TextAnalyticsClient Class

The Language service API is a suite of natural language processing (NLP) skills built with the best-in-class Microsoft machine learning algorithms. The API can be used to analyze unstructured text for tasks such as sentiment analysis, key phrase extraction, entities recognition, and language detection, and more.

Further documentation can be found in https://docs.microsoft.com/azure/cognitive-services/language-service/overview

Inheritance

azure.ai.textanalytics._base_client.TextAnalyticsClientBase

TextAnalyticsClient

Constructor

TextAnalyticsClient(endpoint: str, credential: AzureKeyCredential | TokenCredential, *, default_language: str | None = None, default_country_hint: str | None = None, api_version: str | TextAnalyticsApiVersion | None = None, **kwargs: Any)

Parameters

Name	Description
endpoint Required	str Supported Cognitive Services or Language resource endpoints (protocol and hostname, for example: 'https://.cognitiveservices.azure.com').
credential Required	AzureKeyCredential or TokenCredential Credentials needed for the client to connect to Azure. This can be the an instance of AzureKeyCredential if using a Cognitive Services/Language API key or a token credential from identity.

Keyword-Only Parameters

Name	Description
default_country_hint	str Sets the default country_hint to use for all operations. Defaults to "US". If you don't want to use a country hint, pass the string "none".
default_language	str Sets the default language to use for all operations. Defaults to "en".
api_version	str or TextAnalyticsApiVersion The API version of the service to use for requests. It defaults to the latest service version. Setting to an older version may result in reduced feature compatibility.

Examples

Creating the TextAnalyticsClient with endpoint and API key.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient
   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint, AzureKeyCredential(key))

Creating the TextAnalyticsClient with endpoint and token credential from Azure Active Directory.

   import os
   from azure.ai.textanalytics import TextAnalyticsClient
   from azure.identity import DefaultAzureCredential

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   credential = DefaultAzureCredential()

   text_analytics_client = TextAnalyticsClient(endpoint, credential=credential)

Methods

analyze_sentiment	Analyze sentiment for a batch of documents. Turn on opinion mining with show_opinion_mining. Returns a sentiment prediction, as well as sentiment scores for each sentiment class (Positive, Negative, and Neutral) for the document and each sentence within it. See https://aka.ms/azsdk/textanalytics/data-limits for service data limits. New in version v3.1: The show_opinion_mining, disable_service_logs, and string_index_type keyword arguments.
begin_abstract_summary	Start a long-running abstractive summarization operation. For a conceptual discussion of abstractive summarization, see the service documentation: https://learn.microsoft.com/azure/cognitive-services/language-service/summarization/overview New in version 2023-04-01: The begin_abstract_summary client method.
begin_analyze_actions	Start a long-running operation to perform a variety of text analysis actions over a batch of documents. We recommend you use this function if you're looking to analyze larger documents, and / or combine multiple text analysis actions into one call. Otherwise, we recommend you use the action specific endpoints, for example analyze_sentiment. Note See the service documentation for regional support of custom action features: https://aka.ms/azsdk/textanalytics/customfunctionalities New in version v3.1: The begin_analyze_actions client method. New in version 2022-05-01: The RecognizeCustomEntitiesAction, SingleLabelClassifyAction, MultiLabelClassifyAction, and AnalyzeHealthcareEntitiesAction input options and the corresponding RecognizeCustomEntitiesResult, ClassifyDocumentResult, and AnalyzeHealthcareEntitiesResult result objects New in version 2023-04-01: The ExtractiveSummaryAction and AbstractiveSummaryAction input options and the corresponding ExtractiveSummaryResult and AbstractiveSummaryResult result objects.
begin_analyze_healthcare_entities	Analyze healthcare entities and identify relationships between these entities in a batch of documents. Entities are associated with references that can be found in existing knowledge bases, such as UMLS, CHV, MSH, etc. We also extract the relations found between entities, for example in "The subject took 100 mg of ibuprofen", we would extract the relationship between the "100 mg" dosage and the "ibuprofen" medication. New in version v3.1: The begin_analyze_healthcare_entities client method. New in version 2022-05-01: The display_name keyword argument.
begin_extract_summary	Start a long-running extractive summarization operation. For a conceptual discussion of extractive summarization, see the service documentation: https://learn.microsoft.com/azure/cognitive-services/language-service/summarization/overview New in version 2023-04-01: The begin_extract_summary client method.
begin_multi_label_classify	Start a long-running custom multi label classification operation. For information on regional support of custom features and how to train a model to classify your documents, see https://aka.ms/azsdk/textanalytics/customfunctionalities New in version 2022-05-01: The begin_multi_label_classify client method.
begin_recognize_custom_entities	Start a long-running custom named entity recognition operation. For information on regional support of custom features and how to train a model to recognize custom entities, see https://aka.ms/azsdk/textanalytics/customentityrecognition New in version 2022-05-01: The begin_recognize_custom_entities client method.
begin_single_label_classify	Start a long-running custom single label classification operation. For information on regional support of custom features and how to train a model to classify your documents, see https://aka.ms/azsdk/textanalytics/customfunctionalities New in version 2022-05-01: The begin_single_label_classify client method.
close	Close sockets opened by the client. Calling this method is unnecessary when using the client as a context manager.
detect_language	Detect language for a batch of documents. Returns the detected language and a numeric score between zero and one. Scores close to one indicate 100% certainty that the identified language is true. See https://aka.ms/talangs for the list of enabled languages. See https://aka.ms/azsdk/textanalytics/data-limits for service data limits. New in version v3.1: The disable_service_logs keyword argument.
extract_key_phrases	Extract key phrases from a batch of documents. Returns a list of strings denoting the key phrases in the input text. For example, for the input text "The food was delicious and there were wonderful staff", the API returns the main talking points: "food" and "wonderful staff" See https://aka.ms/azsdk/textanalytics/data-limits for service data limits. New in version v3.1: The disable_service_logs keyword argument.
recognize_entities	Recognize entities for a batch of documents. Identifies and categorizes entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. For the list of supported entity types, check: https://aka.ms/taner See https://aka.ms/azsdk/textanalytics/data-limits for service data limits. New in version v3.1: The disable_service_logs and string_index_type keyword arguments.
recognize_linked_entities	Recognize linked entities from a well-known knowledge base for a batch of documents. Identifies and disambiguates the identity of each entity found in text (for example, determining whether an occurrence of the word Mars refers to the planet, or to the Roman god of war). Recognized entities are associated with URLs to a well-known knowledge base, like Wikipedia. See https://aka.ms/azsdk/textanalytics/data-limits for service data limits. New in version v3.1: The disable_service_logs and string_index_type keyword arguments.
recognize_pii_entities	Recognize entities containing personal information for a batch of documents. Returns a list of personal information entities ("SSN", "Bank Account", etc) in the document. For the list of supported entity types, check https://aka.ms/azsdk/language/pii See https://aka.ms/azsdk/textanalytics/data-limits for service data limits. New in version v3.1: The recognize_pii_entities client method.

