better:重构no_reply，不再使用小模型，而是采用激活度决定是否结束等待
This commit is contained in:
SengokuCola
4
.gitignore
vendored
4
.gitignore
vendored
@@ -316,4 +316,6 @@ run_pet.bat
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!/plugins/hello_world_plugin
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!/plugins/hello_world_plugin
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!/plugins/take_picture_plugin
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!/plugins/take_picture_plugin
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config.toml
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config.toml
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interested_rates.txt
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@@ -618,3 +618,333 @@
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@@ -10,26 +10,27 @@ from src.plugin_system import BaseAction, ActionActivationType, ChatMode
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from src.common.logger import get_logger
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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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# 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.plugin_system.apis import message_api
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from src.config.config import global_config
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from src.config.config import global_config
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from json_repair import repair_json
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from src.chat.message_receive.message import MessageRecv
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from src.chat.message_receive.chat_stream import get_chat_manager
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from src.chat.memory_system.Hippocampus import hippocampus_manager
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import math
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from src.chat.utils.utils import is_mentioned_bot_in_message
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logger = get_logger("core_actions")
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logger = get_logger("core_actions")
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class NoReplyAction(BaseAction):
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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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"""不回复动作，根据新消息的兴趣值或数量决定何时结束等待.
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||||||
新的等待逻辑：
|
新的等待逻辑:
|
||||||
- 每0.2秒检查是否有新消息（提高响应性）
|
1. 新消息累计兴趣值超过阈值 (默认10) 则结束等待
|
||||||
- 如果累计消息数量达到阈值（默认20条），直接结束等待
|
2. 累计新消息数量达到随机阈值 (默认5-10条) 则结束等待
|
||||||
- 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.ALWAYS
|
||||||
# focus_activation_type = ActionActivationType.RANDOM
|
|
||||||
normal_activation_type = ActionActivationType.NEVER
|
normal_activation_type = ActionActivationType.NEVER
|
||||||
mode_enable = ChatMode.FOCUS
|
mode_enable = ChatMode.FOCUS
|
||||||
parallel_action = False
|
parallel_action = False
|
||||||
@@ -41,21 +42,11 @@ class NoReplyAction(BaseAction):
|
|||||||
# 连续no_reply计数器
|
# 连续no_reply计数器
|
||||||
_consecutive_count = 0
|
_consecutive_count = 0
|
||||||
|
|
||||||
# LLM判断的最小间隔时间
|
# 新增：兴趣值退出阈值
|
||||||
_min_judge_interval = 1.0 # 最快1秒一次LLM判断
|
_interest_exit_threshold = 3.0
|
||||||
|
# 新增：消息数量退出阈值
|
||||||
# 自动结束的消息数量阈值
|
_min_exit_message_count = 5
|
||||||
_auto_exit_message_count = 20 # 累计20条消息自动结束
|
_max_exit_message_count = 10
|
||||||
|
|
||||||
# 最大等待超时时间
|
|
||||||
_max_timeout = 600 # 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
|
