EnrichmentJobClientCompositeOperations

class oci.generative_ai_data.EnrichmentJobClientCompositeOperations(client, **kwargs)

This class provides a wrapper around EnrichmentJobClient and offers convenience methods for operations that would otherwise need to be chained together. For example, instead of performing an action on a resource (e.g. launching an instance, creating a load balancer) and then using a waiter to wait for the resource to enter a given state, you can call a single method in this class to accomplish the same functionality

Methods

__init__(client, **kwargs)	Creates a new EnrichmentJobClientCompositeOperations object
cancel_enrichment_job_and_wait_for_state(…)	Calls cancel_enrichment_job() and waits for the EnrichmentJob acted upon to enter the given state(s).

__init__(client, **kwargs)

Creates a new EnrichmentJobClientCompositeOperations object

Parameters:client (EnrichmentJobClient) – The service client which will be wrapped by this object

cancel_enrichment_job_and_wait_for_state(semantic_store_id, enrichment_job_id, wait_for_states=[], operation_kwargs={}, waiter_kwargs={})

Calls cancel_enrichment_job() and waits for the EnrichmentJob acted upon to enter the given state(s).

Parameters:

• semantic_store_id (str) – (required) The OCID of the semantic store
• enrichment_job_id (str) – (required) The OCID of the enrichment job
• wait_for_states (list[str]) – An array of states to wait on. These should be valid values for lifecycle_state
• operation_kwargs (dict) – A dictionary of keyword arguments to pass to cancel_enrichment_job()
• waiter_kwargs (dict) – A dictionary of keyword arguments to pass to the oci.wait_until() function. For example, you could pass max_interval_seconds or max_interval_seconds as dictionary keys to modify how long the waiter function will wait between retries and the maximum amount of time it will wait