start_entities_detection_job ¶

def start_entities_detection_job(
    self,
    input_data_config: "capo_comprehend.types.input_data_config.InputDataConfig",
    output_data_config: "capo_comprehend.types.output_data_config.OutputDataConfig",
    data_access_role_arn: "capo_comprehend.types.iam_role_arn.IamRoleArn",
    language_code: "capo_comprehend.types.language_code.LanguageCode",
    *,
    config_overrides: Optional[ComprehendClientConfig] = None,
    job_name: Optional["capo_comprehend.types.job_name.JobName"] = None,
    entity_recognizer_arn: Optional[
        "capo_comprehend.types.entity_recognizer_arn.EntityRecognizerArn"
    ] = None,
    client_request_token: Optional[
        "capo_comprehend.types.client_request_token_string.ClientRequestTokenString"
    ] = None,
    volume_kms_key_id: Optional["capo_comprehend.types.kms_key_id.KmsKeyId"] = None,
    vpc_config: Optional["capo_comprehend.types.vpc_config.VpcConfig"] = None,
    tags: Optional["capo_comprehend.types.tag_list.TagList"] = None,
    flywheel_arn: Optional[
        "capo_comprehend.types.comprehend_flywheel_arn.ComprehendFlywheelArn"
    ] = None,
) -> "capo_comprehend.types.start_entities_detection_job_response.StartEntitiesDetectionJobResponse":
    """<p>Starts an asynchronous entity detection job for a collection of documents. Use the operation to track the status of a job.</p> <p>This API can be used for either standard entity detection or custom entity recognition. In order to be used for custom entity recognition, the optional <code>EntityRecognizerArn</code> must be used in order to provide access to the recognizer being used to detect the custom entity.</p>

    Args:
        input_data_config: <p>Specifies the format and location of the input data for the job.</p>
        output_data_config: <p>Specifies where to send the output files.</p>
        data_access_role_arn: <p>The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data. For more information, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/security_iam_id-based-policy-examples.html#auth-role-permissions">Role-based permissions</a>.</p>
        job_name: <p>The identifier of the job.</p>
        entity_recognizer_arn: <p>The Amazon Resource Name (ARN) that identifies the specific entity recognizer to be used by the <code>StartEntitiesDetectionJob</code>. This ARN is optional and is only used for a custom entity recognition job.</p>
        language_code: <p>The language of the input documents. All documents must be in the same language. You can specify any of the languages supported by Amazon Comprehend. If custom entities recognition is used, this parameter is ignored and the language used for training the model is used instead.</p>
        client_request_token: <p>A unique identifier for the request. If you don't set the client request token, Amazon Comprehend generates one.</p>
        volume_kms_key_id: <p>ID for the Amazon Web Services Key Management Service (KMS) key that Amazon Comprehend uses to encrypt data on the storage volume attached to the ML compute instance(s) that process the analysis job. The VolumeKmsKeyId can be either of the following formats:</p> <ul> <li> <p>KMS Key ID: <code>"1234abcd-12ab-34cd-56ef-1234567890ab"</code> </p> </li> <li> <p>Amazon Resource Name (ARN) of a KMS Key: <code>"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"</code> </p> </li> </ul>
        vpc_config: <p>Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for your entity detection job. For more information, see <a href="https://docs.aws.amazon.com/vpc/latest/userguide/what-is-amazon-vpc.html">Amazon VPC</a>. </p>
        tags: <p>Tags to associate with the entities detection job. A tag is a key-value pair that adds metadata to a resource used by Amazon Comprehend. For example, a tag with "Sales" as the key might be added to a resource to indicate its use by the sales department.</p>
        flywheel_arn: <p>The Amazon Resource Number (ARN) of the flywheel associated with the model to use.</p>

    Raises:
        capo_comprehend.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred. Retry your request.</p>
        capo_comprehend.errors.invalid_request_exception.InvalidRequestException: <p>The request is invalid.</p>
        capo_comprehend.errors.kms_key_validation_exception.KmsKeyValidationException: <p>The KMS customer managed key (CMK) entered cannot be validated. Verify the key and re-enter it.</p>
        capo_comprehend.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource name is already in use. Use a different name and try your request again.</p>
        capo_comprehend.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource ARN was not found. Check the ARN and try your request again.</p>
        capo_comprehend.errors.resource_unavailable_exception.ResourceUnavailableException: <p>The specified resource is not available. Check the resource and try your request again.</p>
        capo_comprehend.errors.too_many_requests_exception.TooManyRequestsException: <p>The number of requests exceeds the limit. Resubmit your request later.</p>
        capo_comprehend.errors.too_many_tags_exception.TooManyTagsException: <p>The request contains more tags than can be associated with a resource (50 tags per resource). The maximum number of tags includes both existing tags and those included in your current request. </p>
        capo_comprehend.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_comprehend.types.start_entities_detection_job_request.StartEntitiesDetectionJobRequest]",
    ) -> OperationResponse[
        "capo_comprehend.types.start_entities_detection_job_response.StartEntitiesDetectionJobResponse"
    ]:
        import capo_comprehend._operations.comprehend_20171127.start_entities_detection_job

        output, http_response = (
            capo_comprehend._operations.comprehend_20171127.start_entities_detection_job.start_entities_detection_job(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartEntitiesDetectionJob")
    )
    input_: capo_comprehend.types.start_entities_detection_job_request.StartEntitiesDetectionJobRequest = {
        "input_data_config": input_data_config,
        "output_data_config": output_data_config,
        "data_access_role_arn": data_access_role_arn,
        "language_code": language_code,
    }
    if job_name is not None:
        input_["job_name"] = job_name
    if entity_recognizer_arn is not None:
        input_["entity_recognizer_arn"] = entity_recognizer_arn
    if client_request_token is None:
        client_request_token = str(uuid.uuid4())
    input_["client_request_token"] = client_request_token
    if volume_kms_key_id is not None:
        input_["volume_kms_key_id"] = volume_kms_key_id
    if vpc_config is not None:
        input_["vpc_config"] = vpc_config
    if tags is not None:
        input_["tags"] = tags
    if flywheel_arn is not None:
        input_["flywheel_arn"] = flywheel_arn

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