refactor ai_auto_response into xiaoniu group bot
This commit is contained in:
liuwei
@@ -1,49 +1,56 @@
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from __future__ import annotations
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import re
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import time
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import xml.etree.ElementTree as ET
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from typing import Any, Dict, List, Optional, Tuple
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from loguru import logger
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import os
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import requests
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from typing import Dict, Any, List, Optional, Tuple
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from base.plugin_common.message_plugin_interface import MessagePluginInterface
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from base.plugin_common.plugin_interface import PluginStatus
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from utils.robot_cmd.robot_command import Feature, PermissionStatus, GroupBotManager
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from utils.robot_cmd.robot_command import GroupBotManager, PermissionStatus
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from utils.wechat.contact_manager import ContactManager
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from wechat_ipad import WechatAPIClient
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from wechat_ipad.models.message import MessageType
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import xml.etree.ElementTree as ET
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from .bot_ai import InterventionBot
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from .context_builder import ContextBuilder
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from .flow_manager import FlowManager
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from .llm_client import LLMClient
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from .memory_store import MemoryStore
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from .persona_engine import PersonaEngine
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from .response_planner import ResponsePlanner
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from .triggers import TriggerRouter
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from .vector_memory import VectorMemoryStore
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class AIAutoResponsePlugin(MessagePluginInterface):
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"""AI自动对话插件"""
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# 功能权限常量
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FEATURE_KEY = "AI_AUTO_RESPONSE"
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FEATURE_DESCRIPTION = "🤖 AI自动对话功能 [自动对话]"
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FEATURE_DESCRIPTION = "🐮 小牛拟人群聊BOT [群聊拟真、及时答疑、长期记忆]"
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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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return "小牛群聊BOT"
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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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return "2.0.0"
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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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return "拟人化群聊BOT，支持心流、长期记忆和回归成员识别"
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@property
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def author(self) -> str:
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return "liu.wei"
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return "ABOT Team"
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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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return None
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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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return []
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@property
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def feature_key(self) -> Optional[str]:
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@@ -55,207 +62,452 @@ class AIAutoResponsePlugin(MessagePluginInterface):
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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 = 100 # 每个群最多存储的消息数量
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# 注册功能权限
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self.feature = self.register_feature()
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# DIFY API配置
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self.dify_api_url = ""
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self.dify_api_key = "" # 需要在配置文件中设置
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self.group_messages: Dict[str, List[Dict]] = {}
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self.enable = True
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self.last_reply_at: Dict[str, float] = {}
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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.debug(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", "")
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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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self.db_manager = context.get("db_manager")
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self.enable = bool(self._config.get("enable", True))
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self.persona_engine = PersonaEngine(self.get_plugin_path(), self._config.get("persona", {}))
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self.flow_manager = FlowManager({
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**(self._config.get("flow", {}) or {}),
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"night_silent_hours": (self._config.get("cooldown", {}) or {}).get("night_silent_hours", []),
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})
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merged_trigger_config = dict(self._config.get("priority", {}) or {})
