mirror of
https://github.com/BerriAI/litellm.git
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rename llms/OpenAI/ -> llms/openai/ (#7154)
* 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
This commit is contained in:
Ishaan Jaff
GitHub
parent
e903fe6038
commit
bfb6891eb7
48 changed files with 53 additions and 59 deletions
314
litellm/llms/openai/completion/handler.py
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314
litellm/llms/openai/completion/handler.py
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@ -0,0 +1,314 @@
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import json
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from typing import Callable, List, Optional, Union
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from openai import AsyncOpenAI, OpenAI
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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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from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
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from litellm.llms.base import BaseLLM
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from litellm.types.llms.openai import AllMessageValues, OpenAITextCompletionUserMessage
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from litellm.types.utils import ModelResponse, TextCompletionResponse
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from ..common_utils import OpenAIError
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from .transformation import OpenAITextCompletionConfig
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class OpenAITextCompletion(BaseLLM):
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openai_text_completion_global_config = OpenAITextCompletionConfig()
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def __init__(self) -> None:
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super().__init__()
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def validate_environment(self, api_key):
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headers = {
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"content-type": "application/json",
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}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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return headers
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def completion(
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self,
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model_response: ModelResponse,
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api_key: str,
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model: str,
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messages: Union[List[AllMessageValues], List[OpenAITextCompletionUserMessage]],
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timeout: float,
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logging_obj: LiteLLMLoggingObj,
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optional_params: dict,
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print_verbose: Optional[Callable] = None,
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api_base: Optional[str] = None,
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acompletion: bool = False,
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litellm_params=None,
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logger_fn=None,
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client=None,
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organization: Optional[str] = None,
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headers: Optional[dict] = None,
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):
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try:
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if headers is None:
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headers = self.validate_environment(api_key=api_key)
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if model is None or messages is None:
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raise OpenAIError(status_code=422, message="Missing model or messages")
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# don't send max retries to the api, if set
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prompt = self.openai_text_completion_global_config._transform_prompt(
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messages
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)
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data = {"model": model, "prompt": prompt, **optional_params}
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max_retries = data.pop("max_retries", 2)
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## LOGGING
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logging_obj.pre_call(
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input=messages,
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api_key=api_key,
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additional_args={
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"headers": headers,
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"api_base": api_base,
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"complete_input_dict": data,
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},
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)
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if acompletion is True:
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if optional_params.get("stream", False):
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return self.async_streaming(
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logging_obj=logging_obj,
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api_base=api_base,
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api_key=api_key,
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data=data,
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headers=headers,
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model_response=model_response,
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model=model,
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timeout=timeout,
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max_retries=max_retries,
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client=client,
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organization=organization,
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)
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else:
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return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response, prompt=prompt, api_key=api_key, logging_obj=logging_obj, model=model, timeout=timeout, max_retries=max_retries, organization=organization, client=client) # type: ignore
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elif optional_params.get("stream", False):
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return self.streaming(
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logging_obj=logging_obj,
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api_base=api_base,
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api_key=api_key,
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data=data,
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headers=headers,
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model_response=model_response,
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model=model,
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timeout=timeout,
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max_retries=max_retries, # type: ignore
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client=client,
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organization=organization,
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)
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else:
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if client is None:
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openai_client = OpenAI(
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api_key=api_key,
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base_url=api_base,
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http_client=litellm.client_session,
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timeout=timeout,
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max_retries=max_retries, # type: ignore
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organization=organization,
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)
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else:
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openai_client = client
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raw_response = openai_client.completions.with_raw_response.create(**data) # type: ignore
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response = raw_response.parse()
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response_json = response.model_dump()
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## LOGGING
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logging_obj.post_call(
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input=prompt,
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api_key=api_key,
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original_response=response_json,
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additional_args={
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"headers": headers,
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"api_base": api_base,
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},
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)
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## RESPONSE OBJECT
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return TextCompletionResponse(**response_json)
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except Exception as e:
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status_code = getattr(e, "status_code", 500)
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error_headers = getattr(e, "headers", None)
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error_text = getattr(e, "text", str(e))
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error_response = getattr(e, "response", None)
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if error_headers is None and error_response:
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error_headers = getattr(error_response, "headers", None)
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raise OpenAIError(
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status_code=status_code, message=error_text, headers=error_headers
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)
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async def acompletion(
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self,
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logging_obj,
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api_base: str,
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data: dict,
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headers: dict,
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model_response: ModelResponse,
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prompt: str,
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api_key: str,
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model: str,
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timeout: float,
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max_retries: int,
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organization: Optional[str] = None,
