForce 
 ( 
 true 
 ) 
  
 . 
 build 
 (); 
  
 OperationFuture<Empty 
 , 
  
 DeleteOperationMetadata 
>  
 operationFuture 
  
 = 
  
 featurestoreServiceClient 
 . 
  deleteEntityTypeAsync 
 
 ( 
 deleteEntityTypeRequest 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Operation name: %s%n" 
 , 
  
 operationFuture 
 . 
 getInitialFuture 
 (). 
 get 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Waiting for operation to finish..." 
 ); 
  
 operationFuture 
 . 
 get 
 ( 
 timeout 
 , 
  
 TimeUnit 
 . 
 SECONDS 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Deleted Entity Type." 
 ); 
  
 } 
  
 } 
 }

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  /** 
 * TODO(developer): Uncomment these variables before running the sample.\ 
 * (Not necessary if passing values as arguments) 
 */ 
 // const project = 'YOUR_PROJECT_ID'; 
 // const featurestoreId = 'YOUR_FEATURESTORE_ID'; 
 // const entityTypeId = 'YOUR_ENTITY_TYPE_ID'; 
 // const force = <BOOLEAN>; 
 // const location = 'YOUR_PROJECT_LOCATION'; 
 // const apiEndpoint = 'YOUR_API_ENDPOINT'; 
 // const timeout = <TIMEOUT_IN_MILLI_SECONDS>; 
 // Imports the Google Cloud Featurestore Service Client library 
 const 
  
 { 
 FeaturestoreServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ). 
 v1 
 ; 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 apiEndpoint 
 , 
 }; 
 // Instantiates a client 
 const 
  
 featurestoreServiceClient 
  
 = 
  
 new 
  
  FeaturestoreServiceClient 
 
 ( 
  
 clientOptions 
 ); 
 async 
  
 function 
  
 deleteEntityType 
 () 
  
 { 
  
 // Configure the name resource 
  
 const 
  
 name 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 /featurestores/ 
 ${ 
 featurestoreId 
 } 
 /entityTypes/ 
 ${ 
 entityTypeId 
 } 
 ` 
 ; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 name 
 : 
  
 name 
 , 
  
 force 
 : 
  
 Boolean 
 ( 
 force 
 ), 
  
 }; 
  
 // Delete EntityType request 
  
 const 
  
 [ 
 operation 
 ] 
  
 = 
  
 await 
  
 featurestoreServiceClient 
 . 
 deleteEntityType 
 ( 
  
 request 
 , 
  
 { 
 timeout 
 : 
  
 Number 
 ( 
 timeout 
 )} 
  
 ); 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 operation 
 . 
 promise 
 (); 
  
 console 
 . 
 log 
 ( 
 'Delete entity type response' 
 ); 
  
 console 
 . 
 log 
 ( 
 'Raw response:' 
 ); 
  
 console 
 . 
 log 
 ( 
 JSON 
 . 
 stringify 
 ( 
 response 
 , 
  
 null 
 , 
  
 2 
 )); 
 } 
 deleteEntityType 
 ();

Additional languages

To learn how to install and use the Vertex AI SDK for Python, see Use the Vertex AI SDK for Python . For more information, see the Vertex AI SDK for Python API reference documentation.

What's next

Learn how to manage features .
Learn how to monitor feature values imported over time .
View the Vertex AI Feature Store (Legacy) entity types quota .
Troubleshoot common Vertex AI Feature Store (Legacy) issues.

Manage entity types Stay organized with collections Save and categorize content based on your preferences.

Create an entity type

Web UI

Terraform

REST

curl

PowerShell

Python

Java

Node.js

List entity types

Web UI

REST

curl

PowerShell

Java

Node.js

Additional languages

Delete an entity type

Web UI

REST

curl

PowerShell

Java

Node.js

Additional languages

What's next

Manage entity types