// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
use std::fmt::Write;
/// See [`AnalyzeDocumentInput`](crate::input::AnalyzeDocumentInput).
pub mod analyze_document_input {
/// A builder for [`AnalyzeDocumentInput`](crate::input::AnalyzeDocumentInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) document: std::option::Option<crate::model::Document>,
pub(crate) feature_types: std::option::Option<std::vec::Vec<crate::model::FeatureType>>,
pub(crate) human_loop_config: std::option::Option<crate::model::HumanLoopConfig>,
pub(crate) queries_config: std::option::Option<crate::model::QueriesConfig>,
}
impl Builder {
/// <p>The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can't pass image bytes. The document must be an image in JPEG, PNG, PDF, or TIFF format.</p>
/// <p>If you're using an AWS SDK to call Amazon Textract, you might not need to base64-encode image bytes that are passed using the <code>Bytes</code> field. </p>
pub fn document(mut self, input: crate::model::Document) -> Self {
self.document = Some(input);
self
}
/// <p>The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can't pass image bytes. The document must be an image in JPEG, PNG, PDF, or TIFF format.</p>
/// <p>If you're using an AWS SDK to call Amazon Textract, you might not need to base64-encode image bytes that are passed using the <code>Bytes</code> field. </p>
pub fn set_document(mut self, input: std::option::Option<crate::model::Document>) -> Self {
self.document = input;
self
}
/// Appends an item to `feature_types`.
///
/// To override the contents of this collection use [`set_feature_types`](Self::set_feature_types).
///
/// <p>A list of the types of analysis to perform. Add TABLES to the list to return information about the tables that are detected in the input document. Add FORMS to return detected form data. Add SIGNATURES to return the locations of detected signatures. To perform both forms and table analysis, add TABLES and FORMS to <code>FeatureTypes</code>. To detect signatures within form data and table data, add SIGNATURES to either TABLES or FORMS. All lines and words detected in the document are included in the response (including text that isn't related to the value of <code>FeatureTypes</code>). </p>
pub fn feature_types(mut self, input: crate::model::FeatureType) -> Self {
let mut v = self.feature_types.unwrap_or_default();
v.push(input);
self.feature_types = Some(v);
self
}
/// <p>A list of the types of analysis to perform. Add TABLES to the list to return information about the tables that are detected in the input document. Add FORMS to return detected form data. Add SIGNATURES to return the locations of detected signatures. To perform both forms and table analysis, add TABLES and FORMS to <code>FeatureTypes</code>. To detect signatures within form data and table data, add SIGNATURES to either TABLES or FORMS. All lines and words detected in the document are included in the response (including text that isn't related to the value of <code>FeatureTypes</code>). </p>
pub fn set_feature_types(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::FeatureType>>,
) -> Self {
self.feature_types = input;
self
}
/// <p>Sets the configuration for the human in the loop workflow for analyzing documents.</p>
pub fn human_loop_config(mut self, input: crate::model::HumanLoopConfig) -> Self {
self.human_loop_config = Some(input);
self
}
/// <p>Sets the configuration for the human in the loop workflow for analyzing documents.</p>
pub fn set_human_loop_config(
mut self,
input: std::option::Option<crate::model::HumanLoopConfig>,
) -> Self {
self.human_loop_config = input;
self
}
/// <p>Contains Queries and the alias for those Queries, as determined by the input. </p>
pub fn queries_config(mut self, input: crate::model::QueriesConfig) -> Self {
self.queries_config = Some(input);
self
}
/// <p>Contains Queries and the alias for those Queries, as determined by the input. </p>
pub fn set_queries_config(
mut self,
input: std::option::Option<crate::model::QueriesConfig>,
) -> Self {
self.queries_config = input;
self
}
/// Consumes the builder and constructs a [`AnalyzeDocumentInput`](crate::input::AnalyzeDocumentInput).
pub fn build(
self,
) -> Result<crate::input::AnalyzeDocumentInput, aws_smithy_http::operation::error::BuildError>
{
Ok(crate::input::AnalyzeDocumentInput {
document: self.document,
feature_types: self.feature_types,
human_loop_config: self.human_loop_config,
queries_config: self.queries_config,
})
}
}
}
impl AnalyzeDocumentInput {
/// Consumes the builder and constructs an Operation<[`AnalyzeDocument`](crate::operation::AnalyzeDocument)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::AnalyzeDocument,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::AnalyzeDocumentInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::AnalyzeDocumentInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.AnalyzeDocument",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_analyze_document(&self)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::AnalyzeDocument::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"AnalyzeDocument",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`AnalyzeDocumentInput`](crate::input::AnalyzeDocumentInput).
pub fn builder() -> crate::input::analyze_document_input::Builder {
crate::input::analyze_document_input::Builder::default()
}
}
/// See [`AnalyzeExpenseInput`](crate::input::AnalyzeExpenseInput).
pub mod analyze_expense_input {
/// A builder for [`AnalyzeExpenseInput`](crate::input::AnalyzeExpenseInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) document: std::option::Option<crate::model::Document>,
}
impl Builder {
/// <p>The input document, either as bytes or as an S3 object.</p>
/// <p>You pass image bytes to an Amazon Textract API operation by using the <code>Bytes</code> property. For example, you would use the <code>Bytes</code> property to pass a document loaded from a local file system. Image bytes passed by using the <code>Bytes</code> property must be base64 encoded. Your code might not need to encode document file bytes if you're using an AWS SDK to call Amazon Textract API operations. </p>
/// <p>You pass images stored in an S3 bucket to an Amazon Textract API operation by using the <code>S3Object</code> property. Documents stored in an S3 bucket don't need to be base64 encoded.</p>
/// <p>The AWS Region for the S3 bucket that contains the S3 object must match the AWS Region that you use for Amazon Textract operations.</p>
/// <p>If you use the AWS CLI to call Amazon Textract operations, passing image bytes using the Bytes property isn't supported. You must first upload the document to an Amazon S3 bucket, and then call the operation using the S3Object property.</p>
/// <p>For Amazon Textract to process an S3 object, the user must have permission to access the S3 object. </p>
pub fn document(mut self, input: crate::model::Document) -> Self {
self.document = Some(input);
self
}
/// <p>The input document, either as bytes or as an S3 object.</p>
/// <p>You pass image bytes to an Amazon Textract API operation by using the <code>Bytes</code> property. For example, you would use the <code>Bytes</code> property to pass a document loaded from a local file system. Image bytes passed by using the <code>Bytes</code> property must be base64 encoded. Your code might not need to encode document file bytes if you're using an AWS SDK to call Amazon Textract API operations. </p>
/// <p>You pass images stored in an S3 bucket to an Amazon Textract API operation by using the <code>S3Object</code> property. Documents stored in an S3 bucket don't need to be base64 encoded.</p>
/// <p>The AWS Region for the S3 bucket that contains the S3 object must match the AWS Region that you use for Amazon Textract operations.</p>
/// <p>If you use the AWS CLI to call Amazon Textract operations, passing image bytes using the Bytes property isn't supported. You must first upload the document to an Amazon S3 bucket, and then call the operation using the S3Object property.</p>
/// <p>For Amazon Textract to process an S3 object, the user must have permission to access the S3 object. </p>
pub fn set_document(mut self, input: std::option::Option<crate::model::Document>) -> Self {
self.document = input;
self
}
/// Consumes the builder and constructs a [`AnalyzeExpenseInput`](crate::input::AnalyzeExpenseInput).
pub fn build(
self,
) -> Result<crate::input::AnalyzeExpenseInput, aws_smithy_http::operation::error::BuildError>
{
Ok(crate::input::AnalyzeExpenseInput {
document: self.document,
})
}
}
}
impl AnalyzeExpenseInput {
/// Consumes the builder and constructs an Operation<[`AnalyzeExpense`](crate::operation::AnalyzeExpense)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::AnalyzeExpense,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::AnalyzeExpenseInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::AnalyzeExpenseInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.AnalyzeExpense",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_analyze_expense(&self)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::AnalyzeExpense::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"AnalyzeExpense",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`AnalyzeExpenseInput`](crate::input::AnalyzeExpenseInput).
pub fn builder() -> crate::input::analyze_expense_input::Builder {
crate::input::analyze_expense_input::Builder::default()
}
}
/// See [`AnalyzeIdInput`](crate::input::AnalyzeIdInput).
pub mod analyze_id_input {
/// A builder for [`AnalyzeIdInput`](crate::input::AnalyzeIdInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) document_pages: std::option::Option<std::vec::Vec<crate::model::Document>>,
}
impl Builder {
/// Appends an item to `document_pages`.
///
/// To override the contents of this collection use [`set_document_pages`](Self::set_document_pages).
