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_value_1', 'uris_value_2'] 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_value_1', 'ids_value_2' ] 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)
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_value_1', 'ids_value_2'] 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)
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_value_1', 'uris_value_2'] 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)
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)
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)
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)
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)
async def sample_update_feature(): # Create a client client = aiplatform_v1.FeaturestoreServiceAsyncClient() # Initialize request argument(s) feature = aiplatform_v1.Feature() feature.value_type = "BYTES" request = aiplatform_v1.UpdateFeatureRequest(feature=feature, ) # Make the request response = await client.update_feature(request=request) # Handle the response print(response)
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)
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)
async def sample_create_feature(): # Create a client client = aiplatform_v1.FeaturestoreServiceAsyncClient() # Initialize request argument(s) feature = aiplatform_v1.Feature() feature.value_type = "BYTES" request = aiplatform_v1.CreateFeatureRequest( parent="parent_value", feature=feature, 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)