FeaturestoreService¶

async batch_read_feature_values(request: Optional[Union[BatchReadFeatureValuesRequest, dict]] = None, *, featurestore: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Batch reads Feature values from a Featurestore.

This API enables batch reading Feature values, where each read instance in the batch may read Feature values of entities from one or more EntityTypes. Point-in-time correctness is guaranteed for Feature values of each read instance as of each instance’s read timestamp.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_batch_read_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    csv_read_instances = aiplatform_v1.CsvSource()
    csv_read_instances.gcs_source.uris = ['uris_value1', 'uris_value2']

    destination = aiplatform_v1.FeatureValueDestination()
    destination.bigquery_destination.output_uri = "output_uri_value"

    entity_type_specs = aiplatform_v1.EntityTypeSpec()
    entity_type_specs.entity_type_id = "entity_type_id_value"
    entity_type_specs.feature_selector.id_matcher.ids = ['ids_value1', 'ids_value2']

    request = aiplatform_v1.BatchReadFeatureValuesRequest(
        csv_read_instances=csv_read_instances,
        featurestore="featurestore_value",
        destination=destination,
        entity_type_specs=entity_type_specs,
    )

    # Make the request
    operation = client.batch_read_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.BatchReadFeatureValuesRequest, dict]]) – The request object. Request message for [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.BatchReadFeatureValues].
featurestore (str) –
Required. The resource name of the Featurestore from which to query Feature values. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

This corresponds to the featurestore field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.BatchReadFeatureValuesResponse Response message for: [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.BatchReadFeatureValues].

Return type:

async cancel_operation(request: Optional[CancelOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (CancelOperationRequest) – The request object. Request message for CancelOperation method.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

None

static common_billing_account_path(billing_account: str) → str¶: Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str¶: Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str¶: Returns a fully-qualified location string.

static common_organization_path(organization: str) → str¶: Returns a fully-qualified organization string.

static common_project_path(project: str) → str¶: Returns a fully-qualified project string.

async create_entity_type(request: Optional[Union[CreateEntityTypeRequest, dict]] = None, *, parent: Optional[str] = None, entity_type: Optional[EntityType] = None, entity_type_id: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Creates a new EntityType in a given Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateEntityTypeRequest(
        parent="parent_value",
        entity_type_id="entity_type_id_value",
    )

    # Make the request
    operation = client.create_entity_type(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.CreateEntityTypeRequest, dict]]) – The request object. Request message for [FeaturestoreService.CreateEntityType][google.cloud.aiplatform.v1.FeaturestoreService.CreateEntityType].
parent (str) –
Required. The resource name of the Featurestore to create EntityTypes. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
entity_type (google.cloud.aiplatform_v1.types.EntityType) – The EntityType to create. This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
entity_type_id (str) –
Required. The ID to use for the EntityType, which will become the final component of the EntityType’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 featurestore.

This corresponds to the entity_type_id field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.EntityType An entity type is a type of object in a system that needs to be modeled and: have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Return type:

async create_feature(request: Optional[Union[CreateFeatureRequest, dict]] = None, *, parent: Optional[str] = None, feature: Optional[Feature] = None, feature_id: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Creates a new Feature in a given EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateFeatureRequest(
        parent="parent_value",
        feature_id="feature_id_value",
    )

    # Make the request
    operation = client.create_feature(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.CreateFeatureRequest, dict]]) – The request object. Request message for [FeaturestoreService.CreateFeature][google.cloud.aiplatform.v1.FeaturestoreService.CreateFeature]. Request message for [FeatureRegistryService.CreateFeature][google.cloud.aiplatform.v1.FeatureRegistryService.CreateFeature].
parent (str) –
Required. The resource name of the EntityType or FeatureGroup to create a Feature. Format for entity_type as parent: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} Format for feature_group as parent: projects/{project}/locations/{location}/featureGroups/{feature_group}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
feature (google.cloud.aiplatform_v1.types.Feature) – Required. The Feature to create. This corresponds to the feature field on the request instance; if request is provided, this should not be set.
feature_id (str) –
Required. The ID to use for the Feature, which will become the final component of the Feature’s resource name.

This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number.

The value must be unique within an EntityType/FeatureGroup.

This corresponds to the feature_id field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.Feature Feature Metadata information.: For example, color is a feature that describes an apple.

Return type:

async create_featurestore(request: Optional[Union[CreateFeaturestoreRequest, dict]] = None, *, parent: Optional[str] = None, featurestore: Optional[Featurestore] = None, featurestore_id: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Creates a new Featurestore in a given project and location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateFeaturestoreRequest(
        parent="parent_value",
        featurestore_id="featurestore_id_value",
    )

    # Make the request
    operation = client.create_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.CreateFeaturestoreRequest, dict]]) – The request object. Request message for [FeaturestoreService.CreateFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.CreateFeaturestore].
parent (str) –
Required. The resource name of the Location to create Featurestores. Format: projects/{project}/locations/{location}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
featurestore (google.cloud.aiplatform_v1.types.Featurestore) – Required. The Featurestore to create. This corresponds to the featurestore field on the request instance; if request is provided, this should not be set.
featurestore_id (str) –
Required. The ID to use for this Featurestore, which will become the final component of the Featurestore’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 the project and location.

This corresponds to the featurestore_id field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.Featurestore Vertex AI Feature Store provides a centralized repository for organizing,: storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Return type:

async delete_entity_type(request: Optional[Union[DeleteEntityTypeRequest, dict]] = None, *, name: Optional[str] = None, force: Optional[bool] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Deletes a single EntityType. The EntityType must not have any Features or force must be set to true for the request to succeed.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteEntityTypeRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_entity_type(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.DeleteEntityTypeRequest, dict]]) – The request object. Request message for [FeaturestoreService.DeleteEntityType][google.cloud.aiplatform.v1.FeaturestoreService.DeleteEntityType].
name (str) –
Required. The name of the EntityType to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

This corresponds to the name field on the request instance; if request is provided, this should not be set.
force (bool) –
If set to true, any Features for this EntityType will also be deleted. (Otherwise, the request will only work if the EntityType has no Features.)

This corresponds to the force field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated

empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {
rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

}

Return type:

async delete_feature(request: Optional[Union[DeleteFeatureRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Deletes a single Feature.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteFeatureRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_feature(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.DeleteFeatureRequest, dict]]) – The request object. Request message for [FeaturestoreService.DeleteFeature][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeature]. Request message for [FeatureRegistryService.DeleteFeature][google.cloud.aiplatform.v1.FeatureRegistryService.DeleteFeature].
name (str) –
Required. The name of the Features to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature}

This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated

empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {
rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

}

Return type:

async delete_feature_values(request: Optional[Union[DeleteFeatureValuesRequest, dict]] = None, *, entity_type: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Delete Feature values from Featurestore.

