Instantiating an asynchronous Text Analytics Client
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.instantiation} + * + *
+ * TextAnalyticsAsyncClient textAnalyticsAsyncClient = new TextAnalyticsClientBuilder()
+ * .credential(new AzureKeyCredential("{key}"))
+ * .endpoint("{endpoint}")
+ * .buildAsyncClient();
+ *
+ *
*
* View {@link TextAnalyticsClientBuilder} for additional ways to construct the client.
* @@ -72,7 +79,7 @@ public final class TextAnalyticsAsyncClient { private final String defaultCountryHint; private final String defaultLanguage; - // Please see here + // Please see here // for more information on Azure resource provider namespaces. static final String COGNITIVE_TRACING_NAMESPACE_VALUE = "Microsoft.CognitiveServices"; final DetectLanguageAsyncClient detectLanguageAsyncClient; @@ -139,7 +146,14 @@ public String getDefaultLanguage() { *Detects language in a document. Subscribes to the call asynchronously and prints out the detected language * details when a response is received.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.detectLanguage#string} + * + *
+ * 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()));
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -163,12 +177,20 @@ public MonoDetects 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.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.detectLanguage#string-string} + * + *
+ * 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()));
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
* data limits.
- * @param countryHint Accepts two letter country codes specified by ISO 3166-1 alpha-2. Defaults to "US" if not
+ * @param 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
* {@code countryHint} = "" or "none".
*
@@ -207,7 +229,28 @@ public Mono
+ * 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());
+ * }
+ * });
+ *
+ *
*
* @param documents The list of documents to detect languages for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -226,7 +269,7 @@ public MonoDetects language in a batch of {@link DetectLanguageInput document} with provided request options. Subscribes * to the call asynchronously and prints out the detected language details when a response is received.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.detectLanguageBatch#Iterable-TextAnalyticsRequestOptions} + * + *
+ * 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());
+ * }
+ * });
+ *
+ *
*
* @param documents The list of {@link DetectLanguageInput documents} to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -280,7 +351,17 @@ public MonoRecognize entities in a document. Subscribes to the call asynchronously and prints out the recognized entity * details when a response is received.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.recognizeEntities#string} + * + *
+ * 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())));
+ *
+ *
*
* @param document The document to recognize entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -306,7 +387,17 @@ public MonoRecognize entities in a document with provided language code. Subscribes to the call asynchronously and prints * out the entity details when a response is received.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.recognizeEntities#string-string} + * + *
+ * 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())));
+ *
+ *
*
* @param document the text to recognize entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -332,7 +423,25 @@ public MonoRecognize 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.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.recognizeCategorizedEntitiesBatch#Iterable-String-TextAnalyticsRequestOptions} + * + *
+ * 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())));
+ * });
+ *
+ *
*
* @param documents A list of documents to recognize entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -369,7 +478,34 @@ public MonoRecognize entities in a list of {@link TextDocumentInput document}. Subscribes to the call asynchronously * and prints out the entity details when a response is received.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.recognizeCategorizedEntitiesBatch#Iterable-TextAnalyticsRequestOptions} + * + *
+ * 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())));
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to recognize entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -402,7 +538,18 @@ public Mono
+ * 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()));
+ * });
+ *
+ *
*
* @param document The document to recognize PII entities details for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -429,7 +576,19 @@ public MonoRecognize 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.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.recognizePiiEntities#string-string} + * + *
+ * 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()));
+ * });
+ *
+ *
*
* @param document the text to recognize PII entities details for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -458,12 +617,25 @@ public Mono
+ * 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()));
+ * });
+ *
+ *
*
* @param document the text to recognize PII entities details for.
* For text length limits, maximum batch size, and supported text encoding, see
* data limits.
- * @param language The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
+ * @param language The 2-letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
* @param options The additional configurable {@link RecognizePiiEntitiesOptions options} that may be passed when
* recognizing PII entities.
