707 lines
31 KiB
Python
707 lines
31 KiB
Python
import asyncio
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import time
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import traceback
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import random
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from typing import List, Optional, Dict, Any, Tuple, TYPE_CHECKING
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from rich.traceback import install
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from src.config.config import global_config
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from src.common.logger import get_logger
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from src.common.data_models.info_data_model import ActionPlannerInfo
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from src.common.data_models.message_data_model import ReplyContentType
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from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
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from src.chat.utils.prompt_builder import global_prompt_manager
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from src.chat.utils.timer_calculator import Timer
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from src.chat.brain_chat.brain_planner import BrainPlanner
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from src.chat.planner_actions.action_modifier import ActionModifier
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from src.chat.planner_actions.action_manager import ActionManager
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from src.chat.heart_flow.hfc_utils import CycleDetail
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from src.express.expression_learner import expression_learner_manager
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from src.person_info.person_info import Person
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from src.plugin_system.base.component_types import EventType, ActionInfo
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from src.plugin_system.core import events_manager
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from src.plugin_system.apis import generator_api, send_api, message_api, database_api
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from src.chat.utils.chat_message_builder import (
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build_readable_messages_with_id,
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get_raw_msg_before_timestamp_with_chat,
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)
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from src.chat.brain_chat.brain_reply_checker import BrainReplyChecker, BrainLLMReplyChecker
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if TYPE_CHECKING:
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.common.data_models.message_data_model import ReplySetModel
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ERROR_LOOP_INFO = {
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"loop_plan_info": {
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"action_result": {
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"action_type": "error",
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"action_data": {},
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"reasoning": "循环处理失败",
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},
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},
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"loop_action_info": {
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"action_taken": False,
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"reply_text": "",
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"command": "",
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"taken_time": time.time(),
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},
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}
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install(extra_lines=3)
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# 注释：原来的动作修改超时常量已移除，因为改为顺序执行
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logger = get_logger("bc") # Logger Name Changed
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class BrainChatting:
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"""
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管理一个连续的私聊Brain Chat循环
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用于在特定聊天流中生成回复。
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"""
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def __init__(self, chat_id: str):
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"""
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BrainChatting 初始化函数
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参数:
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chat_id: 聊天流唯一标识符(如stream_id)
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on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数
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performance_version: 性能记录版本号，用于区分不同启动版本
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"""
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# 基础属性
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self.stream_id: str = chat_id # 聊天流ID
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self.chat_stream: ChatStream = get_chat_manager().get_stream(self.stream_id) # type: ignore
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if not self.chat_stream:
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raise ValueError(f"无法找到聊天流: {self.stream_id}")
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self.log_prefix = f"[{get_chat_manager().get_stream_name(self.stream_id) or self.stream_id}]"
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self.expression_learner = expression_learner_manager.get_expression_learner(self.stream_id)
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self.action_manager = ActionManager()
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self.action_planner = BrainPlanner(chat_id=self.stream_id, action_manager=self.action_manager)
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self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.stream_id)
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# 循环控制内部状态
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self.running: bool = False
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self._loop_task: Optional[asyncio.Task] = None # 主循环任务
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# 轻量级回复检查器（比 PFC 更宽松）
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self.reply_checker = BrainReplyChecker(chat_id=self.stream_id)