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merged_trigger_config.update(self._config.get("topics", {}) or {})
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self.trigger_router = TriggerRouter(merged_trigger_config)
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merged_memory_config = dict(self._config.get("mode", {}) or {})
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merged_memory_config.update(self._config.get("memory", {}) or {})
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self.memory_store = MemoryStore(self.db_manager, merged_memory_config)
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self.vector_memory = VectorMemoryStore(self._config.get("memory", {}) or {})
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self.context_builder = ContextBuilder()
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self.response_planner = ResponsePlanner()
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self.llm_client = LLMClient(self._config.get("api", {}) or {})
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self.filters = self._config.get("filters", {}) or {}
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self.mode_config = self._config.get("mode", {}) or {}
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self.cooldown_config = self._config.get("cooldown", {}) or {}
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self._synced_member_context_versions: Dict[str, str] = {}
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self.log_debug = bool((self._config.get("logging", {}) or {}).get("debug", True))
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self.LOG.debug(f"[{self.name}] 初始化完成")
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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.debug(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, self.feature) == PermissionStatus.DISABLED:
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room_id = message.get("roomid", "")
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if not room_id:
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return False
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if GroupBotManager.get_group_permission(room_id, self.feature) == 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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msg_type = message.get("type")
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# 获取发送者昵称
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sender_id = message.get("sender", "")
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try:
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members = ContactManager.get_instance().get_group_members(roomid)
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sender_name = members.get(sender_id, sender_id)
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except Exception:
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sender_name = sender_id
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msg_type = message.get("type")
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if msg_type not in (MessageType.TEXT, MessageType.APP):
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return False
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# 仅追加文本(1)与应用消息(49)，并对49提取标题
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content_to_store = None
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try:
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if msg_type == MessageType.TEXT:
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content_to_store = content
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elif msg_type == MessageType.APP:
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try:
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root = ET.fromstring(content)
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title_elem = root.find('.//title')
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if title_elem is not None and title_elem.text:
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content_to_store = title_elem.text
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else:
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content_to_store = "[应用消息]"
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except Exception as e:
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self.LOG.error(f"解析消息类型49出错: {e}")
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content_to_store = "[应用消息]"
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except Exception as e:
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self.LOG.error(f"处理消息类型出错: {e}")
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content_to_store = None
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full_msg = message.get("full_wx_msg")
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if full_msg and full_msg.from_self():
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return False
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if content_to_store is not None:
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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_to_store,
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"sender": sender_id,
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"sender_name": sender_name
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}
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# 添加新消息
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if content_to_store is not None:
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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(roomid, 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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content = self._normalize_content(message)
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if not content:
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return False
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if self._should_ignore(content):
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return False
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return True
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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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room_id = message.get("roomid", "")
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sender = message.get("sender", "")
