初始化

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雅诺狐
2025-08-11 19:34:18 +08:00
parent ff7d1177fa
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import asyncio
import time
import traceback
import random
from typing import List, Optional, Dict, Any, Tuple
from rich.traceback import install
from src.config.config import global_config
from src.common.logger import get_logger
from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
from src.chat.utils.prompt_builder import global_prompt_manager
from src.chat.utils.timer_calculator import Timer
from src.chat.planner_actions.planner import ActionPlanner
from src.chat.planner_actions.action_modifier import ActionModifier
from src.chat.planner_actions.action_manager import ActionManager
from src.chat.chat_loop.hfc_utils import CycleDetail
from src.person_info.relationship_builder_manager import relationship_builder_manager
from src.chat.express.expression_learner import expression_learner_manager
from src.person_info.person_info import get_person_info_manager
from src.plugin_system.base.component_types import ActionInfo, ChatMode, EventType
from src.plugin_system.core import events_manager
from src.plugin_system.apis import generator_api, send_api, message_api, database_api
from src.chat.willing.willing_manager import get_willing_manager
from src.mais4u.mai_think import mai_thinking_manager
from src.mais4u.constant_s4u import ENABLE_S4U
from src.plugins.built_in.core_actions.no_reply import NoReplyAction
from src.chat.chat_loop.hfc_utils import send_typing, stop_typing
ERROR_LOOP_INFO = {
"loop_plan_info": {
"action_result": {
"action_type": "error",
"action_data": {},
"reasoning": "循环处理失败",
},
},
"loop_action_info": {
"action_taken": False,
"reply_text": "",
"command": "",
"taken_time": time.time(),
},
}
NO_ACTION = {
"action_result": {
"action_type": "no_action",
"action_data": {},
"reasoning": "规划器初始化默认",
"is_parallel": True,
},
"chat_context": "",
"action_prompt": "",
}
install(extra_lines=3)
# 注释:原来的动作修改超时常量已移除,因为改为顺序执行
logger = get_logger("hfc") # Logger Name Changed
class HeartFChatting:
"""
管理一个连续的Focus Chat循环
用于在特定聊天流中生成回复。
其生命周期现在由其关联的 SubHeartflow 的 FOCUSED 状态控制。
"""
def __init__(
self,
chat_id: str,
):
"""
HeartFChatting 初始化函数
参数:
chat_id: 聊天流唯一标识符(如stream_id)
on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数
performance_version: 性能记录版本号,用于区分不同启动版本
"""
# 基础属性
self.stream_id: str = chat_id # 聊天流ID
self.chat_stream: ChatStream = get_chat_manager().get_stream(self.stream_id) # type: ignore
if not self.chat_stream:
raise ValueError(f"无法找到聊天流: {self.stream_id}")
self.log_prefix = f"[{get_chat_manager().get_stream_name(self.stream_id) or self.stream_id}]"
self.relationship_builder = relationship_builder_manager.get_or_create_builder(self.stream_id)
self.expression_learner = expression_learner_manager.get_expression_learner(self.stream_id)
self.loop_mode = ChatMode.NORMAL # 初始循环模式为普通模式
self.last_action = "no_action"
self.action_manager = ActionManager()
self.action_planner = ActionPlanner(chat_id=self.stream_id, action_manager=self.action_manager)
self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.stream_id)
# 循环控制内部状态
self.running: bool = False
self._loop_task: Optional[asyncio.Task] = None # 主循环任务
self._energy_task: Optional[asyncio.Task] = None
# 添加循环信息管理相关的属性
self.history_loop: List[CycleDetail] = []
self._cycle_counter = 0
self._current_cycle_detail: CycleDetail = None # type: ignore
self.reply_timeout_count = 0
self.plan_timeout_count = 0
self.last_read_time = time.time() - 1
self.willing_manager = get_willing_manager()
logger.info(f"{self.log_prefix} HeartFChatting 初始化完成")
self.energy_value = 5
self.focus_energy = 1
self.no_reply_consecutive = 0
async def start(self):
"""检查是否需要启动主循环,如果未激活则启动。"""
# 如果循环已经激活,直接返回
if self.running:
logger.debug(f"{self.log_prefix} HeartFChatting 已激活,无需重复启动")
