async def sample_export_feature_values():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceAsyncClient()
# Initialize request argument(s)
destination = aiplatform_v1beta1.FeatureValueDestination()
destination.bigquery_destination.output_uri = "output_uri_value"
feature_selector = aiplatform_v1beta1.FeatureSelector()
feature_selector.id_matcher.ids = ['ids_value_1', 'ids_value_2']
request = aiplatform_v1beta1.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)
def sample_read_feature_values():
# Create a client
client = aiplatform_v1beta1.FeaturestoreOnlineServingServiceClient()
# Initialize request argument(s)
feature_selector = aiplatform_v1beta1.FeatureSelector()
feature_selector.id_matcher.ids = ['ids_value_1', 'ids_value_2']
request = aiplatform_v1beta1.ReadFeatureValuesRequest(
entity_type="entity_type_value",
entity_id="entity_id_value",
feature_selector=feature_selector,
)
# Make the request
response = client.read_feature_values(request=request)
# Handle the response
print(response)
async def sample_streaming_read_feature_values():
# Create a client
client = aiplatform_v1beta1.FeaturestoreOnlineServingServiceAsyncClient()
# Initialize request argument(s)
feature_selector = aiplatform_v1beta1.FeatureSelector()
feature_selector.id_matcher.ids = ['ids_value_1', 'ids_value_2']
request = aiplatform_v1beta1.StreamingReadFeatureValuesRequest(
entity_type="entity_type_value",
entity_ids=['entity_ids_value_1', 'entity_ids_value_2'],
feature_selector=feature_selector,
)
# Make the request
stream = await client.streaming_read_feature_values(request=request)
# Handle the response
async for response in stream:
print(response)