analyze_sentiment

Analyze sentiment for a batch of documents. Turn on opinion mining with show_opinion_mining.

Returns a sentiment prediction, as well as sentiment scores for each sentiment class (Positive, Negative, and Neutral) for the document and each sentence within it.

See https://aka.ms/azsdk/textanalytics/data-limits for service data limits.

New in version v3.1: The show_opinion_mining, disable_service_logs, and string_index_type keyword arguments.

analyze_sentiment(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, disable_service_logs: bool | None = None, language: str | None = None, model_version: str | None = None, show_opinion_mining: bool | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> List[AnalyzeSentimentResult | DocumentError]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.

Keyword-Only Parameters

Name	Description
show_opinion_mining	bool Whether to mine the opinions of a sentence and conduct more granular analysis around the aspects of a product or service (also known as aspect-based sentiment analysis). If set to true, the returned SentenceSentiment objects will have property mined_opinions containing the result of this analysis. Only available for API version v3.1 and up.
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
model_version	str The model version to use for the analysis, e.g. "latest". If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning
show_stats	bool If set to true, response will contain document level statistics in the statistics field of the document-level response.
string_index_type	str Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodeUnit or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

Type	Description
list[AnalyzeSentimentResult, DocumentError]	The combined list of AnalyzeSentimentResult and DocumentError in the order the original documents were passed in.

Exceptions

Type	Description
HttpResponseError
TypeError
ValueError

Examples

Analyze sentiment in a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))

   documents = [
       """I had the best day of my life. I decided to go sky-diving and it made me appreciate my whole life so much more.
       I developed a deep-connection with my instructor as well, and I feel as if I've made a life-long friend in her.""",
       """This was a waste of my time. All of the views on this drop are extremely boring, all I saw was grass. 0/10 would
       not recommend to any divers, even first timers.""",
       """This was pretty good! The sights were ok, and I had fun with my instructors! Can't complain too much about my experience""",
       """I only have one word for my experience: WOW!!! I can't believe I have had such a wonderful skydiving company right
       in my backyard this whole time! I will definitely be a repeat customer, and I want to take my grandmother skydiving too,
       I know she'll love it!"""
   ]


   result = text_analytics_client.analyze_sentiment(documents, show_opinion_mining=True)
   docs = [doc for doc in result if not doc.is_error]

   print("Let's visualize the sentiment of each of these documents")
   for idx, doc in enumerate(docs):
       print(f"Document text: {documents[idx]}")
       print(f"Overall sentiment: {doc.sentiment}")

begin_abstract_summary

Start a long-running abstractive summarization operation.

For a conceptual discussion of abstractive summarization, see the service documentation: https://learn.microsoft.com/azure/cognitive-services/language-service/summarization/overview

New in version 2023-04-01: The begin_abstract_summary client method.

begin_abstract_summary(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, model_version: str | None = None, string_index_type: str | None = None, sentence_count: int | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[AbstractiveSummaryResult | DocumentError]]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.

Keyword-Only Parameters

Name	Description
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
show_stats	bool If set to true, response will contain document level statistics.
sentence_count	Optional[int] It controls the approximate number of sentences in the output summaries.
model_version	Optional[str] The model version to use for the analysis, e.g. "latest". If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning
string_index_type	Optional[str] Specifies the method used to interpret string offsets.
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.
polling_interval	int Waiting time between two polls for LRO operations if no Retry-After header is present. Defaults to 5 seconds.
continuation_token	str Call continuation_token() on the poller object to save the long-running operation (LRO) state into an opaque token. Pass the value as the continuation_token keyword argument to restart the LRO from a saved state.
display_name	str An optional display name to set for the requested analysis.

Returns

Type	Description
TextAnalysisLROPoller[ItemPaged[ AbstractiveSummaryResult, DocumentError]]	An instance of an TextAnalysisLROPoller. Call result() on the this object to return a heterogeneous pageable of AbstractiveSummaryResult and DocumentError.

