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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

google-native.aiplatform/v1beta1.FeatureView

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

    Creates a new FeatureView in a given FeatureOnlineStore. Auto-naming is currently not supported for this resource.

    Create FeatureView Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new FeatureView(name: string, args: FeatureViewArgs, opts?: CustomResourceOptions);
    @overload
    def FeatureView(resource_name: str,
                    args: FeatureViewArgs,
                    opts: Optional[ResourceOptions] = None)
    
    @overload
    def FeatureView(resource_name: str,
                    opts: Optional[ResourceOptions] = None,
                    feature_online_store_id: Optional[str] = None,
                    feature_view_id: Optional[str] = None,
                    big_query_source: Optional[GoogleCloudAiplatformV1beta1FeatureViewBigQuerySourceArgs] = None,
                    etag: Optional[str] = None,
                    feature_registry_source: Optional[GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceArgs] = None,
                    labels: Optional[Mapping[str, str]] = None,
                    location: Optional[str] = None,
                    project: Optional[str] = None,
                    run_sync_immediately: Optional[bool] = None,
                    sync_config: Optional[GoogleCloudAiplatformV1beta1FeatureViewSyncConfigArgs] = None,
                    vector_search_config: Optional[GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigArgs] = None)
    func NewFeatureView(ctx *Context, name string, args FeatureViewArgs, opts ...ResourceOption) (*FeatureView, error)
    public FeatureView(string name, FeatureViewArgs args, CustomResourceOptions? opts = null)
    public FeatureView(String name, FeatureViewArgs args)
    public FeatureView(String name, FeatureViewArgs args, CustomResourceOptions options)
    
    type: google-native:aiplatform/v1beta1:FeatureView
    properties: # The arguments to resource properties.
    options: # Bag of options to control resource's behavior.
    
    

    Parameters

    name string
    The unique name of the resource.
    args FeatureViewArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args FeatureViewArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args FeatureViewArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args FeatureViewArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args FeatureViewArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Example

    The following reference example uses placeholder values for all input properties.

    var google_nativeFeatureViewResource = new GoogleNative.Aiplatform.V1Beta1.FeatureView("google-nativeFeatureViewResource", new()
    {
        FeatureOnlineStoreId = "string",
        FeatureViewId = "string",
        BigQuerySource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewBigQuerySourceArgs
        {
            EntityIdColumns = new[]
            {
                "string",
            },
            Uri = "string",
        },
        Etag = "string",
        FeatureRegistrySource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceArgs
        {
            FeatureGroups = new[]
            {
                new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroupArgs
                {
                    FeatureGroupId = "string",
                    FeatureIds = new[]
                    {
                        "string",
                    },
                },
            },
        },
        Labels = 
        {
            { "string", "string" },
        },
        Location = "string",
        Project = "string",
        RunSyncImmediately = false,
        SyncConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewSyncConfigArgs
        {
            Cron = "string",
        },
        VectorSearchConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigArgs
        {
            BruteForceConfig = null,
            CrowdingColumn = "string",
            DistanceMeasureType = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureType.DistanceMeasureTypeUnspecified,
            EmbeddingColumn = "string",
            EmbeddingDimension = 0,
            FilterColumns = new[]
            {
                "string",
            },
            TreeAhConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigArgs
            {
                LeafNodeEmbeddingCount = "string",
            },
        },
    });
    
    example, err := aiplatformv1beta1.NewFeatureView(ctx, "google-nativeFeatureViewResource", &aiplatformv1beta1.FeatureViewArgs{
    FeatureOnlineStoreId: pulumi.String("string"),
    FeatureViewId: pulumi.String("string"),
    BigQuerySource: &aiplatform.GoogleCloudAiplatformV1beta1FeatureViewBigQuerySourceArgs{
    EntityIdColumns: pulumi.StringArray{
    pulumi.String("string"),
    },
    Uri: pulumi.String("string"),
    },
    Etag: pulumi.String("string"),
    FeatureRegistrySource: &aiplatform.GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceArgs{
    FeatureGroups: aiplatform.GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroupArray{
    &aiplatform.GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroupArgs{
    FeatureGroupId: pulumi.String("string"),
    FeatureIds: pulumi.StringArray{
    pulumi.String("string"),
    },
    },
    },
    },
    Labels: pulumi.StringMap{
    "string": pulumi.String("string"),
    },
    Location: pulumi.String("string"),
    Project: pulumi.String("string"),
    RunSyncImmediately: pulumi.Bool(false),
    SyncConfig: &aiplatform.GoogleCloudAiplatformV1beta1FeatureViewSyncConfigArgs{
    Cron: pulumi.String("string"),
    },
    VectorSearchConfig: &aiplatform.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigArgs{
    BruteForceConfig: nil,
    CrowdingColumn: pulumi.String("string"),
    DistanceMeasureType: aiplatformv1beta1.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureTypeDistanceMeasureTypeUnspecified,
    EmbeddingColumn: pulumi.String("string"),
    EmbeddingDimension: pulumi.Int(0),
    FilterColumns: pulumi.StringArray{
    pulumi.String("string"),
    },
    TreeAhConfig: &aiplatform.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigArgs{
    LeafNodeEmbeddingCount: pulumi.String("string"),
    },
    },
    })
    
