管理及搜尋功能

瞭解如何管理及搜尋功能。

建立特徵

為現有實體類型建立單一特徵。如要在單一要求中建立多項功能,請參閱「批次建立功能」。

網路使用者介面

  1. 在 Google Cloud 控制台的 Vertex AI 專區,前往「Features」頁面。

    前往「Features」(功能) 頁面

  2. 從「Region」(區域) 下拉式清單中選取一個區域。
  3. 在特徵表格中,查看「實體類型」欄,然後按一下要新增特徵的實體類型。
  4. 按一下「新增功能」,開啟「新增功能」窗格。
  5. 指定特徵的名稱、值類型,並視需要輸入說明。
  6. 如要啟用特徵值監控功能 (預先發布版),請在「特徵監控」下方選取「覆寫實體類型監控設定」,然後輸入快照之間的天數。這項設定會覆寫特徵實體類型上現有或未來的任何監控設定。詳情請參閱「監控特徵值」。
  7. 如要新增更多功能,請按一下「新增其他功能」
  8. 按一下 [儲存]

REST

如要為現有實體型別建立特徵,請使用 featurestores.entityTypes.features.create 方法傳送 POST 要求。

使用任何要求資料之前,請先替換以下項目:

  • LOCATION_ID:特徵儲存庫所在的區域,例如 us-central1
  • PROJECT_ID:您的專案 ID
  • FEATURESTORE_ID:特徵商店的 ID。
  • ENTITY_TYPE_ID:實體類型 ID。
  • FEATURE_ID:功能的 ID。
  • DESCRIPTION:功能說明。
  • VALUE_TYPE:特徵的值類型。

HTTP 方法和網址:

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

JSON 要求主體:

{
  "description": "DESCRIPTION",
  "valueType": "VALUE_TYPE"
}

如要傳送要求,請選擇以下其中一個選項:

curl

將要求主體儲存在名為 request.json 的檔案中,然後執行下列指令:

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/ENTITY_TYPE_ID?featureId=FEATURE_ID"

PowerShell

將要求主體儲存在名為 request.json 的檔案中,然後執行下列指令:

$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/ENTITY_TYPE_ID?featureId=FEATURE_ID" | Select-Object -Expand Content

畫面會顯示類似以下的輸出。您可以使用回應中的 OPERATION_ID 取得作業狀態

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

Python

如要瞭解如何安裝或更新 Python 適用的 Vertex AI SDK,請參閱「安裝 Python 適用的 Vertex AI SDK」。 詳情請參閱 Python API 參考說明文件

from google.cloud import aiplatform


def create_feature_sample(
    project: str,
    location: str,
    feature_id: str,
    value_type: str,
    entity_type_id: str,
    featurestore_id: str,
):

    aiplatform.init(project=project, location=location)

    my_feature = aiplatform.Feature.create(
        feature_id=feature_id,
        value_type=value_type,
        entity_type_name=entity_type_id,
        featurestore_id=featurestore_id,
    )

    my_feature.wait()

    return my_feature

Java

在試用這個範例之前,請先按照Java使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Java API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.CreateFeatureOperationMetadata;
import com.google.cloud.aiplatform.v1.CreateFeatureRequest;
import com.google.cloud.aiplatform.v1.EntityTypeName;
import com.google.cloud.aiplatform.v1.Feature;
import com.google.cloud.aiplatform.v1.Feature.ValueType;
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 CreateFeatureSample {

  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 featureId = "YOUR_FEATURE_ID";
    String description = "YOUR_FEATURE_DESCRIPTION";
    ValueType valueType = ValueType.STRING;
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";
    int timeout = 900;
    createFeatureSample(
        project,
        featurestoreId,
        entityTypeId,
        featureId,
        description,
        valueType,
        location,
        endpoint,
        timeout);
  }

  static void createFeatureSample(
      String project,
      String featurestoreId,
      String entityTypeId,
      String featureId,
      String description,
      ValueType valueType,
      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)) {

      Feature feature =
          Feature.newBuilder().setDescription(description).setValueType(valueType).build();

