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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_recognize_pii_entities_with_redaction_policies.py
DESCRIPTION:
This sample demonstrates how to run **PII entity recognition** with redaction policies,
including synthetic replacement for selected PII categories.
USAGE:
python sample_recognize_pii_entities_with_redaction_policies.py
REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
AZURE_TEXT_ENDPOINT
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
NOTE:
If you prefer to use AzureKeyCredential instead, set:
- AZURE_TEXT_ENDPOINT
- AZURE_TEXT_KEY
"""
# [START recognize_pii_entities_with_redaction_policies]
import os
from azure.identity import DefaultAzureCredential
from azure.ai.textanalytics import TextAnalysisClient
from azure.ai.textanalytics.models import (
MultiLanguageInput,
MultiLanguageTextInput,
TextPiiEntitiesRecognitionInput,
PiiActionContent,
EntityMaskPolicyType,
CharacterMaskPolicyType,
SyntheticReplacementPolicyType,
AnalyzeTextPiiResult,
)
def sample_recognize_pii_entities_with_redaction_policies():
# Settings
endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
credential = DefaultAzureCredential()
client = TextAnalysisClient(endpoint, credential=credential)
# Input text
text_1 = "My name is John Doe. My SSN is 123-45-6789. My email is john@example.com."
text_2 = "Contact John Doe at john@example.com. SSN: 123-45-6789."
body = TextPiiEntitiesRecognitionInput(
text_input=MultiLanguageTextInput(
multi_language_inputs=[
MultiLanguageInput(id="1", text=text_1, language="en"),
MultiLanguageInput(id="2", text=text_2, language="en"),
]
),
action_content=PiiActionContent(
pii_categories=["All"],
redaction_policies=[
# Default policy: mask all PII categories (asterisks)
EntityMaskPolicyType(policy_name="defaultPolicy", is_default=True),
# For SSN: only mask characters except first 4 digits
CharacterMaskPolicyType(
policy_name="ssnMaskPolicy",
unmask_length=4,
unmask_from_end=False,
entity_types=["USSocialSecurityNumber"],
),
# For Person & Email: replace with synthetic data (fake names/emails)
SyntheticReplacementPolicyType(
policy_name="syntheticPolicy",
entity_types=["Person", "Email"],
),
],
),
)
# Sync call
result = client.analyze_text(body=body)
# Display results
if isinstance(result, AnalyzeTextPiiResult) and result.results:
for doc in result.results.documents:
print(f"\nDocument ID: {doc.id}")
print(f"Redacted Text:\n {doc.redacted_text}\n")
print("Detected Entities:")
for entity in doc.entities or []:
print(f" Text: {entity.text}")
print(f" Category: {entity.category}")
print(f" Masked As: {entity.mask}")
print(f" Confidence: {entity.confidence_score}")
print("")
else:
print("No results returned or unexpected result type.")
# [END recognize_pii_entities_with_redaction_policies]
def main():
sample_recognize_pii_entities_with_redaction_policies()
if __name__ == "__main__":
main()
|
