list_ingestion_jobs ¶

def list_ingestion_jobs(
    self,
    knowledge_base_id: "capo_bedrock_agent.types.id.Id",
    data_source_id: "capo_bedrock_agent.types.id.Id",
    *,
    config_overrides: Optional[BedrockAgentClientConfig] = None,
    filters: Optional[
        "capo_bedrock_agent.types.ingestion_job_filters.IngestionJobFilters"
    ] = None,
    sort_by: Optional[
        "capo_bedrock_agent.types.ingestion_job_sort_by.IngestionJobSortBy"
    ] = None,
    max_results: Optional["capo_bedrock_agent.types.max_results.MaxResults"] = None,
    next_token: Optional["capo_bedrock_agent.types.next_token.NextToken"] = None,
) -> "capo_bedrock_agent.types.list_ingestion_jobs_response.ListIngestionJobsResponse":
    """<p>Lists the data ingestion jobs for a data source. The list also includes information about each job.</p>

    Args:
        knowledge_base_id: <p>The unique identifier of the knowledge base for the list of data ingestion jobs.</p>
        data_source_id: <p>The unique identifier of the data source for the list of data ingestion jobs.</p>
        filters: <p>Contains information about the filters for filtering the data.</p>
        sort_by: <p>Contains details about how to sort the data.</p>
        max_results: <p>The maximum number of results to return in the response. If the total number of results is greater than this value, use the token returned in the response in the <code>nextToken</code> field when making another request to return the next batch of results.</p>
        next_token: <p>If the total number of results is greater than the <code>maxResults</code> value provided in the request, enter the token returned in the <code>nextToken</code> field in the response in this field to return the next batch of results.</p>

    Raises:
        capo_bedrock_agent.errors.access_denied_exception.AccessDeniedException: <p>The request is denied because of missing access permissions.</p>
        capo_bedrock_agent.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred. Retry your request.</p>
        capo_bedrock_agent.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource Amazon Resource Name (ARN) was not found. Check the Amazon Resource Name (ARN) and try your request again.</p>
        capo_bedrock_agent.errors.throttling_exception.ThrottlingException: <p>The number of requests exceeds the limit. Resubmit your request later.</p>
        capo_bedrock_agent.errors.validation_exception.ValidationException: <p>Input validation failed. Check your request parameters and retry the request.</p>
        capo_bedrock_agent.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_bedrock_agent.types.list_ingestion_jobs_request.ListIngestionJobsRequest]",
    ) -> OperationResponse[
        "capo_bedrock_agent.types.list_ingestion_jobs_response.ListIngestionJobsResponse"
    ]:
        import capo_bedrock_agent._operations.amazon_bedrock_agent_build_time_lambda.list_ingestion_jobs

        output, http_response = (
            capo_bedrock_agent._operations.amazon_bedrock_agent_build_time_lambda.list_ingestion_jobs.list_ingestion_jobs(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListIngestionJobs")
    )
    input_: capo_bedrock_agent.types.list_ingestion_jobs_request.ListIngestionJobsRequest = {
        "knowledge_base_id": knowledge_base_id,
        "data_source_id": data_source_id,
    }
    if filters is not None:
        input_["filters"] = filters
    if sort_by is not None:
        input_["sort_by"] = sort_by
    if max_results is not None:
        input_["max_results"] = max_results
    if next_token is not None:
        input_["next_token"] = next_token

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output