The progress of the deletion is tracked by the returned operation. The deleted feature values are guaranteed to be invisible to subsequent read operations after the operation is marked as successfully done.

If a delete feature values operation fails, the feature values returned from reads and exports may be inconsistent. If consistency is required, the caller must retry the same delete request again and wait till the new operation returned is marked as successfully done.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    select_entity = aiplatform_v1.SelectEntity()
    select_entity.entity_id_selector.csv_source.gcs_source.uris = ['uris_value1', 'uris_value2']

    request = aiplatform_v1.DeleteFeatureValuesRequest(
        select_entity=select_entity,
        entity_type="entity_type_value",
    )

    # Make the request
    operation = client.delete_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.DeleteFeatureValuesRequest, dict]]) – The request object. Request message for [FeaturestoreService.DeleteFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeatureValues].
entity_type (str) –
Required. The resource name of the EntityType grouping the Features for which values are being deleted from. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}

This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.DeleteFeatureValuesResponse Response message for: [FeaturestoreService.DeleteFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeatureValues].

Return type:

async delete_featurestore(request: Optional[Union[DeleteFeaturestoreRequest, dict]] = None, *, name: Optional[str] = None, force: Optional[bool] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Deletes a single Featurestore. The Featurestore must not contain any EntityTypes or force must be set to true for the request to succeed.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteFeaturestoreRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.DeleteFeaturestoreRequest, dict]]) – The request object. Request message for [FeaturestoreService.DeleteFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeaturestore].
name (str) –
Required. The name of the Featurestore to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

This corresponds to the name field on the request instance; if request is provided, this should not be set.
force (bool) –
If set to true, any EntityTypes and Features for this Featurestore will also be deleted. (Otherwise, the request will only work if the Featurestore has no EntityTypes.)

This corresponds to the force field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated

empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {
rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

}

Return type:

async delete_operation(request: Optional[DeleteOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (DeleteOperationRequest) – The request object. Request message for DeleteOperation method.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

None

static entity_type_path(project: str, location: str, featurestore: str, entity_type: str) → str¶: Returns a fully-qualified entity_type string.

async export_feature_values(request: Optional[Union[ExportFeatureValuesRequest, dict]] = None, *, entity_type: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Exports Feature values from all the entities of a target EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_export_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    destination = aiplatform_v1.FeatureValueDestination()
    destination.bigquery_destination.output_uri = "output_uri_value"

    feature_selector = aiplatform_v1.FeatureSelector()
    feature_selector.id_matcher.ids = ['ids_value1', 'ids_value2']

    request = aiplatform_v1.ExportFeatureValuesRequest(
        entity_type="entity_type_value",
        destination=destination,
        feature_selector=feature_selector,
    )

    # Make the request
    operation = client.export_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.ExportFeatureValuesRequest, dict]]) – The request object. Request message for [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ExportFeatureValues].
entity_type (str) –
Required. The resource name of the EntityType from which to export Feature values. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.ExportFeatureValuesResponse Response message for: [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ExportFeatureValues].

Return type:

FeaturestoreServiceAsyncClient

static feature_path(project: str, location: str, featurestore: str, entity_type: str, feature: str) → str¶: Returns a fully-qualified feature string.

static featurestore_path(project: str, location: str, featurestore: str) → str¶: Returns a fully-qualified featurestore string.

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]¶

Creates an instance of this client using the provided credentials: file.

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]¶

Creates an instance of this client using the provided credentials: info.

Parameters:

info (dict) – The service account private key info.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

FeaturestoreServiceAsyncClient

classmethod from_service_account_json(filename: str, *args, **kwargs)¶

Creates an instance of this client using the provided credentials: file.

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

FeaturestoreServiceAsyncClient

async get_entity_type(request: Optional[Union[GetEntityTypeRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → EntityType[source]¶

Gets details of a single EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetEntityTypeRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_entity_type(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.GetEntityTypeRequest, dict]]) – The request object. Request message for [FeaturestoreService.GetEntityType][google.cloud.aiplatform.v1.FeaturestoreService.GetEntityType].
name (str) –
Required. The name of the EntityType resource. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Return type:

async get_feature(request: Optional[Union[GetFeatureRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Feature[source]¶

Gets details of a single Feature.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetFeatureRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_feature(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.GetFeatureRequest, dict]]) – The request object. Request message for [FeaturestoreService.GetFeature][google.cloud.aiplatform.v1.FeaturestoreService.GetFeature]. Request message for [FeatureRegistryService.GetFeature][google.cloud.aiplatform.v1.FeatureRegistryService.GetFeature].
name (str) –
Required. The name of the Feature resource. Format for entity_type as parent: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} Format for feature_group as parent: projects/{project}/locations/{location}/featureGroups/{feature_group}

This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Feature Metadata information. For example, color is a feature that describes an apple.

Return type:

google.cloud.aiplatform_v1.types.Featurestore

async get_featurestore(request: Optional[Union[GetFeaturestoreRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Featurestore[source]¶

Gets details of a single Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetFeaturestoreRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_featurestore(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.GetFeaturestoreRequest, dict]]) – The request object. Request message for [FeaturestoreService.GetFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.GetFeaturestore].
name (str) –
Required. The name of the Featurestore resource.

This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Return type:

async get_iam_policy(request: Optional[GetIamPolicyRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Parameters:

request (GetIamPolicyRequest) – The request object. Request message for GetIamPolicy method.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

async get_location(request: Optional[GetLocationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Location[source]¶

Gets information about a location.

Parameters:

request (GetLocationRequest) – The request object. Request message for GetLocation method.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Location object.

Return type:

Location

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[ClientOptions] = None)[source]¶

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not “true”, the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “always”, use the default mTLS endpoint; if the environment variable is “never”, use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameters:

client_options (google.api_core.client_options.ClientOptions) – Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.

Returns:

returns the API endpoint and the: client cert source to use.

Return type:

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises:

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

async get_operation(request: Optional[GetOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Gets the latest state of a long-running operation.

Parameters:

request (GetOperationRequest) – The request object. Request message for GetOperation method.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An Operation object.

Return type:

Operation

classmethod get_transport_class(label: Optional[str] = None) → Type[FeaturestoreServiceTransport]¶

Returns an appropriate transport class.