///
/// <p>The document being passed to AnalyzeID.</p>
pub fn document_pages(mut self, input: crate::model::Document) -> Self {
let mut v = self.document_pages.unwrap_or_default();
v.push(input);
self.document_pages = Some(v);
self
}
/// <p>The document being passed to AnalyzeID.</p>
pub fn set_document_pages(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::Document>>,
) -> Self {
self.document_pages = input;
self
}
/// Consumes the builder and constructs a [`AnalyzeIdInput`](crate::input::AnalyzeIdInput).
pub fn build(
self,
) -> Result<crate::input::AnalyzeIdInput, aws_smithy_http::operation::error::BuildError>
{
Ok(crate::input::AnalyzeIdInput {
document_pages: self.document_pages,
})
}
}
}
impl AnalyzeIdInput {
/// Consumes the builder and constructs an Operation<[`AnalyzeID`](crate::operation::AnalyzeID)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::AnalyzeID,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::AnalyzeIdInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::AnalyzeIdInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.AnalyzeID",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_analyze_id(&self)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op =
aws_smithy_http::operation::Operation::new(request, crate::operation::AnalyzeID::new())
.with_metadata(aws_smithy_http::operation::Metadata::new(
"AnalyzeID",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`AnalyzeIdInput`](crate::input::AnalyzeIdInput).
pub fn builder() -> crate::input::analyze_id_input::Builder {
crate::input::analyze_id_input::Builder::default()
}
}
/// See [`DetectDocumentTextInput`](crate::input::DetectDocumentTextInput).
pub mod detect_document_text_input {
/// A builder for [`DetectDocumentTextInput`](crate::input::DetectDocumentTextInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) document: std::option::Option<crate::model::Document>,
}
impl Builder {
/// <p>The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can't pass image bytes. The document must be an image in JPEG or PNG format.</p>
/// <p>If you're using an AWS SDK to call Amazon Textract, you might not need to base64-encode image bytes that are passed using the <code>Bytes</code> field. </p>
pub fn document(mut self, input: crate::model::Document) -> Self {
self.document = Some(input);
self
}
/// <p>The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can't pass image bytes. The document must be an image in JPEG or PNG format.</p>
/// <p>If you're using an AWS SDK to call Amazon Textract, you might not need to base64-encode image bytes that are passed using the <code>Bytes</code> field. </p>
pub fn set_document(mut self, input: std::option::Option<crate::model::Document>) -> Self {
self.document = input;
self
}
/// Consumes the builder and constructs a [`DetectDocumentTextInput`](crate::input::DetectDocumentTextInput).
pub fn build(
self,
) -> Result<
crate::input::DetectDocumentTextInput,
aws_smithy_http::operation::error::BuildError,
> {
Ok(crate::input::DetectDocumentTextInput {
document: self.document,
})
}
}
}
impl DetectDocumentTextInput {
/// Consumes the builder and constructs an Operation<[`DetectDocumentText`](crate::operation::DetectDocumentText)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::DetectDocumentText,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::DetectDocumentTextInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::DetectDocumentTextInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.DetectDocumentText",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_detect_document_text(&self)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::DetectDocumentText::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"DetectDocumentText",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`DetectDocumentTextInput`](crate::input::DetectDocumentTextInput).
pub fn builder() -> crate::input::detect_document_text_input::Builder {
crate::input::detect_document_text_input::Builder::default()
}
}
/// See [`GetDocumentAnalysisInput`](crate::input::GetDocumentAnalysisInput).
pub mod get_document_analysis_input {
/// A builder for [`GetDocumentAnalysisInput`](crate::input::GetDocumentAnalysisInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
pub(crate) max_results: std::option::Option<i32>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A unique identifier for the text-detection job. The <code>JobId</code> is returned from <code>StartDocumentAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>A unique identifier for the text-detection job. The <code>JobId</code> is returned from <code>StartDocumentAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// <p>The maximum number of results to return per paginated call. The largest value that you can specify is 1,000. If you specify a value greater than 1,000, a maximum of 1,000 results is returned. The default value is 1,000.</p>
pub fn max_results(mut self, input: i32) -> Self {
self.max_results = Some(input);
self
}
/// <p>The maximum number of results to return per paginated call. The largest value that you can specify is 1,000. If you specify a value greater than 1,000, a maximum of 1,000 results is returned. The default value is 1,000.</p>
pub fn set_max_results(mut self, input: std::option::Option<i32>) -> Self {
self.max_results = input;
self
}
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`GetDocumentAnalysisInput`](crate::input::GetDocumentAnalysisInput).
pub fn build(
self,
) -> Result<
crate::input::GetDocumentAnalysisInput,
aws_smithy_http::operation::error::BuildError,
> {
Ok(crate::input::GetDocumentAnalysisInput {
job_id: self.job_id,
max_results: self.max_results,
next_token: self.next_token,
})
}
}
}
impl GetDocumentAnalysisInput {
/// Consumes the builder and constructs an Operation<[`GetDocumentAnalysis`](crate::operation::GetDocumentAnalysis)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::GetDocumentAnalysis,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::GetDocumentAnalysisInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::GetDocumentAnalysisInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.GetDocumentAnalysis",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_get_document_analysis(&self)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::GetDocumentAnalysis::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"GetDocumentAnalysis",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`GetDocumentAnalysisInput`](crate::input::GetDocumentAnalysisInput).
pub fn builder() -> crate::input::get_document_analysis_input::Builder {
crate::input::get_document_analysis_input::Builder::default()
}
}
/// See [`GetDocumentTextDetectionInput`](crate::input::GetDocumentTextDetectionInput).
pub mod get_document_text_detection_input {
/// A builder for [`GetDocumentTextDetectionInput`](crate::input::GetDocumentTextDetectionInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
pub(crate) max_results: std::option::Option<i32>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A unique identifier for the text detection job. The <code>JobId</code> is returned from <code>StartDocumentTextDetection</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>A unique identifier for the text detection job. The <code>JobId</code> is returned from <code>StartDocumentTextDetection</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// <p>The maximum number of results to return per paginated call. The largest value you can specify is 1,000. If you specify a value greater than 1,000, a maximum of 1,000 results is returned. The default value is 1,000.</p>
pub fn max_results(mut self, input: i32) -> Self {
self.max_results = Some(input);
self
}
/// <p>The maximum number of results to return per paginated call. The largest value you can specify is 1,000. If you specify a value greater than 1,000, a maximum of 1,000 results is returned. The default value is 1,000.</p>
pub fn set_max_results(mut self, input: std::option::Option<i32>) -> Self {
self.max_results = input;
self
}
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`GetDocumentTextDetectionInput`](crate::input::GetDocumentTextDetectionInput).
pub fn build(
self,
) -> Result<
crate::input::GetDocumentTextDetectionInput,
aws_smithy_http::operation::error::BuildError,
> {
Ok(crate::input::GetDocumentTextDetectionInput {
job_id: self.job_id,
max_results: self.max_results,
next_token: self.next_token,
})
}
}
}
impl GetDocumentTextDetectionInput {
/// Consumes the builder and constructs an Operation<[`GetDocumentTextDetection`](crate::operation::GetDocumentTextDetection)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::GetDocumentTextDetection,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::GetDocumentTextDetectionInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::GetDocumentTextDetectionInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.GetDocumentTextDetection",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_get_document_text_detection(
&self,
)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::GetDocumentTextDetection::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"GetDocumentTextDetection",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`GetDocumentTextDetectionInput`](crate::input::GetDocumentTextDetectionInput).
pub fn builder() -> crate::input::get_document_text_detection_input::Builder {
crate::input::get_document_text_detection_input::Builder::default()
}
}
/// See [`GetExpenseAnalysisInput`](crate::input::GetExpenseAnalysisInput).
pub mod get_expense_analysis_input {
/// A builder for [`GetExpenseAnalysisInput`](crate::input::GetExpenseAnalysisInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
pub(crate) max_results: std::option::Option<i32>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A unique identifier for the text detection job. The <code>JobId</code> is returned from <code>StartExpenseAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>A unique identifier for the text detection job. The <code>JobId</code> is returned from <code>StartExpenseAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// <p>The maximum number of results to return per paginated call. The largest value you can specify is 20. If you specify a value greater than 20, a maximum of 20 results is returned. The default value is 20.</p>
pub fn max_results(mut self, input: i32) -> Self {
self.max_results = Some(input);
self
}
/// <p>The maximum number of results to return per paginated call. The largest value you can specify is 20. If you specify a value greater than 20, a maximum of 20 results is returned. The default value is 20.</p>
pub fn set_max_results(mut self, input: std::option::Option<i32>) -> Self {
self.max_results = input;
self
}
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`GetExpenseAnalysisInput`](crate::input::GetExpenseAnalysisInput).
