fix：小小拆分
This commit is contained in:
SengokuCola
510
src/plugins/built_in/core_actions/no_reply.py
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510
src/plugins/built_in/core_actions/no_reply.py
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@@ -0,0 +1,510 @@
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import random
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import time
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import json
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from typing import Tuple
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# 导入新插件系统
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from src.plugin_system import BaseAction, ActionActivationType, ChatMode
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# 导入依赖的系统组件
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from src.common.logger import get_logger
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# 导入API模块 - 标准Python包方式
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from src.plugin_system.apis import message_api, llm_api
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from src.config.config import global_config
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from datetime import datetime
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from json_repair import repair_json
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logger = get_logger("core_actions")
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class NoReplyAction(BaseAction):
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"""不回复动作，使用智能判断机制决定何时结束等待
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新的等待逻辑：
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- 每0.2秒检查是否有新消息（提高响应性）
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- 如果累计消息数量达到阈值（默认20条），直接结束等待
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- 有新消息时进行LLM判断，但最快1秒一次（防止过于频繁）
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- 如果判断需要回复，则结束等待；否则继续等待
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- 达到最大超时时间后强制结束
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"""
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focus_activation_type = ActionActivationType.ALWAYS
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# focus_activation_type = ActionActivationType.RANDOM
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normal_activation_type = ActionActivationType.NEVER
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mode_enable = ChatMode.FOCUS
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parallel_action = False
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# 动作基本信息
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action_name = "no_reply"
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action_description = "暂时不回复消息"
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# 连续no_reply计数器
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_consecutive_count = 0
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# LLM判断的最小间隔时间
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_min_judge_interval = 1.0 # 最快1秒一次LLM判断
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# 自动结束的消息数量阈值
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_auto_exit_message_count = 20 # 累计20条消息自动结束
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# 最大等待超时时间
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_max_timeout = 1200 # 1200秒
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# 跳过LLM判断的配置
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_skip_judge_when_tired = True
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_skip_probability = 0.5
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# 新增：回复频率退出专注模式的配置
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_frequency_check_window = 600 # 频率检查窗口时间（秒）
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# 动作参数定义
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action_parameters = {"reason": "不回复的原因"}
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# 动作使用场景
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action_require = ["你发送了消息，目前无人回复"]
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# 关联类型
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associated_types = []
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async def execute(self) -> Tuple[bool, str]:
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"""执行不回复动作，有新消息时进行判断，但最快1秒一次"""
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import asyncio
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try:
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# 增加连续计数
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NoReplyAction._consecutive_count += 1
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count = NoReplyAction._consecutive_count
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reason = self.action_data.get("reason", "")
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start_time = time.time()
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last_judge_time = 0 # 上次进行LLM判断的时间
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min_judge_interval = self._min_judge_interval # 最小判断间隔，从配置获取
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check_interval = 0.2 # 检查新消息的间隔，设为0.2秒提高响应性
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# 累积判断历史
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judge_history = [] # 存储每次判断的结果和理由
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# 获取no_reply开始时的上下文消息（10条），用于后续记录
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context_messages = message_api.get_messages_by_time_in_chat(
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chat_id=self.chat_id,
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start_time=start_time - 600, # 获取开始前10分钟内的消息
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end_time=start_time,
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limit=10,
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limit_mode="latest",
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)
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# 构建上下文字符串
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context_str = ""
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if context_messages:
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context_str = message_api.build_readable_messages(
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messages=context_messages, timestamp_mode="normal_no_YMD", truncate=False, show_actions=True
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)
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context_str = f"当时选择no_reply前的聊天上下文：\n{context_str}\n"
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logger.info(f"{self.log_prefix} 选择不回复(第{count}次)，开始智能等待，原因: {reason}")
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while True:
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current_time = time.time()
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elapsed_time = current_time - start_time
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# 检查是否超时
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if elapsed_time >= self._max_timeout:
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logger.info(f"{self.log_prefix} 达到最大等待时间{self._max_timeout}秒，结束等待")
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exit_reason = (
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f"{global_config.bot.nickname}（你）等待了{self._max_timeout}秒，可以考虑一下是否要进行回复"
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)
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await self.store_action_info(
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action_build_into_prompt=True,
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action_prompt_display=exit_reason,
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action_done=True,
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)
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return True, exit_reason
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# **新增**：检查回复频率，决定是否退出专注模式
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should_exit_focus = await self._check_frequency_and_exit_focus(current_time)
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if should_exit_focus:
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logger.info(f"{self.log_prefix} 检测到回复频率过高，退出专注模式")
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# 标记退出专注模式
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self.action_data["_system_command"] = "stop_focus_chat"
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exit_reason = f"{global_config.bot.nickname}（你）发现自己回复太频繁了，决定退出专注模式，稍作休息"
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await self.store_action_info(
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action_build_into_prompt=True,
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action_prompt_display=exit_reason,
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action_done=True,
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)
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return True, exit_reason
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# 检查是否有新消息
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new_message_count = message_api.count_new_messages(
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chat_id=self.chat_id, start_time=start_time, end_time=current_time
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)
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# 如果累计消息数量达到阈值，直接结束等待
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if new_message_count >= self._auto_exit_message_count:
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logger.info(f"{self.log_prefix} 累计消息数量达到{new_message_count}条，直接结束等待")
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exit_reason = f"{global_config.bot.nickname}（你）看到了{new_message_count}条新消息，可以考虑一下是否要进行回复"
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await self.store_action_info(
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action_build_into_prompt=True,
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action_prompt_display=exit_reason,
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action_done=True,
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)
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return True, f"累计消息数量达到{new_message_count}条，直接结束等待 (等待时间: {elapsed_time:.1f}秒)"
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# 判定条件：累计3条消息或等待超过5秒且有新消息
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time_since_last_judge = current_time - last_judge_time
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should_judge = (
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new_message_count >= 3 # 累计3条消息
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or (new_message_count > 0 and time_since_last_judge >= 5.0) # 等待超过5秒且有新消息
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)
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if should_judge and time_since_last_judge >= min_judge_interval:
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# 判断触发原因
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trigger_reason = ""
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if new_message_count >= 3:
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trigger_reason = f"累计{new_message_count}条消息"
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elif time_since_last_judge >= 5.0:
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trigger_reason = f"等待{time_since_last_judge:.1f}秒且有{new_message_count}条新消息"
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logger.info(f"{self.log_prefix} 触发判定({trigger_reason})，进行智能判断...")
