azure-ai-textanalytics 5.3.0

Description:

azureaitextanalytics 5.3.0

Azure Text Analytics client library for Python
The Azure Cognitive Service for Language is a cloud-based service that provides Natural Language Processing (NLP) features for understanding and analyzing text, and includes the following main features:

Sentiment Analysis
Named Entity Recognition
Language Detection
Key Phrase Extraction
Entity Linking
Multiple Analysis
Personally Identifiable Information (PII) Detection
Text Analytics for Health
Custom Named Entity Recognition
Custom Text Classification
Extractive Text Summarization
Abstractive Text Summarization

Source code
| Package (PyPI)
| Package (Conda)
| API reference documentation
| Product documentation
| Samples
Getting started
Prerequisites

Python 3.7 later is required to use this package.
You must have an Azure subscription and a
Cognitive Services or Language service resource to use this package.

Create a Cognitive Services or Language service resource
The Language service supports both multi-service and single-service access.
Create a Cognitive Services resource if you plan to access multiple cognitive services under a single endpoint/key. For Language service access only, create a Language service resource.
You can create the resource using the Azure Portal or Azure CLI following the steps in this document.
Interaction with the service using the client library begins with a client.
To create a client object, you will need the Cognitive Services or Language service endpoint to
your resource and a credential that allows you access:
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

credential = AzureKeyCredential("<api_key>")
text_analytics_client = TextAnalyticsClient(endpoint="https://<resource-name>.cognitiveservices.azure.com/", credential=credential)

Note that for some Cognitive Services resources the endpoint might look different from the above code snippet.
For example, https://<region>.api.cognitive.microsoft.com/.
Install the package
Install the Azure Text Analytics client library for Python with pip:
pip install azure-ai-textanalytics


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))



Note that 5.2.X and newer targets the Azure Cognitive Service for Language APIs. These APIs include the text analysis and natural language processing features found in the previous versions of the Text Analytics client library.
In addition, the service API has changed from semantic to date-based versioning. This version of the client library defaults to the latest supported API version, which currently is 2023-04-01.

This table shows the relationship between SDK versions and supported API versions of the service



SDK version
Supported API version of service




5.3.X - Latest stable release
3.0, 3.1, 2022-05-01, 2023-04-01 (default)


5.2.X
3.0, 3.1, 2022-05-01 (default)


5.1.0
3.0, 3.1 (default)


5.0.0
3.0



API version can be selected by passing the api_version keyword argument into the client.
For the latest Language service features, consider selecting the most recent beta API version. For production scenarios, the latest stable version is recommended. Setting to an older version may result in reduced feature compatibility.
Authenticate the client
Get the endpoint
You can find the endpoint for your Language service resource using the
Azure Portal
or Azure CLI:
# Get the endpoint for the Language service resource
az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "properties.endpoint"

Get the API Key
You can get the API key from the Cognitive Services or Language service resource in the Azure Portal.
Alternatively, you can use Azure CLI snippet below to get the API key of your resource.
az cognitiveservices account keys list --name "resource-name" --resource-group "resource-group-name"
Create a TextAnalyticsClient with an API Key Credential
Once you have the value for the API key, you can pass it as a string into an instance of AzureKeyCredential. Use the key as the credential parameter
to authenticate the client:

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))


Create a TextAnalyticsClient with an Azure Active Directory Credential
To use an Azure Active Directory (AAD) token credential,
provide an instance of the desired credential type obtained from the
azure-identity library.
Note that regional endpoints do not support AAD authentication. Create a custom subdomain
name for your resource in order to use this type of authentication.
Authentication with AAD requires some initial setup:

Install azure-identity
Register a new AAD application
Grant access to the Language service by assigning the "Cognitive Services Language Reader" role to your service principal.

After setup, you can choose which type of credential from azure.identity to use.
As an example, DefaultAzureCredential
can be used to authenticate the client:
Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables:
AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET
Use the returned token credential to authenticate the client:

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)


Key concepts
TextAnalyticsClient
The Text Analytics client library provides a TextAnalyticsClient to do analysis on batches of documents.
It provides both synchronous and asynchronous operations to access a specific use of text analysis, such as language detection or key phrase extraction.
Input
A document is a single unit to be analyzed by the predictive models in the Language service.
The input for each operation is passed as a list of documents.
Each document can be passed as a string in the list, e.g.
documents = ["I hated the movie. It was so slow!", "The movie made it into my top ten favorites. What a great movie!"]

or, if you wish to pass in a per-item document id or language/country_hint, they can be passed as a list of
DetectLanguageInput or
TextDocumentInput
or a dict-like representation of the object:
documents = [
{"id": "1", "language": "en", "text": "I hated the movie. It was so slow!"},
{"id": "2", "language": "en", "text": "The movie made it into my top ten favorites. What a great movie!"},
]