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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, self.feature) == 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 (完整的消息对象)
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chat_history = self.group_messages[roomid]
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timestamp = message.get("timestamp", "")
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content = self._normalize_content(message)
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sender_name = self._get_sender_name(room_id, sender)
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self._log_event(
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"recv",
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room_id=room_id,
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sender=sender,
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sender_name=sender_name,
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is_at=message.get("is_at", False),
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content_preview=self._preview(content),
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msg_type=str(message.get("type")),
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)
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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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normalized_message = {
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"sender": sender,
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"sender_name": sender_name,
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"content": content,
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"timestamp": message.get("timestamp"),
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}
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self._append_group_message(room_id, normalized_message)
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# 生成回复
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response = self._generate_response_with_dify(content, chat_history)
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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 False, "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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memory_hints = self.memory_store.build_memory_hints(room_id, sender)
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self._sync_member_memory(room_id, sender, sender_name, memory_hints.get("member_context", {}))
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self._log_event(
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"memory",
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room_id=room_id,
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sender=sender,
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returning_state=memory_hints.get("returning_member_state", "") or "none",
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has_member_context=bool(memory_hints.get("member_context")),
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is_followup=memory_hints.get("is_followup", False),
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last_active_at=memory_hints.get("last_active_at", "") or "",
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)
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trigger = self.trigger_router.route(message | {"content": content}, memory_hints)
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flow_state = self.flow_manager.apply_message_event(room_id, {
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"is_at": message.get("is_at", False),
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"is_question": trigger.is_question,
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"is_followup": trigger.is_followup,
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"topic_hit": bool(trigger.topic),
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"topic": trigger.topic,
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"is_returning_member": trigger.is_returning_member,
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"message_after_bot": True,
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})
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self._log_event(
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"decision",
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room_id=room_id,
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sender=sender,
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trigger_type=trigger.trigger_type,
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priority=trigger.priority,
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reasons="|".join(trigger.reasons),
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flow_state=flow_state.state,
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flow_score=round(flow_state.score, 2),
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topic=trigger.topic or "",
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)
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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, current_message: str, chat_history: List[Dict[str, Any]]) -> 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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# 构建上下文消息
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# 取更多上下文以帮助理解语境
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recent_msgs = chat_history[-10:] if len(chat_history) > 10 else chat_history
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context_str_list = []
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for msg in recent_msgs:
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# 优先使用昵称，如果没有则使用sender ID
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sender = msg.get("sender_name") or msg.get("sender", "Unknown")
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content = msg.get("message", "")
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context_str_list.append(f"{sender}: {content}")
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context = "\n".join(context_str_list)
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# 构建提示词 - 增强拟人化指令
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prompt = (
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f"当前群聊上下文（格式为 '发言人: 内容'，最后一句是最新消息）：\n{context}\n\n"
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f"指令：\n"
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f"1. 参考上下文。\n"
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f"2. 保持简短（1-2句话），口语化，不要长篇大论。\n"
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f"3. 不要重复之前的回复。\n"
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f"4. 如果最后一句不是对你说的，且你觉得没必要强行接话，可以回个表情或简短的语气词，或者委婉结束话题。\n"
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f"请生成回复："
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allow_proactive = bool(self.mode_config.get("allow_proactive_reply", True))
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reply_mode = self.response_planner.choose_reply_mode(trigger.__dict__, flow_state.state)
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should_reply = self.response_planner.should_reply(trigger.__dict__, flow_state.state, allow_proactive)
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if not should_reply:
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self._log_event(
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"skip",
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room_id=room_id,
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sender=sender,
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reason="planner_skip",
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trigger_type=trigger.trigger_type,
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reply_mode=reply_mode,
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flow_state=flow_state.state,
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)
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return False, "skip"
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if not self._pass_cooldown(room_id, trigger.__dict__):
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self._log_event(
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"skip",
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room_id=room_id,
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sender=sender,
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reason="cooldown",
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trigger_type=trigger.trigger_type,
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reply_mode=reply_mode,
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)
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return False, "cooldown"
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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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recent_messages = self.group_messages.get(room_id) or self.memory_store.get_recent_messages(room_id)
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vector_memories = []
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if self.vector_memory.should_search(reply_mode, trigger.trigger_type, memory_hints.get("returning_member_state", "")):
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vector_memories = self.vector_memory.search(content, room_id, sender)
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self._log_event(
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"context",
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room_id=room_id,
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sender=sender,
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reply_mode=reply_mode,
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recent_message_count=len(recent_messages),
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vector_hit_count=len(vector_memories),
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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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context = self.context_builder.build(
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room_id=room_id,
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sender=sender,
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sender_name=sender_name,
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content=content,
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recent_messages=recent_messages,
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member_context=memory_hints.get("member_context", {}),
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trigger=trigger.__dict__,
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flow_state=flow_state.state,
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reply_mode=reply_mode,
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vector_memories=vector_memories,
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)
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response = requests.post(self.dify_api_url, headers=headers, json=payload)
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system_prompt = self.persona_engine.build_system_prompt()
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user_prompt = self._build_user_prompt(context, memory_hints)
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response = self._sanitize_response(self.llm_client.chat(system_prompt, user_prompt, user_id=f"{room_id}:{sender}"))
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if not response:
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self._log_event(
|
||||
"model_empty",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
model=self.llm_client.model,
|
||||
last_error=self.llm_client.last_error,
|
||||
reply_mode=reply_mode,
|
||||
)
|
||||
return False, "empty_response"
|
||||
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
return result.get("answer", "")
|
||||
else:
|
||||
self.LOG.error(f"DIFY API调用失败: {response.status_code} - {response.text}")
|
||||
return ""
|
||||
await bot.send_text_message(room_id, response, sender)
|
||||
self.last_reply_at[room_id] = time.time()
|
||||
self.flow_manager.note_bot_reply(room_id)
|
||||
self.memory_store.note_bot_reply(room_id, sender, trigger.topic)
|
||||
self._upsert_interaction_memory(room_id, sender, sender_name, content, response, trigger.trigger_type, trigger.topic)
|
||||
self._log_event(
|
||||
"sent",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
sender_name=sender_name,
|
||||
trigger_type=trigger.trigger_type,
|
||||
reply_mode=reply_mode,
|
||||
response_preview=self._preview(response),
|
||||
response_len=len(response),
|
||||
)
|
||||
return False, "replied"
|
||||
|
||||
