async def sample_create_context(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.CreateContextRequest(parent="parent_value", ) # Make the request response = await client.create_context(request=request) # Handle the response print(response)
async def sample_get_metadata_store(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.GetMetadataStoreRequest(name="name_value", ) # Make the request response = await client.get_metadata_store(request=request) # Handle the response print(response)
async def sample_update_execution(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.UpdateExecutionRequest() # Make the request response = await client.update_execution(request=request) # Handle the response print(response)
async def sample_list_executions(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.ListExecutionsRequest(parent="parent_value", ) # Make the request page_result = client.list_executions(request=request) # Handle the response async for response in page_result: print(response)
async def sample_query_artifact_lineage_subgraph(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.QueryArtifactLineageSubgraphRequest( artifact="artifact_value", ) # Make the request response = await client.query_artifact_lineage_subgraph(request=request) # Handle the response print(response)
async def sample_add_context_children(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.AddContextChildrenRequest( context="context_value", ) # Make the request response = await client.add_context_children(request=request) # Handle the response print(response)
async def sample_query_execution_inputs_and_outputs(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.QueryExecutionInputsAndOutputsRequest( execution="execution_value", ) # Make the request response = await client.query_execution_inputs_and_outputs(request=request) # Handle the response print(response)
async def sample_add_context_artifacts_and_executions(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.AddContextArtifactsAndExecutionsRequest( context="context_value", ) # Make the request response = await client.add_context_artifacts_and_executions( request=request) # Handle the response print(response)
async def sample_add_execution_events(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.AddExecutionEventsRequest( execution="execution_value", ) # Make the request response = await client.add_execution_events(request=request) # Handle the response print(response)
async def sample_delete_context(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.DeleteContextRequest(name="name_value", ) # Make the request operation = client.delete_context(request=request) print("Waiting for operation to complete...") response = await operation.result() # Handle the response print(response)
async def sample_create_metadata_store(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.CreateMetadataStoreRequest(parent="parent_value", ) # Make the request operation = client.create_metadata_store(request=request) print("Waiting for operation to complete...") response = await operation.result() # Handle the response print(response)
async def sample_create_metadata_schema(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) metadata_schema = aiplatform_v1.MetadataSchema() metadata_schema.schema = "schema_value" request = aiplatform_v1.CreateMetadataSchemaRequest( parent="parent_value", metadata_schema=metadata_schema, ) # Make the request response = await client.create_metadata_schema(request=request) # Handle the response print(response)
async def sample_purge_contexts(): # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.PurgeContextsRequest( parent="parent_value", filter="filter_value", ) # Make the request operation = client.purge_contexts(request=request) print("Waiting for operation to complete...") response = await operation.result() # Handle the response print(response)