phoenix/litellm-mirror
0
0
Fork 1
mirror of https://github.com/BerriAI/litellm.git synced 2025-04-25 10:44:24 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

fix(router.py): reset caching correctly

Browse source
This commit is contained in:
Krrish Dholakia 2023-12-11 19:57:34 -08:00
parent 6cb4ef5659
commit 3e908bf507
6 changed files with 119 additions and 91 deletions
Download patch file Download diff file Expand all files Collapse all files

44
litellm/utils.py
View file

@ -964,7 +964,7 @@ class Logging:
end_time=end_time,
print_verbose=print_verbose,
)
if callback == "cache":
if callback == "cache" and litellm.cache is not None:
# this only logs streaming once, complete_streaming_response exists i.e when stream ends
print_verbose("success_callback: reaches cache for logging!")
kwargs = self.model_call_details
@ -1052,7 +1052,7 @@ class Logging:
start_time, end_time, result = self._success_handler_helper_fn(start_time=start_time, end_time=end_time, result=result)
for callback in litellm._async_success_callback:
try:
if callback == "cache":
if callback == "cache" and litellm.cache is not None:
# set_cache once complete streaming response is built
print_verbose("async success_callback: reaches cache for logging!")
kwargs = self.model_call_details
@ -1238,7 +1238,7 @@ class Logging:
print_verbose=print_verbose,
callback_func=callback
)
except:
except Exception as e:
print_verbose(
f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}"
)
@ -1649,6 +1649,19 @@ def client(original_function):
result._response_ms = (end_time - start_time).total_seconds() * 1000 # return response latency in ms like openai
return result
except Exception as e:
traceback_exception = traceback.format_exc()
crash_reporting(*args, **kwargs, exception=traceback_exception)
end_time = datetime.datetime.now()
if logging_obj:
try:
logging_obj.failure_handler(e, traceback_exception, start_time, end_time) # DO NOT MAKE THREADED - router retry fallback relies on this!
except Exception as e:
raise e
try:
await logging_obj.async_failure_handler(e, traceback_exception, start_time, end_time)
except Exception as e:
raise e
call_type = original_function.__name__
if call_type == CallTypes.acompletion.value:
num_retries = (
@ -1658,27 +1671,24 @@ def client(original_function):
)
litellm.num_retries = None # set retries to None to prevent infinite loops
context_window_fallback_dict = kwargs.get("context_window_fallback_dict", {})
if num_retries:
kwargs["num_retries"] = num_retries
kwargs["original_function"] = original_function
if (isinstance(e, openai.RateLimitError)): # rate limiting specific error
kwargs["retry_strategy"] = "exponential_backoff_retry"
elif (isinstance(e, openai.APIError)): # generic api error
kwargs["retry_strategy"] = "constant_retry"
return await litellm.acompletion_with_retries(*args, **kwargs)
try:
kwargs["num_retries"] = num_retries
kwargs["original_function"] = original_function
if (isinstance(e, openai.RateLimitError)): # rate limiting specific error
kwargs["retry_strategy"] = "exponential_backoff_retry"
elif (isinstance(e, openai.APIError)): # generic api error
kwargs["retry_strategy"] = "constant_retry"
return await litellm.acompletion_with_retries(*args, **kwargs)
except:
pass
elif isinstance(e, litellm.exceptions.ContextWindowExceededError) and context_window_fallback_dict and model in context_window_fallback_dict:
if len(args) > 0:
args[0] = context_window_fallback_dict[model]
else:
kwargs["model"] = context_window_fallback_dict[model]
return await original_function(*args, **kwargs)
traceback_exception = traceback.format_exc()
crash_reporting(*args, **kwargs, exception=traceback_exception)
end_time = datetime.datetime.now()
if logging_obj:
logging_obj.failure_handler(e, traceback_exception, start_time, end_time) # DO NOT MAKE THREADED - router retry fallback relies on this!
await logging_obj.async_failure_handler(e, traceback_exception, start_time, end_time)
raise e
is_coroutine = inspect.iscoroutinefunction(original_function)