pub fn build(
self,
) -> Result<
crate::input::GetExpenseAnalysisInput,
aws_smithy_http::operation::error::BuildError,
> {
Ok(crate::input::GetExpenseAnalysisInput {
job_id: self.job_id,
max_results: self.max_results,
next_token: self.next_token,
})
}
}
}
impl GetExpenseAnalysisInput {
/// Consumes the builder and constructs an Operation<[`GetExpenseAnalysis`](crate::operation::GetExpenseAnalysis)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::GetExpenseAnalysis,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::GetExpenseAnalysisInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::GetExpenseAnalysisInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.GetExpenseAnalysis",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_get_expense_analysis(&self)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::GetExpenseAnalysis::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"GetExpenseAnalysis",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`GetExpenseAnalysisInput`](crate::input::GetExpenseAnalysisInput).
pub fn builder() -> crate::input::get_expense_analysis_input::Builder {
crate::input::get_expense_analysis_input::Builder::default()
}
}
/// See [`GetLendingAnalysisInput`](crate::input::GetLendingAnalysisInput).
pub mod get_lending_analysis_input {
/// A builder for [`GetLendingAnalysisInput`](crate::input::GetLendingAnalysisInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
pub(crate) max_results: std::option::Option<i32>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A unique identifier for the lending or text-detection job. The <code>JobId</code> is returned from <code>StartLendingAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p>A unique identifier for the lending or text-detection job. The <code>JobId</code> is returned from <code>StartLendingAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// <p>The maximum number of results to return per paginated call. The largest value that you can specify is 30. If you specify a value greater than 30, a maximum of 30 results is returned. The default value is 30.</p>
pub fn max_results(mut self, input: i32) -> Self {
self.max_results = Some(input);
self
}
/// <p>The maximum number of results to return per paginated call. The largest value that you can specify is 30. If you specify a value greater than 30, a maximum of 30 results is returned. The default value is 30.</p>
pub fn set_max_results(mut self, input: std::option::Option<i32>) -> Self {
self.max_results = input;
self
}
/// <p>If the previous response was incomplete, Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of lending results.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>If the previous response was incomplete, Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of lending results.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`GetLendingAnalysisInput`](crate::input::GetLendingAnalysisInput).
pub fn build(
self,
) -> Result<
crate::input::GetLendingAnalysisInput,
aws_smithy_http::operation::error::BuildError,
> {
Ok(crate::input::GetLendingAnalysisInput {
job_id: self.job_id,
max_results: self.max_results,
next_token: self.next_token,
})
}
}
}
impl GetLendingAnalysisInput {
/// Consumes the builder and constructs an Operation<[`GetLendingAnalysis`](crate::operation::GetLendingAnalysis)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::GetLendingAnalysis,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::GetLendingAnalysisInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::GetLendingAnalysisInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.GetLendingAnalysis",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_get_lending_analysis(&self)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::GetLendingAnalysis::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"GetLendingAnalysis",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`GetLendingAnalysisInput`](crate::input::GetLendingAnalysisInput).
pub fn builder() -> crate::input::get_lending_analysis_input::Builder {
crate::input::get_lending_analysis_input::Builder::default()
}
}
/// See [`GetLendingAnalysisSummaryInput`](crate::input::GetLendingAnalysisSummaryInput).
pub mod get_lending_analysis_summary_input {
/// A builder for [`GetLendingAnalysisSummaryInput`](crate::input::GetLendingAnalysisSummaryInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) job_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p> A unique identifier for the lending or text-detection job. The <code>JobId</code> is returned from StartLendingAnalysis. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn job_id(mut self, input: impl Into<std::string::String>) -> Self {
self.job_id = Some(input.into());
self
}
/// <p> A unique identifier for the lending or text-detection job. The <code>JobId</code> is returned from StartLendingAnalysis. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn set_job_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_id = input;
self
}
/// Consumes the builder and constructs a [`GetLendingAnalysisSummaryInput`](crate::input::GetLendingAnalysisSummaryInput).
pub fn build(
self,
) -> Result<
crate::input::GetLendingAnalysisSummaryInput,
aws_smithy_http::operation::error::BuildError,
> {
Ok(crate::input::GetLendingAnalysisSummaryInput {
job_id: self.job_id,
})
}
}
}
impl GetLendingAnalysisSummaryInput {
/// Consumes the builder and constructs an Operation<[`GetLendingAnalysisSummary`](crate::operation::GetLendingAnalysisSummary)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::GetLendingAnalysisSummary,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::GetLendingAnalysisSummaryInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::GetLendingAnalysisSummaryInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.GetLendingAnalysisSummary",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_get_lending_analysis_summary(
&self,
)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::GetLendingAnalysisSummary::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"GetLendingAnalysisSummary",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`GetLendingAnalysisSummaryInput`](crate::input::GetLendingAnalysisSummaryInput).
pub fn builder() -> crate::input::get_lending_analysis_summary_input::Builder {
crate::input::get_lending_analysis_summary_input::Builder::default()
}
}
/// See [`StartDocumentAnalysisInput`](crate::input::StartDocumentAnalysisInput).
pub mod start_document_analysis_input {
/// A builder for [`StartDocumentAnalysisInput`](crate::input::StartDocumentAnalysisInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) document_location: std::option::Option<crate::model::DocumentLocation>,
pub(crate) feature_types: std::option::Option<std::vec::Vec<crate::model::FeatureType>>,
pub(crate) client_request_token: std::option::Option<std::string::String>,
pub(crate) job_tag: std::option::Option<std::string::String>,
pub(crate) notification_channel: std::option::Option<crate::model::NotificationChannel>,
pub(crate) output_config: std::option::Option<crate::model::OutputConfig>,
pub(crate) kms_key_id: std::option::Option<std::string::String>,
pub(crate) queries_config: std::option::Option<crate::model::QueriesConfig>,
}
impl Builder {
/// <p>The location of the document to be processed.</p>
pub fn document_location(mut self, input: crate::model::DocumentLocation) -> Self {
self.document_location = Some(input);
self
}
/// <p>The location of the document to be processed.</p>
pub fn set_document_location(
mut self,
input: std::option::Option<crate::model::DocumentLocation>,
) -> Self {
self.document_location = input;
self
}
/// Appends an item to `feature_types`.
///
/// To override the contents of this collection use [`set_feature_types`](Self::set_feature_types).
///
/// <p>A list of the types of analysis to perform. Add TABLES to the list to return information about the tables that are detected in the input document. Add FORMS to return detected form data. To perform both types of analysis, add TABLES and FORMS to <code>FeatureTypes</code>. All lines and words detected in the document are included in the response (including text that isn't related to the value of <code>FeatureTypes</code>). </p>
pub fn feature_types(mut self, input: crate::model::FeatureType) -> Self {
let mut v = self.feature_types.unwrap_or_default();
v.push(input);
self.feature_types = Some(v);
self
}
/// <p>A list of the types of analysis to perform. Add TABLES to the list to return information about the tables that are detected in the input document. Add FORMS to return detected form data. To perform both types of analysis, add TABLES and FORMS to <code>FeatureTypes</code>. All lines and words detected in the document are included in the response (including text that isn't related to the value of <code>FeatureTypes</code>). </p>
pub fn set_feature_types(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::FeatureType>>,
) -> Self {
self.feature_types = input;
self
}
/// <p>The idempotent token that you use to identify the start request. If you use the same token with multiple <code>StartDocumentAnalysis</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
pub fn client_request_token(mut self, input: impl Into<std::string::String>) -> Self {
self.client_request_token = Some(input.into());
self
}
/// <p>The idempotent token that you use to identify the start request. If you use the same token with multiple <code>StartDocumentAnalysis</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
pub fn set_client_request_token(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.client_request_token = input;
self
}
/// <p>An identifier that you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn job_tag(mut self, input: impl Into<std::string::String>) -> Self {
self.job_tag = Some(input.into());
self
}
/// <p>An identifier that you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn set_job_tag(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_tag = input;
self
}
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
pub fn notification_channel(mut self, input: crate::model::NotificationChannel) -> Self {
self.notification_channel = Some(input);
self
}
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
pub fn set_notification_channel(
mut self,
input: std::option::Option<crate::model::NotificationChannel>,
) -> Self {
self.notification_channel = input;
self
}
/// <p>Sets if the output will go to a customer defined bucket. By default, Amazon Textract will save the results internally to be accessed by the GetDocumentAnalysis operation.</p>
pub fn output_config(mut self, input: crate::model::OutputConfig) -> Self {
self.output_config = Some(input);
self
}
/// <p>Sets if the output will go to a customer defined bucket. By default, Amazon Textract will save the results internally to be accessed by the GetDocumentAnalysis operation.</p>
pub fn set_output_config(
mut self,
input: std::option::Option<crate::model::OutputConfig>,
) -> Self {
self.output_config = input;
self
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
pub fn kms_key_id(mut self, input: impl Into<std::string::String>) -> Self {
self.kms_key_id = Some(input.into());
self
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
pub fn set_kms_key_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.kms_key_id = input;
self
}
/// <p></p>
pub fn queries_config(mut self, input: crate::model::QueriesConfig) -> Self {
self.queries_config = Some(input);
self
}
/// <p></p>
pub fn set_queries_config(
mut self,
input: std::option::Option<crate::model::QueriesConfig>,
) -> Self {
self.queries_config = input;
self
}
/// Consumes the builder and constructs a [`StartDocumentAnalysisInput`](crate::input::StartDocumentAnalysisInput).