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client=None,
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):
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try:
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if client is None:
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openai_aclient = AsyncOpenAI(
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api_key=api_key,
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base_url=api_base,
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http_client=litellm.aclient_session,
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timeout=timeout,
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max_retries=max_retries,
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organization=organization,
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)
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else:
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openai_aclient = client
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raw_response = await openai_aclient.completions.with_raw_response.create(
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**data
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)
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response = raw_response.parse()
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response_json = response.model_dump()
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## LOGGING
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logging_obj.post_call(
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input=prompt,
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api_key=api_key,
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original_response=response,
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additional_args={
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"headers": headers,
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"api_base": api_base,
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},
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)
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## RESPONSE OBJECT
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response_obj = TextCompletionResponse(**response_json)
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response_obj._hidden_params.original_response = json.dumps(response_json)
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return response_obj
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except Exception as e:
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status_code = getattr(e, "status_code", 500)
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error_headers = getattr(e, "headers", None)
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error_text = getattr(e, "text", str(e))
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error_response = getattr(e, "response", None)
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if error_headers is None and error_response:
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error_headers = getattr(error_response, "headers", None)
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raise OpenAIError(
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status_code=status_code, message=error_text, headers=error_headers
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)
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def streaming(
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self,
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logging_obj,
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api_key: str,
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data: dict,
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headers: dict,
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model_response: ModelResponse,
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model: str,
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timeout: float,
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api_base: Optional[str] = None,
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max_retries=None,
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client=None,
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organization=None,
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):
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if client is None:
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openai_client = OpenAI(
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api_key=api_key,
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base_url=api_base,
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http_client=litellm.client_session,
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timeout=timeout,
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max_retries=max_retries, # type: ignore
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organization=organization,
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)
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else:
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openai_client = client
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try:
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raw_response = openai_client.completions.with_raw_response.create(**data)
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response = raw_response.parse()
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except Exception as e:
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status_code = getattr(e, "status_code", 500)
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error_headers = getattr(e, "headers", None)
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error_text = getattr(e, "text", str(e))
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error_response = getattr(e, "response", None)
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if error_headers is None and error_response:
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error_headers = getattr(error_response, "headers", None)
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raise OpenAIError(
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status_code=status_code, message=error_text, headers=error_headers
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)
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streamwrapper = CustomStreamWrapper(
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completion_stream=response,
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model=model,
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custom_llm_provider="text-completion-openai",
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logging_obj=logging_obj,
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stream_options=data.get("stream_options", None),
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)
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try:
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for chunk in streamwrapper:
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yield chunk
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except Exception as e:
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status_code = getattr(e, "status_code", 500)
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error_headers = getattr(e, "headers", None)
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error_text = getattr(e, "text", str(e))
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error_response = getattr(e, "response", None)
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if error_headers is None and error_response:
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error_headers = getattr(error_response, "headers", None)
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raise OpenAIError(
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status_code=status_code, message=error_text, headers=error_headers
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)
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async def async_streaming(
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self,
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logging_obj,
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api_key: str,
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data: dict,
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headers: dict,
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model_response: ModelResponse,
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model: str,
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timeout: float,
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max_retries: int,
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api_base: Optional[str] = None,
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client=None,
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organization=None,
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):
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if client is None:
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openai_client = AsyncOpenAI(
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api_key=api_key,
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base_url=api_base,
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http_client=litellm.aclient_session,
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timeout=timeout,
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max_retries=max_retries,
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organization=organization,
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)
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else:
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openai_client = client
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raw_response = await openai_client.completions.with_raw_response.create(**data)
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response = raw_response.parse()
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streamwrapper = CustomStreamWrapper(
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completion_stream=response,
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model=model,
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custom_llm_provider="text-completion-openai",
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logging_obj=logging_obj,
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stream_options=data.get("stream_options", None),
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)
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try:
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async for transformed_chunk in streamwrapper:
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yield transformed_chunk
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except Exception as e:
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status_code = getattr(e, "status_code", 500)
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error_headers = getattr(e, "headers", None)
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error_text = getattr(e, "text", str(e))
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error_response = getattr(e, "response", None)
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if error_headers is None and error_response:
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error_headers = getattr(error_response, "headers", None)
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raise OpenAIError(
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status_code=status_code, message=error_text, headers=error_headers
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)
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