*
@@ -486,7 +658,35 @@ public MonoRecognize 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.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.recognizePiiEntitiesBatch#Iterable-String-RecognizePiiEntitiesOptions} + * + *
+ * 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()));
+ * });
+ * });
+ *
+ *
*
* @param documents A list of documents to recognize PII entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -525,7 +725,35 @@ public Mono
+ * 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()));
+ * });
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to recognize PII entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -557,7 +785,21 @@ public MonoRecognize linked entities in a document. Subscribes to the call asynchronously and prints out the * entity details when a response is received.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.recognizeLinkedEntities#string} + * + *
+ * 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()));
+ * }));
+ *
+ *
*
* @param document The document to recognize linked entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -580,7 +822,21 @@ public MonoRecognize 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.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.recognizeLinkedEntities#string-string} + * + *
+ * 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()));
+ * }));
+ *
+ *
*
* @param document The document to recognize linked entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -607,7 +863,33 @@ public MonoRecognize 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.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.recognizeLinkedEntitiesBatch#Iterable-String-TextAnalyticsRequestOptions} + * + *
+ * 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()));
+ * }));
+ * });
+ *
+ *
*
* @param documents A list of documents to recognize linked entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -645,7 +927,38 @@ public Mono
+ * 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()));
+ * }));
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to recognize linked entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -676,7 +989,13 @@ public MonoExtract key phrases in a document. Subscribes to the call asynchronously and prints out the * key phrases when a response is received.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.extractKeyPhrases#string} + * + *
+ * System.out.println("Extracted phrases:");
+ * textAnalyticsAsyncClient.extractKeyPhrases("Bonjour tout le monde").subscribe(keyPhrase ->
+ * System.out.printf("%s.%n", keyPhrase));
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -700,7 +1019,13 @@ public MonoExtract 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.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.extractKeyPhrases#string-string} + * + *
+ * System.out.println("Extracted phrases:");
+ * textAnalyticsAsyncClient.extractKeyPhrases("Bonjour tout le monde", "fr")
+ * .subscribe(keyPhrase -> System.out.printf("%s.%n", keyPhrase));
+ *
+ *
*
* @param document The document to be analyzed. For text length limits, maximum batch size, and supported text
* encoding, see
@@ -727,7 +1052,26 @@ public MonoExtract 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.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.extractKeyPhrasesBatch#Iterable-String-TextAnalyticsRequestOptions} + * + *
+ * 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));
+ * });
+ * });
+ *
+ *
*
* @param documents A list of documents to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -764,7 +1108,34 @@ public MonoExtract key phrases in a list of {@link TextDocumentInput document} with provided request options. * Subscribes to the call asynchronously and prints out the key phrases when a response is received.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.extractKeyPhrasesBatch#Iterable-TextAnalyticsRequestOptions} + * + *
+ * 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);
+ * }
+ * }
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -796,7 +1167,24 @@ public MonoAnalyze the sentiment in a document. Subscribes to the call asynchronously and prints out the * sentiment details when a response is received.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.analyzeSentiment#string} + * + *
+ * 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());
+ * }
+ * });
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -820,7 +1208,23 @@ public MonoAnalyze 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.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.analyzeSentiment#String-String} + * + *
+ * 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());
+ * }
+ * });
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -850,7 +1254,27 @@ public Mono
+ * 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());
+ * }
+ * });
+ * }
+ * });
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -894,7 +1318,36 @@ public MonoAnalyze 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.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.analyzeSentimentBatch#Iterable-String-TextAnalyticsRequestOptions} + * + *
+ * 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()));
+ * });
+ * });
+ *
+ *
*
* @param documents A list of documents to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -932,7 +1385,51 @@ public Mono
+ * 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));
+ * });
+ * }
+ * });
+ * });
+ *
+ *
*
* @param documents A list of documents to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -969,7 +1466,41 @@ public Mono
+ * 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()));
+ * });
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -1005,7 +1536,47 @@ public Mono
+ * 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());
+ * }
+ * });
+ * });
+ * });
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -1037,7 +1608,7 @@ public Mono
+ * 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 = %s, erroneous document count = %s,"
+ * + " transaction count = %s, valid document count = %s.%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());
+ * });
+ * });
+ * });
+ * }));
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to be analyzed.
* @param options The additional configurable {@link AnalyzeHealthcareEntitiesOptions options} that may be passed
@@ -1105,7 +1743,48 @@ public MonoCode Sample
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.beginAnalyzeActions#Iterable-TextAnalyticsActions-String-AnalyzeActionsOptions} + * + *
+ * 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));
+ * });
+ * }
+ * });
+ * }));
+ *
+ *
*
* @param documents A list of documents to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -1143,7 +1822,49 @@ public PollerFluxCode Sample
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.beginAnalyzeActions#Iterable-TextAnalyticsActions-AnalyzeActionsOptions} + * + *
+ * 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));
+ * });
+ * }
+ * });
+ * }));
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to be analyzed.
* @param actions The {@link TextAnalyticsActions actions} that contains all actions to be executed.
diff --git a/sdk/textanalytics/azure-ai-textanalytics/src/main/java/com/azure/ai/textanalytics/TextAnalyticsClient.java b/sdk/textanalytics/azure-ai-textanalytics/src/main/java/com/azure/ai/textanalytics/TextAnalyticsClient.java
index f7f6a1ceadf7c..0d92134c70e7d 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/src/main/java/com/azure/ai/textanalytics/TextAnalyticsClient.java
+++ b/sdk/textanalytics/azure-ai-textanalytics/src/main/java/com/azure/ai/textanalytics/TextAnalyticsClient.java
@@ -51,8 +51,51 @@
* key phrases extraction, and sentiment analysis of a document or a list of documents.
*
* Instantiating a synchronous Text Analytics Client
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.instantiation} + * + *
+ * List<String> documents = Arrays.asList(
+ * "Elon Musk is the CEO of SpaceX and Tesla.",
+ * "My SSN is 859-98-0987"
+ * );
*
+ * SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
+ * textAnalyticsClient.beginAnalyzeActions(
+ * documents,
+ * new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
+ * .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
+ * .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
+ * "en",
+ * new AnalyzeActionsOptions().setIncludeStatistics(false));
+ * 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));
+ * });
+ * }
+ * });
+ * });
+ *
+ *
* View {@link TextAnalyticsClientBuilder this} for additional ways to construct the client.
* * @see TextAnalyticsClientBuilder @@ -99,7 +142,13 @@ public String getDefaultLanguage() { * *Code Sample
*Detects the language of single document.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.detectLanguage#String} + * + *
+ * DetectedLanguage detectedLanguage = textAnalyticsClient.detectLanguage("Bonjour tout le monde");
+ * System.out.printf("Detected language name: %s, ISO 6391 name: %s, confidence score: %f.%n",
+ * detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore());
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -120,7 +169,14 @@ public DetectedLanguage detectLanguage(String document) {
*
* Code Sample
*Detects the language of documents with a provided country hint.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.detectLanguage#String-String} + * + *
+ * DetectedLanguage detectedLanguage = textAnalyticsClient.detectLanguage(
+ * "This text is in English", "US");
+ * System.out.printf("Detected language name: %s, ISO 6391 name: %s, confidence score: %f.%n",
+ * detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore());
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -143,7 +199,31 @@ public DetectedLanguage detectLanguage(String document, String countryHint) {
*
* Code Sample
*Detects the language in a list of documents with a provided country hint and request options.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.detectLanguageBatch#Iterable-String-TextAnalyticsRequestOptions} + * + *
+ * List<String> documents = Arrays.asList(
+ * "This is written in English",
+ * "Este es un documento escrito en Español."
