Merge branch 'new-storage' into plugin

README.md

@@ -1,18 +1,18 @@
 # 麦麦！MaiCore-MaiMBot (编辑中)
 <br />
-<div style="text-align: center">
+<div align="center">
-
-  ![Python Version](https://img.shields.io/badge/Python-3.10+-blue)
-  ![License](https://img.shields.io/github/license/SengokuCola/MaiMBot?label=协议)
-  ![Status](https://img.shields.io/badge/状态-开发中-yellow)
-  ![Contributors](https://img.shields.io/github/contributors/MaiM-with-u/MaiBot.svg?style=flat&label=贡献者)
-  ![forks](https://img.shields.io/github/forks/MaiM-with-u/MaiBot.svg?style=flat&label=分支数)
-  ![stars](https://img.shields.io/github/stars/MaiM-with-u/MaiBot?style=flat&label=星标数)
-  ![issues](https://img.shields.io/github/issues/MaiM-with-u/MaiBot)
 
+![Python Version](https://img.shields.io/badge/Python-3.10+-blue)
+![License](https://img.shields.io/github/license/SengokuCola/MaiMBot?label=协议)
+![Status](https://img.shields.io/badge/状态-开发中-yellow)
+![Contributors](https://img.shields.io/github/contributors/MaiM-with-u/MaiBot.svg?style=flat&label=贡献者)
+![forks](https://img.shields.io/github/forks/MaiM-with-u/MaiBot.svg?style=flat&label=分支数)
+![stars](https://img.shields.io/github/stars/MaiM-with-u/MaiBot?style=flat&label=星标数)
+![issues](https://img.shields.io/github/issues/MaiM-with-u/MaiBot)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/DrSmoothl/MaiBot)
   </div>
 
-<p style="text-align: center">
+<p align="center">
   <a href="https://github.com/MaiM-with-u/MaiBot/">
     <img src="depends-data/maimai.png" alt="Logo" style="width: 200px">
   </a>
@@ -21,8 +21,8 @@
     画师：略nd
   </a>
 
-  <h3 style="text-align: center">MaiBot(麦麦)</h3>
+  <h3 align="center">MaiBot(麦麦)</h3>
-  <p style="text-align: center">
+  <p align="center">
     一款专注于<strong> 群组聊天 </strong>的赛博网友
     <br />
     <a href="https://docs.mai-mai.org"><strong>探索本项目的文档 »</strong></a>

src/api/reload_config.py

@@ -1,6 +1,6 @@
 from fastapi import HTTPException
 from rich.traceback import install
-from src.config.config import BotConfig
+from src.config.config import Config
 from src.common.logger_manager import get_logger
 import os
 
@@ -14,8 +14,8 @@ async def reload_config():
         from src.config import config as config_module
 
         logger.debug("正在重载配置文件...")
-        bot_config_path = os.path.join(BotConfig.get_config_dir(), "bot_config.toml")
+        bot_config_path = os.path.join(Config.get_config_dir(), "bot_config.toml")
-        config_module.global_config = BotConfig.load_config(config_path=bot_config_path)
+        config_module.global_config = Config.load_config(config_path=bot_config_path)
         logger.debug("配置文件重载成功")
         return {"status": "reloaded"}
     except FileNotFoundError as e:

src/chat/emoji_system/emoji_manager.py

@@ -5,12 +5,15 @@ import os
 import random
 import time
 import traceback
-from typing import Optional, Tuple
+from typing import Optional, Tuple, List, Any
 from PIL import Image
 import io
 import re
 
-from ...common.database import db
+# from gradio_client import file
+
+from ...common.database.database_model import Emoji
+from ...common.database.database import db as peewee_db
 from ...config.config import global_config
 from ..utils.utils_image import image_path_to_base64, image_manager
 from ..models.utils_model import LLMRequest
@@ -51,7 +54,7 @@ class MaiEmoji:
         self.is_deleted = False  # 标记是否已被删除
         self.format = ""
 
-    async def initialize_hash_format(self):
+    async def initialize_hash_format(self) -> Optional[bool]:
         """从文件创建表情包实例, 计算哈希值和格式"""
         try:
             # 使用 full_path 检查文件是否存在
@@ -104,7 +107,7 @@ class MaiEmoji:
             self.is_deleted = True
             return None
 
-    async def register_to_db(self):
+    async def register_to_db(self) -> bool:
         """
         注册表情包
         将表情包对应的文件，从当前路径移动到EMOJI_REGISTED_DIR目录下
@@ -143,22 +146,22 @@ class MaiEmoji:
             # --- 数据库操作 ---
             try:
                 # 准备数据库记录 for emoji collection
-                emoji_record = {
+                emotion_str = ",".join(self.emotion) if self.emotion else ""
-                    "filename": self.filename,
-                    "path": self.path,  # 存储目录路径
-                    "full_path": self.full_path,  # 存储完整文件路径
-                    "embedding": self.embedding,
-                    "description": self.description,
-                    "emotion": self.emotion,
-                    "hash": self.hash,
-                    "format": self.format,
-                    "timestamp": int(self.register_time),
-                    "usage_count": self.usage_count,
-                    "last_used_time": self.last_used_time,
-                }
 
-                # 使用upsert确保记录存在或被更新
+                Emoji.create(
-                db["emoji"].update_one({"hash": self.hash}, {"$set": emoji_record}, upsert=True)
+                    hash=self.hash,
+                    full_path=self.full_path,
+                    format=self.format,
+                    description=self.description,
+                    emotion=emotion_str,  # Store as comma-separated string
+                    query_count=0,  # Default value
+                    is_registered=True,
+                    is_banned=False,  # Default value
+                    record_time=self.register_time,  # Use MaiEmoji's register_time for DB record_time
+                    register_time=self.register_time,
+                    usage_count=self.usage_count,
+                    last_used_time=self.last_used_time,
+                )
 
                 logger.success(f"[注册] 表情包信息保存到数据库: {self.filename} ({self.emotion})")
 
@@ -166,14 +169,6 @@ class MaiEmoji:
 
             except Exception as db_error:
                 logger.error(f"[错误] 保存数据库失败 ({self.filename}): {str(db_error)}")
-                # 数据库保存失败，是否需要将文件移回？为了简化，暂时只记录错误
-                # 可以考虑在这里尝试删除已移动的文件，避免残留
-                try:
-                    if os.path.exists(self.full_path):  # full_path 此时是目标路径
-                        os.remove(self.full_path)
-                        logger.warning(f"[回滚] 已删除移动失败后残留的文件: {self.full_path}")
-                except Exception as remove_error:
-                    logger.error(f"[错误] 回滚删除文件失败: {remove_error}")
                 return False
 
         except Exception as e:
@@ -181,7 +176,7 @@ class MaiEmoji:
             logger.error(traceback.format_exc())
             return False
 
-    async def delete(self):
+    async def delete(self) -> bool:
         """删除表情包
 
         删除表情包的文件和数据库记录
@@ -201,10 +196,14 @@ class MaiEmoji:
                     # 文件删除失败，但仍然尝试删除数据库记录
 
             # 2. 删除数据库记录
-            result = db.emoji.delete_one({"hash": self.hash})
+            try:
-            deleted_in_db = result.deleted_count > 0
+                will_delete_emoji = Emoji.get(Emoji.emoji_hash == self.hash)
+                result = will_delete_emoji.delete_instance()  # Returns the number of rows deleted.
+            except Emoji.DoesNotExist:
+                logger.warning(f"[删除] 数据库中未找到哈希值为 {self.hash} 的表情包记录。")
+                result = 0  # Indicate no DB record was deleted
 
-            if deleted_in_db:
+            if result > 0:
                 logger.info(f"[删除] 表情包数据库记录 {self.filename} (Hash: {self.hash})")
                 # 3. 标记对象已被删除
                 self.is_deleted = True
@@ -224,7 +223,7 @@ class MaiEmoji:
             return False
 
 
-def _emoji_objects_to_readable_list(emoji_objects):
+def _emoji_objects_to_readable_list(emoji_objects: List["MaiEmoji"]) -> List[str]:
     """将表情包对象列表转换为可读的字符串列表
 
     参数:
@@ -243,47 +242,48 @@ def _emoji_objects_to_readable_list(emoji_objects):
     return emoji_info_list
 
 
-def _to_emoji_objects(data):
+def _to_emoji_objects(data: Any) -> Tuple[List["MaiEmoji"], int]:
     emoji_objects = []
     load_errors = 0
+    # data is now an iterable of Peewee Emoji model instances
     emoji_data_list = list(data)
 
-    for emoji_data in emoji_data_list:
+    for emoji_data in emoji_data_list:  # emoji_data is an Emoji model instance
-        full_path = emoji_data.get("full_path")
+        full_path = emoji_data.full_path
         if not full_path:
-            logger.warning(f"[加载错误] 数据库记录缺少 'full_path' 字段: {emoji_data.get('_id')}")
+            logger.warning(
+                f"[加载错误] 数据库记录缺少 'full_path' 字段: ID {emoji_data.id if hasattr(emoji_data, 'id') else 'Unknown'}"
+            )
             load_errors += 1
-            continue  # 跳过缺少 full_path 的记录
+            continue
 
         try:
-            # 使用 full_path 初始化 MaiEmoji 对象
             emoji = MaiEmoji(full_path=full_path)
 
-            # 设置从数据库加载的属性
+            emoji.hash = emoji_data.emoji_hash
-            emoji.hash = emoji_data.get("hash", "")
-            # 如果 hash 为空，也跳过？取决于业务逻辑
             if not emoji.hash:
                 logger.warning(f"[加载错误] 数据库记录缺少 'hash' 字段: {full_path}")
                 load_errors += 1
                 continue
 
-            emoji.description = emoji_data.get("description", "")
+            emoji.description = emoji_data.description
-            emoji.emotion = emoji_data.get("emotion", [])
+            # Deserialize emotion string from DB to list
-            emoji.usage_count = emoji_data.get("usage_count", 0)
+            emoji.emotion = emoji_data.emotion.split(",") if emoji_data.emotion else []
-            # 优先使用 last_used_time，否则用 timestamp，最后用当前时间
+            emoji.usage_count = emoji_data.usage_count
-            last_used = emoji_data.get("last_used_time")
-            timestamp = emoji_data.get("timestamp")
-            emoji.last_used_time = (
-                last_used if last_used is not None else (timestamp if timestamp is not None else time.time())
-            )
-            emoji.register_time = timestamp if timestamp is not None else time.time()
-            emoji.format = emoji_data.get("format", "")  # 加载格式
 
-            # 不需要再手动设置 path 和 filename，__init__ 会自动处理
+            db_last_used_time = emoji_data.last_used_time
+            db_register_time = emoji_data.register_time
+
+            # If last_used_time from DB is None, use MaiEmoji's initialized register_time or current time
+            emoji.last_used_time = db_last_used_time if db_last_used_time is not None else emoji.register_time
+            # If register_time from DB is None, use MaiEmoji's initialized register_time (which is time.time())
+            emoji.register_time = db_register_time if db_register_time is not None else emoji.register_time
+
+            emoji.format = emoji_data.format
 
             emoji_objects.append(emoji)
 
-        except ValueError as ve:  # 捕获 __init__ 可能的错误
+        except ValueError as ve:
             logger.error(f"[加载错误] 初始化 MaiEmoji 失败 ({full_path}): {ve}")
             load_errors += 1
         except Exception as e:
@@ -292,13 +292,13 @@ def _to_emoji_objects(data):
     return emoji_objects, load_errors
 
 
-def _ensure_emoji_dir():
+def _ensure_emoji_dir() -> None:
     """确保表情存储目录存在"""
     os.makedirs(EMOJI_DIR, exist_ok=True)
     os.makedirs(EMOJI_REGISTED_DIR, exist_ok=True)
 
 
-async def clear_temp_emoji():
+async def clear_temp_emoji() -> None:
     """清理临时表情包
     清理/data/emoji和/data/image目录下的所有文件
     当目录中文件数超过100时，会全部删除
@@ -320,7 +320,7 @@ async def clear_temp_emoji():
     logger.success("[清理] 完成")
 
 
-async def clean_unused_emojis(emoji_dir, emoji_objects):
+async def clean_unused_emojis(emoji_dir: str, emoji_objects: List["MaiEmoji"]) -> None:
     """清理指定目录中未被 emoji_objects 追踪的表情包文件"""
     if not os.path.exists(emoji_dir):
         logger.warning(f"[清理] 目标目录不存在，跳过清理: {emoji_dir}")
@@ -360,74 +360,52 @@ async def clean_unused_emojis(emoji_dir, emoji_objects):
 class EmojiManager:
     _instance = None
 
-    def __new__(cls):
+    def __new__(cls) -> "EmojiManager":
         if cls._instance is None:
             cls._instance = super().__new__(cls)
             cls._instance._initialized = False
         return cls._instance
 
-    def __init__(self):
+    def __init__(self) -> None:
         self._initialized = None
         self._scan_task = None
-        self.vlm = LLMRequest(model=global_config.vlm, temperature=0.3, max_tokens=1000, request_type="emoji")
+
+        self.vlm = LLMRequest(model=global_config.model.vlm, temperature=0.3, max_tokens=1000, request_type="emoji")
         self.llm_emotion_judge = LLMRequest(
-            model=global_config.llm_normal, max_tokens=600, request_type="emoji"
+            model=global_config.model.normal, max_tokens=600, request_type="emoji"
         )  # 更高的温度，更少的token（后续可以根据情绪来调整温度）
 
         self.emoji_num = 0
-        self.emoji_num_max = global_config.max_emoji_num
+        self.emoji_num_max = global_config.emoji.max_reg_num
-        self.emoji_num_max_reach_deletion = global_config.max_reach_deletion
+        self.emoji_num_max_reach_deletion = global_config.emoji.do_replace
         self.emoji_objects: list[MaiEmoji] = []  # 存储MaiEmoji对象的列表，使用类型注解明确列表元素类型
 
         logger.info("启动表情包管理器")
 
-    def initialize(self):
+    def initialize(self) -> None:
         """初始化数据库连接和表情目录"""
-        if not self._initialized:
+        peewee_db.connect(reuse_if_open=True)
-            try:
+        if peewee_db.is_closed():
-                self._ensure_emoji_collection()
+            raise RuntimeError("数据库连接失败")
         _ensure_emoji_dir()
-                self._initialized = True
+        Emoji.create_table(safe=True)  # Ensures table exists
-                # 更新表情包数量
-                # 启动时执行一次完整性检查
-                # await self.check_emoji_file_integrity()
-            except Exception as e:
-                logger.exception(f"初始化表情管理器失败: {e}")
 
-    def _ensure_db(self):
+    def _ensure_db(self) -> None:
         """确保数据库已初始化"""
         if not self._initialized:
             self.initialize()
         if not self._initialized:
             raise RuntimeError("EmojiManager not initialized")
 
-    @staticmethod
+    def record_usage(self, emoji_hash: str) -> None:
-    def _ensure_emoji_collection():
-        """确保emoji集合存在并创建索引
-
-        这个函数用于确保MongoDB数据库中存在emoji集合,并创建必要的索引。
-
-        索引的作用是加快数据库查询速度:
-        - embedding字段的2dsphere索引: 用于加速向量相似度搜索,帮助快速找到相似的表情包
-        - tags字段的普通索引: 加快按标签搜索表情包的速度
-        - filename字段的唯一索引: 确保文件名不重复,同时加快按文件名查找的速度
-
-        没有索引的话,数据库每次查询都需要扫描全部数据,建立索引后可以大大提高查询效率。
-        """
-        if "emoji" not in db.list_collection_names():
-            db.create_collection("emoji")
-            db.emoji.create_index([("embedding", "2dsphere")])
-            db.emoji.create_index([("filename", 1)], unique=True)
-
-    def record_usage(self, emoji_hash: str):
         """记录表情使用次数"""
         try:
-            db.emoji.update_one({"hash": emoji_hash}, {"$inc": {"usage_count": 1}})
+            emoji_update = Emoji.get(Emoji.emoji_hash == emoji_hash)
-            for emoji in self.emoji_objects:
+            emoji_update.usage_count += 1
-                if emoji.hash == emoji_hash:
+            emoji_update.last_used_time = time.time()  # Update last used time
-                    emoji.usage_count += 1
+            emoji_update.save()  # Persist changes to DB
-                    break
+        except Emoji.DoesNotExist:
-
+            logger.error(f"记录表情使用失败: 未找到 hash 为 {emoji_hash} 的表情包")
         except Exception as e:
             logger.error(f"记录表情使用失败: {str(e)}")
 
@@ -447,7 +425,6 @@ class EmojiManager:
 
             if not all_emojis:
                 logger.warning("内存中没有任何表情包对象")
-                # 可以考虑再查一次数据库？或者依赖定期任务更新
                 return None
 
             # 计算每个表情包与输入文本的最大情感相似度
@@ -463,40 +440,38 @@ class EmojiManager:
 
                 # 计算与每个emotion标签的相似度，取最大值
                 max_similarity = 0
-                best_matching_emotion = ""  # 记录最匹配的 emotion 喵~
+                best_matching_emotion = ""
                 for emotion in emotions:
                     # 使用编辑距离计算相似度
                     distance = self._levenshtein_distance(text_emotion, emotion)
                     max_len = max(len(text_emotion), len(emotion))
                     similarity = 1 - (distance / max_len if max_len > 0 else 0)
-                    if similarity > max_similarity:  # 如果找到更相似的喵~
+                    if similarity > max_similarity:
                         max_similarity = similarity
-                        best_matching_emotion = emotion  # 就记下这个 emotion 喵~
+                        best_matching_emotion = emotion
 
-                if best_matching_emotion:  # 确保有匹配的情感才添加喵~
+                if best_matching_emotion:
-                    emoji_similarities.append((emoji, max_similarity, best_matching_emotion))  # 把 emotion 也存起来喵~
+                    emoji_similarities.append((emoji, max_similarity, best_matching_emotion))
 
             # 按相似度降序排序
             emoji_similarities.sort(key=lambda x: x[1], reverse=True)
 
             # 获取前10个最相似的表情包
-            top_emojis = (
+            top_emojis = emoji_similarities[:10] if len(emoji_similarities) > 10 else emoji_similarities
-                emoji_similarities[:10] if len(emoji_similarities) > 10 else emoji_similarities
-            )  # 改个名字，更清晰喵~
 
             if not top_emojis:
                 logger.warning("未找到匹配的表情包")
                 return None
 
             # 从前几个中随机选择一个
-            selected_emoji, similarity, matched_emotion = random.choice(top_emojis)  # 把匹配的 emotion 也拿出来喵~
+            selected_emoji, similarity, matched_emotion = random.choice(top_emojis)
 
             # 更新使用次数
-            self.record_usage(selected_emoji.hash)
+            self.record_usage(selected_emoji.emoji_hash)
 
             _time_end = time.time()
 
-            logger.info(  # 使用匹配到的 emotion 记录日志喵~
+            logger.info(
                 f"为[{text_emotion}]找到表情包: {matched_emotion} ({selected_emoji.filename}), Similarity: {similarity:.4f}"
             )
             # 返回完整文件路径和描述
@@ -534,7 +509,7 @@ class EmojiManager:
 
         return previous_row[-1]
 
-    async def check_emoji_file_integrity(self):
+    async def check_emoji_file_integrity(self) -> None:
         """检查表情包文件完整性
         遍历self.emoji_objects中的所有对象，检查文件是否存在
         如果文件已被删除，则执行对象的删除方法并从列表中移除
@@ -599,7 +574,7 @@ class EmojiManager:
             logger.error(f"[错误] 检查表情包完整性失败: {str(e)}")
             logger.error(traceback.format_exc())
 
-    async def start_periodic_check_register(self):
+    async def start_periodic_check_register(self) -> None:
         """定期检查表情包完整性和数量"""
         await self.get_all_emoji_from_db()
         while True:
@@ -613,18 +588,18 @@ class EmojiManager:
                 logger.warning(f"[警告] 表情包目录不存在: {EMOJI_DIR}")
                 os.makedirs(EMOJI_DIR, exist_ok=True)
                 logger.info(f"[创建] 已创建表情包目录: {EMOJI_DIR}")
-                await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60)
+                await asyncio.sleep(global_config.emoji.check_interval * 60)
                 continue
 
             # 检查目录是否为空
             files = os.listdir(EMOJI_DIR)
             if not files:
                 logger.warning(f"[警告] 表情包目录为空: {EMOJI_DIR}")
-                await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60)
+                await asyncio.sleep(global_config.emoji.check_interval * 60)
                 continue
 
             # 检查是否需要处理表情包(数量超过最大值或不足)
-            if (self.emoji_num > self.emoji_num_max and global_config.max_reach_deletion) or (
+            if (self.emoji_num > self.emoji_num_max and global_config.emoji.do_replace) or (
                 self.emoji_num < self.emoji_num_max
             ):
                 try:
@@ -651,15 +626,16 @@ class EmojiManager:
                 except Exception as e:
                     logger.error(f"[错误] 扫描表情包目录失败: {str(e)}")
 
-            await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60)
+            await asyncio.sleep(global_config.emoji.check_interval * 60)
 
-    async def get_all_emoji_from_db(self):
+    async def get_all_emoji_from_db(self) -> None:
         """获取所有表情包并初始化为MaiEmoji类对象，更新 self.emoji_objects"""
         try:
             self._ensure_db()
-            logger.info("[数据库] 开始加载所有表情包记录...")
+            logger.info("[数据库] 开始加载所有表情包记录 (Peewee)...")
 
-            emoji_objects, load_errors = _to_emoji_objects(db.emoji.find())
+            emoji_peewee_instances = Emoji.select()
+            emoji_objects, load_errors = _to_emoji_objects(emoji_peewee_instances)
 
             # 更新内存中的列表和数量
             self.emoji_objects = emoji_objects
@@ -674,7 +650,7 @@ class EmojiManager:
             self.emoji_objects = []  # 加载失败则清空列表
             self.emoji_num = 0
 
-    async def get_emoji_from_db(self, emoji_hash=None):
+    async def get_emoji_from_db(self, emoji_hash: Optional[str] = None) -> List["MaiEmoji"]:
         """获取指定哈希值的表情包并初始化为MaiEmoji类对象列表 (主要用于调试或特定查找)
 
         参数:
@@ -686,15 +662,16 @@ class EmojiManager:
         try:
             self._ensure_db()
 
-            query = {}
             if emoji_hash:
-                query = {"hash": emoji_hash}
+                query = Emoji.select().where(Emoji.emoji_hash == emoji_hash)
             else:
                 logger.warning(
                     "[查询] 未提供 hash，将尝试加载所有表情包，建议使用 get_all_emoji_from_db 更新管理器状态。"
                 )
+                query = Emoji.select()
 
-            emoji_objects, load_errors = _to_emoji_objects(db.emoji.find(query))
+            emoji_peewee_instances = query
+            emoji_objects, load_errors = _to_emoji_objects(emoji_peewee_instances)
 
             if load_errors > 0:
                 logger.warning(f"[查询] 加载过程中出现 {load_errors} 个错误。")
@@ -705,7 +682,7 @@ class EmojiManager:
             logger.error(f"[错误] 从数据库获取表情包对象失败: {str(e)}")
             return []
 
-    async def get_emoji_from_manager(self, emoji_hash) -> Optional[MaiEmoji]:
+    async def get_emoji_from_manager(self, emoji_hash: str) -> Optional["MaiEmoji"]:
         """从内存中的 emoji_objects 列表获取表情包
 
         参数:
@@ -758,7 +735,7 @@ class EmojiManager:
             logger.error(traceback.format_exc())
             return False
 
-    async def replace_a_emoji(self, new_emoji: MaiEmoji):
+    async def replace_a_emoji(self, new_emoji: "MaiEmoji") -> bool:
         """替换一个表情包
 
         Args:
@@ -788,7 +765,7 @@ class EmojiManager:
 
             # 构建提示词
             prompt = (
-                f"{global_config.BOT_NICKNAME}的表情包存储已满({self.emoji_num}/{self.emoji_num_max})，"
+                f"{global_config.bot.nickname}的表情包存储已满({self.emoji_num}/{self.emoji_num_max})，"
                 f"需要决定是否删除一个旧表情包来为新表情包腾出空间。\n\n"
                 f"新表情包信息：\n"
                 f"描述: {new_emoji.description}\n\n"
@@ -819,7 +796,7 @@ class EmojiManager:
 
                     # 删除选定的表情包
                     logger.info(f"[决策] 删除表情包: {emoji_to_delete.description}")
-                    delete_success = await self.delete_emoji(emoji_to_delete.hash)
+                    delete_success = await self.delete_emoji(emoji_to_delete.emoji_hash)
 
                     if delete_success:
                         # 修复：等待异步注册完成
@@ -847,7 +824,7 @@ class EmojiManager:
             logger.error(traceback.format_exc())
             return False
 
-    async def build_emoji_description(self, image_base64: str) -> Tuple[str, list]:
+    async def build_emoji_description(self, image_base64: str) -> Tuple[str, List[str]]:
         """获取表情包描述和情感列表
 
         Args:
@@ -871,10 +848,10 @@ class EmojiManager:
                 description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
 
             # 审核表情包
-            if global_config.EMOJI_CHECK:
+            if global_config.emoji.content_filtration:
                 prompt = f'''
                     这是一个表情包，请对这个表情包进行审核，标准如下：
-                    1. 必须符合"{global_config.EMOJI_CHECK_PROMPT}"的要求
+                    1. 必须符合"{global_config.emoji.filtration_prompt}"的要求
                     2. 不能是色情、暴力、等违法违规内容，必须符合公序良俗
                     3. 不能是任何形式的截图，聊天记录或视频截图
                     4. 不要出现5个以上文字

src/chat/focus_chat/expressors/default_expressor.py

@@ -76,9 +76,10 @@ def init_prompt():
 class DefaultExpressor:
     def __init__(self, chat_id: str):
         self.log_prefix = "expressor"
+        # TODO: API-Adapter修改标记
         self.express_model = LLMRequest(
-            model=global_config.llm_normal,
+            model=global_config.model.normal,
-            temperature=global_config.llm_normal["temp"],
+            temperature=global_config.model.normal["temp"],
             max_tokens=256,
             request_type="response_heartflow",
         )
@@ -102,8 +103,8 @@ class DefaultExpressor:
         messageinfo = anchor_message.message_info
         thinking_time_point = parse_thinking_id_to_timestamp(thinking_id)
         bot_user_info = UserInfo(
-            user_id=global_config.BOT_QQ,
+            user_id=global_config.bot.qq_account,
-            user_nickname=global_config.BOT_NICKNAME,
+            user_nickname=global_config.bot.nickname,
             platform=messageinfo.platform,
         )
         # logger.debug(f"创建思考消息：{anchor_message}")
@@ -192,7 +193,7 @@ class DefaultExpressor:
         try:
             # 1. 获取情绪影响因子并调整模型温度
             arousal_multiplier = mood_manager.get_arousal_multiplier()
-            current_temp = float(global_config.llm_normal["temp"]) * arousal_multiplier
+            current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier
             self.express_model.params["temperature"] = current_temp  # 动态调整温度
 
             # 2. 获取信息捕捉器
@@ -231,6 +232,7 @@ class DefaultExpressor:
 
             try:
                 with Timer("LLM生成", {}):  # 内部计时器，可选保留
+                    # TODO: API-Adapter修改标记
                     # logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n")
                     content, reasoning_content, model_name = await self.express_model.generate_response(prompt)
 
@@ -482,8 +484,8 @@ class DefaultExpressor:
         """构建单个发送消息"""
 
         bot_user_info = UserInfo(
-            user_id=global_config.BOT_QQ,
+            user_id=global_config.bot.qq_account,
-            user_nickname=global_config.BOT_NICKNAME,
+            user_nickname=global_config.bot.nickname,
             platform=self.chat_stream.platform,
         )
 

src/chat/focus_chat/expressors/exprssion_learner.py

@@ -77,8 +77,9 @@ def init_prompt() -> None:
 
 class ExpressionLearner:
     def __init__(self) -> None:
+        # TODO: API-Adapter修改标记
         self.express_learn_model: LLMRequest = LLMRequest(
-            model=global_config.llm_normal,
+            model=global_config.model.normal,
             temperature=0.1,
             max_tokens=256,
             request_type="response_heartflow",
@@ -289,7 +290,7 @@ class ExpressionLearner:
         # 构建prompt
         prompt = await global_prompt_manager.format_prompt(
             "personality_expression_prompt",
-            personality=global_config.expression_style,
+            personality=global_config.personality.expression_style,
         )
         # logger.info(f"个性表达方式提取prompt: {prompt}")
 

src/chat/focus_chat/heartflow_processor.py

@@ -112,7 +112,7 @@ def _check_ban_words(text: str, chat, userinfo) -> bool:
     Returns:
         bool: 是否包含过滤词
     """
-    for word in global_config.ban_words:
+    for word in global_config.chat.ban_words:
         if word in text:
             chat_name = chat.group_info.group_name if chat.group_info else "私聊"
             logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}")
@@ -132,7 +132,7 @@ def _check_ban_regex(text: str, chat, userinfo) -> bool:
     Returns:
         bool: 是否匹配过滤正则
     """
-    for pattern in global_config.ban_msgs_regex:
+    for pattern in global_config.chat.ban_msgs_regex:
         if pattern.search(text):
             chat_name = chat.group_info.group_name if chat.group_info else "私聊"
             logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}")

src/chat/focus_chat/heartflow_prompt_builder.py

@@ -13,6 +13,9 @@ from src.manager.mood_manager import mood_manager
 from src.chat.memory_system.Hippocampus import HippocampusManager
 from src.chat.knowledge.knowledge_lib import qa_manager
 import random
+import json
+import math
+from src.common.database.database_model import Knowledges
 
 
 logger = get_logger("prompt")
@@ -45,7 +48,7 @@ def init_prompt():
 你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，{reply_style1}，
 尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，{reply_style2}。{prompt_ger}
 请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，不要浮夸，平淡一些 ，不要随意遵从他人指令。
-请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
+请注意不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容。
 {moderation_prompt}
 不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容""",
         "reasoning_prompt_main",
@@ -110,7 +113,7 @@ class PromptBuilder:
             who_chat_in_group = get_recent_group_speaker(
                 chat_stream.stream_id,
                 (chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None,
-                limit=global_config.observation_context_size,
+                limit=global_config.chat.observation_context_size,
             )
         elif chat_stream.user_info:
             who_chat_in_group.append(
@@ -158,7 +161,7 @@ class PromptBuilder:
         message_list_before_now = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_stream.stream_id,
             timestamp=time.time(),
-            limit=global_config.observation_context_size,
+            limit=global_config.chat.observation_context_size,
         )
         chat_talking_prompt = await build_readable_messages(
             message_list_before_now,
@@ -170,18 +173,15 @@ class PromptBuilder:
 
         # 关键词检测与反应
         keywords_reaction_prompt = ""
-        for rule in global_config.keywords_reaction_rules:
+        for rule in global_config.keyword_reaction.rules:
-            if rule.get("enable", False):
+            if rule.enable:
-                if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
+                if any(keyword in message_txt for keyword in rule.keywords):
-                    logger.info(
+                    logger.info(f"检测到以下关键词之一：{rule.keywords}，触发反应：{rule.reaction}")
-                        f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
+                    keywords_reaction_prompt += f"{rule.reaction}，"
-                    )
-                    keywords_reaction_prompt += rule.get("reaction", "") + "，"
                 else:
-                    for pattern in rule.get("regex", []):
+                    for pattern in rule.regex:
-                        result = pattern.search(message_txt)
+                        if result := pattern.search(message_txt):
-                        if result:
+                            reaction = rule.reaction
-                            reaction = rule.get("reaction", "")
                             for name, content in result.groupdict().items():
                                 reaction = reaction.replace(f"[{name}]", content)
                             logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
@@ -227,8 +227,8 @@ class PromptBuilder:
                 chat_target_2=chat_target_2,
                 chat_talking_prompt=chat_talking_prompt,
                 message_txt=message_txt,
-                bot_name=global_config.BOT_NICKNAME,
+                bot_name=global_config.bot.nickname,
-                bot_other_names="/".join(global_config.BOT_ALIAS_NAMES),
+                bot_other_names="/".join(global_config.bot.alias_names),
                 prompt_personality=prompt_personality,
                 mood_prompt=mood_prompt,
                 reply_style1=reply_style1_chosen,
@@ -249,8 +249,8 @@ class PromptBuilder:
                 prompt_info=prompt_info,
                 chat_talking_prompt=chat_talking_prompt,
                 message_txt=message_txt,
-                bot_name=global_config.BOT_NICKNAME,
+                bot_name=global_config.bot.nickname,
-                bot_other_names="/".join(global_config.BOT_ALIAS_NAMES),
+                bot_other_names="/".join(global_config.bot.alias_names),
                 prompt_personality=prompt_personality,
                 mood_prompt=mood_prompt,
                 reply_style1=reply_style1_chosen,
@@ -269,30 +269,6 @@ class PromptBuilder:
         logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
         # 1. 先从LLM获取主题，类似于记忆系统的做法
         topics = []
-        # try:
-        #     # 先尝试使用记忆系统的方法获取主题
-        #     hippocampus = HippocampusManager.get_instance()._hippocampus
-        #     topic_num = min(5, max(1, int(len(message) * 0.1)))
-        #     topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
-
-        #     # 提取关键词
-        #     topics = re.findall(r"<([^>]+)>", topics_response[0])
-        #     if not topics:
-        #         topics = []
-        #     else:
-        #         topics = [
-        #             topic.strip()
-        #             for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
-        #             if topic.strip()
-        #         ]
-
-        #     logger.info(f"从LLM提取的主题: {', '.join(topics)}")
-        # except Exception as e:
-        #     logger.error(f"从LLM提取主题失败: {str(e)}")
-        #     # 如果LLM提取失败，使用jieba分词提取关键词作为备选
-        #     words = jieba.cut(message)
-        #     topics = [word for word in words if len(word) > 1][:5]
-        #     logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
 
         # 如果无法提取到主题，直接使用整个消息
         if not topics:
@@ -402,8 +378,6 @@ class PromptBuilder:
                 for _i, result in enumerate(results, 1):
                     _similarity = result["similarity"]
                     content = result["content"].strip()
-                    # 调试：为内容添加序号和相似度信息
-                    # related_info += f"{i}. [{similarity:.2f}] {content}\n"
                     related_info += f"{content}\n"
                 related_info += "\n"
 
