litellm/enterprise/litellm_enterprise/enterprise_callbacks/llm_guard.py
Krish Dholakia 06906534b3
feat(audio_transcriptions/): calculate duration of audio file for cost calculation + feat (image_generations): cost tracking accuracy improved with output_format, quality, size values fixed per openai model
* feat(audio_transcriptions/): calculate duration of audio file for cost calculation

Fixes https://github.com/BerriAI/litellm/issues/11846

Closes https://github.com/BerriAI/litellm/issues/14605

* fix(cost_calculator.py): correctly use base model, when set

Fixes issue where azure base model was being ignored

* feat(cost_calculator.py): fix default cost tracking quality param for image generation

* feat(image_generations/): return output_format, quality, size

aligns response to openai spec and improves cost tracking accuracy

* fix(cost_calculator.py): refactor cost calculation for image generation to use image response instead of hidden params

* build: update build

* fix: fix cost calculation

* build: update poetry lock

* fix: fix ruff checks

* fix: fix aembedding

* fix: fix ruff errors

* fix: modify to catch errors

* fix: test

* fix: loosen test to handle openai lib out of sync

* fix: fix base models

* fix: fix usage object
2025-11-08 16:24:31 -08:00

175 lines
6.3 KiB
Python

# +------------------------+
#
# LLM Guard
# https://llm-guard.com/
#
# +------------------------+
# Thank you users! We ❤️ you! - Krrish & Ishaan
## This provides an LLM Guard Integration for content moderation on the proxy
from typing import Literal, Optional
import aiohttp
from fastapi import HTTPException
import litellm
from litellm._logging import verbose_proxy_logger
from litellm.integrations.custom_logger import CustomLogger
from litellm.proxy._types import UserAPIKeyAuth
from litellm.secret_managers.main import get_secret_str
from litellm.types.utils import CallTypesLiteral
from litellm.utils import get_formatted_prompt
class _ENTERPRISE_LLMGuard(CustomLogger):
# Class variables or attributes
def __init__(
self,
mock_testing: bool = False,
mock_redacted_text: Optional[dict] = None,
):
self.mock_redacted_text = mock_redacted_text
self.llm_guard_mode = litellm.llm_guard_mode
if mock_testing is True: # for testing purposes only
return
self.llm_guard_api_base = get_secret_str("LLM_GUARD_API_BASE", None)
if self.llm_guard_api_base is None:
raise Exception("Missing `LLM_GUARD_API_BASE` from environment")
elif not self.llm_guard_api_base.endswith("/"):
self.llm_guard_api_base += "/"
def print_verbose(self, print_statement):
try:
verbose_proxy_logger.debug(print_statement)
if litellm.set_verbose:
print(print_statement) # noqa
except Exception:
pass
async def moderation_check(self, text: str):
"""
[TODO] make this more performant for high-throughput scenario
"""
try:
async with aiohttp.ClientSession() as session:
if self.mock_redacted_text is not None:
redacted_text = self.mock_redacted_text
else:
# Make the first request to /analyze
analyze_url = f"{self.llm_guard_api_base}analyze/prompt"
verbose_proxy_logger.debug("Making request to: %s", analyze_url)
analyze_payload = {"prompt": text}
redacted_text = None
async with session.post(
analyze_url, json=analyze_payload
) as response:
redacted_text = await response.json()
verbose_proxy_logger.debug(
f"LLM Guard: Received response - {redacted_text}"
)
if redacted_text is not None:
if (
redacted_text.get("is_valid", None) is not None
and redacted_text["is_valid"] is False
):
raise HTTPException(
status_code=400,
detail={"error": "Violated content safety policy"},
)
else:
pass
else:
raise HTTPException(
status_code=500,
detail={
"error": f"Invalid content moderation response: {redacted_text}"
},
)
except Exception as e:
verbose_proxy_logger.exception(
"litellm.enterprise.enterprise_hooks.llm_guard::moderation_check - Exception occurred - {}".format(
str(e)
)
)
raise e
def should_proceed(self, user_api_key_dict: UserAPIKeyAuth, data: dict) -> bool:
if self.llm_guard_mode == "key-specific":
# check if llm guard enabled for specific keys only
self.print_verbose(
f"user_api_key_dict.permissions: {user_api_key_dict.permissions}"
)
if (
user_api_key_dict.permissions.get("enable_llm_guard_check", False)
is True
):
return True
elif self.llm_guard_mode == "all":
return True
elif self.llm_guard_mode == "request-specific":
self.print_verbose(f"received metadata: {data.get('metadata', {})}")
metadata = data.get("metadata", {})
permissions = metadata.get("permissions", {})
if (
"enable_llm_guard_check" in permissions
and permissions["enable_llm_guard_check"] is True
):
return True
return False
async def async_moderation_hook(
self,
data: dict,
user_api_key_dict: UserAPIKeyAuth,
call_type: CallTypesLiteral,
):
"""
- Calls the LLM Guard Endpoint
- Rejects request if it fails safety check
- Use the sanitized prompt returned
- LLM Guard can handle things like PII Masking, etc.
"""
self.print_verbose(
f"Inside LLM Guard Pre-Call Hook - llm_guard_mode={self.llm_guard_mode}"
)
_proceed = self.should_proceed(user_api_key_dict=user_api_key_dict, data=data)
if _proceed is False:
return
self.print_verbose("Makes LLM Guard Check")
try:
assert call_type in [
"completion",
"embeddings",
"image_generation",
"moderation",
"audio_transcription",
]
except Exception:
self.print_verbose(
f"Call Type - {call_type}, not in accepted list - ['completion','embeddings','image_generation','moderation','audio_transcription']"
)
return data
formatted_prompt = get_formatted_prompt(data=data, call_type=call_type) # type: ignore
self.print_verbose(f"LLM Guard, formatted_prompt: {formatted_prompt}")
return await self.moderation_check(text=formatted_prompt)
async def async_post_call_streaming_hook(
self, user_api_key_dict: UserAPIKeyAuth, response: str
):
if response is not None:
await self.moderation_check(text=response)
return response
# llm_guard = _ENTERPRISE_LLMGuard()
# asyncio.run(
# llm_guard.async_moderation_hook(
# data={"messages": [{"role": "user", "content": "Hey how's it going?"}]}
# )
# )