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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, 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,
NoMaskPolicyType,
)
from typing import cast, List
import re
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_with_no_mask_policy(self, conversations_endpoint, conversations_key):
client = self.create_client(conversations_endpoint, conversations_key)
detected_entities: List[str] = []
# ---- Redaction policy: NoMask (detect PII but do NOT redact) ----------
redaction_policy = NoMaskPolicyType()
# ---- Build input -----------------------------------------------------
ml_input = MultiLanguageConversationInput(
conversations=[
TextConversation(
id="1",
language="en",
conversation_items=[
TextConversationItem(id="1", participant_id="Agent_1", text="Can you provide your 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.",
),
],
)
]
)
# Action with NoMaskPolicyType
pii_action: AnalyzeConversationOperationAction = PiiOperationAction(
action_content=ConversationPiiActionContent(redaction_policy=redaction_policy),
name="Conversation PII with No Mask Policy",
)
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
)
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')}")
if d.get("errors"):
print("Errors:")
for err in d["errors"]:
err = cast(ConversationError, err)
print(f" Code: {err.code} - {err.message}")
# ---- Iterate results and validate: PII present in returned text -------
for actions_page in paged_actions:
for action_result in actions_page.task_results or []:
ar = cast(AnalyzeConversationOperationResult, action_result)
if isinstance(ar, ConversationPiiOperationResult):
for conversation in ar.results.conversations or []:
conversation = cast(ConversationalPiiResult, conversation)
for item in conversation.conversation_items or []:
item = cast(ConversationPiiItemResult, item)
# With NoMask, service returns original text in redacted_content
returned_text = (getattr(item.redacted_content, "text", None) or "").strip()
if item.entities and returned_text:
for entity in item.entities:
entity = cast(NamedEntity, entity)
ent_text = entity.text or ""
detected_entities.append(ent_text)
# Ensure the original PII text is still present
assert (
ent_text in returned_text
), f"Expected entity '{ent_text}' to be present but was not found in: {returned_text}"
# ---- Assertions -------------------------------------------------------
assert len(detected_entities) > 0, "Expected at least one detected PII entity."
assert (d.get("status") or "").lower() in {"succeeded", "partiallysucceeded"}
|
