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Showing content from https://docs.aws.amazon.com/sdkforruby/api/Aws/Comprehend/Client.html below:

Client — AWS SDK for Ruby V2

Instance Method Details #batch_detect_entities(options = {}) ⇒ Types::BatchDetectEntitiesResponse

Inspects the text of a batch of documents for named entities and returns information about them. For more information about named entities, see how-entities

#batch_detect_sentiment(options = {}) ⇒ Types::BatchDetectSentimentResponse

Inspects a batch of documents and returns an inference of the prevailing sentiment, POSITIVE, NEUTRAL, MIXED, or NEGATIVE, in each one.

#batch_detect_syntax(options = {}) ⇒ Types::BatchDetectSyntaxResponse

Inspects the text of a batch of documents for the syntax and part of speech of the words in the document and returns information about them. For more information, see how-syntax.

#classify_document(options = {}) ⇒ Types::ClassifyDocumentResponse

Creates a new document classification request to analyze a single document in real-time, using a previously created and trained custom model and an endpoint.

#create_document_classifier(options = {}) ⇒ Types::CreateDocumentClassifierResponse

Creates a new document classifier that you can use to categorize documents. To create a classifier, you provide a set of training documents that labeled with the categories that you want to use. After the classifier is trained you can use it to categorize a set of labeled documents into the categories. For more information, see how-document-classification.

#create_endpoint(options = {}) ⇒ Types::CreateEndpointResponse

Creates a model-specific endpoint for synchronous inference for a previously trained custom model

#create_entity_recognizer(options = {}) ⇒ Types::CreateEntityRecognizerResponse

Creates an entity recognizer using submitted files. After your CreateEntityRecognizer request is submitted, you can check job status using the API.

#delete_document_classifier(options = {}) ⇒ Struct

Deletes a previously created document classifier

Only those classifiers that are in terminated states (IN_ERROR, TRAINED) will be deleted. If an active inference job is using the model, a ResourceInUseException will be returned.

This is an asynchronous action that puts the classifier into a DELETING state, and it is then removed by a background job. Once removed, the classifier disappears from your account and is no longer available for use.

#delete_endpoint(options = {}) ⇒ Struct

Deletes a model-specific endpoint for a previously-trained custom model. All endpoints must be deleted in order for the model to be deleted.

#delete_entity_recognizer(options = {}) ⇒ Struct

Deletes an entity recognizer.

Only those recognizers that are in terminated states (IN_ERROR, TRAINED) will be deleted. If an active inference job is using the model, a ResourceInUseException will be returned.

This is an asynchronous action that puts the recognizer into a DELETING state, and it is then removed by a background job. Once removed, the recognizer disappears from your account and is no longer available for use.

#describe_endpoint(options = {}) ⇒ Types::DescribeEndpointResponse

Gets the properties associated with a specific endpoint. Use this operation to get the status of an endpoint.

#describe_entity_recognizer(options = {}) ⇒ Types::DescribeEntityRecognizerResponse

Provides details about an entity recognizer including status, S3 buckets containing training data, recognizer metadata, metrics, and so on.

#detect_pii_entities(options = {}) ⇒ Types::DetectPiiEntitiesResponse

Inspects the input text for entities that contain personally identifiable information (PII) and returns information about them.

#detect_sentiment(options = {}) ⇒ Types::DetectSentimentResponse

Inspects text and returns an inference of the prevailing sentiment (POSITIVE, NEUTRAL, MIXED, or NEGATIVE).

#list_entity_recognizers(options = {}) ⇒ Types::ListEntityRecognizersResponse

Gets a list of the properties of all entity recognizers that you created, including recognizers currently in training. Allows you to filter the list of recognizers based on criteria such as status and submission time. This call returns up to 500 entity recognizers in the list, with a default number of 100 recognizers in the list.

The results of this list are not in any particular order. Please get the list and sort locally if needed.

#start_entities_detection_job(options = {}) ⇒ Types::StartEntitiesDetectionJobResponse

Starts an asynchronous entity detection job for a collection of documents. Use the operation to track the status of a job.

This API can be used for either standard entity detection or custom entity recognition. In order to be used for custom entity recognition, the optional EntityRecognizerArn must be used in order to provide access to the recognizer being used to detect the custom entity.

#start_key_phrases_detection_job(options = {}) ⇒ Types::StartKeyPhrasesDetectionJobResponse

Starts an asynchronous key phrase detection job for a collection of documents. Use the operation to track the status of a job.

#start_sentiment_detection_job(options = {}) ⇒ Types::StartSentimentDetectionJobResponse

Starts an asynchronous sentiment detection job for a collection of documents. use the operation to track the status of a job.

#start_topics_detection_job(options = {}) ⇒ Types::StartTopicsDetectionJobResponse

Starts an asynchronous topic detection job. Use the DescribeTopicDetectionJob operation to track the status of a job.

