mirror of
https://github.com/BerriAI/litellm.git
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Merge branch 'BerriAI:main' into main
This commit is contained in:
Sunny Wan
GitHub
commit
f9a5109203
317 changed files with 15980 additions and 5207 deletions
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@ -1159,6 +1159,18 @@ def completion( # type: ignore # noqa: PLR0915
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prompt_id=prompt_id,
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prompt_variables=prompt_variables,
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ssl_verify=ssl_verify,
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merge_reasoning_content_in_choices=kwargs.get(
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"merge_reasoning_content_in_choices", None
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),
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api_version=api_version,
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azure_ad_token=kwargs.get("azure_ad_token"),
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tenant_id=kwargs.get("tenant_id"),
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client_id=kwargs.get("client_id"),
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client_secret=kwargs.get("client_secret"),
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azure_username=kwargs.get("azure_username"),
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azure_password=kwargs.get("azure_password"),
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max_retries=max_retries,
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timeout=timeout,
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)
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logging.update_environment_variables(
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model=model,
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@ -2271,23 +2283,22 @@ def completion( # type: ignore # noqa: PLR0915
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data = {"model": model, "messages": messages, **optional_params}
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## COMPLETION CALL
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response = openai_like_chat_completion.completion(
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response = base_llm_http_handler.completion(
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model=model,
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stream=stream,
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messages=messages,
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headers=headers,
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api_key=api_key,
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acompletion=acompletion,
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api_base=api_base,
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model_response=model_response,
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print_verbose=print_verbose,
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optional_params=optional_params,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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logging_obj=logging,
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acompletion=acompletion,
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timeout=timeout, # type: ignore
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custom_llm_provider="openrouter",
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custom_prompt_dict=custom_prompt_dict,
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timeout=timeout,
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headers=headers,
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encoding=encoding,
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api_key=api_key,
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logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
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client=client,
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)
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## LOGGING
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logging.post_call(
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@ -2853,6 +2864,7 @@ def completion( # type: ignore # noqa: PLR0915
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acompletion=acompletion,
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model_response=model_response,
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encoding=encoding,
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client=client,
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)
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if acompletion is True or optional_params.get("stream", False) is True:
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return generator
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@ -3380,6 +3392,7 @@ def embedding( # noqa: PLR0915
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}
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}
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)
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litellm_params_dict = get_litellm_params(**kwargs)
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logging: Logging = litellm_logging_obj # type: ignore
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@ -3441,6 +3454,7 @@ def embedding( # noqa: PLR0915
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aembedding=aembedding,
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max_retries=max_retries,
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headers=headers or extra_headers,
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litellm_params=litellm_params_dict,
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)
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elif (
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model in litellm.open_ai_embedding_models
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@ -3930,42 +3944,19 @@ async def atext_completion(
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ctx = contextvars.copy_context()
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func_with_context = partial(ctx.run, func)
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_, custom_llm_provider, _, _ = get_llm_provider(
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model=model, api_base=kwargs.get("api_base", None)
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)
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if (
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custom_llm_provider == "openai"
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or custom_llm_provider == "azure"
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or custom_llm_provider == "azure_text"
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or custom_llm_provider == "custom_openai"
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or custom_llm_provider == "anyscale"
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or custom_llm_provider == "mistral"
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or custom_llm_provider == "openrouter"
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or custom_llm_provider == "deepinfra"
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or custom_llm_provider == "perplexity"
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or custom_llm_provider == "groq"
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or custom_llm_provider == "nvidia_nim"
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or custom_llm_provider == "cerebras"
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or custom_llm_provider == "sambanova"
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or custom_llm_provider == "ai21_chat"
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or custom_llm_provider == "ai21"
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or custom_llm_provider == "volcengine"
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or custom_llm_provider == "text-completion-codestral"
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or custom_llm_provider == "deepseek"
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or custom_llm_provider == "text-completion-openai"
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or custom_llm_provider == "huggingface"
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or custom_llm_provider == "ollama"
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or custom_llm_provider == "vertex_ai"
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or custom_llm_provider in litellm.openai_compatible_providers
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): # currently implemented aiohttp calls for just azure and openai, soon all.
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# Await normally
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response = await loop.run_in_executor(None, func_with_context)
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if asyncio.iscoroutine(response):
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response = await response
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init_response = await loop.run_in_executor(None, func_with_context)
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if isinstance(init_response, dict) or isinstance(
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init_response, TextCompletionResponse
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): ## CACHING SCENARIO
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if isinstance(init_response, dict):
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response = TextCompletionResponse(**init_response)
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else:
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response = init_response
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elif asyncio.iscoroutine(init_response):
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response = await init_response
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else:
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# Call the synchronous function using run_in_executor
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response = await loop.run_in_executor(None, func_with_context)
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response = init_response # type: ignore
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if (
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kwargs.get("stream", False) is True
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or isinstance(response, TextCompletionStreamWrapper)
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@ -4554,6 +4545,7 @@ def image_generation( # noqa: PLR0915
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non_default_params = {
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k: v for k, v in kwargs.items() if k not in default_params
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} # model-specific params - pass them straight to the model/provider
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optional_params = get_optional_params_image_gen(
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model=model,
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n=n,
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@ -4565,6 +4557,9 @@ def image_generation( # noqa: PLR0915
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custom_llm_provider=custom_llm_provider,
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**non_default_params,
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)
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litellm_params_dict = get_litellm_params(**kwargs)
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logging: Logging = litellm_logging_obj
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logging.update_environment_variables(
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model=model,
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@ -4635,6 +4630,7 @@ def image_generation( # noqa: PLR0915
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aimg_generation=aimg_generation,
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client=client,
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headers=headers,
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litellm_params=litellm_params_dict,
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)
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elif (
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custom_llm_provider == "openai"
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@ -4663,6 +4659,7 @@ def image_generation( # noqa: PLR0915
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optional_params=optional_params,
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model_response=model_response,
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aimg_generation=aimg_generation,
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client=client,
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)
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elif custom_llm_provider == "vertex_ai":
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vertex_ai_project = (
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@ -5029,6 +5026,7 @@ def transcription(
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custom_llm_provider=custom_llm_provider,
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drop_params=drop_params,
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)
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litellm_params_dict = get_litellm_params(**kwargs)
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litellm_logging_obj.update_environment_variables(
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model=model,
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@ -5082,6 +5080,7 @@ def transcription(
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api_version=api_version,
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azure_ad_token=azure_ad_token,
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max_retries=max_retries,
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litellm_params=litellm_params_dict,
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)
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elif (
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custom_llm_provider == "openai"
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@ -5184,7 +5183,7 @@ async def aspeech(*args, **kwargs) -> HttpxBinaryResponseContent:
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@client
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def speech(
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def speech( # noqa: PLR0915
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model: str,
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input: str,
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voice: Optional[Union[str, dict]] = None,
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@ -5225,7 +5224,7 @@ def speech(
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if max_retries is None:
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max_retries = litellm.num_retries or openai.DEFAULT_MAX_RETRIES
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litellm_params_dict = get_litellm_params(**kwargs)
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logging_obj = kwargs.get("litellm_logging_obj", None)
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logging_obj.update_environment_variables(
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model=model,
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@ -5342,6 +5341,7 @@ def speech(
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timeout=timeout,
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client=client, # pass AsyncOpenAI, OpenAI client
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aspeech=aspeech,
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litellm_params=litellm_params_dict,
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)
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elif custom_llm_provider == "vertex_ai" or custom_llm_provider == "vertex_ai_beta":
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