Merge branch 'MaiM-with-u:dev' into dev

src/chat/chat_loop/heartFC_chat.py

@@ -3,13 +3,13 @@ import time
 import traceback
 import math
 import random
-from typing import List, Optional, Dict, Any, Tuple
+from typing import List, Optional, Dict, Any, Tuple, TYPE_CHECKING
 from rich.traceback import install
 from collections import deque
 
 from src.config.config import global_config
 from src.common.logger import get_logger
-from src.common.data_models.database_data_model import DatabaseMessages
+from src.common.data_models.info_data_model import ActionPlannerInfo
 from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
 from src.chat.utils.prompt_builder import global_prompt_manager
 from src.chat.utils.timer_calculator import Timer
@@ -24,12 +24,15 @@ from src.chat.frequency_control.focus_value_control import focus_value_control
 from src.chat.express.expression_learner import expression_learner_manager
 from src.person_info.relationship_builder_manager import relationship_builder_manager
 from src.person_info.person_info import Person
-from src.plugin_system.base.component_types import ChatMode, EventType
+from src.plugin_system.base.component_types import ChatMode, EventType, ActionInfo
 from src.plugin_system.core import events_manager
 from src.plugin_system.apis import generator_api, send_api, message_api, database_api
 from src.mais4u.mai_think import mai_thinking_manager
 from src.mais4u.s4u_config import s4u_config
 
+if TYPE_CHECKING:
+    from src.common.data_models.database_data_model import DatabaseMessages
+
 
 ERROR_LOOP_INFO = {
     "loop_plan_info": {
@@ -100,7 +103,7 @@ class HeartFChatting:
         self.reply_timeout_count = 0
         self.plan_timeout_count = 0
 
-        self.last_read_time = time.time() - 1
+        self.last_read_time = time.time() - 10
 
         self.focus_energy = 1
         self.no_action_consecutive = 0
@@ -141,7 +144,7 @@ class HeartFChatting:
         except asyncio.CancelledError:
             logger.info(f"{self.log_prefix} HeartFChatting: 结束了聊天")
 
-    def start_cycle(self):
+    def start_cycle(self) -> Tuple[Dict[str, float], str]:
         self._cycle_counter += 1
         self._current_cycle_detail = CycleDetail(self._cycle_counter)
         self._current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
@@ -172,7 +175,8 @@ class HeartFChatting:
                     action_type = action_result.get("action_type", "未知动作")
                 elif isinstance(action_result, list) and action_result:
                     # 新格式：action_result是actions列表
-                    action_type = action_result[0].get("action_type", "未知动作")
+                    # TODO: 把这里写明白
+                    action_type = action_result[0].action_type or "未知动作"
             elif isinstance(loop_plan_info, list) and loop_plan_info:
                 # 直接是actions列表的情况
                 action_type = loop_plan_info[0].get("action_type", "未知动作")
@@ -207,7 +211,7 @@ class HeartFChatting:
                 logger.info(f"{self.log_prefix} 兴趣度充足，等待新消息")
                 self.focus_energy = 1
 
-    async def _should_process_messages(self, new_message: List[DatabaseMessages]) -> tuple[bool, float]:
+    async def _should_process_messages(self, new_message: List["DatabaseMessages"]) -> tuple[bool, float]:
         """
         判断是否应该处理消息
 
@@ -265,7 +269,7 @@ class HeartFChatting:
         return False, 0.0
 
     async def _loopbody(self):
-        recent_messages_dict = message_api.get_messages_by_time_in_chat(
+        recent_messages_list = message_api.get_messages_by_time_in_chat(
             chat_id=self.stream_id,
             start_time=self.last_read_time,
             end_time=time.time(),
@@ -275,7 +279,7 @@ class HeartFChatting:
             filter_command=True,
         )
         # 统一的消息处理逻辑
-        should_process, interest_value = await self._should_process_messages(recent_messages_dict)
+        should_process, interest_value = await self._should_process_messages(recent_messages_list)
 
         if should_process:
             self.last_read_time = time.time()
@@ -290,11 +294,11 @@ class HeartFChatting:
     async def _send_and_store_reply(
         self,
         response_set,
-        action_message,
+        action_message: "DatabaseMessages",
         cycle_timers: Dict[str, float],
         thinking_id,
         actions,
-        selected_expressions: List[int] = None,
+        selected_expressions: Optional[List[int]] = None,
     ) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
         with Timer("回复发送", cycle_timers):
             reply_text = await self._send_response(
@@ -304,11 +308,11 @@ class HeartFChatting:
             )
 
         # 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
-        platform = action_message.get("chat_info_platform")
+        platform = action_message.chat_info.platform
         if platform is None:
             platform = getattr(self.chat_stream, "platform", "unknown")
 
-        person = Person(platform=platform, user_id=action_message.get("user_id", ""))
+        person = Person(platform=platform, user_id=action_message.user_info.user_id)
         person_name = person.person_name
         action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
 
@@ -353,9 +357,13 @@ class HeartFChatting:
             k = 2.0  # 控制曲线陡峭程度
             x0 = 1.0  # 控制曲线中心点
             return 1.0 / (1.0 + math.exp(-k * (interest_val - x0)))
-        
-        normal_mode_probability = calculate_normal_mode_probability(interest_value) * 2 *  self.talk_frequency_control.get_current_talk_frequency()
-        
+
+        normal_mode_probability = (
+            calculate_normal_mode_probability(interest_value)
+            * 2
+            * self.talk_frequency_control.get_current_talk_frequency()
+        )
+
         # 根据概率决定使用哪种模式
         if random.random() < normal_mode_probability:
             mode = ChatMode.NORMAL
@@ -383,17 +391,17 @@ class HeartFChatting:
             except Exception as e:
                 logger.error(f"{self.log_prefix} 记忆构建失败: {e}")
 
+            available_actions: Dict[str, ActionInfo] = {}
             if random.random() > self.focus_value_control.get_current_focus_value() and mode == ChatMode.FOCUS:
                 # 如果激活度没有激活，并且聊天活跃度低，有可能不进行plan，相当于不在电脑前，不进行认真思考
-                actions = [
-                    {
-                        "action_type": "no_action",
-                        "reasoning": "专注不足",
-                        "action_data": {},
-                    }
+                action_to_use_info = [
+                    ActionPlannerInfo(
+                        action_type="no_action",
+                        reasoning="专注不足",
+                        action_data={},
+                    )
                 ]
             else:
-                available_actions = {}
                 # 第一步：动作修改
                 with Timer("动作修改", cycle_timers):
                     try:
@@ -414,105 +422,19 @@ class HeartFChatting:
                 ):
                     return False
                 with Timer("规划器", cycle_timers):
-                    actions, _ = await self.action_planner.plan(
+                    action_to_use_info, _ = await self.action_planner.plan(
                         mode=mode,
                         loop_start_time=self.last_read_time,
                         available_actions=available_actions,
                     )
 
             # 3. 并行执行所有动作
-            async def execute_action(action_info, actions):
-                """执行单个动作的通用函数"""
-                try:
-                    if action_info["action_type"] == "no_action":
-                        # 直接处理no_action逻辑，不再通过动作系统
-                        reason = action_info.get("reasoning", "选择不回复")
-                        logger.info(f"{self.log_prefix} 选择不回复，原因: {reason}")
-
-                        # 存储no_action信息到数据库
-                        await database_api.store_action_info(
-                            chat_stream=self.chat_stream,
-                            action_build_into_prompt=False,
-                            action_prompt_display=reason,
-                            action_done=True,
-                            thinking_id=thinking_id,
-                            action_data={"reason": reason},
-                            action_name="no_action",
-                        )
-
-                        return {"action_type": "no_action", "success": True, "reply_text": "", "command": ""}
-                    elif action_info["action_type"] != "reply":
-                        # 执行普通动作
-                        with Timer("动作执行", cycle_timers):
-                            success, reply_text, command = await self._handle_action(
-                                action_info["action_type"],
-                                action_info["reasoning"],
-                                action_info["action_data"],
-                                cycle_timers,
-                                thinking_id,
-                                action_info["action_message"],
-                            )
-                        return {
-                            "action_type": action_info["action_type"],
-                            "success": success,
-                            "reply_text": reply_text,
-                            "command": command,
-                        }
-                    else:
-                        try:
-                            success, response_set, prompt_selected_expressions = await generator_api.generate_reply(
-                                chat_stream=self.chat_stream,
-                                reply_message=action_info["action_message"],
-                                available_actions=available_actions,
-                                choosen_actions=actions,
-                                reply_reason=action_info.get("reasoning", ""),
-                                enable_tool=global_config.tool.enable_tool,
-                                request_type="replyer",
-                                from_plugin=False,
-                                return_expressions=True,
-                            )
-
-                            if prompt_selected_expressions and len(prompt_selected_expressions) > 1:
-                                _, selected_expressions = prompt_selected_expressions
-                            else:
-                                selected_expressions = []
-
-                            if not success or not response_set:
-                                logger.info(
-                                    f"对 {action_info['action_message'].get('processed_plain_text')} 的回复生成失败"
-                                )
-                                return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
-
-                        except asyncio.CancelledError:
-                            logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消")
-                            return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
-
-                        loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply(
-                            response_set=response_set,
-                            action_message=action_info["action_message"],
-                            cycle_timers=cycle_timers,
-                            thinking_id=thinking_id,
-                            actions=actions,
-                            selected_expressions=selected_expressions,
-                        )
-                        return {
-                            "action_type": "reply",
-                            "success": True,
-                            "reply_text": reply_text,
-                            "loop_info": loop_info,
-                        }
-                except Exception as e:
-                    logger.error(f"{self.log_prefix} 执行动作时出错: {e}")
-                    logger.error(f"{self.log_prefix} 错误信息: {traceback.format_exc()}")
-                    return {
-                        "action_type": action_info["action_type"],
-                        "success": False,
-                        "reply_text": "",
-                        "loop_info": None,
-                        "error": str(e),
-                    }
-
-            action_tasks = [asyncio.create_task(execute_action(action, actions)) for action in actions]
+            action_tasks = [
+                asyncio.create_task(
+                    self._execute_action(action, action_to_use_info, thinking_id, available_actions, cycle_timers)
+                )
+                for action in action_to_use_info
+            ]
 
             # 并行执行所有任务
             results = await asyncio.gather(*action_tasks, return_exceptions=True)
@@ -529,7 +451,7 @@ class HeartFChatting:
                     logger.error(f"{self.log_prefix} 动作执行异常: {result}")
                     continue
 
-                _cur_action = actions[i]
+                _cur_action = action_to_use_info[i]
                 if result["action_type"] != "reply":
                     action_success = result["success"]
                     action_reply_text = result["reply_text"]
@@ -558,7 +480,7 @@ class HeartFChatting:
                 # 没有回复信息，构建纯动作的loop_info
                 loop_info = {
                     "loop_plan_info": {
-                        "action_result": actions,
+                        "action_result": action_to_use_info,
                     },
                     "loop_action_info": {
                         "action_taken": action_success,
@@ -578,7 +500,7 @@ class HeartFChatting:
 
         # await self.willing_manager.after_generate_reply_handle(message_data.get("message_id", ""))
 
-        action_type = actions[0]["action_type"] if actions else "no_action"
+        action_type = action_to_use_info[0].action_type if action_to_use_info else "no_action"
 
         # 管理no_action计数器：当执行了非no_action动作时，重置计数器
         if action_type != "no_action":
@@ -620,7 +542,7 @@ class HeartFChatting:
         action_data: dict,
         cycle_timers: Dict[str, float],
         thinking_id: str,
-        action_message: dict,
+        action_message: Optional["DatabaseMessages"] = None,
     ) -> tuple[bool, str, str]:
         """
         处理规划动作，使用动作工厂创建相应的动作处理器
@@ -672,8 +594,8 @@ class HeartFChatting:
     async def _send_response(
         self,
         reply_set,
-        message_data,
-        selected_expressions: List[int] = None,
+        message_data: "DatabaseMessages",
+        selected_expressions: Optional[List[int]] = None,
     ) -> str:
         new_message_count = message_api.count_new_messages(
             chat_id=self.chat_stream.stream_id, start_time=self.last_read_time, end_time=time.time()
@@ -710,3 +632,97 @@ class HeartFChatting:
             reply_text += data
 
         return reply_text
+
+    async def _execute_action(
+        self,
+        action_planner_info: ActionPlannerInfo,
+        chosen_action_plan_infos: List[ActionPlannerInfo],
+        thinking_id: str,
+        available_actions: Dict[str, ActionInfo],
+        cycle_timers: Dict[str, float],
+    ):
+        """执行单个动作的通用函数"""
+        try:
+            if action_planner_info.action_type == "no_action":
+                # 直接处理no_action逻辑，不再通过动作系统
+                reason = action_planner_info.reasoning or "选择不回复"
+                logger.info(f"{self.log_prefix} 选择不回复，原因: {reason}")
+
+                # 存储no_action信息到数据库
+                await database_api.store_action_info(
+                    chat_stream=self.chat_stream,
+                    action_build_into_prompt=False,
+                    action_prompt_display=reason,
+                    action_done=True,
+                    thinking_id=thinking_id,
+                    action_data={"reason": reason},
+                    action_name="no_action",
+                )
+
+                return {"action_type": "no_action", "success": True, "reply_text": "", "command": ""}
+            elif action_planner_info.action_type != "reply":
+                # 执行普通动作
+                with Timer("动作执行", cycle_timers):
+                    success, reply_text, command = await self._handle_action(
+                        action_planner_info.action_type,
+                        action_planner_info.reasoning or "",
+                        action_planner_info.action_data or {},
+                        cycle_timers,
+                        thinking_id,
+                        action_planner_info.action_message,
+                    )
+                return {
+                    "action_type": action_planner_info.action_type,
+                    "success": success,
+                    "reply_text": reply_text,
+                    "command": command,
+                }
+            else:
+                try:
+                    success, response_set, prompt, selected_expressions = await generator_api.generate_reply(
+                        chat_stream=self.chat_stream,
+                        reply_message=action_planner_info.action_message,
+                        available_actions=available_actions,
+                        chosen_actions=chosen_action_plan_infos,
+                        reply_reason=action_planner_info.reasoning or "",
+                        enable_tool=global_config.tool.enable_tool,
+                        request_type="replyer",
+                        from_plugin=False,
+                        return_expressions=True,
+                    )
+
+                    if not success or not response_set:
+                        if action_planner_info.action_message:
+                            logger.info(f"对 {action_planner_info.action_message.processed_plain_text} 的回复生成失败")
+                        else:
+                            logger.info("回复生成失败")
+                        return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
+
+                except asyncio.CancelledError:
+                    logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消")
+                    return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
+
+                loop_info, reply_text, _ = await self._send_and_store_reply(
+                    response_set=response_set,
+                    action_message=action_planner_info.action_message,  # type: ignore
+                    cycle_timers=cycle_timers,
+                    thinking_id=thinking_id,
+                    actions=chosen_action_plan_infos,
+                    selected_expressions=selected_expressions,
+                )
+                return {
+                    "action_type": "reply",
+                    "success": True,
+                    "reply_text": reply_text,
+                    "loop_info": loop_info,
+                }
+        except Exception as e:
+            logger.error(f"{self.log_prefix} 执行动作时出错: {e}")
+            logger.error(f"{self.log_prefix} 错误信息: {traceback.format_exc()}")
+            return {
+                "action_type": action_planner_info.action_type,
+                "success": False,
+                "reply_text": "",
+                "loop_info": None,
+                "error": str(e),
+            }

src/chat/express/expression_selector.py

@@ -303,4 +303,4 @@ init_prompt()
 try:
     expression_selector = ExpressionSelector()
 except Exception as e:
-    print(f"ExpressionSelector初始化失败: {e}")
+    logger.error(f"ExpressionSelector初始化失败: {e}")

src/chat/frequency_control/focus_value_control.py

@@ -4,44 +4,43 @@ from src.chat.frequency_control.utils import parse_stream_config_to_chat_id
 
 
 class FocusValueControl:
-    def __init__(self,chat_id:str):
+    def __init__(self, chat_id: str):
         self.chat_id = chat_id
-        self.focus_value_adjust = 1
-        
-        
+        self.focus_value_adjust: float = 1
+
     def get_current_focus_value(self) -> float:
         return get_current_focus_value(self.chat_id) * self.focus_value_adjust
-        
+
 
 class FocusValueControlManager:
     def __init__(self):
-        self.focus_value_controls = {}
-        
-    def get_focus_value_control(self,chat_id:str) -> FocusValueControl:
+        self.focus_value_controls: dict[str, FocusValueControl] = {}
+
+    def get_focus_value_control(self, chat_id: str) -> FocusValueControl:
         if chat_id not in self.focus_value_controls:
             self.focus_value_controls[chat_id] = FocusValueControl(chat_id)
         return self.focus_value_controls[chat_id]
 