    var google_nativeFeatureViewResource = new FeatureView("google-nativeFeatureViewResource", FeatureViewArgs.builder()        
        .featureOnlineStoreId("string")
        .featureViewId("string")
        .bigQuerySource(GoogleCloudAiplatformV1beta1FeatureViewBigQuerySourceArgs.builder()
            .entityIdColumns("string")
            .uri("string")
            .build())
        .etag("string")
        .featureRegistrySource(GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceArgs.builder()
            .featureGroups(GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroupArgs.builder()
                .featureGroupId("string")
                .featureIds("string")
                .build())
            .build())
        .labels(Map.of("string", "string"))
        .location("string")
        .project("string")
        .runSyncImmediately(false)
        .syncConfig(GoogleCloudAiplatformV1beta1FeatureViewSyncConfigArgs.builder()
            .cron("string")
            .build())
        .vectorSearchConfig(GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigArgs.builder()
            .bruteForceConfig()
            .crowdingColumn("string")
            .distanceMeasureType("DISTANCE_MEASURE_TYPE_UNSPECIFIED")
            .embeddingColumn("string")
            .embeddingDimension(0)
            .filterColumns("string")
            .treeAhConfig(GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigArgs.builder()
                .leafNodeEmbeddingCount("string")
                .build())
            .build())
        .build());
    
    google_native_feature_view_resource = google_native.aiplatform.v1beta1.FeatureView("google-nativeFeatureViewResource",
        feature_online_store_id="string",
        feature_view_id="string",
        big_query_source=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureViewBigQuerySourceArgs(
            entity_id_columns=["string"],
            uri="string",
        ),
        etag="string",
        feature_registry_source=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceArgs(
            feature_groups=[google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroupArgs(
                feature_group_id="string",
                feature_ids=["string"],
            )],
        ),
        labels={
            "string": "string",
        },
        location="string",
        project="string",
        run_sync_immediately=False,
        sync_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureViewSyncConfigArgs(
            cron="string",
        ),
        vector_search_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigArgs(
            brute_force_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfigArgs(),
            crowding_column="string",
            distance_measure_type=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureType.DISTANCE_MEASURE_TYPE_UNSPECIFIED,
            embedding_column="string",
            embedding_dimension=0,
            filter_columns=["string"],
            tree_ah_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigArgs(
                leaf_node_embedding_count="string",
            ),
        ))
    
    const google_nativeFeatureViewResource = new google_native.aiplatform.v1beta1.FeatureView("google-nativeFeatureViewResource", {
        featureOnlineStoreId: "string",
        featureViewId: "string",
        bigQuerySource: {
            entityIdColumns: ["string"],
            uri: "string",
        },
        etag: "string",
        featureRegistrySource: {
            featureGroups: [{
                featureGroupId: "string",
                featureIds: ["string"],
            }],
        },
        labels: {
            string: "string",
        },
        location: "string",
        project: "string",
        runSyncImmediately: false,
        syncConfig: {
            cron: "string",
        },
        vectorSearchConfig: {
            bruteForceConfig: {},
            crowdingColumn: "string",
            distanceMeasureType: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureType.DistanceMeasureTypeUnspecified,
            embeddingColumn: "string",
            embeddingDimension: 0,
            filterColumns: ["string"],
            treeAhConfig: {
                leafNodeEmbeddingCount: "string",
            },
        },
    });
    
    type: google-native:aiplatform/v1beta1:FeatureView
    properties:
        bigQuerySource:
            entityIdColumns:
                - string
            uri: string
        etag: string
        featureOnlineStoreId: string
        featureRegistrySource:
            featureGroups:
                - featureGroupId: string
                  featureIds:
                    - string
        featureViewId: string
        labels:
            string: string
        location: string
        project: string
        runSyncImmediately: false
        syncConfig:
            cron: string
        vectorSearchConfig:
            bruteForceConfig: {}
            crowdingColumn: string
            distanceMeasureType: DISTANCE_MEASURE_TYPE_UNSPECIFIED
            embeddingColumn: string
            embeddingDimension: 0
            filterColumns:
                - string
            treeAhConfig:
                leafNodeEmbeddingCount: string
    