+ * );
+ *
+ * DetectLanguageResultCollection resultCollection =
+ * textAnalyticsClient.detectLanguageBatch(documents, "US", null);
+ *
+ * // Batch statistics
+ * TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
+ * System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * // Batch result of languages
+ * resultCollection.forEach(detectLanguageResult -> {
+ * System.out.printf("Document ID: %s%n", detectLanguageResult.getId());
+ * DetectedLanguage detectedLanguage = detectLanguageResult.getPrimaryLanguage();
+ * System.out.printf("Primary language name: %s, ISO 6391 name: %s, confidence score: %f.%n",
+ * detectedLanguage.getName(), detectedLanguage.getIso6391Name(),
+ * detectedLanguage.getConfidenceScore());
+ * });
+ *
+ *
*
* @param documents The list of documents to detect languages for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -172,7 +252,39 @@ public DetectLanguageResultCollection detectLanguageBatch(
* Code Sample
*Detects the languages with http response in a list of {@link DetectLanguageInput document} with provided * request options.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.detectLanguageBatch#Iterable-TextAnalyticsRequestOptions-Context} + * + *
+ * List<DetectLanguageInput> detectLanguageInputs = Arrays.asList(
+ * new DetectLanguageInput("1", "This is written in English.", "US"),
+ * new DetectLanguageInput("2", "Este es un documento escrito en Español.", "es")
+ * );
+ *
+ * Response<DetectLanguageResultCollection> response =
+ * textAnalyticsClient.detectLanguageBatchWithResponse(detectLanguageInputs,
+ * new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
+ *
+ * // Response's status code
+ * System.out.printf("Status code of request response: %d%n", response.getStatusCode());
+ * DetectLanguageResultCollection detectedLanguageResultCollection = response.getValue();
+ *
+ * // Batch statistics
+ * TextDocumentBatchStatistics batchStatistics = detectedLanguageResultCollection.getStatistics();
+ * System.out.printf(
+ * "Documents statistics: document count = %s, erroneous document count = %s, transaction count = %s,"
+ * + " valid document count = %s.%n",
+ * batchStatistics.getDocumentCount(), batchStatistics.getInvalidDocumentCount(),
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * // Batch result of languages
+ * detectedLanguageResultCollection.forEach(detectLanguageResult -> {
+ * System.out.printf("Document ID: %s%n", detectLanguageResult.getId());
+ * DetectedLanguage detectedLanguage = detectLanguageResult.getPrimaryLanguage();
+ * System.out.printf("Primary language name: %s, ISO 6391 name: %s, confidence score: %f.%n",
+ * detectedLanguage.getName(), detectedLanguage.getIso6391Name(),
+ * detectedLanguage.getConfidenceScore());
+ * });
+ *
+ *
*
* @param documents The list of {@link DetectLanguageInput documents} to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -205,7 +317,16 @@ public ResponseCode Sample
*Recognize the entities of documents
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizeCategorizedEntities#String} + * + *
+ * final CategorizedEntityCollection recognizeEntitiesResult =
+ * textAnalyticsClient.recognizeEntities("Satya Nadella is the CEO of Microsoft");
+ * for (CategorizedEntity entity : recognizeEntitiesResult) {
+ * System.out.printf("Recognized entity: %s, entity category: %s, confidence score: %f.%n",
+ * entity.getText(), entity.getCategory(), entity.getConfidenceScore());
+ * }
+ *
+ *
*
* @param document The document to recognize entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -230,7 +351,17 @@ public CategorizedEntityCollection recognizeEntities(String document) {
*
* Code Sample
*Recognizes the entities in a document with a provided language code.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizeCategorizedEntities#String-String} + * + *
+ * final CategorizedEntityCollection recognizeEntitiesResult =
+ * textAnalyticsClient.recognizeEntities("Satya Nadella is the CEO of Microsoft", "en");
+ *
+ * for (CategorizedEntity entity : recognizeEntitiesResult) {
+ * System.out.printf("Recognized entity: %s, entity category: %s, confidence score: %f.%n",
+ * entity.getText(), entity.getCategory(), entity.getConfidenceScore());
+ * }
+ *
+ *
*
* @param document The document to recognize entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -254,7 +385,27 @@ public CategorizedEntityCollection recognizeEntities(String document, String lan
*
* Code Sample
*Recognizes the entities in a list of documents with a provided language code and request options.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizeCategorizedEntitiesBatch#Iterable-String-TextAnalyticsRequestOptions} + * + *
+ * List<String> documents = Arrays.asList(
+ * "I had a wonderful trip to Seattle last week.",
+ * "I work at Microsoft.");
+ *
+ * RecognizeEntitiesResultCollection resultCollection =
+ * textAnalyticsClient.recognizeEntitiesBatch(documents, "en", null);
+ *
+ * // Batch statistics
+ * TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
+ * System.out.printf(
+ * "A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * resultCollection.forEach(recognizeEntitiesResult ->
+ * recognizeEntitiesResult.getEntities().forEach(entity ->
+ * System.out.printf("Recognized entity: %s, entity category: %s, confidence score: %f.%n",
+ * entity.getText(), entity.getCategory(), entity.getConfidenceScore())));
+ *
+ *
*
* @param documents A list of documents to recognize entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -282,7 +433,33 @@ public RecognizeEntitiesResultCollection recognizeEntitiesBatch(
* Code Sample
*Recognizes the entities with http response in a list of {@link TextDocumentInput document} with provided * request options.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizeEntitiesBatch#Iterable-TextAnalyticsRequestOptions-Context} + * + *
+ * List<TextDocumentInput> textDocumentInputs = 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")
+ * );
+ *
+ * Response<RecognizeEntitiesResultCollection> response =
+ * textAnalyticsClient.recognizeEntitiesBatchWithResponse(textDocumentInputs,
+ * new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
+ *
+ * // Response's status code
+ * System.out.printf("Status code of request response: %d%n", response.getStatusCode());
+ * RecognizeEntitiesResultCollection recognizeEntitiesResultCollection = response.getValue();
+ *
+ * // Batch statistics
