feat: allow to pass "none" as reasoning effort

requirements.txt

@@ -1 +1 @@
-lightning_sdk >= 2025.09.11
+lightning_sdk >= 2025.09.16

src/litai/llm.py

@@ -269,7 +269,7 @@ def chat(  # noqa: D417
         stream: bool = False,
         tools: Optional[Sequence[Union[LitTool, "StructuredTool"]]] = None,
         auto_call_tools: bool = False,
-        reasoning_effort: Optional[Literal["low", "medium", "high"]] = None,
+        reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None,
         **kwargs: Any,
     ) -> str:
         """Sends a message to the LLM and retrieves a response.
@@ -290,13 +290,14 @@ def chat(  # noqa: D417
             categorized by conversation ID.
             full_response (bool): Whether the entire response should be returned from the chat.
             auto_call_tools (bool): Tools will be executed automatically whenever applicable. Defaults to False.
-            reasoning_effort (Optional[Literal["low", "medium", "high"]]): The level of reasoning effort for the model.
+            reasoning_effort (Optional[Literal["none", "low", "medium", "high"]]):
+                The level of reasoning effort for the model.
             **kwargs (Any): Additional keyword arguments
 
         Returns:
             str: The response from the LLM.
         """
-        if reasoning_effort is not None and reasoning_effort not in ["low", "medium", "high"]:
+        if reasoning_effort is not None and reasoning_effort not in ["none", "low", "medium", "high"]:
             raise ValueError("reasoning_effort must be 'low', 'medium', 'high', or None")
         self._wait_for_model()
         lit_tools = LitTool.convert_tools(tools)

tests/test_llm.py

@@ -119,6 +119,7 @@ def test_llm_chat(mock_llm_class):
         system_prompt="You are a helpful assistant.",
         metadata={"user_api": "123456"},
         my_kwarg="test-kwarg",
+        reasoning_effort="none",
     )
 
     assert isinstance(response, str)
@@ -134,7 +135,7 @@ def test_llm_chat(mock_llm_class):
         full_response=False,
         my_kwarg="test-kwarg",
         tools=None,
-        reasoning_effort=None,
+        reasoning_effort="none",
     )
     test_kwargs = mock_llm_instance.chat.call_args.kwargs
     assert test_kwargs.get("my_kwarg") == "test-kwarg"