diff --git a/src/chat/brain_chat/brain_chat.py b/src/chat/brain_chat/brain_chat.py
index d486e865..3624646f 100644
--- a/src/chat/brain_chat/brain_chat.py
+++ b/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,
diff --git a/src/chat/frequency_control/frequency_control.py b/src/chat/frequency_control/frequency_control.py
index ca63e503..95242972 100644
--- a/src/chat/frequency_control/frequency_control.py
+++ b/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",
)
diff --git a/src/chat/heart_flow/heartFC_chat.py b/src/chat/heart_flow/heartFC_chat.py
index c60b8647..6bbda587 100644
--- a/src/chat/heart_flow/heartFC_chat.py
+++ b/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,
diff --git a/src/chat/replyer/group_generator.py b/src/chat/replyer/group_generator.py
index 617caee9..bcda39b9 100644
--- a/src/chat/replyer/group_generator.py
+++ b/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
diff --git a/src/chat/utils/memory_forget_task.py b/src/chat/utils/memory_forget_task.py
new file mode 100644
index 00000000..11d49171
--- /dev/null
+++ b/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
+
diff --git a/src/common/database/database_model.py b/src/common/database/database_model.py
index 186bb65c..424ec125 100644
--- a/src/common/database/database_model.py
+++ b/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,
diff --git a/src/main.py b/src/main.py
index 98c36147..0515e5f1 100644
--- a/src/main.py
+++ b/src/main.py
@@ -70,6 +70,10 @@ 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()
diff --git a/src/memory_system/Memory_chest.py b/src/memory_system/Memory_chest.py
deleted file mode 100644
index dac3995b..00000000
--- a/src/memory_system/Memory_chest.py
+++ /dev/null
@@ -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_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_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：
-
-第一部分内容，整合后的概念，如果第一部分为none，请输出none
-
-
-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
-
-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：
-
-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。
-
-
-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
-
-
-
-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()
\ No newline at end of file
diff --git a/src/memory_system/curious.py b/src/memory_system/curious.py
index c10d6b84..044c9cdb 100644
--- a/src/memory_system/curious.py
+++ b/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
- await global_conflict_tracker.record_conflict(
- 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}")
+ # 冲突追踪器功能已移除
+ logger.info(f"检测到问题（冲突追踪器已移除）: {question}")
return True
except Exception as e:
diff --git a/src/memory_system/memory_retrieval.py b/src/memory_system/memory_retrieval.py
index 4014826c..025aba37 100644
--- a/src/memory_system/memory_retrieval.py
+++ b/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")
- return False, "未找到相关信息", thinking_steps
+ if is_timeout:
+ 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,
- max_iterations: int
+ context: str
) -> 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,
- timeout=30.0
+ max_iterations=5,
+ timeout=120.0
)
- # 存储到数据库
- _store_thinking_back(
- chat_id=chat_id,
- question=question,
- context=context,
- found_answer=found_answer,
- answer=answer,
- thinking_steps=thinking_steps
- )
+ # 存储到数据库（超时时不存储）
+ if not is_timeout:
+ _store_thinking_back(
+ chat_id=chat_id,
+ question=question,
+ context=context,
+ found_answer=found_answer,
+ 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
- max_iterations = _get_max_iterations_by_question_count(len(questions))
- logger.info(f"问题数量: {len(questions)}，设置最大迭代次数: {max_iterations}")
+ # 第二步：并行处理所有问题（固定使用5次迭代/120秒超时）
+ 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")
# 并行处理所有问题
question_tasks = [
_process_single_question(
question=question,
chat_id=chat_id,
- context=message,
- max_iterations=max_iterations
+ context=message
)
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 ""
diff --git a/src/memory_system/memory_utils.py b/src/memory_system/memory_utils.py
index 0033dbc6..59c4a143 100644
--- a/src/memory_system/memory_utils.py
+++ b/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
\ No newline at end of file
diff --git a/src/memory_system/question_maker.py b/src/memory_system/question_maker.py
deleted file mode 100644
index bd9c6929..00000000
--- a/src/memory_system/question_maker.py
+++ /dev/null
@@ -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
\ No newline at end of file
diff --git a/src/memory_system/questions.py b/src/memory_system/questions.py
deleted file mode 100644
index af4fb344..00000000
--- a/src/memory_system/questions.py
+++ /dev/null
@@ -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()
\ No newline at end of file
diff --git a/src/memory_system/retrieval_tools/query_jargon.py b/src/memory_system/retrieval_tools/query_jargon.py
index 998973be..0f007f8c 100644
--- a/src/memory_system/retrieval_tools/query_jargon.py
+++ b/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,
- fuzzy: bool = False
+ chat_id: str
) -> 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]
- translation = result.get("translation", "").strip()
- meaning = result.get("meaning", "").strip()
- search_type = "模糊搜索" if fuzzy else "精确匹配"
- output = f'"{content}可能为黑话或者网络简写，翻译为：{translation}，含义为：{meaning}"'
- logger.info(f"在jargon库中找到匹配（当前会话或全局，{search_type}）: {content}")
+ # 如果是模糊匹配，显示找到的实际jargon内容
+ if is_fuzzy_match:
+ # 处理多个结果
+ output_parts = [f"未精确匹配到'{content}'"]
+ for result in results:
+ 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，{search_type}）: {content}")
+ 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}")
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