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(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
).result()
self.assertIsNotNone(response)
credential.update("xxx") # Make authentication fail
with self.assertRaises(ClientAuthenticationError):
response = client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
).result()
credential.update(
text_analytics_account_key) # Authenticate successfully again
response = client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
).result()
self.assertIsNotNone(response)
def test_passing_only_string_key_phrase_task(self, client):
docs = [
u"Microsoft was founded by Bill Gates and Paul Allen",
u"Microsoft fue fundado por Bill Gates y Paul Allen"
]
response = client.begin_analyze(
docs,
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
polling_interval=self._interval(),
).result()
results_pages = list(response)
self.assertEqual(len(results_pages), 1)
key_phrase_task_results = results_pages[
0].key_phrase_extraction_results
self.assertEqual(len(key_phrase_task_results), 1)
results = key_phrase_task_results[0].results
self.assertEqual(len(results), 2)
self.assertIn("Paul Allen", results[0].key_phrases)
self.assertIn("Bill Gates", results[0].key_phrases)
self.assertIn("Microsoft", results[0].key_phrases)
self.assertIsNotNone(results[0].id)
def test_output_same_order_as_input_multiple_tasks(self, client):
docs = [
TextDocumentInput(id="1", text="one"),
TextDocumentInput(id="2", text="two"),
TextDocumentInput(id="3", text="three"),
TextDocumentInput(id="4", text="four"),
TextDocumentInput(id="5", text="five")
]
response = client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
).result()
results_pages = list(response)
self.assertEqual(len(results_pages), 1)
task_types = [
"entities_recognition_results", "key_phrase_extraction_results",
"pii_entities_recognition_results"
]
for task_type in task_types:
task_results = getattr(results_pages[0], task_type)
self.assertEqual(len(task_results), 1)
results = task_results[0].results
self.assertEqual(len(results), 5)
for idx, doc in enumerate(results):
self.assertEqual(str(idx + 1), doc.id)
def test_whole_batch_dont_use_language_hint(self, client):
docs = [
u"This was the best day of my life.",
u"I did not like the hotel we stayed at. It was too expensive.",
u"The restaurant was not as good as I hoped."
]
response = list(
client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
language="",
polling_interval=self._interval(),
).result())
task_types = [
"entities_recognition_results", "key_phrase_extraction_results",
"pii_entities_recognition_results"
]
for task_type in task_types:
task_results = getattr(response[0], task_type)
self.assertEqual(len(task_results), 1)
results = task_results[0].results
for r in results:
self.assertFalse(r.is_error)
def test_all_successful_passing_dict_key_phrase_task(self, client):
docs = [{
"id": "1",
"language": "en",
"text": "Microsoft was founded by Bill Gates and Paul Allen"
}, {
"id": "2",
"language": "es",
"text": "Microsoft fue fundado por Bill Gates y Paul Allen"
}]
response = client.begin_analyze(
docs,
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
show_stats=True,
polling_interval=self._interval(),
).result()
results_pages = list(response)
self.assertEqual(len(results_pages), 1)
task_results = results_pages[0].key_phrase_extraction_results
self.assertEqual(len(task_results), 1)
results = task_results[0].results
self.assertEqual(len(results), 2)
for phrases in results:
self.assertIn("Paul Allen", phrases.key_phrases)
self.assertIn("Bill Gates", phrases.key_phrases)
self.assertIn("Microsoft", phrases.key_phrases)
self.assertIsNotNone(phrases.id)
def test_whole_batch_language_hint_and_obj_per_item_hints(self, client):
docs = [
TextDocumentInput(id="1",
text="I should take my cat to the veterinarian.",
language="en"),
TextDocumentInput(id="2",
text="Este es un document escrito en Español.",
language="en"),
TextDocumentInput(id="3", text="猫は幸せ"),
]
response = list(
client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
language="en",
polling_interval=self._interval(),
).result())
task_types = [
"entities_recognition_results", "key_phrase_extraction_results",
"pii_entities_recognition_results"
]
for task_type in task_types:
task_results = getattr(response[0], task_type)
self.assertEqual(len(task_results), 1)
results = task_results[0].results
for r in results:
self.assertFalse(r.is_error)
def test_client_passed_default_language_hint(self, client):
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 = list(
client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
).result())
task_types = [
"entities_recognition_results", "key_phrase_extraction_results",
"pii_entities_recognition_results"
]
for task_type in task_types:
tasks = getattr(
response[0],
task_type) # only expecting a single page of results here
self.assertEqual(len(tasks), 1)
self.assertEqual(len(tasks[0].results), 3)
for r in tasks[0].results:
self.assertFalse(r.is_error)
def test_user_agent(self, client):
def callback(resp):
self.assertIn(
"azsdk-python-ai-textanalytics/{} Python/{} ({})".format(
VERSION, platform.python_version(), platform.platform()),
resp.http_request.headers["User-Agent"])
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."