# 动作参数定义
|
||||||
action_parameters = {"reason": "不回复的原因"}
|
action_parameters = {"reason": "不回复的原因"}
|
||||||
@@ -67,7 +58,7 @@ class NoReplyAction(BaseAction):
|
|||||||
associated_types = []
|
associated_types = []
|
||||||
|
|
||||||
async def execute(self) -> Tuple[bool, str]:
|
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"""
|
"""执行不回复动作"""
|
||||||
import asyncio
|
import asyncio
|
||||||
|
|
||||||
try:
|
try:
|
||||||
@@ -77,30 +68,14 @@ class NoReplyAction(BaseAction):
|
|||||||
|
|
||||||
reason = self.action_data.get("reason", "")
|
reason = self.action_data.get("reason", "")
|
||||||
start_time = time.time()
|
start_time = time.time()
|
||||||
last_judge_time = start_time # 上次进行LLM判断的时间
|
check_interval = 1.0 # 每秒检查一次
|
||||||
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
|
# 随机生成本次等待需要的新消息数量阈值
|
||||||
judge_history = [] # 存储每次判断的结果和理由
|
exit_message_count_threshold = random.randint(self._min_exit_message_count, self._max_exit_message_count)
|
||||||
|
logger.info(
|
||||||
# 获取no_reply开始时的上下文消息（10条），用于后续记录
|
f"{self.log_prefix} 本次no_reply需要 {exit_message_count_threshold} 条新消息或累计兴趣值超过 {self._interest_exit_threshold} 才能打断"
|
||||||
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)，开始摸鱼，原因: {reason}")
|
logger.info(f"{self.log_prefix} 选择不回复(第{count}次)，开始摸鱼，原因: {reason}")
|
||||||
|
|
||||||
# 进入等待状态
|
# 进入等待状态
|
||||||
@@ -108,196 +83,52 @@ class NoReplyAction(BaseAction):
|
|||||||
current_time = time.time()
|
current_time = time.time()
|
||||||
elapsed_time = current_time - start_time
|
elapsed_time = current_time - start_time
|
||||||
|
|
||||||
if global_config.chat.chat_mode == "auto" and self.is_group:
|
# 1. 检查新消息
|
||||||
# 检查是否超时
|
recent_messages_dict = message_api.get_messages_by_time_in_chat(
|
||||||
if elapsed_time >= self._max_timeout or self._check_no_activity_and_exit_focus(current_time):
|
|
||||||
logger.info(
|
|
||||||
f"{self.log_prefix} 等待时间过久（{self._max_timeout}秒）或过去10分钟完全没有发言，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）等待了{self._max_timeout}秒，或完全没有说话，感觉群里没有新内容，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
|
chat_id=self.chat_id, start_time=start_time, end_time=current_time
|
||||||
)
|
)
|
||||||
|
new_message_count = len(recent_messages_dict)
|
||||||
|
|
||||||
# 如果累计消息数量达到阈值，直接结束等待
|
# 2. 检查消息数量是否达到阈值
|
||||||
if new_message_count >= self._auto_exit_message_count:
|
if new_message_count >= exit_message_count_threshold:
|
||||||
logger.info(f"{self.log_prefix} 累计消息数量达到{new_message_count}条，直接结束等待")
|
logger.info(f"{self.log_prefix} 累计消息数量达到{new_message_count}条(>{exit_message_count_threshold})，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"
|
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(
|
await self.store_action_info(
|
||||||
action_build_into_prompt=True,
|
action_build_into_prompt=False,
|
||||||
action_prompt_display=exit_reason,
|
action_prompt_display=exit_reason,
|
||||||
action_done=True,
|
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)"
|
return True, f"累计消息数量达到{new_message_count}条，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
|
# 3. 检查累计兴趣值
|
||||||
time_since_last_judge = current_time - last_judge_time
|
if new_message_count > 0:
|
||||||
should_judge, trigger_reason = self._should_trigger_judge(new_message_count, time_since_last_judge)
|
accumulated_interest = await self._calculate_accumulated_interest(recent_messages_dict)
|
||||||
|
logger.info(f"{self.log_prefix} 当前累计兴趣值: {accumulated_interest:.2f}")
|
||||||
if should_judge and time_since_last_judge >= min_judge_interval:
|
if accumulated_interest >= self._interest_exit_threshold:
|
||||||
logger.info(f"{self.log_prefix} 触发判定({trigger_reason})，进行智能判断...")