 
-
 def get_current_focus_value(chat_id: Optional[str] = None) -> float:
     """
     根据当前时间和聊天流获取对应的 focus_value
     """
     if not global_config.chat.focus_value_adjust:
         return global_config.chat.focus_value
-    
+
     if chat_id:
         stream_focus_value = get_stream_specific_focus_value(chat_id)
         if stream_focus_value is not None:
             return stream_focus_value
-        
+
     global_focus_value = get_global_focus_value()
     if global_focus_value is not None:
         return global_focus_value
-    
+
     return global_config.chat.focus_value
 
+
 def get_stream_specific_focus_value(chat_id: str) -> Optional[float]:
     """
     获取特定聊天流在当前时间的专注度
@@ -140,4 +139,5 @@ def get_global_focus_value() -> Optional[float]:
 
     return None
 
-focus_value_control = FocusValueControlManager()
+
+focus_value_control = FocusValueControlManager()

src/chat/frequency_control/talk_frequency_control.py

@@ -2,20 +2,21 @@ from typing import Optional
 from src.config.config import global_config
 from src.chat.frequency_control.utils import parse_stream_config_to_chat_id
 
+
 class TalkFrequencyControl:
-    def __init__(self,chat_id:str):
+    def __init__(self, chat_id: str):
         self.chat_id = chat_id
-        self.talk_frequency_adjust = 1
-        
+        self.talk_frequency_adjust: float = 1
+
     def get_current_talk_frequency(self) -> float:
         return get_current_talk_frequency(self.chat_id) * self.talk_frequency_adjust
-        
+
 
 class TalkFrequencyControlManager:
     def __init__(self):
         self.talk_frequency_controls = {}
-        
-    def get_talk_frequency_control(self,chat_id:str) -> TalkFrequencyControl:
+
+    def get_talk_frequency_control(self, chat_id: str) -> TalkFrequencyControl:
         if chat_id not in self.talk_frequency_controls:
             self.talk_frequency_controls[chat_id] = TalkFrequencyControl(chat_id)
         return self.talk_frequency_controls[chat_id]
@@ -44,6 +45,7 @@ def get_current_talk_frequency(chat_id: Optional[str] = None) -> float:
     global_frequency = get_global_frequency()
     return global_config.chat.talk_frequency if global_frequency is None else global_frequency
 
+
 def get_time_based_frequency(time_freq_list: list[str]) -> Optional[float]:
     """
     根据时间配置列表获取当前时段的频率
@@ -124,6 +126,7 @@ def get_stream_specific_frequency(chat_stream_id: str):
 
     return None
 
+
 def get_global_frequency() -> Optional[float]:
     """
     获取全局默认频率配置
@@ -141,4 +144,5 @@ def get_global_frequency() -> Optional[float]:
 
     return None
 
-talk_frequency_control = TalkFrequencyControlManager()
+
+talk_frequency_control = TalkFrequencyControlManager()

src/chat/heart_flow/heartflow_message_processor.py

@@ -12,7 +12,7 @@ from src.chat.message_receive.storage import MessageStorage
 from src.chat.heart_flow.heartflow import heartflow
 from src.chat.utils.utils import is_mentioned_bot_in_message
 from src.chat.utils.timer_calculator import Timer
-from src.chat.utils.chat_message_builder import replace_user_references_sync
+from src.chat.utils.chat_message_builder import replace_user_references
 from src.common.logger import get_logger
 from src.mood.mood_manager import mood_manager
 from src.person_info.person_info import Person
@@ -131,7 +131,7 @@ class HeartFCMessageReceiver:
             processed_plain_text = re.sub(picid_pattern, "[图片]", message.processed_plain_text)
             
             # 应用用户引用格式替换，将回复<aaa:bbb>和@<aaa:bbb>格式转换为可读格式
-            processed_plain_text = replace_user_references_sync(
+            processed_plain_text = replace_user_references(
                 processed_plain_text,
                 message.message_info.platform, # type: ignore
                 replace_bot_name=True

src/chat/knowledge/__init__.py

@@ -0,0 +1,82 @@
+from src.chat.knowledge.embedding_store import EmbeddingManager
+from src.chat.knowledge.qa_manager import QAManager
+from src.chat.knowledge.kg_manager import KGManager
+from src.chat.knowledge.global_logger import logger
+from src.config.config import global_config
+import os
+
+INVALID_ENTITY = [
+    "",
+    "你",
+    "他",
+    "她",
+    "它",
+    "我们",
+    "你们",
+    "他们",
+    "她们",
+    "它们",
+]
+
+RAG_GRAPH_NAMESPACE = "rag-graph"
+RAG_ENT_CNT_NAMESPACE = "rag-ent-cnt"
+RAG_PG_HASH_NAMESPACE = "rag-pg-hash"
+
+
+ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+DATA_PATH = os.path.join(ROOT_PATH, "data")
+
+
+qa_manager = None
+inspire_manager = None
+
+def lpmm_start_up():  # sourcery skip: extract-duplicate-method
+    # 检查LPMM知识库是否启用
+    if global_config.lpmm_knowledge.enable:
+        logger.info("正在初始化Mai-LPMM")
+        logger.info("创建LLM客户端")
+
+        # 初始化Embedding库
+        embed_manager = EmbeddingManager()
+        logger.info("正在从文件加载Embedding库")
+        try:
+            embed_manager.load_from_file()
+        except Exception as e:
+            logger.warning(f"此消息不会影响正常使用：从文件加载Embedding库时，{e}")
+            # logger.warning("如果你是第一次导入知识，或者还未导入知识，请忽略此错误")
+        logger.info("Embedding库加载完成")
+        # 初始化KG
+        kg_manager = KGManager()
+        logger.info("正在从文件加载KG")
+        try:
+            kg_manager.load_from_file()
+        except Exception as e:
+            logger.warning(f"此消息不会影响正常使用：从文件加载KG时，{e}")
+            # logger.warning("如果你是第一次导入知识，或者还未导入知识，请忽略此错误")
+        logger.info("KG加载完成")
+
+        logger.info(f"KG节点数量：{len(kg_manager.graph.get_node_list())}")
+        logger.info(f"KG边数量：{len(kg_manager.graph.get_edge_list())}")
+
+        # 数据比对：Embedding库与KG的段落hash集合
+        for pg_hash in kg_manager.stored_paragraph_hashes:
+            # 使用与EmbeddingStore中一致的命名空间格式
+            key = f"paragraph-{pg_hash}"
+            if key not in embed_manager.stored_pg_hashes:
+                logger.warning(f"KG中存在Embedding库中不存在的段落：{key}")
+        global qa_manager
+        # 问答系统（用于知识库）
+        qa_manager = QAManager(
+            embed_manager,
+            kg_manager,
+        )
+
+        # # 记忆激活（用于记忆库）
+        # global inspire_manager
+        # inspire_manager = MemoryActiveManager(
+        #     embed_manager,
+        #     llm_client_list[global_config["embedding"]["provider"]],
+        # )
+    else:
+        logger.info("LPMM知识库已禁用，跳过初始化")
+        # 创建空的占位符对象，避免导入错误

src/chat/knowledge/ie_process.py

@@ -5,7 +5,7 @@ from typing import List, Union
 
 from .global_logger import logger
 from . import prompt_template
-from .knowledge_lib import INVALID_ENTITY
+from . import INVALID_ENTITY
 from src.llm_models.utils_model import LLMRequest
 from json_repair import repair_json
 

src/chat/knowledge/knowledge_lib.py

@@ -1,80 +0,0 @@
-from src.chat.knowledge.embedding_store import EmbeddingManager
-from src.chat.knowledge.qa_manager import QAManager
-from src.chat.knowledge.kg_manager import KGManager
-from src.chat.knowledge.global_logger import logger
-from src.config.config import global_config
-import os
-
-INVALID_ENTITY = [
-    "",
-    "你",
-    "他",
-    "她",
-    "它",
-    "我们",
-    "你们",
-    "他们",
-    "她们",
-    "它们",
-]
-
-RAG_GRAPH_NAMESPACE = "rag-graph"
-RAG_ENT_CNT_NAMESPACE = "rag-ent-cnt"
-RAG_PG_HASH_NAMESPACE = "rag-pg-hash"
-
-
-ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
-DATA_PATH = os.path.join(ROOT_PATH, "data")
-
-
-qa_manager = None
-inspire_manager = None
-
-# 检查LPMM知识库是否启用
-if global_config.lpmm_knowledge.enable:
-    logger.info("正在初始化Mai-LPMM")
-    logger.info("创建LLM客户端")
-
-    # 初始化Embedding库
-    embed_manager = EmbeddingManager()
-    logger.info("正在从文件加载Embedding库")
-    try:
-        embed_manager.load_from_file()
-    except Exception as e:
-        logger.warning(f"此消息不会影响正常使用：从文件加载Embedding库时，{e}")
-        # logger.warning("如果你是第一次导入知识，或者还未导入知识，请忽略此错误")
-    logger.info("Embedding库加载完成")
-    # 初始化KG
-    kg_manager = KGManager()
-    logger.info("正在从文件加载KG")
-    try:
-        kg_manager.load_from_file()
-    except Exception as e:
-        logger.warning(f"此消息不会影响正常使用：从文件加载KG时，{e}")
-        # logger.warning("如果你是第一次导入知识，或者还未导入知识，请忽略此错误")
-    logger.info("KG加载完成")
-
-    logger.info(f"KG节点数量：{len(kg_manager.graph.get_node_list())}")
-    logger.info(f"KG边数量：{len(kg_manager.graph.get_edge_list())}")
-
-    # 数据比对：Embedding库与KG的段落hash集合
-    for pg_hash in kg_manager.stored_paragraph_hashes:
-        # 使用与EmbeddingStore中一致的命名空间格式
-        key = f"paragraph-{pg_hash}"
-        if key not in embed_manager.stored_pg_hashes:
-            logger.warning(f"KG中存在Embedding库中不存在的段落：{key}")
-
-    # 问答系统（用于知识库）
-    qa_manager = QAManager(
-        embed_manager,
-        kg_manager,
-    )
-
-    # # 记忆激活（用于记忆库）
-    # inspire_manager = MemoryActiveManager(
-    #     embed_manager,
-    #     llm_client_list[global_config["embedding"]["provider"]],
-    # )
-else:
-    logger.info("LPMM知识库已禁用，跳过初始化")
-    # 创建空的占位符对象，避免导入错误

src/chat/knowledge/open_ie.py

@@ -4,7 +4,7 @@ import glob
 from typing import Any, Dict, List
 
 
-from .knowledge_lib import INVALID_ENTITY, ROOT_PATH, DATA_PATH
+from . import INVALID_ENTITY, ROOT_PATH, DATA_PATH
 # from src.manager.local_store_manager import local_storage
 
 

src/chat/knowledge/qa_manager.py

@@ -60,7 +60,7 @@ class QAManager:
         for res in relation_search_res:
             if store_item := self.embed_manager.relation_embedding_store.store.get(res[0]):
                 rel_str = store_item.str
-            print(f"找到相关关系，相似度：{(res[1] * 100):.2f}%  -  {rel_str}")
+            logger.info(f"找到相关关系，相似度：{(res[1] * 100):.2f}%  -  {rel_str}")
 
         # TODO: 使用LLM过滤三元组结果
         # logger.info(f"LLM过滤三元组用时：{time.time() - part_start_time:.2f}s")
@@ -94,7 +94,7 @@ class QAManager:
 
         for res in result:
             raw_paragraph = self.embed_manager.paragraphs_embedding_store.store[res[0]].str
-            print(f"找到相关文段，相关系数：{res[1]:.8f}\n{raw_paragraph}\n\n")
+            logger.info(f"找到相关文段，相关系数：{res[1]:.8f}\n{raw_paragraph}\n\n")
 
         return result, ppr_node_weights
 

src/chat/memory_system/Hippocampus.py

@@ -30,9 +30,7 @@ def cosine_similarity(v1, v2):
     dot_product = np.dot(v1, v2)
     norm1 = np.linalg.norm(v1)
     norm2 = np.linalg.norm(v2)
-    if norm1 == 0 or norm2 == 0:
-        return 0
-    return dot_product / (norm1 * norm2)
+    return 0 if norm1 == 0 or norm2 == 0 else dot_product / (norm1 * norm2)
 
 
 install(extra_lines=3)
@@ -142,11 +140,10 @@ class MemoryGraph:
         # 获取当前节点的记忆项
         node_data = self.get_dot(topic)
         if node_data:
-            concept, data = node_data
+            _, data = node_data
             if "memory_items" in data:
-                memory_items = data["memory_items"]
                 # 直接使用完整的记忆内容
-                if memory_items:
+                if memory_items := data["memory_items"]:
                     first_layer_items.append(memory_items)
 
         # 只在depth=2时获取第二层记忆
@@ -154,11 +151,10 @@ class MemoryGraph:
             # 获取相邻节点的记忆项
             for neighbor in neighbors:
                 if node_data := self.get_dot(neighbor):
-                    concept, data = node_data
+                    _, data = node_data
                     if "memory_items" in data:
-                        memory_items = data["memory_items"]
                         # 直接使用完整的记忆内容
-                        if memory_items:
+                        if memory_items := data["memory_items"]:
                             second_layer_items.append(memory_items)
 
         return first_layer_items, second_layer_items
@@ -224,27 +220,17 @@ class MemoryGraph:
         # 获取话题节点数据
         node_data = self.G.nodes[topic]
 
+        # 删除整个节点
+        self.G.remove_node(topic)
         # 如果节点存在memory_items
         if "memory_items" in node_data:
-            memory_items = node_data["memory_items"]
-
-            # 既然每个节点现在是一个完整的记忆内容，直接删除整个节点
-            if memory_items:
-                # 删除整个节点
-                self.G.remove_node(topic)
+            if memory_items := node_data["memory_items"]:
                 return (
                     f"删除了节点 {topic} 的完整记忆: {memory_items[:50]}..."
                     if len(memory_items) > 50
                     else f"删除了节点 {topic} 的完整记忆: {memory_items}"
                 )
-            else:
-                # 如果没有记忆项，删除该节点
-                self.G.remove_node(topic)
-                return None
-        else:
-            # 如果没有memory_items字段，删除该节点
-            self.G.remove_node(topic)
-            return None
+        return None
 
 
 # 海马体
@@ -392,9 +378,8 @@ class Hippocampus:
             # 如果相似度超过阈值，获取该节点的记忆
             if similarity >= 0.3:  # 可以调整这个阈值
                 node_data = self.memory_graph.G.nodes[node]
-                memory_items = node_data.get("memory_items", "")
                 # 直接使用完整的记忆内容
-                if memory_items:
+                if memory_items := node_data.get("memory_items", ""):
                     memories.append((node, memory_items, similarity))
 