}]
poller = client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
)
self.assertIn(
"azsdk-python-ai-textanalytics/{} Python/{} ({})".format(
VERSION, platform.python_version(),
platform.platform()), poller._polling_method._initial_response.
http_request.headers["User-Agent"])
poller.result(
) # need to call this before tearDown runs even though we don't need the response for the test.
def test_all_successful_passing_text_document_input_key_phrase_task(
self, client):
docs = [
TextDocumentInput(
id="1",
text="Microsoft was founded by Bill Gates and Paul Allen",
language="en"),
TextDocumentInput(
id="2",
text="Microsoft fue fundado por Bill Gates y Paul Allen",
language="es")
]
response = client.begin_analyze(
docs,
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
polling_interval=self._interval(),
).result()
results_pages = list(response)
self.assertEqual(len(results_pages), 1)
key_phrase_task_results = results_pages[
0].key_phrase_extraction_results
self.assertEqual(len(key_phrase_task_results), 1)
results = key_phrase_task_results[0].results
self.assertEqual(len(results), 2)
for phrases in results:
self.assertIn("Paul Allen", phrases.key_phrases)
self.assertIn("Bill Gates", phrases.key_phrases)
self.assertIn("Microsoft", phrases.key_phrases)
self.assertIsNotNone(phrases.id)
def test_invalid_language_hint_docs(self, client):
response = list(
client.begin_analyze(
[{
"id":
"1",
"language":
"notalanguage",
"text":
"This should fail because we're passing in an invalid language hint"
}],
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
).result())
task_types = [
"entities_recognition_results", "key_phrase_extraction_results",
"pii_entities_recognition_results"
]
for task_type in task_types:
tasks = getattr(
response[0],
task_type) # only expecting a single page of results here
self.assertEqual(len(tasks), 1)
for r in tasks[0].results:
self.assertTrue(r.is_error)
def test_bad_request_on_empty_document(self, client):
docs = [u""]
with self.assertRaises(HttpResponseError):
response = client.begin_analyze(
docs,
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
polling_interval=self._interval(),
)
def test_bad_credentials(self, client):
with self.assertRaises(ClientAuthenticationError):
response = client.begin_analyze(
["This is written in English."],
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
)
def test_not_passing_list_for_docs(self, client):
docs = {"id": "1", "text": "hello world"}
with pytest.raises(TypeError) as excinfo:
client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
)
assert "Input documents cannot be a dict" in str(excinfo.value)
def test_bad_document_input(self, client):
docs = "This is the wrong type"
with self.assertRaises(TypeError):
response = client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
)
def test_missing_input_records_error(self, client):
docs = []
with pytest.raises(ValueError) as excinfo:
client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
)
assert "Input documents can not be empty or None" in str(excinfo.value)
def test_pass_cls(self, client):
def callback(pipeline_response, deserialized, _):
return "cls result"
res = client.begin_analyze(
documents=["Test passing cls to endpoint"],
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
cls=callback,
polling_interval=self._interval(),
).result()
assert res == "cls result"
def test_too_many_documents(self, client):
docs = list(itertools.repeat(
"input document",
26)) # Maximum number of documents per request is 25
with pytest.raises(HttpResponseError) as excinfo:
client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
)
assert excinfo.value.status_code == 400
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"
}]
response = client.begin_analyze(
docs,
entities_recognition_tasks=[
EntitiesRecognitionTask(model_version="latest")
],
key_phrase_extraction_tasks=[
KeyPhraseExtractionTask(model_version="latest")
],
pii_entities_recognition_tasks=[
PiiEntitiesRecognitionTask(model_version="latest")
],
show_stats=True,
polling_interval=self._interval(),
).result()
results_pages = list(response)
self.assertEqual(len(results_pages), 1)
task_types = [
"entities_recognition_results", "key_phrase_extraction_results",
"pii_entities_recognition_results"
]
for task_type in task_types:
task_results = getattr(results_pages[0], task_type)
self.assertEqual(len(task_results), 1)
results = task_results[0].results
self.assertEqual(len(results), len(docs))
def test_out_of_order_ids_multiple_tasks(self, client):
docs = [{
"id": "56",
"text": ":)"
}, {
"id": "0",
"text": ":("
}, {
"id": "19",
"text": ":P"
}, {
"id": "1",
"text": ":D"
}]
response = client.begin_analyze(
docs,
entities_recognition_tasks=[
EntitiesRecognitionTask(model_version="bad")
], # at this moment this should cause all documents to be errors, which isn't correct behavior but I'm using it here to test document ordering with errors. :)
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
).result()
results_pages = list(response)
self.assertEqual(len(results_pages), 1)
task_types = [
"entities_recognition_results", "key_phrase_extraction_results",
"pii_entities_recognition_results"
]
in_order = ["56", "0", "19", "1"]
for task_type in task_types:
task_results = getattr(results_pages[0], task_type)
self.assertEqual(len(task_results), 1)
results = task_results[0].results
self.assertEqual(len(results), len(docs))
for idx, resp in enumerate(results):
self.assertEqual(resp.id, in_order[idx])
def test_duplicate_ids_error(self,
client): # TODO: verify behavior of service
# Duplicate Ids
docs = [{
"id": "1",
"text": "hello world"
}, {
"id": "1",
"text": "I did not like the hotel we stayed at."