+ * TextDocumentBatchStatistics batchStatistics = recognizeEntitiesResultCollection.getStatistics();
+ * System.out.printf(
+ * "A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * recognizeEntitiesResultCollection.forEach(recognizeEntitiesResult ->
+ * recognizeEntitiesResult.getEntities().forEach(entity ->
+ * System.out.printf("Recognized entity: %s, entity category: %s, confidence score: %f.%n",
+ * entity.getText(), entity.getCategory(), entity.getConfidenceScore())));
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to recognize entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -315,7 +492,18 @@ public ResponseCode Sample
*Recognize the PII entities details in a document.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizePiiEntities#String} + * + *
+ * PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities("My SSN is 859-98-0987");
+ * System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
+ * for (PiiEntity entity : piiEntityCollection) {
+ * 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());
+ * }
+ *
+ *
*
* @param document The document to recognize PII entities details for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -341,7 +529,17 @@ public PiiEntityCollection recognizePiiEntities(String document) {
* Code Sample
*Recognizes the PII entities details in a document with a provided language code.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizePiiEntities#String-String} + * + *
+ * PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities(
+ * "My SSN is 859-98-0987", "en");
+ * 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()));
+ *
+ *
*
* @param document The document to recognize PII entities details for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -369,7 +567,18 @@ public PiiEntityCollection recognizePiiEntities(String document, String language
* Recognizes the PII entities details in a document with a provided language code and * {@link RecognizePiiEntitiesOptions}.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizePiiEntities#String-String-RecognizePiiEntitiesOptions} + * + *
+ * PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities(
+ * "My SSN is 859-98-0987", "en",
+ * new RecognizePiiEntitiesOptions().setDomainFilter(PiiEntityDomain.PROTECTED_HEALTH_INFORMATION));
+ * 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()));
+ *
+ *
*
* @param document The document to recognize PII entities details for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -397,7 +606,31 @@ public PiiEntityCollection recognizePiiEntities(String document, String language
* Recognizes the PII entities details in a list of documents with a provided language code * and request options.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizePiiEntitiesBatch#Iterable-String-RecognizePiiEntitiesOptions} + * + *
+ * List<String> documents = Arrays.asList(
+ * "My SSN is 859-98-0987",
+ * "Visa card 4111 1111 1111 1111"
+ * );
+ *
+ * RecognizePiiEntitiesResultCollection resultCollection = textAnalyticsClient.recognizePiiEntitiesBatch(
+ * documents, "en", new RecognizePiiEntitiesOptions().setIncludeStatistics(true));
+ *
+ * // Batch statistics
+ * TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
+ * System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * resultCollection.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()));
+ * });
+ *
+ *
*
* @param documents A list of documents to recognize PII entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -425,7 +658,34 @@ public RecognizePiiEntitiesResultCollection recognizePiiEntitiesBatch(
* Recognizes the PII entities details with http response in a list of {@link TextDocumentInput document} * with provided request options.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizePiiEntitiesBatch#Iterable-RecognizePiiEntitiesOptions-Context} + * + *
+ * List<TextDocumentInput> textDocumentInputs = Arrays.asList(
+ * new TextDocumentInput("0", "My SSN is 859-98-0987"),
+ * new TextDocumentInput("1", "Visa card 4111 1111 1111 1111")
+ * );
+ *
+ * Response<RecognizePiiEntitiesResultCollection> response =
+ * textAnalyticsClient.recognizePiiEntitiesBatchWithResponse(textDocumentInputs,
+ * new RecognizePiiEntitiesOptions().setIncludeStatistics(true), Context.NONE);
+ *
+ * RecognizePiiEntitiesResultCollection resultCollection = response.getValue();
+ *
+ * // Batch statistics
+ * TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
+ * System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * resultCollection.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()));
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to recognize PII entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -457,7 +717,20 @@ public ResponseCode Sample
*Recognize the linked entities of documents
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizeLinkedEntities#String} + * + *
+ * final String document = "Old Faithful is a geyser at Yellowstone Park.";
+ * System.out.println("Linked Entities:");
+ * textAnalyticsClient.recognizeLinkedEntities(document).forEach(linkedEntity -> {
+ * 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()));
+ * });
+ *
+ *
*
* @param document The document to recognize linked entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -481,7 +754,19 @@ public LinkedEntityCollection recognizeLinkedEntities(String document) {
*
* Code Sample
*Recognizes the linked entities in a document with a provided language code.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizeLinkedEntities#String-String} + * + *
+ * String document = "Old Faithful is a geyser at Yellowstone Park.";
+ * textAnalyticsClient.recognizeLinkedEntities(document, "en").forEach(linkedEntity -> {
+ * 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()));
+ * });
+ *
+ *
*
* @param document The document to recognize linked entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -509,7 +794,33 @@ public LinkedEntityCollection recognizeLinkedEntities(String document, String la
* Code Sample
*Recognizes the linked entities in a list of documents with a provided language code and request options. *
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizeLinkedEntitiesBatch#Iterable-String-TextAnalyticsRequestOptions} + * + *
+ * List<String> documents = Arrays.asList(
+ * "Old Faithful is a geyser at Yellowstone Park.",
+ * "Mount Shasta has lenticular clouds."
