litellm/tests/guardrails_tests/test_presidio_pii.py
Mateo Wang 2c733c00f5
chore(ci): modernize model references in tests and configs (#27856)
* test: modernize models used in CircleCI e2e test suites

Replaces obsolete models (gpt-4o, gpt-4o-mini, gpt-3.5-turbo,
claude-3-5-sonnet-20240620, claude-sonnet-4-20250514) with current
equivalents across the e2e_openai_endpoints and
proxy_e2e_anthropic_messages_tests CircleCI jobs.

- gpt-4o -> gpt-5.5 (responses API e2e tests)
- gpt-4o-mini -> gpt-5-mini (websocket responses, oai_misc_config)
- gpt-4o-mini-2024-07-18 -> gpt-4.1-mini-2025-04-14 (fine-tuning,
  still actively fine-tunable)
- gpt-4 / gpt-3.5-turbo target_model_names example -> gpt-5.5 /
  gpt-5-mini
- bedrock claude-3-5-sonnet-20240620 batch entry -> haiku-4-5-20251001
  (also aligning oai_misc_config model_name with what
  test_bedrock_batches_api.py actually requests)
- bedrock claude-sonnet-4-20250514 (deprecated, retires 2026-06-15)
  -> claude-sonnet-4-5-20250929

* test: point bedrock-claude-sonnet-4 alias at Sonnet 4.6, not 4.5

Greptile/Cursor flagged that after the previous commit, the
bedrock-claude-sonnet-4 alias collided with bedrock-claude-sonnet-4.5
(both pointed to claude-sonnet-4-5-20250929). Rename to
bedrock-claude-sonnet-4.6 and point it at the Sonnet 4.6 Bedrock ID
(us.anthropic.claude-sonnet-4-6, already in the litellm model
registry) so the alias name matches the underlying model version.

* test: modernize models across remaining CI-mounted configs & tests

Expands the modernization sweep to all CircleCI-mounted proxy configs
and to test directories where the model literal is a fixture/route key
(not the test's subject).

Config changes:
- proxy_server_config.yaml: bump gpt-3.5-turbo / gpt-3.5-turbo-1106 /
  gpt-4o / gemini-1.5-flash / dall-e-3 underlying models; rename
  gpt-3.5-turbo-end-user-test alias to gpt-5-mini-end-user-test; bump
  text-embedding-ada-002 underlying to text-embedding-3-small. User-
  facing aliases (gpt-3.5-turbo, gpt-4, text-embedding-ada-002, etc.)
  preserved for backward compatibility with tests.
- simple_config.yaml, otel_test_config.yaml, spend_tracking_config.yaml:
  bump gpt-3.5-turbo underlying to gpt-5-mini.
- pass_through_config.yaml: claude-3-5-sonnet / claude-3-7-sonnet /
  claude-3-haiku entries replaced with claude-sonnet-4-5 / claude-
  haiku-4-5 / claude-opus-4-7.
- oai_misc_config.yaml: align alias name with the gpt-5-mini rename.

Test changes (proactive: claude-sonnet-4-20250514 / claude-opus-4-
20250514 retire 2026-06-15):
- tests/llm_translation/test_anthropic_completion.py: bump 3 references
  + paired Vertex AI ID to claude-sonnet-4-5.
- tests/llm_translation/test_optional_params.py: bump 2 references.
- tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py
  and test_bedrock_anthropic_messages_test.py: bump router fixtures
  using the deprecated model IDs.
- tests/pass_through_unit_tests/base_anthropic_messages_tool_search_test.py:
  modernize docstring examples.
- tests/test_end_users.py: update references to renamed alias.

* test: modernize placeholder model literals in router_unit_tests

Mass replace_all on fixture/placeholder model literals across the
router_unit_tests/ suite (model name is a routing key / label, not the
test subject). Sub-agent sweep so far — additional commits will follow
for logging_callback_tests/, enterprise/, top-level tests/test_*.py,
and other CI-mounted dirs.

