create_evaluation_job ¶

def create_evaluation_job(
    self,
    job_name: "capo_bedrock.types.evaluation_job_name.EvaluationJobName",
    role_arn: "capo_bedrock.types.role_arn.RoleArn",
    evaluation_config: "capo_bedrock.types.evaluation_config.EvaluationConfig",
    inference_config: "capo_bedrock.types.evaluation_inference_config.EvaluationInferenceConfig",
    output_data_config: "capo_bedrock.types.evaluation_output_data_config.EvaluationOutputDataConfig",
    *,
    config_overrides: Optional[BedrockClientConfig] = None,
    job_description: Optional[
        "capo_bedrock.types.evaluation_job_description.EvaluationJobDescription"
    ] = None,
    client_request_token: Optional[
        "capo_bedrock.types.idempotency_token.IdempotencyToken"
    ] = None,
    customer_encryption_key_id: Optional[
        "capo_bedrock.types.kms_key_id.KmsKeyId"
    ] = None,
    job_tags: Optional["capo_bedrock.types.tag_list.TagList"] = None,
    application_type: Optional[
        "capo_bedrock.types.application_type.ApplicationType"
    ] = None,
) -> (
    "capo_bedrock.types.create_evaluation_job_response.CreateEvaluationJobResponse"
):
    """<p>Creates an evaluation job.</p>

    Args:
        job_name: <p>A name for the evaluation job. Names must unique with your Amazon Web Services account, and your account's Amazon Web Services region.</p>
        job_description: <p>A description of the evaluation job.</p>
        client_request_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>
        role_arn: <p>The Amazon Resource Name (ARN) of an IAM service role that Amazon Bedrock can assume to perform tasks on your behalf. To learn more about the required permissions, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-evaluation-security.html">Required permissions for model evaluations</a>.</p>
        customer_encryption_key_id: <p>Specify your customer managed encryption key Amazon Resource Name (ARN) that will be used to encrypt your evaluation job.</p>
        job_tags: <p>Tags to attach to the model evaluation job.</p>
        application_type: <p>Specifies whether the evaluation job is for evaluating a model or evaluating a knowledge base (retrieval and response generation).</p>
        evaluation_config: <p>Contains the configuration details of either an automated or human-based evaluation job.</p>
        inference_config: <p>Contains the configuration details of the inference model for the evaluation job.</p> <p>For model evaluation jobs, automated jobs support a single model or <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html">inference profile</a>, and jobs that use human workers support two models or inference profiles.</p>
        output_data_config: <p>Contains the configuration details of the Amazon S3 bucket for storing the results of the evaluation job.</p>

    Raises:
        capo_bedrock.errors.access_denied_exception.AccessDeniedException: <p>The request is denied because of missing access permissions.</p>
        capo_bedrock.errors.conflict_exception.ConflictException: <p>Error occurred because of a conflict while performing an operation.</p>
        capo_bedrock.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred. Retry your request.</p>
        capo_bedrock.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.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>The number of requests exceeds the service quota. Resubmit your request later.</p>
        capo_bedrock.errors.throttling_exception.ThrottlingException: <p>The number of requests exceeds the limit. Resubmit your request later.</p>
        capo_bedrock.errors.validation_exception.ValidationException: <p>Input validation failed. Check your request parameters and retry the request.</p>
        capo_bedrock.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_bedrock.types.create_evaluation_job_request.CreateEvaluationJobRequest]",
    ) -> OperationResponse[
        "capo_bedrock.types.create_evaluation_job_response.CreateEvaluationJobResponse"
    ]:
        import capo_bedrock._operations.amazon_bedrock_control_plane_service.create_evaluation_job

        output, http_response = (
            capo_bedrock._operations.amazon_bedrock_control_plane_service.create_evaluation_job.create_evaluation_job(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_bedrock.types.create_evaluation_job_request.CreateEvaluationJobRequest",
    ) -> dict[str, Any]:
        import capo_bedrock.types.create_evaluation_job_request

        return capo_bedrock.types.create_evaluation_job_request.redact_sensitive(
            input_
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("CreateEvaluationJob", redact_input=_redact_input),
    )
    input_: capo_bedrock.types.create_evaluation_job_request.CreateEvaluationJobRequest = {
        "job_name": job_name,
        "role_arn": role_arn,
        "evaluation_config": evaluation_config,
        "inference_config": inference_config,
        "output_data_config": output_data_config,
    }
    if job_description is not None:
        input_["job_description"] = job_description
    if client_request_token is None:
        client_request_token = str(uuid.uuid4())
    input_["client_request_token"] = client_request_token
    if customer_encryption_key_id is not None:
        input_["customer_encryption_key_id"] = customer_encryption_key_id
    if job_tags is not None:
        input_["job_tags"] = job_tags
    if application_type is not None:
        input_["application_type"] = application_type

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