CfnDocumentClassifierPropsMixin

class aws_cdk.mixins_preview.aws_comprehend.mixins.CfnDocumentClassifierPropsMixin(props, *, strategy=None)

Bases: Mixin

This resource creates and trains a document classifier to categorize documents.

You provide a set of training documents that are labeled with the categories that you want to identify. After the classifier is trained you can use it to categorize a set of labeled documents into the categories. For more information, see Document Classification in the Comprehend Developer Guide.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-comprehend-documentclassifier.html
CloudformationResource:: AWS::Comprehend::DocumentClassifier
Mixin:: true
ExampleMetadata:: fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview import mixins
from aws_cdk.mixins_preview.aws_comprehend import mixins as comprehend_mixins

cfn_document_classifier_props_mixin = comprehend_mixins.CfnDocumentClassifierPropsMixin(comprehend_mixins.CfnDocumentClassifierMixinProps(
    data_access_role_arn="dataAccessRoleArn",
    document_classifier_name="documentClassifierName",
    input_data_config=comprehend_mixins.CfnDocumentClassifierPropsMixin.DocumentClassifierInputDataConfigProperty(
        augmented_manifests=[comprehend_mixins.CfnDocumentClassifierPropsMixin.AugmentedManifestsListItemProperty(
            attribute_names=["attributeNames"],
            s3_uri="s3Uri",
            split="split"
        )],
        data_format="dataFormat",
        document_reader_config=comprehend_mixins.CfnDocumentClassifierPropsMixin.DocumentReaderConfigProperty(
            document_read_action="documentReadAction",
            document_read_mode="documentReadMode",
            feature_types=["featureTypes"]
        ),
        documents=comprehend_mixins.CfnDocumentClassifierPropsMixin.DocumentClassifierDocumentsProperty(
            s3_uri="s3Uri",
            test_s3_uri="testS3Uri"
        ),
        document_type="documentType",
        label_delimiter="labelDelimiter",
        s3_uri="s3Uri",
        test_s3_uri="testS3Uri"
    ),
    language_code="languageCode",
    mode="mode",
    model_kms_key_id="modelKmsKeyId",
    model_policy="modelPolicy",
    output_data_config=comprehend_mixins.CfnDocumentClassifierPropsMixin.DocumentClassifierOutputDataConfigProperty(
        kms_key_id="kmsKeyId",
        s3_uri="s3Uri"
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )],
    version_name="versionName",
    volume_kms_key_id="volumeKmsKeyId",
    vpc_config=comprehend_mixins.CfnDocumentClassifierPropsMixin.VpcConfigProperty(
        security_group_ids=["securityGroupIds"],
        subnets=["subnets"]
    )
),
    strategy=mixins.PropertyMergeStrategy.OVERRIDE
)

Create a mixin to apply properties to AWS::Comprehend::DocumentClassifier.

Parameters:

props (Union[CfnDocumentClassifierMixinProps, Dict[str, Any]]) – L1 properties to apply.
strategy (Optional[PropertyMergeStrategy]) – (experimental) Strategy for merging nested properties. Default: - PropertyMergeStrategy.MERGE

Methods

apply_to(construct)

Apply the mixin properties to the construct.

Parameters:: construct (IConstruct)
Return type:: IConstruct

supports(construct)

Check if this mixin supports the given construct.

Parameters:: construct (IConstruct)
Return type:: bool

Attributes

CFN_PROPERTY_KEYS = ['dataAccessRoleArn', 'documentClassifierName', 'inputDataConfig', 'languageCode', 'mode', 'modelKmsKeyId', 'modelPolicy', 'outputDataConfig', 'tags', 'versionName', 'volumeKmsKeyId', 'vpcConfig']

Static Methods

classmethod is_mixin(x)

(experimental) Checks if x is a Mixin.

Parameters:: x (Any) – Any object.
Return type:: bool
Returns:: true if x is an object created from a class which extends Mixin.
Stability:: experimental

AugmentedManifestsListItemProperty

class CfnDocumentClassifierPropsMixin.AugmentedManifestsListItemProperty(*, attribute_names=None, s3_uri=None, split=None)

Bases: object

An augmented manifest file that provides training data for your custom model.

An augmented manifest file is a labeled dataset that is produced by Amazon SageMaker Ground Truth.

Parameters:

attribute_names (Optional[Sequence[str]]) – The JSON attribute that contains the annotations for your training documents. The number of attribute names that you specify depends on whether your augmented manifest file is the output of a single labeling job or a chained labeling job. If your file is the output of a single labeling job, specify the LabelAttributeName key that was used when the job was created in Ground Truth. If your file is the output of a chained labeling job, specify the LabelAttributeName key for one or more jobs in the chain. Each LabelAttributeName key provides the annotations from an individual job.
s3_uri (Optional[str]) – The Amazon S3 location of the augmented manifest file.
split (Optional[str]) – The purpose of the data you’ve provided in the augmented manifest. You can either train or test this data. If you don’t specify, the default is train. TRAIN - all of the documents in the manifest will be used for training. If no test documents are provided, Amazon Comprehend will automatically reserve a portion of the training documents for testing. TEST - all of the documents in the manifest will be used for testing.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-augmentedmanifestslistitem.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_comprehend import mixins as comprehend_mixins

augmented_manifests_list_item_property = comprehend_mixins.CfnDocumentClassifierPropsMixin.AugmentedManifestsListItemProperty(
    attribute_names=["attributeNames"],
    s3_uri="s3Uri",
    split="split"
)

Attributes

attribute_names

The JSON attribute that contains the annotations for your training documents.

The number of attribute names that you specify depends on whether your augmented manifest file is the output of a single labeling job or a chained labeling job.

If your file is the output of a single labeling job, specify the LabelAttributeName key that was used when the job was created in Ground Truth.

If your file is the output of a chained labeling job, specify the LabelAttributeName key for one or more jobs in the chain. Each LabelAttributeName key provides the annotations from an individual job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-augmentedmanifestslistitem.html#cfn-comprehend-documentclassifier-augmentedmanifestslistitem-attributenames

s3_uri

The Amazon S3 location of the augmented manifest file.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-augmentedmanifestslistitem.html#cfn-comprehend-documentclassifier-augmentedmanifestslistitem-s3uri

split

The purpose of the data you’ve provided in the augmented manifest.

You can either train or test this data. If you don’t specify, the default is train.

TRAIN - all of the documents in the manifest will be used for training. If no test documents are provided, Amazon Comprehend will automatically reserve a portion of the training documents for testing.

TEST - all of the documents in the manifest will be used for testing.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-augmentedmanifestslistitem.html#cfn-comprehend-documentclassifier-augmentedmanifestslistitem-split

DocumentClassifierDocumentsProperty

class CfnDocumentClassifierPropsMixin.DocumentClassifierDocumentsProperty(*, s3_uri=None, test_s3_uri=None)

Bases: object

The location of the training documents.

This parameter is required in a request to create a semi-structured document classification model.

Parameters:

s3_uri (Optional[str]) – The S3 URI location of the training documents specified in the S3Uri CSV file.
test_s3_uri (Optional[str]) – The S3 URI location of the test documents included in the TestS3Uri CSV file. This field is not required if you do not specify a test CSV file.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierdocuments.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_comprehend import mixins as comprehend_mixins

document_classifier_documents_property = comprehend_mixins.CfnDocumentClassifierPropsMixin.DocumentClassifierDocumentsProperty(
    s3_uri="s3Uri",
    test_s3_uri="testS3Uri"
)

Attributes

s3_uri

The S3 URI location of the training documents specified in the S3Uri CSV file.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierdocuments.html#cfn-comprehend-documentclassifier-documentclassifierdocuments-s3uri

test_s3_uri

The S3 URI location of the test documents included in the TestS3Uri CSV file.

This field is not required if you do not specify a test CSV file.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierdocuments.html#cfn-comprehend-documentclassifier-documentclassifierdocuments-tests3uri

DocumentClassifierInputDataConfigProperty

class CfnDocumentClassifierPropsMixin.DocumentClassifierInputDataConfigProperty(*, augmented_manifests=None, data_format=None, document_reader_config=None, documents=None, document_type=None, label_delimiter=None, s3_uri=None, test_s3_uri=None)

Bases: object

The input properties for training a document classifier.

For more information on how the input file is formatted, see Preparing training data in the Comprehend Developer Guide.

Parameters:

augmented_manifests (Union[IResolvable, Sequence[Union[IResolvable, AugmentedManifestsListItemProperty, Dict[str, Any]]], None]) – A list of augmented manifest files that provide training data for your custom model. An augmented manifest file is a labeled dataset that is produced by Amazon SageMaker Ground Truth. This parameter is required if you set DataFormat to AUGMENTED_MANIFEST .
data_format (Optional[str]) – The format of your training data:. - COMPREHEND_CSV : A two-column CSV file, where labels are provided in the first column, and documents are provided in the second. If you use this value, you must provide the S3Uri parameter in your request. - AUGMENTED_MANIFEST : A labeled dataset that is produced by Amazon SageMaker Ground Truth. This file is in JSON lines format. Each line is a complete JSON object that contains a training document and its associated labels. If you use this value, you must provide the AugmentedManifests parameter in your request. If you don’t specify a value, Amazon Comprehend uses COMPREHEND_CSV as the default.
document_reader_config (Union[IResolvable, DocumentReaderConfigProperty, Dict[str, Any], None])
documents (Union[IResolvable, DocumentClassifierDocumentsProperty, Dict[str, Any], None]) – The S3 location of the training documents. This parameter is required in a request to create a native document model.
document_type (Optional[str]) – The type of input documents for training the model. Provide plain-text documents to create a plain-text model, and provide semi-structured documents to create a native document model.
label_delimiter (Optional[str]) – Indicates the delimiter used to separate each label for training a multi-label classifier. The default delimiter between labels is a pipe (|). You can use a different character as a delimiter (if it’s an allowed character) by specifying it under Delimiter for labels. If the training documents use a delimiter other than the default or the delimiter you specify, the labels on that line will be combined to make a single unique label, such as LABELLABELLABEL.
s3_uri (Optional[str]) – The Amazon S3 URI for the input data. The S3 bucket must be in the 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 input 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. This parameter is required if you set DataFormat to COMPREHEND_CSV .
test_s3_uri (Optional[str]) – This specifies the Amazon S3 location that contains the test annotations for the document classifier. The URI must be in the same AWS Region as the API endpoint that you are calling.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_comprehend import mixins as comprehend_mixins

document_classifier_input_data_config_property = comprehend_mixins.CfnDocumentClassifierPropsMixin.DocumentClassifierInputDataConfigProperty(
    augmented_manifests=[comprehend_mixins.CfnDocumentClassifierPropsMixin.AugmentedManifestsListItemProperty(
        attribute_names=["attributeNames"],
        s3_uri="s3Uri",
        split="split"
    )],
    data_format="dataFormat",
    document_reader_config=comprehend_mixins.CfnDocumentClassifierPropsMixin.DocumentReaderConfigProperty(
        document_read_action="documentReadAction",
        document_read_mode="documentReadMode",
        feature_types=["featureTypes"]
    ),
    documents=comprehend_mixins.CfnDocumentClassifierPropsMixin.DocumentClassifierDocumentsProperty(
        s3_uri="s3Uri",
        test_s3_uri="testS3Uri"
    ),
    document_type="documentType",
    label_delimiter="labelDelimiter",
    s3_uri="s3Uri",
    test_s3_uri="testS3Uri"
)

Attributes

augmented_manifests

A list of augmented manifest files that provide training data for your custom model.

An augmented manifest file is a labeled dataset that is produced by Amazon SageMaker Ground Truth.

This parameter is required if you set DataFormat to AUGMENTED_MANIFEST .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-augmentedmanifests

data_format

.

COMPREHEND_CSV : A two-column CSV file, where labels are provided in the first column, and documents are provided in the second. If you use this value, you must provide the S3Uri parameter in your request.
AUGMENTED_MANIFEST : A labeled dataset that is produced by Amazon SageMaker Ground Truth. This file is in JSON lines format. Each line is a complete JSON object that contains a training document and its associated labels.

If you use this value, you must provide the AugmentedManifests parameter in your request.

If you don’t specify a value, Amazon Comprehend uses COMPREHEND_CSV as the default.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-dataformat
Type:: The format of your training data

document_reader_config

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-documentreaderconfig

Type:: see

document_type

The type of input documents for training the model.

Provide plain-text documents to create a plain-text model, and provide semi-structured documents to create a native document model.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-documenttype

documents

The S3 location of the training documents.

This parameter is required in a request to create a native document model.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-documents

label_delimiter

Indicates the delimiter used to separate each label for training a multi-label classifier.

The default delimiter between labels is a pipe (|). You can use a different character as a delimiter (if it’s an allowed character) by specifying it under Delimiter for labels. If the training documents use a delimiter other than the default or the delimiter you specify, the labels on that line will be combined to make a single unique label, such as LABELLABELLABEL.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-labeldelimiter

s3_uri

The Amazon S3 URI for the input data.

The S3 bucket must be in the 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 input 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.

This parameter is required if you set DataFormat to COMPREHEND_CSV .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-s3uri

test_s3_uri

This specifies the Amazon S3 location that contains the test annotations for the document classifier.

The URI must be in the same AWS Region as the API endpoint that you are calling.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-tests3uri

DocumentClassifierOutputDataConfigProperty

class CfnDocumentClassifierPropsMixin.DocumentClassifierOutputDataConfigProperty(*, kms_key_id=None, s3_uri=None)

Bases: object

Provide the location for output data from a custom classifier job.

This field is mandatory if you are training a native document model.

Parameters:

kms_key_id (Optional[str]) – 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"
s3_uri (Optional[str]) – When you use the OutputDataConfig object while creating a custom classifier, you specify the Amazon S3 location where you want to write the confusion matrix and other output files. 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 this output file. When the custom classifier job is finished, the service creates the 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 confusion matrix.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifieroutputdataconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_comprehend import mixins as comprehend_mixins

document_classifier_output_data_config_property = comprehend_mixins.CfnDocumentClassifierPropsMixin.DocumentClassifierOutputDataConfigProperty(
    kms_key_id="kmsKeyId",
    s3_uri="s3Uri"
)

Attributes

kms_key_id

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"

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifieroutputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifieroutputdataconfig-kmskeyid

s3_uri

When you use the OutputDataConfig object while creating a custom classifier, you specify the Amazon S3 location where you want to write the confusion matrix and other output files.

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 this output file.

When the custom classifier job is finished, the service creates the 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 confusion matrix.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifieroutputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifieroutputdataconfig-s3uri

DocumentReaderConfigProperty

class CfnDocumentClassifierPropsMixin.DocumentReaderConfigProperty(*, document_read_action=None, document_read_mode=None, feature_types=None)

Bases: object

Provides configuration parameters to override the default actions for extracting text from PDF documents and image files.

By default, Amazon Comprehend performs the following actions to extract text from files, based on the input file type:

Word files - Amazon Comprehend parser extracts the text.
Digital PDF files - Amazon Comprehend parser extracts the text.
Image files and scanned PDF files - Amazon Comprehend uses the Amazon Textract DetectDocumentText API to extract the text.

