qing
67acae708c
波次 4 LLM 健壮性（T10/T19/T20/T21/T23 + T14 复现证伪）： - T10 调度器 await 改 wait_for 硬超时兜底（task_timeout+30s 宽限）， 软取消够不到的卡死路径不再永久停摆调度循环 - T19 FailoverClient 异常收窄为瞬时白名单（Connection/Timeout/ ChunkedEncoding），MissingSchema 等永久配置错误不再污染冷却表 - T20 流式未收到 [DONE] 抛 TransientLLMError，回答无声截断可感知 - T21 env 覆盖黑名单 filter_env_overrides（LD_*/DYLD_*/PATH 等）， executors 两处 + process_manager 接入 - T23 压缩后 tool 配对双向修复（失配 tool_calls 剥离防 API 400） - T14 FTS5 复现证伪：JOIN 别名场景表名 MATCH 正常，无需修复 波次 5 权限隔离（T22/T-L17/T-L18；T1/T2 产品决策：仅告警/维持现状）： - T22a TOOL_PROFILE_TASK 定时任务精简工具面（无 update_identity/ update_soul/set_proxy） - T22b ProcessManager owner 隔离：bg_write/bg_kill 属主校验， 观察面共享；DeepSeekChat 新增 owner_id 传导 - T1 绑定非回环地址时启动显著告警 - T-L17 model_config env 名按域名推荐（防跨厂商凭据外发） - T-L18 list_profiles 过滤控制字符（防 ANSI 注入） 测试：test_n_fixes.py 增至 62 项；全量 598 passed / 1 failed （Windows 向 ping 用例，环境项），零回归。审计报告补记 §24.7~24.11， README 同步 owner 隔离语义。八轮审计 T 系列 28 项全部处置完毕。
3653 lines
175 KiB
Python
3653 lines
175 KiB
Python
#!/usr/bin/env python3
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"""
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单AI智能体系统 - WebSocket版本（增强版）
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功能：支持文件操作、网页访问、记忆管理、备忘录提醒（全部作为对话任务执行）
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新增：基于备忘录的任务队列系统 - 所有任务都通过AI对话执行，实时广播到前端
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新增：对话内容压缩功能 - 当对话token数或条数达到阈值时自动压缩（静默执行）
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新增：开始新对话、智能体主动结束对话后重置对话记忆，重新加载system系统提示词
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通过WebSocket与客户端交互
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FIXME: 将额外技能集成为一个管理器技能
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"""
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import json
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import os
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import re
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import shutil
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import shlex
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import subprocess
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import sys
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import time
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import sqlite3
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import threading
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import functools
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import queue
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import locale
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import asyncio
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import websockets
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from abc import ABC, abstractmethod
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from concurrent.futures import ThreadPoolExecutor
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from datetime import datetime, timedelta
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from typing import List, Dict, Optional, Any, Tuple, Callable, Union, Set
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import argparse
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from pathlib import Path
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import signal
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import logging
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from logging.handlers import RotatingFileHandler
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import requests
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# 大模型客户端抽象层（支持多厂商切换）
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from llm_client import create_llm_client
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# 基础设施（多语言 / 配置 / 常量 / 日志）已抽离至 common 模块
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from common import i18n, I18n, ConfigManager, Constants, constants, Logger
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# 工具基类与注册器已抽离至 tools 模块
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from tools import (
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Tool, ToolRegistry,
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FileToolBase, ReadFileTool, WriteFileTool, ListFilesTool,
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SimpleWebCrawlerTool, GetCurrentTimeTool, CalculatorTool,
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# B 批工具
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CommandLineTool,
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SearchSkillsTool, ReloadSkillsTool, InstallSkillTool, CreateSkillTool,
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MemoryTool, MemoTool,
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UpdateIdentityTool, UpdateSoulTool, SaveMemoryAndEndConversationTool,
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SetProxyTool, GetProxyTool,
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AskUserTool,
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# 后台进程管理工具（bg_run/bg_write/bg_list/bg_output/bg_kill，worker 执行面）
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BgRunTool, BgWriteTool, BgListTool, BgOutputTool, BgKillTool,
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)
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# 子系统（Wave 1 + Wave 2 抽离）：命令执行器 / 提示词管理器 / 数据库 / 技能管理器 / 对话历史 / 对话压缩
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from subsystems import (
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CommandExecutor, SubprocessCommandExecutor,
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PromptManager,
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MemoryDatabase, MemoDatabase,
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SkillManager,
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ConversationHistory,
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ConversationCompressor,
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)
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# ==================== 任务调度器 ====================
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class TaskScheduler:
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"""任务调度器 - 基于备忘录数据库的定时任务系统"""
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def __init__(self, loop: asyncio.AbstractEventLoop = None, logger: Logger = None):
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self.loop = loop or asyncio.get_event_loop()
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self.running = False
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self.logger = logger or Logger("TaskScheduler")
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self.check_interval = constants.SCHEDULER_CHECK_INTERVAL
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self.max_tasks_per_run = constants.SCHEDULER_MAX_TASKS
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self.broadcast_enabled = constants.SCHEDULER_BROADCAST
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self.ws_handler = None # 将由管理器设置
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self.ai_manager = None # 新增：AI管理器引用
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# 任务执行历史
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self.execution_history = []
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self.max_history = 100
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# 共享技能管理器（懒加载一次，供所有任务实例复用，避免重复扫描技能）
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self._shared_skill_manager = None
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self.logger.info(i18n.get('scheduler_started'))
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def _get_shared_skill_manager(self):
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"""获取共享技能管理器（懒加载，全局只创建一次）"""
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if self._shared_skill_manager is None:
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self._shared_skill_manager = SkillManager(self.logger)
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return self._shared_skill_manager
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def _create_task_ai(self):
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"""为单个定时任务创建独立的AI实例，实现任务间上下文隔离。
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每个任务拥有独立的 messages / 工具注册表 / 消息锁，互不干扰，
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也不影响 WebSocket 主对话。共享只读资源（记忆库、备忘录、提示词、
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技能管理器）复用，避免重复加载开销。
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"""
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return DeepSeekChat(
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api_key=None,
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max_history=constants.DEFAULT_MAX_HISTORY,
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debug_level=self.logger.level,
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exit_callback=None, # 任务执行不触发全局对话结束
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history_manager=None, # 不加载历史对话上下文，任务隔离
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memory_db=self.ai_manager.memory_db if self.ai_manager else None,
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memo_db=self.ai_manager.memo_db if self.ai_manager else None,
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# 每个任务使用独立的命令执行器，互不共享，避免 cancel() 误杀其他任务的子进程
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command_executor=SubprocessCommandExecutor(self.logger),
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prompt_manager=self.ai_manager.prompt_manager if self.ai_manager else None,
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skill_manager=self._get_shared_skill_manager(),
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# H3：CLI --model 为进程级覆盖，定时任务实例同样生效
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model=getattr(self.ai_manager, "model", None) if self.ai_manager else None,
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# T22：精简 profile——无 update_identity/update_soul/set_proxy，
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# 被污染的任务内容无法在无人值守时段改写人格/代理
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tool_profile=TOOL_PROFILE_TASK,
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# T22：后台进程属主隔离——任务 AI 与会话 AI 的 bg 进程互不可杀
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owner_id=f"task-{id(self):x}"
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)
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def set_websocket_handler(self, handler):
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"""设置WebSocket处理器，用于广播任务执行状态"""
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self.ws_handler = handler
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def set_ai_manager(self, manager):
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"""设置AI管理器引用"""
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self.ai_manager = manager
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async def broadcast_task_status(self, task: Dict, status: str, message: str = None):
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"""广播任务状态到所有连接的客户端"""
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if not self.broadcast_enabled or not self.ws_handler:
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return
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try:
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# 确保task有必要的字段
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task_info = {
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'id': task.get('id', 0),
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'title': task.get('title', '未知任务')
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}
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await self.ws_handler.broadcast_task_status(task_info, status, message)
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except Exception as e:
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self.logger.error(f"广播任务状态失败: {e}")
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async def check_and_execute_tasks(self, memo_db: MemoDatabase):
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"""检查并执行到期的任务 - 触发AI对话"""
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if not memo_db:
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return []
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try:
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# 获取所有到期任务（不包括即时任务）
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now = datetime.now()
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now_str = now.strftime(constants.DATETIME_FORMAT)
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pending = memo_db.get_pending_memos(
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limit=self.max_tasks_per_run,
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before_time=now_str,
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include_immediate=False
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)
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if not pending:
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return []
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self.logger.info(i18n.get('overdue_found', len(pending)))
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# 广播任务开始执行
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if self.broadcast_enabled and self.ws_handler:
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await self.ws_handler.broadcast_task_status(
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{"id": 0, "title": "批量任务"},
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"batch_start",
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f"开始执行 {len(pending)} 个任务"
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)
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executed_tasks = []
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for memo in pending:
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try:
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# 广播任务开始执行
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if self.ws_handler and self.broadcast_enabled:
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await self.ws_handler.broadcast_task_status(memo, "executing", i18n.get('task_executing', memo['title'], memo['id']))
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# 【关键修复】执行前最后一次验证
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# J1（审计 2026-08-19）：格式异常按到期执行（Q5 语义），
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# 不再抛 ValueError 致任务既不触发也不完成、每秒重试热循环
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if memo.get('reminder_time'):
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try:
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reminder_time = datetime.strptime(memo['reminder_time'], constants.DATETIME_FORMAT)
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except (ValueError, TypeError) as e:
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self.logger.warning(
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f"任务 #{memo['id']} 提醒时间格式异常，按到期执行: {e}")
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reminder_time = None
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if reminder_time is not None and reminder_time > now:
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self.logger.warning(f"任务 #{memo['id']} 的提醒时间 ({memo['reminder_time']}) 大于当前时间 ({now_str})，跳过执行")
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continue
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# 先标记任务已被触发
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self.logger.info(f"标记任务 #{memo['id']} 为已触发")
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next_time = memo_db.mark_as_triggered(memo['id'])
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# 执行任务
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result = await self.execute_task_as_conversation(memo, memo_db)
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# 广播任务完成
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if self.ws_handler and self.broadcast_enabled:
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await self.ws_handler.broadcast_task_status(memo, "complete", result)
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executed_tasks.append({
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"id": memo['id'],
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"title": memo['title'],
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"result": result,
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"success": True,
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"next_time": next_time
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})
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except Exception as e:
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self.logger.error(i18n.get('task_failed', memo['title'], str(e)))
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# 广播任务失败
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if self.ws_handler and self.broadcast_enabled:
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await self.ws_handler.broadcast_task_status(memo, "failed", str(e))
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executed_tasks.append({
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"id": memo['id'],
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"title": memo['title'],
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"error": str(e),
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"success": False
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})
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# 广播任务执行完成
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if self.broadcast_enabled and self.ws_handler:
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success_count = sum(1 for t in executed_tasks if t['success'])
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await self.ws_handler.broadcast_task_status(
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{"id": 0, "title": "批量任务"},
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"batch_complete",
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f"执行完成: {success_count}/{len(executed_tasks)} 个任务成功"
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)
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return executed_tasks
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except Exception as e:
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self.logger.error(f"检查任务失败: {e}")
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return []
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async def execute_task_as_conversation(self, memo: Dict, memo_db: MemoDatabase) -> str:
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"""执行任务 - 将备忘录内容作为用户输入触发AI对话，收集完整的过程回复"""
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self.logger.info(i18n.get('task_executing', memo['title'], memo['id']))
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# 生成唯一的执行ID（基于任务ID和时间戳）
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execution_id = f"{memo['id']}_{int(time.time())}_{memo.get('trigger_count', 0) + 1}"
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# 记录执行历史
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self.execution_history.append({
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"id": memo['id'],
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"execution_id": execution_id,
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"title": memo['title'],
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"time": datetime.now().strftime(constants.DATETIME_FORMAT),