    FeatureView Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    The FeatureView resource accepts the following input properties:

    FeatureOnlineStoreId string
    FeatureViewId string
    Required. The ID to use for the FeatureView, which will become the final component of the FeatureView's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within a FeatureOnlineStore.
    BigQuerySource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewBigQuerySource
    Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
    Etag string
    Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    FeatureRegistrySource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySource
    Optional. Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
    Labels Dictionary<string, string>
    Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    Location string
    Project string
    RunSyncImmediately bool
    Immutable. If set to true, one on demand sync will be run immediately, regardless whether the FeatureView.sync_config is configured or not.
    SyncConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewSyncConfig
    Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving.
    VectorSearchConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig
    Optional. Configuration for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
    FeatureOnlineStoreId string
    FeatureViewId string
    Required. The ID to use for the FeatureView, which will become the final component of the FeatureView's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within a FeatureOnlineStore.
    BigQuerySource GoogleCloudAiplatformV1beta1FeatureViewBigQuerySourceArgs
    Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
    Etag string
    Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    FeatureRegistrySource GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceArgs
    Optional. Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
    Labels map[string]string
    Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    Location string
    Project string
    RunSyncImmediately bool
    Immutable. If set to true, one on demand sync will be run immediately, regardless whether the FeatureView.sync_config is configured or not.
    SyncConfig GoogleCloudAiplatformV1beta1FeatureViewSyncConfigArgs
    Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving.
    VectorSearchConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigArgs
    Optional. Configuration for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
    featureOnlineStoreId String
    featureViewId String
    Required. The ID to use for the FeatureView, which will become the final component of the FeatureView's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within a FeatureOnlineStore.
    bigQuerySource GoogleCloudAiplatformV1beta1FeatureViewBigQuerySource
    Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
    etag String
    Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    featureRegistrySource GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySource
    Optional. Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
    labels Map<String,String>
    Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    location String
    project String
    runSyncImmediately Boolean
    Immutable. If set to true, one on demand sync will be run immediately, regardless whether the FeatureView.sync_config is configured or not.
    syncConfig GoogleCloudAiplatformV1beta1FeatureViewSyncConfig
    Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving.
    vectorSearchConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig
    Optional. Configuration for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
    featureOnlineStoreId string
    featureViewId string
    Required. The ID to use for the FeatureView, which will become the final component of the FeatureView's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within a FeatureOnlineStore.
    bigQuerySource GoogleCloudAiplatformV1beta1FeatureViewBigQuerySource
    Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
    etag string
    Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    featureRegistrySource GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySource
    Optional. Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
    labels {[key: string]: string}
    Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    location string
    project string
    runSyncImmediately boolean
    Immutable. If set to true, one on demand sync will be run immediately, regardless whether the FeatureView.sync_config is configured or not.
    syncConfig GoogleCloudAiplatformV1beta1FeatureViewSyncConfig
    Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving.
    vectorSearchConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig
    Optional. Configuration for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
    feature_online_store_id str
    feature_view_id str
    Required. The ID to use for the FeatureView, which will become the final component of the FeatureView's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within a FeatureOnlineStore.
    big_query_source GoogleCloudAiplatformV1beta1FeatureViewBigQuerySourceArgs
    Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
    etag str
    Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    feature_registry_source GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceArgs
    Optional. Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
    labels Mapping[str, str]
    Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    location str
    project str
    run_sync_immediately bool
    Immutable. If set to true, one on demand sync will be run immediately, regardless whether the FeatureView.sync_config is configured or not.
    sync_config GoogleCloudAiplatformV1beta1FeatureViewSyncConfigArgs
    Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving.
    vector_search_config GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigArgs
    Optional. Configuration for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
    featureOnlineStoreId String
    featureViewId String
    Required. The ID to use for the FeatureView, which will become the final component of the FeatureView's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within a FeatureOnlineStore.
    bigQuerySource Property Map
    Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
    etag String
    Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    featureRegistrySource Property Map
    Optional. Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
    labels Map<String>
    Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    location String
    project String
    runSyncImmediately Boolean
    Immutable. If set to true, one on demand sync will be run immediately, regardless whether the FeatureView.sync_config is configured or not.
    syncConfig Property Map
    Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving.
    vectorSearchConfig Property Map
    Optional. Configuration for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.

