def test_disable_service_logs(self, client):
actions = [
RecognizeEntitiesAction(disable_service_logs=True),
ExtractKeyPhrasesAction(disable_service_logs=True),
RecognizePiiEntitiesAction(disable_service_logs=True),
RecognizeLinkedEntitiesAction(disable_service_logs=True),
AnalyzeSentimentAction(disable_service_logs=True),
]
for action in actions:
assert action.disable_service_logs
def callback(resp):
tasks = json.loads(resp.http_request.body)["tasks"]
assert len(tasks) == len(actions)
for task in tasks.values():
assert task[0]["parameters"]["loggingOptOut"]
client.begin_analyze_actions(
documents=["Test for logging disable"],
actions=actions,
polling_interval=self._interval(),
raw_response_hook=callback,
).result()
async def test_multiple_pages_of_results_returned_successfully(
self, client):
single_doc = "hello world"
docs = [{
"id": str(idx),
"text": val
} for (idx, val) in enumerate(list(itertools.repeat(single_doc, 25)))
] # max number of documents is 25
async with client:
result = await (await client.begin_analyze_actions(
docs,
actions=[
RecognizeEntitiesAction(),
ExtractKeyPhrasesAction(),
RecognizePiiEntitiesAction(),
RecognizeLinkedEntitiesAction(),
AnalyzeSentimentAction()
],
show_stats=True,
polling_interval=self._interval())).result()
pages = []
async for p in result:
pages.append(p)
recognize_entities_results = []
extract_key_phrases_results = []
recognize_pii_entities_results = []
recognize_linked_entities_results = []
analyze_sentiment_results = []
for idx, action_result in enumerate(pages):
if idx % 5 == 0:
assert action_result.action_type == AnalyzeActionsType.RECOGNIZE_ENTITIES
recognize_entities_results.append(action_result)
elif idx % 5 == 1:
assert action_result.action_type == AnalyzeActionsType.EXTRACT_KEY_PHRASES
extract_key_phrases_results.append(action_result)
elif idx % 5 == 2:
assert action_result.action_type == AnalyzeActionsType.RECOGNIZE_PII_ENTITIES
recognize_pii_entities_results.append(action_result)
elif idx % 5 == 3:
assert action_result.action_type == AnalyzeActionsType.RECOGNIZE_LINKED_ENTITIES
recognize_linked_entities_results.append(action_result)
else:
assert action_result.action_type == AnalyzeActionsType.ANALYZE_SENTIMENT
analyze_sentiment_results.append(action_result)
if idx < 5: # first page of task results
assert len(action_result.document_results) == 20
else:
assert len(action_result.document_results) == 5
assert all([
action_result for action_result in recognize_entities_results
if len(action_result.document_results) == len(docs)
])
assert all([
action_result for action_result in extract_key_phrases_results
if len(action_result.document_results) == len(docs)
])
assert all([
action_result
for action_result in recognize_pii_entities_results
if len(action_result.document_results) == len(docs)
])
assert all([
action_result
for action_result in recognize_linked_entities_results
if len(action_result.document_results) == len(docs)
])
assert all([
action_result for action_result in analyze_sentiment_results
if len(action_result.document_results) == len(docs)
])
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_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("------------------------------------------")
def sample_analyze_actions():
# [START analyze]
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import (
TextAnalyticsClient,
RecognizeEntitiesAction,
RecognizeLinkedEntitiesAction,
RecognizePiiEntitiesAction,
ExtractKeyPhrasesAction,
AnalyzeSentimentAction,
)
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_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}")
recognize_entities_result = action_results[0]
print("...Results of Recognize Entities Action:")
if recognize_entities_result.is_error:
print("...Is an error with code '{}' and message '{}'".format(
recognize_entities_result.code,
recognize_entities_result.message))
else:
for entity in recognize_entities_result.entities:
print(f"......Entity: {entity.text}")
print(f".........Category: {entity.category}")
print(f".........Confidence Score: {entity.confidence_score}")
print(f".........Offset: {entity.offset}")
recognize_pii_entities_result = action_results[1]
print("...Results of Recognize PII Entities action:")
if recognize_pii_entities_result.is_error:
print("...Is an error with code '{}' and message '{}'".format(
recognize_pii_entities_result.code,
recognize_pii_entities_result.message))
else:
for entity in recognize_pii_entities_result.entities:
print(f"......Entity: {entity.text}")
print(f".........Category: {entity.category}")
print(f".........Confidence Score: {entity.confidence_score}")