      CreateFeatureRequest createFeatureRequest =
          CreateFeatureRequest.newBuilder()
              .setParent(
                  EntityTypeName.of(project, location, featurestoreId, entityTypeId).toString())
              .setFeature(feature)
              .setFeatureId(featureId)
              .build();

      OperationFuture<Feature, CreateFeatureOperationMetadata> featureFuture =
          featurestoreServiceClient.createFeatureAsync(createFeatureRequest);
      System.out.format("Operation name: %s%n", featureFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      Feature featureResponse = featureFuture.get(timeout, TimeUnit.SECONDS);
      System.out.println("Create Feature Response");
      System.out.format("Name: %s%n", featureResponse.getName());
      featurestoreServiceClient.close();
    }
  }
}

Node.js

在試用這個範例之前,請先按照Node.js使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Node.js API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

/**
 * 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 featureId = 'YOUR_FEATURE_ID';
// const valueType = 'FEATURE_VALUE_DATA_TYPE';
// 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 createFeature() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}/featurestores/${featurestoreId}/entityTypes/${entityTypeId}`;

  const feature = {
    valueType: valueType,
    description: description,
  };

  const request = {
    parent: parent,
    feature: feature,
    featureId: featureId,
  };

  // Create Feature request
  const [operation] = await featurestoreServiceClient.createFeature(request, {
    timeout: Number(timeout),
  });
  const [response] = await operation.promise();

  console.log('Create feature response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
createFeature();

批次建立特徵

為現有類型大量建立特徵。如果是批次建立要求,Vertex AI 特徵儲存庫 (舊版) 會一次建立多個特徵,與 featurestores.entityTypes.features.create 方法相比,建立大量特徵時速度更快。

網路使用者介面

請參閱建立功能

REST

如要為現有實體類型建立一或多項特徵,請使用 featurestores.entityTypes.features.batchCreate 方法傳送 POST 要求,如以下範例所示。

使用任何要求資料之前,請先替換以下項目:

  • LOCATION_ID:特徵儲存庫所在的區域,例如 us-central1
  • PROJECT_ID:您的專案 ID
  • FEATURESTORE_ID:特徵商店的 ID。
  • ENTITY_TYPE_ID:實體類型 ID。
  • PARENT:要在其中建立特徵的實體類型資源名稱。 必要格式:
    projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/entityTypes/ENTITY_TYPE_ID
  • FEATURE_ID:功能的 ID。
  • DESCRIPTION:功能說明。
  • VALUE_TYPE:特徵的值類型。
  • DURATION:(選用) 快照之間的時間間隔,以秒為單位。值必須以 `s` 結尾。

HTTP 方法和網址:

POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/entityTypes/ENTITY_TYPE_ID/features:batchCreate

JSON 要求主體:

{
  "requests": [
    {
      "parent" : "PARENT_1",
      "feature": {
        "description": "DESCRIPTION_1",
        "valueType": "VALUE_TYPE_1",
        "monitoringConfig": {
          "snapshotAnalysis": {
            "monitoringInterval": "DURATION"
          }
        }
      },
      "featureId": "FEATURE_ID_1"
    },
    {
      "parent" : "PARENT_2",
      "feature": {
        "description": "DESCRIPTION_2",
        "valueType": "VALUE_TYPE_2",
        "monitoringConfig": {
          "snapshotAnalysis": {
            "monitoringInterval": "DURATION"
          }
        }
      },
      "featureId": "FEATURE_ID_2"
    }
  ]
}

如要傳送要求,請選擇以下其中一個選項:

curl

將要求主體儲存在名為 request.json 的檔案中,然後執行下列指令:

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/ENTITY_TYPE_ID/features:batchCreate"

PowerShell

將要求主體儲存在名為 request.json 的檔案中,然後執行下列指令:

$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/ENTITY_TYPE_ID/features:batchCreate" | Select-Object -Expand Content

畫面會顯示類似以下的輸出。您可以使用回應中的 OPERATION_ID 取得作業狀態

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

Python

如要瞭解如何安裝或更新 Python 適用的 Vertex AI SDK,請參閱「安裝 Python 適用的 Vertex AI SDK」。 詳情請參閱 Python API 參考說明文件

from google.cloud import aiplatform


def batch_create_features_sample(
    project: str,
    location: str,
    entity_type_id: str,
    featurestore_id: str,
    sync: bool = True,
):

    aiplatform.init(project=project, location=location)

    my_entity_type = aiplatform.featurestore.EntityType(
        entity_type_name=entity_type_id, featurestore_id=featurestore_id
    )