@@ -432,14 +406,14 @@ class PromptBuilder:
                 return related_info
             else:
                 logger.debug("从LPMM知识库获取知识失败，使用旧版数据库进行检索")
-                knowledge_from_old = await self.get_prompt_info_old(message, threshold=0.38)
+                knowledge_from_old = await self.get_prompt_info_old(message, threshold=threshold)
                 related_info += knowledge_from_old
                 logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
                 return related_info
         except Exception as e:
             logger.error(f"获取知识库内容时发生异常: {str(e)}")
             try:
-                knowledge_from_old = await self.get_prompt_info_old(message, threshold=0.38)
+                knowledge_from_old = await self.get_prompt_info_old(message, threshold=threshold)
                 related_info += knowledge_from_old
                 logger.debug(
                     f"异常后使用旧版数据库获取知识，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}"
@@ -455,69 +429,69 @@ class PromptBuilder:
     ) -> Union[str, list]:
         if not query_embedding:
             return "" if not return_raw else []
-        # 使用余弦相似度计算
-        pipeline = [
-            {
-                "$addFields": {
-                    "dotProduct": {
-                        "$reduce": {
-                            "input": {"$range": [0, {"$size": "$embedding"}]},
-                            "initialValue": 0,
-                            "in": {
-                                "$add": [
-                                    "$$value",
-                                    {
-                                        "$multiply": [
-                                            {"$arrayElemAt": ["$embedding", "$$this"]},
-                                            {"$arrayElemAt": [query_embedding, "$$this"]},
-                                        ]
-                                    },
-                                ]
-                            },
-                        }
-                    },
-                    "magnitude1": {
-                        "$sqrt": {
-                            "$reduce": {
-                                "input": "$embedding",
-                                "initialValue": 0,
-                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
-                            }
-                        }
-                    },
-                    "magnitude2": {
-                        "$sqrt": {
-                            "$reduce": {
-                                "input": query_embedding,
-                                "initialValue": 0,
-                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
-                            }
-                        }
-                    },
-                }
-            },
-            {"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
-            {
-                "$match": {
-                    "similarity": {"$gte": threshold}  # 只保留相似度大于等于阈值的结果
-                }
-            },
-            {"$sort": {"similarity": -1}},
-            {"$limit": limit},
-            {"$project": {"content": 1, "similarity": 1}},
-        ]
 
-        results = list(db.knowledges.aggregate(pipeline))
+        results_with_similarity = []
-        logger.debug(f"知识库查询结果数量: {len(results)}")
+        try:
+            # Fetch all knowledge entries
+            # This might be inefficient for very large databases.
+            # Consider strategies like FAISS or other vector search libraries if performance becomes an issue.
+            all_knowledges = Knowledges.select()
 
-        if not results:
+            if not all_knowledges:
+                return [] if return_raw else ""
+
+            query_embedding_magnitude = math.sqrt(sum(x * x for x in query_embedding))
+            if query_embedding_magnitude == 0:  # Avoid division by zero
+                return "" if not return_raw else []
+
+            for knowledge_item in all_knowledges:
+                try:
+                    db_embedding_str = knowledge_item.embedding
+                    db_embedding = json.loads(db_embedding_str)
+
+                    if len(db_embedding) != len(query_embedding):
+                        logger.warning(
+                            f"Embedding length mismatch for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}. Skipping."
+                        )
+                        continue
+
+                    # Calculate Cosine Similarity
+                    dot_product = sum(q * d for q, d in zip(query_embedding, db_embedding))
+                    db_embedding_magnitude = math.sqrt(sum(x * x for x in db_embedding))
+
+                    if db_embedding_magnitude == 0:  # Avoid division by zero
+                        similarity = 0.0
+                    else:
+                        similarity = dot_product / (query_embedding_magnitude * db_embedding_magnitude)
+
+                    if similarity >= threshold:
+                        results_with_similarity.append({"content": knowledge_item.content, "similarity": similarity})
+                except json.JSONDecodeError:
+                    logger.error(
+                        f"Failed to parse embedding for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}"
+                    )
+                except Exception as e:
+                    logger.error(f"Error processing knowledge item: {e}")
+
+            # Sort by similarity in descending order
+            results_with_similarity.sort(key=lambda x: x["similarity"], reverse=True)
+
+            # Limit results
+            limited_results = results_with_similarity[:limit]
+
+            logger.debug(f"知识库查询结果数量 (after Peewee processing): {len(limited_results)}")
+
+            if not limited_results:
                 return "" if not return_raw else []
 
             if return_raw:
-            return results
+                return limited_results
             else:
-            # 返回所有找到的内容，用换行分隔
+                return "\n".join(str(result["content"]) for result in limited_results)
-            return "\n".join(str(result["content"]) for result in results)
+
+        except Exception as e:
+            logger.error(f"Error querying Knowledges with Peewee: {e}")
+            return "" if not return_raw else []
 
 
 init_prompt()

src/chat/focus_chat/info_processors/chattinginfo_processor.py

@@ -26,8 +26,9 @@ class ChattingInfoProcessor(BaseProcessor):
     def __init__(self):
         """初始化观察处理器"""
         super().__init__()
+        # TODO: API-Adapter修改标记
         self.llm_summary = LLMRequest(
-            model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
+            model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
         )
 
     async def process_info(
@@ -110,12 +111,12 @@ class ChattingInfoProcessor(BaseProcessor):
                 "created_at": datetime.now().timestamp(),
             }
 
-            obs.mid_memorys.append(mid_memory)
+            obs.mid_memories.append(mid_memory)
-            if len(obs.mid_memorys) > obs.max_mid_memory_len:
+            if len(obs.mid_memories) > obs.max_mid_memory_len:
-                obs.mid_memorys.pop(0)  # 移除最旧的
+                obs.mid_memories.pop(0)  # 移除最旧的
 
             mid_memory_str = "之前聊天的内容概述是：\n"
-            for mid_memory_item in obs.mid_memorys:  # 重命名循环变量以示区分
+            for mid_memory_item in obs.mid_memories:  # 重命名循环变量以示区分
                 time_diff = int((datetime.now().timestamp() - mid_memory_item["created_at"]) / 60)
                 mid_memory_str += (
                     f"距离现在{time_diff}分钟前(聊天记录id:{mid_memory_item['id']})：{mid_memory_item['theme']}\n"

src/chat/focus_chat/info_processors/mind_processor.py

@@ -71,8 +71,8 @@ class MindProcessor(BaseProcessor):
         self.subheartflow_id = subheartflow_id
 
         self.llm_model = LLMRequest(
-            model=global_config.llm_sub_heartflow,
+            model=global_config.model.sub_heartflow,
-            temperature=global_config.llm_sub_heartflow["temp"],
+            temperature=global_config.model.sub_heartflow["temp"],
             max_tokens=800,
             request_type="sub_heart_flow",
         )

src/chat/focus_chat/info_processors/tool_processor.py

@@ -49,7 +49,7 @@ class ToolProcessor(BaseProcessor):
         self.subheartflow_id = subheartflow_id
         self.log_prefix = f"[{subheartflow_id}:ToolExecutor] "
         self.llm_model = LLMRequest(
-            model=global_config.llm_tool_use,
+            model=global_config.model.tool_use,
             max_tokens=500,
             request_type="tool_execution",
         )

src/chat/focus_chat/memory_activator.py

@@ -34,8 +34,9 @@ def init_prompt():
 
 class MemoryActivator:
     def __init__(self):
+        # TODO: API-Adapter修改标记
         self.summary_model = LLMRequest(
-            model=global_config.llm_summary, temperature=0.7, max_tokens=50, request_type="chat_observation"
+            model=global_config.model.summary, temperature=0.7, max_tokens=50, request_type="chat_observation"
         )
         self.running_memory = []
 

src/chat/heart_flow/heartflow.py

@@ -35,8 +35,9 @@ class Heartflow:
         self.subheartflow_manager: SubHeartflowManager = SubHeartflowManager(self.current_state)
 
         # LLM模型配置
+        # TODO: API-Adapter修改标记
         self.llm_model = LLMRequest(
-            model=global_config.llm_heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow"
+            model=global_config.model.heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow"
         )
 
         # 外部依赖模块

src/chat/heart_flow/interest_chatting.py

@@ -20,9 +20,9 @@ MAX_REPLY_PROBABILITY = 1
 class InterestChatting:
     def __init__(
         self,
-        decay_rate=global_config.default_decay_rate_per_second,
+        decay_rate=global_config.focus_chat.default_decay_rate_per_second,
         max_interest=MAX_INTEREST,
-        trigger_threshold=global_config.reply_trigger_threshold,
+        trigger_threshold=global_config.focus_chat.reply_trigger_threshold,
         max_probability=MAX_REPLY_PROBABILITY,
     ):
         # 基础属性初始化

src/chat/heart_flow/mai_state_manager.py

@@ -18,19 +18,14 @@ enable_unlimited_hfc_chat = True  # 调试用：无限专注聊天
 prevent_offline_state = True
 # 目前默认不启用OFFLINE状态
 
-# 不同状态下普通聊天的最大消息数
+MAX_NORMAL_CHAT_NUM_PEEKING = int(global_config.chat.base_normal_chat_num / 2)
-base_normal_chat_num = global_config.base_normal_chat_num
+MAX_NORMAL_CHAT_NUM_NORMAL = global_config.chat.base_normal_chat_num
-base_focused_chat_num = global_config.base_focused_chat_num
+MAX_NORMAL_CHAT_NUM_FOCUSED = global_config.chat.base_normal_chat_num + 1
-
-
-MAX_NORMAL_CHAT_NUM_PEEKING = int(base_normal_chat_num / 2)
-MAX_NORMAL_CHAT_NUM_NORMAL = base_normal_chat_num
-MAX_NORMAL_CHAT_NUM_FOCUSED = base_normal_chat_num + 1
 
 # 不同状态下专注聊天的最大消息数
-MAX_FOCUSED_CHAT_NUM_PEEKING = int(base_focused_chat_num / 2)
+MAX_FOCUSED_CHAT_NUM_PEEKING = int(global_config.chat.base_focused_chat_num / 2)
-MAX_FOCUSED_CHAT_NUM_NORMAL = base_focused_chat_num
+MAX_FOCUSED_CHAT_NUM_NORMAL = global_config.chat.base_focused_chat_num
-MAX_FOCUSED_CHAT_NUM_FOCUSED = base_focused_chat_num + 2
+MAX_FOCUSED_CHAT_NUM_FOCUSED = global_config.chat.base_focused_chat_num + 2
 
 # -- 状态定义 --
 

src/chat/heart_flow/observation/chatting_observation.py

@@ -55,19 +55,20 @@ class ChattingObservation(Observation):
         self.talking_message = []
         self.talking_message_str = ""
         self.talking_message_str_truncate = ""
-        self.name = global_config.BOT_NICKNAME
+        self.name = global_config.bot.nickname
-        self.nick_name = global_config.BOT_ALIAS_NAMES
+        self.nick_name = global_config.bot.alias_names
-        self.max_now_obs_len = global_config.observation_context_size
+        self.max_now_obs_len = global_config.chat.observation_context_size
-        self.overlap_len = global_config.compressed_length
+        self.overlap_len = global_config.focus_chat.compressed_length
-        self.mid_memorys = []
+        self.mid_memories = []
-        self.max_mid_memory_len = global_config.compress_length_limit
+        self.max_mid_memory_len = global_config.focus_chat.compress_length_limit
         self.mid_memory_info = ""
         self.person_list = []
         self.oldest_messages = []
         self.oldest_messages_str = ""
         self.compressor_prompt = ""
+        # TODO: API-Adapter修改标记
         self.llm_summary = LLMRequest(
-            model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
+            model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
         )
 
     async def initialize(self):
@@ -85,7 +86,7 @@ class ChattingObservation(Observation):
             for id in ids:
                 print(f"id：{id}")
                 try:
-                    for mid_memory in self.mid_memorys:
+                    for mid_memory in self.mid_memories:
                         if mid_memory["id"] == id:
                             mid_memory_by_id = mid_memory
                             msg_str = ""
@@ -103,7 +104,7 @@ class ChattingObservation(Observation):
 
         else:
             mid_memory_str = "之前的聊天内容：\n"
-            for mid_memory in self.mid_memorys:
+            for mid_memory in self.mid_memories:
                 mid_memory_str += f"{mid_memory['theme']}\n"
             return mid_memory_str + "现在群里正在聊：\n" + self.talking_message_str
 

src/chat/heart_flow/subheartflow_manager.py

@@ -76,8 +76,9 @@ class SubHeartflowManager:
 
         # 为 LLM 状态评估创建一个 LLMRequest 实例
         # 使用与 Heartflow 相同的模型和参数
+        # TODO: API-Adapter修改标记
         self.llm_state_evaluator = LLMRequest(
-            model=global_config.llm_heartflow,  # 与 Heartflow 一致
+            model=global_config.model.heartflow,  # 与 Heartflow 一致
             temperature=0.6,  # 与 Heartflow 一致
             max_tokens=1000,  # 与 Heartflow 一致 (虽然可能不需要这么多)
             request_type="subheartflow_state_eval",  # 保留特定的请求类型
@@ -278,7 +279,7 @@ class SubHeartflowManager:
             focused_limit = current_state.get_focused_chat_max_num()
 
             # --- 新增：检查是否允许进入 FOCUS 模式 --- #
-            if not global_config.allow_focus_mode:
+            if not global_config.chat.allow_focus_mode:
                 if int(time.time()) % 60 == 0:  # 每60秒输出一次日志避免刷屏
                     logger.trace("未开启 FOCUSED 状态 (allow_focus_mode=False)")
                 return  # 如果不允许，直接返回
@@ -766,7 +767,7 @@ class SubHeartflowManager:
         focused_limit = current_mai_state.get_focused_chat_max_num()
 
         # --- 检查是否允许 FOCUS 模式 --- #
-        if not global_config.allow_focus_mode:
+        if not global_config.chat.allow_focus_mode:
             # Log less frequently to avoid spam
             # if int(time.time()) % 60 == 0:
             #     logger.debug(f"{log_prefix_task} 配置不允许进入 FOCUSED 状态")

src/chat/memory_system/Hippocampus.py

@@ -10,7 +10,7 @@ import jieba
 import networkx as nx
 import numpy as np
 from collections import Counter
-from ...common.database import db
+from ...common.database.database import memory_db as db
 from ...chat.models.utils_model import LLMRequest
 from src.common.logger_manager import get_logger
 from src.chat.memory_system.sample_distribution import MemoryBuildScheduler  # 分布生成器
@@ -19,9 +19,10 @@ from ..utils.chat_message_builder import (
     build_readable_messages,
 )  # 导入 build_readable_messages
 from ..utils.utils import translate_timestamp_to_human_readable
-from .memory_config import MemoryConfig
 from rich.traceback import install
 
+from ...config.config import global_config
+
 install(extra_lines=3)
 
 
@@ -195,18 +196,16 @@ class Hippocampus:
         self.llm_summary = None
         self.entorhinal_cortex = None
         self.parahippocampal_gyrus = None
-        self.config = None
 
-    def initialize(self, global_config):
+    def initialize(self):
-        # 使用导入的 MemoryConfig dataclass 和其 from_global_config 方法
-        self.config = MemoryConfig.from_global_config(global_config)
         # 初始化子组件
         self.entorhinal_cortex = EntorhinalCortex(self)
         self.parahippocampal_gyrus = ParahippocampalGyrus(self)
         # 从数据库加载记忆图
         self.entorhinal_cortex.sync_memory_from_db()
-        self.llm_topic_judge = LLMRequest(self.config.llm_topic_judge, request_type="memory")
+        # TODO: API-Adapter修改标记
-        self.llm_summary = LLMRequest(self.config.llm_summary, request_type="memory")
+        self.llm_topic_judge = LLMRequest(global_config.model.topic_judge, request_type="memory")
+        self.llm_summary = LLMRequest(global_config.model.summary, request_type="memory")
 
     def get_all_node_names(self) -> list:
         """获取记忆图中所有节点的名字列表"""
@@ -792,7 +791,6 @@ class EntorhinalCortex:
     def __init__(self, hippocampus: Hippocampus):
         self.hippocampus = hippocampus
         self.memory_graph = hippocampus.memory_graph
-        self.config = hippocampus.config
 
     def get_memory_sample(self):
         """从数据库获取记忆样本"""
@@ -801,13 +799,13 @@ class EntorhinalCortex:
 
         # 创建双峰分布的记忆调度器
         sample_scheduler = MemoryBuildScheduler(
-            n_hours1=self.config.memory_build_distribution[0],
+            n_hours1=global_config.memory.memory_build_distribution[0],
-            std_hours1=self.config.memory_build_distribution[1],
+            std_hours1=global_config.memory.memory_build_distribution[1],
-            weight1=self.config.memory_build_distribution[2],
+            weight1=global_config.memory.memory_build_distribution[2],
-            n_hours2=self.config.memory_build_distribution[3],
+            n_hours2=global_config.memory.memory_build_distribution[3],
-            std_hours2=self.config.memory_build_distribution[4],
+            std_hours2=global_config.memory.memory_build_distribution[4],
-            weight2=self.config.memory_build_distribution[5],
+            weight2=global_config.memory.memory_build_distribution[5],
-            total_samples=self.config.build_memory_sample_num,
+            total_samples=global_config.memory.memory_build_sample_num,
         )
 
         timestamps = sample_scheduler.get_timestamp_array()
@@ -818,7 +816,7 @@ class EntorhinalCortex:
         for timestamp in timestamps:
             # 调用修改后的 random_get_msg_snippet
             messages = self.random_get_msg_snippet(
-                timestamp, self.config.build_memory_sample_length, max_memorized_time_per_msg
+                timestamp, global_config.memory.memory_build_sample_length, max_memorized_time_per_msg
             )
             if messages:
                 time_diff = (datetime.datetime.now().timestamp() - timestamp) / 3600
@@ -1099,7 +1097,6 @@ class ParahippocampalGyrus:
     def __init__(self, hippocampus: Hippocampus):
         self.hippocampus = hippocampus
         self.memory_graph = hippocampus.memory_graph
-        self.config = hippocampus.config
 
     async def memory_compress(self, messages: list, compress_rate=0.1):
         """压缩和总结消息内容，生成记忆主题和摘要。
@@ -1159,7 +1156,7 @@ class ParahippocampalGyrus:
 
         # 3. 过滤掉包含禁用关键词的topic
         filtered_topics = [
-            topic for topic in topics if not any(keyword in topic for keyword in self.config.memory_ban_words)
+            topic for topic in topics if not any(keyword in topic for keyword in global_config.memory.memory_ban_words)
         ]
 
         logger.debug(f"过滤后话题: {filtered_topics}")
@@ -1222,7 +1219,7 @@ class ParahippocampalGyrus:
             bar = "█" * filled_length + "-" * (bar_length - filled_length)
             logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
 
-            compress_rate = self.config.memory_compress_rate
+            compress_rate = global_config.memory.memory_compress_rate
             try:
                 compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
             except Exception as e:
@@ -1322,7 +1319,7 @@ class ParahippocampalGyrus:
             edge_data = self.memory_graph.G[source][target]
             last_modified = edge_data.get("last_modified")
 
-            if current_time - last_modified > 3600 * self.config.memory_forget_time:
+            if current_time - last_modified > 3600 * global_config.memory.memory_forget_time:
                 current_strength = edge_data.get("strength", 1)
                 new_strength = current_strength - 1
 
@@ -1430,8 +1427,8 @@ class ParahippocampalGyrus:
     async def operation_consolidate_memory(self):
         """整合记忆：合并节点内相似的记忆项"""
         start_time = time.time()
-        percentage = self.config.consolidate_memory_percentage
+        percentage = global_config.memory.consolidate_memory_percentage
-        similarity_threshold = self.config.consolidation_similarity_threshold
+        similarity_threshold = global_config.memory.consolidation_similarity_threshold
         logger.info(f"[整合] 开始检查记忆节点... 检查比例: {percentage:.2%}, 合并阈值: {similarity_threshold}")
 
         # 获取所有至少有2条记忆项的节点
@@ -1544,7 +1541,6 @@ class ParahippocampalGyrus:
 class HippocampusManager:
     _instance = None
     _hippocampus = None
-    _global_config = None
     _initialized = False
 
     @classmethod
@@ -1559,19 +1555,15 @@ class HippocampusManager:
             raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
         return cls._hippocampus
 
-    def initialize(self, global_config):
+    def initialize(self):
         """初始化海马体实例"""
         if self._initialized:
             return self._hippocampus
 
-        self._global_config = global_config
         self._hippocampus = Hippocampus()
-        self._hippocampus.initialize(global_config)
+        self._hippocampus.initialize()
         self._initialized = True
 
-        # 输出记忆系统参数信息
-        config = self._hippocampus.config
-
         # 输出记忆图统计信息
         memory_graph = self._hippocampus.memory_graph.G
         node_count = len(memory_graph.nodes())
@@ -1579,9 +1571,9 @@ class HippocampusManager:
 
         logger.success(f"""--------------------------------
                     记忆系统参数配置:
-                    构建间隔: {global_config.build_memory_interval}秒|样本数: {config.build_memory_sample_num},长度: {config.build_memory_sample_length}|压缩率: {config.memory_compress_rate}
+                    构建间隔: {global_config.memory.memory_build_interval}秒|样本数: {global_config.memory.memory_build_sample_num},长度: {global_config.memory.memory_build_sample_length}|压缩率: {global_config.memory.memory_compress_rate}
-                    记忆构建分布: {config.memory_build_distribution}
+                    记忆构建分布: {global_config.memory.memory_build_distribution}
-                    遗忘间隔: {global_config.forget_memory_interval}秒|遗忘比例: {global_config.memory_forget_percentage}|遗忘: {config.memory_forget_time}小时之后
+                    遗忘间隔: {global_config.memory.forget_memory_interval}秒|遗忘比例: {global_config.memory.memory_forget_percentage}|遗忘: {global_config.memory.memory_forget_time}小时之后
                     记忆图统计信息: 节点数量: {node_count}, 连接数量: {edge_count}
                     --------------------------------""")  # noqa: E501
 

src/chat/memory_system/debug_memory.py

@@ -7,7 +7,6 @@ import os
 # 添加项目根目录到系统路径
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))))
 from src.chat.memory_system.Hippocampus import HippocampusManager
-from src.config.config import global_config
 from rich.traceback import install
 
 install(extra_lines=3)
@@ -19,7 +18,7 @@ async def test_memory_system():
         # 初始化记忆系统
         print("开始初始化记忆系统...")
         hippocampus_manager = HippocampusManager.get_instance()
-        hippocampus_manager.initialize(global_config=global_config)
+        hippocampus_manager.initialize()
         print("记忆系统初始化完成")
 
         # 测试记忆构建

src/chat/memory_system/manually_alter_memory.py

@@ -34,7 +34,7 @@ root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
 sys.path.append(root_path)
 
 from src.common.logger import get_module_logger  # noqa E402
-from src.common.database import db  # noqa E402
+from common.database.database import db  # noqa E402
 
 logger = get_module_logger("mem_alter")
 console = Console()

src/chat/memory_system/memory_config.py

@@ -1,48 +0,0 @@
-from dataclasses import dataclass
-from typing import List
-
-
-@dataclass
-class MemoryConfig:
-    """记忆系统配置类"""
-
-    # 记忆构建相关配置
-    memory_build_distribution: List[float]  # 记忆构建的时间分布参数
-    build_memory_sample_num: int  # 每次构建记忆的样本数量
-    build_memory_sample_length: int  # 每个样本的消息长度
-    memory_compress_rate: float  # 记忆压缩率
-
-    # 记忆遗忘相关配置
-    memory_forget_time: int  # 记忆遗忘时间（小时）
-
-    # 记忆过滤相关配置
-    memory_ban_words: List[str]  # 记忆过滤词列表
-
-    # 新增：记忆整合相关配置
-    consolidation_similarity_threshold: float  # 相似度阈值
-    consolidate_memory_percentage: float  # 检查节点比例
-    consolidate_memory_interval: int  # 记忆整合间隔
-
-    llm_topic_judge: str  # 话题判断模型
-    llm_summary: str  # 话题总结模型
-
-    @classmethod
-    def from_global_config(cls, global_config):
-        """从全局配置创建记忆系统配置"""
-        # 使用 getattr 提供默认值，防止全局配置缺少这些项
-        return cls(
-            memory_build_distribution=getattr(
-                global_config, "memory_build_distribution", (24, 12, 0.5, 168, 72, 0.5)
-            ),  # 添加默认值
-            build_memory_sample_num=getattr(global_config, "build_memory_sample_num", 5),
-            build_memory_sample_length=getattr(global_config, "build_memory_sample_length", 30),
-            memory_compress_rate=getattr(global_config, "memory_compress_rate", 0.1),
-            memory_forget_time=getattr(global_config, "memory_forget_time", 24 * 7),
-            memory_ban_words=getattr(global_config, "memory_ban_words", []),
-            # 新增加载整合配置，并提供默认值
-            consolidation_similarity_threshold=getattr(global_config, "consolidation_similarity_threshold", 0.7),
-            consolidate_memory_percentage=getattr(global_config, "consolidate_memory_percentage", 0.01),
-            consolidate_memory_interval=getattr(global_config, "consolidate_memory_interval", 1000),
-            llm_topic_judge=getattr(global_config, "llm_topic_judge", "default_judge_model"),  # 添加默认模型名
-            llm_summary=getattr(global_config, "llm_summary", "default_summary_model"),  # 添加默认模型名
-        )

src/chat/message_receive/bot.py

@@ -41,7 +41,7 @@ class ChatBot:
             chat_id = str(message.chat_stream.stream_id)
             private_name = str(message.message_info.user_info.user_nickname)
 
-            if global_config.enable_pfc_chatting:
+            if global_config.experimental.enable_pfc_chatting:
                 await self.pfc_manager.get_or_create_conversation(chat_id, private_name)
 
         except Exception as e:
@@ -78,19 +78,19 @@ class ChatBot:
             userinfo = message.message_info.user_info
 
             # 用户黑名单拦截
-            if userinfo.user_id in global_config.ban_user_id:
+            if userinfo.user_id in global_config.chat_target.ban_user_id:
                 logger.debug(f"用户{userinfo.user_id}被禁止回复")
                 return
 
             if groupinfo is None:
                 logger.trace("检测到私聊消息，检查")
                 # 好友黑名单拦截
-                if userinfo.user_id not in global_config.talk_allowed_private:
+                if userinfo.user_id not in global_config.experimental.talk_allowed_private:
                     logger.debug(f"用户{userinfo.user_id}没有私聊权限")
                     return
 
             # 群聊黑名单拦截
-            if groupinfo is not None and groupinfo.group_id not in global_config.talk_allowed_groups:
+            if groupinfo is not None and groupinfo.group_id not in global_config.chat_target.talk_allowed_groups:
                 logger.trace(f"群{groupinfo.group_id}被禁止回复")
                 return
 
@@ -112,7 +112,7 @@ class ChatBot:
                 if groupinfo is None:
                     logger.trace("检测到私聊消息")
                     # 是否在配置信息中开启私聊模式
-                    if global_config.enable_friend_chat:
+                    if global_config.experimental.enable_friend_chat:
                         logger.trace("私聊模式已启用")
                         # 是否进入PFC
                         if global_config.enable_pfc_chatting:

src/chat/message_receive/chat_stream.py

@@ -5,7 +5,8 @@ import copy
 from typing import Dict, Optional
 
 
-from ...common.database import db
+from ...common.database.database import db
+from ...common.database.database_model import ChatStreams  # 新增导入
 from maim_message import GroupInfo, UserInfo
 
 from src.common.logger_manager import get_logger
@@ -82,7 +83,13 @@ class ChatManager:
     def __init__(self):
         if not self._initialized:
             self.streams: Dict[str, ChatStream] = {}  # stream_id -> ChatStream
-            self._ensure_collection()
+            try:
+                db.connect(reuse_if_open=True)
+                # 确保 ChatStreams 表存在
+                db.create_tables([ChatStreams], safe=True)
+            except Exception as e:
+                logger.error(f"数据库连接或 ChatStreams 表创建失败: {e}")
+
             self._initialized = True
             # 在事件循环中启动初始化
             # asyncio.create_task(self._initialize())
@@ -107,15 +114,6 @@ class ChatManager:
             except Exception as e:
                 logger.error(f"聊天流自动保存失败: {str(e)}")
 
-    @staticmethod
-    def _ensure_collection():
-        """确保数据库集合存在并创建索引"""
-        if "chat_streams" not in db.list_collection_names():
-            db.create_collection("chat_streams")
-            # 创建索引
-            db.chat_streams.create_index([("stream_id", 1)], unique=True)
-            db.chat_streams.create_index([("platform", 1), ("user_info.user_id", 1), ("group_info.group_id", 1)])
-
     @staticmethod
     def _generate_stream_id(platform: str, user_info: UserInfo, group_info: Optional[GroupInfo] = None) -> str:
         """生成聊天流唯一ID"""
@@ -151,16 +149,43 @@ class ChatManager:
                 stream = self.streams[stream_id]
                 # 更新用户信息和群组信息
                 stream.update_active_time()
-                stream = copy.deepcopy(stream)
+                stream = copy.deepcopy(stream)  # 返回副本以避免外部修改影响缓存
                 stream.user_info = user_info
                 if group_info:
                     stream.group_info = group_info
                 return stream
 
             # 检查数据库中是否存在
-            data = db.chat_streams.find_one({"stream_id": stream_id})
+            def _db_find_stream_sync(s_id: str):
-            if data:
+                return ChatStreams.get_or_none(ChatStreams.stream_id == s_id)
-                stream = ChatStream.from_dict(data)
+
+            model_instance = await asyncio.to_thread(_db_find_stream_sync, stream_id)
+
+            if model_instance:
+                # 从 Peewee 模型转换回 ChatStream.from_dict 期望的格式
+                user_info_data = {
+                    "platform": model_instance.user_platform,
+                    "user_id": model_instance.user_id,
+                    "user_nickname": model_instance.user_nickname,
+                    "user_cardname": model_instance.user_cardname or "",
+                }
+                group_info_data = None
+                if model_instance.group_id:  # 假设 group_id 为空字符串表示没有群组信息
+                    group_info_data = {
+                        "platform": model_instance.group_platform,
+                        "group_id": model_instance.group_id,
+                        "group_name": model_instance.group_name,
+                    }
+
+                data_for_from_dict = {
+                    "stream_id": model_instance.stream_id,
+                    "platform": model_instance.platform,
+                    "user_info": user_info_data,
+                    "group_info": group_info_data,
+                    "create_time": model_instance.create_time,
+                    "last_active_time": model_instance.last_active_time,
+                }
+                stream = ChatStream.from_dict(data_for_from_dict)
                 # 更新用户信息和群组信息
                 stream.user_info = user_info
                 if group_info:
@@ -175,7 +200,7 @@ class ChatManager:
                     group_info=group_info,
                 )
         except Exception as e:
-            logger.error(f"创建聊天流失败: {e}")
+            logger.error(f"获取或创建聊天流失败: {e}", exc_info=True)
             raise e
 
         # 保存到内存和数据库
@@ -205,15 +230,38 @@ class ChatManager:
         elif stream.user_info and stream.user_info.user_nickname:
             return f"{stream.user_info.user_nickname}的私聊"
         else:
-            # 如果没有群名或用户昵称，返回 None 或其他默认值
             return None
 
     @staticmethod
     async def _save_stream(stream: ChatStream):
         """保存聊天流到数据库"""
         if not stream.saved:
-            db.chat_streams.update_one({"stream_id": stream.stream_id}, {"$set": stream.to_dict()}, upsert=True)
+            stream_data_dict = stream.to_dict()
+
+            def _db_save_stream_sync(s_data_dict: dict):
+                user_info_d = s_data_dict.get("user_info")
+                group_info_d = s_data_dict.get("group_info")
+
+                fields_to_save = {
+                    "platform": s_data_dict["platform"],
+                    "create_time": s_data_dict["create_time"],
+                    "last_active_time": s_data_dict["last_active_time"],
+                    "user_platform": user_info_d["platform"] if user_info_d else "",
+                    "user_id": user_info_d["user_id"] if user_info_d else "",
+                    "user_nickname": user_info_d["user_nickname"] if user_info_d else "",
+                    "user_cardname": user_info_d.get("user_cardname", "") if user_info_d else None,
+                    "group_platform": group_info_d["platform"] if group_info_d else "",
+                    "group_id": group_info_d["group_id"] if group_info_d else "",
+                    "group_name": group_info_d["group_name"] if group_info_d else "",
+                }
+
+                ChatStreams.replace(stream_id=s_data_dict["stream_id"], **fields_to_save).execute()
+
+            try:
+                await asyncio.to_thread(_db_save_stream_sync, stream_data_dict)
                 stream.saved = True
+            except Exception as e:
+                logger.error(f"保存聊天流 {stream.stream_id} 到数据库失败 (Peewee): {e}", exc_info=True)
 
     async def _save_all_streams(self):
         """保存所有聊天流"""
@@ -222,10 +270,44 @@ class ChatManager:
 
     async def load_all_streams(self):
         """从数据库加载所有聊天流"""
-        all_streams = db.chat_streams.find({})
+
-        for data in all_streams:
+        def _db_load_all_streams_sync():
+            loaded_streams_data = []
+            for model_instance in ChatStreams.select():
+                user_info_data = {
+                    "platform": model_instance.user_platform,
+                    "user_id": model_instance.user_id,
+                    "user_nickname": model_instance.user_nickname,
+                    "user_cardname": model_instance.user_cardname or "",
+                }
+                group_info_data = None
+                if model_instance.group_id:
+                    group_info_data = {
+                        "platform": model_instance.group_platform,
+                        "group_id": model_instance.group_id,
+                        "group_name": model_instance.group_name,
+                    }
+
+                data_for_from_dict = {
+                    "stream_id": model_instance.stream_id,
+                    "platform": model_instance.platform,
+                    "user_info": user_info_data,
+                    "group_info": group_info_data,
+                    "create_time": model_instance.create_time,
+                    "last_active_time": model_instance.last_active_time,
+                }
+                loaded_streams_data.append(data_for_from_dict)
+            return loaded_streams_data
+
+        try:
+            all_streams_data_list = await asyncio.to_thread(_db_load_all_streams_sync)
+            self.streams.clear()
+            for data in all_streams_data_list:
                 stream = ChatStream.from_dict(data)
+                stream.saved = True
                 self.streams[stream.stream_id] = stream
+        except Exception as e:
+            logger.error(f"从数据库加载所有聊天流失败 (Peewee): {e}", exc_info=True)
 
