feat：为focus的no_reply决策加入更多信息，可以自定义频率
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SengokuCola
@@ -5,6 +5,7 @@
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这是系统的内置插件，提供基础的聊天交互功能
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"""
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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 List, Tuple, Type
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@@ -76,7 +77,7 @@ class ReplyAction(BaseAction):
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)
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# 根据新消息数量决定是否使用reply_to
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need_reply = new_message_count >= 4
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need_reply = new_message_count >= random.randint(2, 5)
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logger.info(
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f"{self.log_prefix} 从{start_time}到{current_time}共有{new_message_count}条新消息，{'使用' if need_reply else '不使用'}reply_to"
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)
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@@ -170,13 +171,16 @@ class NoReplyAction(BaseAction):
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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（5条），用于后续记录
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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 - 300, # 获取开始前5分钟内的消息
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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=5,
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limit=10,
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limit_mode="latest",
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)
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@@ -184,7 +188,7 @@ class NoReplyAction(BaseAction):
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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=False
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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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@@ -252,23 +256,83 @@ class NoReplyAction(BaseAction):
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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
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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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for i, (timestamp, judge_result, reason) in enumerate(judge_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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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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print(bot_message_count)
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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
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if over_count <= 5:
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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 <= 10:
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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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elif over_count <= 20:
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frequency_block = "你发现自己说话太多了，感觉很累，需要好好休息一下，不想频繁回复。\n"
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else:
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frequency_block = "你感到非常疲惫，今天话说得太多了，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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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")
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else:
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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}")
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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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# 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. 如果出现了紧急或重要的情况，应该回复
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4. 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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5. 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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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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@@ -284,6 +348,8 @@ class NoReplyAction(BaseAction):
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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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@@ -307,9 +373,22 @@ class NoReplyAction(BaseAction):
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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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# 构建包含判断历史的详细信息
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history_summary = ""
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if len(judge_history) > 1:
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history_summary = f"\n\n判断过程：\n"
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for i, (timestamp, past_result, past_reason) in enumerate(judge_history[:-1], 1):
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elapsed_seconds = int(timestamp - start_time)
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history_summary += f"{i}. 等待{elapsed_seconds}秒时：{past_result}，理由：{past_reason}\n"
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history_summary += f"{len(judge_history)}. 等待{elapsed_time:.0f}秒时：{judge_result}，理由：{reason}"
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full_prompt = f"{global_config.bot.nickname}（你）的想法是：{reason}{history_summary}"
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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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