    FEATURE_CONFIGS = {
        "age": {"value_type": "INT64", "description": "User age"},
        "gender": {"value_type": "STRING", "description": "User gender"},
        "liked_genres": {
            "value_type": "STRING_ARRAY",
            "description": "An array of genres this user liked",
        },
    }

    my_entity_type.batch_create_features(feature_configs=FEATURE_CONFIGS, sync=sync)

Java

在試用這個範例之前,請先按照Java使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Java API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.BatchCreateFeaturesOperationMetadata;
import com.google.cloud.aiplatform.v1.BatchCreateFeaturesRequest;
import com.google.cloud.aiplatform.v1.BatchCreateFeaturesResponse;
import com.google.cloud.aiplatform.v1.CreateFeatureRequest;
import com.google.cloud.aiplatform.v1.EntityTypeName;
import com.google.cloud.aiplatform.v1.Feature;
import com.google.cloud.aiplatform.v1.Feature.ValueType;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class BatchCreateFeaturesSample {

  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;
    batchCreateFeaturesSample(project, featurestoreId, entityTypeId, location, endpoint, timeout);
  }

  static void batchCreateFeaturesSample(
      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)) {

      List<CreateFeatureRequest> createFeatureRequests = new ArrayList<>();

      Feature titleFeature =
          Feature.newBuilder()
              .setDescription("The title of the movie")
              .setValueType(ValueType.STRING)
              .build();
      Feature genresFeature =
          Feature.newBuilder()
              .setDescription("The genres of the movie")
              .setValueType(ValueType.STRING)
              .build();
      Feature averageRatingFeature =
          Feature.newBuilder()
              .setDescription("The average rating for the movie, range is [1.0-5.0]")
              .setValueType(ValueType.DOUBLE)
              .build();

      createFeatureRequests.add(
          CreateFeatureRequest.newBuilder().setFeature(titleFeature).setFeatureId("title").build());

      createFeatureRequests.add(
          CreateFeatureRequest.newBuilder()
              .setFeature(genresFeature)
              .setFeatureId("genres")
              .build());

      createFeatureRequests.add(
          CreateFeatureRequest.newBuilder()
              .setFeature(averageRatingFeature)
              .setFeatureId("average_rating")
              .build());

      BatchCreateFeaturesRequest batchCreateFeaturesRequest =
          BatchCreateFeaturesRequest.newBuilder()
              .setParent(
                  EntityTypeName.of(project, location, featurestoreId, entityTypeId).toString())
              .addAllRequests(createFeatureRequests)
              .build();

      OperationFuture<BatchCreateFeaturesResponse, BatchCreateFeaturesOperationMetadata>
          batchCreateFeaturesFuture =
              featurestoreServiceClient.batchCreateFeaturesAsync(batchCreateFeaturesRequest);
      System.out.format(
          "Operation name: %s%n", batchCreateFeaturesFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      BatchCreateFeaturesResponse batchCreateFeaturesResponse =
          batchCreateFeaturesFuture.get(timeout, TimeUnit.SECONDS);
      System.out.println("Batch Create Features Response");
      System.out.println(batchCreateFeaturesResponse);
      featurestoreServiceClient.close();
    }
  }
}

Node.js

在試用這個範例之前,請先按照Node.js使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Node.js API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

/**
 * 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 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 batchCreateFeatures() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}/featurestores/${featurestoreId}/entityTypes/${entityTypeId}`;

  const ageFeature = {
    valueType: 'INT64',
    description: 'User age',
  };

  const ageFeatureRequest = {
    feature: ageFeature,
    featureId: 'age',
  };

  const genderFeature = {
    valueType: 'STRING',
    description: 'User gender',
  };

  const genderFeatureRequest = {
    feature: genderFeature,
    featureId: 'gender',
  };

  const likedGenresFeature = {
    valueType: 'STRING_ARRAY',
    description: 'An array of genres that this user liked',
  };

  const likedGenresFeatureRequest = {
    feature: likedGenresFeature,
    featureId: 'liked_genres',
  };

  const requests = [
    ageFeatureRequest,
    genderFeatureRequest,
    likedGenresFeatureRequest,
  ];

  const request = {
    parent: parent,
    requests: requests,
  };