         # 按相似度降序排序
@@ -411,7 +396,7 @@ class Hippocampus:
                 如果为False，使用LLM提取关键词，速度较慢但更准确。
         """
         if not text:
-            return []
+            return [], []
 
         # 使用LLM提取关键词 - 根据详细文本长度分布优化topic_num计算
         text_length = len(text)
@@ -587,7 +572,7 @@ class Hippocampus:
         unique_memories = []
         for topic, memory_items, activation_value in all_memories:
             # memory_items现在是完整的字符串格式
-            memory = memory_items if memory_items else ""
+            memory = memory_items or ""
             if memory not in seen_memories:
                 seen_memories.add(memory)
                 unique_memories.append((topic, memory_items, activation_value))
@@ -599,7 +584,7 @@ class Hippocampus:
         result = []
         for topic, memory_items, _ in unique_memories:
             # memory_items现在是完整的字符串格式
-            memory = memory_items if memory_items else ""
+            memory = memory_items or ""
             result.append((topic, memory))
             logger.debug(f"选中记忆: {memory} (来自节点: {topic})")
 
@@ -1435,13 +1420,11 @@ class HippocampusManager:
         if not self._initialized:
             raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
         try:
-            response, keywords, keywords_lite = await self._hippocampus.get_activate_from_text(
-                text, max_depth, fast_retrieval
-            )
+            return await self._hippocampus.get_activate_from_text(text, max_depth, fast_retrieval)
         except Exception as e:
             logger.error(f"文本产生激活值失败: {e}")
             logger.error(traceback.format_exc())
-        return 0.0, [], []
+            return 0.0, [], []
 
     def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
         """从关键词获取相关记忆的公共接口"""
@@ -1473,6 +1456,7 @@ class MemoryBuilder:
         self.last_processed_time: float = 0.0
 
     def should_trigger_memory_build(self) -> bool:
+        # sourcery skip: assign-if-exp, boolean-if-exp-identity, reintroduce-else
         """检查是否应该触发记忆构建"""
         current_time = time.time()
 

src/chat/memory_system/instant_memory.py

@@ -11,7 +11,7 @@ from datetime import datetime, timedelta
 from src.llm_models.utils_model import LLMRequest
 from src.common.logger import get_logger
 from src.common.database.database_model import Memory  # Peewee Models导入
-from src.config.config import model_config
+from src.config.config import model_config, global_config
 
 
 logger = get_logger(__name__)
@@ -42,7 +42,7 @@ class InstantMemory:
             request_type="memory.summary",
         )
 
-    async def if_need_build(self, text):
+    async def if_need_build(self, text: str):
         prompt = f"""
 请判断以下内容中是否有值得记忆的信息，如果有，请输出1，否则输出0
 {text}
@@ -51,8 +51,9 @@ class InstantMemory:
 
         try:
             response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
-            print(prompt)
-            print(response)
+            if global_config.debug.show_prompt:
+                print(prompt)
+                print(response)
 
             return "1" in response
         except Exception as e:
@@ -94,7 +95,7 @@ class InstantMemory:
             logger.error(f"构建记忆出现错误：{str(e)} {traceback.format_exc()}")
             return None
 
-    async def create_and_store_memory(self, text):
+    async def create_and_store_memory(self, text: str):
         if_need = await self.if_need_build(text)
         if if_need:
             logger.info(f"需要记忆：{text}")
@@ -126,24 +127,25 @@ class InstantMemory:
         from json_repair import repair_json
 
         prompt = f"""
-        请根据以下发言内容，判断是否需要提取记忆
-        {target}
-        请用json格式输出，包含以下字段：
-        其中，time的要求是：
-        可以选择具体日期时间，格式为YYYY-MM-DD HH:MM:SS，或者大致时间，格式为YYYY-MM-DD
-        可以选择相对时间，例如：今天，昨天，前天，5天前，1个月前
-        可以选择留空进行模糊搜索
-        {{
-            "need_memory": 1,
-            "keywords": "希望获取的记忆关键词，用/划分",
-            "time": "希望获取的记忆大致时间"
-        }}
-        请只输出json格式，不要输出其他多余内容
-        """
+请根据以下发言内容，判断是否需要提取记忆
+{target}
+请用json格式输出，包含以下字段：
+其中，time的要求是：
+可以选择具体日期时间，格式为YYYY-MM-DD HH:MM:SS，或者大致时间，格式为YYYY-MM-DD
+可以选择相对时间，例如：今天，昨天，前天，5天前，1个月前
+可以选择留空进行模糊搜索
+{{
+    "need_memory": 1,
+    "keywords": "希望获取的记忆关键词，用/划分",
+    "time": "希望获取的记忆大致时间"
+}}
+请只输出json格式，不要输出其他多余内容
+"""
         try:
             response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
-            print(prompt)
-            print(response)
+            if global_config.debug.show_prompt:
+                print(prompt)
+                print(response)
             if not response:
                 return None
             try:

src/chat/message_receive/bot.py

@@ -145,7 +145,7 @@ class ChatBot:
             logger.error(f"处理命令时出错: {e}")
             return False, None, True  # 出错时继续处理消息
 
-    async def hanle_notice_message(self, message: MessageRecv):
+    async def handle_notice_message(self, message: MessageRecv):
         if message.message_info.message_id == "notice":
             message.is_notify = True
             logger.info("notice消息")
@@ -212,7 +212,7 @@ class ChatBot:
             # logger.debug(str(message_data))
             message = MessageRecv(message_data)
 
-            if await self.hanle_notice_message(message):
+            if await self.handle_notice_message(message):
                 # return
                 pass
 

src/chat/message_receive/message.py

@@ -115,7 +115,7 @@ class MessageRecv(Message):
         self.priority_mode = "interest"
         self.priority_info = None
         self.interest_value: float = None  # type: ignore
-        
+
         self.key_words = []
         self.key_words_lite = []
 
@@ -213,9 +213,9 @@ class MessageRecvS4U(MessageRecv):
         self.is_screen = False
         self.is_internal = False
         self.voice_done = None
-        
+
         self.chat_info = None
-    
+
     async def process(self) -> None:
         self.processed_plain_text = await self._process_message_segments(self.message_segment)
 
@@ -420,7 +420,7 @@ class MessageSending(MessageProcessBase):
         thinking_start_time: float = 0,
         apply_set_reply_logic: bool = False,
         reply_to: Optional[str] = None,
-        selected_expressions:List[int] = None,
+        selected_expressions: Optional[List[int]] = None,
     ):
         # 调用父类初始化
         super().__init__(
@@ -445,7 +445,7 @@ class MessageSending(MessageProcessBase):
         self.display_message = display_message
 
         self.interest_value = 0.0
-        
+
         self.selected_expressions = selected_expressions
 
     def build_reply(self):

src/chat/planner_actions/action_manager.py

@@ -2,6 +2,7 @@ from typing import Dict, Optional, Type
 
 from src.chat.message_receive.chat_stream import ChatStream
 from src.common.logger import get_logger
+from src.common.data_models.database_data_model import DatabaseMessages
 from src.plugin_system.core.component_registry import component_registry
 from src.plugin_system.base.component_types import ComponentType, ActionInfo
 from src.plugin_system.base.base_action import BaseAction
@@ -37,7 +38,7 @@ class ActionManager:
         chat_stream: ChatStream,
         log_prefix: str,
         shutting_down: bool = False,
-        action_message: Optional[dict] = None,
+        action_message: Optional[DatabaseMessages] = None,
     ) -> Optional[BaseAction]:
         """
         创建动作处理器实例
@@ -83,7 +84,7 @@ class ActionManager:
                 log_prefix=log_prefix,
                 shutting_down=shutting_down,
                 plugin_config=plugin_config,
-                action_message=action_message,
+                action_message=action_message.flatten() if action_message else None,
             )
 
             logger.debug(f"创建Action实例成功: {action_name}")
@@ -123,4 +124,4 @@ class ActionManager:
         """恢复到默认动作集"""
         actions_to_restore = list(self._using_actions.keys())
         self._using_actions = component_registry.get_default_actions()
-        logger.debug(f"恢复动作集: 从 {actions_to_restore} 恢复到默认动作集 {list(self._using_actions.keys())}")
+        logger.debug(f"恢复动作集: 从 {actions_to_restore} 恢复到默认动作集 {list(self._using_actions.keys())}")

src/chat/planner_actions/planner.py

@@ -1,7 +1,7 @@
 import json
 import time
 import traceback
-from typing import Dict, Any, Optional, Tuple, List
+from typing import Dict, Optional, Tuple, List
 from rich.traceback import install
 from datetime import datetime
 from json_repair import repair_json
@@ -9,6 +9,8 @@ from json_repair import repair_json
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config, model_config
 from src.common.logger import get_logger
+from src.common.data_models.database_data_model import DatabaseMessages
+from src.common.data_models.info_data_model import ActionPlannerInfo
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.chat_message_builder import (
     build_readable_actions,
@@ -97,7 +99,9 @@ class ActionPlanner:
         self.plan_retry_count = 0
         self.max_plan_retries = 3
 
-    def find_message_by_id(self, message_id: str, message_id_list: list) -> Optional[Dict[str, Any]]:
+    def find_message_by_id(
+        self, message_id: str, message_id_list: List[DatabaseMessages]
+    ) -> Optional[DatabaseMessages]:
         # sourcery skip: use-next
         """
         根据message_id从message_id_list中查找对应的原始消息
@@ -110,37 +114,37 @@ class ActionPlanner:
             找到的原始消息字典，如果未找到则返回None
         """
         for item in message_id_list:
-            if item.get("id") == message_id:
-                return item.get("message")
+            if item.message_id == message_id:
+                return item
         return None
 
-    def get_latest_message(self, message_id_list: list) -> Optional[Dict[str, Any]]:
+    def get_latest_message(self, message_id_list: List[DatabaseMessages]) -> Optional[DatabaseMessages]:
         """
         获取消息列表中的最新消息
-        
+
         Args:
             message_id_list: 消息ID列表，格式为[{'id': str, 'message': dict}, ...]
-            
+
         Returns:
             最新的消息字典，如果列表为空则返回None
         """
-        return message_id_list[-1].get("message") if message_id_list else None
+        return message_id_list[-1] if message_id_list else None
 
     async def plan(
         self,
         mode: ChatMode = ChatMode.FOCUS,
-        loop_start_time:float = 0.0,
+        loop_start_time: float = 0.0,
         available_actions: Optional[Dict[str, ActionInfo]] = None,
-    ) -> Tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
+    ) -> Tuple[List[ActionPlannerInfo], Optional[DatabaseMessages]]:
         """
         规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
         """
 
-        action = "no_action"  # 默认动作
-        reasoning = "规划器初始化默认"
+        action: str = "no_action"  # 默认动作
+        reasoning: str = "规划器初始化默认"
         action_data = {}
         current_available_actions: Dict[str, ActionInfo] = {}
-        target_message: Optional[Dict[str, Any]] = None  # 初始化target_message变量
+        target_message: Optional[DatabaseMessages] = None  # 初始化target_message变量
         prompt: str = ""
         message_id_list: list = []
 
@@ -208,19 +212,21 @@ class ActionPlanner:
                             # 如果获取的target_message为None，输出warning并重新plan
                             if target_message is None:
                                 self.plan_retry_count += 1
-                                logger.warning(f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息，重试次数: {self.plan_retry_count}/{self.max_plan_retries}")
+                                logger.warning(
+                                    f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息，重试次数: {self.plan_retry_count}/{self.max_plan_retries}"
+                                )
                                 # 仍有重试次数
                                 if self.plan_retry_count < self.max_plan_retries:
                                     # 递归重新plan
                                     return await self.plan(mode, loop_start_time, available_actions)
-                                logger.error(f"{self.log_prefix}连续{self.max_plan_retries}次plan获取target_message失败，选择最新消息作为target_message")
+                                logger.error(
+                                    f"{self.log_prefix}连续{self.max_plan_retries}次plan获取target_message失败，选择最新消息作为target_message"
+                                )
                                 target_message = self.get_latest_message(message_id_list)
                             self.plan_retry_count = 0  # 重置计数器
                         else:
                             logger.warning(f"{self.log_prefix}动作'{action}'缺少target_message_id")
 
-
-
                     if action != "no_action" and action != "reply" and action not in current_available_actions:
                         logger.warning(
                             f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_action'"
@@ -244,38 +250,37 @@ class ActionPlanner:
         if mode == ChatMode.NORMAL and action in current_available_actions:
             is_parallel = current_available_actions[action].parallel_action
 
-
         action_data["loop_start_time"] = loop_start_time
 
         actions = [
-            {
-                "action_type": action,
-                "reasoning": reasoning,
-                "action_data": action_data,
-                "action_message": target_message,
-                "available_actions": available_actions,
-            }
+            ActionPlannerInfo(
+                action_type=action,
+                reasoning=reasoning,
+                action_data=action_data,
+                action_message=target_message,
+                available_actions=available_actions,
+            )
         ]
 
         if action != "reply" and is_parallel:
-            actions.append({
-                "action_type": "reply",
-                "action_message": target_message,
-                "available_actions": available_actions
-            })
+            actions.append(
+                ActionPlannerInfo(
+                    action_type="reply",
+                    action_message=target_message,
+                    available_actions=available_actions,
+                )
+            )
 
-        return actions,target_message
-    
-    
+        return actions, target_message
 
     async def build_planner_prompt(
         self,
         is_group_chat: bool,  # Now passed as argument
         chat_target_info: Optional[dict],  # Now passed as argument
         current_available_actions: Dict[str, ActionInfo],
-        refresh_time :bool = False,
+        refresh_time: bool = False,
         mode: ChatMode = ChatMode.FOCUS,
-    ) -> tuple[str, list]:  # sourcery skip: use-join
+    ) -> tuple[str, List[DatabaseMessages]]:  # sourcery skip: use-join
         """构建 Planner LLM 的提示词 (获取模板并填充数据)"""
         try:
             message_list_before_now = get_raw_msg_before_timestamp_with_chat(
@@ -305,13 +310,12 @@ class ActionPlanner:
             actions_before_now_block = f"你刚刚选择并执行过的action是：\n{actions_before_now_block}"
             if refresh_time:
                 self.last_obs_time_mark = time.time()
-            
+
             mentioned_bonus = ""
             if global_config.chat.mentioned_bot_inevitable_reply:
                 mentioned_bonus = "\n- 有人提到你"
             if global_config.chat.at_bot_inevitable_reply:
                 mentioned_bonus = "\n- 有人提到你，或者at你"
-            
 
             if mode == ChatMode.FOCUS:
                 no_action_block = """
@@ -332,7 +336,7 @@ class ActionPlanner:
 """
 
             chat_context_description = "你现在正在一个群聊中"
-            chat_target_name = None 
+            chat_target_name = None
             if not is_group_chat and chat_target_info:
                 chat_target_name = (
                     chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方"
@@ -388,7 +392,7 @@ class ActionPlanner:
                 action_options_text=action_options_block,
                 moderation_prompt=moderation_prompt_block,
                 identity_block=identity_block,
-                plan_style = global_config.personality.plan_style
+                plan_style=global_config.personality.plan_style,
             )
             return prompt, message_id_list
         except Exception as e:

src/chat/replyer/default_generator.py

@@ -9,6 +9,7 @@ from datetime import datetime
 from src.mais4u.mai_think import mai_thinking_manager
 from src.common.logger import get_logger
 from src.common.data_models.database_data_model import DatabaseMessages
+from src.common.data_models.info_data_model import ActionPlannerInfo
 from src.config.config import global_config, model_config
 from src.individuality.individuality import get_individuality
 from src.llm_models.utils_model import LLMRequest
@@ -21,7 +22,7 @@ from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.chat_message_builder import (
     build_readable_messages,
     get_raw_msg_before_timestamp_with_chat,
-    replace_user_references_sync,
+    replace_user_references,
 )
 from src.chat.express.expression_selector import expression_selector
 from src.chat.memory_system.memory_activator import MemoryActivator
@@ -157,12 +158,12 @@ class DefaultReplyer:
         extra_info: str = "",
         reply_reason: str = "",
         available_actions: Optional[Dict[str, ActionInfo]] = None,
-        chosen_actions: Optional[List[Dict[str, Any]]] = None,
+        chosen_actions: Optional[List[ActionPlannerInfo]] = None,
         enable_tool: bool = True,
         from_plugin: bool = True,
         stream_id: Optional[str] = None,
-        reply_message: Optional[Dict[str, Any]] = None,
-    ) -> Tuple[bool, Optional[Dict[str, Any]], Optional[str], List[Dict[str, Any]]]:
+        reply_message: Optional[DatabaseMessages] = None,
+    ) -> Tuple[bool, Optional[Dict[str, Any]], Optional[str], Optional[List[int]]]:
         # sourcery skip: merge-nested-ifs
         """
         回复器 (Replier): 负责生成回复文本的核心逻辑。
@@ -181,7 +182,7 @@ class DefaultReplyer:
         """
 
         prompt = None
-        selected_expressions = None
+        selected_expressions: Optional[List[int]] = None
         if available_actions is None:
             available_actions = {}
         try:
@@ -374,7 +375,12 @@ class DefaultReplyer:
         )
 
         if global_config.memory.enable_instant_memory:
-            asyncio.create_task(self.instant_memory.create_and_store_memory(chat_history))
+            chat_history_str = build_readable_messages(
+                messages=chat_history,
+                replace_bot_name=True,
+                timestamp_mode="normal"
+            )
+            asyncio.create_task(self.instant_memory.create_and_store_memory(chat_history_str))
 
             instant_memory = await self.instant_memory.get_memory(target)
             logger.info(f"即时记忆：{instant_memory}")
@@ -527,7 +533,7 @@ class DefaultReplyer:
         Returns:
             Tuple[str, str]: (核心对话prompt, 背景对话prompt)
         """
-        core_dialogue_list = []
+        core_dialogue_list: List[DatabaseMessages] = []
         bot_id = str(global_config.bot.qq_account)
 