See service limitations for the input, including document length limits, maximum batch size, and supported text encoding.
Return Value
The return value for a single document can be a result or error object.
A heterogeneous list containing a collection of result and error objects is returned from each operation.
These results/errors are index-matched with the order of the provided documents.
A result, such as AnalyzeSentimentResult,
is the result of a text analysis operation and contains a prediction or predictions about a document input.
The error object, DocumentError, indicates that the service had trouble processing the document and contains
the reason it was unsuccessful.
Document Error Handling
You can filter for a result or error object in the list by using the is_error attribute. For a result object this is always False and for a
DocumentError this is True.
For example, to filter out all DocumentErrors you might use list comprehension:
response = text_analytics_client.analyze_sentiment(documents)
successful_responses = [doc for doc in response if not doc.is_error]

You can also use the kind attribute to filter between result types:
poller = text_analytics_client.begin_analyze_actions(documents, actions)
response = poller.result()
for result in response:
if result.kind == "SentimentAnalysis":
print(f"Sentiment is {result.sentiment}")
elif result.kind == "KeyPhraseExtraction":
print(f"Key phrases: {result.key_phrases}")
elif result.is_error is True:
print(f"Document error: {result.code}, {result.message}")

Long-Running Operations
Long-running operations are operations which consist of an initial request sent to the service to start an operation,
followed by polling the service at intervals to determine whether the operation has completed or failed, and if it has
succeeded, to get the result.
Methods that support healthcare analysis, custom text analysis, or multiple analyses are modeled as long-running operations.
The client exposes a begin_<method-name> method that returns a poller object. Callers should wait
for the operation to complete by calling result() on the poller object returned from the begin_<method-name> method.
Sample code snippets are provided to illustrate using long-running operations below.
Examples
The following section provides several code snippets covering some of the most common Language service tasks, including:

Analyze Sentiment
Recognize Entities
Recognize Linked Entities
Recognize PII Entities
Extract Key Phrases
Detect Language
Healthcare Entities Analysis
Multiple Analysis
Custom Entity Recognition
Custom Single Label Classification
Custom Multi Label Classification
Extractive Summarization
Abstractive Summarization

Analyze Sentiment
analyze_sentiment looks at its input text and determines whether its sentiment is positive, negative, neutral or mixed. It's response includes per-sentence sentiment analysis and confidence scores.

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}")


The returned response is a heterogeneous list of result and error objects: list[AnalyzeSentimentResult, DocumentError]
Please refer to the service documentation for a conceptual discussion of sentiment analysis. To see how to conduct more granular analysis into the opinions related to individual aspects (such as attributes of a product or service) in a text, see here.
Recognize Entities
recognize_entities recognizes and categories entities in its input text as people, places, organizations, date/time, quantities, percentages, currencies, and more.

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)
)
)


The returned response is a heterogeneous list of result and error objects: list[RecognizeEntitiesResult, DocumentError]
Please refer to the service documentation for a conceptual discussion of named entity recognition
and supported types.
Recognize Linked Entities
recognize_linked_entities recognizes and disambiguates the identity of each entity found in its input 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.

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


The returned response is a heterogeneous list of result and error objects: list[RecognizeLinkedEntitiesResult, DocumentError]
Please refer to the service documentation for a conceptual discussion of entity linking
and supported types.
Recognize PII Entities
recognize_pii_entities recognizes and categorizes Personally Identifiable Information (PII) entities in its input text, such as
Social Security Numbers, bank account information, credit card numbers, and more.

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
))


The returned response is a heterogeneous list of result and error objects: list[RecognizePiiEntitiesResult, DocumentError]
Please refer to the service documentation for supported PII entity types.
Note: The Recognize PII Entities service is available in API version v3.1 and newer.
Extract Key Phrases
extract_key_phrases determines the main talking points in its input text. For example, for the input text "The food was delicious and there were wonderful staff", the API returns: "food" and "wonderful staff".

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)
))


The returned response is a heterogeneous list of result and error objects: list[ExtractKeyPhrasesResult, DocumentError]
Please refer to the service documentation for a conceptual discussion of key phrase extraction.
Detect Language
detect_language determines the language of its input text, including the confidence score of the predicted language.

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
))


The returned response is a heterogeneous list of result and error objects: list[DetectLanguageResult, DocumentError]
Please refer to the service documentation for a conceptual discussion of language detection
and language and regional support.
Healthcare Entities Analysis
Long-running operation begin_analyze_healthcare_entities extracts entities recognized within the healthcare domain, and identifies relationships between entities within the input document and links to known sources of information in various well known databases, such as UMLS, CHV, MSH, etc.