|
logger.info(
|
||||||
|
f"{self.log_prefix} 累计兴趣值达到{accumulated_interest:.2f}(>{self._interest_exit_threshold})，结束等待"
|
||||||
# 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,
|
|
||||||
)
|
)
|
||||||
|
exit_reason = f"{global_config.bot.nickname}（你）感觉到了大家浓厚的兴趣（兴趣值{accumulated_interest:.1f}），决定重新加入讨论"
|
||||||
# 获取身份信息
|
await self.store_action_info(
|
||||||
bot_name = global_config.bot.nickname
|
action_build_into_prompt=False,
|
||||||
bot_nickname = ""
|
action_prompt_display=exit_reason,
|
||||||
if global_config.bot.alias_names:
|
action_done=True,
|
||||||
bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
|
)
|
||||||
bot_core_personality = global_config.personality.personality_core
|
return True, f"累计兴趣值达到{accumulated_interest:.2f}，结束等待 (等待时间: {elapsed_time:.1f}秒)"
|
||||||
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 = self._get_fatigue_status(current_time)
|
|
||||||
|
|
||||||
# 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:
|
|
||||||
logger.info(f"{self.log_prefix} 疲劳,继续等待。")
|
|
||||||
last_judge_time = time.time() # 更新判断时间，避免立即重新判断
|
|
||||||
start_time = current_time # 更新消息检查的起始时间，以避免重复判断
|
|
||||||
continue # 跳过本次LLM判断，继续循环等待
|
|
||||||
|
|
||||||
# 构建判断上下文
|
|
||||||
chat_context = "QQ群" if self.is_group else "私聊"
|
|
||||||
judge_prompt = f"""
|
|
||||||
{identity_block}
|
|
||||||
|
|
||||||
你现在正在{chat_context}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)
|
|
||||||
|
|
||||||
logger.debug(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.debug(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)
|
|
||||||
|
|
||||||
if judge_result:
|
|
||||||
logger.info(f"{self.log_prefix} 决定继续参与讨论，结束等待，原因: {reason}")
|
|
||||||
else:
|
|
||||||
logger.info(f"{self.log_prefix} 决定不参与讨论，继续等待，原因: {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
|
# 每10秒输出一次等待状态
|
||||||
if elapsed_time < 60:
|
if int(elapsed_time) > 0 and int(elapsed_time) % 10 == 0:
|
||||||
if int(elapsed_time) % 10 == 0 and int(elapsed_time) > 0:
|
logger.debug(f"{self.log_prefix} 已等待{elapsed_time:.0f}秒，累计{new_message_count}条消息，继续等待...")
|
||||||
logger.debug(f"{self.log_prefix} 已等待{elapsed_time:.0f}秒，等待新消息...")
|
# 使用 asyncio.sleep(1) 来避免在同一秒内重复打印日志
|
||||||
await asyncio.sleep(1)
|
await asyncio.sleep(1)
|
||||||
else:
|
|
||||||
if int(elapsed_time) % 180 == 0 and int(elapsed_time) > 0:
|
|
||||||
logger.info(f"{self.log_prefix} 已等待{elapsed_time / 60:.0f}分钟，等待新消息...")
|
|
||||||
await asyncio.sleep(1)
|
|
||||||
|
|
||||||
# 短暂等待后继续检查
|
# 短暂等待后继续检查
|
||||||
await asyncio.sleep(check_interval)
|
await asyncio.sleep(check_interval)
|
||||||
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error(f"{self.log_prefix} 不回复动作执行失败: {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)}"
|
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"
|
full_prompt = f"no_reply执行异常: {exit_reason}，你思考是否要进行回复"
|
||||||
await self.store_action_info(
|
await self.store_action_info(
|
||||||
action_build_into_prompt=True,
|
action_build_into_prompt=True,
|
||||||
action_prompt_display=full_prompt,
|
action_prompt_display=full_prompt,
|
||||||
@@ -305,214 +136,53 @@ class NoReplyAction(BaseAction):
|
|||||||
)
|
)
|
||||||