pub fn build(
self,
) -> Result<
crate::input::StartDocumentAnalysisInput,
aws_smithy_http::operation::error::BuildError,
> {
Ok(crate::input::StartDocumentAnalysisInput {
document_location: self.document_location,
feature_types: self.feature_types,
client_request_token: self.client_request_token,
job_tag: self.job_tag,
notification_channel: self.notification_channel,
output_config: self.output_config,
kms_key_id: self.kms_key_id,
queries_config: self.queries_config,
})
}
}
}
impl StartDocumentAnalysisInput {
/// Consumes the builder and constructs an Operation<[`StartDocumentAnalysis`](crate::operation::StartDocumentAnalysis)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::StartDocumentAnalysis,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::StartDocumentAnalysisInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::StartDocumentAnalysisInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.StartDocumentAnalysis",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_start_document_analysis(
&self,
)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::StartDocumentAnalysis::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"StartDocumentAnalysis",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`StartDocumentAnalysisInput`](crate::input::StartDocumentAnalysisInput).
pub fn builder() -> crate::input::start_document_analysis_input::Builder {
crate::input::start_document_analysis_input::Builder::default()
}
}
/// See [`StartDocumentTextDetectionInput`](crate::input::StartDocumentTextDetectionInput).
pub mod start_document_text_detection_input {
/// A builder for [`StartDocumentTextDetectionInput`](crate::input::StartDocumentTextDetectionInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) document_location: std::option::Option<crate::model::DocumentLocation>,
pub(crate) client_request_token: std::option::Option<std::string::String>,
pub(crate) job_tag: std::option::Option<std::string::String>,
pub(crate) notification_channel: std::option::Option<crate::model::NotificationChannel>,
pub(crate) output_config: std::option::Option<crate::model::OutputConfig>,
pub(crate) kms_key_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The location of the document to be processed.</p>
pub fn document_location(mut self, input: crate::model::DocumentLocation) -> Self {
self.document_location = Some(input);
self
}
/// <p>The location of the document to be processed.</p>
pub fn set_document_location(
mut self,
input: std::option::Option<crate::model::DocumentLocation>,
) -> Self {
self.document_location = input;
self
}
/// <p>The idempotent token that's used to identify the start request. If you use the same token with multiple <code>StartDocumentTextDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
pub fn client_request_token(mut self, input: impl Into<std::string::String>) -> Self {
self.client_request_token = Some(input.into());
self
}
/// <p>The idempotent token that's used to identify the start request. If you use the same token with multiple <code>StartDocumentTextDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
pub fn set_client_request_token(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.client_request_token = input;
self
}
/// <p>An identifier that you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn job_tag(mut self, input: impl Into<std::string::String>) -> Self {
self.job_tag = Some(input.into());
self
}
/// <p>An identifier that you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn set_job_tag(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_tag = input;
self
}
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
pub fn notification_channel(mut self, input: crate::model::NotificationChannel) -> Self {
self.notification_channel = Some(input);
self
}
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
pub fn set_notification_channel(
mut self,
input: std::option::Option<crate::model::NotificationChannel>,
) -> Self {
self.notification_channel = input;
self
}
/// <p>Sets if the output will go to a customer defined bucket. By default Amazon Textract will save the results internally to be accessed with the GetDocumentTextDetection operation.</p>
pub fn output_config(mut self, input: crate::model::OutputConfig) -> Self {
self.output_config = Some(input);
self
}
/// <p>Sets if the output will go to a customer defined bucket. By default Amazon Textract will save the results internally to be accessed with the GetDocumentTextDetection operation.</p>
pub fn set_output_config(
mut self,
input: std::option::Option<crate::model::OutputConfig>,
) -> Self {
self.output_config = input;
self
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
pub fn kms_key_id(mut self, input: impl Into<std::string::String>) -> Self {
self.kms_key_id = Some(input.into());
self
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
pub fn set_kms_key_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.kms_key_id = input;
self
}
/// Consumes the builder and constructs a [`StartDocumentTextDetectionInput`](crate::input::StartDocumentTextDetectionInput).
pub fn build(
self,
) -> Result<
crate::input::StartDocumentTextDetectionInput,
aws_smithy_http::operation::error::BuildError,
> {
Ok(crate::input::StartDocumentTextDetectionInput {
document_location: self.document_location,
client_request_token: self.client_request_token,
job_tag: self.job_tag,
notification_channel: self.notification_channel,
output_config: self.output_config,
kms_key_id: self.kms_key_id,
})
}
}
}
impl StartDocumentTextDetectionInput {
/// Consumes the builder and constructs an Operation<[`StartDocumentTextDetection`](crate::operation::StartDocumentTextDetection)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::StartDocumentTextDetection,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::StartDocumentTextDetectionInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::StartDocumentTextDetectionInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.StartDocumentTextDetection",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_start_document_text_detection(&self)?
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::StartDocumentTextDetection::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"StartDocumentTextDetection",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`StartDocumentTextDetectionInput`](crate::input::StartDocumentTextDetectionInput).
pub fn builder() -> crate::input::start_document_text_detection_input::Builder {
crate::input::start_document_text_detection_input::Builder::default()
}
}
/// See [`StartExpenseAnalysisInput`](crate::input::StartExpenseAnalysisInput).
pub mod start_expense_analysis_input {
/// A builder for [`StartExpenseAnalysisInput`](crate::input::StartExpenseAnalysisInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) document_location: std::option::Option<crate::model::DocumentLocation>,
pub(crate) client_request_token: std::option::Option<std::string::String>,
pub(crate) job_tag: std::option::Option<std::string::String>,
pub(crate) notification_channel: std::option::Option<crate::model::NotificationChannel>,
pub(crate) output_config: std::option::Option<crate::model::OutputConfig>,
pub(crate) kms_key_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The location of the document to be processed.</p>
pub fn document_location(mut self, input: crate::model::DocumentLocation) -> Self {
self.document_location = Some(input);
self
}
/// <p>The location of the document to be processed.</p>
pub fn set_document_location(
mut self,
input: std::option::Option<crate::model::DocumentLocation>,
) -> Self {
self.document_location = input;
self
}
/// <p>The idempotent token that's used to identify the start request. If you use the same token with multiple <code>StartDocumentTextDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a> </p>
pub fn client_request_token(mut self, input: impl Into<std::string::String>) -> Self {
self.client_request_token = Some(input.into());
self
}
/// <p>The idempotent token that's used to identify the start request. If you use the same token with multiple <code>StartDocumentTextDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a> </p>
pub fn set_client_request_token(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.client_request_token = input;
self
}
/// <p>An identifier you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn job_tag(mut self, input: impl Into<std::string::String>) -> Self {
self.job_tag = Some(input.into());
self
}
/// <p>An identifier you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn set_job_tag(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_tag = input;
self
}
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
pub fn notification_channel(mut self, input: crate::model::NotificationChannel) -> Self {
self.notification_channel = Some(input);
self
}
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
pub fn set_notification_channel(
mut self,
input: std::option::Option<crate::model::NotificationChannel>,
) -> Self {
self.notification_channel = input;
self
}
/// <p>Sets if the output will go to a customer defined bucket. By default, Amazon Textract will save the results internally to be accessed by the <code>GetExpenseAnalysis</code> operation.</p>
pub fn output_config(mut self, input: crate::model::OutputConfig) -> Self {
self.output_config = Some(input);
self
}
/// <p>Sets if the output will go to a customer defined bucket. By default, Amazon Textract will save the results internally to be accessed by the <code>GetExpenseAnalysis</code> operation.</p>
pub fn set_output_config(
mut self,
input: std::option::Option<crate::model::OutputConfig>,
) -> Self {
self.output_config = input;
self
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
pub fn kms_key_id(mut self, input: impl Into<std::string::String>) -> Self {
self.kms_key_id = Some(input.into());
self
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
pub fn set_kms_key_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.kms_key_id = input;
self
}
/// Consumes the builder and constructs a [`StartExpenseAnalysisInput`](crate::input::StartExpenseAnalysisInput).