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# 使用 planner 模型的一次性 LLM 检查器
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self.llm_reply_checker = BrainLLMReplyChecker(chat_id=self.stream_id, max_retries=1)
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# 添加循环信息管理相关的属性
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self.history_loop: List[CycleDetail] = []
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self._cycle_counter = 0
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self._current_cycle_detail: CycleDetail = None # type: ignore
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self.last_read_time = time.time() - 2
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self.more_plan = False
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# 最近一次是否成功进行了 reply，用于选择 BrainPlanner 的 Prompt
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self._last_successful_reply: bool = False
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# 类似 PFC 的 block_and_ignore：在该时间点之前不主动参与该聊天
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self._ignore_until_timestamp: Optional[float] = None
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async def start(self):
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"""检查是否需要启动主循环，如果未激活则启动。"""
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# 如果循环已经激活，直接返回
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if self.running:
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logger.debug(f"{self.log_prefix} BrainChatting 已激活，无需重复启动")
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return
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try:
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# 标记为活动状态，防止重复启动
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self.running = True
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self._loop_task = asyncio.create_task(self._main_chat_loop())
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self._loop_task.add_done_callback(self._handle_loop_completion)
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logger.info(f"{self.log_prefix} BrainChatting 启动完成")
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except Exception as e:
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# 启动失败时重置状态
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self.running = False
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self._loop_task = None
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logger.error(f"{self.log_prefix} BrainChatting 启动失败: {e}")
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raise
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def _handle_loop_completion(self, task: asyncio.Task):
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"""当 _hfc_loop 任务完成时执行的回调。"""
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try:
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if exception := task.exception():
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logger.error(f"{self.log_prefix} BrainChatting: 脱离了聊天(异常): {exception}")
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logger.error(traceback.format_exc()) # Log full traceback for exceptions
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else:
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logger.info(f"{self.log_prefix} BrainChatting: 脱离了聊天 (外部停止)")
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except asyncio.CancelledError:
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logger.info(f"{self.log_prefix} BrainChatting: 结束了聊天")
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def start_cycle(self) -> Tuple[Dict[str, float], str]:
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self._cycle_counter += 1
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self._current_cycle_detail = CycleDetail(self._cycle_counter)
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self._current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
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cycle_timers = {}
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return cycle_timers, self._current_cycle_detail.thinking_id
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def end_cycle(self, loop_info, cycle_timers):
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self._current_cycle_detail.set_loop_info(loop_info)
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self.history_loop.append(self._current_cycle_detail)
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self._current_cycle_detail.timers = cycle_timers
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self._current_cycle_detail.end_time = time.time()
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def print_cycle_info(self, cycle_timers):
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# 记录循环信息和计时器结果
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timer_strings = []
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for name, elapsed in cycle_timers.items():
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formatted_time = f"{elapsed * 1000:.2f}毫秒" if elapsed < 1 else f"{elapsed:.2f}秒"
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timer_strings.append(f"{name}: {formatted_time}")
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logger.info(
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f"{self.log_prefix} 第{self._current_cycle_detail.cycle_id}次思考,"
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f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒" # type: ignore
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+ (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
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)
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async def _loopbody(self): # sourcery skip: hoist-if-from-if
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# 如果当前处于 block_and_ignore 冷却期，直接跳过本轮思考
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if self._ignore_until_timestamp and time.time() < self._ignore_until_timestamp:
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await asyncio.sleep(0.5)
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return True
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elif self._ignore_until_timestamp and time.time() >= self._ignore_until_timestamp:
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logger.info(f"{self.log_prefix} block_and_ignore 冷却结束，恢复该聊天的正常思考")
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self._ignore_until_timestamp = None
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recent_messages_list = message_api.get_messages_by_time_in_chat(
