585 lines
24 KiB
Python
585 lines
24 KiB
Python
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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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 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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from .context_builder import ContextBuilder
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from .flow_manager import FlowManager
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from .group_memory import GroupMemoryService
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from .group_profile import GroupProfileResolver
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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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FEATURE_KEY = "AI_AUTO_RESPONSE"
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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 "小牛群聊BOT"
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@property
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def version(self) -> str:
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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 "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 "ABOT Team"
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@property
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def command_prefix(self) -> Optional[str]:
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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 []
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@property
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def feature_key(self) -> Optional[str]:
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return self.FEATURE_KEY
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@property
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def feature_description(self) -> Optional[str]:
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return self.FEATURE_DESCRIPTION
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def __init__(self):
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super().__init__()
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self.feature = self.register_feature()
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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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self.LOG = logger
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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.group_memory_service = GroupMemoryService(self.db_manager, self._config.get("group_profiles", {}) or {})
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self.group_profile_resolver = GroupProfileResolver(self._config.get("group_profiles", {}) or {})
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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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self.status = PluginStatus.RUNNING
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return True
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def stop(self) -> bool:
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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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if not self.enable:
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return False
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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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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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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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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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if self._is_targeting_other_user(message):
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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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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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content = self._normalize_content(message)
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sender_name = self._get_sender_name(room_id, sender)
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group_name = self._get_group_name(room_id, message)
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group_memory_profile = self.group_memory_service.build_group_memory_profile(room_id, group_name)
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group_profile = self.group_profile_resolver.resolve(room_id, group_name, group_memory_profile)
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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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group_mode=group_profile.get("mode", ""),
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knowledge_domain=group_profile.get("knowledge_domain", ""),
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memory_domain=group_profile.get("group_memory_domain", ""),
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humor_style=group_profile.get("humor_style", ""),
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sharpness_style=group_profile.get("sharpness_style", ""),
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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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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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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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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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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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group_mode=group_profile.get("mode", ""),
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knowledge_domain=group_profile.get("knowledge_domain", ""),
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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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context = self.context_builder.build(
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room_id=room_id,
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group_profile=group_profile,
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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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system_prompt = self.persona_engine.build_system_prompt(group_profile)
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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(
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"model_empty",
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room_id=room_id,
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sender=sender,
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model=self.llm_client.model,
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last_error=self.llm_client.last_error,
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reply_mode=reply_mode,
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)
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return False, "empty_response"
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response = self._finalize_reply(response, reply_mode)
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await bot.send_text_message(room_id, response, sender)
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self.last_reply_at[room_id] = time.time()
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self.flow_manager.note_bot_reply(room_id)
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self.memory_store.note_bot_reply(room_id, sender, trigger.topic)
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self._upsert_interaction_memory(room_id, sender, sender_name, content, response, trigger.trigger_type, trigger.topic)
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self._log_event(
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"sent",
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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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trigger_type=trigger.trigger_type,
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reply_mode=reply_mode,
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response_preview=self._preview(response),
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response_len=len(response),
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)
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return False, "replied"
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def _append_group_message(self, room_id: str, message: Dict) -> None:
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items = self.group_messages.setdefault(room_id, [])
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items.append(message)
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size = int(self.mode_config.get("recent_context_size", 30))
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if len(items) > size:
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self.group_messages[room_id] = items[-size:]
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def _normalize_content(self, message: Dict[str, Any]) -> str:
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msg_type = message.get("type")
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content = str(message.get("content", "")).strip()
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if msg_type == MessageType.TEXT:
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return self._strip_at_prefix(content)
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if msg_type == MessageType.APP:
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try:
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root = ET.fromstring(content)
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title = root.find(".//title")
