mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-15 23:31:29 +00:00
* 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.
589 lines
20 KiB
Python
589 lines
20 KiB
Python
import sys
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import os
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import pytest
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from litellm import mock_completion
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from unittest.mock import patch
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sys.path.insert(0, os.path.abspath("../.."))
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import litellm
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from litellm.proxy.guardrails.guardrail_hooks.presidio import (
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_OPTIONAL_PresidioPIIMasking,
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PresidioPerRequestConfig,
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)
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from litellm.types.guardrails import PiiEntityType, PiiAction
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from litellm.proxy._types import UserAPIKeyAuth
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from litellm.caching.caching import DualCache
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from litellm.exceptions import BlockedPiiEntityError
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@pytest.mark.asyncio
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async def test_presidio_with_entities_config():
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"""Test for Presidio guardrail with entities config - requires actual Presidio API"""
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# Setup the guardrail with specific entities config
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litellm._turn_on_debug()
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pii_entities_config = {
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PiiEntityType.CREDIT_CARD: PiiAction.MASK,
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PiiEntityType.EMAIL_ADDRESS: PiiAction.MASK,
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}
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presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
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pii_entities_config=pii_entities_config,
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presidio_analyzer_api_base=os.environ.get("PRESIDIO_ANALYZER_API_BASE"),
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presidio_anonymizer_api_base=os.environ.get("PRESIDIO_ANONYMIZER_API_BASE"),
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)
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# Test text with different PII types
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test_text = "My credit card number is 4111-1111-1111-1111, my email is test@example.com, and my phone is 555-123-4567"
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# Test the analyze request configuration
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analyze_request = presidio_guardrail._get_presidio_analyze_request_payload(
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text=test_text, presidio_config=None, request_data={}
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)
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# Verify entities were passed correctly
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assert "entities" in analyze_request
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assert set(analyze_request["entities"]) == set(pii_entities_config.keys())
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# Test the check_pii method - this will call the actual Presidio API
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redacted_text = await presidio_guardrail.check_pii(
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text=test_text, output_parse_pii=True, presidio_config=None, request_data={}
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)
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# Verify PII has been masked/replaced/redacted in the result
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assert "4111-1111-1111-1111" not in redacted_text
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assert "test@example.com" not in redacted_text
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# Since this entity is not in the config, it should not be masked
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assert "555-123-4567" in redacted_text
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# The specific replacements will vary based on Presidio's implementation
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print(f"Redacted text: {redacted_text}")
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@pytest.mark.asyncio
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async def test_presidio_apply_guardrail():
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"""Test for Presidio guardrail apply guardrail - requires actual Presidio API"""
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litellm._turn_on_debug()
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presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
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pii_entities_config={},
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presidio_analyzer_api_base=os.environ.get("PRESIDIO_ANALYZER_API_BASE"),
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presidio_anonymizer_api_base=os.environ.get("PRESIDIO_ANONYMIZER_API_BASE"),
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)
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test_text = (
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"My credit card number is 4111-1111-1111-1111 and my email is test@example.com"
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)
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response = await presidio_guardrail.apply_guardrail(
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inputs={"texts": [test_text]},
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request_data={},
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input_type="request",
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)
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print("response from apply guardrail for presidio: ", response)
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# Extract the modified text from the response
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modified_text = response["texts"][0] if response.get("texts") else ""
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# assert the default config masks the credit card and email
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assert "4111-1111-1111-1111" not in modified_text
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assert "test@example.com" not in modified_text
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@pytest.mark.asyncio
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async def test_presidio_with_blocked_entities():
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"""Test for Presidio guardrail with blocked entities - requires actual Presidio API"""
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# Setup the guardrail with specific entities config - BLOCK for credit card
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litellm._turn_on_debug()
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pii_entities_config = {
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PiiEntityType.CREDIT_CARD: PiiAction.BLOCK, # This entity should cause a block
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PiiEntityType.EMAIL_ADDRESS: PiiAction.MASK, # This entity should be masked
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}
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presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
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pii_entities_config=pii_entities_config,
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presidio_analyzer_api_base=os.environ.get("PRESIDIO_ANALYZER_API_BASE"),
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presidio_anonymizer_api_base=os.environ.get("PRESIDIO_ANONYMIZER_API_BASE"),
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)
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# Test text with blocked PII type
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test_text = (
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"My credit card number is 4111-1111-1111-1111 and my email is test@example.com"
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)
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# Verify the analyze request configuration
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analyze_request = presidio_guardrail._get_presidio_analyze_request_payload(
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text=test_text, presidio_config=None, request_data={}
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)
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# Verify entities were passed correctly
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assert "entities" in analyze_request
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assert set(analyze_request["entities"]) == set(pii_entities_config.keys())