Mappings applied:
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 / claude-3-opus-20240229 /
  claude-3-haiku-20240307 / claude-3-5-sonnet-20240620 ->
  claude-sonnet-4-5-20250929 / claude-opus-4-7 /
  claude-haiku-4-5-20251001 as appropriate

Explicitly preserved:
- gpt-4o-mini-* variants (transcribe, tts, etc.) where they're current
- gpt-4-turbo / gpt-4-vision-preview / gpt-4-0613 (subject literals)
- JSONL batch body literals
- Mock LLM response model fields (must match upstream)
- Fake/mock identifiers

* test: modernize placeholder model literals across remaining CI suites

Sub-agent sweep across logging_callback_tests/, guardrails_tests/,
enterprise/, pass_through_unit_tests/, otel_tests/,
llm_responses_api_testing/, batches_tests/, spend_tracking_tests/,
litellm_utils_tests/, unified_google_tests/, and a few top-level
tests/test_*.py files where the model literal is a fixture or
placeholder (router model_list, mock standard logging payload, mock
callback data) rather than the test's subject.

Mappings applied (see scope notes below):
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5.5 (corrected from initial gpt-5 — bare gpt-5
  is not a valid OpenAI alias; only gpt-5.5 / gpt-5.4 / gpt-5.2-codex
  / gpt-5-mini exist)
- gpt-4o-mini (bare) -> gpt-5-mini
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 -> claude-sonnet-4-5-20250929
- claude-3-opus-20240229 -> claude-opus-4-7
- claude-3-haiku-20240307 -> claude-haiku-4-5-20251001
- claude-3-5-sonnet-20240620/20241022 -> claude-sonnet-4-5-20250929
- claude-3-7-sonnet-20250219 -> claude-sonnet-4-6
- gemini-1.5-flash -> gemini-2.5-flash
- gemini-1.5-pro -> gemini-2.5-pro

Explicitly preserved (not modernized):
- llm_translation/ tests where model is the SUBJECT (provider-specific
  translation/transformation logic). Only the deprecated 20250514
  references were already bumped in a prior commit.
- Cost-calc / tokenizer subject tests in test_utils.py (skip-ranges
  documented by the sub-agent).
- Bedrock model IDs in test_health_check.py path-stripping tests.
- JSONL batch request bodies and mock LLM response bodies (must match
  upstream literal).
- Langfuse expected-request-body JSON fixtures (cost values are exact-
  match-asserted; changing the model would shift response_cost).
- gpt-3.5-turbo-instruct (text-completion endpoint; no modern OpenAI
  equivalent).
- Top-level tests calling the proxy through user-facing aliases
  (gpt-3.5-turbo, gpt-4, text-embedding-ada-002, dall-e-3) — aliases
  in proxy_server_config.yaml stay; only the underlying model was
  bumped.
- tests/test_gpt5_azure_temperature_support.py (the test's whole point
  is model-name handling).
- Fake / mock / openai/fake identifiers.

Notable side fixes:
- test_spend_accuracy_tests.py: UPSTREAM_MODEL now matches what
  spend_tracking_config.yaml's proxy actually routes to (gpt-5-mini),
  resolving a latent inconsistency.
- proxy_server_config.yaml: bare `gpt-5` alias renamed to `gpt-5.5`
  (bare gpt-5 is not a valid OpenAI alias).
- test_batches_logging_unit_tests.py: explicit_models list entries
  kept distinct (gpt-5-mini + gpt-5.5) after bulk rename.

* test: fix CI failures from model modernization sweep

CI surfaced 4 categories of regression from the bulk modernization:

1. Azure deployment names are customer-specific. Reverted:
   - tests/litellm_utils_tests/test_health_check.py: azure/text-
     embedding-3-small -> azure/text-embedding-ada-002 (the CI Azure
     account does not have a text-embedding-3-small deployment).
   - tests/logging_callback_tests/test_custom_callback_router.py:
     same revert for two router fixtures driving aembedding.

2. gpt-5 family does not accept temperature != 1. Tests that pass a
   custom temperature swapped from gpt-5-mini to gpt-4.1-mini (modern
   non-reasoning OpenAI mini that still accepts temperature/logprobs):
   - tests/logging_callback_tests/test_datadog.py
   - tests/logging_callback_tests/test_langsmith_unit_test.py
   - tests/logging_callback_tests/test_otel_logging.py

3. proxy_server_config.yaml's gpt-3.5-turbo-large alias was routing to
   gpt-5.5 (a reasoning model that rejects logprobs). The proxy test
   tests/test_openai_endpoints.py::test_chat_completion_streaming
   exercises logprobs/top_logprobs through that alias. Bumped the
   underlying model to gpt-4.1 (non-reasoning, still modern).