return
try:
# 标记为活动状态,防止重复启动
self.running = True
self._energy_task = asyncio.create_task(self._energy_loop())
self._energy_task.add_done_callback(self._handle_energy_completion)
self._loop_task = asyncio.create_task(self._main_chat_loop())
self._loop_task.add_done_callback(self._handle_loop_completion)
logger.info(f"{self.log_prefix} HeartFChatting 启动完成")
except Exception as e:
# 启动失败时重置状态
self.running = False
self._loop_task = None
logger.error(f"{self.log_prefix} HeartFChatting 启动失败: {e}")
raise
def _handle_loop_completion(self, task: asyncio.Task):
"""当 _hfc_loop 任务完成时执行的回调。"""
try:
if exception := task.exception():
logger.error(f"{self.log_prefix} HeartFChatting: 脱离了聊天(异常): {exception}")
logger.error(traceback.format_exc()) # Log full traceback for exceptions
else:
logger.info(f"{self.log_prefix} HeartFChatting: 脱离了聊天 (外部停止)")
except asyncio.CancelledError:
logger.info(f"{self.log_prefix} HeartFChatting: 结束了聊天")
def start_cycle(self):
self._cycle_counter += 1
self._current_cycle_detail = CycleDetail(self._cycle_counter)
self._current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
cycle_timers = {}
return cycle_timers, self._current_cycle_detail.thinking_id
def end_cycle(self, loop_info, cycle_timers):
self._current_cycle_detail.set_loop_info(loop_info)
self.history_loop.append(self._current_cycle_detail)
self._current_cycle_detail.timers = cycle_timers
self._current_cycle_detail.end_time = time.time()
def _handle_energy_completion(self, task: asyncio.Task):
if exception := task.exception():
logger.error(f"{self.log_prefix} HeartFChatting: 能量循环异常: {exception}")
logger.error(traceback.format_exc())
else:
logger.info(f"{self.log_prefix} HeartFChatting: 能量循环完成")
async def _energy_loop(self):
while self.running:
await asyncio.sleep(10)
if self.loop_mode == ChatMode.NORMAL:
self.energy_value -= 0.3
self.energy_value = max(self.energy_value, 0.3)
if self.loop_mode == ChatMode.FOCUS:
self.energy_value -= 0.6
self.energy_value = max(self.energy_value, 0.3)
def print_cycle_info(self, cycle_timers):
# 记录循环信息和计时器结果
timer_strings = []
for name, elapsed in cycle_timers.items():
formatted_time = f"{elapsed * 1000:.2f}毫秒" if elapsed < 1 else f"{elapsed:.2f}"
timer_strings.append(f"{name}: {formatted_time}")
logger.info(
f"{self.log_prefix}{self._current_cycle_detail.cycle_id}次思考,"
f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒, " # type: ignore
f"选择动作: {self._current_cycle_detail.loop_plan_info.get('action_result', {}).get('action_type', '未知动作')}"
+ (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
)
def _determine_form_type(self) -> str:
"""判断使用哪种形式的no_reply"""
# 如果连续no_reply次数少于3次使用waiting形式
if self.no_reply_consecutive <= 3:
self.focus_energy = 1
else:
# 计算最近三次记录的兴趣度总和
total_recent_interest = sum(NoReplyAction._recent_interest_records)
# 获取当前聊天频率和意愿系数
talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
# 计算调整后的阈值
adjusted_threshold = 3 / talk_frequency
logger.info(f"{self.log_prefix} 最近三次兴趣度总和: {total_recent_interest:.2f}, 调整后阈值: {adjusted_threshold:.2f}")
# 如果兴趣度总和小于阈值进入breaking形式
if total_recent_interest < adjusted_threshold:
logger.info(f"{self.log_prefix} 兴趣度不足进入breaking形式")
self.focus_energy = random.randint(3, 6)
else:
logger.info(f"{self.log_prefix} 兴趣度充足")
self.focus_energy = 1
async def _execute_no_reply(self, new_message:List[Dict[str, Any]]) -> Tuple[bool, str]:
"""执行breaking形式的no_reply原有逻辑"""
new_message_count = len(new_message)
# 检查消息数量是否达到阈值
talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
modified_exit_count_threshold = self.focus_energy / talk_frequency
if new_message_count >= modified_exit_count_threshold:
# 记录兴趣度到列表
total_interest = 0.0
for msg_dict in new_message:
interest_value = msg_dict.get("interest_value", 0.0)
if msg_dict.get("processed_plain_text", ""):
total_interest += interest_value