extract_key_phrases_result = action_results[2]
print("...Results of Extract Key Phrases action:")
if extract_key_phrases_result.is_error:
print("...Is an error with code '{}' and message '{}'".format(
extract_key_phrases_result.code,
extract_key_phrases_result.message))
else:
print(
f"......Key Phrases: {extract_key_phrases_result.key_phrases}")
recognize_linked_entities_result = action_results[3]
print("...Results of Recognize Linked Entities action:")
if recognize_linked_entities_result.is_error:
print("...Is an error with code '{}' and message '{}'".format(
recognize_linked_entities_result.code,
recognize_linked_entities_result.message))
else:
for linked_entity in recognize_linked_entities_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}")
analyze_sentiment_result = action_results[4]
print("...Results of Analyze Sentiment action:")
if analyze_sentiment_result.is_error:
print("...Is an error with code '{}' and message '{}'".format(
analyze_sentiment_result.code,
analyze_sentiment_result.message))
else:
print(
f"......Overall sentiment: {analyze_sentiment_result.sentiment}"
)
print(
"......Scores: positive={}; neutral={}; negative={} \n".format(
analyze_sentiment_result.confidence_scores.positive,
analyze_sentiment_result.confidence_scores.neutral,
analyze_sentiment_result.confidence_scores.negative,
))
print("------------------------------------------")
async def analyze_async(self):
# [START analyze_async]
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics.aio import TextAnalyticsClient
from azure.ai.textanalytics import (
RecognizeEntitiesAction,
RecognizeLinkedEntitiesAction,
RecognizePiiEntitiesAction,
ExtractKeyPhrasesAction,
AnalyzeSentimentAction,
)
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!'\
]
async with text_analytics_client:
poller = await text_analytics_client.begin_analyze_actions(
documents,
display_name="Sample Text Analysis",
actions=[
RecognizeEntitiesAction(),
RecognizePiiEntitiesAction(),
ExtractKeyPhrasesAction(),
RecognizeLinkedEntitiesAction(),
AnalyzeSentimentAction()
])
pages = await poller.result()
# To enumerate / zip for async, unless you install a third party library,
# you have to read in all of the elements into memory first.
# If you're not looking to enumerate / zip, we recommend you just asynchronously
# loop over it immediately, without going through this step of reading them into memory
document_results = []
async for page in pages:
document_results.append(page)
for doc, action_results in zip(documents, document_results):
print("\nDocument text: {}".format(doc))
recognize_entities_result = action_results[0]
print("...Results of Recognize Entities Action:")
if recognize_entities_result.is_error:
print("...Is an error with code '{}' and message '{}'".
format(recognize_entities_result.code,
recognize_entities_result.message))
else:
for entity in recognize_entities_result.entities:
print("......Entity: {}".format(entity.text))
print(".........Category: {}".format(entity.category))
print(".........Confidence Score: {}".format(
entity.confidence_score))
print(".........Offset: {}".format(entity.offset))
recognize_pii_entities_result = action_results[1]
print("...Results of Recognize PII Entities action:")
if recognize_pii_entities_result.is_error:
print("...Is an error with code '{}' and message '{}'".
format(recognize_pii_entities_result.code,
recognize_pii_entities_result.message))
else:
for entity in recognize_pii_entities_result.entities:
print("......Entity: {}".format(entity.text))
print(".........Category: {}".format(entity.category))
print(".........Confidence Score: {}".format(
entity.confidence_score))
extract_key_phrases_result = action_results[2]
print("...Results of Extract Key Phrases action:")
if extract_key_phrases_result.is_error:
print("...Is an error with code '{}' and message '{}'".
format(extract_key_phrases_result.code,
extract_key_phrases_result.message))
else:
print("......Key Phrases: {}".format(
extract_key_phrases_result.key_phrases))
recognize_linked_entities_result = action_results[3]
print("...Results of Recognize Linked Entities action:")
if recognize_linked_entities_result.is_error:
print("...Is an error with code '{}' and message '{}'".
format(recognize_linked_entities_result.code,
recognize_linked_entities_result.message))
else:
for linked_entity in recognize_linked_entities_result.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))
analyze_sentiment_result = action_results[4]
print("...Results of Analyze Sentiment action:")
if analyze_sentiment_result.is_error:
print("...Is an error with code '{}' and message '{}'".