Parameters:: label – The name of the desired transport. If none is provided, then the first transport in the registry is used.
Returns:: The transport class to use.

async import_feature_values(request: Optional[Union[ImportFeatureValuesRequest, dict]] = None, *, entity_type: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Imports Feature values into the Featurestore from a source storage. The progress of the import is tracked by the returned operation. The imported features are guaranteed to be visible to subsequent read operations after the operation is marked as successfully done.

If an import operation fails, the Feature values returned from reads and exports may be inconsistent. If consistency is required, the caller must retry the same import request again and wait till the new operation returned is marked as successfully done.

There are also scenarios where the caller can cause inconsistency.

Source data for import contains multiple distinct

Feature values for the same entity ID and timestamp.

Source is modified during an import. This includes

adding, updating, or removing source data and/or metadata. Examples of updating metadata include but are not limited to changing storage location, storage class, or retention policy.

Online serving cluster is under-provisioned.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_import_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    avro_source = aiplatform_v1.AvroSource()
    avro_source.gcs_source.uris = ['uris_value1', 'uris_value2']

    feature_specs = aiplatform_v1.FeatureSpec()
    feature_specs.id = "id_value"

    request = aiplatform_v1.ImportFeatureValuesRequest(
        avro_source=avro_source,
        feature_time_field="feature_time_field_value",
        entity_type="entity_type_value",
        feature_specs=feature_specs,
    )

    # Make the request
    operation = client.import_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.ImportFeatureValuesRequest, dict]]) – The request object. Request message for [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ImportFeatureValues].
entity_type (str) –
Required. The resource name of the EntityType grouping the Features for which values are being imported. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}

This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.ImportFeatureValuesResponse Response message for: [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ImportFeatureValues].

Return type:

google.cloud.aiplatform_v1.services.featurestore_service.pagers.ListEntityTypesAsyncPager

async list_entity_types(request: Optional[Union[ListEntityTypesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListEntityTypesAsyncPager[source]¶

Lists EntityTypes in a given Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_entity_types():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListEntityTypesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_entity_types(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.ListEntityTypesRequest, dict]]) – The request object. Request message for [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1.FeaturestoreService.ListEntityTypes].
parent (str) –
Required. The resource name of the Featurestore to list EntityTypes. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for: [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1.FeaturestoreService.ListEntityTypes].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

async list_features(request: Optional[Union[ListFeaturesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListFeaturesAsyncPager[source]¶

Lists Features in a given EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListFeaturesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_features(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.ListFeaturesRequest, dict]]) – The request object. Request message for [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures]. Request message for [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatures].
parent (str) –
Required. The resource name of the Location to list Features. Format for entity_type as parent: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} Format for feature_group as parent: projects/{project}/locations/{location}/featureGroups/{feature_group}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for: [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures]. Response message for [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatures].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1.services.featurestore_service.pagers.ListFeaturesAsyncPager

async list_featurestores(request: Optional[Union[ListFeaturestoresRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListFeaturestoresAsyncPager[source]¶

Lists Featurestores in a given project and location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_featurestores():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListFeaturestoresRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_featurestores(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.ListFeaturestoresRequest, dict]]) – The request object. Request message for [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1.FeaturestoreService.ListFeaturestores].
parent (str) –
Required. The resource name of the Location to list Featurestores. Format: projects/{project}/locations/{location}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for: [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1.FeaturestoreService.ListFeaturestores].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1.services.featurestore_service.pagers.ListFeaturestoresAsyncPager

async list_locations(request: Optional[ListLocationsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListLocationsResponse[source]¶

Lists information about the supported locations for this service.

Parameters:

request (ListLocationsRequest) – The request object. Request message for ListLocations method.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for ListLocations method.

Return type:

ListLocationsResponse

async list_operations(request: Optional[ListOperationsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListOperationsResponse[source]¶

Lists operations that match the specified filter in the request.

Parameters:

request (ListOperationsRequest) – The request object. Request message for ListOperations method.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for ListOperations method.

Return type:

ListOperationsResponse

static parse_common_billing_account_path(path: str) → Dict[str, str]¶: Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str]¶: Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str]¶: Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str]¶: Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str]¶: Parse a project path into its component segments.

static parse_entity_type_path(path: str) → Dict[str, str]¶: Parses a entity_type path into its component segments.

static parse_feature_path(path: str) → Dict[str, str]¶: Parses a feature path into its component segments.

static parse_featurestore_path(path: str) → Dict[str, str]¶: Parses a featurestore path into its component segments.

async search_features(request: Optional[Union[SearchFeaturesRequest, dict]] = None, *, location: Optional[str] = None, query: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → SearchFeaturesAsyncPager[source]¶

Searches Features matching a query in a given project.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_search_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.SearchFeaturesRequest(
        location="location_value",
    )

    # Make the request
    page_result = client.search_features(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.SearchFeaturesRequest, dict]]) – The request object. Request message for [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1.FeaturestoreService.SearchFeatures].
location (str) –
Required. The resource name of the Location to search Features. Format: projects/{project}/locations/{location}

This corresponds to the location field on the request instance; if request is provided, this should not be set.
query (str) –
Query string that is a conjunction of field-restricted queries and/or field-restricted filters. Field-restricted queries and filters can be combined using AND to form a conjunction.

A field query is in the form FIELD:QUERY. This implicitly checks if QUERY exists as a substring within Feature’s FIELD. The QUERY and the FIELD are converted to a sequence of words (i.e. tokens) for comparison. This is done by:
- Removing leading/trailing whitespace and tokenizing the search value. Characters that are not one of alphanumeric [a-zA-Z0-9], underscore _, or asterisk * are treated as delimiters for tokens. * is treated as a wildcard that matches characters within a token.
- Ignoring case.
- Prepending an asterisk to the first and appending an asterisk to the last token in QUERY.
A QUERY must be either a singular token or a phrase. A phrase is one or multiple words enclosed in double quotation marks (“). With phrases, the order of the words is important. Words in the phrase must be matching in order and consecutively.

