mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-10 22:41:41 +00:00
refactor moderations api usage
This commit is contained in:
Ishaan Jaff
parent
5a9001f04c
commit
b076d3174c
4 changed files with 186 additions and 102 deletions
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@ -1052,6 +1052,7 @@ from .proxy.proxy_cli import run_server
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from .router import Router
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from .assistants.main import *
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from .batches.main import *
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from .moderations.main import *
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from .batch_completion.main import * # type: ignore
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from .rerank_api.main import *
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from .llms.anthropic.experimental_pass_through.messages.handler import *
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102
litellm/main.py
102
litellm/main.py
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@ -4432,108 +4432,6 @@ def adapter_completion(
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return translated_response
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##### Moderation #######################
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def moderation(
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input: str, model: Optional[str] = None, api_key: Optional[str] = None, **kwargs
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) -> OpenAIModerationResponse:
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# only supports open ai for now
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api_key = (
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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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openai_client = kwargs.get("client", None)
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if openai_client is None:
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openai_client = openai.OpenAI(
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api_key=api_key,
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)
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if model is not None:
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response = openai_client.moderations.create(input=input, model=model)
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else:
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response = openai_client.moderations.create(input=input)
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response_dict: Dict = response.model_dump()
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return litellm.utils.LiteLLMResponseObjectHandler.convert_to_moderation_response(
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response_object=response_dict,
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)
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@client
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async def amoderation(
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input: str,
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model: Optional[str] = None,
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api_key: Optional[str] = None,
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custom_llm_provider: Optional[str] = None,
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**kwargs,
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) -> OpenAIModerationResponse:
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from openai import AsyncOpenAI
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# only supports open ai for now
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api_key = (
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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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openai_client = kwargs.get("client", None)
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if openai_client is None or not isinstance(openai_client, AsyncOpenAI):
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# call helper to get OpenAI client
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# _get_openai_client maintains in-memory caching logic for OpenAI clients
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_openai_client: AsyncOpenAI = openai_chat_completions._get_openai_client( # type: ignore
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is_async=True,
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api_key=api_key,
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)
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else:
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_openai_client = openai_client
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optional_params = GenericLiteLLMParams(**kwargs)
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litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get(
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"litellm_logging_obj", None
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)
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try:
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(
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model,
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custom_llm_provider,
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_dynamic_api_key,
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_dynamic_api_base,
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) = litellm.get_llm_provider(
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model=model or "",
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custom_llm_provider=custom_llm_provider,
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api_base=optional_params.api_base,
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api_key=optional_params.api_key,
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)
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except litellm.BadRequestError:
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# `model` is optional field for moderation - get_llm_provider will throw BadRequestError if model is not set / not recognized
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pass
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# update litellm_logging_obj with environment variables
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custom_llm_provider = custom_llm_provider or litellm.LlmProviders.OPENAI.value
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if litellm_logging_obj is not None:
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litellm_logging_obj.update_environment_variables(
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model=model,
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user=kwargs.get("user", None),
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optional_params={},
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litellm_params={
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**kwargs,
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},
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custom_llm_provider=custom_llm_provider,
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)
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if model is not None:
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response = await _openai_client.moderations.create(input=input, model=model)
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else:
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response = await _openai_client.moderations.create(input=input)
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response_dict: Dict = response.model_dump()
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return litellm.utils.LiteLLMResponseObjectHandler.convert_to_moderation_response(
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response_object=response_dict,
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)
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##### Image Generation #######################
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@client
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async def aimage_generation(*args, **kwargs) -> ImageResponse:
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147
litellm/moderations/main.py
Normal file
147
litellm/moderations/main.py
Normal file
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@ -0,0 +1,147 @@
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from typing import Dict, Optional
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import openai
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import litellm
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############ Instantiated classes ############
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from litellm.main import openai_chat_completions as openai_api_client
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from litellm.moderations.utils import ModerationAPIUtils
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import OpenAIModerationResponse
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from litellm.types.router import GenericLiteLLMParams
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from litellm.utils import client
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############# Moderations API #######################
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@client
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def moderation(
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input: str,
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model: Optional[str] = None,
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api_key: Optional[str] = None,
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custom_llm_provider: Optional[str] = None,
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**kwargs,
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) -> OpenAIModerationResponse:
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# only supports open ai for now
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api_key = (
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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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optional_params = GenericLiteLLMParams(**kwargs)
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try:
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(
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model,
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custom_llm_provider,
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_dynamic_api_key,
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_dynamic_api_base,