  // Batch Create Features request
  const [operation] = await featurestoreServiceClient.batchCreateFeatures(
    request,
    {timeout: Number(timeout)}
  );
  const [response] = await operation.promise();

  console.log('Batch create features response');
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
batchCreateFeatures();

列出特徵

列出特定位置的所有功能。如要在特定位置搜尋所有實體類型和特徵商店的特徵,請參閱「搜尋特徵」方法。

網路使用者介面

  1. 在 Google Cloud 控制台的 Vertex AI 專區,前往「Features」頁面。

    前往「Features」(功能) 頁面

  2. 從「Region」(區域) 下拉式清單中選取一個區域。
  3. 在功能表格中,查看「功能」欄,瞭解所選區域的專案功能。

REST

如要列出單一實體類型的所有特徵,請使用 featurestores.entityTypes.features.list 方法傳送 GET 要求。

使用任何要求資料之前,請先替換以下項目:

  • LOCATION_ID:特徵儲存庫所在的區域,例如 us-central1
  • PROJECT_ID:您的專案 ID
  • FEATURESTORE_ID:特徵商店的 ID。
  • ENTITY_TYPE_ID:實體類型 ID。

HTTP 方法和網址:

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

如要傳送要求,請選擇以下其中一個選項:

curl

執行下列指令:

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/ENTITY_TYPE_ID/features"

PowerShell

執行下列指令:

$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/ENTITY_TYPE_ID/features" | Select-Object -Expand Content

您應該會收到如下的 JSON 回應:

{
  "features": [
    {
      "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/entityTypes/ENTITY_TYPE_ID/features/FEATURE_ID_1",
      "description": "DESCRIPTION",
      "valueType": "VALUE_TYPE",
      "createTime": "2021-03-01T22:41:20.626644Z",
      "updateTime": "2021-03-01T22:41:20.626644Z",
      "labels": {
        "environment": "testing"
      },
      "etag": "AMEw9yP0qJeLao6P3fl9cKEGY4ie5-SanQaiN7c_Ca4QOa0u7AxwO6i75Vbp0Cr51MSf"
    },
    {
      "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/entityTypes/ENTITY_TYPE_ID/features/FEATURE_ID_2",
      "description": "DESCRIPTION",
      "valueType": "VALUE_TYPE",
      "createTime": "2021-02-25T01:27:00.544230Z",
      "updateTime": "2021-02-25T01:27:00.544230Z",
      "labels": {
        "environment": "testing"
      },
      "etag": "AMEw9yMdrLZ7Waty0ane-DkHq4kcsIVC-piqJq7n6A_Y-BjNzPY4rNlokDHNyUqC7edw"
    },
    {
      "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/entityTypes/ENTITY_TYPE_ID/features/FEATURE_ID_3",
      "description": "DESCRIPTION",
      "valueType": "VALUE_TYPE",
      "createTime": "2021-03-01T22:41:20.628493Z",
      "updateTime": "2021-03-01T22:41:20.628493Z",
      "labels": {
        "environment": "testing"
      },
      "etag": "AMEw9yM-sAkv-u-jzkUOToaAVovK7GKbrubd9DbmAonik-ojTWG8-hfSRYt6jHKRTQ35"
    }
  ]
}

Java

在試用這個範例之前,請先按照Java使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Java API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。


import com.google.cloud.aiplatform.v1.EntityTypeName;
import com.google.cloud.aiplatform.v1.Feature;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
import com.google.cloud.aiplatform.v1.ListFeaturesRequest;
import java.io.IOException;

public class ListFeaturesSample {

  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 entityTypeId = "YOUR_ENTITY_TYPE_ID";
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";

    listFeaturesSample(project, featurestoreId, entityTypeId, location, endpoint);
  }

  static void listFeaturesSample(
      String project, String featurestoreId, String entityTypeId, 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)) {