pub fn build(
self,
) -> Result<
crate::input::StartExpenseAnalysisInput,
aws_smithy_http::operation::error::BuildError,
> {
Ok(crate::input::StartExpenseAnalysisInput {
document_location: self.document_location,
client_request_token: self.client_request_token,
job_tag: self.job_tag,
notification_channel: self.notification_channel,
output_config: self.output_config,
kms_key_id: self.kms_key_id,
})
}
}
}
impl StartExpenseAnalysisInput {
/// Consumes the builder and constructs an Operation<[`StartExpenseAnalysis`](crate::operation::StartExpenseAnalysis)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::StartExpenseAnalysis,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::StartExpenseAnalysisInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::StartExpenseAnalysisInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.StartExpenseAnalysis",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_start_expense_analysis(
&self,
)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::StartExpenseAnalysis::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"StartExpenseAnalysis",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`StartExpenseAnalysisInput`](crate::input::StartExpenseAnalysisInput).
pub fn builder() -> crate::input::start_expense_analysis_input::Builder {
crate::input::start_expense_analysis_input::Builder::default()
}
}
/// See [`StartLendingAnalysisInput`](crate::input::StartLendingAnalysisInput).
pub mod start_lending_analysis_input {
/// A builder for [`StartLendingAnalysisInput`](crate::input::StartLendingAnalysisInput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) document_location: std::option::Option<crate::model::DocumentLocation>,
pub(crate) client_request_token: std::option::Option<std::string::String>,
pub(crate) job_tag: std::option::Option<std::string::String>,
pub(crate) notification_channel: std::option::Option<crate::model::NotificationChannel>,
pub(crate) output_config: std::option::Option<crate::model::OutputConfig>,
pub(crate) kms_key_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The Amazon S3 bucket that contains the document to be processed. It's used by asynchronous operations.</p>
/// <p>The input document can be an image file in JPEG or PNG format. It can also be a file in PDF format.</p>
pub fn document_location(mut self, input: crate::model::DocumentLocation) -> Self {
self.document_location = Some(input);
self
}
/// <p>The Amazon S3 bucket that contains the document to be processed. It's used by asynchronous operations.</p>
/// <p>The input document can be an image file in JPEG or PNG format. It can also be a file in PDF format.</p>
pub fn set_document_location(
mut self,
input: std::option::Option<crate::model::DocumentLocation>,
) -> Self {
self.document_location = input;
self
}
/// <p>The idempotent token that you use to identify the start request. If you use the same token with multiple <code>StartLendingAnalysis</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-sync.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
pub fn client_request_token(mut self, input: impl Into<std::string::String>) -> Self {
self.client_request_token = Some(input.into());
self
}
/// <p>The idempotent token that you use to identify the start request. If you use the same token with multiple <code>StartLendingAnalysis</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-sync.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
pub fn set_client_request_token(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.client_request_token = input;
self
}
/// <p>An identifier that you specify to be included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn job_tag(mut self, input: impl Into<std::string::String>) -> Self {
self.job_tag = Some(input.into());
self
}
/// <p>An identifier that you specify to be included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn set_job_tag(mut self, input: std::option::Option<std::string::String>) -> Self {
self.job_tag = input;
self
}
/// <p>The Amazon Simple Notification Service (Amazon SNS) topic to which Amazon Textract publishes the completion status of an asynchronous document operation. </p>
pub fn notification_channel(mut self, input: crate::model::NotificationChannel) -> Self {
self.notification_channel = Some(input);
self
}
/// <p>The Amazon Simple Notification Service (Amazon SNS) topic to which Amazon Textract publishes the completion status of an asynchronous document operation. </p>
pub fn set_notification_channel(
mut self,
input: std::option::Option<crate::model::NotificationChannel>,
) -> Self {
self.notification_channel = input;
self
}
/// <p>Sets whether or not your output will go to a user created bucket. Used to set the name of the bucket, and the prefix on the output file.</p>
/// <p> <code>OutputConfig</code> is an optional parameter which lets you adjust where your output will be placed. By default, Amazon Textract will store the results internally and can only be accessed by the Get API operations. With <code>OutputConfig</code> enabled, you can set the name of the bucket the output will be sent to the file prefix of the results where you can download your results. Additionally, you can set the <code>KMSKeyID</code> parameter to a customer master key (CMK) to encrypt your output. Without this parameter set Amazon Textract will encrypt server-side using the AWS managed CMK for Amazon S3.</p>
/// <p>Decryption of Customer Content is necessary for processing of the documents by Amazon Textract. If your account is opted out under an AI services opt out policy then all unencrypted Customer Content is immediately and permanently deleted after the Customer Content has been processed by the service. No copy of of the output is retained by Amazon Textract. For information about how to opt out, see <a href="https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_ai-opt-out.html"> Managing AI services opt-out policy. </a> </p>
/// <p>For more information on data privacy, see the <a href="https://aws.amazon.com/compliance/data-privacy-faq/">Data Privacy FAQ</a>.</p>
pub fn output_config(mut self, input: crate::model::OutputConfig) -> Self {
self.output_config = Some(input);
self
}
/// <p>Sets whether or not your output will go to a user created bucket. Used to set the name of the bucket, and the prefix on the output file.</p>
/// <p> <code>OutputConfig</code> is an optional parameter which lets you adjust where your output will be placed. By default, Amazon Textract will store the results internally and can only be accessed by the Get API operations. With <code>OutputConfig</code> enabled, you can set the name of the bucket the output will be sent to the file prefix of the results where you can download your results. Additionally, you can set the <code>KMSKeyID</code> parameter to a customer master key (CMK) to encrypt your output. Without this parameter set Amazon Textract will encrypt server-side using the AWS managed CMK for Amazon S3.</p>
/// <p>Decryption of Customer Content is necessary for processing of the documents by Amazon Textract. If your account is opted out under an AI services opt out policy then all unencrypted Customer Content is immediately and permanently deleted after the Customer Content has been processed by the service. No copy of of the output is retained by Amazon Textract. For information about how to opt out, see <a href="https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_ai-opt-out.html"> Managing AI services opt-out policy. </a> </p>
/// <p>For more information on data privacy, see the <a href="https://aws.amazon.com/compliance/data-privacy-faq/">Data Privacy FAQ</a>.</p>
pub fn set_output_config(
mut self,
input: std::option::Option<crate::model::OutputConfig>,
) -> Self {
self.output_config = input;
self
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side, using SSE-S3. </p>
pub fn kms_key_id(mut self, input: impl Into<std::string::String>) -> Self {
self.kms_key_id = Some(input.into());
self
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side, using SSE-S3. </p>
pub fn set_kms_key_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.kms_key_id = input;
self
}
/// Consumes the builder and constructs a [`StartLendingAnalysisInput`](crate::input::StartLendingAnalysisInput).
