better：重构personinfo，使用Person类和类属性

src/chat/chat_loop/heartFC_chat.py

@@ -17,7 +17,7 @@ from src.chat.planner_actions.action_manager import ActionManager
 from src.chat.chat_loop.hfc_utils import CycleDetail
 from src.person_info.relationship_builder_manager import relationship_builder_manager
 from src.chat.express.expression_learner import expression_learner_manager
-from src.person_info.person_info import get_person_info_manager
+from src.person_info.person_info import Person
 from src.person_info.group_relationship_manager import get_group_relationship_manager
 from src.plugin_system.base.component_types import ChatMode, EventType
 from src.plugin_system.core import events_manager
@@ -307,19 +307,13 @@ class HeartFChatting:
         with Timer("回复发送", cycle_timers):
             reply_text = await self._send_response(response_set, action_message)
         
-        # 存储reply action信息
-        person_info_manager = get_person_info_manager()
-        
         # 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")
         if platform is None:
             platform = getattr(self.chat_stream, "platform", "unknown")
         
-        person_id = person_info_manager.get_person_id(
+        person = Person(platform = platform ,user_id = action_message.get("user_id", ""))
-            platform,
+        person_name = person.person_name
-            action_message.get("user_id", ""),
-        )
-        person_name = await person_info_manager.get_value(person_id, "person_name")
         action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
 
         await database_api.store_action_info(

src/chat/express/expression_learner.py

@@ -281,7 +281,7 @@ class ExpressionLearner:
         logger.info(f"在 {group_name} 学习到表达风格:\n{learnt_expressions_str}")
 
         if not learnt_expressions:
-            logger.info(f"没有学习到表达风格")
+            logger.info("没有学习到表达风格")
             return []
 
         # 按chat_id分组

src/chat/express/expression_selector.py

@@ -3,7 +3,7 @@ import time
 import random
 import hashlib
 
-from typing import List, Dict, Tuple, Optional, Any
+from typing import List, Dict, Optional, Any
 from json_repair import repair_json
 
 from src.llm_models.utils_model import LLMRequest
@@ -22,16 +22,22 @@ def init_prompt():
 
 你的名字是{bot_name}{target_message}
 
-你知道以下这些表达方式，梗和说话方式：
+以下是可选的表达情境：
 {all_situations}
 
-现在，请你根据聊天记录从中挑选合适的表达方式，梗和说话方式，组织一条回复风格指导，指导的目的是在组织回复的时候提供一些语言风格和梗上的参考。
+请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的，最多{max_num}个情境。
-请在reply_style_guide中以平文本输出指导，不要浮夸，并在selected_expressions中说明在指导中你挑选了哪些表达方式，梗和说话方式，以json格式输出：
+考虑因素包括：
-例子：
+1. 聊天的情绪氛围（轻松、严肃、幽默等）
+2. 话题类型（日常、技术、游戏、情感等）
+3. 情境与当前语境的匹配度
+{target_message_extra_block}
+
+请以JSON格式输出，只需要输出选中的情境编号：
+例如：
 {{
-    "reply_style_guide": "...",
+    "selected_situations": [2, 3, 5, 7, 19]
-    "selected_expressions": [2, 3, 4, 7]
 }}
+
 请严格按照JSON格式输出，不要包含其他内容：
 """
     Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
@@ -190,14 +196,14 @@ class ExpressionSelector:
         chat_info: str,
         max_num: int = 10,
         target_message: Optional[str] = None,
-    ) -> Tuple[str, List[Dict[str, Any]]]:
+    ) -> List[Dict[str, Any]]:
         # sourcery skip: inline-variable, list-comprehension
         """使用LLM选择适合的表达方式"""
         
         # 检查是否允许在此聊天流中使用表达
         if not self.can_use_expression_for_chat(chat_id):
             logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表")
-            return "", []
+            return []
 
         # 1. 获取20个随机表达方式（现在按权重抽取）
         style_exprs = self.get_random_expressions(chat_id, 10)
@@ -216,7 +222,7 @@ class ExpressionSelector:
 
         if not all_expressions:
             logger.warning("没有找到可用的表达方式")
-            return "", []
+            return []
 
         all_situations_str = "\n".join(all_situations)
 
@@ -255,24 +261,23 @@ class ExpressionSelector:
 
             if not content:
                 logger.warning("LLM返回空结果")
-                return "", []
+                return []
 
             # 5. 解析结果
             result = repair_json(content)
             if isinstance(result, str):
                 result = json.loads(result)
 
-            if not isinstance(result, dict) or "reply_style_guide" not in result or "selected_expressions" not in result:
+            if not isinstance(result, dict) or "selected_situations" not in result:
                 logger.error("LLM返回格式错误")
                 logger.info(f"LLM返回结果: \n{content}")
-                return "", []
+                return []
 
-            reply_style_guide = result["reply_style_guide"]
+            selected_indices = result["selected_situations"]
-            selected_expressions = result["selected_expressions"]
 
             # 根据索引获取完整的表达方式
             valid_expressions = []
-            for idx in selected_expressions:
+            for idx in selected_indices:
                 if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
                     expression = all_expressions[idx - 1]  # 索引从1开始
                     valid_expressions.append(expression)
@@ -282,11 +287,11 @@ class ExpressionSelector:
                 self.update_expressions_count_batch(valid_expressions, 0.006)
 
             # logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个")
-            return reply_style_guide, valid_expressions
+            return valid_expressions
 
         except Exception as e:
             logger.error(f"LLM处理表达方式选择时出错: {e}")
-            return "", []
+            return []
     
 
 

src/chat/express/expression_selector_new.py

@@ -22,22 +22,16 @@ def init_prompt():
 
 你的名字是{bot_name}{target_message}
 
-以下是可选的表达情境：
+你知道以下这些表达方式，梗和说话方式：
 {all_situations}
 
-请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的，最多{max_num}个情境。
+现在，请你根据聊天记录从中挑选合适的表达方式，梗和说话方式，组织一条回复风格指导，指导的目的是在组织回复的时候提供一些语言风格和梗上的参考。
-考虑因素包括：
+请在reply_style_guide中以平文本输出指导，不要浮夸，并在selected_expressions中说明在指导中你挑选了哪些表达方式，梗和说话方式，以json格式输出：
-1. 聊天的情绪氛围（轻松、严肃、幽默等）
+例子：
-2. 话题类型（日常、技术、游戏、情感等）
-3. 情境与当前语境的匹配度
-{target_message_extra_block}
-
-请以JSON格式输出，只需要输出选中的情境编号：
-例如：
 {{
-    "selected_situations": [2, 3, 5, 7, 19]
+    "reply_style_guide": "...",
+    "selected_expressions": [2, 3, 4, 7]
 }}
-
 请严格按照JSON格式输出，不要包含其他内容：
 """
     Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
@@ -196,14 +190,14 @@ class ExpressionSelector:
         chat_info: str,
         max_num: int = 10,
         target_message: Optional[str] = None,
-    ) -> List[Dict[str, Any]]:
+    ) -> Tuple[str, List[Dict[str, Any]]]:
         # sourcery skip: inline-variable, list-comprehension
         """使用LLM选择适合的表达方式"""
         
         # 检查是否允许在此聊天流中使用表达
         if not self.can_use_expression_for_chat(chat_id):
             logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表")
-            return []
+            return "", []
 
         # 1. 获取20个随机表达方式（现在按权重抽取）
         style_exprs = self.get_random_expressions(chat_id, 10)
@@ -222,7 +216,7 @@ class ExpressionSelector:
 
         if not all_expressions:
             logger.warning("没有找到可用的表达方式")
-            return []
+            return "", []
 
         all_situations_str = "\n".join(all_situations)
 
@@ -261,23 +255,24 @@ class ExpressionSelector:
 
             if not content:
                 logger.warning("LLM返回空结果")
-                return []
+                return "", []
 
             # 5. 解析结果
             result = repair_json(content)
             if isinstance(result, str):
                 result = json.loads(result)
 
-            if not isinstance(result, dict) or "selected_situations" not in result:
+            if not isinstance(result, dict) or "reply_style_guide" not in result or "selected_expressions" not in result:
                 logger.error("LLM返回格式错误")
                 logger.info(f"LLM返回结果: \n{content}")
-                return []
+                return "", []
             
-            selected_indices = result["selected_situations"]
+            reply_style_guide = result["reply_style_guide"]
+            selected_expressions = result["selected_expressions"]
 
             # 根据索引获取完整的表达方式
             valid_expressions = []
-            for idx in selected_indices:
+            for idx in selected_expressions:
                 if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
                     expression = all_expressions[idx - 1]  # 索引从1开始
                     valid_expressions.append(expression)
@@ -287,11 +282,11 @@ class ExpressionSelector:
                 self.update_expressions_count_batch(valid_expressions, 0.006)
 
             # logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个")
-            return valid_expressions
+            return reply_style_guide, valid_expressions
 
         except Exception as e:
             logger.error(f"LLM处理表达方式选择时出错: {e}")
-            return []
+            return "", []
     
 
 

src/chat/heart_flow/heartflow_message_processor.py

@@ -14,34 +14,14 @@ 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.common.logger import get_logger
-from src.person_info.relationship_manager import get_relationship_manager
 from src.mood.mood_manager import mood_manager
+from src.person_info.person_info import Person
 
 if TYPE_CHECKING:
     from src.chat.heart_flow.sub_heartflow import SubHeartflow
 
 logger = get_logger("chat")
 
-
-async def _process_relationship(message: MessageRecv) -> None:
-    """处理用户关系逻辑
-
-    Args:
-        message: 消息对象，包含用户信息
-    """
-    platform = message.message_info.platform
-    user_id = message.message_info.user_info.user_id  # type: ignore
-    nickname = message.message_info.user_info.user_nickname  # type: ignore
-    cardname = message.message_info.user_info.user_cardname or nickname  # type: ignore
-
-    relationship_manager = get_relationship_manager()
-    is_known = await relationship_manager.is_known_some_one(platform, user_id)
-
-    if not is_known:
-        logger.info(f"首次认识用户: {nickname}")
-        await relationship_manager.first_knowing_some_one(platform, user_id, nickname, cardname)  # type: ignore
-
-
 async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool, list[str]]:
     """计算消息的兴趣度
 
@@ -165,7 +145,7 @@ class HeartFCMessageReceiver:
 
             # 4. 关系处理
             if global_config.relationship.enable_relationship:
-                await _process_relationship(message)
+                person = Person(platform=message.message_info.platform, user_id=message.message_info.user_info.user_id,nickname=userinfo.user_nickname)
 
         except Exception as e:
             logger.error(f"消息处理失败: {e}")

src/chat/replyer/default_generator.py

@@ -9,7 +9,6 @@ from datetime import datetime
 from src.mais4u.mai_think import mai_thinking_manager
 from src.common.logger import get_logger
 from src.config.config import global_config, model_config
-from src.config.api_ada_configs import TaskConfig
 from src.individuality.individuality import get_individuality
 from src.llm_models.utils_model import LLMRequest
 from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending
@@ -27,8 +26,7 @@ from src.chat.express.expression_selector import expression_selector
 from src.chat.memory_system.memory_activator import MemoryActivator
 from src.chat.memory_system.instant_memory import InstantMemory
 from src.mood.mood_manager import mood_manager
-from src.person_info.relationship_fetcher import relationship_fetcher_manager
+from src.person_info.person_info import Person
-from src.person_info.person_info import get_person_info_manager
 from src.plugin_system.base.component_types import ActionInfo, EventType
 from src.plugin_system.apis import llm_api
 