    Outputs

    All input properties are implicitly available as output properties. Additionally, the FeatureView resource produces the following output properties:

    CreateTime string
    Timestamp when this FeatureView was created.
    Id string
    The provider-assigned unique ID for this managed resource.
    Name string
    Name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}
    UpdateTime string
    Timestamp when this FeatureView was last updated.
    CreateTime string
    Timestamp when this FeatureView was created.
    Id string
    The provider-assigned unique ID for this managed resource.
    Name string
    Name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}
    UpdateTime string
    Timestamp when this FeatureView was last updated.
    createTime String
    Timestamp when this FeatureView was created.
    id String
    The provider-assigned unique ID for this managed resource.
    name String
    Name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}
    updateTime String
    Timestamp when this FeatureView was last updated.
    createTime string
    Timestamp when this FeatureView was created.
    id string
    The provider-assigned unique ID for this managed resource.
    name string
    Name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}
    updateTime string
    Timestamp when this FeatureView was last updated.
    create_time str
    Timestamp when this FeatureView was created.
    id str
    The provider-assigned unique ID for this managed resource.
    name str
    Name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}
    update_time str
    Timestamp when this FeatureView was last updated.
    createTime String
    Timestamp when this FeatureView was created.
    id String
    The provider-assigned unique ID for this managed resource.
    name String
    Name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}
    updateTime String
    Timestamp when this FeatureView was last updated.

    Supporting Types

    GoogleCloudAiplatformV1beta1FeatureViewBigQuerySource, GoogleCloudAiplatformV1beta1FeatureViewBigQuerySourceArgs

    EntityIdColumns List<string>
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    Uri string
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
    EntityIdColumns []string
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    Uri string
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
    entityIdColumns List<String>
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    uri String
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
    entityIdColumns string[]
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    uri string
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
    entity_id_columns Sequence[str]
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    uri str
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
    entityIdColumns List<String>
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    uri String
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.

    GoogleCloudAiplatformV1beta1FeatureViewBigQuerySourceResponse, GoogleCloudAiplatformV1beta1FeatureViewBigQuerySourceResponseArgs

    EntityIdColumns List<string>
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    Uri string
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
    EntityIdColumns []string
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    Uri string
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
    entityIdColumns List<String>
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    uri String
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
    entityIdColumns string[]
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    uri string
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
    entity_id_columns Sequence[str]
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    uri str
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
    entityIdColumns List<String>
    Columns to construct entity_id / row keys. Start by supporting 1 only.
    uri String
    The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.

    GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySource, GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceArgs

    featureGroups List<Property Map>
    List of features that need to be synced to Online Store.

    GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroup, GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroupArgs

    FeatureGroupId string
    Identifier of the feature group.
    FeatureIds List<string>
    Identifiers of features under the feature group.
    FeatureGroupId string
    Identifier of the feature group.
    FeatureIds []string
    Identifiers of features under the feature group.
    featureGroupId String
    Identifier of the feature group.
    featureIds List<String>
    Identifiers of features under the feature group.
    featureGroupId string
    Identifier of the feature group.
    featureIds string[]
    Identifiers of features under the feature group.
    feature_group_id str
    Identifier of the feature group.
    feature_ids Sequence[str]
    Identifiers of features under the feature group.
    featureGroupId String
    Identifier of the feature group.
    featureIds List<String>
    Identifiers of features under the feature group.

    GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroupResponse, GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroupResponseArgs

    FeatureGroupId string
    Identifier of the feature group.
    FeatureIds List<string>
    Identifiers of features under the feature group.
    FeatureGroupId string
    Identifier of the feature group.
    FeatureIds []string
    Identifiers of features under the feature group.
    featureGroupId String
    Identifier of the feature group.
    featureIds List<String>
    Identifiers of features under the feature group.
    featureGroupId string
    Identifier of the feature group.
    featureIds string[]
    Identifiers of features under the feature group.
    feature_group_id str
    Identifier of the feature group.
    feature_ids Sequence[str]
    Identifiers of features under the feature group.
    featureGroupId String
    Identifier of the feature group.
    featureIds List<String>
    Identifiers of features under the feature group.

    GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceResponse, GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceResponseArgs

    featureGroups List<Property Map>
    List of features that need to be synced to Online Store.