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
]


Note: Healthcare Entities Analysis is only available with API version v3.1 and newer.
Multiple Analysis
Long-running operation begin_analyze_actions performs multiple analyses over one set of documents in a single request. Currently it is supported using any combination of the following Language APIs in a single request:

Entities Recognition
PII Entities Recognition
Linked Entity Recognition
Key Phrase Extraction
Sentiment Analysis
Custom Entity Recognition (API version 2022-05-01 and newer)
Custom Single Label Classification (API version 2022-05-01 and newer)
Custom Multi Label Classification (API version 2022-05-01 and newer)
Healthcare Entities Analysis (API version 2022-05-01 and newer)
Extractive Summarization (API version 2023-04-01 and newer)
Abstractive Summarization (API version 2023-04-01 and newer)


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 [email protected]! '
'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("------------------------------------------")


The returned response is an object encapsulating multiple iterables, each representing results of individual analyses.
Note: Multiple analysis is available in API version v3.1 and newer.
Optional Configuration
Optional keyword arguments can be passed in at the client and per-operation level.
The azure-core reference documentation
describes available configurations for retries, logging, transport protocols, and more.
Troubleshooting
General
The Text Analytics client will raise exceptions defined in Azure Core.
Logging
This library uses the standard
logging library for logging.
Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO
level.
Detailed DEBUG level logging, including request/response bodies and unredacted
headers, can be enabled on a client with the logging_enable keyword argument:
import sys
import logging
from azure.identity import DefaultAzureCredential
from azure.ai.textanalytics import TextAnalyticsClient

# Create a logger for the 'azure' SDK
logger = logging.getLogger('azure')
logger.setLevel(logging.DEBUG)

# Configure a console output
handler = logging.StreamHandler(stream=sys.stdout)
logger.addHandler(handler)

endpoint = "https://<resource-name>.cognitiveservices.azure.com/"
credential = DefaultAzureCredential()

# This client will log detailed information about its HTTP sessions, at DEBUG level
text_analytics_client = TextAnalyticsClient(endpoint, credential, logging_enable=True)
result = text_analytics_client.analyze_sentiment(["I did not like the restaurant. The food was too spicy."])

Similarly, logging_enable can enable detailed logging for a single operation,
even when it isn't enabled for the client:
result = text_analytics_client.analyze_sentiment(documents, logging_enable=True)

Next steps
More sample code
These code samples show common scenario operations with the Azure Text Analytics client library.
Authenticate the client with a Cognitive Services/Language service API key or a token credential from azure-identity:

sample_authentication.py (async version)

Common scenarios

Analyze sentiment: sample_analyze_sentiment.py (async version)
Recognize entities: sample_recognize_entities.py (async version)
Recognize personally identifiable information: sample_recognize_pii_entities.py (async version)
Recognize linked entities: sample_recognize_linked_entities.py (async version)
Extract key phrases: sample_extract_key_phrases.py (async version)
Detect language: sample_detect_language.py (async version)
Healthcare Entities Analysis: sample_analyze_healthcare_entities.py (async version)
Multiple Analysis: sample_analyze_actions.py (async version)
Custom Entity Recognition: sample_recognize_custom_entities.py (async_version)
Custom Single Label Classification: sample_single_label_classify.py (async_version)
Custom Multi Label Classification: sample_multi_label_classify.py (async_version)
Extractive text summarization: sample_extract_summary.py (async version)
Abstractive text summarization: sample_abstract_summary.py (async version)

Advanced scenarios

Opinion Mining: sample_analyze_sentiment_with_opinion_mining.py (async_version)

Additional documentation
For more extensive documentation on Azure Cognitive Service for Language, see the Language Service documentation on docs.microsoft.com.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Release History
5.3.0 (2023-06-15)
This version of the client library defaults to the service API version 2023-04-01.
Breaking Changes

Note: The following changes are only breaking from the previous beta. They are not breaking against previous stable versions.


Renamed model ExtractSummaryAction to ExtractiveSummaryAction.
Renamed model ExtractSummaryResult to ExtractiveSummaryResult.
Renamed client method begin_abstractive_summary to begin_abstract_summary.
Removed dynamic_classification client method and related types: DynamicClassificationResult and ClassificationType.
Removed keyword arguments fhir_version and document_type from begin_analyze_healthcare_entities and AnalyzeHealthcareEntitiesAction.
Removed property fhir_bundle from AnalyzeHealthcareEntitiesResult.
Removed enum HealthcareDocumentType.
Removed property resolutions from CategorizedEntity.
Removed models and enums related to resolutions: ResolutionKind, AgeResolution, AreaResolution,
CurrencyResolution, DateTimeResolution, InformationResolution, LengthResolution,
NumberResolution, NumericRangeResolution, OrdinalResolution, SpeedResolution, TemperatureResolution,
TemporalSpanResolution, VolumeResolution, WeightResolution, AgeUnit, AreaUnit, TemporalModifier,
InformationUnit, LengthUnit, NumberKind, RangeKind, RelativeTo, SpeedUnit, TemperatureUnit,
VolumeUnit, DateTimeSubKind, and WeightUnit.
Removed property detected_language from RecognizeEntitiesResult, RecognizePiiEntitiesResult, AnalyzeHealthcareEntitiesResult,
ExtractKeyPhrasesResult, RecognizeLinkedEntitiesResult, AnalyzeSentimentResult, RecognizeCustomEntitiesResult,
ClassifyDocumentResult, ExtractSummaryResult, and AbstractSummaryResult.
Removed property script from DetectedLanguage.

Features Added

New enum values added for HealthcareEntityCategory and HealthcareEntityRelation.

5.3.0b2 (2023-03-07)
This version of the client library defaults to the service API version 2022-10-01-preview.
Features Added

Added begin_extract_summary client method to perform extractive summarization on documents.
Added begin_abstractive_summary client method to perform abstractive summarization on documents.