Exceptions

Type	Description
HttpResponseError

Examples

Perform abstractive summarization on a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   document = [
       "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, "
       "human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI Cognitive "
       "Services, I have been working with a team of amazing scientists and engineers to turn this quest into a "
       "reality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of "
       "human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the "
       "intersection of all three, there's magic-what we call XYZ-code as illustrated in Figure 1-a joint "
       "representation to create more powerful AI that can speak, hear, see, and understand humans better. "
       "We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, "
       "spanning modalities and languages. The goal is to have pretrained models that can jointly learn "
       "representations to support a broad range of downstream AI tasks, much in the way humans do today. "
       "Over the past five years, we have achieved human performance on benchmarks in conversational speech "
       "recognition, machine translation, conversational question answering, machine reading comprehension, "
       "and image captioning. These five breakthroughs provided us with strong signals toward our more ambitious "
       "aspiration to produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
       "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a foundational "
       "component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks."
   ]

   poller = text_analytics_client.begin_abstract_summary(document)
   abstract_summary_results = poller.result()
   for result in abstract_summary_results:
       if result.kind == "AbstractiveSummarization":
           print("Summaries abstracted:")
           [print(f"{summary.text}\n") for summary in result.summaries]
       elif result.is_error is True:
           print("...Is an error with code '{}' and message '{}'".format(
               result.error.code, result.error.message
           ))

begin_analyze_actions

Start a long-running operation to perform a variety of text analysis actions over a batch of documents.

We recommend you use this function if you're looking to analyze larger documents, and / or combine multiple text analysis actions into one call. Otherwise, we recommend you use the action specific endpoints, for example analyze_sentiment.

Note

See the service documentation for regional support of custom action features:

https://aka.ms/azsdk/textanalytics/customfunctionalities

New in version v3.1: The begin_analyze_actions client method.

New in version 2022-05-01: The RecognizeCustomEntitiesAction, SingleLabelClassifyAction, MultiLabelClassifyAction, and AnalyzeHealthcareEntitiesAction input options and the corresponding RecognizeCustomEntitiesResult, ClassifyDocumentResult, and AnalyzeHealthcareEntitiesResult result objects

New in version 2023-04-01: The ExtractiveSummaryAction and AbstractiveSummaryAction input options and the corresponding ExtractiveSummaryResult and AbstractiveSummaryResult result objects.

begin_analyze_actions(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], actions: List[RecognizeEntitiesAction | RecognizeLinkedEntitiesAction | RecognizePiiEntitiesAction | ExtractKeyPhrasesAction | AnalyzeSentimentAction | RecognizeCustomEntitiesAction | SingleLabelClassifyAction | MultiLabelClassifyAction | AnalyzeHealthcareEntitiesAction | ExtractiveSummaryAction | AbstractiveSummaryAction], *, continuation_token: str | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[List[RecognizeEntitiesResult | RecognizeLinkedEntitiesResult | RecognizePiiEntitiesResult | ExtractKeyPhrasesResult | AnalyzeSentimentResult | RecognizeCustomEntitiesResult | ClassifyDocumentResult | AnalyzeHealthcareEntitiesResult | ExtractiveSummaryResult | AbstractiveSummaryResult | DocumentError]]]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.
actions Required	list[RecognizeEntitiesAction or RecognizePiiEntitiesAction or ExtractKeyPhrasesAction or RecognizeLinkedEntitiesAction or AnalyzeSentimentAction or RecognizeCustomEntitiesAction or SingleLabelClassifyAction or MultiLabelClassifyAction or AnalyzeHealthcareEntitiesAction or ExtractiveSummaryAction or AbstractiveSummaryAction] A heterogeneous list of actions to perform on the input documents. Each action object encapsulates the parameters used for the particular action type. The action results will be in the same order of the input actions.

Keyword-Only Parameters

Name	Description
display_name	str An optional display name to set for the requested analysis.
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
show_stats	bool If set to true, response will contain document level statistics.
polling_interval	int Waiting time between two polls for LRO operations if no Retry-After header is present. Defaults to 5 seconds.
continuation_token	str Call continuation_token() on the poller object to save the long-running operation (LRO) state into an opaque token. Pass the value as the continuation_token keyword argument to restart the LRO from a saved state.

Returns

Type

Description

TextAnalysisLROPoller[ItemPaged[ list[RecognizeEntitiesResult, RecognizeLinkedEntitiesResult, RecognizePiiEntitiesResult, ExtractKeyPhrasesResult, AnalyzeSentimentResult, RecognizeCustomEntitiesResult, ClassifyDocumentResult, AnalyzeHealthcareEntitiesResult, ExtractiveSummaryResult, AbstractiveSummaryResult, DocumentError]]]

An instance of an TextAnalysisLROPoller. Call result() on the poller object to return a pageable heterogeneous list of lists. This list of lists is first ordered by the documents you input, then ordered by the actions you input. For example, if you have documents input ["Hello", "world"], and actions RecognizeEntitiesAction and AnalyzeSentimentAction, when iterating over the list of lists, you will first iterate over the action results for the "Hello" document, getting the RecognizeEntitiesResult of "Hello", then the AnalyzeSentimentResult of "Hello". Then, you will get the RecognizeEntitiesResult and AnalyzeSentimentResult of "world".

Exceptions

Type	Description
HttpResponseError
TypeError
ValueError

Examples

Start a long-running operation to perform a variety of text analysis actions over a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import (
       TextAnalyticsClient,
       RecognizeEntitiesAction,
       RecognizeLinkedEntitiesAction,
       RecognizePiiEntitiesAction,
       ExtractKeyPhrasesAction,
       AnalyzeSentimentAction,
   )

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   documents = [
       'We went to Contoso Steakhouse located at midtown NYC last week for a dinner party, and we adore the spot! '
       'They provide marvelous food and they have a great menu. The chief cook happens to be the owner (I think his name is John Doe) '
       'and he is super nice, coming out of the kitchen and greeted us all.'
       ,

