phoenix/litellm-mirror
0
0
Fork 1
mirror of https://github.com/BerriAI/litellm.git synced 2025-04-25 18:54:30 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

fix(azure.py): fix linting errors

Browse source
This commit is contained in:
Krrish Dholakia 2023-11-16 12:15:50 -08:00
parent 63104f4194
commit f99a161d98
2 changed files with 4 additions and 9 deletions
Download patch file Download diff file Expand all files Collapse all files

11
litellm/llms/azure.py
View file

@ -131,11 +131,11 @@ class AzureChatCompletion(BaseLLM):
) )
if acompletion is True: if acompletion is True:
if optional_params.get("stream", False): if optional_params.get("stream", False):
return self.async_streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model, api_key=api_key, api_version=api_version, azure_ad_token=azure_ad_token) return self.async_streaming(logging_obj=logging_obj, api_base=api_base, data=data, model=model, api_key=api_key, api_version=api_version, azure_ad_token=azure_ad_token)
else: else:
return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response, api_key=api_key, api_version=api_version, model=model, azure_ad_token=azure_ad_token) return self.acompletion(api_base=api_base, data=data, model_response=model_response, api_key=api_key, api_version=api_version, model=model, azure_ad_token=azure_ad_token)
elif "stream" in optional_params and optional_params["stream"] == True: elif "stream" in optional_params and optional_params["stream"] == True:
return self.streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model, api_key=api_key, api_version=api_version, azure_ad_token=azure_ad_token) return self.streaming(logging_obj=logging_obj, api_base=api_base, data=data, model=model, api_key=api_key, api_version=api_version, azure_ad_token=azure_ad_token)
else: else:
azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token) azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token)
response = azure_client.chat.completions.create(**data) # type: ignore response = azure_client.chat.completions.create(**data) # type: ignore
@ -152,7 +152,6 @@ class AzureChatCompletion(BaseLLM):
model: str, model: str,
api_base: str, api_base: str,
data: dict, data: dict,
headers: dict,
model_response: ModelResponse, model_response: ModelResponse,
azure_ad_token: Optional[str]=None, ): azure_ad_token: Optional[str]=None, ):
try: try:
@ -173,8 +172,6 @@ class AzureChatCompletion(BaseLLM):
api_key: str, api_key: str,
api_version: str, api_version: str,
data: dict, data: dict,
headers: dict,
model_response: ModelResponse,
model: str, model: str,
azure_ad_token: Optional[str]=None, azure_ad_token: Optional[str]=None,
): ):
@ -190,8 +187,6 @@ class AzureChatCompletion(BaseLLM):
api_key: str, api_key: str,
api_version: str, api_version: str,
data: dict, data: dict,
headers: dict,
model_response: ModelResponse,
model: str, model: str,
azure_ad_token: Optional[str]=None): azure_ad_token: Optional[str]=None):
azure_client = AsyncAzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token) azure_client = AsyncAzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token)

2
litellm/main.py
View file

@ -1819,7 +1819,7 @@ def embedding(
###### Text Completion ################ ###### Text Completion ################
def text_completion( def text_completion(
prompt: Union[str, List[Union[str, List[Union[str, List[int]]]]]], # Required: The prompt(s) to generate completions for. prompt: Union[str, List[Union[str, List[Union[str, List[int]]]]]], # Required: The prompt(s) to generate completions for.
model: Optional[str] = None, # Required: ID of the model to use. model: str, # Required: ID of the model to use.
best_of: Optional[int] = None, # Optional: Generates best_of completions server-side. best_of: Optional[int] = None, # Optional: Generates best_of completions server-side.
echo: Optional[bool] = None, # Optional: Echo back the prompt in addition to the completion. echo: Optional[bool] = None, # Optional: Echo back the prompt in addition to the completion.
frequency_penalty: Optional[float] = None, # Optional: Penalize new tokens based on their existing frequency. frequency_penalty: Optional[float] = None, # Optional: Penalize new tokens based on their existing frequency.