Breaking Changes

Removed models BaseResolution and BooleanResolution.
Removed enum value BooleanResolution from ResolutionKind.
Renamed model AbstractSummaryAction to AbstractiveSummaryAction.
Renamed model AbstractSummaryResult to AbstractiveSummaryResult.
Removed keyword argument autodetect_default_language from long-running operation APIs.

Other Changes

Improved static typing in the client library.

5.3.0b1 (2022-11-17)
This version of the client library defaults to the service API version 2022-10-01-preview.
Features Added

Added the Extractive Summarization feature and related models: ExtractSummaryAction, ExtractSummaryResult, and SummarySentence.
Access the feature through the begin_analyze_actions API.
Added keyword arguments fhir_version and document_type to begin_analyze_healthcare_entities and AnalyzeHealthcareEntitiesAction.
Added property fhir_bundle to AnalyzeHealthcareEntitiesResult.
Added property confidence_score to HealthcareRelation.
Added enum HealthcareDocumentType.
Added property resolutions to CategorizedEntity.
Added models and enums related to resolutions: BaseResolution, ResolutionKind, AgeResolution, AreaResolution,
BooleanResolution, CurrencyResolution, DateTimeResolution, InformationResolution, LengthResolution,
NumberResolution, NumericRangeResolution, OrdinalResolution, SpeedResolution, TemperatureResolution,
TemporalSpanResolution, VolumeResolution, WeightResolution, AgeUnit, AreaUnit, TemporalModifier,
InformationUnit, LengthUnit, NumberKind, RangeKind, RelativeTo, SpeedUnit, TemperatureUnit,
VolumeUnit, DateTimeSubKind, and WeightUnit.
Added the Abstractive Summarization feature and related models: AbstractSummaryAction, AbstractSummaryResult, AbstractiveSummary,
and SummaryContext. Access the feature through the begin_analyze_actions API.
Added automatic language detection to long-running operation APIs. Pass auto into the document language hint to use this feature.
Added autodetect_default_language to long-running operation APIs. Pass as the default/fallback language for automatic language detection.
Added property detected_language to RecognizeEntitiesResult, RecognizePiiEntitiesResult, AnalyzeHealthcareEntitiesResult,
ExtractKeyPhrasesResult, RecognizeLinkedEntitiesResult, AnalyzeSentimentResult, RecognizeCustomEntitiesResult,
ClassifyDocumentResult, ExtractSummaryResult, and AbstractSummaryResult to indicate the language detected by automatic language detection.
Added property script to DetectedLanguage to indicate the script of the input document.
Added the dynamic_classification client method to perform dynamic classification on documents without needing to train a model.

Other Changes

Removed dependency on msrest.

5.2.1 (2022-10-26)
Bugs Fixed

Returns a more helpful message in the document error when all documents fail for an action in the begin_analyze_actions API.

5.2.0 (2022-09-08)
Other Changes
This version of the client library marks a stable release and defaults to the service API version 2022-05-01.
Includes all changes from 5.2.0b1 to 5.2.0b5.
5.2.0b5 (2022-08-11)
The version of this client library defaults to the API version 2022-05-01.
Features Added

Added begin_recognize_custom_entities client method to recognize custom named entities in documents.
Added begin_single_label_classify client method to perform custom single label classification on documents.
Added begin_multi_label_classify client method to perform custom multi label classification on documents.
Added property details on returned poller objects which contain long-running operation metadata.
Added TextAnalysisLROPoller and AsyncTextAnalysisLROPoller protocols to describe the return types from long-running operations.
Added cancel method on the poller objects. Call it to cancel a long-running operation that's in progress.
Added property kind to RecognizeEntitiesResult, RecognizePiiEntitiesResult, AnalyzeHealthcareEntitiesResult,
DetectLanguageResult, ExtractKeyPhrasesResult, RecognizeLinkedEntitiesResult, AnalyzeSentimentResult,
RecognizeCustomEntitiesResult, ClassifyDocumentResult, and DocumentError.
Added enum TextAnalysisKind.

Breaking Changes

Removed the Extractive Text Summarization feature and related models: ExtractSummaryAction, ExtractSummaryResult, and SummarySentence. To access this beta feature, install the 5.2.0b4 version of the client library.
Removed the FHIR feature and related keyword argument and property: fhir_version and fhir_bundle. To access this beta feature, install the 5.2.0b4 version of the client library.
SingleCategoryClassifyResult and MultiCategoryClassifyResult models have been merged into one model: ClassifyDocumentResult.
Renamed SingleCategoryClassifyAction to SingleLabelClassifyAction
Renamed MultiCategoryClassifyAction to MultiLabelClassifyAction.

Bugs Fixed

A HttpResponseError will be immediately raised when the call quota volume is exceeded in a F0 tier Language resource.

Other Changes

Python 3.6 is no longer supported. Please use Python version 3.7 or later. For more details, see Azure SDK for Python version support policy.