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# 获取最近的消息内容用于判断
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recent_messages = message_api.get_messages_by_time_in_chat(
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chat_id=self.chat_id,
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start_time=start_time,
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end_time=current_time,
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)
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if recent_messages:
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# 使用message_api构建可读的消息字符串
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messages_text = message_api.build_readable_messages(
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messages=recent_messages, timestamp_mode="normal_no_YMD", truncate=False, show_actions=False
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)
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# 参考simple_planner构建更完整的判断信息
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# 获取时间信息
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time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
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# 获取身份信息
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bot_name = global_config.bot.nickname
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bot_nickname = ""
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if global_config.bot.alias_names:
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bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
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bot_core_personality = global_config.personality.personality_core
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identity_block = f"你的名字是{bot_name}{bot_nickname}，你{bot_core_personality}"
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# 构建判断历史字符串（最多显示3条）
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history_block = ""
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if judge_history:
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history_block = "之前的判断历史：\n"
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# 只取最近的3条历史记录
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recent_history = judge_history[-3:] if len(judge_history) > 3 else judge_history
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for i, (timestamp, judge_result, reason) in enumerate(recent_history, 1):
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elapsed_seconds = int(timestamp - start_time)
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history_block += f"{i}. 等待{elapsed_seconds}秒时判断：{judge_result}，理由：{reason}\n"
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history_block += "\n"
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# 检查过去10分钟的发言频率
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frequency_block = ""
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should_skip_llm_judge = False # 是否跳过LLM判断
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try:
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# 获取过去10分钟的所有消息
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past_10min_time = current_time - 600 # 10分钟前
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all_messages_10min = message_api.get_messages_by_time_in_chat(
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chat_id=self.chat_id,
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start_time=past_10min_time,
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end_time=current_time,
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)
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# 手动过滤bot自己的消息
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bot_message_count = 0
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if all_messages_10min:
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user_id = global_config.bot.qq_account
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for message in all_messages_10min:
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# 检查消息发送者是否是bot
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sender_id = message.get("user_id", "")
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if sender_id == user_id:
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bot_message_count += 1
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talk_frequency_threshold = global_config.chat.talk_frequency * 10
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if bot_message_count > talk_frequency_threshold:
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over_count = bot_message_count - talk_frequency_threshold
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# 根据超过的数量设置不同的提示词和跳过概率
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skip_probability = 0
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if over_count <= 3:
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frequency_block = "你感觉稍微有些累，回复的有点多了。\n"
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elif over_count <= 5:
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frequency_block = "你今天说话比较多，感觉有点疲惫，想要稍微休息一下。\n"
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else:
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frequency_block = "你发现自己说话太多了，感觉很累，想要安静一会儿，除非有重要的事情否则不想回复。\n"
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skip_probability = self._skip_probability
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# 根据配置和概率决定是否跳过LLM判断
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if self._skip_judge_when_tired and random.random() < skip_probability:
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should_skip_llm_judge = True
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logger.info(
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f"{self.log_prefix} 发言过多(超过{over_count}条)，随机决定跳过此次LLM判断(概率{skip_probability * 100:.0f}%)"
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)
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logger.info(
