Package com.azure.ai.textanalytics

Class TextAnalyticsAsyncClient

java.lang.Object
com.azure.ai.textanalytics.TextAnalyticsAsyncClient
public final class TextAnalyticsAsyncClient extends Object
This class provides an asynchronous client that contains all the operations that apply to Azure Text Analytics. Operations allowed by the client are language detection, entities recognition, linked entities recognition, key phrases extraction, and sentiment analysis of a document or a list of documents.

Getting Started

In order to interact with the Text Analytics features in Azure AI Language Service, you'll need to create an instance of the TextAnalyticsAsyncClient. To make this possible you'll need the key credential of the service. Alternatively, you can use AAD authentication via Azure Identity to connect to the service.

  1. Azure Key Credential, see AzureKeyCredential.
  2. Azure Active Directory, see TokenCredential.

Sample: Construct Asynchronous Text Analytics Client with Azure Key Credential

The following code sample demonstrates the creation of a TextAnalyticsAsyncClient, using the TextAnalyticsClientBuilder to configure it with a key credential.

 TextAnalyticsAsyncClient textAnalyticsAsyncClient = new TextAnalyticsClientBuilder()
     .credential(new AzureKeyCredential("{key}"))
     .endpoint("{endpoint}")
     .buildAsyncClient();

View TextAnalyticsClientBuilder for additional ways to construct the client.

See methods in client level class below to explore all features that library provides.


Extract information

Text Analytics client can use Natural Language Understanding (NLU) to extract information from unstructured text. For example, identify key phrases or Personally Identifiable, etc. Below you can look at the samples on how to use it.

Key Phrases Extraction

The extractKeyPhrases method can be used to extract key phrases, which returns a list of strings denoting the key phrases in the document. 

 textAnalyticsAsyncClient.extractKeyPhrases("Bonjour tout le monde").subscribe(keyPhrase ->
     System.out.printf("%s.%n", keyPhrase));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.

Named Entities Recognition(NER): Prebuilt Model

The recognizeEntities method can be used to recognize entities, which returns a list of general categorized entities in the provided document.

 String document = "Satya Nadella is the CEO of Microsoft";
 textAnalyticsAsyncClient.recognizeEntities(document)
     .subscribe(entityCollection -> entityCollection.forEach(entity ->
         System.out.printf("Recognized categorized entity: %s, category: %s, confidence score: %f.%n",
         entity.getText(),
         entity.getCategory(),
         entity.getConfidenceScore())));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.

Custom Named Entities Recognition(NER): Custom Model

The beginRecognizeCustomEntities(Iterable, String, String) method can be used to recognize custom entities, which returns a list of custom entities for the provided list of document.

 List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
             + "in oil and natural gas development on federal lands over the past six years has stretched the"
             + " staff of the BLM to a point that it has been unable to meet its environmental protection "
             + "responsibilities."
     );
 }
 textAnalyticsAsyncClient.beginRecognizeCustomEntities(documents, "{project_name}", "{deployment_name}")
     .flatMap(pollResult -> {
         RecognizeCustomEntitiesOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (RecognizeCustomEntitiesResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (RecognizeEntitiesResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (CategorizedEntity entity : documentResult.getEntities()) {
                         System.out.printf(
                             "\tText: %s, category: %s, confidence score: %f.%n",
                             entity.getText(), entity.getCategory(), entity.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.

Linked Entities Recognition

The recognizeLinkedEntities method can be used to find linked entities, which returns a list of recognized entities with links to a well-known knowledge base for the provided document.

 String document = "Old Faithful is a geyser at Yellowstone Park.";
 textAnalyticsAsyncClient.recognizeLinkedEntities(document).subscribe(
     linkedEntityCollection -> linkedEntityCollection.forEach(linkedEntity -> {
         System.out.println("Linked Entities:");
         System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
             linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(),
             linkedEntity.getDataSource());
         linkedEntity.getMatches().forEach(entityMatch -> System.out.printf(
             "Matched entity: %s, confidence score: %f.%n",
             entityMatch.getText(), entityMatch.getConfidenceScore()));
     }));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.

Personally Identifiable Information(PII) Entities Recognition

The recognizePiiEntities method can be used to recognize PII entities, which returns a list of Personally Identifiable Information(PII) entities in the provided document.

For a list of supported entity types, check: this.

 String document = "My SSN is 859-98-0987";
 textAnalyticsAsyncClient.recognizePiiEntities(document).subscribe(piiEntityCollection -> {
     System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
     piiEntityCollection.forEach(entity -> System.out.printf(
         "Recognized Personally Identifiable Information entity: %s, entity category: %s,"
             + " entity subcategory: %s, confidence score: %f.%n",
         entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
 });

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.

Text Analytics for Health: Prebuilt Model

The beginAnalyzeHealthcareEntities method can be used to analyze healthcare entities, entity data sources, and entity relations in a list of documents.

 List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add("The patient is a 54-year-old gentleman with a history of progressive angina "
         + "over the past several months.");
 }
 textAnalyticsAsyncClient.beginAnalyzeHealthcareEntities(documents)
     .flatMap(AsyncPollResponse::getFinalResult)
     .flatMap(pagedFlux -> pagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeHealthcareEntitiesResultCollection -> {
                 analyzeHealthcareEntitiesResultCollection.forEach(healthcareEntitiesResult -> {
                     System.out.println("document id = " + healthcareEntitiesResult.getId());
                     System.out.println("Document entities: ");
                     AtomicInteger ct = new AtomicInteger();
                     healthcareEntitiesResult.getEntities().forEach(healthcareEntity -> {
                         System.out.printf(
                             "\ti = %d, Text: %s, category: %s, confidence score: %f.%n",
                             ct.getAndIncrement(), healthcareEntity.getText(), healthcareEntity.getCategory(),
                             healthcareEntity.getConfidenceScore());

                         IterableStream<EntityDataSource> healthcareEntityDataSources =
                             healthcareEntity.getDataSources();
                         if (healthcareEntityDataSources != null) {
                             healthcareEntityDataSources.forEach(healthcareEntityLink -> System.out.printf(
                                 "\t\tEntity ID in data source: %s, data source: %s.%n",
                                 healthcareEntityLink.getEntityId(), healthcareEntityLink.getName()));
                         }
                     });
                     // Healthcare entity relation groups
                     healthcareEntitiesResult.getEntityRelations().forEach(entityRelation -> {
                         System.out.printf("\tRelation type: %s.%n", entityRelation.getRelationType());
                         entityRelation.getRoles().forEach(role -> {
                             final HealthcareEntity entity = role.getEntity();
                             System.out.printf("\t\tEntity text: %s, category: %s, role: %s.%n",
                                 entity.getText(), entity.getCategory(), role.getName());
                         });
                         System.out.printf("\tRelation confidence score: %f.%n",
                             entityRelation.getConfidenceScore());
                     });
                 });
             }));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.


Summarize text-based content: Document Summarization

Text Analytics client can use Natural Language Understanding (NLU) to summarize lengthy documents. For example, extractive or abstractive summarization. Below you can look at the samples on how to use it.

Extractive summarization

The beginExtractSummary method returns a list of extract summaries for the provided list of document.

This method is supported since service API version TextAnalyticsServiceVersion.V2023_04_01.

 List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
             + " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
             + " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
             + "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
             + " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
             + " (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code"
             + " as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear,"
             + " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
             + " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
             + " pretrained models that can jointly learn representations to support a broad range of downstream"
             + " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
             + " performance on benchmarks in conversational speech recognition, machine translation, "
             + "conversational question answering, machine reading comprehension, and image captioning. These"
             + " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
             + " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
             + "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
             + "foundational component of this aspiration, if grounded with external knowledge sources in "
             + "the downstream AI tasks.");
 }
 textAnalyticsAsyncClient.beginExtractSummary(documents)
     .flatMap(result -> {
         ExtractiveSummaryOperationDetail operationDetail = result.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationDetail.getCreatedAt(), operationDetail.getExpiresAt());
         return result.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux) // this unwrap the Mono<> of Mono<PagedFlux<T>> to return PagedFlux<T>
     .subscribe(
         resultCollection -> {
             for (ExtractiveSummaryResult documentResult : resultCollection) {
                 for (ExtractiveSummarySentence extractiveSummarySentence : documentResult.getSentences()) {
                     System.out.printf(
                         "Sentence text: %s, length: %d, offset: %d, rank score: %f.%n",
                         extractiveSummarySentence.getText(), extractiveSummarySentence.getLength(),
                         extractiveSummarySentence.getOffset(), extractiveSummarySentence.getRankScore());
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.

Abstractive summarization

The beginAbstractSummary method returns a list of abstractive summary for the provided list of document.

This method is supported since service API version TextAnalyticsServiceVersion.V2023_04_01.

 List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
             + " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
             + " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
             + "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
             + " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
             + " (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code"
             + " as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear,"
             + " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
             + " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
             + " pretrained models that can jointly learn representations to support a broad range of downstream"
             + " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
             + " performance on benchmarks in conversational speech recognition, machine translation, "
             + "conversational question answering, machine reading comprehension, and image captioning. These"
             + " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
             + " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
             + "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
             + "foundational component of this aspiration, if grounded with external knowledge sources in "
             + "the downstream AI tasks.");
 }
 textAnalyticsAsyncClient.beginAbstractSummary(documents)
     .flatMap(result -> {
         AbstractiveSummaryOperationDetail operationDetail = result.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationDetail.getCreatedAt(), operationDetail.getExpiresAt());
         return result.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux) // this unwrap the Mono<> of Mono<PagedFlux<T>> to return PagedFlux<T>
     .subscribe(
         resultCollection -> {
             for (AbstractiveSummaryResult documentResult : resultCollection) {
                 System.out.println("\tAbstractive summary sentences:");
                 for (AbstractiveSummary summarySentence : documentResult.getSummaries()) {
                     System.out.printf("\t\t Summary text: %s.%n", summarySentence.getText());
                     for (AbstractiveSummaryContext abstractiveSummaryContext : summarySentence.getContexts()) {
                         System.out.printf("\t\t offset: %d, length: %d%n",
                             abstractiveSummaryContext.getOffset(), abstractiveSummaryContext.getLength());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.


Classify Text

Text Analytics client can use Natural Language Understanding (NLU) to detect the language or classify the sentiment of text you have. For example, language detection, sentiment analysis, or custom text classification. Below you can look at the samples on how to use it.

Analyze Sentiment and Mine Text for Opinions

The analyzeSentiment(String, String, AnalyzeSentimentOptions) analyzeSentiment} method can be used to analyze sentiment on a given input text string, which returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).

 textAnalyticsAsyncClient.analyzeSentiment("The hotel was dark and unclean.", "en",
     new AnalyzeSentimentOptions().setIncludeOpinionMining(true))
     .subscribe(documentSentiment -> {
         for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
             System.out.printf("\tSentence sentiment: %s%n", sentenceSentiment.getSentiment());
             sentenceSentiment.getOpinions().forEach(opinion -> {
                 TargetSentiment targetSentiment = opinion.getTarget();
                 System.out.printf("\tTarget sentiment: %s, target text: %s%n",
                     targetSentiment.getSentiment(), targetSentiment.getText());
                 for (AssessmentSentiment assessmentSentiment : opinion.getAssessments()) {
                     System.out.printf("\t\t'%s' sentiment because of \"%s\". Is the assessment negated: %s.%n",
                         assessmentSentiment.getSentiment(), assessmentSentiment.getText(),
                         assessmentSentiment.isNegated());
                 }
             });
         }
     });

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.

Detect Language

The detectLanguage method returns the detected language and a confidence score between zero and one. Scores close to one indicate 100% certainty that the identified language is true.

This method will use the default country hint that sets up in TextAnalyticsClientBuilder.defaultCountryHint(String). If none is specified, service will use 'US' as the country hint.

 String document = "Bonjour tout le monde";
 textAnalyticsAsyncClient.detectLanguage(document).subscribe(detectedLanguage ->
     System.out.printf("Detected language name: %s, ISO 6391 Name: %s, confidence score: %f.%n",
         detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore()));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.

Single-Label Classification

The beginSingleLabelClassify method returns a list of single-label classification for the provided list of documents.

Note: this method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

 List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
             + "in oil and natural gas development on federal lands over the past six years has stretched the"
             + " staff of the BLM to a point that it has been unable to meet its environmental protection "
             + "responsibilities."
     );
 }
 // See the service documentation for regional support and how to train a model to classify your documents,
 // see https://aka.ms/azsdk/textanalytics/customfunctionalities
 textAnalyticsAsyncClient.beginSingleLabelClassify(documents,
         "{project_name}", "{deployment_name}")
     .flatMap(pollResult -> {
         ClassifyDocumentOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFluxAsyncPollResponse -> pagedFluxAsyncPollResponse.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (ClassifyDocumentResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (ClassifyDocumentResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (ClassificationCategory classification : documentResult.getClassifications()) {
                         System.out.printf("\tCategory: %s, confidence score: %f.%n",
                             classification.getCategory(), classification.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.

Multi-Label Classification

The beginMultiLabelClassify method returns a list of multi-label classification for the provided list of document.

Note: this method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

 List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "I need a reservation for an indoor restaurant in China. Please don't stop the music."
             + " Play music and add it to my playlist");
 }
 textAnalyticsAsyncClient.beginMultiLabelClassify(documents, "{project_name}", "{deployment_name}")
     .flatMap(pollResult -> {
         ClassifyDocumentOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFluxAsyncPollResponse -> pagedFluxAsyncPollResponse.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (ClassifyDocumentResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (ClassifyDocumentResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (ClassificationCategory classification : documentResult.getClassifications()) {
                         System.out.printf("\tCategory: %s, confidence score: %f.%n",
                             classification.getCategory(), classification.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.


Execute multiple actions

The beginAnalyzeActions method execute actions, such as, entities recognition, PII entities recognition, key phrases extraction, and etc, for a list of documents.

 List<String> documents = Arrays.asList(
     "Elon Musk is the CEO of SpaceX and Tesla.",
     "1", "My SSN is 859-98-0987"
 );
 textAnalyticsAsyncClient.beginAnalyzeActions(documents,
         new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
             .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
             .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()))
     .flatMap(AsyncPollResponse::getFinalResult)
     .flatMap(analyzeActionsResultPagedFlux -> analyzeActionsResultPagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeActionsResult -> {
                 analyzeActionsResult.getRecognizeEntitiesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(
                                 entitiesResult -> entitiesResult.getEntities().forEach(
                                     entity -> System.out.printf(
                                         "Recognized entity: %s, entity category: %s, entity subcategory: %s,"
                                             + " confidence score: %f.%n",
                                         entity.getText(), entity.getCategory(), entity.getSubcategory(),
                                         entity.getConfidenceScore())));
                         }
                     });
                 analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
                                 System.out.println("Extracted phrases:");
                                 extractKeyPhraseResult.getKeyPhrases()
                                     .forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
                             });
                         }
                     });
             }));

See this for supported languages in Text Analytics API.

Note: For synchronous sample, refer to TextAnalyticsClient.

See Also:

  • Method Details

    • getDefaultCountryHint

      public String getDefaultCountryHint()
      Gets default country hint code.
      Returns:
      The default country hint code

    • getDefaultLanguage

      public String getDefaultLanguage()
      Gets default language when the builder is setup.
      Returns:
      The default language

    • detectLanguage

      public Mono<DetectedLanguage> detectLanguage(String document)
      Returns the detected language and a confidence score between zero and one. Scores close to one indicate 100% certainty that the identified language is true. This method will use the default country hint that sets up in TextAnalyticsClientBuilder.defaultCountryHint(String). If none is specified, service will use 'US' as the country hint.

      Code sample

      Detects language in a document. Subscribes to the call asynchronously and prints out the detected language details when a response is received.

       String document = "Bonjour tout le monde";
 textAnalyticsAsyncClient.detectLanguage(document).subscribe(detectedLanguage ->
     System.out.printf("Detected language name: %s, ISO 6391 Name: %s, confidence score: %f.%n",
         detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore()));
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono containing the detected language of the document.
      Throws:
      NullPointerException - if the document is null.
      TextAnalyticsException - if the response returned with an error.

    • detectLanguage

      public Mono<DetectedLanguage> detectLanguage(String document, String countryHint)
      Returns a Response contains the detected language and a confidence score between zero and one. Scores close to one indicate 100% certainty that the identified language is true.

      Code sample

      Detects language with http response in a document with a provided country hint. Subscribes to the call asynchronously and prints out the detected language details when a response is received.

       String document = "This text is in English";
 String countryHint = "US";
 textAnalyticsAsyncClient.detectLanguage(document, countryHint).subscribe(detectedLanguage ->
     System.out.printf("Detected language name: %s, ISO 6391 Name: %s, confidence score: %f.%n",
         detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore()));
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      countryHint - Accepts 2-letter country codes specified by ISO 3166-1 alpha-2. Defaults to "US" if not specified. To remove this behavior you can reset this parameter by setting this value to empty string countryHint = "" or "none".
      Returns:
      A Mono contains a detected language of the document.
      Throws:
      NullPointerException - if the document is null.
      TextAnalyticsException - if the response returned with an error.

    • detectLanguageBatch

      public Mono<DetectLanguageResultCollection> detectLanguageBatch(Iterable<String> documents, String countryHint, TextAnalyticsRequestOptions options)
      Returns the detected language for each of documents with the provided country hint and request option.

      Code sample

      Detects language in a list of documents with a provided country hint and request option for the batch. Subscribes to the call asynchronously and prints out the detected language details when a response is received. 

       List<String> documents = Arrays.asList(
     "This is written in English",
     "Este es un documento  escrito en Español."
 );
 textAnalyticsAsyncClient.detectLanguageBatch(documents, "US", null).subscribe(
     batchResult -> {
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = batchResult.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
         // Batch result of languages
         for (DetectLanguageResult detectLanguageResult : batchResult) {
             DetectedLanguage detectedLanguage = detectLanguageResult.getPrimaryLanguage();
             System.out.printf("Detected language name: %s, ISO 6391 Name: %s, confidence score: %f.%n",
                 detectedLanguage.getName(), detectedLanguage.getIso6391Name(),
                 detectedLanguage.getConfidenceScore());
         }
     });
      Parameters:
      documents - The list of documents to detect languages for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      countryHint - Accepts two letter country codes specified by ISO 3166-1 alpha-2. Defaults to "US" if not specified. To remove this behavior you can reset this parameter by setting this value to empty string countryHint = "" or "none".
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a DetectLanguageResultCollection.
      Throws:
      NullPointerException - if documents is null.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs is only available for API version v3.1 and newer.

    • detectLanguageBatchWithResponse

      public Mono<com.azure.core.http.rest.Response<DetectLanguageResultCollection>> detectLanguageBatchWithResponse(Iterable<DetectLanguageInput> documents, TextAnalyticsRequestOptions options)
      Returns the detected language for a batch of document with provided request options.

      Code sample

      Detects language in a batch of document with provided request options. Subscribes to the call asynchronously and prints out the detected language details when a response is received.

       List<DetectLanguageInput> detectLanguageInputs1 = Arrays.asList(
     new DetectLanguageInput("1", "This is written in English.", "US"),
     new DetectLanguageInput("2", "Este es un documento  escrito en Español.", "ES")
 );

 TextAnalyticsRequestOptions requestOptions = new TextAnalyticsRequestOptions().setIncludeStatistics(true);

 textAnalyticsAsyncClient.detectLanguageBatchWithResponse(detectLanguageInputs1, requestOptions)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());

         DetectLanguageResultCollection resultCollection = response.getValue();
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
         // Batch result of languages
         for (DetectLanguageResult detectLanguageResult : resultCollection) {
             DetectedLanguage detectedLanguage = detectLanguageResult.getPrimaryLanguage();
             System.out.printf("Detected language name: %s, ISO 6391 Name: %s, confidence score: %f.%n",
                 detectedLanguage.getName(), detectedLanguage.getIso6391Name(),
                 detectedLanguage.getConfidenceScore());
         }
     });
      Parameters:
      documents - The list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a Response which contains a DetectLanguageResultCollection.
      Throws:
      NullPointerException - if documents is null.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs is only available for API version v3.1 and newer.

    • recognizeEntities

      public Mono<CategorizedEntityCollection> recognizeEntities(String document)
      Returns a list of general categorized entities in the provided document. For a list of supported entity types, check: this. For a list of enabled languages, check: this. This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code sample

      Recognize entities in a document. Subscribes to the call asynchronously and prints out the recognized entity details when a response is received.

       String document = "Satya Nadella is the CEO of Microsoft";
 textAnalyticsAsyncClient.recognizeEntities(document)
     .subscribe(entityCollection -> entityCollection.forEach(entity ->
         System.out.printf("Recognized categorized entity: %s, category: %s, confidence score: %f.%n",
         entity.getText(),
         entity.getCategory(),
         entity.getConfidenceScore())));
      Parameters:
      document - The document to recognize entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono contains a recognized categorized entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizeEntities

      public Mono<CategorizedEntityCollection> recognizeEntities(String document, String language)
      Returns a list of general categorized entities in the provided document. For a list of supported entity types, check: this. For a list of enabled languages, check: this.

      Code sample

      Recognize entities in a document with provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       String document = "Satya Nadella is the CEO of Microsoft";
 textAnalyticsAsyncClient.recognizeEntities(document, "en")
     .subscribe(entityCollection -> entityCollection.forEach(entity ->
         System.out.printf("Recognized categorized entity: %s, category: %s, confidence score: %f.%n",
         entity.getText(),
         entity.getCategory(),
         entity.getConfidenceScore())));
      Parameters:
      document - the text to recognize entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      Returns:
      A Mono contains a recognized categorized entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizeEntitiesBatch

      public Mono<RecognizeEntitiesResultCollection> recognizeEntitiesBatch(Iterable<String> documents, String language, TextAnalyticsRequestOptions options)
      Returns a list of general categorized entities for the provided list of documents with the provided language code and request options.

      Code sample

      Recognize entities in a document with the provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<String> documents = Arrays.asList(
     "I had a wonderful trip to Seattle last week.", "I work at Microsoft.");

 textAnalyticsAsyncClient.recognizeEntitiesBatch(documents, "en", null)
     .subscribe(batchResult -> {
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = batchResult.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
         // Batch Result of entities
         batchResult.forEach(recognizeEntitiesResult ->
             recognizeEntitiesResult.getEntities().forEach(entity -> System.out.printf(
                 "Recognized categorized entity: %s, category: %s, confidence score: %f.%n",
                     entity.getText(), entity.getCategory(), entity.getConfidenceScore())));
     });
      Parameters:
      documents - A list of documents to recognize entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a RecognizeEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs is only available for API version v3.1 and newer.

    • recognizeEntitiesBatchWithResponse

      public Mono<com.azure.core.http.rest.Response<RecognizeEntitiesResultCollection>> recognizeEntitiesBatchWithResponse(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options)
      Returns a list of general categorized entities for the provided list of document with provided request options.

      Code sample

      Recognize entities in a list of document. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "I had a wonderful trip to Seattle last week.").setLanguage("en"),
     new TextDocumentInput("1", "I work at Microsoft.").setLanguage("en"));

 TextAnalyticsRequestOptions requestOptions = new TextAnalyticsRequestOptions().setIncludeStatistics(true);

 textAnalyticsAsyncClient.recognizeEntitiesBatchWithResponse(textDocumentInputs1, requestOptions)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());
         RecognizeEntitiesResultCollection resultCollection = response.getValue();

         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         resultCollection.forEach(recognizeEntitiesResult ->
             recognizeEntitiesResult.getEntities().forEach(entity -> System.out.printf(
                 "Recognized categorized entity: %s, category: %s, confidence score: %f.%n",
                 entity.getText(),
                 entity.getCategory(),
                 entity.getConfidenceScore())));
     });
      Parameters:
      documents - A list of documents to recognize entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a Response which contains a RecognizeEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs is only available for API version v3.1 and newer.