NoReplyAction._recent_interest_records.append(total_interest)
logger.info(
f"{self.log_prefix} 累计消息数量达到{new_message_count}条(>{modified_exit_count_threshold}),结束等待"
)
return True
# 检查累计兴趣值
if new_message_count > 0:
accumulated_interest = 0.0
for msg_dict in new_message:
text = msg_dict.get("processed_plain_text", "")
interest_value = msg_dict.get("interest_value", 0.0)
if text:
accumulated_interest += interest_value
# 只在兴趣值变化时输出log
if not hasattr(self, "_last_accumulated_interest") or accumulated_interest != self._last_accumulated_interest:
logger.info(f"{self.log_prefix} breaking形式当前累计兴趣值: {accumulated_interest:.2f}, 当前聊天频率: {talk_frequency:.2f}")
self._last_accumulated_interest = accumulated_interest
if accumulated_interest >= 3 / talk_frequency:
# 记录兴趣度到列表
NoReplyAction._recent_interest_records.append(accumulated_interest)
logger.info(
f"{self.log_prefix} 累计兴趣值达到{accumulated_interest:.2f}(>{5 / talk_frequency}),结束等待"
)
return True
# 每10秒输出一次等待状态
if int(time.time() - self.last_read_time) > 0 and int(time.time() - self.last_read_time) % 10 == 0:
logger.info(
f"{self.log_prefix} 已等待{time.time() - self.last_read_time:.0f}秒,累计{new_message_count}条消息,继续等待..."
)
async def _loopbody(self):
recent_messages_dict = message_api.get_messages_by_time_in_chat(
chat_id=self.stream_id,
start_time=self.last_read_time,
end_time=time.time(),
limit = 10,
limit_mode="latest",
filter_mai=True,
filter_command=True,
)
new_message_count = len(recent_messages_dict)
if self.loop_mode == ChatMode.FOCUS:
if self.last_action == "no_reply":
if not await self._execute_no_reply(recent_messages_dict):
self.energy_value -= 0.3 / global_config.chat.focus_value
logger.info(f"{self.log_prefix} 能量值减少,当前能量值:{self.energy_value:.1f}")
await asyncio.sleep(0.5)
return True
self.last_read_time = time.time()
if await self._observe():
self.energy_value += 1 / global_config.chat.focus_value
logger.info(f"{self.log_prefix} 能量值增加,当前能量值:{self.energy_value:.1f}")
if self.energy_value <= 1:
self.energy_value = 1
self.loop_mode = ChatMode.NORMAL
return True
return True
elif self.loop_mode == ChatMode.NORMAL:
if global_config.chat.focus_value != 0:
if new_message_count > 3 / pow(global_config.chat.focus_value, 0.5):
self.loop_mode = ChatMode.FOCUS
self.energy_value = (
10 + (new_message_count / (3 / pow(global_config.chat.focus_value, 0.5))) * 10
)
return True
if self.energy_value >= 30:
self.loop_mode = ChatMode.FOCUS
return True
if new_message_count >= self.focus_energy:
earliest_messages_data = recent_messages_dict[0]
self.last_read_time = earliest_messages_data.get("time")
if_think = await self.normal_response(earliest_messages_data)
if if_think:
factor = max(global_config.chat.focus_value, 0.1)
self.energy_value *= 1.1 * factor
logger.info(f"{self.log_prefix} 进行了思考,能量值按倍数增加,当前能量值:{self.energy_value:.1f}")
else:
self.energy_value += 0.1 * global_config.chat.focus_value
logger.debug(f"{self.log_prefix} 没有进行思考,能量值线性增加,当前能量值:{self.energy_value:.1f}")
logger.debug(f"{self.log_prefix} 当前能量值:{self.energy_value:.1f}")
return True
await asyncio.sleep(0.5)
return True
async def build_reply_to_str(self, message_data: dict):
person_info_manager = get_person_info_manager()
person_id = person_info_manager.get_person_id(
message_data.get("chat_info_platform"), # type: ignore
message_data.get("user_id"), # type: ignore
)
person_name = await person_info_manager.get_value(person_id, "person_name")
return f"{person_name}:{message_data.get('processed_plain_text')}"
async def _send_and_store_reply(
self,
response_set,
reply_to_str,
loop_start_time,
action_message,
cycle_timers: Dict[str, float],
thinking_id,
plan_result,
) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
with Timer("回复发送", cycle_timers):
reply_text = await self._send_response(response_set, reply_to_str, loop_start_time, action_message)
# 存储reply action信息
person_info_manager = get_person_info_manager()