 
 # 创建全局单例

src/chat/message_receive/message_buffer.py

@@ -38,7 +38,7 @@ class MessageBuffer:
 
     async def start_caching_messages(self, message: MessageRecv):
         """添加消息，启动缓冲"""
-        if not global_config.message_buffer:
+        if not global_config.chat.message_buffer:
             person_id = person_info_manager.get_person_id(
                 message.message_info.user_info.platform, message.message_info.user_info.user_id
             )
@@ -107,7 +107,7 @@ class MessageBuffer:
 
     async def query_buffer_result(self, message: MessageRecv) -> bool:
         """查询缓冲结果，并清理"""
-        if not global_config.message_buffer:
+        if not global_config.chat.message_buffer:
             return True
         person_id_ = self.get_person_id_(
             message.message_info.platform, message.message_info.user_info.user_id, message.message_info.group_info

src/chat/message_receive/message_sender.py

@@ -279,7 +279,7 @@ class MessageManager:
                     )
 
                 # 检查是否超时
-                if thinking_time > global_config.thinking_timeout:
+                if thinking_time > global_config.normal_chat.thinking_timeout:
                     logger.warning(
                         f"[{chat_id}] 消息思考超时 ({thinking_time:.1f}秒)，移除消息 {message_earliest.message_info.message_id}"
                     )

src/chat/message_receive/storage.py

@@ -1,9 +1,10 @@
 import re
 from typing import Union
 
-from ...common.database import db
+# from ...common.database.database import db  # db is now Peewee's SqliteDatabase instance
 from .message import MessageSending, MessageRecv
 from .chat_stream import ChatStream
+from ...common.database.database_model import Messages, RecalledMessages  # Import Peewee models
 from src.common.logger import get_module_logger
 
 logger = get_module_logger("message_storage")
@@ -29,34 +30,56 @@ class MessageStorage:
             else:
                 filtered_detailed_plain_text = ""
 
-            message_data = {
+            chat_info_dict = chat_stream.to_dict()
-                "message_id": message.message_info.message_id,
+            user_info_dict = message.message_info.user_info.to_dict()
-                "time": message.message_info.time,
+
-                "chat_id": chat_stream.stream_id,
+            # message_id 现在是 TextField，直接使用字符串值
-                "chat_info": chat_stream.to_dict(),
+            msg_id = message.message_info.message_id
-                "user_info": message.message_info.user_info.to_dict(),
+
-                # 使用过滤后的文本
+            # 安全地获取 group_info, 如果为 None 则视为空字典
-                "processed_plain_text": filtered_processed_plain_text,
+            group_info_from_chat = chat_info_dict.get("group_info") or {}
-                "detailed_plain_text": filtered_detailed_plain_text,
+            # 安全地获取 user_info, 如果为 None 则视为空字典 (以防万一)
-                "memorized_times": message.memorized_times,
+            user_info_from_chat = chat_info_dict.get("user_info") or {}
-            }
+
-            db.messages.insert_one(message_data)
+            Messages.create(
+                message_id=msg_id,
+                time=float(message.message_info.time),
+                chat_id=chat_stream.stream_id,
+                # Flattened chat_info
+                chat_info_stream_id=chat_info_dict.get("stream_id"),
+                chat_info_platform=chat_info_dict.get("platform"),
+                chat_info_user_platform=user_info_from_chat.get("platform"),
+                chat_info_user_id=user_info_from_chat.get("user_id"),
+                chat_info_user_nickname=user_info_from_chat.get("user_nickname"),
+                chat_info_user_cardname=user_info_from_chat.get("user_cardname"),
+                chat_info_group_platform=group_info_from_chat.get("platform"),
+                chat_info_group_id=group_info_from_chat.get("group_id"),
+                chat_info_group_name=group_info_from_chat.get("group_name"),
+                chat_info_create_time=float(chat_info_dict.get("create_time", 0.0)),
+                chat_info_last_active_time=float(chat_info_dict.get("last_active_time", 0.0)),
+                # Flattened user_info (message sender)
+                user_platform=user_info_dict.get("platform"),
+                user_id=user_info_dict.get("user_id"),
+                user_nickname=user_info_dict.get("user_nickname"),
+                user_cardname=user_info_dict.get("user_cardname"),
+                # Text content
+                processed_plain_text=filtered_processed_plain_text,
+                detailed_plain_text=filtered_detailed_plain_text,
+                memorized_times=message.memorized_times,
+            )
         except Exception:
             logger.exception("存储消息失败")
 
     @staticmethod
     async def store_recalled_message(message_id: str, time: str, chat_stream: ChatStream) -> None:
         """存储撤回消息到数据库"""
-        if "recalled_messages" not in db.list_collection_names():
+        # Table creation is handled by initialize_database in database_model.py
-            db.create_collection("recalled_messages")
-        else:
         try:
-                message_data = {
+            RecalledMessages.create(
-                    "message_id": message_id,
+                message_id=message_id,
-                    "time": time,
+                time=float(time),  # Assuming time is a string representing a float timestamp
-                    "stream_id": chat_stream.stream_id,
+                stream_id=chat_stream.stream_id,
-                }
+            )
-                db.recalled_messages.insert_one(message_data)
         except Exception:
             logger.exception("存储撤回消息失败")
 
@@ -64,7 +87,9 @@ class MessageStorage:
     async def remove_recalled_message(time: str) -> None:
         """删除撤回消息"""
         try:
-            db.recalled_messages.delete_many({"time": {"$lt": time - 300}})
+            # Assuming input 'time' is a string timestamp that can be converted to float
+            current_time_float = float(time)
+            RecalledMessages.delete().where(RecalledMessages.time < (current_time_float - 300)).execute()
         except Exception:
             logger.exception("删除撤回消息失败")
 

src/chat/models/utils_model.py

@@ -12,7 +12,8 @@ import base64
 from PIL import Image
 import io
 import os
-from ...common.database import db
+from src.common.database.database import db  # 确保 db 被导入用于 create_tables
+from src.common.database.database_model import LLMUsage  # 导入 LLMUsage 模型
 from ...config.config import global_config
 from rich.traceback import install
 
@@ -85,8 +86,6 @@ async def _safely_record(request_content: Dict[str, Any], payload: Dict[str, Any
                     f"data:image/{image_format.lower() if image_format else 'jpeg'};base64,"
                     f"{image_base64[:10]}...{image_base64[-10:]}"
                 )
-            # if isinstance(content, str) and len(content) > 100:
-            #     payload["messages"][0]["content"] = content[:100]
     return payload
 
 
@@ -111,8 +110,8 @@ class LLMRequest:
     def __init__(self, model: dict, **kwargs):
         # 将大写的配置键转换为小写并从config中获取实际值
         try:
-            self.api_key = os.environ[model["key"]]
+            self.api_key = os.environ[f"{model['provider']}_KEY"]
-            self.base_url = os.environ[model["base_url"]]
+            self.base_url = os.environ[f"{model['provider']}_BASE_URL"]
         except AttributeError as e:
             logger.error(f"原始 model dict 信息：{model}")
             logger.error(f"配置错误：找不到对应的配置项 - {str(e)}")
@@ -134,13 +133,11 @@ class LLMRequest:
     def _init_database():
         """初始化数据库集合"""
         try:
-            # 创建llm_usage集合的索引
+            # 使用 Peewee 创建表，safe=True 表示如果表已存在则不会抛出错误
-            db.llm_usage.create_index([("timestamp", 1)])
+            db.create_tables([LLMUsage], safe=True)
-            db.llm_usage.create_index([("model_name", 1)])
+            logger.debug("LLMUsage 表已初始化/确保存在。")
-            db.llm_usage.create_index([("user_id", 1)])
-            db.llm_usage.create_index([("request_type", 1)])
         except Exception as e:
-            logger.error(f"创建数据库索引失败: {str(e)}")
+            logger.error(f"创建 LLMUsage 表失败: {str(e)}")
 
     def _record_usage(
         self,
@@ -165,19 +162,19 @@ class LLMRequest:
             request_type = self.request_type
 
         try:
-            usage_data = {
+            # 使用 Peewee 模型创建记录
-                "model_name": self.model_name,
+            LLMUsage.create(
-                "user_id": user_id,
+                model_name=self.model_name,
-                "request_type": request_type,
+                user_id=user_id,
-                "endpoint": endpoint,
+                request_type=request_type,
-                "prompt_tokens": prompt_tokens,
+                endpoint=endpoint,
-                "completion_tokens": completion_tokens,
+                prompt_tokens=prompt_tokens,
-                "total_tokens": total_tokens,
+                completion_tokens=completion_tokens,
-                "cost": self._calculate_cost(prompt_tokens, completion_tokens),
+                total_tokens=total_tokens,
-                "status": "success",
+                cost=self._calculate_cost(prompt_tokens, completion_tokens),
-                "timestamp": datetime.now(),
+                status="success",
-            }
+                timestamp=datetime.now(),  # Peewee 会处理 DateTimeField
-            db.llm_usage.insert_one(usage_data)
+            )
             logger.trace(
                 f"Token使用情况 - 模型: {self.model_name}, "
                 f"用户: {user_id}, 类型: {request_type}, "
@@ -500,11 +497,11 @@ class LLMRequest:
                 logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
 
                 # 对全局配置进行更新
-                if global_config.llm_normal.get("name") == old_model_name:
+                if global_config.model.normal.get("name") == old_model_name:
-                    global_config.llm_normal["name"] = self.model_name
+                    global_config.model.normal["name"] = self.model_name
                     logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}")
-                if global_config.llm_reasoning.get("name") == old_model_name:
+                if global_config.model.reasoning.get("name") == old_model_name:
-                    global_config.llm_reasoning["name"] = self.model_name
+                    global_config.model.reasoning["name"] = self.model_name
                     logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}")
 
                 if payload and "model" in payload:
@@ -636,7 +633,7 @@ class LLMRequest:
             **params_copy,
         }
         if "max_tokens" not in payload and "max_completion_tokens" not in payload:
-            payload["max_tokens"] = global_config.model_max_output_length
+            payload["max_tokens"] = global_config.model.model_max_output_length
         # 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查
         if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION and "max_tokens" in payload:
             payload["max_completion_tokens"] = payload.pop("max_tokens")

src/chat/normal_chat/normal_chat.py

@@ -73,8 +73,8 @@ class NormalChat:
         messageinfo = message.message_info
 
         bot_user_info = UserInfo(
-            user_id=global_config.BOT_QQ,
+            user_id=global_config.bot.qq_account,
-            user_nickname=global_config.BOT_NICKNAME,
+            user_nickname=global_config.bot.nickname,
             platform=messageinfo.platform,
         )
 
@@ -121,8 +121,8 @@ class NormalChat:
                 message_id=thinking_id,
                 chat_stream=self.chat_stream,  # 使用 self.chat_stream
                 bot_user_info=UserInfo(
-                    user_id=global_config.BOT_QQ,
+                    user_id=global_config.bot.qq_account,
-                    user_nickname=global_config.BOT_NICKNAME,
+                    user_nickname=global_config.bot.nickname,
                     platform=message.message_info.platform,
                 ),
                 sender_info=message.message_info.user_info,
@@ -147,7 +147,7 @@ class NormalChat:
     # 改为实例方法
     async def _handle_emoji(self, message: MessageRecv, response: str):
         """处理表情包"""
-        if random() < global_config.emoji_chance:
+        if random() < global_config.normal_chat.emoji_chance:
             emoji_raw = await emoji_manager.get_emoji_for_text(response)
             if emoji_raw:
                 emoji_path, description = emoji_raw
@@ -160,8 +160,8 @@ class NormalChat:
                     message_id="mt" + str(thinking_time_point),
                     chat_stream=self.chat_stream,  # 使用 self.chat_stream
                     bot_user_info=UserInfo(
-                        user_id=global_config.BOT_QQ,
+                        user_id=global_config.bot.qq_account,
-                        user_nickname=global_config.BOT_NICKNAME,
+                        user_nickname=global_config.bot.nickname,
                         platform=message.message_info.platform,
                     ),
                     sender_info=message.message_info.user_info,
@@ -186,7 +186,7 @@ class NormalChat:
             label=emotion,
             stance=stance,  # 使用 self.chat_stream
         )
-        self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
+        self.mood_manager.update_mood_from_emotion(emotion, global_config.mood.mood_intensity_factor)
 
     async def _reply_interested_message(self) -> None:
         """
@@ -430,7 +430,7 @@ class NormalChat:
     def _check_ban_words(text: str, chat: ChatStream, userinfo: UserInfo) -> bool:
         """检查消息中是否包含过滤词"""
         stream_name = chat_manager.get_stream_name(chat.stream_id) or chat.stream_id
-        for word in global_config.ban_words:
+        for word in global_config.chat.ban_words:
             if word in text:
                 logger.info(
                     f"[{stream_name}][{chat.group_info.group_name if chat.group_info else '私聊'}]"
@@ -445,7 +445,7 @@ class NormalChat:
     def _check_ban_regex(text: str, chat: ChatStream, userinfo: UserInfo) -> bool:
         """检查消息是否匹配过滤正则表达式"""
         stream_name = chat_manager.get_stream_name(chat.stream_id) or chat.stream_id
-        for pattern in global_config.ban_msgs_regex:
+        for pattern in global_config.chat.ban_msgs_regex:
             if pattern.search(text):
                 logger.info(
                     f"[{stream_name}][{chat.group_info.group_name if chat.group_info else '私聊'}]"

src/chat/normal_chat/normal_chat_generator.py

@@ -15,21 +15,22 @@ logger = get_logger("llm")
 
 class NormalChatGenerator:
     def __init__(self):
+        # TODO: API-Adapter修改标记
         self.model_reasoning = LLMRequest(
-            model=global_config.llm_reasoning,
+            model=global_config.model.reasoning,
             temperature=0.7,
             max_tokens=3000,
             request_type="response_reasoning",
         )
         self.model_normal = LLMRequest(
-            model=global_config.llm_normal,
+            model=global_config.model.normal,
-            temperature=global_config.llm_normal["temp"],
+            temperature=global_config.model.normal["temp"],
             max_tokens=256,
             request_type="response_reasoning",
         )
 
         self.model_sum = LLMRequest(
-            model=global_config.llm_summary, temperature=0.7, max_tokens=3000, request_type="relation"
+            model=global_config.model.summary, temperature=0.7, max_tokens=3000, request_type="relation"
         )
         self.current_model_type = "r1"  # 默认使用 R1
         self.current_model_name = "unknown model"
@@ -37,7 +38,7 @@ class NormalChatGenerator:
     async def generate_response(self, message: MessageThinking, thinking_id: str) -> Optional[Union[str, List[str]]]:
         """根据当前模型类型选择对应的生成函数"""
         # 从global_config中获取模型概率值并选择模型
-        if random.random() < global_config.model_reasoning_probability:
+        if random.random() < global_config.normal_chat.reasoning_model_probability:
             self.current_model_type = "深深地"
             current_model = self.model_reasoning
         else:
@@ -51,7 +52,7 @@ class NormalChatGenerator:
         model_response = await self._generate_response_with_model(message, current_model, thinking_id)
 
         if model_response:
-            logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}")
+            logger.info(f"{global_config.bot.nickname}的回复是：{model_response}")
             model_response = await self._process_response(model_response)
 
             return model_response
@@ -113,7 +114,7 @@ class NormalChatGenerator:
             - "中立"：不表达明确立场或无关回应
             2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签
             3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒"
-            4. 考虑回复者的人格设定为{global_config.personality_core}
+            4. 考虑回复者的人格设定为{global_config.personality.personality_core}
 
             对话示例：
             被回复：「A就是笨」

src/chat/normal_chat/willing/mode_classical.py

@@ -1,18 +1,20 @@
 import asyncio
+
+from src.config.config import global_config
 from .willing_manager import BaseWillingManager
 
 
 class ClassicalWillingManager(BaseWillingManager):
     def __init__(self):
         super().__init__()
-        self._decay_task: asyncio.Task = None
+        self._decay_task: asyncio.Task | None = None
 
     async def _decay_reply_willing(self):
         """定期衰减回复意愿"""
         while True:
             await asyncio.sleep(1)
             for chat_id in self.chat_reply_willing:
-                self.chat_reply_willing[chat_id] = max(0, self.chat_reply_willing[chat_id] * 0.9)
+                self.chat_reply_willing[chat_id] = max(0.0, self.chat_reply_willing[chat_id] * 0.9)
 
     async def async_task_starter(self):
         if self._decay_task is None:
@@ -23,35 +25,33 @@ class ClassicalWillingManager(BaseWillingManager):
         chat_id = willing_info.chat_id
         current_willing = self.chat_reply_willing.get(chat_id, 0)
 
-        interested_rate = willing_info.interested_rate * self.global_config.response_interested_rate_amplifier
+        interested_rate = willing_info.interested_rate * global_config.normal_chat.response_interested_rate_amplifier
 
         if interested_rate > 0.4:
             current_willing += interested_rate - 0.3
 
-        if willing_info.is_mentioned_bot and current_willing < 1.0:
+        if willing_info.is_mentioned_bot:
-            current_willing += 1
+            current_willing += 1 if current_willing < 1.0 else 0.05
-        elif willing_info.is_mentioned_bot:
-            current_willing += 0.05
 
         is_emoji_not_reply = False
         if willing_info.is_emoji:
-            if self.global_config.emoji_response_penalty != 0:
+            if global_config.normal_chat.emoji_response_penalty != 0:
-                current_willing *= self.global_config.emoji_response_penalty
+                current_willing *= global_config.normal_chat.emoji_response_penalty
             else:
                 is_emoji_not_reply = True
 
         self.chat_reply_willing[chat_id] = min(current_willing, 3.0)
 
         reply_probability = min(
-            max((current_willing - 0.5), 0.01) * self.global_config.response_willing_amplifier * 2, 1
+            max((current_willing - 0.5), 0.01) * global_config.normal_chat.response_willing_amplifier * 2, 1
         )
 
         # 检查群组权限（如果是群聊）
         if (
             willing_info.group_info
-            and willing_info.group_info.group_id in self.global_config.talk_frequency_down_groups
+            and willing_info.group_info.group_id in global_config.chat_target.talk_frequency_down_groups
         ):
-            reply_probability = reply_probability / self.global_config.down_frequency_rate
+            reply_probability = reply_probability / global_config.normal_chat.down_frequency_rate
 
         if is_emoji_not_reply:
             reply_probability = 0
@@ -61,7 +61,7 @@ class ClassicalWillingManager(BaseWillingManager):
     async def before_generate_reply_handle(self, message_id):
         chat_id = self.ongoing_messages[message_id].chat_id
         current_willing = self.chat_reply_willing.get(chat_id, 0)
-        self.chat_reply_willing[chat_id] = max(0, current_willing - 1.8)
+        self.chat_reply_willing[chat_id] = max(0.0, current_willing - 1.8)
 
     async def after_generate_reply_handle(self, message_id):
         if message_id not in self.ongoing_messages:
@@ -70,7 +70,7 @@ class ClassicalWillingManager(BaseWillingManager):
         chat_id = self.ongoing_messages[message_id].chat_id
         current_willing = self.chat_reply_willing.get(chat_id, 0)
         if current_willing < 1:
-            self.chat_reply_willing[chat_id] = min(1, current_willing + 0.4)
+            self.chat_reply_willing[chat_id] = min(1.0, current_willing + 0.4)
 
     async def bombing_buffer_message_handle(self, message_id):
         return await super().bombing_buffer_message_handle(message_id)

src/chat/normal_chat/willing/mode_mxp.py

@@ -19,6 +19,7 @@ Mxp 模式：梦溪畔独家赞助
 下下策是询问一个菜鸟（@梦溪畔）
 """
 
+from src.config.config import global_config
 from .willing_manager import BaseWillingManager
 from typing import Dict
 import asyncio
@@ -50,8 +51,6 @@ class MxpWillingManager(BaseWillingManager):
 
         self.mention_willing_gain = 0.6  # 提及意愿增益
         self.interest_willing_gain = 0.3  # 兴趣意愿增益
-        self.emoji_response_penalty = self.global_config.emoji_response_penalty  # 表情包回复惩罚
-        self.down_frequency_rate = self.global_config.down_frequency_rate  # 降低回复频率的群组惩罚系数
         self.single_chat_gain = 0.12  # 单聊增益
 
         self.fatigue_messages_triggered_num = self.expected_replies_per_min  # 疲劳消息触发数量(int)
@@ -179,10 +178,10 @@ class MxpWillingManager(BaseWillingManager):
             probability = self._willing_to_probability(current_willing)
 
             if w_info.is_emoji:
-                probability *= self.emoji_response_penalty
+                probability *= global_config.normal_chat.emoji_response_penalty
 
-            if w_info.group_info and w_info.group_info.group_id in self.global_config.talk_frequency_down_groups:
+            if w_info.group_info and w_info.group_info.group_id in global_config.chat_target.talk_frequency_down_groups:
-                probability /= self.down_frequency_rate
+                probability /= global_config.normal_chat.down_frequency_rate
 
             self.temporary_willing = current_willing
 

src/chat/normal_chat/willing/willing_manager.py

@@ -1,6 +1,6 @@
 from src.common.logger import LogConfig, WILLING_STYLE_CONFIG, LoguruLogger, get_module_logger
 from dataclasses import dataclass
-from src.config.config import global_config, BotConfig
+from src.config.config import global_config
 from src.chat.message_receive.chat_stream import ChatStream, GroupInfo
 from src.chat.message_receive.message import MessageRecv
 from src.chat.person_info.person_info import person_info_manager, PersonInfoManager
@@ -93,7 +93,6 @@ class BaseWillingManager(ABC):
         self.chat_reply_willing: Dict[str, float] = {}  # 存储每个聊天流的回复意愿(chat_id)
         self.ongoing_messages: Dict[str, WillingInfo] = {}  # 当前正在进行的消息(message_id)
         self.lock = asyncio.Lock()
-        self.global_config: BotConfig = global_config
         self.logger: LoguruLogger = logger
 
     def setup(self, message: MessageRecv, chat: ChatStream, is_mentioned_bot: bool, interested_rate: float):
@@ -173,7 +172,7 @@ def init_willing_manager() -> BaseWillingManager:
     Returns:
         对应mode的WillingManager实例
     """
-    mode = global_config.willing_mode.lower()
+    mode = global_config.normal_chat.willing_mode.lower()
     return BaseWillingManager.create(mode)
 
 

src/chat/person_info/person_info.py

@@ -1,5 +1,6 @@
 from src.common.logger_manager import get_logger
-from ...common.database import db
+from ...common.database.database import db
+from ...common.database.database_model import PersonInfo  # 新增导入
 import copy
 import hashlib
 from typing import Any, Callable, Dict
@@ -16,7 +17,7 @@ matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 from pathlib import Path
 import pandas as pd
-import json
+import json  # 新增导入
 import re
 
 
@@ -38,47 +39,49 @@ logger = get_logger("person_info")
 
 person_info_default = {
     "person_id": None,
-    "person_name": None,
+    "person_name": None,  # 模型中已设为 null=True，此默认值OK
     "name_reason": None,
-    "platform": None,
+    "platform": "unknown",  # 提供非None的默认值
-    "user_id": None,
+    "user_id": "unknown",  # 提供非None的默认值
-    "nickname": None,
+    "nickname": "Unknown",  # 提供非None的默认值
-    # "age" : 0,
     "relationship_value": 0,
-    # "saved" : True,
+    "know_time": 0,  # 修正拼写：konw_time -> know_time
-    # "impression" : None,
-    # "gender" : Unkown,
-    "konw_time": 0,
     "msg_interval": 2000,
-    "msg_interval_list": [],
+    "msg_interval_list": [],  # 将作为 JSON 字符串存储在 Peewee 的 TextField
-    "user_cardname": None,  # 添加群名片
+    "user_cardname": None,  # 注意：此字段不在 PersonInfo Peewee 模型中
-    "user_avatar": None,  # 添加头像信息（例如URL或标识符）
+    "user_avatar": None,  # 注意：此字段不在 PersonInfo Peewee 模型中
-}  # 个人信息的各项与默认值在此定义，以下处理会自动创建/补全每一项
+}
 
 
 class PersonInfoManager:
     def __init__(self):
         self.person_name_list = {}
+        # TODO: API-Adapter修改标记
         self.qv_name_llm = LLMRequest(
-            model=global_config.llm_normal,
+            model=global_config.model.normal,
             max_tokens=256,
             request_type="qv_name",
         )
-        if "person_info" not in db.list_collection_names():
+        try:
-            db.create_collection("person_info")
+            db.connect(reuse_if_open=True)
-            db.person_info.create_index("person_id", unique=True)
+            db.create_tables([PersonInfo], safe=True)
+        except Exception as e:
+            logger.error(f"数据库连接或 PersonInfo 表创建失败: {e}")
 
         # 初始化时读取所有person_name
-        cursor = db.person_info.find({"person_name": {"$exists": True}}, {"person_id": 1, "person_name": 1, "_id": 0})
+        try:
-        for doc in cursor:
+            for record in PersonInfo.select(PersonInfo.person_id, PersonInfo.person_name).where(
-            if doc.get("person_name"):
+                PersonInfo.person_name.is_null(False)
-                self.person_name_list[doc["person_id"]] = doc["person_name"]
+            ):
-        logger.debug(f"已加载 {len(self.person_name_list)} 个用户名称")
+                if record.person_name:
+                    self.person_name_list[record.person_id] = record.person_name
+            logger.debug(f"已加载 {len(self.person_name_list)} 个用户名称 (Peewee)")
+        except Exception as e:
+            logger.error(f"从 Peewee 加载 person_name_list 失败: {e}")
 
     @staticmethod
     def get_person_id(platform: str, user_id: int):
         """获取唯一id"""
-        # 如果platform中存在-，就截取-后面的部分
         if "-" in platform:
             platform = platform.split("-")[1]
 
@@ -86,13 +89,17 @@ class PersonInfoManager:
         key = "_".join(components)
         return hashlib.md5(key.encode()).hexdigest()
 
-    def is_person_known(self, platform: str, user_id: int):
+    async def is_person_known(self, platform: str, user_id: int):
         """判断是否认识某人"""
         person_id = self.get_person_id(platform, user_id)
-        document = db.person_info.find_one({"person_id": person_id})
+
-        if document:
+        def _db_check_known_sync(p_id: str):
-            return True
+            return PersonInfo.get_or_none(PersonInfo.person_id == p_id) is not None
-        else:
+
+        try:
+            return await asyncio.to_thread(_db_check_known_sync, person_id)
+        except Exception as e:
+            logger.error(f"检查用户 {person_id} 是否已知时出错 (Peewee): {e}")
             return False
 
     def get_person_id_by_person_name(self, person_name: str):
@@ -111,73 +118,111 @@ class PersonInfoManager:
             return
 
         _person_info_default = copy.deepcopy(person_info_default)
-        _person_info_default["person_id"] = person_id
+        model_fields = PersonInfo._meta.fields.keys()
+
+        final_data = {"person_id": person_id}
 
         if data:
-            for key in _person_info_default:
+            for key, value in data.items():
-                if key != "person_id" and key in data:
+                if key in model_fields:
-                    _person_info_default[key] = data[key]
+                    final_data[key] = value
 
-        db.person_info.insert_one(_person_info_default)
+        for key, default_value in _person_info_default.items():
+            if key in model_fields and key not in final_data:
+                final_data[key] = default_value
+
+        if "msg_interval_list" in final_data and isinstance(final_data["msg_interval_list"], list):
+            final_data["msg_interval_list"] = json.dumps(final_data["msg_interval_list"])
+        elif "msg_interval_list" not in final_data and "msg_interval_list" in model_fields:
+            final_data["msg_interval_list"] = json.dumps([])
+
+        def _db_create_sync(p_data: dict):
+            try:
+                PersonInfo.create(**p_data)
+                return True
+            except Exception as e:
+                logger.error(f"创建 PersonInfo 记录 {p_data.get('person_id')} 失败 (Peewee): {e}")
+                return False
+
+        await asyncio.to_thread(_db_create_sync, final_data)
 
     async def update_one_field(self, person_id: str, field_name: str, value, data: dict = None):
         """更新某一个字段，会补全"""
-        if field_name not in person_info_default.keys():
+        if field_name not in PersonInfo._meta.fields:
-            logger.debug(f"更新'{field_name}'失败，未定义的字段")
+            if field_name in person_info_default:
+                logger.debug(f"更新'{field_name}'跳过，字段存在于默认配置但不在 PersonInfo Peewee 模型中。")
+                return
+            logger.debug(f"更新'{field_name}'失败，未在 PersonInfo Peewee 模型中定义的字段。")
             return
 
-        document = db.person_info.find_one({"person_id": person_id})
+        def _db_update_sync(p_id: str, f_name: str, val):
-
+            record = PersonInfo.get_or_none(PersonInfo.person_id == p_id)
-        if document:
+            if record:
-            db.person_info.update_one({"person_id": person_id}, {"$set": {field_name: value}})
+                if f_name == "msg_interval_list" and isinstance(val, list):
+                    setattr(record, f_name, json.dumps(val))
                 else:
-            data[field_name] = value
+                    setattr(record, f_name, val)
-            logger.debug(f"更新时{person_id}不存在，已新建")
+                record.save()
-            await self.create_person_info(person_id, data)
+                return True, False
+            return False, True
+
+        found, needs_creation = await asyncio.to_thread(_db_update_sync, person_id, field_name, value)
+
+        if needs_creation:
+            logger.debug(f"更新时 {person_id} 不存在，将新建。")
+            creation_data = data if data is not None else {}
+            creation_data[field_name] = value
+            if "platform" not in creation_data or "user_id" not in creation_data:
+                logger.warning(f"为 {person_id} 创建记录时，platform/user_id 可能缺失。")
+
+            await self.create_person_info(person_id, creation_data)
 
     @staticmethod
     async def has_one_field(person_id: str, field_name: str):
         """判断是否存在某一个字段"""
-        document = db.person_info.find_one({"person_id": person_id}, {field_name: 1})
+        if field_name not in PersonInfo._meta.fields:
-        if document:
+            logger.debug(f"检查字段'{field_name}'失败，未在 PersonInfo Peewee 模型中定义。")
+            return False
+
+        def _db_has_field_sync(p_id: str, f_name: str):
+            record = PersonInfo.get_or_none(PersonInfo.person_id == p_id)
+            if record:
                 return True
-        else:
+            return False
+
+        try:
+            return await asyncio.to_thread(_db_has_field_sync, person_id, field_name)
+        except Exception as e:
+            logger.error(f"检查字段 {field_name} for {person_id} 时出错 (Peewee): {e}")
             return False
 
     @staticmethod
     def _extract_json_from_text(text: str) -> dict:
         """从文本中提取JSON数据的高容错方法"""
         try:
-            # 尝试直接解析
             parsed_json = json.loads(text)
-            # 如果解析结果是列表，尝试取第一个元素
             if isinstance(parsed_json, list):
-                if parsed_json:  # 检查列表是否为空
+                if parsed_json:
                     parsed_json = parsed_json[0]
-                else:  # 如果列表为空，重置为 None，走后续逻辑
+                else:
                     parsed_json = None
-            # 确保解析结果是字典
             if isinstance(parsed_json, dict):
                 return parsed_json
 
         except json.JSONDecodeError:
-            # 解析失败，继续尝试其他方法
             pass
         except Exception as e:
             logger.warning(f"尝试直接解析JSON时发生意外错误: {e}")
-            pass  # 继续尝试其他方法
+            pass
 
-        # 如果直接解析失败或结果不是字典
         try:
-            # 尝试找到JSON对象格式的部分
             json_pattern = r"\{[^{}]*\}"
             matches = re.findall(json_pattern, text)
             if matches:
                 parsed_obj = json.loads(matches[0])
-                if isinstance(parsed_obj, dict):  # 确保是字典
+                if isinstance(parsed_obj, dict):
                     return parsed_obj
 
-            # 如果上面都失败了，尝试提取键值对
             nickname_pattern = r'"nickname"[:\s]+"([^"]+)"'
             reason_pattern = r'"reason"[:\s]+"([^"]+)"'
 
@@ -192,7 +237,6 @@ class PersonInfoManager:
         except Exception as e:
             logger.error(f"后备JSON提取失败: {str(e)}")
 
-        # 如果所有方法都失败了，返回默认字典
         logger.warning(f"无法从文本中提取有效的JSON字典: {text}")
         return {"nickname": "", "reason": ""}
 
@@ -207,9 +251,11 @@ class PersonInfoManager:
         old_name = await self.get_value(person_id, "person_name")
         old_reason = await self.get_value(person_id, "name_reason")
 
-        max_retries = 5  # 最大重试次数
+        max_retries = 5
         current_try = 0
-        existing_names = ""
+        existing_names_str = ""
+        current_name_set = set(self.person_name_list.values())
+
         while current_try < max_retries:
             individuality = Individuality.get_instance()
             prompt_personality = individuality.get_prompt(x_person=2, level=1)
@@ -224,45 +270,58 @@ class PersonInfoManager:
                 qv_name_prompt += f"你之前叫他{old_name}，是因为{old_reason}，"
 
             qv_name_prompt += f"\n其他取名的要求是：{request}，不要太浮夸"
-
             qv_name_prompt += (
                 "\n请根据以上用户信息，想想你叫他什么比较好，不要太浮夸，请最好使用用户的qq昵称，可以稍作修改"
             )
-            if existing_names:
+
-                qv_name_prompt += f"\n请注意，以下名称已被使用，不要使用以下昵称：{existing_names}。\n"
+            if existing_names_str:
+                qv_name_prompt += f"\n请注意，以下名称已被你尝试过或已知存在，请避免：{existing_names_str}。\n"
+
+            if len(current_name_set) < 50 and current_name_set:
+                qv_name_prompt += f"已知的其他昵称有: {', '.join(list(current_name_set)[:10])}等。\n"
+
             qv_name_prompt += "请用json给出你的想法，并给出理由，示例如下："
             qv_name_prompt += """{
                 "nickname": "昵称",
                 "reason": "理由"
             }"""
-            # logger.debug(f"取名提示词：{qv_name_prompt}")
             response = await self.qv_name_llm.generate_response(qv_name_prompt)
             logger.trace(f"取名提示词：{qv_name_prompt}\n取名回复：{response}")
             result = self._extract_json_from_text(response[0])
 
-            if not result["nickname"]:
+            if not result or not result.get("nickname"):
-                logger.error("生成的昵称为空，重试中...")
+                logger.error("生成的昵称为空或结果格式不正确，重试中...")
                 current_try += 1
                 continue
 
-            # 检查生成的昵称是否已存在
+            generated_nickname = result["nickname"]
-            if result["nickname"] not in self.person_name_list.values():
-                # 更新数据库和内存中的列表
-                await self.update_one_field(person_id, "person_name", result["nickname"])
-                # await self.update_one_field(person_id, "nickname", user_nickname)
-                # await self.update_one_field(person_id, "avatar", user_avatar)
-                await self.update_one_field(person_id, "name_reason", result["reason"])
 