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chat_id=self.stream_id,
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start_time=self.last_read_time,
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end_time=time.time(),
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limit=20,
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limit_mode="latest",
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filter_mai=True,
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filter_command=False,
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filter_intercept_message_level=1,
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)
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if len(recent_messages_list) >= 1:
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self.last_read_time = time.time()
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await self._observe(recent_messages_list=recent_messages_list)
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else:
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# Normal模式：消息数量不足，等待
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await asyncio.sleep(0.2)
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return True
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return True
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async def _send_and_store_reply(
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self,
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response_set: "ReplySetModel",
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action_message: "DatabaseMessages",
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cycle_timers: Dict[str, float],
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thinking_id,
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actions,
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selected_expressions: Optional[List[int]] = None,
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) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
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with Timer("回复发送", cycle_timers):
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reply_text = await self._send_response(
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reply_set=response_set,
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message_data=action_message,
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selected_expressions=selected_expressions,
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)
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# 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
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platform = action_message.chat_info.platform
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if platform is None:
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platform = getattr(self.chat_stream, "platform", "unknown")
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person = Person(platform=platform, user_id=action_message.user_info.user_id)
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person_name = person.person_name
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action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
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await database_api.store_action_info(
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chat_stream=self.chat_stream,
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action_build_into_prompt=False,
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action_prompt_display=action_prompt_display,
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action_done=True,
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thinking_id=thinking_id,
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action_data={"reply_text": reply_text},
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action_name="reply",
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)
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# 构建循环信息
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loop_info: Dict[str, Any] = {
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"loop_plan_info": {
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"action_result": actions,
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},
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"loop_action_info": {
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"action_taken": True,
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"reply_text": reply_text,
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"command": "",
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"taken_time": time.time(),
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},
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}
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return loop_info, reply_text, cycle_timers
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async def _observe(
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self, # interest_value: float = 0.0,
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recent_messages_list: Optional[List["DatabaseMessages"]] = None,
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) -> bool: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
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if recent_messages_list is None:
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recent_messages_list = []
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_reply_text = "" # 初始化reply_text变量，避免UnboundLocalError
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# -------------------------------------------------------------------------
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# ReflectTracker Check
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# 在每次回复前检查一次上下文，看是否有反思问题得到了解答
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# -------------------------------------------------------------------------
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from src.express.reflect_tracker import reflect_tracker_manager
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tracker = reflect_tracker_manager.get_tracker(self.stream_id)
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if tracker:
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resolved = await tracker.trigger_tracker()
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if resolved:
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reflect_tracker_manager.remove_tracker(self.stream_id)
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logger.info(f"{self.log_prefix} ReflectTracker resolved and removed.")