person_id = person_info_manager.get_person_id(
action_message.get("chat_info_platform", ""),
action_message.get("user_id", ""),
)
person_name = await person_info_manager.get_value(person_id, "person_name")
action_prompt_display = f"你对{person_name}进行了回复:{reply_text}"
await database_api.store_action_info(
chat_stream=self.chat_stream,
action_build_into_prompt=False,
action_prompt_display=action_prompt_display,
action_done=True,
thinking_id=thinking_id,
action_data={"reply_text": reply_text, "reply_to": reply_to_str},
action_name="reply",
)
# 构建循环信息
loop_info: Dict[str, Any] = {
"loop_plan_info": {
"action_result": plan_result.get("action_result", {}),
},
"loop_action_info": {
"action_taken": True,
"reply_text": reply_text,
"command": "",
"taken_time": time.time(),
},
}
return loop_info, reply_text, cycle_timers
async def _observe(self, message_data: Optional[Dict[str, Any]] = None) -> bool:
if not message_data:
message_data = {}
action_type = "no_action"
reply_text = "" # 初始化reply_text变量避免UnboundLocalError
gen_task = None # 初始化gen_task变量避免UnboundLocalError
reply_to_str = "" # 初始化reply_to_str变量
# 创建新的循环信息
cycle_timers, thinking_id = self.start_cycle()
logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考[模式:{self.loop_mode}]")
if ENABLE_S4U:
await send_typing()
async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
loop_start_time = time.time()
await self.relationship_builder.build_relation()
await self.expression_learner.trigger_learning_for_chat()
available_actions = {}
# 第一步:动作修改
with Timer("动作修改", cycle_timers):
try:
await self.action_modifier.modify_actions()
available_actions = self.action_manager.get_using_actions()
except Exception as e:
logger.error(f"{self.log_prefix} 动作修改失败: {e}")
# 检查是否在normal模式下没有可用动作除了reply相关动作
skip_planner = False
if self.loop_mode == ChatMode.NORMAL:
# 过滤掉reply相关的动作检查是否还有其他动作
non_reply_actions = {
k: v for k, v in available_actions.items() if k not in ["reply", "no_reply", "no_action"]
}
if not non_reply_actions:
skip_planner = True
logger.info(f"{self.log_prefix} Normal模式下没有可用动作直接回复")
# 直接设置为reply动作
action_type = "reply"
reasoning = ""
action_data = {"loop_start_time": loop_start_time}
is_parallel = False
# 构建plan_result用于后续处理
plan_result = {
"action_result": {
"action_type": action_type,
"action_data": action_data,
"reasoning": reasoning,
"timestamp": time.time(),
"is_parallel": is_parallel,
},
"action_prompt": "",
}
target_message = message_data
# 如果normal模式且不跳过规划器开始一个回复生成进程先准备好回复其实是和planer同时进行的
if not skip_planner:
reply_to_str = await self.build_reply_to_str(message_data)
gen_task = asyncio.create_task(
self._generate_response(
message_data=message_data,
available_actions=available_actions,
reply_to=reply_to_str,
request_type="chat.replyer.normal",
)
)
if not skip_planner:
planner_info = self.action_planner.get_necessary_info()
prompt_info = await self.action_planner.build_planner_prompt(
is_group_chat=planner_info[0],
chat_target_info=planner_info[1],
current_available_actions=planner_info[2],
)
if not await events_manager.handle_mai_events(
EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id
):
return False
with Timer("规划器", cycle_timers):
plan_result, target_message = await self.action_planner.plan(mode=self.loop_mode)
action_result: Dict[str, Any] = plan_result.get("action_result", {}) # type: ignore
action_type, action_data, reasoning, is_parallel = (
action_result.get("action_type", "error"),
action_result.get("action_data", {}),
action_result.get("reasoning", "未提供理由"),
action_result.get("is_parallel", True),
)
action_data["loop_start_time"] = loop_start_time
if action_type == "reply":
logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复")
elif is_parallel:
logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复, 同时执行{action_type}动作")
else:
# 只有在gen_task存在时才进行相关操作
if gen_task:
if not gen_task.done():
gen_task.cancel()
logger.debug(f"{self.log_prefix} 已取消预生成的回复任务")
logger.info(
f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复,但选择执行{action_type},不发表回复"
)
elif generation_result := gen_task.result():
content = " ".join([item[1] for item in generation_result if item[0] == "text"])
logger.debug(f"{self.log_prefix} 预生成的回复任务已完成")
logger.info(
f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复:{content},但选择执行{action_type},不发表回复"
)
else:
logger.warning(f"{self.log_prefix} 预生成的回复任务未生成有效内容")
action_message = message_data or target_message
if action_type == "reply":
# 等待回复生成完毕
if self.loop_mode == ChatMode.NORMAL:
# 只有在gen_task存在时才等待
if not gen_task:
reply_to_str = await self.build_reply_to_str(message_data)
gen_task = asyncio.create_task(
self._generate_response(
message_data=message_data,
available_actions=available_actions,
reply_to=reply_to_str,
request_type="chat.replyer.normal",
)
)
gather_timeout = global_config.chat.thinking_timeout
try:
response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout)
except asyncio.TimeoutError:
logger.warning(f"{self.log_prefix} 回复生成超时>{global_config.chat.thinking_timeout}s已跳过")
response_set = None
# 模型炸了或超时,没有回复内容生成
if not response_set:
logger.warning(f"{self.log_prefix}模型未生成回复内容")
return False
else:
logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复 (focus模式)")
# 构建reply_to字符串
reply_to_str = await self.build_reply_to_str(action_message)
# 生成回复
with Timer("回复生成", cycle_timers):
response_set = await self._generate_response(
message_data=action_message,
available_actions=available_actions,
reply_to=reply_to_str,
request_type="chat.replyer.focus",
)
if not response_set:
logger.warning(f"{self.log_prefix}模型未生成回复内容")
return False
loop_info, reply_text, cycle_timers = await self._send_and_store_reply(
response_set, reply_to_str, loop_start_time, action_message, cycle_timers, thinking_id, plan_result
)
return True
else:
# 并行执行:同时进行回复发送和动作执行
# 先置空防止未定义错误
background_reply_task = None
background_action_task = None
# 如果是并行执行且在normal模式下需要等待预生成的回复任务完成并发送回复
if self.loop_mode == ChatMode.NORMAL and is_parallel and gen_task:
async def handle_reply_task() -> Tuple[Optional[Dict[str, Any]], str, Dict[str, float]]:
# 等待预生成的回复任务完成
gather_timeout = global_config.chat.thinking_timeout
try:
response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout)
except asyncio.TimeoutError:
logger.warning(
f"{self.log_prefix} 并行执行:回复生成超时>{global_config.chat.thinking_timeout}s已跳过"
)
return None, "", {}
except asyncio.CancelledError:
logger.debug(f"{self.log_prefix} 并行执行:回复生成任务已被取消")
return None, "", {}
if not response_set:
logger.warning(f"{self.log_prefix} 模型超时或生成回复内容为空")
return None, "", {}
reply_to_str = await self.build_reply_to_str(action_message)
loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply(
response_set,
reply_to_str,
loop_start_time,
action_message,
cycle_timers,
thinking_id,
plan_result,
)
return loop_info, reply_text, cycle_timers_reply
# 执行回复任务并赋值到变量
background_reply_task = asyncio.create_task(handle_reply_task())
# 动作执行任务
async def handle_action_task():
with Timer("动作执行", cycle_timers):
success, reply_text, command = await self._handle_action(
action_type, reasoning, action_data, cycle_timers, thinking_id, action_message
)
return success, reply_text, command
# 执行动作任务并赋值到变量
background_action_task = asyncio.create_task(handle_action_task())
reply_loop_info = None
reply_text_from_reply = ""
action_success = False
action_reply_text = ""
action_command = ""
# 并行执行所有任务
if background_reply_task:
results = await asyncio.gather(
background_reply_task, background_action_task, return_exceptions=True
)
# 处理回复任务结果
reply_result = results[0]
if isinstance(reply_result, BaseException):
logger.error(f"{self.log_prefix} 回复任务执行异常: {reply_result}")
elif reply_result and reply_result[0] is not None:
reply_loop_info, reply_text_from_reply, _ = reply_result
# 处理动作任务结果
action_task_result = results[1]
if isinstance(action_task_result, BaseException):
logger.error(f"{self.log_prefix} 动作任务执行异常: {action_task_result}")
else:
action_success, action_reply_text, action_command = action_task_result
else:
results = await asyncio.gather(background_action_task, return_exceptions=True)
# 只有动作任务
action_task_result = results[0]
if isinstance(action_task_result, BaseException):
logger.error(f"{self.log_prefix} 动作任务执行异常: {action_task_result}")
else:
action_success, action_reply_text, action_command = action_task_result
# 构建最终的循环信息
if reply_loop_info:
# 如果有回复信息使用回复的loop_info作为基础
loop_info = reply_loop_info
# 更新动作执行信息
loop_info["loop_action_info"].update(
{
"action_taken": action_success,
"command": action_command,
"taken_time": time.time(),
}
)
reply_text = reply_text_from_reply
else:
# 没有回复信息构建纯动作的loop_info
loop_info = {
"loop_plan_info": {
"action_result": plan_result.get("action_result", {}),
},
"loop_action_info": {
"action_taken": action_success,
"reply_text": action_reply_text,
"command": action_command,
"taken_time": time.time(),
},
}
reply_text = action_reply_text
self.last_action = action_type
if ENABLE_S4U:
await stop_typing()
await mai_thinking_manager.get_mai_think(self.stream_id).do_think_after_response(reply_text)
self.end_cycle(loop_info, cycle_timers)
self.print_cycle_info(cycle_timers)
if self.loop_mode == ChatMode.NORMAL:
await self.willing_manager.after_generate_reply_handle(message_data.get("message_id", ""))
# 管理no_reply计数器当执行了非no_reply动作时重置计数器
if action_type != "no_reply" and action_type != "no_action":
# 导入NoReplyAction并重置计数器
NoReplyAction.reset_consecutive_count()
self.no_reply_consecutive = 0
logger.info(f"{self.log_prefix} 执行了{action_type}动作重置no_reply计数器")
return True
elif action_type == "no_action":
# 当执行回复动作时也重置no_reply计数
NoReplyAction.reset_consecutive_count()
self.no_reply_consecutive = 0
logger.info(f"{self.log_prefix} 执行了回复动作重置no_reply计数器")
if action_type == "no_reply":
self.no_reply_consecutive += 1
self._determine_form_type()
return True
async def _main_chat_loop(self):
"""主循环,持续进行计划并可能回复消息,直到被外部取消。"""
try:
while self.running:
# 主循环
success = await self._loopbody()
await asyncio.sleep(0.1)
if not success:
break
except asyncio.CancelledError:
# 设置了关闭标志位后被取消是正常流程
logger.info(f"{self.log_prefix} 麦麦已关闭聊天")
except Exception:
logger.error(f"{self.log_prefix} 麦麦聊天意外错误将于3s后尝试重新启动")
print(traceback.format_exc())
await asyncio.sleep(3)
self._loop_task = asyncio.create_task(self._main_chat_loop())
logger.error(f"{self.log_prefix} 结束了当前聊天循环")
async def _handle_action(
self,
action: str,
reasoning: str,
action_data: dict,
cycle_timers: Dict[str, float],
thinking_id: str,
action_message: dict,
) -> tuple[bool, str, str]:
"""
处理规划动作,使用动作工厂创建相应的动作处理器
参数:
action: 动作类型
reasoning: 决策理由
action_data: 动作数据,包含不同动作需要的参数
cycle_timers: 计时器字典
thinking_id: 思考ID
返回:
tuple[bool, str, str]: (是否执行了动作, 思考消息ID, 命令)
"""
try:
# 使用工厂创建动作处理器实例
try:
action_handler = self.action_manager.create_action(
action_name=action,
action_data=action_data,
reasoning=reasoning,
cycle_timers=cycle_timers,
thinking_id=thinking_id,
chat_stream=self.chat_stream,
log_prefix=self.log_prefix,
action_message=action_message,
)
except Exception as e:
logger.error(f"{self.log_prefix} 创建动作处理器时出错: {e}")
traceback.print_exc()
return False, "", ""
if not action_handler:
logger.warning(f"{self.log_prefix} 未能创建动作处理器: {action}")
return False, "", ""
# 处理动作并获取结果
result = await action_handler.handle_action()
success, reply_text = result
command = ""
if reply_text == "timeout":
self.reply_timeout_count += 1
if self.reply_timeout_count > 5:
logger.warning(
f"[{self.log_prefix} ] 连续回复超时次数过多,{global_config.chat.thinking_timeout}秒 内大模型没有返回有效内容请检查你的api是否速度过慢或配置错误。建议不要使用推理模型推理模型生成速度过慢。或者尝试拉高thinking_timeout参数这可能导致回复时间过长。"
)