-                self.person_name_list[person_id] = result["nickname"]
+            is_duplicate = False
-                # logger.debug(f"用户 {person_id} 的名称已更新为 {result['nickname']}，原因：{result['reason']}")
+            if generated_nickname in current_name_set:
+                is_duplicate = True
+            else:
+
+                def _db_check_name_exists_sync(name_to_check):
+                    return PersonInfo.select().where(PersonInfo.person_name == name_to_check).exists()
+
+                if await asyncio.to_thread(_db_check_name_exists_sync, generated_nickname):
+                    is_duplicate = True
+                    current_name_set.add(generated_nickname)
+
+            if not is_duplicate:
+                await self.update_one_field(person_id, "person_name", generated_nickname)
+                await self.update_one_field(person_id, "name_reason", result.get("reason", "未提供理由"))
+
+                self.person_name_list[person_id] = generated_nickname
                 return result
             else:
-                existing_names += f"{result['nickname']}、"
+                if existing_names_str:
-
+                    existing_names_str += "、"
-            logger.debug(f"生成的昵称 {result['nickname']} 已存在，重试中...")
+                existing_names_str += generated_nickname
+                logger.debug(f"生成的昵称 {generated_nickname} 已存在，重试中...")
                 current_try += 1
 
-        logger.error(f"在{max_retries}次尝试后仍未能生成唯一昵称")
+        logger.error(f"在{max_retries}次尝试后仍未能生成唯一昵称 for {person_id}")
         return None
 
     @staticmethod
@@ -272,30 +331,56 @@ class PersonInfoManager:
             logger.debug("删除失败：person_id 不能为空")
             return
 
-        result = db.person_info.delete_one({"person_id": person_id})
+        def _db_delete_sync(p_id: str):
-        if result.deleted_count > 0:
+            try:
-            logger.debug(f"删除成功：person_id={person_id}")
+                query = PersonInfo.delete().where(PersonInfo.person_id == p_id)
+                deleted_count = query.execute()
+                return deleted_count
+            except Exception as e:
+                logger.error(f"删除 PersonInfo {p_id} 失败 (Peewee): {e}")
+                return 0
+
+        deleted_count = await asyncio.to_thread(_db_delete_sync, person_id)
+
+        if deleted_count > 0:
+            logger.debug(f"删除成功：person_id={person_id} (Peewee)")
         else:
-            logger.debug(f"删除失败：未找到 person_id={person_id}")
+            logger.debug(f"删除失败：未找到 person_id={person_id} 或删除未影响行 (Peewee)")
 
     @staticmethod
     async def get_value(person_id: str, field_name: str):
         """获取指定person_id文档的字段值，若不存在该字段，则返回该字段的全局默认值"""
         if not person_id:
             logger.debug("get_value获取失败：person_id不能为空")
+            return person_info_default.get(field_name)
+
+        if field_name not in PersonInfo._meta.fields:
+            if field_name in person_info_default:
+                logger.trace(f"字段'{field_name}'不在Peewee模型中，但存在于默认配置中。返回配置默认值。")
+                return copy.deepcopy(person_info_default[field_name])
+            logger.debug(f"get_value获取失败：字段'{field_name}'未在Peewee模型和默认配置中定义。")
             return None
 
-        if field_name not in person_info_default:
+        def _db_get_value_sync(p_id: str, f_name: str):
-            logger.debug(f"get_value获取失败：字段'{field_name}'未定义")
+            record = PersonInfo.get_or_none(PersonInfo.person_id == p_id)
+            if record:
+                val = getattr(record, f_name)
+                if f_name == "msg_interval_list" and isinstance(val, str):
+                    try:
+                        return json.loads(val)
+                    except json.JSONDecodeError:
+                        logger.warning(f"无法解析 {p_id} 的 msg_interval_list JSON: {val}")
+                        return copy.deepcopy(person_info_default.get(f_name, []))
+                return val
             return None
 
-        document = db.person_info.find_one({"person_id": person_id}, {field_name: 1})
+        value = await asyncio.to_thread(_db_get_value_sync, person_id, field_name)
 
-        if document and field_name in document:
+        if value is not None:
-            return document[field_name]
+            return value
         else:
-            default_value = copy.deepcopy(person_info_default[field_name])
+            default_value = copy.deepcopy(person_info_default.get(field_name))
-            logger.trace(f"获取{person_id}的{field_name}失败，已返回默认值{default_value}")
+            logger.trace(f"获取{person_id}的{field_name}失败或值为None，已返回默认值{default_value} (Peewee)")
             return default_value
 
     @staticmethod
@@ -305,93 +390,84 @@ class PersonInfoManager:
             logger.debug("get_values获取失败：person_id不能为空")
             return {}
 
-        # 检查所有字段是否有效
-        for field in field_names:
-            if field not in person_info_default:
-                logger.debug(f"get_values获取失败：字段'{field}'未定义")
-                return {}
-
-        # 构建查询投影（所有字段都有效才会执行到这里）
-        projection = {field: 1 for field in field_names}
-
-        document = db.person_info.find_one({"person_id": person_id}, projection)
-
         result = {}
-        for field in field_names:
+
-            result[field] = copy.deepcopy(
+        def _db_get_record_sync(p_id: str):
-                document.get(field, person_info_default[field]) if document else person_info_default[field]
+            return PersonInfo.get_or_none(PersonInfo.person_id == p_id)
-            )
+
+        record = await asyncio.to_thread(_db_get_record_sync, person_id)
+
+        for field_name in field_names:
+            if field_name not in PersonInfo._meta.fields:
+                if field_name in person_info_default:
+                    result[field_name] = copy.deepcopy(person_info_default[field_name])
+                    logger.trace(f"字段'{field_name}'不在Peewee模型中，使用默认配置值。")
+                else:
+                    logger.debug(f"get_values查询失败：字段'{field_name}'未在Peewee模型和默认配置中定义。")
+                    result[field_name] = None
+                continue
+
+            if record:
+                value = getattr(record, field_name)
+                if field_name == "msg_interval_list" and isinstance(value, str):
+                    try:
+                        result[field_name] = json.loads(value)
+                    except json.JSONDecodeError:
+                        logger.warning(f"无法解析 {person_id} 的 msg_interval_list JSON: {value}")
+                        result[field_name] = copy.deepcopy(person_info_default.get(field_name, []))
+                elif value is not None:
+                    result[field_name] = value
+                else:
+                    result[field_name] = copy.deepcopy(person_info_default.get(field_name))
+            else:
+                result[field_name] = copy.deepcopy(person_info_default.get(field_name))
 
         return result
 
     @staticmethod
     async def del_all_undefined_field():
-        """删除所有项里的未定义字段"""
+        """删除所有项里的未定义字段 - 对于Peewee (SQL)，此操作通常不适用，因为模式是固定的。"""
-        # 获取所有已定义的字段名
+        logger.info(
-        defined_fields = set(person_info_default.keys())
+            "del_all_undefined_field: 对于使用Peewee的SQL数据库，此操作通常不适用或不需要，因为表结构是预定义的。"
-
-        try:
-            # 遍历集合中的所有文档
-            for document in db.person_info.find({}):
-                # 找出文档中未定义的字段
-                undefined_fields = set(document.keys()) - defined_fields - {"_id"}
-
-                if undefined_fields:
-                    # 构建更新操作，使用$unset删除未定义字段
-                    update_result = db.person_info.update_one(
-                        {"_id": document["_id"]}, {"$unset": {field: 1 for field in undefined_fields}}
         )
-
-                    if update_result.modified_count > 0:
-                        logger.debug(f"已清理文档 {document['_id']} 的未定义字段: {undefined_fields}")
-
-            return
-
-        except Exception as e:
-            logger.error(f"清理未定义字段时出错: {e}")
         return
 
     @staticmethod
     async def get_specific_value_list(
         field_name: str,
-        way: Callable[[Any], bool],  # 接受任意类型值
+        way: Callable[[Any], bool],
     ) -> Dict[str, Any]:
         """
         获取满足条件的字段值字典
-
-        Args:
-            field_name: 目标字段名
-            way: 判断函数 (value: Any) -> bool
-
-        Returns:
-            {person_id: value} | {}
-
-        Example:
-            # 查找所有nickname包含"admin"的用户
-            result = manager.specific_value_list(
-                "nickname",
-                lambda x: "admin" in x.lower()
-            )
         """
-        if field_name not in person_info_default:
+        if field_name not in PersonInfo._meta.fields:
-            logger.error(f"字段检查失败：'{field_name}'未定义")
+            logger.error(f"字段检查失败：'{field_name}'未在 PersonInfo Peewee 模型中定义")
             return {}
 
+        def _db_get_specific_sync(f_name: str):
+            found_results = {}
             try:
-            result = {}
+                for record in PersonInfo.select(PersonInfo.person_id, getattr(PersonInfo, f_name)):
-            for doc in db.person_info.find({field_name: {"$exists": True}}, {"person_id": 1, field_name: 1, "_id": 0}):
+                    value = getattr(record, f_name)
+                    if f_name == "msg_interval_list" and isinstance(value, str):
                         try:
-                    value = doc[field_name]
+                            processed_value = json.loads(value)
-                    if way(value):
+                        except json.JSONDecodeError:
-                        result[doc["person_id"]] = value
+                            logger.warning(f"跳过记录 {record.person_id}，无法解析 msg_interval_list: {value}")
-                except (KeyError, TypeError, ValueError) as e:
-                    logger.debug(f"记录{doc.get('person_id')}处理失败: {str(e)}")
                             continue
+                    else:
+                        processed_value = value
 
-            return result
+                    if way(processed_value):
+                        found_results[record.person_id] = processed_value
+            except Exception as e_query:
+                logger.error(f"数据库查询失败 (Peewee specific_value_list for {f_name}): {str(e_query)}", exc_info=True)
+            return found_results
 
+        try:
+            return await asyncio.to_thread(_db_get_specific_sync, field_name)
         except Exception as e:
-            logger.error(f"数据库查询失败: {str(e)}", exc_info=True)
+            logger.error(f"执行 get_specific_value_list 线程时出错: {str(e)}", exc_info=True)
             return {}
 
     async def personal_habit_deduction(self):
@@ -399,35 +475,31 @@ class PersonInfoManager:
         try:
             while 1:
                 await asyncio.sleep(600)
-                current_time = datetime.datetime.now()
+                current_time_dt = datetime.datetime.now()
-                logger.info(f"个人信息推断启动: {current_time.strftime('%Y-%m-%d %H:%M:%S')}")
+                logger.info(f"个人信息推断启动: {current_time_dt.strftime('%Y-%m-%d %H:%M:%S')}")
 
-                # "msg_interval"推断
+                msg_interval_map_generated = False
-                msg_interval_map = False
+                msg_interval_lists_map = await self.get_specific_value_list(
-                msg_interval_lists = await self.get_specific_value_list(
                     "msg_interval_list", lambda x: isinstance(x, list) and len(x) >= 100
                 )
-                for person_id, msg_interval_list_ in msg_interval_lists.items():
+
+                for person_id, actual_msg_interval_list in msg_interval_lists_map.items():
                     await asyncio.sleep(0.3)
                     try:
                         time_interval = []
-                        for t1, t2 in zip(msg_interval_list_, msg_interval_list_[1:]):
+                        for t1, t2 in zip(actual_msg_interval_list, actual_msg_interval_list[1:]):
                             delta = t2 - t1
                             if delta > 0:
                                 time_interval.append(delta)
 
                         time_interval = [t for t in time_interval if 200 <= t <= 8000]
-                        # --- 修改后的逻辑 ---
-                        # 数据量检查 (至少需要 30 条有效间隔，并且足够进行头尾截断)
-                        if len(time_interval) >= 30 + 10:  # 至少30条有效+头尾各5条
-                            time_interval.sort()
 
-                            # 画图(log) - 这部分保留
+                        if len(time_interval) >= 30 + 10:
-                            msg_interval_map = True
+                            time_interval.sort()
+                            msg_interval_map_generated = True
                             log_dir = Path("logs/person_info")
                             log_dir.mkdir(parents=True, exist_ok=True)
                             plt.figure(figsize=(10, 6))
-                            # 使用截断前的数据画图，更能反映原始分布
                             time_series_original = pd.Series(time_interval)
                             plt.hist(
                                 time_series_original,
@@ -449,34 +521,29 @@ class PersonInfoManager:
                             img_path = log_dir / f"interval_distribution_{person_id[:8]}.png"
                             plt.savefig(img_path)
                             plt.close()
-                            # 画图结束
 
-                            # 去掉头尾各 5 个数据点
                             trimmed_interval = time_interval[5:-5]
-
+                            if trimmed_interval:
-                            # 计算截断后数据的 37% 分位数
+                                msg_interval_val = int(round(np.percentile(trimmed_interval, 37)))
-                            if trimmed_interval:  # 确保截断后列表不为空
+                                await self.update_one_field(person_id, "msg_interval", msg_interval_val)
-                                msg_interval = int(round(np.percentile(trimmed_interval, 37)))
+                                logger.trace(
-                                # 更新数据库
+                                    f"用户{person_id}的msg_interval通过头尾截断和37分位数更新为{msg_interval_val}"
-                                await self.update_one_field(person_id, "msg_interval", msg_interval)
+                                )
-                                logger.trace(f"用户{person_id}的msg_interval通过头尾截断和37分位数更新为{msg_interval}")
                             else:
                                 logger.trace(f"用户{person_id}截断后数据为空，无法计算msg_interval")
                         else:
                             logger.trace(
                                 f"用户{person_id}有效消息间隔数量 ({len(time_interval)}) 不足进行推断 (需要至少 {30 + 10} 条)"
                             )
-                        # --- 修改结束 ---
+                    except Exception as e_inner:
-                    except Exception as e:
+                        logger.trace(f"用户{person_id}消息间隔计算失败: {type(e_inner).__name__}: {str(e_inner)}")
-                        logger.trace(f"用户{person_id}消息间隔计算失败: {type(e).__name__}: {str(e)}")
                         continue
 
-                # 其他...
+                if msg_interval_map_generated:
-
-                if msg_interval_map:
                     logger.trace("已保存分布图到: logs/person_info")
-                current_time = datetime.datetime.now()
+
-                logger.trace(f"个人信息推断结束: {current_time.strftime('%Y-%m-%d %H:%M:%S')}")
+                current_time_dt_end = datetime.datetime.now()
+                logger.trace(f"个人信息推断结束: {current_time_dt_end.strftime('%Y-%m-%d %H:%M:%S')}")
                 await asyncio.sleep(86400)
 
         except Exception as e:
@@ -489,41 +556,27 @@ class PersonInfoManager:
         """
         根据 platform 和 user_id 获取 person_id。
         如果对应的用户不存在，则使用提供的可选信息创建新用户。
-
-        Args:
-            platform: 平台标识
-            user_id: 用户在该平台上的ID
-            nickname: 用户的昵称 (可选，用于创建新用户)
-            user_cardname: 用户的群名片 (可选，用于创建新用户)
-            user_avatar: 用户的头像信息 (可选，用于创建新用户)
-
-        Returns:
-            对应的 person_id。
         """
         person_id = self.get_person_id(platform, user_id)
 
-        # 检查用户是否已存在
+        def _db_check_exists_sync(p_id: str):
-        # 使用静态方法 get_person_id，因此可以直接调用 db
+            return PersonInfo.get_or_none(PersonInfo.person_id == p_id)
-        document = db.person_info.find_one({"person_id": person_id})
 
-        if document is None:
+        record = await asyncio.to_thread(_db_check_exists_sync, person_id)
-            logger.info(f"用户 {platform}:{user_id} (person_id: {person_id}) 不存在，将创建新记录。")
+
+        if record is None:
+            logger.info(f"用户 {platform}:{user_id} (person_id: {person_id}) 不存在，将创建新记录 (Peewee)。")
             initial_data = {
                 "platform": platform,
-                "user_id": user_id,
+                "user_id": str(user_id),
                 "nickname": nickname,
-                "konw_time": int(datetime.datetime.now().timestamp()),  # 添加初次认识时间
+                "know_time": int(datetime.datetime.now().timestamp()),  # 修正拼写：konw_time -> know_time
-                # 注意：这里没有添加 user_cardname 和 user_avatar，因为它们不在 person_info_default 中
-                # 如果需要存储它们，需要先在 person_info_default 中定义
             }
-            # 过滤掉值为 None 的初始数据
+            model_fields = PersonInfo._meta.fields.keys()
-            initial_data = {k: v for k, v in initial_data.items() if v is not None}
+            filtered_initial_data = {k: v for k, v in initial_data.items() if v is not None and k in model_fields}
 
-            # 注意：create_person_info 是静态方法
+            await self.create_person_info(person_id, data=filtered_initial_data)
-            await PersonInfoManager.create_person_info(person_id, data=initial_data)
+            logger.debug(f"已为 {person_id} 创建新记录，初始数据 (filtered for model): {filtered_initial_data}")
-            # 创建后，可以考虑立即为其取名，但这可能会增加延迟
-            # await self.qv_person_name(person_id, nickname, user_cardname, user_avatar)
-            logger.debug(f"已为 {person_id} 创建新记录，初始数据: {initial_data}")
 
         return person_id
 
@@ -533,34 +586,54 @@ class PersonInfoManager:
             logger.debug("get_person_info_by_name 获取失败：person_name 不能为空")
             return None
 
-        # 优先从内存缓存查找 person_id
         found_person_id = None
-        for pid, name in self.person_name_list.items():
+        for pid, name_in_cache in self.person_name_list.items():
-            if name == person_name:
+            if name_in_cache == person_name:
                 found_person_id = pid
-                break  # 找到第一个匹配就停止
+                break
 
         if not found_person_id:
-            # 如果内存没有，尝试数据库查询（可能内存未及时更新或启动时未加载）
-            document = db.person_info.find_one({"person_name": person_name})
-            if document:
-                found_person_id = document.get("person_id")
-            else:
-                logger.debug(f"数据库中也未找到名为 '{person_name}' 的用户")
-                return None  # 数据库也找不到
 
-        # 根据找到的 person_id 获取所需信息
+            def _db_find_by_name_sync(p_name_to_find: str):
-        if found_person_id:
+                return PersonInfo.get_or_none(PersonInfo.person_name == p_name_to_find)
-            required_fields = ["person_id", "platform", "user_id", "nickname", "user_cardname", "user_avatar"]
+
-            person_data = await self.get_values(found_person_id, required_fields)
+            record = await asyncio.to_thread(_db_find_by_name_sync, person_name)
-            if person_data:  # 确保 get_values 成功返回
+            if record:
-                return person_data
+                found_person_id = record.person_id
+                if (
+                    found_person_id not in self.person_name_list
+                    or self.person_name_list[found_person_id] != person_name
+                ):
+                    self.person_name_list[found_person_id] = person_name
             else:
-                logger.warning(f"找到了 person_id '{found_person_id}' 但获取详细信息失败")
+                logger.debug(f"数据库中也未找到名为 '{person_name}' 的用户 (Peewee)")
                 return None
+
+        if found_person_id:
+            required_fields = [
+                "person_id",
+                "platform",
+                "user_id",
+                "nickname",
+                "user_cardname",
+                "user_avatar",
+                "person_name",
+                "name_reason",
+            ]
+            valid_fields_to_get = [
+                f for f in required_fields if f in PersonInfo._meta.fields or f in person_info_default
+            ]
+
+            person_data = await self.get_values(found_person_id, valid_fields_to_get)
+
+            if person_data:
+                final_result = {key: person_data.get(key) for key in required_fields}
+                return final_result
             else:
-            # 这理论上不应该发生，因为上面已经处理了找不到的情况
+                logger.warning(f"找到了 person_id '{found_person_id}' 但 get_values 返回空 (Peewee)")
-            logger.error(f"逻辑错误：未能为 '{person_name}' 确定 person_id")
+                return None
+
+        logger.error(f"逻辑错误：未能为 '{person_name}' 确定 person_id (Peewee)")
         return None
 
 

src/chat/utils/chat_message_builder.py

@@ -190,8 +190,8 @@ async def _build_readable_messages_internal(
 
         person_id = person_info_manager.get_person_id(platform, user_id)
         # 根据 replace_bot_name 参数决定是否替换机器人名称
-        if replace_bot_name and user_id == global_config.BOT_QQ:
+        if replace_bot_name and user_id == global_config.bot.qq_account:
-            person_name = f"{global_config.BOT_NICKNAME}(你)"
+            person_name = f"{global_config.bot.nickname}(你)"
         else:
             person_name = await person_info_manager.get_value(person_id, "person_name")
 
@@ -427,7 +427,7 @@ async def build_anonymous_messages(messages: List[Dict[str, Any]]) -> str:
     output_lines = []
 
     def get_anon_name(platform, user_id):
-        if user_id == global_config.BOT_QQ:
+        if user_id == global_config.bot.qq_account:
             return "SELF"
         person_id = person_info_manager.get_person_id(platform, user_id)
         if person_id not in person_map:
@@ -454,7 +454,7 @@ async def build_anonymous_messages(messages: List[Dict[str, Any]]) -> str:
         def reply_replacer(match, platform=platform):
             # aaa = match.group(1)
             bbb = match.group(2)
-            anon_reply = get_anon_name(platform, bbb)
+            anon_reply = get_anon_name(platform, bbb)  # noqa
             return f"回复 {anon_reply}"
 
         content = re.sub(reply_pattern, reply_replacer, content, count=1)
@@ -465,7 +465,7 @@ async def build_anonymous_messages(messages: List[Dict[str, Any]]) -> str:
         def at_replacer(match, platform=platform):
             # aaa = match.group(1)
             bbb = match.group(2)
-            anon_at = get_anon_name(platform, bbb)
+            anon_at = get_anon_name(platform, bbb)  # noqa
             return f"@{anon_at}"
 
         content = re.sub(at_pattern, at_replacer, content)
@@ -501,7 +501,7 @@ async def get_person_id_list(messages: List[Dict[str, Any]]) -> List[str]:
         user_id = user_info.get("user_id")
 
         # 检查必要信息是否存在 且 不是机器人自己
-        if not all([platform, user_id]) or user_id == global_config.BOT_QQ:
+        if not all([platform, user_id]) or user_id == global_config.bot.qq_account:
             continue
 
         person_id = person_info_manager.get_person_id(platform, user_id)

src/chat/utils/info_catcher.py

@@ -1,15 +1,15 @@
 from src.config.config import global_config
 from src.chat.message_receive.message import MessageRecv, MessageSending, Message
-from src.common.database import db
+from src.common.database.database_model import Messages, ThinkingLog
 import time
 import traceback
 from typing import List
+import json
 
 
 class InfoCatcher:
     def __init__(self):
         self.chat_history = []  # 聊天历史，长度为三倍使用的上下文喵~
-        self.context_length = global_config.observation_context_size
         self.chat_history_in_thinking = []  # 思考期间的聊天内容喵~
         self.chat_history_after_response = []  # 回复后的聊天内容，长度为一倍上下文喵~
 
@@ -60,8 +60,6 @@ class InfoCatcher:
     def catch_after_observe(self, obs_duration: float):  # 这里可以有更多信息
         self.timing_results["sub_heartflow_observe_time"] = obs_duration
 
-    # def catch_shf
-
     def catch_afer_shf_step(self, step_duration: float, past_mind: str, current_mind: str):
         self.timing_results["sub_heartflow_step_time"] = step_duration
         if len(past_mind) > 1:
@@ -72,25 +70,10 @@ class InfoCatcher:
             self.heartflow_data["sub_heartflow_now"] = current_mind
 
     def catch_after_llm_generated(self, prompt: str, response: str, reasoning_content: str = "", model_name: str = ""):
-        # if self.response_mode == "heart_flow": # 条件判断不需要了喵~
-        #     self.heartflow_data["prompt"] = prompt
-        #     self.heartflow_data["response"] = response
-        #     self.heartflow_data["model"] = model_name
-        # elif self.response_mode == "reasoning": # 条件判断不需要了喵~
-        #     self.reasoning_data["thinking_log"] = reasoning_content
-        #     self.reasoning_data["prompt"] = prompt
-        #     self.reasoning_data["response"] = response
-        #     self.reasoning_data["model"] = model_name
-
-        # 直接记录信息喵~
         self.reasoning_data["thinking_log"] = reasoning_content
         self.reasoning_data["prompt"] = prompt
         self.reasoning_data["response"] = response
         self.reasoning_data["model"] = model_name
-        # 如果 heartflow 数据也需要通用字段，可以取消下面的注释喵~
-        # self.heartflow_data["prompt"] = prompt
-        # self.heartflow_data["response"] = response
-        # self.heartflow_data["model"] = model_name
 
         self.response_text = response
 
@@ -102,6 +85,7 @@ class InfoCatcher:
     ):
         self.timing_results["make_response_time"] = response_duration
         self.response_time = time.time()
+        self.response_messages = []
         for msg in response_message:
             self.response_messages.append(msg)
 
@@ -112,107 +96,112 @@ class InfoCatcher:
     @staticmethod
     def get_message_from_db_between_msgs(message_start: Message, message_end: Message):
         try:
-            # 从数据库中获取消息的时间戳
             time_start = message_start.message_info.time
             time_end = message_end.message_info.time
             chat_id = message_start.chat_stream.stream_id
 
             print(f"查询参数: time_start={time_start}, time_end={time_end}, chat_id={chat_id}")
 
-            # 查询数据库，获取 chat_id 相同且时间在 start 和 end 之间的数据
+            messages_between_query = (
-            messages_between = db.messages.find(
+                Messages.select()
-                {"chat_id": chat_id, "time": {"$gt": time_start, "$lt": time_end}}
+                .where((Messages.chat_id == chat_id) & (Messages.time > time_start) & (Messages.time < time_end))
-            ).sort("time", -1)
+                .order_by(Messages.time.desc())
+            )
 
-            result = list(messages_between)
+            result = list(messages_between_query)
             print(f"查询结果数量: {len(result)}")
             if result:
-                print(f"第一条消息时间: {result[0]['time']}")
+                print(f"第一条消息时间: {result[0].time}")
-                print(f"最后一条消息时间: {result[-1]['time']}")
+                print(f"最后一条消息时间: {result[-1].time}")
             return result
         except Exception as e:
             print(f"获取消息时出错: {str(e)}")
+            print(traceback.format_exc())
             return []
 
     def get_message_from_db_before_msg(self, message: MessageRecv):
-        # 从数据库中获取消息
+        message_id_val = message.message_info.message_id
-        message_id = message.message_info.message_id
+        chat_id_val = message.chat_stream.stream_id
-        chat_id = message.chat_stream.stream_id
 
-        # 查询数据库，获取 chat_id 相同且 message_id 小于当前消息的 30 条数据
+        messages_before_query = (
-        messages_before = (
+            Messages.select()
-            db.messages.find({"chat_id": chat_id, "message_id": {"$lt": message_id}})
+            .where((Messages.chat_id == chat_id_val) & (Messages.message_id < message_id_val))
-            .sort("time", -1)
+            .order_by(Messages.time.desc())
-            .limit(self.context_length * 3)
+            .limit(global_config.chat.observation_context_size * 3)
-        )  # 获取更多历史信息
+        )
 
-        return list(messages_before)
+        return list(messages_before_query)
 
     def message_list_to_dict(self, message_list):
-        # 存储简化的聊天记录
         result = []
-        for message in message_list:
+        for msg_item in message_list:
-            if not isinstance(message, dict):
+            processed_msg_item = msg_item
-                message = self.message_to_dict(message)
+            if not isinstance(msg_item, dict):
-            # print(message)
+                processed_msg_item = self.message_to_dict(msg_item)
+
+            if not processed_msg_item:
+                continue
 
             lite_message = {
-                "time": message["time"],
+                "time": processed_msg_item.get("time"),
-                "user_nickname": message["user_info"]["user_nickname"],
+                "user_nickname": processed_msg_item.get("user_nickname"),
-                "processed_plain_text": message["processed_plain_text"],
+                "processed_plain_text": processed_msg_item.get("processed_plain_text"),
             }
             result.append(lite_message)
-
         return result
 
     @staticmethod
-    def message_to_dict(message):
+    def message_to_dict(msg_obj):
-        if not message:
+        if not msg_obj:
             return None
-        if isinstance(message, dict):
+        if isinstance(msg_obj, dict):
-            return message
+            return msg_obj
+
+        if isinstance(msg_obj, Messages):
             return {
-            # "message_id": message.message_info.message_id,
+                "time": msg_obj.time,
-            "time": message.message_info.time,
+                "user_id": msg_obj.user_id,
-            "user_id": message.message_info.user_info.user_id,
+                "user_nickname": msg_obj.user_nickname,
-            "user_nickname": message.message_info.user_info.user_nickname,
+                "processed_plain_text": msg_obj.processed_plain_text,
-            "processed_plain_text": message.processed_plain_text,
-            # "detailed_plain_text": message.detailed_plain_text
             }
 
+        if hasattr(msg_obj, "message_info") and hasattr(msg_obj.message_info, "user_info"):
+            return {
+                "time": msg_obj.message_info.time,
+                "user_id": msg_obj.message_info.user_info.user_id,
+                "user_nickname": msg_obj.message_info.user_info.user_nickname,
+                "processed_plain_text": msg_obj.processed_plain_text,
+            }
+
+        print(f"Warning: message_to_dict received an unhandled type: {type(msg_obj)}")
+        return {}
+
     def done_catch(self):
-        """将收集到的信息存储到数据库的 thinking_log 集合中喵~"""
+        """将收集到的信息存储到数据库的 thinking_log 表中喵~"""
         try:
-            # 将消息对象转换为可序列化的字典喵~
+            trigger_info_dict = self.message_to_dict(self.trigger_response_message)
-
+            response_info_dict = {
-            thinking_log_data = {
-                "chat_id": self.chat_id,
-                "trigger_text": self.trigger_response_text,
-                "response_text": self.response_text,
-                "trigger_info": {
-                    "time": self.trigger_response_time,
-                    "message": self.message_to_dict(self.trigger_response_message),
-                },
-                "response_info": {
                 "time": self.response_time,
                 "message": self.response_messages,
-                },
-                "timing_results": self.timing_results,
-                "chat_history": self.message_list_to_dict(self.chat_history),
-                "chat_history_in_thinking": self.message_list_to_dict(self.chat_history_in_thinking),
-                "chat_history_after_response": self.message_list_to_dict(self.chat_history_after_response),
-                "heartflow_data": self.heartflow_data,
-                "reasoning_data": self.reasoning_data,
             }
+            chat_history_list = self.message_list_to_dict(self.chat_history)
+            chat_history_in_thinking_list = self.message_list_to_dict(self.chat_history_in_thinking)
+            chat_history_after_response_list = self.message_list_to_dict(self.chat_history_after_response)
 
-            # 根据不同的响应模式添加相应的数据喵~ # 现在直接都加上去好了喵~
+            log_entry = ThinkingLog(
-            # if self.response_mode == "heart_flow":
+                chat_id=self.chat_id,
-            #     thinking_log_data["mode_specific_data"] = self.heartflow_data
+                trigger_text=self.trigger_response_text,
-            # elif self.response_mode == "reasoning":
+                response_text=self.response_text,
-            #     thinking_log_data["mode_specific_data"] = self.reasoning_data
+                trigger_info_json=json.dumps(trigger_info_dict) if trigger_info_dict else None,
-
+                response_info_json=json.dumps(response_info_dict),
-            # 将数据插入到 thinking_log 集合中喵~
+                timing_results_json=json.dumps(self.timing_results),
-            db.thinking_log.insert_one(thinking_log_data)
+                chat_history_json=json.dumps(chat_history_list),
+                chat_history_in_thinking_json=json.dumps(chat_history_in_thinking_list),
+                chat_history_after_response_json=json.dumps(chat_history_after_response_list),
+                heartflow_data_json=json.dumps(self.heartflow_data),
+                reasoning_data_json=json.dumps(self.reasoning_data),
+            )
+            log_entry.save()
 
             return True
         except Exception as e:

src/chat/utils/statistic.py

@@ -2,10 +2,12 @@ from collections import defaultdict
 from datetime import datetime, timedelta
 from typing import Any, Dict, Tuple, List
 
+
 from src.common.logger import get_module_logger
 from src.manager.async_task_manager import AsyncTask
 
-from ...common.database import db
+from ...common.database.database import db  # This db is the Peewee database instance
+from ...common.database.database_model import OnlineTime, LLMUsage, Messages  # Import the Peewee model
 from src.manager.local_store_manager import local_storage
 
 logger = get_module_logger("maibot_statistic")
@@ -39,7 +41,7 @@ class OnlineTimeRecordTask(AsyncTask):
     def __init__(self):
         super().__init__(task_name="Online Time Record Task", run_interval=60)
 
-        self.record_id: str | None = None
+        self.record_id: int | None = None  # Changed to int for Peewee's default ID
         """记录ID"""
 
         self._init_database()  # 初始化数据库
@@ -47,49 +49,46 @@ class OnlineTimeRecordTask(AsyncTask):
     @staticmethod
     def _init_database():
         """初始化数据库"""
-        if "online_time" not in db.list_collection_names():
+        with db.atomic():  # Use atomic operations for schema changes
-            # 初始化数据库（在线时长）
+            OnlineTime.create_table(safe=True)  # Creates table if it doesn't exist, Peewee handles indexes from model
-            db.create_collection("online_time")
-            # 创建索引
-            if ("end_timestamp", 1) not in db.online_time.list_indexes():
-                db.online_time.create_index([("end_timestamp", 1)])
 
     async def run(self):
         try:
+            current_time = datetime.now()
+            extended_end_time = current_time + timedelta(minutes=1)
+
             if self.record_id:
                 # 如果有记录，则更新结束时间
-                db.online_time.update_one(
+                query = OnlineTime.update(end_timestamp=extended_end_time).where(OnlineTime.id == self.record_id)
-                    {"_id": self.record_id},
+                updated_rows = query.execute()
-                    {
+                if updated_rows == 0:
-                        "$set": {
+                    # Record might have been deleted or ID is stale, try to find/create
-                            "end_timestamp": datetime.now() + timedelta(minutes=1),
+                    self.record_id = None  # Reset record_id to trigger find/create logic below
-                        }
+
-                    },
+            if not self.record_id:  # Check again if record_id was reset or initially None
-                )
-            else:
                 # 如果没有记录，检查一分钟以内是否已有记录
-                current_time = datetime.now()
+                # Look for a record whose end_timestamp is recent enough to be considered ongoing
-                if recent_record := db.online_time.find_one(
+                recent_record = (
-                    {"end_timestamp": {"$gte": current_time - timedelta(minutes=1)}}
+                    OnlineTime.select()
-                ):
+                    .where(OnlineTime.end_timestamp >= (current_time - timedelta(minutes=1)))
-                    # 如果有记录，则更新结束时间
+                    .order_by(OnlineTime.end_timestamp.desc())
-                    self.record_id = recent_record["_id"]
+                    .first()
-                    db.online_time.update_one(
-                        {"_id": self.record_id},
-                        {
-                            "$set": {
-                                "end_timestamp": current_time + timedelta(minutes=1),
-                            }
-                        },
                 )
+
+                if recent_record:
+                    # 如果有记录，则更新结束时间
+                    self.record_id = recent_record.id
+                    recent_record.end_timestamp = extended_end_time
+                    recent_record.save()
                 else:
                     # 若没有记录，则插入新的在线时间记录
-                    self.record_id = db.online_time.insert_one(
+                    new_record = OnlineTime.create(
-                        {
+                        timestamp=current_time.timestamp(),  # 添加此行
-                            "start_timestamp": current_time,
+                        start_timestamp=current_time,
-                            "end_timestamp": current_time + timedelta(minutes=1),
+                        end_timestamp=extended_end_time,
-                        }
+                        duration=5,  # 初始时长为5分钟
-                    ).inserted_id
+                    )
+                    self.record_id = new_record.id
         except Exception as e:
             logger.error(f"在线时间记录失败，错误信息：{e}")
 
