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// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq)]
pub struct DetectEntitiesInput {
/// <p>A UTF-8 text string. The maximum string size is 100 KB. If you enter text using this parameter, do not use the <code>Bytes</code> parameter.</p>
pub text: ::std::option::Option<::std::string::String>,
/// <p>The language of the input documents. You can specify any of the primary languages supported by Amazon Comprehend. If your request includes the endpoint for a custom entity recognition model, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you specify here.</p>
/// <p>All input documents must be in the same language.</p>
pub language_code: ::std::option::Option<crate::types::LanguageCode>,
/// <p>The Amazon Resource Name of an endpoint that is associated with a custom entity recognition model. Provide an endpoint if you want to detect entities by using your own custom model instead of the default model that is used by Amazon Comprehend.</p>
/// <p>If you specify an endpoint, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you provide in your request.</p>
/// <p>For information about endpoints, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html">Managing endpoints</a>.</p>
pub endpoint_arn: ::std::option::Option<::std::string::String>,
/// <p>This field applies only when you use a custom entity recognition model that was trained with PDF annotations. For other cases, enter your text input in the <code>Text</code> field.</p>
/// <p>Use the <code>Bytes</code> parameter to input a text, PDF, Word or image file. Using a plain-text file in the <code>Bytes</code> parameter is equivelent to using the <code>Text</code> parameter (the <code>Entities</code> field in the response is identical).</p>
/// <p>You can also use the <code>Bytes</code> parameter to input an Amazon Textract <code>DetectDocumentText</code> or <code>AnalyzeDocument</code> output file.</p>
/// <p>Provide the input document as a sequence of base64-encoded bytes. If your code uses an Amazon Web Services SDK to detect entities, the SDK may encode the document file bytes for you.</p>
/// <p>The maximum length of this field depends on the input document type. For details, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/idp-inputs-sync.html"> Inputs for real-time custom analysis</a> in the Comprehend Developer Guide.</p>
/// <p>If you use the <code>Bytes</code> parameter, do not use the <code>Text</code> parameter.</p>
pub bytes: ::std::option::Option<::aws_smithy_types::Blob>,
/// <p>Provides configuration parameters to override the default actions for extracting text from PDF documents and image files.</p>
pub document_reader_config: ::std::option::Option<crate::types::DocumentReaderConfig>,
}
impl DetectEntitiesInput {
/// <p>A UTF-8 text string. The maximum string size is 100 KB. If you enter text using this parameter, do not use the <code>Bytes</code> parameter.</p>
pub fn text(&self) -> ::std::option::Option<&str> {
self.text.as_deref()
}
/// <p>The language of the input documents. You can specify any of the primary languages supported by Amazon Comprehend. If your request includes the endpoint for a custom entity recognition model, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you specify here.</p>
/// <p>All input documents must be in the same language.</p>
pub fn language_code(&self) -> ::std::option::Option<&crate::types::LanguageCode> {
self.language_code.as_ref()
}
/// <p>The Amazon Resource Name of an endpoint that is associated with a custom entity recognition model. Provide an endpoint if you want to detect entities by using your own custom model instead of the default model that is used by Amazon Comprehend.</p>
/// <p>If you specify an endpoint, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you provide in your request.</p>
/// <p>For information about endpoints, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html">Managing endpoints</a>.</p>
pub fn endpoint_arn(&self) -> ::std::option::Option<&str> {
self.endpoint_arn.as_deref()
}
/// <p>This field applies only when you use a custom entity recognition model that was trained with PDF annotations. For other cases, enter your text input in the <code>Text</code> field.</p>
/// <p>Use the <code>Bytes</code> parameter to input a text, PDF, Word or image file. Using a plain-text file in the <code>Bytes</code> parameter is equivelent to using the <code>Text</code> parameter (the <code>Entities</code> field in the response is identical).</p>
/// <p>You can also use the <code>Bytes</code> parameter to input an Amazon Textract <code>DetectDocumentText</code> or <code>AnalyzeDocument</code> output file.</p>
/// <p>Provide the input document as a sequence of base64-encoded bytes. If your code uses an Amazon Web Services SDK to detect entities, the SDK may encode the document file bytes for you.</p>
/// <p>The maximum length of this field depends on the input document type. For details, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/idp-inputs-sync.html"> Inputs for real-time custom analysis</a> in the Comprehend Developer Guide.</p>
/// <p>If you use the <code>Bytes</code> parameter, do not use the <code>Text</code> parameter.</p>
pub fn bytes(&self) -> ::std::option::Option<&::aws_smithy_types::Blob> {
self.bytes.as_ref()
}
/// <p>Provides configuration parameters to override the default actions for extracting text from PDF documents and image files.</p>
pub fn document_reader_config(&self) -> ::std::option::Option<&crate::types::DocumentReaderConfig> {
self.document_reader_config.as_ref()
}
}
impl ::std::fmt::Debug for DetectEntitiesInput {
fn fmt(&self, f: &mut ::std::fmt::Formatter<'_>) -> ::std::fmt::Result {
let mut formatter = f.debug_struct("DetectEntitiesInput");
formatter.field("text", &"*** Sensitive Data Redacted ***");
formatter.field("language_code", &self.language_code);
formatter.field("endpoint_arn", &self.endpoint_arn);
formatter.field("bytes", &self.bytes);
formatter.field("document_reader_config", &self.document_reader_config);
formatter.finish()
}
}
impl DetectEntitiesInput {
/// Creates a new builder-style object to manufacture [`DetectEntitiesInput`](crate::operation::detect_entities::DetectEntitiesInput).