logger.warning(f"{self.log_prefix} 回复生成超时{global_config.chat.thinking_timeout}s已跳过")
return False, "", ""
return success, reply_text, command
except Exception as e:
logger.error(f"{self.log_prefix} 处理{action}时出错: {e}")
traceback.print_exc()
return False, "", ""
async def normal_response(self, message_data: dict) -> bool:
"""
处理接收到的消息。
"兴趣"模式下,判断是否回复并生成内容。
"""
interested_rate = message_data.get("interest_value") or 0.0
self.willing_manager.setup(message_data, self.chat_stream)
reply_probability = await self.willing_manager.get_reply_probability(message_data.get("message_id", ""))
talk_frequency = -1.00
if reply_probability < 1: # 简化逻辑,如果未提及 (reply_probability 为 0),则获取意愿概率
additional_config = message_data.get("additional_config", {})
if additional_config and "maimcore_reply_probability_gain" in additional_config:
reply_probability += additional_config["maimcore_reply_probability_gain"]
reply_probability = min(max(reply_probability, 0), 1) # 确保概率在 0-1 之间
talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
reply_probability = talk_frequency * reply_probability
# 处理表情包
if message_data.get("is_emoji") or message_data.get("is_picid"):
reply_probability = 0
# 打印消息信息
mes_name = self.chat_stream.group_info.group_name if self.chat_stream.group_info else "私聊"
# logger.info(f"[{mes_name}] 当前聊天频率: {talk_frequency:.2f},兴趣值: {interested_rate:.2f},回复概率: {reply_probability * 100:.1f}%")
if reply_probability > 0.05:
logger.info(
f"[{mes_name}]"
f"{message_data.get('user_nickname')}:"
f"{message_data.get('processed_plain_text')}[兴趣:{interested_rate:.2f}][回复概率:{reply_probability * 100:.1f}%]"
)
if random.random() < reply_probability:
await self.willing_manager.before_generate_reply_handle(message_data.get("message_id", ""))
await self._observe(message_data=message_data)
return True
# 意愿管理器注销当前message信息 (无论是否回复,只要处理过就删除)
self.willing_manager.delete(message_data.get("message_id", ""))
return False
async def _generate_response(
self,
message_data: dict,
available_actions: Optional[Dict[str, ActionInfo]],
reply_to: str,
request_type: str = "chat.replyer.normal",
) -> Optional[list]:
"""生成普通回复"""
try:
success, reply_set, _ = await generator_api.generate_reply(
chat_stream=self.chat_stream,
reply_to=reply_to,
available_actions=available_actions,
enable_tool=global_config.tool.enable_tool,
request_type=request_type,
from_plugin=False,
)
if not success or not reply_set:
logger.info(f"{message_data.get('processed_plain_text')} 的回复生成失败")
return None
return reply_set
except Exception as e:
logger.error(f"{self.log_prefix}回复生成出现错误:{str(e)} {traceback.format_exc()}")
return None
async def _send_response(self, reply_set, reply_to, thinking_start_time, message_data) -> str:
current_time = time.time()
new_message_count = message_api.count_new_messages(
chat_id=self.chat_stream.stream_id, start_time=thinking_start_time, end_time=current_time
)
platform = message_data.get("user_platform", "")
user_id = message_data.get("user_id", "")
reply_to_platform_id = f"{platform}:{user_id}"
need_reply = new_message_count >= random.randint(2, 4)
if need_reply:
logger.info(f"{self.log_prefix} 从思考到回复,共有{new_message_count}条新消息,使用引用回复")
else:
logger.info(f"{self.log_prefix} 从思考到回复,共有{new_message_count}条新消息,不使用引用回复")
reply_text = ""
first_replied = False
for reply_seg in reply_set:
data = reply_seg[1]
if not first_replied:
if need_reply:
await send_api.text_to_stream(
text=data,
stream_id=self.chat_stream.stream_id,
reply_to=reply_to,
reply_to_platform_id=reply_to_platform_id,
typing=False,
)
else:
await send_api.text_to_stream(
text=data,
stream_id=self.chat_stream.stream_id,
reply_to_platform_id=reply_to_platform_id,
typing=False,
)
first_replied = True
else:
await send_api.text_to_stream(
text=data,
stream_id=self.chat_stream.stream_id,
reply_to_platform_id=reply_to_platform_id,
typing=True,
)
reply_text += data
return reply_text