Supported FIELDs for field-restricted queries:
- feature_id
- description
- entity_type_id
Examples:
- feature_id: foo –> Matches a Feature with ID containing the substring foo (eg. foo, foofeature, barfoo).
- feature_id: foo*feature –> Matches a Feature with ID containing the substring foo*feature (eg. foobarfeature).
- feature_id: foo AND description: bar –> Matches a Feature with ID containing the substring foo and description containing the substring bar.
Besides field queries, the following exact-match filters are supported. The exact-match filters do not support wildcards. Unlike field-restricted queries, exact-match filters are case-sensitive.
- feature_id: Supports = comparisons.
- description: Supports = comparisons. Multi-token filters should be enclosed in quotes.
- entity_type_id: Supports = comparisons.
- value_type: Supports = and != comparisons.
- labels: Supports key-value equality as well as key presence.
- featurestore_id: Supports = comparisons.
Examples:
- description = "foo bar" –> Any Feature with description exactly equal to foo bar
- value_type = DOUBLE –> Features whose type is DOUBLE.
- labels.active = yes AND labels.env = prod –> Features having both (active: yes) and (env: prod) labels.
- labels.env: * –> Any Feature which has a label with env as the key.
This corresponds to the query field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for: [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1.FeaturestoreService.SearchFeatures].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1.services.featurestore_service.pagers.SearchFeaturesAsyncPager

async set_iam_policy(request: Optional[SetIamPolicyRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Parameters:

request (SetIamPolicyRequest) – The request object. Request message for SetIamPolicy method.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

async test_iam_permissions(request: Optional[TestIamPermissionsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TestIamPermissionsResponse[source]¶

Tests the specified IAM permissions against the IAM access control: policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Parameters:

request (TestIamPermissionsRequest) – The request object. Request message for TestIamPermissions method.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for TestIamPermissions method.

Return type:

TestIamPermissionsResponse

property transport: FeaturestoreServiceTransport¶

Returns the transport used by the client instance.

Returns:: The transport used by the client instance.
Return type:: FeaturestoreServiceTransport

property universe_domain: str¶

Return the universe domain used by the client instance.

Returns:

The universe domain used: by the client instance.

Return type:

str

async update_entity_type(request: Optional[Union[UpdateEntityTypeRequest, dict]] = None, *, entity_type: Optional[EntityType] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → EntityType[source]¶

Updates the parameters of a single EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateEntityTypeRequest(
    )

    # Make the request
    response = await client.update_entity_type(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.UpdateEntityTypeRequest, dict]]) – The request object. Request message for [FeaturestoreService.UpdateEntityType][google.cloud.aiplatform.v1.FeaturestoreService.UpdateEntityType].
entity_type (google.cloud.aiplatform_v1.types.EntityType) –
Required. The EntityType’s name field is used to identify the EntityType to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
update_mask (google.protobuf.field_mask_pb2.FieldMask) –
Field mask is used to specify the fields to be overwritten in the EntityType resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:
- description
- labels
- monitoring_config.snapshot_analysis.disabled
- monitoring_config.snapshot_analysis.monitoring_interval_days
- monitoring_config.snapshot_analysis.staleness_days
- monitoring_config.import_features_analysis.state
- monitoring_config.import_features_analysis.anomaly_detection_baseline
- monitoring_config.numerical_threshold_config.value
- monitoring_config.categorical_threshold_config.value
- offline_storage_ttl_days
This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Return type:

async update_feature(request: Optional[Union[UpdateFeatureRequest, dict]] = None, *, feature: Optional[Feature] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Feature[source]¶

Updates the parameters of a single Feature.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateFeatureRequest(
    )

    # Make the request
    response = await client.update_feature(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.UpdateFeatureRequest, dict]]) – The request object. Request message for [FeaturestoreService.UpdateFeature][google.cloud.aiplatform.v1.FeaturestoreService.UpdateFeature]. Request message for [FeatureRegistryService.UpdateFeature][google.cloud.aiplatform.v1.FeatureRegistryService.UpdateFeature].
feature (google.cloud.aiplatform_v1.types.Feature) –
Required. The Feature’s name field is used to identify the Feature to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature}

This corresponds to the feature field on the request instance; if request is provided, this should not be set.
update_mask (google.protobuf.field_mask_pb2.FieldMask) –
Field mask is used to specify the fields to be overwritten in the Features resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:
- description
- labels
- disable_monitoring (Not supported for FeatureRegistryService Feature)
- point_of_contact (Not supported for FeaturestoreService FeatureStore)
This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Feature Metadata information. For example, color is a feature that describes an apple.

Return type:

async update_featurestore(request: Optional[Union[UpdateFeaturestoreRequest, dict]] = None, *, featurestore: Optional[Featurestore] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Updates the parameters of a single Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateFeaturestoreRequest(
    )

    # Make the request
    operation = client.update_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1.types.UpdateFeaturestoreRequest, dict]]) – The request object. Request message for [FeaturestoreService.UpdateFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.UpdateFeaturestore].
featurestore (google.cloud.aiplatform_v1.types.Featurestore) –
Required. The Featurestore’s name field is used to identify the Featurestore to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

This corresponds to the featurestore field on the request instance; if request is provided, this should not be set.
update_mask (google.protobuf.field_mask_pb2.FieldMask) –
Field mask is used to specify the fields to be overwritten in the Featurestore resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:
- labels
- online_serving_config.fixed_node_count
- online_serving_config.scaling
- online_storage_ttl_days
This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.Featurestore Vertex AI Feature Store provides a centralized repository for organizing,: storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Return type:

async wait_operation(request: Optional[WaitOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.

If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (WaitOperationRequest) – The request object. Request message for WaitOperation method.
retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An Operation object.

Return type:

Operation

class google.cloud.aiplatform_v1.services.featurestore_service.FeaturestoreServiceClient(*, credentials: ~typing.Optional[~google.auth.credentials.Credentials] = None, transport: ~typing.Optional[~typing.Union[str, ~google.cloud.aiplatform_v1.services.featurestore_service.transports.base.FeaturestoreServiceTransport, ~typing.Callable[[...], ~google.cloud.aiplatform_v1.services.featurestore_service.transports.base.FeaturestoreServiceTransport]]] = None, client_options: ~typing.Optional[~typing.Union[~google.api_core.client_options.ClientOptions, dict]] = None, client_info: ~google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)[source]¶

The service that handles CRUD and List for resources for Featurestore.

Instantiates the featurestore service client.

Parameters:

credentials (Optional[google.auth.credentials.Credentials]) – The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.
transport (Optional[Union[str,FeaturestoreServiceTransport,Callable[..., FeaturestoreServiceTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport. If a Callable is given, it will be called with the same set of initialization arguments as used in the FeaturestoreServiceTransport constructor. If set to None, a transport is chosen automatically.
client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]) –
Custom options for the client.

1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “always” (always use the default mTLS endpoint), “never” (always use the default regular endpoint) and “auto” (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value).

2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is “false” or not set, no client certificate will be used.

3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that the api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.
client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises:

google.auth.exceptions.MutualTLSChannelError – If mutual TLS transport creation failed for any reason.

