+
@@ -21,8 +21,8 @@
画师：略nd
-
MaiBot(麦麦)
-+
MaiBot(麦麦)
+
一款专注于 群组聊天 的赛博网友
探索本项目的文档 »
diff --git a/src/api/reload_config.py b/src/api/reload_config.py
index a5f36e3db..1772800b6 100644
--- a/src/api/reload_config.py
+++ b/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")
- config_module.global_config = BotConfig.load_config(config_path=bot_config_path)
+ bot_config_path = os.path.join(Config.get_config_dir(), "bot_config.toml")
+ 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:
diff --git a/src/chat/emoji_system/emoji_manager.py b/src/chat/emoji_system/emoji_manager.py
index 5d800866f..fda0a63fd 100644
--- a/src/chat/emoji_system/emoji_manager.py
+++ b/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 = {
- "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,
- }
+ emotion_str = ",".join(self.emotion) if self.emotion else ""
- # 使用upsert确保记录存在或被更新
- db["emoji"].update_one({"hash": self.hash}, {"$set": emoji_record}, upsert=True)
+ Emoji.create(
+ 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})
- deleted_in_db = result.deleted_count > 0
+ try:
+ 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:
- full_path = emoji_data.get("full_path")
+ for emoji_data in emoji_data_list: # emoji_data is an Emoji model instance
+ 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.get("hash", "")
- # 如果 hash 为空，也跳过？取决于业务逻辑
+ emoji.hash = emoji_data.emoji_hash
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.emotion = emoji_data.get("emotion", [])
- emoji.usage_count = emoji_data.get("usage_count", 0)
- # 优先使用 last_used_time，否则用 timestamp，最后用当前时间
- 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
+ emoji.description = emoji_data.description
+ # Deserialize emotion string from DB to list
+ emoji.emotion = emoji_data.emotion.split(",") if emoji_data.emotion else []
+ emoji.usage_count = emoji_data.usage_count
- # 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_reach_deletion = global_config.max_reach_deletion
+ self.emoji_num_max = global_config.emoji.max_reg_num
+ 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:
- try:
- self._ensure_emoji_collection()
- _ensure_emoji_dir()
- self._initialized = True
- # 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}")
+ peewee_db.connect(reuse_if_open=True)
+ if peewee_db.is_closed():
+ raise RuntimeError("数据库连接失败")
+ _ensure_emoji_dir()
+ Emoji.create_table(safe=True) # Ensures table exists
- 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 _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):
+ def record_usage(self, emoji_hash: str) -> None:
"""记录表情使用次数"""
try:
- db.emoji.update_one({"hash": emoji_hash}, {"$inc": {"usage_count": 1}})
- for emoji in self.emoji_objects:
- if emoji.hash == emoji_hash:
- emoji.usage_count += 1
- break
-
+ emoji_update = Emoji.get(Emoji.emoji_hash == emoji_hash)
+ emoji_update.usage_count += 1
+ emoji_update.last_used_time = time.time() # Update last used time
+ emoji_update.save() # Persist changes to DB
+ 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~
- emoji_similarities.append((emoji, max_similarity, best_matching_emotion)) # 把 emotion 也存起来喵~
+ if best_matching_emotion:
+ 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 = (
- emoji_similarities[:10] if len(emoji_similarities) > 10 else emoji_similarities
- ) # 改个名字，更清晰喵~
+ top_emojis = emoji_similarities[:10] if len(emoji_similarities) > 10 else emoji_similarities
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
diff --git a/src/chat/focus_chat/expressors/default_expressor.py b/src/chat/focus_chat/expressors/default_expressor.py
index 37b634b37..ccbc1ca56 100644
--- a/src/chat/focus_chat/expressors/default_expressor.py
+++ b/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,
- temperature=global_config.llm_normal["temp"],
+ model=global_config.model.normal,
+ 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_nickname=global_config.BOT_NICKNAME,
+ user_id=global_config.bot.qq_account,
+ 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_nickname=global_config.BOT_NICKNAME,
+ user_id=global_config.bot.qq_account,
+ user_nickname=global_config.bot.nickname,
platform=self.chat_stream.platform,
)
diff --git a/src/chat/focus_chat/expressors/exprssion_learner.py b/src/chat/focus_chat/expressors/exprssion_learner.py
index 942162bc8..7766fde56 100644
--- a/src/chat/focus_chat/expressors/exprssion_learner.py
+++ b/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}")
diff --git a/src/chat/focus_chat/heartflow_processor.py b/src/chat/focus_chat/heartflow_processor.py
index bbfa4ce46..a4cf360a5 100644
--- a/src/chat/focus_chat/heartflow_processor.py
+++ b/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}")
diff --git a/src/chat/focus_chat/heartflow_prompt_builder.py b/src/chat/focus_chat/heartflow_prompt_builder.py
index 74bac0a1f..af526eb88 100644
--- a/src/chat/focus_chat/heartflow_prompt_builder.py
+++ b/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:
- if rule.get("enable", False):
- if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
- logger.info(
- f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
- )
- keywords_reaction_prompt += rule.get("reaction", "") + "，"
+ for rule in global_config.keyword_reaction.rules:
+ if rule.enable:
+ if any(keyword in message_txt for keyword in rule.keywords):
+ logger.info(f"检测到以下关键词之一：{rule.keywords}，触发反应：{rule.reaction}")
+ keywords_reaction_prompt += f"{rule.reaction}，"
else:
- for pattern in rule.get("regex", []):
- result = pattern.search(message_txt)
- if result:
- reaction = rule.get("reaction", "")
+ for pattern in rule.regex:
+ if result := pattern.search(message_txt):
+ reaction = rule.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_other_names="/".join(global_config.BOT_ALIAS_NAMES),
+ bot_name=global_config.bot.nickname,
+ 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_other_names="/".join(global_config.BOT_ALIAS_NAMES),
+ bot_name=global_config.bot.nickname,
+ 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)}"
@@ -455,70 +429,70 @@ 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))
- logger.debug(f"知识库查询结果数量: {len(results)}")
+ results_with_similarity = []
+ 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 limited_results
+ else:
+ return "\n".join(str(result["content"]) for result in limited_results)
+
+ except Exception as e:
+ logger.error(f"Error querying Knowledges with Peewee: {e}")
return "" if not return_raw else []
- if return_raw:
- return 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)
-
init_prompt()
prompt_builder = PromptBuilder()
diff --git a/src/chat/focus_chat/info_processors/chattinginfo_processor.py b/src/chat/focus_chat/info_processors/chattinginfo_processor.py
index bb565ee7e..8d1eb9793 100644
--- a/src/chat/focus_chat/info_processors/chattinginfo_processor.py
+++ b/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)
- if len(obs.mid_memorys) > obs.max_mid_memory_len:
- obs.mid_memorys.pop(0) # 移除最旧的
+ obs.mid_memories.append(mid_memory)
+ if len(obs.mid_memories) > obs.max_mid_memory_len:
+ 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"
diff --git a/src/chat/focus_chat/info_processors/mind_processor.py b/src/chat/focus_chat/info_processors/mind_processor.py
index 09228174c..afd7921d4 100644
--- a/src/chat/focus_chat/info_processors/mind_processor.py
+++ b/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,
- temperature=global_config.llm_sub_heartflow["temp"],
+ model=global_config.model.sub_heartflow,
+ temperature=global_config.model.sub_heartflow["temp"],
max_tokens=800,
request_type="sub_heart_flow",
)
diff --git a/src/chat/focus_chat/info_processors/tool_processor.py b/src/chat/focus_chat/info_processors/tool_processor.py
index 92c1b607a..de9a9a216 100644
--- a/src/chat/focus_chat/info_processors/tool_processor.py
+++ b/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",
)
