取得內容推薦

本頁面說明如何使用 Google Cloud 控制台預覽推薦內容,以及如何使用 API 取得推薦結果。請參閱「REST」分頁,瞭解可協助您將推薦內容整合至應用程式的 API 呼叫範例。

您採用的程序取決於您是否有媒體資料或自訂資料:

取得媒體推薦內容

控制台

如要使用 Google Cloud 控制台預覽媒體推薦內容,請按照下列步驟操作:

  1. 前往 Google Cloud 控制台的「AI Applications」頁面。

    AI 應用程式

  2. 按一下您要預覽建議的應用程式名稱。

  3. 依序點選「設定」 >「訓練」。如果「已準備好查詢」為是,表示應用程式已準備好進行預覽。

  4. 按一下「預覽」

  5. 按一下「Document ID」(文件 ID) 欄位。畫面上就會顯示文件 ID 清單。

  6. 針對您要取得建議的文件,按一下對應的文件 ID。 您也可以在「Document ID」(文件 ID) 欄位中輸入文件 ID。

  7. 按一下「選取供應設定」,然後選取要預覽的供應設定。

  8. 選用:輸入您收集使用者事件的使用者訪客 ID (也稱為虛擬使用者 ID)。如果您將這個欄位留空或輸入不存在的訪客 ID,系統會以新使用者的身分預覽推薦內容。

  9. 按一下「Get recommendations」(推薦內容)。畫面上就會顯示建議的文件清單。

  10. 按一下文件即可取得文件詳細資料。

REST

如要使用 API 取得媒體建議,請使用 servingConfigs.recommend 方法:

  1. 找出引擎 ID 和供應設定 ID。如果您已取得引擎 ID 和放送設定 ID,請直接跳至步驟 2。

    1. 前往 Google Cloud 控制台的「AI Applications」頁面。

      AI 應用程式

    2. 按一下應用程式名稱。

    3. 在導覽窗格中,按一下「設定」

    4. 如果您只有一個供應設定,請取得「Serving」分頁中顯示的供應設定 ID。

      如果「服務」分頁中列出多個服務設定,請找出要取得建議的服務設定。供應設定 ID 是「ID」欄中的值。

    5. 按一下「訓練」分頁標籤。引擎 ID 是「App ID」列中的值。

  2. 確認應用程式已準備好供預覽:

    1. 前往 Google Cloud 控制台的「AI Applications」頁面。

      AI 應用程式

    2. 按一下應用程式名稱。

    3. 依序點選「設定」 >「訓練」。如果「已準備好查詢」為是,表示應用程式已準備好進行預覽。

  3. 取得推薦內容。

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d  '{
            "validateOnly": false,
            "userEvent": {
                "eventType": "view-item",
                "userPseudoId": "USER_PSEUDO_ID",
                "documents": [{
                  "id": "DOCUMENT_ID"
                }],
            "filter": "FILTER_STRING"            }
        }' \
      "https://discoveryengine.googleapis.com/v1beta/projects/PROJECT_ID/locations/global/collections/default_collection/dataStores/DATA_STORE_ID/servingConfigs/SERVING_CONFIG_ID:recommend"

    更改下列內容:

    • PROJECT_ID:專案 ID。
    • DATA_STORE_ID:資料儲存庫的 ID。
    • DOCUMENT_ID:您要預覽建議的文件 ID。請使用您在擷取資料時,為這份文件使用的 ID。
    • USER_PSEUDO_ID:使用者的化名 ID。您可以使用 HTTP Cookie 填入這個欄位,這樣系統就能明確識別單一裝置上的訪客。請勿將這個欄位設為多位使用者的相同 ID,否則系統會合併他們的事件記錄,導致模型品質下降。請勿在這個欄位中加入個人識別資訊 (PII)。
    • SERVING_CONFIG_ID:供應設定 ID。
    • FILTER:選用。文字欄位,可讓您使用篩選運算式語法,對特定欄位組合進行篩選。預設值為空字串,表示不會套用篩選器。詳情請參閱「篩選推薦內容」。

您應該會看到類似以下的結果:

{
  "results": [{"id": "sample-id-1"}, {"id": "sample-id-2"}],
  "attributionToken": "abc123"
}