pub fn builder() -> crate::operation::detect_entities::builders::DetectEntitiesInputBuilder {
crate::operation::detect_entities::builders::DetectEntitiesInputBuilder::default()
}
}
/// A builder for [`DetectEntitiesInput`](crate::operation::detect_entities::DetectEntitiesInput).
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::default::Default)]
#[non_exhaustive]
pub struct DetectEntitiesInputBuilder {
pub(crate) text: ::std::option::Option<::std::string::String>,
pub(crate) language_code: ::std::option::Option<crate::types::LanguageCode>,
pub(crate) endpoint_arn: ::std::option::Option<::std::string::String>,
pub(crate) bytes: ::std::option::Option<::aws_smithy_types::Blob>,
pub(crate) document_reader_config: ::std::option::Option<crate::types::DocumentReaderConfig>,
}
impl DetectEntitiesInputBuilder {
/// <p>A UTF-8 text string. The maximum string size is 100 KB. If you enter text using this parameter, do not use the <code>Bytes</code> parameter.</p>
pub fn text(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.text = ::std::option::Option::Some(input.into());
self
}
/// <p>A UTF-8 text string. The maximum string size is 100 KB. If you enter text using this parameter, do not use the <code>Bytes</code> parameter.</p>
pub fn set_text(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.text = input;
self
}
/// <p>A UTF-8 text string. The maximum string size is 100 KB. If you enter text using this parameter, do not use the <code>Bytes</code> parameter.</p>
pub fn get_text(&self) -> &::std::option::Option<::std::string::String> {
&self.text
}
/// <p>The language of the input documents. You can specify any of the primary languages supported by Amazon Comprehend. If your request includes the endpoint for a custom entity recognition model, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you specify here.</p>
/// <p>All input documents must be in the same language.</p>
pub fn language_code(mut self, input: crate::types::LanguageCode) -> Self {
self.language_code = ::std::option::Option::Some(input);
self
}
/// <p>The language of the input documents. You can specify any of the primary languages supported by Amazon Comprehend. If your request includes the endpoint for a custom entity recognition model, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you specify here.</p>
/// <p>All input documents must be in the same language.</p>
pub fn set_language_code(mut self, input: ::std::option::Option<crate::types::LanguageCode>) -> Self {
self.language_code = input;
self
}
/// <p>The language of the input documents. You can specify any of the primary languages supported by Amazon Comprehend. If your request includes the endpoint for a custom entity recognition model, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you specify here.</p>
/// <p>All input documents must be in the same language.</p>
pub fn get_language_code(&self) -> &::std::option::Option<crate::types::LanguageCode> {
&self.language_code
}
/// <p>The Amazon Resource Name of an endpoint that is associated with a custom entity recognition model. Provide an endpoint if you want to detect entities by using your own custom model instead of the default model that is used by Amazon Comprehend.</p>
/// <p>If you specify an endpoint, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you provide in your request.</p>
/// <p>For information about endpoints, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html">Managing endpoints</a>.</p>
pub fn endpoint_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.endpoint_arn = ::std::option::Option::Some(input.into());
self
}
/// <p>The Amazon Resource Name of an endpoint that is associated with a custom entity recognition model. Provide an endpoint if you want to detect entities by using your own custom model instead of the default model that is used by Amazon Comprehend.</p>
/// <p>If you specify an endpoint, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you provide in your request.</p>
/// <p>For information about endpoints, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html">Managing endpoints</a>.</p>
pub fn set_endpoint_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.endpoint_arn = input;
self
}
/// <p>The Amazon Resource Name of an endpoint that is associated with a custom entity recognition model. Provide an endpoint if you want to detect entities by using your own custom model instead of the default model that is used by Amazon Comprehend.</p>
/// <p>If you specify an endpoint, Amazon Comprehend uses the language of your custom model, and it ignores any language code that you provide in your request.</p>
/// <p>For information about endpoints, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html">Managing endpoints</a>.</p>
pub fn get_endpoint_arn(&self) -> &::std::option::Option<::std::string::String> {
&self.endpoint_arn
}
/// <p>This field applies only when you use a custom entity recognition model that was trained with PDF annotations. For other cases, enter your text input in the <code>Text</code> field.</p>
/// <p>Use the <code>Bytes</code> parameter to input a text, PDF, Word or image file. Using a plain-text file in the <code>Bytes</code> parameter is equivelent to using the <code>Text</code> parameter (the <code>Entities</code> field in the response is identical).</p>