       'We enjoyed very much dining in the place! '
       'The Sirloin steak I ordered was tender and juicy, and the place was impeccably clean. You can even pre-order from their '
       'online menu at www.contososteakhouse.com, call 312-555-0176 or send email to order@contososteakhouse.com! '
       'The only complaint I have is the food didn\'t come fast enough. Overall I highly recommend it!'
   ]

   poller = text_analytics_client.begin_analyze_actions(
       documents,
       display_name="Sample Text Analysis",
       actions=[
           RecognizeEntitiesAction(),
           RecognizePiiEntitiesAction(),
           ExtractKeyPhrasesAction(),
           RecognizeLinkedEntitiesAction(),
           AnalyzeSentimentAction(),
       ],
   )

   document_results = poller.result()
   for doc, action_results in zip(documents, document_results):
       print(f"\nDocument text: {doc}")
       for result in action_results:
           if result.kind == "EntityRecognition":
               print("...Results of Recognize Entities Action:")
               for entity in result.entities:
                   print(f"......Entity: {entity.text}")
                   print(f".........Category: {entity.category}")
                   print(f".........Confidence Score: {entity.confidence_score}")
                   print(f".........Offset: {entity.offset}")

           elif result.kind == "PiiEntityRecognition":
               print("...Results of Recognize PII Entities action:")
               for pii_entity in result.entities:
                   print(f"......Entity: {pii_entity.text}")
                   print(f".........Category: {pii_entity.category}")
                   print(f".........Confidence Score: {pii_entity.confidence_score}")

           elif result.kind == "KeyPhraseExtraction":
               print("...Results of Extract Key Phrases action:")
               print(f"......Key Phrases: {result.key_phrases}")

           elif result.kind == "EntityLinking":
               print("...Results of Recognize Linked Entities action:")
               for linked_entity in result.entities:
                   print(f"......Entity name: {linked_entity.name}")
                   print(f".........Data source: {linked_entity.data_source}")
                   print(f".........Data source language: {linked_entity.language}")
                   print(
                       f".........Data source entity ID: {linked_entity.data_source_entity_id}"
                   )
                   print(f".........Data source URL: {linked_entity.url}")
                   print(".........Document matches:")
                   for match in linked_entity.matches:
                       print(f"............Match text: {match.text}")
                       print(f"............Confidence Score: {match.confidence_score}")
                       print(f"............Offset: {match.offset}")
                       print(f"............Length: {match.length}")

           elif result.kind == "SentimentAnalysis":
               print("...Results of Analyze Sentiment action:")
               print(f"......Overall sentiment: {result.sentiment}")
               print(
                   f"......Scores: positive={result.confidence_scores.positive}; \
                   neutral={result.confidence_scores.neutral}; \
                   negative={result.confidence_scores.negative} \n"
               )

           elif result.is_error is True:
               print(
                   f"...Is an error with code '{result.error.code}' and message '{result.error.message}'"
               )

       print("------------------------------------------")

begin_analyze_healthcare_entities

Analyze healthcare entities and identify relationships between these entities in a batch of documents.

Entities are associated with references that can be found in existing knowledge bases, such as UMLS, CHV, MSH, etc.

We also extract the relations found between entities, for example in "The subject took 100 mg of ibuprofen", we would extract the relationship between the "100 mg" dosage and the "ibuprofen" medication.

New in version v3.1: The begin_analyze_healthcare_entities client method.

New in version 2022-05-01: The display_name keyword argument.

begin_analyze_healthcare_entities(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, model_version: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> AnalyzeHealthcareEntitiesLROPoller[ItemPaged[AnalyzeHealthcareEntitiesResult | DocumentError]]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.

Keyword-Only Parameters

Name	Description
model_version	str The model version to use for the analysis, e.g. "latest". If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning
show_stats	bool If set to true, response will contain document level statistics.
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
display_name	str An optional display name to set for the requested analysis.
string_index_type	str Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodeUnit or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets
polling_interval	int Waiting time between two polls for LRO operations if no Retry-After header is present. Defaults to 5 seconds.
continuation_token	str Call continuation_token() on the poller object to save the long-running operation (LRO) state into an opaque token. Pass the value as the continuation_token keyword argument to restart the LRO from a saved state.
disable_service_logs	bool Defaults to true, meaning that the Language service will not log your input text on the service side for troubleshooting. If set to False, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

Type	Description
AnalyzeHealthcareEntitiesLROPoller[ItemPaged[ AnalyzeHealthcareEntitiesResult, DocumentError]]	An instance of an AnalyzeHealthcareEntitiesLROPoller. Call result() on the this object to return a heterogeneous pageable of AnalyzeHealthcareEntitiesResult and DocumentError.

Exceptions

Type	Description
HttpResponseError
TypeError
ValueError

Examples

Recognize healthcare entities in a batch of documents.

   import os
   import typing
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient, HealthcareEntityRelation

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   documents = [
       """
       Patient needs to take 100 mg of ibuprofen, and 3 mg of potassium. Also needs to take
       10 mg of Zocor.
       """,
       """
       Patient needs to take 50 mg of ibuprofen, and 2 mg of Coumadin.
       """
   ]

   poller = text_analytics_client.begin_analyze_healthcare_entities(documents)
   result = poller.result()

   docs = [doc for doc in result if not doc.is_error]

   print("Let's first visualize the outputted healthcare result:")
   for doc in docs:
       for entity in doc.entities:
           print(f"Entity: {entity.text}")
           print(f"...Normalized Text: {entity.normalized_text}")
           print(f"...Category: {entity.category}")
           print(f"...Subcategory: {entity.subcategory}")
           print(f"...Offset: {entity.offset}")
           print(f"...Confidence score: {entity.confidence_score}")
           if entity.data_sources is not None:
               print("...Data Sources:")
               for data_source in entity.data_sources:
                   print(f"......Entity ID: {data_source.entity_id}")
                   print(f"......Name: {data_source.name}")
           if entity.assertion is not None:
               print("...Assertion:")
               print(f"......Conditionality: {entity.assertion.conditionality}")
               print(f"......Certainty: {entity.assertion.certainty}")
               print(f"......Association: {entity.assertion.association}")
       for relation in doc.entity_relations:
           print(f"Relation of type: {relation.relation_type} has the following roles")
           for role in relation.roles:
               print(f"...Role '{role.name}' with entity '{role.entity.text}'")
       print("------------------------------------------")

   print("Now, let's get all of medication dosage relations from the documents")
   dosage_of_medication_relations = [
       entity_relation
       for doc in docs
       for entity_relation in doc.entity_relations if entity_relation.relation_type == HealthcareEntityRelation.DOSAGE_OF_MEDICATION
   ]

begin_extract_summary

Start a long-running extractive summarization operation.