Google 建議您將歸因權杖與使用者在看到搜尋回應和推薦內容後採取的動作建立關聯,這些權杖會隨每個搜尋回應和推薦內容提供。這麼做有助於提升搜尋回應和推薦內容的品質。如要這麼做,請在網站上顯示的搜尋回應或推薦連結的網址 (例如 https://www.example.com/54321/?rtoken=abc123) 後方附加 attributionToken 值。當使用者點選其中一個連結時,請在您記錄的使用者事件中加入 attributionToken 值。

取得使用自訂結構化資料的應用程式推薦內容

控制台

如要使用 Google Cloud 控制台為結構化應用程式預覽自訂建議,請按照下列步驟操作:

  1. 前往 Google Cloud 控制台的「AI Applications」頁面。

    AI 應用程式

  2. 按一下您要預覽建議的應用程式名稱。

  3. 按一下「預覽」

  4. 按一下「Document ID」(文件 ID) 欄位。畫面上就會顯示文件 ID 清單。

  5. 針對您要取得建議的文件,按一下對應的文件 ID。 您也可以在「Document ID」(文件 ID) 欄位中輸入文件 ID。

  6. 按一下「Get recommendations」(推薦內容)。畫面上就會顯示建議的文件清單。

  7. 按一下文件即可取得文件詳細資料。

REST

如要使用 API 為含有結構化資料的應用程式取得自訂建議,請使用 servingConfigs.recommend 方法:

  1. 找出引擎 ID。如果您已取得引擎 ID,請跳至步驟 2。

    1. 前往 Google Cloud 控制台的「AI Applications」頁面。

      AI 應用程式

    2. 按一下應用程式名稱。

    3. 從 Google Cloud 控制台的網址取得引擎 ID。它是 engines//data 之間的文字。舉例來說,如果網址包含

      gen-app-builder/engines/demo_1234567890123/data/records
      

      則引擎 ID 為 demo_1234567890123

  2. 找出資料儲存庫 ID。如果您已取得資料儲存庫 ID,請略過至下一個步驟。

    1. 前往 Google Cloud 控制台的「AI Applications」頁面,然後在導覽選單中按一下「資料儲存庫」

      前往「資料儲存庫」頁面

    2. 點按資料儲存庫的名稱。

    3. 在資料儲存庫的「資料」頁面中,取得資料儲存庫 ID。

  3. 請輪詢 GetEngine 方法,直到傳回 "servingState":"ACTIVE" 為止,以確保引擎已準備好進行預覽。此時引擎已準備好進行預覽。

    curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
    "https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID/locations/global/collections/default_collection/engines/ENGINE_ID
    

    更改下列內容:

    • PROJECT_ID:專案 ID。
    • ENGINE_ID:引擎 ID。
  4. 取得推薦內容。

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
    -d '{
      "userEvent": { "eventType":"view-item", "userPseudoId":"USER_PSEUDO_ID", "documents":[{"id":"DOCUMENT_ID"}]}}' \
      "https://discoveryengine.googleapis.com/v1beta/projects/PROJECT_ID/locations/global/collections/default_collection/dataStores/DATA_STORE_ID/servingConfigs/SERVING_CONFIG_ID:recommend"
    

    更改下列內容:

    • PROJECT_ID:專案 ID。
    • DATA_STORE_ID:資料儲存庫的 ID。
    • DOCUMENT_ID:您要預覽建議的文件 ID。請使用您在擷取資料時,為這份文件使用的 ID。
    • USER_PSEUDO_ID:使用者的化名 ID。您可以使用 HTTP Cookie 填入這個欄位,這樣系統就能明確識別單一裝置上的訪客。請勿將這個欄位設為多位使用者的相同 ID,否則系統會合併他們的事件記錄,導致模型品質下降。請勿在這個欄位中加入個人識別資訊 (PII)。
    • SERVING_CONFIG_ID:供應設定的 ID。供應設定 ID 與引擎 ID 相同,因此請在此處使用引擎 ID。