         # 过滤消息：分离bot和目标用户的对话 vs 其他用户的对话
@@ -559,7 +565,7 @@ class DefaultReplyer:
         if core_dialogue_list:
             # 检查最新五条消息中是否包含bot自己说的消息
             latest_5_messages = core_dialogue_list[-5:] if len(core_dialogue_list) >= 5 else core_dialogue_list
-            has_bot_message = any(str(msg.get("user_id")) == bot_id for msg in latest_5_messages)
+            has_bot_message = any(str(msg.user_info.user_id) == bot_id for msg in latest_5_messages)
 
             # logger.info(f"最新五条消息：{latest_5_messages}")
             # logger.info(f"最新五条消息中是否包含bot自己说的消息：{has_bot_message}")
@@ -634,7 +640,7 @@ class DefaultReplyer:
         return mai_think
 
     async def build_actions_prompt(
-        self, available_actions, choosen_actions: Optional[List[Dict[str, Any]]] = None
+        self, available_actions: Dict[str, ActionInfo], chosen_actions_info: Optional[List[ActionPlannerInfo]] = None
     ) -> str:
         """构建动作提示"""
 
@@ -646,20 +652,21 @@ class DefaultReplyer:
                 action_descriptions += f"- {action_name}: {action_description}\n"
             action_descriptions += "\n"
 
-        choosen_action_descriptions = ""
-        if choosen_actions:
-            for action in choosen_actions:
-                action_name = action.get("action_type", "unknown_action")
+        chosen_action_descriptions = ""
+        if chosen_actions_info:
+            for action_plan_info in chosen_actions_info:
+                action_name = action_plan_info.action_type
                 if action_name == "reply":
                     continue
-                action_description = action.get("reason", "无描述")
-                reasoning = action.get("reasoning", "无原因")
+                if action := available_actions.get(action_name):
+                    action_description = action.description or "无描述"
+                    reasoning = action_plan_info.reasoning or "无原因"
 
-                choosen_action_descriptions += f"- {action_name}: {action_description}，原因：{reasoning}\n"
+                chosen_action_descriptions += f"- {action_name}: {action_description}，原因：{reasoning}\n"
 
-        if choosen_action_descriptions:
+        if chosen_action_descriptions:
             action_descriptions += "根据聊天情况，另一个模型决定在回复的同时做以下这些动作：\n"
-            action_descriptions += choosen_action_descriptions
+            action_descriptions += chosen_action_descriptions
 
         return action_descriptions
 
@@ -668,9 +675,9 @@ class DefaultReplyer:
         extra_info: str = "",
         reply_reason: str = "",
         available_actions: Optional[Dict[str, ActionInfo]] = None,
-        chosen_actions: Optional[List[Dict[str, Any]]] = None,
+        chosen_actions: Optional[List[ActionPlannerInfo]] = None,
         enable_tool: bool = True,
-        reply_message: Optional[Dict[str, Any]] = None,
+        reply_message: Optional[DatabaseMessages] = None,
     ) -> Tuple[str, List[int]]:
         """
         构建回复器上下文
@@ -694,11 +701,11 @@ class DefaultReplyer:
         platform = chat_stream.platform
 
         if reply_message:
-            user_id = reply_message.get("user_id", "")
+            user_id = reply_message.user_info.user_id
             person = Person(platform=platform, user_id=user_id)
             person_name = person.person_name or user_id
             sender = person_name
-            target = reply_message.get("processed_plain_text")
+            target = reply_message.processed_plain_text
         else:
             person_name = "用户"
             sender = "用户"
@@ -710,7 +717,7 @@ class DefaultReplyer:
         else:
             mood_prompt = ""
 
-        target = replace_user_references_sync(target, chat_stream.platform, replace_bot_name=True)
+        target = replace_user_references(target, chat_stream.platform, replace_bot_name=True)
 
         message_list_before_now_long = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_id,
@@ -774,11 +781,13 @@ class DefaultReplyer:
         logger.info(f"回复准备: {'; '.join(timing_logs)}; {almost_zero_str} <0.01s")
 
         expression_habits_block, selected_expressions = results_dict["expression_habits"]
-        relation_info = results_dict["relation_info"]
-        memory_block = results_dict["memory_block"]
-        tool_info = results_dict["tool_info"]
-        prompt_info = results_dict["prompt_info"]  # 直接使用格式化后的结果
-        actions_info = results_dict["actions_info"]
+        expression_habits_block: str
+        selected_expressions: List[int]
+        relation_info: str = results_dict["relation_info"]
+        memory_block: str = results_dict["memory_block"]
+        tool_info: str = results_dict["tool_info"]
+        prompt_info: str = results_dict["prompt_info"]  # 直接使用格式化后的结果
+        actions_info: str = results_dict["actions_info"]
         keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
 
         if extra_info:

src/chat/utils/chat_message_builder.py

@@ -19,8 +19,8 @@ install(extra_lines=3)
 logger = get_logger("chat_message_builder")
 
 
-def replace_user_references_sync(
-    content: str,
+def replace_user_references(
+    content: Optional[str],
     platform: str,
     name_resolver: Optional[Callable[[str, str], str]] = None,
     replace_bot_name: bool = True,
@@ -38,6 +38,8 @@ def replace_user_references_sync(
     Returns:
         str: 处理后的内容字符串
     """
+    if not content:
+        return ""
     if name_resolver is None:
 
         def default_resolver(platform: str, user_id: str) -> str:
@@ -93,80 +95,6 @@ def replace_user_references_sync(
     return content
 
 
-async def replace_user_references_async(
-    content: str,
-    platform: str,
-    name_resolver: Optional[Callable[[str, str], Any]] = None,
-    replace_bot_name: bool = True,
-) -> str:
-    """
-    替换内容中的用户引用格式，包括回复<aaa:bbb>和@<aaa:bbb>格式
-
-    Args:
-        content: 要处理的内容字符串
-        platform: 平台标识
-        name_resolver: 名称解析函数，接收(platform, user_id)参数，返回用户名称
-                       如果为None，则使用默认的person_info_manager
-        replace_bot_name: 是否将机器人的user_id替换为"机器人昵称(你)"
-
-    Returns:
-        str: 处理后的内容字符串
-    """
-    if name_resolver is None:
-
-        async def default_resolver(platform: str, user_id: str) -> str:
-            # 检查是否是机器人自己
-            if replace_bot_name and user_id == global_config.bot.qq_account:
-                return f"{global_config.bot.nickname}(你)"
-            person = Person(platform=platform, user_id=user_id)
-            return person.person_name or user_id  # type: ignore
-
-        name_resolver = default_resolver
-
-    # 处理回复<aaa:bbb>格式
-    reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
-    match = re.search(reply_pattern, content)
-    if match:
-        aaa = match.group(1)
-        bbb = match.group(2)
-        try:
-            # 检查是否是机器人自己
-            if replace_bot_name and bbb == global_config.bot.qq_account:
-                reply_person_name = f"{global_config.bot.nickname}(你)"
-            else:
-                reply_person_name = await name_resolver(platform, bbb) or aaa
-            content = re.sub(reply_pattern, f"回复 {reply_person_name}", content, count=1)
-        except Exception:
-            # 如果解析失败，使用原始昵称
-            content = re.sub(reply_pattern, f"回复 {aaa}", content, count=1)
-
-    # 处理@<aaa:bbb>格式
-    at_pattern = r"@<([^:<>]+):([^:<>]+)>"
-    at_matches = list(re.finditer(at_pattern, content))
-    if at_matches:
-        new_content = ""
-        last_end = 0
-        for m in at_matches:
-            new_content += content[last_end : m.start()]
-            aaa = m.group(1)
-            bbb = m.group(2)
-            try:
-                # 检查是否是机器人自己
-                if replace_bot_name and bbb == global_config.bot.qq_account:
-                    at_person_name = f"{global_config.bot.nickname}(你)"
-                else:
-                    at_person_name = await name_resolver(platform, bbb) or aaa
-                new_content += f"@{at_person_name}"
-            except Exception:
-                # 如果解析失败，使用原始昵称
-                new_content += f"@{aaa}"
-            last_end = m.end()
-        new_content += content[last_end:]
-        content = new_content
-
-    return content
-
-
 def get_raw_msg_by_timestamp(timestamp_start: float, timestamp_end: float, limit: int = 0, limit_mode: str = "latest"):
     """
     获取从指定时间戳到指定时间戳的消息，按时间升序排序，返回消息列表
@@ -498,7 +426,7 @@ def _build_readable_messages_internal(
             person_name = f"{global_config.bot.nickname}(你)"
 
         # 使用独立函数处理用户引用格式
-        if content := replace_user_references_sync(content, platform, replace_bot_name=replace_bot_name):
+        if content := replace_user_references(content, platform, replace_bot_name=replace_bot_name):
             detailed_messages_raw.append((timestamp, person_name, content, False))
 
     if not detailed_messages_raw:
@@ -658,7 +586,10 @@ async def build_readable_messages_with_list(
     允许通过参数控制格式化行为。
     """
     formatted_string, details_list, pic_id_mapping, _ = _build_readable_messages_internal(
-        convert_DatabaseMessages_to_MessageAndActionModel(messages), replace_bot_name, timestamp_mode, truncate
+        [MessageAndActionModel.from_DatabaseMessages(msg) for msg in messages],
+        replace_bot_name,
+        timestamp_mode,
+        truncate,
     )
 
     if pic_mapping_info := build_pic_mapping_info(pic_id_mapping):
@@ -725,19 +656,7 @@ def build_readable_messages(
     if not messages:
         return ""
 
-    copy_messages: List[MessageAndActionModel] = [
-        MessageAndActionModel(
-            msg.time,
-            msg.user_info.user_id,
-            msg.user_info.platform,
-            msg.user_info.user_nickname,
-            msg.user_info.user_cardname,
-            msg.processed_plain_text,
-            msg.display_message,
-            msg.chat_info.platform,
-        )
-        for msg in messages
-    ]
+    copy_messages: List[MessageAndActionModel] = [MessageAndActionModel.from_DatabaseMessages(msg) for msg in messages]
 
     if show_actions and copy_messages:
         # 获取所有消息的时间范围
@@ -942,7 +861,7 @@ async def build_anonymous_messages(messages: List[DatabaseMessages]) -> str:
                 except Exception:
                     return "?"
 
-            content = replace_user_references_sync(content, platform, anon_name_resolver, replace_bot_name=False)
+            content = replace_user_references(content, platform, anon_name_resolver, replace_bot_name=False)
 
             header = f"{anon_name}说 "
             output_lines.append(header)
@@ -996,22 +915,3 @@ async def get_person_id_list(messages: List[Dict[str, Any]]) -> List[str]:
             person_ids_set.add(person_id)
 
     return list(person_ids_set)  # 将集合转换为列表返回
-
-
-def convert_DatabaseMessages_to_MessageAndActionModel(message: List[DatabaseMessages]) -> List[MessageAndActionModel]:
-    """
-    将 DatabaseMessages 列表转换为 MessageAndActionModel 列表。
-    """
-    return [
-        MessageAndActionModel(
-            time=msg.time,
-            user_id=msg.user_info.user_id,
-            user_platform=msg.user_info.platform,
-            user_nickname=msg.user_info.user_nickname,
-            user_cardname=msg.user_info.user_cardname,
-            processed_plain_text=msg.processed_plain_text,
-            display_message=msg.display_message,
-            chat_info_platform=msg.chat_info.platform,
-        )
-        for msg in message
-    ]

src/chat/utils/utils.py

@@ -11,7 +11,6 @@ from collections import Counter
 from typing import Optional, Tuple, Dict, List, Any
 
 from src.common.logger import get_logger
-from src.common.data_models.info_data_model import TargetPersonInfo
 from src.common.data_models.database_data_model import DatabaseMessages
 from src.common.message_repository import find_messages, count_messages
 from src.config.config import global_config, model_config
@@ -641,6 +640,8 @@ def get_chat_type_and_target_info(chat_id: str) -> Tuple[bool, Optional[Dict]]:
                 platform: str = chat_stream.platform
                 user_id: str = user_info.user_id  # type: ignore
 
+                from src.common.data_models.info_data_model import TargetPersonInfo  # 解决循环导入问题
+
                 # Initialize target_info with basic info
                 target_info = TargetPersonInfo(
                     platform=platform,

src/common/data_models/database_data_model.py

@@ -1,4 +1,4 @@
-from typing import Optional, Any
+from typing import Optional, Any, Dict
 from dataclasses import dataclass, field
 
 from . import BaseDataModel
@@ -157,3 +157,42 @@ class DatabaseMessages(BaseDataModel):
     #     assert isinstance(self.interest_value, float) or self.interest_value is None, (
     #         "interest_value must be a float or None"
     #     )
+    def flatten(self) -> Dict[str, Any]:
+        """
+        将消息数据模型转换为字典格式，便于存储或传输
+        """
+        return {
+            "message_id": self.message_id,
+            "time": self.time,
+            "chat_id": self.chat_id,
+            "reply_to": self.reply_to,
+            "interest_value": self.interest_value,
+            "key_words": self.key_words,
+            "key_words_lite": self.key_words_lite,
+            "is_mentioned": self.is_mentioned,
+            "processed_plain_text": self.processed_plain_text,
+            "display_message": self.display_message,
+            "priority_mode": self.priority_mode,
+            "priority_info": self.priority_info,
+            "additional_config": self.additional_config,
+            "is_emoji": self.is_emoji,
+            "is_picid": self.is_picid,
+            "is_command": self.is_command,
+            "is_notify": self.is_notify,
+            "selected_expressions": self.selected_expressions,
+            "user_id": self.user_info.user_id,
+            "user_nickname": self.user_info.user_nickname,
+            "user_cardname": self.user_info.user_cardname,
+            "user_platform": self.user_info.platform,
+            "chat_info_group_id": self.group_info.group_id if self.group_info else None,
+            "chat_info_group_name": self.group_info.group_name if self.group_info else None,
+            "chat_info_group_platform": self.group_info.group_platform if self.group_info else None,
+            "chat_info_stream_id": self.chat_info.stream_id,
+            "chat_info_platform": self.chat_info.platform,
+            "chat_info_create_time": self.chat_info.create_time,
+            "chat_info_last_active_time": self.chat_info.last_active_time,
+            "chat_info_user_platform": self.chat_info.user_info.platform,
+            "chat_info_user_id": self.chat_info.user_info.user_id,
+            "chat_info_user_nickname": self.chat_info.user_info.user_nickname,
+            "chat_info_user_cardname": self.chat_info.user_info.user_cardname,
+        }

src/common/data_models/info_data_model.py

@@ -1,12 +1,25 @@
 from dataclasses import dataclass, field
-from typing import Optional
-
+from typing import Optional, Dict, TYPE_CHECKING
 from . import BaseDataModel
 