@@ -302,16 +300,14 @@ class DefaultReplyer:
         if not global_config.relationship.enable_relationship:
             return ""
 
-        relationship_fetcher = relationship_fetcher_manager.get_fetcher(self.chat_stream.stream_id)
-
         # 获取用户ID
-        person_info_manager = get_person_info_manager()
+        person = Person(platform=self.chat_stream.platform, user_id=sender)
-        person_id = person_info_manager.get_person_id_by_person_name(sender)
+        person_id = person.person_id
         if not person_id:
             logger.warning(f"未找到用户 {sender} 的ID，跳过信息提取")
             return f"你完全不认识{sender}，不理解ta的相关信息。"
 
-        return await relationship_fetcher.build_relation_info(person_id, points_num=5)
+        return person.build_relationship(points_num=5)
 
     async def build_expression_habits(self, chat_history: str, target: str) -> Tuple[str, str]:
         """构建表达习惯块
@@ -330,7 +326,7 @@ class DefaultReplyer:
         style_habits = []
         # 使用从处理器传来的选中表达方式
         # LLM模式：调用LLM选择5-10个，然后随机选5个
-        reply_style_guide, selected_expressions = await expression_selector.select_suitable_expressions_llm(
+        selected_expressions = await expression_selector.select_suitable_expressions_llm(
             self.chat_stream.stream_id, chat_history, max_num=8, target_message=target
         )
 
@@ -354,7 +350,7 @@ class DefaultReplyer:
             )
             expression_habits_block += f"{style_habits_str}\n"
 
-        return (f"{expression_habits_title}\n{expression_habits_block}", reply_style_guide)
+        return f"{expression_habits_title}\n{expression_habits_block}"
 
     async def build_memory_block(self, chat_history: str, target: str) -> str:
         """构建记忆块
@@ -659,18 +655,16 @@ class DefaultReplyer:
             available_actions = {}
         chat_stream = self.chat_stream
         chat_id = chat_stream.stream_id
-        person_info_manager = get_person_info_manager()
         is_group_chat = bool(chat_stream.group_info)
         platform = chat_stream.platform
         user_id = reply_message.get("user_id","")
         
         if user_id:
-            person_id = person_info_manager.get_person_id(platform,user_id)
+            person = Person(platform=platform, user_id=user_id)
-            person_name = await person_info_manager.get_value(person_id, "person_name")
+            person_name = person.person_name or user_id
             sender = person_name
             target = reply_message.get('processed_plain_text')
         else:
-            person_id = ""
             person_name = "用户"
             sender = "用户"
             target = "消息"
@@ -746,7 +740,7 @@ class DefaultReplyer:
                 logger.warning(f"回复生成前信息获取耗时过长: {chinese_name} 耗时: {duration:.1f}s，请使用更快的模型")
         logger.info(f"在回复前的步骤耗时: {'; '.join(timing_logs)}")
 
-        (expression_habits_block, reply_style_guide) = results_dict["expression_habits"]
+        expression_habits_block = results_dict["expression_habits"]
         relation_info = results_dict["relation_info"]
         memory_block = results_dict["memory_block"]
         tool_info = results_dict["tool_info"]
@@ -802,7 +796,7 @@ class DefaultReplyer:
         if global_config.bot.qq_account == user_id and platform == global_config.bot.platform:
             return await global_prompt_manager.format_prompt(
                 "replyer_self_prompt",
-                expression_habits_block=reply_style_guide,
+                expression_habits_block=expression_habits_block,
                 tool_info_block=tool_info,
                 knowledge_prompt=prompt_info,
                 memory_block=memory_block,
@@ -822,7 +816,7 @@ class DefaultReplyer:
         else:
             return await global_prompt_manager.format_prompt(
                 "replyer_prompt",
-                expression_habits_block=reply_style_guide,
+                expression_habits_block=expression_habits_block,
                 tool_info_block=tool_info,
                 knowledge_prompt=prompt_info,
                 memory_block=memory_block,
@@ -885,7 +879,6 @@ class DefaultReplyer:
             self.build_relation_info(sender, target),
         )
         
-        expression_habits_block, reply_style_guide = expression_habits_block
 
         keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
 

src/chat/replyer/replyer_manager.py

@@ -1,7 +1,6 @@
-from typing import Dict, Optional, List, Tuple
+from typing import Dict, Optional
 
 from src.common.logger import get_logger
-from src.config.api_ada_configs import TaskConfig
 from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
 from src.chat.replyer.default_generator import DefaultReplyer
 

src/chat/utils/chat_message_builder.py

@@ -9,7 +9,7 @@ from src.config.config import global_config
 from src.common.message_repository import find_messages, count_messages
 from src.common.database.database_model import ActionRecords
 from src.common.database.database_model import Images
-from src.person_info.person_info import PersonInfoManager, get_person_info_manager
+from src.person_info.person_info import Person,get_person_id
 from src.chat.utils.utils import translate_timestamp_to_human_readable, assign_message_ids
 
 install(extra_lines=3)
@@ -35,14 +35,12 @@ def replace_user_references_sync(
         str: 处理后的内容字符串
     """
     if name_resolver is None:
-        person_info_manager = get_person_info_manager()
-
         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_id = PersonInfoManager.get_person_id(platform, user_id)
+            person = Person(platform=platform, user_id=user_id)
-            return person_info_manager.get_value_sync(person_id, "person_name") or user_id  # type: ignore
+            return person.person_name or user_id  # type: ignore
 
         name_resolver = default_resolver
 
@@ -110,14 +108,12 @@ async def replace_user_references_async(
         str: 处理后的内容字符串
     """
     if name_resolver is None:
-        person_info_manager = get_person_info_manager()
-
         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_id = PersonInfoManager.get_person_id(platform, user_id)
+            person = Person(platform=platform, user_id=user_id)
-            return await person_info_manager.get_value(person_id, "person_name") or user_id  # type: ignore
+            return person.person_name or user_id  # type: ignore
 
         name_resolver = default_resolver
 
@@ -506,14 +502,13 @@ def _build_readable_messages_internal(
         if not all([platform, user_id, timestamp is not None]):
             continue
 
-        person_id = PersonInfoManager.get_person_id(platform, user_id)
+        person = Person(platform=platform, user_id=user_id)
-        person_info_manager = get_person_info_manager()
         # 根据 replace_bot_name 参数决定是否替换机器人名称
         person_name: str
         if replace_bot_name and user_id == global_config.bot.qq_account:
             person_name = f"{global_config.bot.nickname}(你)"
         else:
-            person_name = person_info_manager.get_value_sync(person_id, "person_name")  # type: ignore
+            person_name = person.person_name or user_id  # type: ignore
 
         # 如果 person_name 未设置，则使用消息中的 nickname 或默认名称
         if not person_name:
@@ -1009,7 +1004,7 @@ async def build_anonymous_messages(messages: List[Dict[str, Any]]) -> str:
             # print("SELF11111111111111")
             return "SELF"
         try:
-            person_id = PersonInfoManager.get_person_id(platform, user_id)
+            person_id = get_person_id(platform, user_id)
         except Exception as _e:
             person_id = None
         if not person_id:
@@ -1102,7 +1097,7 @@ async def get_person_id_list(messages: List[Dict[str, Any]]) -> List[str]:
         if platform is None:
             platform = "unknown"
 
-        if person_id := PersonInfoManager.get_person_id(platform, user_id):
+        if person_id := get_person_id(platform, user_id):
             person_ids_set.add(person_id)
 
     return list(person_ids_set)  # 将集合转换为列表返回

src/chat/utils/utils.py

@@ -15,7 +15,7 @@ from src.config.config import global_config, model_config
 from src.chat.message_receive.message import MessageRecv
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.llm_models.utils_model import LLMRequest
-from src.person_info.person_info import PersonInfoManager, get_person_info_manager
+from src.person_info.person_info import Person
 from .typo_generator import ChineseTypoGenerator
 
 logger = get_logger("chat_utils")
@@ -639,12 +639,12 @@ def get_chat_type_and_target_info(chat_id: str) -> Tuple[bool, Optional[Dict]]:
                 # Try to fetch person info
                 try:
                     # Assume get_person_id is sync (as per original code), keep using to_thread
-                    person_id = PersonInfoManager.get_person_id(platform, user_id)
+                    person = Person(platform=platform, user_id=user_id)
+                    person_id = person.person_id
                     person_name = None
                     if person_id:
                         # get_value is async, so await it directly
-                        person_info_manager = get_person_info_manager()
+                        person_name = person.person_name
-                        person_name = person_info_manager.get_value_sync(person_id, "person_name")
 
                     target_info["person_id"] = person_id
                     target_info["person_name"] = person_name

src/common/database/database_model.py

@@ -254,6 +254,7 @@ class PersonInfo(BaseModel):
     用于存储个人信息数据的模型。
     """
 
+    is_known = BooleanField(default=False)  # 是否已认识
     person_id = TextField(unique=True, index=True)  # 个人唯一ID
     person_name = TextField(null=True)  # 个人名称 (允许为空)
     name_reason = TextField(null=True)  # 名称设定的原因
@@ -261,16 +262,26 @@ class PersonInfo(BaseModel):
     user_id = TextField(index=True)  # 用户ID
     nickname = TextField(null=True)  # 用户昵称
     points = TextField(null=True)  # 个人印象的点
-    attitude_to_me = TextField(null=True)  # 对bot的态度
-    rudeness = TextField(null=True)  # 对bot的冒犯程度
-    neuroticism = TextField(null=True)  # 对bot的神经质程度
-    conscientiousness = TextField(null=True)  # 对bot的尽责程度
-    likeness = TextField(null=True)  # 对bot的相似程度
-
     know_times = FloatField(null=True)  # 认识时间 (时间戳)
     know_since = FloatField(null=True)  # 首次印象总结时间
     last_know = FloatField(null=True)  # 最后一次印象总结时间
     
+    
+    attitude_to_me = TextField(null=True)  # 对bot的态度
+    attitude_to_me_confidence = FloatField(null=True)  # 对bot的态度置信度
+    friendly_value = FloatField(null=True)  # 对bot的友好程度
+    friendly_value_confidence = FloatField(null=True)  # 对bot的友好程度置信度
+    rudeness = TextField(null=True)  # 对bot的冒犯程度
+    rudeness_confidence = FloatField(null=True)  # 对bot的冒犯程度置信度
+    neuroticism = TextField(null=True)  # 对bot的神经质程度
+    neuroticism_confidence = FloatField(null=True)  # 对bot的神经质程度置信度
+    conscientiousness = TextField(null=True)  # 对bot的尽责程度
+    conscientiousness_confidence = FloatField(null=True)  # 对bot的尽责程度置信度
+    likeness = TextField(null=True)  # 对bot的相似程度
+    likeness_confidence = FloatField(null=True)  # 对bot的相似程度置信度
+
+
+
     class Meta:
         # database = db # 继承自 BaseModel
         table_name = "person_info"

src/individuality/individuality.py

@@ -6,7 +6,6 @@ import time
 from src.common.logger import get_logger
 from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
-from src.person_info.person_info import get_person_info_manager
 from rich.traceback import install
 
 install(extra_lines=3)
@@ -19,7 +18,6 @@ class Individuality:
 
     def __init__(self):
         self.name = ""
-        self.bot_person_id = ""
         self.meta_info_file_path = "data/personality/meta.json"
         self.personality_data_file_path = "data/personality/personality_data.json"
 
@@ -32,8 +30,6 @@ class Individuality:
         personality_side = global_config.personality.personality_side
         identity = global_config.personality.identity
 
-        person_info_manager = get_person_info_manager()
-        self.bot_person_id = person_info_manager.get_person_id("system", "bot_id")
         self.name = bot_nickname
 
         # 检查配置变化，如果变化则清空
@@ -64,16 +60,6 @@ class Individuality:
         else:
             logger.error("人设构建失败")
 
-        # 如果任何一个发生变化，都需要清空数据库中的info_list（因为这影响整体人设）
-        if personality_changed or identity_changed:
-            logger.info("将清空数据库中原有的关键词缓存")
-            update_data = {
-                "platform": "system",
-                "user_id": "bot_id",
-                "person_name": self.name,
-                "nickname": self.name,
-            }
-            await person_info_manager.update_one_field(self.bot_person_id, "info_list", [], data=update_data)
 
     async def get_personality_block(self) -> str:
         bot_name = global_config.bot.nickname
@@ -130,7 +116,6 @@ class Individuality:
         Returns:
             tuple: (personality_changed, identity_changed)
         """
-        person_info_manager = get_person_info_manager()
         current_personality_hash, current_identity_hash = self._get_config_hash(
             bot_nickname, personality_core, personality_side, identity
         )
@@ -148,17 +133,6 @@ class Individuality:
         if identity_changed:
             logger.info("检测到身份配置发生变化")
 
-        # 如果任何一个发生变化，都需要清空info_list（因为这影响整体人设）
-        if personality_changed or identity_changed:
-            logger.info("将清空原有的关键词缓存")
-            update_data = {
-                "platform": "system",
-                "user_id": "bot_id",
-                "person_name": self.name,
-                "nickname": self.name,
-            }
-            await person_info_manager.update_one_field(self.bot_person_id, "info_list", [], data=update_data)
-
         # 更新元信息文件
         new_meta_info = {
             "personality_hash": current_personality_hash,

src/mais4u/mais4u_chat/s4u_chat.py

@@ -16,7 +16,7 @@ import json
 from .s4u_mood_manager import mood_manager
 from src.person_info.relationship_builder_manager import relationship_builder_manager
 from src.mais4u.s4u_config import s4u_config
-from src.person_info.person_info import PersonInfoManager
+from src.person_info.person_info import get_person_id
 from .super_chat_manager import get_super_chat_manager
 from .yes_or_no import yes_or_no_head
 from src.mais4u.constant_s4u import ENABLE_S4U
@@ -262,7 +262,7 @@ class S4UChat:
         """根据VIP状态和中断逻辑将消息放入相应队列。"""
         user_id = message.message_info.user_info.user_id
         platform = message.message_info.platform
-        person_id = PersonInfoManager.get_person_id(platform, user_id)
+        person_id = get_person_id(platform, user_id)
         
         try:
             is_gift = message.is_gift

src/mais4u/mais4u_chat/s4u_prompt.py

@@ -10,8 +10,7 @@ from datetime import datetime
 import asyncio
 from src.mais4u.s4u_config import s4u_config
 from src.chat.message_receive.message import MessageRecvS4U
-from src.person_info.relationship_fetcher import relationship_fetcher_manager
+from src.person_info.person_info import Person, get_person_id
-from src.person_info.person_info import PersonInfoManager, get_person_info_manager
 from src.chat.message_receive.chat_stream import ChatStream
 from src.mais4u.mais4u_chat.super_chat_manager import get_super_chat_manager
 from src.mais4u.mais4u_chat.screen_manager import screen_manager
@@ -103,7 +102,7 @@ class PromptBuilder:
 
         # 使用从处理器传来的选中表达方式
         # LLM模式：调用LLM选择5-10个，然后随机选5个
-        _,selected_expressions = await expression_selector.select_suitable_expressions_llm(
+        selected_expressions = await expression_selector.select_suitable_expressions_llm(
             chat_stream.stream_id, chat_history, max_num=12, target_message=target
         )
 
@@ -142,18 +141,16 @@ class PromptBuilder:
 
         relation_prompt = ""
         if global_config.relationship.enable_relationship and who_chat_in_group:
-            relationship_fetcher = relationship_fetcher_manager.get_fetcher(chat_stream.stream_id)
-
             # 将 (platform, user_id, nickname) 转换为 person_id
             person_ids = []
             for person in who_chat_in_group:
-                person_id = PersonInfoManager.get_person_id(person[0], person[1])
+                person_id = get_person_id(person[0], person[1])
                 person_ids.append(person_id)
 
-            # 使用 RelationshipFetcher 的 build_relation_info 方法，设置 points_num=3 保持与原来相同的行为
+            # 使用 Person 的 build_relationship 方法，设置 points_num=3 保持与原来相同的行为
-            relation_info_list = await asyncio.gather(
+            relation_info_list = [
-                *[relationship_fetcher.build_relation_info(person_id, points_num=3) for person_id in person_ids]
+                Person(person_id=person_id).build_relationship(points_num=3) for person_id in person_ids
-            )
+            ]
             if relation_info := "".join(relation_info_list):
                 relation_prompt = await global_prompt_manager.format_prompt(
                     "relation_prompt", relation_info=relation_info
@@ -288,11 +285,8 @@ class PromptBuilder:
         
         chat_stream = message.chat_stream
         
-        person_id = PersonInfoManager.get_person_id(
+        person = Person(platform=message.chat_stream.user_info.platform, user_id=message.chat_stream.user_info.user_id)
-            message.chat_stream.user_info.platform, message.chat_stream.user_info.user_id
+        person_name = person.person_name
-        )
-        person_info_manager = get_person_info_manager()
-        person_name = await person_info_manager.get_value(person_id, "person_name")
 
         if message.chat_stream.user_info.user_nickname:
             if person_name:

src/mais4u/mais4u_chat/s4u_stream_generator.py

@@ -5,7 +5,6 @@ from src.config.config import model_config
 from src.chat.message_receive.message import MessageRecvS4U
 from src.mais4u.mais4u_chat.s4u_prompt import prompt_builder
 from src.common.logger import get_logger
-import asyncio
 import re
 
 

src/person_info/__init__.py

@@ -1,4 +0,0 @@
-from .person_info import get_person_info_manager
-from .group_info import get_group_info_manager
-
-__all__ = ["get_person_info_manager", "get_group_info_manager"]

src/person_info/person_info.py

@@ -1,11 +1,11 @@
-import copy
 import hashlib
-import datetime
 import asyncio
 import json
+import time
+import random
 
 from json_repair import repair_json
-from typing import Any, Callable, Dict, Union, Optional
+from typing import Union
 
 from src.common.logger import get_logger
 from src.common.database.database import db
@@ -14,45 +14,276 @@ from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config, model_config
 
 
-"""
-PersonInfoManager 类方法功能摘要：
-1. get_person_id - 根据平台和用户ID生成MD5哈希的唯一person_id
-2. create_person_info - 创建新个人信息文档（自动合并默认值）
-3. update_one_field - 更新单个字段值（若文档不存在则创建）
-4. del_one_document - 删除指定person_id的文档
-5. get_value - 获取单个字段值（返回实际值或默认值）
-6. get_values - 批量获取字段值（任一字段无效则返回空字典）
-7. del_all_undefined_field - 清理全集合中未定义的字段
-8. get_specific_value_list - 根据指定条件，返回person_id,value字典
-"""
-
-
 logger = get_logger("person_info")
 
-JSON_SERIALIZED_FIELDS = ["points"]
+def get_person_id(platform: str, user_id: Union[int, str]) -> str:
+    """获取唯一id"""
+    if "-" in platform:
+        platform = platform.split("-")[1]
+    components = [platform, str(user_id)]
+    key = "_".join(components)
+    return hashlib.md5(key.encode()).hexdigest()
 
-person_info_default = {
+def get_person_id_by_person_name(person_name: str) -> str:
-    "person_id": None,
+    """根据用户名获取用户ID"""
-    "person_name": None,
+    try:
-    "name_reason": None,  # Corrected from person_name_reason to match common usage if intended
+        record = PersonInfo.get_or_none(PersonInfo.person_name == person_name)
-    "platform": "unknown",
+        return record.person_id if record else ""
-    "user_id": "unknown",
+    except Exception as e:
-    "nickname": "Unknown",
+        logger.error(f"根据用户名 {person_name} 获取用户ID时出错 (Peewee): {e}")
-    "know_times": 0,
+        return ""
-    "know_since": None,
+
-    "last_know": None,
+class Person:
-    "attitude_to_me": "0,1",
+    def __init__(self, platform: str = "", user_id: str = "",person_id: str = "",person_name: str = "",nickname: str = ""):
-    "friendly_value": 50,
+        if person_id:
-    "rudeness":50,
+            self.person_id = person_id
-    "neuroticism":"5,1",
+        elif person_name:
-    "conscientiousness": 50,
+            self.person_id = get_person_id_by_person_name(person_name)
-    "likeness": 50,
+            if not self.person_id:
-    "points": None,
+                logger.error(f"根据用户名 {person_name} 获取用户ID时出错，不存在用户{person_name}")
+                return ""
+        elif platform and user_id:
+            self.person_id = get_person_id(platform, user_id)
+        else:
+            logger.error("Person 初始化失败，缺少必要参数")
+            return ""
+        
+        self.is_known = False
+        self.platform = platform
+        self.user_id = user_id
+        
+        # 初始化默认值
+        self.nickname = nickname
+        self.person_name = None
+        self.name_reason = None
+        self.know_times = 0
+        self.know_since = None
+        self.last_know = None
+        self.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
+        
+        # 从数据库加载数据
+        self.load_from_database()
+    
+    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.is_known = record.is_known if record.is_known else False
+                self.nickname = record.nickname if record.nickname else self.nickname
+                
+                if not self.is_known:
+                    if self.nickname:
+                        self.is_known = True
+                        self.person_name = self.nickname
+                        logger.info(f"用户 {self.person_id} 已认识，昵称：{self.nickname}")
+                    else:
+                        logger.warning(f"用户 {self.person_id} 尚未认识，昵称为空")
+                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.know_since = record.know_since if record.know_since else time.time()
+                    self.last_know = record.last_know if record.last_know else time.time()
+                    
+                    # 处理points字段（JSON格式的列表）
+                    if record.points:
+                        try:
+                            self.points = json.loads(record.points)
+                        except (json.JSONDecodeError, TypeError):
+                            logger.warning(f"解析用户 {self.person_id} 的points字段失败，使用默认值")
+                            self.points = []
+                    else:
+                        self.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.info(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"""
+        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,
+                'points': json.dumps(self.points, ensure_ascii=False) if self.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():
+                    if hasattr(record, field):
+                        setattr(record, field, value)
+                record.save()
+                logger.debug(f"已同步用户 {self.person_id} 的信息到数据库")
+            else:
+                # 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,points_num=3):
+        if not self.is_known:
+            return ""
+        
+        # 按时间排序forgotten_points
+        current_points = self.points
+        current_points.sort(key=lambda x: x[2])
+        # 按权重加权随机抽取最多3个不重复的points，point[1]的值在1-10之间，权重越高被抽到概率越大
+        if len(current_points) > points_num:
+            # point[1] 取值范围1-10，直接作为权重
+            weights = [max(1, min(10, int(point[1]))) for point in current_points]
+            # 使用加权采样不放回，保证不重复
+            indices = list(range(len(current_points)))
+            points = []
+            for _ in range(points_num):
+                if not indices:
+                    break
+                sub_weights = [weights[i] for i in indices]
+                chosen_idx = random.choices(indices, weights=sub_weights, k=1)[0]
+                points.append(current_points[chosen_idx])
+                indices.remove(chosen_idx)
+        else:
+            points = current_points
+
+        # 构建points文本
+        points_text = "\n".join([f"{point[2]}：{point[0]}" for point in points])
+
+        nickname_str = ""
+        if self.person_name != nickname_str:
+            nickname_str = f"(ta在{self.platform}上的昵称是{nickname_str})"
+
+        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:
+                neuroticism_info = f"{self.person_name}的情绪十分活跃，容易情绪化,"
+            elif self.neuroticism > 6:
+                neuroticism_info = f"{self.person_name}的情绪比较活跃,"
+            elif self.neuroticism > 4:
+                neuroticism_info = ""
+            elif self.neuroticism > 2:
+                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_info = ""
+        if points_text:
+            points_info = f"你还记得ta最近做的事：{points_text}"
+                
+        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:
@@ -78,157 +309,10 @@ class PersonInfoManager:
         except Exception as e:
             logger.error(f"从 Peewee 加载 person_name_list 失败: {e}")
             
-    @staticmethod
+    def get_person(self, platform: str, user_id: Union[int, str]) -> Person:
-    def get_person_id(platform: str, user_id: Union[int, str]) -> str:
+        person = Person(platform, user_id)
-        """获取唯一id"""
+        return person
-        # 添加空值检查，防止 platform 为 None 时出错
-        if platform is None:
-            platform = "unknown"
-        elif "-" in platform:
-            platform = platform.split("-")[1]
 
-        components = [platform, str(user_id)]
-        key = "_".join(components)
-        return hashlib.md5(key.encode()).hexdigest()
-
-    async def is_person_known(self, platform: str, user_id: int):
-        """判断是否认识某人"""
-        person_id = self.get_person_id(platform, user_id)
-
-        def _db_check_known_sync(p_id: str):
-            return PersonInfo.get_or_none(PersonInfo.person_id == p_id) is not None
-
-        try:
-            return await asyncio.to_thread(_db_check_known_sync, person_id)
-        except Exception as e:
-            logger.error(f"检查用户 {person_id} 是否已知时出错 (Peewee): {e}")
-            return False
-
-    def get_person_id_by_person_name(self, person_name: str) -> str:
-        """根据用户名获取用户ID"""
-        try:
-            record = PersonInfo.get_or_none(PersonInfo.person_name == person_name)
-            return record.person_id if record else ""
-        except Exception as e:
-            logger.error(f"根据用户名 {person_name} 获取用户ID时出错 (Peewee): {e}")
-            return ""
-
-    async def _safe_create_person_info(self, person_id: str, data: Optional[dict] = None):
-        """安全地创建用户信息，处理竞态条件"""
-        if not person_id:
-            logger.debug("创建失败，person_id不存在")
-            return
-
-        _person_info_default = copy.deepcopy(person_info_default)
-        model_fields = PersonInfo._meta.fields.keys()  # type: ignore
-
-        final_data = {"person_id": person_id}
-
-        # Start with defaults for all model fields
-        for key, default_value in _person_info_default.items():
-            if key in model_fields:
-                final_data[key] = default_value
-
-        # Override with provided data
-        if data:
-            for key, value in data.items():
-                if key in model_fields:
-                    final_data[key] = value
-
-        # Ensure person_id is correctly set from the argument
-        final_data["person_id"] = person_id
-
-        # Serialize JSON fields
-        for key in JSON_SERIALIZED_FIELDS:
-            if key in final_data:
-                if isinstance(final_data[key], (list, dict)):
-                    final_data[key] = json.dumps(final_data[key], ensure_ascii=False)
-                elif final_data[key] is None:  # Default for lists is [], store as "[]"
-                    final_data[key] = json.dumps([], ensure_ascii=False)
-
-        def _db_safe_create_sync(p_data: dict):
-            try:
-                # 首先检查是否已存在
-                existing = PersonInfo.get_or_none(PersonInfo.person_id == p_data["person_id"])
-                if existing:
-                    logger.debug(f"用户 {p_data['person_id']} 已存在，跳过创建")
-                    return True
-
-                # 尝试创建
-                PersonInfo.create(**p_data)
-                return True
-            except Exception as e:
-                if "UNIQUE constraint failed" in str(e):
-                    logger.debug(f"检测到并发创建用户 {p_data.get('person_id')}，跳过错误")
-                    return True  # 其他协程已创建，视为成功
-                else:
-                    logger.error(f"创建 PersonInfo 记录 {p_data.get('person_id')} 失败 (Peewee): {e}")
-                    return False
-
-        await asyncio.to_thread(_db_safe_create_sync, final_data)
-
-    async def update_one_field(self, person_id: str, field_name: str, value, data: Optional[Dict] = None):
-        """更新某一个字段，会补全"""
-        if field_name not in PersonInfo._meta.fields:  # type: ignore
-            logger.debug(f"更新'{field_name}'失败，未在 PersonInfo Peewee 模型中定义的字段。")
-            return
-
-        processed_value = value
-        if field_name in JSON_SERIALIZED_FIELDS:
-            if isinstance(value, (list, dict)):
-                processed_value = json.dumps(value, ensure_ascii=False, indent=None)
-            elif value is None:  # Store None as "[]" for JSON list fields
-                processed_value = json.dumps([], ensure_ascii=False, indent=None)
-
-        def _db_update_sync(p_id: str, f_name: str, val_to_set):
-            import time
-
-            start_time = time.time()
-            try:
-                record = PersonInfo.get_or_none(PersonInfo.person_id == p_id)
-                query_time = time.time()
-
-                if record:
-                    setattr(record, f_name, val_to_set)
-                    record.save()
-                    save_time = time.time()
-
-                    total_time = save_time - start_time
-                    if total_time > 0.5:  # 如果超过500ms就记录日志
-                        logger.warning(
-                            f"数据库更新操作耗时 {total_time:.3f}秒 (查询: {query_time - start_time:.3f}s, 保存: {save_time - query_time:.3f}s) person_id={p_id}, field={f_name}"
-                        )
-
-                    return True, False  # Found and updated, no creation needed
-                else:
-                    total_time = time.time() - start_time
-                    if total_time > 0.5:
-                        logger.warning(f"数据库查询操作耗时 {total_time:.3f}秒 person_id={p_id}, field={f_name}")
-                    return False, True  # Not found, needs creation
-            except Exception as e:
-                total_time = time.time() - start_time
-                logger.error(f"数据库操作异常，耗时 {total_time:.3f}秒: {e}")
-                raise
-
-        found, needs_creation = await asyncio.to_thread(_db_update_sync, person_id, field_name, processed_value)
-
-        if needs_creation:
-            logger.info(f"{person_id} 不存在，将新建。")
-            creation_data = data if data is not None else {}
-            # Ensure platform and user_id are present for context if available from 'data'
-            # but primarily, set the field that triggered the update.
-            # The create_person_info will handle defaults and serialization.
-            creation_data[field_name] = value  # Pass original value to create_person_info
-
-            # Ensure platform and user_id are in creation_data if available,
-            # otherwise create_person_info will use defaults.
-            if data and "platform" in data:
-                creation_data["platform"] = data["platform"]
-            if data and "user_id" in data:
-                creation_data["user_id"] = data["user_id"]
-
-            # 使用安全的创建方法，处理竞态条件
-            await self._safe_create_person_info(person_id, creation_data)
 
     @staticmethod
     def _extract_json_from_text(text: str) -> dict:
@@ -279,8 +363,9 @@ class PersonInfoManager:
             logger.debug("取名失败：person_id不能为空")
             return None
 
-        old_name = await self.get_value(person_id, "person_name")
+        person = Person(person_id=person_id)
-        old_reason = await self.get_value(person_id, "name_reason")
+        old_name = person.person_name
+        old_reason = person.name_reason
 
         max_retries = 8
         current_try = 0
@@ -338,8 +423,9 @@ class PersonInfoManager:
                     current_name_set.add(generated_nickname)
 
             if not is_duplicate:
-                await self.update_one_field(person_id, "person_name", generated_nickname)
+                person.person_name = generated_nickname
-                await self.update_one_field(person_id, "name_reason", result.get("reason", "未提供理由"))
+                person.name_reason = result.get("reason", "未提供理由")
+                person.sync_to_database()
 
                 logger.info(
                     f"成功给用户{user_nickname} {person_id} 取名 {generated_nickname}，理由：{result.get('reason', '未提供理由')}"
@@ -357,186 +443,11 @@ class PersonInfoManager:
         # 如果多次尝试后仍未成功，使用唯一的 user_nickname 作为默认值
         unique_nickname = await self._generate_unique_person_name(user_nickname)
         logger.warning(f"在{max_retries}次尝试后未能生成唯一昵称，使用默认昵称 {unique_nickname}")
-        await self.update_one_field(person_id, "person_name", unique_nickname)
+        person.person_name = unique_nickname
-        await self.update_one_field(person_id, "name_reason", "使用用户原始昵称作为默认值")
+        person.name_reason = "使用用户原始昵称作为默认值"
+        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"}
     
 
-    @staticmethod
-    async def get_value(person_id: str, field_name: str):
-        """获取指定用户指定字段的值"""
-        default_value_for_field = person_info_default.get(field_name)
-        if field_name in JSON_SERIALIZED_FIELDS and default_value_for_field is None:
-            default_value_for_field = []  # Ensure JSON fields default to [] if not in DB
-
-        def _db_get_value_sync(p_id: str, f_name: str):
-            record = PersonInfo.get_or_none(PersonInfo.person_id == p_id)
-            if record:
-                val = getattr(record, f_name, None)
-                if f_name in JSON_SERIALIZED_FIELDS:
-                    if isinstance(val, str):
-                        try:
-                            return json.loads(val)
-                        except json.JSONDecodeError:
-                            logger.warning(f"字段 {f_name} for {p_id} 包含无效JSON: {val}. 返回默认值.")
-                            return []  # Default for JSON fields on error
-                    elif val is None:  # Field exists in DB but is None
-                        return []  # Default for JSON fields
-                    # If val is already a list/dict (e.g. if somehow set without serialization)
-                    return val  # Should ideally not happen if update_one_field is always used
-                return val
-            return None  # Record not found
-
-        try:
-            value_from_db = await asyncio.to_thread(_db_get_value_sync, person_id, field_name)
-            if value_from_db is not None:
-                return value_from_db
-            if field_name in person_info_default:
-                return default_value_for_field
-            logger.warning(f"字段 {field_name} 在 person_info_default 中未定义，且在数据库中未找到。")
-            return None  # Ultimate fallback
-        except Exception as e:
-            logger.error(f"获取字段 {field_name} for {person_id} 时出错 (Peewee): {e}")
-            # Fallback to default in case of any error during DB access
-            return default_value_for_field if field_name in person_info_default else None
-
-    @staticmethod
-    def get_value_sync(person_id: str, field_name: str):
-        """同步获取指定用户指定字段的值"""
-        default_value_for_field = person_info_default.get(field_name)
-        if field_name in JSON_SERIALIZED_FIELDS and default_value_for_field is None:
-            default_value_for_field = []
-
-        record = PersonInfo.get_or_none(PersonInfo.person_id == person_id)
-        if record:
-            val = getattr(record, field_name, None)
-            if field_name in JSON_SERIALIZED_FIELDS:
-                if isinstance(val, str):
-                    try:
-                        return json.loads(val)
-                    except json.JSONDecodeError:
-                        logger.warning(f"字段 {field_name} for {person_id} 包含无效JSON: {val}. 返回默认值.")
-                        return []
-                elif val is None:
-                    return []
-                return val
-            return val
-
-        if field_name in person_info_default:
-            return default_value_for_field
-        logger.warning(f"字段 {field_name} 在 person_info_default 中未定义，且在数据库中未找到。")
-        return None
-
-    @staticmethod
-    async def get_values(person_id: str, field_names: list) -> dict:
-        """获取指定person_id文档的多个字段值，若不存在该字段，则返回该字段的全局默认值"""
-        if not person_id:
-            logger.debug("get_values获取失败：person_id不能为空")
-            return {}
-
-        result = {}
-
-        def _db_get_record_sync(p_id: str):
-            return PersonInfo.get_or_none(PersonInfo.person_id == p_id)
-
-        record = await asyncio.to_thread(_db_get_record_sync, person_id)
-
-        for field_name in field_names:
-            if field_name not in PersonInfo._meta.fields:  # type: ignore
-                if field_name in person_info_default:
-                    result[field_name] = copy.deepcopy(person_info_default[field_name])
-                    logger.debug(f"字段'{field_name}'不在Peewee模型中，使用默认配置值。")
-                else:
-                    logger.debug(f"get_values查询失败：字段'{field_name}'未在Peewee模型和默认配置中定义。")
-                    result[field_name] = None
-                continue
-
-            if record:
-                value = getattr(record, field_name)
-                if value is not None:
-                    result[field_name] = value
-                else:
-                    result[field_name] = copy.deepcopy(person_info_default.get(field_name))
-            else:
-                result[field_name] = copy.deepcopy(person_info_default.get(field_name))
-
-        return result
-
-    async def get_or_create_person(
-        self, platform: str, user_id: int, nickname: str, user_cardname: str, user_avatar: Optional[str] = None
-    ) -> str:
-        """
-        根据 platform 和 user_id 获取 person_id。
-        如果对应的用户不存在，则使用提供的可选信息创建新用户。
-        使用try-except处理竞态条件，避免重复创建错误。
-        """
-        person_id = self.get_person_id(platform, user_id)
-
-        def _db_get_or_create_sync(p_id: str, init_data: dict):
-            """原子性的获取或创建操作"""
-            # 首先尝试获取现有记录
-            record = PersonInfo.get_or_none(PersonInfo.person_id == p_id)
-            if record:
-                return record, False  # 记录存在，未创建
-
-            # 记录不存在，尝试创建
-            try:
-                PersonInfo.create(**init_data)
-                return PersonInfo.get(PersonInfo.person_id == p_id), True  # 创建成功
-            except Exception as e:
-                # 如果创建失败（可能是因为竞态条件），再次尝试获取
-                if "UNIQUE constraint failed" in str(e):
-                    logger.debug(f"检测到并发创建用户 {p_id}，获取现有记录")
-                    record = PersonInfo.get_or_none(PersonInfo.person_id == p_id)
-                    if record:
-                        return record, False  # 其他协程已创建，返回现有记录
-                # 如果仍然失败，重新抛出异常
-                raise e
-
-        unique_nickname = await self._generate_unique_person_name(nickname)
-        initial_data = {
-            "person_id": person_id,
-            "platform": platform,
-            "user_id": str(user_id),
-            "nickname": nickname,
-            "person_name": unique_nickname,  # 使用群昵称作为person_name
-            "name_reason": "从群昵称获取",
-            "know_times": 0,
-            "know_since": int(datetime.datetime.now().timestamp()),
-            "last_know": int(datetime.datetime.now().timestamp()),
-            "impression": None,
-            "points": [],
-            "forgotten_points": [],
-        }
-
-        # 序列化JSON字段
-        for key in JSON_SERIALIZED_FIELDS:
-            if key in initial_data:
-                if isinstance(initial_data[key], (list, dict)):
-                    initial_data[key] = json.dumps(initial_data[key], ensure_ascii=False)
-                elif initial_data[key] is None:
-                    initial_data[key] = json.dumps([], ensure_ascii=False)
-
-        model_fields = PersonInfo._meta.fields.keys()  # type: ignore
-        filtered_initial_data = {k: v for k, v in initial_data.items() if v is not None and k in model_fields}
-
-        record, was_created = await asyncio.to_thread(_db_get_or_create_sync, person_id, filtered_initial_data)
-
-        if was_created:
-            logger.info(f"用户 {platform}:{user_id} (person_id: {person_id}) 不存在，将创建新记录 (Peewee)。")
-            logger.info(f"已为 {person_id} 创建新记录，初始数据 (filtered for model): {filtered_initial_data}")
-        else:
-            logger.debug(f"用户 {platform}:{user_id} (person_id: {person_id}) 已存在，返回现有记录。")
-
-        return person_id
-    
-    
-
-person_info_manager = None
-
-def get_person_info_manager():
-    global person_info_manager
-    if person_info_manager is None:
 person_info_manager = PersonInfoManager()
-    return person_info_manager

src/person_info/relationship_builder.py

@@ -7,7 +7,7 @@ from typing import List, Dict, Any
 from src.config.config import global_config
 from src.common.logger import get_logger
 from src.person_info.relationship_manager import get_relationship_manager
-from src.person_info.person_info import get_person_info_manager, PersonInfoManager
+from src.person_info.person_info import Person,get_person_id
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.chat.utils.chat_message_builder import (
     get_raw_msg_by_timestamp_with_chat,
@@ -15,6 +15,7 @@ from src.chat.utils.chat_message_builder import (
     get_raw_msg_before_timestamp_with_chat,
     num_new_messages_since,
 )
+import asyncio
 
 logger = get_logger("relationship_builder")
 
@@ -142,7 +143,8 @@ class RelationshipBuilder:
             }
             segments.append(new_segment)
 
-            person_name = get_person_info_manager().get_value_sync(person_id, "person_name") or person_id
+            person = Person(person_id=person_id)
+            person_name = person.person_name or person_id
             logger.debug(
                 f"{self.log_prefix} 眼熟用户 {person_name} 在 {time.strftime('%H:%M:%S', time.localtime(potential_start_time))} - {time.strftime('%H:%M:%S', time.localtime(message_time))} 之间有 {new_segment['message_count']} 条消息"
             )
@@ -188,8 +190,8 @@ class RelationshipBuilder:
                 "message_count": self._count_messages_in_timerange(potential_start_time, message_time),
             }
             segments.append(new_segment)
-            person_info_manager = get_person_info_manager()
+            person = Person(person_id=person_id)
-            person_name = person_info_manager.get_value_sync(person_id, "person_name") or person_id
+            person_name = person.person_name or person_id
             logger.debug(
                 f"{self.log_prefix} 重新眼熟用户 {person_name} 创建新消息段（超过10条消息间隔）: {new_segment}"
             )
@@ -375,7 +377,7 @@ class RelationshipBuilder:
                     and user_id != global_config.bot.qq_account
                     and msg_time > self.last_processed_message_time
                 ):
-                    person_id = PersonInfoManager.get_person_id(platform, user_id)
+                    person_id = get_person_id(platform, user_id)
                     self._update_message_segments(person_id, msg_time)
                     logger.debug(
                         f"{self.log_prefix} 更新用户 {person_id} 的消息段，消息时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(msg_time))}"
@@ -386,7 +388,8 @@ class RelationshipBuilder:
         users_to_build_relationship = []
         for person_id, segments in self.person_engaged_cache.items():
             total_message_count = self._get_total_message_count(person_id)
-            person_name = get_person_info_manager().get_value_sync(person_id, "person_name") or person_id
+            person = Person(person_id=person_id)
+            person_name = person.person_name or person_id
             
             if total_message_count >= max_build_threshold or (total_message_count >= 5 and (immediate_build == person_id or immediate_build == "all")):
                 users_to_build_relationship.append(person_id)
@@ -403,8 +406,8 @@ class RelationshipBuilder:
         for person_id in users_to_build_relationship:
             segments = self.person_engaged_cache[person_id]
             # 异步执行关系构建
-            import asyncio
+            person = Person(person_id=person_id)
-
+            if person.is_known: 
                 asyncio.create_task(self.update_impression_on_segments(person_id, self.chat_id, segments))
             # 移除已处理的用户缓存
             del self.person_engaged_cache[person_id]

src/person_info/relationship_fetcher.py

@@ -1,468 +0,0 @@
-import time
-import traceback
-import json
-import random
-
-from typing import List, Dict, Any
-from json_repair import repair_json
-
-from src.common.logger import get_logger
-from src.config.config import global_config, model_config
-from src.llm_models.utils_model import LLMRequest
-from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
-from src.chat.message_receive.chat_stream import get_chat_manager
-from src.person_info.person_info import get_person_info_manager
-
-
-logger = get_logger("relationship_fetcher")
-
-
-def init_real_time_info_prompts():
-    """初始化实时信息提取相关的提示词"""
-    relationship_prompt = """
-<聊天记录>
-{chat_observe_info}
-</聊天记录>
-
-{name_block}
-现在，你想要回复{person_name}的消息，消息内容是：{target_message}。请根据聊天记录和你要回复的消息，从你对{person_name}的了解中提取有关的信息：
-1.你需要提供你想要提取的信息具体是哪方面的信息，例如：年龄，性别，你们之间的交流方式，最近发生的事等等。
-2.请注意，请不要重复调取相同的信息，已经调取的信息如下：
-{info_cache_block}
-3.如果当前聊天记录中没有需要查询的信息，或者现有信息已经足够回复，请返回{{"none": "不需要查询"}}
-
-请以json格式输出，例如：
-
-{{
-    "info_type": "信息类型",
-}}
-
-请严格按照json输出格式，不要输出多余内容：
-"""
-    Prompt(relationship_prompt, "real_time_info_identify_prompt")
-
-    fetch_info_prompt = """
-    
-{name_block}
-以下是你在之前与{person_name}的交流中，产生的对{person_name}的了解：
-{person_impression_block}
-{points_text_block}
-
-请从中提取用户"{person_name}"的有关"{info_type}"信息
-请以json格式输出，例如：
-
-{{
-    {info_json_str}
-}}
-
-请严格按照json输出格式，不要输出多余内容：
-"""
-    Prompt(fetch_info_prompt, "real_time_fetch_person_info_prompt")
-
-
-class RelationshipFetcher:
-    def __init__(self, chat_id):
-        self.chat_id = chat_id
-
-        # 信息获取缓存：记录正在获取的信息请求
-        self.info_fetching_cache: List[Dict[str, Any]] = []
-
-        # 信息结果缓存：存储已获取的信息结果，带TTL
-        self.info_fetched_cache: Dict[str, Dict[str, Any]] = {}
-        # 结构：{person_id: {info_type: {"info": str, "ttl": int, "start_time": float, "person_name": str, "unknown": bool}}}
-
-        # LLM模型配置
-        self.llm_model = LLMRequest(
-            model_set=model_config.model_task_config.utils_small, request_type="relation.fetcher"
-        )
-
-        # 小模型用于即时信息提取
-        self.instant_llm_model = LLMRequest(
-            model_set=model_config.model_task_config.utils_small, request_type="relation.fetch"
-        )
-
-        name = get_chat_manager().get_stream_name(self.chat_id)
-        self.log_prefix = f"[{name}] 实时信息"
-
-    def _cleanup_expired_cache(self):
-        """清理过期的信息缓存"""
-        for person_id in list(self.info_fetched_cache.keys()):
-            for info_type in list(self.info_fetched_cache[person_id].keys()):
-                self.info_fetched_cache[person_id][info_type]["ttl"] -= 1
-                if self.info_fetched_cache[person_id][info_type]["ttl"] <= 0:
-                    del self.info_fetched_cache[person_id][info_type]
-            if not self.info_fetched_cache[person_id]:
-                del self.info_fetched_cache[person_id]
-
-    async def build_relation_info(self, person_id, points_num=3):
-        # 清理过期的信息缓存
-        self._cleanup_expired_cache()
-
-        person_info_manager = get_person_info_manager()
-        person_name = await person_info_manager.get_value(person_id, "person_name")
-        attitude_to_me = await person_info_manager.get_value(person_id, "attitude_to_me")
-        neuroticism = await person_info_manager.get_value(person_id, "neuroticism")
-        conscientiousness = await person_info_manager.get_value(person_id, "conscientiousness")
-        likeness = await person_info_manager.get_value(person_id, "likeness")
-
-        nickname_str = await person_info_manager.get_value(person_id, "nickname")
-        platform = await person_info_manager.get_value(person_id, "platform")
-
-        current_points = await person_info_manager.get_value(person_id, "points") or []
-
-        # 按时间排序forgotten_points
-        current_points.sort(key=lambda x: x[2])
-        # 按权重加权随机抽取最多3个不重复的points，point[1]的值在1-10之间，权重越高被抽到概率越大
-        if len(current_points) > points_num:
-            # point[1] 取值范围1-10，直接作为权重
-            weights = [max(1, min(10, int(point[1]))) for point in current_points]
-            # 使用加权采样不放回，保证不重复
-            indices = list(range(len(current_points)))
-            points = []
-            for _ in range(points_num):
-                if not indices:
-                    break
-                sub_weights = [weights[i] for i in indices]
-                chosen_idx = random.choices(indices, weights=sub_weights, k=1)[0]
-                points.append(current_points[chosen_idx])
-                indices.remove(chosen_idx)
-        else:
-            points = current_points
-
-        # 构建points文本
-        points_text = "\n".join([f"{point[2]}：{point[0]}" for point in points])
-
-        nickname_str = ""
-        if person_name != nickname_str:
-            nickname_str = f"(ta在{platform}上的昵称是{nickname_str})"
-
-        relation_info = ""
-        
-        attitude_info = ""
-        attitude_parts = attitude_to_me.split(',')
-        current_attitude_score = float(attitude_parts[0]) if len(attitude_parts) > 0 else 0.0
-        total_confidence = float(attitude_parts[1]) if len(attitude_parts) > 1 else 1.0
-        if attitude_to_me:
-            if current_attitude_score > 8:
-                attitude_info = f"{person_name}对你的态度十分好,"
-            elif current_attitude_score > 5:
-                attitude_info = f"{person_name}对你的态度较好,"
-                
-            
-            if current_attitude_score < -8:
-                attitude_info = f"{person_name}对你的态度十分恶劣,"
-            elif current_attitude_score < -4:
-                attitude_info = f"{person_name}对你的态度不好,"
-            elif current_attitude_score < 0:
-                attitude_info = f"{person_name}对你的态度一般,"
-                
-        neuroticism_info = ""
-        neuroticism_parts = neuroticism.split(',')
-        current_neuroticism_score = float(neuroticism_parts[0]) if len(neuroticism_parts) > 0 else 0.0
-        total_confidence = float(neuroticism_parts[1]) if len(neuroticism_parts) > 1 else 1.0
-        if neuroticism:
-            if current_neuroticism_score > 8:
-                neuroticism_info = f"{person_name}的情绪十分活跃，容易情绪化,"
-            elif current_neuroticism_score > 6:
-                neuroticism_info = f"{person_name}的情绪比较活跃,"
-            elif current_neuroticism_score > 4:
-                neuroticism_info = ""
-            elif current_neuroticism_score > 2:
-                neuroticism_info = f"{person_name}的情绪比较稳定,"
-            else:
-                neuroticism_info = f"{person_name}的情绪非常稳定,毫无波动"
-        
-        points_info = ""
-        if points_text:
-            points_info = f"你还记得ta最近做的事：{points_text}"
-                
-        
-        
-        relation_info = f"{person_name}:{nickname_str}{attitude_info}{neuroticism_info}{points_info}"
-
-
-        return relation_info
-
-    async def _build_fetch_query(self, person_id, target_message, chat_history):
-        nickname_str = ",".join(global_config.bot.alias_names)
-        name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。"
-        person_info_manager = get_person_info_manager()
-        person_name: str = await person_info_manager.get_value(person_id, "person_name")  # type: ignore
-
-        info_cache_block = self._build_info_cache_block()
-
-        prompt = (await global_prompt_manager.get_prompt_async("real_time_info_identify_prompt")).format(
-            chat_observe_info=chat_history,
-            name_block=name_block,
-            info_cache_block=info_cache_block,
-            person_name=person_name,
-            target_message=target_message,
-        )
-
-        try:
-            logger.debug(f"{self.log_prefix} 信息识别prompt: \n{prompt}\n")
-            content, _ = await self.llm_model.generate_response_async(prompt=prompt)
-
-            if content:
-                content_json = json.loads(repair_json(content))
-
-                # 检查是否返回了不需要查询的标志
-                if "none" in content_json:
-                    logger.debug(f"{self.log_prefix} LLM判断当前不需要查询任何信息：{content_json.get('none', '')}")
-                    return None
-
-                if info_type := content_json.get("info_type"):
-                    # 记录信息获取请求
-                    self.info_fetching_cache.append(
-                        {
-                            "person_id": get_person_info_manager().get_person_id_by_person_name(person_name),
-                            "person_name": person_name,
-                            "info_type": info_type,
-                            "start_time": time.time(),
-                            "forget": False,
-                        }
-                    )
-
-                    # 限制缓存大小
-                    if len(self.info_fetching_cache) > 10:
-                        self.info_fetching_cache.pop(0)
-
-                    logger.info(f"{self.log_prefix} 识别到需要调取用户 {person_name} 的[{info_type}]信息")
-                    return info_type
-                else:
-                    logger.warning(f"{self.log_prefix} LLM未返回有效的info_type。响应: {content}")
-
-        except Exception as e:
-            logger.error(f"{self.log_prefix} 执行信息识别LLM请求时出错: {e}")
-            logger.error(traceback.format_exc())
-
-        return None
-
-    def _build_info_cache_block(self) -> str:
-        """构建已获取信息的缓存块"""
-        info_cache_block = ""
-        if self.info_fetching_cache:
-            # 对于每个(person_id, info_type)组合，只保留最新的记录
-            latest_records = {}
-            for info_fetching in self.info_fetching_cache:
-                key = (info_fetching["person_id"], info_fetching["info_type"])
-                if key not in latest_records or info_fetching["start_time"] > latest_records[key]["start_time"]:
-                    latest_records[key] = info_fetching
-
-            # 按时间排序并生成显示文本
-            sorted_records = sorted(latest_records.values(), key=lambda x: x["start_time"])
-            for info_fetching in sorted_records:
-                info_cache_block += (
-                    f"你已经调取了[{info_fetching['person_name']}]的[{info_fetching['info_type']}]信息\n"
-                )
-        return info_cache_block
-
-    async def _extract_single_info(self, person_id: str, info_type: str, person_name: str):
-        """提取单个信息类型
-
-        Args:
-            person_id: 用户ID
-            info_type: 信息类型
-            person_name: 用户名
-        """
-        start_time = time.time()
-        person_info_manager = get_person_info_manager()
-
-        # 首先检查 info_list 缓存
-        info_list = await person_info_manager.get_value(person_id, "info_list") or []
-        cached_info = None
-
-        # 查找对应的 info_type
-        for info_item in info_list:
-            if info_item.get("info_type") == info_type:
-                cached_info = info_item.get("info_content")
-                logger.debug(f"{self.log_prefix} 在info_list中找到 {person_name} 的 {info_type} 信息: {cached_info}")
-                break
-
-        # 如果缓存中有信息，直接使用
-        if cached_info:
-            if person_id not in self.info_fetched_cache:
-                self.info_fetched_cache[person_id] = {}
-
-            self.info_fetched_cache[person_id][info_type] = {
-                "info": cached_info,
-                "ttl": 2,
-                "start_time": start_time,
-                "person_name": person_name,
-                "unknown": cached_info == "none",
-            }
-            logger.info(f"{self.log_prefix} 记得 {person_name} 的 {info_type}: {cached_info}")
-            return
-
-        # 如果缓存中没有，尝试从用户档案中提取
-        try:
-            person_impression = await person_info_manager.get_value(person_id, "impression")
-            points = await person_info_manager.get_value(person_id, "points")
-
-            # 构建印象信息块
-            if person_impression:
-                person_impression_block = (
-                    f"<对{person_name}的总体了解>\n{person_impression}\n</对{person_name}的总体了解>"
-                )
-            else:
-                person_impression_block = ""
-
-            # 构建要点信息块
-            if points:
-                points_text = "\n".join([f"{point[2]}:{point[0]}" for point in points])
-                points_text_block = f"<对{person_name}的近期了解>\n{points_text}\n</对{person_name}的近期了解>"
-            else:
-                points_text_block = ""
-
-            # 如果完全没有用户信息
-            if not points_text_block and not person_impression_block:
-                if person_id not in self.info_fetched_cache:
-                    self.info_fetched_cache[person_id] = {}
-                self.info_fetched_cache[person_id][info_type] = {
-                    "info": "none",
-                    "ttl": 2,
-                    "start_time": start_time,
-                    "person_name": person_name,
-                    "unknown": True,
-                }
-                logger.info(f"{self.log_prefix} 完全不认识 {person_name}")
-                await self._save_info_to_cache(person_id, info_type, "none")
-                return
-
-            # 使用LLM提取信息
-            nickname_str = ",".join(global_config.bot.alias_names)
-            name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。"
-
-            prompt = (await global_prompt_manager.get_prompt_async("real_time_fetch_person_info_prompt")).format(
-                name_block=name_block,
-                info_type=info_type,
-                person_impression_block=person_impression_block,
-                person_name=person_name,
-                info_json_str=f'"{info_type}": "有关{info_type}的信息内容"',
-                points_text_block=points_text_block,
-            )
-
-            # 使用小模型进行即时提取
-            content, _ = await self.instant_llm_model.generate_response_async(prompt=prompt)
-
-            if content:
-                content_json = json.loads(repair_json(content))
-                if info_type in content_json:
-                    info_content = content_json[info_type]
-                    is_unknown = info_content == "none" or not info_content
-
-                    # 保存到运行时缓存
-                    if person_id not in self.info_fetched_cache:
-                        self.info_fetched_cache[person_id] = {}
-                    self.info_fetched_cache[person_id][info_type] = {
-                        "info": "unknown" if is_unknown else info_content,
-                        "ttl": 3,
-                        "start_time": start_time,
-                        "person_name": person_name,
-                        "unknown": is_unknown,
-                    }
-
-                    # 保存到持久化缓存 (info_list)
-                    await self._save_info_to_cache(person_id, info_type, "none" if is_unknown else info_content)
-
-                    if not is_unknown:
-                        logger.info(f"{self.log_prefix} 思考得到，{person_name} 的 {info_type}: {info_content}")
-                    else:
-                        logger.info(f"{self.log_prefix} 思考了也不知道{person_name} 的 {info_type} 信息")
-            else:
-                logger.warning(f"{self.log_prefix} 小模型返回空结果，获取 {person_name} 的 {info_type} 信息失败。")
-
-        except Exception as e:
-            logger.error(f"{self.log_prefix} 执行信息提取时出错: {e}")
-            logger.error(traceback.format_exc())
-
-    async def _save_info_to_cache(self, person_id: str, info_type: str, info_content: str):
-        # sourcery skip: use-next
-        """将提取到的信息保存到 person_info 的 info_list 字段中
-
-        Args:
-            person_id: 用户ID
-            info_type: 信息类型
-            info_content: 信息内容
-        """
-        try:
-            person_info_manager = get_person_info_manager()
-
-            # 获取现有的 info_list
-            info_list = await person_info_manager.get_value(person_id, "info_list") or []
-
-            # 查找是否已存在相同 info_type 的记录
-            found_index = -1
-            for i, info_item in enumerate(info_list):
-                if isinstance(info_item, dict) and info_item.get("info_type") == info_type:
-                    found_index = i
-                    break
-
-            # 创建新的信息记录
-            new_info_item = {
-                "info_type": info_type,
-                "info_content": info_content,
-            }
-
-            if found_index >= 0:
-                # 更新现有记录
-                info_list[found_index] = new_info_item
-                logger.info(f"{self.log_prefix} [缓存更新] 更新 {person_id} 的 {info_type} 信息缓存")
-            else:
-                # 添加新记录
-                info_list.append(new_info_item)
-                logger.info(f"{self.log_prefix} [缓存保存] 新增 {person_id} 的 {info_type} 信息缓存")
-
-            # 保存更新后的 info_list
-            await person_info_manager.update_one_field(person_id, "info_list", info_list)
-
-        except Exception as e:
-            logger.error(f"{self.log_prefix} [缓存保存] 保存信息到缓存失败: {e}")
-            logger.error(traceback.format_exc())
-
-
-class RelationshipFetcherManager:
-    """关系提取器管理器
-
-    管理不同 chat_id 的 RelationshipFetcher 实例
-    """
-
-    def __init__(self):
-        self._fetchers: Dict[str, RelationshipFetcher] = {}
-
-    def get_fetcher(self, chat_id: str) -> RelationshipFetcher:
-        """获取或创建指定 chat_id 的 RelationshipFetcher
-
-        Args:
-            chat_id: 聊天ID
-
-        Returns:
-            RelationshipFetcher: 关系提取器实例
-        """
-        if chat_id not in self._fetchers:
-            self._fetchers[chat_id] = RelationshipFetcher(chat_id)
-        return self._fetchers[chat_id]
-
-    def remove_fetcher(self, chat_id: str):
-        """移除指定 chat_id 的 RelationshipFetcher
-
-        Args:
-            chat_id: 聊天ID
-        """
-        if chat_id in self._fetchers:
-            del self._fetchers[chat_id]
-
-    def clear_all(self):
-        """清空所有 RelationshipFetcher"""
-        self._fetchers.clear()
-
-    def get_active_chat_ids(self) -> List[str]:
-        """获取所有活跃的 chat_id 列表"""
-        return list(self._fetchers.keys())
-
-
-# 全局管理器实例
-relationship_fetcher_manager = RelationshipFetcherManager()
-
-
-init_real_time_info_prompts()

src/person_info/relationship_manager.py

@@ -1,6 +1,5 @@
 from src.common.logger import get_logger
-from .person_info import PersonInfoManager, get_person_info_manager
+from .person_info import Person
-import time
 import random
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config, model_config
@@ -8,11 +7,7 @@ from src.chat.utils.chat_message_builder import build_readable_messages
 import json
 from json_repair import repair_json
 from datetime import datetime
-from difflib import SequenceMatcher
+from typing import List, Dict, Any
-import jieba
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-from typing import List, Dict, Any, Tuple
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 import traceback
 
@@ -52,8 +47,7 @@ def init_prompt():
     }}
 ]
 
-如果没有，就输出none,或返回空数组：
+如果没有，就只输出空数组：[]
-[]
 """,
         "relation_points",
     )
@@ -83,7 +77,9 @@ def init_prompt():
     "attitude": 0,
     "confidence": 0.5
 }}
-现在，请你输出json:
+如果无法看出对方对你的态度，就只输出空数组：[]
+
+现在，请你输出:
 """,
         "attitude_to_me_prompt",
     )
@@ -115,7 +111,9 @@ def init_prompt():
     "neuroticism": 0,
     "confidence": 0.5
 }}
-现在，请你输出json:
+如果无法看出对方的神经质程度，就只输出空数组：[]
+
+现在，请你输出:
 """,
         "neuroticism_prompt",
     )
@@ -124,38 +122,7 @@ class RelationshipManager:
     def __init__(self):
         self.relationship_llm = LLMRequest(
             model_set=model_config.model_task_config.utils, request_type="relationship.person"
-        )  # 用于动作规划
-
-    @staticmethod
-    async def is_known_some_one(platform, user_id):
-        """判断是否认识某人"""
-        person_info_manager = get_person_info_manager()
-        return await person_info_manager.is_person_known(platform, user_id)
-
-    @staticmethod
-    async def first_knowing_some_one(platform: str, user_id: str, user_nickname: str, user_cardname: str):
-        """判断是否认识某人"""
-        person_id = PersonInfoManager.get_person_id(platform, user_id)
-        # 生成唯一的 person_name
-        person_info_manager = get_person_info_manager()
-        unique_nickname = await person_info_manager._generate_unique_person_name(user_nickname)
-        data = {
-            "platform": platform,
-            "user_id": user_id,
-            "nickname": user_nickname,
-            "konw_time": int(time.time()),
-            "person_name": unique_nickname,  # 使用唯一的 person_name
-        }
-        # 先创建用户基本信息，使用安全创建方法避免竞态条件
-        await person_info_manager._safe_create_person_info(person_id=person_id, data=data)
-        # 更新昵称
-        await person_info_manager.update_one_field(
-            person_id=person_id, field_name="nickname", value=user_nickname, data=data
         ) 
-        # 尝试生成更好的名字
-        # await person_info_manager.qv_person_name(
-        # person_id=person_id, user_nickname=user_nickname, user_cardname=user_cardname, user_avatar=user_avatar
-        # )
         
     async def get_points(self,
                         person_name: str,
@@ -163,7 +130,7 @@ class RelationshipManager:
                         readable_messages: str,
                         name_mapping: Dict[str, str],
                         timestamp: float,
-                         current_points: List[Tuple[str, float, str]]):
+                        person: Person):
         alias_str = ", ".join(global_config.bot.alias_names)
         current_time = datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
         
@@ -198,9 +165,7 @@ class RelationshipManager:
             points_data = json.loads(points)
 
             # 只处理正确的格式，错误格式直接跳过
-            if points_data == "none" or not points_data:
+            if points_data == "none" or not points_data or (isinstance(points_data, str) and points_data.lower() == "none") or (isinstance(points_data, list) and len(points_data) == 0):
-                points_list = []
-            elif isinstance(points_data, str) and points_data.lower() == "none":
                 points_list = []
             elif isinstance(points_data, list):
                 points_list = [(item["point"], float(item["weight"]), current_time) for item in points_data]
@@ -238,15 +203,15 @@ class RelationshipManager:
             return
 
                     
-        current_points.extend(points_list)
+        person.points.extend(points_list)
         # 如果points超过10条，按权重随机选择多余的条目移动到forgotten_points
-        if len(current_points) > 20:
+        if len(person.points) > 20:
             # 计算当前时间
             current_time = datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
 
             # 计算每个点的最终权重（原始权重 * 时间权重）
             weighted_points = []
-            for point in current_points:
+            for point in person.points:
                 time_weight = self.calculate_time_weight(point[2], current_time)
                 final_weight = point[1] * time_weight
                 weighted_points.append((point, final_weight))
@@ -270,16 +235,15 @@ class RelationshipManager:
                     idx_to_remove = random.randrange(len(remaining_points))
                     remaining_points[idx_to_remove] = point
                     
-            return remaining_points
+            person.points = remaining_points
-        return current_points
+        return person
     
-    async def get_attitude_to_me(self, person_name, nickname, readable_messages, timestamp, current_attitude):
+    async def get_attitude_to_me(self, person_name, nickname, readable_messages, timestamp, person: Person):
         alias_str = ", ".join(global_config.bot.alias_names)
         current_time = datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
         # 解析当前态度值
-        attitude_parts = current_attitude.split(',')
+        current_attitude_score = person.attitude_to_me
-        current_attitude_score = float(attitude_parts[0]) if len(attitude_parts) > 0 else 0.0
+        total_confidence = person.attitude_to_me_confidence
-        total_confidence = float(attitude_parts[1]) if len(attitude_parts) > 1 else 1.0
         
         prompt = await global_prompt_manager.format_prompt(
             "attitude_to_me_prompt",
@@ -301,23 +265,31 @@ class RelationshipManager:
         attitude = repair_json(attitude)
         attitude_data = json.loads(attitude)
         
+        if attitude_data == "none" or not attitude_data or (isinstance(attitude_data, str) and attitude_data.lower() == "none") or (isinstance(attitude_data, list) and len(attitude_data) == 0):
+            return ""
+        
+        # 确保 attitude_data 是字典格式
+        if not isinstance(attitude_data, dict):
+            logger.warning(f"LLM返回了错误的JSON格式，跳过解析: {type(attitude_data)}, 内容: {attitude_data}")
+            return ""
+        
         attitude_score = attitude_data["attitude"]
         confidence = attitude_data["confidence"]
         
         new_confidence = total_confidence + confidence
-        
         new_attitude_score = (current_attitude_score * total_confidence + attitude_score * confidence)/new_confidence
         
+        person.attitude_to_me = new_attitude_score
+        person.attitude_to_me_confidence = new_confidence
         
-        return f"{new_attitude_score:.3f},{new_confidence:.3f}"
+        return person
     