+ * );
+ *
+ * RecognizeLinkedEntitiesResultCollection resultCollection =
+ * textAnalyticsClient.recognizeLinkedEntitiesBatch(documents, "en", null);
+ *
+ * // Batch statistics
+ * TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
+ * System.out.printf("A batch of documents 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: %f.%n",
+ * entityMatch.getText(), entityMatch.getConfidenceScore()));
+ * }));
+ *
+ *
*
* @param documents A list of documents to recognize linked entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -540,7 +851,39 @@ public RecognizeLinkedEntitiesResultCollection recognizeLinkedEntitiesBatch(
* Code Sample
*Recognizes the linked entities with http response in a list of {@link TextDocumentInput} with request options. *
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.recognizeLinkedEntitiesBatch#Iterable-TextAnalyticsRequestOptions-Context} + * + *
+ * List<TextDocumentInput> textDocumentInputs = Arrays.asList(
+ * new TextDocumentInput("1", "Old Faithful is a geyser at Yellowstone Park.").setLanguage("en"),
+ * new TextDocumentInput("2", "Mount Shasta has lenticular clouds.").setLanguage("en")
+ * );
+ *
+ * Response<RecognizeLinkedEntitiesResultCollection> response =
+ * textAnalyticsClient.recognizeLinkedEntitiesBatchWithResponse(textDocumentInputs,
+ * new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
+ *
+ * // 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(
+ * "A batch of documents 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()));
+ * }));
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to recognize linked entities for.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -572,7 +915,14 @@ public ResponseCode Sample
*Extracts key phrases of documents
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.extractKeyPhrases#String} + * + *
+ * System.out.println("Extracted phrases:");
+ * for (String keyPhrase : textAnalyticsClient.extractKeyPhrases("My cat might need to see a veterinarian.")) {
+ * System.out.printf("%s.%n", keyPhrase);
+ * }
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -594,7 +944,13 @@ public KeyPhrasesCollection extractKeyPhrases(String document) {
*
* Code Sample
*Extracts key phrases in a document with a provided language representation.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.extractKeyPhrases#String-String-Context} + * + *
+ * System.out.println("Extracted phrases:");
+ * textAnalyticsClient.extractKeyPhrases("My cat might need to see a veterinarian.", "en")
+ * .forEach(kegPhrase -> System.out.printf("%s.%n", kegPhrase));
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -621,7 +977,32 @@ public KeyPhrasesCollection extractKeyPhrases(String document, String language)
*
* Code Sample
*Extracts key phrases in a list of documents with a provided language code and request options.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.extractKeyPhrasesBatch#Iterable-String-TextAnalyticsRequestOptions} + * + *
+ * List<String> documents = Arrays.asList(
+ * "My cat might need to see a veterinarian.",
+ * "The pitot tube is used to measure airspeed."
+ * );
+ *
+ * // Extracting batch key phrases
+ * ExtractKeyPhrasesResultCollection resultCollection =
+ * textAnalyticsClient.extractKeyPhrasesBatch(documents, "en", null);
+ *
+ * // Batch statistics
+ * TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
+ * System.out.printf(
+ * "A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * // Extracted key phrase for each of documents from a batch of documents
+ * resultCollection.forEach(extractKeyPhraseResult -> {
+ * System.out.printf("Document ID: %s%n", extractKeyPhraseResult.getId());
+ * // Valid document
+ * System.out.println("Extracted phrases:");
+ * extractKeyPhraseResult.getKeyPhrases().forEach(keyPhrase -> System.out.printf("%s.%n", keyPhrase));
+ * });
+ *
+ *
*
* @param documents A list of documents to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -651,7 +1032,39 @@ public ExtractKeyPhrasesResultCollection extractKeyPhrasesBatch(
*
* Code Sample
*Extracts key phrases with http response in a list of {@link TextDocumentInput} with request options.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.extractKeyPhrasesBatch#Iterable-TextAnalyticsRequestOptions-Context} + * + *
+ * List<TextDocumentInput> textDocumentInputs = Arrays.asList(
+ * new TextDocumentInput("1", "My cat might need to see a veterinarian.").setLanguage("en"),
+ * new TextDocumentInput("2", "The pitot tube is used to measure airspeed.").setLanguage("en")
+ * );
+ *
+ * // Extracting batch key phrases
+ * Response<ExtractKeyPhrasesResultCollection> response =
+ * textAnalyticsClient.extractKeyPhrasesBatchWithResponse(textDocumentInputs,
+ * new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
+ *
+ *
+ * // 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(
+ * "A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * // Extracted key phrase for each of documents from a batch of documents
+ * resultCollection.forEach(extractKeyPhraseResult -> {
+ * System.out.printf("Document ID: %s%n", extractKeyPhraseResult.getId());
+ * // Valid document
+ * System.out.println("Extracted phrases:");
+ * extractKeyPhraseResult.getKeyPhrases().forEach(keyPhrase ->
+ * System.out.printf("%s.%n", keyPhrase));
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -685,7 +1098,28 @@ public ResponseCode Sample
*Analyze the sentiments of documents
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.analyzeSentiment#String} + * + *
