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 type to open the Create entity type pane.
Select a region from the Region drop-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 Enabled and 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 Region drop-down list.
In the features table, view the Entity type column 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: Your 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 Region drop-down list.
In the features table, view the Entity type column and find the entity type to delete.
Click the name of the entity type.
From the action bar, click Delete.
Click Confirm to 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: Your 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.