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# -------------------------------------------------------------------------
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# Expression Reflection Check
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# 检查是否需要提问表达反思
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# -------------------------------------------------------------------------
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from src.express.expression_reflector import expression_reflector_manager
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reflector = expression_reflector_manager.get_or_create_reflector(self.stream_id)
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asyncio.create_task(reflector.check_and_ask())
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async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
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asyncio.create_task(self.expression_learner.trigger_learning_for_chat())
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cycle_timers, thinking_id = self.start_cycle()
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logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")
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# 第一步：动作检查
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available_actions: Dict[str, ActionInfo] = {}
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try:
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await self.action_modifier.modify_actions()
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available_actions = self.action_manager.get_using_actions()
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except Exception as e:
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logger.error(f"{self.log_prefix} 动作修改失败: {e}")
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# 执行planner
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is_group_chat, chat_target_info, _ = self.action_planner.get_necessary_info()
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message_list_before_now = get_raw_msg_before_timestamp_with_chat(
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chat_id=self.stream_id,
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timestamp=time.time(),
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limit=int(global_config.chat.max_context_size * 0.6),
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filter_intercept_message_level=1,
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)
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chat_content_block, message_id_list = build_readable_messages_with_id(
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messages=message_list_before_now,
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timestamp_mode="normal_no_YMD",
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read_mark=self.action_planner.last_obs_time_mark,
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truncate=True,
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show_actions=True,
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)
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prompt_info = await self.action_planner.build_planner_prompt(
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is_group_chat=is_group_chat,
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chat_target_info=chat_target_info,
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current_available_actions=available_actions,
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chat_content_block=chat_content_block,
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message_id_list=message_id_list,
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interest=global_config.personality.interest,
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prompt_key=(
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"brain_planner_prompt_follow_up" if self._last_successful_reply else "brain_planner_prompt_initial"
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),
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log_prompt=True,
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)
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continue_flag, modified_message = await events_manager.handle_mai_events(
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EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id
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)
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if not continue_flag:
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return False
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if modified_message and modified_message._modify_flags.modify_llm_prompt:
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prompt_info = (modified_message.llm_prompt, prompt_info[1])
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with Timer("规划器", cycle_timers):
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action_to_use_info = await self.action_planner.plan(
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loop_start_time=self.last_read_time,
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available_actions=available_actions,
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last_successful_reply=self._last_successful_reply,
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)
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# 3. 并行执行所有动作
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action_tasks = [
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asyncio.create_task(
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self._execute_action(action, action_to_use_info, thinking_id, available_actions, cycle_timers)
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)
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for action in action_to_use_info
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]
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# 并行执行所有任务
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results = await asyncio.gather(*action_tasks, return_exceptions=True)
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# 处理执行结果
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reply_loop_info = None
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reply_text_from_reply = ""
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action_success = False
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action_reply_text = ""
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for result in results:
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if isinstance(result, BaseException):
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logger.error(f"{self.log_prefix} 动作执行异常: {result}")
