Struct aws_sdk_textract::Client

pub struct Client { /* private fields */ }

Client for Amazon Textract

Client for invoking operations on Amazon Textract. Each operation on Amazon Textract is a method on this this struct. .send() MUST be invoked on the generated operations to dispatch the request to the service.

Examples

Constructing a client and invoking an operation

    // create a shared configuration. This can be used & shared between multiple service clients.
    let shared_config = aws_config::load_from_env().await;
    let client = aws_sdk_textract::Client::new(&shared_config);
    // invoke an operation
    /* let rsp = client
        .<operation_name>().
        .<param>("some value")
        .send().await; */

Constructing a client with custom configuration

use aws_config::retry::RetryConfig;
let shared_config = aws_config::load_from_env().await;
let config = aws_sdk_textract::config::Builder::from(&shared_config)
  .retry_config(RetryConfig::disabled())
  .build();
let client = aws_sdk_textract::Client::from_conf(config);

Implementations

impl Client

pub fn with_config(
    client: Client<DynConnector, DynMiddleware<DynConnector>>,
    conf: Config
) -> Self

Creates a client with the given service configuration.

Examples found in repository

src/client.rs (line 70)

    fn from(
        client: aws_smithy_client::Client<
            aws_smithy_client::erase::DynConnector,
            aws_smithy_client::erase::DynMiddleware<aws_smithy_client::erase::DynConnector>,
        >,
    ) -> Self {
        Self::with_config(client, crate::Config::builder().build())
    }

pub fn conf(&self) -> &Config

Returns the client’s configuration.

impl Client

pub fn analyze_document(&self) -> AnalyzeDocument

Constructs a fluent builder for the AnalyzeDocument operation.

• The fluent builder is configurable:
  • document(Document) / set_document(Option<Document>):

    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.

    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 Bytes field.

  • feature_types(Vec<FeatureType>) / set_feature_types(Option<Vec<FeatureType>>):

    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 FeatureTypes. All lines and words detected in the document are included in the response (including text that isn’t related to the value of FeatureTypes).

  • human_loop_config(HumanLoopConfig) / set_human_loop_config(Option<HumanLoopConfig>):

    Sets the configuration for the human in the loop workflow for analyzing documents.

  • queries_config(QueriesConfig) / set_queries_config(Option<QueriesConfig>):

    Contains Queries and the alias for those Queries, as determined by the input.

• On success, responds with AnalyzeDocumentOutput with field(s):
  • document_metadata(Option<DocumentMetadata>):

    Metadata about the analyzed document. An example is the number of pages.

  • blocks(Option<Vec<Block>>):

    The items that are detected and analyzed by AnalyzeDocument.

  • human_loop_activation_output(Option<HumanLoopActivationOutput>):

    Shows the results of the human in the loop evaluation.

  • analyze_document_model_version(Option<String>):

    The version of the model used to analyze the document.

• On failure, responds with SdkError<AnalyzeDocumentError>

pub fn analyze_expense(&self) -> AnalyzeExpense

Constructs a fluent builder for the AnalyzeExpense operation.

• The fluent builder is configurable:
  • document(Document) / set_document(Option<Document>):

    The input document, either as bytes or as an S3 object.

    You pass image bytes to an Amazon Textract API operation by using the Bytes property. For example, you would use the Bytes property to pass a document loaded from a local file system. Image bytes passed by using the Bytes 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.

    You pass images stored in an S3 bucket to an Amazon Textract API operation by using the S3Object property. Documents stored in an S3 bucket don’t need to be base64 encoded.

    The AWS Region for the S3 bucket that contains the S3 object must match the AWS Region that you use for Amazon Textract operations.

    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.

    For Amazon Textract to process an S3 object, the user must have permission to access the S3 object.

• On success, responds with AnalyzeExpenseOutput with field(s):
  • document_metadata(Option<DocumentMetadata>):

    Information about the input document.

  • expense_documents(Option<Vec<ExpenseDocument>>):

    The expenses detected by Amazon Textract.

• On failure, responds with SdkError<AnalyzeExpenseError>

pub fn analyze_id(&self) -> AnalyzeID

Constructs a fluent builder for the AnalyzeID operation.

• The fluent builder is configurable:
  • document_pages(Vec<Document>) / set_document_pages(Option<Vec<Document>>):

    The document being passed to AnalyzeID.

• On success, responds with AnalyzeIdOutput with field(s):
  • identity_documents(Option<Vec<IdentityDocument>>):

    The list of documents processed by AnalyzeID. Includes a number denoting their place in the list and the response structure for the document.

  • document_metadata(Option<DocumentMetadata>):

    Information about the input document.

  • analyze_id_model_version(Option<String>):

    The version of the AnalyzeIdentity API being used to process documents.

• On failure, responds with SdkError<AnalyzeIDError>

pub fn detect_document_text(&self) -> DetectDocumentText

Constructs a fluent builder for the DetectDocumentText operation.

• The fluent builder is configurable:
  • document(Document) / set_document(Option<Document>):

    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.

    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 Bytes field.

• On success, responds with DetectDocumentTextOutput with field(s):
  • document_metadata(Option<DocumentMetadata>):

    Metadata about the document. It contains the number of pages that are detected in the document.

  • blocks(Option<Vec<Block>>):

    An array of Block objects that contain the text that’s detected in the document.

  • detect_document_text_model_version(Option<String>):

• On failure, responds with SdkError<DetectDocumentTextError>

pub fn get_document_analysis(&self) -> GetDocumentAnalysis

Constructs a fluent builder for the GetDocumentAnalysis operation.

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):

    A unique identifier for the text-detection job. The JobId is returned from StartDocumentAnalysis. A JobId value is only valid for 7 days.

  • max_results(i32) / set_max_results(Option<i32>):

    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.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    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.

• On success, responds with GetDocumentAnalysisOutput with field(s):
  • document_metadata(Option<DocumentMetadata>):

    Information about a document that Amazon Textract processed. DocumentMetadata is returned in every page of paginated responses from an Amazon Textract video operation.

  • job_status(Option<JobStatus>):

    The current status of the text detection job.

  • next_token(Option<String>):

    If the response is truncated, Amazon Textract returns this token. You can use this token in the subsequent request to retrieve the next set of text detection results.

  • blocks(Option<Vec<Block>>):

    The results of the text-analysis operation.

  • warnings(Option<Vec<Warning>>):

    A list of warnings that occurred during the document-analysis operation.

  • status_message(Option<String>):

    Returns if the detection job could not be completed. Contains explanation for what error occured.

  • analyze_document_model_version(Option<String>):

• On failure, responds with SdkError<GetDocumentAnalysisError>

pub fn get_document_text_detection(&self) -> GetDocumentTextDetection

Constructs a fluent builder for the GetDocumentTextDetection operation.

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):

    A unique identifier for the text detection job. The JobId is returned from StartDocumentTextDetection. A JobId value is only valid for 7 days.

  • max_results(i32) / set_max_results(Option<i32>):

    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.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    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.

• On success, responds with GetDocumentTextDetectionOutput with field(s):
  • document_metadata(Option<DocumentMetadata>):

    Information about a document that Amazon Textract processed. DocumentMetadata is returned in every page of paginated responses from an Amazon Textract video operation.

  • job_status(Option<JobStatus>):

    The current status of the text detection job.

  • next_token(Option<String>):

    If the response is truncated, Amazon Textract returns this token. You can use this token in the subsequent request to retrieve the next set of text-detection results.

  • blocks(Option<Vec<Block>>):

    The results of the text-detection operation.

  • warnings(Option<Vec<Warning>>):

    A list of warnings that occurred during the text-detection operation for the document.

  • status_message(Option<String>):

    Returns if the detection job could not be completed. Contains explanation for what error occured.

  • detect_document_text_model_version(Option<String>):

• On failure, responds with SdkError<GetDocumentTextDetectionError>

pub fn get_expense_analysis(&self) -> GetExpenseAnalysis

Constructs a fluent builder for the GetExpenseAnalysis operation.

• The fluent builder is configurable:
  • job_id(impl Into<String>) / set_job_id(Option<String>):

    A unique identifier for the text detection job. The JobId is returned from StartExpenseAnalysis. A JobId value is only valid for 7 days.

  • max_results(i32) / set_max_results(Option<i32>):

    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.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    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.

• On success, responds with GetExpenseAnalysisOutput with field(s):
  • document_metadata(Option<DocumentMetadata>):

    Information about a document that Amazon Textract processed. DocumentMetadata is returned in every page of paginated responses from an Amazon Textract operation.

  • job_status(Option<JobStatus>):

    The current status of the text detection job.

  • next_token(Option<String>):

    If the response is truncated, Amazon Textract returns this token. You can use this token in the subsequent request to retrieve the next set of text-detection results.

  • expense_documents(Option<Vec<ExpenseDocument>>):

    The expenses detected by Amazon Textract.

  • warnings(Option<Vec<Warning>>):

    A list of warnings that occurred during the text-detection operation for the document.

  • status_message(Option<String>):

    Returns if the detection job could not be completed. Contains explanation for what error occured.

  • analyze_expense_model_version(Option<String>):

    The current model version of AnalyzeExpense.

• On failure, responds with SdkError<GetExpenseAnalysisError>

pub fn start_document_analysis(&self) -> StartDocumentAnalysis

Constructs a fluent builder for the StartDocumentAnalysis operation.