#stop_dominant_language_detection_job(options = {}) ⇒ Types::StopDominantLanguageDetectionJobResponse

Stops a dominant language detection job in progress.

If the job state is IN_PROGRESS the job is marked for termination and put into the STOP_REQUESTED state. If the job completes before it can be stopped, it is put into the COMPLETED state; otherwise the job is stopped and put into the STOPPED state.

If the job is in the COMPLETED or FAILED state when you call the StopDominantLanguageDetectionJob operation, the operation returns a 400 Internal Request Exception.

When a job is stopped, any documents already processed are written to the output location.

#stop_entities_detection_job(options = {}) ⇒ Types::StopEntitiesDetectionJobResponse

Stops an entities detection job in progress.

If the job state is IN_PROGRESS the job is marked for termination and put into the STOP_REQUESTED state. If the job completes before it can be stopped, it is put into the COMPLETED state; otherwise the job is stopped and put into the STOPPED state.

If the job is in the COMPLETED or FAILED state when you call the StopDominantLanguageDetectionJob operation, the operation returns a 400 Internal Request Exception.

When a job is stopped, any documents already processed are written to the output location.

#stop_key_phrases_detection_job(options = {}) ⇒ Types::StopKeyPhrasesDetectionJobResponse

Stops a key phrases detection job in progress.

If the job state is IN_PROGRESS the job is marked for termination and put into the STOP_REQUESTED state. If the job completes before it can be stopped, it is put into the COMPLETED state; otherwise the job is stopped and put into the STOPPED state.

If the job is in the COMPLETED or FAILED state when you call the StopDominantLanguageDetectionJob operation, the operation returns a 400 Internal Request Exception.

When a job is stopped, any documents already processed are written to the output location.

#stop_sentiment_detection_job(options = {}) ⇒ Types::StopSentimentDetectionJobResponse

Stops a sentiment detection job in progress.

If the job state is IN_PROGRESS the job is marked for termination and put into the STOP_REQUESTED state. If the job completes before it can be stopped, it is put into the COMPLETED state; otherwise the job is be stopped and put into the STOPPED state.

If the job is in the COMPLETED or FAILED state when you call the StopDominantLanguageDetectionJob operation, the operation returns a 400 Internal Request Exception.

When a job is stopped, any documents already processed are written to the output location.

#stop_training_document_classifier(options = {}) ⇒ Struct

Stops a document classifier training job while in progress.

If the training job state is TRAINING, the job is marked for termination and put into the STOP_REQUESTED state. If the training job completes before it can be stopped, it is put into the TRAINED; otherwise the training job is stopped and put into the STOPPED state and the service sends back an HTTP 200 response with an empty HTTP body.

#stop_training_entity_recognizer(options = {}) ⇒ Struct

Stops an entity recognizer training job while in progress.

If the training job state is TRAINING, the job is marked for termination and put into the STOP_REQUESTED state. If the training job completes before it can be stopped, it is put into the TRAINED; otherwise the training job is stopped and putted into the STOPPED state and the service sends back an HTTP 200 response with an empty HTTP body.

#tag_resource(options = {}) ⇒ Struct

Associates a specific tag with an Amazon Comprehend resource. A tag is a key-value pair that adds as a metadata to a resource used by Amazon Comprehend. For example, a tag with "Sales" as the key might be added to a resource to indicate its use by the sales department.

#untag_resource(options = {}) ⇒ Struct

Removes a specific tag associated with an Amazon Comprehend resource.

#update_endpoint(options = {}) ⇒ Struct

Updates information about the specified endpoint.

#wait_until(waiter_name, params = {}) {|waiter| ... } ⇒ Boolean

Waiters polls an API operation until a resource enters a desired state.

Basic Usage

Waiters will poll until they are succesful, they fail by entering a terminal state, or until a maximum number of attempts are made.

# polls in a loop, sleeping between attempts client.waiter_until(waiter_name, params)

Configuration

You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You configure waiters by passing a block to #wait_until:

# poll for ~25 seconds
client.wait_until(...) do |w|
  w.max_attempts = 5
  w.delay = 5
end
Callbacks

You can be notified before each polling attempt and before each delay. If you throw :success or :failure from these callbacks, it will terminate the waiter.

started_at = Time.now
client.wait_until(...) do |w|

  # disable max attempts
  w.max_attempts = nil

  # poll for 1 hour, instead of a number of attempts
  w.before_wait do |attempts, response|
    throw :failure if Time.now - started_at > 3600
  end

end
Handling Errors

When a waiter is successful, it returns true. When a waiter fails, it raises an error. All errors raised extend from Waiters::Errors::WaiterFailed.

begin
  client.wait_until(...)
rescue Aws::Waiters::Errors::WaiterFailed
  # resource did not enter the desired state in time
end
#waiter_names ⇒ Array<Symbol>

Returns the list of supported waiters. The following table lists the supported waiters and the client method they call:

Waiter Name Client Method Default Delay: Default Max Attempts:

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