合并

src/experimental/PFC/action_handlers.py

@@ -142,7 +142,7 @@ class ActionHandler(ABC):
         )
 
         # 限制历史记录长度
-        max_history_len = getattr(global_config, "pfc_max_chat_history_for_checker", 50)
+        max_history_len = global_config.pfc.pfc_max_chat_history_for_checker
         if len(observation_info.chat_history) > max_history_len:
             observation_info.chat_history = observation_info.chat_history[-max_history_len:]
             observation_info.chat_history_count = len(observation_info.chat_history)
@@ -616,7 +616,7 @@ class DirectReplyHandler(BaseTextReplyHandler):
         action_successful = False  # 整体动作是否成功
         final_status = "recall"  # 默认最终状态
         final_reason = "直接回复动作未成功执行"  # 默认最终原因
-        max_reply_attempts: int = getattr(global_config, "pfc_max_reply_attempts", 3)
+        max_reply_attempts: int = global_config.pfc.pfc_max_reply_attempts
 
         (
             sent_text_successfully,
@@ -697,7 +697,7 @@ class SendNewMessageHandler(BaseTextReplyHandler):
         action_successful = False  # 整体动作是否成功
         final_status = "recall"  # 默认最终状态
         final_reason = "发送新消息动作未成功执行"  # 默认最终原因
-        max_reply_attempts: int = getattr(global_config, "pfc_max_reply_attempts", 3)
+        max_reply_attempts: int = global_config.pfc.pfc_max_reply_attempts
 
         (
             sent_text_successfully,

src/experimental/PFC/action_planner.py

@@ -159,14 +159,10 @@ class ActionPlanner:
         self.private_name = private_name
         # 初始化 LLM 请求对象
         try:
-            llm_config = global_config.model.pfc_action_planner
-            if not isinstance(llm_config, dict):
-                raise TypeError(f"LLM config 'pfc_action_planner' is not a dictionary: {llm_config}")
-
             self.llm = LLMRequest(
-                model=llm_config,
+                model=global_config.model.pfc_action_planner,
-                temperature=llm_config.get("temp", 0.7),
+                temperature=global_config.model.pfc_action_planner["temp"],
-                max_tokens=1500,
+                max_tokens=global_config.model.pfc_action_planner["max_tokens"],
                 request_type="action_planning",
             )
         except TypeError as e:

src/experimental/PFC/conversation_loop.py

@@ -45,10 +45,9 @@ async def run_conversation_loop(conversation_instance: "Conversation"):
         try:
             global TIME_ZONE
             if TIME_ZONE is None:
-                configured_tz_loop = getattr(global_config, "TIME_ZONE", "Asia/Shanghai")
+                TIME_ZONE = global_config.schedule.time_zone
-                TIME_ZONE = tz.gettz(configured_tz_loop)
                 if TIME_ZONE is None:
-                    logger.error(f"循环中: 配置的时区 '{configured_tz_loop}' 无效，将使用 'Asia/Shanghai'")
+                    logger.error(f"循环中: 配置的时区 '{global_config.schedule.time_zone}' 无效，将使用 'Asia/Shanghai'")
                     TIME_ZONE = tz.gettz("Asia/Shanghai")
 
             current_time_dt = datetime.datetime.now(TIME_ZONE)

src/experimental/PFC/pfc_emotion.py

@@ -22,7 +22,7 @@ class PfcEmotionUpdater:
         self.mood_mng = mood_manager
 
         # LLM 实例 (根据 global_config.model.summary 配置)
-        llm_config_summary = getattr(global_config, "llm_summary", None)
+        llm_config_summary = global_config.model.summary
         if llm_config_summary and isinstance(llm_config_summary, dict):
             logger.debug(f"[私聊][{self.private_name}] 使用 llm_summary 配置初始化情绪判断LLM。")
             self.llm = LLMRequest(
@@ -37,8 +37,8 @@ class PfcEmotionUpdater:
             logger.error(f"[私聊][{self.private_name}] 未找到 llm_summary 配置或配置无效！情绪判断功能将受限。")
             self.llm = None  # LLM 未初始化
 
-        self.EMOTION_UPDATE_INTENSITY = getattr(global_config, "pfc_emotion_update_intensity", 0.6)
+        self.EMOTION_UPDATE_INTENSITY = global_config.pfc.pfc_emotion_update_intensity
-        self.EMOTION_HISTORY_COUNT = getattr(global_config, "pfc_emotion_history_count", 5)
+        self.EMOTION_HISTORY_COUNT = global_config.pfc.pfc_emotion_history_count
 
     async def update_emotion_based_on_context(
         self,

src/experimental/PFC/pfc_relationship.py

@@ -30,20 +30,20 @@ class PfcRelationshipUpdater:
 