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f"{self.log_prefix} 过去10分钟发言{bot_message_count}条，超过阈值{talk_frequency_threshold}，添加疲惫提示"
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)
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else:
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# 回复次数少时的正向提示
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under_count = talk_frequency_threshold - bot_message_count
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if under_count >= talk_frequency_threshold * 0.8: # 回复很少（少于20%）
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frequency_block = "你感觉精力充沛，状态很好。\n"
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elif under_count >= talk_frequency_threshold * 0.5: # 回复较少（少于50%）
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frequency_block = "你感觉状态不错。\n"
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else: # 刚好达到阈值
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frequency_block = ""
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logger.info(
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f"{self.log_prefix} 过去10分钟发言{bot_message_count}条，未超过阈值{talk_frequency_threshold}，添加正向提示"
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)
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except Exception as e:
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logger.warning(f"{self.log_prefix} 检查发言频率时出错: {e}")
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frequency_block = ""
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# 如果决定跳过LLM判断，直接更新时间并继续等待
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if should_skip_llm_judge:
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last_judge_time = time.time() # 更新判断时间，避免立即重新判断
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start_time = current_time # 更新开始时间，避免重复计算同样的消息
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continue # 跳过本次LLM判断，继续循环等待
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# 构建判断上下文
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judge_prompt = f"""
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{time_block}
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{identity_block}
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你现在正在QQ群参与聊天，以下是聊天内容：
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{context_str}
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在以上的聊天中，你选择了暂时不回复，现在，你看到了新的聊天消息如下：
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{messages_text}
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{history_block}
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请注意：{frequency_block}
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请你判断，是否要结束不回复的状态，重新加入聊天讨论。
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判断标准：
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1. 如果有人直接@你、提到你的名字或明确向你询问，应该回复
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2. 如果话题发生重要变化，需要你参与讨论，应该回复
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3. 如果只是普通闲聊、重复内容或与你无关的讨论，不需要回复
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4. 如果消息内容过于简单（如单纯的表情、"哈哈"等），不需要回复
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5. 参考之前的判断历史，如果情况有明显变化或持续等待时间过长，考虑调整判断
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请用JSON格式回复你的判断，严格按照以下格式：
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{{
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"should_reply": true/false,
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"reason": "详细说明你的判断理由"
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}}
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"""
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try:
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# 获取可用的模型配置
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available_models = llm_api.get_available_models()
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# 使用 utils_small 模型
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small_model = getattr(available_models, "utils_small", None)
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print(judge_prompt)
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if small_model:
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# 使用小模型进行判断
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success, response, reasoning, model_name = await llm_api.generate_with_model(
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prompt=judge_prompt,
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model_config=small_model,
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request_type="plugin.no_reply_judge",
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temperature=0.7, # 进一步降低温度，提高JSON输出的一致性和准确性
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)
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# 更新上次判断时间
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last_judge_time = time.time()
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if success and response:
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response = response.strip()
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logger.info(f"{self.log_prefix} 模型({model_name})原始JSON响应: {response}")
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# 解析LLM的JSON响应，提取判断结果和理由
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judge_result, reason = self._parse_llm_judge_response(response)
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logger.info(
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f"{self.log_prefix} JSON解析结果 - 判断: {judge_result}, 理由: {reason}"
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)
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# 将判断结果保存到历史中
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judge_history.append((current_time, judge_result, reason))
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if judge_result == "需要回复":
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logger.info(f"{self.log_prefix} 模型判断需要回复，结束等待")
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full_prompt = f"{global_config.bot.nickname}（你）的想法是：{reason}"