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# Test that BlockedPiiEntityError is raised when check_pii is called
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with pytest.raises(BlockedPiiEntityError) as excinfo:
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await presidio_guardrail.check_pii(
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text=test_text, output_parse_pii=True, presidio_config=None, request_data={}
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)
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# Verify the error contains the correct entity type
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assert excinfo.value.entity_type == PiiEntityType.CREDIT_CARD
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assert excinfo.value.guardrail_name == presidio_guardrail.guardrail_name
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@pytest.mark.asyncio
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async def test_presidio_pre_call_hook_with_blocked_entities():
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"""Test for Presidio guardrail pre-call hook with blocked entities on a chat completion request"""
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# Setup the guardrail with specific entities config
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pii_entities_config = {
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PiiEntityType.CREDIT_CARD: PiiAction.BLOCK, # This entity should cause a block
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PiiEntityType.EMAIL_ADDRESS: PiiAction.MASK, # This entity should be masked
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}
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presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
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pii_entities_config=pii_entities_config,
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presidio_analyzer_api_base=os.environ.get("PRESIDIO_ANALYZER_API_BASE"),
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presidio_anonymizer_api_base=os.environ.get("PRESIDIO_ANONYMIZER_API_BASE"),
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)
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# Create a sample chat completion request with PII data
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data = {
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{
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"role": "user",
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"content": "My credit card is 4111-1111-1111-1111 and my email is test@example.com.",
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},
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],
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"model": "gpt-5-mini",
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}
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# Mock objects needed for the pre-call hook
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user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
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cache = DualCache()
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# Call the pre-call hook and expect BlockedPiiEntityError
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with pytest.raises(BlockedPiiEntityError) as excinfo:
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await presidio_guardrail.async_pre_call_hook(
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user_api_key_dict=user_api_key_dict,
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cache=cache,
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data=data,
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call_type="completion",
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)
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print(f"got error: {excinfo}")
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# Verify the error contains the correct entity type
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assert excinfo.value.entity_type == PiiEntityType.CREDIT_CARD
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assert excinfo.value.guardrail_name == presidio_guardrail.guardrail_name
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@pytest.mark.asyncio
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@pytest.mark.parametrize("call_type", ["completion", "acompletion"])
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async def test_presidio_pre_call_hook_with_different_call_types(call_type):
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"""Test for Presidio guardrail pre-call hook with both completion and acompletion call types"""
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# Setup the guardrail with specific entities config
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pii_entities_config = {
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PiiEntityType.CREDIT_CARD: PiiAction.MASK,
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PiiEntityType.EMAIL_ADDRESS: PiiAction.MASK,
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}
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presidio_guardrail = _OPTIONAL_PresidioPIIMasking(
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pii_entities_config=pii_entities_config,
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presidio_analyzer_api_base=os.environ.get("PRESIDIO_ANALYZER_API_BASE"),
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presidio_anonymizer_api_base=os.environ.get("PRESIDIO_ANONYMIZER_API_BASE"),
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)
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# Create a sample request with PII data
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data = {
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{
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"role": "user",
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"content": "My credit card is 4111-1111-1111-1111 and my email is test@example.com. My phone number is 555-123-4567",
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},
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],
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"model": "gpt-5-mini",
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}
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# Mock objects needed for the pre-call hook
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user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
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cache = DualCache()
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# Call the pre-call hook with the specified call type
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modified_data = await presidio_guardrail.async_pre_call_hook(
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user_api_key_dict=user_api_key_dict, cache=cache, data=data, call_type=call_type
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)
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# Verify the messages have been modified to mask PII
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assert (
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modified_data["messages"][0]["content"] == "You are a helpful assistant."
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) # System prompt should be unchanged
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user_message = modified_data["messages"][1]["content"]
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assert "4111-1111-1111-1111" not in user_message
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assert "test@example.com" not in user_message
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# Since this entity is not in the config, it should not be masked
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assert "555-123-4567" in user_message
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print(f"Modified user message for call_type={call_type}: {user_message}")
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@pytest.mark.parametrize(
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"base_url",
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[
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"presidio-analyzer-s3pa:10000",
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"https://presidio-analyzer-s3pa:10000",
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"http://presidio-analyzer-s3pa:10000",
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],
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)
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def test_validate_environment_missing_http(base_url):
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pii_masking = _OPTIONAL_PresidioPIIMasking(mock_testing=True)
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# Use patch.dict to temporarily modify environment variables only for this test
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env_vars = {
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"PRESIDIO_ANALYZER_API_BASE": f"{base_url}/analyze",
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"PRESIDIO_ANONYMIZER_API_BASE": f"{base_url}/anonymize",
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}
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with patch.dict(os.environ, env_vars):
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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
|