管理实体类型

了解如何创建、列出和删除实体类型。

创建实体类型

创建实体类型，以便可以创建其相关特征。

网页界面

在 Google Cloud 控制台的“Vertex AI”部分，转到特征页面。

转到“特征”页面

在操作栏中，点击创建实体类型，打开创建实体类型窗口。
从区域下拉列表中选择包含您要在其中创建实体类型的特征存储区的区域。
选择特征存储区。
指定实体类型的名称。
如果您想要包含实体类型的说明，请输入说明。
如需启用特征值监控（预览版），请将监控设置为已启用，然后以天为单位指定快照间隔。此监控配置适用于此实体类型下的所有特征。如需了解详情，请参阅特征值监控。
点击创建。

Terraform

以下示例会创建一个新的特征存储区，然后使用 google_vertex_ai_featurestore_entitytype Terraform 资源在该特征存储区中创建一个名为 featurestore_entitytype 的实体类型。

如需了解如何应用或移除 Terraform 配置，请参阅基本 Terraform 命令。

# 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

如需创建实体类型，请使用 featurestores.entityTypes.create 方法发送 POST 请求。

在使用任何请求数据之前，请先进行以下替换：

LOCATION_ID：特征存储区所在的区域，例如 us-central1。
PROJECT_ID：您的项目 ID。
FEATURESTORE_ID：特征存储区的 ID。
ENTITY_TYPE_ID：实体类型的 ID。
DESCRIPTION：实体类型的说明。

HTTP 方法和网址：

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

请求 JSON 正文：

{
  "description": "DESCRIPTION"
}

如需发送请求，请选择以下方式之一：

curl

注意：以下命令假定您已使用您的用户账号通过运行 gcloud init 或 gcloud auth login 登录 gcloud CLI，或者使用了 Cloud Shell，这会使您自动登录 gcloud CLI。您可以运行 gcloud auth list 来检查当前活跃的账号。

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

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

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

$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

您应该会看到类似如下所示的输出。您可以使用响应中的 OPERATION_ID 来获取操作的状态。

{
  "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

如需了解如何安装或更新 Python，请参阅安装 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python API 参考文档。

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

Python

Python 版 Vertex AI SDK 的安装包含 Vertex AI 客户端库。如需了解如何安装 Python 版 Vertex AI SDK，请参阅安装 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python 版 Vertex AI SDK API 参考文档。

from google.cloud import aiplatform

def create_entity_type_sample(
    project: str,
    featurestore_id: str,
    entity_type_id: str,
    description: str = "sample entity type",
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 300,
):
    # The AI Platform services require regional API endpoints, which need to be
    # in the same region or multi-region overlap with the Feature Store location.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.FeaturestoreServiceClient(client_options=client_options)
    parent = f"projects/{project}/locations/{location}/featurestores/{featurestore_id}"
    create_entity_type_request = aiplatform.gapic.CreateEntityTypeRequest(
        parent=parent,
        entity_type_id=entity_type_id,
        entity_type=aiplatform.gapic.EntityType(description=description),
    )
    lro_response = client.create_entity_type(request=create_entity_type_request)
    print("Long running operation:", lro_response.operation.name)
    create_entity_type_response = lro_response.result(timeout=timeout)
    print("create_entity_type_response:", create_entity_type_response)

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。


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

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

/**
 * 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();

列出实体类型

列出特征存储区中的所有实体类型。

网页界面

在 Google Cloud 控制台的“Vertex AI”部分，转到特征页面。

转到“特征”页面

从区域下拉列表中选择一个区域。
在特征表中，查看实体类型列，以了解您的项目中所选区域的实体类型。

REST

如需列出实体类型，请使用 featurestores.entityTypes.list 方法发送 GET 请求。

在使用任何请求数据之前，请先进行以下替换：

LOCATION_ID：特征存储区所在的区域，例如 us-central1。
PROJECT_ID：您的项目 ID。
FEATURESTORE_ID：特征存储区的 ID。

HTTP 方法和网址：

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

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

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

执行以下命令：

$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

您应会收到如下所示的 JSON 响应：

{
  "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

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。


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

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

/**
 * 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();

其他语言

如需了解如何安装和使用 Python 版 Vertex AI SDK，请参阅使用 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python 版 Vertex AI SDK API 参考文档。

删除实体类型

删除实体类型。如果您使用 Google Cloud 控制台，Vertex AI Feature Store（旧版）会删除实体类型及其所有内容。如果您使用 API，请启用 force 查询参数删除实体类型及其所有内容。

网页界面

在 Google Cloud 控制台的“Vertex AI”部分，转到特征页面。

转到“特征”页面

从区域下拉列表中选择一个区域。
在特征表中，查看实体类型列并找到要删除的实体类型。
点击实体类型的名称。
在操作栏中，点击删除。
点击确认以删除实体类型。

REST

如需删除实体类型，请使用 featurestores.entityTypes.delete 方法发送 DELETE 请求。

在使用任何请求数据之前，请先进行以下替换：

LOCATION_ID：特征存储区所在的区域，例如 us-central1。
PROJECT_ID：您的项目 ID。
FEATURESTORE_ID：特征存储区的 ID。
ENTITY_TYPE_ID：实体类型的 ID。
BOOLEAN：是否删除实体类型（即使该实体包含特征）。force 查询参数是可选的，默认为 false。

HTTP 方法和网址：

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

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

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

执行以下命令：

$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

您应会收到如下所示的 JSON 响应：

{
  "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

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。


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

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

/**
 * 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();

其他语言

如需了解如何安装和使用 Python 版 Vertex AI SDK，请参阅使用 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python 版 Vertex AI SDK API 参考文档。

后续步骤

了解如何管理特征。
了解如何监控一段时间内导入的特征值。
查看 Vertex AI Feature Store（旧版）实体类型配额。
排查 Vertex AI Feature Store（旧版）常见问题。