C#

詳情請參閱 AI Applications C# API 參考說明文件

如要向 AI Applications 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機制」。

using Google.Cloud.DiscoveryEngine.V1Beta;
using Google.Protobuf.WellKnownTypes;

public sealed partial class GeneratedRecommendationServiceClientSnippets
{
    /// <summary>Snippet for Recommend</summary>
    /// <remarks>
    /// This snippet has been automatically generated and should be regarded as a code template only.
    /// It will require modifications to work:
    /// - It may require correct/in-range values for request initialization.
    /// - It may require specifying regional endpoints when creating the service client as shown in
    ///   https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint.
    /// </remarks>
    public void RecommendRequestObject()
    {
        // Create client
        RecommendationServiceClient recommendationServiceClient = RecommendationServiceClient.Create();
        // Initialize request argument(s)
        RecommendRequest request = new RecommendRequest
        {
            ServingConfigAsServingConfigName = ServingConfigName.FromProjectLocationDataStoreServingConfig("[PROJECT]", "[LOCATION]", "[DATA_STORE]", "[SERVING_CONFIG]"),
            UserEvent = new UserEvent(),
            PageSize = 0,
            Filter = "",
            ValidateOnly = false,
            Params = { { "", new Value() }, },
            UserLabels = { { "", "" }, },
        };
        // Make the request
        RecommendResponse response = recommendationServiceClient.Recommend(request);
    }
}

Go

詳情請參閱 AI Applications Go API 參考說明文件

如要向 AI Applications 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機制」。


package main

import (
	"context"

	discoveryengine "cloud.google.com/go/discoveryengine/apiv1beta"
	discoveryenginepb "cloud.google.com/go/discoveryengine/apiv1beta/discoveryenginepb"
)

func main() {
	ctx := context.Background()
	// This snippet has been automatically generated and should be regarded as a code template only.
	// It will require modifications to work:
	// - It may require correct/in-range values for request initialization.
	// - It may require specifying regional endpoints when creating the service client as shown in:
	//   https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options
	c, err := discoveryengine.NewRecommendationClient(ctx)
	if err != nil {
		// TODO: Handle error.
	}
	defer c.Close()

	req := &discoveryenginepb.RecommendRequest{
		// TODO: Fill request struct fields.
		// See https://pkg.go.dev/cloud.google.com/go/discoveryengine/apiv1beta/discoveryenginepb#RecommendRequest.
	}
	resp, err := c.Recommend(ctx, req)
	if err != nil {
		// TODO: Handle error.
	}
	// TODO: Use resp.
	_ = resp
}

Java

詳情請參閱 AI Applications Java API 參考說明文件

如要向 AI Applications 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機制」。

import com.google.cloud.discoveryengine.v1beta.RecommendRequest;
import com.google.cloud.discoveryengine.v1beta.RecommendResponse;
import com.google.cloud.discoveryengine.v1beta.RecommendationServiceClient;
import com.google.cloud.discoveryengine.v1beta.ServingConfigName;
import com.google.cloud.discoveryengine.v1beta.UserEvent;
import com.google.protobuf.Value;
import java.util.HashMap;

public class SyncRecommend {

  public static void main(String[] args) throws Exception {
    syncRecommend();
  }

  public static void syncRecommend() throws Exception {
    // This snippet has been automatically generated and should be regarded as a code template only.
    // It will require modifications to work:
    // - It may require correct/in-range values for request initialization.
    // - It may require specifying regional endpoints when creating the service client as shown in
    // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
    try (RecommendationServiceClient recommendationServiceClient =
        RecommendationServiceClient.create()) {
      RecommendRequest request =
          RecommendRequest.newBuilder()
              .setServingConfig(
                  ServingConfigName.ofProjectLocationDataStoreServingConfigName(
                          "[PROJECT]", "[LOCATION]", "[DATA_STORE]", "[SERVING_CONFIG]")
                      .toString())
              .setUserEvent(UserEvent.newBuilder().build())
              .setPageSize(883849137)
              .setFilter("filter-1274492040")
              .setValidateOnly(true)
              .putAllParams(new HashMap<String, Value>())
              .putAllUserLabels(new HashMap<String, String>())
              .build();
      RecommendResponse response = recommendationServiceClient.recommend(request);
    }
  }
}

