fix: omit None config values from Gemini GenerateContentConfig (#5528)

mem0/llms/gemini.py

@@ -157,12 +157,15 @@ class GeminiLLM(LLMBase):
         # Extract system instruction and reformat messages
         system_instruction, contents = self._reformat_messages(messages)
 
-        # Prepare generation config
-        config_params = {
-            "temperature": self.config.temperature,
-            "max_output_tokens": self.config.max_tokens,
-            "top_p": self.config.top_p,
-        }
+        # Prepare generation config — only include non-None values so the
+        # Gemini SDK uses its own defaults instead of rejecting None.
+        config_params = {}
+        if self.config.temperature is not None:
+            config_params["temperature"] = self.config.temperature
+        if self.config.max_tokens is not None:
+            config_params["max_output_tokens"] = self.config.max_tokens
+        if self.config.top_p is not None:
+            config_params["top_p"] = self.config.top_p
 
         # Add system instruction to config if present
         if system_instruction:

tests/llms/test_gemini.py

@@ -187,3 +187,45 @@ def test_parse_response_empty_parts_with_tools(mock_gemini_client: Mock):
 
     result = llm._parse_response(mock_response, tools=[{"function": {"name": "test"}}])
     assert result == {"content": None, "tool_calls": []}
+
+
+def test_none_config_values_omitted_from_generation_config(mock_gemini_client: Mock):
+    """When temperature/max_tokens/top_p are None, they must not be passed
+    to GenerateContentConfig (verified via model_fields_set)."""
+    config = BaseLlmConfig(model="gemini-2.0-flash", temperature=None, max_tokens=None, top_p=None)
+    llm = GeminiLLM(config)
+
+    mock_part = Mock(text="ok")
+    mock_content = Mock(parts=[mock_part])
+    mock_candidate = Mock(content=mock_content)
+    mock_response = Mock(candidates=[mock_candidate])
+    mock_gemini_client.models.generate_content.return_value = mock_response
+
+    llm.generate_response([{"role": "user", "content": "hi"}])
+
+    config_arg = mock_gemini_client.models.generate_content.call_args.kwargs["config"]
+    assert "temperature" not in config_arg.model_fields_set
+    assert "max_output_tokens" not in config_arg.model_fields_set
+    assert "top_p" not in config_arg.model_fields_set
+
+
+def test_explicit_config_values_passed_to_generation_config(mock_gemini_client: Mock):
+    """When temperature/max_tokens/top_p are explicitly set, they must appear."""
+    config = BaseLlmConfig(model="gemini-2.0-flash", temperature=0.5, max_tokens=200, top_p=0.9)
+    llm = GeminiLLM(config)
+
+    mock_part = Mock(text="ok")
+    mock_content = Mock(parts=[mock_part])
+    mock_candidate = Mock(content=mock_content)
+    mock_response = Mock(candidates=[mock_candidate])
+    mock_gemini_client.models.generate_content.return_value = mock_response
+
+    llm.generate_response([{"role": "user", "content": "hi"}])
+
+    config_arg = mock_gemini_client.models.generate_content.call_args.kwargs["config"]
+    assert "temperature" in config_arg.model_fields_set
+    assert config_arg.temperature == 0.5
+    assert "max_output_tokens" in config_arg.model_fields_set
+    assert config_arg.max_output_tokens == 200
+    assert "top_p" in config_arg.model_fields_set
+    assert config_arg.top_p == 0.9