481 lines
18 KiB
Python
481 lines
18 KiB
Python
"""
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AutoReply 插件 - 基于双LLM架构的智能自动回复
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使用小模型判断是否需要回复，通过后触发AIChat插件生成回复
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"""
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import json
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import time
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import asyncio
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import tomllib
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import aiohttp
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from pathlib import Path
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from datetime import datetime, date
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from dataclasses import dataclass, field
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from typing import Dict, Optional
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from loguru import logger
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from utils.plugin_base import PluginBase
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from utils.decorators import on_text_message
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try:
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from aiohttp_socks import ProxyConnector
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PROXY_SUPPORT = True
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except ImportError:
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PROXY_SUPPORT = False
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@dataclass
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class JudgeResult:
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"""判断结果"""
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relevance: float = 0.0
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willingness: float = 0.0
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social: float = 0.0
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timing: float = 0.0
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continuity: float = 0.0
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reasoning: str = ""
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should_reply: bool = False
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overall_score: float = 0.0
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@dataclass
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class ChatState:
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"""群聊状态"""
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energy: float = 1.0
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last_reply_time: float = 0.0
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last_reset_date: str = ""
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total_messages: int = 0
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total_replies: int = 0
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class AutoReply(PluginBase):
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"""智能自动回复插件"""
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description = "基于双LLM架构的智能自动回复插件"
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author = "ShiHao"
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version = "1.1.0"
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def __init__(self):
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super().__init__()
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self.config = None
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self.chat_states: Dict[str, ChatState] = {}
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self.weights = {}
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self.last_judge_time: Dict[str, float] = {}
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self.judging: Dict[str, bool] = {}
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self.bot_wxid: str = ""
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self.bot_nickname: str = ""
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async def async_init(self):
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"""异步初始化"""
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try:
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config_path = Path(__file__).parent / "config.toml"
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with open(config_path, "rb") as f:
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self.config = tomllib.load(f)
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# 加载权重配置
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self.weights = {
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"relevance": self.config["weights"]["relevance"],
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"willingness": self.config["weights"]["willingness"],
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"social": self.config["weights"]["social"],
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"timing": self.config["weights"]["timing"],
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"continuity": self.config["weights"]["continuity"]
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}
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# 检查权重和
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weight_sum = sum(self.weights.values())
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if abs(weight_sum - 1.0) > 1e-6:
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logger.warning(f"[AutoReply] 判断权重和不为1，当前和为{weight_sum}，已自动归一化")
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self.weights = {k: v / weight_sum for k, v in self.weights.items()}
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# 加载机器人信息
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self._load_bot_info()
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logger.success(f"[AutoReply] 插件已加载，判断模型: {self.config['basic']['judge_model']}")
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logger.info(f"[AutoReply] 回复阈值: {self.config['basic']['reply_threshold']}, 最小间隔: {self.config['rate_limit']['min_interval']}秒")
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except Exception as e:
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logger.error(f"[AutoReply] 初始化失败: {e}")
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self.config = None
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def _load_bot_info(self):
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"""加载机器人信息"""
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try:
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with open("main_config.toml", "rb") as f:
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main_config = tomllib.load(f)
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self.bot_wxid = main_config.get("Bot", {}).get("wxid", "")
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self.bot_nickname = main_config.get("Bot", {}).get("nickname", "")
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except Exception as e:
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logger.warning(f"[AutoReply] 加载机器人信息失败: {e}")
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def _normalize_chat_id(self, chat_id: str) -> str:
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"""将群ID转成history文件使用的安全文件名"""
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return (chat_id or "").replace("@", "_").replace(":", "_")
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def _is_chat_allowed(self, chat_id: str) -> bool:
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"""白名单判断"""
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whitelist_config = self.config.get("whitelist", {})
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if not whitelist_config.get("enabled", False):
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return True