Node.js

詳情請參閱 AI Applications Node.js API 參考說明文件

如要向 AI Applications 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機制」。

/**
 * This snippet has been automatically generated and should be regarded as a code template only.
 * It will require modifications to work.
 * It may require correct/in-range values for request initialization.
 * TODO(developer): Uncomment these variables before running the sample.
 */
/**
 *  Required. Full resource name of a
 *  ServingConfig google.cloud.discoveryengine.v1beta.ServingConfig:
 *  `projects/* /locations/global/collections/* /engines/* /servingConfigs/*`, or
 *  `projects/* /locations/global/collections/* /dataStores/* /servingConfigs/*`
 *  One default serving config is created along with your recommendation engine
 *  creation. The engine ID is used as the ID of the default serving
 *  config. For example, for Engine
 *  `projects/* /locations/global/collections/* /engines/my-engine`, you can use
 *  `projects/* /locations/global/collections/* /engines/my-engine/servingConfigs/my-engine`
 *  for your
 *  RecommendationService.Recommend google.cloud.discoveryengine.v1beta.RecommendationService.Recommend 
 *  requests.
 */
// const servingConfig = 'abc123'
/**
 *  Required. Context about the user, what they are looking at and what action
 *  they took to trigger the Recommend request. Note that this user event
 *  detail won't be ingested to userEvent logs. Thus, a separate userEvent
 *  write request is required for event logging.
 *  Don't set
 *  UserEvent.user_pseudo_id google.cloud.discoveryengine.v1beta.UserEvent.user_pseudo_id 
 *  or
 *  UserEvent.user_info.user_id google.cloud.discoveryengine.v1beta.UserInfo.user_id 
 *  to the same fixed ID for different users. If you are trying to receive
 *  non-personalized recommendations (not recommended; this can negatively
 *  impact model performance), instead set
 *  UserEvent.user_pseudo_id google.cloud.discoveryengine.v1beta.UserEvent.user_pseudo_id 
 *  to a random unique ID and leave
 *  UserEvent.user_info.user_id google.cloud.discoveryengine.v1beta.UserInfo.user_id 
 *  unset.
 */
// const userEvent = {}
/**
 *  Maximum number of results to return. Set this property
 *  to the number of recommendation results needed. If zero, the service
 *  chooses a reasonable default. The maximum allowed value is 100. Values
 *  above 100 are set to 100.
 */
// const pageSize = 1234
/**
 *  Filter for restricting recommendation results with a length limit of 5,000
 *  characters. Currently, only filter expressions on the `filter_tags`
 *  attribute is supported.
 *  Examples:
 *   * `(filter_tags: ANY("Red", "Blue") OR filter_tags: ANY("Hot", "Cold"))`
 *   * `(filter_tags: ANY("Red", "Blue")) AND NOT (filter_tags: ANY("Green"))`
 *  If `attributeFilteringSyntax` is set to true under the `params` field, then
 *  attribute-based expressions are expected instead of the above described
 *  tag-based syntax. Examples:
 *   * (launguage: ANY("en", "es")) AND NOT (categories: ANY("Movie"))
 *   * (available: true) AND
 *     (launguage: ANY("en", "es")) OR (categories: ANY("Movie"))
 *  If your filter blocks all results, the API returns generic
 *  (unfiltered) popular Documents. If you only want results strictly matching
 *  the filters, set `strictFiltering` to `true` in
 *  RecommendRequest.params google.cloud.discoveryengine.v1beta.RecommendRequest.params 
 *  to receive empty results instead.
 *  Note that the API never returns
 *  Document google.cloud.discoveryengine.v1beta.Document s with
 *  `storageStatus` as `EXPIRED` or `DELETED` regardless of filter choices.
 */
// const filter = 'abc123'
/**
 *  Use validate only mode for this recommendation query. If set to `true`, a
 *  fake model is used that returns arbitrary Document IDs.
 *  Note that the validate only mode should only be used for testing the API,
 *  or if the model is not ready.
 */
// const validateOnly = true
/**
 *  Additional domain specific parameters for the recommendations.
 *  Allowed values:
 *  * `returnDocument`: Boolean. If set to `true`, the associated Document
 *     object is returned in
 *     RecommendResponse.RecommendationResult.document google.cloud.discoveryengine.v1beta.RecommendResponse.RecommendationResult.document.
 *  * `returnScore`: Boolean. If set to true, the recommendation score
 *     corresponding to each returned Document is set in
 *     RecommendResponse.RecommendationResult.metadata google.cloud.discoveryengine.v1beta.RecommendResponse.RecommendationResult.metadata.
 *     The given score indicates the probability of a Document conversion given
 *     the user's context and history.
 *  * `strictFiltering`: Boolean. True by default. If set to `false`, the
 *  service
 *     returns generic (unfiltered) popular Documents instead of empty if
 *     your filter blocks all recommendation results.
 *  * `diversityLevel`: String. Default empty. If set to be non-empty, then
 *     it needs to be one of:
 *      *  `no-diversity`
 *      *  `low-diversity`
 *      *  `medium-diversity`
 *      *  `high-diversity`
 *      *  `auto-diversity`
 *     This gives request-level control and adjusts recommendation results
 *     based on Document category.
 *  * `attributeFilteringSyntax`: Boolean. False by default. If set to true,
 *     the `filter` field is interpreted according to the new,
 *     attribute-based syntax.
 */
// const params = [1,2,3,4]
/**
 *  The user labels applied to a resource must meet the following requirements:
 *  * Each resource can have multiple labels, up to a maximum of 64.
 *  * Each label must be a key-value pair.
 *  * Keys have a minimum length of 1 character and a maximum length of 63
 *    characters and cannot be empty. Values can be empty and have a maximum
 *    length of 63 characters.
 *  * Keys and values can contain only lowercase letters, numeric characters,
 *    underscores, and dashes. All characters must use UTF-8 encoding, and
 *    international characters are allowed.
 *  * The key portion of a label must be unique. However, you can use the same
 *    key with multiple resources.
 *  * Keys must start with a lowercase letter or international character.
 *  See Requirements for
 *  labels (https://cloud.google.com/resource-manager/docs/creating-managing-labels#requirements)
 *  for more details.
 */
// const userLabels = [1,2,3,4]