except Exception as e:
|
||||
self.LOG.error(f"生成回复出错: {e}")
|
||||
def _append_group_message(self, room_id: str, message: Dict) -> None:
|
||||
items = self.group_messages.setdefault(room_id, [])
|
||||
items.append(message)
|
||||
size = int(self.mode_config.get("recent_context_size", 30))
|
||||
if len(items) > size:
|
||||
self.group_messages[room_id] = items[-size:]
|
||||
|
||||
def _normalize_content(self, message: Dict[str, Any]) -> str:
|
||||
msg_type = message.get("type")
|
||||
content = str(message.get("content", "")).strip()
|
||||
if msg_type == MessageType.TEXT:
|
||||
return self._strip_at_prefix(content)
|
||||
if msg_type == MessageType.APP:
|
||||
try:
|
||||
root = ET.fromstring(content)
|
||||
title = root.find(".//title")
|
||||
return (title.text or "").strip() if title is not None else "[应用消息]"
|
||||
except Exception:
|
||||
return "[应用消息]"
|
||||
return content
|
||||
|
||||
@staticmethod
|
||||
def _strip_at_prefix(content: str) -> str:
|
||||
return re.sub(r"@.*?[\u2005\s]+", "", content).strip()
|
||||
|
||||
def _should_ignore(self, content: str) -> bool:
|
||||
if len(content) < int(self.filters.get("min_text_length", 1)):
|
||||
return True
|
||||
if content in set(self.filters.get("ignore_exact", [])):
|
||||
return True
|
||||
return any(content.startswith(prefix) for prefix in self.filters.get("ignore_prefixes", []))
|
||||
|
||||
def _get_sender_name(self, room_id: str, sender: str) -> str:
|
||||
try:
|
||||
members = ContactManager.get_instance().get_group_members(room_id)
|
||||
return members.get(sender, sender)
|
||||
except Exception:
|
||||
return sender
|
||||
|
||||
def _pass_cooldown(self, room_id: str, trigger: Dict) -> bool:
|
||||
current_ts = time.time()
|
||||
room_cd = int(self.cooldown_config.get("group_reply_cooldown_sec", 45))
|
||||
user_cd = int(self.cooldown_config.get("same_user_followup_cooldown_sec", 10))
|
||||
last_room_reply = self.last_reply_at.get(room_id, 0.0)
|
||||
if trigger.get("is_question") or trigger.get("is_followup") or trigger.get("trigger_type") == "at_trigger":
|
||||
return (current_ts - last_room_reply) >= user_cd
|
||||
return (current_ts - last_room_reply) >= room_cd
|
||||
|
||||
def _build_user_prompt(self, context: Dict, memory_hints: Dict) -> str:
|
||||
recent_text = "\n".join(context.get("recent_messages", [])[-20:]) or "暂无"
|
||||
reply_mode = context.get("reply_mode", "social_short")
|
||||
length_rule = self._build_length_rule(reply_mode)
|
||||
return (
|
||||
f"当前群聊消息：\n{recent_text}\n\n"
|
||||
f"当前发言：{context.get('current_message', '')}\n"
|
||||
f"触发类型：{context.get('trigger_type', 'none')}\n"
|
||||
f"回复模式：{context.get('reply_mode', 'social_short')}\n"
|
||||
f"当前心流状态：{context.get('flow_state', 'idle')}\n"
|
||||
f"成员稳定记忆：\n{context.get('memory_prompt', '暂无')}\n\n"
|
||||
f"向量召回记忆：\n{context.get('vector_memory_prompt', '') or '暂无'}\n\n"
|
||||
f"补充信息：回归状态={memory_hints.get('returning_member_state', '') or 'none'}\n"
|
||||
f"要求：\n"
|
||||
f"1. 如果是明确问题，先给清楚答案。\n"
|
||||
f"2. 如果只是轻量接话，保持自然短句。\n"
|
||||
f"3. 不要暴露系统记忆来源。\n"
|
||||
f"4. 如果信息不足，不要硬编。\n"
|
||||
f"5. 输出最终可直接发到群里的内容，不要解释你的思路。\n"
|
||||
f"6. {length_rule}\n"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _sanitize_response(response: str) -> str:
|
||||
if not response:
|
||||
return ""
|
||||
response = response.strip()
|
||||
response = re.sub(r"\n{3,}", "\n\n", response)
|
||||
return response[:500].strip()
|
||||
|
||||
@staticmethod
|
||||
def _build_length_rule(reply_mode: str) -> str:
|
||||
if reply_mode == "social_short":
|
||||
return "默认只回一句短话，最好控制在2到12个字，除非非常不自然。"
|
||||
if reply_mode == "qa_fast":
|
||||
return "尽量只回1句话，必要时最多2句，先给结论，不要展开成长教程。"
|
||||
if reply_mode == "qa_with_context":
|
||||
return "优先控制在1到2句，除非对方明显在等详细步骤。"
|
||||
return "尽量短，像群友临时接一句，不要长篇大论。"
|
||||
|
||||
|
||||
def _sync_member_memory(self, room_id: str, sender: str, sender_name: str, member_context: Dict) -> None:
|
||||
if not member_context:
|
||||
return
|
||||
version = str(member_context.get("last_profiled_at", ""))
|
||||
cache_key = f"{room_id}:{sender}"
|
||||
if version and self._synced_member_context_versions.get(cache_key) == version:
|
||||
return
|
||||
text = self.context_builder._build_member_memory_prompt(member_context)
|
||||
if not text or text == "暂无稳定成员画像。":
|
||||
return
|
||||
payload = {
|
||||
"chatroom_id": room_id,
|
||||
"wxid": sender,
|
||||
"display_name": sender_name,
|
||||
"memory_type": "member_context_snapshot",
|
||||
"source_id": cache_key,
|
||||
"last_active_at": member_context.get("last_profiled_at", ""),
|
||||
"topic_tags": member_context.get("topics_of_interest", [])[:5],
|
||||
"summary_text": member_context.get("summary_text", ""),
|
||||
}
|
||||
ok = self.vector_memory.upsert_memory(f"member_context:{cache_key}:{version}", text, payload)
|
||||
self._log_event(
|
||||
"memory_upsert",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
memory_type="member_context_snapshot",
|
||||
ok=ok,
|
||||
)
|
||||
if ok and version:
|
||||
self._synced_member_context_versions[cache_key] = version
|
||||
|
||||
def _upsert_interaction_memory(
|
||||
self,
|
||||
room_id: str,
|
||||
sender: str,
|
||||
sender_name: str,
|
||||
content: str,
|
||||
response: str,
|
||||
trigger_type: str,
|
||||
topic: str,
|
||||
) -> None:
|
||||
text = f"{sender_name}说：{content}\n小牛回复：{response}"
|
||||
payload = {
|
||||
"chatroom_id": room_id,
|
||||
"wxid": sender,
|
||||
"display_name": sender_name,
|
||||
"memory_type": "interaction_memory",
|
||||
"topic_tags": [item for item in [topic, trigger_type] if item],
|
||||
"created_at": time.strftime("%Y-%m-%d %H:%M:%S"),
|
||||
"source_id": f"{room_id}:{sender}:{int(time.time())}",
|
||||
"summary_text": text[:500],
|
||||
}
|
||||
ok = self.vector_memory.upsert_memory(payload["source_id"], text, payload)
|
||||
self._log_event(
|
||||
"memory_upsert",
|
||||
room_id=room_id,
|
||||
sender=sender,
|
||||
memory_type="interaction_memory",
|
||||
ok=ok,
|
||||
trigger_type=trigger_type,
|
||||
)
|
||||
|
||||
def _log_event(self, event: str, **kwargs: Any) -> None:
|
||||
if not self.log_debug:
|
||||
return
|
||||
summary = self._build_log_summary(event, kwargs)
|
||||
self.LOG.info(summary)
|
||||
|
||||
@staticmethod
|
||||
def _preview(text: str, limit: int = 80) -> str:
|
||||
text = (text or "").replace("\n", "\\n").strip()
|
||||
if len(text) <= limit:
|
||||
return text
|
||||
return text[: limit - 3] + "..."
