better：进一步拆分模型配置

src/chat/emoji_system/emoji_manager.py

@@ -374,7 +374,7 @@ class EmojiManager:
 
         self.vlm = LLMRequest(model=global_config.model.vlm, temperature=0.3, max_tokens=1000, request_type="emoji")
         self.llm_emotion_judge = LLMRequest(
-            model=global_config.model.normal, max_tokens=600, request_type="emoji"
+            model=global_config.model.utils, max_tokens=600, request_type="emoji"
         )  # 更高的温度，更少的token（后续可以根据情绪来调整温度）
 
         self.emoji_num = 0

src/chat/focus_chat/expressors/default_expressor.py

@@ -192,9 +192,9 @@ class DefaultExpressor:
         """
         try:
             # 1. 获取情绪影响因子并调整模型温度
-            arousal_multiplier = mood_manager.get_arousal_multiplier()
-            current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier
-            self.express_model.params["temperature"] = current_temp  # 动态调整温度
+            # arousal_multiplier = mood_manager.get_arousal_multiplier()
+            # current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier
+            # self.express_model.params["temperature"] = current_temp  # 动态调整温度
 
             # 2. 获取信息捕捉器
             info_catcher = info_catcher_manager.get_info_catcher(thinking_id)

src/chat/focus_chat/info_processors/chattinginfo_processor.py

@@ -28,7 +28,7 @@ class ChattingInfoProcessor(BaseProcessor):
         super().__init__()
         # TODO: API-Adapter修改标记
         self.model_summary = LLMRequest(
-            model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
+            model=global_config.model.utils_small, temperature=0.7, max_tokens=300, request_type="chat_observation"
         )
 
     async def process_info(

src/chat/normal_chat/normal_chat_generator.py

@@ -20,13 +20,13 @@ class NormalChatGenerator:
             model=global_config.model.normal_chat_1,
             temperature=0.7,
             max_tokens=3000,
-            request_type="response_reasoning",
+            request_type="normal_chat_1",
         )
         self.model_normal = LLMRequest(
-            model=global_config.model.normal,
-            temperature=global_config.model.normal["temp"],
+            model=global_config.model.normal_chat_2,
+            temperature=global_config.model.normal_chat_2["temp"],
             max_tokens=256,
-            request_type="response_reasoning",
+            request_type="normal_chat_2",
         )
 
         self.model_sum = LLMRequest(

src/config/official_configs.py

@@ -377,12 +377,19 @@ class ModelConfig(ConfigBase):
     """模型配置类"""
 
     model_max_output_length: int = 800  # 最大回复长度
+    
+    
+    utils: dict[str, Any] = field(default_factory=lambda: {})
+    """组件模型配置"""
+    
+    utils_small: dict[str, Any] = field(default_factory=lambda: {})
+    """组件小模型配置"""
 
-    reasoning: dict[str, Any] = field(default_factory=lambda: {})
-    """推理模型配置"""
-
-    normal: dict[str, Any] = field(default_factory=lambda: {})
-    """普通模型配置"""
+    normal_chat_1: dict[str, Any] = field(default_factory=lambda: {})
+    """normal_chat首要回复模型模型配置"""
+    
+    normal_chat_2: dict[str, Any] = field(default_factory=lambda: {})
+    """normal_chat次要回复模型配置"""
 
     memory_summary: dict[str, Any] = field(default_factory=lambda: {})
     """记忆的概括模型配置"""
@@ -390,9 +397,6 @@ class ModelConfig(ConfigBase):
     vlm: dict[str, Any] = field(default_factory=lambda: {})
     """视觉语言模型配置"""
 
-    observation: dict[str, Any] = field(default_factory=lambda: {})
-    """观察模型配置"""
-
     focus_working_memory: dict[str, Any] = field(default_factory=lambda: {})
     """专注工作记忆模型配置"""
     

src/experimental/PFC/pfc.py

@@ -44,7 +44,7 @@ class GoalAnalyzer:
     def __init__(self, stream_id: str, private_name: str):
         # TODO: API-Adapter修改标记
         self.llm = LLMRequest(
-            model=global_config.model.normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
+            model=global_config.model.utils, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
         )
 
         self.personality_info = individuality.get_prompt(x_person=2, level=3)

src/experimental/PFC/pfc_KnowledgeFetcher.py

@@ -16,8 +16,8 @@ class KnowledgeFetcher:
     def __init__(self, private_name: str):
         # TODO: API-Adapter修改标记
         self.llm = LLMRequest(
-            model=global_config.model.normal,
-            temperature=global_config.model.normal["temp"],
+            model=global_config.model.utils,
+            temperature=global_config.model.utils["temp"],
             max_tokens=1000,
             request_type="knowledge_fetch",
         )

src/llm_models/utils_model.py

@@ -497,8 +497,8 @@ class LLMRequest:
                 logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
 
                 # 对全局配置进行更新
-                if global_config.model.normal.get("name") == old_model_name:
-                    global_config.model.normal["name"] = self.model_name
+                if global_config.model.normal_chat_2.get("name") == old_model_name:
+                    global_config.model.normal_chat_2["name"] = self.model_name
                     logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}")
                 if global_config.model.normal_chat_1.get("name") == old_model_name:
                     global_config.model.normal_chat_1["name"] = self.model_name

src/person_info/person_info.py

@@ -58,7 +58,7 @@ class PersonInfoManager:
         self.person_name_list = {}
         # TODO: API-Adapter修改标记
         self.qv_name_llm = LLMRequest(
-            model=global_config.model.normal,
+            model=global_config.model.utils,
             max_tokens=256,
             request_type="qv_name",
         )