@@ -201,35 +200,28 @@ class StatisticOutputTask(AsyncTask):
 
         :param collect_period: 统计时间段
         """
-        if len(collect_period) <= 0:
+        if not collect_period:
             return {}
-        else:
+
         # 排序-按照时间段开始时间降序排列（最晚的时间段在前）
         collect_period.sort(key=lambda x: x[1], reverse=True)
 
         stats = {
             period_key: {
-                # 总LLM请求数
                 TOTAL_REQ_CNT: 0,
-                # 请求次数统计
                 REQ_CNT_BY_TYPE: defaultdict(int),
                 REQ_CNT_BY_USER: defaultdict(int),
                 REQ_CNT_BY_MODEL: defaultdict(int),
-                # 输入Token数
                 IN_TOK_BY_TYPE: defaultdict(int),
                 IN_TOK_BY_USER: defaultdict(int),
                 IN_TOK_BY_MODEL: defaultdict(int),
-                # 输出Token数
                 OUT_TOK_BY_TYPE: defaultdict(int),
                 OUT_TOK_BY_USER: defaultdict(int),
                 OUT_TOK_BY_MODEL: defaultdict(int),
-                # 总Token数
                 TOTAL_TOK_BY_TYPE: defaultdict(int),
                 TOTAL_TOK_BY_USER: defaultdict(int),
                 TOTAL_TOK_BY_MODEL: defaultdict(int),
-                # 总开销
                 TOTAL_COST: 0.0,
-                # 请求开销统计
                 COST_BY_TYPE: defaultdict(float),
                 COST_BY_USER: defaultdict(float),
                 COST_BY_MODEL: defaultdict(float),
@@ -238,26 +230,26 @@ class StatisticOutputTask(AsyncTask):
         }
 
         # 以最早的时间戳为起始时间获取记录
-        for record in db.llm_usage.find({"timestamp": {"$gte": collect_period[-1][1]}}):
+        # Assuming LLMUsage.timestamp is a DateTimeField
-            record_timestamp = record.get("timestamp")
+        query_start_time = collect_period[-1][1]
+        for record in LLMUsage.select().where(LLMUsage.timestamp >= query_start_time):
+            record_timestamp = record.timestamp  # This is already a datetime object
             for idx, (_, period_start) in enumerate(collect_period):
                 if record_timestamp >= period_start:
-                    # 如果记录时间在当前时间段内，则它一定在更早的时间段内
-                    # 因此，我们可以直接跳过更早的时间段的判断，直接更新当前以及更早时间段的统计数据
                     for period_key, _ in collect_period[idx:]:
                         stats[period_key][TOTAL_REQ_CNT] += 1
 
-                        request_type = record.get("request_type", "unknown")  # 请求类型
+                        request_type = record.request_type or "unknown"
-                        user_id = str(record.get("user_id", "unknown"))  # 用户ID
+                        user_id = record.user_id or "unknown"  # user_id is TextField, already string
-                        model_name = record.get("model_name", "unknown")  # 模型名称
+                        model_name = record.model_name or "unknown"
 
                         stats[period_key][REQ_CNT_BY_TYPE][request_type] += 1
                         stats[period_key][REQ_CNT_BY_USER][user_id] += 1
                         stats[period_key][REQ_CNT_BY_MODEL][model_name] += 1
 
-                        prompt_tokens = record.get("prompt_tokens", 0)  # 输入Token数
+                        prompt_tokens = record.prompt_tokens or 0
-                        completion_tokens = record.get("completion_tokens", 0)  # 输出Token数
+                        completion_tokens = record.completion_tokens or 0
-                        total_tokens = prompt_tokens + completion_tokens  # Token总数 = 输入Token数 + 输出Token数
+                        total_tokens = prompt_tokens + completion_tokens
 
                         stats[period_key][IN_TOK_BY_TYPE][request_type] += prompt_tokens
                         stats[period_key][IN_TOK_BY_USER][user_id] += prompt_tokens
@@ -271,13 +263,12 @@ class StatisticOutputTask(AsyncTask):
                         stats[period_key][TOTAL_TOK_BY_USER][user_id] += total_tokens
                         stats[period_key][TOTAL_TOK_BY_MODEL][model_name] += total_tokens
 
-                        cost = record.get("cost", 0.0)
+                        cost = record.cost or 0.0
                         stats[period_key][TOTAL_COST] += cost
                         stats[period_key][COST_BY_TYPE][request_type] += cost
                         stats[period_key][COST_BY_USER][user_id] += cost
                         stats[period_key][COST_BY_MODEL][model_name] += cost
-                    break  # 取消更早时间段的判断
+                    break
-
         return stats
 
     @staticmethod
@@ -287,39 +278,38 @@ class StatisticOutputTask(AsyncTask):
 
         :param collect_period: 统计时间段
         """
-        if len(collect_period) <= 0:
+        if not collect_period:
             return {}
-        else:
+
-            # 排序-按照时间段开始时间降序排列（最晚的时间段在前）
         collect_period.sort(key=lambda x: x[1], reverse=True)
 
         stats = {
             period_key: {
-                # 在线时间统计
                 ONLINE_TIME: 0.0,
             }
             for period_key, _ in collect_period
         }
 
-        # 统计在线时间
+        query_start_time = collect_period[-1][1]
-        for record in db.online_time.find({"end_timestamp": {"$gte": collect_period[-1][1]}}):
+        # Assuming OnlineTime.end_timestamp is a DateTimeField
-            end_timestamp: datetime = record.get("end_timestamp")
+        for record in OnlineTime.select().where(OnlineTime.end_timestamp >= query_start_time):
-            for idx, (_, period_start) in enumerate(collect_period):
+            # record.end_timestamp and record.start_timestamp are datetime objects
-                if end_timestamp >= period_start:
+            record_end_timestamp = record.end_timestamp
-                    # 由于end_timestamp会超前标记时间，所以我们需要判断是否晚于当前时间，如果是，则使用当前时间作为结束时间
+            record_start_timestamp = record.start_timestamp
-                    end_timestamp = min(end_timestamp, now)
-                    # 如果记录时间在当前时间段内，则它一定在更早的时间段内
-                    # 因此，我们可以直接跳过更早的时间段的判断，直接更新当前以及更早时间段的统计数据
-                    for period_key, _period_start in collect_period[idx:]:
-                        start_timestamp: datetime = record.get("start_timestamp")
-                        if start_timestamp < _period_start:
-                            # 如果开始时间在查询边界之前，则使用开始时间
-                            stats[period_key][ONLINE_TIME] += (end_timestamp - _period_start).total_seconds()
-                        else:
-                            # 否则，使用开始时间
-                            stats[period_key][ONLINE_TIME] += (end_timestamp - start_timestamp).total_seconds()
-                    break  # 取消更早时间段的判断
 
+            for idx, (_, period_boundary_start) in enumerate(collect_period):
+                if record_end_timestamp >= period_boundary_start:
+                    # Calculate effective end time for this record in relation to 'now'
+                    effective_end_time = min(record_end_timestamp, now)
+
+                    for period_key, current_period_start_time in collect_period[idx:]:
+                        # Determine the portion of the record that falls within this specific statistical period
+                        overlap_start = max(record_start_timestamp, current_period_start_time)
+                        overlap_end = effective_end_time  # Already capped by 'now' and record's own end
+
+                        if overlap_end > overlap_start:
+                            stats[period_key][ONLINE_TIME] += (overlap_end - overlap_start).total_seconds()
+                    break
         return stats
 
     def _collect_message_count_for_period(self, collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]:
@@ -328,55 +318,57 @@ class StatisticOutputTask(AsyncTask):
 
         :param collect_period: 统计时间段
         """
-        if len(collect_period) <= 0:
+        if not collect_period:
             return {}
-        else:
+
-            # 排序-按照时间段开始时间降序排列（最晚的时间段在前）
         collect_period.sort(key=lambda x: x[1], reverse=True)
 
         stats = {
             period_key: {
-                # 消息统计
                 TOTAL_MSG_CNT: 0,
                 MSG_CNT_BY_CHAT: defaultdict(int),
             }
             for period_key, _ in collect_period
         }
 
-        # 统计消息量
+        query_start_timestamp = collect_period[-1][1].timestamp()  # Messages.time is a DoubleField (timestamp)
-        for message in db.messages.find({"time": {"$gte": collect_period[-1][1].timestamp()}}):
+        for message in Messages.select().where(Messages.time >= query_start_timestamp):
-            chat_info = message.get("chat_info", None)  # 聊天信息
+            message_time_ts = message.time  # This is a float timestamp
-            user_info = message.get("user_info", None)  # 用户信息（消息发送人）
-            message_time = message.get("time", 0)  # 消息时间
 
-            group_info = chat_info.get("group_info") if chat_info else None  # 尝试获取群聊信息
+            chat_id = None
-            if group_info is not None:
+            chat_name = None
-                # 若有群聊信息
+
-                chat_id = f"g{group_info.get('group_id')}"
+            # Logic based on Peewee model structure, aiming to replicate original intent
-                chat_name = group_info.get("group_name", f"群{group_info.get('group_id')}")
+            if message.chat_info_group_id:
-            elif user_info:
+                chat_id = f"g{message.chat_info_group_id}"
-                # 若没有群聊信息，则尝试获取用户信息
+                chat_name = message.chat_info_group_name or f"群{message.chat_info_group_id}"
-                chat_id = f"u{user_info['user_id']}"
+            elif message.user_id:  # Fallback to sender's info for chat_id if not a group_info based chat
-                chat_name = user_info["user_nickname"]
+                # This uses the message SENDER's ID as per original logic's fallback
+                chat_id = f"u{message.user_id}"  # SENDER's user_id
+                chat_name = message.user_nickname  # SENDER's nickname
             else:
-                continue  # 如果没有群组信息也没有用户信息，则跳过
+                # If neither group_id nor sender_id is available for chat identification
+                logger.warning(
+                    f"Message (PK: {message.id if hasattr(message, 'id') else 'N/A'}) lacks group_id and user_id for chat stats."
+                )
+                continue
 
+            if not chat_id:  # Should not happen if above logic is correct
+                continue
+
+            # Update name_mapping
             if chat_id in self.name_mapping:
-                if chat_name != self.name_mapping[chat_id][0] and message_time > self.name_mapping[chat_id][1]:
+                if chat_name != self.name_mapping[chat_id][0] and message_time_ts > self.name_mapping[chat_id][1]:
-                    # 如果用户名称不同，且新消息时间晚于之前记录的时间，则更新用户名称
+                    self.name_mapping[chat_id] = (chat_name, message_time_ts)
-                    self.name_mapping[chat_id] = (chat_name, message_time)
             else:
-                self.name_mapping[chat_id] = (chat_name, message_time)
+                self.name_mapping[chat_id] = (chat_name, message_time_ts)
 
-            for idx, (_, period_start) in enumerate(collect_period):
+            for idx, (_, period_start_dt) in enumerate(collect_period):
-                if message_time >= period_start.timestamp():
+                if message_time_ts >= period_start_dt.timestamp():
-                    # 如果记录时间在当前时间段内，则它一定在更早的时间段内
-                    # 因此，我们可以直接跳过更早的时间段的判断，直接更新当前以及更早时间段的统计数据
                     for period_key, _ in collect_period[idx:]:
                         stats[period_key][TOTAL_MSG_CNT] += 1
                         stats[period_key][MSG_CNT_BY_CHAT][chat_id] += 1
                     break
-
         return stats
 
     def _collect_all_statistics(self, now: datetime) -> Dict[str, Dict[str, Any]]:

src/chat/utils/utils.py

@@ -13,7 +13,7 @@ from src.manager.mood_manager import mood_manager
 from ..message_receive.message import MessageRecv
 from ..models.utils_model import LLMRequest
 from .typo_generator import ChineseTypoGenerator
-from ...common.database import db
+from ...common.database.database import db
 from ...config.config import global_config
 
 logger = get_module_logger("chat_utils")
@@ -43,8 +43,8 @@ def db_message_to_str(message_dict: dict) -> str:
 
 def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
     """检查消息是否提到了机器人"""
-    keywords = [global_config.BOT_NICKNAME]
+    keywords = [global_config.bot.nickname]
-    nicknames = global_config.BOT_ALIAS_NAMES
+    nicknames = global_config.bot.alias_names
     reply_probability = 0.0
     is_at = False
     is_mentioned = False
@@ -64,18 +64,18 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
             )
 
     # 判断是否被@
-    if re.search(f"@[\s\S]*?（id:{global_config.BOT_QQ}）", message.processed_plain_text):
+    if re.search(f"@[\s\S]*?（id:{global_config.bot.qq_account}）", message.processed_plain_text):
         is_at = True
         is_mentioned = True
 
-    if is_at and global_config.at_bot_inevitable_reply:
+    if is_at and global_config.normal_chat.at_bot_inevitable_reply:
         reply_probability = 1.0
         logger.info("被@，回复概率设置为100%")
     else:
         if not is_mentioned:
             # 判断是否被回复
             if re.match(
-                f"\[回复 [\s\S]*?\({str(global_config.BOT_QQ)}\)：[\s\S]*?]，说：", message.processed_plain_text
+                f"\[回复 [\s\S]*?\({str(global_config.bot.qq_account)}\)：[\s\S]*?]，说：", message.processed_plain_text
             ):
                 is_mentioned = True
             else:
@@ -88,7 +88,7 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
                 for nickname in nicknames:
                     if nickname in message_content:
                         is_mentioned = True
-        if is_mentioned and global_config.mentioned_bot_inevitable_reply:
+        if is_mentioned and global_config.normal_chat.mentioned_bot_inevitable_reply:
             reply_probability = 1.0
             logger.info("被提及，回复概率设置为100%")
     return is_mentioned, reply_probability
@@ -96,7 +96,8 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
 
 async def get_embedding(text, request_type="embedding"):
     """获取文本的embedding向量"""
-    llm = LLMRequest(model=global_config.embedding, request_type=request_type)
+    # TODO: API-Adapter修改标记
+    llm = LLMRequest(model=global_config.model.embedding, request_type=request_type)
     # return llm.get_embedding_sync(text)
     try:
         embedding = await llm.get_embedding(text)
@@ -163,7 +164,7 @@ def get_recent_group_speaker(chat_stream_id: int, sender, limit: int = 12) -> li
         user_info = UserInfo.from_dict(msg_db_data["user_info"])
         if (
             (user_info.platform, user_info.user_id) != sender
-            and user_info.user_id != global_config.BOT_QQ
+            and user_info.user_id != global_config.bot.qq_account
             and (user_info.platform, user_info.user_id, user_info.user_nickname) not in who_chat_in_group
             and len(who_chat_in_group) < 5
         ):  # 排除重复，排除消息发送者，排除bot，限制加载的关系数目
@@ -321,7 +322,7 @@ def random_remove_punctuation(text: str) -> str:
 
 def process_llm_response(text: str) -> list[str]:
     # 先保护颜文字
-    if global_config.enable_kaomoji_protection:
+    if global_config.response_splitter.enable_kaomoji_protection:
         protected_text, kaomoji_mapping = protect_kaomoji(text)
         logger.trace(f"保护颜文字后的文本: {protected_text}")
     else:
@@ -340,8 +341,8 @@ def process_llm_response(text: str) -> list[str]:
     logger.debug(f"{text}去除括号处理后的文本: {cleaned_text}")
 
     # 对清理后的文本进行进一步处理
-    max_length = global_config.response_max_length * 2
+    max_length = global_config.response_splitter.max_length * 2
-    max_sentence_num = global_config.response_max_sentence_num
+    max_sentence_num = global_config.response_splitter.max_sentence_num
     # 如果基本上是中文，则进行长度过滤
     if get_western_ratio(cleaned_text) < 0.1:
         if len(cleaned_text) > max_length:
@@ -349,20 +350,20 @@ def process_llm_response(text: str) -> list[str]:
             return ["懒得说"]
 
     typo_generator = ChineseTypoGenerator(
-        error_rate=global_config.chinese_typo_error_rate,
+        error_rate=global_config.chinese_typo.error_rate,
-        min_freq=global_config.chinese_typo_min_freq,
+        min_freq=global_config.chinese_typo.min_freq,
-        tone_error_rate=global_config.chinese_typo_tone_error_rate,
+        tone_error_rate=global_config.chinese_typo.tone_error_rate,
-        word_replace_rate=global_config.chinese_typo_word_replace_rate,
+        word_replace_rate=global_config.chinese_typo.word_replace_rate,
     )
 
-    if global_config.enable_response_splitter:
+    if global_config.response_splitter.enable:
         split_sentences = split_into_sentences_w_remove_punctuation(cleaned_text)
     else:
         split_sentences = [cleaned_text]
 
     sentences = []
     for sentence in split_sentences:
-        if global_config.chinese_typo_enable:
+        if global_config.chinese_typo.enable:
             typoed_text, typo_corrections = typo_generator.create_typo_sentence(sentence)
             sentences.append(typoed_text)
             if typo_corrections:
@@ -372,7 +373,7 @@ def process_llm_response(text: str) -> list[str]:
 
     if len(sentences) > max_sentence_num:
         logger.warning(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
-        return [f"{global_config.BOT_NICKNAME}不知道哦"]
+        return [f"{global_config.bot.nickname}不知道哦"]
 
     # if extracted_contents:
     #     for content in extracted_contents:

src/chat/utils/utils_image.py

@@ -8,7 +8,8 @@ import io
 import numpy as np
 
 
-from ...common.database import db
+from ...common.database.database import db
+from ...common.database.database_model import Images, ImageDescriptions
 from ...config.config import global_config
 from ..models.utils_model import LLMRequest
 
@@ -32,40 +33,23 @@ class ImageManager:
 
     def __init__(self):
         if not self._initialized:
-            self._ensure_image_collection()
-            self._ensure_description_collection()
             self._ensure_image_dir()
+
+            self._initialized = True
+            self._llm = LLMRequest(model=global_config.model.vlm, temperature=0.4, max_tokens=300, request_type="image")
+
+            try:
+                db.connect(reuse_if_open=True)
+                db.create_tables([Images, ImageDescriptions], safe=True)
+            except Exception as e:
+                logger.error(f"数据库连接或表创建失败: {e}")
+
             self._initialized = True
-            self._llm = LLMRequest(model=global_config.vlm, temperature=0.4, max_tokens=300, request_type="image")
 
     def _ensure_image_dir(self):
         """确保图像存储目录存在"""
         os.makedirs(self.IMAGE_DIR, exist_ok=True)
 
-    @staticmethod
-    def _ensure_image_collection():
-        """确保images集合存在并创建索引"""
-        if "images" not in db.list_collection_names():
-            db.create_collection("images")
-
-        # 删除旧索引
-        db.images.drop_indexes()
-        # 创建新的复合索引
-        db.images.create_index([("hash", 1), ("type", 1)], unique=True)
-        db.images.create_index([("url", 1)])
-        db.images.create_index([("path", 1)])
-
-    @staticmethod
-    def _ensure_description_collection():
-        """确保image_descriptions集合存在并创建索引"""
-        if "image_descriptions" not in db.list_collection_names():
-            db.create_collection("image_descriptions")
-
-        # 删除旧索引
-        db.image_descriptions.drop_indexes()
-        # 创建新的复合索引
-        db.image_descriptions.create_index([("hash", 1), ("type", 1)], unique=True)
-
     @staticmethod
     def _get_description_from_db(image_hash: str, description_type: str) -> Optional[str]:
         """从数据库获取图片描述
@@ -77,8 +61,14 @@ class ImageManager:
         Returns:
             Optional[str]: 描述文本，如果不存在则返回None
         """
-        result = db.image_descriptions.find_one({"hash": image_hash, "type": description_type})
+        try:
-        return result["description"] if result else None
+            record = ImageDescriptions.get_or_none(
+                (ImageDescriptions.image_description_hash == image_hash) & (ImageDescriptions.type == description_type)
+            )
+            return record.description if record else None
+        except Exception as e:
+            logger.error(f"从数据库获取描述失败 (Peewee): {str(e)}")
+            return None
 
     @staticmethod
     def _save_description_to_db(image_hash: str, description: str, description_type: str) -> None:
@@ -90,20 +80,17 @@ class ImageManager:
             description_type: 描述类型 ('emoji' 或 'image')
         """
         try:
-            db.image_descriptions.update_one(
+            current_timestamp = time.time()
-                {"hash": image_hash, "type": description_type},
+            defaults = {"description": description, "timestamp": current_timestamp}
-                {
+            desc_obj, created = ImageDescriptions.get_or_create(
-                    "$set": {
+                hash=image_hash, type=description_type, defaults=defaults
-                        "description": description,
-                        "timestamp": int(time.time()),
-                        "hash": image_hash,  # 确保hash字段存在
-                        "type": description_type,  # 确保type字段存在
-                    }
-                },
-                upsert=True,
             )
+            if not created:  # 如果记录已存在，则更新
+                desc_obj.description = description
+                desc_obj.timestamp = current_timestamp
+                desc_obj.save()
         except Exception as e:
-            logger.error(f"保存描述到数据库失败: {str(e)}")
+            logger.error(f"保存描述到数据库失败 (Peewee): {str(e)}")
 
     async def get_emoji_description(self, image_base64: str) -> str:
         """获取表情包描述，带查重和保存功能"""
@@ -116,51 +103,64 @@ class ImageManager:
             # 查询缓存的描述
             cached_description = self._get_description_from_db(image_hash, "emoji")
             if cached_description:
-                # logger.debug(f"缓存表情包描述: {cached_description}")
                 return f"[表情包，含义看起来是：{cached_description}]"
 
             # 调用AI获取描述
             if image_format == "gif" or image_format == "GIF":
-                image_base64 = self.transform_gif(image_base64)
+                image_base64_processed = self.transform_gif(image_base64)
+                if image_base64_processed is None:
+                    logger.warning("GIF转换失败，无法获取描述")
+                    return "[表情包(GIF处理失败)]"
                 prompt = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，使用1-2个词描述一下表情包表达的情感和内容，简短一些"
-                description, _ = await self._llm.generate_response_for_image(prompt, image_base64, "jpg")
+                description, _ = await self._llm.generate_response_for_image(prompt, image_base64_processed, "jpg")
             else:
                 prompt = "这是一个表情包，请用使用几个词描述一下表情包所表达的情感和内容，简短一些"
                 description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
 
+            if description is None:
+                logger.warning("AI未能生成表情包描述")
+                return "[表情包(描述生成失败)]"
+
+            # 再次检查缓存，防止并发写入时重复生成
             cached_description = self._get_description_from_db(image_hash, "emoji")
             if cached_description:
                 logger.warning(f"虽然生成了描述，但是找到缓存表情包描述: {cached_description}")
                 return f"[表情包，含义看起来是：{cached_description}]"
 
             # 根据配置决定是否保存图片
-            if global_config.save_emoji:
+            if global_config.emoji.save_emoji:
                 # 生成文件名和路径
-                timestamp = int(time.time())
+                current_timestamp = time.time()
-                filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
+                filename = f"{int(current_timestamp)}_{image_hash[:8]}.{image_format}"
-                if not os.path.exists(os.path.join(self.IMAGE_DIR, "emoji")):
+                emoji_dir = os.path.join(self.IMAGE_DIR, "emoji")
-                    os.makedirs(os.path.join(self.IMAGE_DIR, "emoji"))
+                os.makedirs(emoji_dir, exist_ok=True)
-                file_path = os.path.join(self.IMAGE_DIR, "emoji", filename)
+                file_path = os.path.join(emoji_dir, filename)
 
                 try:
                     # 保存文件
                     with open(file_path, "wb") as f:
                         f.write(image_bytes)
 
-                    # 保存到数据库
+                    # 保存到数据库 (Images表)
-                    image_doc = {
+                    try:
-                        "hash": image_hash,
+                        img_obj = Images.get((Images.emoji_hash == image_hash) & (Images.type == "emoji"))
-                        "path": file_path,
+                        img_obj.path = file_path
-                        "type": "emoji",
+                        img_obj.description = description
-                        "description": description,
+                        img_obj.timestamp = current_timestamp
-                        "timestamp": timestamp,
+                        img_obj.save()
-                    }
+                    except Images.DoesNotExist:
-                    db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True)
+                        Images.create(
-                    logger.trace(f"保存表情包: {file_path}")
+                            hash=image_hash,
+                            path=file_path,
+                            type="emoji",
+                            description=description,
+                            timestamp=current_timestamp,
+                        )
+                    logger.trace(f"保存表情包元数据: {file_path}")
                 except Exception as e:
-                    logger.error(f"保存表情包文件失败: {str(e)}")
+                    logger.error(f"保存表情包文件或元数据失败: {str(e)}")
 
-            # 保存描述到数据库
+            # 保存描述到数据库 (ImageDescriptions表)
             self._save_description_to_db(image_hash, description, "emoji")
 
             return f"[表情包：{description}]"
@@ -188,6 +188,11 @@ class ImageManager:
             )
             description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
 
+            if description is None:
+                logger.warning("AI未能生成图片描述")
+                return "[图片(描述生成失败)]"
+
+            # 再次检查缓存
             cached_description = self._get_description_from_db(image_hash, "image")
             if cached_description:
                 logger.warning(f"虽然生成了描述，但是找到缓存图片描述 {cached_description}")
@@ -195,38 +200,40 @@ class ImageManager:
 
             logger.debug(f"描述是{description}")
 
-            if description is None:
-                logger.warning("AI未能生成图片描述")
-                return "[图片]"
-
             # 根据配置决定是否保存图片
-            if global_config.save_pic:
+            if global_config.emoji.save_pic:
                 # 生成文件名和路径
-                timestamp = int(time.time())
+                current_timestamp = time.time()
-                filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
+                filename = f"{int(current_timestamp)}_{image_hash[:8]}.{image_format}"
-                if not os.path.exists(os.path.join(self.IMAGE_DIR, "image")):
+                image_dir = os.path.join(self.IMAGE_DIR, "image")
-                    os.makedirs(os.path.join(self.IMAGE_DIR, "image"))
+                os.makedirs(image_dir, exist_ok=True)
-                file_path = os.path.join(self.IMAGE_DIR, "image", filename)
+                file_path = os.path.join(image_dir, filename)
 
                 try:
                     # 保存文件
                     with open(file_path, "wb") as f:
                         f.write(image_bytes)
 
-                    # 保存到数据库
+                    # 保存到数据库 (Images表)
-                    image_doc = {
+                    try:
-                        "hash": image_hash,
+                        img_obj = Images.get((Images.emoji_hash == image_hash) & (Images.type == "image"))
-                        "path": file_path,
+                        img_obj.path = file_path
-                        "type": "image",
+                        img_obj.description = description
-                        "description": description,
+                        img_obj.timestamp = current_timestamp
-                        "timestamp": timestamp,
+                        img_obj.save()
-                    }
+                    except Images.DoesNotExist:
-                    db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True)
+                        Images.create(
-                    logger.trace(f"保存图片: {file_path}")
+                            hash=image_hash,
+                            path=file_path,
+                            type="image",
+                            description=description,
+                            timestamp=current_timestamp,
+                        )
+                    logger.trace(f"保存图片元数据: {file_path}")
                 except Exception as e:
-                    logger.error(f"保存图片文件失败: {str(e)}")
+                    logger.error(f"保存图片文件或元数据失败: {str(e)}")
 
-            # 保存描述到数据库
+            # 保存描述到数据库 (ImageDescriptions表)
             self._save_description_to_db(image_hash, description, "image")
 
             return f"[图片：{description}]"

src/chat/zhishi/knowledge_library.py

@@ -16,7 +16,7 @@ root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
 sys.path.append(root_path)
 
 # 现在可以导入src模块
-from src.common.database import db  # noqa E402
+from common.database.database import db  # noqa E402
 
 
 # 加载根目录下的env.edv文件

src/common/database/database.py

@@ -1,5 +1,6 @@
 import os
 from pymongo import MongoClient
+from peewee import SqliteDatabase
 from pymongo.database import Database
 from rich.traceback import install
 
@@ -57,4 +58,15 @@ class DBWrapper:
 
 
 # 全局数据库访问点
-db: Database = DBWrapper()
+memory_db: Database = DBWrapper()
+
+# 定义数据库文件路径
+ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+_DB_DIR = os.path.join(ROOT_PATH, "data")
+_DB_FILE = os.path.join(_DB_DIR, "MaiBot.db")
+
+# 确保数据库目录存在
+os.makedirs(_DB_DIR, exist_ok=True)
+
+# 全局 Peewee SQLite 数据库访问点
+db = SqliteDatabase(_DB_FILE)

