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use apply guardrails for OpenAI moderations
This commit is contained in:
Ishaan Jaffer
parent
1f809fa679
commit
6c4237f2fe
1 changed files with 42 additions and 151 deletions
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@ -22,10 +22,12 @@ from litellm.integrations.custom_guardrail import (
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CustomGuardrail,
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log_guardrail_information,
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)
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.llms.custom_httpx.http_handler import (
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get_async_httpx_client,
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httpxSpecialProvider,
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)
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from litellm.types.utils import GenericGuardrailAPIInputs
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from .base import OpenAIGuardrailBase
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@ -178,170 +180,59 @@ class OpenAIModerationGuardrail(OpenAIGuardrailBase, CustomGuardrail):
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},
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)
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def _extract_user_content_from_data(self, data: Dict[str, Any]) -> Optional[str]:
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async def apply_guardrail(
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self,
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inputs: GenericGuardrailAPIInputs,
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request_data: dict,
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input_type: Literal["request", "response"],
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logging_obj: Optional["LiteLLMLoggingObj"] = None,
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) -> GenericGuardrailAPIInputs:
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"""
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Extract user content from request data, supporting both Chat Completions and Responses API.
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Apply OpenAI moderation guardrail using the unified guardrail interface.
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For Chat Completions: extracts from 'messages' field
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For Responses API: extracts from 'input' field
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This method is called by the UnifiedLLMGuardrails system for all endpoint types
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(chat completions, embeddings, responses API, etc.).
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Args:
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inputs: GenericGuardrailAPIInputs containing texts and/or structured_messages
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request_data: The original request data
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input_type: Whether this is a "request" (pre-call) or "response" (post-call)
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logging_obj: Optional logging object
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Returns:
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The extracted user content string, or None if no content found
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The inputs unchanged (moderation doesn't modify content, only blocks)
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Raises:
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HTTPException: If content violates moderation policy
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"""
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# Try to get messages first (Chat Completions API)
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messages: Optional[List["AllMessageValues"]] = data.get("messages")
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if messages is not None:
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return self.get_user_prompt(messages)
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# Extract text to moderate from inputs
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text_to_moderate: Optional[str] = None
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# Try to get input (Responses API)
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input_data = data.get("input")
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if input_data is not None:
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# input can be a string or a list of message-like objects
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if isinstance(input_data, str):
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return input_data
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elif isinstance(input_data, list):
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# Treat input as messages and extract user content
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return self.get_user_prompt(input_data)
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# Prefer structured_messages if available (has role context)
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if structured_messages := inputs.get("structured_messages"):
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text_to_moderate = self.get_user_prompt(structured_messages)
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return None
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@log_guardrail_information
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async def async_pre_call_hook(
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self,
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user_api_key_dict: "UserAPIKeyAuth",
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cache: Any,
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data: Dict[str, Any],
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call_type: Literal[
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"completion",
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"text_completion",
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"embeddings",
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"image_generation",
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"moderation",
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"audio_transcription",
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"pass_through_endpoint",
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"rerank",
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"mcp_call",
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],
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) -> Optional[Dict[str, Any]]:
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"""
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Pre-call hook to scan user prompts before sending to LLM.
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Raises HTTPException if content should be blocked.
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"""
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verbose_proxy_logger.debug(
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"OpenAI Moderation: Running pre-call prompt scan, on call_type: %s",
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call_type,
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)
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# Fall back to texts
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if not text_to_moderate:
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if texts := inputs.get("texts"):
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# Join all texts for moderation
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text_to_moderate = "\n".join(texts)
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# Skip moderation calls to avoid infinite recursion
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if call_type == "moderation":
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return data
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user_prompt = self._extract_user_content_from_data(data)
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if user_prompt is None:
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verbose_proxy_logger.warning(
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"OpenAI Moderation: not running guardrail. No messages or input in data"
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)
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return data
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if user_prompt:
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if not text_to_moderate:
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verbose_proxy_logger.debug(
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f"OpenAI Moderation: User prompt: {user_prompt[:100]}..." # Log first 100 chars for debugging
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"OpenAI Moderation: No text content to moderate in inputs"
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)
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moderation_response = await self.async_make_request(
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input_text=user_prompt,
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)
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# Check if content is flagged and raise exception if needed
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self._check_moderation_result(moderation_response)
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else:
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verbose_proxy_logger.warning(
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"OpenAI Moderation: No user prompt found"
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)
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return data
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@log_guardrail_information
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async def async_moderation_hook(
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self,
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data: Dict[str, Any],
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user_api_key_dict: "UserAPIKeyAuth",
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call_type: Literal[
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"completion",
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"embeddings",
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"image_generation",
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"moderation",
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"audio_transcription",
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"responses",
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"mcp_call",
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],
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) -> Optional[Dict[str, Any]]:
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"""
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Moderation hook to scan user prompts during call processing.
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Raises HTTPException if content should be blocked.
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"""
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verbose_proxy_logger.debug(
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"OpenAI Moderation: Running moderation hook, on call_type: %s",
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call_type,
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)
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return inputs
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# Make moderation request
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moderation_response = await self.async_make_request(input_text=text_to_moderate)
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# Skip moderation calls to avoid infinite recursion
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if call_type == "moderation":
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return data
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# Extract user content from either messages or input field
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user_prompt = self._extract_user_content_from_data(data)
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# Check if content is flagged and raise exception if needed
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self._check_moderation_result(moderation_response)
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if user_prompt is None:
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verbose_proxy_logger.warning(
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"OpenAI Moderation: not running guardrail. No messages or input in data"
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)
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return data
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# Moderation doesn't modify content, just blocks - return inputs unchanged
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return inputs
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if user_prompt:
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moderation_response = await self.async_make_request(
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input_text=user_prompt,
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)
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# Check if content is flagged and raise exception if needed
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self._check_moderation_result(moderation_response)
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return data
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@log_guardrail_information
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async def async_post_call_hook(
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self,
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data: Dict[str, Any],
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user_api_key_dict: "UserAPIKeyAuth",
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response: "ModelResponse",
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) -> "ModelResponse":
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"""
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Post-call hook to scan LLM responses before returning to user.
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Raises HTTPException if response should be blocked.
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"""
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verbose_proxy_logger.debug(
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"OpenAI Moderation: Running post-call response scan"
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)
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# Extract response text for moderation
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response_text = self._extract_response_text(response)
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if response_text:
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verbose_proxy_logger.debug(
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f"OpenAI Moderation: Response text: {response_text[:100]}..." # Log first 100 chars
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)
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moderation_response = await self.async_make_request(
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input_text=response_text,
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)
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# Check if content is flagged and raise exception if needed
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self._check_moderation_result(moderation_response)
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return response
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@log_guardrail_information
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async def async_post_call_streaming_iterator_hook(
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