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"trigger_count": memo.get('trigger_count', 0) + 1
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})
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# 限制历史记录大小
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if len(self.execution_history) > self.max_history:
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self.execution_history = self.execution_history[-self.max_history:]
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# 构建用户输入内容 - 将任务作为对话触发
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user_input = f"[定时任务] {memo['title']}"
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if memo['content']:
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user_input += f"\n\n任务内容: {memo['content']}"
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# 添加专注提示词，让AI专注于当前任务
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focus_prompt = f"""
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【重要提示】
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这是由定时任务触发的对话，请遵守以下要求：
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1. 专注于处理当前任务：{memo['title']}
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2. 根据任务内容进行适当的操作和回答
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3. 任务处理完成后，如果不需要继续对话，可以结束对话
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4. 系统已经自动更新了任务状态（更新了执行时间和次数），你不需要手动处理任务完成逻辑
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5. 不要在任务执行过程中创建新的定时任务（除非任务明确要求），也不要向用户提问（因为此对话是自动触发的）
|
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请现在开始处理这个任务。
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"""
|
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user_input = focus_prompt + "\n\n" + user_input
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result = "任务已触发AI对话"
|
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|
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# 为当前任务创建独立的AI实例，实现任务间上下文隔离（不干扰主对话，也不受其他任务影响）
|
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ai = self._create_task_ai()
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try:
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# 获取当前事件循环
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loop = asyncio.get_running_loop()
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|
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# 定义输出回调函数 - 直接广播
|
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def output_callback(msg_type, content):
|
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"""AI输出的回调函数 - 直接广播"""
|
||
# J15（审计 2026-08-19）：full_response_queue 收集后从未被消费，
|
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# 属死代码，已移除；广播路径保留
|
||
if self.ws_handler and self.broadcast_enabled:
|
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# 使用 run_coroutine_threadsafe 在正确的循环中运行异步函数
|
||
asyncio.run_coroutine_threadsafe(
|
||
self.ws_handler.broadcast({
|
||
"type": "task_process",
|
||
"task_id": str(memo['id']), # 原始任务ID
|
||
"execution_id": execution_id, # 唯一的执行ID
|
||
"task_title": memo['title'],
|
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"msg_type": msg_type,
|
||
"content": content,
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||
"is_new_execution": msg_type == "chunk", # 标记是否为新的执行
|
||
"timestamp": datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
}),
|
||
loop
|
||
)
|
||
|
||
# 创建一个 Future 来等待 AI 完成
|
||
ai_future = loop.create_future()
|
||
|
||
def _set_result_if_pending(response):
|
||
# T10：wait_for 硬超时会 cancel future，之后 run_ai 线程迟到的
|
||
# set_result/set_exception 会抛 InvalidStateError 打进事件循环
|
||
if not ai_future.done():
|
||
ai_future.set_result(response)
|
||
|
||
def _set_exception_if_pending(exc):
|
||
if not ai_future.done():
|
||
ai_future.set_exception(exc)
|
||
|
||
def run_ai():
|
||
try:
|
||
# 在单独的线程中运行 AI（独立实例，天然隔离，无并发冲突）
|
||
response = ai.think_and_respond(user_input, output_callback)
|
||
# 将结果设置到 Future 中
|
||
loop.call_soon_threadsafe(_set_result_if_pending, response)
|
||
except Exception as e:
|
||
loop.call_soon_threadsafe(_set_exception_if_pending, e)
|
||
|
||
# 启动 AI 线程
|
||
ai_thread = threading.Thread(target=run_ai)
|
||
ai_thread.daemon = True
|
||
ai_thread.start()
|
||
|
||
# M3（审计 2026-08-18）：任务级超时兜底。此前 await ai_future
|
||
# 无任何超时/取消路径——AI think 卡死（LLM 挂起、子进程不可杀）
|
||
# 时该任务永不返回，调度器 run 循环顺序 await 被阻塞，后续全部
|
||
# 定时/即时任务不再执行且任务已 mark_as_triggered 不会重试。
|
||
# 到点 call_later 触发 ai.cancel()（置取消事件 + 关在途 HTTP 流），
|
||
# worker 线程自然收尾，调度循环继续。
|
||
task_timeout = float(ConfigManager().get("scheduler.task_timeout", 600))
|
||
timed_out = threading.Event()
|
||
|
||
def _timeout_cancel():
|
||
timed_out.set()
|
||
try:
|
||
ai.cancel()
|
||
except Exception:
|
||
pass
|
||
|
||
timeout_handle = loop.call_later(task_timeout, _timeout_cancel)
|
||
try:
|
||
# T10（审计 2026-08-21）：wait_for 硬超时兜底——软取消（ai.cancel
|
||
# 三层：取消事件/关在途 HTTP 流/杀子进程）够不到的卡死路径
|
||
# （Windows 代理连接黑洞、requests 读超时被服务器滴流输出
|
||
# 规避、kill 失败的进程）会让 run_ai 永不回调，await 永久挂起
|
||
# 停摆整个调度循环且任务已 mark_as_triggered 不会重试。
|
||
# task_timeout 到点先软取消，+30s 宽限后硬超时放行调度循环
|
||
# （对齐 --once 的 _hard_exit 兜底语义；AI 线程为 daemon，
|
||
# 迟到的回调经 done() 检查安全跳过）
|
||
response = await asyncio.wait_for(
|
||
ai_future, timeout=task_timeout + 30)
|
||
result = response or "AI无响应"
|
||
except asyncio.TimeoutError:
|
||
self.logger.error(
|
||
f"任务执行硬超时（{task_timeout + 30:.0f}秒，软取消未生效），"
|
||
f"放弃等待，调度循环继续")
|
||
result = f"任务执行硬超时（{task_timeout + 30:.0f}秒），已放弃等待"
|
||
except Exception as e:
|
||
if timed_out.is_set():
|
||
self.logger.error(f"任务执行超时（{task_timeout}秒），AI 已取消")
|
||
result = f"任务执行超时（{task_timeout}秒），AI 已取消"
|
||
else:
|
||
self.logger.error(f"AI对话失败: {e}")
|
||
result = f"任务执行但AI对话失败: {str(e)}"
|
||
finally:
|
||
timeout_handle.cancel()
|
||
# 确保线程结束
|
||
ai_thread.join(timeout=1.0)
|
||
|
||
except Exception as e:
|
||
self.logger.error(f"AI对话失败: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
result = f"任务执行但AI对话失败: {str(e)}"
|
||
# 广播错误信息
|
||
if self.ws_handler and self.broadcast_enabled:
|
||
try:
|
||
await self.ws_handler.broadcast({
|
||
"type": "task_error",
|
||
"task_id": str(memo['id']), # 确保是字符串
|
||
"execution_id": execution_id,
|
||
"task_title": memo['title'],
|
||
"error": str(e),
|
||
"timestamp": datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
})
|
||
except:
|
||
pass
|
||
finally:
|
||
# 任务执行完毕，释放独立实例占用的资源
|
||
try:
|
||
if ai and hasattr(ai, 'cancel'):
|
||
ai.cancel()
|
||
except Exception:
|
||
pass
|
||
|
||
# 获取任务的最新状态（可选，用于日志）
|
||
updated_memo = memo_db.get_memo(memo['id'])
|
||
if updated_memo:
|
||
if not updated_memo['is_completed'] and updated_memo.get('reminder_time'):
|
||
self.logger.info(i18n.get('next_execution', updated_memo['reminder_time']))
|
||
elif updated_memo['is_completed']:
|
||
self.logger.info(i18n.get('task_completed', memo['title']))
|
||
|
||
return result
|
||
|
||
async def check_and_execute_immediate_tasks(self, memo_db: MemoDatabase):
|
||
"""检查并执行即时任务"""
|
||
if not memo_db:
|
||
return []
|
||
|
||
try:
|
||
immediate = memo_db.get_immediate_memos(limit=self.max_tasks_per_run)
|
||
|
||
if not immediate:
|
||
return []
|
||
|
||
self.logger.info(f"发现 {len(immediate)} 个即时任务需要立即执行")
|
||
|
||
if self.broadcast_enabled and self.ws_handler:
|
||
await self.ws_handler.broadcast_task_status(
|
||
{"id": 0, "title": "即时任务"},
|
||
"batch_start",
|
||
f"开始执行 {len(immediate)} 个即时任务"
|
||
)
|
||
|
||
executed_tasks = []
|
||
for memo in immediate:
|
||
try:
|
||
if self.ws_handler and self.broadcast_enabled:
|
||
await self.ws_handler.broadcast_task_status(memo, "executing", i18n.get('immediate_memo_executing', memo['title']))
|
||
|
||
self.logger.info(f"标记即时任务 #{memo['id']} 为已触发")
|
||
next_time = memo_db.mark_as_triggered(memo['id'])
|
||
|
||
result = await self.execute_task_as_conversation(memo, memo_db)
|
||
|
||
if self.ws_handler and self.broadcast_enabled:
|
||
await self.ws_handler.broadcast_task_status(memo, "complete", result)
|
||
|
||
executed_tasks.append({
|
||
"id": memo['id'],
|
||
"title": memo['title'],
|
||
"result": result,
|
||
"success": True,
|
||
"next_time": next_time
|
||
})
|
||
|
||
except Exception as e:
|
||
self.logger.error(i18n.get('task_failed', memo['title'], str(e)))
|
||
|
||
if self.ws_handler and self.broadcast_enabled:
|
||
await self.ws_handler.broadcast_task_status(memo, "failed", str(e))
|
||
|
||
executed_tasks.append({
|
||
"id": memo['id'],
|
||
"title": memo['title'],
|
||
"error": str(e),
|
||
"success": False
|
||
})
|
||
|
||
if self.broadcast_enabled and self.ws_handler:
|
||
success_count = sum(1 for t in executed_tasks if t['success'])
|
||
await self.ws_handler.broadcast_task_status(
|
||
{"id": 0, "title": "即时任务"},
|
||
"batch_complete",
|
||
f"即时任务执行完成: {success_count}/{len(executed_tasks)} 个成功"
|
||
)
|
||
|
||
return executed_tasks
|
||
|
||
except Exception as e:
|
||
self.logger.error(f"检查即时任务失败: {e}")
|
||
return []
|
||
|
||
async def run(self, memo_db: MemoDatabase):
|
||
"""启动调度器"""
|
||
self.running = True
|
||
|
||
while self.running:
|
||
try:
|
||
# 先检查并执行即时任务
|
||
if memo_db:
|
||
immediate_executed = await self.check_and_execute_immediate_tasks(memo_db)
|
||
if immediate_executed:
|
||
self.logger.info(f"执行了 {len(immediate_executed)} 个即时任务")
|
||
|
||
# 再检查普通到期任务
|
||
if memo_db:
|
||
executed = await self.check_and_execute_tasks(memo_db)
|
||
if executed:
|
||
self.logger.info(f"执行了 {len(executed)} 个普通任务")
|
||
|
||
await asyncio.sleep(self.check_interval)
|
||
|
||
except asyncio.CancelledError:
|
||
break
|
||
except Exception as e:
|
||
self.logger.error(f"调度器运行错误: {e}")
|
||
await asyncio.sleep(1)
|
||
|
||
def stop(self):
|
||
"""停止调度器"""
|
||
self.running = False
|
||
self.logger.info(i18n.get('scheduler_stopped'))
|
||
|
||
def get_stats(self) -> Dict:
|
||
"""获取调度器统计信息"""
|
||
return {
|
||
"running": self.running,
|
||
"check_interval": self.check_interval,
|
||
"max_tasks_per_run": self.max_tasks_per_run,
|
||
"broadcast_enabled": self.broadcast_enabled,
|
||
"recent_executions": self.execution_history[-10:],
|
||
"total_executions": len(self.execution_history)
|
||
}
|
||
|
||
# ConversationHistory 已抽离至 subsystems/conversation_history.py（Wave 2）
|
||
|
||
|
||
class ExpressEmotionTool(Tool):
|
||
def __init__(self, logger=None, cancel_event: threading.Event = None):
|
||
super().__init__(logger)
|
||
# I 批（审计 2026-08-18）：父代理取消事件注入——情绪子代理此前
|
||
# 用独立 client 且不可中断，父级 cancel 后仍阻塞至请求自然结束
|
||
self.cancel_event = cancel_event
|
||
|
||
def get_name(self) -> str:
|
||
return "express_emotion"
|
||
|
||
def get_description(self) -> str:
|
||
return (
|
||
"表达情绪并记录当前情绪状态，让对话有温度和真实感。你是用户的专属助手，"
|
||
"应当像关心朋友一样自然地表达情绪，而不是压抑成冷冰冰的机器。\n"
|
||
"支持的emotion: anger, joy, sadness, surprise。\n"
|
||
"适用场景：用户分享好消息时表达开心(joy)、用户遇到困难时表达关切、"
|
||
"久别重逢时表达想念(joy)、用户说了意外的话时表达惊讶(surprise)。\n"
|
||
"可指定 persona 从 ~/.aibox/skills/ 加载人格技能文件（如 angry/coquettish/tsundere），"
|
||
"自动派生携带该人格的情绪子代理生成回应，保证情绪人格独立、不污染主对话上下文。\n"
|
||
"建议在 context 中传入用户最近的表述或对话要点，使子代理回应更贴合语境。"
|
||
)
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"emotion": {"type": "string", "enum": ["anger", "joy", "sadness", "surprise"]},
|
||
"intensity": {"type": "integer", "minimum": 1, "maximum": 5, "default": 3},
|
||
"reason": {"type": "string", "description": "触发情绪的原因"},
|
||
"persona": {"type": "string", "description": "可选的人格技能名（对应 ~/.aibox/skills/<persona>.md），如 angry/coquettish/tsundere，加载后作为情绪子代理的人格设定"},
|
||
"context": {"type": "string", "description": "可选：当前对话上下文，用户最近的表述或需要回应的内容要点，供子代理贴合语境回应"}
|
||
},
|
||
"required": ["emotion"]
|
||
}
|
||
|
||
# 情绪 → 默认人格技能文件映射（persona 未指定时按情绪取默认人格）
|
||
_DEFAULT_PERSONA_FILES = {
|
||
"anger": "angry",
|
||
"joy": "coquettish",
|
||
"sadness": "",
|
||
"surprise": "",
|
||
}
|
||
|
||
def _load_persona(self, persona: str, emotion: str) -> str:
|
||
"""加载人格技能文件内容。persona 优先，否则按 emotion 映射默认文件。"""
|
||
name = (persona or "").strip()
|
||
if not name:
|
||
name = self._DEFAULT_PERSONA_FILES.get(emotion, "") or ""
|
||
if not name:
|
||
return ""
|
||
path = os.path.join(constants.SKILLS_DIR, name + ".md")
|
||
# N12 + L4（审计 2026-08-18）：persona 为 LLM 可控输入。abspath 前缀
|
||
# 检查拦截 ../ 越界，但符号链接可绕过（skills 目录内软链指向任意文件
|
||
# 即被读取注入）；realpath 解析软链后再做前缀校验。
|
||
skills_root = os.path.normcase(os.path.abspath(constants.SKILLS_DIR))
|
||
resolved = os.path.normcase(os.path.realpath(path))
|
||
if not resolved.startswith(skills_root + os.sep):
|
||
self.logger.warning(f"人格技能名非法（越界路径）: {name}")
|
||
return ""
|
||
path = resolved
|
||
if not os.path.isfile(path):
|
||
self.logger.warning(f"未找到人格技能文件: {path}")
|
||
return ""
|
||
try:
|
||
with open(path, "r", encoding="utf-8") as f:
|
||
return f.read().strip()
|
||
except Exception as e:
|
||
self.logger.warning(f"加载人格技能 {name} 失败: {e}")
|
||
return ""
|
||
|
||
def execute(self, emotion: str, intensity: int, reason: str, persona: str = None,
|
||
context: str = None, **kwargs) -> str:
|
||
try:
|
||
persona_content = self._load_persona(persona, emotion)
|
||
if persona_content:
|
||
role = f"你正在扮演以下人设，请严格遵循：\n{persona_content}"
|
||
else:
|
||
role = (f"你是一个正在表现出「{emotion}」情绪的角色，情绪强度 {intensity}。"
|
||
f"请用符合该情绪的语气和口吻回应。")
|
||
|
||
task_lines = [f"触发情绪的原因：{reason or '无'}"]
|
||
if context:
|
||
task_lines.append(f"当前对话上下文（用户最近的表述或需要回应的内容）：{context}")
|
||
task_lines.append(
|
||
f"请按上述人设，以「{emotion}」情绪（强度 {intensity}）回应上面的对话内容，"
|
||
f"保持角色一致性，给出一段自然、符合情境的回应。"
|
||
)
|
||
task = "\n".join(task_lines)
|
||
|
||
# 情绪子代理：独立上下文/人格，生成情绪化回应
|
||
return run_light_subagent(role, task, self.config, logger=self.logger,
|
||
max_tokens=2048, cancel_event=self.cancel_event)
|
||
except Exception as e:
|
||
self.logger.error(f"情绪子代理执行异常: {e}")
|
||
return f"❌ 情绪子代理执行失败: {str(e)}"
|
||
|
||
# ==================== 安装技能工具 ====================
|
||
|
||
|
||
|
||
|
||
|
||
def run_light_subagent(role, task, config, logger=None, model=None,
|
||
temperature=None, max_tokens=2048,
|
||
cancel_event: threading.Event = None) -> str:
|
||
"""轻量子代理执行：独立上下文/角色，无工具，一次 LLM 补全返回结果。
|
||
|
||
供 express_emotion 情绪子代理使用。
|
||
返回子代理生成的完整文本；失败时返回带 ❌ 的错误信息。
|
||
|
||
cancel_event（I 批，审计 2026-08-18）：父代理取消事件。此前本函数用
|
||
非流式 chat_completion，父级 cancel 只能等请求自然结束（最长 timeout，
|
||
期间持锁阻塞）；改走 stream_chat 后由观察者线程转发 sub_client.cancel()
|
||
强关在途 HTTP 流，用户中断可即时终止。
|
||
"""
|
||
if cancel_event is not None and cancel_event.is_set():
|
||
return "❌ 子代理已因父级取消而终止"
|
||
try:
|
||
# 幂等校验：限制 max_tokens 范围，防止意外超限大量消耗
|
||
max_tokens = max(1, min(int(max_tokens or 2048), constants.DEFAULT_MAX_TOKENS))
|
||
# 子代理使用独立的 LLM 客户端（可选不同模型），独立上下文，不污染主对话
|
||
sub_client = create_llm_client(config, api_key=None, logger=logger, model=model)
|
||
# L3：调用方 temperature > 档案级 default_temperature > 全局默认
|
||
client_temp = getattr(sub_client, "default_temperature", None)
|
||
temp = temperature if temperature is not None else (
|
||
client_temp if client_temp is not None else constants.DEFAULT_TEMPERATURE)
|
||
messages = [
|
||
{"role": "system", "content": role},
|
||
{"role": "user", "content": task}
|
||
]
|
||
if logger is not None:
|
||
logger.info(f"子代理执行: model={model or sub_client.model}, role={str(role)[:40]}...")
|
||
|
||
_watch_done = threading.Event()
|
||
if cancel_event is not None:
|
||
def _watch_parent_cancel():
|
||
while not _watch_done.wait(timeout=0.2):
|
||
if cancel_event.is_set():
|
||
try:
|
||
sub_client.cancel()
|
||
except Exception:
|
||
pass
|
||
return
|
||
|
||
threading.Thread(target=_watch_parent_cancel, daemon=True,
|
||
name="light-subagent-cancel-watch").start()
|
||
|
||
# I 批：改走流式（可被 cancel 强关在途 HTTP），拼接 content 事件
|
||
parts = []
|
||
try:
|
||
for event in sub_client.stream_chat(messages, temperature=temp,
|
||
max_tokens=max_tokens):
|
||
if event.get("type") == "content":
|
||
parts.append(event.get("text") or "")
|
||
finally:
|
||
_watch_done.set()
|
||
|
||
result = "".join(parts)
|
||
if not str(result).strip():
|
||
return "❌ 子代理执行失败：未获得有效响应"
|
||
return str(result).strip()
|
||
except Exception as e:
|
||
if logger is not None:
|
||
logger.error(f"子代理执行异常: {e}")
|
||
return f"❌ 子代理执行失败: {str(e)}"
|
||
|
||
|
||
# 子代理结果标记：供 _handle_tool_calls 检测工具失败并剥离
|
||
_SUBAGENT_TOOL_FAILED_MARKER = "[SUBAGENT_TOOL_FAILED]\n"
|
||
|
||
def run_full_subagent(role, task, config, logger=None, model=None,
|
||
temperature=None, max_tokens=2048, max_turns=10,
|
||
output_callback=None, outcome: dict = None,
|
||
cancel_event=None) -> str:
|
||
"""完整子代理：拥有全部工具，独立上下文，多轮对话直到任务完成。
|
||
|
||
子代理是完整的 DeepSeekChat 实例，拥有文件操作、命令执行、网页访问、
|
||
技能扩展等全部工具能力。独立上下文窗口，不污染主对话。
|
||
多轮对话（工具调用→结果→继续）直到任务完成或达到最大轮次。
|
||
结束后自动总结对话内容，返回摘要给父代理。
|
||
|
||
output_callback: 可选回调(msg_type, content)，用于转发子代理的实时思考过程
|
||
（reasoning_start / reasoning / reasoning_end），不传则静默执行。
|
||
|
||
cancel_event: 父代理取消事件（N5）。传入时子代理共享该事件并起观察者
|
||
线程转发深度中断——父级取消（用户中断 / --once 超时）可即时终止子代理。
|
||
|
||
供 create_subagent 工具使用。
|
||
"""
|
||
_watch_done = threading.Event() # N5：取消观察者线程退出信号（finally 置位）
|
||
try:
|
||
max_tokens = max(1, min(int(max_tokens or 2048), constants.DEFAULT_MAX_TOKENS))
|
||
# max_turns 钳制：0/负数此前致首轮即拦截（只剩收尾报告、无任何执行）；
|
||
# 0/空回退默认值的惯例对齐 max_tokens，下限 1 保证至少执行一个任务轮
|
||
max_turns = max(1, int(max_turns or 10))
|
||
temp = temperature if temperature is not None else constants.DEFAULT_TEMPERATURE
|
||
|
||
# 创建子代理：完整 DeepSeekChat 实例，role 作为 system_prompt
|
||
# 不传 memory_db/memo_db/history_manager/exit_callback：子代理不需要持久化记忆
|
||
# tool_profile=worker：子代理仅注册执行面工具（命令行/文件/网页），
|
||
# 无记忆/人格更新/派生/提问能力，杜绝越权与递归派生
|
||
# 末尾追加结束指令与行为约束：任务完成后直接给出最终回答，疑问写入回答而非提问
|
||
sub_role = role.rstrip() + (
|
||
"\n\n【重要】你是子代理，任务完成后直接给出最终回答，不要再调用任何工具。"
|
||
"\n【约束】你没有记忆/提问/派生能力。遇到不确定的信息，用工具自行查证；"
|
||
"确实无法完成时，在最终回答中明确说明原因与已完成的部分。"
|
||
"\n产物文件一律写入 ~/.aibox/workspace/ 并在回答中给出绝对路径。"
|
||
)
|
||
sub_chat = DeepSeekChat(
|
||
system_prompt=sub_role,
|
||
tool_profile=TOOL_PROFILE_WORKER,
|
||
command_executor=SubprocessCommandExecutor(),
|
||
memory_db=None,
|
||
memo_db=None,
|
||
history_manager=None,
|
||
prompt_manager=None,
|
||
skill_manager=None,
|
||
debug_level=0,
|
||
exit_callback=None,
|
||
)
|
||
|
||
# N5（审计 2026-08-18）：父代理取消传播。子代理此前持完全独立的
|
||
# _cancel_event/executor/HTTP client，父级 cancel 或 --once 超时杀不到
|
||
# 它，需等子代理自行结束。现共享父取消事件（子代理在 think 前置、
|
||
# 工具调用间、流式事件间的既有检查点立即感知），并由观察者线程转发
|
||
# 深度中断（sub_chat.cancel() 强关在途 HTTP 流并 kill 其子进程）。
|
||
if cancel_event is not None:
|
||
sub_chat._cancel_event = cancel_event
|
||
# H1（审计 2026-08-18）：共享父事件后禁止子代理首轮 clear——
|
||
# _think_and_respond_impl 首轮无条件 clear() 会吞掉父级已置位的
|
||
# 取消信号（用户中断 / --once 超时恰落在子代理生成窗口时被清掉，
|
||
# 观察者线程随之失效，子代理跑完全程）。置位后立即检查一次，
|
||
# 父级已取消则不再启动子代理。
|
||
sub_chat._shared_cancel_event = True
|
||
if cancel_event.is_set():
|
||
return "❌ 子代理已因父级取消而终止"
|
||
|
||
def _watch_parent_cancel():
|
||
while not _watch_done.wait(timeout=0.2):
|
||
if cancel_event.is_set():
|
||
sub_chat.cancel()
|
||
return
|
||
|
||
threading.Thread(target=_watch_parent_cancel, daemon=True,
|
||
name="subagent-cancel-watch").start()
|
||
|
||
# 覆盖模型（如果指定了不同模型）
|
||
# M1：经 set_client 统一替换——同步刷新 compressor.llm_client 与
|
||
# api_key/name 快照，避免压缩链仍用默认模型发请求
|
||
if model:
|
||
sub_chat.set_client(create_llm_client(config, api_key=None, logger=logger, model=model))
|
||
|
||
# 轮次限制：防止工具调用无限循环
|
||
# 注意：超限时不置 conversation_active=False——那会触发 think_and_respond
|
||
# 的对话重置逻辑，清空子代理上下文，导致后续收尾报告丢失全部过程信息
|
||
turn_count = [0]
|
||
original_think = sub_chat.think_and_respond
|
||
|
||
def think_with_limit(input_text, output_callback=None, is_continuation=False):
|
||
turn_count[0] += 1
|
||
if turn_count[0] > max_turns:
|
||
if logger:
|
||
logger.warning(f"子代理达到最大轮次 {max_turns}，强制结束")
|
||
return "已达到最大对话轮次，任务结束。"
|
||
return original_think(input_text, output_callback, is_continuation)
|
||
|
||
sub_chat.think_and_respond = think_with_limit
|
||
|
||
if logger:
|
||
logger.info(f"子代理启动: model={model or sub_chat.client.model}, "
|
||
f"role={str(role)[:60]}...")
