refactor(chat): 重构HeartFChatting为模块化架构
将原本超长的heartFC_chat.py拆分为6个功能内聚的子模块： - hfc_context：上下文数据容器 - cycle_tracker：循环状态记录 - energy_manager：能量值独立管理 - proactive_thinker：主动思考逻辑抽离 - cycle_processor：单次循环处理器 - response_handler / normal_mode_handler：响应策略 删除冗余常量、错误样板及旧逻辑；大幅减少类体积；降低耦合度，提升可维护性。
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minecraft1024a
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src/chat/chat_loop/proactive_thinker.py
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src/chat/chat_loop/proactive_thinker.py
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import asyncio
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import time
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import traceback
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from typing import Optional, Dict, Any, TYPE_CHECKING
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from src.common.logger import get_logger
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from src.config.config import global_config
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from src.plugin_system.base.component_types import ChatMode
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from .hfc_context import HfcContext
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if TYPE_CHECKING:
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from .cycle_processor import CycleProcessor
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logger = get_logger("hfc.proactive")
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class ProactiveThinker:
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def __init__(self, context: HfcContext, cycle_processor: "CycleProcessor"):
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self.context = context
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self.cycle_processor = cycle_processor
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self._proactive_thinking_task: Optional[asyncio.Task] = None
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self.proactive_thinking_prompts = {
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"private": """现在你和你朋友的私聊里面已经隔了{time}没有发送消息了，请你结合上下文以及你和你朋友之前聊过的话题和你的人设来决定要不要主动发送消息，你可以选择：
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1. 继续保持沉默（当{time}以前已经结束了一个话题并且你不想挑起新话题时）
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2. 选择回复（当{time}以前你发送了一条消息且没有人回复你时、你想主动挑起一个话题时）
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请根据当前情况做出选择。如果选择回复，请直接发送你想说的内容；如果选择保持沉默，请只回复"沉默"（注意：这个词不会被发送到群聊中）。""",
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"group": """现在群里面已经隔了{time}没有人发送消息了，请你结合上下文以及群聊里面之前聊过的话题和你的人设来决定要不要主动发送消息，你可以选择：
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1. 继续保持沉默（当{time}以前已经结束了一个话题并且你不想挑起新话题时）
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2. 选择回复（当{time}以前你发送了一条消息且没有人回复你时、你想主动挑起一个话题时）
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请根据当前情况做出选择。如果选择回复，请直接发送你想说的内容；如果选择保持沉默，请只回复"沉默"（注意：这个词不会被发送到群聊中）。""",
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}
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async def start(self):
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if self.context.running and not self._proactive_thinking_task and global_config.chat.enable_proactive_thinking:
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self._proactive_thinking_task = asyncio.create_task(self._proactive_thinking_loop())
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self._proactive_thinking_task.add_done_callback(self._handle_proactive_thinking_completion)
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logger.info(f"{self.context.log_prefix} 主动思考器已启动")
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async def stop(self):
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if self._proactive_thinking_task and not self._proactive_thinking_task.done():
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self._proactive_thinking_task.cancel()
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await asyncio.sleep(0)
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logger.info(f"{self.context.log_prefix} 主动思考器已停止")
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def _handle_proactive_thinking_completion(self, task: asyncio.Task):
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try:
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if exception := task.exception():
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logger.error(f"{self.context.log_prefix} 主动思考循环异常: {exception}")
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else:
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logger.info(f"{self.context.log_prefix} 主动思考循环正常结束")
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except asyncio.CancelledError:
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logger.info(f"{self.context.log_prefix} 主动思考循环被取消")
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async def _proactive_thinking_loop(self):
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while self.context.running:
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await asyncio.sleep(15)
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if self.context.loop_mode != ChatMode.FOCUS:
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continue
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if not self._should_enable_proactive_thinking():
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continue
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current_time = time.time()
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silence_duration = current_time - self.context.last_message_time
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target_interval = self._get_dynamic_thinking_interval()
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if silence_duration >= target_interval:
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try:
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await self._execute_proactive_thinking(silence_duration)
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self.context.last_message_time = current_time
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except Exception as e:
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logger.error(f"{self.context.log_prefix} 主动思考执行出错: {e}")
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logger.error(traceback.format_exc())
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def _should_enable_proactive_thinking(self) -> bool:
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if not self.context.chat_stream:
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return False
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try:
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chat_id = int(self.context.stream_id.split(':')[-1])
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except (ValueError, IndexError):
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chat_id = None