return False, f"不回复动作执行失败: {e}"
|
return False, f"不回复动作执行失败: {e}"
|
||||||
|
|
||||||
def _should_trigger_judge(self, new_message_count: int, time_since_last_judge: float) -> Tuple[bool, str]:
|
async def _calculate_accumulated_interest(self, messages_dicts: list[dict]) -> float:
|
||||||
"""判断是否应该触发智能判断，并返回触发原因。
|
"""将所有新消息文本合并，然后一次性计算兴趣值"""
|
||||||
|
if not messages_dicts:
|
||||||
|
return 0.0
|
||||||
|
|
||||||
Args:
|
|
||||||
new_message_count: 新消息的数量。
|
|
||||||
time_since_last_judge: 距离上次判断的时间。
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
一个元组 (should_judge, reason)。
|
|
||||||
- should_judge: 一个布尔值，指示是否应该触发判断。
|
|
||||||
- reason: 触发判断的原因字符串。
|
|
||||||
"""
|
|
||||||
# 判定条件：累计3条消息或等待超过15秒且有新消息
|
|
||||||
should_judge_flag = new_message_count >= 3 or (new_message_count > 0 and time_since_last_judge >= 15.0)
|
|
||||||
|
|
||||||
if not should_judge_flag:
|
combined_text_parts = []
|
||||||
return False, ""
|
is_any_mentioned = False
|
||||||
|
|
||||||
# 判断触发原因
|
for msg_dict in messages_dicts:
|
||||||
if new_message_count >= 3:
|
|
||||||
return True, f"累计{new_message_count}条消息"
|
|
||||||
elif new_message_count > 0 and time_since_last_judge >= 15.0:
|
|
||||||
return True, f"等待{time_since_last_judge:.1f}秒且有新消息"
|
|
||||||
|
|
||||||
return False, ""
|
|
||||||
|
|
||||||
def _get_fatigue_status(self, current_time: float) -> Tuple[str, bool]:
|
|
||||||
"""
|
|
||||||
根据最近的发言频率生成疲劳提示，并决定是否跳过判断。
|
|
||||||
|
|
||||||
Args:
|
|
||||||
current_time: 当前时间戳。
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
一个元组 (frequency_block, should_skip_judge)。
|
|
||||||
- frequency_block: 疲劳度相关的提示字符串。
|
|
||||||
- should_skip_judge: 是否应该跳过LLM判断的布尔值。
|
|
||||||
"""
|
|
||||||
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:
|
|
||||||
sender_id = message.get("user_id", "")
|
|
||||||
if sender_id == user_id:
|
|
||||||
bot_message_count += 1
|
|
||||||
|
|
||||||
talk_frequency_threshold = global_config.chat.get_current_talk_frequency(self.chat_id) * 10
|
|
||||||
|
|
||||||
if bot_message_count > talk_frequency_threshold:
|
|
||||||
over_count = bot_message_count - talk_frequency_threshold
|
|
||||||
skip_probability = 0
|
|
||||||
frequency_block = ""
|
|
||||||
|
|
||||||
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"
|
|
||||||
elif over_count <= 8:
|
|
||||||
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
|
|
||||||
else:
|
|
||||||
frequency_block = "你感觉非常累，想要安静一会儿。\n"
|
|
||||||
skip_probability = 1
|
|
||||||
|
|
||||||
should_skip_judge = self._skip_judge_when_tired and random.random() < skip_probability
|
|
||||||
|
|
||||||
if should_skip_judge:
|
|
||||||
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"
|
|
||||||
)
|
|
||||||
return frequency_block, should_skip_judge
|
|
||||||
else:
|
|
||||||
# 回复次数少时的正向提示
|
|
||||||
under_count = talk_frequency_threshold - bot_message_count
|
|
||||||
frequency_block = ""
|
|
||||||
if under_count >= talk_frequency_threshold * 0.8:
|
|
||||||
frequency_block = "你感觉精力充沛，状态很好，积极参与聊天。\n"
|
|
||||||
elif under_count >= talk_frequency_threshold * 0.5:
|
|
||||||
frequency_block = "你感觉状态不错。\n"
|
|
||||||
|
|
||||||
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"
