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import functools
import pytest
from devtools_testutils import AzureRecordedTestCase, EnvironmentVariableLoader, recorded_by_proxy
from azure.ai.language.conversations import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
AnalyzeConversationOperationInput,
ConversationActionContent,
ConversationAnalysisInput,
TextConversationItem,
ConversationActionResult,
ConversationPrediction,
ConversationIntent,
ConversationEntity,
StringIndexType,
ResolutionBase,
DateTimeResolution,
AnalyzeConversationActionResult,
ConversationLanguageUnderstandingInput,
)
from typing import cast
from azure.core.credentials import AzureKeyCredential
ConversationsPreparer = functools.partial(
EnvironmentVariableLoader,
"conversations",
conversations_endpoint="https://Sanitized.cognitiveservices.azure.com/",
conversations_key="fake_key",
)
class TestConversations(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 = ConversationAnalysisClient(endpoint, credential)
return client
...
class TestConversationsCase(TestConversations):
@ConversationsPreparer()
@recorded_by_proxy
def test_conversation_prediction_with_language(self, conversations_endpoint, conversations_key):
client = self.create_client(conversations_endpoint, conversations_key)
project_name = "EmailApp"
deployment_name = "production"
# Build request using strongly-typed models; set language to Spanish ("es")
data = ConversationLanguageUnderstandingInput(
conversation_input=ConversationAnalysisInput(
conversation_item=TextConversationItem(
id="1",
participant_id="participant1",
text="Enviar un email a Carol acerca de la presentación de mañana",
language="es",
)
),
action_content=ConversationActionContent(
project_name=project_name,
deployment_name=deployment_name,
string_index_type=StringIndexType.UTF16_CODE_UNIT,
verbose=True,
),
)
# Call API
response: AnalyzeConversationActionResult = client.analyze_conversation(data)
# Cast to discriminator subtype (C#: `as ConversationActionResult`)
conversation_result = cast(ConversationActionResult, response)
prediction = conversation_result.result.prediction
assert isinstance(prediction, ConversationPrediction)
print(f"Top intent: {prediction.top_intent}")
# Intents
print("Intents:")
for intent in prediction.intents or []:
print(f"Category: {intent.category}")
print(f"Confidence: {intent.confidence}")
print()
# Entities
print("Entities:")
for entity in prediction.entities or []:
print(f"Category: {entity.category}")
print(f"Text: {entity.text}")
print(f"Offset: {entity.offset}")
print(f"Length: {entity.length}")
print(f"Confidence: {entity.confidence}")
print()
if entity.resolutions:
for res in entity.resolutions:
if isinstance(res, DateTimeResolution):
print(f"Datetime Sub Kind: {getattr(res, 'date_time_sub_kind', None)}")
print(f"Timex: {res.timex}")
print(f"Value: {res.value}")
print()
# Final assertion (mirror C#)
assert prediction.top_intent == "SendEmail"
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