DocumentReaderConfig does not apply to plain text files or Word files.

For image files and PDF documents, you can override these default actions using the fields listed below. For more information, see Setting text extraction options in the Comprehend Developer Guide.

Parameters:

document_read_action (Optional[str]) – This field defines the Amazon Textract API operation that Amazon Comprehend uses to extract text from PDF files and image files. Enter one of the following values: - TEXTRACT_DETECT_DOCUMENT_TEXT - The Amazon Comprehend service uses the DetectDocumentText API operation. - TEXTRACT_ANALYZE_DOCUMENT - The Amazon Comprehend service uses the AnalyzeDocument API operation.
document_read_mode (Optional[str]) – Determines the text extraction actions for PDF files. Enter one of the following values:. - SERVICE_DEFAULT - use the Amazon Comprehend service defaults for PDF files. - FORCE_DOCUMENT_READ_ACTION - Amazon Comprehend uses the Textract API specified by DocumentReadAction for all PDF files, including digital PDF files.
feature_types (Optional[Sequence[str]]) – Specifies the type of Amazon Textract features to apply. If you chose TEXTRACT_ANALYZE_DOCUMENT as the read action, you must specify one or both of the following values: - TABLES - Returns additional information about any tables that are detected in the input document. - FORMS - Returns additional information about any forms that are detected in the input document.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentreaderconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_comprehend import mixins as comprehend_mixins

document_reader_config_property = comprehend_mixins.CfnDocumentClassifierPropsMixin.DocumentReaderConfigProperty(
    document_read_action="documentReadAction",
    document_read_mode="documentReadMode",
    feature_types=["featureTypes"]
)

Attributes

document_read_action

This field defines the Amazon Textract API operation that Amazon Comprehend uses to extract text from PDF files and image files.

Enter one of the following values:

TEXTRACT_DETECT_DOCUMENT_TEXT - The Amazon Comprehend service uses the DetectDocumentText API operation.
TEXTRACT_ANALYZE_DOCUMENT - The Amazon Comprehend service uses the AnalyzeDocument API operation.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentreaderconfig.html#cfn-comprehend-documentclassifier-documentreaderconfig-documentreadaction

document_read_mode

.

SERVICE_DEFAULT - use the Amazon Comprehend service defaults for PDF files.
FORCE_DOCUMENT_READ_ACTION - Amazon Comprehend uses the Textract API specified by DocumentReadAction for all PDF files, including digital PDF files.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentreaderconfig.html#cfn-comprehend-documentclassifier-documentreaderconfig-documentreadmode
Type:: Determines the text extraction actions for PDF files. Enter one of the following values

feature_types

Specifies the type of Amazon Textract features to apply.

If you chose TEXTRACT_ANALYZE_DOCUMENT as the read action, you must specify one or both of the following values:

TABLES - Returns additional information about any tables that are detected in the input document.
FORMS - Returns additional information about any forms that are detected in the input document.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentreaderconfig.html#cfn-comprehend-documentclassifier-documentreaderconfig-featuretypes

VpcConfigProperty

class CfnDocumentClassifierPropsMixin.VpcConfigProperty(*, security_group_ids=None, subnets=None)

Bases: object

Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for the job.

For more information, see Amazon VPC .

Parameters:

security_group_ids (Optional[Sequence[str]]) – 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 .
subnets (Optional[Sequence[str]]) – 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 .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-vpcconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_comprehend import mixins as comprehend_mixins

vpc_config_property = comprehend_mixins.CfnDocumentClassifierPropsMixin.VpcConfigProperty(
    security_group_ids=["securityGroupIds"],
    subnets=["subnets"]
)

Attributes

security_group_ids

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 .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-vpcconfig.html#cfn-comprehend-documentclassifier-vpcconfig-securitygroupids

subnets

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 .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-vpcconfig.html#cfn-comprehend-documentclassifier-vpcconfig-subnets