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import functools
from devtools_testutils import AzureRecordedTestCase, EnvironmentVariableLoader, recorded_by_proxy
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalysisClient
from azure.ai.textanalytics.models import (
MultiLanguageTextInput,
MultiLanguageInput,
AnalyzeTextOperationAction,
CustomEntitiesActionContent,
CustomEntitiesLROTask,
TextActions,
CustomEntityRecognitionOperationResult, # subclass of AnalyzeTextLROResult
CustomEntityActionResult,
NamedEntity,
)
from azure.core.credentials import AzureKeyCredential
TextAnalysisPreparer = functools.partial(
EnvironmentVariableLoader,
"text_analysis",
text_analysis_endpoint="https://Sanitized.cognitiveservices.azure.com/",
text_analysis_key="fake_key",
)
class TestTextAnalysis(AzureRecordedTestCase):
# Start with any helper functions you might need, for example a client creation method:
def create_client(self, endpoint, key):
credential = AzureKeyCredential(key)
client = TextAnalysisClient(endpoint, credential)
return client
...
class TestTextAnalysisCase(TestTextAnalysis):
@TextAnalysisPreparer()
@recorded_by_proxy
def test_analyze_text_custom_entities_lro_task(self, text_analysis_endpoint, text_analysis_key):
client = self.create_client(text_analysis_endpoint, text_analysis_key)
project_name = "Example-ner-project"
deployment_name = "TestDeployment"
text_a = (
"We love this trail and make the trip every year. The views are breathtaking and well worth the hike! "
"Yesterday was foggy though, so we missed the spectacular views. We tried again today and it was "
"amazing. Everyone in my family liked the trail although it was too challenging for the less "
"athletic among us."
)
text_input = MultiLanguageTextInput(
multi_language_inputs=[MultiLanguageInput(id="A", text=text_a, language="en")]
)
action_content = CustomEntitiesActionContent(project_name=project_name, deployment_name=deployment_name)
actions: list[AnalyzeTextOperationAction] = [
CustomEntitiesLROTask(
name="CustomEntitiesOperationActionSample",
parameters=action_content,
)
]
# Start LRO (sync) – custom poller returns ItemPaged[TextActions]
poller = client.begin_analyze_text_job(
text_input=text_input,
actions=actions,
)
assert poller is not None
paged_actions = poller.result()
details = poller.details
assert "operation_id" in details
assert details.get("status") is not None
assert paged_actions is not None
found_custom_entities = False
for actions_page in paged_actions:
# page payload container
assert isinstance(actions_page, TextActions)
assert actions_page.items_property is not None # wire: "items"
for op_result in actions_page.items_property:
if isinstance(op_result, CustomEntityRecognitionOperationResult):
found_custom_entities = True
result = op_result.results
assert result is not None
assert result.documents is not None
for doc in result.documents:
assert isinstance(doc, CustomEntityActionResult)
assert doc.id is not None
assert doc.entities is not None
for entity in doc.entities:
assert isinstance(entity, NamedEntity)
assert entity.text is not None
assert entity.category is not None
assert entity.offset is not None
assert entity.length is not None
assert entity.confidence_score is not None
assert found_custom_entities, "Expected a CustomEntityRecognitionLROResult in TextActions.items_property"
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