__exit__(type, value, traceback)[source]¶: Releases underlying transport’s resources.

Warning

ONLY use as a context manager if the transport is NOT shared with other clients! Exiting the with block will CLOSE the transport and may cause errors in other clients!

property api_endpoint¶

Return the API endpoint used by the client instance.

Returns:: The API endpoint used by the client instance.
Return type:: str

batch_create_features(request: Optional[Union[BatchCreateFeaturesRequest, dict]] = None, *, parent: Optional[str] = None, requests: Optional[MutableSequence[CreateFeatureRequest]] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Creates a batch of Features in a given EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_batch_create_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    requests = aiplatform_v1.CreateFeatureRequest()
    requests.parent = "parent_value"
    requests.feature_id = "feature_id_value"

    request = aiplatform_v1.BatchCreateFeaturesRequest(
        parent="parent_value",
        requests=requests,
    )

    # Make the request
    operation = client.batch_create_features(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.BatchCreateFeaturesRequest, dict]) – The request object. Request message for [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1.FeaturestoreService.BatchCreateFeatures]. Request message for [FeatureRegistryService.BatchCreateFeatures][google.cloud.aiplatform.v1.FeatureRegistryService.BatchCreateFeatures].
parent (str) –
Required. The resource name of the EntityType/FeatureGroup to create the batch of Features under. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} projects/{project}/locations/{location}/featureGroups/{feature_group}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
requests (MutableSequence[google.cloud.aiplatform_v1.types.CreateFeatureRequest]) –
Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType / FeatureGroup. The parent field in each child request message can be omitted. If parent is set in a child request, then the value must match the parent value in this request message.

This corresponds to the requests field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.BatchCreateFeaturesResponse Response message for: [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1.FeaturestoreService.BatchCreateFeatures].

Return type:

batch_read_feature_values(request: Optional[Union[BatchReadFeatureValuesRequest, dict]] = None, *, featurestore: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Batch reads Feature values from a Featurestore.

This API enables batch reading Feature values, where each read instance in the batch may read Feature values of entities from one or more EntityTypes. Point-in-time correctness is guaranteed for Feature values of each read instance as of each instance’s read timestamp.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_batch_read_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    csv_read_instances = aiplatform_v1.CsvSource()
    csv_read_instances.gcs_source.uris = ['uris_value1', 'uris_value2']

    destination = aiplatform_v1.FeatureValueDestination()
    destination.bigquery_destination.output_uri = "output_uri_value"

    entity_type_specs = aiplatform_v1.EntityTypeSpec()
    entity_type_specs.entity_type_id = "entity_type_id_value"
    entity_type_specs.feature_selector.id_matcher.ids = ['ids_value1', 'ids_value2']

    request = aiplatform_v1.BatchReadFeatureValuesRequest(
        csv_read_instances=csv_read_instances,
        featurestore="featurestore_value",
        destination=destination,
        entity_type_specs=entity_type_specs,
    )

    # Make the request
    operation = client.batch_read_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.BatchReadFeatureValuesRequest, dict]) – The request object. Request message for [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.BatchReadFeatureValues].
featurestore (str) –
Required. The resource name of the Featurestore from which to query Feature values. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

This corresponds to the featurestore field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.BatchReadFeatureValuesResponse Response message for: [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.BatchReadFeatureValues].

Return type:

cancel_operation(request: Optional[CancelOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (CancelOperationRequest) – The request object. Request message for CancelOperation method.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

None

static common_billing_account_path(billing_account: str) → str[source]¶: Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str[source]¶: Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str[source]¶: Returns a fully-qualified location string.

static common_organization_path(organization: str) → str[source]¶: Returns a fully-qualified organization string.

static common_project_path(project: str) → str[source]¶: Returns a fully-qualified project string.

create_entity_type(request: Optional[Union[CreateEntityTypeRequest, dict]] = None, *, parent: Optional[str] = None, entity_type: Optional[EntityType] = None, entity_type_id: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Creates a new EntityType in a given Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_create_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateEntityTypeRequest(
        parent="parent_value",
        entity_type_id="entity_type_id_value",
    )

    # Make the request
    operation = client.create_entity_type(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.CreateEntityTypeRequest, dict]) – The request object. Request message for [FeaturestoreService.CreateEntityType][google.cloud.aiplatform.v1.FeaturestoreService.CreateEntityType].
parent (str) –
Required. The resource name of the Featurestore to create EntityTypes. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
entity_type (google.cloud.aiplatform_v1.types.EntityType) – The EntityType to create. This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
entity_type_id (str) –
Required. The ID to use for the EntityType, which will become the final component of the EntityType’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 featurestore.

This corresponds to the entity_type_id field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.EntityType An entity type is a type of object in a system that needs to be modeled and: have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Return type:

create_feature(request: Optional[Union[CreateFeatureRequest, dict]] = None, *, parent: Optional[str] = None, feature: Optional[Feature] = None, feature_id: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Creates a new Feature in a given EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_create_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateFeatureRequest(
        parent="parent_value",
        feature_id="feature_id_value",
    )

    # Make the request
    operation = client.create_feature(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.CreateFeatureRequest, dict]) – The request object. Request message for [FeaturestoreService.CreateFeature][google.cloud.aiplatform.v1.FeaturestoreService.CreateFeature]. Request message for [FeatureRegistryService.CreateFeature][google.cloud.aiplatform.v1.FeatureRegistryService.CreateFeature].
parent (str) –
Required. The resource name of the EntityType or FeatureGroup to create a Feature. Format for entity_type as parent: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} Format for feature_group as parent: projects/{project}/locations/{location}/featureGroups/{feature_group}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
feature (google.cloud.aiplatform_v1.types.Feature) – Required. The Feature to create. This corresponds to the feature field on the request instance; if request is provided, this should not be set.
feature_id (str) –
Required. The ID to use for the Feature, which will become the final component of the Feature’s resource name.

This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number.

The value must be unique within an EntityType/FeatureGroup.

This corresponds to the feature_id field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.Feature Feature Metadata information.: For example, color is a feature that describes an apple.

Return type:

create_featurestore(request: Optional[Union[CreateFeaturestoreRequest, dict]] = None, *, parent: Optional[str] = None, featurestore: Optional[Featurestore] = None, featurestore_id: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Creates a new Featurestore in a given project and location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_create_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateFeaturestoreRequest(
        parent="parent_value",
        featurestore_id="featurestore_id_value",
    )

    # Make the request
    operation = client.create_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.CreateFeaturestoreRequest, dict]) – The request object. Request message for [FeaturestoreService.CreateFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.CreateFeaturestore].
parent (str) –
Required. The resource name of the Location to create Featurestores. Format: projects/{project}/locations/{location}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
featurestore (google.cloud.aiplatform_v1.types.Featurestore) – Required. The Featurestore to create. This corresponds to the featurestore field on the request instance; if request is provided, this should not be set.
featurestore_id (str) –
Required. The ID to use for this Featurestore, which will become the final component of the Featurestore’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 the project and location.

