在线传送

通过在线传送，您能够以较短的延迟时间为小批量实体提供特征值。对于每个请求，您只能从单个实体类型提供特征值。Vertex AI Feature Store（旧版）仅返回每个特征的最新非 null 值。

通常，您使用在线传送为已部署的模型传送特征值，以进行在线预测。例如，您可能有一家单车共享公司，并且您想要预测特定用户租用单车的时长。您可以添加来自用户的实时输入以及来自特征存储区的数据以执行在线预测。这样，您就可以实时确定资源分配。

Null 值

对于在线传送结果，如果特征的最新值为 null，则 Vertex AI Feature Store（旧版）会返回最新的非 null 值。如果不存在之前的值，则 Vertex AI Feature Store（旧版）会返回 null。

须知事项

确保您所调用的特征存储区具备在线存储区（即节点数必须大于 0）。否则，在线传送请求会返回错误。如需了解详情，请参阅管理特征存储区。

部署来自单个实体的值

从单个实体为特定实体类型提供特征值。

REST

如需从实体获取特征值，请使用 featurestores.entityTypes.readFeatureValues 方法发送 POST 请求。

以下示例为特定实体获取了两个不同特征的最新值。请注意，对于 ids 字段，您可以指定 ["*"]（而不是特征 ID）以选择实体的所有特征。

在使用任何请求数据之前，请先进行以下替换：

  • LOCATION_ID：在其中创建特征存储区的区域。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。
  • ENTITY_ID：要获取其特征值的实体的 ID。
  • FEATURE_ID：要获取其值的特征的 ID。

HTTP 方法和网址：

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

请求 JSON 正文： 

{
  "entityId": "ENTITY_ID",
  "featureSelector": {
    "idMatcher": {
      "ids": ["FEATURE_ID_1", "FEATURE_ID_2"]
    }
  }
}

如需发送请求，请选择以下方式之一：

curl

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/ENTITY_TYPE_ID:readFeatureValues"

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/ENTITY_TYPE_ID:readFeatureValues" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "header": {
    "entityType": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/entityTypes/ENTITY_TYPE_ID",
    "featureDescriptors": [
      {
        "id": "FEATURE_ID_1"
      },
      {
        "id": "FEATURE_ID_2"
      }
    ]
  },
  "entityView": {
    "entityId": "ENTITY_ID",
    "data": [
      {
        "value": {
          "VALUE_TYPE_1": "FEATURE_VALUE_1",
          "metadata": {
            "generateTime": "2019-10-28T15:38:10Z"
          }
        }
      },
      {
        "value": {
          "VALUE_TYPE_2": "FEATURE_VALUE_2",
          "metadata": {
            "generateTime": "2019-10-28T15:38:10Z"
          }
        }
      }
    ]
  }
}

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 typing import List, Union

from google.cloud import aiplatform

def read_feature_values_sample(
    project: str,
    location: str,
    entity_type_id: str,
    featurestore_id: str,
    entity_ids: Union[str, List[str]],
    feature_ids: Union[str, List[str]] = "*",
):

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

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

    my_dataframe = my_entity_type.read(entity_ids=entity_ids, feature_ids=feature_ids)

    return my_dataframe

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 read_feature_values_sample(
    project: str,
    featurestore_id: str,
    entity_type_id: str,
    entity_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # 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.FeaturestoreOnlineServingServiceClient(
        client_options=client_options
    )
    entity_type = f"projects/{project}/locations/{location}/featurestores/{featurestore_id}/entityTypes/{entity_type_id}"
    feature_selector = aiplatform.gapic.FeatureSelector(
        id_matcher=aiplatform.gapic.IdMatcher(ids=["age", "gender", "liked_genres"])
    )
    read_feature_values_request = aiplatform.gapic.ReadFeatureValuesRequest(
        entity_type=entity_type, entity_id=entity_id, feature_selector=feature_selector
    )
    read_feature_values_response = client.read_feature_values(
        request=read_feature_values_request
    )
    print("read_feature_values_response:", read_feature_values_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.cloud.aiplatform.v1.EntityTypeName;
import com.google.cloud.aiplatform.v1.FeatureSelector;
import com.google.cloud.aiplatform.v1.FeaturestoreOnlineServingServiceClient;
import com.google.cloud.aiplatform.v1.FeaturestoreOnlineServingServiceSettings;
import com.google.cloud.aiplatform.v1.IdMatcher;
import com.google.cloud.aiplatform.v1.ReadFeatureValuesRequest;
import com.google.cloud.aiplatform.v1.ReadFeatureValuesResponse;
import java.io.IOException;
import java.util.Arrays;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeoutException;

public class ReadFeatureValuesSample {

  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";
    // Feature Store ID
    String featurestoreId = "YOUR_FEATURESTORE_ID";
    // Entity Type ID
    String entityTypeId = "YOUR_ENTITY_TYPE_ID";
    // Entity ID
    String entityId = "YOUR_ENTITY_ID";
    // Features to read with batch or online serving.
    List<String> featureSelectorIds = Arrays.asList("title", "genres", "average_rating");
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";
    int timeout = 300;

    readFeatureValuesSample(
        project,
        featurestoreId,
        entityTypeId,
        entityId,
        featureSelectorIds,
        location,
        endpoint,
        timeout);
  }

