add group-aware persona bias for xiaoniu bot

plugins/ai_auto_response/main.py

@@ -16,6 +16,8 @@ from wechat_ipad.models.message import MessageType
 
 from .context_builder import ContextBuilder
 from .flow_manager import FlowManager
+from .group_memory import GroupMemoryService
+from .group_profile import GroupProfileResolver
 from .llm_client import LLMClient
 from .memory_store import MemoryStore
 from .persona_engine import PersonaEngine
@@ -72,6 +74,8 @@ class AIAutoResponsePlugin(MessagePluginInterface):
         self.db_manager = context.get("db_manager")
         self.enable = bool(self._config.get("enable", True))
         self.persona_engine = PersonaEngine(self.get_plugin_path(), self._config.get("persona", {}))
+        self.group_memory_service = GroupMemoryService(self.db_manager, self._config.get("group_profiles", {}) or {})
+        self.group_profile_resolver = GroupProfileResolver(self._config.get("group_profiles", {}) or {})
         self.flow_manager = FlowManager({
             **(self._config.get("flow", {}) or {}),
             "night_silent_hours": (self._config.get("cooldown", {}) or {}).get("night_silent_hours", []),
@@ -134,11 +138,19 @@ class AIAutoResponsePlugin(MessagePluginInterface):
         bot: WechatAPIClient = message.get("bot")
         content = self._normalize_content(message)
         sender_name = self._get_sender_name(room_id, sender)
+        group_name = self._get_group_name(room_id, message)
+        group_memory_profile = self.group_memory_service.build_group_memory_profile(room_id, group_name)
+        group_profile = self.group_profile_resolver.resolve(room_id, group_name, group_memory_profile)
         self._log_event(
             "recv",
             room_id=room_id,
             sender=sender,
             sender_name=sender_name,
+            group_mode=group_profile.get("mode", ""),
+            knowledge_domain=group_profile.get("knowledge_domain", ""),
+            memory_domain=group_profile.get("group_memory_domain", ""),
+            humor_style=group_profile.get("humor_style", ""),
+            sharpness_style=group_profile.get("sharpness_style", ""),
             is_at=message.get("is_at", False),
             content_preview=self._preview(content),
             msg_type=str(message.get("type")),
@@ -218,6 +230,8 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             "context",
             room_id=room_id,
             sender=sender,
+            group_mode=group_profile.get("mode", ""),
+            knowledge_domain=group_profile.get("knowledge_domain", ""),
             reply_mode=reply_mode,
             recent_message_count=len(recent_messages),
             vector_hit_count=len(vector_memories),
@@ -225,6 +239,7 @@ class AIAutoResponsePlugin(MessagePluginInterface):
 
         context = self.context_builder.build(
             room_id=room_id,
+            group_profile=group_profile,
             sender=sender,
             sender_name=sender_name,
             content=content,
@@ -236,7 +251,7 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             vector_memories=vector_memories,
         )
 
-        system_prompt = self.persona_engine.build_system_prompt()
+        system_prompt = self.persona_engine.build_system_prompt(group_profile)
         user_prompt = self._build_user_prompt(context, memory_hints)
         response = self._sanitize_response(self.llm_client.chat(system_prompt, user_prompt, user_id=f"{room_id}:{sender}"))
         if not response:
@@ -314,6 +329,11 @@ class AIAutoResponsePlugin(MessagePluginInterface):
         except Exception:
             return sender
 
+    @staticmethod
+    def _get_group_name(room_id: str, message: Dict[str, Any]) -> str:
+        all_contacts = message.get("all_contacts", {}) or {}
+        return str(all_contacts.get(room_id, room_id))
+
     def _pass_cooldown(self, room_id: str, trigger: Dict) -> bool:
         current_ts = time.time()
         room_cd = int(self.cooldown_config.get("group_reply_cooldown_sec", 45))
@@ -333,6 +353,7 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             f"触发类型：{context.get('trigger_type', 'none')}\n"
             f"回复模式：{context.get('reply_mode', 'social_short')}\n"
             f"当前心流状态：{context.get('flow_state', 'idle')}\n"
+            f"当前群画像：\n{context.get('group_profile_prompt', '暂无')}\n\n"
             f"成员稳定记忆：\n{context.get('memory_prompt', '暂无')}\n\n"
             f"向量召回记忆：\n{context.get('vector_memory_prompt', '') or '暂无'}\n\n"
             f"补充信息：回归状态={memory_hints.get('returning_member_state', '') or 'none'}\n"
@@ -348,6 +369,7 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             f"9. 如果你不确定自己是否理解对了，就宁可不展开，只回很短。\n"
             f"10. 把这次回复当作真人聊天里的第一反应，先只给第一层结论，不要主动补第二层解释。\n"
             f"11. 如果一句话已经够了，就立刻停，不要为了完整而补充。\n"
+            f"12. 回答时优先服从当前群画像里的知识域和回答风格，不要跨领域乱发挥。\n"
         )
 
     @staticmethod
@@ -478,7 +500,9 @@ class AIAutoResponsePlugin(MessagePluginInterface):
         if event == "recv":
             return (
                 f"[XIAONIU] RECV room={room} user={sender_name}/{sender} "
-                f"at={self._yn(data.get('is_at'))} msg={data.get('content_preview', '')}"
+                f"at={self._yn(data.get('is_at'))} "
+                f"style={self._style_mark(data.get('humor_style', ''), data.get('sharpness_style', ''))} "
+                f"msg={data.get('content_preview', '')}"
             ).strip()
 
         if event == "memory":
@@ -552,3 +576,9 @@ class AIAutoResponsePlugin(MessagePluginInterface):
         if len(value) <= 10:
             return value
         return value[:4] + "..." + value[-4:]
+
+    @staticmethod
+    def _style_mark(humor_style: str, sharpness_style: str) -> str:
+        humor = "humor" if "中等" in str(humor_style) or "偏上" in str(humor_style) else "plain"
+        sharp = "sharp" if "毒舌" in str(sharpness_style) or "嘴欠" in str(sharpness_style) else "soft"
+        return f"{humor}/{sharp}"