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chat_list = whitelist_config.get("chat_list", [])
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safe_id = self._normalize_chat_id(chat_id)
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# 同时检查原始ID和归一化ID
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return chat_id in chat_list or safe_id in chat_list
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def _is_at_bot(self, message: dict) -> bool:
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"""检查是否@了机器人"""
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# 优先检查 Ats 列表
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at_list = message.get('Ats', [])
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if at_list:
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# 检查机器人wxid是否在@列表中
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if self.bot_wxid and self.bot_wxid in at_list:
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return True
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# 备用：检查内容中是否包含@机器人昵称
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content = message.get('Content', '')
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if self.bot_nickname and f"@{self.bot_nickname}" in content:
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return True
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return False
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def _is_bot_message(self, message: dict) -> bool:
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"""检查是否是机器人自己的消息"""
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sender_wxid = message.get('SenderWxid', '')
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return sender_wxid == self.bot_wxid if self.bot_wxid else False
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@on_text_message(priority=90)
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async def handle_message(self, bot, message: dict):
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"""处理消息"""
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try:
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# 检查是否启用
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if not self.config or not self.config["basic"]["enabled"]:
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return True
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# 只处理群聊消息
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if not message.get('IsGroup', False):
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return True
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from_wxid = message.get('FromWxid', '') # 群聊ID
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sender_wxid = message.get('SenderWxid', '') # 发送者ID
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content = message.get('Content', '').strip()
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# 跳过空消息
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if not content:
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return True
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# 跳过机器人自己的消息
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if self._is_bot_message(message):
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return True
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# 检查白名单
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if not self._is_chat_allowed(from_wxid):
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return True
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# 跳过@机器人的消息（让AIChat正常处理）
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if self._is_at_bot(message):
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logger.debug(f"[AutoReply] 跳过@消息，交由AIChat处理")
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return True
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chat_id = self._normalize_chat_id(from_wxid)
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current_time = time.time()
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# 频率限制：检查是否正在判断中
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if self.judging.get(chat_id, False):
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logger.debug(f"[AutoReply] 群聊 {from_wxid[:15]}... 正在判断中，跳过")
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return True
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# 频率限制：检查时间间隔
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min_interval = self.config.get("rate_limit", {}).get("min_interval", 10)
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last_time = self.last_judge_time.get(chat_id, 0)
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if current_time - last_time < min_interval:
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logger.debug(f"[AutoReply] 距离上次判断仅 {current_time - last_time:.1f}秒，跳过")
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return True
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# 标记正在判断
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self.judging[chat_id] = True
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self.last_judge_time[chat_id] = current_time
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try:
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# 使用小模型判断
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judge_result = await self._judge_with_small_model(from_wxid, content)
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if judge_result.should_reply:
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logger.info(f"[AutoReply] 触发回复 | 群:{from_wxid[:15]}... | 评分:{judge_result.overall_score:.2f} | {judge_result.reasoning[:30]}")
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# 更新状态
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self._update_state(chat_id, replied=True)
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# 设置触发标记，让AIChat处理
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message['_auto_reply_triggered'] = True
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else:
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logger.debug(f"[AutoReply] 不触发 | 群:{from_wxid[:15]}... | 评分:{judge_result.overall_score:.2f}")
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self._update_state(chat_id, replied=False)
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finally:
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# 清除判断中标记
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self.judging[chat_id] = False
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return True
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except Exception as e:
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logger.error(f"[AutoReply] 处理异常: {e}")
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import traceback
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logger.error(traceback.format_exc())
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# 清除判断中标记
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if 'chat_id' in locals():
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self.judging[chat_id] = False
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return True
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async def _judge_with_small_model(self, from_wxid: str, content: str) -> JudgeResult:
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"""使用小模型判断是否需要回复"""
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chat_id = self._normalize_chat_id(from_wxid)
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chat_state = self._get_chat_state(chat_id)
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# 获取最近消息历史
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recent_messages = await self._get_recent_messages(chat_id)
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last_bot_reply = await self._get_last_bot_reply(chat_id)
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# 构建判断提示词