+ * final DocumentSentiment documentSentiment =
+ * textAnalyticsClient.analyzeSentiment("The hotel was dark and unclean.");
+ *
+ * System.out.printf(
+ * "Recognized sentiment: %s, positive score: %.2f, neutral score: %.2f, negative score: %.2f.%n",
+ * documentSentiment.getSentiment(),
+ * documentSentiment.getConfidenceScores().getPositive(),
+ * documentSentiment.getConfidenceScores().getNeutral(),
+ * documentSentiment.getConfidenceScores().getNegative());
+ *
+ * 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());
+ * }
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -708,7 +1142,28 @@ public DocumentSentiment analyzeSentiment(String document) {
* Code Sample
*Analyze the sentiments in a document with a provided language representation.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.analyzeSentiment#String-String} + * + *
+ * final DocumentSentiment documentSentiment = textAnalyticsClient.analyzeSentiment(
+ * "The hotel was dark and unclean.", "en");
+ *
+ * System.out.printf(
+ * "Recognized sentiment: %s, positive score: %.2f, neutral score: %.2f, negative score: %.2f.%n",
+ * documentSentiment.getSentiment(),
+ * documentSentiment.getConfidenceScores().getPositive(),
+ * documentSentiment.getConfidenceScores().getNeutral(),
+ * documentSentiment.getConfidenceScores().getNegative());
+ *
+ * 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());
+ * }
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -737,7 +1192,25 @@ public DocumentSentiment analyzeSentiment(String document, String language) {
* Analyze the sentiment and mine the opinions for each sentence in a document with a provided language * representation and {@link AnalyzeSentimentOptions} options.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.analyzeSentiment#String-String-AnalyzeSentimentOptions} + * + *
+ * final DocumentSentiment documentSentiment = textAnalyticsClient.analyzeSentiment(
+ * "The hotel was dark and unclean.", "en",
+ * new AnalyzeSentimentOptions().setIncludeOpinionMining(true));
+ * 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());
+ * }
+ * });
+ * }
+ *
+ *
*
* @param document The document to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -763,7 +1236,44 @@ public DocumentSentiment analyzeSentiment(String document, String language, Anal
*
* Code Sample
*Analyze the sentiments in a list of documents with a provided language representation and request options.
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.analyzeSentimentBatch#Iterable-String-TextAnalyticsRequestOptions} + * + *
+ * List<String> documents = Arrays.asList(
+ * "The hotel was dark and unclean. The restaurant had amazing gnocchi.",
+ * "The restaurant had amazing gnocchi. The hotel was dark and unclean."
+ * );
+ *
+ * // Analyzing batch sentiments
+ * AnalyzeSentimentResultCollection resultCollection = textAnalyticsClient.analyzeSentimentBatch(
+ * documents, "en", new TextAnalyticsRequestOptions().setIncludeStatistics(true));
+ *
+ * // Batch statistics
+ * TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
+ * System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * // Analyzed sentiment for each of documents from a batch of documents
+ * resultCollection.forEach(analyzeSentimentResult -> {
+ * System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
+ * // Valid document
+ * DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
+ * System.out.printf(
+ * "Recognized document sentiment: %s, positive score: %.2f, neutral score: %.2f,"
+ * + " negative score: %.2f.%n",
+ * documentSentiment.getSentiment(),
+ * documentSentiment.getConfidenceScores().getPositive(),
+ * documentSentiment.getConfidenceScores().getNeutral(),
+ * documentSentiment.getConfidenceScores().getNegative());
+ * 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()));
+ * });
+ *
+ *
*
* @param documents A list of documents to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -798,7 +1308,36 @@ public AnalyzeSentimentResultCollection analyzeSentimentBatch(
* Analyze the sentiments and mine the opinions for each sentence in a list of documents with a provided language * representation and {@link AnalyzeSentimentOptions} options.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.analyzeSentimentBatch#Iterable-String-AnalyzeSentimentOptions} + * + *
+ * List<String> documents = Arrays.asList(
+ * "The hotel was dark and unclean. The restaurant had amazing gnocchi.",
+ * "The restaurant had amazing gnocchi. The hotel was dark and unclean."
+ * );
+ *
+ * // Analyzing batch sentiments
+ * AnalyzeSentimentResultCollection resultCollection = textAnalyticsClient.analyzeSentimentBatch(
+ * documents, "en", new AnalyzeSentimentOptions().setIncludeOpinionMining(true));
+ *
+ * // Analyzed sentiment for each of documents from a batch of documents
+ * 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("\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());
+ * }
+ * });
+ * });
+ * });
+ *
+ *
*
* @param documents A list of documents to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -826,7 +1365,53 @@ public AnalyzeSentimentResultCollection analyzeSentimentBatch(IterableCode Sample
*Analyze sentiment in a list of {@link TextDocumentInput document} with provided request options.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.analyzeSentimentBatch#Iterable-TextAnalyticsRequestOptions-Context} + * + *
+ * List<TextDocumentInput> textDocumentInputs = Arrays.asList(
+ * new TextDocumentInput("1", "The hotel was dark and unclean. The restaurant had amazing gnocchi.")