+if TYPE_CHECKING:
+    from .database_data_model import DatabaseMessages
+    from src.plugin_system.base.component_types import ActionInfo
+
+
 @dataclass
 class TargetPersonInfo(BaseDataModel):
     platform: str = field(default_factory=str)
     user_id: str = field(default_factory=str)
     user_nickname: str = field(default_factory=str)
     person_id: Optional[str] = None
-    person_name: Optional[str] = None
+    person_name: Optional[str] = None
+
+
+@dataclass
+class ActionPlannerInfo(BaseDataModel):
+    action_type: str = field(default_factory=str)
+    reasoning: Optional[str] = None
+    action_data: Optional[Dict] = None
+    action_message: Optional["DatabaseMessages"] = None
+    available_actions: Optional[Dict[str, "ActionInfo"]] = None

src/common/data_models/message_data_model.py

@@ -1,10 +1,15 @@
-from typing import Optional
+from typing import Optional, TYPE_CHECKING
 from dataclasses import dataclass, field
 
 from . import BaseDataModel
 
+if TYPE_CHECKING:
+    from .database_data_model import DatabaseMessages
+
+
 @dataclass
 class MessageAndActionModel(BaseDataModel):
+    chat_id: str = field(default_factory=str)
     time: float = field(default_factory=float)
     user_id: str = field(default_factory=str)
     user_platform: str = field(default_factory=str)
@@ -15,3 +20,17 @@ class MessageAndActionModel(BaseDataModel):
     chat_info_platform: str = field(default_factory=str)
     is_action_record: bool = field(default=False)
     action_name: Optional[str] = None
+
+    @classmethod
+    def from_DatabaseMessages(cls, message: "DatabaseMessages"):
+        return cls(
+            chat_id=message.chat_id,
+            time=message.time,
+            user_id=message.user_info.user_id,
+            user_platform=message.user_info.platform,
+            user_nickname=message.user_info.user_nickname,
+            user_cardname=message.user_info.user_cardname,
+            processed_plain_text=message.processed_plain_text,
+            display_message=message.display_message,
+            chat_info_platform=message.chat_info.platform,
+        )

src/llm_models/model_client/gemini_client.py

@@ -47,10 +47,13 @@ logger = get_logger("Gemini客户端")
 # gemini_thinking参数（默认范围）
 # 不同模型的思考预算范围配置
 THINKING_BUDGET_LIMITS = {
-    "gemini-2.5-flash":       {"min": 1,   "max": 24576, "can_disable": True},
-    "gemini-2.5-flash-lite":  {"min": 512, "max": 24576, "can_disable": True},
-    "gemini-2.5-pro":         {"min": 128, "max": 32768, "can_disable": False},
+    "gemini-2.5-flash": {"min": 1, "max": 24576, "can_disable": True},
+    "gemini-2.5-flash-lite": {"min": 512, "max": 24576, "can_disable": True},
+    "gemini-2.5-pro": {"min": 128, "max": 32768, "can_disable": False},
 }
+# 思维预算特殊值
+THINKING_BUDGET_AUTO = -1  # 自动调整思考预算，由模型决定
+THINKING_BUDGET_DISABLED = 0  # 禁用思考预算（如果模型允许禁用）
 
 gemini_safe_settings = [
     SafetySetting(category=HarmCategory.HARM_CATEGORY_HATE_SPEECH, threshold=HarmBlockThreshold.BLOCK_NONE),
@@ -91,9 +94,7 @@ def _convert_messages(
             for item in message.content:
                 if isinstance(item, tuple):
                     image_format = "jpeg" if item[0].lower() == "jpg" else item[0].lower()
-                    content.append(
-                        Part.from_bytes(data=base64.b64decode(item[1]), mime_type=f"image/{image_format}")
-                    )
+                    content.append(Part.from_bytes(data=base64.b64decode(item[1]), mime_type=f"image/{image_format}"))
                 elif isinstance(item, str):
                     content.append(Part.from_text(text=item))
         else:
@@ -336,47 +337,40 @@ class GeminiClient(BaseClient):
             api_key=api_provider.api_key,
         )  # 这里和openai不一样，gemini会自己决定自己是否需要retry
 
-    # 思维预算特殊值
-    THINKING_BUDGET_AUTO = -1  # 自动调整思考预算，由模型决定
-    THINKING_BUDGET_DISABLED = 0  # 禁用思考预算（如果模型允许禁用）
-
     @staticmethod
-    def clamp_thinking_budget(tb: int, model_id: str):
+    def clamp_thinking_budget(tb: int, model_id: str) -> int:
         """
         按模型限制思考预算范围，仅支持指定的模型（支持带数字后缀的新版本）
         """
         limits = None
-        matched_key = None
 
         # 优先尝试精确匹配
         if model_id in THINKING_BUDGET_LIMITS:
             limits = THINKING_BUDGET_LIMITS[model_id]
-            matched_key = model_id
         else:
             # 按 key 长度倒序，保证更长的（更具体的，如 -lite）优先
             sorted_keys = sorted(THINKING_BUDGET_LIMITS.keys(), key=len, reverse=True)
             for key in sorted_keys:
                 # 必须满足：完全等于 或者 前缀匹配（带 "-" 边界）
-                if model_id == key or model_id.startswith(key + "-"):
-                   limits = THINKING_BUDGET_LIMITS[key]
-                   matched_key = key
-                   break
+                if model_id == key or model_id.startswith(f"{key}-"):
+                    limits = THINKING_BUDGET_LIMITS[key]
+                    break
 
         # 特殊值处理
-        if tb == GeminiClient.THINKING_BUDGET_AUTO:
-            return GeminiClient.THINKING_BUDGET_AUTO
-        if tb == GeminiClient.THINKING_BUDGET_DISABLED:
+        if tb == THINKING_BUDGET_AUTO:
+            return THINKING_BUDGET_AUTO
+        if tb == THINKING_BUDGET_DISABLED:
             if limits and limits.get("can_disable", False):
-                return GeminiClient.THINKING_BUDGET_DISABLED
-            return limits["min"] if limits else GeminiClient.THINKING_BUDGET_AUTO
+                return THINKING_BUDGET_DISABLED
+            return limits["min"] if limits else THINKING_BUDGET_AUTO
 
         # 已知模型裁剪到范围
         if limits:
-           return max(limits["min"], min(tb, limits["max"]))
+            return max(limits["min"], min(tb, limits["max"]))
 
         # 未知模型，返回动态模式
         logger.warning(f"模型 {model_id} 未在 THINKING_BUDGET_LIMITS 中定义，将使用动态模式 tb=-1 兼容。")
-        return GeminiClient.THINKING_BUDGET_AUTO
+        return THINKING_BUDGET_AUTO
 
     async def get_response(
         self,
@@ -424,15 +418,13 @@ class GeminiClient(BaseClient):
         # 将tool_options转换为Gemini API所需的格式
         tools = _convert_tool_options(tool_options) if tool_options else None
 
-        tb = GeminiClient.THINKING_BUDGET_AUTO
-        #空处理
+        tb = THINKING_BUDGET_AUTO
+        # 空处理
         if extra_params and "thinking_budget" in extra_params:
             try:
                 tb = int(extra_params["thinking_budget"])
             except (ValueError, TypeError):
-                logger.warning(
-                f"无效的 thinking_budget 值 {extra_params['thinking_budget']}，将使用默认动态模式 {tb}"
-            )
+                logger.warning(f"无效的 thinking_budget 值 {extra_params['thinking_budget']}，将使用默认动态模式 {tb}")
         # 裁剪到模型支持的范围
         tb = self.clamp_thinking_budget(tb, model_info.model_identifier)
 

src/main.py

@@ -13,6 +13,7 @@ from src.common.logger import get_logger
 from src.individuality.individuality import get_individuality, Individuality
 from src.common.server import get_global_server, Server
 from src.mood.mood_manager import mood_manager
+from src.chat.knowledge import lpmm_start_up
 from rich.traceback import install
 from src.migrate_helper.migrate import check_and_run_migrations
 # from src.api.main import start_api_server
@@ -83,6 +84,9 @@ class MainSystem:
         # 启动API服务器
         # start_api_server()
         # logger.info("API服务器启动成功")
+        
+        # 启动LPMM
+        lpmm_start_up()
 
         # 加载所有actions，包括默认的和插件的
         plugin_manager.load_all_plugins()
@@ -104,7 +108,6 @@ class MainSystem:
         logger.info("情绪管理器初始化成功")
 
         # 初始化聊天管理器
-
         await get_chat_manager()._initialize()
         asyncio.create_task(get_chat_manager()._auto_save_task())
 

src/person_info/person_info.py

@@ -3,6 +3,7 @@ import asyncio
 import json
 import time
 import random
+import math
 
 from json_repair import repair_json
 from typing import Union, Optional
@@ -16,6 +17,7 @@ from src.config.config import global_config, model_config
 
 logger = get_logger("person_info")
 
+
 def get_person_id(platform: str, user_id: Union[int, str]) -> str:
     """获取唯一id"""
     if "-" in platform:
@@ -24,6 +26,7 @@ def get_person_id(platform: str, user_id: Union[int, str]) -> str:
     key = "_".join(components)
     return hashlib.md5(key.encode()).hexdigest()
 
+
 def get_person_id_by_person_name(person_name: str) -> str:
     """根据用户名获取用户ID"""
     try:
@@ -33,7 +36,8 @@ def get_person_id_by_person_name(person_name: str) -> str:
         logger.error(f"根据用户名 {person_name} 获取用户ID时出错 (Peewee): {e}")
         return ""
 
-def is_person_known(person_id: str = None,user_id: str = None,platform: str = None,person_name: str = None) -> bool:
+
+def is_person_known(person_id: str = None, user_id: str = None, platform: str = None, person_name: str = None) -> bool:  # type: ignore
     if person_id:
         person = PersonInfo.get_or_none(PersonInfo.person_id == person_id)
         return person.is_known if person else False
@@ -47,89 +51,84 @@ def is_person_known(person_id: str = None,user_id: str = None,platform: str = No
         return person.is_known if person else False
     else:
         return False
-    
-    
-def get_catagory_from_memory(memory_point:str) -> str:
+
+
+def get_category_from_memory(memory_point: str) -> Optional[str]:
     """从记忆点中获取分类"""
     # 按照最左边的:符号进行分割，返回分割后的第一个部分作为分类
     if not isinstance(memory_point, str):
         return None
     parts = memory_point.split(":", 1)
-    if len(parts) > 1:
-        return parts[0].strip()
-    else:
-        return None
-    
-def get_weight_from_memory(memory_point:str) -> float:
+    return parts[0].strip() if len(parts) > 1 else None
+
+
+def get_weight_from_memory(memory_point: str) -> float:
     """从记忆点中获取权重"""
     # 按照最右边的:符号进行分割，返回分割后的最后一个部分作为权重
     if not isinstance(memory_point, str):
-        return None
+        return -math.inf
     parts = memory_point.rsplit(":", 1)
-    if len(parts) > 1:
-        try:
-            return float(parts[-1].strip())
-        except Exception:
-            return None
-    else:
-        return None
-    
-def get_memory_content_from_memory(memory_point:str) -> str:
+    if len(parts) <= 1:
+        return -math.inf
+    try:
+        return float(parts[-1].strip())
+    except Exception:
+        return -math.inf
+
+
+def get_memory_content_from_memory(memory_point: str) -> str:
     """从记忆点中获取记忆内容"""
     # 按:进行分割，去掉第一段和最后一段，返回中间部分作为记忆内容
     if not isinstance(memory_point, str):
-        return None
+        return ""
     parts = memory_point.split(":")
-    if len(parts) > 2:
-        return ":".join(parts[1:-1]).strip()
-    else:
-        return None
-    
-    
+    return ":".join(parts[1:-1]).strip() if len(parts) > 2 else ""
+
+
 def calculate_string_similarity(s1: str, s2: str) -> float:
     """
     计算两个字符串的相似度
-    
+
     Args:
         s1: 第一个字符串
         s2: 第二个字符串
-        
+
     Returns:
         float: 相似度，范围0-1，1表示完全相同
     """
     if s1 == s2:
         return 1.0
-        
+
     if not s1 or not s2:
         return 0.0
-        
+
     # 计算Levenshtein距离
-    
-    
+
     distance = levenshtein_distance(s1, s2)
     max_len = max(len(s1), len(s2))
-    
+
     # 计算相似度：1 - (编辑距离 / 最大长度)
     similarity = 1 - (distance / max_len if max_len > 0 else 0)
     return similarity
 
+
 def levenshtein_distance(s1: str, s2: str) -> int:
     """
     计算两个字符串的编辑距离
-    
+
     Args:
         s1: 第一个字符串
         s2: 第二个字符串
-        
+
     Returns:
         int: 编辑距离
     """
     if len(s1) < len(s2):
         return levenshtein_distance(s2, s1)
-        
+
     if len(s2) == 0:
         return len(s1)
-        
+
     previous_row = range(len(s2) + 1)
     for i, c1 in enumerate(s1):
         current_row = [i + 1]
@@ -139,44 +138,45 @@ def levenshtein_distance(s1: str, s2: str) -> int:
             substitutions = previous_row[j] + (c1 != c2)
             current_row.append(min(insertions, deletions, substitutions))
         previous_row = current_row
-        
+
     return previous_row[-1]
 