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await self.store_action_info(
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action_build_into_prompt=True,
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action_prompt_display=full_prompt,
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action_done=True,
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)
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return True, f"检测到需要回复的消息，结束等待 (等待时间: {elapsed_time:.1f}秒)"
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else:
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logger.info(f"{self.log_prefix} 模型判断不需要回复，理由: {reason}，继续等待")
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# 更新开始时间，避免重复判断同样的消息
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start_time = current_time
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else:
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logger.warning(f"{self.log_prefix} 模型判断失败，继续等待")
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else:
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logger.warning(f"{self.log_prefix} 未找到可用的模型配置，继续等待")
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last_judge_time = time.time() # 即使失败也更新时间，避免频繁重试
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except Exception as e:
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logger.error(f"{self.log_prefix} 模型判断异常: {e}，继续等待")
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last_judge_time = time.time() # 异常时也更新时间，避免频繁重试
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# 每10秒输出一次等待状态
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if int(elapsed_time) % 10 == 0 and int(elapsed_time) > 0:
|
||||
logger.info(f"{self.log_prefix} 已等待{elapsed_time:.0f}秒，等待新消息...")
|
||||
await asyncio.sleep(1)
|
||||
|
||||
# 短暂等待后继续检查
|
||||
await asyncio.sleep(check_interval)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 不回复动作执行失败: {e}")
|
||||
# 即使执行失败也要记录
|
||||
exit_reason = f"执行异常: {str(e)}"
|
||||
full_prompt = f"{context_str}{exit_reason}，你思考是否要进行回复"
|
||||
await self.store_action_info(
|
||||
action_build_into_prompt=True,
|
||||
action_prompt_display=full_prompt,
|
||||
action_done=True,
|
||||
)
|
||||
return False, f"不回复动作执行失败: {e}"
|
||||
|
||||
async def _check_frequency_and_exit_focus(self, current_time: float) -> bool:
|
||||
"""检查回复频率，决定是否退出专注模式
|
||||
|
||||
Args:
|
||||
current_time: 当前时间戳
|
||||
|
||||
Returns:
|
||||
bool: 是否应该退出专注模式
|
||||
"""
|
||||
try:
|
||||
# 只在auto模式下进行频率检查
|
||||
if global_config.chat.chat_mode != "auto":
|
||||
return False
|
||||
|
||||
# 获取检查窗口内的所有消息
|
||||
window_start_time = current_time - self._frequency_check_window
|
||||
all_messages = message_api.get_messages_by_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
start_time=window_start_time,
|
||||
end_time=current_time,
|
||||
)
|
||||
|
||||
if not all_messages:
|
||||
return False
|
||||
|
||||
# 统计bot自己的回复数量
|
||||
bot_message_count = 0
|
||||
user_id = global_config.bot.qq_account
|
||||
|
||||
for message in all_messages:
|
||||
sender_id = message.get("user_id", "")
|
||||
if sender_id == user_id:
|
||||
bot_message_count += 1
|
||||
|
||||
# 计算当前回复频率（每分钟回复数）
|
||||
window_minutes = self._frequency_check_window / 60
|
||||
current_frequency = bot_message_count / window_minutes
|
||||
|
||||
# 计算阈值频率：使用 exit_focus_threshold * 1.5
|
||||
threshold_multiplier = global_config.chat.exit_focus_threshold * 1.5
|
||||
threshold_frequency = global_config.chat.talk_frequency * threshold_multiplier
|
||||
|
||||
# 判断是否超过阈值
|
||||
if current_frequency > threshold_frequency:
|
||||
logger.info(
|
||||
f"{self.log_prefix} 回复频率检查：当前频率 {current_frequency:.2f}/分钟，超过阈值 {threshold_frequency:.2f}/分钟 (exit_threshold={global_config.chat.exit_focus_threshold} * 1.5)，准备退出专注模式"
|
||||
)
|
||||
return True
|
||||
else:
|
||||
logger.debug(
|
||||
f"{self.log_prefix} 回复频率检查：当前频率 {current_frequency:.2f}/分钟，未超过阈值 {threshold_frequency:.2f}/分钟 (exit_threshold={global_config.chat.exit_focus_threshold} * 1.5)"
|
||||
)
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 检查回复频率时出错: {e}")
|
||||
return False
|
||||
|
||||
def _parse_llm_judge_response(self, response: str) -> tuple[str, str]:
|
||||
"""解析LLM判断响应，使用JSON格式提取判断结果和理由
|
||||
|
||||
Args:
|
||||
response: LLM的原始JSON响应
|
||||
|
||||
Returns:
|
||||
tuple: (判断结果, 理由)
|
||||
"""
|
||||
try:
|
||||
# 使用repair_json修复可能有问题的JSON格式
|
||||
fixed_json_string = repair_json(response)
|
||||
logger.debug(f"{self.log_prefix} repair_json修复后的响应: {fixed_json_string}")
|
||||
|
||||
# 如果repair_json返回的是字符串，需要解析为Python对象
|
||||
if isinstance(fixed_json_string, str):
|
||||
result_json = json.loads(fixed_json_string)
|
||||
else:
|
||||
# 如果repair_json直接返回了字典对象，直接使用
|
||||
result_json = fixed_json_string
|
||||
|
||||
# 从JSON中提取判断结果和理由
|
||||
should_reply = result_json.get("should_reply", False)
|
||||
reason = result_json.get("reason", "无法获取判断理由")
|
||||
|
||||
# 转换布尔值为中文字符串
|
||||
judge_result = "需要回复" if should_reply else "不需要回复"
|
||||
|
||||
logger.debug(f"{self.log_prefix} JSON解析成功 - 判断: {judge_result}, 理由: {reason}")
|
||||
return judge_result, reason
|
||||
|
||||
except (json.JSONDecodeError, KeyError, TypeError) as e:
|
||||
logger.warning(f"{self.log_prefix} JSON解析失败，尝试文本解析: {e}")
|
||||
|
||||
# 如果JSON解析失败，回退到简单的关键词匹配
|
||||
try:
|
||||
response_lower = response.lower()
|
||||
|
||||
if "true" in response_lower or "需要回复" in response:
|
||||
judge_result = "需要回复"
|
||||
reason = "从响应文本中检测到需要回复的指示"
|
||||
elif "false" in response_lower or "不需要回复" in response:
|
||||
judge_result = "不需要回复"
|
||||
reason = "从响应文本中检测到不需要回复的指示"
|
||||
else:
|
||||
judge_result = "不需要回复" # 默认值
|
||||
reason = f"无法解析响应格式，使用默认判断。原始响应: {response[:100]}..."
|
||||
|
||||
logger.debug(f"{self.log_prefix} 文本解析结果 - 判断: {judge_result}, 理由: {reason}")
|
||||
return judge_result, reason
|
||||
|
||||
except Exception as fallback_e:
|
||||
logger.error(f"{self.log_prefix} 文本解析也失败: {fallback_e}")
|
||||
return "不需要回复", f"解析异常: {str(e)}, 回退解析也失败: {str(fallback_e)}"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 解析LLM响应时出错: {e}")