4. tests/logging_callback_tests/test_gcs_pub_sub.py asserts against a
   pinned JSON fixture (gcs_pub_sub_body/spend_logs_payload.json) with
   hardcoded model="gpt-4o" and a model-specific spend value. Reverted
   the litellm.acompletion calls in the test to model="gpt-4o" so the
   fixture's exact-match assertions still hold.

5. tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py:
   anthropic.messages.create routing to openai/gpt-5-mini returned an
   empty content[0] with max_tokens=100 (reasoning-token consumption).
   Swapped to openai/gpt-4.1-mini.

* test: fix Assistants API model + 2 cursor[bot] review nits

1. pass_through_unit_tests/test_custom_logger_passthrough.py: gpt-5.5
   isn't accepted by the /v1/assistants endpoint
   ("unsupported_model"). Switch to gpt-4.1-mini (modern, Assistants-
   API-supported, non-reasoning).

2. example_config_yaml/pass_through_config.yaml: the previous sweep
   bumped the claude-3-7-sonnet alias to claude-opus-4-7, which is a
   tier change (Sonnet -> Opus). Map to claude-sonnet-4-6 to keep the
   Sonnet tier intact. (Cursor bugbot review.)

3. example_config_yaml/simple_config.yaml: model_name was left as
   gpt-3.5-turbo while the underlying was bumped to gpt-5-mini, which
   muddles the "simple" example. Make both sides gpt-5-mini so the
   most basic example is a straight 1:1 mapping again. (Cursor bugbot
   review.)

* fix: revert gpt-4/gpt-3.5-turbo alias underlying to non-reasoning models

tests/test_openai_endpoints.py::test_completion calls the proxy alias
"gpt-4" with temperature=0, and other tests call gpt-3.5-turbo with
custom temperature / logprobs / the legacy /v1/completions endpoint.
The earlier modernization mapped both aliases to gpt-5.5 / gpt-5-mini,
which are reasoning models that reject temperature != 1 and don't
expose /v1/completions. Map the aliases to gpt-4.1 / gpt-4.1-mini
(modern non-reasoning OpenAI models) instead — keeps user-facing
aliases preserved while picking a current underlying that still
supports the parameters/endpoints the tests exercise.
2026-05-15 15:44:28 -07:00

