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fix(openai.py-+-azure.py): fix linting issues

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Krrish Dholakia 2023-11-20 19:29:23 -08:00
parent 311b4f9d2c
commit 1306addfe8
4 changed files with 30 additions and 14 deletions
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2
litellm/exceptions.py
View file

@ -134,8 +134,6 @@ class APIResponseValidationError(APIResponseValidationError): # type: ignore
message=message
)
class OpenAIError(OpenAIError): # type: ignore
def __init__(self, original_exception):
self.status_code = original_exception.http_status

20
litellm/llms/azure.py
View file

@ -136,7 +136,10 @@ class AzureChatCompletion(BaseLLM):
elif "stream" in optional_params and optional_params["stream"] == True:
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, timeout=timeout)
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, http_client=litellm.client_session, timeout=timeout, max_retries=data.pop("max_retries", 2))
max_retries = data.pop("max_retries", 2)
if not isinstance(max_retries, int):
raise AzureOpenAIError(status_code=422, message="max retries must be an int")
azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token, http_client=litellm.client_session, timeout=timeout, max_retries=max_retries)
response = azure_client.chat.completions.create(**data) # type: ignore
return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response)
except AzureOpenAIError as e:
@ -156,7 +159,10 @@ class AzureChatCompletion(BaseLLM):
azure_ad_token: Optional[str]=None, ):
response = None
try:
azure_client = AsyncAzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token, http_client=litellm.aclient_session, timeout=timeout, max_retries=data.pop("max_retries", 2))
max_retries = data.pop("max_retries", 2)
if not isinstance(max_retries, int):
raise AzureOpenAIError(status_code=422, message="max retries must be an int")
azure_client = AsyncAzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token, http_client=litellm.aclient_session, timeout=timeout, max_retries=max_retries)
response = await azure_client.chat.completions.create(**data)
return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response)
except Exception as e:
@ -177,7 +183,10 @@ class AzureChatCompletion(BaseLLM):
timeout: Any,
azure_ad_token: Optional[str]=None,
):
azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token, http_client=litellm.client_session, timeout=timeout, max_retries=data.pop("max_retries", 2))
max_retries = data.pop("max_retries", 2)
if not isinstance(max_retries, int):
raise AzureOpenAIError(status_code=422, message="max retries must be an int")
azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token, http_client=litellm.client_session, timeout=timeout, max_retries=max_retries)
response = azure_client.chat.completions.create(**data)
streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="azure",logging_obj=logging_obj)
for transformed_chunk in streamwrapper:
@ -218,7 +227,10 @@ class AzureChatCompletion(BaseLLM):
"input": input,
**optional_params
}
azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token, http_client=litellm.client_session, max_retries=data.pop("max_retries", 2))
max_retries = data.pop("max_retries", 2)
if not isinstance(max_retries, int):
raise AzureOpenAIError(status_code=422, message="max retries must be an int")
azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token, http_client=litellm.client_session, max_retries=max_retries)
## LOGGING
logging_obj.pre_call(

10
litellm/llms/openai.py
View file

@ -209,7 +209,10 @@ class OpenAIChatCompletion(BaseLLM):
elif optional_params.get("stream", False):
return self.streaming(logging_obj=logging_obj, data=data, model=model, api_base=api_base, api_key=api_key, timeout=timeout)
else:
openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, timeout=timeout, max_retries=data.pop("max_retries", 2))
max_retries = data.pop("max_retries", 2)
if not isinstance(max_retries, int):
raise OpenAIError(status_code=422, message="max retries must be an int")
openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, timeout=timeout, max_retries=max_retries)
response = openai_client.chat.completions.create(**data) # type: ignore
logging_obj.post_call(
input=None,
@ -317,7 +320,10 @@ class OpenAIChatCompletion(BaseLLM):
"input": input,
**optional_params
}
openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, max_retries=data.pop("max_retries", 2))
max_retries = data.pop("max_retries", 2)
if not isinstance(max_retries, int):
raise OpenAIError(status_code=422, message="max retries must be an int")
openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, max_retries=max_retries)
## LOGGING
logging_obj.pre_call(

12
litellm/utils.py
View file

@ -1082,7 +1082,7 @@ class Rules:
if callable(rule):
decision = rule(input)
if decision is False:
raise litellm.APIResponseValidationError("LLM Response failed post-call-rule check", llm_provider=custom_llm_provider, model=model)
raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider=custom_llm_provider, model=model) # type: ignore
return True
def post_call_rules(self, input: str, model: str):
@ -1091,7 +1091,7 @@ class Rules:
if callable(rule):
decision = rule(input)
if decision is False:
raise litellm.APIResponseValidationError("LLM Response failed post-call-rule check", llm_provider=custom_llm_provider, model=model)
raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider=custom_llm_provider, model=model) # type: ignore
return True
####### CLIENT ###################
@ -3135,10 +3135,10 @@ def convert_to_model_response_object(response_object: Optional[dict]=None, model
choice_list.append(choice)
model_response_object.choices = choice_list
if "usage" in response_object:
model_response_object.usage.completion_tokens = response_object["usage"].get("completion_tokens", 0)
model_response_object.usage.prompt_tokens = response_object["usage"].get("prompt_tokens", 0)
model_response_object.usage.total_tokens = response_object["usage"].get("total_tokens", 0)
if "usage" in response_object and response_object["usage"] is not None:
model_response_object.usage.completion_tokens = response_object["usage"].get("completion_tokens", 0) # type: ignore
model_response_object.usage.prompt_tokens = response_object["usage"].get("prompt_tokens", 0) # type: ignore
model_response_object.usage.total_tokens = response_object["usage"].get("total_tokens", 0) # type: ignore
if "id" in response_object:
model_response_object.id = response_object["id"]