fix: do not default to none

src/litai/llm.py

@@ -41,8 +41,6 @@
     "google/gemini-2.5-flash",
 }
 
-# this nmodels support reasoning_effort='none'
-NONE_REASONING_MODELS = ["google/gemini-2.0-flash", "google/gemini-2.5-flash-lite-preview-06-17"]
 
 logger = logging.getLogger(__name__)
 
@@ -301,10 +299,6 @@ def chat(  # noqa: D417
         """
         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")
-        if reasoning_effort is None and (
-            model in NONE_REASONING_MODELS or (self._model in NONE_REASONING_MODELS and model is None)
-        ):
-            reasoning_effort = "none"
 
         self._wait_for_model()
         lit_tools = LitTool.convert_tools(tools)

tests/test_llm.py

@@ -144,49 +144,6 @@ def test_llm_chat(mock_llm_class):
     mock_llm_instance.reset_conversation.assert_called_once()
 
 
-@patch("litai.llm.SDKLLM")
-def test_reasoning_effort_override(mock_llm_class):
-    """Test LigtningLLM chat."""
-    from litai.llm import LLM as LLMCLIENT
-
-    LLMCLIENT._sdkllm_cache.clear()
-    mock_llm_instance = MagicMock()
-    mock_llm_instance.chat.return_value = "Hello! I am a helpful assistant."
-
-    mock_llm_class.return_value = mock_llm_instance
-
-    llm = LLM(model="google/gemini-2.0-flash")
-
-    response = llm.chat(
-        "Hello, who are you?",
-        system_prompt="You are a helpful assistant.",
-        metadata={"user_api": "123456"},
-        my_kwarg="test-kwarg",
-        reasoning_effort=None,
-    )
-
-    assert isinstance(response, str)
-    assert "helpful" in response.lower()
-    mock_llm_instance.chat.assert_called_once_with(
-        prompt="Hello, who are you?",
-        system_prompt="You are a helpful assistant.",
-        max_completion_tokens=None,
-        images=None,
-        conversation=None,
-        metadata={"user_api": "123456"},
-        stream=False,
-        full_response=False,
-        my_kwarg="test-kwarg",
-        tools=None,
-        reasoning_effort="none",
-    )
-    test_kwargs = mock_llm_instance.chat.call_args.kwargs
-    assert test_kwargs.get("my_kwarg") == "test-kwarg"
-
-    llm.reset_conversation("test")
-    mock_llm_instance.reset_conversation.assert_called_once()
-
-
 def test_model_override(monkeypatch):
     """Test override model logic when main model fails."""
     mock_llm = MagicMock()