    GoogleCloudAiplatformV1beta1FeatureViewSyncConfig, GoogleCloudAiplatformV1beta1FeatureViewSyncConfigArgs

    Cron string
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
    Cron string
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
    cron String
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
    cron string
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
    cron str
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
    cron String
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".

    GoogleCloudAiplatformV1beta1FeatureViewSyncConfigResponse, GoogleCloudAiplatformV1beta1FeatureViewSyncConfigResponseArgs

    Cron string
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
    Cron string
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
    cron String
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
    cron string
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
    cron str
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
    cron String
    Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".

    GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig, GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigArgs

    BruteForceConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfig
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    CrowdingColumn string
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    DistanceMeasureType Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureType
    Optional. The distance measure used in nearest neighbor search.
    EmbeddingColumn string
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    EmbeddingDimension int
    Optional. The number of dimensions of the input embedding.
    FilterColumns List<string>
    Optional. Columns of features that're used to filter vector search results.
    TreeAhConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
    BruteForceConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfig
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    CrowdingColumn string
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    DistanceMeasureType GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureType
    Optional. The distance measure used in nearest neighbor search.
    EmbeddingColumn string
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    EmbeddingDimension int
    Optional. The number of dimensions of the input embedding.
    FilterColumns []string
    Optional. Columns of features that're used to filter vector search results.
    TreeAhConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
    bruteForceConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfig
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    crowdingColumn String
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    distanceMeasureType GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureType
    Optional. The distance measure used in nearest neighbor search.
    embeddingColumn String
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    embeddingDimension Integer
    Optional. The number of dimensions of the input embedding.
    filterColumns List<String>
    Optional. Columns of features that're used to filter vector search results.
    treeAhConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
    bruteForceConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfig
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    crowdingColumn string
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    distanceMeasureType GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureType
    Optional. The distance measure used in nearest neighbor search.
    embeddingColumn string
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    embeddingDimension number
    Optional. The number of dimensions of the input embedding.
    filterColumns string[]
    Optional. Columns of features that're used to filter vector search results.
    treeAhConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
    brute_force_config GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfig
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    crowding_column str
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    distance_measure_type GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureType
    Optional. The distance measure used in nearest neighbor search.
    embedding_column str
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    embedding_dimension int
    Optional. The number of dimensions of the input embedding.
    filter_columns Sequence[str]
    Optional. Columns of features that're used to filter vector search results.
    tree_ah_config GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
    bruteForceConfig Property Map
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    crowdingColumn String
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    distanceMeasureType "DISTANCE_MEASURE_TYPE_UNSPECIFIED" | "SQUARED_L2_DISTANCE" | "COSINE_DISTANCE" | "DOT_PRODUCT_DISTANCE"
    Optional. The distance measure used in nearest neighbor search.
    embeddingColumn String
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    embeddingDimension Number
    Optional. The number of dimensions of the input embedding.
    filterColumns List<String>
    Optional. Columns of features that're used to filter vector search results.
    treeAhConfig Property Map
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

    GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureType, GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureTypeArgs

    DistanceMeasureTypeUnspecified
    DISTANCE_MEASURE_TYPE_UNSPECIFIEDShould not be set.
    SquaredL2Distance
    SQUARED_L2_DISTANCEEuclidean (L_2) Distance.
    CosineDistance
    COSINE_DISTANCECosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking.
    DotProductDistance
    DOT_PRODUCT_DISTANCEDot Product Distance. Defined as a negative of the dot product.
    GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureTypeDistanceMeasureTypeUnspecified
    DISTANCE_MEASURE_TYPE_UNSPECIFIEDShould not be set.
    GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureTypeSquaredL2Distance
    SQUARED_L2_DISTANCEEuclidean (L_2) Distance.
    GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureTypeCosineDistance
    COSINE_DISTANCECosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking.
    GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureTypeDotProductDistance
    DOT_PRODUCT_DISTANCEDot Product Distance. Defined as a negative of the dot product.
    DistanceMeasureTypeUnspecified
    DISTANCE_MEASURE_TYPE_UNSPECIFIEDShould not be set.
    SquaredL2Distance
    SQUARED_L2_DISTANCEEuclidean (L_2) Distance.
    CosineDistance
    COSINE_DISTANCECosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking.
    DotProductDistance
    DOT_PRODUCT_DISTANCEDot Product Distance. Defined as a negative of the dot product.
    DistanceMeasureTypeUnspecified
    DISTANCE_MEASURE_TYPE_UNSPECIFIEDShould not be set.
    SquaredL2Distance
    SQUARED_L2_DISTANCEEuclidean (L_2) Distance.
    CosineDistance
    COSINE_DISTANCECosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking.
    DotProductDistance
    DOT_PRODUCT_DISTANCEDot Product Distance. Defined as a negative of the dot product.
    DISTANCE_MEASURE_TYPE_UNSPECIFIED
    DISTANCE_MEASURE_TYPE_UNSPECIFIEDShould not be set.
    SQUARED_L2_DISTANCE
    SQUARED_L2_DISTANCEEuclidean (L_2) Distance.
    COSINE_DISTANCE
    COSINE_DISTANCECosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking.
    DOT_PRODUCT_DISTANCE
    DOT_PRODUCT_DISTANCEDot Product Distance. Defined as a negative of the dot product.
    "DISTANCE_MEASURE_TYPE_UNSPECIFIED"
    DISTANCE_MEASURE_TYPE_UNSPECIFIEDShould not be set.
    "SQUARED_L2_DISTANCE"
    SQUARED_L2_DISTANCEEuclidean (L_2) Distance.
    "COSINE_DISTANCE"
    COSINE_DISTANCECosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking.
    "DOT_PRODUCT_DISTANCE"
    DOT_PRODUCT_DISTANCEDot Product Distance. Defined as a negative of the dot product.

    GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigResponse, GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigResponseArgs

    BruteForceConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfigResponse
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    CrowdingColumn string
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    DistanceMeasureType string
    Optional. The distance measure used in nearest neighbor search.
    EmbeddingColumn string
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    EmbeddingDimension int
    Optional. The number of dimensions of the input embedding.
    FilterColumns List<string>
    Optional. Columns of features that're used to filter vector search results.
    TreeAhConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigResponse
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
    BruteForceConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfigResponse
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    CrowdingColumn string
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    DistanceMeasureType string
    Optional. The distance measure used in nearest neighbor search.
    EmbeddingColumn string
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    EmbeddingDimension int
    Optional. The number of dimensions of the input embedding.
    FilterColumns []string
    Optional. Columns of features that're used to filter vector search results.
    TreeAhConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigResponse
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
    bruteForceConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfigResponse
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    crowdingColumn String
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    distanceMeasureType String
    Optional. The distance measure used in nearest neighbor search.
    embeddingColumn String
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    embeddingDimension Integer
    Optional. The number of dimensions of the input embedding.
    filterColumns List<String>
    Optional. Columns of features that're used to filter vector search results.
    treeAhConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigResponse
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
    bruteForceConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfigResponse
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    crowdingColumn string
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    distanceMeasureType string
    Optional. The distance measure used in nearest neighbor search.
    embeddingColumn string
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    embeddingDimension number
    Optional. The number of dimensions of the input embedding.
    filterColumns string[]
    Optional. Columns of features that're used to filter vector search results.
    treeAhConfig GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigResponse
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
    brute_force_config GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfigResponse
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    crowding_column str
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    distance_measure_type str
    Optional. The distance measure used in nearest neighbor search.
    embedding_column str
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    embedding_dimension int
    Optional. The number of dimensions of the input embedding.
    filter_columns Sequence[str]
    Optional. Columns of features that're used to filter vector search results.
    tree_ah_config GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigResponse
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
    bruteForceConfig Property Map
    Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
    crowdingColumn String
    Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.
    distanceMeasureType String
    Optional. The distance measure used in nearest neighbor search.
    embeddingColumn String
    Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
    embeddingDimension Number
    Optional. The number of dimensions of the input embedding.
    filterColumns List<String>
    Optional. Columns of features that're used to filter vector search results.
    treeAhConfig Property Map
    Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

    GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig, GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigArgs

    LeafNodeEmbeddingCount string
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
    LeafNodeEmbeddingCount string
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
    leafNodeEmbeddingCount String
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
    leafNodeEmbeddingCount string
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
    leaf_node_embedding_count str
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
    leafNodeEmbeddingCount String
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.

    GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigResponse, GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfigResponseArgs

    LeafNodeEmbeddingCount string
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
    LeafNodeEmbeddingCount string
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
    leafNodeEmbeddingCount String
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
    leafNodeEmbeddingCount string
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
    leaf_node_embedding_count str
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
    leafNodeEmbeddingCount String
    Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.

    Package Details

    Repository
    Google Cloud Native pulumi/pulumi-google-native
    License
    Apache-2.0
    google-native logo

    Google Cloud Native is in preview. Google Cloud Classic is fully supported.

    Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi