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sai-chaithugcf-owl-bot[bot]
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gcf-owl-bot[bot]
authored
feat(samples): add all feature values samples (#981)
* feat(samples): add all feature values samples * feat(samples): update all feature values samples * feat(samples): update big query dependency in pom.xml * feat(samples): code review changes * feat(samples): updated feature values samples with close method call and list variable * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>
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-1
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‎aiplatform/snippets/pom.xml

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@@ -62,6 +62,10 @@
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<artifactId>proto-google-cloud-aiplatform-v1beta1</artifactId>
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<version>0.17.0</version>
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</dependency>
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<dependency>
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<groupId>com.google.cloud</groupId>
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<artifactId>google-cloud-bigquery</artifactId>
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<version>2.13.6</version>
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</dependency>
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</dependencies>
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</project>

‎aiplatform/snippets/src/main/java/aiplatform/BatchCreateFeaturesSample.java

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/*
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* Copyright 2022 Google LLC
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*
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*
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* Create features in bulk for an existing entity type. See
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* https://cloud.google.com/vertex-ai/docs/featurestore/setup
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* before running the code snippet
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*/
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package aiplatform;
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// [START aiplatform_batch_create_features_sample]
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import com.google.api.gax.longrunning.OperationFuture;
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import com.google.cloud.aiplatform.v1.BatchCreateFeaturesOperationMetadata;
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import com.google.cloud.aiplatform.v1.BatchCreateFeaturesRequest;
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import com.google.cloud.aiplatform.v1.BatchCreateFeaturesResponse;
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import com.google.cloud.aiplatform.v1.CreateFeatureRequest;
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import com.google.cloud.aiplatform.v1.EntityTypeName;
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import com.google.cloud.aiplatform.v1.Feature;
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import com.google.cloud.aiplatform.v1.Feature.ValueType;
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import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
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import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.concurrent.ExecutionException;
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import java.util.concurrent.TimeUnit;
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import java.util.concurrent.TimeoutException;
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public class BatchCreateFeaturesSample {
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public static void main(String[] args)
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throws IOException, InterruptedException, ExecutionException, TimeoutException {
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// TODO(developer): Replace these variables before running the sample.
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String project = "YOUR_PROJECT_ID";
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String featurestoreId = "YOUR_FEATURESTORE_ID";
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String entityTypeId = "YOUR_ENTITY_TYPE_ID";
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String location = "us-central1";
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String endpoint = "us-central1-aiplatform.googleapis.com:443";
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int timeout = 300;
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batchCreateFeaturesSample(project, featurestoreId, entityTypeId, location, endpoint, timeout);
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}
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static void batchCreateFeaturesSample(
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String project,
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String featurestoreId,
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String entityTypeId,
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String location,
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String endpoint,
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int timeout)
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throws IOException, InterruptedException, ExecutionException, TimeoutException {
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FeaturestoreServiceSettings featurestoreServiceSettings =
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FeaturestoreServiceSettings.newBuilder().setEndpoint(endpoint).build();
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// Initialize client that will be used to send requests. This client only needs to be created
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// once, and can be reused for multiple requests. After completing all of your requests, call
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// the "close" method on the client to safely clean up any remaining background resources.
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try (FeaturestoreServiceClient featurestoreServiceClient =
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FeaturestoreServiceClient.create(featurestoreServiceSettings)) {
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List<CreateFeatureRequest> createFeatureRequests = new ArrayList<>();
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Feature titleFeature =
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Feature.newBuilder()
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.setDescription("The title of the movie")
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.setValueType(ValueType.STRING)
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.build();
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Feature genresFeature =
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Feature.newBuilder()
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.setDescription("The genres of the movie")
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.setValueType(ValueType.STRING)
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.build();
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Feature averageRatingFeature =
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Feature.newBuilder()
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.setDescription("The average rating for the movie, range is [1.0-5.0]")
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.setValueType(ValueType.DOUBLE)
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.build();
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createFeatureRequests.add(
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CreateFeatureRequest.newBuilder().setFeature(titleFeature).setFeatureId("title").build());
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createFeatureRequests.add(
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CreateFeatureRequest.newBuilder()
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.setFeature(genresFeature)
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.setFeatureId("genres")
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.build());
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createFeatureRequests.add(
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CreateFeatureRequest.newBuilder()
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.setFeature(averageRatingFeature)
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.setFeatureId("average_rating")
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.build());
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BatchCreateFeaturesRequest batchCreateFeaturesRequest =
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BatchCreateFeaturesRequest.newBuilder()
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.setParent(
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EntityTypeName.of(project, location, featurestoreId, entityTypeId).toString())
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.addAllRequests(createFeatureRequests)
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.build();
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OperationFuture<BatchCreateFeaturesResponse, BatchCreateFeaturesOperationMetadata>
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batchCreateFeaturesFuture =
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featurestoreServiceClient.batchCreateFeaturesAsync(batchCreateFeaturesRequest);
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System.out.format(
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"Operation name: %s%n", batchCreateFeaturesFuture.getInitialFuture().get().getName());
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System.out.println("Waiting for operation to finish...");
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BatchCreateFeaturesResponse batchCreateFeaturesResponse =
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batchCreateFeaturesFuture.get(timeout, TimeUnit.SECONDS);
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System.out.println("Batch Create Features Response");
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System.out.println(batchCreateFeaturesResponse);
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featurestoreServiceClient.close();
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}
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}
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}
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// [END aiplatform_batch_create_features_sample]

