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import functools
import pytest
from devtools_testutils import AzureRecordedTestCase, EnvironmentVariableLoader, recorded_by_proxy
from azure.ai.language.conversations import ConversationAnalysisClient, AnalyzeConversationLROPoller
from azure.core.paging import ItemPaged
from azure.ai.language.conversations.models import (
# request models
AnalyzeConversationOperationInput,
MultiLanguageConversationInput,
TextConversation,
TextConversationItem,
PiiOperationAction,
ConversationPiiActionContent,
ConversationActions,
AnalyzeConversationOperationResult,
ConversationPiiOperationResult,
ConversationalPiiResult,
ConversationPiiItemResult,
NamedEntity,
InputWarning,
ConversationError,
AnalyzeConversationOperationAction,
)
from typing import cast, List
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_pii(self, conversations_endpoint, conversations_key):
client = self.create_client(conversations_endpoint, conversations_key)
entities_detected: List[NamedEntity] = []
# ---- Build input ------------------------------------
ml_input = MultiLanguageConversationInput(
conversations=[
TextConversation(
id="1",
language="en",
conversation_items=[
TextConversationItem(id="1", participant_id="Agent_1", text="Can you provide you name?"),
TextConversationItem(id="2", participant_id="Customer_1", text="Hi, my name is John Doe."),
TextConversationItem(
id="3",
participant_id="Agent_1",
text="Thank you John, that has been updated in our system.",
),
],
)
]
)
pii_action: AnalyzeConversationOperationAction = PiiOperationAction(
action_content=ConversationPiiActionContent(),
name="Conversation PII",
)
actions: List[AnalyzeConversationOperationAction] = [pii_action]
operation_input = AnalyzeConversationOperationInput(
conversation_input=ml_input,
actions=actions,
)
# ---- Begin LRO --------------------------------------------------------
poller: AnalyzeConversationLROPoller[ItemPaged[ConversationActions]] = client.begin_analyze_conversation_job(
body=operation_input
)
# Operation metadata is available immediately
print(f"Operation ID: {poller.details.get('operation_id')}")
# Wait for completion; result is ItemPaged[ConversationActions]
paged_actions: ItemPaged[ConversationActions] = poller.result()
# Final-state metadata
d = poller.details
print(f"Job ID: {d.get('job_id')}")
print(f"Status: {d.get('status')}")
print(f"Created: {d.get('created_date_time')}")
print(f"Last Updated: {d.get('last_updated_date_time')}")
if d.get("expiration_date_time"):
print(f"Expires: {d.get('expiration_date_time')}")
if d.get("display_name"):
print(f"Display Name: {d.get('display_name')}")
# ---- Iterate pages and action results --------------------------------
for actions_page in paged_actions:
print(
f"Completed: {actions_page.completed}, "
f"In Progress: {actions_page.in_progress}, "
f"Failed: {actions_page.failed}, "
f"Total: {actions_page.total}"
)
for action_result in actions_page.task_results or []:
ar = cast(AnalyzeConversationOperationResult, action_result)
print(f"\nAction Name: {getattr(ar, 'name', None)}")
print(f"Action Status: {getattr(ar, 'status', None)}")
print(f"Kind: {getattr(ar, 'kind', None)}")
if isinstance(ar, ConversationPiiOperationResult):
for conversation in ar.results.conversations or []:
conversation = cast(ConversationalPiiResult, conversation)
print(f"Conversation: #{conversation.id}")
print("Detected Entities:")
for item in conversation.conversation_items or []:
item = cast(ConversationPiiItemResult, item)
for entity in item.entities or []:
entity = cast(NamedEntity, entity)
print(f" Category: {entity.category}")
print(f" Subcategory: {entity.subcategory}")
print(f" Text: {entity.text}")
print(f" Offset: {entity.offset}")
print(f" Length: {entity.length}")
print(f" Confidence score: {entity.confidence_score}\n")
entities_detected.append(entity)
if conversation.warnings:
print("Warnings:")
for warning in conversation.warnings:
warning = cast(InputWarning, warning)
print(f" Code: {warning.code}")
print(f" Message: {warning.message}")
print()
else:
print(" [No supported results to display for this action type]")
# ---- Print errors (from final-state metadata) -------------------------
if d.get("errors"):
print("\nErrors:")
for err in d["errors"]:
err = cast(ConversationError, err)
print(f" Code: {err.code} - {err.message}")
# ---- Assertions -------------------------------------------------------
assert len(entities_detected) > 0, "Expected at least one PII entity."
assert (d.get("status") or "").lower() in {"succeeded", "partiallysucceeded"}
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