mirror of
https://github.com/BerriAI/litellm.git
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12b173fdc5
* fix(utils.py): support dropping temperature param for azure o1 models * fix(main.py): handle azure o1 streaming requests o1 doesn't support streaming, fake it to ensure code works as expected * feat(utils.py): expose `hosted_vllm/` endpoint, with tool handling for vllm Fixes https://github.com/BerriAI/litellm/issues/6088 * refactor(internal_user_endpoints.py): cleanup unused params + update docstring Closes https://github.com/BerriAI/litellm/issues/6100 * fix(main.py): expose custom image generation api support Fixes https://github.com/BerriAI/litellm/issues/6097 * fix: fix linting errors * docs(custom_llm_server.md): add docs on custom api for image gen calls * fix(types/utils.py): handle dict type * fix(types/utils.py): fix linting errors
97 lines
2.7 KiB
Python
97 lines
2.7 KiB
Python
"""
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Handler file for calls to Azure OpenAI's o1 family of models
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Written separately to handle faking streaming for o1 models.
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"""
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import asyncio
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from typing import Any, Callable, List, Optional, Union
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from httpx._config import Timeout
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from litellm.litellm_core_utils.litellm_logging import Logging
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from litellm.llms.bedrock.chat.invoke_handler import MockResponseIterator
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from litellm.types.utils import ModelResponse
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from litellm.utils import CustomStreamWrapper
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from ..azure import AzureChatCompletion
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class AzureOpenAIO1ChatCompletion(AzureChatCompletion):
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async def mock_async_streaming(
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self,
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response: Any,
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model: Optional[str],
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logging_obj: Any,
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):
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model_response = await response
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completion_stream = MockResponseIterator(model_response=model_response)
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streaming_response = CustomStreamWrapper(
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completion_stream=completion_stream,
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model=model,
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custom_llm_provider="azure",
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logging_obj=logging_obj,
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)
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return streaming_response
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def completion(
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self,
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model: str,
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messages: List,
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model_response: ModelResponse,
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api_key: str,
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api_base: str,
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api_version: str,
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api_type: str,
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azure_ad_token: str,
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dynamic_params: bool,
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print_verbose: Callable[..., Any],
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timeout: Union[float, Timeout],
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logging_obj: Logging,
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optional_params,
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litellm_params,
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logger_fn,
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acompletion: bool = False,
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headers: Optional[dict] = None,
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client=None,
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):
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stream: Optional[bool] = optional_params.pop("stream", False)
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response = super().completion(
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model,
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messages,
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model_response,
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api_key,
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api_base,
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api_version,
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api_type,
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azure_ad_token,
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dynamic_params,
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print_verbose,
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timeout,
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logging_obj,
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optional_params,
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litellm_params,
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logger_fn,
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acompletion,
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headers,
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client,
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)
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if stream is True:
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if asyncio.iscoroutine(response):
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return self.mock_async_streaming(
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response=response, model=model, logging_obj=logging_obj # type: ignore
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)
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completion_stream = MockResponseIterator(model_response=response)
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streaming_response = CustomStreamWrapper(
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completion_stream=completion_stream,
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model=model,
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custom_llm_provider="openai",
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logging_obj=logging_obj,
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)
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return streaming_response
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else:
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return response
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