diff --git a/src/chat/focus_chat/memory_activator.py b/src/chat/focus_chat/memory_activator.py
index dae310c06..4fcd37302 100644
--- a/src/chat/focus_chat/memory_activator.py
+++ b/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 = []
diff --git a/src/chat/heart_flow/heartflow.py b/src/chat/heart_flow/heartflow.py
index ad876bcf0..748c8331e 100644
--- a/src/chat/heart_flow/heartflow.py
+++ b/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
diff --git a/src/chat/heart_flow/interest_chatting.py b/src/chat/heart_flow/interest_chatting.py
index 45f7fe952..bce372b5c 100644
--- a/src/chat/heart_flow/interest_chatting.py
+++ b/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
diff --git a/src/chat/heart_flow/mai_state_manager.py b/src/chat/heart_flow/mai_state_manager.py
index 7dea910e9..017656ad2 100644
--- a/src/chat/heart_flow/mai_state_manager.py
+++ b/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
-base_normal_chat_num = global_config.base_normal_chat_num
-base_focused_chat_num = global_config.base_focused_chat_num
-
-
-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
+MAX_NORMAL_CHAT_NUM_PEEKING = int(global_config.chat.base_normal_chat_num / 2)
+MAX_NORMAL_CHAT_NUM_NORMAL = global_config.chat.base_normal_chat_num
+MAX_NORMAL_CHAT_NUM_FOCUSED = global_config.chat.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_NORMAL = base_focused_chat_num
-MAX_FOCUSED_CHAT_NUM_FOCUSED = base_focused_chat_num + 2
+MAX_FOCUSED_CHAT_NUM_PEEKING = int(global_config.chat.base_focused_chat_num / 2)
+MAX_FOCUSED_CHAT_NUM_NORMAL = global_config.chat.base_focused_chat_num
+MAX_FOCUSED_CHAT_NUM_FOCUSED = global_config.chat.base_focused_chat_num + 2
# -- 状态定义 --
diff --git a/src/chat/heart_flow/observation/chatting_observation.py b/src/chat/heart_flow/observation/chatting_observation.py
index 6bb72bca0..9ea18b471 100644
--- a/src/chat/heart_flow/observation/chatting_observation.py
+++ b/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.nick_name = global_config.BOT_ALIAS_NAMES
- self.max_now_obs_len = global_config.observation_context_size
- self.overlap_len = global_config.compressed_length
- self.mid_memorys = []
- self.max_mid_memory_len = global_config.compress_length_limit
+ self.name = global_config.bot.nickname
+ self.nick_name = global_config.bot.alias_names
+ self.max_now_obs_len = global_config.chat.observation_context_size
+ self.overlap_len = global_config.focus_chat.compressed_length
+ self.mid_memories = []
+ 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
diff --git a/src/chat/heart_flow/subheartflow_manager.py b/src/chat/heart_flow/subheartflow_manager.py
index a4bff8338..bf4ddf7e1 100644
--- a/src/chat/heart_flow/subheartflow_manager.py
+++ b/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")
diff --git a/src/chat/memory_system/Hippocampus.py b/src/chat/memory_system/Hippocampus.py
index 70eb679c9..2de769205 100644
--- a/src/chat/memory_system/Hippocampus.py
+++ b/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):
- # 使用导入的 MemoryConfig dataclass 和其 from_global_config 方法
- self.config = MemoryConfig.from_global_config(global_config)
+ def initialize(self):
# 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")
- self.llm_summary = LLMRequest(self.config.llm_summary, request_type="memory")
+ # TODO: API-Adapter修改标记
+ 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],
- std_hours1=self.config.memory_build_distribution[1],
- weight1=self.config.memory_build_distribution[2],
- n_hours2=self.config.memory_build_distribution[3],
- std_hours2=self.config.memory_build_distribution[4],
- weight2=self.config.memory_build_distribution[5],
- total_samples=self.config.build_memory_sample_num,
+ n_hours1=global_config.memory.memory_build_distribution[0],
+ std_hours1=global_config.memory.memory_build_distribution[1],
+ weight1=global_config.memory.memory_build_distribution[2],
+ n_hours2=global_config.memory.memory_build_distribution[3],
+ std_hours2=global_config.memory.memory_build_distribution[4],
+ weight2=global_config.memory.memory_build_distribution[5],
+ 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
- similarity_threshold = self.config.consolidation_similarity_threshold
+ percentage = global_config.memory.consolidate_memory_percentage
+ 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}
- 记忆构建分布: {config.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
+ 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: {global_config.memory.memory_build_distribution}
+ 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
diff --git a/src/chat/memory_system/debug_memory.py b/src/chat/memory_system/debug_memory.py
index baf745409..b09e703a1 100644
--- a/src/chat/memory_system/debug_memory.py
+++ b/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
diff --git a/src/chat/memory_system/manually_alter_memory.py b/src/chat/memory_system/manually_alter_memory.py
index ce5abbba7..9bbf59f5b 100644
--- a/src/chat/memory_system/manually_alter_memory.py
+++ b/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()
diff --git a/src/chat/memory_system/memory_config.py b/src/chat/memory_system/memory_config.py
deleted file mode 100644
index b82e54ec1..000000000
--- a/src/chat/memory_system/memory_config.py
+++ /dev/null
@@ -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
- )
diff --git a/src/chat/message_receive/bot.py b/src/chat/message_receive/bot.py
index 3c9e4420c..0e35f6f6e 100644
--- a/src/chat/message_receive/bot.py
+++ b/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:
diff --git a/src/chat/message_receive/chat_stream.py b/src/chat/message_receive/chat_stream.py
index 53ebd5026..723d6da47 100644
--- a/src/chat/message_receive/chat_stream.py
+++ b/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})
- if data:
- stream = ChatStream.from_dict(data)
+ def _db_find_stream_sync(s_id: str):
+ return ChatStreams.get_or_none(ChatStreams.stream_id == s_id)
+
+ 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.saved = 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:
- stream = ChatStream.from_dict(data)
- self.streams[stream.stream_id] = stream
+
+ 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
diff --git a/src/chat/message_receive/message_buffer.py b/src/chat/message_receive/message_buffer.py
index f3cf63d0a..2df256ce5 100644
--- a/src/chat/message_receive/message_buffer.py
+++ b/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
diff --git a/src/chat/message_receive/message_sender.py b/src/chat/message_receive/message_sender.py
index 5db34fdea..cf5877989 100644
--- a/src/chat/message_receive/message_sender.py
+++ b/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}"
)
diff --git a/src/chat/message_receive/storage.py b/src/chat/message_receive/storage.py
index cae029a11..d0041cd51 100644
--- a/src/chat/message_receive/storage.py
+++ b/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")
@@ -29,42 +30,66 @@ class MessageStorage:
else:
filtered_detailed_plain_text = ""
- message_data = {
- "message_id": message.message_info.message_id,
- "time": message.message_info.time,
- "chat_id": chat_stream.stream_id,
- "chat_info": chat_stream.to_dict(),
- "user_info": message.message_info.user_info.to_dict(),
- # 使用过滤后的文本
- "processed_plain_text": filtered_processed_plain_text,
- "detailed_plain_text": filtered_detailed_plain_text,
- "memorized_times": message.memorized_times,
- }
- db.messages.insert_one(message_data)
+ chat_info_dict = chat_stream.to_dict()
+ user_info_dict = message.message_info.user_info.to_dict()
+
+ # message_id 现在是 TextField，直接使用字符串值
+ msg_id = message.message_info.message_id
+
+ # 安全地获取 group_info, 如果为 None 则视为空字典
+ group_info_from_chat = chat_info_dict.get("group_info") or {}
+ # 安全地获取 user_info, 如果为 None 则视为空字典 (以防万一)
+ user_info_from_chat = chat_info_dict.get("user_info") or {}
+
+ 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():
- db.create_collection("recalled_messages")
- else:
- try:
- message_data = {
- "message_id": message_id,
- "time": time,
- "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")
+ # Table creation is handled by initialize_database in database_model.py
+ try:
+ RecalledMessages.create(
+ message_id=message_id,
+ time=float(time), # Assuming time is a string representing a float timestamp
+ stream_id=chat_stream.stream_id,
+ )
+ except Exception:
+ logger.exception("存储撤回消息失败")
@staticmethod
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")
diff --git a/src/chat/models/utils_model.py b/src/chat/models/utils_model.py
index e662a8e33..f6528856d 100644
--- a/src/chat/models/utils_model.py
+++ b/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.base_url = os.environ[model["base_url"]]
+ self.api_key = os.environ[f"{model['provider']}_KEY"]
+ 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
- db.llm_usage.create_index([("timestamp", 1)])
- db.llm_usage.create_index([("model_name", 1)])
- db.llm_usage.create_index([("user_id", 1)])
- db.llm_usage.create_index([("request_type", 1)])
+ # 使用 Peewee 创建表，safe=True 表示如果表已存在则不会抛出错误
+ db.create_tables([LLMUsage], safe=True)
+ logger.debug("LLMUsage 表已初始化/确保存在。")
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 = {
- "model_name": self.model_name,
- "user_id": user_id,
- "request_type": request_type,
- "endpoint": endpoint,
- "prompt_tokens": prompt_tokens,
- "completion_tokens": completion_tokens,
- "total_tokens": total_tokens,
- "cost": self._calculate_cost(prompt_tokens, completion_tokens),
- "status": "success",
- "timestamp": datetime.now(),
- }
- db.llm_usage.insert_one(usage_data)
+ # 使用 Peewee 模型创建记录
+ LLMUsage.create(
+ model_name=self.model_name,
+ user_id=user_id,
+ request_type=request_type,
+ endpoint=endpoint,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=total_tokens,
+ cost=self._calculate_cost(prompt_tokens, completion_tokens),
+ status="success",
+ timestamp=datetime.now(), # Peewee 会处理 DateTimeField
+ )
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:
- global_config.llm_normal["name"] = self.model_name
+ if global_config.model.normal.get("name") == old_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:
- global_config.llm_reasoning["name"] = self.model_name
+ if global_config.model.reasoning.get("name") == old_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")
diff --git a/src/chat/normal_chat/normal_chat.py b/src/chat/normal_chat/normal_chat.py
index 9dc2454ff..96cc2b8cb 100644
--- a/src/chat/normal_chat/normal_chat.py
+++ b/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_nickname=global_config.BOT_NICKNAME,
+ user_id=global_config.bot.qq_account,
+ 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_nickname=global_config.BOT_NICKNAME,
+ user_id=global_config.bot.qq_account,
+ 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_nickname=global_config.BOT_NICKNAME,
+ user_id=global_config.bot.qq_account,
+ 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'}]"
diff --git a/src/chat/normal_chat/normal_chat_generator.py b/src/chat/normal_chat/normal_chat_generator.py
index aec65ed1d..631f7baa5 100644
--- a/src/chat/normal_chat/normal_chat_generator.py
+++ b/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,
- temperature=global_config.llm_normal["temp"],
+ model=global_config.model.normal,
+ 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」
diff --git a/src/chat/normal_chat/willing/mode_classical.py b/src/chat/normal_chat/willing/mode_classical.py
index e96aa77a7..a9f04273a 100644
--- a/src/chat/normal_chat/willing/mode_classical.py
+++ b/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:
- current_willing += 1
- elif willing_info.is_mentioned_bot:
- current_willing += 0.05
+ if willing_info.is_mentioned_bot:
+ current_willing += 1 if current_willing < 1.0 else 0.05
is_emoji_not_reply = False
if willing_info.is_emoji:
- if self.global_config.emoji_response_penalty != 0:
- current_willing *= self.global_config.emoji_response_penalty
+ if global_config.normal_chat.emoji_response_penalty != 0:
+ 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)
diff --git a/src/chat/normal_chat/willing/mode_mxp.py b/src/chat/normal_chat/willing/mode_mxp.py
index 78120ac53..1e7d5856d 100644
--- a/src/chat/normal_chat/willing/mode_mxp.py
+++ b/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:
- probability /= self.down_frequency_rate
+ if w_info.group_info and w_info.group_info.group_id in global_config.chat_target.talk_frequency_down_groups:
+ probability /= global_config.normal_chat.down_frequency_rate
self.temporary_willing = current_willing
diff --git a/src/chat/normal_chat/willing/willing_manager.py b/src/chat/normal_chat/willing/willing_manager.py
index 37e623d11..bbc5dcc0a 100644
--- a/src/chat/normal_chat/willing/willing_manager.py
+++ b/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)
diff --git a/src/chat/person_info/person_info.py b/src/chat/person_info/person_info.py
index c8394a195..562cdc235 100644
--- a/src/chat/person_info/person_info.py
+++ b/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,
- "user_id": None,
- "nickname": None,
- # "age" : 0,
+ "platform": "unknown", # 提供非None的默认值
+ "user_id": "unknown", # 提供非None的默认值
+ "nickname": "Unknown", # 提供非None的默认值
"relationship_value": 0,
- # "saved" : True,
- # "impression" : None,
- # "gender" : Unkown,
- "konw_time": 0,
+ "know_time": 0, # 修正拼写：konw_time -> know_time
"msg_interval": 2000,
- "msg_interval_list": [],
- "user_cardname": None, # 添加群名片
- "user_avatar": None, # 添加头像信息（例如URL或标识符）
-} # 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
+ "msg_interval_list": [], # 将作为 JSON 字符串存储在 Peewee 的 TextField
+ "user_cardname": None, # 注意：此字段不在 PersonInfo Peewee 模型中
+ "user_avatar": None, # 注意：此字段不在 PersonInfo Peewee 模型中
+}
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():
- db.create_collection("person_info")
- db.person_info.create_index("person_id", unique=True)
+ try:
+ db.connect(reuse_if_open=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})
- for doc in cursor:
- if doc.get("person_name"):
- 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")
+ try:
+ for record in PersonInfo.select(PersonInfo.person_id, PersonInfo.person_name).where(
+ PersonInfo.person_name.is_null(False)
+ ):
+ 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:
- return True
- else:
+
+ def _db_check_known_sync(p_id: str):
+ return PersonInfo.get_or_none(PersonInfo.person_id == p_id) is not None
+
+ try:
+ return await asyncio.to_thread(_db_check_known_sync, person_id)
+ except Exception as e:
+ logger.error(f"检查用户 {person_id} 是否已知时出错 (Peewee): {e}")
return False
def get_person_id_by_person_name(self, person_name: str):
@@ -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:
- if key != "person_id" and key in data:
- _person_info_default[key] = data[key]
+ for key, value in data.items():
+ if key in model_fields:
+ 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():
- logger.debug(f"更新'{field_name}'失败，未定义的字段")
+ if field_name not in PersonInfo._meta.fields:
+ 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 record:
+ if f_name == "msg_interval_list" and isinstance(val, list):
+ setattr(record, f_name, json.dumps(val))
+ else:
+ setattr(record, f_name, val)
+ record.save()
+ return True, False
+ return False, True
- if document:
- db.person_info.update_one({"person_id": person_id}, {"$set": {field_name: value}})
- else:
- data[field_name] = value
- logger.debug(f"更新时{person_id}不存在，已新建")
- await self.create_person_info(person_id, data)
+ 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 document:
- return True
- else:
+ if field_name not in PersonInfo._meta.fields:
+ 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
+ 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"]:
- logger.error("生成的昵称为空，重试中...")
+ if not result or not result.get("nickname"):
+ 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
- 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"])
+ generated_nickname = result["nickname"]
- self.person_name_list[person_id] = result["nickname"]
- # logger.debug(f"用户 {person_id} 的名称已更新为 {result['nickname']}，原因：{result['reason']}")
+ is_duplicate = False
+ 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 += "、"
+ 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.debug(f"生成的昵称 {result['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})
- if result.deleted_count > 0:
- logger.debug(f"删除成功：person_id={person_id}")
+ def _db_delete_sync(p_id: str):
+ try:
+ 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:
- logger.debug(f"get_value获取失败：字段'{field_name}'未定义")
+ def _db_get_value_sync(p_id: str, f_name: str):
+ record = PersonInfo.get_or_none(PersonInfo.person_id == p_id)
+ if record:
+ val = getattr(record, f_name)
+ 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:
- return document[field_name]
+ if value is not None:
+ return value
else:
- default_value = copy.deepcopy(person_info_default[field_name])
- logger.trace(f"获取{person_id}的{field_name}失败，已返回默认值{default_value}")
+ default_value = copy.deepcopy(person_info_default.get(field_name))
+ 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(
- document.get(field, person_info_default[field]) if document else person_info_default[field]
- )
+
+ def _db_get_record_sync(p_id: str):
+ return PersonInfo.get_or_none(PersonInfo.person_id == p_id)
+
+ record = await asyncio.to_thread(_db_get_record_sync, person_id)
+
+ for field_name in field_names:
+ if field_name not in PersonInfo._meta.fields:
+ 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"""
- # 获取所有已定义的字段名
- defined_fields = set(person_info_default.keys())
-
- 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"]}, {"$unset": {field: 1 for field in undefined_fields}}
- )
-
- if update_result.modified_count > 0:
- logger.debug(f"已清理文档 {document['_id']} 的未定义字段: {undefined_fields}")
-
- return
-
- except Exception as e:
- logger.error(f"清理未定义字段时出错: {e}")
- return
+ """删除所有项里的未定义字段 - 对于Peewee (SQL)，此操作通常不适用，因为模式是固定的。"""
+ logger.info(
+ "del_all_undefined_field: 对于使用Peewee的SQL数据库，此操作通常不适用或不需要，因为表结构是预定义的。"
+ )
+ return
@staticmethod
async def get_specific_value_list(
field_name: str,
- way: Callable[[Any], bool], # 接受任意类型值
+ way: Callable[[Any], bool],
) -> Dict[str, Any]:
"""
获取满足条件的字段值字典
-
- Args:
- field_name: 目标字段名
- way: 判断函数 (value: Any) -> bool
-
- Returns:
- {person_id: value} | {}
-
- Example:
- # 查找所有nickname包含"admin"的用户
- result = manager.specific_value_list(
- "nickname",
- lambda x: "admin" in x.lower()
- )
"""
- if field_name not in person_info_default:
- logger.error(f"字段检查失败：'{field_name}'未定义")
+ if field_name not in PersonInfo._meta.fields:
+ logger.error(f"字段检查失败：'{field_name}'未在 PersonInfo Peewee 模型中定义")
return {}
+ def _db_get_specific_sync(f_name: str):
+ found_results = {}
+ try:
+ for record in PersonInfo.select(PersonInfo.person_id, getattr(PersonInfo, f_name)):
+ value = getattr(record, f_name)
+ if f_name == "msg_interval_list" and isinstance(value, str):
+ try:
+ processed_value = json.loads(value)
+ except json.JSONDecodeError:
+ logger.warning(f"跳过记录 {record.person_id}，无法解析 msg_interval_list: {value}")
+ continue
+ else:
+ processed_value = value
+
+ if way(processed_value):
+ found_results[record.person_id] = processed_value
+ except Exception as e_query:
+ logger.error(f"数据库查询失败 (Peewee specific_value_list for {f_name}): {str(e_query)}", exc_info=True)
+ return found_results
+
try:
- result = {}
- for doc in db.person_info.find({field_name: {"$exists": True}}, {"person_id": 1, field_name: 1, "_id": 0}):
- try:
- value = doc[field_name]
- if way(value):
- result[doc["person_id"]] = value
- except (KeyError, TypeError, ValueError) as e:
- logger.debug(f"记录{doc.get('person_id')}处理失败: {str(e)}")
- continue
-
- return result
-
+ 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()
- logger.info(f"个人信息推断启动: {current_time.strftime('%Y-%m-%d %H:%M:%S')}")
+ current_time_dt = datetime.datetime.now()
+ 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 = False
- msg_interval_lists = await self.get_specific_value_list(
+ msg_interval_map_generated = False
+ msg_interval_lists_map = 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
- msg_interval_map = True
+ if len(time_interval) >= 30 + 10:
+ 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]
-
- # 计算截断后数据的 37% 分位数
- if trimmed_interval: # 确保截断后列表不为空
- msg_interval = int(round(np.percentile(trimmed_interval, 37)))
- # 更新数据库
- 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}")
+ if trimmed_interval:
+ msg_interval_val = int(round(np.percentile(trimmed_interval, 37)))
+ await self.update_one_field(person_id, "msg_interval", msg_interval_val)
+ 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_val}"
+ )
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:
- logger.trace(f"用户{person_id}消息间隔计算失败: {type(e).__name__}: {str(e)}")
+ except Exception as e_inner:
+ logger.trace(f"用户{person_id}消息间隔计算失败: {type(e_inner).__name__}: {str(e_inner)}")
continue
- # 其他...