}]
with self.assertRaises(HttpResponseError):
result = client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
).result()
def test_mixing_inputs(self, client):
docs = [
{
"id": "1",
"text": "Microsoft was founded by Bill Gates and Paul Allen."
},
TextDocumentInput(
id="2",
text=
"I did not like the hotel we stayed at. It was too expensive."
), u"You cannot mix string input with the above inputs"
]
with self.assertRaises(TypeError):
response = client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
polling_interval=self._interval(),
).result()
def test_bad_model_version_error_multiple_tasks(
self, client): # TODO: verify behavior of service
docs = [{
"id": "1",
"language": "english",
"text": "I did not like the hotel we stayed at."
}]
response = client.begin_analyze(
docs,
entities_recognition_tasks=[
EntitiesRecognitionTask(model_version="latest")
],
key_phrase_extraction_tasks=[
KeyPhraseExtractionTask(model_version="bad")
],
pii_entities_recognition_tasks=[
PiiEntitiesRecognitionTask(model_version="bad")
],
polling_interval=self._interval(),
).result()
results_pages = list(response)
self.assertEqual(len(results_pages), 1)
task_types = [
"entities_recognition_results", "key_phrase_extraction_results",
"pii_entities_recognition_results"
]
for task_type in task_types:
tasks = getattr(
results_pages[0],
task_type) # only expecting a single page of results here
self.assertEqual(len(tasks), 1)
for r in tasks[0].results:
self.assertTrue(
r.is_error
) # This is not the optimal way to represent this failure. We are discussing a solution with the service team.
def test_bad_model_version_error_all_tasks(
self, client): # TODO: verify behavior of service
docs = [{
"id": "1",
"language": "english",
"text": "I did not like the hotel we stayed at."
}]
with self.assertRaises(HttpResponseError):
response = client.begin_analyze(
docs,
entities_recognition_tasks=[
EntitiesRecognitionTask(model_version="bad")
],
key_phrase_extraction_tasks=[
KeyPhraseExtractionTask(model_version="bad")
],
pii_entities_recognition_tasks=[
PiiEntitiesRecognitionTask(model_version="bad")
],
polling_interval=self._interval(),
).result()
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
result = client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
show_stats=True,
polling_interval=self._interval(),
).result()
pages = list(result)
self.assertEqual(len(pages), 2) # default page size is 20
# self.assertIsNotNone(result.statistics) # statistics not working at the moment, but a bug has been filed on the service to correct this.
task_types = [
"entities_recognition_results", "key_phrase_extraction_results",
"pii_entities_recognition_results"
]
expected_results_per_page = [20, 5]
for idx, page in enumerate(pages):
for task_type in task_types:
task_results = getattr(page, task_type)
self.assertEqual(len(task_results), 1)
results = task_results[0].results
self.assertEqual(len(results), expected_results_per_page[idx])
for doc in results:
self.assertFalse(doc.is_error)
def test_whole_batch_language_hint_and_dict_per_item_hints(self, client):
docs = [{
"id": "1",
"language": "en",
"text": "I will go to the park."
}, {
"id": "2",
"language": "en",
"text": "I did not like the hotel we stayed at."
}, {
"id": "3",
"text": "The restaurant had really good food."
}]
response = list(
client.begin_analyze(
docs,
entities_recognition_tasks=[EntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
language="en",
polling_interval=self._interval(),
).result())
task_types = [
"entities_recognition_results", "key_phrase_extraction_results",
"pii_entities_recognition_results"
]
for task_type in task_types:
task_results = getattr(response[0], task_type)
self.assertEqual(len(task_results), 1)
results = task_results[0].results
for r in results:
self.assertFalse(r.is_error)
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 EntitiesRecognitionTask, \
PiiEntitiesRecognitionTask, \
KeyPhraseExtractionTask
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(
documents,
display_name="Sample Text Analysis",
entities_recognition_tasks=[EntitiesRecognitionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()])
result = await poller.result()
async for page in result:
for task in page.entities_recognition_results:
print("Results of Entities Recognition task:")
docs = [doc for doc in task.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("------------------------------------------")
for task in page.pii_entities_recognition_results:
print("Results of PII Entities Recognition task:")
docs = [doc for doc in task.results if not doc.is_error]
for idx, doc in enumerate(docs):
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("------------------------------------------")
for task in page.key_phrase_extraction_results:
print("Results of Key Phrase Extraction task:")
docs = [doc for doc in task.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("------------------------------------------")