For a conceptual discussion of extractive summarization, see the service documentation: https://learn.microsoft.com/azure/cognitive-services/language-service/summarization/overview

New in version 2023-04-01: The begin_extract_summary client method.

begin_extract_summary(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, model_version: str | None = None, string_index_type: str | None = None, max_sentence_count: int | None = None, order_by: Literal['Rank', 'Offset'] | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[ExtractiveSummaryResult | DocumentError]]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.

Keyword-Only Parameters

Name	Description
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
show_stats	bool If set to true, response will contain document level statistics.
max_sentence_count	Optional[int] Maximum number of sentences to return. Defaults to 3.
order_by	Optional[str] Possible values include: "Offset", "Rank". Default value: "Offset".
model_version	Optional[str] The model version to use for the analysis, e.g. "latest". If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning
string_index_type	Optional[str] Specifies the method used to interpret string offsets.
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.
polling_interval	int Waiting time between two polls for LRO operations if no Retry-After header is present. Defaults to 5 seconds.
continuation_token	str Call continuation_token() on the poller object to save the long-running operation (LRO) state into an opaque token. Pass the value as the continuation_token keyword argument to restart the LRO from a saved state.
display_name	str An optional display name to set for the requested analysis.

Returns

Type	Description
TextAnalysisLROPoller[ItemPaged[ ExtractiveSummaryResult, DocumentError]]	An instance of an TextAnalysisLROPoller. Call result() on the this object to return a heterogeneous pageable of ExtractiveSummaryResult and DocumentError.

Exceptions

Type	Description
HttpResponseError

Examples

Perform extractive summarization on a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   document = [
       "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, "
       "human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI Cognitive "
       "Services, I have been working with a team of amazing scientists and engineers to turn this quest into a "
       "reality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of "
       "human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the "
       "intersection of all three, there's magic-what we call XYZ-code as illustrated in Figure 1-a joint "
       "representation to create more powerful AI that can speak, hear, see, and understand humans better. "
       "We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, "
       "spanning modalities and languages. The goal is to have pretrained models that can jointly learn "
       "representations to support a broad range of downstream AI tasks, much in the way humans do today. "
       "Over the past five years, we have achieved human performance on benchmarks in conversational speech "
       "recognition, machine translation, conversational question answering, machine reading comprehension, "
       "and image captioning. These five breakthroughs provided us with strong signals toward our more ambitious "
       "aspiration to produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
       "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a foundational "
       "component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks."
   ]

   poller = text_analytics_client.begin_extract_summary(document)
   extract_summary_results = poller.result()
   for result in extract_summary_results:
       if result.kind == "ExtractiveSummarization":
           print("Summary extracted: \n{}".format(
               " ".join([sentence.text for sentence in result.sentences]))
           )
       elif result.is_error is True:
           print("...Is an error with code '{}' and message '{}'".format(
               result.error.code, result.error.message
           ))

begin_multi_label_classify

Start a long-running custom multi label classification operation.

For information on regional support of custom features and how to train a model to classify your documents, see https://aka.ms/azsdk/textanalytics/customfunctionalities

New in version 2022-05-01: The begin_multi_label_classify client method.

begin_multi_label_classify(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], project_name: str, deployment_name: str, *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[ClassifyDocumentResult | DocumentError]]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.
project_name Required	str Required. This field indicates the project name for the model.
deployment_name Required	str This field indicates the deployment name for the model.

Keyword-Only Parameters

Name	Description
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
show_stats	bool If set to true, response will contain document level statistics.
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.
polling_interval	int Waiting time between two polls for LRO operations if no Retry-After header is present. Defaults to 5 seconds.
continuation_token	str Call continuation_token() on the poller object to save the long-running operation (LRO) state into an opaque token. Pass the value as the continuation_token keyword argument to restart the LRO from a saved state.
display_name	str An optional display name to set for the requested analysis.

Returns

Type	Description
TextAnalysisLROPoller[ItemPaged[ ClassifyDocumentResult, DocumentError]]	An instance of an TextAnalysisLROPoller. Call result() on the this object to return a heterogeneous pageable of ClassifyDocumentResult and DocumentError.

Exceptions

Type	Description
HttpResponseError

Examples

Perform multi label classification on a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]
   project_name = os.environ["MULTI_LABEL_CLASSIFY_PROJECT_NAME"]
   deployment_name = os.environ["MULTI_LABEL_CLASSIFY_DEPLOYMENT_NAME"]
   path_to_sample_document = os.path.abspath(
       os.path.join(
           os.path.abspath(__file__),
           "..",
           "./text_samples/custom_classify_sample.txt",
       )
   )

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   with open(path_to_sample_document) as fd:
       document = [fd.read()]

   poller = text_analytics_client.begin_multi_label_classify(
       document,
       project_name=project_name,
       deployment_name=deployment_name
   )

   document_results = poller.result()
   for doc, classification_result in zip(document, document_results):
       if classification_result.kind == "CustomDocumentClassification":
           classifications = classification_result.classifications
           print(f"\nThe movie plot '{doc}' was classified as the following genres:\n")
           for classification in classifications:
               print("'{}' with confidence score {}.".format(
                   classification.category, classification.confidence_score
               ))
       elif classification_result.is_error is True:
           print("Movie plot '{}' has an error with code '{}' and message '{}'".format(
               doc, classification_result.error.code, classification_result.error.message
           ))

begin_recognize_custom_entities

Start a long-running custom named entity recognition operation.