589 lines
20 KiB
Python

import sys
import os
import pytest
from litellm import mock_completion
from unittest.mock import patch
sys.path.insert(0, os.path.abspath("../.."))
import litellm
from litellm.proxy.guardrails.guardrail_hooks.presidio import (
_OPTIONAL_PresidioPIIMasking,
PresidioPerRequestConfig,
)
from litellm.types.guardrails import PiiEntityType, PiiAction
from litellm.proxy._types import UserAPIKeyAuth
from litellm.caching.caching import DualCache
from litellm.exceptions import BlockedPiiEntityError
@pytest.mark.asyncio
async def test_presidio_with_entities_config():
"""Test for Presidio guardrail with entities config - requires actual Presidio API"""
# Setup the guardrail with specific entities config
litellm._turn_on_debug()
pii_entities_config = {
PiiEntityType.CREDIT_CARD: PiiAction.MASK,
PiiEntityType.EMAIL_ADDRESS: PiiAction.MASK,
}
presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
pii_entities_config=pii_entities_config,
presidio_analyzer_api_base=os.environ.get("PRESIDIO_ANALYZER_API_BASE"),
presidio_anonymizer_api_base=os.environ.get("PRESIDIO_ANONYMIZER_API_BASE"),
)
# Test text with different PII types
test_text = "My credit card number is 4111-1111-1111-1111, my email is test@example.com, and my phone is 555-123-4567"
# Test the analyze request configuration
analyze_request = presidio_guardrail._get_presidio_analyze_request_payload(
text=test_text, presidio_config=None, request_data={}
)
# Verify entities were passed correctly
assert "entities" in analyze_request
assert set(analyze_request["entities"]) == set(pii_entities_config.keys())
# Test the check_pii method - this will call the actual Presidio API
redacted_text = await presidio_guardrail.check_pii(
text=test_text, output_parse_pii=True, presidio_config=None, request_data={}
)
# Verify PII has been masked/replaced/redacted in the result
assert "4111-1111-1111-1111" not in redacted_text
assert "test@example.com" not in redacted_text
# Since this entity is not in the config, it should not be masked
assert "555-123-4567" in redacted_text
# The specific replacements will vary based on Presidio's implementation
print(f"Redacted text: {redacted_text}")
@pytest.mark.asyncio
async def test_presidio_apply_guardrail():
"""Test for Presidio guardrail apply guardrail - requires actual Presidio API"""
litellm._turn_on_debug()
presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
pii_entities_config={},
presidio_analyzer_api_base=os.environ.get("PRESIDIO_ANALYZER_API_BASE"),
presidio_anonymizer_api_base=os.environ.get("PRESIDIO_ANONYMIZER_API_BASE"),
)
test_text = (
"My credit card number is 4111-1111-1111-1111 and my email is test@example.com"
)
response = await presidio_guardrail.apply_guardrail(
inputs={"texts": [test_text]},
request_data={},
input_type="request",
)
print("response from apply guardrail for presidio: ", response)
# Extract the modified text from the response
modified_text = response["texts"][0] if response.get("texts") else ""
# assert the default config masks the credit card and email
assert "4111-1111-1111-1111" not in modified_text
assert "test@example.com" not in modified_text
@pytest.mark.asyncio
async def test_presidio_with_blocked_entities():
"""Test for Presidio guardrail with blocked entities - requires actual Presidio API"""
# Setup the guardrail with specific entities config - BLOCK for credit card
litellm._turn_on_debug()
pii_entities_config = {
PiiEntityType.CREDIT_CARD: PiiAction.BLOCK, # This entity should cause a block
PiiEntityType.EMAIL_ADDRESS: PiiAction.MASK, # This entity should be masked
}
presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
pii_entities_config=pii_entities_config,
presidio_analyzer_api_base=os.environ.get("PRESIDIO_ANALYZER_API_BASE"),
presidio_anonymizer_api_base=os.environ.get("PRESIDIO_ANONYMIZER_API_BASE"),
)
# Test text with blocked PII type
test_text = (
"My credit card number is 4111-1111-1111-1111 and my email is test@example.com"
)
# Verify the analyze request configuration
analyze_request = presidio_guardrail._get_presidio_analyze_request_payload(
text=test_text, presidio_config=None, request_data={}
)
# Verify entities were passed correctly
assert "entities" in analyze_request
assert set(analyze_request["entities"]) == set(pii_entities_config.keys())