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continue
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if result["action_type"] != "reply":
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action_success = result["success"]
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action_reply_text = result["reply_text"]
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elif result["action_type"] == "reply":
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if result["success"]:
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reply_loop_info = result["loop_info"]
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reply_text_from_reply = result["reply_text"]
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else:
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logger.warning(f"{self.log_prefix} 回复动作执行失败")
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# 构建最终的循环信息
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if reply_loop_info:
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# 如果有回复信息，使用回复的loop_info作为基础
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loop_info = reply_loop_info
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# 更新动作执行信息
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loop_info["loop_action_info"].update(
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{
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"action_taken": action_success,
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"taken_time": time.time(),
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}
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)
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_reply_text = reply_text_from_reply
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else:
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# 没有回复信息，构建纯动作的loop_info
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loop_info = {
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"loop_plan_info": {
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"action_result": action_to_use_info,
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},
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"loop_action_info": {
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"action_taken": action_success,
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"reply_text": action_reply_text,
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"taken_time": time.time(),
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},
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}
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_reply_text = action_reply_text
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self.end_cycle(loop_info, cycle_timers)
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self.print_cycle_info(cycle_timers)
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return True
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async def _main_chat_loop(self):
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"""主循环，持续进行计划并可能回复消息，直到被外部取消。"""
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try:
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while self.running:
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# 主循环
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success = await self._loopbody()
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await asyncio.sleep(0.1)
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if not success:
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break
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except asyncio.CancelledError:
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# 设置了关闭标志位后被取消是正常流程
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logger.info(f"{self.log_prefix} 麦麦已关闭聊天")
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except Exception:
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logger.error(f"{self.log_prefix} 麦麦聊天意外错误，将于3s后尝试重新启动")
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print(traceback.format_exc())
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await asyncio.sleep(3)
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self._loop_task = asyncio.create_task(self._main_chat_loop())
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logger.error(f"{self.log_prefix} 结束了当前聊天循环")
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async def _handle_action(
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self,
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action: str,
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reasoning: str,
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action_data: dict,
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cycle_timers: Dict[str, float],
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thinking_id: str,
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action_message: Optional["DatabaseMessages"] = None,
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) -> tuple[bool, str, str]:
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"""
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处理规划动作，使用动作工厂创建相应的动作处理器
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参数:
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action: 动作类型
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reasoning: 决策理由
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action_data: 动作数据，包含不同动作需要的参数
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cycle_timers: 计时器字典
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thinking_id: 思考ID
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返回:
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tuple[bool, str, str]: (是否执行了动作, 思考消息ID, 命令)
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"""
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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,
|
||
action_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（固定记录一次动作信息）
|
||
# BaseAction 定义了异步方法 execute() 作为统一执行入口
|
||
# 这里调用 execute() 以兼容所有 Action 实现
|
||
result = await action_handler.execute()
|
||
success, action_text = result
|
||
command = ""
|
||
|
||
return success, action_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 _send_response(
|
||
self,
|
||
reply_set: "ReplySetModel",
|
||
message_data: "DatabaseMessages",
|
||
selected_expressions: Optional[List[int]] = None,
|
||
) -> str:
|
||
new_message_count = message_api.count_new_messages(
|
||
chat_id=self.chat_stream.stream_id, start_time=self.last_read_time, end_time=time.time()
|
||
)
|
||
|
||
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")
|
||
|
||
reply_text = ""