src/common/database/database_model.py

@@ -0,0 +1,358 @@
+from peewee import Model, DoubleField, IntegerField, BooleanField, TextField, FloatField, DateTimeField
+from .database import db
+import datetime
+from ..logger_manager import get_logger
+
+logger = get_logger("database_model")
+# 请在此处定义您的数据库实例。
+# 您需要取消注释并配置适合您的数据库的部分。
+# 例如，对于 SQLite:
+# db = SqliteDatabase('MaiBot.db')
+#
+# 对于 PostgreSQL:
+# db = PostgresqlDatabase('your_db_name', user='your_user', password='your_password',
+#                         host='localhost', port=5432)
+#
+# 对于 MySQL:
+# db = MySQLDatabase('your_db_name', user='your_user', password='your_password',
+#                    host='localhost', port=3306)
+
+
+# 定义一个基础模型是一个好习惯，所有其他模型都应继承自它。
+# 这允许您在一个地方为所有模型指定数据库。
+class BaseModel(Model):
+    class Meta:
+        # 将下面的 'db' 替换为您实际的数据库实例变量名。
+        database = db  # 例如: database = my_actual_db_instance
+        pass  # 在用户定义数据库实例之前，此处为占位符
+
+
+class ChatStreams(BaseModel):
+    """
+    用于存储流式记录数据的模型，类似于提供的 MongoDB 结构。
+    """
+
+    # stream_id: "a544edeb1a9b73e3e1d77dff36e41264"
+    # 假设 stream_id 是唯一的，并为其创建索引以提高查询性能。
+    stream_id = TextField(unique=True, index=True)
+
+    # create_time: 1746096761.4490178 (时间戳，精确到小数点后7位)
+    # DoubleField 用于存储浮点数，适合此类时间戳。
+    create_time = DoubleField()
+
+    # group_info 字段:
+    #   platform: "qq"
+    #   group_id: "941657197"
+    #   group_name: "测试"
+    group_platform = TextField()
+    group_id = TextField()
+    group_name = TextField()
+
+    # last_active_time: 1746623771.4825106 (时间戳，精确到小数点后7位)
+    last_active_time = DoubleField()
+
+    # platform: "qq" (顶层平台字段)
+    platform = TextField()
+
+    # user_info 字段:
+    #   platform: "qq"
+    #   user_id: "1787882683"
+    #   user_nickname: "墨梓柒(IceSakurary)"
+    #   user_cardname: ""
+    user_platform = TextField()
+    user_id = TextField()
+    user_nickname = TextField()
+    # user_cardname 可能为空字符串或不存在，设置 null=True 更具灵活性。
+    user_cardname = TextField(null=True)
+
+    class Meta:
+        # 如果 BaseModel.Meta.database 已设置，则此模型将继承该数据库配置。
+        # 如果不使用带有数据库实例的 BaseModel，或者想覆盖它，
+        # 请取消注释并在下面设置数据库实例：
+        # database = db
+        table_name = "chat_streams"  # 可选：明确指定数据库中的表名
+
+
+class LLMUsage(BaseModel):
+    """
+    用于存储 API 使用日志数据的模型。
+    """
+
+    model_name = TextField(index=True)  # 添加索引
+    user_id = TextField(index=True)  # 添加索引
+    request_type = TextField(index=True)  # 添加索引
+    endpoint = TextField()
+    prompt_tokens = IntegerField()
+    completion_tokens = IntegerField()
+    total_tokens = IntegerField()
+    cost = DoubleField()
+    status = TextField()
+    timestamp = DateTimeField(index=True)  # 更改为 DateTimeField 并添加索引
+
+    class Meta:
+        # 如果 BaseModel.Meta.database 已设置，则此模型将继承该数据库配置。
+        # database = db
+        table_name = "llm_usage"
+
+
+class Emoji(BaseModel):
+    """表情包"""
+
+    full_path = TextField(unique=True, index=True)  # 文件的完整路径 (包括文件名)
+    format = TextField()  # 图片格式
+    emoji_hash = TextField(index=True)  # 表情包的哈希值
+    description = TextField()  # 表情包的描述
+    query_count = IntegerField(default=0)  # 查询次数（用于统计表情包被查询描述的次数）
+    is_registered = BooleanField(default=False)  # 是否已注册
+    is_banned = BooleanField(default=False)  # 是否被禁止注册
+    # emotion: list[str]  # 表情包的情感标签 - 存储为文本，应用层处理序列化/反序列化
+    emotion = TextField(null=True)
+    record_time = FloatField()  # 记录时间（被创建的时间）
+    register_time = FloatField(null=True)  # 注册时间（被注册为可用表情包的时间）
+    usage_count = IntegerField(default=0)  # 使用次数（被使用的次数）
+    last_used_time = FloatField(null=True)  # 上次使用时间
+
+    class Meta:
+        # database = db # 继承自 BaseModel
+        table_name = "emoji"
+
+
+class Messages(BaseModel):
+    """
+    用于存储消息数据的模型。
+    """
+
+    message_id = TextField(index=True)  # 消息 ID (更改自 IntegerField)
+    time = DoubleField()  # 消息时间戳
+
+    chat_id = TextField(index=True)  # 对应的 ChatStreams stream_id
+
+    # 从 chat_info 扁平化而来的字段
+    chat_info_stream_id = TextField()
+    chat_info_platform = TextField()
+    chat_info_user_platform = TextField()
+    chat_info_user_id = TextField()
+    chat_info_user_nickname = TextField()
+    chat_info_user_cardname = TextField(null=True)
+    chat_info_group_platform = TextField(null=True)  # 群聊信息可能不存在
+    chat_info_group_id = TextField(null=True)
+    chat_info_group_name = TextField(null=True)
+    chat_info_create_time = DoubleField()
+    chat_info_last_active_time = DoubleField()
+
+    # 从顶层 user_info 扁平化而来的字段 (消息发送者信息)
+    user_platform = TextField()
+    user_id = TextField()
+    user_nickname = TextField()
+    user_cardname = TextField(null=True)
+
+    processed_plain_text = TextField(null=True)  # 处理后的纯文本消息
+    detailed_plain_text = TextField(null=True)  # 详细的纯文本消息
+    memorized_times = IntegerField(default=0)  # 被记忆的次数
+
+    class Meta:
+        # database = db # 继承自 BaseModel
+        table_name = "messages"
+
+
+class Images(BaseModel):
+    """
+    用于存储图像信息的模型。
+    """
+
+    emoji_hash = TextField(index=True)  # 图像的哈希值
+    description = TextField(null=True)  # 图像的描述
+    path = TextField(unique=True)  # 图像文件的路径
+    timestamp = FloatField()  # 时间戳
+    type = TextField()  # 图像类型，例如 "emoji"
+
+    class Meta:
+        # database = db # 继承自 BaseModel
+        table_name = "images"
+
+
+class ImageDescriptions(BaseModel):
+    """
+    用于存储图像描述信息的模型。
+    """
+
+    type = TextField()  # 类型，例如 "emoji"
+    image_description_hash = TextField(index=True)  # 图像的哈希值
+    description = TextField()  # 图像的描述
+    timestamp = FloatField()  # 时间戳
+
+    class Meta:
+        # database = db # 继承自 BaseModel
+        table_name = "image_descriptions"
+
+
+class OnlineTime(BaseModel):
+    """
+    用于存储在线时长记录的模型。
+    """
+
+    # timestamp: "$date": "2025-05-01T18:52:18.191Z" (存储为字符串)
+    timestamp = TextField(default=datetime.datetime.now)  # 时间戳
+    duration = IntegerField()  # 时长，单位分钟
+    start_timestamp = DateTimeField(default=datetime.datetime.now)
+    end_timestamp = DateTimeField(index=True)
+
+    class Meta:
+        # database = db # 继承自 BaseModel
+        table_name = "online_time"
+
+
+class PersonInfo(BaseModel):
+    """
+    用于存储个人信息数据的模型。
+    """
+
+    person_id = TextField(unique=True, index=True)  # 个人唯一ID
+    person_name = TextField(null=True)  # 个人名称 (允许为空)
+    name_reason = TextField(null=True)  # 名称设定的原因
+    platform = TextField()  # 平台
+    user_id = TextField(index=True)  # 用户ID
+    nickname = TextField()  # 用户昵称
+    relationship_value = IntegerField(default=0)  # 关系值
+    know_time = FloatField()  # 认识时间 (时间戳)
+    msg_interval = IntegerField()  # 消息间隔
+    # msg_interval_list: 存储为 JSON 字符串的列表
+    msg_interval_list = TextField(null=True)
+
+    class Meta:
+        # database = db # 继承自 BaseModel
+        table_name = "person_info"
+
+
+class Knowledges(BaseModel):
+    """
+    用于存储知识库条目的模型。
+    """
+
+    content = TextField()  # 知识内容的文本
+    embedding = TextField()  # 知识内容的嵌入向量，存储为 JSON 字符串的浮点数列表
+    # 可以添加其他元数据字段，如 source, create_time 等
+
+    class Meta:
+        # database = db # 继承自 BaseModel
+        table_name = "knowledges"
+
+
+class ThinkingLog(BaseModel):
+    chat_id = TextField(index=True)
+    trigger_text = TextField(null=True)
+    response_text = TextField(null=True)
+
+    # Store complex dicts/lists as JSON strings
+    trigger_info_json = TextField(null=True)
+    response_info_json = TextField(null=True)
+    timing_results_json = TextField(null=True)
+    chat_history_json = TextField(null=True)
+    chat_history_in_thinking_json = TextField(null=True)
+    chat_history_after_response_json = TextField(null=True)
+    heartflow_data_json = TextField(null=True)
+    reasoning_data_json = TextField(null=True)
+
+    # Add a timestamp for the log entry itself
+    # Ensure you have: from peewee import DateTimeField
+    # And: import datetime
+    created_at = DateTimeField(default=datetime.datetime.now)
+
+    class Meta:
+        table_name = "thinking_logs"
+
+
+class RecalledMessages(BaseModel):
+    """
+    用于存储撤回消息记录的模型。
+    """
+
+    message_id = TextField(index=True)  # 被撤回的消息 ID
+    time = DoubleField()  # 撤回操作发生的时间戳
+    stream_id = TextField()  # 对应的 ChatStreams stream_id
+
+    class Meta:
+        table_name = "recalled_messages"
+
+
+def create_tables():
+    """
+    创建所有在模型中定义的数据库表。
+    """
+    with db:
+        db.create_tables(
+            [
+                ChatStreams,
+                LLMUsage,
+                Emoji,
+                Messages,
+                Images,
+                ImageDescriptions,
+                OnlineTime,
+                PersonInfo,
+                Knowledges,
+                ThinkingLog,
+                RecalledMessages,  # 添加新模型
+            ]
+        )
+
+
+def initialize_database():
+    """
+    检查所有定义的表是否存在，如果不存在则创建它们。
+    检查所有表的所有字段是否存在，如果缺失则警告用户并退出程序。
+    """
+    import sys
+
+    models = [
+        ChatStreams,
+        LLMUsage,
+        Emoji,
+        Messages,
+        Images,
+        ImageDescriptions,
+        OnlineTime,
+        PersonInfo,
+        Knowledges,
+        ThinkingLog,
+        RecalledMessages,  # 添加新模型
+    ]
+
+    needs_creation = False
+    try:
+        with db:  # 管理 table_exists 检查的连接
+            for model in models:
+                table_name = model._meta.table_name
+                if not db.table_exists(model):
+                    logger.warning(f"表 '{table_name}' 未找到。")
+                    needs_creation = True
+                    break  # 一个表丢失，无需进一步检查。
+            if not needs_creation:
+                # 检查字段
+                for model in models:
+                    table_name = model._meta.table_name
+                    cursor = db.execute_sql(f"PRAGMA table_info('{table_name}')")
+                    existing_columns = {row[1] for row in cursor.fetchall()}
+                    model_fields = model._meta.fields
+                    for field_name in model_fields:
+                        if field_name not in existing_columns:
+                            logger.error(f"表 '{table_name}' 缺失字段 '{field_name}'，请手动迁移数据库结构后重启程序。")
+                            sys.exit(1)
+    except Exception as e:
+        logger.exception(f"检查表或字段是否存在时出错: {e}")
+        # 如果检查失败（例如数据库不可用），则退出
+        return
+
+    if needs_creation:
+        logger.info("正在初始化数据库：一个或多个表丢失。正在尝试创建所有定义的表...")
+        try:
+            create_tables()  # 此函数有其自己的 'with db:' 上下文管理。
+            logger.info("数据库表创建过程完成。")
+        except Exception as e:
+            logger.exception(f"创建表期间出错: {e}")
+    else:
+        logger.info("所有数据库表及字段均已存在。")
+
+
+# 模块加载时调用初始化函数
+initialize_database()

src/common/message_repository.py

@@ -1,11 +1,19 @@
-from src.common.database import db
+from src.common.database.database_model import Messages  # 更改导入
 from src.common.logger import get_module_logger
 import traceback
 from typing import List, Any, Optional
+from peewee import Model  # 添加 Peewee Model 导入
 
 logger = get_module_logger(__name__)
 
 
+def _model_to_dict(model_instance: Model) -> dict[str, Any]:
+    """
+    将 Peewee 模型实例转换为字典。
+    """
+    return model_instance.__data__
+
+
 def find_messages(
     message_filter: dict[str, Any],
     sort: Optional[List[tuple[str, int]]] = None,
@@ -16,39 +24,84 @@ def find_messages(
     根据提供的过滤器、排序和限制条件查找消息。
 
     Args:
-        message_filter: MongoDB 查询过滤器。
+        message_filter: 查询过滤器字典，键为模型字段名，值为期望值或包含操作符的字典 (例如 {'$gt': value}).
-        sort: MongoDB 排序条件列表，例如 [('time', 1)]。仅在 limit 为 0 时生效。
+        sort: 排序条件列表，例如 [('time', 1)] (1 for asc, -1 for desc)。仅在 limit 为 0 时生效。
         limit: 返回的最大文档数，0表示不限制。
         limit_mode: 当 limit > 0 时生效。 'earliest' 表示获取最早的记录， 'latest' 表示获取最新的记录（结果仍按时间正序排列）。默认为 'latest'。
 
     Returns:
-        消息文档列表，如果出错则返回空列表。
+        消息字典列表，如果出错则返回空列表。
     """
     try:
-        query = db.messages.find(message_filter)
+        query = Messages.select()
+
+        # 应用过滤器
+        if message_filter:
+            conditions = []
+            for key, value in message_filter.items():
+                if hasattr(Messages, key):
+                    field = getattr(Messages, key)
+                    if isinstance(value, dict):
+                        # 处理 MongoDB 风格的操作符
+                        for op, op_value in value.items():
+                            if op == "$gt":
+                                conditions.append(field > op_value)
+                            elif op == "$lt":
+                                conditions.append(field < op_value)
+                            elif op == "$gte":
+                                conditions.append(field >= op_value)
+                            elif op == "$lte":
+                                conditions.append(field <= op_value)
+                            elif op == "$ne":
+                                conditions.append(field != op_value)
+                            elif op == "$in":
+                                conditions.append(field.in_(op_value))
+                            elif op == "$nin":
+                                conditions.append(field.not_in(op_value))
+                            else:
+                                logger.warning(f"过滤器中遇到未知操作符 '{op}' (字段: '{key}')。将跳过此操作符。")
+                    else:
+                        # 直接相等比较
+                        conditions.append(field == value)
+                else:
+                    logger.warning(f"过滤器键 '{key}' 在 Messages 模型中未找到。将跳过此条件。")
+            if conditions:
+                query = query.where(*conditions)
 
         if limit > 0:
             if limit_mode == "earliest":
                 # 获取时间最早的 limit 条记录，已经是正序
-                query = query.sort([("time", 1)]).limit(limit)
+                query = query.order_by(Messages.time.asc()).limit(limit)
-                results = list(query)
+                peewee_results = list(query)
             else:  # 默认为 'latest'
                 # 获取时间最晚的 limit 条记录
-                query = query.sort([("time", -1)]).limit(limit)
+                query = query.order_by(Messages.time.desc()).limit(limit)
-                latest_results = list(query)
+                latest_results_peewee = list(query)
                 # 将结果按时间正序排列
-                # 假设消息文档中总是有 'time' 字段且可排序
+                peewee_results = sorted(latest_results_peewee, key=lambda msg: msg.time)
-                results = sorted(latest_results, key=lambda msg: msg.get("time"))
         else:
             # limit 为 0 时，应用传入的 sort 参数
             if sort:
-                query = query.sort(sort)
+                peewee_sort_terms = []
-            results = list(query)
+                for field_name, direction in sort:
+                    if hasattr(Messages, field_name):
+                        field = getattr(Messages, field_name)
+                        if direction == 1:  # ASC
+                            peewee_sort_terms.append(field.asc())
+                        elif direction == -1:  # DESC
+                            peewee_sort_terms.append(field.desc())
+                        else:
+                            logger.warning(f"字段 '{field_name}' 的排序方向 '{direction}' 无效。将跳过此排序条件。")
+                    else:
+                        logger.warning(f"排序字段 '{field_name}' 在 Messages 模型中未找到。将跳过此排序条件。")
+                if peewee_sort_terms:
+                    query = query.order_by(*peewee_sort_terms)
+            peewee_results = list(query)
 
-        return results
+        return [_model_to_dict(msg) for msg in peewee_results]
     except Exception as e:
         log_message = (
-            f"查找消息失败 (filter={message_filter}, sort={sort}, limit={limit}, limit_mode={limit_mode}): {e}\n"
+            f"使用 Peewee 查找消息失败 (filter={message_filter}, sort={sort}, limit={limit}, limit_mode={limit_mode}): {e}\n"
             + traceback.format_exc()
         )
         logger.error(log_message)
@@ -60,18 +113,57 @@ def count_messages(message_filter: dict[str, Any]) -> int:
     根据提供的过滤器计算消息数量。
 
     Args:
-        message_filter: MongoDB 查询过滤器。
+        message_filter: 查询过滤器字典，键为模型字段名，值为期望值或包含操作符的字典 (例如 {'$gt': value}).
 
     Returns:
         符合条件的消息数量，如果出错则返回 0。
     """
     try:
-        count = db.messages.count_documents(message_filter)
+        query = Messages.select()
+
+        # 应用过滤器
+        if message_filter:
+            conditions = []
+            for key, value in message_filter.items():
+                if hasattr(Messages, key):
+                    field = getattr(Messages, key)
+                    if isinstance(value, dict):
+                        # 处理 MongoDB 风格的操作符
+                        for op, op_value in value.items():
+                            if op == "$gt":
+                                conditions.append(field > op_value)
+                            elif op == "$lt":
+                                conditions.append(field < op_value)
+                            elif op == "$gte":
+                                conditions.append(field >= op_value)
+                            elif op == "$lte":
+                                conditions.append(field <= op_value)
+                            elif op == "$ne":
+                                conditions.append(field != op_value)
+                            elif op == "$in":
+                                conditions.append(field.in_(op_value))
+                            elif op == "$nin":
+                                conditions.append(field.not_in(op_value))
+                            else:
+                                logger.warning(
+                                    f"计数时，过滤器中遇到未知操作符 '{op}' (字段: '{key}')。将跳过此操作符。"
+                                )
+                    else:
+                        # 直接相等比较
+                        conditions.append(field == value)
+                else:
+                    logger.warning(f"计数时，过滤器键 '{key}' 在 Messages 模型中未找到。将跳过此条件。")
+            if conditions:
+                query = query.where(*conditions)
+
+        count = query.count()
         return count
     except Exception as e:
-        log_message = f"计数消息失败 (message_filter={message_filter}): {e}\n" + traceback.format_exc()
+        log_message = f"使用 Peewee 计数消息失败 (message_filter={message_filter}): {e}\n{traceback.format_exc()}"
         logger.error(log_message)
         return 0
 
 
 # 你可以在这里添加更多与 messages 集合相关的数据库操作函数，例如 find_one_message, insert_message 等。
+# 注意：对于 Peewee，插入操作通常是 Messages.create(...) 或 instance.save()。
+# 查找单个消息可以是 Messages.get_or_none(...) 或 query.first()。

src/common/remote.py

@@ -35,7 +35,7 @@ class TelemetryHeartBeatTask(AsyncTask):
         info_dict = {
             "os_type": "Unknown",
             "py_version": platform.python_version(),
-            "mmc_version": global_config.MAI_VERSION,
+            "mmc_version": global_config.MMC_VERSION,
         }
 
         match platform.system():
@@ -133,9 +133,8 @@ class TelemetryHeartBeatTask(AsyncTask):
 
     async def run(self):
         # 发送心跳
-        if global_config.remote_enable:
+        if global_config.telemetry.enable:
-            if self.client_uuid is None:
+            if self.client_uuid is None and not await self._req_uuid():
-                if not await self._req_uuid():
                 logger.error("获取UUID失败，跳过此次心跳")
                 return
 

src/config/config.py

@@ -1,64 +1,68 @@
 import os
-import re
+from dataclasses import field, dataclass
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
 
-import tomli
 import tomlkit
 import shutil
 from datetime import datetime
-from pathlib import Path
+
-from packaging import version
+from tomlkit import TOMLDocument
-from packaging.version import Version, InvalidVersion
+from tomlkit.items import Table
-from packaging.specifiers import SpecifierSet, InvalidSpecifier
 
 from src.common.logger_manager import get_logger
 from rich.traceback import install
 
+from src.config.config_base import ConfigBase
+from src.config.official_configs import (
+    BotConfig,
+    ChatTargetConfig,
+    PersonalityConfig,
+    IdentityConfig,
+    PlatformsConfig,
+    ChatConfig,
+    NormalChatConfig,
+    FocusChatConfig,
+    EmojiConfig,
+    MemoryConfig,
+    MoodConfig,
+    KeywordReactionConfig,
+    ChineseTypoConfig,
+    ResponseSplitterConfig,
+    TelemetryConfig,
+    ExperimentalConfig,
+    ModelConfig,
+)
+
 install(extra_lines=3)
 
 
 # 配置主程序日志格式
 logger = get_logger("config")
 
-# 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
+CONFIG_DIR = "config"
-is_test = True
+TEMPLATE_DIR = "template"
-mai_version_main = "0.6.4"
-mai_version_fix = "snapshot-1"
 
-if mai_version_fix:
+# 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
-    if is_test:
+# 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/
-        mai_version = f"test-{mai_version_main}-{mai_version_fix}"
+MMC_VERSION = "0.7.0-snapshot.1"
-    else:
-        mai_version = f"{mai_version_main}-{mai_version_fix}"
-else:
-    if is_test:
-        mai_version = f"test-{mai_version_main}"
-    else:
-        mai_version = mai_version_main
 
 
 def update_config():
     # 获取根目录路径
-    root_dir = Path(__file__).parent.parent.parent
+    old_config_dir = f"{CONFIG_DIR}/old"
-    template_dir = root_dir / "template"
-    config_dir = root_dir / "config"
-    old_config_dir = config_dir / "old"
 
     # 定义文件路径
-    template_path = template_dir / "bot_config_template.toml"
+    template_path = f"{TEMPLATE_DIR}/bot_config_template.toml"
-    old_config_path = config_dir / "bot_config.toml"
+    old_config_path = f"{CONFIG_DIR}/bot_config.toml"
-    new_config_path = config_dir / "bot_config.toml"
+    new_config_path = f"{CONFIG_DIR}/bot_config.toml"
 
     # 检查配置文件是否存在
-    if not old_config_path.exists():
+    if not os.path.exists(old_config_path):
         logger.info("配置文件不存在，从模板创建新配置")
-        # 创建文件夹
+        os.makedirs(CONFIG_DIR, exist_ok=True)  # 创建文件夹
-        old_config_dir.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(template_path, old_config_path)  # 复制模板文件
-        shutil.copy2(template_path, old_config_path)
         logger.info(f"已创建新配置文件，请填写后重新运行: {old_config_path}")
         # 如果是新创建的配置文件,直接返回
-        return quit()
+        quit()
 
     # 读取旧配置文件和模板文件
     with open(old_config_path, "r", encoding="utf-8") as f:
@@ -75,13 +79,15 @@ def update_config():
             return
         else:
             logger.info(f"检测到版本号不同: 旧版本 v{old_version} -> 新版本 v{new_version}")
+    else:
+        logger.info("已有配置文件未检测到版本号，可能是旧版本。将进行更新")
 
     # 创建old目录（如果不存在）
-    old_config_dir.mkdir(exist_ok=True)
+    os.makedirs(old_config_dir, exist_ok=True)
 
     # 生成带时间戳的新文件名
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    old_backup_path = old_config_dir / f"bot_config_{timestamp}.toml"
+    old_backup_path = f"{old_config_dir}/bot_config_{timestamp}.toml"
 
     # 移动旧配置文件到old目录
     shutil.move(old_config_path, old_backup_path)
@@ -91,24 +97,23 @@ def update_config():
     shutil.copy2(template_path, new_config_path)
     logger.info(f"已创建新配置文件: {new_config_path}")
 
-    # 递归更新配置
+    def update_dict(target: TOMLDocument | dict, source: TOMLDocument | dict):
-    def update_dict(target, source):
+        """
+        将source字典的值更新到target字典中（如果target中存在相同的键）
+        """
         for key, value in source.items():
             # 跳过version字段的更新
             if key == "version":
                 continue
             if key in target:
-                if isinstance(value, dict) and isinstance(target[key], (dict, tomlkit.items.Table)):
+                if isinstance(value, dict) and isinstance(target[key], (dict, Table)):
                     update_dict(target[key], value)
                 else:
                     try:
                         # 对数组类型进行特殊处理
                         if isinstance(value, list):
                             # 如果是空数组，确保它保持为空数组
-                            if not value:
+                            target[key] = tomlkit.array(str(value)) if value else tomlkit.array()
-                                target[key] = tomlkit.array()
-                            else:
-                                target[key] = tomlkit.array(value)
                         else:
                             # 其他类型使用item方法创建新值
                             target[key] = tomlkit.item(value)
@@ -123,619 +128,57 @@ def update_config():
     # 保存更新后的配置（保留注释和格式）
     with open(new_config_path, "w", encoding="utf-8") as f:
         f.write(tomlkit.dumps(new_config))
-    logger.info("配置文件更新完成")
+    logger.info("配置文件更新完成，建议检查新配置文件中的内容，以免丢失重要信息")
+    quit()
 
 
 @dataclass
-class BotConfig:
+class Config(ConfigBase):
-    """机器人配置类"""
+    """总配置类"""
 
-    INNER_VERSION: Version = None
+    MMC_VERSION: str = field(default=MMC_VERSION, repr=False, init=False)  # 硬编码的版本信息
-    MAI_VERSION: str = mai_version  # 硬编码的版本信息
 
-    # bot
+    bot: BotConfig
-    BOT_QQ: Optional[str] = "114514"
+    chat_target: ChatTargetConfig
-    BOT_NICKNAME: Optional[str] = None
+    personality: PersonalityConfig
-    BOT_ALIAS_NAMES: List[str] = field(default_factory=list)  # 别名，可以通过这个叫它
+    identity: IdentityConfig
+    platforms: PlatformsConfig
+    chat: ChatConfig
+    normal_chat: NormalChatConfig
+    focus_chat: FocusChatConfig
+    emoji: EmojiConfig
+    memory: MemoryConfig
+    mood: MoodConfig
+    keyword_reaction: KeywordReactionConfig
+    chinese_typo: ChineseTypoConfig
+    response_splitter: ResponseSplitterConfig
+    telemetry: TelemetryConfig
+    experimental: ExperimentalConfig
+    model: ModelConfig
 
-    # group
-    talk_allowed_groups = set()
-    talk_frequency_down_groups = set()
-    ban_user_id = set()
 
-    # personality
+def load_config(config_path: str) -> Config:
-    personality_core = "用一句话或几句话描述人格的核心特点"  # 建议20字以内，谁再写3000字小作文敲谁脑袋
-    personality_sides: List[str] = field(
-        default_factory=lambda: [
-            "用一句话或几句话描述人格的一些侧面",
-            "用一句话或几句话描述人格的一些侧面",
-            "用一句话或几句话描述人格的一些侧面",
-        ]
-    )
-    expression_style = "描述麦麦说话的表达风格，表达习惯"
-    # identity
-    identity_detail: List[str] = field(
-        default_factory=lambda: [
-            "身份特点",
-            "身份特点",
-        ]
-    )
-    height: int = 170  # 身高 单位厘米
-    weight: int = 50  # 体重 单位千克
-    age: int = 20  # 年龄 单位岁
-    gender: str = "男"  # 性别
-    appearance: str = "用几句话描述外貌特征"  # 外貌特征
-
-    # chat
-    allow_focus_mode: bool = True  # 是否允许专注聊天状态
-
-    base_normal_chat_num: int = 3  # 最多允许多少个群进行普通聊天
-    base_focused_chat_num: int = 2  # 最多允许多少个群进行专注聊天
-
-    observation_context_size: int = 12  # 心流观察到的最长上下文大小，超过这个值的上下文会被压缩
-
-    message_buffer: bool = True  # 消息缓冲器
-
-    ban_words = set()
-    ban_msgs_regex = set()
-
-    # focus_chat
-    reply_trigger_threshold: float = 3.0  # 心流聊天触发阈值，越低越容易触发
-    default_decay_rate_per_second: float = 0.98  # 默认衰减率，越大衰减越慢
-    consecutive_no_reply_threshold = 3
-
-    compressed_length: int = 5  # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
-    compress_length_limit: int = 5  # 最多压缩份数，超过该数值的压缩上下文会被删除
-
-    # normal_chat
-    model_reasoning_probability: float = 0.7  # 麦麦回答时选择推理模型(主要)模型概率
-    model_normal_probability: float = 0.3  # 麦麦回答时选择一般模型(次要)模型概率
-
-    emoji_chance: float = 0.2  # 发送表情包的基础概率
-    thinking_timeout: int = 120  # 思考时间
-
-    willing_mode: str = "classical"  # 意愿模式
-    response_willing_amplifier: float = 1.0  # 回复意愿放大系数
-    response_interested_rate_amplifier: float = 1.0  # 回复兴趣度放大系数
-    down_frequency_rate: float = 3  # 降低回复频率的群组回复意愿降低系数
-    emoji_response_penalty: float = 0.0  # 表情包回复惩罚
-    mentioned_bot_inevitable_reply: bool = False  # 提及 bot 必然回复
-    at_bot_inevitable_reply: bool = False  # @bot 必然回复
-
-    # emoji
-    max_emoji_num: int = 200  # 表情包最大数量
-    max_reach_deletion: bool = True  # 开启则在达到最大数量时删除表情包，关闭则不会继续收集表情包
-    EMOJI_CHECK_INTERVAL: int = 120  # 表情包检查间隔（分钟）
-
-    save_pic: bool = False  # 是否保存图片
-    save_emoji: bool = False  # 是否保存表情包
-    steal_emoji: bool = True  # 是否偷取表情包，让麦麦可以发送她保存的这些表情包
-
-    EMOJI_CHECK: bool = False  # 是否开启过滤
-    EMOJI_CHECK_PROMPT: str = "符合公序良俗"  # 表情包过滤要求
-
-    # memory
-    build_memory_interval: int = 600  # 记忆构建间隔（秒）
-    memory_build_distribution: list = field(
-        default_factory=lambda: [4, 2, 0.6, 24, 8, 0.4]
-    )  # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
-    build_memory_sample_num: int = 10  # 记忆构建采样数量
-    build_memory_sample_length: int = 20  # 记忆构建采样长度
-    memory_compress_rate: float = 0.1  # 记忆压缩率
-
-    forget_memory_interval: int = 600  # 记忆遗忘间隔（秒）
-    memory_forget_time: int = 24  # 记忆遗忘时间（小时）
-    memory_forget_percentage: float = 0.01  # 记忆遗忘比例
-
-    consolidate_memory_interval: int = 1000  # 记忆整合间隔（秒）
-    consolidation_similarity_threshold: float = 0.7  # 相似度阈值
-    consolidate_memory_percentage: float = 0.01  # 检查节点比例
-
-    memory_ban_words: list = field(
-        default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"]
-    )  # 添加新的配置项默认值
-
-    # mood
-    mood_update_interval: float = 1.0  # 情绪更新间隔 单位秒
-    mood_decay_rate: float = 0.95  # 情绪衰减率
-    mood_intensity_factor: float = 0.7  # 情绪强度因子
-
-    # keywords
-    keywords_reaction_rules = []  # 关键词回复规则
-
-    # chinese_typo
-    chinese_typo_enable = True  # 是否启用中文错别字生成器
-    chinese_typo_error_rate = 0.03  # 单字替换概率
-    chinese_typo_min_freq = 7  # 最小字频阈值
-    chinese_typo_tone_error_rate = 0.2  # 声调错误概率
-    chinese_typo_word_replace_rate = 0.02  # 整词替换概率
-
-    # response_splitter
-    enable_kaomoji_protection = False  # 是否启用颜文字保护
-    enable_response_splitter = True  # 是否启用回复分割器
-    response_max_length = 100  # 回复允许的最大长度
-    response_max_sentence_num = 3  # 回复允许的最大句子数
-
-    model_max_output_length: int = 800  # 最大回复长度
-
-    # remote
-    remote_enable: bool = True  # 是否启用远程控制
-
-    # experimental
-    enable_friend_chat: bool = False  # 是否启用好友聊天
-    # enable_think_flow: bool = False  # 是否启用思考流程
-    talk_allowed_private = set()
-    enable_pfc_chatting: bool = False  # 是否启用PFC聊天
-
-    # 模型配置
-    llm_reasoning: dict[str, str] = field(default_factory=lambda: {})
-    # llm_reasoning_minor: dict[str, str] = field(default_factory=lambda: {})
-    llm_normal: Dict[str, str] = field(default_factory=lambda: {})
-    llm_topic_judge: Dict[str, str] = field(default_factory=lambda: {})
-    llm_summary: Dict[str, str] = field(default_factory=lambda: {})
-    embedding: Dict[str, str] = field(default_factory=lambda: {})
-    vlm: Dict[str, str] = field(default_factory=lambda: {})
-    moderation: Dict[str, str] = field(default_factory=lambda: {})
-
-    llm_observation: Dict[str, str] = field(default_factory=lambda: {})
-    llm_sub_heartflow: Dict[str, str] = field(default_factory=lambda: {})
-    llm_heartflow: Dict[str, str] = field(default_factory=lambda: {})
-    llm_tool_use: Dict[str, str] = field(default_factory=lambda: {})
-    llm_plan: Dict[str, str] = field(default_factory=lambda: {})
-
-    api_urls: Dict[str, str] = field(default_factory=lambda: {})
-
-    @staticmethod
-    def get_config_dir() -> str:
-        """获取配置文件目录"""
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        root_dir = os.path.abspath(os.path.join(current_dir, "..", ".."))
-        config_dir = os.path.join(root_dir, "config")
-        if not os.path.exists(config_dir):
-            os.makedirs(config_dir)
-        return config_dir
-
-    @classmethod
-    def convert_to_specifierset(cls, value: str) -> SpecifierSet:
-        """将 字符串 版本表达式转换成 SpecifierSet
-        Args:
-            value[str]: 版本表达式(字符串)
-        Returns:
-            SpecifierSet
     """
-
+    加载配置文件
-        try:
+    :param config_path: 配置文件路径
-            converted = SpecifierSet(value)
+    :return: Config对象
-        except InvalidSpecifier:
-            logger.error(f"{value} 分类使用了错误的版本约束表达式\n", "请阅读 https://semver.org/lang/zh-CN/ 修改代码")
-            exit(1)
-
-        return converted
-
-    @classmethod
-    def get_config_version(cls, toml: dict) -> Version:
-        """提取配置文件的 SpecifierSet 版本数据
-        Args:
-            toml[dict]: 输入的配置文件字典
-        Returns:
-            Version
     """
+    # 读取配置文件
+    with open(config_path, "r", encoding="utf-8") as f:
+        config_data = tomlkit.load(f)
 