This corresponds to the featurestore_id field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.Featurestore Vertex AI Feature Store provides a centralized repository for organizing,: storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Return type:

delete_entity_type(request: Optional[Union[DeleteEntityTypeRequest, dict]] = None, *, name: Optional[str] = None, force: Optional[bool] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Deletes a single EntityType. The EntityType must not have any Features or force must be set to true for the request to succeed.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_delete_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteEntityTypeRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_entity_type(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.DeleteEntityTypeRequest, dict]) – The request object. Request message for [FeaturestoreService.DeleteEntityType][google.cloud.aiplatform.v1.FeaturestoreService.DeleteEntityType].
name (str) –
Required. The name of the EntityType to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

This corresponds to the name field on the request instance; if request is provided, this should not be set.
force (bool) –
If set to true, any Features for this EntityType will also be deleted. (Otherwise, the request will only work if the EntityType has no Features.)

This corresponds to the force field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated

empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {
rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

}

Return type:

delete_feature(request: Optional[Union[DeleteFeatureRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Deletes a single Feature.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_delete_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteFeatureRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_feature(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.DeleteFeatureRequest, dict]) – The request object. Request message for [FeaturestoreService.DeleteFeature][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeature]. Request message for [FeatureRegistryService.DeleteFeature][google.cloud.aiplatform.v1.FeatureRegistryService.DeleteFeature].
name (str) –
Required. The name of the Features to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature}

This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated

empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {
rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

}

Return type:

delete_feature_values(request: Optional[Union[DeleteFeatureValuesRequest, dict]] = None, *, entity_type: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Delete Feature values from Featurestore.

The progress of the deletion is tracked by the returned operation. The deleted feature values are guaranteed to be invisible to subsequent read operations after the operation is marked as successfully done.

If a delete feature values operation fails, the feature values returned from reads and exports may be inconsistent. If consistency is required, the caller must retry the same delete request again and wait till the new operation returned is marked as successfully done.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_delete_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    select_entity = aiplatform_v1.SelectEntity()
    select_entity.entity_id_selector.csv_source.gcs_source.uris = ['uris_value1', 'uris_value2']

    request = aiplatform_v1.DeleteFeatureValuesRequest(
        select_entity=select_entity,
        entity_type="entity_type_value",
    )

    # Make the request
    operation = client.delete_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.DeleteFeatureValuesRequest, dict]) – The request object. Request message for [FeaturestoreService.DeleteFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeatureValues].
entity_type (str) –
Required. The resource name of the EntityType grouping the Features for which values are being deleted from. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}

This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.DeleteFeatureValuesResponse Response message for: [FeaturestoreService.DeleteFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeatureValues].

Return type:

delete_featurestore(request: Optional[Union[DeleteFeaturestoreRequest, dict]] = None, *, name: Optional[str] = None, force: Optional[bool] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Deletes a single Featurestore. The Featurestore must not contain any EntityTypes or force must be set to true for the request to succeed.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_delete_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteFeaturestoreRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.DeleteFeaturestoreRequest, dict]) – The request object. Request message for [FeaturestoreService.DeleteFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeaturestore].
name (str) –
Required. The name of the Featurestore to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

This corresponds to the name field on the request instance; if request is provided, this should not be set.
force (bool) –
If set to true, any EntityTypes and Features for this Featurestore will also be deleted. (Otherwise, the request will only work if the Featurestore has no EntityTypes.)

This corresponds to the force field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated

empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {
rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

}

Return type:

delete_operation(request: Optional[DeleteOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (DeleteOperationRequest) – The request object. Request message for DeleteOperation method.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

None

static entity_type_path(project: str, location: str, featurestore: str, entity_type: str) → str[source]¶: Returns a fully-qualified entity_type string.

export_feature_values(request: Optional[Union[ExportFeatureValuesRequest, dict]] = None, *, entity_type: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Exports Feature values from all the entities of a target EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_export_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    destination = aiplatform_v1.FeatureValueDestination()
    destination.bigquery_destination.output_uri = "output_uri_value"

    feature_selector = aiplatform_v1.FeatureSelector()
    feature_selector.id_matcher.ids = ['ids_value1', 'ids_value2']

    request = aiplatform_v1.ExportFeatureValuesRequest(
        entity_type="entity_type_value",
        destination=destination,
        feature_selector=feature_selector,
    )

    # Make the request
    operation = client.export_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.ExportFeatureValuesRequest, dict]) – The request object. Request message for [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ExportFeatureValues].
entity_type (str) –
Required. The resource name of the EntityType from which to export Feature values. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.ExportFeatureValuesResponse Response message for: [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ExportFeatureValues].

Return type:

FeaturestoreServiceClient

static feature_path(project: str, location: str, featurestore: str, entity_type: str, feature: str) → str[source]¶: Returns a fully-qualified feature string.

static featurestore_path(project: str, location: str, featurestore: str) → str[source]¶: Returns a fully-qualified featurestore string.

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]¶

Creates an instance of this client using the provided credentials: file.

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]¶

Creates an instance of this client using the provided credentials: info.

Parameters:

info (dict) – The service account private key info.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

FeaturestoreServiceClient

classmethod from_service_account_json(filename: str, *args, **kwargs)¶

Creates an instance of this client using the provided credentials: file.

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

FeaturestoreServiceClient

get_entity_type(request: Optional[Union[GetEntityTypeRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → EntityType[source]¶

Gets details of a single EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_get_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetEntityTypeRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.GetEntityTypeRequest, dict]) – The request object. Request message for [FeaturestoreService.GetEntityType][google.cloud.aiplatform.v1.FeaturestoreService.GetEntityType].
name (str) –
Required. The name of the EntityType resource. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Return type:

get_feature(request: Optional[Union[GetFeatureRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Feature[source]¶

Gets details of a single Feature.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_get_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetFeatureRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.GetFeatureRequest, dict]) – The request object. Request message for [FeaturestoreService.GetFeature][google.cloud.aiplatform.v1.FeaturestoreService.GetFeature]. Request message for [FeatureRegistryService.GetFeature][google.cloud.aiplatform.v1.FeatureRegistryService.GetFeature].
name (str) –
Required. The name of the Feature resource. Format for entity_type as parent: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} Format for feature_group as parent: projects/{project}/locations/{location}/featureGroups/{feature_group}

This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Feature Metadata information. For example, color is a feature that describes an apple.