      ListFeaturesRequest listFeaturesRequest =
          ListFeaturesRequest.newBuilder()
              .setParent(
                  EntityTypeName.of(project, location, featurestoreId, entityTypeId).toString())
              .build();
      System.out.println("List Features Response");
      for (Feature element :
          featurestoreServiceClient.listFeatures(listFeaturesRequest).iterateAll()) {
        System.out.println(element);
      }
      featurestoreServiceClient.close();
    }
  }
}

Node.js

在試用這個範例之前,請先按照Node.js使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Node.js API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

/**
 * 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 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 listFeatures() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}/featurestores/${featurestoreId}/entityTypes/${entityTypeId}`;

  const request = {
    parent: parent,
  };

  // List Features request
  const [response] = await featurestoreServiceClient.listFeatures(request, {
    timeout: Number(timeout),
  });

  console.log('List features response');
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
listFeatures();

其他語言

如要瞭解如何安裝及使用 Python 適用的 Vertex AI SDK,請參閱「使用 Python 適用的 Vertex AI SDK」。詳情請參閱 Vertex AI SDK for Python API 參考說明文件

搜尋功能

根據一或多項屬性搜尋特徵,例如特徵 ID、實體類型 ID 或特徵說明。Vertex AI 特徵儲存庫 (舊版) 會在指定位置的所有特徵儲存庫和實體類型中搜尋。您也可以依特定特徵存放區、值類型和標籤進行篩選,藉此限制結果。

如要列出所有功能,請參閱列出功能

網路使用者介面

  1. 在 Google Cloud 控制台的 Vertex AI 專區,前往「Features」頁面。

    前往「Features」(功能) 頁面

  2. 從「Region」(區域) 下拉式清單中選取一個區域。
  3. 按一下功能表格的「篩選器」欄位。
  4. 選取要篩選的屬性,例如「特徵」,這會傳回 ID 中任何位置含有相符字串的特徵。
  5. 輸入篩選器的值,然後按 Enter 鍵。Vertex AI 特徵儲存庫 (舊版) 會在特徵表格中傳回結果。
  6. 如要新增其他篩選器,請再次點選「篩選器」欄位。

REST

如要搜尋特徵,請使用 featurestores.searchFeatures 方法傳送 GET 要求。下列範例使用多個搜尋參數,以 featureId:test AND valueType=STRING 形式撰寫。查詢會傳回 ID 包含 test 且值為 STRING 類型的特徵。

使用任何要求資料之前,請先替換以下項目:

  • LOCATION_ID:特徵儲存庫所在的區域,例如 us-central1
  • PROJECT_ID:您的專案 ID

HTTP 方法和網址:

GET https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores:searchFeatures?query="featureId:test%20AND%20valueType=STRING"

如要傳送要求,請選擇以下其中一個選項:

curl

執行下列指令:

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:searchFeatures?query="featureId:test%20AND%20valueType=STRING""

PowerShell

執行下列指令:

$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:searchFeatures?query="featureId:test%20AND%20valueType=STRING"" | Select-Object -Expand Content

您應該會收到如下的 JSON 回應:

{
  "features": [
    {
      "name": "projects/PROJECT_NUMBER/locations/LOCATION_IDfeature-delete.html/featurestores/featurestore_demo/entityTypes/testing/features/test1",
      "description": "featurestore test1",
      "createTime": "2021-02-26T18:16:09.528185Z",
      "updateTime": "2021-02-26T18:16:09.528185Z",
      "labels": {
        "environment": "testing"
      }
    }
  ]
}

Java

在試用這個範例之前,請先按照Java使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Java API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。


import com.google.cloud.aiplatform.v1.Feature;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.cloud.aiplatform.v1.SearchFeaturesRequest;
import java.io.IOException;

public class SearchFeaturesSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String query = "YOUR_QUERY";
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";
    searchFeaturesSample(project, query, location, endpoint);
  }

  static void searchFeaturesSample(String project, String query, 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)) {

      SearchFeaturesRequest searchFeaturesRequest =
          SearchFeaturesRequest.newBuilder()
              .setLocation(LocationName.of(project, location).toString())
              .setQuery(query)
              .build();
      System.out.println("Search Features Response");
      for (Feature element :
          featurestoreServiceClient.searchFeatures(searchFeaturesRequest).iterateAll()) {
        System.out.println(element);
      }
      featurestoreServiceClient.close();
    }
  }
}

