def test_rotate_subscription_key(self, resource_group, location, text_analytics_account, text_analytics_account_key):
credential = AzureKeyCredential(text_analytics_account_key)
client = TextAnalyticsClient(text_analytics_account, credential)
docs = [{"id": "1", "text": "I will go to the park."},
{"id": "2", "text": "I did not like the hotel we stayed at."},
{"id": "3", "text": "The restaurant had really good food."}]
response = client.begin_analyze_batch_actions(
docs,
actions=[
RecognizeEntitiesAction(),
],
polling_interval=self._interval(),
).result()
self.assertIsNotNone(response)
credential.update("xxx") # Make authentication fail
with self.assertRaises(ClientAuthenticationError):
response = client.begin_analyze_batch_actions(
docs,
actions=[
RecognizeEntitiesAction(),
],
polling_interval=self._interval(),
).result()
credential.update(text_analytics_account_key) # Authenticate successfully again
response = client.begin_analyze_batch_actions(
docs,
actions=[
RecognizeEntitiesAction(),
],
polling_interval=self._interval(),
).result()
self.assertIsNotNone(response)
def analyze(self):
# [START analyze]
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import (
TextAnalyticsClient,
RecognizeEntitiesAction,
RecognizeLinkedEntitiesAction,
RecognizePiiEntitiesAction,
ExtractKeyPhrasesAction,
AnalyzeSentimentAction,
PiiEntityDomainType,
)
endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_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_batch_actions(
documents,
display_name="Sample Text Analysis",
actions=[
RecognizeEntitiesAction(),
RecognizePiiEntitiesAction(domain_filter=PiiEntityDomainType.PROTECTED_HEALTH_INFORMATION),
ExtractKeyPhrasesAction(),
RecognizeLinkedEntitiesAction(),
AnalyzeSentimentAction()
],
)
result = poller.result()
action_results = [action_result for action_result in list(result) if not action_result.is_error]
first_action_result = action_results[0]
print("Results of Entities Recognition action:")
docs = [doc for doc in first_action_result.document_results if not doc.is_error]
for idx, doc in enumerate(docs):
print("\nDocument text: {}".format(documents[idx]))
for entity in doc.entities:
print("Entity: {}".format(entity.text))
print("...Category: {}".format(entity.category))
print("...Confidence Score: {}".format(entity.confidence_score))
print("...Offset: {}".format(entity.offset))
print("...Length: {}".format(entity.length))
print("------------------------------------------")
second_action_result = action_results[1]
print("Results of PII Entities Recognition action:")
docs = [doc for doc in second_action_result.document_results if not doc.is_error]
for idx, doc in enumerate(docs):
print("Document text: {}".format(documents[idx]))
print("Document text with redactions: {}".format(doc.redacted_text))
for entity in doc.entities:
print("Entity: {}".format(entity.text))
print("...Category: {}".format(entity.category))
print("...Confidence Score: {}\n".format(entity.confidence_score))
print("...Offset: {}".format(entity.offset))
print("...Length: {}".format(entity.length))
print("------------------------------------------")
third_action_result = action_results[2]
print("Results of Key Phrase Extraction action:")
docs = [doc for doc in third_action_result.document_results if not doc.is_error]
for idx, doc in enumerate(docs):
print("Document text: {}\n".format(documents[idx]))
print("Key Phrases: {}\n".format(doc.key_phrases))
print("------------------------------------------")
fourth_action_result = action_results[3]
print("Results of Linked Entities Recognition action:")
docs = [doc for doc in fourth_action_result.document_results if not doc.is_error]
for idx, doc in enumerate(docs):
print("Document text: {}\n".format(documents[idx]))
for linked_entity in doc.entities:
print("Entity name: {}".format(linked_entity.name))
print("...Data source: {}".format(linked_entity.data_source))
print("...Data source language: {}".format(linked_entity.language))
print("...Data source entity ID: {}".format(linked_entity.data_source_entity_id))
print("...Data source URL: {}".format(linked_entity.url))
print("...Document matches:")
for match in linked_entity.matches:
print("......Match text: {}".format(match.text))
print(".........Confidence Score: {}".format(match.confidence_score))
print(".........Offset: {}".format(match.offset))
print(".........Length: {}".format(match.length))
print("------------------------------------------")
fifth_action_result = action_results[4]
print("Results of Sentiment Analysis action:")
docs = [doc for doc in fifth_action_result.document_results if not doc.is_error]
for doc in docs:
print("Overall sentiment: {}".format(doc.sentiment))
print("Scores: positive={}; neutral={}; negative={} \n".format(
doc.confidence_scores.positive,
doc.confidence_scores.neutral,
doc.confidence_scores.negative,
))
print("------------------------------------------")