    • recognizePiiEntities

      public Mono<PiiEntityCollection> recognizePiiEntities(String document)
      Returns a list of Personally Identifiable Information(PII) entities in the provided document. For a list of supported entity types, check: this. For a list of enabled languages, check: this. This method will use the default language that is set using TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code sample

      Recognize the PII entities details in a document. Subscribes to the call asynchronously and prints out the recognized entity details when a response is received.

       String document = "My SSN is 859-98-0987";
 textAnalyticsAsyncClient.recognizePiiEntities(document).subscribe(piiEntityCollection -> {
     System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
     piiEntityCollection.forEach(entity -> System.out.printf(
         "Recognized Personally Identifiable Information entity: %s, entity category: %s,"
             + " entity subcategory: %s, confidence score: %f.%n",
         entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
 });
      Parameters:
      document - The document to recognize PII entities details for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono contains a recognized PII entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
      UnsupportedOperationException - if recognizePiiEntities is called with service API version TextAnalyticsServiceVersion.V3_0. recognizePiiEntities is only available for API version v3.1 and newer.

    • recognizePiiEntities

      public Mono<PiiEntityCollection> recognizePiiEntities(String document, String language)
      Returns a list of Personally Identifiable Information(PII) entities in the provided document with provided language code. For a list of supported entity types, check: this. For a list of enabled languages, check: this.

      Code sample

      Recognize the PII entities details in a document with provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       String document = "My SSN is 859-98-0987";
 textAnalyticsAsyncClient.recognizePiiEntities(document, "en")
     .subscribe(piiEntityCollection -> {
         System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
         piiEntityCollection.forEach(entity -> System.out.printf(
             "Recognized Personally Identifiable Information entity: %s, entity category: %s,"
                 + " entity subcategory: %s, confidence score: %f.%n",
             entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
     });
      Parameters:
      document - the text to recognize PII entities details for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      Returns:
      A Mono contains a recognized PII entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
      UnsupportedOperationException - if recognizePiiEntities is called with service API version TextAnalyticsServiceVersion.V3_0. recognizePiiEntities is only available for API version v3.1 and newer.

    • recognizePiiEntities

      public Mono<PiiEntityCollection> recognizePiiEntities(String document, String language, RecognizePiiEntitiesOptions options)
      Returns a list of Personally Identifiable Information(PII) entities in the provided document with provided language code. For a list of supported entity types, check: this. For a list of enabled languages, check: this.

      Code sample

      Recognize the PII entities details in a document with provided language code and RecognizePiiEntitiesOptions. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       String document = "My SSN is 859-98-0987";
 textAnalyticsAsyncClient.recognizePiiEntities(document, "en",
     new RecognizePiiEntitiesOptions().setDomainFilter(PiiEntityDomain.PROTECTED_HEALTH_INFORMATION))
     .subscribe(piiEntityCollection -> {
         System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
         piiEntityCollection.forEach(entity -> System.out.printf(
             "Recognized Personally Identifiable Information entity: %s, entity category: %s,"
                 + " entity subcategory: %s, confidence score: %f.%n",
             entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
     });
      Parameters:
      document - the text to recognize PII entities details for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2-letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when recognizing PII entities.
      Returns:
      A Mono contains a recognized PII entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
      UnsupportedOperationException - if recognizePiiEntities is called with service API version TextAnalyticsServiceVersion.V3_0. recognizePiiEntities is only available for API version v3.1 and newer.

    • recognizePiiEntitiesBatch

      public Mono<RecognizePiiEntitiesResultCollection> recognizePiiEntitiesBatch(Iterable<String> documents, String language, RecognizePiiEntitiesOptions options)
      Returns a list of Personally Identifiable Information(PII) entities for the provided list of documents with the provided language code and request options.

      Code sample

      Recognize Personally Identifiable Information entities in a document with the provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<String> documents = Arrays.asList(
     "My SSN is 859-98-0987.",
     "Visa card 0111 1111 1111 1111."
 );

 // Show statistics and model version
 RecognizePiiEntitiesOptions requestOptions = new RecognizePiiEntitiesOptions().setIncludeStatistics(true)
     .setModelVersion("latest");

 textAnalyticsAsyncClient.recognizePiiEntitiesBatch(documents, "en", requestOptions)
     .subscribe(piiEntitiesResults -> {
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = piiEntitiesResults.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         piiEntitiesResults.forEach(recognizePiiEntitiesResult -> {
             PiiEntityCollection piiEntityCollection = recognizePiiEntitiesResult.getEntities();
             System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
             piiEntityCollection.forEach(entity -> System.out.printf(
                 "Recognized Personally Identifiable Information entity: %s, entity category: %s,"
                     + " entity subcategory: %s, confidence score: %f.%n",
                 entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
         });
     });
      Parameters:
      documents - A list of documents to recognize PII entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when recognizing PII entities.
      Returns:
      A Mono contains a RecognizePiiEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if recognizePiiEntitiesBatch is called with service API version TextAnalyticsServiceVersion.V3_0. recognizePiiEntitiesBatch is only available for API version v3.1 and newer.

    • recognizePiiEntitiesBatchWithResponse

      public Mono<com.azure.core.http.rest.Response<RecognizePiiEntitiesResultCollection>> recognizePiiEntitiesBatchWithResponse(Iterable<TextDocumentInput> documents, RecognizePiiEntitiesOptions options)
      Returns a list of Personally Identifiable Information entities for the provided list of document with provided request options.

      Code sample

      Recognize the PII entities details with http response in a list of document with provided request options. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "My SSN is 859-98-0987."),
     new TextDocumentInput("1", "Visa card 0111 1111 1111 1111."));

 // Show statistics and model version
 RecognizePiiEntitiesOptions requestOptions = new RecognizePiiEntitiesOptions().setIncludeStatistics(true)
     .setModelVersion("latest");

 textAnalyticsAsyncClient.recognizePiiEntitiesBatchWithResponse(textDocumentInputs1, requestOptions)
     .subscribe(response -> {
         RecognizePiiEntitiesResultCollection piiEntitiesResults = response.getValue();
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = piiEntitiesResults.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         piiEntitiesResults.forEach(recognizePiiEntitiesResult -> {
             PiiEntityCollection piiEntityCollection = recognizePiiEntitiesResult.getEntities();
             System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
             piiEntityCollection.forEach(entity -> System.out.printf(
                 "Recognized Personally Identifiable Information entity: %s, entity category: %s,"
                     + " entity subcategory: %s, confidence score: %f.%n",
                 entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
         });
     });
      Parameters:
      documents - A list of documents to recognize PII entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The additional configurable options that may be passed when recognizing PII entities.
      Returns:
      A Mono contains a Response which contains a RecognizePiiEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if recognizePiiEntitiesBatchWithResponse is called with service API version TextAnalyticsServiceVersion.V3_0. recognizePiiEntitiesBatchWithResponse is only available for API version v3.1 and newer.

    • recognizeLinkedEntities

      public Mono<LinkedEntityCollection> recognizeLinkedEntities(String document)
      Returns a list of recognized entities with links to a well-known knowledge base for the provided document. See this for supported languages in Text Analytics API. This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Recognize linked entities in a document. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       String document = "Old Faithful is a geyser at Yellowstone Park.";
 textAnalyticsAsyncClient.recognizeLinkedEntities(document).subscribe(
     linkedEntityCollection -> linkedEntityCollection.forEach(linkedEntity -> {
         System.out.println("Linked Entities:");
         System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
             linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(),
             linkedEntity.getDataSource());
         linkedEntity.getMatches().forEach(entityMatch -> System.out.printf(
             "Matched entity: %s, confidence score: %f.%n",
             entityMatch.getText(), entityMatch.getConfidenceScore()));
     }));
      Parameters:
      document - The document to recognize linked entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono contains a recognized linked entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizeLinkedEntities

      public Mono<LinkedEntityCollection> recognizeLinkedEntities(String document, String language)
      Returns a list of recognized entities with links to a well-known knowledge base for the provided document. See this for supported languages in Text Analytics API.

      Recognize linked entities in a text with provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       String document = "Old Faithful is a geyser at Yellowstone Park.";
 textAnalyticsAsyncClient.recognizeLinkedEntities(document, "en").subscribe(
     linkedEntityCollection -> linkedEntityCollection.forEach(linkedEntity -> {
         System.out.println("Linked Entities:");
         System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
             linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(),
             linkedEntity.getDataSource());
         linkedEntity.getMatches().forEach(entityMatch -> System.out.printf(
             "Matched entity: %s, confidence score: %f.%n",
             entityMatch.getText(), entityMatch.getConfidenceScore()));
     }));
      Parameters:
      document - The document to recognize linked entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the document. If not set, uses "en" for English as default.
      Returns:
      A Mono contains a recognized linked entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizeLinkedEntitiesBatch

      public Mono<RecognizeLinkedEntitiesResultCollection> recognizeLinkedEntitiesBatch(Iterable<String> documents, String language, TextAnalyticsRequestOptions options)
      Returns a list of recognized entities with links to a well-known knowledge base for the list of documents with provided language code and request options. See this for supported languages in Text Analytics API.

      Recognize linked entities in a list of documents with provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<String> documents = Arrays.asList(
     "Old Faithful is a geyser at Yellowstone Park.",
     "Mount Shasta has lenticular clouds."
 );

 textAnalyticsAsyncClient.recognizeLinkedEntitiesBatch(documents, "en", null)
     .subscribe(batchResult -> {
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = batchResult.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         batchResult.forEach(recognizeLinkedEntitiesResult ->
             recognizeLinkedEntitiesResult.getEntities().forEach(linkedEntity -> {
                 System.out.println("Linked Entities:");
                 System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
                     linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(),
                     linkedEntity.getDataSource());
                 linkedEntity.getMatches().forEach(entityMatch -> System.out.printf(
                     "Matched entity: %s, confidence score: %f.%n",
                     entityMatch.getText(), entityMatch.getConfidenceScore()));
             }));
     });
      Parameters:
      documents - A list of documents to recognize linked entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the text. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a RecognizeLinkedEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs is only available for API version v3.1 and newer.

    • recognizeLinkedEntitiesBatchWithResponse

      public Mono<com.azure.core.http.rest.Response<RecognizeLinkedEntitiesResultCollection>> recognizeLinkedEntitiesBatchWithResponse(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options)
      Returns a list of recognized entities with links to a well-known knowledge base for the list of document with provided request options. See this supported languages in Language service API.

      Recognize linked entities in a list of document and provided request options to show statistics. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "Old Faithful is a geyser at Yellowstone Park.").setLanguage("en"),
     new TextDocumentInput("1", "Mount Shasta has lenticular clouds.").setLanguage("en"));

 TextAnalyticsRequestOptions requestOptions = new TextAnalyticsRequestOptions().setIncludeStatistics(true);

 textAnalyticsAsyncClient.recognizeLinkedEntitiesBatchWithResponse(textDocumentInputs1, requestOptions)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());
         RecognizeLinkedEntitiesResultCollection resultCollection = response.getValue();

         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         resultCollection.forEach(recognizeLinkedEntitiesResult ->
             recognizeLinkedEntitiesResult.getEntities().forEach(linkedEntity -> {
                 System.out.println("Linked Entities:");
                 System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
                     linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(),
                     linkedEntity.getDataSource());
                 linkedEntity.getMatches().forEach(entityMatch -> System.out.printf(
                     "Matched entity: %s, confidence score: %.2f.%n",
                     entityMatch.getText(), entityMatch.getConfidenceScore()));
             }));
     });
      Parameters:
      documents - A list of documents to recognize linked entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a Response which contains a RecognizeLinkedEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs is only available for API version v3.1 and newer.

    • extractKeyPhrases

      public Mono<KeyPhrasesCollection> extractKeyPhrases(String document)
      Returns a list of strings denoting the key phrases in the document. This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Extract key phrases in a document. Subscribes to the call asynchronously and prints out the key phrases when a response is received.

       textAnalyticsAsyncClient.extractKeyPhrases("Bonjour tout le monde").subscribe(keyPhrase ->
     System.out.printf("%s.%n", keyPhrase));
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono contains a KeyPhrasesCollection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • extractKeyPhrases

      public Mono<KeyPhrasesCollection> extractKeyPhrases(String document, String language)
      Returns a list of strings denoting the key phrases in the document. See this for the list of enabled languages.