  /*
   * Reads Feature values of a specific entity of an EntityType.
   * See: https://cloud.google.com/vertex-ai/docs/featurestore/serving-online
   */
  public static void readFeatureValuesSample(
      String project,
      String featurestoreId,
      String entityTypeId,
      String entityId,
      List<String> featureSelectorIds,
      String location,
      String endpoint,
      int timeout)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    FeaturestoreOnlineServingServiceSettings featurestoreOnlineServiceSettings =
        FeaturestoreOnlineServingServiceSettings.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 (FeaturestoreOnlineServingServiceClient featurestoreOnlineServiceClient =
        FeaturestoreOnlineServingServiceClient.create(featurestoreOnlineServiceSettings)) {
      ReadFeatureValuesRequest readFeatureValuesRequest =
          ReadFeatureValuesRequest.newBuilder()
              .setEntityType(
                  EntityTypeName.of(project, location, featurestoreId, entityTypeId).toString())
              .setEntityId(entityId)
              .setFeatureSelector(
                  FeatureSelector.newBuilder()
                      .setIdMatcher(IdMatcher.newBuilder().addAllIds(featureSelectorIds)))
              .build();

      ReadFeatureValuesResponse readFeatureValuesResponse =
          featurestoreOnlineServiceClient.readFeatureValues(readFeatureValuesRequest);
      System.out.println("Read Feature Values Response" + readFeatureValuesResponse);
    }
  }
}

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 entityId = 'ENTITY_ID_TO_SERVE';
// 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 {FeaturestoreOnlineServingServiceClient} =
  require('@google-cloud/aiplatform').v1;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: apiEndpoint,
};

// Instantiates a client
const featurestoreOnlineServingServiceClient =
  new FeaturestoreOnlineServingServiceClient(clientOptions);

async function readFeatureValues() {
  // Configure the entityType resource
  const entityType = `projects/${project}/locations/${location}/featurestores/${featurestoreId}/entityTypes/${entityTypeId}`;

  const featureSelector = {
    idMatcher: {
      ids: ['age', 'gender', 'liked_genres'],
    },
  };

  const request = {
    entityType: entityType,
    entityId: entityId,
    featureSelector: featureSelector,
  };

  // Read Feature Values Request
  const [response] =
    await featurestoreOnlineServingServiceClient.readFeatureValues(request, {
      timeout: Number(timeout),
    });

  console.log('Read feature values response');
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
readFeatureValues();

从多个实体传送值

从一个或多个实体为特定实体类型提供特征值。为了获得更好的性能，请使用 streamingReadFeatureValues 方法，而不是向 readFeatureValues 方法发送并行请求。

REST

如需从多个实体获取特征值，请使用 featurestores.entityTypes.streamingReadFeatureValues 方法发送 POST 请求。

以下示例为两个不同实体获取了两个不同特征的最新值。请注意，对于 ids 字段，您可以指定 ["*"]（而不是特征 ID）以选择实体的所有特征。

在使用任何请求数据之前，请先进行以下替换：

  • LOCATION_ID：在其中创建特征存储区的区域。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。
  • ENTITY_ID：要获取其特征值的实体的 ID。
  • FEATURE_ID：要获取其值的特征的 ID。

HTTP 方法和网址：

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

请求 JSON 正文： 

{
  "entityIds": ["ENTITY_ID_1", "ENTITY_ID_2"],
  "featureSelector": {
    "idMatcher": {
      "ids": ["FEATURE_ID_1", "FEATURE_ID_2"]
    }
  }
}

如需发送请求，请选择以下方式之一：

curl

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/ENTITY_TYPE_ID:streamingReadFeatureValues"

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/ENTITY_TYPE_ID:streamingReadFeatureValues" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

[{
  "header": {
    "entityType": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/entityTypes/ENTITY_TYPE_ID",
    "featureDescriptors": [
      {
        "id": "FEATURE_ID_1"
      },
      {
        "id": "FEATURE_ID_2"
      }
    ]
  }
},
{
  "entityView": {
    "entityId": "ENTITY_ID_1",
    "data": [
      {
        "value": {
          "VALUE_TYPE_1": "FEATURE_VALUE_A",
          "metadata": {
            "generateTime": "2019-10-28T15:38:10Z"
          }
        }
      },
      {
        "value": {
          "VALUE_TYPE_2": "FEATURE_VALUE_B",
          "metadata": {
            "generateTime": "2019-10-28T15:38:10Z"
          }
        }
      }
    ]
  }
},
{
  "entityView": {
    "entityId": "ENTITY_ID_2",
    "data": [
      {
        "value": {
          "VALUE_TYPE_1": "FEATURE_VALUE_C",
          "metadata": {
            "generateTime": "2019-10-28T21:21:37Z"
          }
        }
      },
      {
        "value": {
          "VALUE_TYPE_2": "FEATURE_VALUE_D",
          "metadata": {
            "generateTime": "2019-10-28T21:21:37Z"
          }
        }
      }
    ]
  }
}]

其他语言

您可以安装并使用以下 Vertex AI 客户端库来调用 Vertex AI API。Cloud 客户端库使用每种受支持语言的自然规范和样式，为开发者带来优化的体验。

后续步骤