-        if "inner" in toml:
+    # 创建Config对象
     try:
-                config_version: str = toml["inner"]["version"]
+        return Config.from_dict(config_data)
-            except KeyError as e:
+    except Exception as e:
-                logger.error("配置文件中 inner 段 不存在, 这是错误的配置文件")
+        logger.critical("配置文件解析失败")
-                raise KeyError(f"配置文件中 inner 段 不存在 {e}, 这是错误的配置文件") from e
+        raise e
-        else:
-            toml["inner"] = {"version": "0.0.0"}
-            config_version = toml["inner"]["version"]
-
-        try:
-            ver = version.parse(config_version)
-        except InvalidVersion as e:
-            logger.error(
-                "配置文件中 inner段 的 version 键是错误的版本描述\n"
-                "请阅读 https://semver.org/lang/zh-CN/ 修改配置，并参考本项目指定的模板进行修改\n"
-                "本项目在不同的版本下有不同的模板，请注意识别"
-            )
-            raise InvalidVersion("配置文件中 inner段 的 version 键是错误的版本描述\n") from e
-
-        return ver
-
-    @classmethod
-    def load_config(cls, config_path: str = None) -> "BotConfig":
-        """从TOML配置文件加载配置"""
-        config = cls()
-
-        def personality(parent: dict):
-            personality_config = parent["personality"]
-            if config.INNER_VERSION in SpecifierSet(">=1.2.4"):
-                config.personality_core = personality_config.get("personality_core", config.personality_core)
-                config.personality_sides = personality_config.get("personality_sides", config.personality_sides)
-            if config.INNER_VERSION in SpecifierSet(">=1.7.0"):
-                config.expression_style = personality_config.get("expression_style", config.expression_style)
-
-        def identity(parent: dict):
-            identity_config = parent["identity"]
-            if config.INNER_VERSION in SpecifierSet(">=1.2.4"):
-                config.identity_detail = identity_config.get("identity_detail", config.identity_detail)
-                config.height = identity_config.get("height", config.height)
-                config.weight = identity_config.get("weight", config.weight)
-                config.age = identity_config.get("age", config.age)
-                config.gender = identity_config.get("gender", config.gender)
-                config.appearance = identity_config.get("appearance", config.appearance)
-
-        def emoji(parent: dict):
-            emoji_config = parent["emoji"]
-            config.EMOJI_CHECK_INTERVAL = emoji_config.get("check_interval", config.EMOJI_CHECK_INTERVAL)
-            config.EMOJI_CHECK_PROMPT = emoji_config.get("check_prompt", config.EMOJI_CHECK_PROMPT)
-            config.EMOJI_CHECK = emoji_config.get("enable_check", config.EMOJI_CHECK)
-            if config.INNER_VERSION in SpecifierSet(">=1.1.1"):
-                config.max_emoji_num = emoji_config.get("max_emoji_num", config.max_emoji_num)
-                config.max_reach_deletion = emoji_config.get("max_reach_deletion", config.max_reach_deletion)
-            if config.INNER_VERSION in SpecifierSet(">=1.4.2"):
-                config.save_pic = emoji_config.get("save_pic", config.save_pic)
-                config.save_emoji = emoji_config.get("save_emoji", config.save_emoji)
-                config.steal_emoji = emoji_config.get("steal_emoji", config.steal_emoji)
-
-        def bot(parent: dict):
-            # 机器人基础配置
-            bot_config = parent["bot"]
-            bot_qq = bot_config.get("qq")
-            config.BOT_QQ = str(bot_qq)
-            config.BOT_NICKNAME = bot_config.get("nickname", config.BOT_NICKNAME)
-            config.BOT_ALIAS_NAMES = bot_config.get("alias_names", config.BOT_ALIAS_NAMES)
-
-        def chat(parent: dict):
-            chat_config = parent["chat"]
-            config.allow_focus_mode = chat_config.get("allow_focus_mode", config.allow_focus_mode)
-            config.base_normal_chat_num = chat_config.get("base_normal_chat_num", config.base_normal_chat_num)
-            config.base_focused_chat_num = chat_config.get("base_focused_chat_num", config.base_focused_chat_num)
-            config.observation_context_size = chat_config.get(
-                "observation_context_size", config.observation_context_size
-            )
-            config.message_buffer = chat_config.get("message_buffer", config.message_buffer)
-            config.ban_words = chat_config.get("ban_words", config.ban_words)
-            for r in chat_config.get("ban_msgs_regex", config.ban_msgs_regex):
-                config.ban_msgs_regex.add(re.compile(r))
-
-        def normal_chat(parent: dict):
-            normal_chat_config = parent["normal_chat"]
-            config.model_reasoning_probability = normal_chat_config.get(
-                "model_reasoning_probability", config.model_reasoning_probability
-            )
-            config.model_normal_probability = normal_chat_config.get(
-                "model_normal_probability", config.model_normal_probability
-            )
-            config.emoji_chance = normal_chat_config.get("emoji_chance", config.emoji_chance)
-            config.thinking_timeout = normal_chat_config.get("thinking_timeout", config.thinking_timeout)
-
-            config.willing_mode = normal_chat_config.get("willing_mode", config.willing_mode)
-            config.response_willing_amplifier = normal_chat_config.get(
-                "response_willing_amplifier", config.response_willing_amplifier
-            )
-            config.response_interested_rate_amplifier = normal_chat_config.get(
-                "response_interested_rate_amplifier", config.response_interested_rate_amplifier
-            )
-            config.down_frequency_rate = normal_chat_config.get("down_frequency_rate", config.down_frequency_rate)
-            config.emoji_response_penalty = normal_chat_config.get(
-                "emoji_response_penalty", config.emoji_response_penalty
-            )
-
-            config.mentioned_bot_inevitable_reply = normal_chat_config.get(
-                "mentioned_bot_inevitable_reply", config.mentioned_bot_inevitable_reply
-            )
-            config.at_bot_inevitable_reply = normal_chat_config.get(
-                "at_bot_inevitable_reply", config.at_bot_inevitable_reply
-            )
-
-        def focus_chat(parent: dict):
-            focus_chat_config = parent["focus_chat"]
-            config.compressed_length = focus_chat_config.get("compressed_length", config.compressed_length)
-            config.compress_length_limit = focus_chat_config.get("compress_length_limit", config.compress_length_limit)
-            config.reply_trigger_threshold = focus_chat_config.get(
-                "reply_trigger_threshold", config.reply_trigger_threshold
-            )
-            config.default_decay_rate_per_second = focus_chat_config.get(
-                "default_decay_rate_per_second", config.default_decay_rate_per_second
-            )
-            config.consecutive_no_reply_threshold = focus_chat_config.get(
-                "consecutive_no_reply_threshold", config.consecutive_no_reply_threshold
-            )
-
-        def model(parent: dict):
-            # 加载模型配置
-            model_config: dict = parent["model"]
-
-            config_list = [
-                "llm_reasoning",
-                # "llm_reasoning_minor",
-                "llm_normal",
-                "llm_topic_judge",
-                "llm_summary",
-                "vlm",
-                "embedding",
-                "llm_tool_use",
-                "llm_observation",
-                "llm_sub_heartflow",
-                "llm_plan",
-                "llm_heartflow",
-                "llm_PFC_action_planner",
-                "llm_PFC_chat",
-                "llm_PFC_reply_checker",
-            ]
-
-            for item in config_list:
-                if item in model_config:
-                    cfg_item: dict = model_config[item]
-
-                    # base_url 的例子： SILICONFLOW_BASE_URL
-                    # key 的例子： SILICONFLOW_KEY
-                    cfg_target = {
-                        "name": "",
-                        "base_url": "",
-                        "key": "",
-                        "stream": False,
-                        "pri_in": 0,
-                        "pri_out": 0,
-                        "temp": 0.7,
-                    }
-
-                    if config.INNER_VERSION in SpecifierSet("<=0.0.0"):
-                        cfg_target = cfg_item
-
-                    elif config.INNER_VERSION in SpecifierSet(">=0.0.1"):
-                        stable_item = ["name", "pri_in", "pri_out"]
-
-                        stream_item = ["stream"]
-                        if config.INNER_VERSION in SpecifierSet(">=1.0.1"):
-                            stable_item.append("stream")
-
-                        pricing_item = ["pri_in", "pri_out"]
-
-                        # 从配置中原始拷贝稳定字段
-                        for i in stable_item:
-                            # 如果 字段 属于计费项 且获取不到，那默认值是 0
-                            if i in pricing_item and i not in cfg_item:
-                                cfg_target[i] = 0
-
-                            if i in stream_item and i not in cfg_item:
-                                cfg_target[i] = False
-
-                            else:
-                                # 没有特殊情况则原样复制
-                                try:
-                                    cfg_target[i] = cfg_item[i]
-                                except KeyError as e:
-                                    logger.error(f"{item} 中的必要字段不存在，请检查")
-                                    raise KeyError(f"{item} 中的必要字段 {e} 不存在，请检查") from e
-
-                        # 如果配置中有temp参数，就使用配置中的值
-                        if "temp" in cfg_item:
-                            cfg_target["temp"] = cfg_item["temp"]
-                        else:
-                            # 如果没有temp参数，就删除默认值
-                            cfg_target.pop("temp", None)
-
-                        provider = cfg_item.get("provider")
-                        if provider is None:
-                            logger.error(f"provider 字段在模型配置 {item} 中不存在，请检查")
-                            raise KeyError(f"provider 字段在模型配置 {item} 中不存在，请检查")
-
-                        cfg_target["base_url"] = f"{provider}_BASE_URL"
-                        cfg_target["key"] = f"{provider}_KEY"
-
-                    # 如果 列表中的项目在 model_config 中，利用反射来设置对应项目
-                    setattr(config, item, cfg_target)
-                else:
-                    logger.error(f"模型 {item} 在config中不存在，请检查，或尝试更新配置文件")
-                    raise KeyError(f"模型 {item} 在config中不存在，请检查，或尝试更新配置文件")
-
-        def memory(parent: dict):
-            memory_config = parent["memory"]
-            config.build_memory_interval = memory_config.get("build_memory_interval", config.build_memory_interval)
-            config.forget_memory_interval = memory_config.get("forget_memory_interval", config.forget_memory_interval)
-            config.memory_ban_words = set(memory_config.get("memory_ban_words", []))
-            config.memory_forget_time = memory_config.get("memory_forget_time", config.memory_forget_time)
-            config.memory_forget_percentage = memory_config.get(
-                "memory_forget_percentage", config.memory_forget_percentage
-            )
-            config.memory_compress_rate = memory_config.get("memory_compress_rate", config.memory_compress_rate)
-            if config.INNER_VERSION in SpecifierSet(">=0.0.11"):
-                config.memory_build_distribution = memory_config.get(
-                    "memory_build_distribution", config.memory_build_distribution
-                )
-                config.build_memory_sample_num = memory_config.get(
-                    "build_memory_sample_num", config.build_memory_sample_num
-                )
-                config.build_memory_sample_length = memory_config.get(
-                    "build_memory_sample_length", config.build_memory_sample_length
-                )
-            if config.INNER_VERSION in SpecifierSet(">=1.5.1"):
-                config.consolidate_memory_interval = memory_config.get(
-                    "consolidate_memory_interval", config.consolidate_memory_interval
-                )
-                config.consolidation_similarity_threshold = memory_config.get(
-                    "consolidation_similarity_threshold", config.consolidation_similarity_threshold
-                )
-                config.consolidate_memory_percentage = memory_config.get(
-                    "consolidate_memory_percentage", config.consolidate_memory_percentage
-                )
-
-        def remote(parent: dict):
-            remote_config = parent["remote"]
-            config.remote_enable = remote_config.get("enable", config.remote_enable)
-
-        def mood(parent: dict):
-            mood_config = parent["mood"]
-            config.mood_update_interval = mood_config.get("mood_update_interval", config.mood_update_interval)
-            config.mood_decay_rate = mood_config.get("mood_decay_rate", config.mood_decay_rate)
-            config.mood_intensity_factor = mood_config.get("mood_intensity_factor", config.mood_intensity_factor)
-
-        def keywords_reaction(parent: dict):
-            keywords_reaction_config = parent["keywords_reaction"]
-            if keywords_reaction_config.get("enable", False):
-                config.keywords_reaction_rules = keywords_reaction_config.get("rules", config.keywords_reaction_rules)
-                for rule in config.keywords_reaction_rules:
-                    if rule.get("enable", False) and "regex" in rule:
-                        rule["regex"] = [re.compile(r) for r in rule.get("regex", [])]
-
-        def chinese_typo(parent: dict):
-            chinese_typo_config = parent["chinese_typo"]
-            config.chinese_typo_enable = chinese_typo_config.get("enable", config.chinese_typo_enable)
-            config.chinese_typo_error_rate = chinese_typo_config.get("error_rate", config.chinese_typo_error_rate)
-            config.chinese_typo_min_freq = chinese_typo_config.get("min_freq", config.chinese_typo_min_freq)
-            config.chinese_typo_tone_error_rate = chinese_typo_config.get(
-                "tone_error_rate", config.chinese_typo_tone_error_rate
-            )
-            config.chinese_typo_word_replace_rate = chinese_typo_config.get(
-                "word_replace_rate", config.chinese_typo_word_replace_rate
-            )
-
-        def response_splitter(parent: dict):
-            response_splitter_config = parent["response_splitter"]
-            config.enable_response_splitter = response_splitter_config.get(
-                "enable_response_splitter", config.enable_response_splitter
-            )
-            config.response_max_length = response_splitter_config.get("response_max_length", config.response_max_length)
-            config.response_max_sentence_num = response_splitter_config.get(
-                "response_max_sentence_num", config.response_max_sentence_num
-            )
-            if config.INNER_VERSION in SpecifierSet(">=1.4.2"):
-                config.enable_kaomoji_protection = response_splitter_config.get(
-                    "enable_kaomoji_protection", config.enable_kaomoji_protection
-                )
-            if config.INNER_VERSION in SpecifierSet(">=1.6.0"):
-                config.model_max_output_length = response_splitter_config.get(
-                    "model_max_output_length", config.model_max_output_length
-                )
-
-        def groups(parent: dict):
-            groups_config = parent["groups"]
-            # config.talk_allowed_groups = set(groups_config.get("talk_allowed", []))
-            config.talk_allowed_groups = set(str(group) for group in groups_config.get("talk_allowed", []))
-            # config.talk_frequency_down_groups = set(groups_config.get("talk_frequency_down", []))
-            config.talk_frequency_down_groups = set(
-                str(group) for group in groups_config.get("talk_frequency_down", [])
-            )
-            # config.ban_user_id = set(groups_config.get("ban_user_id", []))
-            config.ban_user_id = set(str(user) for user in groups_config.get("ban_user_id", []))
-
-        def experimental(parent: dict):
-            experimental_config = parent["experimental"]
-            config.enable_friend_chat = experimental_config.get("enable_friend_chat", config.enable_friend_chat)
-            # config.enable_think_flow = experimental_config.get("enable_think_flow", config.enable_think_flow)
-            config.talk_allowed_private = set(str(user) for user in experimental_config.get("talk_allowed_private", []))
-            if config.INNER_VERSION in SpecifierSet(">=1.1.0"):
-                config.enable_pfc_chatting = experimental_config.get("pfc_chatting", config.enable_pfc_chatting)
-
-        # 版本表达式：>=1.0.0,<2.0.0
-        # 允许字段：func: method, support: str, notice: str, necessary: bool
-        # 如果使用 notice 字段，在该组配置加载时，会展示该字段对用户的警示
-        # 例如："notice": "personality 将在 1.3.2 后被移除"，那么在有效版本中的用户就会虽然可以
-        # 正常执行程序，但是会看到这条自定义提示
-
-        # 版本格式：主版本号.次版本号.修订号，版本号递增规则如下：
-        #     主版本号：当你做了不兼容的 API 修改，
-        #     次版本号：当你做了向下兼容的功能性新增，
-        #     修订号：当你做了向下兼容的问题修正。
-        # 先行版本号及版本编译信息可以加到"主版本号.次版本号.修订号"的后面，作为延伸。
-
-        # 如果你做了break的修改，就应该改动主版本号
-        # 如果做了一个兼容修改，就不应该要求这个选项是必须的！
-        include_configs = {
-            "bot": {"func": bot, "support": ">=0.0.0"},
-            "groups": {"func": groups, "support": ">=0.0.0"},
-            "personality": {"func": personality, "support": ">=0.0.0"},
-            "identity": {"func": identity, "support": ">=1.2.4"},
-            "emoji": {"func": emoji, "support": ">=0.0.0"},
-            "model": {"func": model, "support": ">=0.0.0"},
-            "memory": {"func": memory, "support": ">=0.0.0", "necessary": False},
-            "mood": {"func": mood, "support": ">=0.0.0"},
-            "remote": {"func": remote, "support": ">=0.0.10", "necessary": False},
-            "keywords_reaction": {"func": keywords_reaction, "support": ">=0.0.2", "necessary": False},
-            "chinese_typo": {"func": chinese_typo, "support": ">=0.0.3", "necessary": False},
-            "response_splitter": {"func": response_splitter, "support": ">=0.0.11", "necessary": False},
-            "experimental": {"func": experimental, "support": ">=0.0.11", "necessary": False},
-            "chat": {"func": chat, "support": ">=1.6.0", "necessary": False},
-            "normal_chat": {"func": normal_chat, "support": ">=1.6.0", "necessary": False},
-            "focus_chat": {"func": focus_chat, "support": ">=1.6.0", "necessary": False},
-        }
-
-        # 原地修改，将 字符串版本表达式 转换成 版本对象
-        for key in include_configs:
-            item_support = include_configs[key]["support"]
-            include_configs[key]["support"] = cls.convert_to_specifierset(item_support)
-
-        if os.path.exists(config_path):
-            with open(config_path, "rb") as f:
-                try:
-                    toml_dict = tomli.load(f)
-                except tomli.TOMLDecodeError as e:
-                    logger.critical(f"配置文件bot_config.toml填写有误，请检查第{e.lineno}行第{e.colno}处：{e.msg}")
-                    exit(1)
-
-                # 获取配置文件版本
-                config.INNER_VERSION = cls.get_config_version(toml_dict)
-
-                # 如果在配置中找到了需要的项，调用对应项的闭包函数处理
-                for key in include_configs:
-                    if key in toml_dict:
-                        group_specifierset: SpecifierSet = include_configs[key]["support"]
-
-                        # 检查配置文件版本是否在支持范围内
-                        if config.INNER_VERSION in group_specifierset:
-                            # 如果版本在支持范围内，检查是否存在通知
-                            if "notice" in include_configs[key]:
-                                logger.warning(include_configs[key]["notice"])
-
-                            include_configs[key]["func"](toml_dict)
-
-                        else:
-                            # 如果版本不在支持范围内，崩溃并提示用户
-                            logger.error(
-                                f"配置文件中的 '{key}' 字段的版本 ({config.INNER_VERSION}) 不在支持范围内。\n"
-                                f"当前程序仅支持以下版本范围: {group_specifierset}"
-                            )
-                            raise InvalidVersion(f"当前程序仅支持以下版本范围: {group_specifierset}")
-
-                    # 如果 necessary 项目存在，而且显式声明是 False，进入特殊处理
-                    elif "necessary" in include_configs[key] and include_configs[key].get("necessary") is False:
-                        # 通过 pass 处理的项虽然直接忽略也是可以的，但是为了不增加理解困难，依然需要在这里显式处理
-                        if key == "keywords_reaction":
-                            pass
-
-                    else:
-                        # 如果用户根本没有需要的配置项，提示缺少配置
-                        logger.error(f"配置文件中缺少必需的字段: '{key}'")
-                        raise KeyError(f"配置文件中缺少必需的字段: '{key}'")
-
-                # identity_detail字段非空检查
-                if not config.identity_detail:
-                    logger.error("配置文件错误：[identity] 部分的 identity_detail 不能为空字符串")
-                    raise ValueError("配置文件错误：[identity] 部分的 identity_detail 不能为空字符串")
-
-                logger.success(f"成功加载配置文件: {config_path}")
-
-        return config
 
 
 # 获取配置文件路径
-logger.info(f"MaiCore当前版本: {mai_version}")
+logger.info(f"MaiCore当前版本: {MMC_VERSION}")
 update_config()
 
-bot_config_floder_path = BotConfig.get_config_dir()
+logger.info("正在品鉴配置文件...")
-logger.info(f"正在品鉴配置文件目录: {bot_config_floder_path}")
+global_config = load_config(config_path=f"{CONFIG_DIR}/bot_config.toml")
-
+logger.info("非常的新鲜，非常的美味！")
-bot_config_path = os.path.join(bot_config_floder_path, "bot_config.toml")
-
-if os.path.exists(bot_config_path):
-    # 如果开发环境配置文件不存在，则使用默认配置文件
-    logger.info(f"异常的新鲜，异常的美味: {bot_config_path}")
-else:
-    # 配置文件不存在
-    logger.error("配置文件不存在，请检查路径: {bot_config_path}")
-    raise FileNotFoundError(f"配置文件不存在: {bot_config_path}")
-
-global_config = BotConfig.load_config(config_path=bot_config_path)

src/config/config_base.py

@@ -0,0 +1,116 @@
+from dataclasses import dataclass, fields, MISSING
+from typing import TypeVar, Type, Any, get_origin, get_args
+
+T = TypeVar("T", bound="ConfigBase")
+
+TOML_DICT_TYPE = {
+    int,
+    float,
+    str,
+    bool,
+    list,
+    dict,
+}
+
+
+@dataclass
+class ConfigBase:
+    """配置类的基类"""
+
+    @classmethod
+    def from_dict(cls: Type[T], data: dict[str, Any]) -> T:
+        """从字典加载配置字段"""
+        if not isinstance(data, dict):
+            raise TypeError(f"Expected a dictionary, got {type(data).__name__}")
+
+        init_args: dict[str, Any] = {}
+
+        for f in fields(cls):
+            field_name = f.name
+
+            if field_name.startswith("_"):
+                # 跳过以 _ 开头的字段
+                continue
+
+            if field_name not in data:
+                if f.default is not MISSING or f.default_factory is not MISSING:
+                    # 跳过未提供且有默认值/默认构造方法的字段
+                    continue
+                else:
+                    raise ValueError(f"Missing required field: '{field_name}'")
+
+            value = data[field_name]
+            field_type = f.type
+
+            try:
+                init_args[field_name] = cls._convert_field(value, field_type)
+            except TypeError as e:
+                raise TypeError(f"Field '{field_name}' has a type error: {e}") from e
+            except Exception as e:
+                raise RuntimeError(f"Failed to convert field '{field_name}' to target type: {e}") from e
+
+        return cls(**init_args)
+
+    @classmethod
+    def _convert_field(cls, value: Any, field_type: Type[Any]) -> Any:
+        """
+        转换字段值为指定类型
+
+        1. 对于嵌套的 dataclass，递归调用相应的 from_dict 方法
+        2. 对于泛型集合类型（list, set, tuple），递归转换每个元素
+        3. 对于基础类型（int, str, float, bool），直接转换
+        4. 对于其他类型，尝试直接转换，如果失败则抛出异常
+        """
+
+        # 如果是嵌套的 dataclass，递归调用 from_dict 方法
+        if isinstance(field_type, type) and issubclass(field_type, ConfigBase):
+            if not isinstance(value, dict):
+                raise TypeError(f"Expected a dictionary for {field_type.__name__}, got {type(value).__name__}")
+            return field_type.from_dict(value)
+
+        # 处理泛型集合类型（list, set, tuple）
+        field_origin_type = get_origin(field_type)
+        field_type_args = get_args(field_type)
+
+        if field_origin_type in {list, set, tuple}:
+            # 检查提供的value是否为list
+            if not isinstance(value, list):
+                raise TypeError(f"Expected an list for {field_type.__name__}, got {type(value).__name__}")
+
+            if field_origin_type is list:
+                return [cls._convert_field(item, field_type_args[0]) for item in value]
+            elif field_origin_type is set:
+                return {cls._convert_field(item, field_type_args[0]) for item in value}
+            elif field_origin_type is tuple:
+                # 检查提供的value长度是否与类型参数一致
+                if len(value) != len(field_type_args):
+                    raise TypeError(
+                        f"Expected {len(field_type_args)} items for {field_type.__name__}, got {len(value)}"
+                    )
+                return tuple(cls._convert_field(item, arg) for item, arg in zip(value, field_type_args))
+
+        if field_origin_type is dict:
+            # 检查提供的value是否为dict
+            if not isinstance(value, dict):
+                raise TypeError(f"Expected a dictionary for {field_type.__name__}, got {type(value).__name__}")
+
+            # 检查字典的键值类型
+            if len(field_type_args) != 2:
+                raise TypeError(f"Expected a dictionary with two type arguments for {field_type.__name__}")
+            key_type, value_type = field_type_args
+
+            return {cls._convert_field(k, key_type): cls._convert_field(v, value_type) for k, v in value.items()}
+
+        # 处理基础类型，例如 int, str 等
+        if field_type is Any or isinstance(value, field_type):
+            return value
+
+        # 其他类型，尝试直接转换
+        try:
+            return field_type(value)
+        except (ValueError, TypeError) as e:
+            raise TypeError(f"Cannot convert {type(value).__name__} to {field_type.__name__}") from e
+
+    def __str__(self):
+        """返回配置类的字符串表示"""
+        return f"{self.__class__.__name__}({', '.join(f'{f.name}={getattr(self, f.name)}' for f in fields(self))})"

src/config/official_configs.py

@@ -0,0 +1,399 @@
+from dataclasses import dataclass, field
+from typing import Any
+
+from src.config.config_base import ConfigBase
+
+"""
+须知：
+1. 本文件中记录了所有的配置项
+2. 所有新增的class都需要继承自ConfigBase
+3. 所有新增的class都应在config.py中的Config类中添加字段
+4. 对于新增的字段，若为可选项，则应在其后添加field()并设置default_factory或default
+"""
+
+
+@dataclass
+class BotConfig(ConfigBase):
+    """QQ机器人配置类"""
+
+    qq_account: str
+    """QQ账号"""
+
+    nickname: str
+    """昵称"""
+
+    alias_names: list[str] = field(default_factory=lambda: [])
+    """别名列表"""
+
+
+@dataclass
+class ChatTargetConfig(ConfigBase):
+    """
+    聊天目标配置类
+    此类中有聊天的群组和用户配置
+    """
+
+    talk_allowed_groups: set[str] = field(default_factory=lambda: set())
+    """允许聊天的群组列表"""
+
+    talk_frequency_down_groups: set[str] = field(default_factory=lambda: set())
+    """降低聊天频率的群组列表"""
+
+    ban_user_id: set[str] = field(default_factory=lambda: set())
+    """禁止聊天的用户列表"""
+
+
+@dataclass
+class PersonalityConfig(ConfigBase):
+    """人格配置类"""
+
+    personality_core: str
+    """核心人格"""
+
+    expression_style: str
+    """表达风格"""
+
+    personality_sides: list[str] = field(default_factory=lambda: [])
+    """人格侧写"""
+
+
+@dataclass
+class IdentityConfig(ConfigBase):
+    """个体特征配置类"""
+
+    height: int = 170
+    """身高（单位：厘米）"""
+
+    weight: float = 50
+    """体重（单位：千克）"""
+
+    age: int = 18
+    """年龄（单位：岁）"""
+
+    gender: str = "女"
+    """性别（男/女）"""
+
+    appearance: str = "可爱"
+    """外貌描述"""
+
+    identity_detail: list[str] = field(default_factory=lambda: [])
+    """身份特征"""
+
+
+@dataclass
+class PlatformsConfig(ConfigBase):
+    """平台配置类"""
+
+    qq: str
+    """QQ适配器连接URL配置"""
+
+
+@dataclass
+class ChatConfig(ConfigBase):
+    """聊天配置类"""
+
+    allow_focus_mode: bool = True
+    """是否允许专注聊天状态"""
+
+    base_normal_chat_num: int = 3
+    """最多允许多少个群进行普通聊天"""
+
+    base_focused_chat_num: int = 2
+    """最多允许多少个群进行专注聊天"""
+
+    observation_context_size: int = 12
+    """可观察到的最长上下文大小，超过这个值的上下文会被压缩"""
+
+    message_buffer: bool = True
+    """消息缓冲器"""
+
+    ban_words: set[str] = field(default_factory=lambda: set())
+    """过滤词列表"""
+
+    ban_msgs_regex: set[str] = field(default_factory=lambda: set())
+    """过滤正则表达式列表"""
+
+
+@dataclass
+class NormalChatConfig(ConfigBase):
+    """普通聊天配置类"""
+
+    reasoning_model_probability: float = 0.3
+    """
+    发言时选择推理模型的概率（0-1之间）
+    选择普通模型的概率为 1 - reasoning_normal_model_probability
+    """
+
+    emoji_chance: float = 0.2
+    """发送表情包的基础概率"""
+
+    thinking_timeout: int = 120
+    """最长思考时间"""
+
+    willing_mode: str = "classical"
+    """意愿模式"""
+
+    response_willing_amplifier: float = 1.0
+    """回复意愿放大系数"""
+
+    response_interested_rate_amplifier: float = 1.0
+    """回复兴趣度放大系数"""
+
+    down_frequency_rate: float = 3.0
+    """降低回复频率的群组回复意愿降低系数"""
+
+    emoji_response_penalty: float = 0.0
+    """表情包回复惩罚系数"""
+
+    mentioned_bot_inevitable_reply: bool = False
+    """提及 bot 必然回复"""
+
+    at_bot_inevitable_reply: bool = False
+    """@bot 必然回复"""
+
+
+@dataclass
+class FocusChatConfig(ConfigBase):
+    """专注聊天配置类"""
+
+    reply_trigger_threshold: float = 3.0
+    """心流聊天触发阈值，越低越容易触发"""
+
+    default_decay_rate_per_second: float = 0.98
+    """默认衰减率，越大衰减越快"""
+
+    consecutive_no_reply_threshold: int = 3
+    """连续不回复的次数阈值"""
+
+    compressed_length: int = 5
+    """心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5"""
+
+    compress_length_limit: int = 5
+    """最多压缩份数，超过该数值的压缩上下文会被删除"""
+
+
+@dataclass
+class EmojiConfig(ConfigBase):
+    """表情包配置类"""
+
+    max_reg_num: int = 200
+    """表情包最大注册数量"""
+
+    do_replace: bool = True
+    """达到最大注册数量时替换旧表情包"""
+
+    check_interval: int = 120
+    """表情包检查间隔（分钟）"""
+
+    save_pic: bool = False
+    """是否保存图片"""
+
+    cache_emoji: bool = True
+    """是否缓存表情包"""
+
+    steal_emoji: bool = True
+    """是否偷取表情包，让麦麦可以发送她保存的这些表情包"""
+
+    content_filtration: bool = False
+    """是否开启表情包过滤"""
+
+    filtration_prompt: str = "符合公序良俗"
+    """表情包过滤要求"""
+
+
+@dataclass
+class MemoryConfig(ConfigBase):
+    """记忆配置类"""
+
+    memory_build_interval: int = 600
+    """记忆构建间隔（秒）"""
+
+    memory_build_distribution: tuple[
+        float,
+        float,
+        float,
+        float,
+        float,
+        float,
+    ] = field(default_factory=lambda: (6.0, 3.0, 0.6, 32.0, 12.0, 0.4))
+    """记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重"""
+
+    memory_build_sample_num: int = 8
+    """记忆构建采样数量"""
+
+    memory_build_sample_length: int = 40
+    """记忆构建采样长度"""
+
+    memory_compress_rate: float = 0.1
+    """记忆压缩率"""
+
+    forget_memory_interval: int = 1000
+    """记忆遗忘间隔（秒）"""
+
+    memory_forget_time: int = 24
+    """记忆遗忘时间（小时）"""
+
+    memory_forget_percentage: float = 0.01
+    """记忆遗忘比例"""
+
+    consolidate_memory_interval: int = 1000
+    """记忆整合间隔（秒）"""
+
+    consolidation_similarity_threshold: float = 0.7
+    """整合相似度阈值"""
+
+    consolidate_memory_percentage: float = 0.01
+    """整合检查节点比例"""
+
+    memory_ban_words: list[str] = field(default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"])
+    """不允许记忆的词列表"""
+
+
+@dataclass
+class MoodConfig(ConfigBase):
+    """情绪配置类"""
+
+    mood_update_interval: int = 1
+    """情绪更新间隔（秒）"""
+
+    mood_decay_rate: float = 0.95
+    """情绪衰减率"""
+
+    mood_intensity_factor: float = 0.7
+    """情绪强度因子"""
+
+
+@dataclass
+class KeywordRuleConfig(ConfigBase):
+    """关键词规则配置类"""
+
+    enable: bool = True
+    """是否启用关键词规则"""
+
+    keywords: list[str] = field(default_factory=lambda: [])
+    """关键词列表"""
+
+    regex: list[str] = field(default_factory=lambda: [])
+    """正则表达式列表"""
+
+    reaction: str = ""
+    """关键词触发的反应"""
+
+
+@dataclass
+class KeywordReactionConfig(ConfigBase):
+    """关键词配置类"""
+
+    enable: bool = True
+    """是否启用关键词反应"""
+
+    rules: list[KeywordRuleConfig] = field(default_factory=lambda: [])
+    """关键词反应规则列表"""
+
+
+@dataclass
+class ChineseTypoConfig(ConfigBase):
+    """中文错别字配置类"""
+
+    enable: bool = True
+    """是否启用中文错别字生成器"""
+
+    error_rate: float = 0.01
+    """单字替换概率"""
+
+    min_freq: int = 9
+    """最小字频阈值"""
+
+    tone_error_rate: float = 0.1
+    """声调错误概率"""
+
+    word_replace_rate: float = 0.006
+    """整词替换概率"""
+
+
+@dataclass
+class ResponseSplitterConfig(ConfigBase):
+    """回复分割器配置类"""
+
+    enable: bool = True
+    """是否启用回复分割器"""
+
+    max_length: int = 256
+    """回复允许的最大长度"""
+
+    max_sentence_num: int = 3
+    """回复允许的最大句子数"""
+
+    enable_kaomoji_protection: bool = False
+    """是否启用颜文字保护"""
+
+
+@dataclass
+class TelemetryConfig(ConfigBase):
+    """遥测配置类"""
+
+    enable: bool = True
+    """是否启用遥测"""
+
+
+@dataclass
+class ExperimentalConfig(ConfigBase):
+    """实验功能配置类"""
+
+    enable_friend_chat: bool = False
+    """是否启用好友聊天"""
+
+    talk_allowed_private: set[str] = field(default_factory=lambda: set())
+    """允许聊天的私聊列表"""
+
+    pfc_chatting: bool = False
+    """是否启用PFC"""
+
+
+@dataclass
+class ModelConfig(ConfigBase):
+    """模型配置类"""
+
+    model_max_output_length: int = 800  # 最大回复长度
+
+    reasoning: dict[str, Any] = field(default_factory=lambda: {})
+    """推理模型配置"""
+
+    normal: dict[str, Any] = field(default_factory=lambda: {})
+    """普通模型配置"""
+
+    topic_judge: dict[str, Any] = field(default_factory=lambda: {})
+    """主题判断模型配置"""
+
+    summary: dict[str, Any] = field(default_factory=lambda: {})
+    """摘要模型配置"""
+
+    vlm: dict[str, Any] = field(default_factory=lambda: {})
+    """视觉语言模型配置"""
+
+    heartflow: dict[str, Any] = field(default_factory=lambda: {})
+    """心流模型配置"""
+
+    observation: dict[str, Any] = field(default_factory=lambda: {})
+    """观察模型配置"""
+
+    sub_heartflow: dict[str, Any] = field(default_factory=lambda: {})
+    """子心流模型配置"""
+
+    plan: dict[str, Any] = field(default_factory=lambda: {})
+    """计划模型配置"""
+
+    embedding: dict[str, Any] = field(default_factory=lambda: {})
+    """嵌入模型配置"""
+
+    pfc_action_planner: dict[str, Any] = field(default_factory=lambda: {})
+    """PFC动作规划模型配置"""
+
+    pfc_chat: dict[str, Any] = field(default_factory=lambda: {})
+    """PFC聊天模型配置"""
+
+    pfc_reply_checker: dict[str, Any] = field(default_factory=lambda: {})
+    """PFC回复检查模型配置"""
+
+    tool_use: dict[str, Any] = field(default_factory=lambda: {})
+    """工具使用模型配置"""

src/experimental/PFC/action_planner.py

@@ -114,7 +114,7 @@ class ActionPlanner:
             request_type="action_planning",
         )
         self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
-        self.name = global_config.BOT_NICKNAME
+        self.name = global_config.bot.nickname
         self.private_name = private_name
         self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
         # self.action_planner_info = ActionPlannerInfo() # 移除未使用的变量
@@ -140,7 +140,7 @@ class ActionPlanner:
         # (这部分逻辑不变)
         time_since_last_bot_message_info = ""
         try:
-            bot_id = str(global_config.BOT_QQ)
+            bot_id = str(global_config.bot.qq_account)
             if hasattr(observation_info, "chat_history") and observation_info.chat_history:
                 for i in range(len(observation_info.chat_history) - 1, -1, -1):
                     msg = observation_info.chat_history[i]

src/experimental/PFC/chat_observer.py

@@ -10,7 +10,7 @@ from src.experimental.PFC.chat_states import (
     create_new_message_notification,
     create_cold_chat_notification,
 )
-from src.experimental.PFC.message_storage import MongoDBMessageStorage
+from src.experimental.PFC.message_storage import PeeweeMessageStorage
 from rich.traceback import install
 
 install(extra_lines=3)
@@ -53,7 +53,7 @@ class ChatObserver:
 
         self.stream_id = stream_id
         self.private_name = private_name
-        self.message_storage = MongoDBMessageStorage()
+        self.message_storage = PeeweeMessageStorage()
 
         # self.last_user_speak_time: Optional[float] = None  # 对方上次发言时间
         # self.last_bot_speak_time: Optional[float] = None  # 机器人上次发言时间
@@ -323,7 +323,7 @@ class ChatObserver:
         for msg in messages:
             try:
                 user_info = UserInfo.from_dict(msg.get("user_info", {}))
-                if user_info.user_id == global_config.BOT_QQ:
+                if user_info.user_id == global_config.bot.qq_account:
                     self.update_bot_speak_time(msg["time"])
                 else:
                     self.update_user_speak_time(msg["time"])

src/experimental/PFC/message_sender.py

@@ -42,8 +42,8 @@ class DirectMessageSender:
 