• The fluent builder is configurable:
  • document_location(DocumentLocation) / set_document_location(Option<DocumentLocation>):

    The location of the document to be processed.

  • feature_types(Vec<FeatureType>) / set_feature_types(Option<Vec<FeatureType>>):

    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 FeatureTypes. All lines and words detected in the document are included in the response (including text that isn’t related to the value of FeatureTypes).

  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):

    The idempotent token that you use to identify the start request. If you use the same token with multiple StartDocumentAnalysis requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidentally started more than once. For more information, see Calling Amazon Textract Asynchronous Operations.

  • job_tag(impl Into<String>) / set_job_tag(Option<String>):

    An identifier that you specify that’s included in the completion notification published to the Amazon SNS topic. For example, you can use JobTag to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).

  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):

    The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to.

  • output_config(OutputConfig) / set_output_config(Option<OutputConfig>):

    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.

  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):

    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.

  • queries_config(QueriesConfig) / set_queries_config(Option<QueriesConfig>):

• On success, responds with StartDocumentAnalysisOutput with field(s):
  • job_id(Option<String>):

    The identifier for the document text detection job. Use JobId to identify the job in a subsequent call to GetDocumentAnalysis. A JobId value is only valid for 7 days.

• On failure, responds with SdkError<StartDocumentAnalysisError>

pub fn start_document_text_detection(&self) -> StartDocumentTextDetection

Constructs a fluent builder for the StartDocumentTextDetection operation.

• The fluent builder is configurable:
  • document_location(DocumentLocation) / set_document_location(Option<DocumentLocation>):

    The location of the document to be processed.

  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):

    The idempotent token that’s used to identify the start request. If you use the same token with multiple StartDocumentTextDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidentally started more than once. For more information, see Calling Amazon Textract Asynchronous Operations.

  • job_tag(impl Into<String>) / set_job_tag(Option<String>):

    An identifier that you specify that’s included in the completion notification published to the Amazon SNS topic. For example, you can use JobTag to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).

  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):

    The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to.

  • output_config(OutputConfig) / set_output_config(Option<OutputConfig>):

    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.

  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):

    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.

• On success, responds with StartDocumentTextDetectionOutput with field(s):
  • job_id(Option<String>):

    The identifier of the text detection job for the document. Use JobId to identify the job in a subsequent call to GetDocumentTextDetection. A JobId value is only valid for 7 days.

• On failure, responds with SdkError<StartDocumentTextDetectionError>

pub fn start_expense_analysis(&self) -> StartExpenseAnalysis

Constructs a fluent builder for the StartExpenseAnalysis operation.

• The fluent builder is configurable:
  • document_location(DocumentLocation) / set_document_location(Option<DocumentLocation>):

    The location of the document to be processed.

  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):

    The idempotent token that’s used to identify the start request. If you use the same token with multiple StartDocumentTextDetection requests, the same JobId is returned. Use ClientRequestToken to prevent the same job from being accidentally started more than once. For more information, see Calling Amazon Textract Asynchronous Operations

  • job_tag(impl Into<String>) / set_job_tag(Option<String>):

    An identifier you specify that’s included in the completion notification published to the Amazon SNS topic. For example, you can use JobTag to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).

  • notification_channel(NotificationChannel) / set_notification_channel(Option<NotificationChannel>):

    The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to.

  • output_config(OutputConfig) / set_output_config(Option<OutputConfig>):

    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 GetExpenseAnalysis operation.

  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):

    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.

• On success, responds with StartExpenseAnalysisOutput with field(s):
  • job_id(Option<String>):

    A unique identifier for the text detection job. The JobId is returned from StartExpenseAnalysis. A JobId value is only valid for 7 days.

• On failure, responds with SdkError<StartExpenseAnalysisError>

impl Client

pub fn new(sdk_config: &SdkConfig) -> Self

Creates a new client from an SDK Config.

Panics

• This method will panic if the sdk_config is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
• This method will panic if the sdk_config is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.

pub fn from_conf(conf: Config) -> Self

Creates a new client from the service Config.

Panics

• This method will panic if the conf is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
• This method will panic if the conf is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.

Examples found in repository

src/client.rs (line 1384)

    pub fn new(sdk_config: &aws_types::sdk_config::SdkConfig) -> Self {
        Self::from_conf(sdk_config.into())
    }

Trait Implementations

impl Clone for Client

fn clone(&self) -> Self

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Client

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl From<Client<DynConnector, DynMiddleware<DynConnector>, Standard>> for Client

fn from(client: Client<DynConnector, DynMiddleware<DynConnector>>) -> Self

Converts to this type from the input type.