Return type:

google.cloud.aiplatform_v1.types.Featurestore

get_featurestore(request: Optional[Union[GetFeaturestoreRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Featurestore[source]¶

Gets details of a single Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_get_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetFeaturestoreRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.GetFeaturestoreRequest, dict]) – The request object. Request message for [FeaturestoreService.GetFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.GetFeaturestore].
name (str) –
Required. The name of the Featurestore resource.

This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Return type:

get_iam_policy(request: Optional[GetIamPolicyRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Parameters:

request (GetIamPolicyRequest) – The request object. Request message for GetIamPolicy method.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

get_location(request: Optional[GetLocationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Location[source]¶

Gets information about a location.

Parameters:

request (GetLocationRequest) – The request object. Request message for GetLocation method.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Location object.

Return type:

Location

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[ClientOptions] = None)[source]¶

Deprecated. Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not “true”, the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “always”, use the default mTLS endpoint; if the environment variable is “never”, use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameters:

client_options (google.api_core.client_options.ClientOptions) – Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.

Returns:

returns the API endpoint and the: client cert source to use.

Return type:

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises:

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

get_operation(request: Optional[GetOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Gets the latest state of a long-running operation.

Parameters:

request (GetOperationRequest) – The request object. Request message for GetOperation method.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An Operation object.

Return type:

Operation

import_feature_values(request: Optional[Union[ImportFeatureValuesRequest, dict]] = None, *, entity_type: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Imports Feature values into the Featurestore from a source storage. The progress of the import is tracked by the returned operation. The imported features are guaranteed to be visible to subsequent read operations after the operation is marked as successfully done.

If an import operation fails, the Feature values returned from reads and exports may be inconsistent. If consistency is required, the caller must retry the same import request again and wait till the new operation returned is marked as successfully done.

There are also scenarios where the caller can cause inconsistency.

Source data for import contains multiple distinct

Feature values for the same entity ID and timestamp.

Source is modified during an import. This includes

adding, updating, or removing source data and/or metadata. Examples of updating metadata include but are not limited to changing storage location, storage class, or retention policy.

Online serving cluster is under-provisioned.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_import_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    avro_source = aiplatform_v1.AvroSource()
    avro_source.gcs_source.uris = ['uris_value1', 'uris_value2']

    feature_specs = aiplatform_v1.FeatureSpec()
    feature_specs.id = "id_value"

    request = aiplatform_v1.ImportFeatureValuesRequest(
        avro_source=avro_source,
        feature_time_field="feature_time_field_value",
        entity_type="entity_type_value",
        feature_specs=feature_specs,
    )

    # Make the request
    operation = client.import_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.ImportFeatureValuesRequest, dict]) – The request object. Request message for [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ImportFeatureValues].
entity_type (str) –
Required. The resource name of the EntityType grouping the Features for which values are being imported. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}

This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.ImportFeatureValuesResponse Response message for: [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ImportFeatureValues].

Return type:

google.cloud.aiplatform_v1.services.featurestore_service.pagers.ListEntityTypesPager

list_entity_types(request: Optional[Union[ListEntityTypesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListEntityTypesPager[source]¶

Lists EntityTypes in a given Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_list_entity_types():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListEntityTypesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_entity_types(request=request)

    # Handle the response
    for response in page_result:
        print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.ListEntityTypesRequest, dict]) – The request object. Request message for [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1.FeaturestoreService.ListEntityTypes].
parent (str) –
Required. The resource name of the Featurestore to list EntityTypes. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for: [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1.FeaturestoreService.ListEntityTypes].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

list_features(request: Optional[Union[ListFeaturesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListFeaturesPager[source]¶

Lists Features in a given EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_list_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListFeaturesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_features(request=request)

    # Handle the response
    for response in page_result:
        print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.ListFeaturesRequest, dict]) – The request object. Request message for [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures]. Request message for [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatures].
parent (str) –
Required. The resource name of the Location to list Features. Format for entity_type as parent: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} Format for feature_group as parent: projects/{project}/locations/{location}/featureGroups/{feature_group}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for: [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures]. Response message for [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatures].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1.services.featurestore_service.pagers.ListFeaturesPager

list_featurestores(request: Optional[Union[ListFeaturestoresRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListFeaturestoresPager[source]¶

Lists Featurestores in a given project and location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_list_featurestores():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListFeaturestoresRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_featurestores(request=request)

    # Handle the response
    for response in page_result:
        print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.ListFeaturestoresRequest, dict]) – The request object. Request message for [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1.FeaturestoreService.ListFeaturestores].
parent (str) –
Required. The resource name of the Location to list Featurestores. Format: projects/{project}/locations/{location}

This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for: [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1.FeaturestoreService.ListFeaturestores].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1.services.featurestore_service.pagers.ListFeaturestoresPager

list_locations(request: Optional[ListLocationsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListLocationsResponse[source]¶

Lists information about the supported locations for this service.

Parameters:

request (ListLocationsRequest) – The request object. Request message for ListLocations method.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for ListLocations method.

Return type:

ListLocationsResponse

list_operations(request: Optional[ListOperationsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListOperationsResponse[source]¶

Lists operations that match the specified filter in the request.

Parameters:

request (ListOperationsRequest) – The request object. Request message for ListOperations method.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for ListOperations method.

Return type:

ListOperationsResponse

static parse_common_billing_account_path(path: str) → Dict[str, str][source]¶: Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str][source]¶: Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str][source]¶: Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str][source]¶: Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str][source]¶: Parse a project path into its component segments.

static parse_entity_type_path(path: str) → Dict[str, str][source]¶: Parses a entity_type path into its component segments.

static parse_feature_path(path: str) → Dict[str, str][source]¶: Parses a feature path into its component segments.

static parse_featurestore_path(path: str) → Dict[str, str][source]¶: Parses a featurestore path into its component segments.

search_features(request: Optional[Union[SearchFeaturesRequest, dict]] = None, *, location: Optional[str] = None, query: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → SearchFeaturesPager[source]¶

Searches Features matching a query in a given project.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_search_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.SearchFeaturesRequest(
        location="location_value",
    )

    # Make the request
    page_result = client.search_features(request=request)

    # Handle the response
    for response in page_result:
        print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.SearchFeaturesRequest, dict]) – The request object. Request message for [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1.FeaturestoreService.SearchFeatures].
location (str) –
Required. The resource name of the Location to search Features. Format: projects/{project}/locations/{location}

This corresponds to the location field on the request instance; if request is provided, this should not be set.
query (str) –
Query string that is a conjunction of field-restricted queries and/or field-restricted filters. Field-restricted queries and filters can be combined using AND to form a conjunction.