      Extract key phrases in a document with a provided language code. Subscribes to the call asynchronously and prints out the key phrases when a response is received.

       System.out.println("Extracted phrases:");
 textAnalyticsAsyncClient.extractKeyPhrases("Bonjour tout le monde", "fr")
     .subscribe(keyPhrase -> System.out.printf("%s.%n", keyPhrase));
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the text. If not set, uses "en" for English as default.
      Returns:
      A Mono contains a KeyPhrasesCollection
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • extractKeyPhrasesBatch

      public Mono<ExtractKeyPhrasesResultCollection> extractKeyPhrasesBatch(Iterable<String> documents, String language, TextAnalyticsRequestOptions options)
      Returns a list of strings denoting the key phrases in the document with provided language code and request options. See this for the list of enabled languages.

      Extract key phrases in a list of documents with a provided language and request options. Subscribes to the call asynchronously and prints out the key phrases when a response is received.

       List<String> documents = Arrays.asList(
     "Hello world. This is some input text that I love.",
     "Bonjour tout le monde");

 textAnalyticsAsyncClient.extractKeyPhrasesBatch(documents, "en", null).subscribe(
     extractKeyPhraseResults -> {
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = extractKeyPhraseResults.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         extractKeyPhraseResults.forEach(extractKeyPhraseResult -> {
             System.out.println("Extracted phrases:");
             extractKeyPhraseResult.getKeyPhrases().forEach(keyPhrase -> System.out.printf("%s.%n", keyPhrase));
         });
     });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the text. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a ExtractKeyPhrasesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs is only available for API version v3.1 and newer.

    • extractKeyPhrasesBatchWithResponse

      public Mono<com.azure.core.http.rest.Response<ExtractKeyPhrasesResultCollection>> extractKeyPhrasesBatchWithResponse(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options)
      Returns a list of strings denoting the key phrases in the document with provided request options. See this for the list of enabled languages.

      Extract key phrases in a list of document with provided request options. Subscribes to the call asynchronously and prints out the key phrases when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "I had a wonderful trip to Seattle last week.").setLanguage("en"),
     new TextDocumentInput("1", "I work at Microsoft.").setLanguage("en"));

 TextAnalyticsRequestOptions requestOptions = new TextAnalyticsRequestOptions().setIncludeStatistics(true);

 textAnalyticsAsyncClient.extractKeyPhrasesBatchWithResponse(textDocumentInputs1, requestOptions)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());
         ExtractKeyPhrasesResultCollection resultCollection = response.getValue();

         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         for (ExtractKeyPhraseResult extractKeyPhraseResult : resultCollection) {
             System.out.println("Extracted phrases:");
             for (String keyPhrase : extractKeyPhraseResult.getKeyPhrases()) {
                 System.out.printf("%s.%n", keyPhrase);
             }
         }
     });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a Response that contains a ExtractKeyPhrasesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs is only available for API version v3.1 and newer.

    • analyzeSentiment

      public Mono<DocumentSentiment> analyzeSentiment(String document)
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code Sample

      Analyze the sentiment in a document. Subscribes to the call asynchronously and prints out the sentiment details when a response is received.

       String document = "The hotel was dark and unclean.";
 textAnalyticsAsyncClient.analyzeSentiment(document).subscribe(documentSentiment -> {
     System.out.printf("Recognized document sentiment: %s.%n", documentSentiment.getSentiment());

     for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
         System.out.printf(
             "Recognized sentence sentiment: %s, positive score: %.2f, neutral score: %.2f, "
                 + "negative score: %.2f.%n",
             sentenceSentiment.getSentiment(),
             sentenceSentiment.getConfidenceScores().getPositive(),
             sentenceSentiment.getConfidenceScores().getNeutral(),
             sentenceSentiment.getConfidenceScores().getNegative());
     }
 });
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono contains the analyzed document sentiment of the document.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • analyzeSentiment

      public Mono<DocumentSentiment> analyzeSentiment(String document, String language)
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it.

      Code Sample

      Analyze the sentiments in a document with a provided language representation. Subscribes to the call asynchronously and prints out the sentiment details when a response is received.

       String document = "The hotel was dark and unclean.";
 textAnalyticsAsyncClient.analyzeSentiment(document, "en")
     .subscribe(documentSentiment -> {
         System.out.printf("Recognized sentiment label: %s.%n", documentSentiment.getSentiment());
         for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
             System.out.printf("Recognized sentence sentiment: %s, positive score: %.2f, neutral score: %.2f, "
                     + "negative score: %.2f.%n",
                 sentenceSentiment.getSentiment(),
                 sentenceSentiment.getConfidenceScores().getPositive(),
                 sentenceSentiment.getConfidenceScores().getNeutral(),
                 sentenceSentiment.getConfidenceScores().getNegative());
         }
     });
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the text. If not set, uses "en" for English as default.
      Returns:
      A Mono contains the analyzed document sentiment of the document.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • analyzeSentiment

      public Mono<DocumentSentiment> analyzeSentiment(String document, String language, AnalyzeSentimentOptions options)
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).

      Code Sample

      Analyze the sentiment and mine the opinions for each sentence in a document with a provided language representation and AnalyzeSentimentOptions options. Subscribes to the call asynchronously and prints out the sentiment and sentence opinions details when a response is received.

       textAnalyticsAsyncClient.analyzeSentiment("The hotel was dark and unclean.", "en",
     new AnalyzeSentimentOptions().setIncludeOpinionMining(true))
     .subscribe(documentSentiment -> {
         for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
             System.out.printf("\tSentence sentiment: %s%n", sentenceSentiment.getSentiment());
             sentenceSentiment.getOpinions().forEach(opinion -> {
                 TargetSentiment targetSentiment = opinion.getTarget();
                 System.out.printf("\tTarget sentiment: %s, target text: %s%n",
                     targetSentiment.getSentiment(), targetSentiment.getText());
                 for (AssessmentSentiment assessmentSentiment : opinion.getAssessments()) {
                     System.out.printf("\t\t'%s' sentiment because of \"%s\". Is the assessment negated: %s.%n",
                         assessmentSentiment.getSentiment(), assessmentSentiment.getText(),
                         assessmentSentiment.isNegated());
                 }
             });
         }
     });
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the text. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing sentiments.
      Returns:
      A Mono contains the analyzed document sentiment of the document.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() or AnalyzeSentimentOptions.isIncludeOpinionMining() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs and includeOpinionMining are only available for API version v3.1 and newer.

    • analyzeSentimentBatch

      @Deprecated public Mono<AnalyzeSentimentResultCollection> analyzeSentimentBatch(Iterable<String> documents, String language, TextAnalyticsRequestOptions options)
      Deprecated.
      Please use the analyzeSentimentBatch(Iterable, String, AnalyzeSentimentOptions).
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it.

      Analyze sentiment in a list of documents with provided language code and request options. Subscribes to the call asynchronously and prints out the sentiment details when a response is received.

       List<String> documents = Arrays.asList(
     "The hotel was dark and unclean.",
     "The restaurant had amazing gnocchi."
 );

 textAnalyticsAsyncClient.analyzeSentimentBatch(documents, "en",
     new TextAnalyticsRequestOptions().setIncludeStatistics(true)).subscribe(
         response -> {
             // Batch statistics
             TextDocumentBatchStatistics batchStatistics = response.getStatistics();
             System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
                 batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

             response.forEach(analyzeSentimentResult -> {
                 System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
                 DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
                 System.out.printf("Recognized document sentiment: %s.%n", documentSentiment.getSentiment());
                 documentSentiment.getSentences().forEach(sentenceSentiment ->
                     System.out.printf("Recognized sentence sentiment: %s, positive score: %.2f, "
                             + "neutral score: %.2f, negative score: %.2f.%n",
                         sentenceSentiment.getSentiment(),
                         sentenceSentiment.getConfidenceScores().getPositive(),
                         sentenceSentiment.getConfidenceScores().getNeutral(),
                         sentenceSentiment.getConfidenceScores().getNegative()));
             });
         });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the document. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.

    • analyzeSentimentBatch

      public Mono<AnalyzeSentimentResultCollection> analyzeSentimentBatch(Iterable<String> documents, String language, AnalyzeSentimentOptions options)
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).

      Code Sample

      Analyze the sentiments and mine the opinions for each sentence in a list of documents with a provided language representation and AnalyzeSentimentOptions options. Subscribes to the call asynchronously and prints out the sentiment and sentence opinions details when a response is received.

       List<TextDocumentInput> documents = Arrays.asList(
     new TextDocumentInput("0", "Elon Musk is the CEO of SpaceX and Tesla.").setLanguage("en"),
     new TextDocumentInput("1", "My SSN is 859-98-0987").setLanguage("en")
 );

 SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
     textAnalyticsClient.beginAnalyzeActions(
         documents,
         new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
            .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
            .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
         new AnalyzeActionsOptions().setIncludeStatistics(false),
         Context.NONE);
 syncPoller.waitForCompletion();
 AnalyzeActionsResultPagedIterable result = syncPoller.getFinalResult();
 result.forEach(analyzeActionsResult -> {
     System.out.println("Entities recognition action results:");
     analyzeActionsResult.getRecognizeEntitiesResults().forEach(
         actionResult -> {
             if (!actionResult.isError()) {
                 actionResult.getDocumentsResults().forEach(
                     entitiesResult -> entitiesResult.getEntities().forEach(
                         entity -> System.out.printf(
                             "Recognized entity: %s, entity category: %s, entity subcategory: %s,"
                                 + " confidence score: %f.%n",
                             entity.getText(), entity.getCategory(), entity.getSubcategory(),
                             entity.getConfidenceScore())));
             }
         });
     System.out.println("Key phrases extraction action results:");
     analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
         actionResult -> {
             if (!actionResult.isError()) {
                 actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
                     System.out.println("Extracted phrases:");
                     extractKeyPhraseResult.getKeyPhrases()
                         .forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
                 });
             }
         });
 });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the document. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing sentiments.
      Returns:
      A Mono contains a AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() or AnalyzeSentimentOptions.isIncludeOpinionMining() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs and includeOpinionMining are only available for API version v3.1 and newer.

    • analyzeSentimentBatchWithResponse

      @Deprecated public Mono<com.azure.core.http.rest.Response<AnalyzeSentimentResultCollection>> analyzeSentimentBatchWithResponse(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options)
      Deprecated.
      Please use the analyzeSentimentBatchWithResponse(Iterable, AnalyzeSentimentOptions).
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it.

      Analyze sentiment in a list of document with provided request options. Subscribes to the call asynchronously and prints out the sentiment details when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "The hotel was dark and unclean.").setLanguage("en"),
     new TextDocumentInput("1", "The restaurant had amazing gnocchi.").setLanguage("en"));

 TextAnalyticsRequestOptions requestOptions = new TextAnalyticsRequestOptions().setIncludeStatistics(true);

 textAnalyticsAsyncClient.analyzeSentimentBatchWithResponse(textDocumentInputs1, requestOptions)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());
         AnalyzeSentimentResultCollection resultCollection = response.getValue();

         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(),
             batchStatistics.getValidDocumentCount());

         resultCollection.forEach(analyzeSentimentResult -> {
             System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
             DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
             System.out.printf("Recognized document sentiment: %s.%n", documentSentiment.getSentiment());
             documentSentiment.getSentences().forEach(sentenceSentiment ->
                 System.out.printf("Recognized sentence sentiment: %s, positive score: %.2f, "
                         + "neutral score: %.2f, negative score: %.2f.%n",
                     sentenceSentiment.getSentiment(),
                     sentenceSentiment.getConfidenceScores().getPositive(),
                     sentenceSentiment.getConfidenceScores().getNeutral(),
                     sentenceSentiment.getConfidenceScores().getNegative()));
         });
     });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a Response that contains a AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.

    • analyzeSentimentBatchWithResponse

      public Mono<com.azure.core.http.rest.Response<AnalyzeSentimentResultCollection>> analyzeSentimentBatchWithResponse(Iterable<TextDocumentInput> documents, AnalyzeSentimentOptions options)
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).