-
- if msg_interval_map:
+ if msg_interval_map_generated:
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
- # 使用静态方法 get_person_id，因此可以直接调用 db
- document = db.person_info.find_one({"person_id": person_id})
+ def _db_check_exists_sync(p_id: str):
+ return PersonInfo.get_or_none(PersonInfo.person_id == p_id)
- if document is None:
- logger.info(f"用户 {platform}:{user_id} (person_id: {person_id}) 不存在，将创建新记录。")
+ record = await asyncio.to_thread(_db_check_exists_sync, person_id)
+
+ 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
- # 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
+ "know_time": int(datetime.datetime.now().timestamp()), # 修正拼写：konw_time -> know_time
}
- # 过滤掉值为 None 的初始数据
- initial_data = {k: v for k, v in initial_data.items() if v is not None}
+ model_fields = PersonInfo._meta.fields.keys()
+ 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 PersonInfoManager.create_person_info(person_id, data=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}")
+ await self.create_person_info(person_id, data=filtered_initial_data)
+ logger.debug(f"已为 {person_id} 创建新记录，初始数据 (filtered for model): {filtered_initial_data}")
return person_id
@@ -533,35 +586,55 @@ 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():
- if name == person_name:
+ for pid, name_in_cache in self.person_name_list.items():
+ 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
- if found_person_id:
- required_fields = ["person_id", "platform", "user_id", "nickname", "user_cardname", "user_avatar"]
- person_data = await self.get_values(found_person_id, required_fields)
- if person_data: # 确保 get_values 成功返回
- return person_data
+ def _db_find_by_name_sync(p_name_to_find: str):
+ return PersonInfo.get_or_none(PersonInfo.person_name == p_name_to_find)
+
+ record = await asyncio.to_thread(_db_find_by_name_sync, person_name)
+ if record:
+ 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
- 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.error(f"逻辑错误：未能为 '{person_name}' 确定 person_id")
- 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:
+ logger.warning(f"找到了 person_id '{found_person_id}' 但 get_values 返回空 (Peewee)")
+ return None
+
+ logger.error(f"逻辑错误：未能为 '{person_name}' 确定 person_id (Peewee)")
+ return None
person_info_manager = PersonInfoManager()
diff --git a/src/chat/utils/chat_message_builder.py b/src/chat/utils/chat_message_builder.py
index e5ccd82a7..d3a062680 100644
--- a/src/chat/utils/chat_message_builder.py
+++ b/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:
- person_name = f"{global_config.BOT_NICKNAME}(你)"
+ if replace_bot_name and user_id == global_config.bot.qq_account:
+ 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)
diff --git a/src/chat/utils/info_catcher.py b/src/chat/utils/info_catcher.py
index 174bb5b49..93cda5113 100644
--- a/src/chat/utils/info_catcher.py
+++ b/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 = db.messages.find(
- {"chat_id": chat_id, "time": {"$gt": time_start, "$lt": time_end}}
- ).sort("time", -1)
+ messages_between_query = (
+ Messages.select()
+ .where((Messages.chat_id == chat_id) & (Messages.time > time_start) & (Messages.time < time_end))
+ .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"最后一条消息时间: {result[-1]['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}")
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 = message.message_info.message_id
- chat_id = message.chat_stream.stream_id
+ message_id_val = message.message_info.message_id
+ chat_id_val = 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 = (
- db.messages.find({"chat_id": chat_id, "message_id": {"$lt": message_id}})
- .sort("time", -1)
- .limit(self.context_length * 3)
- ) # 获取更多历史信息
+ messages_before_query = (
+ Messages.select()
+ .where((Messages.chat_id == chat_id_val) & (Messages.message_id < message_id_val))
+ .order_by(Messages.time.desc())
+ .limit(global_config.chat.observation_context_size * 3)
+ )
- 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:
- if not isinstance(message, dict):
- message = self.message_to_dict(message)
- # print(message)
+ for msg_item in message_list:
+ processed_msg_item = msg_item
+ if not isinstance(msg_item, dict):
+ processed_msg_item = self.message_to_dict(msg_item)
+
+ if not processed_msg_item:
+ continue
lite_message = {
- "time": message["time"],
- "user_nickname": message["user_info"]["user_nickname"],
- "processed_plain_text": message["processed_plain_text"],
+ "time": processed_msg_item.get("time"),
+ "user_nickname": processed_msg_item.get("user_nickname"),
+ "processed_plain_text": processed_msg_item.get("processed_plain_text"),
}
result.append(lite_message)
-
return result
@staticmethod
- def message_to_dict(message):
- if not message:
+ def message_to_dict(msg_obj):
+ if not msg_obj:
return None
- if isinstance(message, dict):
- return message
- return {
- # "message_id": message.message_info.message_id,
- "time": message.message_info.time,
- "user_id": message.message_info.user_info.user_id,
- "user_nickname": message.message_info.user_info.user_nickname,
- "processed_plain_text": message.processed_plain_text,
- # "detailed_plain_text": message.detailed_plain_text
- }
+ if isinstance(msg_obj, dict):
+ return msg_obj
- def done_catch(self):
- """将收集到的信息存储到数据库的 thinking_log 集合中喵~"""
- try:
- # 将消息对象转换为可序列化的字典喵~
-
- 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,
+ if isinstance(msg_obj, Messages):
+ return {
+ "time": msg_obj.time,
+ "user_id": msg_obj.user_id,
+ "user_nickname": msg_obj.user_nickname,
+ "processed_plain_text": msg_obj.processed_plain_text,
}
- # 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~
- # if self.response_mode == "heart_flow":
- # thinking_log_data["mode_specific_data"] = self.heartflow_data
- # elif self.response_mode == "reasoning":
- # thinking_log_data["mode_specific_data"] = self.reasoning_data
+ 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,
+ }
- # 将数据插入到 thinking_log 集合中喵~
- db.thinking_log.insert_one(thinking_log_data)
+ 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 表中喵~"""
+ try:
+ trigger_info_dict = self.message_to_dict(self.trigger_response_message)
+ response_info_dict = {
+ "time": self.response_time,
+ "message": self.response_messages,
+ }
+ 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)
+
+ log_entry = ThinkingLog(
+ chat_id=self.chat_id,
+ trigger_text=self.trigger_response_text,
+ response_text=self.response_text,
+ trigger_info_json=json.dumps(trigger_info_dict) if trigger_info_dict else None,
+ response_info_json=json.dumps(response_info_dict),
+ timing_results_json=json.dumps(self.timing_results),
+ 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:
diff --git a/src/chat/utils/statistic.py b/src/chat/utils/statistic.py
index 3f9832926..a657ae85b 100644
--- a/src/chat/utils/statistic.py
+++ b/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():
- # 初始化数据库（在线时长）
- 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)])
+ with db.atomic(): # Use atomic operations for schema changes
+ OnlineTime.create_table(safe=True) # Creates table if it doesn't exist, Peewee handles indexes from model
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(
- {"_id": self.record_id},
- {
- "$set": {
- "end_timestamp": datetime.now() + timedelta(minutes=1),
- }
- },
- )
- else:
+ query = OnlineTime.update(end_timestamp=extended_end_time).where(OnlineTime.id == self.record_id)
+ updated_rows = query.execute()
+ if updated_rows == 0:
+ # Record might have been deleted or ID is stale, try to find/create
+ 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
# 如果没有记录，检查一分钟以内是否已有记录
- current_time = datetime.now()
- if recent_record := db.online_time.find_one(
- {"end_timestamp": {"$gte": current_time - timedelta(minutes=1)}}
- ):
+ # Look for a record whose end_timestamp is recent enough to be considered ongoing
+ recent_record = (
+ OnlineTime.select()
+ .where(OnlineTime.end_timestamp >= (current_time - timedelta(minutes=1)))
+ .order_by(OnlineTime.end_timestamp.desc())
+ .first()
+ )
+
+ if recent_record:
# 如果有记录，则更新结束时间
- self.record_id = recent_record["_id"]
- db.online_time.update_one(
- {"_id": self.record_id},
- {
- "$set": {
- "end_timestamp": current_time + timedelta(minutes=1),
- }
- },
- )
+ 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(
- {
- "start_timestamp": current_time,
- "end_timestamp": current_time + timedelta(minutes=1),
- }
- ).inserted_id
+ new_record = OnlineTime.create(
+ timestamp=current_time.timestamp(), # 添加此行