|
|
||||||
)
|
|
||||||
return frequency_block, False
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"{self.log_prefix} 检查发言频率时出错: {e}")
|
|
||||||
return "", False
|
|
||||||
|
|
||||||
def _check_no_activity_and_exit_focus(self, current_time: float) -> bool:
|
|
||||||
"""检查过去10分钟是否完全没有发言，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模式下进行检查
|
|
||||||
if global_config.chat.chat_mode != "auto":
|
|
||||||
return False
|
|
||||||
|
|
||||||
# 获取过去10分钟的所有消息
|
|
||||||
past_10min_time = current_time - 600 # 10分钟前
|
|
||||||
all_messages = message_api.get_messages_by_time_in_chat(
|
|
||||||
chat_id=self.chat_id,
|
|
||||||
start_time=past_10min_time,
|
|
||||||
end_time=current_time,
|
|
||||||
)
|
|
||||||
|
|
||||||
if not all_messages:
|
|
||||||
# 如果完全没有消息，也不需要退出专注模式
|
|
||||||
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
|
|
||||||
|
|
||||||
# 如果过去10分钟bot一条消息也没有发送，退出专注模式
|
|
||||||
if bot_message_count == 0:
|
|
||||||
logger.info(f"{self.log_prefix} 过去10分钟bot完全没有发言，准备退出专注模式")
|
|
||||||
return True
|
|
||||||
else:
|
|
||||||
logger.debug(f"{self.log_prefix} 过去10分钟bot发言{bot_message_count}条，继续保持专注模式")
|
|
||||||
return False
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"{self.log_prefix} 检查无活动状态时出错: {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:
|
try:
|
||||||
response_lower = response.lower()
|
text = msg_dict.get("processed_plain_text", "")
|
||||||
|
if text:
|
||||||
|
combined_text_parts.append(text)
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"{self.log_prefix} 处理单条消息以计算兴趣值时出错: {e}")
|
||||||
|
|
||||||
if "true" in response_lower or "需要回复" in response:
|
full_text = " ".join(combined_text_parts).strip()
|
||||||
judge_result = "需要回复"
|
if not full_text:
|
||||||
reason = "从响应文本中检测到需要回复的指示"
|
return 0.0
|
||||||
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}")
|
# --- 使用合并后的文本计算兴趣值 ---
|
||||||
return judge_result, reason
|
|
||||||
|
if global_config.bot.nickname in full_text:
|
||||||
|
is_any_mentioned = True
|
||||||
|
|
||||||
except Exception as fallback_e:
|
interested_rate = 0.0
|
||||||
logger.error(f"{self.log_prefix} 文本解析也失败: {fallback_e}")
|
if global_config.memory.enable_memory:
|
||||||
return "不需要回复", f"解析异常: {str(e)}, 回退解析也失败: {str(fallback_e)}"
|
try:
|
||||||
|
interested_rate = await hippocampus_manager.get_activate_from_text(
|
||||||
|
full_text,
|
||||||
|
fast_retrieval=True,
|
||||||
|
)
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"{self.log_prefix} 记忆激活计算失败: {e}")
|
||||||
|
|
||||||
except Exception as e:
|
text_len = len(full_text)
|
||||||
logger.error(f"{self.log_prefix} 解析LLM响应时出错: {e}")
|
# 根据文本长度调整兴趣度
|
||||||
return "不需要回复", f"解析异常: {str(e)}"
|
base_interest = 0.01 + (0.05 - 0.01) * (math.log10(text_len + 1) / math.log10(1000 + 1))
|
||||||
|
base_interest = min(max(base_interest, 0.01), 0.05)
|
||||||
|
interested_rate += base_interest
|
||||||
|
|
||||||
|
if is_any_mentioned:
|
||||||
|
interested_rate += 1
|
||||||
|
|
||||||
|
return interested_rate
|
||||||
|
|
||||||
@classmethod
|
@classmethod
|
||||||
def reset_consecutive_count(cls):
|
def reset_consecutive_count(cls):
|
||||||
|
|||||||
Reference in New Issue
Block a user