5.2.0b4 (2022-05-18)
Note that this is the first version of the client library that targets the Azure Cognitive Service for Language APIs which includes the existing text analysis and natural language processing features found in the Text Analytics client library.
In addition, the service API has changed from semantic to date-based versioning. This version of the client library defaults to the latest supported API version, which currently is 2022-04-01-preview. Support for v3.2-preview.2 is removed, however, all functionalities are included in the latest version.
Features Added

Added support for Healthcare Entities Analysis through the begin_analyze_actions API with the AnalyzeHealthcareEntitiesAction type.
Added keyword argument fhir_version to begin_analyze_healthcare_entities and AnalyzeHealthcareEntitiesAction. Use the keyword to indicate the version for the fhir_bundle contained on the AnalyzeHealthcareEntitiesResult.
Added property fhir_bundle to AnalyzeHealthcareEntitiesResult.
Added keyword argument display_name to begin_analyze_healthcare_entities.

5.2.0b3 (2022-03-08)
Bugs Fixed

string_index_type now correctly defaults to the Python default UnicodeCodePoint for AnalyzeSentimentAction and RecognizeCustomEntitiesAction.
Fixed a bug in begin_analyze_actions where incorrect action types were being sent in the request if targeting the older API version v3.1 in the beta version of the client library.
string_index_type option Utf16CodePoint is corrected to Utf16CodeUnit.

Other Changes

Python 2.7 is no longer supported. Please use Python version 3.6 or later.

5.2.0b2 (2021-11-02)
This version of the SDK defaults to the latest supported API version, which currently is v3.2-preview.2.
Features Added

Added support for Custom Entities Recognition through the begin_analyze_actions API with the RecognizeCustomEntitiesAction and RecognizeCustomEntitiesResult types.
Added support for Custom Single Classification through the begin_analyze_actions API with the SingleCategoryClassifyAction and SingleCategoryClassifyActionResult types.
Added support for Custom Multi Classification through the begin_analyze_actions API with the MultiCategoryClassifyAction and MultiCategoryClassifyActionResult types.
Multiple of the same action type is now supported with begin_analyze_actions.

Bugs Fixed

Restarting a long-running operation from a saved state is now supported for the begin_analyze_actions and begin_recognize_healthcare_entities methods.
In the event of an action level error, available partial results are now returned for any successful actions in begin_analyze_actions.

Other Changes

Package requires azure-core version 1.19.1 or greater

5.2.0b1 (2021-08-09)
This version of the SDK defaults to the latest supported API version, which currently is v3.2-preview.1.
Features Added

Added support for Extractive Summarization actions through the ExtractSummaryAction type.

Bugs Fixed

RecognizePiiEntitiesAction option disable_service_logs now correctly defaults to True.

Other Changes

Python 3.5 is no longer supported.

5.1.0 (2021-07-07)
This version of the SDK defaults to the latest supported API version, which currently is v3.1.
Includes all changes from 5.1.0b1 to 5.1.0b7.
Note: this version will be the last to officially support Python 3.5, future versions will require Python 2.7 or Python 3.6+.
Features Added

Added catagories_filter to RecognizePiiEntitiesAction
Added HealthcareEntityCategory
Added AAD support for the begin_analyze_healthcare_entities methods.

Breaking Changes

Changed: the response structure of being_analyze_actions. Now, we return a list of results, where each result is a list of the action results for the document, in the order the documents and actions were passed.
Changed: begin_analyze_actions now accepts a single action per type. A ValueError is raised if duplicate actions are passed.
Removed: AnalyzeActionsType
Removed: AnalyzeActionsResult
Removed: AnalyzeActionsError
Removed: HealthcareEntityRelationRoleType
Changed: renamed HealthcareEntityRelationType to HealthcareEntityRelation
Changed: renamed PiiEntityCategoryType to PiiEntityCategory
Changed: renamed PiiEntityDomainType to PiiEntityDomain

5.1.0b7 (2021-05-18)
Breaking Changes

Renamed begin_analyze_batch_actions to begin_analyze_actions.
Renamed AnalyzeBatchActionsType to AnalyzeActionsType.
Renamed AnalyzeBatchActionsResult to AnalyzeActionsResult.
Renamed AnalyzeBatchActionsError to AnalyzeActionsError.
Renamed AnalyzeHealthcareEntitiesResultItem to AnalyzeHealthcareEntitiesResult.
Fixed AnalyzeHealthcareEntitiesResult's statistics to be the correct type, TextDocumentStatistics
Remove RequestStatistics, use TextDocumentBatchStatistics instead

New Features

Added enums EntityConditionality, EntityCertainty, and EntityAssociation.
Added AnalyzeSentimentAction as a supported action type for begin_analyze_batch_actions.
Added kwarg disable_service_logs. If set to true, you opt-out of having your text input logged on the service side for troubleshooting.

5.1.0b6 (2021-03-09)
Breaking Changes

By default, we now target the service's v3.1-preview.4 endpoint through enum value TextAnalyticsApiVersion.V3_1_PREVIEW
Removed property related_entities on HealthcareEntity and added entity_relations onto the document response level for healthcare
Renamed properties aspect and opinions to target and assessments respectively in class MinedOpinion.
Renamed classes AspectSentiment and OpinionSentiment to TargetSentiment and AssessmentSentiment respectively.