+
 class Person:
     @classmethod
     def register_person(cls, platform: str, user_id: str, nickname: str):
         """
         注册新用户的类方法
         必须输入 platform、user_id 和 nickname 参数
-        
+
         Args:
             platform: 平台名称
             user_id: 用户ID
             nickname: 用户昵称
-            
+
         Returns:
             Person: 新注册的Person实例
         """
         if not platform or not user_id or not nickname:
             logger.error("注册用户失败：platform、user_id 和 nickname 都是必需参数")
             return None
-            
+
         # 生成唯一的person_id
         person_id = get_person_id(platform, user_id)
-        
+
         if is_person_known(person_id=person_id):
             logger.debug(f"用户 {nickname} 已存在")
             return Person(person_id=person_id)
-        
+
         # 创建Person实例
         person = cls.__new__(cls)
-        
+
         # 设置基本属性
         person.person_id = person_id
         person.platform = platform
         person.user_id = user_id
         person.nickname = nickname
-        
+
         # 初始化默认值
         person.is_known = True  # 注册后立即标记为已认识
         person.person_name = nickname  # 使用nickname作为初始person_name
@@ -185,34 +185,34 @@ class Person:
         person.know_since = time.time()
         person.last_know = time.time()
         person.memory_points = []
-        
+
         # 初始化性格特征相关字段
         person.attitude_to_me = 0
         person.attitude_to_me_confidence = 1
-        
+
         person.neuroticism = 5
         person.neuroticism_confidence = 1
-        
+
         person.friendly_value = 50
         person.friendly_value_confidence = 1
-        
+
         person.rudeness = 50
         person.rudeness_confidence = 1
-        
+
         person.conscientiousness = 50
         person.conscientiousness_confidence = 1
-        
+
         person.likeness = 50
         person.likeness_confidence = 1
-        
+
         # 同步到数据库
         person.sync_to_database()
-        
+
         logger.info(f"成功注册新用户：{person_id}，平台：{platform}，昵称：{nickname}")
-        
+
         return person
-    
-    def __init__(self, platform: str = "", user_id: str = "",person_id: str = "",person_name: str = ""):
+
+    def __init__(self, platform: str = "", user_id: str = "", person_id: str = "", person_name: str = ""):
         if platform == global_config.bot.platform and user_id == global_config.bot.qq_account:
             self.is_known = True
             self.person_id = get_person_id(platform, user_id)
@@ -221,10 +221,10 @@ class Person:
             self.nickname = global_config.bot.nickname
             self.person_name = global_config.bot.nickname
             return
-        
+
         self.user_id = ""
         self.platform = ""
-        
+
         if person_id:
             self.person_id = person_id
         elif person_name:
@@ -232,7 +232,7 @@ class Person:
             if not self.person_id:
                 self.is_known = False
                 logger.warning(f"根据用户名 {person_name} 获取用户ID时，不存在用户{person_name}")
-                return 
+                return
         elif platform and user_id:
             self.person_id = get_person_id(platform, user_id)
             self.user_id = user_id
@@ -240,17 +240,16 @@ class Person:
         else:
             logger.error("Person 初始化失败，缺少必要参数")
             raise ValueError("Person 初始化失败，缺少必要参数")
-        
+
         if not is_person_known(person_id=self.person_id):
             self.is_known = False
             logger.debug(f"用户 {platform}:{user_id}:{person_name}:{person_id} 尚未认识")
             self.person_name = f"未知用户{self.person_id[:4]}"
             return
             # raise ValueError(f"用户 {platform}:{user_id}:{person_name}:{person_id} 尚未认识")
-            
-        
+
         self.is_known = False
-        
+
         # 初始化默认值
         self.nickname = ""
         self.person_name: Optional[str] = None
@@ -259,47 +258,47 @@ class Person:
         self.know_since = None
         self.last_know = None
         self.memory_points = []
-        
+
         # 初始化性格特征相关字段
-        self.attitude_to_me:float = 0
-        self.attitude_to_me_confidence:float = 1
-        
-        self.neuroticism:float = 5
-        self.neuroticism_confidence:float = 1
-        
-        self.friendly_value:float = 50
-        self.friendly_value_confidence:float = 1
-        
-        self.rudeness:float = 50
-        self.rudeness_confidence:float = 1
-        
-        self.conscientiousness:float = 50
-        self.conscientiousness_confidence:float = 1
-        
-        self.likeness:float = 50
-        self.likeness_confidence:float = 1
-        
+        self.attitude_to_me: float = 0
+        self.attitude_to_me_confidence: float = 1
+
+        self.neuroticism: float = 5
+        self.neuroticism_confidence: float = 1
+
+        self.friendly_value: float = 50
+        self.friendly_value_confidence: float = 1
+
+        self.rudeness: float = 50
+        self.rudeness_confidence: float = 1
+
+        self.conscientiousness: float = 50
+        self.conscientiousness_confidence: float = 1
+
+        self.likeness: float = 50
+        self.likeness_confidence: float = 1
+
         # 从数据库加载数据
         self.load_from_database()
-        
+
     def del_memory(self, category: str, memory_content: str, similarity_threshold: float = 0.95):
         """
         删除指定分类和记忆内容的记忆点
-        
+
         Args:
             category: 记忆分类
             memory_content: 要删除的记忆内容
             similarity_threshold: 相似度阈值，默认0.95（95%）
-            
+
         Returns:
             int: 删除的记忆点数量
         """
         if not self.memory_points:
             return 0
-            
+
         deleted_count = 0
         memory_points_to_keep = []
-        
+
         for memory_point in self.memory_points:
             # 跳过None值
             if memory_point is None:
@@ -310,80 +309,76 @@ class Person:
                 # 格式不正确，保留原样
                 memory_points_to_keep.append(memory_point)
                 continue
-                
+
             memory_category = parts[0].strip()
             memory_text = parts[1].strip()
             memory_weight = parts[2].strip()
-            
+
             # 检查分类是否匹配
             if memory_category != category:
                 memory_points_to_keep.append(memory_point)
                 continue
-                
+
             # 计算记忆内容的相似度
             similarity = calculate_string_similarity(memory_content, memory_text)
-            
+
             # 如果相似度达到阈值，则删除（不添加到保留列表）
             if similarity >= similarity_threshold:
                 deleted_count += 1
                 logger.debug(f"删除记忆点: {memory_point} (相似度: {similarity:.4f})")
             else:
                 memory_points_to_keep.append(memory_point)
-        
+
         # 更新memory_points
         self.memory_points = memory_points_to_keep
-        
+
         # 同步到数据库
         if deleted_count > 0:
             self.sync_to_database()
             logger.info(f"成功删除 {deleted_count} 个记忆点，分类: {category}")
-        
+
         return deleted_count
-    
-
-
 
     def get_all_category(self):
         category_list = []
         for memory in self.memory_points:
             if memory is None:
                 continue
-            category = get_catagory_from_memory(memory)
+            category = get_category_from_memory(memory)
             if category and category not in category_list:
                 category_list.append(category)
         return category_list
-        
-    
-    def get_memory_list_by_category(self,category:str):
+
+    def get_memory_list_by_category(self, category: str):
         memory_list = []
         for memory in self.memory_points:
             if memory is None:
                 continue
-            if get_catagory_from_memory(memory) == category:
+            if get_category_from_memory(memory) == category:
                 memory_list.append(memory)
         return memory_list
-    
-    def get_random_memory_by_category(self,category:str,num:int=1):
+
+    def get_random_memory_by_category(self, category: str, num: int = 1):
         memory_list = self.get_memory_list_by_category(category)
         if len(memory_list) < num:
             return memory_list
         return random.sample(memory_list, num)
-    
+
     def load_from_database(self):
         """从数据库加载个人信息数据"""
         try:
             # 查询数据库中的记录
             record = PersonInfo.get_or_none(PersonInfo.person_id == self.person_id)
-            
+
             if record:
-                self.user_id = record.user_id if record.user_id else ""
-                self.platform = record.platform if record.platform else "" 
-                self.is_known = record.is_known if record.is_known else False
-                self.nickname = record.nickname if record.nickname else ""
-                self.person_name = record.person_name if record.person_name else self.nickname
-                self.name_reason = record.name_reason if record.name_reason else None
-                self.know_times = record.know_times if record.know_times else 0
-                
+                self.user_id = record.user_id or ""
+                self.platform = record.platform or ""
+                self.is_known = record.is_known or False
+                self.nickname = record.nickname or ""
+                self.person_name = record.person_name or self.nickname
+                self.name_reason = record.name_reason or None
+                self.know_times = record.know_times or 0
+
                 # 处理points字段（JSON格式的列表）
                 if record.memory_points:
                     try:
@@ -398,53 +393,53 @@ class Person:
                         self.memory_points = []
                 else:
                     self.memory_points = []
-                
+
                 # 加载性格特征相关字段
                 if record.attitude_to_me and not isinstance(record.attitude_to_me, str):
                     self.attitude_to_me = record.attitude_to_me
-                
+
                 if record.attitude_to_me_confidence is not None:
                     self.attitude_to_me_confidence = float(record.attitude_to_me_confidence)
-                
+
                 if record.friendly_value is not None:
                     self.friendly_value = float(record.friendly_value)
-                
+
                 if record.friendly_value_confidence is not None:
                     self.friendly_value_confidence = float(record.friendly_value_confidence)
-                
+
                 if record.rudeness is not None:
                     self.rudeness = float(record.rudeness)
-                
+
                 if record.rudeness_confidence is not None:
                     self.rudeness_confidence = float(record.rudeness_confidence)
-                
+
                 if record.neuroticism and not isinstance(record.neuroticism, str):
                     self.neuroticism = float(record.neuroticism)
-                
+
                 if record.neuroticism_confidence is not None:
                     self.neuroticism_confidence = float(record.neuroticism_confidence)
-                
+
                 if record.conscientiousness is not None:
                     self.conscientiousness = float(record.conscientiousness)
-                
+
                 if record.conscientiousness_confidence is not None:
                     self.conscientiousness_confidence = float(record.conscientiousness_confidence)
-                
+
                 if record.likeness is not None:
                     self.likeness = float(record.likeness)
-                
+
                 if record.likeness_confidence is not None:
                     self.likeness_confidence = float(record.likeness_confidence)
-                
+
                 logger.debug(f"已从数据库加载用户 {self.person_id} 的信息")
             else:
                 self.sync_to_database()
                 logger.info(f"用户 {self.person_id} 在数据库中不存在，使用默认值并创建")
-                
+
         except Exception as e:
             logger.error(f"从数据库加载用户 {self.person_id} 信息时出错: {e}")
             # 出错时保持默认值
-    
+
     def sync_to_database(self):
         """将所有属性同步回数据库"""
         if not self.is_known:
@@ -452,34 +447,38 @@ class Person:
         try:
             # 准备数据
             data = {
-                'person_id': self.person_id,
-                'is_known': self.is_known,
-                'platform': self.platform,
-                'user_id': self.user_id,
-                'nickname': self.nickname,
-                'person_name': self.person_name,
-                'name_reason': self.name_reason,
-                'know_times': self.know_times,
-                'know_since': self.know_since,
-                'last_know': self.last_know,
-                'memory_points': json.dumps([point for point in self.memory_points if point is not None], ensure_ascii=False) if self.memory_points else json.dumps([], ensure_ascii=False),
-                'attitude_to_me': self.attitude_to_me,
-                'attitude_to_me_confidence': self.attitude_to_me_confidence,
-                'friendly_value': self.friendly_value,
-                'friendly_value_confidence': self.friendly_value_confidence,
-                'rudeness': self.rudeness,
-                'rudeness_confidence': self.rudeness_confidence,
-                'neuroticism': self.neuroticism,
-                'neuroticism_confidence': self.neuroticism_confidence,
-                'conscientiousness': self.conscientiousness,
-                'conscientiousness_confidence': self.conscientiousness_confidence,
-                'likeness': self.likeness,
-                'likeness_confidence': self.likeness_confidence,
+                "person_id": self.person_id,
+                "is_known": self.is_known,
+                "platform": self.platform,
+                "user_id": self.user_id,
+                "nickname": self.nickname,
+                "person_name": self.person_name,
+                "name_reason": self.name_reason,
+                "know_times": self.know_times,
+                "know_since": self.know_since,
+                "last_know": self.last_know,
+                "memory_points": json.dumps(
+                    [point for point in self.memory_points if point is not None], ensure_ascii=False
+                )
+                if self.memory_points
+                else json.dumps([], ensure_ascii=False),
+                "attitude_to_me": self.attitude_to_me,
+                "attitude_to_me_confidence": self.attitude_to_me_confidence,
+                "friendly_value": self.friendly_value,
+                "friendly_value_confidence": self.friendly_value_confidence,
+                "rudeness": self.rudeness,
+                "rudeness_confidence": self.rudeness_confidence,
+                "neuroticism": self.neuroticism,
+                "neuroticism_confidence": self.neuroticism_confidence,
+                "conscientiousness": self.conscientiousness,
+                "conscientiousness_confidence": self.conscientiousness_confidence,
+                "likeness": self.likeness,
+                "likeness_confidence": self.likeness_confidence,
             }
-            
+
             # 检查记录是否存在
             record = PersonInfo.get_or_none(PersonInfo.person_id == self.person_id)
-            
+
             if record:
                 # 更新现有记录
                 for field, value in data.items():
@@ -491,10 +490,10 @@ class Person:
                 # 创建新记录
                 PersonInfo.create(**data)
                 logger.debug(f"已创建用户 {self.person_id} 的信息到数据库")
-                
+
         except Exception as e:
             logger.error(f"同步用户 {self.person_id} 信息到数据库时出错: {e}")
-            
+
     def build_relationship(self):
         if not self.is_known:
             return ""
@@ -505,22 +504,21 @@ class Person:
             nickname_str = f"(ta在{self.platform}上的昵称是{self.nickname})"
 
         relation_info = ""
-        
+
         attitude_info = ""
         if self.attitude_to_me:
             if self.attitude_to_me > 8:
                 attitude_info = f"{self.person_name}对你的态度十分好,"
             elif self.attitude_to_me > 5:
                 attitude_info = f"{self.person_name}对你的态度较好,"
-                
-            
+
             if self.attitude_to_me < -8:
                 attitude_info = f"{self.person_name}对你的态度十分恶劣,"
             elif self.attitude_to_me < -4:
                 attitude_info = f"{self.person_name}对你的态度不好,"
             elif self.attitude_to_me < 0:
                 attitude_info = f"{self.person_name}对你的态度一般,"
-                
+
         neuroticism_info = ""
         if self.neuroticism:
             if self.neuroticism > 8:
@@ -533,29 +531,28 @@ class Person:
                 neuroticism_info = f"{self.person_name}的情绪比较稳定,"
             else:
                 neuroticism_info = f"{self.person_name}的情绪非常稳定,毫无波动"
-        
+
         points_text = ""
         category_list = self.get_all_category()
         for category in category_list:
-            random_memory = self.get_random_memory_by_category(category,1)[0]
+            random_memory = self.get_random_memory_by_category(category, 1)[0]
             if random_memory:
                 points_text = f"有关 {category} 的记忆：{get_memory_content_from_memory(random_memory)}"
                 break
-        
+
         points_info = ""
         if points_text:
             points_info = f"你还记得有关{self.person_name}的最近记忆：{points_text}"
-                
+
         if not (nickname_str or attitude_info or neuroticism_info or points_info):
             return ""
         relation_info = f"{self.person_name}:{nickname_str}{attitude_info}{neuroticism_info}{points_info}"
-        
+
         return relation_info
 
 
 class PersonInfoManager:
     def __init__(self):
-        
         self.person_name_list = {}
         self.qv_name_llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="relation.qv_name")
         try:
@@ -580,8 +577,6 @@ class PersonInfoManager:
             logger.debug(f"已加载 {len(self.person_name_list)} 个用户名称 (Peewee)")
         except Exception as e:
             logger.error(f"从 Peewee 加载 person_name_list 失败: {e}")
-        
-
 
     @staticmethod
     def _extract_json_from_text(text: str) -> dict:
@@ -717,6 +712,6 @@ class PersonInfoManager:
         person.sync_to_database()
         self.person_name_list[person_id] = unique_nickname
         return {"nickname": unique_nickname, "reason": "使用用户原始昵称作为默认值"}
-    
+
 
 person_info_manager = PersonInfoManager()

src/person_info/relationship_builder.py

@@ -3,7 +3,8 @@ import traceback
 import os
 import pickle
 import random
-from typing import List, Dict, Any
+import asyncio
+from typing import List, Dict, Any, TYPE_CHECKING
 from src.config.config import global_config
 from src.common.logger import get_logger
 from src.person_info.relationship_manager import get_relationship_manager
@@ -15,7 +16,9 @@ from src.chat.utils.chat_message_builder import (
     get_raw_msg_before_timestamp_with_chat,
     num_new_messages_since,
 )
-import asyncio
+
+if TYPE_CHECKING:
+    from src.common.data_models.database_data_model import DatabaseMessages
 
 logger = get_logger("relationship_builder")
 