|
||||
return "不需要回复", f"解析异常: {str(e)}"
|
||||
|
||||
@classmethod
|
||||
def reset_consecutive_count(cls):
|
||||
"""重置连续计数器"""
|
||||
cls._consecutive_count = 0
|
||||
logger.debug("NoReplyAction连续计数器已重置")
|
||||
@@ -7,7 +7,6 @@
|
||||
|
||||
import random
|
||||
import time
|
||||
import json
|
||||
from typing import List, Tuple, Type
|
||||
|
||||
# 导入新插件系统
|
||||
@@ -18,10 +17,8 @@ from src.plugin_system.base.config_types import ConfigField
|
||||
from src.common.logger import get_logger
|
||||
|
||||
# 导入API模块 - 标准Python包方式
|
||||
from src.plugin_system.apis import emoji_api, generator_api, message_api, llm_api
|
||||
from src.config.config import global_config
|
||||
from datetime import datetime
|
||||
from json_repair import repair_json
|
||||
from src.plugin_system.apis import emoji_api, generator_api, message_api
|
||||
from src.plugins.built_in.core_actions.no_reply import NoReplyAction
|
||||
|
||||
logger = get_logger("core_actions")
|
||||
|
||||
@@ -112,496 +109,6 @@ class ReplyAction(BaseAction):
|
||||
return False, f"回复失败: {str(e)}"
|
||||
|
||||
|
||||
class NoReplyAction(BaseAction):
|
||||
"""不回复动作，使用智能判断机制决定何时结束等待
|
||||
|
||||
新的等待逻辑：
|
||||
- 每0.2秒检查是否有新消息（提高响应性）
|
||||
- 如果累计消息数量达到阈值（默认20条），直接结束等待
|
||||
- 有新消息时进行LLM判断，但最快1秒一次（防止过于频繁）
|
||||
- 如果判断需要回复，则结束等待；否则继续等待
|
||||
- 达到最大超时时间后强制结束
|
||||
"""
|
||||
|
||||
focus_activation_type = ActionActivationType.ALWAYS
|
||||
# focus_activation_type = ActionActivationType.RANDOM
|
||||
normal_activation_type = ActionActivationType.NEVER
|
||||
mode_enable = ChatMode.FOCUS
|
||||
parallel_action = False
|
||||
|
||||
# 动作基本信息
|
||||
action_name = "no_reply"
|
||||
action_description = "暂时不回复消息"
|
||||
|
||||
# 连续no_reply计数器
|
||||
_consecutive_count = 0
|
||||
|
||||
# LLM判断的最小间隔时间
|
||||
_min_judge_interval = 1.0 # 最快1秒一次LLM判断
|
||||
|
||||
# 自动结束的消息数量阈值
|
||||
_auto_exit_message_count = 20 # 累计20条消息自动结束
|
||||
|
||||
# 最大等待超时时间
|
||||
_max_timeout = 1200 # 1200秒
|
||||
|
||||
# 跳过LLM判断的配置
|
||||
_skip_judge_when_tired = True
|
||||
_skip_probability = 0.5
|
||||
|
||||
# 新增：回复频率退出专注模式的配置
|
||||
_frequency_check_window = 600 # 频率检查窗口时间（秒）
|
||||
|
||||
# 动作参数定义
|
||||
action_parameters = {"reason": "不回复的原因"}
|
||||
|
||||
# 动作使用场景
|
||||
action_require = ["你发送了消息，目前无人回复"]
|
||||
|
||||
# 关联类型
|
||||
associated_types = []
|
||||
|
||||
async def execute(self) -> Tuple[bool, str]:
|
||||
"""执行不回复动作，有新消息时进行判断，但最快1秒一次"""
|
||||
import asyncio
|
||||
|
||||
try:
|
||||
# 增加连续计数
|
||||
NoReplyAction._consecutive_count += 1
|
||||
count = NoReplyAction._consecutive_count
|
||||
|
||||
reason = self.action_data.get("reason", "")
|
||||
start_time = time.time()
|
||||
last_judge_time = 0 # 上次进行LLM判断的时间
|
||||
min_judge_interval = self._min_judge_interval # 最小判断间隔，从配置获取
|
||||
check_interval = 0.2 # 检查新消息的间隔，设为0.2秒提高响应性
|
||||
|
||||
# 累积判断历史
|
||||
judge_history = [] # 存储每次判断的结果和理由
|
||||
|
||||
# 获取no_reply开始时的上下文消息（10条），用于后续记录
|
||||
context_messages = message_api.get_messages_by_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
start_time=start_time - 600, # 获取开始前10分钟内的消息
|
||||
end_time=start_time,
|
||||
limit=10,
|
||||
limit_mode="latest",
|
||||
)
|
||||
|
||||
# 构建上下文字符串
|
||||
context_str = ""
|
||||
if context_messages:
|
||||
context_str = message_api.build_readable_messages(
|
||||
messages=context_messages, timestamp_mode="normal_no_YMD", truncate=False, show_actions=True
|
||||
)
|
||||
context_str = f"当时选择no_reply前的聊天上下文：\n{context_str}\n"
|
||||
|
||||
logger.info(f"{self.log_prefix} 选择不回复(第{count}次)，开始智能等待，原因: {reason}")
|
||||
|
||||
while True:
|
||||
current_time = time.time()
|
||||
elapsed_time = current_time - start_time
|
||||
|
||||
# 检查是否超时
|
||||
if elapsed_time >= self._max_timeout:
|
||||
logger.info(f"{self.log_prefix} 达到最大等待时间{self._max_timeout}秒，结束等待")
|
||||
exit_reason = (
|
||||
f"{global_config.bot.nickname}（你）等待了{self._max_timeout}秒，可以考虑一下是否要进行回复"
|
||||
)
|
||||
await self.store_action_info(
|
||||
action_build_into_prompt=True,
|
||||
action_prompt_display=exit_reason,
|
||||
action_done=True,
|
||||
)
|
||||
return True, exit_reason
|
||||
|
||||
# **新增**：检查回复频率，决定是否退出专注模式
|
||||
should_exit_focus = await self._check_frequency_and_exit_focus(current_time)
|
||||
if should_exit_focus:
|
||||
logger.info(f"{self.log_prefix} 检测到回复频率过高，退出专注模式")
|
||||
# 标记退出专注模式
|
||||
self.action_data["_system_command"] = "stop_focus_chat"
|
||||
exit_reason = f"{global_config.bot.nickname}（你）发现自己回复太频繁了，决定退出专注模式，稍作休息"
|
||||
await self.store_action_info(
|
||||
action_build_into_prompt=True,
|
||||
action_prompt_display=exit_reason,
|
||||
action_done=True,
|
||||
)
|
||||
return True, exit_reason
|
||||
|
||||
# 检查是否有新消息
|
||||
new_message_count = message_api.count_new_messages(
|
||||
chat_id=self.chat_id, start_time=start_time, end_time=current_time
|
||||
)
|
||||
|
||||
# 如果累计消息数量达到阈值，直接结束等待
|
||||
if new_message_count >= self._auto_exit_message_count:
|
||||
logger.info(f"{self.log_prefix} 累计消息数量达到{new_message_count}条，直接结束等待")
|
||||
exit_reason = f"{global_config.bot.nickname}（你）看到了{new_message_count}条新消息，可以考虑一下是否要进行回复"
|
||||
await self.store_action_info(
|
||||
action_build_into_prompt=True,
|
||||
action_prompt_display=exit_reason,
|
||||
action_done=True,
|
||||
)
|
||||
return True, f"累计消息数量达到{new_message_count}条，直接结束等待 (等待时间: {elapsed_time:.1f}秒)"
|
||||
|
||||
# 判定条件：累计3条消息或等待超过5秒且有新消息
|
||||
time_since_last_judge = current_time - last_judge_time
|
||||
should_judge = (
|
||||
new_message_count >= 3 # 累计3条消息
|
||||
or (new_message_count > 0 and time_since_last_judge >= 5.0) # 等待超过5秒且有新消息
|
||||
)
|
||||
|
||||
if should_judge and time_since_last_judge >= min_judge_interval:
|
||||
# 判断触发原因
|
||||
trigger_reason = ""
|
||||
if new_message_count >= 3:
|
||||
trigger_reason = f"累计{new_message_count}条消息"
|
||||
elif time_since_last_judge >= 5.0:
|
||||
trigger_reason = f"等待{time_since_last_judge:.1f}秒且有{new_message_count}条新消息"
|
||||
|
||||
logger.info(f"{self.log_prefix} 触发判定({trigger_reason})，进行智能判断...")
|
||||
|
||||
# 获取最近的消息内容用于判断
|
||||
recent_messages = message_api.get_messages_by_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
start_time=start_time,
|
||||
end_time=current_time,
|
||||
)
|
||||
|
||||
if recent_messages:
|
||||
# 使用message_api构建可读的消息字符串
|
||||
messages_text = message_api.build_readable_messages(
|
||||
messages=recent_messages, timestamp_mode="normal_no_YMD", truncate=False, show_actions=False
|
||||
)
|
||||
|
||||
# 参考simple_planner构建更完整的判断信息
|
||||
# 获取时间信息
|
||||
time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
|
||||
|
||||
# 获取身份信息
|
||||
bot_name = global_config.bot.nickname
|
||||
bot_nickname = ""
|
||||
if global_config.bot.alias_names:
|
||||
bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
|
||||
bot_core_personality = global_config.personality.personality_core
|
||||
identity_block = f"你的名字是{bot_name}{bot_nickname}，你{bot_core_personality}"
|
||||
|
||||
# 构建判断历史字符串（最多显示3条）
|
||||
history_block = ""
|
||||
if judge_history:
|
||||
history_block = "之前的判断历史：\n"
|
||||
# 只取最近的3条历史记录
|
||||
recent_history = judge_history[-3:] if len(judge_history) > 3 else judge_history
|
||||
for i, (timestamp, judge_result, reason) in enumerate(recent_history, 1):
|
||||
elapsed_seconds = int(timestamp - start_time)
|
||||
history_block += f"{i}. 等待{elapsed_seconds}秒时判断：{judge_result}，理由：{reason}\n"
|
||||
history_block += "\n"
|
||||
|
||||
# 检查过去10分钟的发言频率
|
||||
frequency_block = ""
|
||||
should_skip_llm_judge = False # 是否跳过LLM判断
|
||||
|
||||
try:
|
||||
# 获取过去10分钟的所有消息
|
||||
past_10min_time = current_time - 600 # 10分钟前
|
||||
all_messages_10min = message_api.get_messages_by_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
start_time=past_10min_time,
|
||||
end_time=current_time,
|
||||
)
|
||||
|
||||
# 手动过滤bot自己的消息
|
||||
bot_message_count = 0
|
||||
if all_messages_10min:
|
||||
user_id = global_config.bot.qq_account
|
||||
|
||||
for message in all_messages_10min:
|
||||
# 检查消息发送者是否是bot
|
||||
sender_id = message.get("user_id", "")
|
||||
|
||||
if sender_id == user_id:
|
||||
bot_message_count += 1
|
||||
|
||||
talk_frequency_threshold = global_config.chat.talk_frequency * 10
|
||||
|
||||
if bot_message_count > talk_frequency_threshold:
|
||||
over_count = bot_message_count - talk_frequency_threshold
|
||||
|
||||
# 根据超过的数量设置不同的提示词和跳过概率
|
||||
skip_probability = 0
|
||||
if over_count <= 3:
|
||||
frequency_block = "你感觉稍微有些累，回复的有点多了。\n"
|
||||
elif over_count <= 5:
|
||||
frequency_block = "你今天说话比较多，感觉有点疲惫，想要稍微休息一下。\n"
|
||||
else:
|
||||
frequency_block = "你发现自己说话太多了，感觉很累，想要安静一会儿，除非有重要的事情否则不想回复。\n"
|
||||
skip_probability = self._skip_probability
|
||||
|
||||
# 根据配置和概率决定是否跳过LLM判断
|
||||
if self._skip_judge_when_tired and random.random() < skip_probability:
|
||||
should_skip_llm_judge = True
|
||||
logger.info(
|
||||
f"{self.log_prefix} 发言过多(超过{over_count}条)，随机决定跳过此次LLM判断(概率{skip_probability * 100:.0f}%)"
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 过去10分钟发言{bot_message_count}条，超过阈值{talk_frequency_threshold}，添加疲惫提示"
|
||||
)
|
||||
else:
|
||||
# 回复次数少时的正向提示
|
||||
under_count = talk_frequency_threshold - bot_message_count
|
||||
|
||||
if under_count >= talk_frequency_threshold * 0.8: # 回复很少（少于20%）
|
||||
frequency_block = "你感觉精力充沛，状态很好。\n"
|
||||
elif under_count >= talk_frequency_threshold * 0.5: # 回复较少（少于50%）
