[ aws . comprehend ]

start-topics-detection-job

Description

Starts an asynchronous topic detection job. Use the DescribeTopicDetectionJob operation to track the status of a job.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  start-topics-detection-job
--input-data-config <value>
--output-data-config <value>
--data-access-role-arn <value>
[--job-name <value>]
[--number-of-topics <value>]
[--client-request-token <value>]
[--volume-kms-key-id <value>]
[--vpc-config <value>]
[--tags <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options

--input-data-config (structure)

Specifies the format and location of the input data for the job.

S3Uri -> (string)

The Amazon S3 URI for the input data. The URI must be in same region as the API endpoint that you are calling. The URI can point to a single input file or it can provide the prefix for a collection of data files.

For example, if you use the URI S3://bucketName/prefix , if the prefix is a single file, Amazon Comprehend uses that file as input. If more than one file begins with the prefix, Amazon Comprehend uses all of them as input.

InputFormat -> (string)

Specifies how the text in an input file should be processed:

• ONE_DOC_PER_FILE - Each file is considered a separate document. Use this option when you are processing large documents, such as newspaper articles or scientific papers.

• ONE_DOC_PER_LINE - Each line in a file is considered a separate document. Use this option when you are processing many short documents, such as text messages.

DocumentReaderConfig -> (structure)

The document reader config field applies only for InputDataConfig of StartEntitiesDetectionJob.

Use DocumentReaderConfig to provide specifications about how you want your inference documents read. Currently it applies for PDF documents in StartEntitiesDetectionJob custom inference.

DocumentReadAction -> (string)

This enum field will start with two values which will apply to PDFs:

• TEXTRACT_DETECT_DOCUMENT_TEXT - The service calls DetectDocumentText for PDF documents per page.

• TEXTRACT_ANALYZE_DOCUMENT - The service calls AnalyzeDocument for PDF documents per page.

DocumentReadMode -> (string)

This enum field provides two values:

• SERVICE_DEFAULT - use service defaults for Document reading. For Digital PDF it would mean using an internal parser instead of Textract APIs

• FORCE_DOCUMENT_READ_ACTION - Always use specified action for DocumentReadAction, including Digital PDF.

FeatureTypes -> (list)

Specifies how the text in an input file should be processed:

(string)

A list of the types of analyses to perform. This field specifies what feature types need to be extracted from the document where entity recognition is expected.

• TABLES - Add TABLES to the list to return information about the tables that are detected in the input document.

• FORMS - Add FORMS to return detected form data.

Shorthand Syntax:

S3Uri=string,InputFormat=string,DocumentReaderConfig={DocumentReadAction=string,DocumentReadMode=string,FeatureTypes=[string,string]}

JSON Syntax:

{
  "S3Uri": "string",
  "InputFormat": "ONE_DOC_PER_FILE"|"ONE_DOC_PER_LINE",
  "DocumentReaderConfig": {
    "DocumentReadAction": "TEXTRACT_DETECT_DOCUMENT_TEXT"|"TEXTRACT_ANALYZE_DOCUMENT",
    "DocumentReadMode": "SERVICE_DEFAULT"|"FORCE_DOCUMENT_READ_ACTION",
    "FeatureTypes": ["TABLES"|"FORMS", ...]
  }
}

--output-data-config (structure)

Specifies where to send the output files. The output is a compressed archive with two files, topic-terms.csv that lists the terms associated with each topic, and doc-topics.csv that lists the documents associated with each topic

S3Uri -> (string)

When you use the OutputDataConfig object with asynchronous operations, you specify the Amazon S3 location where you want to write the output data. The URI must be in the same region as the API endpoint that you are calling. The location is used as the prefix for the actual location of the output file.

When the topic detection job is finished, the service creates an output file in a directory specific to the job. The S3Uri field contains the location of the output file, called output.tar.gz . It is a compressed archive that contains the ouput of the operation.

For a PII entity detection job, the output file is plain text, not a compressed archive. The output file name is the same as the input file, with .out appended at the end.