A field query is in the form FIELD:QUERY. This implicitly checks if QUERY exists as a substring within Feature’s FIELD. The QUERY and the FIELD are converted to a sequence of words (i.e. tokens) for comparison. This is done by:
- Removing leading/trailing whitespace and tokenizing the search value. Characters that are not one of alphanumeric [a-zA-Z0-9], underscore _, or asterisk * are treated as delimiters for tokens. * is treated as a wildcard that matches characters within a token.
- Ignoring case.
- Prepending an asterisk to the first and appending an asterisk to the last token in QUERY.
A QUERY must be either a singular token or a phrase. A phrase is one or multiple words enclosed in double quotation marks (“). With phrases, the order of the words is important. Words in the phrase must be matching in order and consecutively.

Supported FIELDs for field-restricted queries:
- feature_id
- description
- entity_type_id
Examples:
- feature_id: foo –> Matches a Feature with ID containing the substring foo (eg. foo, foofeature, barfoo).
- feature_id: foo*feature –> Matches a Feature with ID containing the substring foo*feature (eg. foobarfeature).
- feature_id: foo AND description: bar –> Matches a Feature with ID containing the substring foo and description containing the substring bar.
Besides field queries, the following exact-match filters are supported. The exact-match filters do not support wildcards. Unlike field-restricted queries, exact-match filters are case-sensitive.
- feature_id: Supports = comparisons.
- description: Supports = comparisons. Multi-token filters should be enclosed in quotes.
- entity_type_id: Supports = comparisons.
- value_type: Supports = and != comparisons.
- labels: Supports key-value equality as well as key presence.
- featurestore_id: Supports = comparisons.
Examples:
- description = "foo bar" –> Any Feature with description exactly equal to foo bar
- value_type = DOUBLE –> Features whose type is DOUBLE.
- labels.active = yes AND labels.env = prod –> Features having both (active: yes) and (env: prod) labels.
- labels.env: * –> Any Feature which has a label with env as the key.
This corresponds to the query field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for: [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1.FeaturestoreService.SearchFeatures].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1.services.featurestore_service.pagers.SearchFeaturesPager

set_iam_policy(request: Optional[SetIamPolicyRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Parameters:

request (SetIamPolicyRequest) – The request object. Request message for SetIamPolicy method.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

test_iam_permissions(request: Optional[TestIamPermissionsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TestIamPermissionsResponse[source]¶

Tests the specified IAM permissions against the IAM access control: policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Parameters:

request (TestIamPermissionsRequest) – The request object. Request message for TestIamPermissions method.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Response message for TestIamPermissions method.

Return type:

TestIamPermissionsResponse

property transport: FeaturestoreServiceTransport¶

Returns the transport used by the client instance.

Returns:

The transport used by the client: instance.

Return type:

FeaturestoreServiceTransport

property universe_domain: str¶

Return the universe domain used by the client instance.

Returns:: The universe domain used by the client instance.
Return type:: str

update_entity_type(request: Optional[Union[UpdateEntityTypeRequest, dict]] = None, *, entity_type: Optional[EntityType] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → EntityType[source]¶

Updates the parameters of a single EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_update_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateEntityTypeRequest(
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.UpdateEntityTypeRequest, dict]) – The request object. Request message for [FeaturestoreService.UpdateEntityType][google.cloud.aiplatform.v1.FeaturestoreService.UpdateEntityType].
entity_type (google.cloud.aiplatform_v1.types.EntityType) –
Required. The EntityType’s name field is used to identify the EntityType to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
update_mask (google.protobuf.field_mask_pb2.FieldMask) –
Field mask is used to specify the fields to be overwritten in the EntityType resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:
- description
- labels
- monitoring_config.snapshot_analysis.disabled
- monitoring_config.snapshot_analysis.monitoring_interval_days
- monitoring_config.snapshot_analysis.staleness_days
- monitoring_config.import_features_analysis.state
- monitoring_config.import_features_analysis.anomaly_detection_baseline
- monitoring_config.numerical_threshold_config.value
- monitoring_config.categorical_threshold_config.value
- offline_storage_ttl_days
This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Return type:

update_feature(request: Optional[Union[UpdateFeatureRequest, dict]] = None, *, feature: Optional[Feature] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Feature[source]¶

Updates the parameters of a single Feature.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_update_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateFeatureRequest(
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.UpdateFeatureRequest, dict]) – The request object. Request message for [FeaturestoreService.UpdateFeature][google.cloud.aiplatform.v1.FeaturestoreService.UpdateFeature]. Request message for [FeatureRegistryService.UpdateFeature][google.cloud.aiplatform.v1.FeatureRegistryService.UpdateFeature].
feature (google.cloud.aiplatform_v1.types.Feature) –
Required. The Feature’s name field is used to identify the Feature to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature}

This corresponds to the feature field on the request instance; if request is provided, this should not be set.
update_mask (google.protobuf.field_mask_pb2.FieldMask) –
Field mask is used to specify the fields to be overwritten in the Features resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:
- description
- labels
- disable_monitoring (Not supported for FeatureRegistryService Feature)
- point_of_contact (Not supported for FeaturestoreService FeatureStore)
This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

Feature Metadata information. For example, color is a feature that describes an apple.

Return type:

update_featurestore(request: Optional[Union[UpdateFeaturestoreRequest, dict]] = None, *, featurestore: Optional[Featurestore] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Updates the parameters of a single Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_update_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateFeaturestoreRequest(
    )

    # Make the request
    operation = client.update_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1.types.UpdateFeaturestoreRequest, dict]) – The request object. Request message for [FeaturestoreService.UpdateFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.UpdateFeaturestore].
featurestore (google.cloud.aiplatform_v1.types.Featurestore) –
Required. The Featurestore’s name field is used to identify the Featurestore to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

This corresponds to the featurestore field on the request instance; if request is provided, this should not be set.
update_mask (google.protobuf.field_mask_pb2.FieldMask) –
Field mask is used to specify the fields to be overwritten in the Featurestore resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:
- labels
- online_serving_config.fixed_node_count
- online_serving_config.scaling
- online_storage_ttl_days
This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1.types.Featurestore Vertex AI Feature Store provides a centralized repository for organizing,: storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Return type: