feat：记忆遗忘，优化记忆提取

src/chat/brain_chat/brain_chat.py

@@ -21,7 +21,6 @@ from src.person_info.person_info import Person
 from src.plugin_system.base.component_types import EventType, ActionInfo
 from src.plugin_system.core import events_manager
 from src.plugin_system.apis import generator_api, send_api, message_api, database_api
-from src.memory_system.Memory_chest import global_memory_chest
 from src.chat.utils.chat_message_builder import (
     build_readable_messages_with_id,
     get_raw_msg_before_timestamp_with_chat,

src/chat/frequency_control/frequency_control.py

@@ -63,14 +63,14 @@ class FrequencyControl:
         )
         
         
-        if time.time() - self.last_frequency_adjust_time < 120 or len(msg_list) <= 5:
+        if time.time() - self.last_frequency_adjust_time < 160 or len(msg_list) <= 20:
             return
         else:
             new_msg_list = get_raw_msg_by_timestamp_with_chat(
                 chat_id=self.chat_id,
                 timestamp_start=self.last_frequency_adjust_time,
                 timestamp_end=time.time(),
-                limit=5,
+                limit=20,
                 limit_mode="latest",
             )
             

src/chat/heart_flow/heartFC_chat.py

@@ -19,15 +19,12 @@ from src.chat.planner_actions.action_manager import ActionManager
 from src.chat.heart_flow.hfc_utils import CycleDetail
 from src.express.expression_learner import expression_learner_manager
 from src.chat.frequency_control.frequency_control import frequency_control_manager
-from src.memory_system.question_maker import QuestionMaker
-from src.memory_system.questions import global_conflict_tracker
 from src.memory_system.curious import check_and_make_question
 from src.jargon import extract_and_store_jargon
 from src.person_info.person_info import Person
 from src.plugin_system.base.component_types import EventType, ActionInfo
 from src.plugin_system.core import events_manager
 from src.plugin_system.apis import generator_api, send_api, message_api, database_api
-from src.memory_system.Memory_chest import global_memory_chest
 from src.chat.utils.chat_message_builder import (
     build_readable_messages_with_id,
     get_raw_msg_before_timestamp_with_chat,

src/chat/replyer/group_generator.py

@@ -6,8 +6,6 @@ import re
 
 from typing import List, Optional, Dict, Any, Tuple
 from datetime import datetime
-from src.memory_system.Memory_chest import global_memory_chest
-from src.memory_system.questions import global_conflict_tracker
 from src.common.logger import get_logger
 from src.common.data_models.database_data_model import DatabaseMessages
 from src.common.data_models.info_data_model import ActionPlannerInfo

src/chat/utils/memory_forget_task.py

@@ -0,0 +1,376 @@
+"""
+记忆遗忘任务
+每5分钟进行一次遗忘检查，根据不同的遗忘阶段删除记忆
+"""
+import time
+import random
+from typing import List
+
+from src.common.logger import get_logger
+from src.common.database.database_model import ChatHistory
+from src.manager.async_task_manager import AsyncTask
+
+logger = get_logger("memory_forget_task")
+
+
+class MemoryForgetTask(AsyncTask):
+    """记忆遗忘任务，每5分钟执行一次"""
+    
+    def __init__(self):
+        # 每5分钟执行一次（300秒）
+        super().__init__(task_name="Memory Forget Task", wait_before_start=0, run_interval=300)
+    
+    async def run(self):
+        """执行遗忘检查"""
+        try:
+            current_time = time.time()
+            logger.info("[记忆遗忘] 开始遗忘检查...")
+            
+            # 执行4个阶段的遗忘检查
+            await self._forget_stage_1(current_time)
+            await self._forget_stage_2(current_time)
+            await self._forget_stage_3(current_time)
+            await self._forget_stage_4(current_time)
+            
+            logger.info("[记忆遗忘] 遗忘检查完成")
+        except Exception as e:
+            logger.error(f"[记忆遗忘] 执行遗忘检查时出错: {e}", exc_info=True)
+    
+    async def _forget_stage_1(self, current_time: float):
+        """
+        第一次遗忘检查：
+        搜集所有：记忆还未被遗忘检查过（forget_times=0），且已经是30分钟之外的记忆
+        取count最高25%和最低25%，删除，然后标记被遗忘检查次数为1
+        """
+        try:
+            # 30分钟 = 1800秒
+            time_threshold = current_time - 1800
+            
+            # 查询符合条件的记忆：forget_times=0 且 end_time < time_threshold
+            candidates = list(
+                ChatHistory.select()
+                .where(
+                    (ChatHistory.forget_times == 0) &
+                    (ChatHistory.end_time < time_threshold)
+                )
+            )
+            
+            if not candidates:
+                logger.debug("[记忆遗忘-阶段1] 没有符合条件的记忆")
+                return
+            
+            logger.info(f"[记忆遗忘-阶段1] 找到 {len(candidates)} 条符合条件的记忆")
+            
+            # 按count排序
+            candidates.sort(key=lambda x: x.count, reverse=True)
+            
+            # 计算要删除的数量（最高25%和最低25%）
+            total_count = len(candidates)
+            delete_count = int(total_count * 0.25)  # 25%
+            
+            if delete_count == 0:
+                logger.debug("[记忆遗忘-阶段1] 删除数量为0，跳过")
+                return
+            
+            # 选择要删除的记录（处理count相同的情况：随机选择）
+            to_delete = []
+            to_delete.extend(self._handle_same_count_random(candidates, delete_count, "high"))
+            to_delete.extend(self._handle_same_count_random(candidates, delete_count, "low"))
+            
+            # 去重（避免重复删除），使用id去重
+            seen_ids = set()
+            unique_to_delete = []
+            for record in to_delete:
+                if record.id not in seen_ids:
+                    seen_ids.add(record.id)
+                    unique_to_delete.append(record)
+            to_delete = unique_to_delete
+            
+            # 删除记录并更新forget_times
+            deleted_count = 0
+            for record in to_delete:
+                try:
+                    record.delete_instance()
+                    deleted_count += 1
+                except Exception as e:
+                    logger.error(f"[记忆遗忘-阶段1] 删除记录失败: {e}")
+            
+            # 更新剩余记录的forget_times为1
+            to_delete_ids = {r.id for r in to_delete}
+            remaining = [r for r in candidates if r.id not in to_delete_ids]
+            if remaining:
+                # 批量更新
+                ids_to_update = [r.id for r in remaining]
+                ChatHistory.update(forget_times=1).where(
+                    ChatHistory.id.in_(ids_to_update)
+                ).execute()
+            
+            logger.info(f"[记忆遗忘-阶段1] 完成：删除了 {deleted_count} 条记忆，更新了 {len(remaining)} 条记忆的forget_times为1")
+            
+        except Exception as e:
+            logger.error(f"[记忆遗忘-阶段1] 执行失败: {e}", exc_info=True)
+    
+    async def _forget_stage_2(self, current_time: float):
+        """
+        第二次遗忘检查：
+        搜集所有：记忆遗忘检查为1，且已经是8小时之外的记忆
+        取count最高7%和最低7%，删除，然后标记被遗忘检查次数为2
+        """
+        try:
+            # 8小时 = 28800秒
+            time_threshold = current_time - 28800
+            
+            # 查询符合条件的记忆：forget_times=1 且 end_time < time_threshold
+            candidates = list(
+                ChatHistory.select()
+                .where(
+                    (ChatHistory.forget_times == 1) &
+                    (ChatHistory.end_time < time_threshold)
+                )
+            )
+            
+            if not candidates:
+                logger.debug("[记忆遗忘-阶段2] 没有符合条件的记忆")
+                return
+            
+            logger.info(f"[记忆遗忘-阶段2] 找到 {len(candidates)} 条符合条件的记忆")
+            
+            # 按count排序
+            candidates.sort(key=lambda x: x.count, reverse=True)
+            
+            # 计算要删除的数量（最高7%和最低7%）
+            total_count = len(candidates)
+            delete_count = int(total_count * 0.07)  # 7%
+            
+            if delete_count == 0:
+                logger.debug("[记忆遗忘-阶段2] 删除数量为0，跳过")
+                return
+            
+            # 选择要删除的记录
+            to_delete = []
+            to_delete.extend(self._handle_same_count_random(candidates, delete_count, "high"))
+            to_delete.extend(self._handle_same_count_random(candidates, delete_count, "low"))
+            
+            # 去重
+            to_delete = list(set(to_delete))
+            
+            # 删除记录
+            deleted_count = 0
+            for record in to_delete:
+                try:
+                    record.delete_instance()
+                    deleted_count += 1
+                except Exception as e:
+                    logger.error(f"[记忆遗忘-阶段2] 删除记录失败: {e}")
+            
+            # 更新剩余记录的forget_times为2
+            to_delete_ids = {r.id for r in to_delete}
+            remaining = [r for r in candidates if r.id not in to_delete_ids]
+            if remaining:
+                ids_to_update = [r.id for r in remaining]
+                ChatHistory.update(forget_times=2).where(
+                    ChatHistory.id.in_(ids_to_update)
+                ).execute()
+            
+            logger.info(f"[记忆遗忘-阶段2] 完成：删除了 {deleted_count} 条记忆，更新了 {len(remaining)} 条记忆的forget_times为2")
+            
+        except Exception as e:
+            logger.error(f"[记忆遗忘-阶段2] 执行失败: {e}", exc_info=True)
+    
+    async def _forget_stage_3(self, current_time: float):
+        """
+        第三次遗忘检查：
+        搜集所有：记忆遗忘检查为2，且已经是48小时之外的记忆
+        取count最高5%和最低5%，删除，然后标记被遗忘检查次数为3
+        """
+        try:
+            # 48小时 = 172800秒
+            time_threshold = current_time - 172800
+            
+            # 查询符合条件的记忆：forget_times=2 且 end_time < time_threshold
+            candidates = list(
+                ChatHistory.select()
+                .where(
+                    (ChatHistory.forget_times == 2) &
+                    (ChatHistory.end_time < time_threshold)
+                )
+            )
+            
+            if not candidates:
+                logger.debug("[记忆遗忘-阶段3] 没有符合条件的记忆")
+                return
+            
+            logger.info(f"[记忆遗忘-阶段3] 找到 {len(candidates)} 条符合条件的记忆")
+            
+            # 按count排序
+            candidates.sort(key=lambda x: x.count, reverse=True)
+            
+            # 计算要删除的数量（最高5%和最低5%）
+            total_count = len(candidates)
+            delete_count = int(total_count * 0.05)  # 5%
+            
+            if delete_count == 0:
+                logger.debug("[记忆遗忘-阶段3] 删除数量为0，跳过")
+                return
+            
+            # 选择要删除的记录
+            to_delete = []
+            to_delete.extend(self._handle_same_count_random(candidates, delete_count, "high"))
+            to_delete.extend(self._handle_same_count_random(candidates, delete_count, "low"))
+            
+            # 去重
+            to_delete = list(set(to_delete))
+            
+            # 删除记录
+            deleted_count = 0
+            for record in to_delete:
+                try:
+                    record.delete_instance()
+                    deleted_count += 1
+                except Exception as e:
+                    logger.error(f"[记忆遗忘-阶段3] 删除记录失败: {e}")
+            
+            # 更新剩余记录的forget_times为3
+            to_delete_ids = {r.id for r in to_delete}
+            remaining = [r for r in candidates if r.id not in to_delete_ids]
+            if remaining:
+                ids_to_update = [r.id for r in remaining]
+                ChatHistory.update(forget_times=3).where(
+                    ChatHistory.id.in_(ids_to_update)
+                ).execute()
+            
+            logger.info(f"[记忆遗忘-阶段3] 完成：删除了 {deleted_count} 条记忆，更新了 {len(remaining)} 条记忆的forget_times为3")
+            
+        except Exception as e:
+            logger.error(f"[记忆遗忘-阶段3] 执行失败: {e}", exc_info=True)
+    
+    async def _forget_stage_4(self, current_time: float):
+        """
+        第四次遗忘检查：
+        搜集所有：记忆遗忘检查为3，且已经是7天之外的记忆
+        取count最高2%和最低2%，删除，然后标记被遗忘检查次数为4
+        """
+        try:
+            # 7天 = 604800秒
+            time_threshold = current_time - 604800
+            
+            # 查询符合条件的记忆：forget_times=3 且 end_time < time_threshold
+            candidates = list(
+                ChatHistory.select()
+                .where(
+                    (ChatHistory.forget_times == 3) &
+                    (ChatHistory.end_time < time_threshold)
+                )
+            )
+            
+            if not candidates:
+                logger.debug("[记忆遗忘-阶段4] 没有符合条件的记忆")
+                return
+            
+            logger.info(f"[记忆遗忘-阶段4] 找到 {len(candidates)} 条符合条件的记忆")
+            
+            # 按count排序
+            candidates.sort(key=lambda x: x.count, reverse=True)
+            
+            # 计算要删除的数量（最高2%和最低2%）
+            total_count = len(candidates)
+            delete_count = int(total_count * 0.02)  # 2%
+            
+            if delete_count == 0:
+                logger.debug("[记忆遗忘-阶段4] 删除数量为0，跳过")
+                return
+            
+            # 选择要删除的记录
+            to_delete = []
+            to_delete.extend(self._handle_same_count_random(candidates, delete_count, "high"))
+            to_delete.extend(self._handle_same_count_random(candidates, delete_count, "low"))
+            
+            # 去重
+            to_delete = list(set(to_delete))
+            
+            # 删除记录
+            deleted_count = 0
+            for record in to_delete:
+                try:
+                    record.delete_instance()
+                    deleted_count += 1
+                except Exception as e:
+                    logger.error(f"[记忆遗忘-阶段4] 删除记录失败: {e}")
+            
+            # 更新剩余记录的forget_times为4
+            to_delete_ids = {r.id for r in to_delete}
+            remaining = [r for r in candidates if r.id not in to_delete_ids]
+            if remaining:
+                ids_to_update = [r.id for r in remaining]
+                ChatHistory.update(forget_times=4).where(
+                    ChatHistory.id.in_(ids_to_update)
+                ).execute()
+            
+            logger.info(f"[记忆遗忘-阶段4] 完成：删除了 {deleted_count} 条记忆，更新了 {len(remaining)} 条记忆的forget_times为4")
+            
+        except Exception as e:
+            logger.error(f"[记忆遗忘-阶段4] 执行失败: {e}", exc_info=True)
+    
+    def _handle_same_count_random(self, candidates: List[ChatHistory], delete_count: int, mode: str) -> List[ChatHistory]:
+        """
+        处理count相同的情况，随机选择要删除的记录
+        
+        Args:
+            candidates: 候选记录列表（已按count排序）
+            delete_count: 要删除的数量
+            mode: "high" 表示选择最高count的记录，"low" 表示选择最低count的记录
+        
+        Returns:
+            要删除的记录列表
+        """
+        if not candidates or delete_count == 0:
+            return []
+        
+        to_delete = []
+        
+        if mode == "high":
+            # 从最高count开始选择
+            start_idx = 0
+            while start_idx < len(candidates) and len(to_delete) < delete_count:
+                # 找到所有count相同的记录
+                current_count = candidates[start_idx].count
+                same_count_records = []
+                idx = start_idx
+                while idx < len(candidates) and candidates[idx].count == current_count:
+                    same_count_records.append(candidates[idx])
+                    idx += 1
+                
+                # 如果相同count的记录数量 <= 还需要删除的数量，全部选择
+                needed = delete_count - len(to_delete)
+                if len(same_count_records) <= needed:
+                    to_delete.extend(same_count_records)
+                else:
+                    # 随机选择需要的数量
+                    to_delete.extend(random.sample(same_count_records, needed))
+                
+                start_idx = idx
+        
+        else:  # mode == "low"
+            # 从最低count开始选择
+            start_idx = len(candidates) - 1
+            while start_idx >= 0 and len(to_delete) < delete_count:
+                # 找到所有count相同的记录
+                current_count = candidates[start_idx].count
+                same_count_records = []
+                idx = start_idx
+                while idx >= 0 and candidates[idx].count == current_count:
+                    same_count_records.append(candidates[idx])
+                    idx -= 1
+                
+                # 如果相同count的记录数量 <= 还需要删除的数量，全部选择
+                needed = delete_count - len(to_delete)
+                if len(same_count_records) <= needed:
+                    to_delete.extend(same_count_records)
+                else:
+                    # 随机选择需要的数量
+                    to_delete.extend(random.sample(same_count_records, needed))
+                
+                start_idx = idx
+        
+        return to_delete
+

src/common/database/database_model.py

@@ -317,35 +317,6 @@ class Expression(BaseModel):
     class Meta:
         table_name = "expression"
 
-class MemoryChest(BaseModel):
-    """
-    用于存储记忆仓库的模型
-    """
-
-    title = TextField()  # 标题
-    content = TextField()  # 内容
-    chat_id = TextField(null=True)  # 聊天ID
-    locked = BooleanField(default=False)  # 是否锁定
-
-    class Meta:
-        table_name = "memory_chest"
-
-class MemoryConflict(BaseModel):
-    """
-    用于存储记忆整合过程中冲突内容的模型
-    """
-
-    conflict_content = TextField()  # 冲突内容
-    answer = TextField(null=True)  # 回答内容
-    create_time = FloatField()  # 创建时间
-    update_time = FloatField()  # 更新时间
-    context = TextField(null=True)  # 上下文
-    chat_id = TextField(null=True)  # 聊天ID
-    raise_time = FloatField(null=True)  # 触发次数
-
-    class Meta:
-        table_name = "memory_conflicts"
-        
 class Jargon(BaseModel):
     """
     用于存储俚语的模型
@@ -378,6 +349,7 @@ class ChatHistory(BaseModel):
     keywords = TextField()  # 关键词：这段对话的关键词，JSON格式存储
     summary = TextField()  # 概括：对这段话的平文本概括
     count = IntegerField(default=0)  # 被检索次数
+    forget_times = IntegerField(default=0)  # 被遗忘检查的次数
     
     class Meta:
         table_name = "chat_history"
@@ -410,8 +382,6 @@ MODELS = [
     PersonInfo,
     Expression,
     ActionRecords,
-    MemoryChest,
-    MemoryConflict,
     Jargon,
     ChatHistory,
     ThinkingBack,

src/main.py

@@ -71,6 +71,10 @@ class MainSystem:
         # 添加遥测心跳任务
         await async_task_manager.add_task(TelemetryHeartBeatTask())
         
+        # 添加记忆遗忘任务
+        from src.chat.utils.memory_forget_task import MemoryForgetTask
+        await async_task_manager.add_task(MemoryForgetTask())
+
         # 启动API服务器
         # start_api_server()
         # logger.info("API服务器启动成功")

src/memory_system/Memory_chest.py

@@ -1,666 +0,0 @@
-import asyncio
-import json
-import re
-import time
-import random
-
-from src.llm_models.utils_model import LLMRequest
-from src.config.config import model_config
-from src.common.database.database_model import MemoryChest as MemoryChestModel
-from src.common.logger import get_logger
-from src.config.config import global_config
-from src.plugin_system.apis.message_api import build_readable_messages
-from src.plugin_system.apis.message_api import get_raw_msg_by_timestamp_with_chat
-from json_repair import repair_json
-from src.memory_system.questions import global_conflict_tracker
-
-
-from .memory_utils import (
-    find_best_matching_memory,
-    check_title_exists_fuzzy,
-    get_all_titles,
-    find_most_similar_memory_by_chat_id,
-    compute_merge_similarity_threshold
-
-)
-
-logger = get_logger("memory")
-
-class MemoryChest:
-    def __init__(self):
-        
-        self.LLMRequest = LLMRequest(
-            model_set=model_config.model_task_config.utils_small,
-            request_type="memory_chest",
-        )
-        
-        self.LLMRequest_build = LLMRequest(
-            model_set=model_config.model_task_config.utils,
-            request_type="memory_chest_build",
-        )
-        
-  
-        self.running_content_list = {}  # {chat_id: {"content": running_content, "last_update_time": timestamp, "create_time": timestamp}}
-        self.fetched_memory_list = []  # [(chat_id, (question, answer, timestamp)), ...]
-
-    def remove_one_memory_by_age_weight(self) -> bool:
-        """
-        删除一条记忆：按“越老/越新更易被删”的权重随机选择（老=较小id，新=较大id）。
-
-        返回：是否删除成功
-        """
-        try:
-            memories = list(MemoryChestModel.select())
-            if not memories:
-                return False
-
-            # 排除锁定项
-            candidates = [m for m in memories if not getattr(m, "locked", False)]
-            if not candidates:
-                return False
-
-            # 按 id 排序，使用 id 近似时间顺序（小 -> 老，大 -> 新）
-            candidates.sort(key=lambda m: m.id)
-            n = len(candidates)
-            if n == 1:
-                MemoryChestModel.delete().where(MemoryChestModel.id == candidates[0].id).execute()
-                logger.info(f"[记忆管理] 已删除一条记忆(权重抽样)：{candidates[0].title}")
-                return True
-
-            # 计算U型权重：中间最低，两端最高
-            # r ∈ [0,1] 为位置归一化，w = 0.1 + 0.9 * (abs(r-0.5)*2)**1.5
-            weights = []
-            for idx, _m in enumerate(candidates):
-                r = idx / (n - 1)
-                w = 0.1 + 0.9 * (abs(r - 0.5) * 2) ** 1.5
-                weights.append(w)
-
-            import random as _random
-            selected = _random.choices(candidates, weights=weights, k=1)[0]
-
-            MemoryChestModel.delete().where(MemoryChestModel.id == selected.id).execute()
-            logger.info(f"[记忆管理] 已删除一条记忆(权重抽样)：{selected.title}")
-            return True
-        except Exception as e:
-            logger.error(f"[记忆管理] 按年龄权重删除记忆时出错: {e}")
-            return False
-        
-    async def get_answer_by_question(self, chat_id: str = "", question: str = "") -> str:
-        """
-        根据问题获取答案
-        """
-        logger.info(f"正在回忆问题答案: {question}")
-        
-        title = await self.select_title_by_question(question)
-        
-        if not title:
-            return ""
-        
-        for memory in MemoryChestModel.select():
-            if memory.title == title:
-                content =  memory.content
-                
-        if random.random() < 0.5:        
-            type = "要求原文能够较为全面的回答问题"
-        else:
-            type = "要求提取简短的内容"
-            
-        prompt = f"""
-目标文段：
-{content}
-
-你现在需要从目标文段中找出合适的信息来回答问题：{question}
-请务必从目标文段中提取相关信息的**原文**并输出，{type}
-如果没有原文能够回答问题，输出"无有效信息"即可，不要输出其他内容：
-"""
-
-        if global_config.debug.show_prompt:
-            logger.info(f"记忆仓库获取答案 prompt: {prompt}")
-        else:
-            logger.debug(f"记忆仓库获取答案 prompt: {prompt}")
-
-        answer, (reasoning_content, model_name, tool_calls) = await self.LLMRequest.generate_response_async(prompt)
-        
-        if "无有效" in answer or "无有效信息" in answer or "无信息" in answer:
-            logger.info(f"没有能够回答{question}的记忆")
-            return ""
-        
-        logger.info(f"记忆仓库对问题 “{question}” 获取答案: {answer}")
-
-        # 将问题和答案存到fetched_memory_list
-        if chat_id and answer:
-            self.fetched_memory_list.append((chat_id, (question, answer, time.time())))
-
-            # 清理fetched_memory_list
-            self._cleanup_fetched_memory_list()
-
-        return answer
-
-    def get_chat_memories_as_string(self, chat_id: str) -> str:
-        """
-        获取某个chat_id的所有记忆，并构建成字符串
-
-        Args:
-            chat_id: 聊天ID
-
-        Returns:
-            str: 格式化的记忆字符串，格式：问题：xxx,答案:xxxxx\n问题：xxx,答案:xxxxx\n...
-        """
-        try:
-            memories = []
-
-            # 从fetched_memory_list中获取该chat_id的所有记忆
-            for cid, (question, answer, timestamp) in self.fetched_memory_list:
-                if cid == chat_id:
-                    memories.append(f"问题：{question},答案:{answer}")
-
-            # 按时间戳排序（最新的在后面）
-            memories.sort()
-
-            # 用换行符连接所有记忆
-            result = "\n".join(memories)
-
-            # logger.info(f"chat_id {chat_id} 共有 {len(memories)} 条记忆")
-            return result
-
-        except Exception as e:
-            logger.error(f"获取chat_id {chat_id} 的记忆时出错: {e}")
-            return ""
-
-
-    async def select_title_by_question(self, question: str) -> str:
-        """
-        根据消息内容选择最匹配的标题
-
-        Args:
-            question: 问题
-
-        Returns:
-            str: 选择的标题
-        """
-        # 获取所有标题并构建格式化字符串（排除锁定的记忆）
-        titles = get_all_titles(exclude_locked=True)
-        formatted_titles = ""
-        for title in titles:
-            formatted_titles += f"{title}\n"
-
-        prompt = f"""
-所有主题：
-{formatted_titles}
-
-请根据以下问题，选择一个能够回答问题的主题：
-问题：{question}
-请你输出主题，不要输出其他内容，完整输出主题名：
-"""
-
-        if global_config.debug.show_prompt:
-            logger.info(f"记忆仓库选择标题 prompt: {prompt}")
-        else:
-            logger.debug(f"记忆仓库选择标题 prompt: {prompt}")
-            
-            
-        title, (reasoning_content, model_name, tool_calls) = await self.LLMRequest.generate_response_async(prompt)
-
-        # 根据 title 获取 titles 里的对应项
-        selected_title = None
-
-        # 使用模糊查找匹配标题
-        best_match = find_best_matching_memory(title, similarity_threshold=0.8)
-        if best_match:
-            selected_title = best_match[0]  # 获取匹配的标题
-            logger.info(f"记忆仓库选择标题: {selected_title} (相似度: {best_match[2]:.3f})")
-        else:
-            logger.warning(f"未找到相似度 >= 0.7 的标题匹配: {title}")
-            selected_title = None
-
-        return selected_title
-
-    def _cleanup_fetched_memory_list(self):
-        """
-        清理fetched_memory_list，移除超过10分钟的记忆和超过10条的最旧记忆
-        """
-        try:
-            current_time = time.time()
-            ten_minutes_ago = current_time - 600  # 10分钟 = 600秒
-
-            # 移除超过10分钟的记忆
-            self.fetched_memory_list = [
-                (chat_id, (question, answer, timestamp))
-                for chat_id, (question, answer, timestamp) in self.fetched_memory_list
-                if timestamp > ten_minutes_ago
-            ]
-
-            # 如果记忆条数超过10条，移除最旧的5条
-            if len(self.fetched_memory_list) > 10:
-                # 按时间戳排序，移除最旧的5条
-                self.fetched_memory_list.sort(key=lambda x: x[1][2])  # 按timestamp排序
-                self.fetched_memory_list = self.fetched_memory_list[5:]  # 保留最新的5条
-
-            logger.debug(f"fetched_memory_list清理后，当前有 {len(self.fetched_memory_list)} 条记忆")
-
-        except Exception as e:
-            logger.error(f"清理fetched_memory_list时出错: {e}")
-
-    async def _save_to_database_and_clear(self, chat_id: str, content: str):
-        """
-        生成标题，保存到数据库，并清空对应chat_id的running_content
-
-        Args:
-            chat_id: 聊天ID
-            content: 要保存的内容
-        """
-        try:
-            # 生成标题
-            title = ""
-            title_prompt = f"""
-请为以下内容生成一个描述全面的标题，要求描述内容的主要概念和事件：
-{content}
-
-标题不要分点，不要换行，不要输出其他内容
-请只输出标题，不要输出其他内容：
-"""
-
-            if global_config.debug.show_prompt:
-                logger.info(f"记忆仓库生成标题 prompt: {title_prompt}")
-            else:
-                logger.debug(f"记忆仓库生成标题 prompt: {title_prompt}")
-
-            title, (reasoning_content, model_name, tool_calls) = await self.LLMRequest_build.generate_response_async(title_prompt)
-
-            
-            await asyncio.sleep(0.5)
-            
-            if title:
-                # 保存到数据库
-                MemoryChestModel.create(
-                    title=title.strip(),
-                    content=content,
-                    chat_id=chat_id
-                )
-                logger.info(f"已保存记忆仓库内容，标题: {title.strip()}, chat_id: {chat_id}")
-
-                # 清空内容并刷新时间戳，但保留条目用于增量计算
-                if chat_id in self.running_content_list:
-                    current_time = time.time()
-                    self.running_content_list[chat_id] = {
-                        "content": "",
-                        "last_update_time": current_time,
-                        "create_time": current_time
-                    }
-                    logger.info(f"已保存并刷新chat_id {chat_id} 的时间戳，准备下一次增量构建")
-            else:
-                logger.warning(f"生成标题失败，chat_id: {chat_id}")
-
-        except Exception as e:
-            logger.error(f"保存记忆仓库内容时出错: {e}")
-    
-    async def choose_merge_target(self, memory_title: str, chat_id: str = None) -> tuple[list[str], list[str]]:
-        """
-        选择与给定记忆标题相关的记忆目标（基于文本相似度）
-
-        Args:
-            memory_title: 要匹配的记忆标题
-            chat_id: 聊天ID，用于筛选同chat_id的记忆
-
-        Returns:
-            tuple[list[str], list[str]]: (选中的记忆标题列表, 选中的记忆内容列表)
-        """
-        try:
-            if not chat_id:
-                logger.warning("未提供chat_id，无法进行记忆匹配")
-                return [], []
-            
-            # 动态计算相似度阈值（占比越高阈值越低）
-            dynamic_threshold = compute_merge_similarity_threshold()
-
-            # 使用相似度匹配查找最相似的记忆（基于动态阈值）
-            similar_memory = find_most_similar_memory_by_chat_id(
-                target_title=memory_title,
-                target_chat_id=chat_id,
-                similarity_threshold=dynamic_threshold
-            )
-            
-            if similar_memory:
-                selected_title, selected_content, similarity = similar_memory
-                logger.info(f"为 '{memory_title}' 找到相似记忆: '{selected_title}' (相似度: {similarity:.3f} 阈值: {dynamic_threshold:.2f})")
-                return [selected_title], [selected_content]
-            else:
-                logger.info(f"为 '{memory_title}' 未找到相似度 >= {dynamic_threshold:.2f} 的记忆")
-                return [], []
-            
-        except Exception as e:
-            logger.error(f"选择合并目标时出错: {e}")
-            return [], []
-    
-    def _get_memories_by_titles(self, titles: list[str]) -> list[str]:
-        """
-        根据标题列表查找对应的记忆内容
-
-        Args:
-            titles: 记忆标题列表
-
-        Returns:
-            list[str]: 记忆内容列表
-        """
-        try:
-            contents = []
-            for title in titles:
-                if not title or not title.strip():
-                    continue
-
-                # 使用模糊查找匹配记忆
-                try:
-                    best_match = find_best_matching_memory(title.strip(), similarity_threshold=0.8)
-                    if best_match:
-                        # 检查记忆是否被锁定
-                        memory_title = best_match[0]
-                        memory_content = best_match[1]
-
-                        # 查询数据库中的锁定状态
-                        for memory in MemoryChestModel.select():
-                            if memory.title == memory_title and memory.locked:
-                                logger.warning(f"记忆 '{memory_title}' 已锁定，跳过合并")
-                                continue
-
-                        contents.append(memory_content)
-                        logger.debug(f"找到记忆: {memory_title} (相似度: {best_match[2]:.3f})")
-                    else:
-                        logger.warning(f"未找到相似度 >= 0.8 的标题匹配: '{title}'")
-                except Exception as e:
-                    logger.error(f"查找标题 '{title}' 的记忆时出错: {e}")
-                    continue
-
-            # logger.info(f"成功找到 {len(contents)} 条记忆内容")
-            return contents
-
-        except Exception as e:
-            logger.error(f"根据标题查找记忆时出错: {e}")
-            return []
-    
-    def _parse_merged_parts(self, merged_response: str) -> tuple[str, str]:
-        """
-        解析合并记忆的part1和part2内容
-
-        Args:
-            merged_response: LLM返回的合并记忆响应
-
-        Returns:
-            tuple[str, str]: (part1_content, part2_content)
-        """
-        try:
-            # 使用正则表达式提取part1和part2内容
-            import re
-
-            # 提取part1内容
-            part1_pattern = r'<part1>(.*?)</part1>'
-            part1_match = re.search(part1_pattern, merged_response, re.DOTALL)
-            part1_content = part1_match.group(1).strip() if part1_match else ""
-
-            # 提取part2内容
-            part2_pattern = r'<part2>(.*?)</part2>'
-            part2_match = re.search(part2_pattern, merged_response, re.DOTALL)
-            part2_content = part2_match.group(1).strip() if part2_match else ""
-
-            # 检查是否包含none或None（不区分大小写）
-            def is_none_content(content: str) -> bool:
-                if not content:
-                    return True
-                # 检查是否只包含"none"或"None"（不区分大小写）
-                return re.match(r'^\s*none\s*$', content, re.IGNORECASE) is not None
-
-            # 如果包含none，则设置为空字符串
-            if is_none_content(part1_content):
-                part1_content = ""
-                logger.info("part1内容为none，设置为空")
-
-            if is_none_content(part2_content):
-                part2_content = ""
-                logger.info("part2内容为none，设置为空")
-
-            return part1_content, part2_content
-
-        except Exception as e:
-            logger.error(f"解析合并记忆part1/part2时出错: {e}")
-            return "", ""
-
-    def _parse_merge_target_json(self, json_text: str) -> list[str]:
-        """
-        解析choose_merge_target生成的JSON响应
-        
-        Args:
-            json_text: LLM返回的JSON文本
-            
-        Returns:
-            list[str]: 解析出的记忆标题列表
-        """
-        try:
-            # 清理JSON文本，移除可能的额外内容
-            repaired_content = repair_json(json_text)
-            
-            # 尝试直接解析JSON
-            try:
-                parsed_data = json.loads(repaired_content)
-                if isinstance(parsed_data, list):
-                    # 如果是列表，提取selected_title字段
-                    titles = []
-                    for item in parsed_data:
-                        if isinstance(item, dict) and "selected_title" in item:
-                            value = item.get("selected_title", "")
-                            if isinstance(value, str) and value.strip():
-                                titles.append(value)
-                    return titles
-                elif isinstance(parsed_data, dict) and "selected_title" in parsed_data:
-                    # 如果是单个对象
-                    value = parsed_data.get("selected_title", "")
-                    if isinstance(value, str) and value.strip():
-                        return [value]
-                    else:
-                        # 空字符串表示没有相关记忆
-                        return []
-            except json.JSONDecodeError:
-                pass
-            
-            # 如果直接解析失败，尝试提取JSON对象
-            # 查找所有包含selected_title的JSON对象
-            pattern = r'\{[^}]*"selected_title"[^}]*\}'
-            matches = re.findall(pattern, repaired_content)
-            
-            titles = []
-            for match in matches:
-                try:
-                    obj = json.loads(match)
-                    if "selected_title" in obj:
-                        value = obj.get("selected_title", "")
-                        if isinstance(value, str) and value.strip():
-                            titles.append(value)
-                except json.JSONDecodeError:
-                    continue
-            
-            if titles:
-                return titles
-            
-            logger.warning(f"无法解析JSON响应: {json_text[:200]}...")
-            return []
-            
-        except Exception as e:
-            logger.error(f"解析合并目标JSON时出错: {e}")
-            return []
-            
-    async def merge_memory(self,memory_list: list[str], chat_id: str = None) -> tuple[str, str]:
-        """
-        合并记忆
-        """
-        try:
-            # 在记忆整合前先清理空chat_id的记忆
-            cleaned_count = self.cleanup_empty_chat_id_memories()
-            if cleaned_count > 0:
-                logger.info(f"记忆整合前清理了 {cleaned_count} 条空chat_id记忆")
-            
-            content = ""
-            for memory in memory_list:
-                content += f"{memory}\n"
-            
-            prompt = f"""
-以下是多段记忆内容，请将它们进行整合和修改：
-{content}
---------------------------------
-请将上面的多段记忆内容，合并成两部分内容，第一部分是可以整合，不冲突的概念和知识，第二部分是相互有冲突的概念和知识
-请主要关注概念和知识，而不是聊天的琐事
-重要！！你要关注的概念和知识必须是较为不常见的信息，或者时效性较强的信息！！
-不要！！关注常见的只是，或者已经过时的信息！！
-1.不要关注诸如某个用户做了什么，说了什么，不要关注某个用户的行为，而是关注其中的概念性信息
-2.概念要求精确，不啰嗦，像科普读物或教育课本那样
-3.如果有图片，请只关注图片和文本结合的知识和概念性内容
-4.记忆为一段纯文本，逻辑清晰，指出概念的含义，并说明关系
-**第一部分**
-1.如果两个概念在描述同一件事情，且相互之间逻辑不冲突（请你严格判断），且相互之间没有矛盾，请将它们整合成一个概念，并输出到第一部分
-2.如果某个概念在时间上更新了另一个概念，请用新概念更新就概念来整合，并输出到第一部分
-3.如果没有可整合的概念，请你输出none
-**第二部分**
-1.如果记忆中有无法整合的地方，例如概念不一致，有逻辑上的冲突，请你输出到第二部分
-2.如果两个概念在描述同一件事情，但相互之间逻辑冲突，请将它们输出到第二部分
-3.如果没有无法整合的概念，请你输出none
-
-**输出格式要求**
-请你按以下格式输出：
-<part1>
-第一部分内容，整合后的概念，如果第一部分为none，请输出none
-</part1>
-<part2>
-第二部分内容，无法整合，冲突的概念，如果第二部分为none，请输出none  
-</part2>
-不要输出其他内容，现在请你输出,不要输出其他内容，注意一定要直白，白话，口语化不要浮夸，修辞。：
-"""
-
-            if global_config.debug.show_prompt:
-                logger.info(f"合并记忆 prompt: {prompt}")
-            else:
-                logger.debug(f"合并记忆 prompt: {prompt}")
-
-            merged_memory, (reasoning_content, model_name, tool_calls) = await self.LLMRequest_build.generate_response_async(prompt)
-
-            # 解析part1和part2
-            part1_content, part2_content = self._parse_merged_parts(merged_memory)
-
-            # 处理part2：独立记录冲突内容（无论part1是否为空）
-            if part2_content and part2_content.strip() != "none":
-                logger.info(f"合并记忆part2记录冲突内容: {len(part2_content)} 字符")
-                # 记录冲突到数据库
-                await global_conflict_tracker.record_memory_merge_conflict(part2_content,chat_id)
-
-            # 处理part1：生成标题并保存
-            if part1_content and part1_content.strip() != "none":
-                merged_title = await self._generate_title_for_merged_memory(part1_content)
-
-                # 保存part1到数据库
-                MemoryChestModel.create(
-                    title=merged_title,
-                    content=part1_content,
-                    chat_id=chat_id
-                )
-
-                logger.info(f"合并记忆part1已保存: {merged_title}")
-
-                return merged_title, part1_content
-            else:
-                logger.warning("合并记忆part1为空，跳过保存")
-                return "", ""
-        except Exception as e:
-            logger.error(f"合并记忆时出错: {e}")
-            return "", ""
-    
-    async def _generate_title_for_merged_memory(self, merged_content: str) -> str:
-        """
-        为合并后的记忆生成标题
-        
-        Args:
-            merged_content: 合并后的记忆内容
-            
-        Returns:
-            str: 生成的标题
-        """
-        try:
-            prompt = f"""
-请为以下内容生成一个描述全面的标题，要求描述内容的主要概念和事件：
-例如：
-<example>
-标题：达尔文的自然选择理论
-内容：达尔文的自然选择是生物进化理论的重要组成部分，它解释了生物进化过程中的自然选择机制。
-</example>
-<example>
-标题：麦麦的禁言插件和支持版本
-内容：
-麦麦的禁言插件是一款能够实现禁言的插件
-麦麦的禁言插件可能不支持0.10.2
-MutePlugin 是禁言插件的名称
-</example>
-
-
-需要对以下内容生成标题：
-{merged_content}
-
-
-标题不要分点，不要换行，不要输出其他内容，不要浮夸，以白话简洁的风格输出标题
-请只输出标题，不要输出其他内容：
-"""
-            
-            if global_config.debug.show_prompt:
-                logger.info(f"生成合并记忆标题 prompt: {prompt}")
-            else:
-                logger.debug(f"生成合并记忆标题 prompt: {prompt}")
-            
-            title_response, (reasoning_content, model_name, tool_calls) = await self.LLMRequest.generate_response_async(prompt)
-            
-            # 清理标题，移除可能的引号或多余字符
-            title = title_response.strip().strip('"').strip("'").strip()
-            
-            if title:
-                # 检查是否存在相似标题
-                if check_title_exists_fuzzy(title, similarity_threshold=0.9):
-                    logger.warning(f"生成的标题 '{title}' 与现有标题相似，使用时间戳后缀")
-                    title = f"{title}_{int(time.time())}"
-                
-                logger.info(f"生成合并记忆标题: {title}")
-                return title
-            else:
-                logger.warning("生成合并记忆标题失败，使用默认标题")
-                return f"合并记忆_{int(time.time())}"
-                
-        except Exception as e:
-            logger.error(f"生成合并记忆标题时出错: {e}")
-            return f"合并记忆_{int(time.time())}"
-    
-    def cleanup_empty_chat_id_memories(self) -> int:
-        """
-        清理chat_id为空的记忆记录
-        
-        Returns:
-            int: 被清理的记忆数量
-        """
-        try:
-            # 查找所有chat_id为空的记忆
-            empty_chat_id_memories = MemoryChestModel.select().where(
-                (MemoryChestModel.chat_id.is_null()) | 
-                (MemoryChestModel.chat_id == "") |
-                (MemoryChestModel.chat_id == "None")
-            )
-            
-            count = 0
-            for memory in empty_chat_id_memories:
-                logger.info(f"清理空chat_id记忆: 标题='{memory.title}', ID={memory.id}")
-                memory.delete_instance()
-                count += 1
-            
-            if count > 0:
-                logger.info(f"已清理 {count} 条chat_id为空的记忆记录")
-            else:
-                logger.debug("未发现需要清理的空chat_id记忆记录")
-                
-            return count
-            
-        except Exception as e:
-            logger.error(f"清理空chat_id记忆时出错: {e}")
-            return 0
-    
-    
-global_memory_chest = MemoryChest()

src/memory_system/curious.py

@@ -7,7 +7,6 @@ from src.chat.utils.chat_message_builder import (
 )
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import model_config, global_config
-from src.memory_system.questions import global_conflict_tracker
 from src.memory_system.memory_utils import parse_md_json
 
 logger = get_logger("curious")
@@ -137,7 +136,7 @@ class CuriousDetector:
     
     async def make_question_from_detection(self, question: str, context: str = "") -> bool:
         """
-        将检测到的问题记录到冲突追踪器中
+        将检测到的问题记录（已移除冲突追踪器功能）
         
         Args:
             question: 检测到的问题
@@ -150,14 +149,8 @@ class CuriousDetector:
             if not question or not question.strip():
                 return False
             
-            # 记录问题到冲突追踪器
+            # 冲突追踪器功能已移除
-            await global_conflict_tracker.record_conflict(
+            logger.info(f"检测到问题（冲突追踪器已移除）: {question}")
-                conflict_content=question.strip(),
-                context=context,
-                chat_id=self.chat_id
-            )
-            
-            logger.info(f"已记录问题到冲突追踪器: {question}")
             return True
             
         except Exception as e:

src/memory_system/memory_retrieval.py

@@ -67,7 +67,8 @@ def init_memory_retrieval_prompt():
     # 第二步：ReAct Agent prompt（工具描述会在运行时动态生成）
     Prompt(
         """
-你的名字是{bot_name}，你正在参与聊天，你需要搜集信息来回答问题，帮助你参与聊天。
+你的名字是{bot_name}。现在是{time_now}。
+你正在参与聊天，你需要搜集信息来回答问题，帮助你参与聊天。
 你需要通过思考(Think)、行动(Action)、观察(Observation)的循环来回答问题。
 
 当前问题：{question}
@@ -160,7 +161,7 @@ async def _react_agent_solve_question(
     chat_id: str,
     max_iterations: int = 5,
     timeout: float = 30.0
-) -> Tuple[bool, str, List[Dict[str, Any]]]:
+) -> Tuple[bool, str, List[Dict[str, Any]], bool]:
     """使用ReAct架构的Agent来解决问题
     
     Args:
@@ -170,16 +171,18 @@ async def _react_agent_solve_question(
         timeout: 超时时间（秒）
         
     Returns:
-        Tuple[bool, str, List[Dict[str, Any]]]: (是否找到答案, 答案内容, 思考步骤列表)
+        Tuple[bool, str, List[Dict[str, Any]], bool]: (是否找到答案, 答案内容, 思考步骤列表, 是否超时)
     """
     start_time = time.time()
     collected_info = ""
     thinking_steps = []
+    is_timeout = False
     
     for iteration in range(max_iterations):
         # 检查超时
         if time.time() - start_time > timeout:
             logger.warning(f"ReAct Agent超时，已迭代{iteration}次")
+            is_timeout = True
             break
         
         logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代，问题: {question}")
@@ -191,10 +194,14 @@ async def _react_agent_solve_question(
         # 获取bot_name
         bot_name = global_config.bot.nickname
         
+        # 获取当前时间
+        time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+        
         # 构建prompt（动态生成工具描述）
         prompt = await global_prompt_manager.format_prompt(
             "memory_retrieval_react_prompt",
             bot_name=bot_name,
+            time_now=time_now,
             question=question,
             collected_info=collected_info if collected_info else "暂无信息",
             tools_description=tool_registry.get_tools_description(),
@@ -247,14 +254,14 @@ async def _react_agent_solve_question(
                 step["observations"] = ["找到答案"]
                 thinking_steps.append(step)
                 logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 找到最终答案: {answer}")
-                return True, answer, thinking_steps
+                return True, answer, thinking_steps, False
             elif action_type == "no_answer":
                 # Agent确认无法找到答案
                 answer = thought  # 使用thought说明无法找到答案的原因
                 step["observations"] = ["确认无法找到答案"]
                 thinking_steps.append(step)
                 logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 确认无法找到答案: {answer}")
-                return False, answer, thinking_steps
+                return False, answer, thinking_steps, False
         
         # 并行执行所有工具
         tool_registry = get_tool_registry()
@@ -316,8 +323,11 @@ async def _react_agent_solve_question(
     # 只有Agent明确返回final_answer时，才认为找到了答案
     if collected_info:
         logger.warning(f"ReAct Agent达到最大迭代次数或超时，但未明确返回final_answer。已收集信息: {collected_info[:100]}...")
-    logger.warning("ReAct Agent达到最大迭代次数或超时，直接视为no_answer")
+    if is_timeout:
-    return False, "未找到相关信息", thinking_steps
+        logger.warning("ReAct Agent超时，直接视为no_answer")
+    else:
+        logger.warning("ReAct Agent达到最大迭代次数，直接视为no_answer")
+    return False, "未找到相关信息", thinking_steps, is_timeout
 
 
 def _get_recent_query_history(chat_id: str, time_window_seconds: float = 300.0) -> str:
@@ -513,28 +523,10 @@ def _store_thinking_back(
         logger.error(f"存储思考过程失败: {e}")
 
 
-def _get_max_iterations_by_question_count(question_count: int) -> int:
-    """根据问题数量获取最大迭代次数
-    
-    Args:
-        question_count: 问题数量
-        
-    Returns:
-        int: 最大迭代次数
-    """
-    if question_count == 1:
-        return 6
-    elif question_count == 2:
-        return 3
-    else:  # 3个或以上
-        return 2
-
-
 async def _process_single_question(
     question: str,
     chat_id: str,
-    context: str,
+    context: str
-    max_iterations: int
 ) -> Optional[str]:
     """处理单个问题的查询（包含缓存检查逻辑）
     
@@ -542,7 +534,6 @@ async def _process_single_question(
         question: 要查询的问题
         chat_id: 聊天ID
         context: 上下文信息
-        max_iterations: 最大迭代次数
         
     Returns:
         Optional[str]: 如果找到答案，返回格式化的结果字符串，否则返回None
@@ -584,22 +575,25 @@ async def _process_single_question(
         else:
             logger.info(f"未找到缓存答案，使用ReAct Agent查询，问题: {question[:50]}...")
         
-        found_answer, answer, thinking_steps = await _react_agent_solve_question(
+        found_answer, answer, thinking_steps, is_timeout = await _react_agent_solve_question(
             question=question,
             chat_id=chat_id,
-            max_iterations=max_iterations,
+            max_iterations=5,
-            timeout=30.0
+            timeout=120.0
         )
         
-        # 存储到数据库
+        # 存储到数据库（超时时不存储）
-        _store_thinking_back(
+        if not is_timeout:
-            chat_id=chat_id,
+            _store_thinking_back(
-            question=question,
+                chat_id=chat_id,
-            context=context,
+                question=question,
-            found_answer=found_answer,
+                context=context,
-            answer=answer,
+                found_answer=found_answer,
-            thinking_steps=thinking_steps
+                answer=answer,
-        )
+                thinking_steps=thinking_steps
+            )
+        else:
+            logger.info(f"ReAct Agent超时，不存储到数据库，问题: {question[:50]}...")
         
         if found_answer and answer:
             return f"问题：{question}\n答案：{answer}"
@@ -659,8 +653,6 @@ async def build_memory_retrieval_prompt(
         
         logger.info(f"记忆检索问题生成提示词: {question_prompt}")
         logger.info(f"记忆检索问题生成响应: {response}")
-        logger.info(f"记忆检索问题生成推理: {reasoning_content}")
-        logger.info(f"记忆检索问题生成模型: {model_name}")
         
         if not success:
             logger.error(f"LLM生成问题失败: {response}")
@@ -679,23 +671,21 @@ async def build_memory_retrieval_prompt(
                 retrieved_memory = "\n\n".join(cached_memories)
                 end_time = time.time()
                 logger.info(f"无当次查询，返回缓存记忆，耗时: {(end_time - start_time):.3f}秒，包含 {len(cached_memories)} 条缓存记忆")
-                return f"你回忆起了以下信息：\n{retrieved_memory}\n请在回复时参考这些回忆的信息。\n"
+                return f"你回忆起了以下信息：\n{retrieved_memory}\n如果与回复内容相关，可以参考这些回忆的信息。\n"
             else:
                 return ""
         
         logger.info(f"解析到 {len(questions)} 个问题: {questions}")
         
-        # 第二步：根据问题数量确定最大迭代次数
+        # 第二步：并行处理所有问题（固定使用5次迭代/120秒超时）
-        max_iterations = _get_max_iterations_by_question_count(len(questions))
+        logger.info(f"问题数量: {len(questions)}，固定设置最大迭代次数: 5，超时时间: 120秒")
-        logger.info(f"问题数量: {len(questions)}，设置最大迭代次数: {max_iterations}")
         
         # 并行处理所有问题
         question_tasks = [
             _process_single_question(
                 question=question,
                 chat_id=chat_id,
-                context=message,
+                context=message
-                max_iterations=max_iterations
             )
             for question in questions
         ]
@@ -730,7 +720,7 @@ async def build_memory_retrieval_prompt(
         if all_results:
             retrieved_memory = "\n\n".join(all_results)
             logger.info(f"记忆检索成功，耗时: {(end_time - start_time):.3f}秒，包含 {len(all_results)} 条记忆（含缓存）")
-            return f"你回忆起了以下信息：\n{retrieved_memory}\n请在回复时参考这些回忆的信息。\n"
+            return f"你回忆起了以下信息：\n{retrieved_memory}\n如果与回复内容相关，可以参考这些回忆的信息。\n"
         else:
             logger.debug("所有问题均未找到答案，且无缓存记忆")
             return ""

src/memory_system/memory_utils.py

@@ -6,40 +6,12 @@
 import json
 import re
 from difflib import SequenceMatcher
-from typing import List, Tuple, Optional
 
-from src.common.database.database_model import MemoryChest as MemoryChestModel
 from src.common.logger import get_logger
-from json_repair import repair_json
-from src.config.config import global_config
 
 
 logger = get_logger("memory_utils")
 
-def get_all_titles(exclude_locked: bool = False) -> list[str]:
-    """
-    获取记忆仓库中的所有标题
-
-    Args:
-        exclude_locked: 是否排除锁定的记忆，默认为 False
-
-    Returns:
-        list: 包含所有标题的列表
-    """
-    try:
-        # 查询所有记忆记录的标题
-        titles = []
-        for memory in MemoryChestModel.select():
-            if memory.title:
-                # 如果 exclude_locked 为 True 且记忆已锁定，则跳过
-                if exclude_locked and memory.locked:
-                    continue
-                titles.append(memory.title)
-        return titles
-    except Exception as e:
-        print(f"获取记忆标题时出错: {e}")
-        return []
-
 def parse_md_json(json_text: str) -> list[str]:
     """从Markdown格式的内容中提取JSON对象和推理内容"""
     json_objects = []
@@ -134,259 +106,3 @@ def preprocess_text(text: str) -> str:
         logger.error(f"预处理文本时出错: {e}")
         return text
 
-
-def fuzzy_find_memory_by_title(target_title: str, similarity_threshold: float = 0.9) -> List[Tuple[str, str, float]]:
-    """
-    根据标题模糊查找记忆
-    
-    Args:
-        target_title: 目标标题
-        similarity_threshold: 相似度阈值，默认0.9
-        
-    Returns:
-        List[Tuple[str, str, float]]: 匹配的记忆列表，每个元素为(title, content, similarity_score)
-    """
-    try:
-        # 获取所有记忆
-        all_memories = MemoryChestModel.select()
-        
-        matches = []
-        for memory in all_memories:
-            similarity = calculate_similarity(target_title, memory.title)
-            if similarity >= similarity_threshold:
-                matches.append((memory.title, memory.content, similarity))
-        
-        # 按相似度降序排序
-        matches.sort(key=lambda x: x[2], reverse=True)
-        
-        # logger.info(f"模糊查找标题 '{target_title}' 找到 {len(matches)} 个匹配项")
-        return matches
-        
-    except Exception as e:
-        logger.error(f"模糊查找记忆时出错: {e}")
-        return []
-
-
-def find_best_matching_memory(target_title: str, similarity_threshold: float = 0.9) -> Optional[Tuple[str, str, float]]:
-    """
-    查找最佳匹配的记忆
-    
-    Args:
-        target_title: 目标标题
-        similarity_threshold: 相似度阈值
-        
-    Returns:
-        Optional[Tuple[str, str, float]]: 最佳匹配的记忆(title, content, similarity)或None
-    """
-    try:
-        matches = fuzzy_find_memory_by_title(target_title, similarity_threshold)
-        
-        if matches:
-            best_match = matches[0]  # 已经按相似度排序，第一个是最佳匹配
-            # logger.info(f"找到最佳匹配: '{best_match[0]}' (相似度: {best_match[2]:.3f})")
-            return best_match
-        else:
-            logger.info(f"未找到相似度 >= {similarity_threshold} 的记忆")
-            return None
-            
-    except Exception as e:
-        logger.error(f"查找最佳匹配记忆时出错: {e}")
-        return None
-
-
-def check_title_exists_fuzzy(target_title: str, similarity_threshold: float = 0.9) -> bool:
-    """
-    检查标题是否已存在（模糊匹配）
-    
-    Args:
-        target_title: 目标标题
-        similarity_threshold: 相似度阈值，默认0.9（较高阈值避免误判）
-        
-    Returns:
-        bool: 是否存在相似标题
-    """
-    try:
-        matches = fuzzy_find_memory_by_title(target_title, similarity_threshold)
-        exists = len(matches) > 0
-        
-        if exists:
-            logger.info(f"发现相似标题: '{matches[0][0]}' (相似度: {matches[0][2]:.3f})")
-        else:
-            logger.debug("未发现相似标题")
-            
-        return exists
-        
-    except Exception as e:
-        logger.error(f"检查标题是否存在时出错: {e}")
-        return False
-
-
-def get_memories_by_chat_id_weighted(target_chat_id: str, same_chat_weight: float = 0.95, other_chat_weight: float = 0.05) -> List[Tuple[str, str, str]]:
-    """
-    根据chat_id进行加权抽样获取记忆列表
-    
-    Args:
-        target_chat_id: 目标聊天ID
-        same_chat_weight: 同chat_id记忆的权重，默认0.95（95%概率）
-        other_chat_weight: 其他chat_id记忆的权重，默认0.05（5%概率）
-        
-    Returns:
-        List[Tuple[str, str, str]]: 选中的记忆列表，每个元素为(title, content, chat_id)
-    """
-    try:
-        # 获取所有记忆
-        all_memories = MemoryChestModel.select()
-        
-        # 按chat_id分组
-        same_chat_memories = []
-        other_chat_memories = []
-        
-        for memory in all_memories:
-            if memory.title and not memory.locked:  # 排除锁定的记忆
-                if memory.chat_id == target_chat_id:
-                    same_chat_memories.append((memory.title, memory.content, memory.chat_id))
-                else:
-                    other_chat_memories.append((memory.title, memory.content, memory.chat_id))
-        
-        # 如果没有同chat_id的记忆，返回空列表
-        if not same_chat_memories:
-            logger.warning(f"未找到chat_id为 '{target_chat_id}' 的记忆")
-            return []
-        
-        # 计算抽样数量
-        total_same = len(same_chat_memories)
-        total_other = len(other_chat_memories)
-        
-        # 根据权重计算抽样数量
-        if total_other > 0:
-            # 计算其他chat_id记忆的抽样数量（至少1个，最多不超过总数的10%）
-            other_sample_count = max(1, min(total_other, int(total_same * other_chat_weight / same_chat_weight)))
-        else:
-            other_sample_count = 0
-        
-        # 随机抽样
-        selected_memories = []
-        
-        # 选择同chat_id的记忆（全部选择，因为权重很高）
-        selected_memories.extend(same_chat_memories)
-        
-        # 随机选择其他chat_id的记忆
-        if other_sample_count > 0 and total_other > 0:
-            import random
-            other_selected = random.sample(other_chat_memories, min(other_sample_count, total_other))
-            selected_memories.extend(other_selected)
-        
-        logger.info(f"加权抽样结果: 同chat_id记忆 {len(same_chat_memories)} 条，其他chat_id记忆 {min(other_sample_count, total_other)} 条")
-        
-        return selected_memories
-        
-    except Exception as e:
-        logger.error(f"按chat_id加权抽样记忆时出错: {e}")
-        return []
-
-
-def get_memory_titles_by_chat_id_weighted(target_chat_id: str, same_chat_weight: float = 0.95, other_chat_weight: float = 0.05) -> List[str]:
-    """
-    根据chat_id进行加权抽样获取记忆标题列表（用于合并选择）
-    
-    Args:
-        target_chat_id: 目标聊天ID
-        same_chat_weight: 同chat_id记忆的权重，默认0.95（95%概率）
-        other_chat_weight: 其他chat_id记忆的权重，默认0.05（5%概率）
-        
-    Returns:
-        List[str]: 选中的记忆标题列表
-    """
-    try:
-        memories = get_memories_by_chat_id_weighted(target_chat_id, same_chat_weight, other_chat_weight)
-        titles = [memory[0] for memory in memories]  # 提取标题
-        return titles
-        
-    except Exception as e:
-        logger.error(f"按chat_id加权抽样记忆标题时出错: {e}")
-        return []
-
-
-def find_most_similar_memory_by_chat_id(target_title: str, target_chat_id: str, similarity_threshold: float = 0.5) -> Optional[Tuple[str, str, float]]:
-    """
-    在指定chat_id的记忆中查找最相似的记忆
-    
-    Args:
-        target_title: 目标标题
-        target_chat_id: 目标聊天ID
-        similarity_threshold: 相似度阈值，默认0.7
-        
-    Returns:
-        Optional[Tuple[str, str, float]]: 最相似的记忆(title, content, similarity)或None
-    """
-    try:
-        # 获取指定chat_id的所有记忆
-        same_chat_memories = []
-        for memory in MemoryChestModel.select():
-            if memory.title and not memory.locked and memory.chat_id == target_chat_id:
-                same_chat_memories.append((memory.title, memory.content))
-        
-        if not same_chat_memories:
-            logger.warning(f"未找到chat_id为 '{target_chat_id}' 的记忆")
-            return None
-        
-        # 计算相似度并找到最佳匹配
-        best_match = None
-        best_similarity = 0.0
-        
-        for title, content in same_chat_memories:
-            # 跳过目标标题本身
-            if title.strip() == target_title.strip():
-                continue
-                
-            similarity = calculate_similarity(target_title, title)
-            
-            if similarity > best_similarity:
-                best_similarity = similarity
-                best_match = (title, content, similarity)
-        
-        # 检查是否超过阈值
-        if best_match and best_similarity >= similarity_threshold:
-            logger.info(f"找到最相似记忆: '{best_match[0]}' (相似度: {best_similarity:.3f})")
-            return best_match
-        else:
-            logger.info(f"未找到相似度 >= {similarity_threshold} 的记忆，最高相似度: {best_similarity:.3f}")
-            return None
-            
-    except Exception as e:
-        logger.error(f"查找最相似记忆时出错: {e}")
-        return None
-    
-    
-    
-def compute_merge_similarity_threshold() -> float:
-    """
-    根据当前记忆数量占比动态计算合并相似度阈值。
-
-    规则：占比越高，阈值越低。
-    - < 60%: 0.80（更严格，避免早期误合并）
-    - < 80%: 0.70
-    - < 100%: 0.60
-    - < 120%: 0.50
-    - >= 120%: 0.45（最宽松，加速收敛）
-    """
-    try:
-        current_count = MemoryChestModel.select().count()
-        max_count = max(1, int(global_config.memory.max_memory_number))
-        percentage = current_count / max_count
-
-        if percentage < 0.6:
-            return 0.70
-        elif percentage < 0.8:
-            return 0.60
-        elif percentage < 1.0:
-            return 0.50
-        elif percentage < 1.5:
-            return 0.40
-        elif percentage < 2:
-            return 0.30
-        else:
-            return 0.25
-    except Exception:
-        # 发生异常时使用保守阈值
-        return 0.70

src/memory_system/question_maker.py

@@ -1,98 +0,0 @@
-import time
-import random
-from typing import List, Optional, Tuple
-from src.chat.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat, build_readable_messages
-from src.common.database.database_model import MemoryConflict
-from src.config.config import global_config
-
-
-class QuestionMaker:
-    def __init__(self, chat_id: str, context: str = "") -> None:
-        """问题生成器。
-
-        - chat_id: 会话 ID，用于筛选该会话下的冲突记录。
-        - context: 额外上下文，可用于后续扩展。
-
-        用法示例：
-        >>> qm = QuestionMaker(chat_id="some_chat")
-        >>> question, chat_ctx, conflict_ctx = await qm.make_question()
-        """
-        self.chat_id = chat_id
-        self.context = context
-
-    def get_context(self, timestamp: float = time.time()) -> str:
-        """获取指定时间点之前的对话上下文字符串。"""
-        latest_30_msgs = get_raw_msg_before_timestamp_with_chat(
-            chat_id=self.chat_id,
-            timestamp=timestamp,
-            limit=30,
-        )   
-
-        all_dialogue_prompt_str = build_readable_messages(
-            latest_30_msgs,
-            replace_bot_name=True,
-            timestamp_mode="normal_no_YMD",
-        )
-        return all_dialogue_prompt_str
-
-
-    async def get_all_conflicts(self) -> List[MemoryConflict]:
-        """获取当前会话下的所有记忆冲突记录。"""
-        conflicts: List[MemoryConflict] = list(MemoryConflict.select().where(MemoryConflict.chat_id == self.chat_id))
-        return conflicts
-    
-    async def get_un_answered_conflict(self) -> List[MemoryConflict]:
-        """获取未回答的记忆冲突记录（answer 为空）。"""
-        conflicts = await self.get_all_conflicts()
-        return [conflict for conflict in conflicts if not conflict.answer]
-
-    async def get_random_unanswered_conflict(self) -> Optional[MemoryConflict]:
-        """按权重随机选取一个未回答的冲突并自增 raise_time。
-
-        选择规则：
-        - 若存在 `raise_time == 0` 的项：按权重抽样（0 次权重 1.0，≥1 次权重 0.01）。
-        - 若不存在，返回 None。
-        - 每次成功选中后，将该条目的 `raise_time` 自增 1 并保存。
-        """
-        conflicts = await self.get_un_answered_conflict()
-        if not conflicts:
-            return None
-
-        conflicts_with_zero = [c for c in conflicts if (getattr(c, "raise_time", 0) or 0) == 0]
-        if conflicts_with_zero:
-            # 权重规则：raise_time == 0 -> 1.0；raise_time >= 1 -> 0.01
-            weights = []
-            for conflict in conflicts:
-                current_raise_time = getattr(conflict, "raise_time", 0) or 0
-                weight = 1.0 if current_raise_time == 0 else 0.01
-                weights.append(weight)
-
-            # 按权重随机选择
-            chosen_conflict = random.choices(conflicts, weights=weights, k=1)[0]
-
-            # 选中后，自增 raise_time 并保存
-            chosen_conflict.raise_time = (getattr(chosen_conflict, "raise_time", 0) or 0) + 1
-            chosen_conflict.save()
-
-            return chosen_conflict
-        else:
-            # 如果没有 raise_time == 0 的冲突，返回 None
-            return None
-
-    async def make_question(self) -> Tuple[Optional[str], Optional[str], Optional[str]]:
-        """生成一条用于询问用户的冲突问题与上下文。
-
-        返回三元组 (question, chat_context, conflict_context)：
-        - question: 冲突文本；若本次未选中任何冲突则为 None。
-        - chat_context: 该冲突创建时间点前的会话上下文字符串；若无则为 None。
-        - conflict_context: 冲突在 DB 中存储的上下文；若无则为 None。
-        """
-        conflict = await self.get_random_unanswered_conflict()
-        if not conflict:
-            return None, None, None
-        question = conflict.conflict_content
-        conflict_context = conflict.context
-        create_time = conflict.create_time
-        chat_context = self.get_context(create_time)
-
-        return question, chat_context, conflict_context

src/memory_system/questions.py

@@ -1,192 +0,0 @@
-import time
-from src.common.logger import get_logger
-from src.common.database.database_model import MemoryConflict
-from src.llm_models.utils_model import LLMRequest
-from src.config.config import model_config
-from src.memory_system.memory_utils import parse_md_json
-
-logger = get_logger("conflict_tracker")
-
-class ConflictTracker:
-    """
-    记忆整合冲突追踪器
-
-    用于记录和存储记忆整合过程中的冲突内容
-    """
-    def __init__(self):
-        self.LLMRequest_tracker = LLMRequest(
-            model_set=model_config.model_task_config.utils,
-            request_type="conflict_tracker",
-        )
-        
-    async def record_conflict(self, conflict_content: str, context: str = "", chat_id: str = "") -> bool:
-        """
-        记录冲突内容
-
-        Args:
-            conflict_content: 冲突内容
-            context: 上下文
-            chat_id: 聊天ID
-
-        Returns:
-            bool: 是否成功记录
-        """
-        try:
-            if not conflict_content or conflict_content.strip() == "":
-                return False
-
-            # 直接记录，不进行跟踪
-            MemoryConflict.create(
-                conflict_content=conflict_content,
-                create_time=time.time(),
-                update_time=time.time(),
-                answer="",
-                chat_id=chat_id,
-            )
-
-            logger.info(f"记录冲突内容: {len(conflict_content)} 字符")
-            return True
-
-        except Exception as e:
-            logger.error(f"记录冲突内容时出错: {e}")
-            return False
-
-    async def add_or_update_conflict(
-        self,
-        conflict_content: str,
-        create_time: float,
-        update_time: float,
-        answer: str = "",
-        context: str = "",
-        chat_id: str = None
-    ) -> bool:
-        """
-        根据conflict_content匹配数据库内容，如果找到相同的就更新update_time和answer，
-        如果没有相同的，就新建一条保存全部内容
-        """
-        try:
-            # 尝试根据conflict_content查找现有记录
-            existing_conflict = MemoryConflict.get_or_none(
-                MemoryConflict.conflict_content == conflict_content,
-                MemoryConflict.chat_id == chat_id
-            )
-            
-            if existing_conflict:
-                # 如果找到相同的conflict_content，更新update_time和answer
-                existing_conflict.update_time = update_time
-                existing_conflict.answer = answer
-                existing_conflict.save()
-                return True
-            else:
-                # 如果没有找到相同的，创建新记录
-                MemoryConflict.create(
-                    conflict_content=conflict_content,
-                    create_time=create_time,
-                    update_time=update_time,
-                    answer=answer,
-                    context=context,
-                    chat_id=chat_id,
-                )
-                return True
-        except Exception as e:
-            # 记录错误并返回False
-            logger.error(f"添加或更新冲突记录时出错: {e}")
-            return False
-
-    async def record_memory_merge_conflict(self, part2_content: str, chat_id: str = None) -> bool:
-        """
-        记录记忆整合过程中的冲突内容（part2）
-
-        Args:
-            part2_content: 冲突内容（part2）
-
-        Returns:
-            bool: 是否成功记录
-        """
-        if not part2_content or part2_content.strip() == "":
-            return False
-
-        prompt = f"""以下是一段有冲突的信息，请你根据这些信息总结出几个具体的提问:
-冲突信息：
-{part2_content}
-
-要求：
-1.提问必须具体，明确
-2.提问最好涉及指向明确的事物，而不是代称
-3.如果缺少上下文，不要强行提问，可以忽略
-4.请忽略涉及违法，暴力，色情，政治等敏感话题的内容
-
-请用json格式输出，不要输出其他内容，仅输出提问理由和具体提的提问:
-**示例**
-// 理由文本
-```json
-{{
-    "question":"提问",
-}}
-```
-```json
-{{
-    "question":"提问"
-}}
-```
-...提问数量在1-3个之间，不要重复，现在请输出："""
-
-        question_response, (reasoning_content, model_name, tool_calls) = await self.LLMRequest_tracker.generate_response_async(prompt)
-            
-        # 解析JSON响应
-        questions, reasoning_content = parse_md_json(question_response)   
-
-        print(prompt)
-        print(question_response)
-
-        for question in questions:
-            await self.record_conflict(
-                conflict_content=question["question"], 
-                context=reasoning_content, 
-                chat_id=chat_id,
-            )
-        return True
-
-    async def get_conflict_count(self) -> int:
-        """
-        获取冲突记录数量
-
-        Returns:
-            int: 记录数量
-        """
-        try:
-            return MemoryConflict.select().count()
-        except Exception as e:
-            logger.error(f"获取冲突记录数量时出错: {e}")
-            return 0
-
-    async def delete_conflict(self, conflict_content: str, chat_id: str) -> bool:
-        """
-        删除指定的冲突记录
-
-        Args:
-            conflict_content: 冲突内容
-            chat_id: 聊天ID
-
-        Returns:
-            bool: 是否成功删除
-        """
-        try:
-            conflict = MemoryConflict.get_or_none(
-                MemoryConflict.conflict_content == conflict_content,
-                MemoryConflict.chat_id == chat_id
-            )
-            
-            if conflict:
-                conflict.delete_instance()
-                logger.info(f"已删除冲突记录: {conflict_content}")
-                return True
-            else:
-                logger.warning(f"未找到要删除的冲突记录: {conflict_content}")
-                return False
-        except Exception as e:
-            logger.error(f"删除冲突记录时出错: {e}")
-            return False
-
-# 全局冲突追踪器实例
-global_conflict_tracker = ConflictTracker()

src/memory_system/retrieval_tools/query_jargon.py

@@ -11,15 +11,13 @@ logger = get_logger("memory_retrieval_tools")
 
 async def query_jargon(
     keyword: str, 
-    chat_id: str,
+    chat_id: str
-    fuzzy: bool = False
 ) -> str:
     """根据关键词在jargon库中查询
     
     Args:
         keyword: 关键词（黑话/俚语/缩写）
         chat_id: 聊天ID
-        fuzzy: 是否使用模糊搜索，默认False（精确匹配）
         
     Returns:
         str: 查询结果
@@ -29,27 +27,53 @@ async def query_jargon(
         if not content:
             return "关键词为空"
         
-        # 执行搜索（仅搜索当前会话或全局）
+        # 先尝试精确匹配
         results = search_jargon(
             keyword=content,
             chat_id=chat_id,
-            limit=1,
+            limit=10,
             case_sensitive=False,
-            fuzzy=fuzzy
+            fuzzy=False
         )
         
+        is_fuzzy_match = False
+        
+        # 如果精确匹配未找到，尝试模糊搜索
+        if not results:
+            results = search_jargon(
+                keyword=content,
+                chat_id=chat_id,
+                limit=10,
+                case_sensitive=False,
+                fuzzy=True
+            )
+            is_fuzzy_match = True
+        
         if results:
-            result = results[0]
+            # 如果是模糊匹配，显示找到的实际jargon内容
-            translation = result.get("translation", "").strip()
+            if is_fuzzy_match:
-            meaning = result.get("meaning", "").strip()
+                # 处理多个结果
-            search_type = "模糊搜索" if fuzzy else "精确匹配"
+                output_parts = [f"未精确匹配到'{content}'"]
-            output = f'"{content}可能为黑话或者网络简写，翻译为：{translation}，含义为：{meaning}"'
+                for result in results:
-            logger.info(f"在jargon库中找到匹配（当前会话或全局，{search_type}）: {content}")
+                    found_content = result.get("content", "").strip()
+                    meaning = result.get("meaning", "").strip()
+                    if found_content and meaning:
+                        output_parts.append(f"找到 '{found_content}' 的含义为：{meaning}")
+                output = "，".join(output_parts)
+                logger.info(f"在jargon库中找到匹配（当前会话或全局，模糊搜索）: {content}，找到{len(results)}条结果")
+            else:
+                # 精确匹配，可能有多条（相同content但不同chat_id的情况）
+                output_parts = []
+                for result in results:
+                    meaning = result.get("meaning", "").strip()
+                    if meaning:
+                        output_parts.append(f"'{content}' 为黑话或者网络简写，含义为：{meaning}")
+                output = "；".join(output_parts) if len(output_parts) > 1 else output_parts[0]
+                logger.info(f"在jargon库中找到匹配（当前会话或全局，精确匹配）: {content}，找到{len(results)}条结果")
             return output
         
         # 未命中
-        search_type = "模糊搜索" if fuzzy else "精确匹配"
+        logger.info(f"在jargon库中未找到匹配（当前会话或全局，精确匹配和模糊搜索都未找到）: {content}")
-        logger.info(f"在jargon库中未找到匹配（当前会话或全局，{search_type}）: {content}")
         return f"未在jargon库中找到'{content}'的解释"
         
     except Exception as e:
@@ -61,19 +85,13 @@ def register_tool():
     """注册工具"""
     register_memory_retrieval_tool(
         name="query_jargon",
-        description="根据关键词在jargon库中查询黑话/俚语/缩写的含义。支持大小写不敏感搜索和模糊搜索。仅搜索当前会话或全局jargon。",
+        description="根据关键词在jargon库中查询黑话/俚语/缩写的含义。支持大小写不敏感搜索，默认会先尝试精确匹配，如果找不到则自动使用模糊搜索。仅搜索当前会话或全局jargon。",
         parameters=[
             {
                 "name": "keyword",
                 "type": "string",
-                "description": "关键词（黑话/俚语/缩写），支持模糊搜索",
+                "description": "关键词（黑话/俚语/缩写）",
                 "required": True
-            },
-            {
-                "name": "fuzzy",
-                "type": "boolean",
-                "description": "是否使用模糊搜索（部分匹配），默认False（精确匹配）。当精确匹配找不到时，可以尝试使用模糊搜索。",
-                "required": False
             }
         ],
         execute_func=query_jargon