+ * .setLanguage("en"),
+ * new TextDocumentInput("2", "The restaurant had amazing gnocchi. The hotel was dark and unclean.")
+ * .setLanguage("en")
+ * );
+ *
+ * // Analyzing batch sentiments
+ * Response<AnalyzeSentimentResultCollection> response =
+ * textAnalyticsClient.analyzeSentimentBatchWithResponse(textDocumentInputs,
+ * new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
+ *
+ * // 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("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * // Analyzed sentiment for each of documents from a batch of documents
+ * resultCollection.forEach(analyzeSentimentResult -> {
+ * System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
+ * // Valid document
+ * DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
+ * System.out.printf(
+ * "Recognized document sentiment: %s, positive score: %.2f, neutral score: %.2f, "
+ * + "negative score: %.2f.%n",
+ * documentSentiment.getSentiment(),
+ * documentSentiment.getConfidenceScores().getPositive(),
+ * documentSentiment.getConfidenceScores().getNeutral(),
+ * documentSentiment.getConfidenceScores().getNegative());
+ * 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());
+ * });
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -864,7 +1449,51 @@ public ResponseAnalyze sentiment and mine the opinions for each sentence in a list of * {@link TextDocumentInput document} with provided {@link AnalyzeSentimentOptions} options.
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.analyzeSentimentBatch#Iterable-AnalyzeSentimentOptions-Context} + * + *
+ * List<TextDocumentInput> textDocumentInputs = Arrays.asList(
+ * new TextDocumentInput("1", "The hotel was dark and unclean. The restaurant had amazing gnocchi.")
+ * .setLanguage("en"),
+ * new TextDocumentInput("2", "The restaurant had amazing gnocchi. The hotel was dark and unclean.")
+ * .setLanguage("en")
+ * );
+ *
+ * AnalyzeSentimentOptions options = new AnalyzeSentimentOptions().setIncludeOpinionMining(true)
+ * .setIncludeStatistics(true);
+ *
+ * // Analyzing batch sentiments
+ * Response<AnalyzeSentimentResultCollection> response =
+ * textAnalyticsClient.analyzeSentimentBatchWithResponse(textDocumentInputs, options, Context.NONE);
+ *
+ * // 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("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
+ * batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
+ *
+ * // Analyzed sentiment for each of documents from a batch of documents
+ * 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("\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());
+ * }
+ * });
+ * });
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -897,7 +1526,7 @@ public Response
+ * 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."));
+ * }
+ *
+ * // Request options: show statistics and model version
+ * AnalyzeHealthcareEntitiesOptions options = new AnalyzeHealthcareEntitiesOptions()
+ * .setIncludeStatistics(true);
+ *
+ * SyncPoller<AnalyzeHealthcareEntitiesOperationDetail, AnalyzeHealthcareEntitiesPagedIterable>
+ * syncPoller = textAnalyticsClient.beginAnalyzeHealthcareEntities(documents, options, Context.NONE);
+ *
+ * syncPoller.waitForCompletion();
+ * AnalyzeHealthcareEntitiesPagedIterable result = syncPoller.getFinalResult();
+ *
+ * // Task operation statistics
+ * final AnalyzeHealthcareEntitiesOperationDetail operationResult = syncPoller.poll().getValue();
+ * System.out.printf("Operation created time: %s, expiration time: %s.%n",
+ * operationResult.getCreatedAt(), operationResult.getExpiresAt());
+ *
+ * result.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 = %s, erroneous document count = %s,"
+ * + " transaction count = %s, valid document count = %s.%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());
+ * });
+ * });
+ * });
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to be analyzed.
* @param options The additional configurable {@link AnalyzeHealthcareEntitiesOptions options} that may be passed
@@ -966,7 +1661,51 @@ public ResponseCode Sample
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.beginAnalyzeActions#Iterable-TextAnalyticsActions-String-AnalyzeActionsOptions} + * + *
+ * List<String> documents = Arrays.asList(
+ * "Elon Musk is the CEO of SpaceX and Tesla.",
+ * "My SSN is 859-98-0987"
+ * );
+ *
+ * SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
+ * textAnalyticsClient.beginAnalyzeActions(
+ * documents,
+ * new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
+ * .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
+ * .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
+ * "en",
+ * new AnalyzeActionsOptions().setIncludeStatistics(false));
+ * 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));
+ * });
+ * }
+ * });
+ * });
+ *
+ *
*
* @param documents A list of documents to be analyzed.
* For text length limits, maximum batch size, and supported text encoding, see
@@ -1004,7 +1743,51 @@ public SyncPollerCode Sample
- * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.beginAnalyzeActions#Iterable-TextAnalyticsActions-AnalyzeActionsOptions-Context} + * + *
+ * 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));
+ * });
+ * }
+ * });
+ * });
+ *
+ *
*
* @param documents A list of {@link TextDocumentInput documents} to be analyzed.