+ start_timestamp=current_time,
+ end_timestamp=extended_end_time,
+ duration=5, # 初始时长为5分钟
+ )
+ 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)
+
+ # 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]}}):
- record_timestamp = record.get("timestamp")
+ # Assuming LLMUsage.timestamp is a DateTimeField
+ 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
- user_id = str(record.get("user_id", "unknown")) # 用户ID
- model_name = record.get("model_name", "unknown") # 模型名称
+ request_type = record.request_type or "unknown"
+ user_id = record.user_id or "unknown" # user_id is TextField, already string
+ 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
- completion_tokens = record.get("completion_tokens", 0) # 输出Token数
- total_tokens = prompt_tokens + completion_tokens # Token总数 = 输入Token数 + 输出Token数
+ prompt_tokens = record.prompt_tokens or 0
+ completion_tokens = record.completion_tokens or 0
+ 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)
+
+ 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
- for record in db.online_time.find({"end_timestamp": {"$gte": collect_period[-1][1]}}):
- end_timestamp: datetime = record.get("end_timestamp")
- for idx, (_, period_start) in enumerate(collect_period):
- if end_timestamp >= period_start:
- # 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
- 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
+ query_start_time = collect_period[-1][1]
+ # Assuming OnlineTime.end_timestamp is a DateTimeField
+ for record in OnlineTime.select().where(OnlineTime.end_timestamp >= query_start_time):
+ # record.end_timestamp and record.start_timestamp are datetime objects
+ record_end_timestamp = record.end_timestamp
+ record_start_timestamp = record.start_timestamp
+ 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)
+
+ 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
- for message in db.messages.find({"time": {"$gte": collect_period[-1][1].timestamp()}}):
- chat_info = message.get("chat_info", None) # 聊天信息
- 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
+ query_start_timestamp = collect_period[-1][1].timestamp() # Messages.time is a DoubleField (timestamp)
+ for message in Messages.select().where(Messages.time >= query_start_timestamp):
+ message_time_ts = message.time # This is a float timestamp
- group_info = chat_info.get("group_info") if chat_info else None # 尝试获取群聊信息
- if group_info is not None:
- # 若有群聊信息
- chat_id = f"g{group_info.get('group_id')}"
- chat_name = group_info.get("group_name", f"群{group_info.get('group_id')}")
- elif user_info:
- # 若没有群聊信息，则尝试获取用户信息
- chat_id = f"u{user_info['user_id']}"
- chat_name = user_info["user_nickname"]
+ chat_id = None
+ chat_name = None
+
+ # Logic based on Peewee model structure, aiming to replicate original intent
+ if message.chat_info_group_id:
+ chat_id = f"g{message.chat_info_group_id}"
+ chat_name = message.chat_info_group_name or f"群{message.chat_info_group_id}"
+ elif message.user_id: # Fallback to sender's info for chat_id if not a group_info based chat
+ # 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]:
- # 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)
+ if chat_name != self.name_mapping[chat_id][0] and message_time_ts > self.name_mapping[chat_id][1]:
+ self.name_mapping[chat_id] = (chat_name, message_time_ts)
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):
- if message_time >= period_start.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 idx, (_, period_start_dt) in enumerate(collect_period):
+ if message_time_ts >= period_start_dt.timestamp():
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]]:
diff --git a/src/chat/utils/utils.py b/src/chat/utils/utils.py
index 8fe8334b8..c400a9948 100644
--- a/src/chat/utils/utils.py
+++ b/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]
- nicknames = global_config.BOT_ALIAS_NAMES
+ keywords = [global_config.bot.nickname]
+ 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_sentence_num = global_config.response_max_sentence_num
+ max_length = global_config.response_splitter.max_length * 2
+ 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,
- min_freq=global_config.chinese_typo_min_freq,
- tone_error_rate=global_config.chinese_typo_tone_error_rate,
- word_replace_rate=global_config.chinese_typo_word_replace_rate,
+ error_rate=global_config.chinese_typo.error_rate,
+ min_freq=global_config.chinese_typo.min_freq,
+ tone_error_rate=global_config.chinese_typo.tone_error_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:
diff --git a/src/chat/utils/utils_image.py b/src/chat/utils/utils_image.py
index 455038246..c317fbbd6 100644
--- a/src/chat/utils/utils_image.py
+++ b/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})
- return result["description"] if result else None
+ try:
+ 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(
- {"hash": image_hash, "type": description_type},
- {
- "$set": {
- "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,
+ current_timestamp = time.time()
+ defaults = {"description": description, "timestamp": current_timestamp}
+ desc_obj, created = ImageDescriptions.get_or_create(
+ hash=image_hash, type=description_type, defaults=defaults
)
+ 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())
- filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
- if not os.path.exists(os.path.join(self.IMAGE_DIR, "emoji")):
- os.makedirs(os.path.join(self.IMAGE_DIR, "emoji"))
- file_path = os.path.join(self.IMAGE_DIR, "emoji", filename)
+ current_timestamp = time.time()
+ filename = f"{int(current_timestamp)}_{image_hash[:8]}.{image_format}"
+ emoji_dir = os.path.join(self.IMAGE_DIR, "emoji")
+ os.makedirs(emoji_dir, exist_ok=True)
+ 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
- image_doc = {
- "hash": image_hash,
- "path": file_path,
- "type": "emoji",
- "description": description,
- "timestamp": timestamp,
- }
- db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True)
- logger.trace(f"保存表情包: {file_path}")
+ # 保存到数据库 (Images表)
+ try:
+ img_obj = Images.get((Images.emoji_hash == image_hash) & (Images.type == "emoji"))
+ img_obj.path = file_path
+ img_obj.description = description
+ img_obj.timestamp = current_timestamp
+ img_obj.save()
+ except Images.DoesNotExist:
+ Images.create(
+ 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())
- filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
- if not os.path.exists(os.path.join(self.IMAGE_DIR, "image")):
- os.makedirs(os.path.join(self.IMAGE_DIR, "image"))
- file_path = os.path.join(self.IMAGE_DIR, "image", filename)
+ current_timestamp = time.time()
+ filename = f"{int(current_timestamp)}_{image_hash[:8]}.{image_format}"
+ image_dir = os.path.join(self.IMAGE_DIR, "image")
+ os.makedirs(image_dir, exist_ok=True)
+ 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
- image_doc = {
- "hash": image_hash,
- "path": file_path,
- "type": "image",
- "description": description,
- "timestamp": timestamp,
- }
- db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True)
- logger.trace(f"保存图片: {file_path}")
+ # 保存到数据库 (Images表)
+ try:
+ img_obj = Images.get((Images.emoji_hash == image_hash) & (Images.type == "image"))
+ img_obj.path = file_path
+ img_obj.description = description
+ img_obj.timestamp = current_timestamp
+ img_obj.save()
+ except Images.DoesNotExist:
+ Images.create(
+ 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}]"
diff --git a/src/chat/zhishi/knowledge_library.py b/src/chat/zhishi/knowledge_library.py
index 6fa1d3e1a..0068a153c 100644
--- a/src/chat/zhishi/knowledge_library.py
+++ b/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
diff --git a/src/common/database.py b/src/common/database/database.py
similarity index 81%
rename from src/common/database.py
rename to src/common/database/database.py
index 752f746db..a2dab739d 100644
--- a/src/common/database.py
+++ b/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)
diff --git a/src/common/database/database_model.py b/src/common/database/database_model.py
new file mode 100644
index 000000000..bd7a2d319
--- /dev/null
+++ b/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()
diff --git a/src/common/message_repository.py b/src/common/message_repository.py
index 03f192cea..ee69b22b0 100644
--- a/src/common/message_repository.py
+++ b/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。
- sort: MongoDB 排序条件列表，例如 [('time', 1)]。仅在 limit 为 0 时生效。
+ 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 {'$gt': value}).