New Features

Added RecognizeLinkedEntitiesAction as a supported action type for begin_analyze_batch_actions.
Added parameter categories_filter to the recognize_pii_entities client method.
Added enum PiiEntityCategoryType.
Add property normalized_text to HealthcareEntity. This property is a normalized version of the text property that already
exists on the HealthcareEntity
Add property assertion onto HealthcareEntity. This contains assertions about the entity itself, i.e. if the entity represents a diagnosis,
is this diagnosis conditional on a symptom?

Known Issues

begin_analyze_healthcare_entities is currently in gated preview and can not be used with AAD credentials. For more information, see the Text Analytics for Health documentation.
At time of this SDK release, the service is not respecting the value passed through model_version to begin_analyze_healthcare_entities, it only uses the latest model.

5.1.0b5 (2021-02-10)
Breaking Changes

Rename begin_analyze to begin_analyze_batch_actions.
Now instead of separate parameters for all of the different types of actions you can pass to begin_analyze_batch_actions, we accept one parameter actions,
which is a list of actions you would like performed. The results of the actions are returned in the same order as when inputted.
The response object from begin_analyze_batch_actions has also changed. Now, after the completion of your long running operation, we return a paged iterable
of action results, in the same order they've been inputted. The actual document results for each action are included under property document_results of
each action result.

New Features

Renamed begin_analyze_healthcare to begin_analyze_healthcare_entities.
Renamed AnalyzeHealthcareResult to AnalyzeHealthcareEntitiesResult and AnalyzeHealthcareResultItem to AnalyzeHealthcareEntitiesResultItem.
Renamed HealthcareEntityLink to HealthcareEntityDataSource and renamed its properties id to entity_id and data_source to name.
Removed relations from AnalyzeHealthcareEntitiesResultItem and added related_entities to HealthcareEntity.
Moved the cancellation logic for the Analyze Healthcare Entities service from
the service client to the poller object returned from begin_analyze_healthcare_entities.
Exposed Analyze Healthcare Entities operation metadata on the poller object returned from begin_analyze_healthcare_entities.
No longer need to specify api_version=TextAnalyticsApiVersion.V3_1_PREVIEW_3 when calling begin_analyze and begin_analyze_healthcare_entities. begin_analyze_healthcare_entities is still in gated preview though.
Added a new parameter string_index_type to the service client methods begin_analyze_healthcare_entities, analyze_sentiment, recognize_entities, recognize_pii_entities, and recognize_linked_entities which tells the service how to interpret string offsets.
Added property length to CategorizedEntity, SentenceSentiment, LinkedEntityMatch, AspectSentiment, OpinionSentiment, PiiEntity and
HealthcareEntity.

5.1.0b4 (2021-01-12)
Bug Fixes

Package requires azure-core version 1.8.2 or greater

5.1.0b3 (2020-11-19)
New Features

We have added method begin_analyze, which supports long-running batch process of Named Entity Recognition, Personally identifiable Information, and Key Phrase Extraction. To use, you must specify api_version=TextAnalyticsApiVersion.V3_1_PREVIEW_3 when creating your client.
We have added method begin_analyze_healthcare, which supports the service's Health API. Since the Health API is currently only available in a gated preview, you need to have your subscription on the service's allow list, and you must specify api_version=TextAnalyticsApiVersion.V3_1_PREVIEW_3 when creating your client. Note that since this is a gated preview, AAD is not supported. More information here.

5.1.0b2 (2020-10-06)
Breaking changes

Removed property length from CategorizedEntity, SentenceSentiment, LinkedEntityMatch, AspectSentiment, OpinionSentiment, and PiiEntity.
To get the length of the text in these models, just call len() on the text property.
When a parameter or endpoint is not compatible with the API version you specify, we will now return a ValueError instead of a NotImplementedError.
Client side validation of input is now disabled by default. This means there will be no ValidationErrors thrown by the client SDK in the case of malformed input. The error will now be thrown by the service through an HttpResponseError.

5.1.0b1 (2020-09-17)
New features

We are now targeting the service's v3.1-preview API as the default. If you would like to still use version v3.0 of the service,
pass in v3.0 to the kwarg api_version when creating your TextAnalyticsClient
We have added an API recognize_pii_entities which returns entities containing personally identifiable information for a batch of documents. Only available for API version v3.1-preview and up.
Added offset and length properties for CategorizedEntity, SentenceSentiment, and LinkedEntityMatch. These properties are only available for API versions v3.1-preview and up.

length is the number of characters in the text of these models
offset is the offset of the text from the start of the document


We now have added support for opinion mining. To use this feature, you need to make sure you are using the service's
v3.1-preview API. To get this support pass show_opinion_mining as True when calling the analyze_sentiment endpoint
Add property bing_entity_search_api_id to the LinkedEntity class. This property is only available for v3.1-preview and up, and it is to be
used in conjunction with the Bing Entity Search API to fetch additional relevant information about the returned entity.