      Code Sample

      Analyze sentiment and mine the opinions for each sentence in a list of document with provided AnalyzeSentimentOptions options. Subscribes to the call asynchronously and prints out the sentiment and sentence opinions details when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "The hotel was dark and unclean.").setLanguage("en"),
     new TextDocumentInput("1", "The restaurant had amazing gnocchi.").setLanguage("en"));

 AnalyzeSentimentOptions options = new AnalyzeSentimentOptions()
     .setIncludeOpinionMining(true).setIncludeStatistics(true);
 textAnalyticsAsyncClient.analyzeSentimentBatchWithResponse(textDocumentInputs1, options)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());
         AnalyzeSentimentResultCollection resultCollection = response.getValue();

         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(),
             batchStatistics.getValidDocumentCount());

         resultCollection.forEach(analyzeSentimentResult -> {
             System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
             DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
             documentSentiment.getSentences().forEach(sentenceSentiment -> {
                 System.out.printf("\tSentence sentiment: %s%n", sentenceSentiment.getSentiment());
                 sentenceSentiment.getOpinions().forEach(opinion -> {
                     TargetSentiment targetSentiment = opinion.getTarget();
                     System.out.printf("\t\tTarget sentiment: %s, target text: %s%n",
                         targetSentiment.getSentiment(), targetSentiment.getText());
                     for (AssessmentSentiment assessmentSentiment : opinion.getAssessments()) {
                         System.out.printf(
                             "\t\t\t'%s' assessment sentiment because of \"%s\". Is the assessment negated: %s.%n",
                             assessmentSentiment.getSentiment(), assessmentSentiment.getText(),
                             assessmentSentiment.isNegated());
                     }
                 });
             });
         });
     });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The additional configurable options that may be passed when analyzing sentiments.
      Returns:
      A Mono contains a Response that contains a AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if TextAnalyticsRequestOptions.isServiceLogsDisabled() or AnalyzeSentimentOptions.isIncludeOpinionMining() is true in service API version TextAnalyticsServiceVersion.V3_0. disableServiceLogs and includeOpinionMining are only available for API version v3.1 and newer.

    • beginAnalyzeHealthcareEntities

      public com.azure.core.util.polling.PollerFlux<AnalyzeHealthcareEntitiesOperationDetail,AnalyzeHealthcareEntitiesPagedFlux> beginAnalyzeHealthcareEntities(Iterable<String> documents)
      Analyze healthcare entities, entity data sources, and entity relations in a list of documents. This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add("The patient is a 54-year-old gentleman with a history of progressive angina "
         + "over the past several months.");
 }
 textAnalyticsAsyncClient.beginAnalyzeHealthcareEntities(documents)
     .flatMap(AsyncPollResponse::getFinalResult)
     .flatMap(pagedFlux -> pagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeHealthcareEntitiesResultCollection -> {
                 analyzeHealthcareEntitiesResultCollection.forEach(healthcareEntitiesResult -> {
                     System.out.println("document id = " + healthcareEntitiesResult.getId());
                     System.out.println("Document entities: ");
                     AtomicInteger ct = new AtomicInteger();
                     healthcareEntitiesResult.getEntities().forEach(healthcareEntity -> {
                         System.out.printf(
                             "\ti = %d, Text: %s, category: %s, confidence score: %f.%n",
                             ct.getAndIncrement(), healthcareEntity.getText(), healthcareEntity.getCategory(),
                             healthcareEntity.getConfidenceScore());

                         IterableStream<EntityDataSource> healthcareEntityDataSources =
                             healthcareEntity.getDataSources();
                         if (healthcareEntityDataSources != null) {
                             healthcareEntityDataSources.forEach(healthcareEntityLink -> System.out.printf(
                                 "\t\tEntity ID in data source: %s, data source: %s.%n",
                                 healthcareEntityLink.getEntityId(), healthcareEntityLink.getName()));
                         }
                     });
                     // Healthcare entity relation groups
                     healthcareEntitiesResult.getEntityRelations().forEach(entityRelation -> {
                         System.out.printf("\tRelation type: %s.%n", entityRelation.getRelationType());
                         entityRelation.getRoles().forEach(role -> {
                             final HealthcareEntity entity = role.getEntity();
                             System.out.printf("\t\tEntity text: %s, category: %s, role: %s.%n",
                                 entity.getText(), entity.getCategory(), role.getName());
                         });
                         System.out.printf("\tRelation confidence score: %f.%n",
                             entityRelation.getConfidenceScore());
                     });
                 });
             }));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits..
      Returns:
      A PollerFlux that polls the analyze healthcare operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of AnalyzeHealthcareEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginAnalyzeHealthcareEntities is called with service API version TextAnalyticsServiceVersion.V3_0. beginAnalyzeHealthcareEntities is only available for API version v3.1 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginAnalyzeHealthcareEntities

      public com.azure.core.util.polling.PollerFlux<AnalyzeHealthcareEntitiesOperationDetail,AnalyzeHealthcareEntitiesPagedFlux> beginAnalyzeHealthcareEntities(Iterable<String> documents, String language, AnalyzeHealthcareEntitiesOptions options)
      Analyze healthcare entities, entity data sources, and entity relations in a list of documents with provided request options. See this supported languages in Language service API.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add("The patient is a 54-year-old gentleman with a history of progressive angina "
         + "over the past several months.");
 }

 AnalyzeHealthcareEntitiesOptions options = new AnalyzeHealthcareEntitiesOptions()
     .setIncludeStatistics(true);

 textAnalyticsAsyncClient.beginAnalyzeHealthcareEntities(documents, "en", options)
     .flatMap(AsyncPollResponse::getFinalResult)
     .flatMap(pagedFlux -> pagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeHealthcareEntitiesResultCollection -> {
                 // Model version
                 System.out.printf("Results of Azure Text Analytics \"Analyze Healthcare\" Model, version: %s%n",
                     analyzeHealthcareEntitiesResultCollection.getModelVersion());

                 TextDocumentBatchStatistics healthcareTaskStatistics =
                     analyzeHealthcareEntitiesResultCollection.getStatistics();
                 // Batch statistics
                 System.out.printf("Documents statistics: document count = %d, erroneous document count = %d,"
                         + " transaction count = %d, valid document count = %d.%n",
                     healthcareTaskStatistics.getDocumentCount(),
                     healthcareTaskStatistics.getInvalidDocumentCount(),
                     healthcareTaskStatistics.getTransactionCount(),
                     healthcareTaskStatistics.getValidDocumentCount());

                 analyzeHealthcareEntitiesResultCollection.forEach(healthcareEntitiesResult -> {
                     System.out.println("document id = " + healthcareEntitiesResult.getId());
                     System.out.println("Document entities: ");
                     AtomicInteger ct = new AtomicInteger();
                     healthcareEntitiesResult.getEntities().forEach(healthcareEntity -> {
                         System.out.printf(
                             "\ti = %d, Text: %s, category: %s, confidence score: %f.%n",
                             ct.getAndIncrement(), healthcareEntity.getText(), healthcareEntity.getCategory(),
                             healthcareEntity.getConfidenceScore());

                         IterableStream<EntityDataSource> healthcareEntityDataSources =
                             healthcareEntity.getDataSources();
                         if (healthcareEntityDataSources != null) {
                             healthcareEntityDataSources.forEach(healthcareEntityLink -> System.out.printf(
                                 "\t\tEntity ID in data source: %s, data source: %s.%n",
                                 healthcareEntityLink.getEntityId(), healthcareEntityLink.getName()));
                         }
                     });
                     // Healthcare entity relation groups
                     healthcareEntitiesResult.getEntityRelations().forEach(entityRelation -> {
                         System.out.printf("\tRelation type: %s.%n", entityRelation.getRelationType());
                         entityRelation.getRoles().forEach(role -> {
                             final HealthcareEntity entity = role.getEntity();
                             System.out.printf("\t\tEntity text: %s, category: %s, role: %s.%n",
                                 entity.getText(), entity.getCategory(), role.getName());
                         });
                         System.out.printf("\tRelation confidence score: %f.%n",
                             entityRelation.getConfidenceScore());
                     });
                 });
             }));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2-letter ISO 639-1 representation of language for the documents. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing healthcare entities.
      Returns:
      A PollerFlux that polls the analyze healthcare operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of AnalyzeHealthcareEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginAnalyzeHealthcareEntities is called with service API version TextAnalyticsServiceVersion.V3_0. beginAnalyzeHealthcareEntities is only available for API version v3.1 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginAnalyzeHealthcareEntities

      public com.azure.core.util.polling.PollerFlux<AnalyzeHealthcareEntitiesOperationDetail,AnalyzeHealthcareEntitiesPagedFlux> beginAnalyzeHealthcareEntities(Iterable<TextDocumentInput> documents, AnalyzeHealthcareEntitiesOptions options)
      Analyze healthcare entities, entity data sources, and entity relations in a list of document and provided request options to show statistics. Subscribes to the call asynchronously and prints out the entity details when a response is received. See this supported languages in Language service API.

      Code Sample

       List<TextDocumentInput> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(new TextDocumentInput(Integer.toString(i),
         "The patient is a 54-year-old gentleman with a history of progressive angina "
             + "over the past several months."));
 }

 AnalyzeHealthcareEntitiesOptions options = new AnalyzeHealthcareEntitiesOptions()
     .setIncludeStatistics(true);

 textAnalyticsAsyncClient.beginAnalyzeHealthcareEntities(documents, options)
     .flatMap(pollResult -> {
         AnalyzeHealthcareEntitiesOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(analyzeActionsResultPagedFlux -> analyzeActionsResultPagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeHealthcareEntitiesResultCollection -> {
                 // Model version
                 System.out.printf("Results of Azure Text Analytics \"Analyze Healthcare\" Model, version: %s%n",
                     analyzeHealthcareEntitiesResultCollection.getModelVersion());

                 TextDocumentBatchStatistics healthcareTaskStatistics =
                     analyzeHealthcareEntitiesResultCollection.getStatistics();
                 // Batch statistics
                 System.out.printf("Documents statistics: document count = %d, erroneous document count = %d,"
                                       + " transaction count = %d, valid document count = %d.%n",
                     healthcareTaskStatistics.getDocumentCount(),
                     healthcareTaskStatistics.getInvalidDocumentCount(),
                     healthcareTaskStatistics.getTransactionCount(),
                     healthcareTaskStatistics.getValidDocumentCount());

                 analyzeHealthcareEntitiesResultCollection.forEach(healthcareEntitiesResult -> {
                     System.out.println("document id = " + healthcareEntitiesResult.getId());
                     System.out.println("Document entities: ");
                     AtomicInteger ct = new AtomicInteger();
                     healthcareEntitiesResult.getEntities().forEach(healthcareEntity -> {
                         System.out.printf(
                             "\ti = %d, Text: %s, category: %s, confidence score: %f.%n",
                             ct.getAndIncrement(), healthcareEntity.getText(), healthcareEntity.getCategory(),
                             healthcareEntity.getConfidenceScore());

                         IterableStream<EntityDataSource> healthcareEntityDataSources =
                             healthcareEntity.getDataSources();
                         if (healthcareEntityDataSources != null) {
                             healthcareEntityDataSources.forEach(healthcareEntityLink -> System.out.printf(
                                 "\t\tEntity ID in data source: %s, data source: %s.%n",
                                 healthcareEntityLink.getEntityId(), healthcareEntityLink.getName()));
                         }
                     });
                     // Healthcare entity relation groups
                     healthcareEntitiesResult.getEntityRelations().forEach(entityRelation -> {
                         System.out.printf("\tRelation type: %s.%n", entityRelation.getRelationType());
                         entityRelation.getRoles().forEach(role -> {
                             final HealthcareEntity entity = role.getEntity();
                             System.out.printf("\t\tEntity text: %s, category: %s, role: %s.%n",
                                 entity.getText(), entity.getCategory(), role.getName());
                         });
                         System.out.printf("\tRelation confidence score: %f.%n",
                             entityRelation.getConfidenceScore());
                     });
                 });
             }));
      Parameters:
      documents - A list of documents to be analyzed.
      options - The additional configurable options that may be passed when analyzing healthcare entities.
      Returns:
      A PollerFlux that polls the analyze healthcare operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of AnalyzeHealthcareEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginAnalyzeHealthcareEntities is called with service API version TextAnalyticsServiceVersion.V3_0. beginAnalyzeHealthcareEntities is only available for API version v3.1 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginRecognizeCustomEntities

      public com.azure.core.util.polling.PollerFlux<RecognizeCustomEntitiesOperationDetail,RecognizeCustomEntitiesPagedFlux> beginRecognizeCustomEntities(Iterable<String> documents, String projectName, String deploymentName)
      Returns a list of custom entities for the provided list of document.