format(analyze_sentiment_result.code,
analyze_sentiment_result.message))
else:
print("......Overall sentiment: {}".format(
analyze_sentiment_result.sentiment))
print(
"......Scores: positive={}; neutral={}; negative={} \n"
.format(
analyze_sentiment_result.confidence_scores.
positive,
analyze_sentiment_result.confidence_scores.neutral,
analyze_sentiment_result.confidence_scores.
negative,
))
print("------------------------------------------")
async def test_show_stats_and_model_version_multiple_tasks(self, client):
docs = [{
"id": "56",
"text": ":)"
}, {
"id": "0",
"text": ":("
}, {
"id": "19",
"text": ":P"
}, {
"id": "1",
"text": ":D"
}]
def callback(resp):
assert resp.raw_response
tasks = resp.raw_response['tasks']
assert tasks['completed'] == 6
assert tasks['inProgress'] == 0
assert tasks['failed'] == 0
assert tasks['total'] == 6
num_tasks = 0
for key, task in tasks.items():
if "Tasks" in key:
num_tasks += 1
assert len(task) == 1
task_stats = task[0]['results']['statistics']
assert task_stats['documentsCount'] == 4
assert task_stats['validDocumentsCount'] == 4
assert task_stats['erroneousDocumentsCount'] == 0
assert task_stats['transactionsCount'] == 4
assert num_tasks == 6
async with client:
response = await (await client.begin_analyze_actions(
docs,
actions=[
RecognizeEntitiesAction(model_version="latest"),
ExtractKeyPhrasesAction(model_version="latest"),
RecognizePiiEntitiesAction(model_version="latest"),
RecognizeLinkedEntitiesAction(model_version="latest"),
AnalyzeSentimentAction(model_version="latest"),
ExtractSummaryAction(model_version="latest")
],
show_stats=True,
polling_interval=self._interval(),
raw_response_hook=callback,
)).result()
pages = []
async for p in response:
pages.append(p)
assert len(pages) == len(docs)
action_order = [
_AnalyzeActionsType.RECOGNIZE_ENTITIES,
_AnalyzeActionsType.EXTRACT_KEY_PHRASES,
_AnalyzeActionsType.RECOGNIZE_PII_ENTITIES,
_AnalyzeActionsType.RECOGNIZE_LINKED_ENTITIES,
_AnalyzeActionsType.ANALYZE_SENTIMENT,
_AnalyzeActionsType.EXTRACT_SUMMARY
]
for document_results in pages:
assert len(document_results) == len(action_order)
for document_result in document_results:
assert document_result.statistics
assert document_result.statistics.character_count
assert document_result.statistics.transaction_count
async def analyze_async(self):
# [START analyze_async]
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics.aio import TextAnalyticsClient
from azure.ai.textanalytics import (
RecognizeEntitiesAction,
RecognizePiiEntitiesAction,
ExtractKeyPhrasesAction,
AnalyzeBatchActionsType
)
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!"
]
async with text_analytics_client:
poller = await text_analytics_client.begin_analyze_batch_actions(
documents,
display_name="Sample Text Analysis",
actions=[
RecognizeEntitiesAction(),
RecognizePiiEntitiesAction(),
ExtractKeyPhrasesAction()
]
)
result = await poller.result()
async for action_result in result:
if action_result.is_error:
raise ValueError(
"Action has failed with message: {}".format(
action_result.error.message
)
)
if action_result.action_type == AnalyzeBatchActionsType.RECOGNIZE_ENTITIES:
print("Results of Entities Recognition action:")
for idx, doc in enumerate(action_result.document_results):
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("------------------------------------------")
if action_result.action_type == AnalyzeBatchActionsType.RECOGNIZE_PII_ENTITIES:
print("Results of PII Entities Recognition action:")
for idx, doc in enumerate(action_result.document_results):
print("Document text: {}".format(documents[idx]))
for entity in doc.entities:
print("Entity: {}".format(entity.text))
print("Category: {}".format(entity.category))
print("Confidence Score: {}\n".format(entity.confidence_score))
print("------------------------------------------")
if action_result.action_type == AnalyzeBatchActionsType.EXTRACT_KEY_PHRASES:
print("Results of Key Phrase Extraction action:")
for idx, doc in enumerate(action_result.document_results):
print("Document text: {}\n".format(documents[idx]))
print("Key Phrases: {}\n".format(doc.key_phrases))
print("------------------------------------------")