pub fn build(
self,
) -> Result<
crate::input::StartLendingAnalysisInput,
aws_smithy_http::operation::error::BuildError,
> {
Ok(crate::input::StartLendingAnalysisInput {
document_location: self.document_location,
client_request_token: self.client_request_token,
job_tag: self.job_tag,
notification_channel: self.notification_channel,
output_config: self.output_config,
kms_key_id: self.kms_key_id,
})
}
}
}
impl StartLendingAnalysisInput {
/// Consumes the builder and constructs an Operation<[`StartLendingAnalysis`](crate::operation::StartLendingAnalysis)>
#[allow(unused_mut)]
#[allow(clippy::let_and_return)]
#[allow(clippy::needless_borrow)]
pub async fn make_operation(
&self,
_config: &crate::config::Config,
) -> std::result::Result<
aws_smithy_http::operation::Operation<
crate::operation::StartLendingAnalysis,
aws_http::retry::AwsResponseRetryClassifier,
>,
aws_smithy_http::operation::error::BuildError,
> {
let params_result = crate::endpoint::Params::builder()
.set_region(_config.region.as_ref().map(|r| r.as_ref().to_owned()))
.set_use_dual_stack(_config.use_dual_stack)
.set_use_fips(_config.use_fips)
.set_endpoint(_config.endpoint_url.clone())
.build()
.map_err(|err| {
aws_smithy_http::endpoint::ResolveEndpointError::from_source(
"could not construct endpoint parameters",
err,
)
});
let (endpoint_result, params) = match params_result {
Ok(params) => (
_config.endpoint_resolver.resolve_endpoint(¶ms),
Some(params),
),
Err(e) => (Err(e), None),
};
let mut request = {
fn uri_base(
_input: &crate::input::StartLendingAnalysisInput,
output: &mut String,
) -> Result<(), aws_smithy_http::operation::error::BuildError> {
write!(output, "/").expect("formatting should succeed");
Ok(())
}
#[allow(clippy::unnecessary_wraps)]
fn update_http_builder(
input: &crate::input::StartLendingAnalysisInput,
builder: http::request::Builder,
) -> std::result::Result<
http::request::Builder,
aws_smithy_http::operation::error::BuildError,
> {
let mut uri = String::new();
uri_base(input, &mut uri)?;
Ok(builder.method("POST").uri(uri))
}
let mut builder = update_http_builder(&self, http::request::Builder::new())?;
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::CONTENT_TYPE,
"application/x-amz-json-1.1",
);
builder = aws_smithy_http::header::set_request_header_if_absent(
builder,
http::header::HeaderName::from_static("x-amz-target"),
"Textract.StartLendingAnalysis",
);
builder
};
let mut properties = aws_smithy_http::property_bag::SharedPropertyBag::new();
#[allow(clippy::useless_conversion)]
let body = aws_smithy_http::body::SdkBody::from(
crate::operation_ser::serialize_operation_crate_operation_start_lending_analysis(
&self,
)?,
);
if let Some(content_length) = body.content_length() {
request = aws_smithy_http::header::set_request_header_if_absent(
request,
http::header::CONTENT_LENGTH,
content_length,
);
}
let request = request.body(body).expect("should be valid request");
let mut request = aws_smithy_http::operation::Request::from_parts(request, properties);
request.properties_mut().insert(endpoint_result);
if let Some(params) = params {
request.properties_mut().insert(params);
}
request
.properties_mut()
.insert(aws_smithy_http::http_versions::DEFAULT_HTTP_VERSION_LIST.clone());
let mut user_agent = aws_http::user_agent::AwsUserAgent::new_from_environment(
aws_types::os_shim_internal::Env::real(),
crate::API_METADATA.clone(),
);
if let Some(app_name) = _config.app_name() {
user_agent = user_agent.with_app_name(app_name.clone());
}
request.properties_mut().insert(user_agent);
let mut signing_config = aws_sig_auth::signer::OperationSigningConfig::default_config();
request.properties_mut().insert(signing_config);
request
.properties_mut()
.insert(aws_types::SigningService::from_static(
_config.signing_service(),
));
if let Some(region) = &_config.region {
request
.properties_mut()
.insert(aws_types::region::SigningRegion::from(region.clone()));
}
if let Some(region) = &_config.region {
request.properties_mut().insert(region.clone());
}
aws_http::auth::set_credentials_cache(
&mut request.properties_mut(),
_config.credentials_cache.clone(),
);
let op = aws_smithy_http::operation::Operation::new(
request,
crate::operation::StartLendingAnalysis::new(),
)
.with_metadata(aws_smithy_http::operation::Metadata::new(
"StartLendingAnalysis",
"textract",
));
let op = op.with_retry_classifier(aws_http::retry::AwsResponseRetryClassifier::new());
Ok(op)
}
/// Creates a new builder-style object to manufacture [`StartLendingAnalysisInput`](crate::input::StartLendingAnalysisInput).
pub fn builder() -> crate::input::start_lending_analysis_input::Builder {
crate::input::start_lending_analysis_input::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartLendingAnalysisInput {
/// <p>The Amazon S3 bucket that contains the document to be processed. It's used by asynchronous operations.</p>
/// <p>The input document can be an image file in JPEG or PNG format. It can also be a file in PDF format.</p>
#[doc(hidden)]
pub document_location: std::option::Option<crate::model::DocumentLocation>,
/// <p>The idempotent token that you use to identify the start request. If you use the same token with multiple <code>StartLendingAnalysis</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-sync.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
#[doc(hidden)]
pub client_request_token: std::option::Option<std::string::String>,
/// <p>An identifier that you specify to be included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
#[doc(hidden)]
pub job_tag: std::option::Option<std::string::String>,
/// <p>The Amazon Simple Notification Service (Amazon SNS) topic to which Amazon Textract publishes the completion status of an asynchronous document operation. </p>
#[doc(hidden)]
pub notification_channel: std::option::Option<crate::model::NotificationChannel>,
/// <p>Sets whether or not your output will go to a user created bucket. Used to set the name of the bucket, and the prefix on the output file.</p>
/// <p> <code>OutputConfig</code> is an optional parameter which lets you adjust where your output will be placed. By default, Amazon Textract will store the results internally and can only be accessed by the Get API operations. With <code>OutputConfig</code> enabled, you can set the name of the bucket the output will be sent to the file prefix of the results where you can download your results. Additionally, you can set the <code>KMSKeyID</code> parameter to a customer master key (CMK) to encrypt your output. Without this parameter set Amazon Textract will encrypt server-side using the AWS managed CMK for Amazon S3.</p>
/// <p>Decryption of Customer Content is necessary for processing of the documents by Amazon Textract. If your account is opted out under an AI services opt out policy then all unencrypted Customer Content is immediately and permanently deleted after the Customer Content has been processed by the service. No copy of of the output is retained by Amazon Textract. For information about how to opt out, see <a href="https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_ai-opt-out.html"> Managing AI services opt-out policy. </a> </p>
/// <p>For more information on data privacy, see the <a href="https://aws.amazon.com/compliance/data-privacy-faq/">Data Privacy FAQ</a>.</p>
#[doc(hidden)]
pub output_config: std::option::Option<crate::model::OutputConfig>,
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side, using SSE-S3. </p>
#[doc(hidden)]
pub kms_key_id: std::option::Option<std::string::String>,
}
impl StartLendingAnalysisInput {
/// <p>The Amazon S3 bucket that contains the document to be processed. It's used by asynchronous operations.</p>
/// <p>The input document can be an image file in JPEG or PNG format. It can also be a file in PDF format.</p>
pub fn document_location(&self) -> std::option::Option<&crate::model::DocumentLocation> {
self.document_location.as_ref()
}
/// <p>The idempotent token that you use to identify the start request. If you use the same token with multiple <code>StartLendingAnalysis</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-sync.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
pub fn client_request_token(&self) -> std::option::Option<&str> {
self.client_request_token.as_deref()
}
/// <p>An identifier that you specify to be included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn job_tag(&self) -> std::option::Option<&str> {
self.job_tag.as_deref()
}
/// <p>The Amazon Simple Notification Service (Amazon SNS) topic to which Amazon Textract publishes the completion status of an asynchronous document operation. </p>
pub fn notification_channel(&self) -> std::option::Option<&crate::model::NotificationChannel> {
self.notification_channel.as_ref()
}
/// <p>Sets whether or not your output will go to a user created bucket. Used to set the name of the bucket, and the prefix on the output file.</p>
/// <p> <code>OutputConfig</code> is an optional parameter which lets you adjust where your output will be placed. By default, Amazon Textract will store the results internally and can only be accessed by the Get API operations. With <code>OutputConfig</code> enabled, you can set the name of the bucket the output will be sent to the file prefix of the results where you can download your results. Additionally, you can set the <code>KMSKeyID</code> parameter to a customer master key (CMK) to encrypt your output. Without this parameter set Amazon Textract will encrypt server-side using the AWS managed CMK for Amazon S3.</p>
/// <p>Decryption of Customer Content is necessary for processing of the documents by Amazon Textract. If your account is opted out under an AI services opt out policy then all unencrypted Customer Content is immediately and permanently deleted after the Customer Content has been processed by the service. No copy of of the output is retained by Amazon Textract. For information about how to opt out, see <a href="https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_ai-opt-out.html"> Managing AI services opt-out policy. </a> </p>
/// <p>For more information on data privacy, see the <a href="https://aws.amazon.com/compliance/data-privacy-faq/">Data Privacy FAQ</a>.</p>
pub fn output_config(&self) -> std::option::Option<&crate::model::OutputConfig> {
self.output_config.as_ref()
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side, using SSE-S3. </p>
pub fn kms_key_id(&self) -> std::option::Option<&str> {
self.kms_key_id.as_deref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartExpenseAnalysisInput {
/// <p>The location of the document to be processed.</p>
#[doc(hidden)]
pub document_location: std::option::Option<crate::model::DocumentLocation>,