-    async def get_neuroticism(self, person_name, nickname, readable_messages, timestamp, current_neuroticism):
+    async def get_neuroticism(self, person_name, nickname, readable_messages, timestamp, person: Person):
         alias_str = ", ".join(global_config.bot.alias_names)
         current_time = datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
         # 解析当前态度值
-        neuroticism_parts = current_neuroticism.split(',')
+        current_neuroticism_score = person.neuroticism
-        current_neuroticism_score = float(neuroticism_parts[0]) if len(neuroticism_parts) > 0 else 0.0
+        total_confidence = person.neuroticism_confidence
-        total_confidence = float(neuroticism_parts[1]) if len(neuroticism_parts) > 1 else 1.0
         
         prompt = await global_prompt_manager.format_prompt(
             "neuroticism_prompt",
@@ -339,6 +311,14 @@ class RelationshipManager:
         neuroticism = repair_json(neuroticism)
         neuroticism_data = json.loads(neuroticism)
         
+        if neuroticism_data == "none" or not neuroticism_data or (isinstance(neuroticism_data, str) and neuroticism_data.lower() == "none") or (isinstance(neuroticism_data, list) and len(neuroticism_data) == 0):
+            return ""
+        
+        # 确保 neuroticism_data 是字典格式
+        if not isinstance(neuroticism_data, dict):
+            logger.warning(f"LLM返回了错误的JSON格式，跳过解析: {type(neuroticism_data)}, 内容: {neuroticism_data}")
+            return ""
+        
         neuroticism_score = neuroticism_data["neuroticism"]
         confidence = neuroticism_data["confidence"]
         
@@ -346,8 +326,10 @@ class RelationshipManager:
         
         new_neuroticism_score = (current_neuroticism_score * total_confidence + neuroticism_score * confidence)/new_confidence
         
+        person.neuroticism = new_neuroticism_score
+        person.neuroticism_confidence = new_confidence
         
-        return f"{new_neuroticism_score:.3f},{new_confidence:.3f}"
+        return person
         
 
     async def update_person_impression(self, person_id, timestamp, bot_engaged_messages: List[Dict[str, Any]]):
@@ -360,21 +342,13 @@ class RelationshipManager:
             timestamp: 时间戳 (用于记录交互时间)
             bot_engaged_messages: bot参与的消息列表
         """
-        person_info_manager = get_person_info_manager()
+        person = Person(person_id=person_id)
-        person_name = await person_info_manager.get_value(person_id, "person_name")
+        person_name = person.person_name
-        nickname = await person_info_manager.get_value(person_id, "nickname")
+        nickname = person.nickname
-        know_times: float = await person_info_manager.get_value(person_id, "know_times") or 0  # type: ignore
+        know_times: float = person.know_times
-        current_points = await person_info_manager.get_value(person_id, "points") or []
-        attitude_to_me = await person_info_manager.get_value(person_id, "attitude_to_me") or "0,1"
-        neuroticism = await person_info_manager.get_value(person_id, "neuroticism") or "5,1"
-        
-        # personality_block =get_individuality().get_personality_prompt(x_person=2, level=2)
-        # identity_block =get_individuality().get_identity_prompt(x_person=2, level=2)
 
         user_messages = bot_engaged_messages
 
-        current_time = datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
-
         # 匿名化消息
         # 创建用户名称映射
         name_mapping = {}
@@ -383,33 +357,23 @@ class RelationshipManager:
 
         # 遍历消息，构建映射
         for msg in user_messages:
-            await person_info_manager.get_or_create_person(
+            msg_person = Person(user_id=msg.get("user_id"), platform=msg.get("chat_info_platform"))
-                platform=msg.get("chat_info_platform"),  # type: ignore
-                user_id=msg.get("user_id"),  # type: ignore
-                nickname=msg.get("user_nickname"),  # type: ignore
-                user_cardname=msg.get("user_cardname"),  # type: ignore
-            )
-            replace_user_id: str = msg.get("user_id")  # type: ignore
-            replace_platform: str = msg.get("chat_info_platform")  # type: ignore
-            replace_person_id = PersonInfoManager.get_person_id(replace_platform, replace_user_id)
-            replace_person_name = await person_info_manager.get_value(replace_person_id, "person_name")
-
             # 跳过机器人自己
-            if replace_user_id == global_config.bot.qq_account:
+            if msg_person.user_id == global_config.bot.qq_account:
                 name_mapping[f"{global_config.bot.nickname}"] = f"{global_config.bot.nickname}"
                 continue
 
             # 跳过目标用户
-            if replace_person_name == person_name:
+            if msg_person.person_name == person_name:
-                name_mapping[replace_person_name] = f"{person_name}"
+                name_mapping[msg_person.person_name] = f"{person_name}"
                 continue
 
             # 其他用户映射
-            if replace_person_name not in name_mapping:
+            if msg_person.person_name not in name_mapping:
                 if current_user > "Z":
                     current_user = "A"
                     user_count += 1
-                name_mapping[replace_person_name] = f"用户{current_user}{user_count if user_count > 1 else ''}"
+                name_mapping[msg_person.person_name] = f"用户{current_user}{user_count if user_count > 1 else ''}"
                 current_user = chr(ord(current_user) + 1)
 
         readable_messages = build_readable_messages(
@@ -420,23 +384,16 @@ class RelationshipManager:
             # print(f"original_name: {original_name}, mapped_name: {mapped_name}")
             readable_messages = readable_messages.replace(f"{original_name}", f"{mapped_name}")
             
+        print(name_mapping)
         
+        person = await self.get_points(person_name, nickname, readable_messages, name_mapping, timestamp, person)
+        person = await self.get_attitude_to_me(person_name, nickname, readable_messages, timestamp, person)
+        person = await self.get_neuroticism(person_name, nickname, readable_messages, timestamp, person)
 
-        remaining_points = await self.get_points(person_name, nickname, readable_messages, name_mapping, timestamp, current_points)
+        person.know_times = know_times + 1
-        attitude_to_me = await self.get_attitude_to_me(person_name, nickname, readable_messages, timestamp, attitude_to_me)
+        person.last_know = timestamp
-        neuroticism = await self.get_neuroticism(person_name, nickname, readable_messages, timestamp, neuroticism)
             
-        # 更新数据库
+        person.sync_to_database()
-        await person_info_manager.update_one_field(
-            person_id, "points", json.dumps(remaining_points, ensure_ascii=False, indent=None)
-        )
-        await person_info_manager.update_one_field(person_id, "neuroticism", neuroticism)
-        await person_info_manager.update_one_field(person_id, "attitude_to_me", attitude_to_me)
-        await person_info_manager.update_one_field(person_id, "know_times", know_times + 1)
-        await person_info_manager.update_one_field(person_id, "last_know", timestamp)
-        know_since = await person_info_manager.get_value(person_id, "know_since") or 0
-        if know_since == 0:
-            await person_info_manager.update_one_field(person_id, "know_since", timestamp)
         
         
 

src/plugin_system/apis/generator_api.py

@@ -12,7 +12,6 @@ import traceback
 from typing import Tuple, Any, Dict, List, Optional
 from rich.traceback import install
 from src.common.logger import get_logger
-from src.config.api_ada_configs import TaskConfig
 from src.chat.replyer.default_generator import DefaultReplyer
 from src.chat.message_receive.chat_stream import ChatStream
 from src.chat.utils.utils import process_llm_response

src/plugin_system/apis/person_api.py

@@ -7,9 +7,9 @@
     value = await person_api.get_person_value(person_id, "nickname")
 """
 
-from typing import Any, Optional
+from typing import Any
 from src.common.logger import get_logger
-from src.person_info.person_info import get_person_info_manager, PersonInfoManager
+from src.person_info.person_info import Person
 
 logger = get_logger("person_api")
 
@@ -33,7 +33,7 @@ def get_person_id(platform: str, user_id: int) -> str:
         person_id = person_api.get_person_id("qq", 123456)
     """
     try:
-        return PersonInfoManager.get_person_id(platform, user_id)
+        return Person(platform=platform, user_id=user_id).person_id
     except Exception as e:
         logger.error(f"[PersonAPI] 获取person_id失败: platform={platform}, user_id={user_id}, error={e}")
         return ""
@@ -55,85 +55,14 @@ async def get_person_value(person_id: str, field_name: str, default: Any = None)
         impression = await person_api.get_person_value(person_id, "impression")
     """
     try:
-        person_info_manager = get_person_info_manager()
+        person = Person(person_id=person_id)
-        value = await person_info_manager.get_value(person_id, field_name)
+        value = getattr(person, field_name)
         return value if value is not None else default
     except Exception as e:
         logger.error(f"[PersonAPI] 获取用户信息失败: person_id={person_id}, field={field_name}, error={e}")
         return default
 
 
-async def get_person_values(person_id: str, field_names: list, default_dict: Optional[dict] = None) -> dict:
-    """批量获取用户信息字段值
-
-    Args:
-        person_id: 用户的唯一标识ID
-        field_names: 要获取的字段名列表
-        default_dict: 默认值字典，键为字段名，值为默认值
-
-    Returns:
-        dict: 字段名到值的映射字典
-
-    示例:
-        values = await person_api.get_person_values(
-            person_id,
-            ["nickname", "impression", "know_times"],
-            {"nickname": "未知用户", "know_times": 0}
-        )
-    """
-    try:
-        person_info_manager = get_person_info_manager()
-        values = await person_info_manager.get_values(person_id, field_names)
-
-        # 如果获取成功，返回结果
-        if values:
-            return values
-
-        # 如果获取失败，构建默认值字典
-        result = {}
-        if default_dict:
-            for field in field_names:
-                result[field] = default_dict.get(field, None)
-        else:
-            for field in field_names:
-                result[field] = None
-
-        return result
-
-    except Exception as e:
-        logger.error(f"[PersonAPI] 批量获取用户信息失败: person_id={person_id}, fields={field_names}, error={e}")
-        # 返回默认值字典
-        result = {}
-        if default_dict:
-            for field in field_names:
-                result[field] = default_dict.get(field, None)
-        else:
-            for field in field_names:
-                result[field] = None
-        return result
-
-
-async def is_person_known(platform: str, user_id: int) -> bool:
-    """判断是否认识某个用户
-
-    Args:
-        platform: 平台名称
-        user_id: 用户ID
-
-    Returns:
-        bool: 是否认识该用户
-
-    示例:
-        known = await person_api.is_person_known("qq", 123456)
-    """
-    try:
-        person_info_manager = get_person_info_manager()
-        return await person_info_manager.is_person_known(platform, user_id)
-    except Exception as e:
-        logger.error(f"[PersonAPI] 检查用户是否已知失败: platform={platform}, user_id={user_id}, error={e}")
-        return False
-
-
 def get_person_id_by_name(person_name: str) -> str:
     """根据用户名获取person_id
 
@@ -147,8 +76,8 @@ def get_person_id_by_name(person_name: str) -> str:
         person_id = person_api.get_person_id_by_name("张三")
     """
     try:
-        person_info_manager = get_person_info_manager()
+        person = Person(person_name=person_name)
-        return person_info_manager.get_person_id_by_person_name(person_name)
+        return person.person_id
     except Exception as e:
         logger.error(f"[PersonAPI] 根据用户名获取person_id失败: person_name={person_name}, error={e}")
         return ""