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# pylint: disable=line-too-long,useless-suppression
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 (
ConversationalAITask,
ConversationalAIAnalysisInput,
TextConversation,
TextConversationItem,
ConversationalAIActionContent,
AnalyzeConversationActionResult,
StringIndexType,
ConversationalAITaskResult,
ConversationalAIResult,
ConversationalAIAnalysis,
ConversationalAIIntent,
ConversationalAIEntity,
ConversationItemRange,
DateTimeResolution,
EntitySubtype,
EntityTag,
)
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_multi_turn_prediction(self, conversations_endpoint, conversations_key):
client = self.create_client(conversations_endpoint, conversations_key)
project_name = "EmailApp"
deployment_name = "production"
data = ConversationalAITask(
analysis_input=ConversationalAIAnalysisInput(
conversations=[
TextConversation(
id="order",
language="en-GB",
conversation_items=[
TextConversationItem(id="1", participant_id="user", text="Hi"),
TextConversationItem(id="2", participant_id="bot", text="Hello, how can I help you?"),
TextConversationItem(
id="3",
participant_id="user",
text="Send an email to Carol about tomorrow's demo",
),
],
)
]
),
parameters=ConversationalAIActionContent(
project_name=project_name,
deployment_name=deployment_name,
string_index_type=StringIndexType.UTF16_CODE_UNIT,
),
)
# Call API
response: AnalyzeConversationActionResult = client.analyze_conversation(data)
# Narrow to ConversationalAI task result (C# style: `as ConversationalAITaskResult`)
ai_task_result = cast(ConversationalAITaskResult, response)
ai_result: ConversationalAIResult = ai_task_result.result
# Basic sanity
assert ai_result is not None
assert ai_result.conversations is not None
# Iterate conversations
for conversation in ai_result.conversations or []:
conversation = cast(ConversationalAIAnalysis, conversation)
print(f"Conversation ID: {conversation.id}\n")
# Intents
print("Intents:")
for intent in conversation.intents or []:
intent = cast(ConversationalAIIntent, intent)
print(f" Name: {intent.name}")
print(f" Type: {getattr(intent, 'type', None)}")
print(" Conversation Item Ranges:")
for rng in intent.conversation_item_ranges or []:
rng = cast(ConversationItemRange, rng)
print(f" - Offset: {rng.offset}, Count: {rng.count}")
print("\n Entities (Scoped to Intent):")
for ent in intent.entities or []:
ent = cast(ConversationalAIEntity, ent)
print(f" Name: {ent.name}")
print(f" Text: {ent.text}")
print(f" Confidence: {ent.confidence_score}")
print(f" Offset: {ent.offset}, Length: {ent.length}")
print(f" Conversation Item ID: {ent.conversation_item_id}, Index: {ent.conversation_item_index}")
# Date/time resolutions
if ent.resolutions:
for res in ent.resolutions:
if isinstance(res, DateTimeResolution):
print(
f" - [DateTimeResolution] SubKind: {getattr(res, 'date_time_sub_kind', None)}, "
f"Timex: {res.timex}, Value: {res.value}"
)
# Extra information (entity subtype + tags)
if ent.extra_information:
for extra in ent.extra_information:
if isinstance(extra, EntitySubtype):
print(f" - [EntitySubtype] Value: {extra.value}")
for tag in extra.tags or []:
tag = cast(EntityTag, tag)
print(f" • Tag: {tag.name}, Confidence: {tag.confidence_score}")
print()
print()
# Global entities
print("Global Entities:")
for ent in conversation.entities or []:
ent = cast(ConversationalAIEntity, ent)
print(f" Name: {ent.name}")
print(f" Text: {ent.text}")
print(f" Confidence: {ent.confidence_score}")
print(f" Offset: {ent.offset}, Length: {ent.length}")
print(f" Conversation Item ID: {ent.conversation_item_id}, Index: {ent.conversation_item_index}")
if ent.extra_information:
for extra in ent.extra_information:
if isinstance(extra, EntitySubtype):
print(f" - [EntitySubtype] Value: {extra.value}")
for tag in extra.tags or []:
tag = cast(EntityTag, tag)
print(f" • Tag: {tag.name}, Confidence: {tag.confidence_score}")
print()
print("-" * 40)
|
