fix: do not use mutable defaults in litellm/main.py

litellm/main.py

@@ -313,7 +313,7 @@ class AsyncCompletions:
 async def acompletion(
     model: str,
     # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
-    messages: List = [],
+    messages: Optional[List] = None,
     functions: Optional[List] = None,
     function_call: Optional[str] = None,
     timeout: Optional[Union[float, int]] = None,
@@ -395,6 +395,7 @@ async def acompletion(
         - The `completion` function is called using `run_in_executor` to execute synchronously in the event loop.
         - If `stream` is True, the function returns an async generator that yields completion lines.
     """
+    messages = messages or []
     fallbacks = kwargs.get("fallbacks", None)
     mock_timeout = kwargs.get("mock_timeout", None)
 
@@ -530,18 +531,14 @@ def _handle_mock_potential_exceptions(
         raise litellm.MockException(
             status_code=getattr(mock_response, "status_code", 500),  # type: ignore
             message=getattr(mock_response, "text", str(mock_response)),
-            llm_provider=getattr(
+            llm_provider=getattr(mock_response, "llm_provider", custom_llm_provider or "openai"),  # type: ignore
-                mock_response, "llm_provider", custom_llm_provider or "openai"
-            ),  # type: ignore
             model=model,  # type: ignore
             request=httpx.Request(method="POST", url="https://api.openai.com/v1/"),
         )
     elif isinstance(mock_response, str) and mock_response == "litellm.RateLimitError":
         raise litellm.RateLimitError(
             message="this is a mock rate limit error",
-            llm_provider=getattr(
+            llm_provider=getattr(mock_response, "llm_provider", custom_llm_provider or "openai"),  # type: ignore
-                mock_response, "llm_provider", custom_llm_provider or "openai"
-            ),  # type: ignore
             model=model,
         )
     elif (
@@ -550,9 +547,7 @@ def _handle_mock_potential_exceptions(
     ):
         raise litellm.ContextWindowExceededError(
             message="this is a mock context window exceeded error",
-            llm_provider=getattr(
+            llm_provider=getattr(mock_response, "llm_provider", custom_llm_provider or "openai"),  # type: ignore
-                mock_response, "llm_provider", custom_llm_provider or "openai"
-            ),  # type: ignore
             model=model,
         )
     elif (
@@ -561,9 +556,7 @@ def _handle_mock_potential_exceptions(
     ):
         raise litellm.InternalServerError(
             message="this is a mock internal server error",
-            llm_provider=getattr(
+            llm_provider=getattr(mock_response, "llm_provider", custom_llm_provider or "openai"),  # type: ignore
-                mock_response, "llm_provider", custom_llm_provider or "openai"
-            ),  # type: ignore
             model=model,
         )
     elif isinstance(mock_response, str) and mock_response.startswith(
@@ -778,7 +771,7 @@ def mock_completion(
 def completion(  # type: ignore # noqa: PLR0915
     model: str,
     # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
-    messages: List = [],
+    messages: Optional[List] = None,
     timeout: Optional[Union[float, str, httpx.Timeout]] = None,
     temperature: Optional[float] = None,
     top_p: Optional[float] = None,
@@ -863,6 +856,7 @@ def completion(  # type: ignore # noqa: PLR0915
         - It supports various optional parameters for customizing the completion behavior.
         - If 'mock_response' is provided, a mock completion response is returned for testing or debugging.
     """
+    messages = messages or []
     ### VALIDATE Request ###
     if model is None:
         raise ValueError("model param not passed in.")
@@ -945,7 +939,7 @@ def completion(  # type: ignore # noqa: PLR0915
     prompt_variables = cast(Optional[dict], kwargs.get("prompt_variables", None))
     ### COPY MESSAGES ### - related issue https://github.com/BerriAI/litellm/discussions/4489
     messages = get_completion_messages(
-        messages=messages,
+        messages=messages or [],
         ensure_alternating_roles=ensure_alternating_roles or False,
         user_continue_message=user_continue_message,
         assistant_continue_message=assistant_continue_message,
@@ -1206,7 +1200,7 @@ def completion(  # type: ignore # noqa: PLR0915
             kwargs.pop("mock_timeout", None)  # remove for any fallbacks triggered
             return mock_completion(
                 model,
-                messages,
+                messages or [],
                 stream=stream,
                 n=n,
                 mock_response=mock_response,
@@ -1218,7 +1212,8 @@ def completion(  # type: ignore # noqa: PLR0915
                 mock_timeout=mock_timeout,
                 timeout=timeout,
             )
-
+        if messages is None:
+            messages = []
         if custom_llm_provider == "azure":
             # azure configs
             ## check dynamic params ##
@@ -3283,7 +3278,7 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse:
 @client
 def embedding(  # noqa: PLR0915
     model,
-    input=[],
+    input: Optional[List] = None,
     # Optional params
     dimensions: Optional[int] = None,
     encoding_format: Optional[str] = None,
@@ -3325,6 +3320,7 @@ def embedding(  # noqa: PLR0915
     Raises:
     - exception_type: If an exception occurs during the API call.
     """
+    input = input or []
     azure = kwargs.get("azure", None)
     client = kwargs.pop("client", None)
     max_retries = kwargs.get("max_retries", None)
@@ -3520,12 +3516,7 @@ def embedding(  # noqa: PLR0915
             api_base = api_base or litellm.api_base or get_secret("DATABRICKS_API_BASE")  # type: ignore
 
             # set API KEY
-            api_key = (
+            api_key = api_key or litellm.api_key or litellm.databricks_key or get_secret("DATABRICKS_API_KEY")  # type: ignore
-                api_key
-                or litellm.api_key
-                or litellm.databricks_key
-                or get_secret("DATABRICKS_API_KEY")
-            )  # type: ignore
 
             ## EMBEDDING CALL
             response = databricks_embedding.embedding(
@@ -3599,12 +3590,7 @@ def embedding(  # noqa: PLR0915
                 client=client,
             )
         elif custom_llm_provider == "huggingface":
-            api_key = (
+            api_key = api_key or litellm.huggingface_key or get_secret("HUGGINGFACE_API_KEY") or litellm.api_key  # type: ignore
-                api_key
-                or litellm.huggingface_key
-                or get_secret("HUGGINGFACE_API_KEY")
-                or litellm.api_key
-            )  # type: ignore
             response = huggingface_embed.embedding(
                 model=model,
                 input=input,
@@ -3765,12 +3751,7 @@ def embedding(  # noqa: PLR0915
                 api_key=api_key,
             )
         elif custom_llm_provider == "ollama":
-            api_base = (
+            api_base = litellm.api_base or api_base or get_secret_str("OLLAMA_API_BASE") or "http://localhost:11434"  # type: ignore
-                litellm.api_base
-                or api_base
-                or get_secret_str("OLLAMA_API_BASE")
-                or "http://localhost:11434"
-            )  # type: ignore
 
             if isinstance(input, str):
                 input = [input]
@@ -5146,12 +5127,7 @@ def transcription(
         custom_llm_provider == "openai"
         or custom_llm_provider in litellm.openai_compatible_providers
     ):
-        api_base = (
+        api_base = api_base or litellm.api_base or get_secret("OPENAI_API_BASE") or "https://api.openai.com/v1"  # type: ignore
-            api_base
-            or litellm.api_base
-            or get_secret("OPENAI_API_BASE")
-            or "https://api.openai.com/v1"
-        )  # type: ignore
         openai.organization = (
             litellm.organization
             or get_secret("OPENAI_ORGANIZATION")