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proactive_thinking_ids = getattr(global_config.chat, 'proactive_thinking_enable_ids', [])
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if proactive_thinking_ids and (chat_id is None or chat_id not in proactive_thinking_ids):
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return False
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is_group_chat = self.context.chat_stream.group_info is not None
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if is_group_chat and not global_config.chat.proactive_thinking_in_group:
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return False
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if not is_group_chat and not global_config.chat.proactive_thinking_in_private:
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return False
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return True
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def _get_dynamic_thinking_interval(self) -> float:
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try:
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from src.utils.timing_utils import get_normal_distributed_interval
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base_interval = global_config.chat.proactive_thinking_interval
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delta_sigma = getattr(global_config.chat, 'delta_sigma', 120)
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if base_interval < 0:
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base_interval = abs(base_interval)
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if delta_sigma < 0:
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delta_sigma = abs(delta_sigma)
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if base_interval == 0 and delta_sigma == 0:
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return 300
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elif base_interval == 0:
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sigma_percentage = delta_sigma / 1000
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return get_normal_distributed_interval(0, sigma_percentage, 1, 86400, use_3sigma_rule=True)
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elif delta_sigma == 0:
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return base_interval
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sigma_percentage = delta_sigma / base_interval
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return get_normal_distributed_interval(base_interval, sigma_percentage, 1, 86400, use_3sigma_rule=True)
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except ImportError:
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logger.warning(f"{self.context.log_prefix} timing_utils不可用，使用固定间隔")
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return max(300, abs(global_config.chat.proactive_thinking_interval))
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except Exception as e:
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logger.error(f"{self.context.log_prefix} 动态间隔计算出错: {e}，使用固定间隔")
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return max(300, abs(global_config.chat.proactive_thinking_interval))
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def _format_duration(self, seconds: float) -> str:
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hours = int(seconds // 3600)
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minutes = int((seconds % 3600) // 60)
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secs = int(seconds % 60)
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parts = []
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if hours > 0:
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parts.append(f"{hours}小时")
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if minutes > 0:
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parts.append(f"{minutes}分")
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if secs > 0 or not parts:
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parts.append(f"{secs}秒")
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return "".join(parts)
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async def _execute_proactive_thinking(self, silence_duration: float):
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formatted_time = self._format_duration(silence_duration)
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logger.info(f"{self.context.log_prefix} 触发主动思考，已沉默{formatted_time}")
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try:
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proactive_prompt = self._get_proactive_prompt(formatted_time)
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thinking_message = {
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"processed_plain_text": proactive_prompt,
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"user_id": "system_proactive_thinking",
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"user_platform": "system",
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"timestamp": time.time(),
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"message_type": "proactive_thinking",
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"user_nickname": "系统主动思考",
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"chat_info_platform": "system",
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"message_id": f"proactive_{int(time.time())}",
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}
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logger.info(f"{self.context.log_prefix} 开始主动思考...")
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await self.cycle_processor.observe(message_data=thinking_message)
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logger.info(f"{self.context.log_prefix} 主动思考完成")
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except Exception as e:
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logger.error(f"{self.context.log_prefix} 主动思考执行异常: {e}")
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logger.error(traceback.format_exc())
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def _get_proactive_prompt(self, formatted_time: str) -> str:
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if hasattr(global_config.chat, 'proactive_thinking_prompt_template') and global_config.chat.proactive_thinking_prompt_template.strip():
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return global_config.chat.proactive_thinking_prompt_template.format(time=formatted_time)
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chat_type = "group" if self.context.chat_stream and self.context.chat_stream.group_info else "private"
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prompt_template = self.proactive_thinking_prompts.get(chat_type, self.proactive_thinking_prompts["group"])
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return prompt_template.format(time=formatted_time)
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