For information on regional support of custom features and how to train a model to recognize custom entities, see https://aka.ms/azsdk/textanalytics/customentityrecognition

New in version 2022-05-01: The begin_recognize_custom_entities client method.

begin_recognize_custom_entities(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], project_name: str, deployment_name: str, *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[RecognizeCustomEntitiesResult | DocumentError]]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.
project_name Required	str Required. This field indicates the project name for the model.
deployment_name Required	str This field indicates the deployment name for the model.

Keyword-Only Parameters

Name	Description
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
show_stats	bool If set to true, response will contain document level statistics.
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.
string_index_type	str Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodeUnit or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets
polling_interval	int Waiting time between two polls for LRO operations if no Retry-After header is present. Defaults to 5 seconds.
continuation_token	str Call continuation_token() on the poller object to save the long-running operation (LRO) state into an opaque token. Pass the value as the continuation_token keyword argument to restart the LRO from a saved state.
display_name	str An optional display name to set for the requested analysis.

Returns

Type	Description
TextAnalysisLROPoller[ItemPaged[ RecognizeCustomEntitiesResult, DocumentError]]	An instance of an TextAnalysisLROPoller. Call result() on the this object to return a heterogeneous pageable of RecognizeCustomEntitiesResult and DocumentError.

Exceptions

Type	Description
HttpResponseError

Examples

Recognize custom entities in a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]
   project_name = os.environ["CUSTOM_ENTITIES_PROJECT_NAME"]
   deployment_name = os.environ["CUSTOM_ENTITIES_DEPLOYMENT_NAME"]
   path_to_sample_document = os.path.abspath(
       os.path.join(
           os.path.abspath(__file__),
           "..",
           "./text_samples/custom_entities_sample.txt",
       )
   )

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   with open(path_to_sample_document) as fd:
       document = [fd.read()]

   poller = text_analytics_client.begin_recognize_custom_entities(
       document,
       project_name=project_name,
       deployment_name=deployment_name
   )

   document_results = poller.result()
   for custom_entities_result in document_results:
       if custom_entities_result.kind == "CustomEntityRecognition":
           for entity in custom_entities_result.entities:
               print(
                   "Entity '{}' has category '{}' with confidence score of '{}'".format(
                       entity.text, entity.category, entity.confidence_score
                   )
               )
       elif custom_entities_result.is_error is True:
           print("...Is an error with code '{}' and message '{}'".format(
               custom_entities_result.error.code, custom_entities_result.error.message
               )
           )

begin_single_label_classify

Start a long-running custom single label classification operation.

For information on regional support of custom features and how to train a model to classify your documents, see https://aka.ms/azsdk/textanalytics/customfunctionalities

New in version 2022-05-01: The begin_single_label_classify client method.

begin_single_label_classify(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], project_name: str, deployment_name: str, *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[ClassifyDocumentResult | DocumentError]]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.
project_name Required	str Required. This field indicates the project name for the model.
deployment_name Required	str This field indicates the deployment name for the model.

Keyword-Only Parameters

Name	Description
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
show_stats	bool If set to true, response will contain document level statistics.
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.
polling_interval	int Waiting time between two polls for LRO operations if no Retry-After header is present. Defaults to 5 seconds.
continuation_token	str Call continuation_token() on the poller object to save the long-running operation (LRO) state into an opaque token. Pass the value as the continuation_token keyword argument to restart the LRO from a saved state.
display_name	str An optional display name to set for the requested analysis.

Returns

Type	Description
TextAnalysisLROPoller[ItemPaged[ ClassifyDocumentResult, DocumentError]]	An instance of an TextAnalysisLROPoller. Call result() on the this object to return a heterogeneous pageable of ClassifyDocumentResult and DocumentError.

Exceptions

Type	Description
HttpResponseError

Examples

Perform single label classification on a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]
   project_name = os.environ["SINGLE_LABEL_CLASSIFY_PROJECT_NAME"]
   deployment_name = os.environ["SINGLE_LABEL_CLASSIFY_DEPLOYMENT_NAME"]
   path_to_sample_document = os.path.abspath(
       os.path.join(
           os.path.abspath(__file__),
           "..",
           "./text_samples/custom_classify_sample.txt",
       )
   )

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   with open(path_to_sample_document) as fd:
       document = [fd.read()]

   poller = text_analytics_client.begin_single_label_classify(
       document,
       project_name=project_name,
       deployment_name=deployment_name
   )

   document_results = poller.result()
   for doc, classification_result in zip(document, document_results):
       if classification_result.kind == "CustomDocumentClassification":
           classification = classification_result.classifications[0]
           print("The document text '{}' was classified as '{}' with confidence score {}.".format(
               doc, classification.category, classification.confidence_score)
           )
       elif classification_result.is_error is True:
           print("Document text '{}' has an error with code '{}' and message '{}'".format(
               doc, classification_result.error.code, classification_result.error.message
           ))

close

Close sockets opened by the client. Calling this method is unnecessary when using the client as a context manager.

close() -> None

Keyword-Only Parameters

Name	Description
show_opinion_mining	bool Whether to mine the opinions of a sentence and conduct more granular analysis around the aspects of a product or service (also known as aspect-based sentiment analysis). If set to true, the returned SentenceSentiment objects will have property mined_opinions containing the result of this analysis. Only available for API version v3.1 and up.
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
model_version	str The model version to use for the analysis, e.g. "latest". If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning
show_stats	bool If set to true, response will contain document level statistics in the statistics field of the document-level response.
string_index_type	str Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodeUnit or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Exceptions

Type	Description
HttpResponseError
TypeError
ValueError

detect_language

Detect language for a batch of documents.

Returns the detected language and a numeric score between zero and one. Scores close to one indicate 100% certainty that the identified language is true. See https://aka.ms/talangs for the list of enabled languages.