@@ -429,7 +432,7 @@ class RelationshipBuilder:
             if dropped_count > 0:
                 logger.debug(f"为 {person_id} 随机丢弃了 {dropped_count} / {original_segment_count} 个消息段")
 
-            processed_messages = []
+            processed_messages: List["DatabaseMessages"] = []
 
             # 对筛选后的消息段进行排序，确保时间顺序
             segments_to_process.sort(key=lambda x: x["start_time"])
@@ -449,17 +452,18 @@ class RelationshipBuilder:
                     # 如果 processed_messages 不为空，说明这不是第一个被处理的消息段，在消息列表前添加间隔标识
                     if processed_messages:
                         # 创建一个特殊的间隔消息
-                        gap_message = {
-                            "time": start_time - 0.1,  # 稍微早于段开始时间
-                            "user_id": "system",
-                            "user_platform": "system",
-                            "user_nickname": "系统",
-                            "user_cardname": "",
-                            "display_message": f"...（中间省略一些消息）{start_date} 之后的消息如下...",
-                            "is_action_record": True,
-                            "chat_info_platform": segment_messages[0].chat_info.platform or "",
-                            "chat_id": chat_id,
-                        }
+                        gap_message = DatabaseMessages(
+                            time=start_time - 0.1,
+                            user_id="system",
+                            user_platform="system",
+                            user_nickname="系统",
+                            user_cardname="",
+                            display_message=f"...（中间省略一些消息）{start_date} 之后的消息如下...",
+                            is_action_record=True,
+                            chat_info_platform=segment_messages[0].chat_info.platform or "",
+                            chat_id=chat_id,
+                        )
+
                         processed_messages.append(gap_message)
 
                     # 添加该段的所有消息
@@ -467,11 +471,11 @@ class RelationshipBuilder:
 
             if processed_messages:
                 # 按时间排序所有消息（包括间隔标识）
-                processed_messages.sort(key=lambda x: x["time"])
+                processed_messages.sort(key=lambda x: x.time)
 
                 logger.debug(f"为 {person_id} 获取到总共 {len(processed_messages)} 条消息（包含间隔标识）用于印象更新")
                 relationship_manager = get_relationship_manager()
-                
+
                 build_frequency = 0.3 * global_config.relationship.relation_frequency
                 if random.random() < build_frequency:
                     # 调用原有的更新方法

src/person_info/relationship_manager.py

@@ -3,16 +3,18 @@ import traceback
 
 from json_repair import repair_json
 from datetime import datetime
-from typing import List
+from typing import List, TYPE_CHECKING
 
 from src.common.logger import get_logger
-from src.common.data_models.database_data_model import DatabaseMessages
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config, model_config
 from src.chat.utils.chat_message_builder import build_readable_messages
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from .person_info import Person
 
+if TYPE_CHECKING:
+    from src.common.data_models.database_data_model import DatabaseMessages
+
 logger = get_logger("relation")
 
 
@@ -177,7 +179,7 @@ class RelationshipManager:
 
         return person
 
-    async def update_person_impression(self, person_id, timestamp, bot_engaged_messages: List[DatabaseMessages]):
+    async def update_person_impression(self, person_id, timestamp, bot_engaged_messages: List["DatabaseMessages"]):
         """更新用户印象
 
         Args:
@@ -192,8 +194,6 @@ class RelationshipManager:
         # nickname = person.nickname
         know_times: float = person.know_times
 
-        user_messages = bot_engaged_messages
-
         # 匿名化消息
         # 创建用户名称映射
         name_mapping = {}
@@ -201,13 +201,14 @@ class RelationshipManager:
         user_count = 1
 
         # 遍历消息，构建映射
-        for msg in user_messages:
+        for msg in bot_engaged_messages:
             if msg.user_info.user_id == "system":
                 continue
             try:
                 user_id = msg.user_info.user_id
                 platform = msg.chat_info.platform
-                assert isinstance(user_id, str) and isinstance(platform, str)
+                assert user_id, "用户ID不能为空"
+                assert platform, "平台不能为空"
                 msg_person = Person(user_id=user_id, platform=platform)
 
             except Exception as e:
@@ -233,7 +234,7 @@ class RelationshipManager:
                 current_user = chr(ord(current_user) + 1)
 
         readable_messages = build_readable_messages(
-            messages=user_messages, replace_bot_name=True, timestamp_mode="normal_no_YMD", truncate=True
+            messages=bot_engaged_messages, replace_bot_name=True, timestamp_mode="normal_no_YMD", truncate=True
         )
 
         for original_name, mapped_name in name_mapping.items():

src/plugin_system/apis/generator_api.py

@@ -9,7 +9,7 @@
 """
 
 import traceback
-from typing import Tuple, Any, Dict, List, Optional
+from typing import Tuple, Any, Dict, List, Optional, TYPE_CHECKING
 from rich.traceback import install
 from src.common.logger import get_logger
 from src.chat.replyer.default_generator import DefaultReplyer
@@ -18,6 +18,10 @@ from src.chat.utils.utils import process_llm_response
 from src.chat.replyer.replyer_manager import replyer_manager
 from src.plugin_system.base.component_types import ActionInfo
 
+if TYPE_CHECKING:
+    from src.common.data_models.info_data_model import ActionPlannerInfo
+    from src.common.data_models.database_data_model import DatabaseMessages
+
 install(extra_lines=3)
 
 logger = get_logger("generator_api")
@@ -73,11 +77,11 @@ async def generate_reply(
     chat_stream: Optional[ChatStream] = None,
     chat_id: Optional[str] = None,
     action_data: Optional[Dict[str, Any]] = None,
-    reply_message: Optional[Dict[str, Any]] = None,
+    reply_message: Optional["DatabaseMessages"] = None,
     extra_info: str = "",
     reply_reason: str = "",
     available_actions: Optional[Dict[str, ActionInfo]] = None,
-            choosen_actions: Optional[List[Dict[str, Any]]] = None,
+    chosen_actions: Optional[List["ActionPlannerInfo"]] = None,
     enable_tool: bool = False,
     enable_splitter: bool = True,
     enable_chinese_typo: bool = True,
@@ -85,7 +89,7 @@ async def generate_reply(
     request_type: str = "generator_api",
     from_plugin: bool = True,
     return_expressions: bool = False,
-) -> Tuple[bool, List[Tuple[str, Any]], Optional[Tuple[str, List[Dict[str, Any]]]]]:
+) -> Tuple[bool, List[Tuple[str, Any]], Optional[str], Optional[List[int]]]:
     """生成回复
 
     Args:
@@ -96,7 +100,7 @@ async def generate_reply(
         extra_info: 额外信息，用于补充上下文
         reply_reason: 回复原因
         available_actions: 可用动作
-        choosen_actions: 已选动作
+        chosen_actions: 已选动作
         enable_tool: 是否启用工具调用
         enable_splitter: 是否启用消息分割器
         enable_chinese_typo: 是否启用错字生成器
@@ -110,16 +114,14 @@ async def generate_reply(
     try:
         # 获取回复器
         logger.debug("[GeneratorAPI] 开始生成回复")
-        replyer = get_replyer(
-            chat_stream, chat_id, request_type=request_type
-        )
+        replyer = get_replyer(chat_stream, chat_id, request_type=request_type)
         if not replyer:
             logger.error("[GeneratorAPI] 无法获取回复器")
-            return False, [], None
+            return False, [], None, None
 
         if not extra_info and action_data:
             extra_info = action_data.get("extra_info", "")
-            
+
         if not reply_reason and action_data:
             reply_reason = action_data.get("reason", "")
 
@@ -127,7 +129,7 @@ async def generate_reply(
         success, llm_response_dict, prompt, selected_expressions = await replyer.generate_reply_with_context(
             extra_info=extra_info,
             available_actions=available_actions,
-            chosen_actions=choosen_actions,
+            chosen_actions=chosen_actions,
             enable_tool=enable_tool,
             reply_message=reply_message,
             reply_reason=reply_reason,
@@ -136,7 +138,7 @@ async def generate_reply(
         )
         if not success:
             logger.warning("[GeneratorAPI] 回复生成失败")
-            return False, [], None
+            return False, [], None, None
         assert llm_response_dict is not None, "llm_response_dict不应为None"  # 虽然说不会出现llm_response为空的情况
         if content := llm_response_dict.get("content", ""):
             reply_set = process_human_text(content, enable_splitter, enable_chinese_typo)
@@ -144,28 +146,34 @@ async def generate_reply(
             reply_set = []
         logger.debug(f"[GeneratorAPI] 回复生成成功，生成了 {len(reply_set)} 个回复项")
 
-        if return_prompt:
-            if return_expressions:
-                return success, reply_set, (prompt, selected_expressions)
-            else:
-                return success, reply_set, prompt
-        else:
-            if return_expressions:
-                return success, reply_set, (None, selected_expressions)
-            else:
-                return success, reply_set, None
-    
+        # if return_prompt:
+        #     if return_expressions:
+        #         return success, reply_set, prompt, selected_expressions
+        #     else:
+        #         return success, reply_set, prompt, None
+        # else:
+        #     if return_expressions:
+        #         return success, reply_set, (None, selected_expressions)
+        #     else:
+        #         return success, reply_set, None
+        return (
+            success,
+            reply_set,
+            prompt if return_prompt else None,
+            selected_expressions if return_expressions else None,
+        )
+
     except ValueError as ve:
         raise ve
 
     except UserWarning as uw:
         logger.warning(f"[GeneratorAPI] 中断了生成: {uw}")
-        return False, [], None
+        return False, [], None, None
 
     except Exception as e:
         logger.error(f"[GeneratorAPI] 生成回复时出错: {e}")
         logger.error(traceback.format_exc())
-        return False, [], None
+        return False, [], None, None
 
 
 async def rewrite_reply(

src/plugin_system/apis/send_api.py

@@ -21,15 +21,17 @@
 
 import traceback
 import time
-from typing import Optional, Union, Dict, Any, List
-from src.common.logger import get_logger
+from typing import Optional, Union, Dict, Any, List, TYPE_CHECKING
 
-# 导入依赖
+from src.common.logger import get_logger
+from src.config.config import global_config
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.chat.message_receive.uni_message_sender import HeartFCSender
 from src.chat.message_receive.message import MessageSending, MessageRecv
 from maim_message import Seg, UserInfo
-from src.config.config import global_config
+
+if TYPE_CHECKING:
+    from src.common.data_models.database_data_model import DatabaseMessages
 
 logger = get_logger("send_api")
 
@@ -46,10 +48,10 @@ async def _send_to_target(
     display_message: str = "",
     typing: bool = False,
     set_reply: bool = False,
-    reply_message: Optional[Dict[str, Any]] = None,
+    reply_message: Optional["DatabaseMessages"] = None,
     storage_message: bool = True,
     show_log: bool = True,
-    selected_expressions:List[int] = None,
+    selected_expressions: Optional[List[int]] = None,
 ) -> bool:
     """向指定目标发送消息的内部实现
 
@@ -70,7 +72,7 @@ async def _send_to_target(
         if set_reply and not reply_message:
             logger.warning("[SendAPI] 使用引用回复，但未提供回复消息")
             return False
-        
+
         if show_log:
             logger.debug(f"[SendAPI] 发送{message_type}消息到 {stream_id}")
 
@@ -98,13 +100,13 @@ async def _send_to_target(
         message_segment = Seg(type=message_type, data=content)  # type: ignore
 
         if reply_message:
-            anchor_message = message_dict_to_message_recv(reply_message)
+            anchor_message = message_dict_to_message_recv(reply_message.flatten())
             if anchor_message:
                 anchor_message.update_chat_stream(target_stream)
                 assert anchor_message.message_info.user_info, "用户信息缺失"
                 reply_to_platform_id = (
                     f"{anchor_message.message_info.platform}:{anchor_message.message_info.user_info.user_id}"
-                )            
+                )
         else:
             reply_to_platform_id = ""
             anchor_message = None
@@ -192,12 +194,11 @@ def message_dict_to_message_recv(message_dict: Dict[str, Any]) -> Optional[Messa
     }
 
     message_recv = MessageRecv(message_dict_recv)
-    
+
     logger.info(f"[SendAPI] 找到匹配的回复消息，发送者: {message_dict.get('user_nickname', '')}")
     return message_recv
 
 
-
 # =============================================================================
 # 公共API函数 - 预定义类型的发送函数
 # =============================================================================
@@ -208,9 +209,9 @@ async def text_to_stream(
     stream_id: str,
     typing: bool = False,
     set_reply: bool = False,
-    reply_message: Optional[Dict[str, Any]] = None,
+    reply_message: Optional["DatabaseMessages"] = None,
     storage_message: bool = True,
-    selected_expressions:List[int] = None,
+    selected_expressions: Optional[List[int]] = None,
 ) -> bool:
     """向指定流发送文本消息
 
@@ -237,7 +238,13 @@ async def text_to_stream(
     )
 
 
-async def emoji_to_stream(emoji_base64: str, stream_id: str, storage_message: bool = True, set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None) -> bool:
+async def emoji_to_stream(
+    emoji_base64: str,
+    stream_id: str,
+    storage_message: bool = True,
+    set_reply: bool = False,
+    reply_message: Optional["DatabaseMessages"] = None,
+) -> bool:
     """向指定流发送表情包
 
     Args:
@@ -248,10 +255,25 @@ async def emoji_to_stream(emoji_base64: str, stream_id: str, storage_message: bo
     Returns:
         bool: 是否发送成功
     """
-    return await _send_to_target("emoji", emoji_base64, stream_id, "", typing=False, storage_message=storage_message, set_reply=set_reply,reply_message=reply_message)
+    return await _send_to_target(
+        "emoji",
+        emoji_base64,
+        stream_id,
+        "",
+        typing=False,
+        storage_message=storage_message,
+        set_reply=set_reply,
+        reply_message=reply_message,
+    )
 
 
-async def image_to_stream(image_base64: str, stream_id: str, storage_message: bool = True, set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None) -> bool:
+async def image_to_stream(
+    image_base64: str,
+    stream_id: str,
+    storage_message: bool = True,
+    set_reply: bool = False,
+    reply_message: Optional["DatabaseMessages"] = None,
+) -> bool:
     """向指定流发送图片
 
     Args:
@@ -262,11 +284,25 @@ async def image_to_stream(image_base64: str, stream_id: str, storage_message: bo
     Returns:
         bool: 是否发送成功
     """
-    return await _send_to_target("image", image_base64, stream_id, "", typing=False, storage_message=storage_message, set_reply=set_reply,reply_message=reply_message)
+    return await _send_to_target(
+        "image",
+        image_base64,
+        stream_id,
+        "",
+        typing=False,
+        storage_message=storage_message,
+        set_reply=set_reply,
+        reply_message=reply_message,
+    )
 
 
 async def command_to_stream(
-    command: Union[str, dict], stream_id: str, storage_message: bool = True, display_message: str = "", set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None
+    command: Union[str, dict],
+    stream_id: str,
+    storage_message: bool = True,
+    display_message: str = "",
+    set_reply: bool = False,
+    reply_message: Optional["DatabaseMessages"] = None,
 ) -> bool:
     """向指定流发送命令
 
@@ -279,7 +315,14 @@ async def command_to_stream(
         bool: 是否发送成功
     """
     return await _send_to_target(
-        "command", command, stream_id, display_message, typing=False, storage_message=storage_message, set_reply=set_reply,reply_message=reply_message
+        "command",
+        command,
+        stream_id,
+        display_message,
+        typing=False,
+        storage_message=storage_message,
+        set_reply=set_reply,
+        reply_message=reply_message,
     )
 