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) = litellm.get_llm_provider(
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model=model or "",
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custom_llm_provider=custom_llm_provider,
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api_base=optional_params.api_base,
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api_key=optional_params.api_key,
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)
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except litellm.BadRequestError:
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# `model` is optional field for moderation - get_llm_provider will throw BadRequestError if model is not set / not recognized
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pass
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openai_client = kwargs.get("client", None)
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if openai_client is None:
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openai_client = openai.OpenAI(
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api_key=api_key,
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)
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# update litellm_logging_obj with request params (used for logging the correct values in the logging callbacks)
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ModerationAPIUtils.init_litellm_logging_obj_for_moderations_call(
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custom_llm_provider=custom_llm_provider,
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model=model,
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user=kwargs.get("user", None),
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**kwargs,
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)
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if model is not None:
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response = openai_client.moderations.create(input=input, model=model)
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else:
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response = openai_client.moderations.create(input=input)
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response_dict: Dict = response.model_dump()
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return litellm.utils.LiteLLMResponseObjectHandler.convert_to_moderation_response(
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response_object=response_dict,
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)
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@client
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async def amoderation(
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input: str,
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model: Optional[str] = None,
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api_key: Optional[str] = None,
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custom_llm_provider: Optional[str] = None,
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**kwargs,
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) -> OpenAIModerationResponse:
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from openai import AsyncOpenAI
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############################################################
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######### Pre-Request Setup #################################
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############################################################
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# only supports open ai for now
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api_key = (
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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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openai_client = kwargs.get("client", None)
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if openai_client is None or not isinstance(openai_client, AsyncOpenAI):
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# call helper to get OpenAI client
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# _get_openai_client maintains in-memory caching logic for OpenAI clients
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_openai_client: AsyncOpenAI = openai_api_client._get_openai_client( # type: ignore
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is_async=True,
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api_key=api_key,
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)
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else:
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_openai_client = openai_client
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optional_params = GenericLiteLLMParams(**kwargs)
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try:
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(
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model,
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custom_llm_provider,
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_dynamic_api_key,
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_dynamic_api_base,
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) = litellm.get_llm_provider(
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model=model or "",
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custom_llm_provider=custom_llm_provider,
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api_base=optional_params.api_base,
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api_key=optional_params.api_key,
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)
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except litellm.BadRequestError:
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# `model` is optional field for moderation - get_llm_provider will throw BadRequestError if model is not set / not recognized
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pass
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# update litellm_logging_obj with request params (used for logging the correct values in the logging callbacks)
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ModerationAPIUtils.init_litellm_logging_obj_for_moderations_call(
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custom_llm_provider=custom_llm_provider,
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model=model,
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user=kwargs.get("user", None),
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**kwargs,
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)
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############################################################
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######### Make API Call #################################
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############################################################
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if model is not None:
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response = await _openai_client.moderations.create(input=input, model=model)
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else:
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response = await _openai_client.moderations.create(input=input)
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############################################################
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######### Post-Request Processing #################################
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############################################################
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response_dict: Dict = response.model_dump()
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return litellm.utils.LiteLLMResponseObjectHandler.convert_to_moderation_response(
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response_object=response_dict,
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)
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38
litellm/moderations/utils.py
Normal file
38
litellm/moderations/utils.py
Normal file
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@ -0,0 +1,38 @@
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from typing import Optional
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import litellm
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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class ModerationAPIUtils:
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@staticmethod
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def init_litellm_logging_obj_for_moderations_call(
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custom_llm_provider: Optional[str] = None,
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model: Optional[str] = None,
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user: Optional[str] = None,
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**kwargs,
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):
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"""
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Initialize the litellm_logging_obj for a moderations call
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Ensures the correct `custom_llm_provider`, model, and user are set in the litellm_logging_obj
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This will be used downstream when constructing the standard_logging_payload
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"""
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litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get(
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"litellm_logging_obj", None
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)
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if litellm_logging_obj:
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custom_llm_provider = (
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custom_llm_provider or litellm.LlmProviders.OPENAI.value
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)
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litellm_logging_obj.update_environment_variables(
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model=model,
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user=kwargs.get("user", None),
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optional_params={},
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litellm_params={
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**kwargs,
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},
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custom_llm_provider=custom_llm_provider,
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)
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