‎aiplatform/snippets/src/main/java/aiplatform/BatchReadFeatureValuesSample.java

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/*
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* Copyright 2022 Google LLC
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*
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*
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* Batch read feature values from a featurestore, as determined by your
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* read instances list file, to export data. See
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* https://cloud.google.com/vertex-ai/docs/featurestore/setup before running
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* the code snippet
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*/
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package aiplatform;
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// [START aiplatform_batch_read_feature_values_sample]
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import com.google.api.gax.longrunning.OperationFuture;
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import com.google.cloud.aiplatform.v1.BatchReadFeatureValuesOperationMetadata;
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import com.google.cloud.aiplatform.v1.BatchReadFeatureValuesRequest;
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import com.google.cloud.aiplatform.v1.BatchReadFeatureValuesRequest.EntityTypeSpec;
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import com.google.cloud.aiplatform.v1.BatchReadFeatureValuesResponse;
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import com.google.cloud.aiplatform.v1.BigQueryDestination;
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import com.google.cloud.aiplatform.v1.CsvSource;
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import com.google.cloud.aiplatform.v1.FeatureSelector;
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import com.google.cloud.aiplatform.v1.FeatureValueDestination;
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import com.google.cloud.aiplatform.v1.FeaturestoreName;
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import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
38+
import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
39+
import com.google.cloud.aiplatform.v1.GcsSource;
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import com.google.cloud.aiplatform.v1.IdMatcher;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.List;
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import java.util.concurrent.ExecutionException;
46+
import java.util.concurrent.TimeUnit;
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import java.util.concurrent.TimeoutException;
48+
49+
public class BatchReadFeatureValuesSample {
50+
51+
public static void main(String[] args)
52+
throws IOException, InterruptedException, ExecutionException, TimeoutException {
53+
// TODO(developer): Replace these variables before running the sample.
54+
String project = "YOUR_PROJECT_ID";
55+
String featurestoreId = "YOUR_FEATURESTORE_ID";
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String entityTypeId = "YOUR_ENTITY_TYPE_ID";
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String inputCsvFile = "YOU_INPUT_CSV_FILE";
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String destinationTableUri = "YOUR_DESTINATION_TABLE_URI";
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List<String> featureSelectorIds = Arrays.asList("title", "genres", "average_rating");
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String location = "us-central1";
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String endpoint = "us-central1-aiplatform.googleapis.com:443";
62+
int timeout = 300;
63+
batchReadFeatureValuesSample(
64+
project,
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featurestoreId,
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entityTypeId,
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inputCsvFile,
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destinationTableUri,
69+
featureSelectorIds,
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location,
71+
endpoint,
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timeout);
73+
}
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static void batchReadFeatureValuesSample(
76+
String project,
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String featurestoreId,
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String entityTypeId,
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String inputCsvFile,
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String destinationTableUri,
