202 lines
7.4 KiB
Python
202 lines
7.4 KiB
Python
from loguru import logger
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import os
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import json
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import requests
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from typing import Dict, Any, List, Optional, Tuple
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from plugin_common.message_plugin_interface import MessagePluginInterface
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from plugin_common.plugin_interface import PluginStatus
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from utils.decorator.plugin_decorators import plugin_stats_decorator
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from utils.robot_cmd.robot_command import Feature, PermissionStatus, GroupBotManager
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from utils.decorator.points_decorator import plugin_points_cost
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from wechat_ipad import WechatAPIClient
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from .bot_ai import InterventionBot
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class AIAutoResponsePlugin(MessagePluginInterface):
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"""AI自动对话插件"""
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@property
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def name(self) -> str:
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return "AI自动对话"
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@property
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def version(self) -> str:
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return "1.0.0"
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@property
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def description(self) -> str:
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return "提供AI自动对话功能，可以在群聊中自动介入对话"
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@property
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def author(self) -> str:
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return "Trae AI"
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@property
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def command_prefix(self) -> Optional[str]:
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return "" # 不需要前缀，直接匹配命令
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@property
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def commands(self) -> List[str]:
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return self._commands
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def __init__(self):
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super().__init__()
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self.intervention_bot = None
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self.group_messages = {} # 存储每个群的最近消息
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self.max_messages = 20 # 每个群最多存储的消息数量
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# DIFY API配置
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self.dify_api_url = "http://192.168.2.240/v1/chat-messages"
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self.dify_api_key = "" # 需要在配置文件中设置
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def initialize(self, context: Dict[str, Any]) -> bool:
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"""初始化插件"""
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self.LOG = logger
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self.LOG.info(f"正在初始化 {self.name} 插件...")
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# 保存上下文对象
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self.event_system = context.get("event_system")
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# 加载配置
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config_path = os.path.join(os.path.dirname(__file__), "config.toml")
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self.enable = self._config.get("enable", True)
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# 从配置中获取DIFY API密钥
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self.dify_api_key = self._config.get("dify_api_key", "")
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self.dify_api_url = self._config.get("dify_api_url", "http://192.168.2.240/v1/chat-messages")
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# 初始化介入机器人
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self.intervention_bot = InterventionBot(config_path)
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return True
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def start(self) -> bool:
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"""启动插件"""
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self.LOG.info(f"[{self.name}] 插件已启动")
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self.status = PluginStatus.RUNNING
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return True
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def stop(self) -> bool:
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"""停止插件"""
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self.LOG.info(f"[{self.name}] 插件已停止")
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self.status = PluginStatus.STOPPED
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return True
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def can_process(self, message: Dict[str, Any]) -> bool:
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"""检查是否可以处理该消息"""
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if not self.enable:
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return False
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content = str(message.get("content", "")).strip()
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roomid = message.get("roomid", "")
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if GroupBotManager.get_group_permission(roomid, Feature.AI_AUTO) == PermissionStatus.DISABLED:
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return False
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# 如果是群消息，且该群启用了自动回复，则处理
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if roomid:
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self.LOG.debug(f"[{roomid}] 进入AI自动回复逻辑")
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# 存储消息
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if roomid not in self.group_messages:
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self.group_messages[roomid] = []
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# 添加新消息
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current_message = {
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"timestamp": message.get("timestamp", ""),
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"message": content,
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"sender": message.get("sender", "")
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}
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# 添加新消息
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self.group_messages[roomid].append(current_message)
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# 限制消息数量
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if len(self.group_messages[roomid]) > self.max_messages:
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self.group_messages[roomid] = self.group_messages[roomid][-self.max_messages:]
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# 判断是否需要介入
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messages = [msg["message"] for msg in self.group_messages[roomid]]
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timestamp = message.get("timestamp", "")
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# 传递完整的聊天记录给should_intervene方法
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can = self.intervention_bot.should_intervene(timestamp, content, messages, self.group_messages[roomid])
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if can:
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self.LOG.debug(f"[{roomid}] 触发自动回复规则，准备生成回复")
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return True
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else:
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self.LOG.debug(f"[{roomid}] 跳过聊天")
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return False
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return False
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async def process_message(self, message: Dict[str, Any]) -> Tuple[bool, Optional[str]]:
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"""处理消息"""
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content = str(message.get("content", "")).strip()
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self.LOG.debug(f"插件执行： {self.name}：{content}")
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sender = message.get("sender")
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roomid = message.get("roomid", "")
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bot: WechatAPIClient = message.get("bot")
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# 检查权限
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if roomid and GroupBotManager.get_group_permission(roomid, Feature.AI_AUTO) == PermissionStatus.DISABLED:
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return False, "没有权限"
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# 处理自动回复
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try:
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# 获取最近的消息
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messages = [msg["message"] for msg in self.group_messages[roomid]]
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timestamp = message.get("timestamp", "")
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# 记录触发原因
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if self.intervention_bot.rule_high_reply_rate(timestamp, self.group_messages[roomid]):
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self.LOG.info(f"[{roomid}] 触发高频率回复规则，准备生成回复")
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# 生成回复
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response = self._generate_response_with_dify(content, messages)
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if response:
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# 发送回复
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await bot.send_text_message(roomid, response, sender)
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return True, "自动回复成功"
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else:
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return False, "生成回复失败"
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except Exception as e:
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self.LOG.error(f"处理AI自动对话出错: {e}")
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return False, f"处理出错: {e}"
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def _generate_response_with_dify(self, message: str, messages: List[str]) -> str:
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"""使用DIFY API生成自动回复内容"""
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try:
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# 检测话题类型
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topic_type = self.intervention_bot.detect_topic(message)
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# 构建上下文消息
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context_messages = messages[-5:] if len(messages) > 5 else messages
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context = "\n".join(context_messages)
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# 构建提示词
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prompt = f"请根据以下群聊上下文，生成一个自然、友好的回复，主要关注最后一句消息，前面的作为参考信息。\n上下文：\n{context}\n\n当前话题类型：{topic_type or '一般聊天'}\n\n请生成回复："
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# 调用DIFY API
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.dify_api_key}"
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}
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payload = {
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"inputs": {},
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"query": prompt,
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"response_mode": "blocking",
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"user": "ai_auto_response"
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}
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response = requests.post(self.dify_api_url, headers=headers, json=payload)
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if response.status_code == 200:
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result = response.json()
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return result.get("answer", "")
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else:
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self.LOG.error(f"DIFY API调用失败: {response.status_code} - {response.text}")
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return ""
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except Exception as e:
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self.LOG.error(f"生成回复出错: {e}")
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return ""
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