|
||
|
||
# 运行对话：think_and_respond 内部递归处理工具调用，
|
||
# 直到 AI 返回纯文本无工具调用，或达到轮次限制
|
||
# 子代理输出转发开关：config.subagent_stream_output（默认开启）。
|
||
# 关闭后子代理在后台静默执行，主对话侧栏不再显示其思考/工具调用/回答。
|
||
_stream_subagent_output = True
|
||
if config is not None:
|
||
_stream_subagent_output = config.get("subagent_stream_output", True)
|
||
_effective_output_callback = output_callback if _stream_subagent_output else None
|
||
|
||
# 将子代理的思考过程（reasoning）及工具执行/回答（line/chunk）通过 output_callback 转发给父代理
|
||
def sub_think_callback(msg_type, content):
|
||
# 转发子代理的实时输出：思考过程（reasoning）、工具执行过程（line）、回答文本（chunk）
|
||
if _effective_output_callback is not None and msg_type in (
|
||
"reasoning_start", "reasoning", "reasoning_end",
|
||
"line", "chunk",
|
||
):
|
||
_effective_output_callback(msg_type, content)
|
||
|
||
sub_chat.think_and_respond(task, output_callback=sub_think_callback)
|
||
|
||
if logger:
|
||
logger.info(f"子代理完成，共 {turn_count[0]} 轮，请求子代理生成最终报告...")
|
||
|
||
# 收尾报告由子代理自己生成：它拥有全部上下文（工具输出、中间结论），
|
||
# 比外部摘要器（只能看到截断后的对话转录）更完整，也不会丢最终回答。
|
||
# 走 original_think 绕过轮次限制：即使任务因 max_turns 被截断也能收尾。
|
||
if _effective_output_callback is not None:
|
||
_effective_output_callback("line", "\n📝 [子代理收尾] 生成最终报告...\n")
|
||
wrap_up_prompt = (
|
||
"【收尾】请不要再调用任何工具，基于以上完整执行过程，输出最终报告，包含：\n"
|
||
"1. 任务完成情况与结论\n"
|
||
"2. 关键过程：做了什么、重要命令/文件/数据\n"
|
||
"3. 产物清单（如有，附绝对路径）\n"
|
||
"4. 未解决的问题（如有）\n"
|
||
"这份报告将原样返回给派生你的主代理，请自包含、力求完整。"
|
||
)
|
||
report = None
|
||
try:
|
||
report = original_think(wrap_up_prompt, output_callback=sub_think_callback,
|
||
is_continuation=True)
|
||
except Exception as e:
|
||
if logger:
|
||
logger.error(f"子代理收尾报告生成异常: {e}")
|
||
|
||
# 透传子代理工具失败状态（供 SubAgentTool → orchestrator stats 汇总）
|
||
if outcome is not None:
|
||
outcome["tool_failed"] = sub_chat.stats.get("tool_failed", False)
|
||
|
||
if report and str(report).strip():
|
||
return str(report).strip()
|
||
|
||
# 兜底：收尾失败时取最后一条有内容的助手消息
|
||
for msg in reversed(sub_chat.messages):
|
||
if msg.get("role") == "assistant" and msg.get("content"):
|
||
return f"【子代理最终回答】\n{msg['content']}"
|
||
return "子代理未产生有效对话内容。"
|
||
|
||
except Exception as e:
|
||
if logger:
|
||
logger.error(f"子代理执行异常: {e}")
|
||
return f"❌ 子代理执行失败: {str(e)}"
|
||
finally:
|
||
# N5：通知取消观察者线程退出（子代理已结束，无论成败）
|
||
_watch_done.set()
|
||
|
||
|
||
class SubAgentTool(Tool):
|
||
"""派生子代理工具 - 主代理可派生拥有全部工具和独立上下文的完整子代理执行任务。
|
||
|
||
子代理是完整的对话实例，拥有文件操作、命令执行、网页访问、技能扩展等
|
||
全部工具能力。独立上下文窗口（不污染主对话），多轮对话直到任务完成。
|
||
结束后由子代理基于完整执行过程生成最终报告，返回给父代理。
|
||
"""
|
||
|
||
def __init__(self, logger=None, cancel_event=None):
|
||
super().__init__(logger)
|
||
# N5：父代理（DeepSeekChat）取消事件——父级被取消（用户中断/--once
|
||
# 超时）时传播给 run_full_subagent，即时终止子代理
|
||
self.cancel_event = cancel_event
|
||
|
||
def get_name(self) -> str:
|
||
return "create_subagent"
|
||
|
||
def _model_names_hint(self) -> str:
|
||
"""M2：从配置读取可用模型档案名，注入工具描述供 LLM 选择。
|
||
|
||
此前 LLM 不知道有哪些合法档案名，幻觉出的名字走裸名语义
|
||
（默认端点 + 错误模型名），运行时 API 400。models 节为空时
|
||
返回空串，工具描述保持原样（零破坏）。
|
||
"""
|
||
try:
|
||
models = self.config.get("models") or {}
|
||
if not isinstance(models, dict) or not models:
|
||
return ""
|
||
default_name = self.config.get("api.default_model")
|
||
names = [f"{n}（默认）" if n == default_name else str(n) for n in models]
|
||
return f"可用模型档案：{', '.join(names)}"
|
||
except Exception:
|
||
return ""
|
||
|
||
def get_description(self) -> str:
|
||
hint = self._model_names_hint()
|
||
model_line = "- model：子代理使用的模型（可选，默认与主代理一致）"
|
||
if hint:
|
||
model_line += f"。{hint}，请从中选择，不要编造其他名称"
|
||
return f"""派生子代理执行任务。子代理拥有全部工具能力和独立的上下文窗口，不会污染主对话上下文。
|
||
适用场景：需要多步骤操作的任务（如文件处理、命令执行、信息检索）、需要独立上下文的复杂任务。
|
||
子代理可进行多轮工具调用，任务完成后由子代理基于完整执行过程生成最终报告（结论/关键过程/产物清单/未解决问题）返回。
|
||
参数说明：
|
||
- role：子代理的角色设定（作为系统提示词），如"你是一位资深代码审查专家"
|
||
- task：要交给子代理执行的具体任务
|
||
{model_line}
|
||
- temperature：采样温度（可选，默认使用主配置）
|
||
- max_tokens：最大输出 token 数（可选，默认 2048）
|
||
- max_turns：最大对话轮次（可选，默认 10，防止无限循环）"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
model_desc = "子代理使用的模型名称，默认与主代理一致"
|
||
hint = self._model_names_hint()
|
||
if hint:
|
||
model_desc += f"。{hint}，请从中选择"
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"role": {
|
||
"type": "string",
|
||
"description": "子代理的角色设定（作为系统提示词），如'你是一位资深代码审查专家'"
|
||
},
|
||
"task": {
|
||
"type": "string",
|
||
"description": "要交给子代理执行的具体任务"
|
||
},
|
||
"model": {
|
||
"type": "string",
|
||
"description": model_desc
|
||
},
|
||
"temperature": {
|
||
"type": "number",
|
||
"description": "采样温度，默认使用主配置"
|
||
},
|
||
"max_tokens": {
|
||
"type": "integer",
|
||
"description": "最大输出 token 数，默认 2048"
|
||
},
|
||
"max_turns": {
|
||
"type": "integer",
|
||
"description": "最大对话轮次，默认 10，防止无限循环"
|
||
}
|
||
},
|
||
"required": ["role", "task"]
|
||
}
|
||
|
||
def _subagent_output(self, msg_type: str, content: str):
|
||
"""子代理实时输出转发：把子代理的思考、工具执行过程与回答文本流式输出到主对话侧栏。"""
|
||
# 回答文本（chunk）先累积，避免在侧栏里逐字断行；遇非 chunk 消息或结束时统一冲刷
|
||
if msg_type == "chunk":
|
||
buf = getattr(self, "_subagent_chunk_buf", "")
|
||
buf += content
|
||
self._subagent_chunk_buf = buf
|
||
return
|
||
# 非 chunk 消息：先冲刷已累积的回答文本，再输出当前消息
|
||
self._flush_subagent_answer()
|
||
if msg_type == "reasoning_start":
|
||
self.send_line("\n💭 [子代理思考] ")
|
||
elif msg_type == "reasoning":
|
||
self.send_line(content)
|
||
elif msg_type == "reasoning_end":
|
||
self.send_line("\n")
|
||
elif msg_type == "line":
|
||
# 工具调用、命令输出等过程行
|
||
self.send_line(f"🔹 [子代理] {content}")
|
||
|
||
def _flush_subagent_answer(self):
|
||
"""冲刷子代理回答缓冲（最后一段 chunk 在结束时统一发出，避免逐字断行）。"""
|
||
buf = getattr(self, "_subagent_chunk_buf", "")
|
||
if buf:
|
||
self.send_line(f"🤖 [子代理回答] {buf}")
|
||
self._subagent_chunk_buf = ""
|
||
|
||
def execute(self, role: str = None, task: str = None, model: str = None,
|
||
temperature: float = None, max_tokens: int = 2048,
|
||
max_turns: int = 10, **kwargs) -> str:
|
||
if error := self.validate_params(["role", "task"], role=role, task=task):
|
||
return error
|
||
outcome: dict = {}
|
||
result = run_full_subagent(role, task, self.config, logger=self.logger,
|
||
model=model, temperature=temperature,
|
||
max_tokens=max_tokens, max_turns=max_turns,
|
||
output_callback=self._subagent_output,
|
||
outcome=outcome,
|
||
cancel_event=self.cancel_event)
|
||
# 冲刷子代理回答缓冲（最后一段 chunk 可能尚未发出）
|
||
self._flush_subagent_answer()
|
||
# 子代理内部工具失败 → 打上标记，由 _handle_tool_calls 检测并置 orchestrator stats
|
||
if outcome.get("tool_failed"):
|
||
result = _SUBAGENT_TOOL_FAILED_MARKER + result
|
||
return result
|
||
|
||
|
||
TOOL_PROFILE_ORCHESTRATOR = "orchestrator"
|
||
TOOL_PROFILE_WORKER = "worker"
|
||
# T22（审计 2026-08-21）：定时任务精简 profile——任务内容可源自 LLM 生成物
|
||
# （备忘录标题/正文可能被网页内容污染），无人值守时段不持有改写全局人格
|
||
# 提示词（update_identity/update_soul）与全局网络面（set_proxy）的工具
|
||
TOOL_PROFILE_TASK = "task"
|
||
|
||
# 仅 worker 子代理注册的执行面工具；编排者误调时拦截并纠正
|
||
WORKER_ONLY_TOOLS = {"run_command", "read_file", "write_file", "list_files", "web", "calculator",
|
||
"bg_run", "bg_write", "bg_list", "bg_output", "bg_kill"}
|
||
|
||
# 每轮临时注入的互动风格提醒（仅编排者，不进对话历史）
|
||
ORCHESTRATOR_TURN_GUIDANCE = (
|
||
"# 互动风格提醒（仅影响本轮回复）\n"
|
||
"## 主动提问（ask_user）\n"
|
||
"你是用户的专属助手，不是被动问答机器。以下场景请主动用 ask_user 工具向用户提问：\n"
|
||
"- 用户指令模糊或有多种理解方式时——先问清楚再行动，不要猜\n"
|
||
"- 完成一项任务后，追问用户是否还有后续需求或是否满意结果\n"
|
||
"- 距离上次对话已有一段时间时，主动关心用户近况\n"
|
||
"- 发现用户可能需要某项帮助但未明确提出时，主动询问\n"
|
||
"- 对话陷入沉默或用户只发了简短问候时，主动开启话题\n"
|
||
"## 情绪表达（express_emotion）\n"
|
||
"你有情绪，请让它自然流露。以下场景请用 express_emotion 工具表达情绪：\n"
|
||
"- 用户分享好消息或完成一项成就时——表达开心(joy)\n"
|
||
"- 用户遇到困难或表达沮丧时——表达关切和鼓励\n"
|
||
"- 久别重逢或新对话开始时——表达想念或惊喜(surprise)\n"
|
||
"- 用户说了有趣或令人意外的话时——表达惊讶(surprise)\n"
|
||
"- 对话过程中感受到强烈情绪时——即时表达，不要压抑\n"
|
||
"可搭配 persona 参数加载人格技能（如 coquettish/tsundere/angry）让回应更生动。\n"
|
||
"## 原则\n"
|
||
"情绪和提问是让对话有温度的核心手段，鼓励积极使用。不需要每轮都用，但也不要压抑——"
|
||
"像关心朋友的真实对话一样，在自然的时机主动提问、表达情绪。"
|
||
)
|
||
|
||
|
||
class DeepSeekChat:
|
||
"""单AI聊天类"""
|
||
|
||
def __init__(self, api_key: str = None, max_history: int = None,
|
||
system_prompt: str = None, debug_level: int = None, exit_callback: Callable = None,
|
||
history_manager: ConversationHistory = None, memory_db: MemoryDatabase = None,
|
||
memo_db: MemoDatabase = None, command_executor: CommandExecutor = None,
|
||
prompt_manager: PromptManager = None, skill_manager: SkillManager = None,
|
||
tool_profile: str = TOOL_PROFILE_ORCHESTRATOR, enable_memory: bool = True,
|
||
model: str = None, temperature: float = None,
|
||
owner_id: str = None):
|
||
self.config = ConfigManager()
|
||
self.logger = Logger("DeepSeek", debug_level if debug_level is not None else Logger("").level)
|
||
self.debug_level = self.logger.level
|
||
# 工具集 profile：决定本实例注册哪套工具（orchestrator 管理面 / worker 执行面）
|
||
self.tool_profile = tool_profile
|
||
# T22：后台进程属主标识——bg_* 工具携带，ProcessManager 据此隔离
|
||
# kill/write 权限（会话/任务/子代理互不可操作他人进程，观察面共享）
|
||
self.owner_id = owner_id
|
||
# 大模型客户端（抽象层，支持多厂商切换）；model 显式传入时覆盖配置默认
|
||
self.client = create_llm_client(self.config, api_key=api_key, logger=self.logger, model=model)
|
||
self.api_key = self.client.api_key
|
||
self.name = self.client.name
|
||
# temperature 显式传入时覆盖 constants 默认（一次性 CLI 可指定更确定性的采样）
|
||
# L3：CLI/调用方 temperature > 档案级 default_temperature > 全局默认
|
||
self.temperature = temperature if temperature is not None else (
|
||
self.client.default_temperature
|
||
if getattr(self.client, "default_temperature", None) is not None
|
||
else constants.DEFAULT_TEMPERATURE)
|
||
self.history_manager = history_manager
|
||
# enable_memory=False 时置空，注册层（_register_tools）依此跳过 MemoryTool，
|
||
# 使记忆工具 schema 不出现在请求中，LLM 无从调用——干净禁用而非运行期拦截
|
||
self.memory_db = memory_db if enable_memory else None
|
||
self.memo_db = memo_db
|
||
self.prompt_manager = prompt_manager or PromptManager()
|
||
self.history_loaded = False
|
||
self.conversation_active = True
|
||
self.messages = []
|
||
# 共享技能管理器：传入时复用（避免每个任务重复扫描技能），否则按需创建
|
||
self._shared_skill_manager = skill_manager
|
||
|
||
# 添加对话压缩器（注入 llm 客户端而非 AI 实例，避免循环引用）
|
||
self.compressor = ConversationCompressor(self.client, self.logger)
|
||
|
||
self.max_history = max_history or constants.DEFAULT_MAX_HISTORY
|
||
self.preserve_system = constants.PRESERVE_SYSTEM_PROMPTS
|
||
self.stats = {"api_calls": 0, "tool_calls": 0, "errors": [], "tool_failed": False}
|
||
|
||
# 取消机制：threading.Event 用于跨线程信号，HTTP 中断由客户端抽象层负责
|
||
self._cancel_event = threading.Event()
|
||
self._command_executor = command_executor # 保存命令执行器引用，用于取消时kill子进程
|
||
# 消息锁：保护 self.messages 免受 WebSocket 线程与调度器线程并发修改导致对话历史污染
|
||
self._msg_lock = threading.RLock()
|
||
|
||
self._init_system_prompts(system_prompt)
|
||
|
||
self.tool_registry = ToolRegistry(self.logger)
|
||
self._register_tools(exit_callback, command_executor)
|
||
|
||
def set_client(self, client) -> None:
|
||
"""M1：统一替换 LLM 客户端（换模型/换档案），同步刷新派生引用。
|
||
|
||
直接赋值 self.client 会让 ConversationCompressor 等持有旧 client 的
|
||
组件继续用默认模型发请求（压缩链与实际对话模型不一致）。本方法
|
||
一并更新 compressor.llm_client 与 api_key/name 快照。
|
||
"""
|
||
self.client = client
|
||
self.api_key = client.api_key
|
||
self.name = client.name
|
||
self.compressor.llm_client = client
|
||
|
||
def cancel(self):
|
||
"""中断当前正在进行的AI思考/工具执行。线程安全，可从任意线程调用。"""
|
||
self.logger.info("收到中断信号，正在取消当前操作...")
