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feat(completion()): adding num_retries

Browse source 
https://github.com/BerriAI/litellm/issues/728
This commit is contained in:
Krrish Dholakia 2023-10-31 19:14:46 -07:00
parent cedc756d2e
commit 125642563c
2 changed files with 92 additions and 70 deletions
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24
litellm/main.py
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@ -254,9 +254,10 @@ def completion(
metadata = kwargs.get('metadata', None)
fallbacks = kwargs.get('fallbacks', None)
headers = kwargs.get("headers", None)
num_retries = kwargs.get("num_retries", None)
######## end of unpacking kwargs ###########
openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key"]
litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list"]
litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries"]
default_params = openai_params + litellm_params
non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider
if mock_response:
@ -1325,9 +1326,19 @@ def completion(
return response
except Exception as e:
## Map to OpenAI Exception
raise exception_type(
model=model, custom_llm_provider=custom_llm_provider, original_exception=e, completion_kwargs=args,
)
try:
raise exception_type(
model=model, custom_llm_provider=custom_llm_provider, original_exception=e, completion_kwargs=args,
)
except Exception as e:
if num_retries:
if (isinstance(e, openai.APIError)
or isinstance(e, openai.Timeout)
or isinstance(e, openai.Timeout)
or isinstance(e, openai.ServiceUnavailableError)):
return completion_with_retries(num_retries=num_retries, **args)
else:
raise e
def completion_with_retries(*args, **kwargs):
@ -1338,8 +1349,9 @@ def completion_with_retries(*args, **kwargs):
import tenacity
except:
raise Exception("tenacity import failed please run `pip install tenacity`")
retryer = tenacity.Retrying(stop=tenacity.stop_after_attempt(3), reraise=True)
num_retries = kwargs.pop("num_retries", 3)
retryer = tenacity.Retrying(stop=tenacity.stop_after_attempt(num_retries), reraise=True)
return retryer(completion, *args, **kwargs)