/// <p>The idempotent token that's used to identify the start request. If you use the same token with multiple <code>StartDocumentTextDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a> </p>
#[doc(hidden)]
pub client_request_token: std::option::Option<std::string::String>,
/// <p>An identifier you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
#[doc(hidden)]
pub job_tag: std::option::Option<std::string::String>,
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
#[doc(hidden)]
pub notification_channel: std::option::Option<crate::model::NotificationChannel>,
/// <p>Sets if the output will go to a customer defined bucket. By default, Amazon Textract will save the results internally to be accessed by the <code>GetExpenseAnalysis</code> operation.</p>
#[doc(hidden)]
pub output_config: std::option::Option<crate::model::OutputConfig>,
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
#[doc(hidden)]
pub kms_key_id: std::option::Option<std::string::String>,
}
impl StartExpenseAnalysisInput {
/// <p>The location of the document to be processed.</p>
pub fn document_location(&self) -> std::option::Option<&crate::model::DocumentLocation> {
self.document_location.as_ref()
}
/// <p>The idempotent token that's used to identify the start request. If you use the same token with multiple <code>StartDocumentTextDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a> </p>
pub fn client_request_token(&self) -> std::option::Option<&str> {
self.client_request_token.as_deref()
}
/// <p>An identifier you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn job_tag(&self) -> std::option::Option<&str> {
self.job_tag.as_deref()
}
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
pub fn notification_channel(&self) -> std::option::Option<&crate::model::NotificationChannel> {
self.notification_channel.as_ref()
}
/// <p>Sets if the output will go to a customer defined bucket. By default, Amazon Textract will save the results internally to be accessed by the <code>GetExpenseAnalysis</code> operation.</p>
pub fn output_config(&self) -> std::option::Option<&crate::model::OutputConfig> {
self.output_config.as_ref()
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
pub fn kms_key_id(&self) -> std::option::Option<&str> {
self.kms_key_id.as_deref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartDocumentTextDetectionInput {
/// <p>The location of the document to be processed.</p>
#[doc(hidden)]
pub document_location: std::option::Option<crate::model::DocumentLocation>,
/// <p>The idempotent token that's used to identify the start request. If you use the same token with multiple <code>StartDocumentTextDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
#[doc(hidden)]
pub client_request_token: std::option::Option<std::string::String>,
/// <p>An identifier that you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
#[doc(hidden)]
pub job_tag: std::option::Option<std::string::String>,
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
#[doc(hidden)]
pub notification_channel: std::option::Option<crate::model::NotificationChannel>,
/// <p>Sets if the output will go to a customer defined bucket. By default Amazon Textract will save the results internally to be accessed with the GetDocumentTextDetection operation.</p>
#[doc(hidden)]
pub output_config: std::option::Option<crate::model::OutputConfig>,
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
#[doc(hidden)]
pub kms_key_id: std::option::Option<std::string::String>,
}
impl StartDocumentTextDetectionInput {
/// <p>The location of the document to be processed.</p>
pub fn document_location(&self) -> std::option::Option<&crate::model::DocumentLocation> {
self.document_location.as_ref()
}
/// <p>The idempotent token that's used to identify the start request. If you use the same token with multiple <code>StartDocumentTextDetection</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
pub fn client_request_token(&self) -> std::option::Option<&str> {
self.client_request_token.as_deref()
}
/// <p>An identifier that you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn job_tag(&self) -> std::option::Option<&str> {
self.job_tag.as_deref()
}
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
pub fn notification_channel(&self) -> std::option::Option<&crate::model::NotificationChannel> {
self.notification_channel.as_ref()
}
/// <p>Sets if the output will go to a customer defined bucket. By default Amazon Textract will save the results internally to be accessed with the GetDocumentTextDetection operation.</p>
pub fn output_config(&self) -> std::option::Option<&crate::model::OutputConfig> {
self.output_config.as_ref()
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
pub fn kms_key_id(&self) -> std::option::Option<&str> {
self.kms_key_id.as_deref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct StartDocumentAnalysisInput {
/// <p>The location of the document to be processed.</p>
#[doc(hidden)]
pub document_location: std::option::Option<crate::model::DocumentLocation>,
/// <p>A list of the types of analysis to perform. Add TABLES to the list to return information about the tables that are detected in the input document. Add FORMS to return detected form data. To perform both types of analysis, add TABLES and FORMS to <code>FeatureTypes</code>. All lines and words detected in the document are included in the response (including text that isn't related to the value of <code>FeatureTypes</code>). </p>
#[doc(hidden)]
pub feature_types: std::option::Option<std::vec::Vec<crate::model::FeatureType>>,
/// <p>The idempotent token that you use to identify the start request. If you use the same token with multiple <code>StartDocumentAnalysis</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
#[doc(hidden)]
pub client_request_token: std::option::Option<std::string::String>,
/// <p>An identifier that you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
#[doc(hidden)]
pub job_tag: std::option::Option<std::string::String>,
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
#[doc(hidden)]
pub notification_channel: std::option::Option<crate::model::NotificationChannel>,
/// <p>Sets if the output will go to a customer defined bucket. By default, Amazon Textract will save the results internally to be accessed by the GetDocumentAnalysis operation.</p>
#[doc(hidden)]
pub output_config: std::option::Option<crate::model::OutputConfig>,
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
#[doc(hidden)]
pub kms_key_id: std::option::Option<std::string::String>,
/// <p></p>
#[doc(hidden)]
pub queries_config: std::option::Option<crate::model::QueriesConfig>,
}
impl StartDocumentAnalysisInput {
/// <p>The location of the document to be processed.</p>
pub fn document_location(&self) -> std::option::Option<&crate::model::DocumentLocation> {
self.document_location.as_ref()
}
/// <p>A list of the types of analysis to perform. Add TABLES to the list to return information about the tables that are detected in the input document. Add FORMS to return detected form data. To perform both types of analysis, add TABLES and FORMS to <code>FeatureTypes</code>. All lines and words detected in the document are included in the response (including text that isn't related to the value of <code>FeatureTypes</code>). </p>
pub fn feature_types(&self) -> std::option::Option<&[crate::model::FeatureType]> {
self.feature_types.as_deref()
}
/// <p>The idempotent token that you use to identify the start request. If you use the same token with multiple <code>StartDocumentAnalysis</code> requests, the same <code>JobId</code> is returned. Use <code>ClientRequestToken</code> to prevent the same job from being accidentally started more than once. For more information, see <a href="https://docs.aws.amazon.com/textract/latest/dg/api-async.html">Calling Amazon Textract Asynchronous Operations</a>.</p>
pub fn client_request_token(&self) -> std::option::Option<&str> {
self.client_request_token.as_deref()
}
/// <p>An identifier that you specify that's included in the completion notification published to the Amazon SNS topic. For example, you can use <code>JobTag</code> to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).</p>
pub fn job_tag(&self) -> std::option::Option<&str> {
self.job_tag.as_deref()
}
/// <p>The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to. </p>
pub fn notification_channel(&self) -> std::option::Option<&crate::model::NotificationChannel> {
self.notification_channel.as_ref()
}
/// <p>Sets if the output will go to a customer defined bucket. By default, Amazon Textract will save the results internally to be accessed by the GetDocumentAnalysis operation.</p>
pub fn output_config(&self) -> std::option::Option<&crate::model::OutputConfig> {
self.output_config.as_ref()
}
/// <p>The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.</p>
pub fn kms_key_id(&self) -> std::option::Option<&str> {
self.kms_key_id.as_deref()
}
/// <p></p>
pub fn queries_config(&self) -> std::option::Option<&crate::model::QueriesConfig> {
self.queries_config.as_ref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetLendingAnalysisSummaryInput {
/// <p> A unique identifier for the lending or text-detection job. The <code>JobId</code> is returned from StartLendingAnalysis. A <code>JobId</code> value is only valid for 7 days.</p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
}
impl GetLendingAnalysisSummaryInput {
/// <p> A unique identifier for the lending or text-detection job. The <code>JobId</code> is returned from StartLendingAnalysis. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetLendingAnalysisInput {
/// <p>A unique identifier for the lending or text-detection job. The <code>JobId</code> is returned from <code>StartLendingAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
/// <p>The maximum number of results to return per paginated call. The largest value that you can specify is 30. If you specify a value greater than 30, a maximum of 30 results is returned. The default value is 30.</p>
#[doc(hidden)]
pub max_results: std::option::Option<i32>,
/// <p>If the previous response was incomplete, Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of lending results.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl GetLendingAnalysisInput {
/// <p>A unique identifier for the lending or text-detection job. The <code>JobId</code> is returned from <code>StartLendingAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
/// <p>The maximum number of results to return per paginated call. The largest value that you can specify is 30. If you specify a value greater than 30, a maximum of 30 results is returned. The default value is 30.</p>
pub fn max_results(&self) -> std::option::Option<i32> {
self.max_results
}