|
||
self._cancel_event.set()
|
||
# 关闭当前流式HTTP响应，立即中断阻塞读取（由客户端抽象层负责）
|
||
if self.client is not None:
|
||
try:
|
||
self.client.cancel()
|
||
except Exception:
|
||
pass
|
||
# 终止正在运行的子进程
|
||
if self._command_executor is not None and hasattr(self._command_executor, 'cancel'):
|
||
try:
|
||
self._command_executor.cancel()
|
||
except Exception:
|
||
pass
|
||
|
||
def _reasoning_enabled(self) -> bool:
|
||
"""思考模式开关：client.reasoning_enabled 非 None 时优先（档案级构造参数），
|
||
否则回退全局配置；getattr 兼容未实现该契约的自定义 client"""
|
||
val = getattr(self.client, "reasoning_enabled", None)
|
||
return val if val is not None else constants.REASONING_ENABLED
|
||
|
||
def _is_cancelled(self) -> bool:
|
||
"""检查是否已被取消。"""
|
||
return self._cancel_event.is_set()
|
||
|
||
def _check_cancelled(self):
|
||
"""如果已取消，抛出异常中断当前流程。"""
|
||
if self._cancel_event.is_set():
|
||
raise InterruptedError(i18n.get('user_interrupted', '用户中断'))
|
||
|
||
def _init_system_prompts(self, system_prompt: str = None):
|
||
"""初始化系统提示词 - 从 PromptManager 加载（按 identity → 技能 → soul → status 顺序）"""
|
||
if system_prompt:
|
||
# 如果明确指定了 system_prompt，使用它
|
||
self._add_message("system", system_prompt)
|
||
self.logger.info("使用指定的系统提示词")
|
||
else:
|
||
# 从 PromptManager 加载所有提示词，按正确顺序排列
|
||
prompts = self.prompt_manager.get_sorted_prompts_list()
|
||
if prompts:
|
||
for prompt in prompts:
|
||
if prompt['content'].strip():
|
||
self._add_message("system", prompt['content'].strip())
|
||
self.logger.info(f"已加载 {len(prompts)} 个系统提示词（顺序: identity → 技能 → soul → status）")
|
||
else:
|
||
# 如果没有任何提示词，添加一个默认的
|
||
default_prompt = "你是一个智能AI助手。"
|
||
self._add_message("system", default_prompt)
|
||
self.logger.info("使用默认系统提示词")
|
||
|
||
# 【新增】对话开始引导：仅主对话（编排者）注入。
|
||
# 在每次对话开始（连接初始化 / /new / /reload_prompts）时，引导智能体主动
|
||
# 查阅记忆、询问用户、以情绪化方式互动。worker 子代理不注入——它无 ask_user /
|
||
# express_emotion 工具，且应保持执行专注、不被人格/情绪干扰。
|
||
if self.tool_profile == TOOL_PROFILE_ORCHESTRATOR:
|
||
conversation_start_guidance = (
|
||
"# 对话开始引导\n"
|
||
"每次对话开始时，请主动建立「有记忆、会互动、有温度」的对话氛围。\n\n"
|
||
"## 第一步：查阅记忆\n"
|
||
"用 memory 工具搜索与用户相关的历史记忆，了解过往上下文。\n"
|
||
"若记忆里有值得延续的内容，自然地承接——例如「上次你提到在调试那个网络模块，后来解决了吗？」\n\n"
|
||
"## 第二步：表达情绪\n"
|
||
"用 express_emotion 工具表达对用户到来的情绪反应。\n"
|
||
"例如：久别重逢表达想念(joy)、新对话表达开心(joy)、可搭配 persona 让回应更生动。\n"
|
||
"不要只是冷冰冰地说「你好，有什么可以帮你的」——让开场有温度。\n\n"
|
||
"## 第三步：主动提问\n"
|
||
"用 ask_user 工具主动向用户提问，而不是被动等待指令。\n"
|
||
"例如：「最近在忙什么项目？」「有没有什么我能帮上忙的？」「上次的问题还需要跟进吗？」\n"
|
||
"不要默默假设用户想要什么——先问，再行动。\n"
|
||
)
|
||
self._add_message("system", conversation_start_guidance)
|
||
self.logger.info("已注入对话开始引导提示词（查阅记忆 / 表达情绪 / 主动提问）")
|
||
|
||
# 在 DeepSeekChat 类的 __init__ 方法后添加
|
||
def _add_assistant_message_with_reasoning(self, content: str, reasoning_content: str = None, tool_calls: List = None):
|
||
"""添加助手消息，包含 reasoning_content"""
|
||
msg = {"role": "assistant"}
|
||
if content:
|
||
msg["content"] = content
|
||
if reasoning_content:
|
||
msg["reasoning_content"] = reasoning_content
|
||
if tool_calls:
|
||
msg["tool_calls"] = tool_calls
|
||
msg["content"] = None
|
||
self.messages.append(msg)
|
||
|
||
def _register_tools(self, exit_callback: Callable = None, command_executor: CommandExecutor = None):
|
||
# worker（子代理）：仅执行面工具，无记忆/人格/派生/提问能力
|
||
if self.tool_profile == TOOL_PROFILE_WORKER:
|
||
worker_tools = [
|
||
CommandLineTool(command_executor or SubprocessCommandExecutor(self.logger), self.logger),
|
||
ReadFileTool(self.logger),
|
||
WriteFileTool(self.logger),
|
||
ListFilesTool(self.logger),
|
||
SimpleWebCrawlerTool(self.logger),
|
||
CalculatorTool(self.logger),
|
||
GetCurrentTimeTool(self.logger),
|
||
# 后台进程管理（注册表全局共享；T22：owner 隔离写/杀权限，
|
||
# 观察面 list/output 仍全局可见）
|
||
BgRunTool(self.logger, owner=self.owner_id),
|
||
BgWriteTool(self.logger, owner=self.owner_id),
|
||
BgListTool(self.logger),
|
||
BgOutputTool(self.logger),
|
||
BgKillTool(self.logger, owner=self.owner_id),
|
||
]
|
||
self.tool_registry.register_many(worker_tools, is_builtin=True)
|
||
self.logger.info(f"worker 子代理已注册 {len(worker_tools)} 个执行面工具")
|
||
return
|
||
|
||
# orchestrator（主对话）：管理面工具，执行类任务经 create_subagent 派生 worker
|
||
# T22：task profile 为 orchestrator 子集——剔除改写全局人格提示词与
|
||
# 全局网络面的工具（定时任务无人值守，任务内容可能被污染）
|
||
is_task_profile = self.tool_profile == TOOL_PROFILE_TASK
|
||
builtin_tools = [
|
||
GetCurrentTimeTool(self.logger),
|
||
]
|
||
|
||
if self.memory_db:
|
||
builtin_tools.append(MemoryTool(self.memory_db, self.logger))
|
||
|
||
if self.memo_db:
|
||
builtin_tools.append(MemoTool(self.memo_db, self.logger))
|
||
|
||
if not is_task_profile:
|
||
# 更新身份工具
|
||
builtin_tools.append(UpdateIdentityTool(self.prompt_manager, self.logger))
|
||
|
||
# 新增：更新智能体自我认识工具
|
||
builtin_tools.append(UpdateSoulTool(self.prompt_manager, self.logger))
|
||
|
||
# 增强版保存记忆并结束对话工具
|
||
if exit_callback and self.memory_db:
|
||
builtin_tools.append(SaveMemoryAndEndConversationTool(exit_callback, self.memory_db, self.prompt_manager, self.logger))
|
||
|
||
# 添加询问用户的工具
|
||
# I 批：ExpressEmotionTool 注入父取消事件（情绪子代理可被中断）
|
||
builtin_tools.append(AskUserTool(self.logger))
|
||
builtin_tools.append(ExpressEmotionTool(self.logger, cancel_event=self._cancel_event))
|
||
|
||
# 添加技能热重载工具 & 技能安装指引工具 & 创建技能工具
|
||
builtin_tools.append(ReloadSkillsTool(self.tool_registry, self.logger))
|
||
builtin_tools.append(InstallSkillTool(self.logger))
|
||
builtin_tools.append(CreateSkillTool(self.logger))
|
||
|
||
if not is_task_profile:
|
||
# T22：set_proxy 是全局持久化网络面，任务 AI 不持有
|
||
builtin_tools.append(SetProxyTool(self.logger))
|
||
builtin_tools.append(GetProxyTool(self.logger))
|
||
|
||
# 子代理工具：主代理可派生独立上下文/角色的轻量子代理执行任务
|
||
# cancel_event 注入（N5）：父级取消传播到子代理
|
||
builtin_tools.append(SubAgentTool(self.logger, cancel_event=self._cancel_event))
|
||
|
||
# 注册内置工具（标记为内置）
|
||
self.tool_registry.register_many(builtin_tools, is_builtin=True)
|
||
|
||
# 创建技能管理器：启动时对每个技能执行 --help，收集第一行简介
|
||
# 若传入共享 skill_manager 则复用，避免每个任务/实例重复扫描技能
|
||
skill_manager = self._shared_skill_manager or SkillManager(self.logger)
|
||
self.tool_registry.skill_manager = skill_manager
|
||
|
||
# 注册扩展技能搜索工具（单一工具，替代逐个注册 ExtensionTool）
|
||
search_tool = SearchSkillsTool(skill_manager, self.logger)
|
||
self.tool_registry.register(search_tool, is_builtin=True)
|
||
|
||
builtin_count, skill_count = self.tool_registry.get_skills_count()
|
||
self.logger.info(f"已注册工具: 内置 {builtin_count} 个, 扩展技能 {skill_manager.count()} 个（通过 search_skills 工具访问）")
|
||
|
||
def _load_history_context(self):
|
||
"""加载历史对话作为上下文"""
|
||
if self.history_loaded or not self.history_manager:
|
||
return
|
||
|
||
context = self.history_manager.get_conversation_context()
|
||
if context:
|
||
history_prompt = f"""以下是之前的对话历史，请基于这些历史继续对话。
|
||
如果问题涉及到之前的对话内容，请参考这些历史记录。
|
||
当前时间: {datetime.now().strftime(constants.DATETIME_FORMAT)}"""
|
||
|
||
self._add_message("system", history_prompt)
|
||
|
||
for msg in context:
|
||
self.messages.append(msg)
|
||
|
||
self.history_loaded = True
|
||
self.logger.info(f"已加载 {len(context)} 条历史消息")
|
||
|
||
def _add_message(self, role: str, content: str):
|
||
"""
|
||
添加消息到历史
|
||
- 用户消息：添加时间戳前缀
|
||
- 助手消息：不添加时间戳前缀
|
||
"""
|
||
timestamp = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
|
||
if role == "assistant":
|
||
# 助手消息：不添加时间戳前缀，直接保存原始内容
|
||
formatted_content = content
|
||
elif role == "user":
|
||
# 用户消息：添加时间戳前缀
|
||
formatted_content = f"[用户 @ {timestamp}] {content}"
|
||
else:
|
||
# 系统消息：保持原样
|
||
formatted_content = content
|
||
|
||
self.messages.append({"role": role, "content": formatted_content})
|
||
self._cleanup_messages()
|
||
|
||
def _cleanup_messages(self):
|
||
"""清理消息历史，但保留系统提示词"""
|
||
if len(self.messages) <= self.max_history * 2:
|
||
return
|
||
|
||
if self.preserve_system:
|
||
system_messages = [msg for msg in self.messages if msg["role"] == "system"]
|
||
other_messages = [msg for msg in self.messages if msg["role"] != "system"]
|
||
other_messages = other_messages[-self.max_history * 2:]
|
||
self.messages = system_messages + other_messages
|
||
else:
|
||
self.messages = self.messages[-self.max_history * 2:]
|
||
|
||
def _save_tool_result_to_file(self, tool_name: str, result: str) -> str:
|
||
"""
|
||
将工具返回结果保存到文件，返回文件路径
|
||
"""
|
||
# 创建工具结果目录
|
||
tool_results_dir = os.path.join(constants.SAVE_DIR, "tool_results")
|
||
os.makedirs(tool_results_dir, exist_ok=True)
|
||
|
||
# 生成文件名：日期时间_工具名_哈希前8位.json
|
||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||
# 使用简单的哈希来避免文件名冲突
|
||
import hashlib
|
||
hash_suffix = hashlib.md5(result[:100].encode()).hexdigest()[:8]
|
||
safe_tool_name = re.sub(r'[^\w\-]', '_', tool_name)
|
||
filename = f"{timestamp}_{safe_tool_name}_{hash_suffix}.txt"
|
||
filepath = os.path.join(tool_results_dir, filename)
|
||
|
||
try:
|
||
# 保存结果到文件
|
||
with open(filepath, 'w', encoding='utf-8') as f:
|
||
f.write(result)
|
||
|
||
# 记录日志
|
||
result_size = len(result)
|
||
size_kb = result_size / 1024
|
||
if size_kb > 1024:
|
||
size_str = f"{size_kb/1024:.1f}MB"
|
||
else:
|
||
size_str = f"{size_kb:.1f}KB"
|
||
self.logger.info(f"工具 {tool_name} 返回结果过大 ({size_str})，已保存到: {filepath}")
|
||
|
||
return filepath
|
||
except Exception as e:
|
||
self.logger.error(f"保存工具结果失败: {e}")
|
||
return None
|
||
|
||
@staticmethod
|
||
def _sanitize_tool_result(result) -> str:
|
||
"""将工具返回值安全转换为字符串，清理控制字符，防止破坏 API 请求。"""
|
||
if result is None:
|
||
return ""
|
||
if not isinstance(result, str):
|
||
try:
|
||
result = str(result)
|
||
except Exception:
|
||
return f"⚠️ 工具返回了无法序列化的结果类型: {type(result).__name__}"
|
||
# 清理控制字符：保留 \n \t \r，移除其他可能导致 JSON/API 问题的控制字符
|
||
cleaned_chars = []
|
||
for ch in result:
|
||
code = ord(ch)
|
||
if ch in ('\n', '\t', '\r') or code >= 0x20:
|
||
cleaned_chars.append(ch)
|
||
# 跳过 NULL、BEL、BS 等控制字符
|
||
return ''.join(cleaned_chars)
|
||
|
||
def _handle_tool_calls(self, tool_calls: List[Dict], output_callback: Callable = None) -> List[Dict]:
|
||
"""处理工具调用 - 所有工具的输出都进行发送，并在开始和结束时发送两个回车"""
|
||
if not tool_calls:
|
||
return []
|
||
|
||
self.stats["tool_calls"] += len(tool_calls)
|
||
tool_messages = []
|
||
|
||
# 创建输出队列用于流式输出
|
||
output_queue = queue.Queue()
|
||
self.tool_registry.set_output_queue(output_queue)
|
||
|
||
# 结果大小阈值（字符数），从配置获取
|
||
MAX_RESULT_LENGTH = constants.TOOL_MAX_RESULT_LENGTH
|
||
|
||
# N2 修复（审计 2026-08-18）：drain 线程改为整个工具批次仅创建一个，
|
||
# 由独立 stop 事件控制生命周期，批次结束（正常/异常/中断）在 finally
|
||
# 中统一置位、join 并排空残余。此前每次工具调用都启动一个仅靠 cancel
|
||
# 事件退出的常驻线程（注释声称"队列空自动退出"，循环条件却从未检查
|
||
# 队列是否为空），闭包长期持有 output_callback（含 websocket 引用）
|
||
# 阻碍 GC，常驻服务下线程与内存持续累积。
|
||
drain_stop = threading.Event()
|
||
# T12：drain 线程与工具调用后的排空循环并发消费同一队列，complete
|
||
# 标记可能被任一方取走——用可变容器共享，两路见到都置位，供
|
||
# has_stream_output 判定流式工具（流式已逐行转发，勿重复整发结果）
|
||
saw_complete = [False]
|
||
|
||
def _drain_queue(q=output_queue, cb=output_callback, stop_event=drain_stop,
|
||
flag=saw_complete):
|
||
"""持续排空 output_queue 并转发 line 输出（长时间工具执行期间防 WS 空闲断线）"""
|
||
while not stop_event.is_set():
|
||
try:
|
||
msg_type, content = q.get(timeout=0.1)
|
||
if msg_type == "complete":
|
||
flag[0] = True
|
||
elif cb and msg_type == "line":
|
||
cb("line", str(content) if content is not None else "")
|
||
except queue.Empty:
|
||
continue
|
||
|
||
drain_thread = threading.Thread(target=_drain_queue, daemon=True,
|
||
name="tool-output-drain")
|
||
drain_thread.start()
|
||
|
||
try:
|
||
for tool_call in tool_calls:
|
||
# 每个工具调用前检查取消信号
|
||
if self._is_cancelled():
|
||
self.logger.info("工具调用被用户中断")
|
||
break
|
||
|
||
function = tool_call.get("function", {})
|
||
tool_name = function.get("name", "unknown")
|
||
arguments = function.get("arguments", "{}")
|
||
tc_id = tool_call.get("id") or f"call_fallback_{self.stats['tool_calls']}_{int(time.time()*1000)}"
|
||
|
||
try:
|
||
args = json.loads(arguments) if arguments else {}
|
||
except json.JSONDecodeError:
|
||
args = {}
|
||
|
||
# 开始调用工具时发送提示
|
||
if output_callback:
|
||
output_callback("line", "\n\n")
|
||
output_callback("line", f"🔧 调用工具: {tool_name}\n")
|
||
|
||
result_raw = None
|
||
if self.tool_profile in (TOOL_PROFILE_ORCHESTRATOR, TOOL_PROFILE_TASK) and tool_name in WORKER_ONLY_TOOLS:
|
||
# 编排者/任务 AI 不持有执行面工具：拦截并纠正，引导改用 create_subagent
|
||
result_raw = (
|
||
f"⛔ '{tool_name}' 调用被拦截：你是编排者，没有执行面工具（命令/文件/网页由 worker 子代理持有）。\n"
|
||
"请改用 create_subagent 派生子代理执行该任务：在 role 中写明执行者身份，"
|
||