See https://aka.ms/azsdk/textanalytics/data-limits for service data limits.

New in version v3.1: The disable_service_logs keyword argument.

detect_language(documents: List[str] | List[DetectLanguageInput] | List[Dict[str, str]], *, country_hint: str | None = None, disable_service_logs: bool | None = None, model_version: str | None = None, show_stats: bool | None = None, **kwargs: Any) -> List[DetectLanguageResult | DocumentError]

Parameters

Name	Description
documents Required	list[str] or list[DetectLanguageInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and country_hint on a per-item basis you must use as input a list[DetectLanguageInput] or a list of dict representations of DetectLanguageInput, like {"id": "1", "country_hint": "us", "text": "hello world"}.

Keyword-Only Parameters

Name	Description
country_hint	str Country of origin hint for the entire batch. Accepts two letter country codes specified by ISO 3166-1 alpha-2. Per-document country hints will take precedence over whole batch hints. Defaults to "US". If you don't want to use a country hint, pass the string "none".
model_version	str The model version to use for the analysis, e.g. "latest". If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning
show_stats	bool If set to true, response will contain document level statistics in the statistics field of the document-level response.
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

Type	Description
list[DetectLanguageResult, DocumentError]	The combined list of DetectLanguageResult and DocumentError in the order the original documents were passed in.

Exceptions

Type	Description
HttpResponseError
TypeError
ValueError

Examples

Detecting language in a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
   documents = [
       """
       The concierge Paulette was extremely helpful. Sadly when we arrived the elevator was broken, but with Paulette's help we barely noticed this inconvenience.
       She arranged for our baggage to be brought up to our room with no extra charge and gave us a free meal to refurbish all of the calories we lost from
       walking up the stairs :). Can't say enough good things about my experience!
       """,
       """
       最近由于工作压力太大，我们决定去富酒店度假。那儿的温泉实在太舒服了，我跟我丈夫都完全恢复了工作前的青春精神！加油！
       """
   ]

   result = text_analytics_client.detect_language(documents)
   reviewed_docs = [doc for doc in result if not doc.is_error]

   print("Let's see what language each review is in!")

   for idx, doc in enumerate(reviewed_docs):
       print("Review #{} is in '{}', which has ISO639-1 name '{}'\n".format(
           idx, doc.primary_language.name, doc.primary_language.iso6391_name
       ))

extract_key_phrases

Extract key phrases from a batch of documents.

Returns a list of strings denoting the key phrases in the input text. For example, for the input text "The food was delicious and there were wonderful staff", the API returns the main talking points: "food" and "wonderful staff"

See https://aka.ms/azsdk/textanalytics/data-limits for service data limits.

New in version v3.1: The disable_service_logs keyword argument.

extract_key_phrases(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, disable_service_logs: bool | None = None, language: str | None = None, model_version: str | None = None, show_stats: bool | None = None, **kwargs: Any) -> List[ExtractKeyPhrasesResult | DocumentError]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.

Keyword-Only Parameters

Name	Description
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
model_version	str The model version to use for the analysis, e.g. "latest". If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning
show_stats	bool If set to true, response will contain document level statistics in the statistics field of the document-level response.
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

Type	Description
list[ExtractKeyPhrasesResult, DocumentError]	The combined list of ExtractKeyPhrasesResult and DocumentError in the order the original documents were passed in.

Exceptions

Type	Description
HttpResponseError
TypeError
ValueError

Examples

Extract the key phrases in a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
   articles = [
       """
       Washington, D.C. Autumn in DC is a uniquely beautiful season. The leaves fall from the trees
       in a city chock-full of forests, leaving yellow leaves on the ground and a clearer view of the
       blue sky above...
       """,
       """
       Redmond, WA. In the past few days, Microsoft has decided to further postpone the start date of
       its United States workers, due to the pandemic that rages with no end in sight...
       """,
       """
       Redmond, WA. Employees at Microsoft can be excited about the new coffee shop that will open on campus
       once workers no longer have to work remotely...
       """
   ]

   result = text_analytics_client.extract_key_phrases(articles)
   for idx, doc in enumerate(result):
       if not doc.is_error:
           print("Key phrases in article #{}: {}".format(
               idx + 1,
               ", ".join(doc.key_phrases)
           ))

recognize_entities

Recognize entities for a batch of documents.

Identifies and categorizes entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. For the list of supported entity types, check: https://aka.ms/taner

See https://aka.ms/azsdk/textanalytics/data-limits for service data limits.

New in version v3.1: The disable_service_logs and string_index_type keyword arguments.

recognize_entities(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, disable_service_logs: bool | None = None, language: str | None = None, model_version: str | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> List[RecognizeEntitiesResult | DocumentError]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.

Keyword-Only Parameters

Name	Description
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
model_version	str The model version to use for the analysis, e.g. "latest". If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning
show_stats	bool If set to true, response will contain document level statistics in the statistics field of the document-level response.
string_index_type	str Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodeUnit or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

Type	Description
list[RecognizeEntitiesResult, DocumentError]	The combined list of RecognizeEntitiesResult and DocumentError in the order the original documents were passed in.

