start_ingestion_job ¶

def start_ingestion_job(
    self,
    knowledge_base_id: "capo_bedrock_agent.types.id.Id",
    data_source_id: "capo_bedrock_agent.types.id.Id",
    *,
    config_overrides: Optional[BedrockAgentClientConfig] = None,
    client_token: Optional[
        "capo_bedrock_agent.types.client_token.ClientToken"
    ] = None,
    description: Optional[
        "capo_bedrock_agent.types.description.Description"
    ] = None,
) -> "capo_bedrock_agent.types.start_ingestion_job_response.StartIngestionJobResponse":
    """<p>Begins a data ingestion job. Data sources are ingested into your knowledge base so that Large Language Models (LLMs) can use your data.</p>

    Args:
        knowledge_base_id: <p>The unique identifier of the knowledge base for the data ingestion job.</p>
        data_source_id: <p>The unique identifier of the data source you want to ingest into your knowledge base.</p>
        client_token: <p>A unique, case-sensitive identifier to ensure that the API request completes no more than one time. If this token matches a previous request, Amazon Bedrock ignores the request, but does not return an error. For more information, see <a href="https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Run_Instance_Idempotency.html">Ensuring idempotency</a>.</p>
        description: <p>A description of the data ingestion job.</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.conflict_exception.ConflictException: <p>There was a conflict performing an operation.</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.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>The number of requests exceeds the service quota. Resubmit your request later.</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.start_ingestion_job_request.StartIngestionJobRequest]",
    ) -> OperationResponse[
        "capo_bedrock_agent.types.start_ingestion_job_response.StartIngestionJobResponse"
    ]:
        import capo_bedrock_agent._operations.amazon_bedrock_agent_build_time_lambda.start_ingestion_job

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartIngestionJob")
    )
    input_: capo_bedrock_agent.types.start_ingestion_job_request.StartIngestionJobRequest = {
        "knowledge_base_id": knowledge_base_id,
        "data_source_id": data_source_id,
    }
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token
    if description is not None:
        input_["description"] = description

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