chore: initial public snapshot for github upload
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from typing import TYPE_CHECKING
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import litellm
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from litellm.proxy.guardrails.guardrail_hooks.openai.moderations import (
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OpenAIModerationGuardrail,
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)
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from litellm.types.guardrails import SupportedGuardrailIntegrations
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if TYPE_CHECKING:
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from litellm.types.guardrails import Guardrail, LitellmParams
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def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"):
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guardrail_name = guardrail.get("guardrail_name")
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if not guardrail_name:
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raise ValueError("OpenAI Moderation: guardrail_name is required")
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openai_moderation_guardrail = OpenAIModerationGuardrail(
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guardrail_name=guardrail_name,
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**{
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**litellm_params.model_dump(exclude_none=True),
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"api_key": litellm_params.api_key,
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"api_base": litellm_params.api_base,
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"default_on": litellm_params.default_on,
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"event_hook": litellm_params.mode,
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"model": litellm_params.model,
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},
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)
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litellm.logging_callback_manager.add_litellm_callback(openai_moderation_guardrail)
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return openai_moderation_guardrail
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guardrail_initializer_registry = {
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SupportedGuardrailIntegrations.OPENAI_MODERATION.value: initialize_guardrail,
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}
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guardrail_class_registry = {
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SupportedGuardrailIntegrations.OPENAI_MODERATION.value: OpenAIModerationGuardrail,
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}
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from typing import TYPE_CHECKING, List, Optional
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if TYPE_CHECKING:
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from litellm.types.llms.openai import AllMessageValues
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class OpenAIGuardrailBase:
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"""
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Base class for OpenAI guardrails.
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"""
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def get_user_prompt(self, messages: List["AllMessageValues"]) -> Optional[str]:
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"""
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Get the last consecutive block of messages from the user.
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Example:
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messages = [
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{"role": "user", "content": "Hello, how are you?"},
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{"role": "assistant", "content": "I'm good, thank you!"},
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{"role": "user", "content": "What is the weather in Tokyo?"},
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]
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get_user_prompt(messages) -> "What is the weather in Tokyo?"
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"""
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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convert_content_list_to_str,
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)
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if not messages:
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return None
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# Iterate from the end to find the last consecutive block of user messages
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user_messages = []
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for message in reversed(messages):
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if message.get("role") == "user":
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user_messages.append(message)
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else:
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# Stop when we hit a non-user message
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break
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if not user_messages:
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return None
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# Reverse to get the messages in chronological order
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user_messages.reverse()
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user_prompt = ""
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for message in user_messages:
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text_content = convert_content_list_to_str(message)
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user_prompt += text_content + "\n"
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result = user_prompt.strip()
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return result if result else None
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@@ -0,0 +1,241 @@
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#!/usr/bin/env python3
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"""
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OpenAI Moderation Guardrail Integration for LiteLLM
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"""
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from typing import (
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TYPE_CHECKING,
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Literal,
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Optional,
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Type,
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)
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from fastapi import HTTPException
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from litellm._logging import verbose_proxy_logger
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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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if TYPE_CHECKING:
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from litellm.types.llms.openai import OpenAIModerationResponse
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from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel
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class OpenAIModerationGuardrail(OpenAIGuardrailBase, CustomGuardrail):
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"""
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LiteLLM Built-in Guardrail for OpenAI Content Moderation.
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This guardrail scans prompts and responses using the OpenAI Moderation API to detect
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harmful content, including violence, hate, harassment, self-harm, sexual content, etc.
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Configuration:
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guardrail_name: Name of the guardrail instance
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api_key: OpenAI API key
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api_base: OpenAI API endpoint
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model: OpenAI moderation model to use
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default_on: Whether to enable by default
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"""
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def __init__(
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self,
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guardrail_name: str,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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model: Optional[
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Literal["omni-moderation-latest", "text-moderation-latest"]
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] = None,
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**kwargs,
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):
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"""Initialize OpenAI Moderation guardrail handler."""