|
||||
frequency_block = "你感觉状态不错。\n"
|
||||
else: # 刚好达到阈值
|
||||
frequency_block = ""
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 过去10分钟发言{bot_message_count}条，未超过阈值{talk_frequency_threshold}，添加正向提示"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"{self.log_prefix} 检查发言频率时出错: {e}")
|
||||
frequency_block = ""
|
||||
|
||||
# 如果决定跳过LLM判断，直接更新时间并继续等待
|
||||
if should_skip_llm_judge:
|
||||
last_judge_time = time.time() # 更新判断时间，避免立即重新判断
|
||||
start_time = current_time # 更新开始时间，避免重复计算同样的消息
|
||||
continue # 跳过本次LLM判断，继续循环等待
|
||||
|
||||
# 构建判断上下文
|
||||
judge_prompt = f"""
|
||||
{time_block}
|
||||
{identity_block}
|
||||
|
||||
你现在正在QQ群参与聊天，以下是聊天内容：
|
||||
{context_str}
|
||||
在以上的聊天中，你选择了暂时不回复，现在，你看到了新的聊天消息如下：
|
||||
{messages_text}
|
||||
|
||||
{history_block}
|
||||
请注意：{frequency_block}
|
||||
请你判断，是否要结束不回复的状态，重新加入聊天讨论。
|
||||
|
||||
判断标准：
|
||||
1. 如果有人直接@你、提到你的名字或明确向你询问，应该回复
|
||||
2. 如果话题发生重要变化，需要你参与讨论，应该回复
|
||||
3. 如果只是普通闲聊、重复内容或与你无关的讨论，不需要回复
|
||||
4. 如果消息内容过于简单（如单纯的表情、"哈哈"等），不需要回复
|
||||
5. 参考之前的判断历史，如果情况有明显变化或持续等待时间过长，考虑调整判断
|
||||
|
||||
请用JSON格式回复你的判断，严格按照以下格式：
|
||||
{{
|
||||
"should_reply": true/false,
|
||||
"reason": "详细说明你的判断理由"
|
||||
}}
|
||||
"""
|
||||
|
||||
try:
|
||||
# 获取可用的模型配置
|
||||
available_models = llm_api.get_available_models()
|
||||
|
||||
# 使用 utils_small 模型
|
||||
small_model = getattr(available_models, "utils_small", None)
|
||||
|
||||
print(judge_prompt)
|
||||
|
||||
if small_model:
|
||||
# 使用小模型进行判断
|
||||
success, response, reasoning, model_name = await llm_api.generate_with_model(
|
||||
prompt=judge_prompt,
|
||||
model_config=small_model,
|
||||
request_type="plugin.no_reply_judge",
|
||||
temperature=0.7, # 进一步降低温度，提高JSON输出的一致性和准确性
|
||||
)
|
||||
|
||||
# 更新上次判断时间
|
||||
last_judge_time = time.time()
|
||||
|
||||
if success and response:
|
||||
response = response.strip()
|
||||
logger.info(f"{self.log_prefix} 模型({model_name})原始JSON响应: {response}")
|
||||
|
||||
# 解析LLM的JSON响应，提取判断结果和理由
|
||||
judge_result, reason = self._parse_llm_judge_response(response)
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} JSON解析结果 - 判断: {judge_result}, 理由: {reason}"
|
||||
)
|
||||
|
||||
# 将判断结果保存到历史中
|
||||
judge_history.append((current_time, judge_result, reason))
|
||||
|
||||
if judge_result == "需要回复":
|
||||
logger.info(f"{self.log_prefix} 模型判断需要回复，结束等待")
|
||||
|
||||
full_prompt = f"{global_config.bot.nickname}（你）的想法是：{reason}"
|
||||
await self.store_action_info(
|
||||
action_build_into_prompt=True,
|
||||
action_prompt_display=full_prompt,
|
||||
action_done=True,
|
||||
)
|
||||
return True, f"检测到需要回复的消息，结束等待 (等待时间: {elapsed_time:.1f}秒)"
|
||||
else:
|
||||
logger.info(f"{self.log_prefix} 模型判断不需要回复，理由: {reason}，继续等待")
|
||||
# 更新开始时间，避免重复判断同样的消息
|
||||
start_time = current_time
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} 模型判断失败，继续等待")
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} 未找到可用的模型配置，继续等待")
|
||||
last_judge_time = time.time() # 即使失败也更新时间，避免频繁重试
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 模型判断异常: {e}，继续等待")
|
||||
last_judge_time = time.time() # 异常时也更新时间，避免频繁重试
|
||||
|
||||
# 每10秒输出一次等待状态
|
||||
if int(elapsed_time) % 10 == 0 and int(elapsed_time) > 0:
|
||||
logger.info(f"{self.log_prefix} 已等待{elapsed_time:.0f}秒，等待新消息...")