|
||
first_replied = False
|
||
for reply_content in reply_set.reply_data:
|
||
if reply_content.content_type != ReplyContentType.TEXT:
|
||
continue
|
||
data: str = reply_content.content # type: ignore
|
||
if not first_replied:
|
||
await send_api.text_to_stream(
|
||
text=data,
|
||
stream_id=self.chat_stream.stream_id,
|
||
reply_message=message_data,
|
||
set_reply=need_reply,
|
||
typing=False,
|
||
selected_expressions=selected_expressions,
|
||
)
|
||
first_replied = True
|
||
else:
|
||
await send_api.text_to_stream(
|
||
text=data,
|
||
stream_id=self.chat_stream.stream_id,
|
||
reply_message=message_data,
|
||
set_reply=False,
|
||
typing=True,
|
||
selected_expressions=selected_expressions,
|
||
)
|
||
reply_text += data
|
||
|
||
return reply_text
|
||
|
||
async def _execute_action(
|
||
self,
|
||
action_planner_info: ActionPlannerInfo,
|
||
chosen_action_plan_infos: List[ActionPlannerInfo],
|
||
thinking_id: str,
|
||
available_actions: Dict[str, ActionInfo],
|
||
cycle_timers: Dict[str, float],
|
||
):
|
||
"""执行单个动作的通用函数"""
|
||
try:
|
||
with Timer(f"动作{action_planner_info.action_type}", cycle_timers):
|
||
if action_planner_info.action_type == "no_reply":
|
||
# 直接处理no_reply逻辑，不再通过动作系统
|
||
reason = action_planner_info.reasoning or "选择不回复"
|
||
# logger.info(f"{self.log_prefix} 选择不回复，原因: {reason}")
|
||
|
||
# 存储no_reply信息到数据库
|
||
await database_api.store_action_info(
|
||
chat_stream=self.chat_stream,
|
||
action_build_into_prompt=False,
|
||
action_prompt_display=reason,
|
||
action_done=True,
|
||
thinking_id=thinking_id,
|
||
action_data={"reason": reason},
|
||
action_name="no_reply",
|
||
)
|
||
return {"action_type": "no_reply", "success": True, "reply_text": "", "command": ""}
|
||
|
||
elif action_planner_info.action_type == "reply":
|
||
# 使用规则 + 一次 LLM ReplyChecker 包一层重试逻辑
|
||
retry_count = 0
|
||
|
||
while True:
|
||
try:
|
||
success, llm_response = await generator_api.generate_reply(
|
||
chat_stream=self.chat_stream,
|
||
reply_message=action_planner_info.action_message,
|
||
available_actions=available_actions,
|
||
chosen_actions=chosen_action_plan_infos,
|
||
reply_reason=action_planner_info.reasoning or "",
|
||
enable_tool=global_config.tool.enable_tool,
|
||
request_type="replyer",
|
||
from_plugin=False,
|
||
)
|
||
|
||
if not success or not llm_response or not llm_response.reply_set:
|
||
if action_planner_info.action_message:
|
||
logger.info(
|
||
f"对 {action_planner_info.action_message.processed_plain_text} 的回复生成失败"
|
||
)
|
||
else:
|
||
logger.info("回复生成失败")
|
||
return {
|
||
"action_type": "reply",
|
||
"success": False,
|
||
"reply_text": "",
|
||
"loop_info": 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 {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
|
||
|
||
response_set = llm_response.reply_set
|
||
|
||
# 预先拼接一次纯文本，供检查使用（与发送逻辑解耦）
|
||
preview_text = ""
|
||
for reply_content in response_set.reply_data:
|
||
if reply_content.content_type != ReplyContentType.TEXT:
|
||
continue
|
||
data: str = reply_content.content # type: ignore
|
||
preview_text += data
|
||
|
||
# 规则检查（不调用 LLM）
|
||
rule_suitable, rule_reason, rule_need_retry = self.reply_checker.check(
|
||
reply_text=preview_text, retry_count=retry_count
|
||
)
|
||
|
||
# LLM 检查（使用 planner 模型，一次机会）
|
||
llm_suitable, llm_reason, llm_need_retry = await self.llm_reply_checker.check(
|
||
reply_text=preview_text, retry_count=retry_count
|
||
)
|
||
|
||
# 是否需要重生成：只要有一方建议重试，且还在重试次数之内
|
||
if (rule_need_retry or llm_need_retry) and retry_count < max(
|
||
self.reply_checker.max_retries, self.llm_reply_checker.max_retries
|
||
):
|
||
retry_count += 1
|
||
logger.info(
|
||
f"{self.log_prefix} ReplyChecker 建议重试（第 {retry_count} 次），"
|
||
f"rule: {rule_reason}; llm: {llm_reason}"
|
||
)
|
||
continue
|
||
|
||
# 到这里为止，不再重试：即使有一方认为“不太理想”，也只记录原因并放行
|
||
if not rule_suitable or not llm_suitable:
|
||
logger.info(
|
||
f"{self.log_prefix} ReplyChecker 判断回复可能不太理想，"
|
||
f"rule: {rule_reason}; llm: {llm_reason}，本次仍将发送。"
|
||
)
|
||
|
||
selected_expressions = llm_response.selected_expressions
|
||
loop_info, reply_text, _ = await self._send_and_store_reply(
|
||
response_set=response_set,
|
||
action_message=action_planner_info.action_message, # type: ignore
|
||
cycle_timers=cycle_timers,
|
||
thinking_id=thinking_id,
|
||
actions=chosen_action_plan_infos,
|
||
selected_expressions=selected_expressions,
|
||
)
|
||
# 标记这次循环已经成功进行了回复，下一轮 Planner 使用 follow_up Prompt
|
||
self._last_successful_reply = True
|
||
return {
|
||
"action_type": "reply",
|
||
"success": True,
|
||
"reply_text": reply_text,
|
||
"loop_info": loop_info,
|
||
}
|
||
|
||
# 其他动作
|
||
else:
|
||
# 内建 wait / listening / block_and_ignore：不通过插件系统，直接在这里处理
|
||
if action_planner_info.action_type in ["wait", "listening", "block_and_ignore"]:
|
||
reason = action_planner_info.reasoning or ""
|
||
|
||
if action_planner_info.action_type == "block_and_ignore":
|
||
# 设置一段时间的忽略窗口，例如 10 分钟
|
||
ignore_minutes = 10
|
||
self._ignore_until_timestamp = time.time() + ignore_minutes * 60
|
||
logger.info(
|
||
f"{self.log_prefix} 收到 block_and_ignore 动作，将在接下来 {ignore_minutes} 分钟内不再主动参与该聊天"
|
||
)
|
||
|
||
# 统一将这三种策略动作记录到数据库，便于后续分析
|
||
await database_api.store_action_info(
|
||
chat_stream=self.chat_stream,
|
||
action_build_into_prompt=False,
|
||
action_prompt_display=reason or f"执行动作: {action_planner_info.action_type}",
|
||
action_done=True,
|
||
thinking_id=thinking_id,
|
||
action_data={"reason": reason},
|
||
action_name=action_planner_info.action_type,
|
||
)
|
||
|
||
# 这些动作本身不产生文本回复
|
||
self._last_successful_reply = False
|
||
return {
|
||
"action_type": action_planner_info.action_type,
|
||
"success": True,
|
||
"reply_text": "",
|
||
"command": "",
|
||
}
|
||
|
||
# 其余动作：走原有插件 Action 体系
|
||
with Timer("动作执行", cycle_timers):
|
||
success, reply_text, command = await self._handle_action(
|
||
action_planner_info.action_type,
|
||
action_planner_info.reasoning or "",
|
||
action_planner_info.action_data or {},
|
||
cycle_timers,
|
||
thinking_id,
|
||
action_planner_info.action_message,
|
||
)
|
||
# 非 reply 类动作执行成功时，清空最近成功回复标记，让下一轮回到 initial Prompt
|
||
if success and action_planner_info.action_type != "reply":
|
||
self._last_successful_reply = False
|
||
|
||
return {
|
||
"action_type": action_planner_info.action_type,
|
||
"success": success,
|
||
"reply_text": reply_text,
|
||
"command": command,
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix} 执行动作时出错: {e}")
|
||
logger.error(f"{self.log_prefix} 错误信息: {traceback.format_exc()}")
|
||
return {
|
||
"action_type": action_planner_info.action_type,
|
||
"success": False,
|
||
"reply_text": "",
|
||
"loop_info": None,
|
||
"error": str(e),
|
||
}
|