+ sort: 排序条件列表，例如 [('time', 1)] (1 for asc, -1 for desc)。仅在 limit 为 0 时生效。
limit: 返回的最大文档数，0表示不限制。
limit_mode: 当 limit > 0 时生效。 'earliest' 表示获取最早的记录， 'latest' 表示获取最新的记录（结果仍按时间正序排列）。默认为 'latest'。
Returns:
- 消息文档列表，如果出错则返回空列表。
+ 消息字典列表，如果出错则返回空列表。
"""
try:
- query = db.messages.find(message_filter)
+ query = Messages.select()
+
+ # 应用过滤器
+ if message_filter:
+ conditions = []
+ for key, value in message_filter.items():
+ if hasattr(Messages, key):
+ field = getattr(Messages, key)
+ if isinstance(value, dict):
+ # 处理 MongoDB 风格的操作符
+ for op, op_value in value.items():
+ if op == "$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)
- results = list(query)
+ query = query.order_by(Messages.time.asc()).limit(limit)
+ 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)
- latest_results = list(query)
+ query = query.order_by(Messages.time.desc()).limit(limit)
+ 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
- results = sorted(latest_results, key=lambda msg: msg.get("time"))
+ peewee_results = sorted(latest_results_peewee, key=lambda msg: msg.time)
else:
# limit 为 0 时，应用传入的 sort 参数
if sort:
- query = query.sort(sort)
- results = list(query)
+ peewee_sort_terms = []
+ 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()。
diff --git a/src/common/remote.py b/src/common/remote.py
index 1d26df01b..b1108be9c 100644
--- a/src/common/remote.py
+++ b/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():
@@ -133,10 +133,9 @@ class TelemetryHeartBeatTask(AsyncTask):
async def run(self):
# 发送心跳
- if global_config.remote_enable:
- if self.client_uuid is None:
- if not await self._req_uuid():
- logger.error("获取UUID失败，跳过此次心跳")
- return
+ if global_config.telemetry.enable:
+ if self.client_uuid is None and not await self._req_uuid():
+ logger.error("获取UUID失败，跳过此次心跳")
+ return
await self._send_heartbeat()
diff --git a/src/config/config.py b/src/config/config.py
index b186f3b83..e6b7c5326 100644
--- a/src/config/config.py
+++ b/src/config/config.py
@@ -1,64 +1,68 @@
import os
-import re
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
+from dataclasses import field, dataclass
-import tomli
import tomlkit
import shutil
from datetime import datetime
-from pathlib import Path
-from packaging import version
-from packaging.version import Version, InvalidVersion
-from packaging.specifiers import SpecifierSet, InvalidSpecifier
+
+from tomlkit import TOMLDocument
+from tomlkit.items import Table
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
-is_test = True
-mai_version_main = "0.6.4"
-mai_version_fix = "snapshot-1"
+CONFIG_DIR = "config"
+TEMPLATE_DIR = "template"
-if mai_version_fix:
- if is_test:
- mai_version = f"test-{mai_version_main}-{mai_version_fix}"
- 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
+# 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
+# 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/
+MMC_VERSION = "0.7.0-snapshot.1"
def update_config():
# 获取根目录路径
- root_dir = Path(__file__).parent.parent.parent
- template_dir = root_dir / "template"
- config_dir = root_dir / "config"
- old_config_dir = config_dir / "old"
+ old_config_dir = f"{CONFIG_DIR}/old"
# 定义文件路径
- template_path = template_dir / "bot_config_template.toml"
- old_config_path = config_dir / "bot_config.toml"
- new_config_path = config_dir / "bot_config.toml"
+ template_path = f"{TEMPLATE_DIR}/bot_config_template.toml"
+ old_config_path = f"{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
- old_config_dir.mkdir(parents=True, exist_ok=True)
- shutil.copy2(template_path, old_config_path)
+ os.makedirs(CONFIG_DIR, exist_ok=True) # 创建文件夹
+ shutil.copy2(template_path, old_config_path) # 复制模板文件
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, source):
+ def update_dict(target: TOMLDocument | dict, source: TOMLDocument | dict):
+ """
+ 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()
- else:
- target[key] = tomlkit.array(value)
+ target[key] = tomlkit.array(str(value)) if value else tomlkit.array()
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:
- """机器人配置类"""
-
- INNER_VERSION: Version = None
- MAI_VERSION: str = mai_version # 硬编码的版本信息
-
- # bot
- BOT_QQ: Optional[str] = "114514"
- BOT_NICKNAME: Optional[str] = None
- BOT_ALIAS_NAMES: List[str] = field(default_factory=list) # 别名，可以通过这个叫它
-
- # group
- talk_allowed_groups = set()
- talk_frequency_down_groups = set()
- ban_user_id = set()
-
- # personality
- 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
- """
-
- try:
- converted = SpecifierSet(value)
- 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
- """
-
- if "inner" in toml:
- try:
- config_version: str = toml["inner"]["version"]
- except KeyError as e:
- logger.error("配置文件中 inner 段 不存在, 这是错误的配置文件")
- 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
- 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
+class Config(ConfigBase):
+ """总配置类"""
+
+ MMC_VERSION: str = field(default=MMC_VERSION, repr=False, init=False) # 硬编码的版本信息
+
+ bot: BotConfig
+ chat_target: ChatTargetConfig
+ personality: PersonalityConfig
+ 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
+
+
+def load_config(config_path: str) -> Config:
+ """
+ 加载配置文件
+ :param config_path: 配置文件路径
+ :return: Config对象
+ """
+ # 读取配置文件
+ with open(config_path, "r", encoding="utf-8") as f:
+ config_data = tomlkit.load(f)
+
+ # 创建Config对象
+ try:
+ return Config.from_dict(config_data)
+ except Exception as e:
+ logger.critical("配置文件解析失败")
+ raise e
# 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(f"正在品鉴配置文件目录: {bot_config_floder_path}")
-
-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)
+logger.info("正在品鉴配置文件...")
