def sample_batch_read_feature_values():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
csv_read_instances = aiplatform_v1beta1.CsvSource()
csv_read_instances.gcs_source.uris = ['uris_value_1', 'uris_value_2']
destination = aiplatform_v1beta1.FeatureValueDestination()
destination.bigquery_destination.output_uri = "output_uri_value"
entity_type_specs = aiplatform_v1beta1.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_v1beta1.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)
def sample_import_feature_values():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
avro_source = aiplatform_v1beta1.AvroSource()
avro_source.gcs_source.uris = ['uris_value_1', 'uris_value_2']
feature_specs = aiplatform_v1beta1.FeatureSpec()
feature_specs.id = "id_value"
request = aiplatform_v1beta1.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)
def sample_get_feature():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetFeatureRequest(name="name_value", )
# Make the request
response = client.get_feature(request=request)
# Handle the response
print(response)
def sample_update_entity_type():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.UpdateEntityTypeRequest()
# Make the request
response = client.update_entity_type(request=request)
# Handle the response
print(response)
def sample_list_features():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListFeaturesRequest(parent="parent_value", )
# Make the request
page_result = client.list_features(request=request)
# Handle the response
for response in page_result:
print(response)
def sample_search_features():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.SearchFeaturesRequest(
location="location_value", )
# Make the request
page_result = client.search_features(request=request)
# Handle the response
for response in page_result:
print(response)
def sample_update_feature():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
feature = aiplatform_v1beta1.Feature()
feature.value_type = "BYTES"
request = aiplatform_v1beta1.UpdateFeatureRequest(feature=feature, )
# Make the request
response = client.update_feature(request=request)
# Handle the response
print(response)
def sample_delete_featurestore():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.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)
def sample_create_entity_type():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.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)
def sample_create_feature():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
feature = aiplatform_v1beta1.Feature()
feature.value_type = "BYTES"
request = aiplatform_v1beta1.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 = operation.result()
# Handle the response
print(response)
def featurestore_client():
featurestore_client = aiplatform_v1beta1.FeaturestoreServiceClient(
client_options={
"api_endpoint": "us-central1-aiplatform.googleapis.com"
})
yield featurestore_client
