Improve configuration handling with better logging and preset override clarity

app_with_mcp.py

@@ -191,8 +191,9 @@ async def minion_respond_async(query: str, preset_model: str, api_type: str,
                              use_tools: bool):
     """Respond to query using specified configuration with optional MCP tools"""
     
-    # If a preset model is selected, use preset configuration as base but allow overrides
+    # Build configuration based on preset or custom settings
     if preset_model != "Custom":
+        # Use preset as base, but allow UI overrides for temperature and max_tokens
         config_obj = preset_configs.get(preset_model, default_config)
         llm_config_dict = {
             'api_type': config_obj.api_type,
@@ -200,21 +201,31 @@ async def minion_respond_async(query: str, preset_model: str, api_type: str,
             'base_url': config_obj.base_url,
             'api_version': config_obj.api_version,
             'model': config_obj.model,
-            'temperature': temperature,  # Use UI value, not preset value
-            'max_tokens': max_tokens,    # Use UI value, not preset value
+            'temperature': temperature,  # Always use UI value for dynamic parameters
+            'max_tokens': max_tokens,    # Always use UI value for dynamic parameters
             'vision_enabled': config_obj.vision_enabled
         }
-        print(f"Using preset '{preset_model}' with temperature={temperature}, max_tokens={max_tokens}")
+        print(f"🔧 Using preset '{preset_model}' with overrides:")
+        print(f"   - Temperature: {temperature} (preset: {config_obj.temperature})")
+        print(f"   - Max tokens: {max_tokens} (preset: {config_obj.max_tokens})")
+        print(f"   - Model: {config_obj.model}")
+        print(f"   - API Type: {config_obj.api_type}")
     else:
-        # Use custom configuration
+        # Use completely custom configuration
         llm_config_dict = create_custom_llm_config(
             api_type, api_key, base_url, api_version, model, temperature, max_tokens
         )
-        print(f"Using custom config with temperature={temperature}, max_tokens={max_tokens}")
+        print(f"🔧 Using custom configuration:")
+        print(f"   - Temperature: {temperature}")
+        print(f"   - Max tokens: {max_tokens}")
+        print(f"   - Model: {model}")
+        print(f"   - API Type: {api_type}")
     
     # Always rebuild brain with current configuration
+    print(f"🧠 Building brain with final config:")
+    print(f"   - Final temperature: {llm_config_dict['temperature']}")
+    print(f"   - Final max_tokens: {llm_config_dict['max_tokens']}")
     brain = build_brain_with_config(llm_config_dict)
-    print(f"Brain rebuilt with config: api_type={llm_config_dict['api_type']}, model={llm_config_dict['model']}")
     
     # Handle empty route selection for auto route
     route_param = route if route else None
@@ -231,10 +242,11 @@ async def minion_respond_async(query: str, preset_model: str, api_type: str,
         try:
             tools = await get_available_tools()
             kwargs['tools'] = tools
-            print(f"Using {len(tools)} tools: {[tool.name for tool in tools]}")
+            print(f"🔧 Using {len(tools)} tools: {[tool.name for tool in tools]}")
         except Exception as e:
-            print(f"Warning: Failed to get tools: {e}")
+            print(f"⚠️ Warning: Failed to get tools: {e}")
     
+    print(f"🚀 Executing brain.step with route='{route_param}', check={check_enabled}")
     obs, score, *_ = await brain.step(**kwargs)
     return obs