Litellm dev 01 20 2025 p1 (#7884)

* fix(initial-test-to-return-api-timeout-value-in-openai-timeout-exception): Makes it easier for user to debug why request timed out

* feat(openai.py): return timeout value + time taken on openai timeout errors

helps debug timeout errors

* fix(utils.py): fix num retries extraction logic when num_retries = 0

* fix(config_settings.md): litellm_logging.py

support printing payload to console if 'LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD' is true

 Enables easier debug

* test(test_auth_checks.py'): remove common checks userapikeyauth enforcement check

* fix(litellm_logging.py): fix linting error

litellm/llms/openai/openai.py

@@ -1,4 +1,5 @@
 import hashlib
+import time
 import types
 from typing import (
     Any,
@@ -390,12 +391,14 @@ class OpenAIChatCompletion(BaseLLM):
         - call chat.completions.create.with_raw_response when litellm.return_response_headers is True
         - call chat.completions.create by default
         """
+        start_time = time.time()
         try:
             raw_response = (
                 await openai_aclient.chat.completions.with_raw_response.create(
                     **data, timeout=timeout
                 )
             )
+            end_time = time.time()
 
             if hasattr(raw_response, "headers"):
                 headers = dict(raw_response.headers)
@@ -403,6 +406,11 @@ class OpenAIChatCompletion(BaseLLM):
                 headers = {}
             response = raw_response.parse()
             return headers, response
+        except openai.APITimeoutError as e:
+            end_time = time.time()
+            time_delta = round(end_time - start_time, 2)
+            e.message += f" - timeout value={timeout}, time taken={time_delta} seconds"
+            raise e
         except Exception as e:
             raise e
 
@@ -521,6 +529,7 @@ class OpenAIChatCompletion(BaseLLM):
             for _ in range(
                 2
             ):  # if call fails due to alternating messages, retry with reformatted message
+
                 if provider_config is not None:
                     data = provider_config.transform_request(
                         model=model,
@@ -725,6 +734,7 @@ class OpenAIChatCompletion(BaseLLM):
         for _ in range(
             2
         ):  # if call fails due to alternating messages, retry with reformatted message
+
             try:
                 openai_aclient: AsyncOpenAI = self._get_openai_client(  # type: ignore
                     is_async=True,
@@ -792,9 +802,10 @@ class OpenAIChatCompletion(BaseLLM):
                 error_headers = getattr(e, "headers", None)
                 if error_headers is None and exception_response:
                     error_headers = getattr(exception_response, "headers", None)
+                message = getattr(e, "message", str(e))
 
                 raise OpenAIError(
-                    status_code=status_code, message=str(e), headers=error_headers
+                    status_code=status_code, message=message, headers=error_headers
                 )
 
     def streaming(