      This method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

      This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
             + "in oil and natural gas development on federal lands over the past six years has stretched the"
             + " staff of the BLM to a point that it has been unable to meet its environmental protection "
             + "responsibilities."
     );
 }
 textAnalyticsAsyncClient.beginRecognizeCustomEntities(documents, "{project_name}", "{deployment_name}")
     .flatMap(pollResult -> {
         RecognizeCustomEntitiesOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (RecognizeCustomEntitiesResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (RecognizeEntitiesResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (CategorizedEntity entity : documentResult.getEntities()) {
                         System.out.printf(
                             "\tText: %s, category: %s, confidence score: %f.%n",
                             entity.getText(), entity.getCategory(), entity.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      projectName - The name of the project which owns the model being consumed.
      deploymentName - The name of the deployment being consumed.
      Returns:
      A PollerFlux that polls the recognize custom entities operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of RecognizeCustomEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginRecognizeCustomEntities is called with service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. beginRecognizeCustomEntities is only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginRecognizeCustomEntities

      public com.azure.core.util.polling.PollerFlux<RecognizeCustomEntitiesOperationDetail,RecognizeCustomEntitiesPagedFlux> beginRecognizeCustomEntities(Iterable<String> documents, String projectName, String deploymentName, String language, RecognizeCustomEntitiesOptions options)
      Returns a list of custom entities for the provided list of document with provided request options.

      This method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

      See this supported languages in Language service API.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
             + "in oil and natural gas development on federal lands over the past six years has stretched the"
             + " staff of the BLM to a point that it has been unable to meet its environmental protection "
             + "responsibilities."
     );
 }
 RecognizeCustomEntitiesOptions options = new RecognizeCustomEntitiesOptions().setIncludeStatistics(true);
 textAnalyticsAsyncClient.beginRecognizeCustomEntities(documents, "{project_name}",
     "{deployment_name}", "en", options)
     .flatMap(pollResult -> {
         RecognizeCustomEntitiesOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (RecognizeCustomEntitiesResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (RecognizeEntitiesResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (CategorizedEntity entity : documentResult.getEntities()) {
                         System.out.printf(
                             "\tText: %s, category: %s, confidence score: %f.%n",
                             entity.getText(), entity.getCategory(), entity.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      projectName - The name of the project which owns the model being consumed.
      deploymentName - The name of the deployment being consumed.
      language - The 2-letter ISO 639-1 representation of language for the documents. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when recognizing custom entities.
      Returns:
      A PollerFlux that polls the recognize custom entities operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of RecognizeCustomEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginRecognizeCustomEntities is called with service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. beginRecognizeCustomEntities is only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginRecognizeCustomEntities

      public com.azure.core.util.polling.PollerFlux<RecognizeCustomEntitiesOperationDetail,RecognizeCustomEntitiesPagedFlux> beginRecognizeCustomEntities(Iterable<TextDocumentInput> documents, String projectName, String deploymentName, RecognizeCustomEntitiesOptions options)
      Returns a list of custom entities for the provided list of document with provided request options.

      This method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

      Code Sample

       List<TextDocumentInput> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(new TextDocumentInput(Integer.toString(i),
         "A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
             + "in oil and natural gas development on federal lands over the past six years has stretched the"
             + " staff of the BLM to a point that it has been unable to meet its environmental protection "
             + "responsibilities."));
 }
 RecognizeCustomEntitiesOptions options = new RecognizeCustomEntitiesOptions().setIncludeStatistics(true);
 textAnalyticsAsyncClient.beginRecognizeCustomEntities(documents, "{project_name}",
     "{deployment_name}", options)
     .flatMap(pollResult -> {
         RecognizeCustomEntitiesOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (RecognizeCustomEntitiesResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (RecognizeEntitiesResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (CategorizedEntity entity : documentResult.getEntities()) {
                         System.out.printf(
                             "\tText: %s, category: %s, confidence score: %f.%n",
                             entity.getText(), entity.getCategory(), entity.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      projectName - The name of the project which owns the model being consumed.
      deploymentName - The name of the deployment being consumed.
      options - The additional configurable options that may be passed when recognizing custom entities.
      Returns:
      A PollerFlux that polls the recognize custom entities until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of RecognizeCustomEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginRecognizeCustomEntities is called with service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. beginRecognizeCustomEntities is only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginSingleLabelClassify

      public com.azure.core.util.polling.PollerFlux<ClassifyDocumentOperationDetail,ClassifyDocumentPagedFlux> beginSingleLabelClassify(Iterable<String> documents, String projectName, String deploymentName)
      Returns a list of single-label classification for the provided list of document.

      This method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

      This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
             + "in oil and natural gas development on federal lands over the past six years has stretched the"
             + " staff of the BLM to a point that it has been unable to meet its environmental protection "
             + "responsibilities."
     );
 }
 // See the service documentation for regional support and how to train a model to classify your documents,
 // see https://aka.ms/azsdk/textanalytics/customfunctionalities
 textAnalyticsAsyncClient.beginSingleLabelClassify(documents,
         "{project_name}", "{deployment_name}")
     .flatMap(pollResult -> {
         ClassifyDocumentOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFluxAsyncPollResponse -> pagedFluxAsyncPollResponse.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (ClassifyDocumentResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (ClassifyDocumentResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (ClassificationCategory classification : documentResult.getClassifications()) {
                         System.out.printf("\tCategory: %s, confidence score: %f.%n",
                             classification.getCategory(), classification.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      projectName - The name of the project which owns the model being consumed.
      deploymentName - The name of the deployment being consumed.
      Returns:
      A PollerFlux that polls the single-label classification operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of ClassifyDocumentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginSingleLabelClassify is called with service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. beginSingleLabelClassify is only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginSingleLabelClassify

      public com.azure.core.util.polling.PollerFlux<ClassifyDocumentOperationDetail,ClassifyDocumentPagedFlux> beginSingleLabelClassify(Iterable<String> documents, String projectName, String deploymentName, String language, SingleLabelClassifyOptions options)
      Returns a list of single-label classification for the provided list of document with provided request options.

      This method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

      See this supported languages in Language service API.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
             + "in oil and natural gas development on federal lands over the past six years has stretched the"
             + " staff of the BLM to a point that it has been unable to meet its environmental protection "
             + "responsibilities."
     );
 }
 SingleLabelClassifyOptions options = new SingleLabelClassifyOptions().setIncludeStatistics(true);
 // See the service documentation for regional support and how to train a model to classify your documents,
 // see https://aka.ms/azsdk/textanalytics/customfunctionalities
 textAnalyticsAsyncClient.beginSingleLabelClassify(documents,
     "{project_name}", "{deployment_name}", "en", options)
     .flatMap(pollResult -> {
         ClassifyDocumentOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFluxAsyncPollResponse -> pagedFluxAsyncPollResponse.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (ClassifyDocumentResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (ClassifyDocumentResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (ClassificationCategory classification : documentResult.getClassifications()) {
                         System.out.printf("\tCategory: %s, confidence score: %f.%n",
                             classification.getCategory(), classification.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      projectName - The name of the project which owns the model being consumed.
      deploymentName - The name of the deployment being consumed.
      language - The 2-letter ISO 639-1 representation of language for the documents. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing single-label classification.
      Returns:
      A PollerFlux that polls the single-label classification operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of ClassifyDocumentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginSingleLabelClassify is called with service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. beginSingleLabelClassify is only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginSingleLabelClassify

      public com.azure.core.util.polling.PollerFlux<ClassifyDocumentOperationDetail,ClassifyDocumentPagedFlux> beginSingleLabelClassify(Iterable<TextDocumentInput> documents, String projectName, String deploymentName, SingleLabelClassifyOptions options)
      Returns a list of single-label classification for the provided list of document with provided request options.

      This method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

      Code Sample

       List<TextDocumentInput> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(new TextDocumentInput(Integer.toString(i),
         "A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
         + "in oil and natural gas development on federal lands over the past six years has stretched the"
         + " staff of the BLM to a point that it has been unable to meet its environmental protection "
         + "responsibilities."));
 }
 SingleLabelClassifyOptions options = new SingleLabelClassifyOptions().setIncludeStatistics(true);
 // See the service documentation for regional support and how to train a model to classify your documents,
 // see https://aka.ms/azsdk/textanalytics/customfunctionalities
 textAnalyticsAsyncClient.beginSingleLabelClassify(documents,
     "{project_name}", "{deployment_name}", options)
     .flatMap(pollResult -> {
         ClassifyDocumentOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFluxAsyncPollResponse -> pagedFluxAsyncPollResponse.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (ClassifyDocumentResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (ClassifyDocumentResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (ClassificationCategory classification : documentResult.getClassifications()) {
                         System.out.printf("\tCategory: %s, confidence score: %f.%n",
                             classification.getCategory(), classification.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      projectName - The name of the project which owns the model being consumed.
      deploymentName - The name of the deployment being consumed.
      options - The additional configurable options that may be passed when analyzing single-label classification.
      Returns:
      A PollerFlux that polls the single-label classification operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of ClassifyDocumentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginSingleLabelClassify is called with service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. beginSingleLabelClassify is only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginMultiLabelClassify

      public com.azure.core.util.polling.PollerFlux<ClassifyDocumentOperationDetail,ClassifyDocumentPagedFlux> beginMultiLabelClassify(Iterable<String> documents, String projectName, String deploymentName)
      Returns a list of multi-label classification for the provided list of document.

      This method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

      This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "I need a reservation for an indoor restaurant in China. Please don't stop the music."
             + " Play music and add it to my playlist");
 }
 textAnalyticsAsyncClient.beginMultiLabelClassify(documents, "{project_name}", "{deployment_name}")
     .flatMap(pollResult -> {
         ClassifyDocumentOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFluxAsyncPollResponse -> pagedFluxAsyncPollResponse.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (ClassifyDocumentResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (ClassifyDocumentResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (ClassificationCategory classification : documentResult.getClassifications()) {
                         System.out.printf("\tCategory: %s, confidence score: %f.%n",
                             classification.getCategory(), classification.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      projectName - The name of the project which owns the model being consumed.
      deploymentName - The name of the deployment being consumed.
      Returns:
      A PollerFlux that polls the multi-label classification operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of ClassifyDocumentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginMultiLabelClassify is called with service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. beginMultiLabelClassify is only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginMultiLabelClassify

      public com.azure.core.util.polling.PollerFlux<ClassifyDocumentOperationDetail,ClassifyDocumentPagedFlux> beginMultiLabelClassify(Iterable<String> documents, String projectName, String deploymentName, String language, MultiLabelClassifyOptions options)
      Returns a list of multi-label classification for the provided list of document with provided request options.

      This method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

      See this supported languages in Language service API.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "I need a reservation for an indoor restaurant in China. Please don't stop the music."
             + " Play music and add it to my playlist");
 }
 MultiLabelClassifyOptions options = new MultiLabelClassifyOptions().setIncludeStatistics(true);
 textAnalyticsAsyncClient.beginMultiLabelClassify(documents, "{project_name}",
     "{deployment_name}", "en", options)
     .flatMap(pollResult -> {
         ClassifyDocumentOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFluxAsyncPollResponse -> pagedFluxAsyncPollResponse.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (ClassifyDocumentResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (ClassifyDocumentResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (ClassificationCategory classification : documentResult.getClassifications()) {
                         System.out.printf("\tCategory: %s, confidence score: %f.%n",
                             classification.getCategory(), classification.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      projectName - The name of the project which owns the model being consumed.
      deploymentName - The name of the deployment being consumed.
      language - The 2-letter ISO 639-1 representation of language for the documents. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing multi-label classification.
      Returns:
      A PollerFlux that polls the multi-label classification operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of ClassifyDocumentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginMultiLabelClassify is called with service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. beginMultiLabelClassify is only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginMultiLabelClassify

      public com.azure.core.util.polling.PollerFlux<ClassifyDocumentOperationDetail,ClassifyDocumentPagedFlux> beginMultiLabelClassify(Iterable<TextDocumentInput> documents, String projectName, String deploymentName, MultiLabelClassifyOptions options)
      Returns a list of multi-label classification for the provided list of document with provided request options.