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List<String> featureSelectorIds,
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String location,
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String endpoint,
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int timeout)
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throws IOException, InterruptedException, ExecutionException, TimeoutException {
86+
FeaturestoreServiceSettings featurestoreServiceSettings =
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FeaturestoreServiceSettings.newBuilder().setEndpoint(endpoint).build();
88+
89+
// Initialize client that will be used to send requests. This client only needs to be created
90+
// once, and can be reused for multiple requests. After completing all of your requests, call
91+
// the "close" method on the client to safely clean up any remaining background resources.
92+
try (FeaturestoreServiceClient featurestoreServiceClient =
93+
FeaturestoreServiceClient.create(featurestoreServiceSettings)) {
94+
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List<EntityTypeSpec> entityTypeSpecs = new ArrayList<>();
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FeatureSelector featureSelector =
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FeatureSelector.newBuilder()
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.setIdMatcher(IdMatcher.newBuilder().addAllIds(featureSelectorIds).build())
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.build();
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EntityTypeSpec entityTypeSpec =
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EntityTypeSpec.newBuilder()
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.setEntityTypeId(entityTypeId)
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.setFeatureSelector(featureSelector)
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.build();
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entityTypeSpecs.add(entityTypeSpec);
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BigQueryDestination bigQueryDestination =
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BigQueryDestination.newBuilder().setOutputUri(destinationTableUri).build();
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GcsSource gcsSource = GcsSource.newBuilder().addUris(inputCsvFile).build();
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BatchReadFeatureValuesRequest batchReadFeatureValuesRequest =
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BatchReadFeatureValuesRequest.newBuilder()
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.setFeaturestore(FeaturestoreName.of(project, location, featurestoreId).toString())
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.setCsvReadInstances(CsvSource.newBuilder().setGcsSource(gcsSource))
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.setDestination(
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FeatureValueDestination.newBuilder().setBigqueryDestination(bigQueryDestination))
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.addAllEntityTypeSpecs(entityTypeSpecs)
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.build();
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OperationFuture<BatchReadFeatureValuesResponse, BatchReadFeatureValuesOperationMetadata>
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batchReadFeatureValuesFuture =
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featurestoreServiceClient.batchReadFeatureValuesAsync(batchReadFeatureValuesRequest);
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System.out.format(
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"Operation name: %s%n", batchReadFeatureValuesFuture.getInitialFuture().get().getName());
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System.out.println("Waiting for operation to finish...");
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BatchReadFeatureValuesResponse batchReadFeatureValuesResponse =
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batchReadFeatureValuesFuture.get(timeout, TimeUnit.SECONDS);
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System.out.println("Batch Read Feature Values Response");
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System.out.println(batchReadFeatureValuesResponse);
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featurestoreServiceClient.close();
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}
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}
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}
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// [END aiplatform_batch_read_feature_values_sample]