             # 获取麦麦的信息
             bot_user_info = UserInfo(
-                user_id=global_config.BOT_QQ,
+                user_id=global_config.bot.qq_account,
-                user_nickname=global_config.BOT_NICKNAME,
+                user_nickname=global_config.bot.nickname,
                 platform=chat_stream.platform,
             )
 

src/experimental/PFC/message_storage.py

@@ -1,6 +1,9 @@
 from abc import ABC, abstractmethod
 from typing import List, Dict, Any
-from src.common.database import db
+
+# from src.common.database.database import db  # Peewee db 导入
+from src.common.database.database_model import Messages  # Peewee Messages 模型导入
+from playhouse.shortcuts import model_to_dict  # 用于将模型实例转换为字典
 
 
 class MessageStorage(ABC):
@@ -47,28 +50,35 @@ class MessageStorage(ABC):
         pass
 
 
-class MongoDBMessageStorage(MessageStorage):
+class PeeweeMessageStorage(MessageStorage):
-    """MongoDB消息存储实现"""
+    """Peewee消息存储实现"""
 
     async def get_messages_after(self, chat_id: str, message_time: float) -> List[Dict[str, Any]]:
-        query = {"chat_id": chat_id, "time": {"$gt": message_time}}
+        query = (
-        # print(f"storage_check_message: {message_time}")
+            Messages.select()
+            .where((Messages.chat_id == chat_id) & (Messages.time > message_time))
+            .order_by(Messages.time.asc())
+        )
 
-        return list(db.messages.find(query).sort("time", 1))
+        # print(f"storage_check_message: {message_time}")
+        messages_models = list(query)
+        return [model_to_dict(msg) for msg in messages_models]
 
     async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]:
-        query = {"chat_id": chat_id, "time": {"$lt": time_point}}
+        query = (
-
+            Messages.select()
-        messages = list(db.messages.find(query).sort("time", -1).limit(limit))
+            .where((Messages.chat_id == chat_id) & (Messages.time < time_point))
+            .order_by(Messages.time.desc())
+            .limit(limit)
+        )
 
+        messages_models = list(query)
         # 将消息按时间正序排列
-        messages.reverse()
+        messages_models.reverse()
-        return messages
+        return [model_to_dict(msg) for msg in messages_models]
 
     async def has_new_messages(self, chat_id: str, after_time: float) -> bool:
-        query = {"chat_id": chat_id, "time": {"$gt": after_time}}
+        return Messages.select().where((Messages.chat_id == chat_id) & (Messages.time > after_time)).exists()
-
-        return db.messages.find_one(query) is not None
 
 
 # # 创建一个内存消息存储实现，用于测试

src/experimental/PFC/pfc.py

@@ -42,13 +42,14 @@ class GoalAnalyzer:
     """对话目标分析器"""
 
     def __init__(self, stream_id: str, private_name: str):
+        # TODO: API-Adapter修改标记
         self.llm = LLMRequest(
-            model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
+            model=global_config.model.normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
         )
 
         self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
-        self.name = global_config.BOT_NICKNAME
+        self.name = global_config.bot.nickname
-        self.nick_name = global_config.BOT_ALIAS_NAMES
+        self.nick_name = global_config.bot.alias_names
         self.private_name = private_name
         self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
 

src/experimental/PFC/pfc_KnowledgeFetcher.py

@@ -14,9 +14,10 @@ class KnowledgeFetcher:
     """知识调取器"""
 
     def __init__(self, private_name: str):
+        # TODO: API-Adapter修改标记
         self.llm = LLMRequest(
-            model=global_config.llm_normal,
+            model=global_config.model.normal,
-            temperature=global_config.llm_normal["temp"],
+            temperature=global_config.model.normal["temp"],
             max_tokens=1000,
             request_type="knowledge_fetch",
         )

src/experimental/PFC/reply_checker.py

@@ -16,7 +16,7 @@ class ReplyChecker:
         self.llm = LLMRequest(
             model=global_config.llm_PFC_reply_checker, temperature=0.50, max_tokens=1000, request_type="reply_check"
         )
-        self.name = global_config.BOT_NICKNAME
+        self.name = global_config.bot.nickname
         self.private_name = private_name
         self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
         self.max_retries = 3  # 最大重试次数
@@ -43,7 +43,7 @@ class ReplyChecker:
             bot_messages = []
             for msg in reversed(chat_history):
                 user_info = UserInfo.from_dict(msg.get("user_info", {}))
-                if str(user_info.user_id) == str(global_config.BOT_QQ):  # 确保比较的是字符串
+                if str(user_info.user_id) == str(global_config.bot.qq_account):  # 确保比较的是字符串
                     bot_messages.append(msg.get("processed_plain_text", ""))
                 if len(bot_messages) >= 2:  # 只和最近的两条比较
                     break

src/experimental/PFC/reply_generator.py

@@ -93,7 +93,7 @@ class ReplyGenerator:
             request_type="reply_generation",
         )
         self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
-        self.name = global_config.BOT_NICKNAME
+        self.name = global_config.bot.nickname
         self.private_name = private_name
         self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
         self.reply_checker = ReplyChecker(stream_id, private_name)

src/experimental/PFC/waiter.py

@@ -19,7 +19,7 @@ class Waiter:
 
     def __init__(self, stream_id: str, private_name: str):
         self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
-        self.name = global_config.BOT_NICKNAME
+        self.name = global_config.bot.nickname
         self.private_name = private_name
         # self.wait_accumulated_time = 0 # 不再需要累加计时
 

src/experimental/only_message_process.py

@@ -16,7 +16,7 @@ class MessageProcessor:
     @staticmethod
     def _check_ban_words(text: str, chat, userinfo) -> bool:
         """检查消息中是否包含过滤词"""
-        for word in global_config.ban_words:
+        for word in global_config.chat.ban_words:
             if word in text:
                 logger.info(
                     f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
@@ -28,7 +28,7 @@ class MessageProcessor:
     @staticmethod
     def _check_ban_regex(text: str, chat, userinfo) -> bool:
         """检查消息是否匹配过滤正则表达式"""
-        for pattern in global_config.ban_msgs_regex:
+        for pattern in global_config.chat.ban_msgs_regex:
             if pattern.search(text):
                 logger.info(
                     f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"

src/main.py

@@ -40,7 +40,7 @@ class MainSystem:
 
     async def initialize(self):
         """初始化系统组件"""
-        logger.debug(f"正在唤醒{global_config.BOT_NICKNAME}......")
+        logger.debug(f"正在唤醒{global_config.bot.nickname}......")
 
         # 其他初始化任务
         await asyncio.gather(self._init_components())
@@ -84,7 +84,7 @@ class MainSystem:
         asyncio.create_task(chat_manager._auto_save_task())
 
         # 使用HippocampusManager初始化海马体
-        self.hippocampus_manager.initialize(global_config=global_config)
+        self.hippocampus_manager.initialize()
         # await asyncio.sleep(0.5) #防止logger输出飞了
 
         # 将bot.py中的chat_bot.message_process消息处理函数注册到api.py的消息处理基类中
@@ -92,15 +92,15 @@ class MainSystem:
 
         # 初始化个体特征
         self.individuality.initialize(
-            bot_nickname=global_config.BOT_NICKNAME,
+            bot_nickname=global_config.bot.nickname,
-            personality_core=global_config.personality_core,
+            personality_core=global_config.personality.personality_core,
-            personality_sides=global_config.personality_sides,
+            personality_sides=global_config.personality.personality_sides,
-            identity_detail=global_config.identity_detail,
+            identity_detail=global_config.identity.identity_detail,
-            height=global_config.height,
+            height=global_config.identity.height,
-            weight=global_config.weight,
+            weight=global_config.identity.weight,
-            age=global_config.age,
+            age=global_config.identity.age,
-            gender=global_config.gender,
+            gender=global_config.identity.gender,
-            appearance=global_config.appearance,
+            appearance=global_config.identity.appearance,
         )
         logger.success("个体特征初始化成功")
 
@@ -141,7 +141,7 @@ class MainSystem:
     async def build_memory_task():
         """记忆构建任务"""
         while True:
-            await asyncio.sleep(global_config.build_memory_interval)
+            await asyncio.sleep(global_config.memory.memory_build_interval)
             logger.info("正在进行记忆构建")
             await HippocampusManager.get_instance().build_memory()
 
@@ -149,16 +149,18 @@ class MainSystem:
     async def forget_memory_task():
         """记忆遗忘任务"""
         while True:
-            await asyncio.sleep(global_config.forget_memory_interval)
+            await asyncio.sleep(global_config.memory.forget_memory_interval)
             print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
-            await HippocampusManager.get_instance().forget_memory(percentage=global_config.memory_forget_percentage)
+            await HippocampusManager.get_instance().forget_memory(
+                percentage=global_config.memory.memory_forget_percentage
+            )
             print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
 
     @staticmethod
     async def consolidate_memory_task():
         """记忆整合任务"""
         while True:
-            await asyncio.sleep(global_config.consolidate_memory_interval)
+            await asyncio.sleep(global_config.memory.consolidate_memory_interval)
             print("\033[1;32m[记忆整合]\033[0m 开始整合记忆...")
             await HippocampusManager.get_instance().consolidate_memory()
             print("\033[1;32m[记忆整合]\033[0m 记忆整合完成")

src/manager/mood_manager.py

@@ -34,14 +34,14 @@ class MoodUpdateTask(AsyncTask):
     def __init__(self):
         super().__init__(
             task_name="Mood Update Task",
-            wait_before_start=global_config.mood_update_interval,
+            wait_before_start=global_config.mood.mood_update_interval,
-            run_interval=global_config.mood_update_interval,
+            run_interval=global_config.mood.mood_update_interval,
         )
 
         # 从配置文件获取衰减率
-        self.decay_rate_valence: float = 1 - global_config.mood_decay_rate
+        self.decay_rate_valence: float = 1 - global_config.mood.mood_decay_rate
         """愉悦度衰减率"""
-        self.decay_rate_arousal: float = 1 - global_config.mood_decay_rate
+        self.decay_rate_arousal: float = 1 - global_config.mood.mood_decay_rate
         """唤醒度衰减率"""
 
         self.last_update = time.time()

src/tools/not_used/change_mood.py

@@ -44,7 +44,7 @@ class ChangeMoodTool(BaseTool):
             _ori_response = ",".join(response_set)
             # _stance, emotion = await gpt._get_emotion_tags(ori_response, message_processed_plain_text)
             emotion = "平静"
-            mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
+            mood_manager.update_mood_from_emotion(emotion, global_config.mood.mood_intensity_factor)
             return {"name": "change_mood", "content": f"你的心情刚刚变化了，现在的心情是: {emotion}"}
         except Exception as e:
             logger.error(f"心情改变工具执行失败: {str(e)}")

src/tools/tool_can_use/get_knowledge.py

@@ -1,8 +1,10 @@
 from src.tools.tool_can_use.base_tool import BaseTool
 from src.chat.utils.utils import get_embedding
-from src.common.database import db
+from src.common.database.database_model import Knowledges  # Updated import
 from src.common.logger_manager import get_logger
-from typing import Any, Union
+from typing import Any, Union, List  # Added List
+import json  # Added for parsing embedding
+import math  # Added for cosine similarity
 
 logger = get_logger("get_knowledge_tool")
 
@@ -30,6 +32,7 @@ class SearchKnowledgeTool(BaseTool):
         Returns:
             dict: 工具执行结果
         """
+        query = ""  # Initialize query to ensure it's defined in except block
         try:
             query = function_args.get("query")
             threshold = function_args.get("threshold", 0.4)
@@ -48,9 +51,19 @@ class SearchKnowledgeTool(BaseTool):
             logger.error(f"知识库搜索工具执行失败: {str(e)}")
             return {"type": "info", "id": query, "content": f"知识库搜索失败，炸了: {str(e)}"}
 
+    @staticmethod
+    def _cosine_similarity(vec1: List[float], vec2: List[float]) -> float:
+        """计算两个向量之间的余弦相似度"""
+        dot_product = sum(p * q for p, q in zip(vec1, vec2))
+        magnitude1 = math.sqrt(sum(p * p for p in vec1))
+        magnitude2 = math.sqrt(sum(q * q for q in vec2))
+        if magnitude1 == 0 or magnitude2 == 0:
+            return 0.0
+        return dot_product / (magnitude1 * magnitude2)
+
     @staticmethod
     def get_info_from_db(
-        query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
+        query_embedding: list[float], limit: int = 1, threshold: float = 0.5, return_raw: bool = False
     ) -> Union[str, list]:
         """从数据库中获取相关信息
 
@@ -66,66 +79,51 @@ class SearchKnowledgeTool(BaseTool):
         if not query_embedding:
             return "" if not return_raw else []
 
-        # 使用余弦相似度计算
+        similar_items = []
-        pipeline = [
+        try:
-            {
+            all_knowledges = Knowledges.select()
-                "$addFields": {
+            for item in all_knowledges:
-                    "dotProduct": {
+                try:
-                        "$reduce": {
+                    item_embedding_str = item.embedding
-                            "input": {"$range": [0, {"$size": "$embedding"}]},
+                    if not item_embedding_str:
-                            "initialValue": 0,
+                        logger.warning(f"Knowledge item ID {item.id} has empty embedding string.")
-                            "in": {
+                        continue
-                                "$add": [
+                    item_embedding = json.loads(item_embedding_str)
-                                    "$$value",
+                    if not isinstance(item_embedding, list) or not all(
-                                    {
+                        isinstance(x, (int, float)) for x in item_embedding
-                                        "$multiply": [
+                    ):
-                                            {"$arrayElemAt": ["$embedding", "$$this"]},
+                        logger.warning(f"Knowledge item ID {item.id} has invalid embedding format after JSON parsing.")
-                                            {"$arrayElemAt": [query_embedding, "$$this"]},
+                        continue
-                                        ]
+                except json.JSONDecodeError:
-                                    },
+                    logger.warning(f"Failed to parse embedding for knowledge item ID {item.id}")
-                                ]
+                    continue
-                            },
+                except AttributeError:
-                        }
+                    logger.warning(f"Knowledge item ID {item.id} missing 'embedding' attribute or it's not a string.")
-                    },
+                    continue
-                    "magnitude1": {
-                        "$sqrt": {
-                            "$reduce": {
-                                "input": "$embedding",
-                                "initialValue": 0,
-                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
-                            }
-                        }
-                    },
-                    "magnitude2": {
-                        "$sqrt": {
-                            "$reduce": {
-                                "input": query_embedding,
-                                "initialValue": 0,
-                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
-                            }
-                        }
-                    },
-                }
-            },
-            {"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
-            {
-                "$match": {
-                    "similarity": {"$gte": threshold}  # 只保留相似度大于等于阈值的结果
-                }
-            },
-            {"$sort": {"similarity": -1}},
-            {"$limit": limit},
-            {"$project": {"content": 1, "similarity": 1}},
-        ]
 
-        results = list(db.knowledges.aggregate(pipeline))
+                similarity = SearchKnowledgeTool._cosine_similarity(query_embedding, item_embedding)
-        logger.debug(f"知识库查询结果数量: {len(results)}")
+
+                if similarity >= threshold:
+                    similar_items.append({"content": item.content, "similarity": similarity, "raw_item": item})
+
+            # 按相似度降序排序
+            similar_items.sort(key=lambda x: x["similarity"], reverse=True)
+
+            # 应用限制
+            results = similar_items[:limit]
+            logger.debug(f"知识库查询后，符合条件的结果数量: {len(results)}")
+
+        except Exception as e:
+            logger.error(f"从 Peewee 数据库获取知识信息失败: {str(e)}")
+            return "" if not return_raw else []
 
         if not results:
             return "" if not return_raw else []
 
         if return_raw:
-            return results
+            # Peewee 模型实例不能直接序列化为 JSON，如果需要原始模型，调用者需要处理
+            # 这里返回包含内容和相似度的字典列表
+            return [{"content": r["content"], "similarity": r["similarity"]} for r in results]
         else:
             # 返回所有找到的内容，用换行分隔
             return "\n".join(str(result["content"]) for result in results)

src/tools/tool_use.py

@@ -15,7 +15,7 @@ logger = get_logger("tool_use")
 class ToolUser:
     def __init__(self):
         self.llm_model_tool = LLMRequest(
-            model=global_config.llm_tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use"
+            model=global_config.model.tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use"
         )
 
     @staticmethod
@@ -37,7 +37,7 @@ class ToolUser:
             # print(f"intol111111111111111111111111111111111222222222222mid_memory_info：{mid_memory_info}")
 
         # 这些信息应该从调用者传入，而不是从self获取
-        bot_name = global_config.BOT_NICKNAME
+        bot_name = global_config.bot.nickname
         prompt = ""
         prompt += mid_memory_info
         prompt += "你正在思考如何回复群里的消息。\n"

template/bot_config_meta.toml

@@ -1,104 +0,0 @@
-[inner.version]
-describe = "版本号"
-important = true
-can_edit = false
-
-[bot.qq]
-describe = "机器人的QQ号"
-important = true
-can_edit = true
-
-[bot.nickname]
-describe = "机器人的昵称"
-important = true
-can_edit = true
-
-[bot.alias_names]
-describe = "机器人的别名列表，该选项还在调试中，暂时未生效"
-important = false
-can_edit = true
-
-[groups.talk_allowed]
-describe = "可以回复消息的群号码列表"
-important = true
-can_edit = true
-
-[groups.talk_frequency_down]
-describe = "降低回复频率的群号码列表"
-important = false
-can_edit = true
-
-[groups.ban_user_id]
-describe = "禁止回复和读取消息的QQ号列表"
-important = false
-can_edit = true
-
-[personality.personality_core]
-describe = "用一句话或几句话描述人格的核心特点，建议20字以内"
-important = true
-can_edit = true
-
-[personality.personality_sides]
-describe = "用一句话或几句话描述人格的一些细节，条数任意，不能为0，该选项还在调试中"
-important = false
-can_edit = true
-
-[identity.identity_detail]
-describe = "身份特点列表，条数任意，不能为0，该选项还在调试中"
-important = false
-can_edit = true
-
-[identity.age]
-describe = "年龄，单位岁"
-important = false
-can_edit = true
-
-[identity.gender]
-describe = "性别"
-important = false
-can_edit = true
-
-[identity.appearance]
-describe = "外貌特征描述，该选项还在调试中，暂时未生效"
-important = false
-can_edit = true
-
-[platforms.nonebot-qq]
-describe = "nonebot-qq适配器提供的链接"
-important = true
-can_edit = true
-
-[chat.allow_focus_mode]
-describe = "是否允许专注聊天状态"
-important = false
-can_edit = true
-
-[chat.base_normal_chat_num]
-describe = "最多允许多少个群进行普通聊天"
-important = false
-can_edit = true
-
-[chat.base_focused_chat_num]
-describe = "最多允许多少个群进行专注聊天"
-important = false
-can_edit = true
-
-[chat.observation_context_size]
-describe = "观察到的最长上下文大小，建议15，太短太长都会导致脑袋尖尖"
-important = false
-can_edit = true
-
-[chat.message_buffer]
-describe = "启用消息缓冲器，启用此项以解决消息的拆分问题，但会使麦麦的回复延迟"
-important = false
-can_edit = true
-
-[chat.ban_words]
-describe = "需要过滤的消息列表"
-important = false
-can_edit = true
-
-[chat.ban_msgs_regex]
-describe = "需要过滤的消息（原始消息）匹配的正则表达式，匹配到的消息将被过滤（支持CQ码）"
-important = false
-can_edit = true 

template/bot_config_template.toml

@@ -1,18 +1,10 @@
 [inner]
-version = "1.7.0"
+version = "2.0.0"
 
 #----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
 #如果你想要修改配置文件，请在修改后将version的值进行变更
-#如果新增项目，请在BotConfig类下新增相应的变量
+#如果新增项目，请阅读src/config/official_configs.py中的说明
-#1.如果你修改的是[]层级项目，例如你新增了 [memory],那么请在config.py的 load_config函数中的include_configs字典中新增"内容":{
+#
-#"func":memory,
-#"support":">=0.0.0",  #新的版本号
-#"necessary":False      #是否必须
-#}
-#2.如果你修改的是[]下的项目，例如你新增了[memory]下的 memory_ban_words ,那么请在config.py的 load_config函数中的 memory函数下新增版本判断:
-            # if config.INNER_VERSION in SpecifierSet(">=0.0.2"):
-            #     config.memory_ban_words = set(memory_config.get("memory_ban_words", []))
-
 # 版本格式：主版本号.次版本号.修订号，版本号递增规则如下：
 #     主版本号：当你做了不兼容的 API 修改，
 #     次版本号：当你做了向下兼容的功能性新增，
@@ -21,11 +13,11 @@ version = "1.7.0"
 #----以上是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
 
 [bot]
-qq = 1145141919810
+qq_account = 1145141919810
 nickname = "麦麦"
 alias_names = ["麦叠", "牢麦"] #该选项还在调试中，暂时未生效
 
-[groups]
+[chat_target]
 talk_allowed = [
     123,
     123,
@@ -53,10 +45,13 @@ identity_detail = [
     "身份特点",
     "身份特点",
 ]# 条数任意，不能为0, 该选项还在调试中
+
 #外貌特征
-age = 20 # 年龄 单位岁 
+age = 18 # 年龄 单位岁
-gender = "男" # 性别 
+gender = "女" # 性别
-appearance = "用几句话描述外貌特征" # 外貌特征 该选项还在调试中，暂时未生效
+height = "170" # 身高（单位cm）
+weight = "50" # 体重（单位kg）
+appearance = "用一句或几句话描述外貌特征" # 外貌特征 该选项还在调试中，暂时未生效
 
 [platforms] # 必填项目，填写每个平台适配器提供的链接
 qq="http://127.0.0.1:18002/api/message"
@@ -85,11 +80,10 @@ ban_msgs_regex = [
 
 [normal_chat] #普通聊天
 #一般回复参数
-model_reasoning_probability = 0.7 # 麦麦回答时选择推理模型 模型的概率
+reasoning_model_probability = 0.3 # 麦麦回答时选择推理模型的概率（与之相对的，普通模型的概率为1 - reasoning_model_probability）
-model_normal_probability = 0.3 # 麦麦回答时选择一般模型 模型的概率
 
 emoji_chance = 0.2 # 麦麦一般回复时使用表情包的概率，设置为1让麦麦自己决定发不发
-thinking_timeout = 100 # 麦麦最长思考时间，超过这个时间的思考会放弃（往往是api反应太慢）
+thinking_timeout = 120 # 麦麦最长思考时间，超过这个时间的思考会放弃（往往是api反应太慢）
 
 willing_mode = "classical" # 回复意愿模式 —— 经典模式：classical，mxp模式：mxp，自定义模式：custom（需要你自己实现）
 response_willing_amplifier = 1 # 麦麦回复意愿放大系数，一般为1
@@ -100,8 +94,8 @@ mentioned_bot_inevitable_reply = false # 提及 bot 必然回复
 at_bot_inevitable_reply = false # @bot 必然回复
 
 [focus_chat] #专注聊天
-reply_trigger_threshold = 3.6 # 专注聊天触发阈值，越低越容易进入专注聊天
+reply_trigger_threshold = 3.0 # 专注聊天触发阈值，越低越容易进入专注聊天
-default_decay_rate_per_second = 0.95 # 默认衰减率，越大衰减越快，越高越难进入专注聊天
+default_decay_rate_per_second = 0.98 # 默认衰减率，越大衰减越快，越高越难进入专注聊天
 consecutive_no_reply_threshold = 3 # 连续不回复的阈值，越低越容易结束专注聊天
 
 # 以下选项暂时无效
@@ -110,20 +104,20 @@ compress_length_limit = 5 #最多压缩份数，超过该数值的压缩上下
 
 
 [emoji]
-max_emoji_num = 40 # 表情包最大数量
+max_reg_num = 40 # 表情包最大注册数量
-max_reach_deletion = true # 开启则在达到最大数量时删除表情包，关闭则达到最大数量时不删除，只是不会继续收集表情包
+do_replace = true # 开启则在达到最大数量时删除（替换）表情包，关闭则达到最大数量时不会继续收集表情包
-check_interval = 10 # 检查表情包（注册，破损，删除）的时间间隔(分钟)
+check_interval = 120 # 检查表情包（注册，破损，删除）的时间间隔(分钟)
 save_pic = false # 是否保存图片
-save_emoji = false # 是否保存表情包
+cache_emoji = true # 是否缓存表情包
 steal_emoji = true # 是否偷取表情包，让麦麦可以发送她保存的这些表情包
-enable_check = false  # 是否启用表情包过滤，只有符合该要求的表情包才会被保存
+content_filtration = false  # 是否启用表情包过滤，只有符合该要求的表情包才会被保存
-check_prompt = "符合公序良俗" # 表情包过滤要求，只有符合该要求的表情包才会被保存
+filtration_prompt = "符合公序良俗" # 表情包过滤要求，只有符合该要求的表情包才会被保存
 
 [memory]
-build_memory_interval = 2000 # 记忆构建间隔 单位秒   间隔越低，麦麦学习越多，但是冗余信息也会增多
+memory_build_interval = 2000 # 记忆构建间隔 单位秒   间隔越低，麦麦学习越多，但是冗余信息也会增多
-build_memory_distribution = [6.0,3.0,0.6,32.0,12.0,0.4] # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
+memory_build_distribution = [6.0, 3.0, 0.6, 32.0, 12.0, 0.4] # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
-build_memory_sample_num = 8 # 采样数量，数值越高记忆采样次数越多
+memory_build_sample_num = 8 # 采样数量，数值越高记忆采样次数越多
-build_memory_sample_length = 40 # 采样长度，数值越高一段记忆内容越丰富
+memory_build_sample_length = 40 # 采样长度，数值越高一段记忆内容越丰富
 memory_compress_rate = 0.1 # 记忆压缩率 控制记忆精简程度 建议保持默认,调高可以获得更多信息，但是冗余信息也会增多
 
 forget_memory_interval = 1000 # 记忆遗忘间隔 单位秒   间隔越低，麦麦遗忘越频繁，记忆更精简，但更难学习
@@ -135,49 +129,45 @@ consolidation_similarity_threshold = 0.7 # 相似度阈值
 consolidation_check_percentage = 0.01 # 检查节点比例
 
 #不希望记忆的词，已经记忆的不会受到影响
-memory_ban_words = [ 
+memory_ban_words = [ "表情包", "图片", "回复", "聊天记录" ]
-    # "403","张三"
-]
 
 [mood]
 mood_update_interval = 1.0 # 情绪更新间隔 单位秒
 mood_decay_rate = 0.95 # 情绪衰减率
 mood_intensity_factor = 1.0 # 情绪强度因子
 
-[keywords_reaction] # 针对某个关键词作出反应
+[keyword_reaction] # 针对某个关键词作出反应
 enable = true  # 关键词反应功能的总开关
 
-[[keywords_reaction.rules]] # 如果想要新增多个关键词，直接复制本条，修改keywords和reaction即可
+[[keyword_reaction.rules]] # 如果想要新增多个关键词，直接复制本条，修改keywords和reaction即可
 enable = true # 是否启用此条（为了人类在未来AI战争能更好地识别AI（bushi），默认开启）
 keywords = ["人机", "bot", "机器", "入机", "robot", "机器人","ai","AI"] # 会触发反应的关键词
 reaction = "有人说你是人机或者机器人，否定这一事实，攻击或反问他。注意，只有认为你是机器人才回复，否则不要否认" # 触发之后添加的提示词
 
-[[keywords_reaction.rules]] # 就像这样复制
+[[keyword_reaction.rules]] # 就像这样复制
 enable = false # 仅作示例，不会触发
 keywords = ["测试关键词回复","test",""]
 reaction = "回答“测试成功”" # 修复错误的引号
 
-[[keywords_reaction.rules]] # 使用正则表达式匹配句式
+[[keyword_reaction.rules]] # 使用正则表达式匹配句式
 enable = false # 仅作示例，不会触发
 regex = ["^(?P<n>\\S{1,20})是这样的$"] # 将匹配到的词汇命名为n，反应中对应的[n]会被替换为匹配到的内容，若不了解正则表达式请勿编写
 reaction = "请按照以下模板造句：[n]是这样的，xx只要xx就可以，可是[n]要考虑的事情就很多了，比如什么时候xx，什么时候xx，什么时候xx。（请自由发挥替换xx部分，只需保持句式结构，同时表达一种将[n]过度重视的反讽意味）"
 
 [chinese_typo]
 enable = true # 是否启用中文错别字生成器
-error_rate=0.001 # 单字替换概率
+error_rate=0.01 # 单字替换概率
 min_freq=9 # 最小字频阈值
 tone_error_rate=0.1 # 声调错误概率
 word_replace_rate=0.006 # 整词替换概率
 
 [response_splitter]
-enable_response_splitter = true # 是否启用回复分割器
+enable = true # 是否启用回复分割器
-response_max_length = 256 # 回复允许的最大长度
+max_length = 256 # 回复允许的最大长度
-response_max_sentence_num = 4 # 回复允许的最大句子数
+max_sentence_num = 4 # 回复允许的最大句子数
 enable_kaomoji_protection = false # 是否启用颜文字保护
 
-model_max_output_length = 256 # 模型单次返回的最大token数
+[telemetry] #发送统计信息，主要是看全球有多少只麦麦
-
-[remote] #发送统计信息，主要是看全球有多少只麦麦
 enable = true
 
 [experimental] #实验性功能
@@ -194,14 +184,17 @@ pfc_chatting = false # 是否启用PFC聊天，该功能仅作用于私聊，与
 # stream = <true|false> : 用于指定模型是否是使用流式输出
 # 如果不指定，则该项是 False
 
+[model]
+model_max_output_length = 800 # 模型单次返回的最大token数
+
 #这个模型必须是推理模型
-[model.llm_reasoning] # 一般聊天模式的推理回复模型
+[model.reasoning] # 一般聊天模式的推理回复模型
 name = "Pro/deepseek-ai/DeepSeek-R1"
 provider = "SILICONFLOW"
 pri_in = 1.0 #模型的输入价格（非必填，可以记录消耗）
 pri_out = 4.0 #模型的输出价格（非必填，可以记录消耗）
 
-[model.llm_normal] #V3 回复模型 专注和一般聊天模式共用的回复模型
+[model.normal] #V3 回复模型 专注和一般聊天模式共用的回复模型
 name = "Pro/deepseek-ai/DeepSeek-V3"
 provider = "SILICONFLOW"
 pri_in = 2 #模型的输入价格（非必填，可以记录消耗）
@@ -209,13 +202,13 @@ pri_out = 8 #模型的输出价格（非必填，可以记录消耗）
 #默认temp 0.2 如果你使用的是老V3或者其他模型，请自己修改temp参数
 temp = 0.2 #模型的温度，新V3建议0.1-0.3
 
-[model.llm_topic_judge] #主题判断模型：建议使用qwen2.5 7b
+[model.topic_judge] #主题判断模型：建议使用qwen2.5 7b
 name = "Pro/Qwen/Qwen2.5-7B-Instruct"
 provider = "SILICONFLOW"
 pri_in = 0.35
 pri_out = 0.35
 
-[model.llm_summary] #概括模型，建议使用qwen2.5 32b 及以上
+[model.summary] #概括模型，建议使用qwen2.5 32b 及以上
 name = "Qwen/Qwen2.5-32B-Instruct"
 provider = "SILICONFLOW"
 pri_in = 1.26
@@ -227,27 +220,27 @@ provider = "SILICONFLOW"
 pri_in = 0.35
 pri_out = 0.35
 
-[model.llm_heartflow] # 用于控制麦麦是否参与聊天的模型
+[model.heartflow] # 用于控制麦麦是否参与聊天的模型
 name = "Qwen/Qwen2.5-32B-Instruct"
 provider = "SILICONFLOW"
 pri_in = 1.26
 pri_out = 1.26
 
-[model.llm_observation] #观察模型，压缩聊天内容，建议用免费的
+[model.observation] #观察模型，压缩聊天内容，建议用免费的
 # name = "Pro/Qwen/Qwen2.5-7B-Instruct"
 name = "Qwen/Qwen2.5-7B-Instruct"
 provider = "SILICONFLOW"
 pri_in = 0
 pri_out = 0
 
-[model.llm_sub_heartflow] #心流：认真水群时,生成麦麦的内心想法，必须使用具有工具调用能力的模型
+[model.sub_heartflow] #心流：认真水群时,生成麦麦的内心想法，必须使用具有工具调用能力的模型
 name = "Pro/deepseek-ai/DeepSeek-V3"
 provider = "SILICONFLOW"
 pri_in = 2
 pri_out = 8
 temp = 0.3 #模型的温度，新V3建议0.1-0.3
 
-[model.llm_plan] #决策：认真水群时,负责决定麦麦该做什么
+[model.plan] #决策：认真水群时,负责决定麦麦该做什么
 name = "Pro/deepseek-ai/DeepSeek-V3"
 provider = "SILICONFLOW"
 pri_in = 2
@@ -265,7 +258,7 @@ pri_out = 0
 #私聊PFC：需要开启PFC功能，默认三个模型均为硅基流动v3，如果需要支持多人同时私聊或频繁调用，建议把其中的一个或两个换成官方v3或其它模型，以免撞到429
 
 #PFC决策模型
-[model.llm_PFC_action_planner]
+[model.pfc_action_planner]
 name = "Pro/deepseek-ai/DeepSeek-V3"
 provider = "SILICONFLOW"
 temp = 0.3
@@ -273,7 +266,7 @@ pri_in = 2
 pri_out = 8
 
 #PFC聊天模型
-[model.llm_PFC_chat]
+[model.pfc_chat]
 name = "Pro/deepseek-ai/DeepSeek-V3"
 provider = "SILICONFLOW"
 temp = 0.3
@@ -281,7 +274,7 @@ pri_in = 2
 pri_out = 8
 
 #PFC检查模型
-[model.llm_PFC_reply_checker]
+[model.pfc_reply_checker]
 name = "Pro/deepseek-ai/DeepSeek-V3"
 provider = "SILICONFLOW"
 pri_in = 2
@@ -294,7 +287,7 @@ pri_out = 8
 #以下模型暂时没有使用！！
 #以下模型暂时没有使用！！
 
-[model.llm_tool_use] #工具调用模型，需要使用支持工具调用的模型，建议使用qwen2.5 32b
+[model.tool_use] #工具调用模型，需要使用支持工具调用的模型，建议使用qwen2.5 32b
 name = "Qwen/Qwen2.5-32B-Instruct"
 provider = "SILICONFLOW"
 pri_in = 1.26

tests/test_config.py

@@ -0,0 +1,7 @@
+from src.config.config import global_config
+
+
+class TestConfig:
+    def test_load(self):
+        config = global_config
+        print(config)