|
||||
|
||||
def _build_log_summary(self, event: str, data: Dict[str, Any]) -> str:
|
||||
room = self._short_id(data.get("room_id", ""))
|
||||
sender_name = data.get("sender_name", "") or self._short_id(data.get("sender", ""))
|
||||
sender = self._short_id(data.get("sender", ""))
|
||||
|
||||
if event == "recv":
|
||||
return (
|
||||
f"[XIAONIU] RECV room={room} user={sender_name}/{sender} "
|
||||
f"at={self._yn(data.get('is_at'))} msg={data.get('content_preview', '')}"
|
||||
).strip()
|
||||
|
||||
if event == "memory":
|
||||
return (
|
||||
f"[XIAONIU] MEMORY room={room} user={sender} "
|
||||
f"ctx={self._yn(data.get('has_member_context'))} "
|
||||
f"follow={self._yn(data.get('is_followup'))} "
|
||||
f"return={data.get('returning_state', 'none')}"
|
||||
).strip()
|
||||
|
||||
if event == "decision":
|
||||
return (
|
||||
f"[XIAONIU] DECIDE room={room} user={sender} "
|
||||
f"trigger={data.get('trigger_type', 'none')} "
|
||||
f"flow={data.get('flow_state', '')}:{data.get('flow_score', '')} "
|
||||
f"topic={data.get('topic', '-') or '-'} "
|
||||
f"reasons={data.get('reasons', '-') or '-'}"
|
||||
).strip()
|
||||
|
||||
if event == "skip":
|
||||
return (
|
||||
f"[XIAONIU] SKIP room={room} user={sender} "
|
||||
f"reason={data.get('reason', '')} "
|
||||
f"trigger={data.get('trigger_type', 'none')} "
|
||||
f"mode={data.get('reply_mode', '')}"
|
||||
).strip()
|
||||
|
||||
if event == "context":
|
||||
return (
|
||||
f"[XIAONIU] CTX room={room} user={sender} "
|
||||
f"mode={data.get('reply_mode', '')} "
|
||||
f"recent={data.get('recent_message_count', 0)} "
|
||||
f"vector={data.get('vector_hit_count', 0)}"
|
||||
).strip()
|
||||
|
||||
if event == "model_empty":
|
||||
return (
|
||||
f"[XIAONIU] MODEL_EMPTY room={room} user={sender} "
|
||||
f"model={data.get('model', '')} "
|
||||
f"mode={data.get('reply_mode', '')} "
|
||||
f"err={data.get('last_error', '')}"
|
||||
).strip()
|
||||
|
||||
if event == "sent":
|
||||
return (
|
||||
f"[XIAONIU] SENT room={room} user={sender_name}/{sender} "
|
||||
f"trigger={data.get('trigger_type', 'none')} "
|
||||
f"mode={data.get('reply_mode', '')} "
|
||||
f"len={data.get('response_len', 0)} "
|
||||
f"reply={data.get('response_preview', '')}"
|
||||
).strip()
|
||||
|
||||
if event == "memory_upsert":
|
||||
return (
|
||||
f"[XIAONIU] MEM_UPSERT room={room} user={sender} "
|
||||
f"type={data.get('memory_type', '')} "
|
||||
f"ok={self._yn(data.get('ok'))} "
|
||||
f"trigger={data.get('trigger_type', '-') or '-'}"
|
||||
).strip()
|
||||
|
||||
compact = " ".join(f"{key}={data[key]}" for key in sorted(data) if data.get(key) not in (None, ""))
|
||||
return f"[XIAONIU] {event.upper()} {compact}".strip()
|
||||
|
||||
@staticmethod
|
||||
def _yn(value: Any) -> str:
|
||||
return "Y" if bool(value) else "N"
|
||||
|
||||
@staticmethod
|
||||
def _short_id(value: str) -> str:
|
||||
value = str(value or "")
|
||||
if len(value) <= 10:
|
||||
return value
|
||||
return value[:4] + "..." + value[-4:]
|
||||
|
||||
Reference in New Issue
Block a user