start_topics_detection_job ¶

def start_topics_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",
    *,
    config_overrides: Optional[ComprehendClientConfig] = None,
    job_name: Optional["capo_comprehend.types.job_name.JobName"] = None,
    number_of_topics: Optional[
        "capo_comprehend.types.number_of_topics_integer.NumberOfTopicsInteger"
    ] = 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,
) -> "capo_comprehend.types.start_topics_detection_job_response.StartTopicsDetectionJobResponse":
    """<p>Starts an asynchronous topic detection job. Use the <code>DescribeTopicDetectionJob</code> operation to track the status of a job.</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. The output is a compressed archive with two files, <code>topic-terms.csv</code> that lists the terms associated with each topic, and <code>doc-topics.csv</code> that lists the documents associated with each topic</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>
        number_of_topics: <p>The number of topics to detect.</p>
        client_request_token: <p>A unique identifier for the request. If you do not 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 topic 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 topics 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>

    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.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_topics_detection_job_request.StartTopicsDetectionJobRequest]",
    ) -> OperationResponse[
        "capo_comprehend.types.start_topics_detection_job_response.StartTopicsDetectionJobResponse"
    ]:
        import capo_comprehend._operations.comprehend_20171127.start_topics_detection_job

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartTopicsDetectionJob")
    )
    input_: capo_comprehend.types.start_topics_detection_job_request.StartTopicsDetectionJobRequest = {
        "input_data_config": input_data_config,
        "output_data_config": output_data_config,
        "data_access_role_arn": data_access_role_arn,
    }
    if job_name is not None:
        input_["job_name"] = job_name
    if number_of_topics is not None:
        input_["number_of_topics"] = number_of_topics
    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

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