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from litellm.types.guardrails import GuardrailEventHooks
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# Initialize parent CustomGuardrail
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supported_event_hooks = [
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GuardrailEventHooks.pre_call,
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GuardrailEventHooks.during_call,
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GuardrailEventHooks.post_call,
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]
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super().__init__(
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guardrail_name=guardrail_name,
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supported_event_hooks=supported_event_hooks,
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**kwargs,
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)
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self.async_handler = get_async_httpx_client(
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llm_provider=httpxSpecialProvider.GuardrailCallback
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)
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# Store configuration
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self.api_key = api_key or self._get_api_key()
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self.api_base = api_base or "https://api.openai.com/v1"
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self.model: Literal["omni-moderation-latest", "text-moderation-latest"] = (
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model or "omni-moderation-latest"
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)
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if not self.api_key:
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raise ValueError(
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"OpenAI Moderation: api_key is required. Set OPENAI_API_KEY environment variable or pass it in configuration."
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)
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verbose_proxy_logger.debug(
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f"Initialized OpenAI Moderation Guardrail: {guardrail_name} with model: {self.model}"
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)
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def _get_api_key(self) -> Optional[str]:
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"""Get API key from environment variables or litellm configuration"""
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import os
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import litellm
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from litellm.secret_managers.main import get_secret_str
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return (
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os.environ.get("OPENAI_API_KEY")
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or litellm.api_key
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or litellm.openai_key
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or get_secret_str("OPENAI_API_KEY")
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)
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async def async_make_request(self, input_text: str) -> "OpenAIModerationResponse":
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"""
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Make a request to the OpenAI Moderation API.
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"""
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request_body = {"model": self.model, "input": input_text}
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verbose_proxy_logger.debug("OpenAI Moderation guard request: %s", request_body)
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response = await self.async_handler.post(
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url=f"{self.api_base}/moderations",
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headers={
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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},
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json=request_body,
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)
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verbose_proxy_logger.debug(
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"OpenAI Moderation guard response: %s", response.json()
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)
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if response.status_code != 200:
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raise HTTPException(
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status_code=response.status_code,
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detail={
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"error": "OpenAI Moderation API request failed",
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"details": response.text,
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},
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)
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from litellm.types.llms.openai import OpenAIModerationResponse
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return OpenAIModerationResponse(**response.json())
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def _check_moderation_result(
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self, moderation_response: "OpenAIModerationResponse"
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) -> None:
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"""
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Check if the moderation response indicates harmful content and raise exception if needed.
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"""
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if not moderation_response.results:
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return
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result = moderation_response.results[0]
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if result.flagged:
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# Build detailed violation information
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violated_categories = []
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if result.categories:
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for category, is_violated in result.categories.items():
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if is_violated:
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violated_categories.append(category)
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violation_details = {
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"violated_categories": violated_categories,
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"category_scores": result.category_scores or {},
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}
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verbose_proxy_logger.warning(
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"OpenAI Moderation: Content flagged for violations: %s",
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violation_details,
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)
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raise HTTPException(
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status_code=400,
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detail={
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"error": "Violated OpenAI moderation policy",
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"moderation_result": violation_details,
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},
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)
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@log_guardrail_information
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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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Apply OpenAI moderation guardrail using the unified guardrail interface.
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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 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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# Extract text to moderate from inputs
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text_to_moderate: Optional[str] = None
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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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# 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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if not text_to_moderate:
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verbose_proxy_logger.debug(
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"OpenAI Moderation: No text content to moderate in inputs"
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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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# Check if content is flagged and raise exception if needed
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self._check_moderation_result(moderation_response)
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# Moderation doesn't modify content, just blocks - return inputs unchanged
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return inputs
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@staticmethod
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def get_config_model() -> Optional[Type["GuardrailConfigModel"]]:
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"""
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Get the config model for the OpenAI Moderation guardrail.
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"""
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from litellm.types.proxy.guardrails.guardrail_hooks.openai.openai_moderation import (
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OpenAIModerationGuardrailConfigModel,
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)
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return OpenAIModerationGuardrailConfigModel
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