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chore: import upstream snapshot with attribution
2026-07-13 13:25:44 +08:00

212 lines
6.6 KiB
Python

import pytest
import opik
import opik.hooks
from opik import opik_context
from opik.anonymizer import factory, anonymizer
from . import verifiers
from ..testlib import generate_project_name
PROJECT_NAME = generate_project_name("e2e", __name__)
# Email pattern
EMAIL_RULE = (
r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
"[EMAIL_REDACTED]",
)
# Credit card pattern (simplified)
CC_RULE = (r"\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b", "[CARD_REDACTED]")
# SSN pattern
SSN_RULE = {"regex": r"\b\d{3}-\d{2}-\d{4}\b", "replace": "[SSN_REDACTED]"}
@pytest.mark.parametrize("override_project_name", [True, False])
def test_tracked_function__regexp_rules_anonymization__happy_flow(
opik_client, override_project_name
):
"""Test that sensitive fields are masked in input, output and metadata."""
project_name = (
generate_project_name("e2e", "anonymization", "override")
if override_project_name
else None
)
# create and register anonymizer
opik.hooks.clear_anonymizers()
rules_anonymizer = factory.create_anonymizer([EMAIL_RULE, CC_RULE, SSN_RULE])
opik.hooks.add_anonymizer(rules_anonymizer)
# Setup
ID_STORAGE = {}
# Example PII values used in this test:
# Email: john.doe@example.com
# Credit Card: 1234 5678 9012 3456
# SSN: 123-45-6789
@opik.track(
tags=["outer-tag1", "outer-tag2"],
metadata={
"outer-metadata-key": "outer-metadata-value",
"card-number": "1234 5678 9012 3456",
},
project_name=project_name,
)
def f_outer(x: str, email: str):
ID_STORAGE["f_outer-trace-id"] = opik_context.get_current_trace_data().id
ID_STORAGE["f_outer-span-id"] = opik_context.get_current_span_data().id
f_inner("inner-input")
return email
@opik.track(
tags=["inner-tag1", "inner-tag2"],
metadata={"inner-metadata-key": "inner-metadata-value", "ssn": "123-45-6789"},
project_name=project_name,
)
def f_inner(y):
ID_STORAGE["f_inner-span-id"] = opik_context.get_current_span_data().id
return "inner-output"
# Call
f_outer("outer-input", email="john.doe@example.com")
opik.flush_tracker()
anonymized_outer_metadata = {
"outer-metadata-key": "outer-metadata-value",
"card-number": "[CARD_REDACTED]",
}
# Verify trace
verifiers.verify_trace(
opik_client=opik_client,
trace_id=ID_STORAGE["f_outer-trace-id"],
name="f_outer",
input={"x": "outer-input", "email": "[EMAIL_REDACTED]"},
output={"output": "[EMAIL_REDACTED]"},
metadata=anonymized_outer_metadata,
tags=["outer-tag1", "outer-tag2"],
project_name=project_name or PROJECT_NAME,
)
# Verify the top level span
verifiers.verify_span(
opik_client=opik_client,
span_id=ID_STORAGE["f_outer-span-id"],
parent_span_id=None,
trace_id=ID_STORAGE["f_outer-trace-id"],
name="f_outer",
input={"x": "outer-input", "email": "[EMAIL_REDACTED]"},
output={"output": "[EMAIL_REDACTED]"},
metadata=anonymized_outer_metadata,
tags=["outer-tag1", "outer-tag2"],
project_name=project_name or PROJECT_NAME,
)
anonymized_inner_metadata = {
"inner-metadata-key": "inner-metadata-value",
"ssn": "[SSN_REDACTED]",
}
# Verify nested span
verifiers.verify_span(
opik_client=opik_client,
span_id=ID_STORAGE["f_inner-span-id"],
parent_span_id=ID_STORAGE["f_outer-span-id"],
trace_id=ID_STORAGE["f_outer-trace-id"],
name="f_inner",
input={"y": "inner-input"},
output={"output": "inner-output"},
metadata=anonymized_inner_metadata,
tags=["inner-tag1", "inner-tag2"],
project_name=project_name or PROJECT_NAME,
)
@pytest.mark.parametrize("override_project_name", [True, False])
def test_tracked_function__rules_anonymization_remove_sensitive_key__happy_flow(
opik_client, override_project_name
):
"""Test that sensitive keys are removed from metadata and other sensitive fields are masked in input and output."""
project_name = (
generate_project_name("e2e", "anonymization", "override")
if override_project_name
else None
)
class ApiKeyAnonymizer(anonymizer.Anonymizer):
def anonymize(self, data, **kwargs):
field_name = kwargs.get("field_name")
object_type = kwargs.get("object_type")
if (
field_name == "metadata"
and object_type in ["span", "trace"]
and "api_key" in data
):
del data["api_key"]
return data
# create and register anonymizer
opik.hooks.clear_anonymizers()
api_key_anonymizer = ApiKeyAnonymizer()
opik.hooks.add_anonymizer(api_key_anonymizer)
rules_anonymizer = factory.create_anonymizer([EMAIL_RULE, CC_RULE, SSN_RULE])
opik.hooks.add_anonymizer(rules_anonymizer)
# Setup
ID_STORAGE = {}
@opik.track(
tags=["outer-tag1", "outer-tag2"],
metadata={
"outer-metadata-key": "outer-metadata-value",
"card-number": "1234 5678 9012 3456",
"api_key": "secret-api-key",
},
project_name=project_name,
)
def f_outer(x: str, email: str):
ID_STORAGE["f_outer-trace-id"] = opik_context.get_current_trace_data().id
ID_STORAGE["f_outer-span-id"] = opik_context.get_current_span_data().id
return email
# Call
f_outer("outer-input", email="john.doe@example.com")
opik.flush_tracker()
anonymized_metadata = {
"outer-metadata-key": "outer-metadata-value",
"card-number": "[CARD_REDACTED]",
}
# Verify trace
verifiers.verify_trace(
opik_client=opik_client,
trace_id=ID_STORAGE["f_outer-trace-id"],
name="f_outer",
input={"x": "outer-input", "email": "[EMAIL_REDACTED]"},
output={"output": "[EMAIL_REDACTED]"},
metadata=anonymized_metadata,
tags=["outer-tag1", "outer-tag2"],
project_name=project_name or PROJECT_NAME,
)
# Verify the top level span
verifiers.verify_span(
opik_client=opik_client,
span_id=ID_STORAGE["f_outer-span-id"],
parent_span_id=None,
trace_id=ID_STORAGE["f_outer-trace-id"],
name="f_outer",
input={"x": "outer-input", "email": "[EMAIL_REDACTED]"},
output={"output": "[EMAIL_REDACTED]"},
metadata=anonymized_metadata,
tags=["outer-tag1", "outer-tag2"],
project_name=project_name or PROJECT_NAME,
)