KmsKeyId -> (string)

ID for the AWS Key Management Service (KMS) key that Amazon Comprehend uses to encrypt the output results from an analysis job. The KmsKeyId can be one of the following formats:

• KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab"

• Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

• KMS Key Alias: "alias/ExampleAlias"

• ARN of a KMS Key Alias: "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

Shorthand Syntax:

S3Uri=string,KmsKeyId=string

JSON Syntax:

{
  "S3Uri": "string",
  "KmsKeyId": "string"
}

--data-access-role-arn (string)

The Amazon Resource Name (ARN) of the AWS Identity and Access Management (IAM) role that grants Amazon Comprehend read access to your input data. For more information, see https://docs.aws.amazon.com/comprehend/latest/dg/access-control-managing-permissions.html#auth-role-permissions .

--job-name (string)

The identifier of the job.

--number-of-topics (integer)

The number of topics to detect.

--client-request-token (string)

A unique identifier for the request. If you do not set the client request token, Amazon Comprehend generates one.

--volume-kms-key-id (string)

ID for the AWS 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:

• KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab"

• Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

--vpc-config (structure)

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 Amazon VPC .

SecurityGroupIds -> (list)

The ID number for a security group on an instance of your private VPC. Security groups on your VPC function serve as a virtual firewall to control inbound and outbound traffic and provides security for the resources that you’ll be accessing on the VPC. This ID number is preceded by “sg-“, for instance: “sg-03b388029b0a285ea”. For more information, see Security Groups for your VPC .

(string)

Subnets -> (list)

The ID for each subnet being used in your private VPC. This subnet is a subset of the a range of IPv4 addresses used by the VPC and is specific to a given availability zone in the VPC’s region. This ID number is preceded by “subnet-“, for instance: “subnet-04ccf456919e69055”. For more information, see VPCs and Subnets .

(string)

Shorthand Syntax:

SecurityGroupIds=string,string,Subnets=string,string

JSON Syntax:

{
  "SecurityGroupIds": ["string", ...],
  "Subnets": ["string", ...]
}

--tags (list)

Tags to be associated 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.

(structure)

A key-value pair that adds as a metadata to a resource used by Amazon Comprehend. For example, a tag with the key-value pair ‘Department’:’Sales’ might be added to a resource to indicate its use by a particular department.

Key -> (string)

The initial part of a key-value pair that forms a tag associated with a given resource. For instance, if you want to show which resources are used by which departments, you might use “Department” as the key portion of the pair, with multiple possible values such as “sales,” “legal,” and “administration.”

Value -> (string)

The second part of a key-value pair that forms a tag associated with a given resource. For instance, if you want to show which resources are used by which departments, you might use “Department” as the initial (key) portion of the pair, with a value of “sales” to indicate the sales department.

Shorthand Syntax:

Key=string,Value=string ...

JSON Syntax:

[
  {
    "Key": "string",
    "Value": "string"
  }
  ...
]

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See ‘aws help’ for descriptions of global parameters.

Output

JobId -> (string)

The identifier generated for the job. To get the status of the job, use this identifier with the DescribeTopicDetectionJob operation.

JobArn -> (string)

The Amazon Resource Name (ARN) of the topics detection job. It is a unique, fully qualified identifier for the job. It includes the AWS account, Region, and the job ID. The format of the ARN is as follows:

arn:<partition>:comprehend:<region>:<account-id>:topics-detection-job/<job-id>

The following is an example job ARN:

arn:aws:comprehend:us-west-2:111122223333:document-classification-job/1234abcd12ab34cd56ef1234567890ab

JobStatus -> (string)

The status of the job:

• SUBMITTED - The job has been received and is queued for processing.

• IN_PROGRESS - Amazon Comprehend is processing the job.

• COMPLETED - The job was successfully completed and the output is available.

• FAILED - The job did not complete. To get details, use the DescribeTopicDetectionJob operation.