5.0.0 (2020-07-27)

Re-release of GA version 1.0.0 with an updated version

1.0.0 (2020-06-09)

First stable release of the azure-ai-textanalytics package. Targets the service's v3.0 API.

1.0.0b6 (2020-05-27)
New features

We now have a warnings property on each document-level response object returned from the endpoints. It is a list of TextAnalyticsWarnings.
Added text property to SentenceSentiment

Breaking changes

Now targets only the service's v3.0 API, instead of the v3.0-preview.1 API
score attribute of DetectedLanguage has been renamed to confidence_score
Removed grapheme_offset and grapheme_length from CategorizedEntity, SentenceSentiment, and LinkedEntityMatch
TextDocumentStatistics attribute grapheme_count has been renamed to character_count

1.0.0b5

This was a broken release

1.0.0b4 (2020-04-07)
Breaking changes

Removed the recognize_pii_entities endpoint and all related models (RecognizePiiEntitiesResult and PiiEntity)
from this library.
Removed TextAnalyticsApiKeyCredential and now using AzureKeyCredential from azure.core.credentials as key credential
score attribute has been renamed to confidence_score for the CategorizedEntity, LinkedEntityMatch, and
PiiEntity models
All input parameters inputs have been renamed to documents

1.0.0b3 (2020-03-10)
Breaking changes

SentimentScorePerLabel has been renamed to SentimentConfidenceScores
AnalyzeSentimentResult and SentenceSentiment attribute sentiment_scores has been renamed to confidence_scores
TextDocumentStatistics attribute character_count has been renamed to grapheme_count
LinkedEntity attribute id has been renamed to data_source_entity_id
Parameters country_hint and language are now passed as keyword arguments
The keyword argument response_hook has been renamed to raw_response_hook
length and offset attributes have been renamed to grapheme_length and grapheme_offset for the SentenceSentiment,
CategorizedEntity, PiiEntity, and LinkedEntityMatch models

New features

Pass country_hint="none" to not use the default country hint of "US".

Dependency updates

Adopted azure-core version 1.3.0 or greater

1.0.0b2 (2020-02-11)
Breaking changes

The single text, module-level operations single_detect_language(), single_recognize_entities(), single_extract_key_phrases(), single_analyze_sentiment(), single_recognize_pii_entities(), and single_recognize_linked_entities()
have been removed from the client library. Use the batching methods for optimal performance in production environments.
To use an API key as the credential for authenticating the client, a new credential class TextAnalyticsApiKeyCredential("<api_key>") must be passed in for the credential parameter.
Passing the API key as a string is no longer supported.
detect_languages() is renamed to detect_language().
The TextAnalyticsError model has been simplified to an object with only attributes code, message, and target.
NamedEntity has been renamed to CategorizedEntity and its attributes type to category and subtype to subcategory.
RecognizePiiEntitiesResult now contains on the object a list of PiiEntity instead of NamedEntity.
AnalyzeSentimentResult attribute document_scores has been renamed to sentiment_scores.
SentenceSentiment attribute sentence_scores has been renamed to sentiment_scores.
SentimentConfidenceScorePerLabel has been renamed to SentimentScorePerLabel.
DetectLanguageResult no longer has attribute detected_languages. Use primary_language to access the detected language in text.

New features

Credential class TextAnalyticsApiKeyCredential provides an update_key() method which allows you to update the API key for long-lived clients.

Fixes and improvements

__repr__ has been added to all of the response objects.
If you try to access a result attribute on a DocumentError object, an AttributeError is raised with a custom error message that provides the document ID and error of the invalid document.

1.0.0b1 (2020-01-09)
Version (1.0.0b1) is the first preview of our efforts to create a user-friendly and Pythonic client library for Azure Text Analytics. For more information about this, and preview releases of other Azure SDK libraries, please visit
https://azure.github.io/azure-sdk/releases/latest/python.html.
Breaking changes: New API design


New namespace/package name:

The namespace/package name for Azure Text Analytics client library has changed from azure.cognitiveservices.language.textanalytics to azure.ai.textanalytics



New operations and naming:

detect_language is renamed to detect_languages
entities is renamed to recognize_entities
key_phrases is renamed to extract_key_phrases
sentiment is renamed to analyze_sentiment
New operation recognize_pii_entities finds personally identifiable information entities in text
New operation recognize_linked_entities provides links from a well-known knowledge base for each recognized entity
New module-level operations single_detect_language, single_recognize_entities, single_extract_key_phrases, single_analyze_sentiment, single_recognize_pii_entities, and single_recognize_linked_entities perform
function on a single string instead of a batch of text documents and can be imported from the azure.ai.textanalytics namespace.
New client and module-level async APIs added to subnamespace azure.ai.textanalytics.aio.
MultiLanguageInput has been renamed to TextDocumentInput
LanguageInput has been renamed to DetectLanguageInput
DocumentLanguage has been renamed to DetectLanguageResult
DocumentEntities has been renamed to RecognizeEntitiesResult
DocumentLinkedEntities has been renamed to RecognizeLinkedEntitiesResult
DocumentKeyPhrases has been renamed to ExtractKeyPhrasesResult
DocumentSentiment has been renamed to AnalyzeSentimentResult
DocumentStatistics has been renamed to TextDocumentStatistics
RequestStatistics has been renamed to TextDocumentBatchStatistics
Entity has been renamed to NamedEntity
Match has been renamed to LinkedEntityMatch
The batching methods' documents parameter has been renamed inputs