Node.js

在試用這個範例之前,請先按照Node.js使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Node.js API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const project = 'YOUR_PROJECT_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 searchFeatures() {
  // Configure the locationResource resource
  const locationResource = `projects/${project}/locations/${location}`;

  const request = {
    location: locationResource,
    query: query,
  };

  // Search Features request
  const [response] = await featurestoreServiceClient.searchFeatures(request, {
    timeout: Number(timeout),
  });

  console.log('Search features response');
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
searchFeatures();

其他語言

如要瞭解如何安裝及使用 Python 適用的 Vertex AI SDK,請參閱「使用 Python 適用的 Vertex AI SDK」。詳情請參閱 Vertex AI SDK for Python API 參考說明文件

查看功能詳細資料

查看特徵的詳細資料,例如值類型或說明。如果您使用 Google Cloud 控制台並啟用特徵監控功能,也可以查看特徵值的分布變化趨勢。

網路使用者介面

  1. 在 Google Cloud 控制台的 Vertex AI 專區,前往「Features」頁面。

    前往「Features」(功能) 頁面

  2. 從「Region」(區域) 下拉式清單中選取一個區域。
  3. 在功能表格中,查看「功能」欄,找出要查看詳細資料的功能。
  4. 按一下功能名稱即可查看詳細資料。
  5. 如要查看指標,請按一下「指標」。Vertex AI 特徵儲存庫 (舊版) 提供特徵的特徵分布指標。

REST

如要取得有關特徵的詳細資料,請使用 featurestores.entityTypes.features.get 方法傳送 GET 要求。

使用任何要求資料之前,請先替換以下項目:

  • LOCATION_ID:特徵儲存庫所在的區域,例如 us-central1
  • PROJECT_ID:您的專案 ID
  • FEATURESTORE_ID:特徵商店的 ID。
  • ENTITY_TYPE_ID:實體類型 ID。
  • FEATURE_ID:功能的 ID。

HTTP 方法和網址:

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

如要傳送要求,請選擇以下其中一個選項:

curl

執行下列指令:

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/ENTITY_TYPE_ID/features/FEATURE_ID"

PowerShell

執行下列指令:

$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/ENTITY_TYPE_ID/features/FEATURE_ID" | Select-Object -Expand Content

您應該會收到如下的 JSON 回應:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/entityTypes/ENTITY_TYPE_ID/features/FEATURE_ID",
  "description": "DESCRIPTION",
  "valueType": "VALUE_TYPE",
  "createTime": "2021-03-01T22:41:20.628493Z",
  "updateTime": "2021-03-01T22:41:20.628493Z",
  "labels": {
    "environment": "testing"
  },
  "etag": "AMEw9yOZbdYKHTyjV22ziZR1vUX3nWOi0o2XU3-OADahSdfZ8Apklk_qPruhF-o1dOSD",
  "monitoringConfig": {}
}

Java

在試用這個範例之前,請先按照Java使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Java API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。


import com.google.cloud.aiplatform.v1.Feature;
import com.google.cloud.aiplatform.v1.FeatureName;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
import com.google.cloud.aiplatform.v1.GetFeatureRequest;
import java.io.IOException;

public class GetFeatureSample {

  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 entityTypeId = "YOUR_ENTITY_TYPE_ID";
    String featureId = "YOUR_FEATURE_ID";
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";

    getFeatureSample(project, featurestoreId, entityTypeId, featureId, location, endpoint);
  }

  static void getFeatureSample(
      String project,
      String featurestoreId,
      String entityTypeId,
      String featureId,
      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)) {

      GetFeatureRequest getFeatureRequest =
          GetFeatureRequest.newBuilder()
              .setName(
                  FeatureName.of(project, location, featurestoreId, entityTypeId, featureId)
                      .toString())
              .build();

      Feature feature = featurestoreServiceClient.getFeature(getFeatureRequest);
      System.out.println("Get Feature Response");
      System.out.println(feature);
      featurestoreServiceClient.close();
    }
  }
}