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return (title.text or "").strip() if title is not None else "[应用消息]"
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except Exception:
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return "[应用消息]"
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return content
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@staticmethod
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def _strip_at_prefix(content: str) -> str:
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return re.sub(r"@.*?[\u2005\s]+", "", content).strip()
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def _should_ignore(self, content: str) -> bool:
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if len(content) < int(self.filters.get("min_text_length", 1)):
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return True
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if content in set(self.filters.get("ignore_exact", [])):
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return True
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return any(content.startswith(prefix) for prefix in self.filters.get("ignore_prefixes", []))
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def _is_targeting_other_user(self, message: Dict[str, Any]) -> bool:
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if message.get("is_at", False):
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return False
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raw_content = str(message.get("content", "") or "")
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return "@" in raw_content
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def _get_sender_name(self, room_id: str, sender: str) -> str:
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try:
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members = ContactManager.get_instance().get_group_members(room_id)
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return members.get(sender, sender)
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except Exception:
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return sender
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@staticmethod
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def _get_group_name(room_id: str, message: Dict[str, Any]) -> str:
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all_contacts = message.get("all_contacts", {}) or {}
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return str(all_contacts.get(room_id, room_id))
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def _pass_cooldown(self, room_id: str, trigger: Dict) -> bool:
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current_ts = time.time()
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room_cd = int(self.cooldown_config.get("group_reply_cooldown_sec", 45))
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user_cd = int(self.cooldown_config.get("same_user_followup_cooldown_sec", 10))
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last_room_reply = self.last_reply_at.get(room_id, 0.0)
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if trigger.get("is_question") or trigger.get("is_followup") or trigger.get("trigger_type") == "at_trigger":
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return (current_ts - last_room_reply) >= user_cd
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return (current_ts - last_room_reply) >= room_cd
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def _build_user_prompt(self, context: Dict, memory_hints: Dict) -> str:
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recent_text = "\n".join(context.get("recent_messages", [])[-8:]) or "暂无"
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reply_mode = context.get("reply_mode", "social_short")
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length_rule = self._build_length_rule(reply_mode)
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return (
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f"当前群聊消息：\n{recent_text}\n\n"
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f"当前发言：{context.get('current_message', '')}\n"
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f"触发类型：{context.get('trigger_type', 'none')}\n"
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f"回复模式：{context.get('reply_mode', 'social_short')}\n"
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f"当前心流状态：{context.get('flow_state', 'idle')}\n"
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f"当前群画像：\n{context.get('group_profile_prompt', '暂无')}\n\n"
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f"成员稳定记忆：\n{context.get('memory_prompt', '暂无')}\n\n"
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f"向量召回记忆：\n{context.get('vector_memory_prompt', '') or '暂无'}\n\n"
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f"补充信息：回归状态={memory_hints.get('returning_member_state', '') or 'none'}\n"
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f"要求：\n"
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f"1. 如果是明确问题，先给清楚答案。\n"
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f"2. 如果只是轻量接话，保持自然短句。\n"
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f"3. 不要暴露系统记忆来源。\n"
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f"4. 如果信息不足，不要硬编。\n"
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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"
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f"6. {length_rule}\n"
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f"7. 优先直接回应“当前发言”本身，不要被较早上下文带跑。\n"
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f"8. 成员记忆和向量召回只有在与当前问题直接相关时才允许使用，否则忽略。\n"
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f"9. 如果你不确定自己是否理解对了，就宁可不展开，只回很短。\n"
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f"10. 把这次回复当作真人聊天里的第一反应，先只给第一层结论，不要主动补第二层解释。\n"
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f"11. 如果一句话已经够了，就立刻停，不要为了完整而补充。\n"
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f"12. 回答时优先服从当前群画像里的知识域和回答风格，不要跨领域乱发挥。\n"
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)
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@staticmethod
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def _sanitize_response(response: str) -> str:
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if not response:
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return ""
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response = response.strip()
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response = re.sub(r"\n{3,}", "\n\n", response)
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return response[:500].strip()
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def _finalize_reply(self, response: str, reply_mode: str) -> str:
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text = (response or "").strip()
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if not text:
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return ""
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text = re.sub(r"\s+", " ", text)
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text = text.replace("\n", " ").strip()
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if reply_mode == "social_short":
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text = self._take_first_sentence(text, 12)
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elif reply_mode == "qa_fast":
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text = self._take_first_sentence(text, 28)
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elif reply_mode == "qa_with_context":
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text = self._take_first_sentence(text, 36)
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else:
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text = self._take_first_sentence(text, 24)
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return text.strip()
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@staticmethod
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def _build_length_rule(reply_mode: str) -> str:
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if reply_mode == "social_short":
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return "默认只回一句短话，最好控制在2到8个字，除非非常不自然。"
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if reply_mode == "qa_fast":
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return "尽量只回1句话，总长度优先控制在28字内，先给结论，不要主动补解释。"
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if reply_mode == "qa_with_context":
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return "优先控制在1句话，必要时最多2句，总长度优先控制在36字内，只给第一层答案。"
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return "尽量短，像群友临时接一句，不要长篇大论。"
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@staticmethod
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def _take_first_sentence(text: str, limit: int) -> str:
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parts = re.split(r"(?<=[。！？!?；;])", text)
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first = parts[0].strip() if parts and parts[0].strip() else text.strip()
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if len(first) <= limit:
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return first
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clipped = first[:limit].rstrip("，,、；;：:")
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return clipped
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|
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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'))} "
|
||
f"style={self._style_mark(data.get('humor_style', ''), data.get('sharpness_style', ''))} "
|
||
f"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:]
|
||
|
||
@staticmethod
|
||
def _style_mark(humor_style: str, sharpness_style: str) -> str:
|
||
humor = "humor" if "中等" in str(humor_style) or "偏上" in str(humor_style) else "plain"
|
||
sharp = "sharp" if "毒舌" in str(sharpness_style) or "嘴欠" in str(sharpness_style) else "soft"
|
||
return f"{humor}/{sharp}"
|