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bfb6891eb7
* rename OpenAI -> openai * fix file rename * fix rename changes * fix organization of openai/transcription * fix import OA fine tuning API * fix openai ft handler * fix handler import
117 lines
3.5 KiB
Python
117 lines
3.5 KiB
Python
"""Abstraction function for OpenAI's realtime API"""
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import os
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from typing import Any, Optional
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import litellm
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from litellm import get_llm_provider
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.router import GenericLiteLLMParams
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from ..litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
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from ..llms.azure.realtime.handler import AzureOpenAIRealtime
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from ..llms.openai.realtime.handler import OpenAIRealtime
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from ..utils import client as wrapper_client
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azure_realtime = AzureOpenAIRealtime()
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openai_realtime = OpenAIRealtime()
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@wrapper_client
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async def _arealtime(
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model: str,
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websocket: Any, # fastapi websocket
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api_base: Optional[str] = None,
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api_key: Optional[str] = None,
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api_version: Optional[str] = None,
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azure_ad_token: Optional[str] = None,
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client: Optional[Any] = None,
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timeout: Optional[float] = None,
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**kwargs,
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):
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"""
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Private function to handle the realtime API call.
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For PROXY use only.
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"""
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litellm_logging_obj: LiteLLMLogging = kwargs.get("litellm_logging_obj") # type: ignore
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litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
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proxy_server_request = kwargs.get("proxy_server_request", None)
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model_info = kwargs.get("model_info", None)
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metadata = kwargs.get("metadata", {})
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user = kwargs.get("user", None)
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litellm_params = GenericLiteLLMParams(**kwargs)
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model, _custom_llm_provider, dynamic_api_key, dynamic_api_base = get_llm_provider(
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model=model,
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api_base=api_base,
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api_key=api_key,
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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=user,
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optional_params={},
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litellm_params={
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"litellm_call_id": litellm_call_id,
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"proxy_server_request": proxy_server_request,
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"model_info": model_info,
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"metadata": metadata,
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"preset_cache_key": None,
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"stream_response": {},
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},
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custom_llm_provider=_custom_llm_provider,
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)
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if _custom_llm_provider == "azure":
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api_base = (
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dynamic_api_base
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or litellm_params.api_base
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or litellm.api_base
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or get_secret_str("AZURE_API_BASE")
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)
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# set API KEY
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api_key = (
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dynamic_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("AZURE_API_KEY")
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)
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await azure_realtime.async_realtime(
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model=model,
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websocket=websocket,
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api_base=api_base,
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api_key=api_key,
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api_version="2024-10-01-preview",
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azure_ad_token=None,
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client=None,
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timeout=timeout,
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logging_obj=litellm_logging_obj,
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)
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elif _custom_llm_provider == "openai":
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api_base = (
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dynamic_api_base
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or litellm_params.api_base
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or litellm.api_base
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or "https://api.openai.com/"
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)
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# set API KEY
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api_key = (
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dynamic_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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await openai_realtime.async_realtime(
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model=model,
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websocket=websocket,
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logging_obj=litellm_logging_obj,
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api_base=api_base,
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api_key=api_key,
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client=None,
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timeout=timeout,
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)
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else:
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raise ValueError(f"Unsupported model: {model}")
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