Exceptions

Type	Description
HttpResponseError
TypeError
ValueError

Examples

Recognize entities in a batch of documents.

   import os
   import typing
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
   reviews = [
       """I work for Foo Company, and we hired Contoso for our annual founding ceremony. The food
       was amazing and we all can't say enough good words about the quality and the level of service.""",
       """We at the Foo Company re-hired Contoso after all of our past successes with the company.
       Though the food was still great, I feel there has been a quality drop since their last time
       catering for us. Is anyone else running into the same problem?""",
       """Bar Company is over the moon about the service we received from Contoso, the best sliders ever!!!!"""
   ]

   result = text_analytics_client.recognize_entities(reviews)
   result = [review for review in result if not review.is_error]
   organization_to_reviews: typing.Dict[str, typing.List[str]] = {}

   for idx, review in enumerate(result):
       for entity in review.entities:
           print(f"Entity '{entity.text}' has category '{entity.category}'")
           if entity.category == 'Organization':
               organization_to_reviews.setdefault(entity.text, [])
               organization_to_reviews[entity.text].append(reviews[idx])

   for organization, reviews in organization_to_reviews.items():
       print(
           "\n\nOrganization '{}' has left us the following review(s): {}".format(
               organization, "\n\n".join(reviews)
           )
       )

recognize_linked_entities

Recognize linked entities from a well-known knowledge base for a batch of documents.

Identifies and disambiguates the identity of each entity found in text (for example, determining whether an occurrence of the word Mars refers to the planet, or to the Roman god of war). Recognized entities are associated with URLs to a well-known knowledge base, like Wikipedia.

See https://aka.ms/azsdk/textanalytics/data-limits for service data limits.

New in version v3.1: The disable_service_logs and string_index_type keyword arguments.

recognize_linked_entities(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, disable_service_logs: bool | None = None, language: str | None = None, model_version: str | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> List[RecognizeLinkedEntitiesResult | DocumentError]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.

Keyword-Only Parameters

Name	Description
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
model_version	str The model version to use for the analysis, e.g. "latest". If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning
show_stats	bool If set to true, response will contain document level statistics in the statistics field of the document-level response.
string_index_type	str Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodeUnit or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets
disable_service_logs	bool If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

Type	Description
list[RecognizeLinkedEntitiesResult, DocumentError]	The combined list of RecognizeLinkedEntitiesResult and DocumentError in the order the original documents were passed in.

Exceptions

Type	Description
HttpResponseError
TypeError
ValueError

Examples

Recognize linked entities in a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
   documents = [
       """
       Microsoft was founded by Bill Gates with some friends he met at Harvard. One of his friends,
       Steve Ballmer, eventually became CEO after Bill Gates as well. Steve Ballmer eventually stepped
       down as CEO of Microsoft, and was succeeded by Satya Nadella.
       Microsoft originally moved its headquarters to Bellevue, Washington in January 1979, but is now
       headquartered in Redmond.
       """
   ]

   result = text_analytics_client.recognize_linked_entities(documents)
   docs = [doc for doc in result if not doc.is_error]

   print(
       "Let's map each entity to it's Wikipedia article. I also want to see how many times each "
       "entity is mentioned in a document\n\n"
   )
   entity_to_url = {}
   for doc in docs:
       for entity in doc.entities:
           print("Entity '{}' has been mentioned '{}' time(s)".format(
               entity.name, len(entity.matches)
           ))
           if entity.data_source == "Wikipedia":
               entity_to_url[entity.name] = entity.url

recognize_pii_entities

Recognize entities containing personal information for a batch of documents.

Returns a list of personal information entities ("SSN", "Bank Account", etc) in the document. For the list of supported entity types, check https://aka.ms/azsdk/language/pii

See https://aka.ms/azsdk/textanalytics/data-limits for service data limits.

New in version v3.1: The recognize_pii_entities client method.

recognize_pii_entities(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, categories_filter: List[str | PiiEntityCategory] | None = None, disable_service_logs: bool | None = None, domain_filter: str | PiiEntityDomain | None = None, language: str | None = None, model_version: str | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> List[RecognizePiiEntitiesResult | DocumentError]

Parameters

Name	Description
documents Required	list[str] or list[TextDocumentInput] or list[dict[str, str]] The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {"id": "1", "language": "en", "text": "hello world"}.

Keyword-Only Parameters

Name	Description
language	str The 2 letter ISO 639-1 representation of language for the entire batch. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Language API.
model_version	str The model version to use for the analysis, e.g. "latest". If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning
show_stats	bool If set to true, response will contain document level statistics in the statistics field of the document-level response.
domain_filter	str or PiiEntityDomain Filters the response entities to ones only included in the specified domain. I.e., if set to 'phi', will only return entities in the Protected Healthcare Information domain. See https://aka.ms/azsdk/language/pii for more information.
categories_filter	list[str or PiiEntityCategory] Instead of filtering over all PII entity categories, you can pass in a list of the specific PII entity categories you want to filter out. For example, if you only want to filter out U.S. social security numbers in a document, you can pass in [PiiEntityCategory.US_SOCIAL_SECURITY_NUMBER] for this kwarg.
string_index_type	str Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodeUnit or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets
disable_service_logs	bool Defaults to true, meaning that the Language service will not log your input text on the service side for troubleshooting. If set to False, the Language service logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the service's natural language processing functions. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

Type	Description
list[RecognizePiiEntitiesResult, DocumentError]	The combined list of RecognizePiiEntitiesResult and DocumentError in the order the original documents were passed in.

Exceptions

Type	Description
HttpResponseError
TypeError
ValueError

Examples

Recognize personally identifiable information entities in a batch of documents.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint, credential=AzureKeyCredential(key)
   )
   documents = [
       """Parker Doe has repaid all of their loans as of 2020-04-25.
       Their SSN is 859-98-0987. To contact them, use their phone number
       555-555-5555. They are originally from Brazil and have Brazilian CPF number 998.214.865-68"""
   ]

   result = text_analytics_client.recognize_pii_entities(documents)
   docs = [doc for doc in result if not doc.is_error]

   print(
       "Let's compare the original document with the documents after redaction. "
       "I also want to comb through all of the entities that got redacted"
   )
   for idx, doc in enumerate(docs):
       print(f"Document text: {documents[idx]}")
       print(f"Redacted document text: {doc.redacted_text}")
       for entity in doc.entities:
           print("...Entity '{}' with category '{}' got redacted".format(
               entity.text, entity.category
           ))