         # LLM 实例 (为关系评估创建一个新的)
         # 尝试读取 llm_PFC_relationship_eval 配置，如果不存在则回退
-        llm_config_rel_eval = getattr(global_config, "llm_PFC_relationship_eval", None)
+        llm_config_rel_eval = global_config.model.PFC_relationship_eval
         if llm_config_rel_eval and isinstance(llm_config_rel_eval, dict):
             logger.info(f"[私聊][{self.private_name}] 使用 llm_PFC_relationship_eval 配置初始化关系评估LLM。")
             self.llm = LLMRequest(
-                model=llm_config_rel_eval,
+                model=global_config.model.PFC_relationship_eval,
-                temperature=llm_config_rel_eval.get("temp", 0.5),  # 判断任务通常用较低温度
+                temperature=global_config.model.PFC_relationship_eval["temp"],
-                max_tokens=llm_config_rel_eval.get("max_tokens", 512),
+                max_tokens=global_config.model.PFC_relationship_eval["max_tokens"],
                 request_type="pfc_relationship_evaluation",
             )
         else:
             logger.warning(
                 f"[私聊][{self.private_name}] 未找到 llm_PFC_relationship_eval 配置或配置无效，将回退使用 llm_PFC_action_planner 的配置。"
             )
-            llm_config_action_planner = getattr(global_config, "llm_PFC_action_planner", None)
+            llm_config_action_planner = global_config.model.pfc_action_planner
             if llm_config_action_planner and isinstance(llm_config_action_planner, dict):
                 self.llm = LLMRequest(
                     model=llm_config_action_planner,  # 使用 action_planner 的模型配置
@@ -56,14 +56,14 @@ class PfcRelationshipUpdater:
                 self.llm = None  # LLM 未初始化
 
         # 从 global_config 读取参数，若无则使用默认值
-        self.REL_INCREMENTAL_INTERVAL = getattr(global_config, "pfc_relationship_incremental_interval", 10)
+        self.REL_INCREMENTAL_INTERVAL = global_config.pfc.pfc_relationship_incremental_interval
-        self.REL_INCREMENTAL_MSG_COUNT = getattr(global_config, "pfc_relationship_incremental_msg_count", 10)
+        self.REL_INCREMENTAL_MSG_COUNT = global_config.pfc.pfc_relationship_incremental_msg_count
-        self.REL_INCREMENTAL_DEFAULT_CHANGE = getattr(global_config, "pfc_relationship_incremental_default_change", 1.0)
+        self.REL_INCREMENTAL_DEFAULT_CHANGE = global_config.pfc.pfc_relationship_incremental_default_change
-        self.REL_INCREMENTAL_MAX_CHANGE = getattr(global_config, "pfc_relationship_incremental_max_change", 5.0)
+        self.REL_INCREMENTAL_MAX_CHANGE = global_config.pfc.pfc_relationship_incremental_max_change
 
-        self.REL_FINAL_MSG_COUNT = getattr(global_config, "pfc_relationship_final_msg_count", 30)
+        self.REL_FINAL_MSG_COUNT = global_config.pfc.pfc_relationship_final_msg_count
-        self.REL_FINAL_DEFAULT_CHANGE = getattr(global_config, "pfc_relationship_final_default_change", 5.0)
+        self.REL_FINAL_DEFAULT_CHANGE = global_config.pfc.pfc_relationship_final_default_change
-        self.REL_FINAL_MAX_CHANGE = getattr(global_config, "pfc_relationship_final_max_change", 50.0)
+        self.REL_FINAL_MAX_CHANGE = global_config.pfc.pfc_relationship_final_max_change
 
     async def update_relationship_incremental(
         self,

src/experimental/PFC/pfc_utils.py

@@ -53,7 +53,7 @@ async def find_most_relevant_historical_message(
         log_source_of_limit = "传入的绝对时间上限"
     else:
         # 如果没有传入绝对时间上限，可以设置一个默认的回退逻辑
-        fallback_exclude_seconds = getattr(global_config, "pfc_historical_fallback_exclude_seconds", 7200)  # 默认2小时
+        fallback_exclude_seconds = global_config.pfc.pfc_historical_fallback_exclude_seconds
         effective_search_upper_limit = time.time() - fallback_exclude_seconds
         log_source_of_limit = f"回退逻辑 (排除最近 {fallback_exclude_seconds} 秒)"
 

src/experimental/PFC/reply_generator.py

@@ -240,7 +240,7 @@ class ReplyGenerator:
         # 我们先做一个合理的假设： “最近聊天记录” 字符串 chat_history_text 是基于
         # observation_info.chat_history 的一个有限的尾部片段生成的。
         # 假设这个片段的长度由 global_config.pfc.pfc_recent_history_display_count 控制，默认为20条。
-        recent_history_display_count = getattr(global_config, "pfc_recent_history_display_count", 20)
+        recent_history_display_count = global_config.pfc.pfc_recent_history_display_count
 
         if observation_info and observation_info.chat_history and len(observation_info.chat_history) > 0:
             # 获取用于生成“最近聊天记录”的实际消息片段

src/plugins/group_nickname/nickname_utils.py

@@ -25,7 +25,7 @@ def select_nicknames_for_prompt(all_nicknames_info: Dict[str, List[Dict[str, int
         return []
 
     candidates = []  # 存储 (用户名, 绰号, 次数, 权重)
-    smoothing_factor = getattr(global_config, "nickname_probability_smoothing", 1.0)  # 平滑因子，避免权重为0
+    smoothing_factor = global_config.group_nickname.nickname_probability_smoothing
 
     for user_name, nicknames in all_nicknames_info.items():
         if nicknames and isinstance(nicknames, list):
@@ -48,7 +48,7 @@ def select_nicknames_for_prompt(all_nicknames_info: Dict[str, List[Dict[str, int
         return []
 
     # 确定需要选择的数量
-    max_nicknames = getattr(global_config, "max_nicknames_in_prompt", 5)
+    max_nicknames = global_config.group_nickname.max_nicknames_in_prompt
     num_to_select = min(max_nicknames, len(candidates))
 
     try: