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fixes to embedding logging

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Krrish Dholakia 2023-08-19 21:17:17 -07:00
parent 2350865a47
commit 8d819daafa
1 changed files with 4 additions and 6 deletions
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10
litellm/main.py
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@ -676,9 +676,10 @@ def batch_completion(*args, **kwargs):
@timeout( # type: ignore @timeout( # type: ignore
60 60
) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout` ) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
def embedding(model, input=[], azure=False, force_timeout=60, logger_fn=None): def embedding(model, input=[], azure=False, force_timeout=60, litellm_call_id=None, logger_fn=None):
try: try:
response = None response = None
logging = Logging(model=model, messages=input, optional_params={}, litellm_params={"azure": azure, "force_timeout": force_timeout, "logger_fn": logger_fn, "litellm_call_id": litellm_call_id})
if azure == True: if azure == True:
# azure configs # azure configs
openai.api_type = "azure" openai.api_type = "azure"
@ -686,7 +687,7 @@ def embedding(model, input=[], azure=False, force_timeout=60, logger_fn=None):
openai.api_version = get_secret("AZURE_API_VERSION") openai.api_version = get_secret("AZURE_API_VERSION")
openai.api_key = get_secret("AZURE_API_KEY") openai.api_key = get_secret("AZURE_API_KEY")
## LOGGING ## LOGGING
logging.pre_call(model=model, input=input, azure=azure, logger_fn=logger_fn) logging.pre_call(input=input, api_key=openai.api_key, additional_args={"api_type": openai.api_type, "api_base": openai.api_base, "api_version": openai.api_version})
## EMBEDDING CALL ## EMBEDDING CALL
response = openai.Embedding.create(input=input, engine=model) response = openai.Embedding.create(input=input, engine=model)
print_verbose(f"response_value: {str(response)[:50]}") print_verbose(f"response_value: {str(response)[:50]}")
@ -696,19 +697,16 @@ def embedding(model, input=[], azure=False, force_timeout=60, logger_fn=None):
openai.api_version = None openai.api_version = None
openai.api_key = get_secret("OPENAI_API_KEY") openai.api_key = get_secret("OPENAI_API_KEY")
## LOGGING ## LOGGING
logging(model=model, input=input, azure=azure, logger_fn=logger_fn) logging.pre_call(input=input, api_key=openai.api_key, additional_args={"api_type": openai.api_type, "api_base": openai.api_base, "api_version": openai.api_version})
## EMBEDDING CALL ## EMBEDDING CALL
response = openai.Embedding.create(input=input, model=model) response = openai.Embedding.create(input=input, model=model)
print_verbose(f"response_value: {str(response)[:50]}") print_verbose(f"response_value: {str(response)[:50]}")
else: else:
logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
args = locals() args = locals()
raise ValueError(f"No valid embedding model args passed in - {args}") raise ValueError(f"No valid embedding model args passed in - {args}")
return response return response
except Exception as e: except Exception as e:
# log the original exception
logging(model=model, input=input, azure=azure, logger_fn=logger_fn, exception=e)
## Map to OpenAI Exception ## Map to OpenAI Exception
raise exception_type(model=model, original_exception=e, custom_llm_provider="azure" if azure==True else None) raise exception_type(model=model, original_exception=e, custom_llm_provider="azure" if azure==True else None)
raise e raise e