litellm/tests/local_testing/test_llm_guard.py
yuneng-jiang 6a0d03914c
test: drop the cwd-relative sys.path.insert calls from the test suite (#37802)
* test: drop the cwd-relative sys.path.insert calls from the test suite

TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.

Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.

Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.

* test: drop the duplicate imports the sys.path sweep exposed to F811

* test(pre-call-utils): restore the os import the new bedrock tests need
2026-08-22 09:25:58 -07:00

215 lines
6 KiB
Python

# What is this?
## This tests the llm guard integration
# What is this?
## Unit test for presidio pii masking
import sys, os, asyncio, time, random
from datetime import datetime
import traceback
from dotenv import load_dotenv
load_dotenv()
import pytest
from fastapi import HTTPException
import litellm
from litellm_enterprise.enterprise_callbacks.llm_guard import _ENTERPRISE_LLMGuard
from litellm import Router, mock_completion
from litellm.proxy.utils import ProxyLogging, hash_token
from litellm.proxy._types import UserAPIKeyAuth
from litellm.caching.caching import DualCache
### UNIT TESTS FOR LLM GUARD ###
@pytest.mark.asyncio
async def test_llm_guard_valid_response():
"""
A valid (is_valid=True) LLM Guard response must apply the returned
sanitized_prompt back onto the request data so the provider receives the
redacted content.
"""
litellm.llm_guard_mode = "all"
input_a_anonymizer_results = {
"sanitized_prompt": "hello world",
"is_valid": True,
"scanners": {"Regex": 0.0},
}
llm_guard = _ENTERPRISE_LLMGuard(
mock_testing=True, mock_redacted_text=input_a_anonymizer_results
)
_api_key = "sk-12345"
_api_key = hash_token("sk-12345")
user_api_key_dict = UserAPIKeyAuth(api_key=_api_key)
local_cache = DualCache()
data = {
"messages": [
{
"role": "user",
"content": "hello world, my name is Jane Doe. My number is: 23r323r23r2wwkl",
}
]
}
result = await llm_guard.async_moderation_hook(
data=data,
user_api_key_dict=user_api_key_dict,
call_type="completion",
)
assert result is data
assert data["messages"][0]["content"] == "hello world"
@pytest.mark.asyncio
async def test_llm_guard_sanitizes_multimodal_and_input():
"""
Sanitization must reach text parts of multimodal message content and the
``input`` field (embeddings/moderation) while leaving non-text parts intact.
"""
litellm.llm_guard_mode = "all"
llm_guard = _ENTERPRISE_LLMGuard(
mock_testing=True,
mock_redacted_text={
"sanitized_prompt": "email: [REDACTED]",
"is_valid": True,
"scanners": {"Regex": 0.0},
},
)
user_api_key_dict = UserAPIKeyAuth(api_key=hash_token("sk-12345"))
image_part = {"type": "image_url", "image_url": {"url": "https://example.com/a.png"}}
data = {
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": "email: person@example.com"},
image_part,
],
}
]
}
result = await llm_guard.async_moderation_hook(
data=data, user_api_key_dict=user_api_key_dict, call_type="completion"
)
assert result["messages"][0]["content"][0]["text"] == "email: [REDACTED]"
assert result["messages"][0]["content"][1] == image_part
input_data = {"input": ["email: person@example.com", "another prompt"]}
input_result = await llm_guard.async_moderation_hook(
data=input_data, user_api_key_dict=user_api_key_dict, call_type="embeddings"
)
assert input_result["input"] == ["email: [REDACTED]", "email: [REDACTED]"]
@pytest.mark.asyncio
async def test_llm_guard_error_raising():
"""
Tests to see llm guard raises an error for a flagged response
"""
input_b_anonymizer_results = {
"sanitized_prompt": "hello world",
"is_valid": False,
"scanners": {"Regex": 0.0},
}
llm_guard = _ENTERPRISE_LLMGuard(
mock_testing=True, mock_redacted_text=input_b_anonymizer_results
)
_api_key = "sk-12345"
_api_key = hash_token("sk-12345")
user_api_key_dict = UserAPIKeyAuth(api_key=_api_key)
local_cache = DualCache()
with pytest.raises(HTTPException) as exc_info:
await llm_guard.async_moderation_hook(
data={
"messages": [
{
"role": "user",
"content": "hello world, my name is Jane Doe. My number is: 23r323r23r2wwkl",
}
]
},
user_api_key_dict=user_api_key_dict,
call_type="completion",
)
assert exc_info.value.status_code == 400
assert exc_info.value.detail == {"error": "Violated content safety policy"}
def test_llm_guard_key_specific_mode():
"""
Tests to see if llm guard 'key-specific' permissions work
"""
litellm.llm_guard_mode = "key-specific"
llm_guard = _ENTERPRISE_LLMGuard(mock_testing=True)
_api_key = "sk-12345"
# NOT ENABLED
user_api_key_dict = UserAPIKeyAuth(
api_key=_api_key,
)
request_data = {}
should_proceed = llm_guard.should_proceed(
user_api_key_dict=user_api_key_dict, data=request_data
)
assert should_proceed == False
# ENABLED
user_api_key_dict = UserAPIKeyAuth(
api_key=_api_key, permissions={"enable_llm_guard_check": True}
)
request_data = {}
should_proceed = llm_guard.should_proceed(
user_api_key_dict=user_api_key_dict, data=request_data
)
assert should_proceed == True
def test_llm_guard_request_specific_mode():
"""
Tests to see if llm guard 'request-specific' permissions work
"""
litellm.llm_guard_mode = "request-specific"
llm_guard = _ENTERPRISE_LLMGuard(mock_testing=True)
_api_key = "sk-12345"
# NOT ENABLED
user_api_key_dict = UserAPIKeyAuth(
api_key=_api_key,
)
request_data = {}
should_proceed = llm_guard.should_proceed(
user_api_key_dict=user_api_key_dict, data=request_data
)
assert should_proceed == False
# ENABLED
user_api_key_dict = UserAPIKeyAuth(
api_key=_api_key, permissions={"enable_llm_guard_check": True}
)
request_data = {"metadata": {"permissions": {"enable_llm_guard_check": True}}}
should_proceed = llm_guard.should_proceed(
user_api_key_dict=user_api_key_dict, data=request_data
)
assert should_proceed == True