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reasoning_part = ',\n "reasoning": "简短分析原因(20字内)"' if self.config["judge"]["include_reasoning"] else ""
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judge_prompt = f"""你是群聊机器人的决策系统，判断是否应该主动回复。
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## 当前状态
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- 精力: {chat_state.energy:.1f}/1.0
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- 上次发言: {self._get_minutes_since_last_reply(chat_id)}分钟前
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## 最近对话
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{recent_messages}
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## 上次机器人回复
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{last_bot_reply or "暂无"}
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## 待判断消息
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{content}
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## 评估维度(0-10分)
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1. relevance: 内容是否有趣、值得回复
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2. willingness: 基于精力的回复意愿
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3. social: 回复是否社交适宜
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4. timing: 时机是否恰当
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5. continuity: 与上次回复的关联度
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回复阈值: {self.config['basic']['reply_threshold']}
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仅返回JSON:
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{{
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"relevance": 分数,
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"willingness": 分数,
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"social": 分数,
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"timing": 分数,
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"continuity": 分数{reasoning_part}
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}}"""
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# 调用API
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max_retries = self.config["judge"].get("max_retries", 2)
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for attempt in range(max_retries + 1):
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try:
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result = await self._call_judge_api(judge_prompt)
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# 解析JSON
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content_text = result.strip()
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# 移除可能的markdown代码块标记
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if content_text.startswith("```"):
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content_text = content_text.split("```")[1]
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if content_text.startswith("json"):
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content_text = content_text[4:]
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content_text = content_text.strip()
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judge_data = json.loads(content_text)
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# 计算综合评分
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overall_score = (
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judge_data.get("relevance", 0) * self.weights["relevance"] +
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judge_data.get("willingness", 0) * self.weights["willingness"] +
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judge_data.get("social", 0) * self.weights["social"] +
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judge_data.get("timing", 0) * self.weights["timing"] +
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judge_data.get("continuity", 0) * self.weights["continuity"]
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) / 10.0
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should_reply = overall_score >= self.config["basic"]["reply_threshold"]
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return JudgeResult(
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relevance=judge_data.get("relevance", 0),
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willingness=judge_data.get("willingness", 0),
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social=judge_data.get("social", 0),
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timing=judge_data.get("timing", 0),
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continuity=judge_data.get("continuity", 0),
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reasoning=judge_data.get("reasoning", ""),
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should_reply=should_reply,
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overall_score=overall_score
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)
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except json.JSONDecodeError as e:
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logger.warning(f"[AutoReply] JSON解析失败 (尝试 {attempt + 1}/{max_retries + 1}): {e}")
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if attempt == max_retries:
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return JudgeResult(should_reply=False, reasoning="JSON解析失败")
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except Exception as e:
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logger.error(f"[AutoReply] 判断异常: {e}")
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return JudgeResult(should_reply=False, reasoning=f"异常: {str(e)}")
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return JudgeResult(should_reply=False, reasoning="重试失败")
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async def _call_judge_api(self, prompt: str) -> str:
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"""调用判断模型API"""
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api_url = self.config["basic"]["judge_api_url"]
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api_key = self.config["basic"]["judge_api_key"]
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model = self.config["basic"]["judge_model"]
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}"
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}
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payload = {
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"model": model,
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"messages": [
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{"role": "system", "content": "你是群聊回复决策系统。严格按JSON格式返回，不要输出其他内容。"},
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{"role": "user", "content": prompt}
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],
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"temperature": 0.5,
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"max_tokens": 200
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}
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# 配置代理
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connector = None
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proxy_config = self.config.get("proxy", {})
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if proxy_config.get("enabled", False) and PROXY_SUPPORT:
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proxy_type = proxy_config.get("type", "http")
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proxy_host = proxy_config.get("host", "127.0.0.1")
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proxy_port = proxy_config.get("port", 7890)
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proxy_url = f"{proxy_type}://{proxy_host}:{proxy_port}"
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connector = ProxyConnector.from_url(proxy_url)
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timeout = aiohttp.ClientTimeout(total=30)