// Imports the Discoveryengine library
const {RecommendationServiceClient} = require('@google-cloud/discoveryengine').v1beta;

// Instantiates a client
const discoveryengineClient = new RecommendationServiceClient();

async function callRecommend() {
  // Construct request
  const request = {
    servingConfig,
    userEvent,
  };

  // Run request
  const response = await discoveryengineClient.recommend(request);
  console.log(response);
}

callRecommend();

PHP

詳情請參閱 AI Applications PHP API 參考說明文件

如要向 AI Applications 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機制」。

use Google\ApiCore\ApiException;
use Google\Cloud\DiscoveryEngine\V1beta\Client\RecommendationServiceClient;
use Google\Cloud\DiscoveryEngine\V1beta\RecommendRequest;
use Google\Cloud\DiscoveryEngine\V1beta\RecommendResponse;
use Google\Cloud\DiscoveryEngine\V1beta\UserEvent;

/**
 * Makes a recommendation, which requires a contextual user event.
 *
 * @param string $formattedServingConfig Full resource name of a
 *                                       [ServingConfig][google.cloud.discoveryengine.v1beta.ServingConfig]:
 *                                       `projects/&#42;/locations/global/collections/&#42;/engines/&#42;/servingConfigs/*`, or
 *                                       `projects/&#42;/locations/global/collections/&#42;/dataStores/&#42;/servingConfigs/*`
 *
 *                                       One default serving config is created along with your recommendation engine
 *                                       creation. The engine ID is used as the ID of the default serving
 *                                       config. For example, for Engine
 *                                       `projects/&#42;/locations/global/collections/&#42;/engines/my-engine`, you can use
 *                                       `projects/&#42;/locations/global/collections/&#42;/engines/my-engine/servingConfigs/my-engine`
 *                                       for your
 *                                       [RecommendationService.Recommend][google.cloud.discoveryengine.v1beta.RecommendationService.Recommend]
 *                                       requests. Please see
 *                                       {@see RecommendationServiceClient::servingConfigName()} for help formatting this field.
 * @param string $userEventEventType     User event type. Allowed values are:
 *
 *                                       Generic values:
 *
 *                                       * `search`: Search for Documents.
 *                                       * `view-item`: Detailed page view of a Document.
 *                                       * `view-item-list`: View of a panel or ordered list of Documents.
 *                                       * `view-home-page`: View of the home page.
 *                                       * `view-category-page`: View of a category page, e.g. Home > Men > Jeans
 *
 *                                       Retail-related values:
 *
 *                                       * `add-to-cart`: Add an item(s) to cart, e.g. in Retail online shopping
 *                                       * `purchase`: Purchase an item(s)
 *
 *                                       Media-related values:
 *
 *                                       * `media-play`: Start/resume watching a video, playing a song, etc.
 *                                       * `media-complete`: Finished or stopped midway through a video, song, etc.
 * @param string $userEventUserPseudoId  A unique identifier for tracking visitors.
 *
 *                                       For example, this could be implemented with an HTTP cookie, which should be
 *                                       able to uniquely identify a visitor on a single device. This unique
 *                                       identifier should not change if the visitor log in/out of the website.
 *
 *                                       Do not set the field to the same fixed ID for different users. This mixes
 *                                       the event history of those users together, which results in degraded model
 *                                       quality.
 *
 *                                       The field must be a UTF-8 encoded string with a length limit of 128
 *                                       characters. Otherwise, an `INVALID_ARGUMENT` error is returned.
 *
 *                                       The field should not contain PII or user-data. We recommend to use Google
 *                                       Analytics [Client
 *                                       ID](https://developers.google.com/analytics/devguides/collection/analyticsjs/field-reference#clientId)
 *                                       for this field.
 */
function recommend_sample(
    string $formattedServingConfig,
    string $userEventEventType,
    string $userEventUserPseudoId
): void {
    // Create a client.
    $recommendationServiceClient = new RecommendationServiceClient();