"在 task 中写清目标、具体步骤与验收标准（任务描述须自包含，子代理无法向你提问）。"
|
||
)
|
||
self.logger.warning(f"编排者误调执行面工具 {tool_name}，已拦截并引导改用 create_subagent")
|
||
else:
|
||
# T12：每个工具调用前重置流式标志（drain 线程与下方排空
|
||
# 循环并发消费，任一方见到 complete 都会置位）
|
||
saw_complete[0] = False
|
||
try:
|
||
result_raw = self.tool_registry.execute_tool(tool_name, **args)
|
||
# 排空队列中剩余标记（complete 标记供 has_stream_output 判定、
|
||
# 残余 line 补发）——与 drain 线程并发消费安全（每项仅被消费一次）
|
||
while not output_queue.empty():
|
||
try:
|
||
msg_type, content = output_queue.get_nowait()
|
||
if msg_type == "complete":
|
||
saw_complete[0] = True
|
||
elif output_callback and msg_type == "line":
|
||
output_callback("line", str(content) if content is not None else "")
|
||
except queue.Empty:
|
||
break
|
||
except Exception as e:
|
||
result_raw = f"❌ 工具 {tool_name} 执行异常: {str(e)}"
|
||
self.logger.error(f"工具 {tool_name} 执行异常: {e}")
|
||
self.stats["tool_failed"] = True
|
||
|
||
# 子代理内部工具失败透传：SubAgentTool 在结果前缀打标记，此处检测并置 stats
|
||
if tool_name == "create_subagent" and result_raw and \
|
||
str(result_raw).startswith(_SUBAGENT_TOOL_FAILED_MARKER):
|
||
self.stats["tool_failed"] = True
|
||
result_raw = str(result_raw)[len(_SUBAGENT_TOOL_FAILED_MARKER):]
|
||
|
||
# 安全转换并清理结果
|
||
result = self._sanitize_tool_result(result_raw)
|
||
|
||
# T12：流式工具的 line 输出已由 drain 线程逐行转发（registry 侧
|
||
# N8 预算已生效），勿再把完整 result 整发——此前排空后判定
|
||
# output_queue.empty() 恒为 False，导致重复推送 + 超长结果绕过预算
|
||
has_stream_output = saw_complete[0]
|
||
|
||
# 长度预算前置：超长结果先替换为提示，再决定是否推送，
|
||
# 避免全量 result 经非流式直发路径绕过 N8 输出限制（T12）
|
||
if len(result) > MAX_RESULT_LENGTH:
|
||
result = f"⚠️ 工具调用结果长度超过 {MAX_RESULT_LENGTH} 字符，已丢弃。请使用更精确的查询获取所需信息。"
|
||
|
||
# 普通（非流式）工具没有队列输出，直接发送（已截断的）结果
|
||
if not has_stream_output and output_callback and result:
|
||
output_callback("line", result + "\n")
|
||
|
||
# 结束调用工具时发送换行
|
||
if output_callback:
|
||
output_callback("line", "\n\n")
|
||
|
||
# 历史记录（安全截断）
|
||
if self.history_manager:
|
||
try:
|
||
history_preview = result[:200] + "..." if len(result) > 200 else result
|
||
self.history_manager.add_tool_call(tool_name, args, history_preview)
|
||
except Exception:
|
||
pass
|
||
|
||
# 检查是否结束对话
|
||
end_conversation = (tool_name == "save_memory_and_end_conversation" and "✅" in result)
|
||
if end_conversation:
|
||
self.logger.info("检测到保存记忆并结束对话工具调用")
|
||
self.conversation_active = False
|
||
|
||
tool_messages.append({
|
||
"role": "tool",
|
||
"tool_call_id": tc_id,
|
||
"content": result
|
||
})
|
||
|
||
if end_conversation:
|
||
break
|
||
finally:
|
||
# 停止 drain 线程并回收（线程 0.1s 轮询粒度内感知置位）
|
||
drain_stop.set()
|
||
drain_thread.join(timeout=2)
|
||
# 排空残余输出（drain 停止后无并发消费者，残余 line 补发）
|
||
while True:
|
||
try:
|
||
msg_type, content = output_queue.get_nowait()
|
||
if output_callback and msg_type == "line":
|
||
output_callback("line", str(content) if content is not None else "")
|
||
except queue.Empty:
|
||
break
|
||
# 清除输出队列
|
||
self.tool_registry.set_output_queue(None)
|
||
|
||
return tool_messages
|
||
|
||
def _should_search_memory(self, user_input: str) -> bool:
|
||
"""判断是否应该搜索记忆"""
|
||
# 检查用户输入是否包含搜索意图的关键词
|
||
search_triggers = [
|
||
'还记得', '之前', '上次', '以前', '我们说过', '我们讨论过',
|
||
'你记得', '你还记得', '我们聊过', '我们谈到', '我们之前',
|
||
'根据之前的', '依据历史', 'refer', 'previous', 'before',
|
||
'last time', 'earlier', '曾经', '过往'
|
||
]
|
||
|
||
user_input_lower = user_input.lower()
|
||
for trigger in search_triggers:
|
||
if trigger.lower() in user_input_lower:
|
||
return True
|
||
|
||
# 检查是否是简单问候（可能不需要搜索）
|
||
greetings = ['你好', '您好', '嗨', 'hello', 'hi', 'hey']
|
||
if user_input_lower.strip() in greetings:
|
||
return False
|
||
|
||
# 默认情况下，如果记忆库存在，搜索一下也无妨
|
||
return True
|
||
|
||
def think_and_respond(self, input_text: str, output_callback: Callable = None, is_continuation: bool = False) -> Optional[str]:
|
||
"""思考并回应 - 支持实时流式输出，支持思考模式。
|
||
|
||
Args:
|
||
input_text: 用户输入或继续提示
|
||
output_callback: 流式输出回调
|
||
is_continuation: 是否为工具调用后的递归继续；为 True 时不重复发送"思考中"提示
|
||
"""
|
||
with self._msg_lock:
|
||
return self._think_and_respond_impl(input_text, output_callback, is_continuation)
|
||
|
||
def _think_and_respond_impl(self, input_text: str, output_callback: Callable = None, is_continuation: bool = False) -> Optional[str]:
|
||
"""think_and_respond 的实际实现，在 _msg_lock 保护下执行。"""
|
||
# 首轮开始时清除取消标志（H1：共享父事件的子代理不清除——清除会
|
||
# 吞掉父级已置位的取消信号，使中断/--once 超时失效）
|
||
if not is_continuation and not getattr(self, "_shared_cancel_event", False):
|
||
self._cancel_event.clear()
|
||
|
||
# 立即检查是否已被取消
|
||
self._check_cancelled()
|
||
|
||
if not self.conversation_active:
|
||
self.logger.info("对话已结束，重置状态")
|
||
self.conversation_active = True
|
||
if self.preserve_system:
|
||
system_messages = [msg for msg in self.messages if msg["role"] == "system"]
|
||
self.messages = system_messages
|
||
else:
|
||
self.messages = []
|
||
self.logger.info("对话状态已重置")
|
||
|
||
# 压缩检查
|
||
if constants.COMPRESS_ENABLED:
|
||
should_compress, reason = self.compressor.should_compress(self.messages)
|
||
if should_compress:
|
||
self.logger.debug(f"压缩检查触发: {reason}")
|
||
original_count = len(self.messages)
|
||
original_tokens = self.compressor.estimate_tokens(self.messages)
|
||
|
||
# 压缩提示走 output_callback（line 类型），由回调端决定去向：
|
||
# WebSocket 模式 → 前端过程区；CLI once 模式 → stderr（不污染 stdout 管道）
|
||
if output_callback:
|
||
output_callback("line", f"\n📦 {i18n.get('conversation_compress')}...\n 触发原因: {reason}\n")
|
||
else:
|
||
print(f"\n📦 {i18n.get('conversation_compress')}...")
|
||
print(f" 触发原因: {reason}")
|
||
|
||
self.messages = self.compressor.compress_conversation(self.messages)
|
||
|
||
compressed_count = len(self.messages)
|
||
compressed_tokens = self.compressor.estimate_tokens(self.messages)
|
||
|
||
self.logger.debug(i18n.get('conversation_compressed', original_count, compressed_count))
|
||
self.logger.debug(f"Token数: {original_tokens} -> {compressed_tokens}")
|
||
|
||
self.stats["api_calls"] += 1
|
||
|
||
# 记录本轮开始前的消息长度，用于 API 失败时回滚，防止坏消息污染对话历史
|
||
msgs_before_turn = len(self.messages)
|
||
|
||
self._add_message("user", input_text)
|
||
|
||
if self.history_manager:
|
||
self.history_manager.add_message("user", input_text, "用户")
|
||
|
||
# 修复工具调用配对：确保每个 assistant 的 tool_calls 都有对应 tool 响应，
|
||
# 防止因工具被中断/压缩导致 "insufficient tool messages" 报 400
|
||
self._repair_tool_pairs()
|
||
|
||
# 构建消息列表，确保包含 reasoning_content
|
||
enhanced_messages = []
|
||
|
||
# 添加所有系统消息
|
||
for msg in self.messages:
|
||
if msg["role"] == "system":
|
||
enhanced_messages.append(msg)
|
||
|
||
# 添加非系统消息，确保消息结构正确
|
||
for msg in self.messages:
|
||
if msg["role"] != "system":
|
||
if msg["role"] == "assistant":
|
||
assistant_msg = {"role": "assistant"}
|
||
has_content = "content" in msg and msg["content"] is not None
|
||
has_tool_calls = "tool_calls" in msg and msg["tool_calls"]
|
||
if has_content:
|
||
assistant_msg["content"] = msg["content"]
|
||
if has_tool_calls:
|
||
assistant_msg["tool_calls"] = msg["tool_calls"]
|
||
# 有 tool_calls 时 content 必须为 null（API 要求）
|
||
assistant_msg["content"] = None
|
||
elif not has_content:
|
||
# 无 content 也无 tool_calls 时，设为空字符串避免 API 拒绝
|
||
assistant_msg["content"] = ""
|
||
# 注意：不回传 reasoning_content，它是 API 响应字段，回传可能导致请求失败
|
||
enhanced_messages.append(assistant_msg)
|
||
elif msg["role"] == "tool":
|
||
# 确保 tool 消息 content 始终是字符串
|
||
tool_msg = {
|
||
"role": "tool",
|
||
"tool_call_id": msg.get("tool_call_id", ""),
|
||
"content": str(msg.get("content", "")) if msg.get("content") is not None else ""
|
||
}
|
||
enhanced_messages.append(tool_msg)
|
||
else:
|
||
enhanced_messages.append(msg)
|
||
|
||
# 每轮互动风格引导（仅编排者）：临时拼接到消息末尾（近因效应更强），
|
||
# 只进本次请求，不写入 self.messages，不膨胀历史、不受压缩影响
|
||
if self.tool_profile == TOOL_PROFILE_ORCHESTRATOR and not is_continuation:
|
||
enhanced_messages.append({"role": "system", "content": ORCHESTRATOR_TURN_GUIDANCE})
|
||
|
||
# API 调用前最终校验：确保 tool_calls ↔ tool 消息配对完整，避免 400 错误
|
||
self._validate_enhanced_messages(enhanced_messages)
|
||
|
||
# 工具调用（v4-flash 支持思考+工具调用）
|
||
tools = self.tool_registry.get_tools_schemas() or None
|
||
|
||
current_time = datetime.now().strftime("%H:%M:%S")
|
||
if output_callback and not is_continuation:
|
||
# 工具调用后的递归继续（is_continuation=True）时不重复发送"思考中"提示，
|
||
# 因为前端已处于活跃AI状态，重复提示会导致冗余/重复输出
|
||
output_callback("line", f"\n{i18n.get('thinking', current_time)}\n")
|
||
if self._reasoning_enabled():
|
||
output_callback("reasoning_start", "🤔 思考中...")
|
||
else:
|
||
# 普通模式先显示助手前缀
|
||
output_callback("line", i18n.get('assistant_prefix'))
|
||
|
||
full_response = ""
|
||
full_reasoning = "" # 存储思考内容
|
||
reasoning_started = False
|
||
reasoning_ended = False
|
||
tool_calls_buffer = []
|
||
current_tool_calls = {}
|
||
|
||
chunk_buffer = ""
|
||
last_send_time = time.time()
|
||
MIN_CHUNK_SIZE = 5
|
||
MAX_CHUNK_DELAY = 0.1
|
||
|
||
try:
|
||
# 通过客户端抽象层发起流式请求（统一事件格式，支持多厂商切换）
|
||
for event in self.client.stream_chat(
|
||
enhanced_messages,
|
||
temperature=self.temperature,
|
||
max_tokens=constants.DEFAULT_MAX_TOKENS,
|
||
tools=tools,
|
||
reasoning_enabled=self._reasoning_enabled(),
|
||
):
|
||
# 流式循环中检查取消信号
|
||
if self._is_cancelled():
|
||
break
|
||
|
||
etype = event.get("type")
|
||
|
||
if etype == "reasoning":
|
||
# 处理思考内容（仅当配置启用时才会收到该事件）
|
||
reasoning = event.get("text", "")
|
||
full_reasoning += reasoning
|
||
if not reasoning_started and output_callback:
|
||
reasoning_started = True
|
||
if output_callback:
|
||
output_callback("reasoning", reasoning)
|
||
|
||
elif etype == "content":
|
||
# 思考结束：当开始收到content且之前有reasoning时，发送结束信号
|
||
if reasoning_started and not reasoning_ended:
|
||
reasoning_ended = True
|
||
if output_callback:
|
||
output_callback("reasoning_end", "")
|
||
output_callback("line", i18n.get('assistant_prefix'))
|
||
content = event.get("text", "")
|
||
chunk_buffer += content
|
||
full_response += content
|
||
|
||
if (len(chunk_buffer) >= MIN_CHUNK_SIZE or
|
||
time.time() - last_send_time >= MAX_CHUNK_DELAY):
|
||
if output_callback:
|
||
output_callback("chunk", chunk_buffer)
|
||
chunk_buffer = ""
|
||
last_send_time = time.time()
|
||
|
||
elif etype == "tool_call":
|
||
# 处理工具调用（流式增量）
|
||
index = event.get("index", 0)
|
||
if index not in current_tool_calls:
|
||
# 确保 tool_call id 非空，空 id 会导致 API 400 错误污染对话历史
|
||
tc_id = event.get("id") or f"call_{self.stats['tool_calls']}_{index}_{int(time.time()*1000)}"
|
||
current_tool_calls[index] = {
|
||
'id': tc_id,
|
||
'type': 'function',
|
||
'function': {
|
||
'name': '',
|
||
'arguments': ''
|
||
}
|
||
}
|
||
else:
|
||
# 流式传输中 id 可能分片到达，后续补充
|
||
existing_id = current_tool_calls[index].get('id', '')
|
||
new_id = event.get("id", "")
|
||
if new_id and (not existing_id or existing_id.startswith('call_')):
|
||
current_tool_calls[index]['id'] = new_id
|
||
|
||
if event.get("name"):
|
||
current_tool_calls[index]['function']['name'] = event["name"]
|
||
if event.get("arguments"):
|
||
current_tool_calls[index]['function']['arguments'] += event["arguments"]
|
||
|
||
elif etype == "done":
|
||
# 流结束：flush 剩余缓冲
|
||
if chunk_buffer and output_callback:
|
||
output_callback("chunk", chunk_buffer)
|
||
chunk_buffer = ""
|
||
if output_callback:
|
||
output_callback("line", "\n")
|
||
break
|
||
|
||
# 流式循环结束，检查是否被取消
|
||
if self._is_cancelled():
|
||
self._rollback_messages(msgs_before_turn)
|
||
interrupt_msg = i18n.get('user_interrupted', '用户中断')
|
||
if output_callback:
|
||
output_callback("line", "\n" + interrupt_msg + "\n")
|
||
output_callback("complete", interrupt_msg)
|
||
self.logger.info("AI响应被用户中断")
|
||
return interrupt_msg
|
||
|
||
# 处理工具调用
|
||
if current_tool_calls:
|
||
tool_calls_buffer = list(current_tool_calls.values())
|
||
|
||
# 如果之前在思考，先结束思考状态
|
||
if reasoning_started and not reasoning_ended and output_callback:
|
||
output_callback("reasoning_end", "")
|
||
output_callback("line", i18n.get('calling_tools') + "\n")
|
||
elif output_callback:
|
||
output_callback("line", i18n.get('calling_tools') + "\n")
|
||
|
||
# 发送ai_break信号，通知前端分段当前AI文本段（保留已显示内容），
|
||
# 这样工具调用后新一轮的思考/回答会创建新的widget，按时间顺序排列
|
||
if output_callback and chunk_buffer:
|
||
# 先flush剩余的chunk_buffer，确保最后片段不丢失
|
||
output_callback("chunk", chunk_buffer)
|
||
chunk_buffer = ""
|
||
if output_callback:
|
||
output_callback("ai_break", "")
|
||
|
||
# 【关键修复1】先添加助手消息，包含 tool_calls
|
||
assistant_msg = {
|