 
@@ -289,7 +332,7 @@ async def custom_to_stream(
     stream_id: str,
     display_message: str = "",
     typing: bool = False,
-    reply_message: Optional[Dict[str, Any]] = None,
+    reply_message: Optional["DatabaseMessages"] = None,
     set_reply: bool = False,
     storage_message: bool = True,
     show_log: bool = True,

src/plugin_system/base/base_action.py

@@ -2,13 +2,15 @@ import time
 import asyncio
 
 from abc import ABC, abstractmethod
-from typing import Tuple, Optional, Dict, Any
+from typing import Tuple, Optional, TYPE_CHECKING
 
 from src.common.logger import get_logger
 from src.chat.message_receive.chat_stream import ChatStream
-from src.plugin_system.base.component_types import ActionActivationType, ChatMode, ActionInfo, ComponentType
+from src.plugin_system.base.component_types import ActionActivationType, ActionInfo, ComponentType
 from src.plugin_system.apis import send_api, database_api, message_api
 
+if TYPE_CHECKING:
+    from src.common.data_models.database_data_model import DatabaseMessages
 
 logger = get_logger("base_action")
 
@@ -206,7 +208,11 @@ class BaseAction(ABC):
             return False, f"等待新消息失败: {str(e)}"
 
     async def send_text(
-        self, content: str, set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None, typing: bool = False
+        self,
+        content: str,
+        set_reply: bool = False,
+        reply_message: Optional["DatabaseMessages"] = None,
+        typing: bool = False,
     ) -> bool:
         """发送文本消息
 
@@ -229,7 +235,9 @@ class BaseAction(ABC):
             typing=typing,
         )
 
-    async def send_emoji(self, emoji_base64: str, set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None) -> bool:
+    async def send_emoji(
+        self, emoji_base64: str, set_reply: bool = False, reply_message: Optional["DatabaseMessages"] = None
+    ) -> bool:
         """发送表情包
 
         Args:
@@ -242,9 +250,13 @@ class BaseAction(ABC):
             logger.error(f"{self.log_prefix} 缺少聊天ID")
             return False
 
-        return await send_api.emoji_to_stream(emoji_base64, self.chat_id,set_reply=set_reply,reply_message=reply_message)
+        return await send_api.emoji_to_stream(
+            emoji_base64, self.chat_id, set_reply=set_reply, reply_message=reply_message
+        )
 
-    async def send_image(self, image_base64: str, set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None) -> bool:
+    async def send_image(
+        self, image_base64: str, set_reply: bool = False, reply_message: Optional["DatabaseMessages"] = None
+    ) -> bool:
         """发送图片
 
         Args:
@@ -257,9 +269,18 @@ class BaseAction(ABC):
             logger.error(f"{self.log_prefix} 缺少聊天ID")
             return False
 
-        return await send_api.image_to_stream(image_base64, self.chat_id,set_reply=set_reply,reply_message=reply_message)
+        return await send_api.image_to_stream(
+            image_base64, self.chat_id, set_reply=set_reply, reply_message=reply_message
+        )
 
-    async def send_custom(self, message_type: str, content: str, typing: bool = False, set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None) -> bool:
+    async def send_custom(
+        self,
+        message_type: str,
+        content: str,
+        typing: bool = False,
+        set_reply: bool = False,
+        reply_message: Optional["DatabaseMessages"] = None,
+    ) -> bool:
         """发送自定义类型消息
 
         Args:
@@ -308,7 +329,13 @@ class BaseAction(ABC):
         )
 
     async def send_command(
-        self, command_name: str, args: Optional[dict] = None, display_message: str = "", storage_message: bool = True,set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None
+        self,
+        command_name: str,
+        args: Optional[dict] = None,
+        display_message: str = "",
+        storage_message: bool = True,
+        set_reply: bool = False,
+        reply_message: Optional["DatabaseMessages"] = None,
     ) -> bool:
         """发送命令消息
 

src/plugin_system/base/base_command.py

@@ -1,10 +1,13 @@
 from abc import ABC, abstractmethod
-from typing import Dict, Tuple, Optional, Any
+from typing import Dict, Tuple, Optional, TYPE_CHECKING
 from src.common.logger import get_logger
 from src.plugin_system.base.component_types import CommandInfo, ComponentType
 from src.chat.message_receive.message import MessageRecv
 from src.plugin_system.apis import send_api
 
+if TYPE_CHECKING:
+    from src.common.data_models.database_data_model import DatabaseMessages
+
 logger = get_logger("base_command")
 
 
@@ -84,7 +87,13 @@ class BaseCommand(ABC):
 
         return current
 
-    async def send_text(self, content: str, set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None,storage_message: bool = True) -> bool:
+    async def send_text(
+        self,
+        content: str,
+        set_reply: bool = False,
+        reply_message: Optional["DatabaseMessages"] = None,
+        storage_message: bool = True,
+    ) -> bool:
         """发送回复消息
 
         Args:
@@ -100,10 +109,22 @@ class BaseCommand(ABC):
             logger.error(f"{self.log_prefix} 缺少聊天流或stream_id")
             return False
 
-        return await send_api.text_to_stream(text=content, stream_id=chat_stream.stream_id, set_reply=set_reply,reply_message=reply_message,storage_message=storage_message)
+        return await send_api.text_to_stream(
+            text=content,
+            stream_id=chat_stream.stream_id,
+            set_reply=set_reply,
+            reply_message=reply_message,
+            storage_message=storage_message,
+        )
 
     async def send_type(
-        self, message_type: str, content: str, display_message: str = "", typing: bool = False, set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None
+        self,
+        message_type: str,
+        content: str,
+        display_message: str = "",
+        typing: bool = False,
+        set_reply: bool = False,
+        reply_message: Optional["DatabaseMessages"] = None,
     ) -> bool:
         """发送指定类型的回复消息到当前聊天环境
 
@@ -134,7 +155,13 @@ class BaseCommand(ABC):
         )
 
     async def send_command(
-        self, command_name: str, args: Optional[dict] = None, display_message: str = "", storage_message: bool = True,set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None
+        self,
+        command_name: str,
+        args: Optional[dict] = None,
+        display_message: str = "",
+        storage_message: bool = True,
+        set_reply: bool = False,
+        reply_message: Optional["DatabaseMessages"] = None,
     ) -> bool:
         """发送命令消息
 
@@ -177,7 +204,9 @@ class BaseCommand(ABC):
             logger.error(f"{self.log_prefix} 发送命令时出错: {e}")
             return False
 
-    async def send_emoji(self, emoji_base64: str, set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None) -> bool:
+    async def send_emoji(
+        self, emoji_base64: str, set_reply: bool = False, reply_message: Optional["DatabaseMessages"] = None
+    ) -> bool:
         """发送表情包
 
         Args:
@@ -191,9 +220,17 @@ class BaseCommand(ABC):
             logger.error(f"{self.log_prefix} 缺少聊天流或stream_id")
             return False
 
-        return await send_api.emoji_to_stream(emoji_base64, chat_stream.stream_id,set_reply=set_reply,reply_message=reply_message)
+        return await send_api.emoji_to_stream(
+            emoji_base64, chat_stream.stream_id, set_reply=set_reply, reply_message=reply_message
+        )
 
-    async def send_image(self, image_base64: str, set_reply: bool = False,reply_message: Optional[Dict[str, Any]] = None,storage_message: bool = True) -> bool:
+    async def send_image(
+        self,
+        image_base64: str,
+        set_reply: bool = False,
+        reply_message: Optional["DatabaseMessages"] = None,
+        storage_message: bool = True,
+    ) -> bool:
         """发送图片
 
         Args:
@@ -207,7 +244,13 @@ class BaseCommand(ABC):
             logger.error(f"{self.log_prefix} 缺少聊天流或stream_id")
             return False
 
-        return await send_api.image_to_stream(image_base64, chat_stream.stream_id,set_reply=set_reply,reply_message=reply_message,storage_message=storage_message)
+        return await send_api.image_to_stream(
+            image_base64,
+            chat_stream.stream_id,
+            set_reply=set_reply,
+            reply_message=reply_message,
+            storage_message=storage_message,
+        )
 
     @classmethod
     def get_command_info(cls) -> "CommandInfo":

src/plugins/built_in/knowledge/lpmm_get_knowledge.py

@@ -2,7 +2,7 @@ from typing import Dict, Any
 
 from src.common.logger import get_logger
 from src.config.config import global_config
-from src.chat.knowledge.knowledge_lib import qa_manager
+from src.chat.knowledge import qa_manager
 from src.plugin_system import BaseTool, ToolParamType
 
 logger = get_logger("lpmm_get_knowledge_tool")

src/plugins/built_in/relation/relation.py

@@ -1,20 +1,13 @@
-import random
+import json
+from json_repair import repair_json
 from typing import Tuple
 
-# 导入新插件系统
-from src.plugin_system import BaseAction, ActionActivationType, ChatMode
-
-# 导入依赖的系统组件
 from src.common.logger import get_logger
-
-# 导入API模块 - 标准Python包方式
-from src.plugin_system.apis import emoji_api, llm_api, message_api
-# NoReplyAction已集成到heartFC_chat.py中，不再需要导入
 from src.config.config import global_config
 from src.person_info.person_info import Person, get_memory_content_from_memory, get_weight_from_memory
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
-import json
-from json_repair import repair_json
+from src.plugin_system import BaseAction, ActionActivationType
+from src.plugin_system.apis import llm_api
 
 
 logger = get_logger("relation")
@@ -39,10 +32,9 @@ def init_prompt():
 {{
     "category": "分类名称"
 }} """,
-        "relation_category"
+        "relation_category",
     )
-    
-    
+
     Prompt(
         """
 以下是有关{category}的现有记忆：
@@ -73,7 +65,7 @@ def init_prompt():
 
 现在，请你根据情况选出合适的修改方式，并输出json，不要输出其他内容：
 """,
-        "relation_category_update"
+        "relation_category_update",
     )
 
 
@@ -98,17 +90,14 @@ class BuildRelationAction(BaseAction):
     """
 
     # 动作参数定义
-    action_parameters = {
-        "person_name":"需要了解或记忆的人的名称",
-        "impression":"需要了解的对某人的记忆或印象"
-    }
+    action_parameters = {"person_name": "需要了解或记忆的人的名称", "impression": "需要了解的对某人的记忆或印象"}
 
     # 动作使用场景
     action_require = [
         "了解对于某人的记忆，并添加到你对对方的印象中",
         "对方与有明确提到有关其自身的事件",
         "对方有提到其个人信息，包括喜好，身份，等等",
-        "对方希望你记住对方的信息"
+        "对方希望你记住对方的信息",
     ]
 
     # 关联类型
@@ -129,9 +118,7 @@ class BuildRelationAction(BaseAction):
             if not person.is_known:
                 logger.warning(f"{self.log_prefix} 用户 {person_name} 不存在，跳过添加记忆")
                 return False, f"用户 {person_name} 不存在，跳过添加记忆"
-            
 
-            
             category_list = person.get_all_category()
             if not category_list:
                 category_list_str = "无分类"
@@ -142,9 +129,8 @@ class BuildRelationAction(BaseAction):
                 "relation_category",
                 category_list=category_list_str,
                 memory_point=impression,
-                person_name=person.person_name
+                person_name=person.person_name,
             )
-            
 
             if global_config.debug.show_prompt:
                 logger.info(f"{self.log_prefix} 生成的LLM Prompt: {prompt}")
@@ -161,84 +147,76 @@ class BuildRelationAction(BaseAction):
             success, category, _, _ = await llm_api.generate_with_model(
                 prompt, model_config=chat_model_config, request_type="relation.category"
             )
-            
-            
 
             category_data = json.loads(repair_json(category))
             category = category_data.get("category", "")
             if not category:
                 logger.warning(f"{self.log_prefix} LLM未给出分类，跳过添加记忆")
                 return False, "LLM未给出分类，跳过添加记忆"
-            
-            
+
             # 第二部分：更新记忆
-            
+
             memory_list = person.get_memory_list_by_category(category)
             if not memory_list:
                 logger.info(f"{self.log_prefix} {person.person_name} 的  {category}  的记忆为空，进行创建")
                 person.memory_points.append(f"{category}:{impression}:1.0")
                 person.sync_to_database()
-                
+
                 return True, f"未找到分类为{category}的记忆点，进行添加"
-            
+
             memory_list_str = ""
             memory_list_id = {}
-            id = 1
-            for memory in memory_list:
+            for id, memory in enumerate(memory_list, start=1):
                 memory_content = get_memory_content_from_memory(memory)
                 memory_list_str += f"{id}. {memory_content}\n"
                 memory_list_id[id] = memory
-                id += 1
-            
             prompt = await global_prompt_manager.format_prompt(
                 "relation_category_update",
                 category=category,
                 memory_list=memory_list_str,
                 memory_point=impression,
-                person_name=person.person_name
+                person_name=person.person_name,
             )
-            
+
             if global_config.debug.show_prompt:
                 logger.info(f"{self.log_prefix} 生成的LLM Prompt: {prompt}")
             else:
                 logger.debug(f"{self.log_prefix} 生成的LLM Prompt: {prompt}")
 
-            chat_model_config = models.get("utils")            
+            chat_model_config = models.get("utils")
             success, update_memory, _, _ = await llm_api.generate_with_model(
-                prompt, model_config=chat_model_config, request_type="relation.category.update"
+                prompt, model_config=chat_model_config, request_type="relation.category.update" # type: ignore
             )
-            
+
             update_memory_data = json.loads(repair_json(update_memory))
             new_memory = update_memory_data.get("new_memory", "")
             memory_id = update_memory_data.get("memory_id", "")
             integrate_memory = update_memory_data.get("integrate_memory", "")
-            
+
             if new_memory:
                 # 新记忆
                 person.memory_points.append(f"{category}:{new_memory}:1.0")
                 person.sync_to_database()
-                
+
                 return True, f"为{person.person_name}新增记忆点: {new_memory}"
             elif memory_id and integrate_memory:
                 # 现存或冲突记忆
                 memory = memory_list_id[memory_id]
                 memory_content = get_memory_content_from_memory(memory)
-                del_count = person.del_memory(category,memory_content)
-                
+                del_count = person.del_memory(category, memory_content)
+
                 if del_count > 0:
                     logger.info(f"{self.log_prefix} 删除记忆点: {memory_content}")
 
                     memory_weight = get_weight_from_memory(memory)
                     person.memory_points.append(f"{category}:{integrate_memory}:{memory_weight + 1.0}")
                     person.sync_to_database()
-                    
+
                     return True, f"更新{person.person_name}的记忆点: {memory_content} -> {integrate_memory}"
-                    
+
                 else:
                     logger.warning(f"{self.log_prefix} 删除记忆点失败: {memory_content}")
                     return False, f"删除{person.person_name}的记忆点失败: {memory_content}"
-                
-                
 
             return True, "关系动作执行成功"
 
@@ -248,4 +226,4 @@ class BuildRelationAction(BaseAction):
 
 
 # 还缺一个关系的太多遗忘和对应的提取
-init_prompt()
+init_prompt()

src/plugins/built_in/tts_plugin/plugin.py

@@ -2,7 +2,7 @@ from src.plugin_system.apis.plugin_register_api import register_plugin
 from src.plugin_system.base.base_plugin import BasePlugin
 from src.plugin_system.base.component_types import ComponentInfo
 from src.common.logger import get_logger
-from src.plugin_system.base.base_action import BaseAction, ActionActivationType, ChatMode
+from src.plugin_system.base.base_action import BaseAction, ActionActivationType
 from src.plugin_system.base.config_types import ConfigField
 from typing import Tuple, List, Type
 

template/model_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "1.3.0"
+version = "1.3.1"
 
 # 配置文件版本号迭代规则同bot_config.toml