* @param actions The {@link TextAnalyticsActions actions} that contains all actions to be executed.
diff --git a/sdk/textanalytics/azure-ai-textanalytics/src/main/java/com/azure/ai/textanalytics/TextAnalyticsClientBuilder.java b/sdk/textanalytics/azure-ai-textanalytics/src/main/java/com/azure/ai/textanalytics/TextAnalyticsClientBuilder.java
index 0d14ba586e7c5..a1542eac17764 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/src/main/java/com/azure/ai/textanalytics/TextAnalyticsClientBuilder.java
+++ b/sdk/textanalytics/azure-ai-textanalytics/src/main/java/com/azure/ai/textanalytics/TextAnalyticsClientBuilder.java
@@ -52,11 +52,25 @@
*
* Instantiating an asynchronous Text Analytics Client
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsAsyncClient.instantiation} + * + *
+ * TextAnalyticsAsyncClient textAnalyticsAsyncClient = new TextAnalyticsClientBuilder()
+ * .credential(new AzureKeyCredential("{key}"))
+ * .endpoint("{endpoint}")
+ * .buildAsyncClient();
+ *
+ *
*
* Instantiating a synchronous Text Analytics Client
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.instantiation} + * + *
+ * TextAnalyticsClient textAnalyticsClient = new TextAnalyticsClientBuilder()
+ * .credential(new AzureKeyCredential("{key}"))
+ * .endpoint("{endpoint}")
+ * .buildClient();
+ *
+ *
*
* * Another way to construct the client is using a {@link HttpPipeline}. The pipeline gives the client an authenticated @@ -65,7 +79,19 @@ * on how the {@link TextAnalyticsClient} and {@link TextAnalyticsAsyncClient} is built. *
* - * {@codesnippet com.azure.ai.textanalytics.TextAnalyticsClient.pipeline.instantiation} + * + *
+ * HttpPipeline pipeline = new HttpPipelineBuilder()
+ * .policies(/* add policies */)
+ * .build();
+ *
+ * TextAnalyticsClient textAnalyticsClient = new TextAnalyticsClientBuilder()
+ * .credential(new AzureKeyCredential("{key}"))
+ * .endpoint("{endpoint}")
+ * .pipeline(pipeline)
+ * .buildClient();
+ *
+ *
*
* @see TextAnalyticsAsyncClient
* @see TextAnalyticsClient
diff --git a/sdk/textanalytics/azure-ai-textanalytics/src/samples/java/com/azure/ai/textanalytics/ReadmeSamples.java b/sdk/textanalytics/azure-ai-textanalytics/src/samples/java/com/azure/ai/textanalytics/ReadmeSamples.java
index c58ddc77a1927..05dd5809d1d79 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/src/samples/java/com/azure/ai/textanalytics/ReadmeSamples.java
+++ b/sdk/textanalytics/azure-ai-textanalytics/src/samples/java/com/azure/ai/textanalytics/ReadmeSamples.java
@@ -49,47 +49,56 @@ public class ReadmeSamples {
* Code snippet for configuring http client.
*/
public void configureHttpClient() {
+ // BEGIN: readme-sample-configureHttpClient
HttpClient client = new NettyAsyncHttpClientBuilder()
.port(8080)
.wiretap(true)
.build();
+ // END: readme-sample-configureHttpClient
}
/**
* Code snippet for getting sync client using the AzureKeyCredential authentication.
*/
public void useAzureKeyCredentialSyncClient() {
+ // BEGIN: readme-sample-createTextAnalyticsClientWithKeyCredential
TextAnalyticsClient textAnalyticsClient = new TextAnalyticsClientBuilder()
.credential(new AzureKeyCredential("{key}"))
.endpoint("{endpoint}")
.buildClient();
+ // END: readme-sample-createTextAnalyticsClientWithKeyCredential
}
/**
* Code snippet for getting async client using AzureKeyCredential authentication.
*/
public void useAzureKeyCredentialAsyncClient() {
- TextAnalyticsAsyncClient textAnalyticsClient = new TextAnalyticsClientBuilder()
+ // BEGIN: readme-sample-createTextAnalyticsAsyncClientWithKeyCredential
+ TextAnalyticsAsyncClient textAnalyticsAsyncClient = new TextAnalyticsClientBuilder()
.credential(new AzureKeyCredential("{key}"))
.endpoint("{endpoint}")
.buildAsyncClient();
+ // END: readme-sample-createTextAnalyticsAsyncClientWithKeyCredential
}
/**
* Code snippet for getting async client using AAD authentication.
*/
public void useAadAsyncClient() {
+ // BEGIN: readme-sample-createTextAnalyticsAsyncClientWithAAD
TokenCredential defaultCredential = new DefaultAzureCredentialBuilder().build();
- TextAnalyticsAsyncClient textAnalyticsClient = new TextAnalyticsClientBuilder()
+ TextAnalyticsAsyncClient textAnalyticsAsyncClient = new TextAnalyticsClientBuilder()
.endpoint("{endpoint}")
.credential(defaultCredential)
.buildAsyncClient();
+ // END: readme-sample-createTextAnalyticsAsyncClientWithAAD
}
/**
* Code snippet for rotating AzureKeyCredential of the client
*/
public void rotatingAzureKeyCredential() {
+ // BEGIN: readme-sample-rotatingAzureKeyCredential
AzureKeyCredential credential = new AzureKeyCredential("{key}");
TextAnalyticsClient textAnalyticsClient = new TextAnalyticsClientBuilder()
.credential(credential)
@@ -97,12 +106,14 @@ public void rotatingAzureKeyCredential() {
.buildClient();
credential.update("{new_key}");
+ // END: readme-sample-rotatingAzureKeyCredential
}
/**
* Code snippet for handling exception
*/
public void handlingException() {
+ // BEGIN: readme-sample-handlingException
List