‎aiplatform/snippets/src/main/java/aiplatform/ExportFeatureValuesSample.java

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/*
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* Copyright 2022 Google LLC
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
6+
* You may obtain a copy of the License at
7+
*
8+
* http://www.apache.org/licenses/LICENSE-2.0
9+
*
10+
* Unless required by applicable law or agreed to in writing, software
11+
* distributed under the License is distributed on an "AS IS" BASIS,
12+
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13+
* See the License for the specific language governing permissions and
14+
* limitations under the License.
15+
*
16+
*
17+
* Bulk export feature values from a featurestore. See
18+
* https://cloud.google.com/vertex-ai/docs/featurestore/setup before running
19+
* the code snippet
20+
*/
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package aiplatform;
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// [START aiplatform_export_feature_values_sample]
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import com.google.api.gax.longrunning.OperationFuture;
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import com.google.cloud.aiplatform.v1.BigQueryDestination;
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import com.google.cloud.aiplatform.v1.EntityTypeName;
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import com.google.cloud.aiplatform.v1.ExportFeatureValuesOperationMetadata;
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import com.google.cloud.aiplatform.v1.ExportFeatureValuesRequest;
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import com.google.cloud.aiplatform.v1.ExportFeatureValuesRequest.FullExport;
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import com.google.cloud.aiplatform.v1.ExportFeatureValuesResponse;
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import com.google.cloud.aiplatform.v1.FeatureSelector;
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import com.google.cloud.aiplatform.v1.FeatureValueDestination;
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import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
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import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
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import com.google.cloud.aiplatform.v1.IdMatcher;
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import java.io.IOException;
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import java.util.Arrays;
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import java.util.List;
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import java.util.concurrent.ExecutionException;
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import java.util.concurrent.TimeUnit;
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import java.util.concurrent.TimeoutException;
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public class ExportFeatureValuesSample {
46+
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public static void main(String[] args)
48+
throws IOException, InterruptedException, ExecutionException, TimeoutException {
49+
// TODO(developer): Replace these variables before running the sample.
50+
String project = "YOUR_PROJECT_ID";
51+
String featurestoreId = "YOUR_FEATURESTORE_ID";
52+
String entityTypeId = "YOUR_ENTITY_TYPE_ID";
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String destinationTableUri = "YOUR_DESTINATION_TABLE_URI";
54+
List<String> featureSelectorIds = Arrays.asList("title", "genres", "average_rating");
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String location = "us-central1";
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String endpoint = "us-central1-aiplatform.googleapis.com:443";
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int timeout = 300;
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exportFeatureValuesSample(
59+
project,
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featurestoreId,
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entityTypeId,
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destinationTableUri,
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featureSelectorIds,
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location,
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endpoint,
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timeout);
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}
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static void exportFeatureValuesSample(
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String project,
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String featurestoreId,
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String entityTypeId,
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String destinationTableUri,
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List<String> featureSelectorIds,
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String location,
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String endpoint,
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int timeout)
78+
throws IOException, InterruptedException, ExecutionException, TimeoutException {
79+
FeaturestoreServiceSettings featurestoreServiceSettings =
80+
FeaturestoreServiceSettings.newBuilder().setEndpoint(endpoint).build();
81+
82+
// Initialize client that will be used to send requests. This client only needs to be created
83+
// once, and can be reused for multiple requests. After completing all of your requests, call
84+
// the "close" method on the client to safely clean up any remaining background resources.
85+
try (FeaturestoreServiceClient featurestoreServiceClient =
86+
FeaturestoreServiceClient.create(featurestoreServiceSettings)) {
87+
88+
FeatureSelector featureSelector =
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FeatureSelector.newBuilder()
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.setIdMatcher(IdMatcher.newBuilder().addAllIds(featureSelectorIds).build())
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.build();
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ExportFeatureValuesRequest exportFeatureValuesRequest =
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ExportFeatureValuesRequest.newBuilder()
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.setEntityType(
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EntityTypeName.of(project, location, featurestoreId, entityTypeId).toString())
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.setDestination(
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FeatureValueDestination.newBuilder()
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.setBigqueryDestination(
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BigQueryDestination.newBuilder().setOutputUri(destinationTableUri)))
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.setFeatureSelector(featureSelector)
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.setFullExport(FullExport.newBuilder())
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.build();
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OperationFuture<ExportFeatureValuesResponse, ExportFeatureValuesOperationMetadata>
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exportFeatureValuesFuture =
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featurestoreServiceClient.exportFeatureValuesAsync(exportFeatureValuesRequest);
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System.out.format(
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"Operation name: %s%n", exportFeatureValuesFuture.getInitialFuture().get().getName());
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System.out.println("Waiting for operation to finish...");
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ExportFeatureValuesResponse exportFeatureValuesResponse =
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exportFeatureValuesFuture.get(timeout, TimeUnit.SECONDS);
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System.out.println("Export Feature Values Response");
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System.out.println(exportFeatureValuesResponse);
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featurestoreServiceClient.close();
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}
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}
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}
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// [END aiplatform_export_feature_values_sample]

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