# Test that BlockedPiiEntityError is raised when check_pii is called
with pytest.raises(BlockedPiiEntityError) as excinfo:
await presidio_guardrail.check_pii(
text=test_text, output_parse_pii=True, presidio_config=None, request_data={}
)
# Verify the error contains the correct entity type
assert excinfo.value.entity_type == PiiEntityType.CREDIT_CARD
assert excinfo.value.guardrail_name == presidio_guardrail.guardrail_name
@pytest.mark.asyncio
async def test_presidio_pre_call_hook_with_blocked_entities():
"""Test for Presidio guardrail pre-call hook with blocked entities on a chat completion request"""
# Setup the guardrail with specific entities config
pii_entities_config = {
PiiEntityType.CREDIT_CARD: PiiAction.BLOCK, # This entity should cause a block
PiiEntityType.EMAIL_ADDRESS: PiiAction.MASK, # This entity should be masked
}
presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
pii_entities_config=pii_entities_config,
presidio_analyzer_api_base=os.environ.get("PRESIDIO_ANALYZER_API_BASE"),
presidio_anonymizer_api_base=os.environ.get("PRESIDIO_ANONYMIZER_API_BASE"),
)
# Create a sample chat completion request with PII data
data = {
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": "My credit card is 4111-1111-1111-1111 and my email is test@example.com.",
},
],
"model": "gpt-5-mini",
}
# Mock objects needed for the pre-call hook
user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
cache = DualCache()
# Call the pre-call hook and expect BlockedPiiEntityError
with pytest.raises(BlockedPiiEntityError) as excinfo:
await presidio_guardrail.async_pre_call_hook(
user_api_key_dict=user_api_key_dict,
cache=cache,
data=data,
call_type="completion",
)
print(f"got error: {excinfo}")
# Verify the error contains the correct entity type
assert excinfo.value.entity_type == PiiEntityType.CREDIT_CARD
assert excinfo.value.guardrail_name == presidio_guardrail.guardrail_name
@pytest.mark.asyncio
@pytest.mark.parametrize("call_type", ["completion", "acompletion"])
async def test_presidio_pre_call_hook_with_different_call_types(call_type):
"""Test for Presidio guardrail pre-call hook with both completion and acompletion call types"""
# Setup the guardrail with specific entities config
pii_entities_config = {
PiiEntityType.CREDIT_CARD: PiiAction.MASK,
PiiEntityType.EMAIL_ADDRESS: PiiAction.MASK,
}
presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
pii_entities_config=pii_entities_config,
presidio_analyzer_api_base=os.environ.get("PRESIDIO_ANALYZER_API_BASE"),
presidio_anonymizer_api_base=os.environ.get("PRESIDIO_ANONYMIZER_API_BASE"),
)
# Create a sample request with PII data
data = {
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": "My credit card is 4111-1111-1111-1111 and my email is test@example.com. My phone number is 555-123-4567",
},
],
"model": "gpt-5-mini",
}
# Mock objects needed for the pre-call hook
user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
cache = DualCache()
# Call the pre-call hook with the specified call type
modified_data = await presidio_guardrail.async_pre_call_hook(
user_api_key_dict=user_api_key_dict, cache=cache, data=data, call_type=call_type
)
# Verify the messages have been modified to mask PII
assert (
modified_data["messages"][0]["content"] == "You are a helpful assistant."
) # System prompt should be unchanged
user_message = modified_data["messages"][1]["content"]
assert "4111-1111-1111-1111" not in user_message
assert "test@example.com" not in user_message
# Since this entity is not in the config, it should not be masked
assert "555-123-4567" in user_message
print(f"Modified user message for call_type={call_type}: {user_message}")
@pytest.mark.parametrize(
"base_url",
[
"presidio-analyzer-s3pa:10000",
"https://presidio-analyzer-s3pa:10000",
"http://presidio-analyzer-s3pa:10000",
],
)
def test_validate_environment_missing_http(base_url):
pii_masking = _OPTIONAL_PresidioPIIMasking(mock_testing=True)
# Use patch.dict to temporarily modify environment variables only for this test
env_vars = {
"PRESIDIO_ANALYZER_API_BASE": f"{base_url}/analyze",
"PRESIDIO_ANONYMIZER_API_BASE": f"{base_url}/anonymize",
}
with patch.dict(os.environ, env_vars):
pii_masking.validate_environment()
expected_url = base_url
if not (base_url.startswith("https://") or base_url.startswith("http://")):
expected_url = "http://" + base_url
assert (