    // Prepare the request message.
    $userEvent = (new UserEvent())
        ->setEventType($userEventEventType)
        ->setUserPseudoId($userEventUserPseudoId);
    $request = (new RecommendRequest())
        ->setServingConfig($formattedServingConfig)
        ->setUserEvent($userEvent);

    // Call the API and handle any network failures.
    try {
        /** @var RecommendResponse $response */
        $response = $recommendationServiceClient->recommend($request);
        printf('Response data: %s' . PHP_EOL, $response->serializeToJsonString());
    } catch (ApiException $ex) {
        printf('Call failed with message: %s' . PHP_EOL, $ex->getMessage());
    }
}

/**
 * Helper to execute the sample.
 *
 * This sample has been automatically generated and should be regarded as a code
 * template only. It will require modifications to work:
 *  - It may require correct/in-range values for request initialization.
 *  - It may require specifying regional endpoints when creating the service client,
 *    please see the apiEndpoint client configuration option for more details.
 */
function callSample(): void
{
    $formattedServingConfig = RecommendationServiceClient::servingConfigName(
        '[PROJECT]',
        '[LOCATION]',
        '[DATA_STORE]',
        '[SERVING_CONFIG]'
    );
    $userEventEventType = '[EVENT_TYPE]';
    $userEventUserPseudoId = '[USER_PSEUDO_ID]';

    recommend_sample($formattedServingConfig, $userEventEventType, $userEventUserPseudoId);
}

Python

詳情請參閱 AI Applications Python API 參考說明文件

如要向 AI Applications 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機制」。

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import discoveryengine_v1beta


def sample_recommend():
    # Create a client
    client = discoveryengine_v1beta.RecommendationServiceClient()

    # Initialize request argument(s)
    user_event = discoveryengine_v1beta.UserEvent()
    user_event.event_type = "event_type_value"
    user_event.user_pseudo_id = "user_pseudo_id_value"

    request = discoveryengine_v1beta.RecommendRequest(
        serving_config="serving_config_value",
        user_event=user_event,
    )

    # Make the request
    response = client.recommend(request=request)

    # Handle the response
    print(response)

Ruby

詳情請參閱 AI Applications Ruby API 參考說明文件

如要向 AI Applications 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機制」。

require "google/cloud/discovery_engine/v1beta"

##
# Snippet for the recommend call in the RecommendationService service
#
# This snippet has been automatically generated and should be regarded as a code
# template only. It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in https://cloud.google.com/ruby/docs/reference.
#
# This is an auto-generated example demonstrating basic usage of
# Google::Cloud::DiscoveryEngine::V1beta::RecommendationService::Client#recommend.
#
def recommend
  # Create a client object. The client can be reused for multiple calls.
  client = Google::Cloud::DiscoveryEngine::V1beta::RecommendationService::Client.new

  # Create a request. To set request fields, pass in keyword arguments.
  request = Google::Cloud::DiscoveryEngine::V1beta::RecommendRequest.new

  # Call the recommend method.
  result = client.recommend request

  # The returned object is of type Google::Cloud::DiscoveryEngine::V1beta::RecommendResponse.
  p result
end