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async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
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async with session.post(api_url, headers=headers, json=payload) as response:
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if response.status != 200:
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error_text = await response.text()
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raise Exception(f"API调用失败: {response.status}, {error_text[:100]}")
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result = await response.json()
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return result["choices"][0]["message"]["content"]
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async def _get_history(self, chat_id: str) -> list:
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"""获取群聊历史记录（优先 Redis，降级到文件）"""
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try:
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from utils.plugin_manager import PluginManager
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aichat_plugin = PluginManager().plugins.get("AIChat")
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if not aichat_plugin:
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return []
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# 优先使用 Redis（与 AIChat 保持一致）
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try:
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from utils.redis_cache import get_cache
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redis_cache = get_cache()
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if redis_cache and redis_cache.enabled:
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aichat_config = aichat_plugin.config or {}
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redis_config = aichat_config.get("redis", {})
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if redis_config.get("use_redis_history", True):
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max_history = aichat_config.get("history", {}).get("max_history", 100)
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history = redis_cache.get_group_history(chat_id, max_history)
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if history:
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return history
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except Exception as e:
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logger.debug(f"[AutoReply] Redis 获取历史失败: {e}")
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# 降级到文件存储
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if hasattr(aichat_plugin, 'history_dir') and aichat_plugin.history_dir:
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history_file = aichat_plugin.history_dir / f"{chat_id}.json"
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if history_file.exists():
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with open(history_file, "r", encoding="utf-8") as f:
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return json.load(f)
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except Exception as e:
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logger.debug(f"[AutoReply] 获取历史失败: {e}")
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return []
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async def _get_recent_messages(self, chat_id: str) -> str:
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"""获取最近消息历史"""
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try:
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history = await self._get_history(chat_id)
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if not history:
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return "暂无对话历史"
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count = self.config.get('context', {}).get('messages_count', 5)
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recent = history[-count:] if len(history) > count else history
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messages = []
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for record in recent:
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nickname = record.get('nickname', '未知')
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content = record.get('content', '')
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# 限制单条消息长度
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if len(content) > 100:
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content = content[:100] + "..."
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messages.append(f"{nickname}: {content}")
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return "\n".join(messages) if messages else "暂无对话历史"
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except Exception as e:
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logger.debug(f"[AutoReply] 获取消息历史失败: {e}")
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return "暂无对话历史"
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async def _get_last_bot_reply(self, chat_id: str) -> Optional[str]:
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"""获取上次机器人回复"""
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try:
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history = await self._get_history(chat_id)
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if not history:
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return None
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# 从后往前查找机器人回复
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for record in reversed(history):
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if record.get('nickname') == self.bot_nickname:
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content = record.get('content', '')
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if len(content) > 100:
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content = content[:100] + "..."
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return content
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except Exception as e:
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logger.debug(f"[AutoReply] 获取上次回复失败: {e}")
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return None
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def _get_chat_state(self, chat_id: str) -> ChatState:
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"""获取群聊状态"""
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if chat_id not in self.chat_states:
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self.chat_states[chat_id] = ChatState()
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state = self.chat_states[chat_id]
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today = date.today().isoformat()
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# 每日重置精力
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if state.last_reset_date != today:
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state.last_reset_date = today
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state.energy = min(1.0, state.energy + 0.2)
|
||
|
||
return state
|
||
|
||
def _get_minutes_since_last_reply(self, chat_id: str) -> int:
|
||
"""获取距离上次回复的分钟数"""
|
||
state = self._get_chat_state(chat_id)
|
||
if state.last_reply_time == 0:
|
||
return 999
|
||
return int((time.time() - state.last_reply_time) / 60)
|
||
|
||
def _update_state(self, chat_id: str, replied: bool):
|
||
"""更新群聊状态"""
|
||
state = self._get_chat_state(chat_id)
|
||
state.total_messages += 1
|
||
|
||
if replied:
|
||
state.last_reply_time = time.time()
|
||
state.total_replies += 1
|
||
decay = self.config.get("energy", {}).get("decay_rate", 0.1)
|
||
state.energy = max(0.1, state.energy - decay)
|
||
else:
|
||
recovery = self.config.get("energy", {}).get("recovery_rate", 0.02)
|
||
state.energy = min(1.0, state.energy + recovery)
|