/// <p>If the previous response was incomplete, Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of lending results.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetExpenseAnalysisInput {
/// <p>A unique identifier for the text detection job. The <code>JobId</code> is returned from <code>StartExpenseAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
/// <p>The maximum number of results to return per paginated call. The largest value you can specify is 20. If you specify a value greater than 20, a maximum of 20 results is returned. The default value is 20.</p>
#[doc(hidden)]
pub max_results: std::option::Option<i32>,
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl GetExpenseAnalysisInput {
/// <p>A unique identifier for the text detection job. The <code>JobId</code> is returned from <code>StartExpenseAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
/// <p>The maximum number of results to return per paginated call. The largest value you can specify is 20. If you specify a value greater than 20, a maximum of 20 results is returned. The default value is 20.</p>
pub fn max_results(&self) -> std::option::Option<i32> {
self.max_results
}
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetDocumentTextDetectionInput {
/// <p>A unique identifier for the text detection job. The <code>JobId</code> is returned from <code>StartDocumentTextDetection</code>. A <code>JobId</code> value is only valid for 7 days.</p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
/// <p>The maximum number of results to return per paginated call. The largest value you can specify is 1,000. If you specify a value greater than 1,000, a maximum of 1,000 results is returned. The default value is 1,000.</p>
#[doc(hidden)]
pub max_results: std::option::Option<i32>,
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl GetDocumentTextDetectionInput {
/// <p>A unique identifier for the text detection job. The <code>JobId</code> is returned from <code>StartDocumentTextDetection</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
/// <p>The maximum number of results to return per paginated call. The largest value you can specify is 1,000. If you specify a value greater than 1,000, a maximum of 1,000 results is returned. The default value is 1,000.</p>
pub fn max_results(&self) -> std::option::Option<i32> {
self.max_results
}
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetDocumentAnalysisInput {
/// <p>A unique identifier for the text-detection job. The <code>JobId</code> is returned from <code>StartDocumentAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
#[doc(hidden)]
pub job_id: std::option::Option<std::string::String>,
/// <p>The maximum number of results to return per paginated call. The largest value that you can specify is 1,000. If you specify a value greater than 1,000, a maximum of 1,000 results is returned. The default value is 1,000.</p>
#[doc(hidden)]
pub max_results: std::option::Option<i32>,
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl GetDocumentAnalysisInput {
/// <p>A unique identifier for the text-detection job. The <code>JobId</code> is returned from <code>StartDocumentAnalysis</code>. A <code>JobId</code> value is only valid for 7 days.</p>
pub fn job_id(&self) -> std::option::Option<&str> {
self.job_id.as_deref()
}
/// <p>The maximum number of results to return per paginated call. The largest value that you can specify is 1,000. If you specify a value greater than 1,000, a maximum of 1,000 results is returned. The default value is 1,000.</p>
pub fn max_results(&self) -> std::option::Option<i32> {
self.max_results
}
/// <p>If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DetectDocumentTextInput {
/// <p>The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can't pass image bytes. The document must be an image in JPEG or PNG format.</p>
/// <p>If you're using an AWS SDK to call Amazon Textract, you might not need to base64-encode image bytes that are passed using the <code>Bytes</code> field. </p>
#[doc(hidden)]
pub document: std::option::Option<crate::model::Document>,
}
impl DetectDocumentTextInput {
/// <p>The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can't pass image bytes. The document must be an image in JPEG or PNG format.</p>
/// <p>If you're using an AWS SDK to call Amazon Textract, you might not need to base64-encode image bytes that are passed using the <code>Bytes</code> field. </p>
pub fn document(&self) -> std::option::Option<&crate::model::Document> {
self.document.as_ref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct AnalyzeIdInput {
/// <p>The document being passed to AnalyzeID.</p>
#[doc(hidden)]
pub document_pages: std::option::Option<std::vec::Vec<crate::model::Document>>,
}
impl AnalyzeIdInput {
/// <p>The document being passed to AnalyzeID.</p>
pub fn document_pages(&self) -> std::option::Option<&[crate::model::Document]> {
self.document_pages.as_deref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct AnalyzeExpenseInput {
/// <p>The input document, either as bytes or as an S3 object.</p>
/// <p>You pass image bytes to an Amazon Textract API operation by using the <code>Bytes</code> property. For example, you would use the <code>Bytes</code> property to pass a document loaded from a local file system. Image bytes passed by using the <code>Bytes</code> property must be base64 encoded. Your code might not need to encode document file bytes if you're using an AWS SDK to call Amazon Textract API operations. </p>
/// <p>You pass images stored in an S3 bucket to an Amazon Textract API operation by using the <code>S3Object</code> property. Documents stored in an S3 bucket don't need to be base64 encoded.</p>
/// <p>The AWS Region for the S3 bucket that contains the S3 object must match the AWS Region that you use for Amazon Textract operations.</p>
/// <p>If you use the AWS CLI to call Amazon Textract operations, passing image bytes using the Bytes property isn't supported. You must first upload the document to an Amazon S3 bucket, and then call the operation using the S3Object property.</p>
/// <p>For Amazon Textract to process an S3 object, the user must have permission to access the S3 object. </p>
#[doc(hidden)]
pub document: std::option::Option<crate::model::Document>,
}
impl AnalyzeExpenseInput {
/// <p>The input document, either as bytes or as an S3 object.</p>
/// <p>You pass image bytes to an Amazon Textract API operation by using the <code>Bytes</code> property. For example, you would use the <code>Bytes</code> property to pass a document loaded from a local file system. Image bytes passed by using the <code>Bytes</code> property must be base64 encoded. Your code might not need to encode document file bytes if you're using an AWS SDK to call Amazon Textract API operations. </p>
/// <p>You pass images stored in an S3 bucket to an Amazon Textract API operation by using the <code>S3Object</code> property. Documents stored in an S3 bucket don't need to be base64 encoded.</p>
/// <p>The AWS Region for the S3 bucket that contains the S3 object must match the AWS Region that you use for Amazon Textract operations.</p>
/// <p>If you use the AWS CLI to call Amazon Textract operations, passing image bytes using the Bytes property isn't supported. You must first upload the document to an Amazon S3 bucket, and then call the operation using the S3Object property.</p>
/// <p>For Amazon Textract to process an S3 object, the user must have permission to access the S3 object. </p>
pub fn document(&self) -> std::option::Option<&crate::model::Document> {
self.document.as_ref()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct AnalyzeDocumentInput {
/// <p>The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can't pass image bytes. The document must be an image in JPEG, PNG, PDF, or TIFF format.</p>
/// <p>If you're using an AWS SDK to call Amazon Textract, you might not need to base64-encode image bytes that are passed using the <code>Bytes</code> field. </p>
#[doc(hidden)]
pub document: std::option::Option<crate::model::Document>,
/// <p>A list of the types of analysis to perform. Add TABLES to the list to return information about the tables that are detected in the input document. Add FORMS to return detected form data. Add SIGNATURES to return the locations of detected signatures. To perform both forms and table analysis, add TABLES and FORMS to <code>FeatureTypes</code>. To detect signatures within form data and table data, add SIGNATURES to either TABLES or FORMS. All lines and words detected in the document are included in the response (including text that isn't related to the value of <code>FeatureTypes</code>). </p>
#[doc(hidden)]
pub feature_types: std::option::Option<std::vec::Vec<crate::model::FeatureType>>,
/// <p>Sets the configuration for the human in the loop workflow for analyzing documents.</p>
#[doc(hidden)]
pub human_loop_config: std::option::Option<crate::model::HumanLoopConfig>,
/// <p>Contains Queries and the alias for those Queries, as determined by the input. </p>
#[doc(hidden)]
pub queries_config: std::option::Option<crate::model::QueriesConfig>,
}
impl AnalyzeDocumentInput {
/// <p>The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can't pass image bytes. The document must be an image in JPEG, PNG, PDF, or TIFF format.</p>
/// <p>If you're using an AWS SDK to call Amazon Textract, you might not need to base64-encode image bytes that are passed using the <code>Bytes</code> field. </p>
pub fn document(&self) -> std::option::Option<&crate::model::Document> {
self.document.as_ref()
}
/// <p>A list of the types of analysis to perform. Add TABLES to the list to return information about the tables that are detected in the input document. Add FORMS to return detected form data. Add SIGNATURES to return the locations of detected signatures. To perform both forms and table analysis, add TABLES and FORMS to <code>FeatureTypes</code>. To detect signatures within form data and table data, add SIGNATURES to either TABLES or FORMS. All lines and words detected in the document are included in the response (including text that isn't related to the value of <code>FeatureTypes</code>). </p>
pub fn feature_types(&self) -> std::option::Option<&[crate::model::FeatureType]> {
self.feature_types.as_deref()
}
/// <p>Sets the configuration for the human in the loop workflow for analyzing documents.</p>
pub fn human_loop_config(&self) -> std::option::Option<&crate::model::HumanLoopConfig> {
self.human_loop_config.as_ref()
}
/// <p>Contains Queries and the alias for those Queries, as determined by the input. </p>
pub fn queries_config(&self) -> std::option::Option<&crate::model::QueriesConfig> {
self.queries_config.as_ref()
}
}