pulumi-gcp-kotlin/com.pulumi.gcp.vertex.kotlin/AiFeatureStoreEntityTypeFeatureArgs

AiFeatureStoreEntityTypeFeatureArgs

data class AiFeatureStoreEntityTypeFeatureArgs(val description: Output<String>? = null, val entitytype: Output<String>? = null, val labels: Output<Map<String, String>>? = null, val name: Output<String>? = null, val valueType: Output<String>? = null) : ConvertibleToJava<AiFeatureStoreEntityTypeFeatureArgs>

Feature Metadata information that describes an attribute of an entity type. For example, apple is an entity type, and color is a feature that describes apple. To get more information about FeaturestoreEntitytypeFeature, see:

Example Usage

Vertex Ai Featurestore Entitytype Feature

package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.vertex.AiFeatureStore;
import com.pulumi.gcp.vertex.AiFeatureStoreArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreOnlineServingConfigArgs;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityType;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeArgs;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeFeature;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeFeatureArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        var featurestore = new AiFeatureStore("featurestore", AiFeatureStoreArgs.builder()
            .labels(Map.of("foo", "bar"))
            .region("us-central1")
            .onlineServingConfig(AiFeatureStoreOnlineServingConfigArgs.builder()
                .fixedNodeCount(2)
                .build())
            .build());
        var entity = new AiFeatureStoreEntityType("entity", AiFeatureStoreEntityTypeArgs.builder()
            .labels(Map.of("foo", "bar"))
            .featurestore(featurestore.id())
            .build());
        var feature = new AiFeatureStoreEntityTypeFeature("feature", AiFeatureStoreEntityTypeFeatureArgs.builder()
            .labels(Map.of("foo", "bar"))
            .entitytype(entity.id())
            .valueType("INT64_ARRAY")
            .build());
    }
}
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Vertex Ai Featurestore Entitytype Feature With Beta Fields

package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.vertex.AiFeatureStore;
import com.pulumi.gcp.vertex.AiFeatureStoreArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreOnlineServingConfigArgs;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityType;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeFeature;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeFeatureArgs;
import com.pulumi.resources.CustomResourceOptions;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        var featurestore = new AiFeatureStore("featurestore", AiFeatureStoreArgs.builder()
            .labels(Map.of("foo", "bar"))
            .region("us-central1")
            .onlineServingConfig(AiFeatureStoreOnlineServingConfigArgs.builder()
                .fixedNodeCount(2)
                .build())
            .build(), CustomResourceOptions.builder()
                .provider(google_beta)
                .build());
        var entity = new AiFeatureStoreEntityType("entity", AiFeatureStoreEntityTypeArgs.builder()
            .labels(Map.of("foo", "bar"))
            .featurestore(featurestore.id())
            .monitoringConfig(AiFeatureStoreEntityTypeMonitoringConfigArgs.builder()
                .snapshotAnalysis(AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs.builder()
                    .disabled(false)
                    .monitoringInterval("86400s")
                    .build())
                .categoricalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs.builder()
                    .value(0.3)
                    .build())
                .numericalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs.builder()
                    .value(0.3)
                    .build())
                .build())
            .build(), CustomResourceOptions.builder()
                .provider(google_beta)
                .build());
        var feature = new AiFeatureStoreEntityTypeFeature("feature", AiFeatureStoreEntityTypeFeatureArgs.builder()
            .labels(Map.of("foo", "bar"))
            .entitytype(entity.id())
            .valueType("INT64_ARRAY")
            .build(), CustomResourceOptions.builder()
                .provider(google_beta)
                .build());
    }
}
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Import

FeaturestoreEntitytypeFeature can be imported using any of these accepted formats:

$ pulumi import gcp:vertex/aiFeatureStoreEntityTypeFeature:AiFeatureStoreEntityTypeFeature default {{entitytype}}/features/{{name}}
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Constructors

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constructor(description: Output<String>? = null, entitytype: Output<String>? = null, labels: Output<Map<String, String>>? = null, name: Output<String>? = null, valueType: Output<String>? = null)

Properties

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val description: Output<String>? = null

Description of the feature.

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val entitytype: Output<String>? = null

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entitytype}.

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val labels: Output<Map<String, String>>? = null

A set of key/value label pairs to assign to the feature.

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val name: Output<String>? = null

The name of the feature. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.

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val valueType: Output<String>? = null

Type of Feature value. Immutable. https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.featurestores.entityTypes.features#ValueType

Functions

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open override fun toJava(): AiFeatureStoreEntityTypeFeatureArgs