pii_masking.presidio_anonymizer_api_base == f"{expected_url}/anonymize/"
), "Got={}, Expected={}".format(
pii_masking.presidio_anonymizer_api_base, f"{expected_url}/anonymize/"
)
assert pii_masking.presidio_analyzer_api_base == f"{expected_url}/analyze/"
@pytest.mark.asyncio
async def test_output_parsing():
"""
- have presidio pii masking - mask an input message
- make llm completion call
- have presidio pii masking - output parse message
- assert that no masked tokens are in the input message
"""
litellm.set_verbose = True
litellm.output_parse_pii = True
pii_masking = _OPTIONAL_PresidioPIIMasking(mock_testing=True)
initial_message = [
{
"role": "user",
"content": "hello world, my name is Jane Doe. My number is: 034453334",
}
]
filtered_message = [
{
"role": "user",
"content": "hello world, my name is <PERSON>. My number is: <PHONE_NUMBER>",
}
]
response = mock_completion(
model="gpt-5-mini",
messages=filtered_message,
mock_response="Hello <PERSON>! How can I assist you today?",
)
new_response = await pii_masking.async_post_call_success_hook(
user_api_key_dict=UserAPIKeyAuth(),
data={
"messages": [
{"role": "system", "content": "You are an helpfull assistant"}
],
"metadata": {
"pii_tokens": {"<PERSON>": "Jane Doe", "<PHONE_NUMBER>": "034453334"}
},
},
response=response,
)
assert (
new_response.choices[0].message.content
== "Hello Jane Doe! How can I assist you today?"
)
# asyncio.run(test_output_parsing())
### UNIT TESTS FOR PRESIDIO PII MASKING ###
input_a_anonymizer_results = {
"text": "hello world, my name is <PERSON>. My number is: <PHONE_NUMBER>",
"items": [
{
"start": 48,
"end": 62,
"entity_type": "PHONE_NUMBER",
"text": "<PHONE_NUMBER>",
"operator": "replace",
},
{
"start": 24,
"end": 32,
"entity_type": "PERSON",
"text": "<PERSON>",
"operator": "replace",
},
],
}
input_b_anonymizer_results = {
"text": "My name is <PERSON>, who are you? Say my name in your response",
"items": [
{
"start": 11,
"end": 19,
"entity_type": "PERSON",
"text": "<PERSON>",
"operator": "replace",
}
],
}
# Test if PII masking works with input A
@pytest.mark.asyncio
async def test_presidio_pii_masking_input_a():
"""
Tests to see if correct parts of sentence anonymized
"""
pii_masking = _OPTIONAL_PresidioPIIMasking(
mock_testing=True, mock_redacted_text=input_a_anonymizer_results
)
_api_key = "sk-12345"
user_api_key_dict = UserAPIKeyAuth(api_key=_api_key)
local_cache = DualCache()
new_data = await pii_masking.async_pre_call_hook(
user_api_key_dict=user_api_key_dict,
cache=local_cache,
data={
"messages": [
{
"role": "user",
"content": "hello world, my name is Jane Doe. My number is: 23r323r23r2wwkl",
}
]
},
call_type="completion",
)
assert "<PERSON>" in new_data["messages"][0]["content"]
assert "<PHONE_NUMBER>" in new_data["messages"][0]["content"]
# Test if PII masking works with input B (also test if the response != A's response)
@pytest.mark.asyncio
async def test_presidio_pii_masking_input_b():
"""
Tests to see if correct parts of sentence anonymized
"""
pii_masking = _OPTIONAL_PresidioPIIMasking(
mock_testing=True, mock_redacted_text=input_b_anonymizer_results
)
_api_key = "sk-12345"
user_api_key_dict = UserAPIKeyAuth(api_key=_api_key)
local_cache = DualCache()
new_data = await pii_masking.async_pre_call_hook(
user_api_key_dict=user_api_key_dict,
cache=local_cache,
data={
"messages": [
{
"role": "user",
"content": "My name is Jane Doe, who are you? Say my name in your response",
}
]
},
call_type="completion",
)
assert "<PERSON>" in new_data["messages"][0]["content"]
assert "<PHONE_NUMBER>" not in new_data["messages"][0]["content"]
@pytest.mark.asyncio
async def test_presidio_pii_masking_logging_output_only_no_pre_api_hook():
from litellm.types.guardrails import GuardrailEventHooks
pii_masking = _OPTIONAL_PresidioPIIMasking(
logging_only=True,
mock_testing=True,
mock_redacted_text=input_b_anonymizer_results,
)
_api_key = "sk-12345"
user_api_key_dict = UserAPIKeyAuth(api_key=_api_key)
local_cache = DualCache()
test_messages = [
{
"role": "user",
"content": "My name is Jane Doe, who are you? Say my name in your response",
}
]
assert (
pii_masking.should_run_guardrail(
data={"messages": test_messages},
event_type=GuardrailEventHooks.pre_call,
)
is False
)
@pytest.mark.asyncio
@patch.dict(
os.environ,
{
"PRESIDIO_ANALYZER_API_BASE": "http://localhost:5002",
"PRESIDIO_ANONYMIZER_API_BASE": "http://localhost:5001",
