litellm/tests/guardrails_tests/test_guardrails_config.py
Alexsander Hamir eaa04cd8ce
fix: use fastuuid helper (#14903)
* fix: use fastuuid helper across the codebase

First batch of changes, simple drop in replacement.

* second batch of changes

* fixed: script mistake on helper file
2025-09-25 15:47:01 -07:00

116 lines
3.5 KiB
Python

# What is this?
## Unit Tests for guardrails config
import asyncio
import inspect
import os
import sys
import time
import traceback
from litellm._uuid import uuid
from datetime import datetime
import pytest
from pydantic import BaseModel
import litellm.litellm_core_utils
import litellm.litellm_core_utils.litellm_logging
sys.path.insert(0, os.path.abspath("../.."))
from typing import Any, List, Literal, Optional, Tuple, Union
from unittest.mock import AsyncMock, MagicMock, patch
import litellm
from litellm import Cache, completion, embedding
from litellm.integrations.custom_logger import CustomLogger
from litellm.types.utils import LiteLLMCommonStrings
class CustomLoggingIntegration(CustomLogger):
def __init__(self) -> None:
super().__init__()
def logging_hook(
self, kwargs: dict, result: Any, call_type: str
) -> Tuple[dict, Any]:
input: Optional[Any] = kwargs.get("input", None)
messages: Optional[List] = kwargs.get("messages", None)
if call_type == "completion":
# assume input is of type messages
if input is not None and isinstance(input, list):
input[0]["content"] = "Hey, my name is [NAME]."
if messages is not None and isinstance(messages, List):
messages[0]["content"] = "Hey, my name is [NAME]."
kwargs["input"] = input
kwargs["messages"] = messages
return kwargs, result
def test_guardrail_masking_logging_only():
"""
Assert response is unmasked.
Assert logged response is masked.
"""
callback = CustomLoggingIntegration()
with patch.object(callback, "log_success_event", new=MagicMock()) as mock_call:
litellm.callbacks = [callback]
messages = [{"role": "user", "content": "Hey, my name is Peter."}]
response = completion(
model="gpt-3.5-turbo", messages=messages, mock_response="Hi Peter!"
)
assert response.choices[0].message.content == "Hi Peter!" # type: ignore
time.sleep(3)
mock_call.assert_called_once()
print(mock_call.call_args.kwargs["kwargs"]["messages"][0]["content"])
assert (
mock_call.call_args.kwargs["kwargs"]["messages"][0]["content"]
== "Hey, my name is [NAME]."
)
def test_guardrail_list_of_event_hooks():
from litellm.integrations.custom_guardrail import CustomGuardrail
from litellm.types.guardrails import GuardrailEventHooks
cg = CustomGuardrail(
guardrail_name="custom-guard", event_hook=["pre_call", "post_call"]
)
data = {"model": "gpt-3.5-turbo", "metadata": {"guardrails": ["custom-guard"]}}
assert cg.should_run_guardrail(data=data, event_type=GuardrailEventHooks.pre_call)
assert cg.should_run_guardrail(data=data, event_type=GuardrailEventHooks.post_call)
assert not cg.should_run_guardrail(
data=data, event_type=GuardrailEventHooks.during_call
)
def test_guardrail_info_response():
from litellm.types.guardrails import (
GuardrailInfoResponse,
LitellmParams,
)
guardrail_info = GuardrailInfoResponse(
guardrail_name="aporia-pre-guard",
litellm_params=LitellmParams(
guardrail="aporia",
mode="pre_call",
),
guardrail_info={
"guardrail_name": "aporia-pre-guard",
"litellm_params": {
"guardrail": "aporia",
"mode": "always_on",
},
},
)
assert guardrail_info.litellm_params.default_on == False