New input types:

detect_languages can take as input a list[DetectLanguageInput] or a list[str]. A list of dict-like objects in the same shape as DetectLanguageInput is still accepted as input.
recognize_entities, recognize_pii_entities, recognize_linked_entities, extract_key_phrases, analyze_sentiment can take as input a list[TextDocumentInput] or list[str].
A list of dict-like objects in the same shape as TextDocumentInput is still accepted as input.



New parameters/keyword arguments:

All operations now take a keyword argument model_version which allows the user to specify a string referencing the desired model version to be used for analysis. If no string specified, it will default to the latest, non-preview version.
detect_languages now takes a parameter country_hint which allows you to specify the country hint for the entire batch. Any per-item country hints will take precedence over a whole batch hint.
recognize_entities, recognize_pii_entities, recognize_linked_entities, extract_key_phrases, analyze_sentiment now take a parameter language which allows you to specify the language for the entire batch.
Any per-item specified language will take precedence over a whole batch hint.
A default_country_hint or default_language keyword argument can be passed at client instantiation to set the default values for all operations.
A response_hook keyword argument can be passed with a callback to use the raw response from the service. Additionally, values returned for TextDocumentBatchStatistics and model_version used must be retrieved using a response hook.
show_stats and model_version parameters move to keyword only arguments.



New return types

The return types for the batching methods (detect_languages, recognize_entities, recognize_pii_entities, recognize_linked_entities, extract_key_phrases, analyze_sentiment) now return a heterogeneous list of
result objects and document errors in the order passed in with the request. To iterate over the list and filter for result or error, a boolean property on each object called is_error can be used to determine whether the returned response object at
that index is a result or an error:
detect_languages now returns a List[Union[DetectLanguageResult, DocumentError]]
recognize_entities now returns a List[Union[RecognizeEntitiesResult, DocumentError]]
recognize_pii_entities now returns a List[Union[RecognizePiiEntitiesResult, DocumentError]]
recognize_linked_entities now returns a List[Union[RecognizeLinkedEntitiesResult, DocumentError]]
extract_key_phrases now returns a List[Union[ExtractKeyPhrasesResult, DocumentError]]
analyze_sentiment now returns a List[Union[AnalyzeSentimentResult, DocumentError]]
The module-level, single text operations will return a single result object or raise the error found on the document:
single_detect_languages returns a DetectLanguageResult
single_recognize_entities returns a RecognizeEntitiesResult
single_recognize_pii_entities returns a RecognizePiiEntitiesResult
single_recognize_linked_entities returns a RecognizeLinkedEntitiesResult
single_extract_key_phrases returns a ExtractKeyPhrasesResult
single_analyze_sentiment returns a AnalyzeSentimentResult



New underlying REST pipeline implementation, based on the new azure-core library.


Client and pipeline configuration is now available via keyword arguments at both the client level, and per-operation. See README for a full list of optional configuration arguments.


Authentication using azure-identity credentials

see the
Azure Identity documentation
for more information



New error hierarchy:

All service errors will now use the base type: azure.core.exceptions.HttpResponseError
There is one exception type derived from this base type for authentication errors:

ClientAuthenticationError: Authentication failed.





0.2.0 (2019-03-12)
Features

Client class can be used as a context manager to keep the underlying HTTP session open for performance
New method "entities"
Model KeyPhraseBatchResultItem has a new parameter statistics
Model KeyPhraseBatchResult has a new parameter statistics
Model LanguageBatchResult has a new parameter statistics
Model LanguageBatchResultItem has a new parameter statistics
Model SentimentBatchResult has a new parameter statistics

Breaking changes

TextAnalyticsAPI main client has been renamed TextAnalyticsClient
TextAnalyticsClient parameter is no longer a region but a complete endpoint

General Breaking changes
This version uses a next-generation code generator that might introduce breaking changes.


Model signatures now use only keyword-argument syntax. All positional arguments must be re-written as keyword-arguments.
To keep auto-completion in most cases, models are now generated for Python 2 and Python 3. Python 3 uses the "*" syntax for keyword-only arguments.


Enum types now use the "str" mixin (class AzureEnum(str, Enum)) to improve the behavior when unrecognized enum values are encountered.
While this is not a breaking change, the distinctions are important, and are documented here:
https://docs.python.org/3/library/enum.html#others
At a glance:

"is" should not be used at all.
"format" will return the string value, where "%s" string formatting will return NameOfEnum.stringvalue. Format syntax should be preferred.



Bugfixes

Compatibility of the sdist with wheel 0.31.0

0.1.0 (2018-01-12)

Initial Release