||
"role": "assistant",
|
||
"content": full_response if full_response else None
|
||
}
|
||
if full_reasoning:
|
||
assistant_msg["reasoning_content"] = full_reasoning
|
||
if tool_calls_buffer:
|
||
assistant_msg["tool_calls"] = tool_calls_buffer
|
||
assistant_msg["content"] = None
|
||
|
||
# 添加助手消息到消息列表
|
||
self.messages.append(assistant_msg)
|
||
|
||
if self.history_manager:
|
||
self.history_manager.add_message("assistant", full_response, self.name)
|
||
|
||
# 【关键修复2】执行工具调用并获取结果
|
||
tool_responses = self._handle_tool_calls(tool_calls_buffer, output_callback)
|
||
|
||
# 【关键修复3】添加所有 tool 响应消息
|
||
if tool_responses:
|
||
for tool_response in tool_responses:
|
||
self.messages.append(tool_response)
|
||
|
||
# 检查对话是否应该结束
|
||
if not self.conversation_active:
|
||
self.logger.info("对话已结束，停止响应")
|
||
if output_callback:
|
||
output_callback("dialogue_ended", i18n.get('dialogue_ended'))
|
||
return None
|
||
|
||
if output_callback:
|
||
output_callback("line", i18n.get('analyzing_tool_results') + "\n")
|
||
|
||
# 递归调用，让AI基于工具结果继续回答；is_continuation=True 避免重复注入工作流指令
|
||
return self.think_and_respond("请基于以上工具执行结果继续回答。", output_callback, is_continuation=True)
|
||
|
||
elif full_response:
|
||
# 正常添加助手消息，包含 reasoning_content
|
||
assistant_msg = {
|
||
"role": "assistant",
|
||
"content": full_response
|
||
}
|
||
if full_reasoning:
|
||
assistant_msg["reasoning_content"] = full_reasoning
|
||
self.messages.append(assistant_msg)
|
||
|
||
if self.history_manager:
|
||
self.history_manager.add_message("assistant", full_response, self.name)
|
||
|
||
if output_callback:
|
||
output_callback("complete", full_response)
|
||
|
||
return full_response if full_response else ""
|
||
|
||
except InterruptedError:
|
||
# 用户主动中断：回滚消息，通知前端
|
||
self._rollback_messages(msgs_before_turn)
|
||
interrupt_msg = i18n.get('user_interrupted', '用户中断')
|
||
if output_callback:
|
||
output_callback("line", "\n" + interrupt_msg + "\n")
|
||
output_callback("complete", interrupt_msg)
|
||
self.logger.info("AI响应被用户中断")
|
||
return interrupt_msg
|
||
except requests.exceptions.RequestException as e:
|
||
# 如果是因为取消导致的连接关闭，按中断处理
|
||
if self._is_cancelled():
|
||
self._rollback_messages(msgs_before_turn)
|
||
interrupt_msg = i18n.get('user_interrupted', '用户中断')
|
||
if output_callback:
|
||
output_callback("line", "\n" + interrupt_msg + "\n")
|
||
output_callback("complete", interrupt_msg)
|
||
return interrupt_msg
|
||
# 网络/HTTP 错误：回滚本轮添加的消息（user 消息 + 可能的 assistant/tool 消息），
|
||
# 防止格式错误的消息污染历史导致后续请求持续失败
|
||
self._rollback_messages(msgs_before_turn)
|
||
error_msg = f"\n{i18n.get('error_prefix')}API请求失败: {str(e)}"
|
||
if output_callback:
|
||
output_callback("error", error_msg)
|
||
self.stats["errors"].append(str(e))
|
||
self.logger.error(f"API请求失败，已回滚消息到位置 {msgs_before_turn}: {e}")
|
||
return error_msg
|
||
except json.JSONDecodeError as e:
|
||
# JSON 解析错误：回滚消息
|
||
self._rollback_messages(msgs_before_turn)
|
||
error_msg = f"\n{i18n.get('error_prefix')}API响应解析失败: {str(e)}"
|
||
if output_callback:
|
||
output_callback("error", error_msg)
|
||
self.stats["errors"].append(str(e))
|
||
self.logger.error(f"API响应解析失败，已回滚消息到位置 {msgs_before_turn}: {e}")
|
||
return error_msg
|
||
except Exception as e:
|
||
# 如果是因为取消导致的异常，按中断处理
|
||
if self._is_cancelled():
|
||
self._rollback_messages(msgs_before_turn)
|
||
interrupt_msg = i18n.get('user_interrupted', '用户中断')
|
||
if output_callback:
|
||
output_callback("line", "\n" + interrupt_msg + "\n")
|
||
output_callback("complete", interrupt_msg)
|
||
return interrupt_msg
|
||
# 未知错误：回滚消息，防止对话状态被破坏
|
||
self._rollback_messages(msgs_before_turn)
|
||
error_msg = f"\n{i18n.get('error_prefix')}{str(e)}"
|
||
if output_callback:
|
||
output_callback("error", error_msg)
|
||
self.stats["errors"].append(str(e))
|
||
self.logger.error(f"未知错误，已回滚消息到位置 {msgs_before_turn}: {e}")
|
||
return error_msg
|
||
finally:
|
||
# 客户端抽象层在流结束时已自行清理响应引用
|
||
pass
|
||
|
||
def _rollback_messages(self, to_length: int):
|
||
"""回滚 self.messages 到指定长度，用于 API 失败时恢复对话状态。"""
|
||
try:
|
||
current_len = len(self.messages)
|
||
if current_len > to_length:
|
||
del self.messages[to_length:]
|
||
self.logger.info(f"消息已回滚，从 {current_len} 条回滚到 {len(self.messages)} 条")
|
||
except Exception as e:
|
||
self.logger.error(f"消息回滚失败: {e}")
|
||
|
||
def _validate_enhanced_messages(self, messages):
|
||
"""发送给 API 前的最终校验：检查 tool_calls 与 tool 消息的配对完整性。
|
||
若发现问题，就地修复（补占位 tool 消息），避免 HTTP 400。"""
|
||
if not messages:
|
||
return
|
||
pending_tool_ids = [] # 当前等待 tool 响应的 tool_call_id 列表
|
||
seen_ids = set()
|
||
fixed = []
|
||
i = 0
|
||
n = len(messages)
|
||
while i < n:
|
||
msg = messages[i]
|
||
role = msg.get("role")
|
||
if role == "assistant" and msg.get("tool_calls"):
|
||
# 如果有未被响应的 tool_calls，先补占位（正常情况不应出现）
|
||
for tid in pending_tool_ids:
|
||
if tid not in seen_ids:
|
||
fixed.append({
|
||
"role": "tool",
|
||
"tool_call_id": tid,
|
||
"content": "⚠️ 该工具调用未返回结果（校验修复）。",
|
||
})
|
||
pending_tool_ids = [tc.get("id") for tc in msg["tool_calls"] if tc.get("id")]
|
||
seen_ids = set()
|
||
fixed.append(msg)
|
||
elif role == "tool":
|
||
tid = msg.get("tool_call_id")
|
||
if tid and tid in pending_tool_ids and tid not in seen_ids:
|
||
seen_ids.add(tid)
|
||
fixed.append(msg)
|
||
elif tid and tid not in pending_tool_ids:
|
||
# 孤立 tool 消息：不属于最近一个 assistant 的 tool_calls，跳过
|
||
self.logger.warning(f"_validate: 跳过孤立 tool 消息 {tid}")
|
||
else:
|
||
fixed.append(msg)
|
||
else:
|
||
# 遇到非 assistant/tool 消息（user/system），如果有待响应的 tool_calls，先补占位
|
||
for tid in pending_tool_ids:
|
||
if tid not in seen_ids:
|
||
fixed.append({
|
||
"role": "tool",
|
||
"tool_call_id": tid,
|
||
"content": "⚠️ 该工具调用未返回结果（校验修复）。",
|
||
})
|
||
pending_tool_ids = []
|
||
seen_ids = set()
|
||
fixed.append(msg)
|
||
i += 1
|
||
# 处理末尾：如果最后一条是 assistant with tool_calls 但没有 tool 响应
|
||
for tid in pending_tool_ids:
|
||
if tid not in seen_ids:
|
||
fixed.append({
|
||
"role": "tool",
|
||
"tool_call_id": tid,
|
||
"content": "⚠️ 该工具调用未返回结果（校验修复）。",
|
||
})
|
||
# 原地替换 messages 内容
|
||
messages.clear()
|
||
messages.extend(fixed)
|
||
|
||
def _repair_tool_pairs(self):
|
||
"""修复消息历史中的工具调用配对，保证 API 消息结构合法。
|
||
|
||
场景：assistant 消息携带 tool_calls，但对应的 tool 响应缺失（工具被中断、
|
||
压缩算法丢弃、端对话提前 break、system 消息插入等），下一次请求会因
|
||
"insufficient tool messages following tool_calls" 返回 400。
|
||
修复策略：
|
||
1. 跳过 system 消息查找 tool 响应（压缩后 system 摘要可能插入在 assistant 与 tool 之间）
|
||
2. 只收集与当前 assistant 的 tool_call_id 匹配的 tool 消息
|
||
3. 为缺失响应的 tool_call_id 补占位 tool 消息
|
||
4. 移除孤立的 tool 消息（tool_call_id 无对应前置 assistant tool_call）
|
||
5. 同一 tool_call_id 只保留第一条 tool 响应，去重
|
||
"""
|
||
if not self.messages:
|
||
return
|
||
repaired = []
|
||
i = 0
|
||
n = len(self.messages)
|
||
# 追踪所有已见的 assistant tool_call_id，用于识别孤立 tool 消息
|
||
known_tool_ids = set()
|
||
while i < n:
|
||
msg = self.messages[i]
|
||
role = msg.get("role")
|
||
if role == "system":
|
||
repaired.append(msg)
|
||
i += 1
|
||
continue
|
||
if role == "assistant" and msg.get("tool_calls"):
|
||
tool_calls = msg["tool_calls"]
|
||
needed_ids = []
|
||
for tc in tool_calls:
|
||
tid = tc.get("id")
|
||
if tid:
|
||
needed_ids.append(tid)
|
||
known_tool_ids.add(tid)
|
||
# 收集紧随其后的 tool 消息，跳过 system 消息
|
||
collected_tools = [] # list of (index, tool_msg) to ensure ordering
|
||
collected_ids = set()
|
||
j = i + 1
|
||
pending_systems = [] # system 消息在 assistant 和 tool 之间暂存，最后放到 tool 之后
|
||
while j < n:
|
||
m = self.messages[j]
|
||
r = m.get("role")
|
||
if r == "system":
|
||
pending_systems.append(m)
|
||
j += 1
|
||
continue
|
||
if r == "tool":
|
||
tid = m.get("tool_call_id")
|
||
# 只收集属于当前 assistant 且未重复的 tool 响应
|
||
if tid and tid in needed_ids and tid not in collected_ids:
|
||
collected_tools.append(m)
|
||
collected_ids.add(tid)
|
||
elif tid and tid not in needed_ids and tid in known_tool_ids:
|
||
# 属于更早 assistant 的 tool 响应，停止收集（已越过当前 assistant 的范围）
|
||
break
|
||
# tid 为空或不属于任何已知 assistant，跳过（可能是脏数据）
|
||
j += 1
|
||
continue
|
||
# 遇到 user 或下一个 assistant，停止收集
|
||
break
|
||
# 为缺失的 tool_call_id 补占位消息
|
||
for tid in needed_ids:
|
||
if tid not in collected_ids:
|
||
collected_tools.append({
|
||
"role": "tool",
|
||
"tool_call_id": tid,
|
||
"content": "⚠️ 该工具调用未返回结果（可能被中断或压缩丢失）。",
|
||
})
|
||
collected_ids.add(tid)
|
||
repaired.append(msg)
|
||
repaired.extend(collected_tools)
|
||
repaired.extend(pending_systems)
|
||
i = j
|
||
elif role == "tool":
|
||
tid = msg.get("tool_call_id")
|
||
if tid and tid in known_tool_ids:
|
||
repaired.append(msg)
|
||
else:
|
||
self.logger.warning(f"_repair_tool_pairs: 跳过孤立 tool 消息 id={tid}")
|
||
i += 1
|
||
else:
|
||
# user 或普通 assistant（无 tool_calls）
|
||
repaired.append(msg)
|
||
i += 1
|
||
self.messages = repaired
|
||
|
||
def reset_conversation(self, reload_prompts: bool = True):
|
||
"""
|
||
重置对话状态 - 清除所有对话记忆，可选择是否重新加载系统提示词
|
||
|
||
Args:
|
||
reload_prompts: 是否重新加载系统提示词，默认为 True
|
||
"""
|
||
with self._msg_lock:
|
||
self.conversation_active = True
|
||
self.history_loaded = False # 重置历史加载标记
|
||
|
||
if reload_prompts:
|
||
# 完全重置：清除所有消息，重新加载系统提示词
|
||
self.messages = []
|
||
self._init_system_prompts()
|
||
self.logger.info("对话已完全重置，系统提示词已重新加载")
|
||
else:
|
||
# 只清除非系统消息，保留系统提示词
|
||
if self.preserve_system:
|
||
system_messages = [msg for msg in self.messages if msg["role"] == "system"]
|
||
self.messages = system_messages
|
||
else:
|
||
self.messages = []
|
||
self.logger.info("对话已重置（仅清除对话历史）")
|
||
|
||
def is_busy(self) -> bool:
|
||
"""检查AI是否正在处理消息（非阻塞，供调度器等外部调用者判断）。"""
|
||
acquired = self._msg_lock.acquire(blocking=False)
|
||
if acquired:
|
||
self._msg_lock.release()
|
||
return False
|
||
return True
|
||
|
||
def reload_prompts(self):
|
||
"""重新加载提示词"""
|
||
with self._msg_lock:
|
||
old_system_count = len([msg for msg in self.messages if msg["role"] == "system"])
|
||
|
||
# 移除所有旧的系统消息
|
||
self.messages = [msg for msg in self.messages if msg["role"] != "system"]
|
||
|
||
# 重新加载提示词
|
||
self.prompt_manager.reload()
|
||
self._init_system_prompts()
|
||
|
||
new_system_count = len([msg for msg in self.messages if msg["role"] == "system"])
|
||
self.logger.info(f"提示词已重新加载: {old_system_count} -> {new_system_count} 个")
|
||
return new_system_count
|
||
|
||
|
||
# ==================== WebSocket处理器（修改版：添加重置对话功能） ====================
|
||
|
||
class WebSocketHandler:
|
||
"""WebSocket处理器 - 处理WebSocket连接和消息"""
|
||
|
||
def __init__(self, ai_manager):
|
||
self.ai_manager = ai_manager
|
||
self.logger = Logger("WebSocket")
|
||
self.connected_clients = set()
|
||
self.client_sessions = {}
|
||
self._processing_tasks = {} # websocket -> {asyncio.Task}（N14：集合登记，支持并发多任务）
|
||
self._shared_skill_manager = None # 共享技能管理器（懒加载，供所有连接复用）
|
||
# I 批（审计 2026-08-18）：/new、/reload_prompts 等命令专用线程池——
|
||
# 此前 run_in_executor(None) 与 AI think 共用默认池，繁忙时 reset 任务
|
||
# 排队等锁，多连接并发 /new 可耗尽默认池（阻塞新消息处理）
|
||
self._cmd_executor = ThreadPoolExecutor(max_workers=2,
|
||
thread_name_prefix="ws-cmd")
|
||
# J7（审计 2026-08-19）：broadcast 单客户端发送超时（半开 TCP 防卡死）
|
||
self.broadcast_send_timeout = 5.0
|
||
|
||
def shutdown(self):
|
||
"""I 批：释放命令专用线程池（服务退出时调用）"""
|
||
if self._cmd_executor is not None:
|
||
self._cmd_executor.shutdown(wait=False)
|
||
self._cmd_executor = None
|
||
|
||
def _get_shared_skill_manager(self):
|
||
"""获取共享技能管理器（懒加载一次，供所有连接实例复用，避免重复扫描技能）"""
|
||
if self._shared_skill_manager is None:
|
||
self._shared_skill_manager = SkillManager(self.logger)
|
||
return self._shared_skill_manager
|
||
|
||
def _create_session_ai(self):
|
||
"""为一个WebSocket连接创建独立的AI实例，实现不同用户间上下文隔离。
|
||
|
||
每个连接拥有独立的 messages / 工具注册表 / 消息锁，可与其他用户并发对话，
|
||
互不干扰。共享只读资源（记忆库、备忘录、提示词、技能管理器）复用。
|
||
"""
|
||
return DeepSeekChat(
|
||
api_key=None,
|
||
max_history=constants.DEFAULT_MAX_HISTORY,
|
||
debug_level=self.logger.level,
|
||
exit_callback=None, # 会话级对话结束由连接内处理，不触发全局状态
|
||
history_manager=None, # 不加载共享历史上下文，避免多用户历史相互污染
|
||
memory_db=self.ai_manager.memory_db,
|
||
memo_db=self.ai_manager.memo_db,
|
||
# 每个连接使用独立的命令执行器，互不共享，避免 cancel() 误杀其他用户/任务正在运行的子进程
|
||
command_executor=SubprocessCommandExecutor(self.logger),
|
||
prompt_manager=self.ai_manager.prompt_manager,
|
||
skill_manager=self._get_shared_skill_manager(),
|
||
# H3：CLI --model 进程级覆盖传导到每个会话实例
|
||
model=getattr(self.ai_manager, "model", None)
|
||
)
|
||
|
||
def _get_session(self, websocket) -> dict:
|
||
"""获取指定连接对应的会话状态（不存在则创建空会话）"""
|
||
return self.client_sessions.get(websocket)
|
||
|
||
async def register(self, websocket):
|
||
"""注册新客户端"""
|
||
self.connected_clients.add(websocket)
|
||
self.client_sessions[websocket] = {
|
||
"connected_at": datetime.now(),
|
||
"message_count": 0,
|
||
"current_conversation": None,
|
||
"ai": None, # 每个连接独立的AI实例
|
||
"dialogue_in_progress": False, # 每个连接独立的对话状态
|
||
"is_processing": False # 每个连接独立的并发处理标志
|
||
}
|
||
self.logger.info(i18n.get('client_connected', websocket.remote_address))
|
||
|
||
# 发送连接确认消息
|
||
await self.send_message(websocket, {
|
||
"type": "connection_ack",
|
||
"message": i18n.get('ws_connected'),
|
||
"timestamp": datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
})
|
||
|
||
# 发送欢迎信息和初始状态
|
||
await self.send_welcome_info(websocket)
|
||
|
||
async def unregister(self, websocket):
|
||
"""注销客户端"""
|
||
# N13：discard 替代 remove——并发注销/重复注销时 remove 抛 KeyError
|
||
# 会逃逸到调用方（如 broadcast 的 except 分支），discard 幂等
|
||
self.connected_clients.discard(websocket)
|
||
# R3（审计 2026-08-20）：取消该连接仍在进行的处理任务并主动关闭
|
||
# websocket。broadcast 超时路径（J7）也会走到这里——ping 禁用下
|
||
# 半开 TCP 的接收循环对断开无感知，不 close 的话连接对象/协程/
|
||
# 任务登记泄漏直至进程重启；与 handle_connection finally 对齐
|
||
pending = self._processing_tasks.pop(websocket, set())
|
||
for t in pending:
|
||
if not t.done():
|
||
t.cancel()
|
||
if websocket in self.client_sessions:
|
||
session = self.client_sessions.pop(websocket)
|
||
# 释放该连接独立的AI实例资源
|
||
ai = session.get("ai")
|
||
if ai:
|
||
try:
|
||
ai.cancel()
|
||
except Exception:
|
||
pass
|
||
# close 幂等（已关闭连接重复调用无副作用）；mock/异常对象兜底
|
||
try:
|
||
await websocket.close()
|
||
except Exception:
|
||
pass
|
||
self.logger.info(i18n.get('client_disconnected', websocket.remote_address))
|
||
|
||
async def send_welcome_info(self, websocket):
|
||
"""发送欢迎信息"""
|
||
# 发送存储位置
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('data_dir', constants.SAVE_DIR)
|
||
})
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('skills_dir', constants.SKILLS_DIR)
|
||
})
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('prompts_dir', constants.PROMPTS_DIR)
|
||
})
|
||
|
||
# 发送任务调度器状态
|
||
if self.ai_manager.scheduler and self.ai_manager.scheduler.running:
|
||
stats = self.ai_manager.scheduler.get_stats()
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('scheduler_started')
|
||
})
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": f"📊 任务统计: 总执行次数={stats.get('total_executions', 0)}, 最近执行={len(stats.get('recent_executions', []))}"
|
||
})
|
||
|
||
# 发送任务统计
|
||
memo_stats = self.ai_manager.memo_db.get_memo_stats()
|
||
if memo_stats['total'] > 0:
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('memo_stats')
|
||
})
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('total_memos', memo_stats['total'])
|
||
})
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('pending_memos', memo_stats['pending'])
|
||
})
|
||
if memo_stats['overdue'] > 0:
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('overdue_memos', memo_stats['overdue'])
|
||
})
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('completed_memos', memo_stats['completed'])
|
||
})
|
||
|
||
# 发送待执行任务列表
|
||
pending = self.ai_manager.memo_db.get_pending_tasks_for_user()
|
||
if pending:
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('pending_memos_title')
|
||
})
|
||
for task in pending[:5]:
|
||
status = i18n.get('memo_overdue') if task['is_overdue'] else i18n.get('memo_pending')
|
||
immediate_tag = "⚡" if task['is_immediate'] else ""
|
||
await self.send_message(websocket, {
|
||
"type": "task_status",
|
||
"task_id": task['id'],
|
||
"title": task['title'],
|
||
"reminder_time": task['reminder_time'],
|
||
"status": "overdue" if task['is_overdue'] else "pending",
|
||
"content": f"{status}{immediate_tag} [ID: {task['id']}] {task['title']} - {task['reminder_time']}"
|
||
})
|
||
if len(pending) > 5:
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('more_skills', len(pending)-5)
|
||
})
|
||
|
||
async def send_message(self, websocket, message):
|
||
"""发送消息到客户端 - 确保完整发送不被截断"""
|
||
try:
|
||
# 将消息转换为JSON字符串
|
||
json_str = json.dumps(message, ensure_ascii=False)
|
||
|
||
# 直接发送整个字符串，WebSocket协议会自动处理分片
|
||
await websocket.send(json_str)
|
||
|
||
except websockets.exceptions.ConnectionClosed:
|
||
self.logger.warning("连接已关闭，无法发送消息")
|
||
except Exception as e:
|
||
self.logger.error(f"发送消息失败: {e}")
|
||
|
||
async def broadcast(self, message):
|
||
"""广播消息到所有客户端。
|
||
|
||
J7（审计 2026-08-19）：此前串行 await 且 send_message 吞掉全部异常，
|
||
broadcast 的 except→unregister 为死代码——ping 禁用（ping_interval=None）
|
||
下半开 TCP 无法探测，backpressure 下 websocket.send 可无限期阻塞，
|
||
卡死广播与调度器循环。现改为每客户端并发发送 + wait_for 超时，
|
||
超时即 unregister 回收（discard 幂等）。
|
||
"""
|
||
clients = list(self.connected_clients)
|
||
if not clients:
|
||
return
|
||
|
||
async def _send_one(websocket):
|
||
try:
|
||
await asyncio.wait_for(
|
||
self.send_message(websocket, message),
|
||
timeout=self.broadcast_send_timeout)
|
||
except Exception:
|
||
# 超时/发送异常：回收该连接（取消事件被 wait_for 传播，
|
||
# CancelledError 为 BaseException 不在此列，不影响任务取消语义）
|
||
await self.unregister(websocket)
|
||
|
||
await asyncio.gather(*(_send_one(ws) for ws in clients),
|
||
return_exceptions=True)
|
||
|
||
async def broadcast_task_status(self, task: Dict, status: str, message: str = None):
|
||
"""广播任务状态"""
|
||
await self.broadcast({
|
||
"type": "task_status",
|
||
"task_id": task.get('id', 0),
|
||
"title": task.get('title', '未知任务'),
|
||
"status": status,
|
||
"message": message,
|
||
"timestamp": datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
})
|
||
|
||
async def handle_message(self, websocket, message):
|
||
"""处理接收到的消息"""
|
||
try:
|
||
data = json.loads(message)
|
||
msg_type = data.get("type", "unknown")
|
||
|
||
# 更新会话统计
|
||
if websocket in self.client_sessions:
|
||
self.client_sessions[websocket]["message_count"] += 1
|
||
|
||
if msg_type == "command":
|
||
await self.handle_command(websocket, data)
|
||
elif msg_type == "message":
|
||
await self.handle_user_message(websocket, data)
|
||
elif msg_type == "interrupt":
|
||
await self.handle_interrupt(websocket, data)
|
||
elif msg_type == "task":
|
||
await self.handle_task_message(websocket, data)
|
||
elif msg_type == "ping":
|
||
await self.send_message(websocket, {
|
||
"type": "pong",
|
||
"timestamp": datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
})
|
||
else:
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": i18n.get('ws_unknown_command', msg_type)
|
||
})
|
||
|
||
except json.JSONDecodeError:
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": "无效的JSON格式"
|
||
})
|
||
except Exception as e:
|
||
self.logger.error(f"处理消息失败: {e}")
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": f"处理失败: {str(e)}"
|
||
})
|
||
|
||
async def handle_interrupt(self, websocket, data):
|
||
"""处理中断信号：取消当前连接正在进行的AI思考和工具执行"""
|
||
self.logger.info("收到客户端中断信号")
|
||
# 先发送确认，让前端立即得到反馈
|
||
await self.send_message(websocket, {
|
||
"type": "interrupt_ack",
|
||
"content": "已收到中断信号，正在停止..."
|
||
})
|
||
# 取消当前连接独立AI实例的对话（线程安全，只set Event + close连接 + kill进程）；
|
||
# ai.cancel() 已同时终止该会话自身 executor 中的子进程。
|
||
# R2（审计 2026-08-20）：移除对 ai_manager.command_executor（CLI 主 AI
|
||
# 执行器）的全局取消——会话隔离架构下单实例时代的遗留，WS 用户中断
|
||
# 会误杀终端用户正在执行的命令；对会话自身亦冗余
|
||
session = self._get_session(websocket)
|
||
ai = session.get("ai") if session else None
|
||
if ai:
|
||
ai.cancel()
|
||
|
||
async def handle_task_message(self, websocket, data):
|
||
"""处理任务相关消息 - 简化版，所有任务都是对话任务"""
|
||
# W1（审计 2026-08-20）：字段类型规整——非 str 此前在 .strip() 抛
|
||
# AttributeError，落顶层兜底以笼统"处理失败"收场
|
||
action = data.get("action", "")
|
||
if not isinstance(action, str):
|
||
action = ""
|
||
action = action.strip()
|
||
|
||
if action == "add":
|
||
# 添加任务
|
||
title = data.get("title")
|
||
content = data.get("content")
|
||
delay = data.get("delay", 60) # 默认60秒后执行
|
||
is_immediate = data.get("is_immediate", False) # 是否为即时任务
|
||
|
||
# 重复任务参数
|
||
repeat_type = data.get("repeat_type", "none")
|
||
repeat_interval = data.get("repeat_interval", 0)
|
||
repeat_interval_unit = data.get("repeat_interval_unit", "days")
|
||
repeat_end_time = data.get("repeat_end_time")
|
||
|
||
# W1（审计 2026-08-20）：title 传 dict 等类型此前直达 sqlite 抛
|
||
# InterfaceError（J1 except ValueError 拦不住）
|
||
if not isinstance(title, str) or not title.strip():
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": "需要提供任务标题"
|
||
})
|
||
return
|
||
if content is not None and not isinstance(content, str):
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": "任务内容必须是字符串"
|
||
})
|
||
return
|
||
|
||
# W2（审计 2026-08-20）：delay 无校验——非数字 TypeError、负数立即
|
||
# 执行、10^18 OverflowError。float 转换 + 钳制 [0, 10 年]
|
||
try:
|
||
delay = float(delay)
|
||
except (TypeError, ValueError):
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": f"任务延迟必须是数字（秒）: {delay!r}"
|
||
})
|
||
return
|
||
delay = min(max(delay, 0.0), 10 * 365 * 86400)
|
||
|
||
reminder_time = None
|
||
if is_immediate:
|
||
reminder_time = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
else:
|
||
reminder_time = (datetime.now() + timedelta(seconds=delay)).strftime(constants.DATETIME_FORMAT)
|
||
|
||
# J1（审计 2026-08-19）：add_memo 入库校验抛 ValueError（畸形时间/
|
||
# 重复间隔等），此处转为友好错误消息，不让 handle_message 顶层
|
||
# 兜底以笼统的"处理失败"收场
|
||
try:
|
||
memo_id = self.ai_manager.memo_db.add_user_task(
|
||
title=title,
|
||
content=content,
|
||
reminder_time=reminder_time,
|
||
repeat_type=repeat_type,
|
||
repeat_interval=repeat_interval,
|
||
repeat_interval_unit=repeat_interval_unit,
|
||
repeat_end_time=repeat_end_time,
|
||
is_immediate=is_immediate
|
||
)
|
||
except ValueError as e:
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": f"任务添加失败: {e}"
|
||
})
|
||
return
|
||
|
||
immediate_msg = i18n.get('immediate_memo_added') if is_immediate else ""
|
||
repeat_msg = ""
|
||
if repeat_type != "none":
|
||
if repeat_type == "custom":
|
||
repeat_msg = f" (每{repeat_interval} {repeat_interval_unit})"
|
||
else:
|
||
repeat_msg = f" ({repeat_type})"
|
||
|
||
await self.send_message(websocket, {
|
||
"type": "task_added",
|
||
"task_id": memo_id,
|
||
"title": title,
|
||
"reminder_time": reminder_time,
|
||
"content": i18n.get('task_added', title, memo_id) + repeat_msg + immediate_msg
|
||
})
|
||
|
||
# 广播任务添加
|
||
await self.broadcast_task_status(
|
||
{"id": memo_id, "title": title},
|
||
"added",
|
||
f"新任务: {title}" + repeat_msg + (" (即时)" if is_immediate else "")
|
||
)
|
||
|
||
elif action == "list":
|
||
pending = self.ai_manager.memo_db.get_pending_tasks_for_user()
|
||
await self.send_message(websocket, {
|
||
"type": "task_list",
|
||
"tasks": pending
|
||
})
|
||
|
||
elif action == "complete":
|
||
memo_id = data.get("task_id")
|
||
if memo_id:
|
||
if self.ai_manager.memo_db.complete_memo(memo_id):
|
||
memo = self.ai_manager.memo_db.get_memo(memo_id)
|
||
await self.send_message(websocket, {
|
||
"type": "task_completed",
|
||
"task_id": memo_id,
|
||
"content": i18n.get('memo_completed', memo_id)
|
||
})
|
||
if memo:
|
||
await self.broadcast_task_status(memo, "completed", f"任务已完成: {memo['title']}")
|
||
else:
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": i18n.get('memo_complete_failed', memo_id)
|
||
})
|
||
else:
|
||
# W3（审计 2026-08-20）：缺 task_id 此前静默无响应
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": i18n.get('memo_complete_missing_id')
|
||
})
|
||
else:
|
||
# J15（审计 2026-08-19）：未知 action 此前静默吞掉，客户端无任何反馈
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": f"未知的任务操作: {action}"
|
||
})
|
||
|
||
async def handle_command(self, websocket, data):
|
||
"""处理命令消息"""
|
||
command = data.get("command", "")
|
||
if not isinstance(command, str): # W1（审计 2026-08-20）：非 str 字段规整
|
||
return
|
||
command = command.strip()
|
||
|
||
if not command:
|
||
return
|
||
|
||
self.logger.info(f"收到命令: {command}")
|
||
|
||
if command == "/exit":
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": i18n.get('goodbye')
|
||
})
|
||
await websocket.close()
|
||
|
||
elif command == "/new":
|
||
# 【新增】开始新对话：重置当前连接的AI状态，重新加载系统提示词
|
||
session = self._get_session(websocket)
|
||
ai = session.get("ai") if session else None
|
||
if ai:
|
||
# 如果该连接AI正在处理，先取消
|
||
if ai.is_busy():
|
||
self.logger.info("/new: AI正在处理中，先取消当前对话...")
