See: newClassifyDocument smart constructor.

Create a value of ClassifyDocument with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:bytes:ClassifyDocument', classifyDocument_bytes - Use the Bytes parameter to input a text, PDF, Word or image file. You can also use the Bytes parameter to input an Amazon Textract DetectDocumentText or AnalyzeDocument output file.

Provide the input document as a sequence of base64-encoded bytes. If your code uses an Amazon Web Services SDK to classify documents, the SDK may encode the document file bytes for you.

The maximum length of this field depends on the input document type. For details, see Inputs for real-time custom analysis in the Comprehend Developer Guide.

If you use the Bytes parameter, do not use the Text parameter.-- -- Note: This Lens automatically encodes and decodes Base64 data. -- The underlying isomorphism will encode to Base64 representation during -- serialisation, and decode from Base64 representation during deserialisation. -- This Lens accepts and returns only raw unencoded data.

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

ClassifyDocument, classifyDocument_text - The document text to be analyzed. If you enter text using this parameter, do not use the Bytes parameter.

ClassifyDocument, classifyDocument_endpointArn - The Amazon Resource Number (ARN) of the endpoint. For information about endpoints, see Managing endpoints.

Use the Bytes parameter to input a text, PDF, Word or image file. You can also use the Bytes parameter to input an Amazon Textract DetectDocumentText or AnalyzeDocument output file.

Provide the input document as a sequence of base64-encoded bytes. If your code uses an Amazon Web Services SDK to classify documents, the SDK may encode the document file bytes for you.

The maximum length of this field depends on the input document type. For details, see Inputs for real-time custom analysis in the Comprehend Developer Guide.

If you use the Bytes parameter, do not use the Text parameter.-- -- Note: This Lens automatically encodes and decodes Base64 data. -- The underlying isomorphism will encode to Base64 representation during -- serialisation, and decode from Base64 representation during deserialisation. -- This Lens accepts and returns only raw unencoded data.

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

The document text to be analyzed. If you enter text using this parameter, do not use the Bytes parameter.

The Amazon Resource Number (ARN) of the endpoint. For information about endpoints, see Managing endpoints.

See: newClassifyDocumentResponse smart constructor.

Create a value of ClassifyDocumentResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:classes:ClassifyDocumentResponse', classifyDocumentResponse_classes - The classes used by the document being analyzed. These are used for multi-class trained models. Individual classes are mutually exclusive and each document is expected to have only a single class assigned to it. For example, an animal can be a dog or a cat, but not both at the same time.

$sel:documentMetadata:ClassifyDocumentResponse', classifyDocumentResponse_documentMetadata - Extraction information about the document. This field is present in the response only if your request includes the Byte parameter.

ClassifyDocumentResponse, classifyDocumentResponse_documentType - The document type for each page in the input document. This field is present in the response only if your request includes the Byte parameter.

$sel:errors:ClassifyDocumentResponse', classifyDocumentResponse_errors - Page-level errors that the system detected while processing the input document. The field is empty if the system encountered no errors.

$sel:labels:ClassifyDocumentResponse', classifyDocumentResponse_labels - The labels used the document being analyzed. These are used for multi-label trained models. Individual labels represent different categories that are related in some manner and are not mutually exclusive. For example, a movie can be just an action movie, or it can be an action movie, a science fiction movie, and a comedy, all at the same time.

$sel:httpStatus:ClassifyDocumentResponse', classifyDocumentResponse_httpStatus - The response's http status code.

The classes used by the document being analyzed. These are used for multi-class trained models. Individual classes are mutually exclusive and each document is expected to have only a single class assigned to it. For example, an animal can be a dog or a cat, but not both at the same time.

Extraction information about the document. This field is present in the response only if your request includes the Byte parameter.

The document type for each page in the input document. This field is present in the response only if your request includes the Byte parameter.

Page-level errors that the system detected while processing the input document. The field is empty if the system encountered no errors.

The labels used the document being analyzed. These are used for multi-label trained models. Individual labels represent different categories that are related in some manner and are not mutually exclusive. For example, a movie can be just an action movie, or it can be an action movie, a science fiction movie, and a comedy, all at the same time.

The response's http status code.