      This method is supported since service API version TextAnalyticsServiceVersion.V2022_05_01.

      Code Sample

       List<TextDocumentInput> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(new TextDocumentInput(Integer.toString(i),
         "I need a reservation for an indoor restaurant in China. Please don't stop the music."
             + " Play music and add it to my playlist"));
 }
 MultiLabelClassifyOptions options = new MultiLabelClassifyOptions().setIncludeStatistics(true);
 textAnalyticsAsyncClient.beginMultiLabelClassify(documents, "{project_name}",
     "{deployment_name}", options)
     .flatMap(pollResult -> {
         ClassifyDocumentOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(pagedFluxAsyncPollResponse -> pagedFluxAsyncPollResponse.byPage())
     .subscribe(
         perPage -> {
             System.out.printf("Response code: %d, Continuation Token: %s.%n",
                 perPage.getStatusCode(), perPage.getContinuationToken());
             for (ClassifyDocumentResultCollection documentsResults : perPage.getElements()) {
                 System.out.printf("Project name: %s, deployment name: %s.%n",
                     documentsResults.getProjectName(), documentsResults.getDeploymentName());
                 for (ClassifyDocumentResult documentResult : documentsResults) {
                     System.out.println("Document ID: " + documentResult.getId());
                     for (ClassificationCategory classification : documentResult.getClassifications()) {
                         System.out.printf("\tCategory: %s, confidence score: %f.%n",
                             classification.getCategory(), classification.getConfidenceScore());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      projectName - The name of the project which owns the model being consumed.
      deploymentName - The name of the deployment being consumed.
      options - The additional configurable options that may be passed when analyzing multi-label classification.
      Returns:
      A PollerFlux that polls the multi-label classification operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of ClassifyDocumentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginMultiLabelClassify is called with service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. beginMultiLabelClassify is only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginAbstractSummary

      public com.azure.core.util.polling.PollerFlux<AbstractiveSummaryOperationDetail,AbstractiveSummaryPagedFlux> beginAbstractSummary(Iterable<String> documents)
      Returns a list of abstractive summary for the provided list of document.

      This method is supported since service API version TextAnalyticsServiceVersion.V2023_04_01.

      This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
             + " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
             + " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
             + "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
             + " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
             + " (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code"
             + " as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear,"
             + " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
             + " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
             + " pretrained models that can jointly learn representations to support a broad range of downstream"
             + " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
             + " performance on benchmarks in conversational speech recognition, machine translation, "
             + "conversational question answering, machine reading comprehension, and image captioning. These"
             + " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
             + " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
             + "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
             + "foundational component of this aspiration, if grounded with external knowledge sources in "
             + "the downstream AI tasks.");
 }
 textAnalyticsAsyncClient.beginAbstractSummary(documents)
     .flatMap(result -> {
         AbstractiveSummaryOperationDetail operationDetail = result.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationDetail.getCreatedAt(), operationDetail.getExpiresAt());
         return result.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux) // this unwrap the Mono<> of Mono<PagedFlux<T>> to return PagedFlux<T>
     .subscribe(
         resultCollection -> {
             for (AbstractiveSummaryResult documentResult : resultCollection) {
                 System.out.println("\tAbstractive summary sentences:");
                 for (AbstractiveSummary summarySentence : documentResult.getSummaries()) {
                     System.out.printf("\t\t Summary text: %s.%n", summarySentence.getText());
                     for (AbstractiveSummaryContext abstractiveSummaryContext : summarySentence.getContexts()) {
                         System.out.printf("\t\t offset: %d, length: %d%n",
                             abstractiveSummaryContext.getOffset(), abstractiveSummaryContext.getLength());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A PollerFlux that polls the abstractive summarization operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of AbstractiveSummaryResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginAbstractSummary is called with service API version TextAnalyticsServiceVersion.V3_0, TextAnalyticsServiceVersion.V3_1, or TextAnalyticsServiceVersion.V2022_05_01. Those actions are only available for API version TextAnalyticsServiceVersion.V2023_04_01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginAbstractSummary

      public com.azure.core.util.polling.PollerFlux<AbstractiveSummaryOperationDetail,AbstractiveSummaryPagedFlux> beginAbstractSummary(Iterable<String> documents, String language, AbstractiveSummaryOptions options)
      Returns a list of abstractive summary for the provided list of document with provided request options.

      This method is supported since service API version TextAnalyticsServiceVersion.V2023_04_01.

      See this supported languages in Language service API.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
             + " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
             + " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
             + "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
             + " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
             + " (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code"
             + " as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear,"
             + " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
             + " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
             + " pretrained models that can jointly learn representations to support a broad range of downstream"
             + " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
             + " performance on benchmarks in conversational speech recognition, machine translation, "
             + "conversational question answering, machine reading comprehension, and image captioning. These"
             + " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
             + " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
             + "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
             + "foundational component of this aspiration, if grounded with external knowledge sources in "
             + "the downstream AI tasks.");
 }
 AbstractiveSummaryOptions options = new AbstractiveSummaryOptions().setSentenceCount(4);
 textAnalyticsAsyncClient.beginAbstractSummary(documents, "en", options)
     .flatMap(result -> {
         AbstractiveSummaryOperationDetail operationDetail = result.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationDetail.getCreatedAt(), operationDetail.getExpiresAt());
         return result.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux) // this unwrap the Mono<> of Mono<PagedFlux<T>> to return PagedFlux<T>
     .subscribe(
         resultCollection -> {
             for (AbstractiveSummaryResult documentResult : resultCollection) {
                 System.out.println("\tAbstractive summary sentences:");
                 for (AbstractiveSummary summarySentence : documentResult.getSummaries()) {
                     System.out.printf("\t\t Summary text: %s.%n", summarySentence.getText());
                     for (AbstractiveSummaryContext abstractiveSummaryContext : summarySentence.getContexts()) {
                         System.out.printf("\t\t offset: %d, length: %d%n",
                             abstractiveSummaryContext.getOffset(), abstractiveSummaryContext.getLength());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2-letter ISO 639-1 representation of language for the documents. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing abstractive summarization.
      Returns:
      A PollerFlux that polls the abstractive summarization operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of AbstractiveSummaryResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginAbstractSummary is called with service API version TextAnalyticsServiceVersion.V3_0, TextAnalyticsServiceVersion.V3_1, or TextAnalyticsServiceVersion.V2022_05_01. Those actions are only available for API version TextAnalyticsServiceVersion.V2023_04_01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginAbstractSummary

      public com.azure.core.util.polling.PollerFlux<AbstractiveSummaryOperationDetail,AbstractiveSummaryPagedFlux> beginAbstractSummary(Iterable<TextDocumentInput> documents, AbstractiveSummaryOptions options)
      Returns a list of abstractive summary for the provided list of document with provided request options.

      This method is supported since service API version TextAnalyticsServiceVersion.V2023_04_01.

      Code Sample

       List<TextDocumentInput> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(new TextDocumentInput(Integer.toString(i),
         "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
             + " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
             + " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
             + "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
             + " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
             + " (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code"
             + " as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear,"
             + " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
             + " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
             + " pretrained models that can jointly learn representations to support a broad range of downstream"
             + " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
             + " performance on benchmarks in conversational speech recognition, machine translation, "
             + "conversational question answering, machine reading comprehension, and image captioning. These"
             + " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
             + " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
             + "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
             + "foundational component of this aspiration, if grounded with external knowledge sources in "
             + "the downstream AI tasks."));
 }
 AbstractiveSummaryOptions options = new AbstractiveSummaryOptions().setSentenceCount(4);
 textAnalyticsAsyncClient.beginAbstractSummary(documents, options)
     .flatMap(result -> {
         AbstractiveSummaryOperationDetail operationDetail = result.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationDetail.getCreatedAt(), operationDetail.getExpiresAt());
         return result.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux) // this unwrap the Mono<> of Mono<PagedFlux<T>> to return PagedFlux<T>
     .subscribe(
         resultCollection -> {
             for (AbstractiveSummaryResult documentResult : resultCollection) {
                 System.out.println("\tAbstractive summary sentences:");
                 for (AbstractiveSummary summarySentence : documentResult.getSummaries()) {
                     System.out.printf("\t\t Summary text: %s.%n", summarySentence.getText());
                     for (AbstractiveSummaryContext abstractiveSummaryContext : summarySentence.getContexts()) {
                         System.out.printf("\t\t offset: %d, length: %d%n",
                             abstractiveSummaryContext.getOffset(), abstractiveSummaryContext.getLength());
                     }
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The additional configurable options that may be passed when analyzing abstractive summarization.
      Returns:
      A PollerFlux that polls the abstractive summarization operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of AbstractiveSummaryResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginAbstractSummary is called with service API version TextAnalyticsServiceVersion.V3_0, TextAnalyticsServiceVersion.V3_1, or TextAnalyticsServiceVersion.V2022_05_01. Those actions are only available for API version TextAnalyticsServiceVersion.V2023_04_01 and newer.r.
      TextAnalyticsException - If analyze operation fails.

    • beginExtractSummary

      public com.azure.core.util.polling.PollerFlux<ExtractiveSummaryOperationDetail,ExtractiveSummaryPagedFlux> beginExtractSummary(Iterable<String> documents)
      Returns a list of extract summaries for the provided list of document.

      This method is supported since service API version TextAnalyticsServiceVersion.V2023_04_01.

      This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
             + " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
             + " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
             + "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
             + " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
             + " (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code"
             + " as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear,"
             + " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
             + " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
             + " pretrained models that can jointly learn representations to support a broad range of downstream"
             + " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
             + " performance on benchmarks in conversational speech recognition, machine translation, "
             + "conversational question answering, machine reading comprehension, and image captioning. These"
             + " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
             + " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
             + "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
             + "foundational component of this aspiration, if grounded with external knowledge sources in "
             + "the downstream AI tasks.");
 }
 textAnalyticsAsyncClient.beginExtractSummary(documents)
     .flatMap(result -> {
         ExtractiveSummaryOperationDetail operationDetail = result.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationDetail.getCreatedAt(), operationDetail.getExpiresAt());
         return result.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux) // this unwrap the Mono<> of Mono<PagedFlux<T>> to return PagedFlux<T>
     .subscribe(
         resultCollection -> {
             for (ExtractiveSummaryResult documentResult : resultCollection) {
                 for (ExtractiveSummarySentence extractiveSummarySentence : documentResult.getSentences()) {
                     System.out.printf(
                         "Sentence text: %s, length: %d, offset: %d, rank score: %f.%n",
                         extractiveSummarySentence.getText(), extractiveSummarySentence.getLength(),
                         extractiveSummarySentence.getOffset(), extractiveSummarySentence.getRankScore());
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.med.
      Returns:
      A PollerFlux that polls the extractive summarization operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of ExtractiveSummaryResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginExtractSummary is called with service API version TextAnalyticsServiceVersion.V3_0, TextAnalyticsServiceVersion.V3_1, or TextAnalyticsServiceVersion.V2022_05_01. Those actions are only available for API version TextAnalyticsServiceVersion.V2023_04_01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginExtractSummary

      public com.azure.core.util.polling.PollerFlux<ExtractiveSummaryOperationDetail,ExtractiveSummaryPagedFlux> beginExtractSummary(Iterable<String> documents, String language, ExtractiveSummaryOptions options)
      Returns a list of extract summaries for the provided list of document with provided request options.

      This method is supported since service API version TextAnalyticsServiceVersion.V2023_04_01.

      See this supported languages in Language service API.