|
||||
await asyncio.sleep(1)
|
||||
|
||||
# 短暂等待后继续检查
|
||||
await asyncio.sleep(check_interval)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 不回复动作执行失败: {e}")
|
||||
# 即使执行失败也要记录
|
||||
exit_reason = f"执行异常: {str(e)}"
|
||||
full_prompt = f"{context_str}{exit_reason}，你思考是否要进行回复"
|
||||
await self.store_action_info(
|
||||
action_build_into_prompt=True,
|
||||
action_prompt_display=full_prompt,
|
||||
action_done=True,
|
||||
)
|
||||
return False, f"不回复动作执行失败: {e}"
|
||||
|
||||
async def _check_frequency_and_exit_focus(self, current_time: float) -> bool:
|
||||
"""检查回复频率，决定是否退出专注模式
|
||||
|
||||
Args:
|
||||
current_time: 当前时间戳
|
||||
|
||||
Returns:
|
||||
bool: 是否应该退出专注模式
|
||||
"""
|
||||
try:
|
||||
# 只在auto模式下进行频率检查
|
||||
if global_config.chat.chat_mode != "auto":
|
||||
return False
|
||||
|
||||
# 获取检查窗口内的所有消息
|
||||
window_start_time = current_time - self._frequency_check_window
|
||||
all_messages = message_api.get_messages_by_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
start_time=window_start_time,
|
||||
end_time=current_time,
|
||||
)
|
||||
|
||||
if not all_messages:
|
||||
return False
|
||||
|
||||
# 统计bot自己的回复数量
|
||||
bot_message_count = 0
|
||||
user_id = global_config.bot.qq_account
|
||||
|
||||
for message in all_messages:
|
||||
sender_id = message.get("user_id", "")
|
||||
if sender_id == user_id:
|
||||
bot_message_count += 1
|
||||
|
||||
# 计算当前回复频率（每分钟回复数）
|
||||
window_minutes = self._frequency_check_window / 60
|
||||
current_frequency = bot_message_count / window_minutes
|
||||
|
||||
# 计算阈值频率：使用 exit_focus_threshold * 1.5
|
||||
threshold_multiplier = global_config.chat.exit_focus_threshold * 1.5
|
||||
threshold_frequency = global_config.chat.talk_frequency * threshold_multiplier
|
||||
|
||||
# 判断是否超过阈值
|
||||
if current_frequency > threshold_frequency:
|
||||
logger.info(
|
||||
f"{self.log_prefix} 回复频率检查：当前频率 {current_frequency:.2f}/分钟，超过阈值 {threshold_frequency:.2f}/分钟 (exit_threshold={global_config.chat.exit_focus_threshold} * 1.5)，准备退出专注模式"
|
||||
)
|
||||
return True
|
||||
else:
|
||||
logger.debug(
|
||||
f"{self.log_prefix} 回复频率检查：当前频率 {current_frequency:.2f}/分钟，未超过阈值 {threshold_frequency:.2f}/分钟 (exit_threshold={global_config.chat.exit_focus_threshold} * 1.5)"
|
||||
)
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 检查回复频率时出错: {e}")
|
||||
return False
|
||||
|
||||
def _parse_llm_judge_response(self, response: str) -> tuple[str, str]:
|
||||
"""解析LLM判断响应，使用JSON格式提取判断结果和理由
|
||||
|
||||
Args:
|
||||
response: LLM的原始JSON响应
|
||||
|
||||
Returns:
|
||||
tuple: (判断结果, 理由)
|
||||
"""
|
||||
try:
|
||||
# 使用repair_json修复可能有问题的JSON格式
|
||||
fixed_json_string = repair_json(response)
|
||||
logger.debug(f"{self.log_prefix} repair_json修复后的响应: {fixed_json_string}")
|
||||
|
||||
# 如果repair_json返回的是字符串，需要解析为Python对象
|
||||
if isinstance(fixed_json_string, str):
|
||||
result_json = json.loads(fixed_json_string)
|
||||
else:
|
||||
# 如果repair_json直接返回了字典对象，直接使用
|
||||
result_json = fixed_json_string
|
||||
|
||||
# 从JSON中提取判断结果和理由
|
||||
should_reply = result_json.get("should_reply", False)
|
||||
reason = result_json.get("reason", "无法获取判断理由")
|
||||
|
||||
# 转换布尔值为中文字符串
|
||||
judge_result = "需要回复" if should_reply else "不需要回复"
|
||||
|
||||
logger.debug(f"{self.log_prefix} JSON解析成功 - 判断: {judge_result}, 理由: {reason}")
|
||||
return judge_result, reason
|
||||
|
||||
except (json.JSONDecodeError, KeyError, TypeError) as e:
|
||||
logger.warning(f"{self.log_prefix} JSON解析失败，尝试文本解析: {e}")
|
||||
|
||||
# 如果JSON解析失败，回退到简单的关键词匹配
|
||||
try:
|
||||
response_lower = response.lower()
|
||||
|
||||
if "true" in response_lower or "需要回复" in response:
|
||||
judge_result = "需要回复"
|
||||
reason = "从响应文本中检测到需要回复的指示"
|
||||
elif "false" in response_lower or "不需要回复" in response:
|
||||
judge_result = "不需要回复"
|
||||
reason = "从响应文本中检测到不需要回复的指示"
|
||||
else:
|
||||
judge_result = "不需要回复" # 默认值
|
||||
reason = f"无法解析响应格式，使用默认判断。原始响应: {response[:100]}..."
|
||||
|
||||
logger.debug(f"{self.log_prefix} 文本解析结果 - 判断: {judge_result}, 理由: {reason}")
|
||||
return judge_result, reason
|
||||
|
||||
except Exception as fallback_e:
|
||||
logger.error(f"{self.log_prefix} 文本解析也失败: {fallback_e}")
|
||||
return "不需要回复", f"解析异常: {str(e)}, 回退解析也失败: {str(fallback_e)}"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 解析LLM响应时出错: {e}")
|
||||
return "不需要回复", f"解析异常: {str(e)}"
|
||||
|
||||
@classmethod
|
||||
def reset_consecutive_count(cls):
|
||||
"""重置连续计数器"""
|
||||
cls._consecutive_count = 0
|
||||
logger.debug("NoReplyAction连续计数器已重置")
|
||||
|
||||
|
||||
class EmojiAction(BaseAction):
|
||||
|
||||
Reference in New Issue
Block a user