06906534b3
* 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
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
# +-------------------------------------------------------------+
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#
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# Use OpenAI /moderations for your LLM calls
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#
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# +-------------------------------------------------------------+
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# Thank you users! We ❤️ you! - Krrish & Ishaan
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import os
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import sys
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import sys
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from fastapi import HTTPException
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import litellm
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from litellm._logging import verbose_proxy_logger
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.proxy._types import UserAPIKeyAuth
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from litellm.types.utils import CallTypesLiteral
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class _ENTERPRISE_OpenAI_Moderation(CustomLogger):
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def __init__(self):
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self.model_name = (
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litellm.openai_moderations_model_name or "text-moderation-latest"
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) # pass the model_name you initialized on litellm.Router()
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pass
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#### CALL HOOKS - proxy only ####
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async def async_moderation_hook(
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self,
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data: dict,
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user_api_key_dict: UserAPIKeyAuth,
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call_type: CallTypesLiteral,
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):
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text = ""
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if "messages" in data and isinstance(data["messages"], list):
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for m in data["messages"]: # assume messages is a list
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if "content" in m and isinstance(m["content"], str):
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text += m["content"]
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from litellm.proxy.proxy_server import llm_router
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if llm_router is None:
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return
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moderation_response = await llm_router.amoderation(
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model=self.model_name, input=text
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)
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verbose_proxy_logger.debug("Moderation response: %s", moderation_response)
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if moderation_response and moderation_response.results[0].flagged is True:
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raise HTTPException(
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status_code=403, detail={"error": "Violated content safety policy"}
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)
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pass
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