      Code Sample

       List<String> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(
         "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
             + " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
             + " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
             + "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
             + " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
             + " (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code"
             + " as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear,"
             + " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
             + " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
             + " pretrained models that can jointly learn representations to support a broad range of downstream"
             + " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
             + " performance on benchmarks in conversational speech recognition, machine translation, "
             + "conversational question answering, machine reading comprehension, and image captioning. These"
             + " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
             + " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
             + "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
             + "foundational component of this aspiration, if grounded with external knowledge sources in "
             + "the downstream AI tasks.");
 }
 ExtractiveSummaryOptions options =
     new ExtractiveSummaryOptions().setMaxSentenceCount(4).setOrderBy(ExtractiveSummarySentencesOrder.RANK);
 textAnalyticsAsyncClient.beginExtractSummary(documents, "en", options)
     .flatMap(result -> {
         ExtractiveSummaryOperationDetail operationDetail = result.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationDetail.getCreatedAt(), operationDetail.getExpiresAt());
         return result.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux) // this unwrap the Mono<> of Mono<PagedFlux<T>> to return PagedFlux<T>
     .subscribe(
         resultCollection -> {
             for (ExtractiveSummaryResult documentResult : resultCollection) {
                 for (ExtractiveSummarySentence extractiveSummarySentence : documentResult.getSentences()) {
                     System.out.printf(
                         "Sentence text: %s, length: %d, offset: %d, rank score: %f.%n",
                         extractiveSummarySentence.getText(), extractiveSummarySentence.getLength(),
                         extractiveSummarySentence.getOffset(), extractiveSummarySentence.getRankScore());
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2-letter ISO 639-1 representation of language for the documents. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing extractive summarization.
      Returns:
      A PollerFlux that polls the extractive summarization operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of ExtractiveSummaryResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginExtractSummary is called with service API version TextAnalyticsServiceVersion.V3_0, TextAnalyticsServiceVersion.V3_1, or TextAnalyticsServiceVersion.V2022_05_01. Those actions are only available for API version TextAnalyticsServiceVersion.V2023_04_01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginExtractSummary

      public com.azure.core.util.polling.PollerFlux<ExtractiveSummaryOperationDetail,ExtractiveSummaryPagedFlux> beginExtractSummary(Iterable<TextDocumentInput> documents, ExtractiveSummaryOptions options)
      Returns a list of extract summaries for the provided list of document with provided request options.

      This method is supported since service API version TextAnalyticsServiceVersion.V2023_04_01.

      Code Sample

       List<TextDocumentInput> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(new TextDocumentInput(Integer.toString(i),
         "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
             + " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
             + " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
             + "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
             + " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
             + " (Y) and multilingual (Z). At the intersection of all three, there’s magic—what we call XYZ-code"
             + " as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear,"
             + " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
             + " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
             + " pretrained models that can jointly learn representations to support a broad range of downstream"
             + " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
             + " performance on benchmarks in conversational speech recognition, machine translation, "
             + "conversational question answering, machine reading comprehension, and image captioning. These"
             + " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
             + " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
             + "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
             + "foundational component of this aspiration, if grounded with external knowledge sources in "
             + "the downstream AI tasks."));
 }
 ExtractiveSummaryOptions options =
     new ExtractiveSummaryOptions().setMaxSentenceCount(4).setOrderBy(ExtractiveSummarySentencesOrder.RANK);
 textAnalyticsAsyncClient.beginExtractSummary(documents, options)
     .flatMap(result -> {
         ExtractiveSummaryOperationDetail operationDetail = result.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationDetail.getCreatedAt(), operationDetail.getExpiresAt());
         return result.getFinalResult();
     })
     .flatMap(pagedFlux -> pagedFlux) // this unwrap the Mono<> of Mono<PagedFlux<T>> to return PagedFlux<T>
     .subscribe(
         resultCollection -> {
             for (ExtractiveSummaryResult documentResult : resultCollection) {
                 for (ExtractiveSummarySentence extractiveSummarySentence : documentResult.getSentences()) {
                     System.out.printf(
                         "Sentence text: %s, length: %d, offset: %d, rank score: %f.%n",
                         extractiveSummarySentence.getText(), extractiveSummarySentence.getLength(),
                         extractiveSummarySentence.getOffset(), extractiveSummarySentence.getRankScore());
                 }
             }
         },
         ex -> System.out.println("Error listing pages: " + ex.getMessage()),
         () -> System.out.println("Successfully listed all pages"));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The additional configurable options that may be passed when analyzing extractive summarization.
      Returns:
      A PollerFlux that polls the extractive summarization operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of ExtractiveSummaryResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginExtractSummary is called with service API version TextAnalyticsServiceVersion.V3_0, TextAnalyticsServiceVersion.V3_1, or TextAnalyticsServiceVersion.V2022_05_01. Those actions are only available for API version TextAnalyticsServiceVersion.V2023_04_01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginAnalyzeActions

      public com.azure.core.util.polling.PollerFlux<AnalyzeActionsOperationDetail,AnalyzeActionsResultPagedFlux> beginAnalyzeActions(Iterable<String> documents, TextAnalyticsActions actions)
      Execute actions, such as, entities recognition, PII entities recognition and key phrases extraction for a list of documents. This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code Sample

       List<String> documents = Arrays.asList(
     "Elon Musk is the CEO of SpaceX and Tesla.",
     "1", "My SSN is 859-98-0987"
 );
 textAnalyticsAsyncClient.beginAnalyzeActions(documents,
         new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
             .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
             .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()))
     .flatMap(AsyncPollResponse::getFinalResult)
     .flatMap(analyzeActionsResultPagedFlux -> analyzeActionsResultPagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeActionsResult -> {
                 analyzeActionsResult.getRecognizeEntitiesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(
                                 entitiesResult -> entitiesResult.getEntities().forEach(
                                     entity -> System.out.printf(
                                         "Recognized entity: %s, entity category: %s, entity subcategory: %s,"
                                             + " confidence score: %f.%n",
                                         entity.getText(), entity.getCategory(), entity.getSubcategory(),
                                         entity.getConfidenceScore())));
                         }
                     });
                 analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
                                 System.out.println("Extracted phrases:");
                                 extractKeyPhraseResult.getKeyPhrases()
                                     .forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
                             });
                         }
                     });
             }));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      actions - The actions that contains all actions to be executed. An action is one task of execution, such as a single task of 'Key Phrases Extraction' on the given document inputs.
      Returns:
      A PollerFlux that polls the analyze a collection of actions operation until it has completed, has failed, or has been cancelled. The completed operation returns a AnalyzeActionsResultPagedFlux.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginAnalyzeActions is called with service API version TextAnalyticsServiceVersion.V3_0. beginAnalyzeActions is only available for API version v3.1 and newer.
      UnsupportedOperationException - if request AnalyzeHealthcareEntitiesAction, RecognizeCustomEntitiesAction, SingleLabelClassifyAction, or MultiLabelClassifyAction in service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. Those actions are only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginAnalyzeActions

      public com.azure.core.util.polling.PollerFlux<AnalyzeActionsOperationDetail,AnalyzeActionsResultPagedFlux> beginAnalyzeActions(Iterable<String> documents, TextAnalyticsActions actions, String language, AnalyzeActionsOptions options)
      Execute actions, such as, entities recognition, PII entities recognition and key phrases extraction for a list of documents with provided request options. See this supported languages in Language service API.

      Code Sample

       List<String> documents = Arrays.asList(
     "Elon Musk is the CEO of SpaceX and Tesla.",
     "1", "My SSN is 859-98-0987"
 );
 textAnalyticsAsyncClient.beginAnalyzeActions(documents,
     new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
         .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
         .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
     "en",
     new AnalyzeActionsOptions().setIncludeStatistics(false))
     .flatMap(AsyncPollResponse::getFinalResult)
     .flatMap(analyzeActionsResultPagedFlux -> analyzeActionsResultPagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeActionsResult -> {
                 analyzeActionsResult.getRecognizeEntitiesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(
                                 entitiesResult -> entitiesResult.getEntities().forEach(
                                     entity -> System.out.printf(
                                         "Recognized entity: %s, entity category: %s, entity subcategory: %s,"
                                             + " confidence score: %f.%n",
                                         entity.getText(), entity.getCategory(), entity.getSubcategory(),
                                         entity.getConfidenceScore())));
                         }
                     });
                 analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
                                 System.out.println("Extracted phrases:");
                                 extractKeyPhraseResult.getKeyPhrases()
                                     .forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
                             });
                         }
                     });
             }));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      actions - The actions that contains all actions to be executed. An action is one task of execution, such as a single task of 'Key Phrases Extraction' on the given document inputs.
      language - The 2 letter ISO 639-1 representation of language for the documents. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing a collection of actions.
      Returns:
      A PollerFlux that polls the analyze a collection of actions operation until it has completed, has failed, or has been cancelled. The completed operation returns a AnalyzeActionsResultPagedFlux.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginAnalyzeActions is called with service API version TextAnalyticsServiceVersion.V3_0. beginAnalyzeActions is only available for API version v3.1 and newer.
      UnsupportedOperationException - if request AnalyzeHealthcareEntitiesAction, RecognizeCustomEntitiesAction, SingleLabelClassifyAction, or MultiLabelClassifyAction in service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. Those actions are only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.

    • beginAnalyzeActions

      public com.azure.core.util.polling.PollerFlux<AnalyzeActionsOperationDetail,AnalyzeActionsResultPagedFlux> beginAnalyzeActions(Iterable<TextDocumentInput> documents, TextAnalyticsActions actions, AnalyzeActionsOptions options)
      Execute actions, such as, entities recognition, PII entities recognition and key phrases extraction for a list of documents with provided request options. See this supported languages in Language service API.

      Code Sample

       List<TextDocumentInput> documents = Arrays.asList(
     new TextDocumentInput("0", "Elon Musk is the CEO of SpaceX and Tesla.").setLanguage("en"),
     new TextDocumentInput("1", "My SSN is 859-98-0987").setLanguage("en")
 );
 textAnalyticsAsyncClient.beginAnalyzeActions(documents,
     new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
         .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
         .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
     new AnalyzeActionsOptions().setIncludeStatistics(false))
     .flatMap(AsyncPollResponse::getFinalResult)
     .flatMap(analyzeActionsResultPagedFlux -> analyzeActionsResultPagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeActionsResult -> {
                 System.out.println("Entities recognition action results:");
                 analyzeActionsResult.getRecognizeEntitiesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(
                                 entitiesResult -> entitiesResult.getEntities().forEach(
                                     entity -> System.out.printf(
                                         "Recognized entity: %s, entity category: %s, entity subcategory: %s,"
                                             + " confidence score: %f.%n",
                                         entity.getText(), entity.getCategory(), entity.getSubcategory(),
                                         entity.getConfidenceScore())));
                         }
                     });
                 System.out.println("Key phrases extraction action results:");
                 analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
                                 System.out.println("Extracted phrases:");
                                 extractKeyPhraseResult.getKeyPhrases()
                                     .forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
                             });
                         }
                     });
             }));
      Parameters:
      documents - A list of documents to be analyzed.
      actions - The actions that contains all actions to be executed. An action is one task of execution, such as a single task of 'Key Phrases Extraction' on the given document inputs.
      options - The additional configurable options that may be passed when analyzing a collection of tasks.
      Returns:
      A PollerFlux that polls the analyze a collection of tasks operation until it has completed, has failed, or has been cancelled. The completed operation returns a AnalyzeActionsResultPagedFlux.
      Throws:
      NullPointerException - if documents or actions is null.
      IllegalArgumentException - if documents is empty.
      UnsupportedOperationException - if beginAnalyzeActions is called with service API version TextAnalyticsServiceVersion.V3_0. beginAnalyzeActions is only available for API version v3.1 and newer.
      UnsupportedOperationException - if request AnalyzeHealthcareEntitiesAction, RecognizeCustomEntitiesAction, SingleLabelClassifyAction, or MultiLabelClassifyAction in service API version TextAnalyticsServiceVersion.V3_0 or TextAnalyticsServiceVersion.V3_1. Those actions are only available for API version 2022-05-01 and newer.
      TextAnalyticsException - If analyze operation fails.