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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_conversation_pii_with_character_mask_policy.py
DESCRIPTION:
This sample demonstrates how to run a PII detection action over a conversation
using the `CharacterMaskPolicyType` synchronously. Detected PII is redacted
by replacing characters with a mask (e.g., '*').
USAGE:
python sample_conversation_pii_with_character_mask_policy.py
REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
AZURE_CONVERSATIONS_ENDPOINT
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
NOTE:
If you want to use AzureKeyCredential instead, set:
- AZURE_CONVERSATIONS_ENDPOINT
- AZURE_CONVERSATIONS_KEY
"""
# [START conversation_pii_with_character_mask_policy]
import os
from azure.identity import DefaultAzureCredential
from azure.ai.language.conversations import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
MultiLanguageConversationInput,
TextConversation,
TextConversationItem,
ParticipantRole,
AnalyzeConversationOperationInput,
PiiOperationAction,
ConversationPiiActionContent,
CharacterMaskPolicyType,
RedactionCharacter,
ConversationPiiOperationResult,
ConversationError,
)
def sample_conversation_pii_with_character_mask_policy():
# settings
endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
# AAD credential
credential = DefaultAzureCredential()
redacted_verified: list[str] = []
client = ConversationAnalysisClient(endpoint, credential=credential)
# build input
ml_input = MultiLanguageConversationInput(
conversations=[
TextConversation(
id="1",
language="en",
conversation_items=[
TextConversationItem(
id="1",
participant_id="Agent_1",
role=ParticipantRole.AGENT,
text="Can you provide your name?",
),
TextConversationItem(
id="2",
participant_id="Customer_1",
role=ParticipantRole.CUSTOMER,
text="Hi, my name is John Doe.",
),
TextConversationItem(
id="3",
participant_id="Agent_1",
role=ParticipantRole.AGENT,
text="Thank you John, that has been updated in our system.",
),
],
)
]
)
# action with CharacterMaskPolicyType
redaction_policy = CharacterMaskPolicyType(redaction_character=RedactionCharacter.ASTERISK)
pii_action = PiiOperationAction(
action_content=ConversationPiiActionContent(redaction_policy=redaction_policy),
name="Conversation PII with Character Mask Policy",
)
operation_input = AnalyzeConversationOperationInput(
conversation_input=ml_input,
actions=[pii_action],
)
# start long-running job
poller = client.begin_analyze_conversation_job(body=operation_input)
print(f"Operation ID: {poller.details.get('operation_id')}")
# wait for completion
paged_actions = poller.result()
# final 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"]:
print(f" Code: {err.code} - {err.message}")
# iterate results and verify redaction
for actions_page in paged_actions:
for action_result in actions_page.task_results or []:
if isinstance(action_result, ConversationPiiOperationResult):
for conversation in action_result.results.conversations or []:
for item in conversation.conversation_items or []:
redacted_text = (item.redacted_content.text or "").strip()
if not redacted_text:
continue
if item.entities:
for entity in item.entities:
ent_text = entity.text or ""
if ent_text in redacted_text:
print(
f"WARNING: Expected '{ent_text}' to be redacted but found in: {redacted_text}"
)
if "*" in redacted_text:
redacted_verified.append(redacted_text)
# [END conversation_pii_with_character_mask_policy]
def main():
sample_conversation_pii_with_character_mask_policy()
if __name__ == "__main__":
main()
|