},
)
async def test_presidio_pii_masking_logging_output_only_logged_response_guardrails_config():
from typing import Dict, List, Optional
import litellm
from litellm.proxy.guardrails.init_guardrails import initialize_guardrails
from litellm.types.guardrails import (
GuardrailItemSpec,
GuardrailEventHooks,
)
litellm.set_verbose = True
# Environment variables are now patched via the decorator instead of setting them directly
guardrails_config: List[Dict[str, GuardrailItemSpec]] = [
{
"pii_masking": {
"callbacks": ["presidio"],
"default_on": True,
"logging_only": True,
}
}
]
litellm_settings = {"guardrails": guardrails_config}
assert len(litellm.guardrail_name_config_map) == 0
initialize_guardrails(
guardrails_config=guardrails_config,
premium_user=True,
config_file_path="",
litellm_settings=litellm_settings,
)
assert len(litellm.guardrail_name_config_map) == 1
pii_masking_obj: Optional[_OPTIONAL_PresidioPIIMasking] = None
for callback in litellm.callbacks:
print(f"CALLBACK: {callback}")
if isinstance(callback, _OPTIONAL_PresidioPIIMasking):
pii_masking_obj = callback
assert pii_masking_obj is not None
assert hasattr(pii_masking_obj, "logging_only")
assert pii_masking_obj.event_hook == GuardrailEventHooks.logging_only
assert pii_masking_obj.should_run_guardrail(
data={}, event_type=GuardrailEventHooks.logging_only
)
@pytest.mark.asyncio
async def test_presidio_language_configuration():
"""Test that presidio_language parameter is properly set and used in analyze requests"""
litellm._turn_on_debug()
# Test with German language using mock testing to avoid API calls
presidio_guardrail_de = _OPTIONAL_PresidioPIIMasking(
pii_entities_config={},
presidio_language="de",
mock_testing=True, # This bypasses the API validation
)
test_text = "Meine Telefonnummer ist +49 30 12345678"
# Test the analyze request configuration
analyze_request = presidio_guardrail_de._get_presidio_analyze_request_payload(
text=test_text, presidio_config=None, request_data={}
)
# Verify the language is set to German
assert analyze_request["language"] == "de"
assert analyze_request["text"] == test_text
# Test with Spanish language
presidio_guardrail_es = _OPTIONAL_PresidioPIIMasking(
pii_entities_config={}, presidio_language="es", mock_testing=True
)
test_text_es = "Mi número de teléfono es +34 912 345 678"
analyze_request_es = presidio_guardrail_es._get_presidio_analyze_request_payload(
text=test_text_es, presidio_config=None, request_data={}
)
# Verify the language is set to Spanish
assert analyze_request_es["language"] == "es"
assert analyze_request_es["text"] == test_text_es
# Test default language (English) when not specified
presidio_guardrail_default = _OPTIONAL_PresidioPIIMasking(
pii_entities_config={}, mock_testing=True
)
test_text_en = "My phone number is +1 555-123-4567"
analyze_request_default = (
presidio_guardrail_default._get_presidio_analyze_request_payload(
text=test_text_en, presidio_config=None, request_data={}
)
)
# Verify the language defaults to English
assert analyze_request_default["language"] == "en"
assert analyze_request_default["text"] == test_text_en
@pytest.mark.asyncio
async def test_presidio_language_configuration_with_per_request_override():
"""Test that per-request language configuration overrides the default configured language"""
litellm._turn_on_debug()
# Set up guardrail with German as default language
presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
pii_entities_config={}, presidio_language="de", mock_testing=True
)
test_text = "Test text with PII"
# Test with per-request config overriding the default language
presidio_config = PresidioPerRequestConfig(language="fr")
analyze_request = presidio_guardrail._get_presidio_analyze_request_payload(
text=test_text, presidio_config=presidio_config, request_data={}
)
# Verify the per-request language (French) overrides the default (German)
assert analyze_request["language"] == "fr"
assert analyze_request["text"] == test_text
# Test without per-request config - should use default language
analyze_request_default = presidio_guardrail._get_presidio_analyze_request_payload(
text=test_text, presidio_config=None, request_data={}
)
# Verify the default language (German) is used
assert analyze_request_default["language"] == "de"
assert analyze_request_default["text"] == test_text