+global_config = load_config(config_path=f"{CONFIG_DIR}/bot_config.toml")
+logger.info("非常的新鲜，非常的美味！")
diff --git a/src/config/config_base.py b/src/config/config_base.py
new file mode 100644
index 000000000..92f6cf9d4
--- /dev/null
+++ b/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))})"
diff --git a/src/config/official_configs.py b/src/config/official_configs.py
new file mode 100644
index 000000000..d92d925d6
--- /dev/null
+++ b/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"""
diff --git a/src/experimental/PFC/action_planner.py b/src/experimental/PFC/action_planner.py
index b4182c9aa..c0bff5887 100644
--- a/src/experimental/PFC/action_planner.py
+++ b/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]
diff --git a/src/experimental/PFC/chat_observer.py b/src/experimental/PFC/chat_observer.py
index 704eeb330..55914d800 100644
--- a/src/experimental/PFC/chat_observer.py
+++ b/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"])
diff --git a/src/experimental/PFC/message_sender.py b/src/experimental/PFC/message_sender.py
index 181bf171b..4b193a41d 100644
--- a/src/experimental/PFC/message_sender.py
+++ b/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_nickname=global_config.BOT_NICKNAME,
+ user_id=global_config.bot.qq_account,
+ user_nickname=global_config.bot.nickname,
platform=chat_stream.platform,
)
diff --git a/src/experimental/PFC/message_storage.py b/src/experimental/PFC/message_storage.py
index cd6a01e34..e2e1dd052 100644
--- a/src/experimental/PFC/message_storage.py
+++ b/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):
- """MongoDB消息存储实现"""
+class PeeweeMessageStorage(MessageStorage):
+ """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": {"$gt": message_time}}
- # print(f"storage_check_message: {message_time}")
+ query = (
+ Messages.select()
+ .where((Messages.chat_id == chat_id) & (Messages.time > message_time))
+ .order_by(Messages.time.asc())
+ )
- return list(db.messages.find(query).sort("time", 1))
+ # print(f"storage_check_message: {message_time}")
+ messages_models = list(query)
+ return [model_to_dict(msg) for msg in messages_models]
async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]:
- query = {"chat_id": chat_id, "time": {"$lt": time_point}}
-
- messages = list(db.messages.find(query).sort("time", -1).limit(limit))
+ query = (
+ Messages.select()
+ .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()
- return messages
+ messages_models.reverse()
+ 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 db.messages.find_one(query) is not None
+ return Messages.select().where((Messages.chat_id == chat_id) & (Messages.time > after_time)).exists()
# # 创建一个内存消息存储实现，用于测试
diff --git a/src/experimental/PFC/pfc.py b/src/experimental/PFC/pfc.py
index 84fb9f8dc..686d4af49 100644
--- a/src/experimental/PFC/pfc.py
+++ b/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.nick_name = global_config.BOT_ALIAS_NAMES
+ self.name = global_config.bot.nickname
+ self.nick_name = global_config.bot.alias_names
self.private_name = private_name
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
diff --git a/src/experimental/PFC/pfc_KnowledgeFetcher.py b/src/experimental/PFC/pfc_KnowledgeFetcher.py
index 8ebc307e2..4c1d8c759 100644
--- a/src/experimental/PFC/pfc_KnowledgeFetcher.py
+++ b/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,
- temperature=global_config.llm_normal["temp"],
+ model=global_config.model.normal,
+ temperature=global_config.model.normal["temp"],
max_tokens=1000,
request_type="knowledge_fetch",
)
diff --git a/src/experimental/PFC/reply_checker.py b/src/experimental/PFC/reply_checker.py
index a76e8a0da..5bca9d601 100644
--- a/src/experimental/PFC/reply_checker.py
+++ b/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
diff --git a/src/experimental/PFC/reply_generator.py b/src/experimental/PFC/reply_generator.py
index 6dcda69af..bac8a769f 100644
--- a/src/experimental/PFC/reply_generator.py
+++ b/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)
diff --git a/src/experimental/PFC/waiter.py b/src/experimental/PFC/waiter.py
index af5cf7ad0..452446589 100644
--- a/src/experimental/PFC/waiter.py
+++ b/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
diff --git a/src/experimental/only_message_process.py b/src/experimental/only_message_process.py
index 3d1432703..62f73c700 100644
--- a/src/experimental/only_message_process.py
+++ b/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}"
diff --git a/src/main.py b/src/main.py
index 34b7eda3d..4f8af28ef 100644
--- a/src/main.py
+++ b/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,
- personality_core=global_config.personality_core,
- personality_sides=global_config.personality_sides,
- identity_detail=global_config.identity_detail,
- height=global_config.height,
- weight=global_config.weight,
- age=global_config.age,
- gender=global_config.gender,
- appearance=global_config.appearance,
+ bot_nickname=global_config.bot.nickname,
+ personality_core=global_config.personality.personality_core,
+ personality_sides=global_config.personality.personality_sides,
+ identity_detail=global_config.identity.identity_detail,
+ height=global_config.identity.height,
+ weight=global_config.identity.weight,
+ age=global_config.identity.age,
+ gender=global_config.identity.gender,
+ 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")
diff --git a/src/manager/mood_manager.py b/src/manager/mood_manager.py
index 42677d4e1..c83fbeb7c 100644
--- a/src/manager/mood_manager.py
+++ b/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,
- run_interval=global_config.mood_update_interval,
+ wait_before_start=global_config.mood.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()
diff --git a/src/tools/not_used/change_mood.py b/src/tools/not_used/change_mood.py
index c34bebb93..69fc3bb78 100644
--- a/src/tools/not_used/change_mood.py
+++ b/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)}")
diff --git a/src/tools/tool_can_use/get_knowledge.py b/src/tools/tool_can_use/get_knowledge.py
index 65acd55c0..fd37f11e7 100644
--- a/src/tools/tool_can_use/get_knowledge.py
+++ b/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
- 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}},
- ]
+ similar_items = []
+ try:
+ all_knowledges = Knowledges.select()
+ for item in all_knowledges:
+ try:
+ item_embedding_str = item.embedding
+ if not item_embedding_str:
+ logger.warning(f"Knowledge item ID {item.id} has empty embedding string.")
+ continue
+ item_embedding = json.loads(item_embedding_str)
+ if not isinstance(item_embedding, list) or not all(
+ isinstance(x, (int, float)) for x in item_embedding
+ ):
+ logger.warning(f"Knowledge item ID {item.id} has invalid embedding format after JSON parsing.")
+ 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
- results = list(db.knowledges.aggregate(pipeline))
- logger.debug(f"知识库查询结果数量: {len(results)}")
+ similarity = SearchKnowledgeTool._cosine_similarity(query_embedding, item_embedding)
+
+ 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)
diff --git a/src/tools/tool_use.py b/src/tools/tool_use.py
index c55170b88..ff36085d6 100644
--- a/src/tools/tool_use.py
+++ b/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"
diff --git a/template/bot_config_meta.toml b/template/bot_config_meta.toml
deleted file mode 100644
index c3541baad..000000000
--- a/template/bot_config_meta.toml
+++ /dev/null
@@ -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
\ No newline at end of file
diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml
index 931afe2ed..64e51da77 100644
--- a/template/bot_config_template.toml
+++ b/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
-#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", []))
-
+#如果新增项目，请阅读src/config/official_configs.py中的说明
+#
# 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
-gender = "男" # 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
+age = 18 # 年龄 单位岁
+gender = "女" # 性别
+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
-model_normal_probability = 0.3 # 麦麦回答时选择一般模型 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）
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
-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
+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 = 0.98 # 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_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
-check_interval = 10 # 检查表情包（注册，破损，删除）的时间间隔(分钟)
+max_reg_num = 40 # 表情包最大注册数量
+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 = 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
-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
+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
+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
-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
-build_memory_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_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_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_sample_num = 8 # 采样数量，数值越高记忆采样次数越多
+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 = [
- # "403","张三"
-]
+memory_ban_words = [ "表情包", "图片", "回复", "聊天记录" ]
[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