/// <p>You can also use the <code>Bytes</code> parameter to input an Amazon Textract <code>DetectDocumentText</code> or <code>AnalyzeDocument</code> output file.</p>
/// <p>Provide the input document as a sequence of base64-encoded bytes. If your code uses an Amazon Web Services SDK to detect entities, the SDK may encode the document file bytes for you.</p>
/// <p>The maximum length of this field depends on the input document type. For details, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/idp-inputs-sync.html"> Inputs for real-time custom analysis</a> in the Comprehend Developer Guide.</p>
/// <p>If you use the <code>Bytes</code> parameter, do not use the <code>Text</code> parameter.</p>
pub fn bytes(mut self, input: ::aws_smithy_types::Blob) -> Self {
self.bytes = ::std::option::Option::Some(input);
self
}
/// <p>This field applies only when you use a custom entity recognition model that was trained with PDF annotations. For other cases, enter your text input in the <code>Text</code> field.</p>
/// <p>Use the <code>Bytes</code> parameter to input a text, PDF, Word or image file. Using a plain-text file in the <code>Bytes</code> parameter is equivelent to using the <code>Text</code> parameter (the <code>Entities</code> field in the response is identical).</p>
/// <p>You can also use the <code>Bytes</code> parameter to input an Amazon Textract <code>DetectDocumentText</code> or <code>AnalyzeDocument</code> output file.</p>
/// <p>Provide the input document as a sequence of base64-encoded bytes. If your code uses an Amazon Web Services SDK to detect entities, the SDK may encode the document file bytes for you.</p>
/// <p>The maximum length of this field depends on the input document type. For details, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/idp-inputs-sync.html"> Inputs for real-time custom analysis</a> in the Comprehend Developer Guide.</p>
/// <p>If you use the <code>Bytes</code> parameter, do not use the <code>Text</code> parameter.</p>
pub fn set_bytes(mut self, input: ::std::option::Option<::aws_smithy_types::Blob>) -> Self {
self.bytes = input;
self
}
/// <p>This field applies only when you use a custom entity recognition model that was trained with PDF annotations. For other cases, enter your text input in the <code>Text</code> field.</p>
/// <p>Use the <code>Bytes</code> parameter to input a text, PDF, Word or image file. Using a plain-text file in the <code>Bytes</code> parameter is equivelent to using the <code>Text</code> parameter (the <code>Entities</code> field in the response is identical).</p>
/// <p>You can also use the <code>Bytes</code> parameter to input an Amazon Textract <code>DetectDocumentText</code> or <code>AnalyzeDocument</code> output file.</p>
/// <p>Provide the input document as a sequence of base64-encoded bytes. If your code uses an Amazon Web Services SDK to detect entities, the SDK may encode the document file bytes for you.</p>
/// <p>The maximum length of this field depends on the input document type. For details, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/idp-inputs-sync.html"> Inputs for real-time custom analysis</a> in the Comprehend Developer Guide.</p>
/// <p>If you use the <code>Bytes</code> parameter, do not use the <code>Text</code> parameter.</p>
pub fn get_bytes(&self) -> &::std::option::Option<::aws_smithy_types::Blob> {
&self.bytes
}
/// <p>Provides configuration parameters to override the default actions for extracting text from PDF documents and image files.</p>
pub fn document_reader_config(mut self, input: crate::types::DocumentReaderConfig) -> Self {
self.document_reader_config = ::std::option::Option::Some(input);
self
}
/// <p>Provides configuration parameters to override the default actions for extracting text from PDF documents and image files.</p>
pub fn set_document_reader_config(mut self, input: ::std::option::Option<crate::types::DocumentReaderConfig>) -> Self {
self.document_reader_config = input;
self
}
/// <p>Provides configuration parameters to override the default actions for extracting text from PDF documents and image files.</p>
pub fn get_document_reader_config(&self) -> &::std::option::Option<crate::types::DocumentReaderConfig> {
&self.document_reader_config
}
/// Consumes the builder and constructs a [`DetectEntitiesInput`](crate::operation::detect_entities::DetectEntitiesInput).
pub fn build(
self,
) -> ::std::result::Result<crate::operation::detect_entities::DetectEntitiesInput, ::aws_smithy_types::error::operation::BuildError> {
::std::result::Result::Ok(crate::operation::detect_entities::DetectEntitiesInput {
text: self.text,
language_code: self.language_code,
endpoint_arn: self.endpoint_arn,
bytes: self.bytes,
document_reader_config: self.document_reader_config,
})
}
}
impl ::std::fmt::Debug for DetectEntitiesInputBuilder {
fn fmt(&self, f: &mut ::std::fmt::Formatter<'_>) -> ::std::fmt::Result {
let mut formatter = f.debug_struct("DetectEntitiesInputBuilder");
formatter.field("text", &"*** Sensitive Data Redacted ***");
formatter.field("language_code", &self.language_code);
formatter.field("endpoint_arn", &self.endpoint_arn);
formatter.field("bytes", &self.bytes);
formatter.field("document_reader_config", &self.document_reader_config);
formatter.finish()
}
}