Node.js

在試用這個範例之前,請先按照Node.js使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Node.js API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

/**
 * 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 featureId = 'YOUR_FEATURE_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 getFeature() {
  // Configure the name resource
  const name = `projects/${project}/locations/${location}/featurestores/${featurestoreId}/entityTypes/${entityTypeId}/features/${featureId}`;

  const request = {
    name: name,
  };

  // Get Feature request
  const [response] = await featurestoreServiceClient.getFeature(request, {
    timeout: Number(timeout),
  });

  console.log('Get feature response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
getFeature();

其他語言

如要瞭解如何安裝及使用 Python 適用的 Vertex AI SDK,請參閱「使用 Python 適用的 Vertex AI SDK」。詳情請參閱 Vertex AI SDK for Python API 參考說明文件

刪除特徵

刪除特徵及其所有值。

網路使用者介面

  1. 在 Google Cloud 控制台的 Vertex AI 專區,前往「Features」頁面。

    前往「Features」(功能) 頁面

  2. 從「Region」(區域) 下拉式清單中選取一個區域。
  3. 在功能表格中查看「功能」欄,找出要刪除的功能。
  4. 按一下功能名稱。
  5. 在動作列中,按一下「刪除」
  6. 按一下「確認」即可刪除特徵及其值。

REST

如要刪除特徵,請使用 featurestores.entityTypes.features.delete 方法傳送 DELETE 要求。

使用任何要求資料之前,請先替換以下項目:

  • LOCATION_ID:特徵儲存庫所在的區域,例如 us-central1
  • PROJECT_ID:您的專案 ID
  • FEATURESTORE_ID:特徵商店的 ID。
  • ENTITY_TYPE_ID:實體類型 ID。
  • FEATURE_ID:功能的 ID。

HTTP 方法和網址:

DELETE https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/entityTypes/ENTITY_TYPE_ID/features/FEATURE_ID

如要傳送要求,請選擇以下其中一個選項:

curl

執行下列指令:

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/features/FEATURE_ID"

PowerShell

執行下列指令:

$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/features/FEATURE_ID" | Select-Object -Expand Content

您應該會收到如下的 JSON 回應:

{
  "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

在試用這個範例之前,請先按照Java使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Java API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.DeleteFeatureRequest;
import com.google.cloud.aiplatform.v1.DeleteOperationMetadata;
import com.google.cloud.aiplatform.v1.FeatureName;
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 DeleteFeatureSample {

  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 featureId = "YOUR_FEATURE_ID";
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";
    int timeout = 300;

    deleteFeatureSample(
        project, featurestoreId, entityTypeId, featureId, location, endpoint, timeout);
  }

  static void deleteFeatureSample(
      String project,
      String featurestoreId,
      String entityTypeId,
      String featureId,
      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)) {

      DeleteFeatureRequest deleteFeatureRequest =
          DeleteFeatureRequest.newBuilder()
              .setName(
                  FeatureName.of(project, location, featurestoreId, entityTypeId, featureId)
                      .toString())
              .build();

      OperationFuture<Empty, DeleteOperationMetadata> operationFuture =
          featurestoreServiceClient.deleteFeatureAsync(deleteFeatureRequest);
      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 Feature.");
      featurestoreServiceClient.close();
    }
  }
}

Node.js

在試用這個範例之前,請先按照Node.js使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Node.js API 參考說明文件

如要向 Vertex AI 進行驗證,請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。

/**
 * 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 featureId = 'YOUR_FEATURE_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 deleteFeature() {
  // Configure the name resource
  const name = `projects/${project}/locations/${location}/featurestores/${featurestoreId}/entityTypes/${entityTypeId}/features/${featureId}`;

  const request = {
    name: name,
  };

  // Delete Feature request
  const [operation] = await featurestoreServiceClient.deleteFeature(request, {
    timeout: Number(timeout),
  });
  const [response] = await operation.promise();

  console.log('Delete feature response');
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
deleteFeature();

其他語言

如要瞭解如何安裝及使用 Python 適用的 Vertex AI SDK,請參閱「使用 Python 適用的 Vertex AI SDK」。詳情請參閱 Vertex AI SDK for Python API 參考說明文件

後續步驟