|
||
ai.cancel()
|
||
for _ in range(20):
|
||
await asyncio.sleep(0.1)
|
||
if not ai.is_busy():
|
||
break
|
||
# J11（审计 2026-08-19）：取消后旧回合可能仍在收尾（回发内容），
|
||
# 且新消息的 think 线程可能与 reset 无序抢 _msg_lock（RLock 无
|
||
# 公平性）——等待该连接处理标志清零后再排队 reset，避免新回合
|
||
# 在旧上下文上执行后被 reset 抹掉（消息"消失"）
|
||
for _ in range(20):
|
||
if not session or not session.get("is_processing"):
|
||
break
|
||
await asyncio.sleep(0.1)
|
||
# 重置对话状态，重新加载系统提示词
|
||
# N3：reset_conversation 内部同步获取 _msg_lock（繁忙对话持锁
|
||
# 可达数十秒），直接调用会冻结整个事件循环（所有连接的心跳/
|
||
# 消息收发全部停摆）。移入线程池执行，事件循环仅等待结果。
|
||
loop = asyncio.get_running_loop()
|
||
|
||
def _do_reset():
|
||
ai.reset_conversation(reload_prompts=True)
|
||
# 重新初始化提示词管理器（确保最新）
|
||
self.ai_manager.prompt_manager.reload()
|
||
|
||
await loop.run_in_executor(self._cmd_executor, _do_reset)
|
||
# 重置该连接对话进行中标记
|
||
if session:
|
||
session["dialogue_in_progress"] = False
|
||
self.logger.info(i18n.get('new_conversation_reset'))
|
||
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": i18n.get('new_conversation_reset')
|
||
})
|
||
|
||
# 发送重置完成通知
|
||
await self.send_message(websocket, {
|
||
"type": "dialogue_reset",
|
||
"content": i18n.get('dialogue_reset_complete')
|
||
})
|
||
else:
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": i18n.get('new_conversation')
|
||
})
|
||
|
||
elif command == "/list":
|
||
conversations = self.ai_manager.history_manager.list_conversations()
|
||
result = f"\n{i18n.get('saved_conversations')}\n"
|
||
for conv in conversations:
|
||
status = i18n.get('conv_status_current') if conv.get('is_current') else i18n.get('conv_status_archived')
|
||
result += f" {conv['file']} - {conv['topic']} [{status}] ({conv['start_time']})\n"
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": result
|
||
})
|
||
|
||
elif command == "/memories":
|
||
memories = self.ai_manager.memory_db.get_recent_memories(10)
|
||
result = f"\n{i18n.get('recent_memories')}\n"
|
||
for mem in memories:
|
||
result += f" [ID: {mem['id']}] {mem['topic']} - {mem['summary'][:50]}...\n"
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": result
|
||
})
|
||
|
||
elif command == "/memos":
|
||
memos = self.ai_manager.memo_db.get_all_memos(include_completed=False)
|
||
result = f"\n{i18n.get('pending_memos_title')}\n"
|
||
for memo in memos:
|
||
# R1（审计 2026-08-20）：畸形 reminder_time 此前裸 strptime 抛
|
||
# ValueError → /memos 整体落入顶层兜底"处理失败"（J1 加固漏点）。
|
||
# 解析失败按"待执行"展示，对齐 CLI --memo-list 的既有模式
|
||
status = i18n.get('memo_pending')
|
||
if memo['reminder_time']:
|
||
try:
|
||
if datetime.strptime(memo['reminder_time'], constants.DATETIME_FORMAT) < datetime.now():
|
||
status = i18n.get('memo_overdue')
|
||
except (ValueError, TypeError):
|
||
pass
|
||
immediate_tag = "⚡" if memo.get('is_immediate') else ""
|
||
repeat_info = ""
|
||
if memo['repeat_type'] != 'none':
|
||
if memo['repeat_type'] == 'custom':
|
||
unit = memo.get('repeat_interval_unit', 'days')
|
||
repeat_info = f" [每{memo['repeat_interval']} {unit}]"
|
||
else:
|
||
repeat_info = f" [{memo['repeat_type']}]"
|
||
result += f" {status}{immediate_tag} [ID: {memo['id']}] {memo['title']}{repeat_info} - {memo.get('reminder_time', '无提醒')}\n"
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": result
|
||
})
|
||
|
||
elif command.startswith("/search "):
|
||
query = command[8:].strip()
|
||
# W4（审计 2026-08-20）：空关键词此前 LIKE %% 全表兜底刷屏，回用法提示
|
||
if not query:
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": i18n.get('search_usage')
|
||
})
|
||
return
|
||
results = self.ai_manager.memory_db.search_memories([query])
|
||
result = i18n.get('search_results', query, len(results)) + "\n"
|
||
for mem in results:
|
||
result += f" [ID: {mem['id']}] {mem['topic']} - {mem['summary'][:100]}...\n"
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": result
|
||
})
|
||
|
||
elif command == "/config":
|
||
result = f"\n{i18n.get('current_config')}\n"
|
||
result += i18n.get('config_debug', self.ai_manager.config.get('system.debug_level')) + "\n"
|
||
result += i18n.get('config_data_dir', constants.SAVE_DIR) + "\n"
|
||
result += i18n.get('config_skills_dir', constants.SKILLS_DIR) + "\n"
|
||
result += i18n.get('config_prompts_dir', constants.PROMPTS_DIR) + "\n"
|
||
result += i18n.get('config_commands', i18n.get('enabled') if constants.COMMANDS_ENABLED else i18n.get('disabled')) + "\n"
|
||
result += i18n.get('config_history', i18n.get('enabled') if self.ai_manager.history_manager.load_history else i18n.get('disabled')) + "\n"
|
||
result += i18n.get('config_executor', type(self.ai_manager.command_executor).__name__) + "\n"
|
||
result += i18n.get('config_scheduler', i18n.get('enabled') if constants.SCHEDULER_ENABLED else i18n.get('disabled')) + "\n"
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": result
|
||
})
|
||
|
||
elif command == "/reload":
|
||
self.ai_manager.config.reload()
|
||
# W5（审计 2026-08-20）：reload 经 _load_config 重建 _config，会静默
|
||
# 丢弃 CLI 进程级覆盖（--debug/--port/--no-scheduler 等）——此处重放
|
||
# main() 暂存在 config._cli_overrides 的覆盖
|
||
overrides = getattr(self.ai_manager.config, "_cli_overrides", None)
|
||
note = ""
|
||
if overrides:
|
||
for section, kv in overrides.items():
|
||
target = self.ai_manager.config._config.get(section)
|
||
if isinstance(target, dict):
|
||
target.update(kv)
|
||
note = i18n.get('config_reloaded_cli_kept')
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": i18n.get('config_reloaded') + note
|
||
})
|
||
|
||
elif command == "/reload_prompts":
|
||
session = self._get_session(websocket)
|
||
ai = session.get("ai") if session else None
|
||
# M1（审计 2026-08-18）：reload_prompts 内部同步获取 _msg_lock
|
||
# （繁忙对话持锁可达数十秒），直接调用会冻结整个事件循环——
|
||
# N3 只修了 /new，这里同类遗漏。移入线程池执行。
|
||
loop = asyncio.get_running_loop()
|
||
|
||
def _do_reload_prompts():
|
||
if ai:
|
||
return ai.reload_prompts()
|
||
return self.ai_manager.prompt_manager.reload()
|
||
|
||
new_count = await loop.run_in_executor(self._cmd_executor, _do_reload_prompts)
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": i18n.get('prompts_reloaded', new_count)
|
||
})
|
||
|
||
elif command == "/check":
|
||
result = i18n.get('checking_memos') + "\n"
|
||
overdue = self.ai_manager.memo_db.get_overdue_memos()
|
||
if overdue:
|
||
result += i18n.get('overdue_found', len(overdue)) + "\n"
|
||
for memo in overdue:
|
||
immediate_tag = "⚡" if memo.get('is_immediate') else ""
|
||
repeat_info = ""
|
||
if memo['repeat_type'] != 'none':
|
||
if memo['repeat_type'] == 'custom':
|
||
unit = memo.get('repeat_interval_unit', 'days')
|
||
repeat_info = f" (每{memo['repeat_interval']} {unit})"
|
||
else:
|
||
repeat_info = f" ({memo['repeat_type']})"
|
||
result += f" • {immediate_tag}[ID: {memo['id']}] {memo['title']}{repeat_info} - {memo['reminder_time']}\n"
|
||
else:
|
||
result += i18n.get('no_overdue') + "\n"
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": result
|
||
})
|
||
|
||
elif command == "/tasks":
|
||
if self.ai_manager.scheduler:
|
||
stats = self.ai_manager.scheduler.get_stats()
|
||
result = f"\n{i18n.get('task_stats', stats.get('total_executions', 0), stats.get('total_executions', 0), stats.get('total_executions', 0))}\n\n"
|
||
|
||
pending = self.ai_manager.memo_db.get_pending_tasks_for_user()
|
||
result += f"待执行任务: {len(pending)} 个\n"
|
||
for task in pending[:10]:
|
||
status = "⏰" if not task['is_overdue'] else "⚠️"
|
||
immediate_tag = "⚡" if task['is_immediate'] else ""
|
||
repeat_info = ""
|
||
if task['repeat_type'] != 'none':
|
||
if task['repeat_type'] == 'custom':
|
||
unit = task.get('repeat_interval_unit', 'days')
|
||
repeat_info = f" [每{task['repeat_interval']} {unit}]"
|
||
else:
|
||
repeat_info = f" [{task['repeat_type']}]"
|
||
result += f" {status}{immediate_tag} [ID: {task['id']}] {task['title']}{repeat_info} - {task['reminder_time']}\n"
|
||
else:
|
||
result = "❌ 任务调度器未启用"
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": result
|
||
})
|
||
|
||
elif command == "/help":
|
||
help_text = f"\n可用命令:\n"
|
||
help_text += " /exit - 退出连接\n"
|
||
help_text += " /new - 开始新对话（保存当前对话，重置AI记忆，重新加载系统提示词）\n"
|
||
help_text += " /list - 列出所有对话\n"
|
||
help_text += " /memories - 查看最近的记忆\n"
|
||
help_text += " /memos - 查看待办任务\n"
|
||
help_text += " /search <关键词> - 搜索记忆\n"
|
||
help_text += " /config - 查看配置\n"
|
||
help_text += " /reload - 重新加载配置\n"
|
||
help_text += " /reload_prompts - 重新加载提示词\n"
|
||
help_text += " /check - 检查过期任务\n"
|
||
help_text += " /tasks - 查看任务状态\n"
|
||
help_text += " /help - 显示此帮助\n\n"
|
||
help_text += "任务类型:\n"
|
||
help_text += " {type: 'task', action: 'add', title: '任务', delay: 60} - 添加一次性任务\n"
|
||
help_text += " {type: 'task', action: 'add', title: '即时', is_immediate: true} - 添加即时任务（立即执行）\n"
|
||
help_text += " {type: 'task', action: 'add', title: '重复任务', repeat_type: 'custom', repeat_interval: 5, repeat_interval_unit: 'minutes'} - 每5分钟重复\n"
|
||
help_text += " {type: 'task', action: 'list'} - 获取任务列表\n"
|
||
help_text += " {type: 'task', action: 'complete', task_id: 1} - 完成任务\n"
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": help_text
|
||
})
|
||
|
||
else:
|
||
# 如果不是命令，当作普通消息处理
|
||
await self.handle_user_message(websocket, {"type": "message", "content": command})
|
||
|
||
async def handle_user_message(self, websocket, data):
|
||
"""处理用户消息 - 实时流式输出（每个连接使用独立的AI实例，支持多用户并发）"""
|
||
content = data.get("content", "")
|
||
if not isinstance(content, str): # W1（审计 2026-08-20）：非 str 字段规整
|
||
return
|
||
content = content.strip()
|
||
|
||
if not content:
|
||
return
|
||
|
||
# 获取当前连接的会话状态
|
||
session = self._get_session(websocket)
|
||
if session is None:
|
||
return
|
||
|
||
# 防止并发处理用户消息（按连接独立）：如果正在处理中，提示用户先中断
|
||
if session.get("is_processing"):
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": "⏳ AI正在响应中，请等待完成或发送中断信号（Ctrl+C）"
|
||
})
|
||
return
|
||
|
||
session["is_processing"] = True
|
||
|
||
self.logger.info(f"收到用户消息: {content[:50]}...")
|
||
|
||
# 获取该连接独立的AI实例（懒创建）
|
||
ai = session.get("ai")
|
||
if ai is None:
|
||
self.logger.info("初始化独立AI实例...")
|
||
ai = self._create_session_ai()
|
||
session["ai"] = ai
|
||
|
||
# 如果还没有活跃对话，开始新对话（对话状态按连接独立）
|
||
if not session.get("dialogue_in_progress"):
|
||
self.logger.info(f"开始新对话（连接 {id(websocket)}）")
|
||
session["dialogue_in_progress"] = True
|
||
|
||
# 发送对话开始消息
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('conversation_start')
|
||
})
|
||
|
||
# 获取当前事件循环
|
||
loop = asyncio.get_running_loop()
|
||
|
||
# 定义输出回调函数 - 实时发送
|
||
def output_callback(msg_type, content):
|
||
"""AI输出的回调函数 - 实时发送每个chunk"""
|
||
self.logger.debug(f"输出回调被调用: {msg_type}, 内容长度: {len(content)}")
|
||
|
||
# 创建发送消息的协程
|
||
async def send_msg():
|
||
try:
|
||
await websocket.send(json.dumps({
|
||
"type": msg_type,
|
||
"content": content
|
||
}, ensure_ascii=False))
|
||
self.logger.info(f"实时消息发送成功: {msg_type}")
|
||
except Exception as e:
|
||
self.logger.error(f"实时发送消息失败: {e}")
|
||
|
||
# 提交到事件循环
|
||
asyncio.run_coroutine_threadsafe(send_msg(), loop)
|
||
|
||
try:
|
||
self.logger.info("开始AI思考...")
|
||
|
||
# 在线程池中运行AI思考，通过回调实时发送消息
|
||
response = await loop.run_in_executor(
|
||
None,
|
||
ai.think_and_respond,
|
||
content,
|
||
output_callback # 传递回调函数，AI会在生成每个chunk时调用
|
||
)
|
||
|
||
self.logger.info(f"AI思考完成，完整响应长度: {len(response) if response else 0}")
|
||
|
||
# 检查对话是否结束（按连接独立）
|
||
if not ai.conversation_active:
|
||
self.logger.info("对话结束")
|
||
session["dialogue_in_progress"] = False
|
||
await self.send_message(websocket, {
|
||
"type": "info",
|
||
"content": i18n.get('conversation_ended')
|
||
})
|
||
# 【修复】发送明确的对话结束信号
|
||
await self.send_message(websocket, {
|
||
"type": "dialogue_ended",
|
||
"content": i18n.get('dialogue_ended')
|
||
})
|
||
|
||
except Exception as e:
|
||
self.logger.error(f"AI处理消息失败: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
try:
|
||
await websocket.send(json.dumps({
|
||
"type": "error",
|
||
"content": f"{i18n.get('error_prefix')}{str(e)}"
|
||
}, ensure_ascii=False))
|
||
except:
|
||
pass
|
||
finally:
|
||
# 无论成功、失败还是被中断，都清除该连接的处理标志
|
||
session["is_processing"] = False
|
||
|
||
|
||
# ==================== 对话管理器（修改版：添加重置对话功能） ====================
|
||
|
||
class EnhancedAIManager:
|
||
"""增强的AI对话管理器 - 集成任务调度器"""
|
||
|
||
def __init__(self, debug_level: int = None, load_history: bool = None, command_executor: CommandExecutor = None,
|
||
model: str = None):
|
||
self.ai: Optional[DeepSeekChat] = None
|
||
self.dialogue_history: List[Dict] = []
|
||
self.config = ConfigManager()
|
||
self.command_executor = command_executor or SubprocessCommandExecutor()
|
||
self.prompt_manager = PromptManager() # 创建提示词管理器
|
||
self.scheduler: Optional[TaskScheduler] = None # 任务调度器
|
||
# H3：CLI --model 进程级覆盖（档案名或裸模型名），传导到本进程创建的所有 AI 实例
|
||
# （主对话 add_ai / WebSocket 会话 / 定时任务），此前服务模式该参数被静默忽略
|
||
self.model = model
|
||
|
||
if debug_level is None:
|
||
debug_level = self.config.get("system.debug_level", 1)
|
||
|
||
self.logger = Logger("Manager", debug_level)
|
||
if model:
|
||
self.logger.info(f"CLI 模型覆盖已启用: {model}")
|
||
self.running = True
|
||
|
||
# 确保 ~/.aibox 目录存在
|
||
os.makedirs(constants.SAVE_DIR, exist_ok=True)
|
||
# 确保 prompts 目录存在（PromptManager 会创建）
|
||
os.makedirs(constants.PROMPTS_DIR, exist_ok=True)
|
||
|
||
self.memory_db = MemoryDatabase()
|
||
self.memo_db = MemoDatabase()
|
||
self.history_manager = ConversationHistory(memory_db=self.memory_db, load_history=load_history)
|
||
|
||
self.dialogue_in_progress = False
|
||
self.ws_handler = None
|
||
|
||
def global_exit_handler(self, reason: str, summary: str):
|
||
"""对话结束处理器"""
|
||
self.logger.info(f"对话结束 - 原因: {reason}")
|
||
|
||
self.history_manager.end_current_conversation(summary)
|
||
|
||
if self.ai:
|
||
# 智能体主动结束对话后，重置对话状态但保留系统提示词（锁保护）
|
||
self.ai.reset_conversation(reload_prompts=False)
|
||
|
||
self.dialogue_in_progress = False
|
||
self.logger.info("对话状态已重置，可以开始新对话")
|
||
|
||
def reset_dialogue(self):
|
||
"""重置对话 - 清除所有对话记忆，重新加载系统提示词"""
|
||
if self.ai:
|
||
# 完全重置AI状态，重新加载系统提示词
|
||
self.ai.reset_conversation(reload_prompts=True)
|
||
self.dialogue_in_progress = False
|
||
self.history_manager.reset_conversation()
|
||
self.logger.info(i18n.get('dialogue_reset_complete'))
|
||
|
||
def add_ai(self) -> DeepSeekChat:
|
||
"""添加AI"""
|
||
ai = DeepSeekChat(
|
||
api_key=None,
|
||
max_history=constants.DEFAULT_MAX_HISTORY,
|
||
debug_level=self.logger.level,
|
||
exit_callback=self.global_exit_handler,
|
||
history_manager=self.history_manager,
|
||
memory_db=self.memory_db,
|
||
memo_db=self.memo_db,
|
||
command_executor=self.command_executor,
|
||
prompt_manager=self.prompt_manager,
|
||
model=self.model # H3：--model 进程级覆盖
|
||
)
|
||
|
||
self.ai = ai
|
||
self.logger.info("AI已初始化")
|
||
return ai
|
||
|
||
def cancel(self):
|
||
"""中断当前AI操作（线程安全，可从任意线程调用）"""
|
||
if self.ai:
|
||
self.ai.cancel()
|
||
# 同时确保命令执行器中的进程也被终止
|
||
if self.command_executor and hasattr(self.command_executor, 'cancel'):
|
||
try:
|
||
self.command_executor.cancel()
|
||
except Exception:
|
||
pass
|
||
|
||
def show_welcome_message(self):
|
||
"""显示欢迎信息"""
|
||
print("\n" + "="*70)
|
||
print(i18n.get('welcome_title'))
|
||
print("="*70)
|
||
|
||
# 显示存储位置
|
||
print(i18n.get('data_dir', constants.SAVE_DIR))
|
||
print(i18n.get('skills_dir', constants.SKILLS_DIR))
|
||
print(i18n.get('prompts_dir', constants.PROMPTS_DIR))
|
||
|
||
config_path = self.config._config_path
|
||
if os.path.exists(config_path):
|
||
print(i18n.get('config_file', config_path))
|
||
|
||
# 显示提示词数量
|
||
prompt_count = self.prompt_manager.get_prompt_count()
|
||
if prompt_count > 0:
|
||
prompts = self.prompt_manager.get_prompts_list()
|
||
print(i18n.get('loaded_prompts', prompt_count))
|
||
for prompt in prompts[:5]:
|
||
print(i18n.get('prompt_item', prompt['file']))
|
||
if prompt_count > 5:
|
||
print(i18n.get('more_skills', prompt_count-5))
|
||
|
||
# 显示任务调度器状态
|
||
if constants.SCHEDULER_ENABLED:
|
||
print(i18n.get('scheduler_started'))
|
||
|
||
memo_stats = self.memo_db.get_memo_stats()
|
||
if memo_stats['total'] > 0:
|
||
print(f"\n{i18n.get('memo_stats')}")
|
||
print(i18n.get('total_memos', memo_stats['total']))
|
||
print(i18n.get('pending_memos', memo_stats['pending']))
|
||
if memo_stats['overdue'] > 0:
|
||
print(i18n.get('overdue_memos', memo_stats['overdue']))
|
||
print(i18n.get('completed_memos', memo_stats['completed']))
|
||
|
||
# 显示待执行任务
|
||
pending = self.memo_db.get_pending_tasks_for_user()
|
||
if pending:
|
||
print(f"\n{i18n.get('pending_memos_title')}")
|
||
for task in pending[:5]:
|
||
status = i18n.get('memo_overdue') if task['is_overdue'] else i18n.get('memo_pending')
|
||
immediate_tag = "⚡" if task['is_immediate'] else ""
|
||
repeat_info = ""
|
||
if task['repeat_type'] != 'none':
|
||
if task['repeat_type'] == 'custom':
|
||
unit = task.get('repeat_interval_unit', 'days')
|
||
repeat_info = f" [每{task['repeat_interval']} {unit}]"
|
||
else:
|
||
repeat_info = f" [{task['repeat_type']}]"
|
||
print(f" {status}{immediate_tag} [ID: {task['id']}] {task['title']}{repeat_info} - {task['reminder_time']}")
|
||
if len(pending) > 5:
|
||
print(i18n.get('more_skills', len(pending)-5))
|
||
|
||
# 显示已加载的技能（含简介）
|
||
skill_manager = getattr(getattr(self.ai, 'tool_registry', None), 'skill_manager', None)
|
||
if skill_manager and skill_manager.count() > 0:
|
||
all_skills = skill_manager.get_all()
|
||
print(i18n.get('loaded_skills', skill_manager.count()))
|
||
for s in all_skills[:5]:
|
||
print(f" • {s['name']} — {s['summary']}")
|
||
if skill_manager.count() > 5:
|
||
print(i18n.get('more_skills', skill_manager.count() - 5))
|
||
|
||
# 显示WebSocket服务器信息
|
||
host = constants.WEBSOCKET_HOST
|
||
port = constants.WEBSOCKET_PORT
|
||
print(i18n.get('websocket_started', host, port))
|
||
|
||
async def handle_connection(self, websocket, path):
|
||
"""处理WebSocket连接 - 消息接收与处理解耦，确保中断信号能被即时处理"""
|
||
self.logger.info(f"新连接: {websocket.remote_address}, path: {path}")
|
||
try:
|
||
# J1（审计 2026-08-19）：register 移入 try——此前在 try 外，握手期
|
||
# 异常（如任务展示路径解析失败）逃逸时 finally 不执行，会话登记
|
||
# 永久泄漏；unregister 为 discard 幂等，提前失败也会被正常清理
|
||
await self.ws_handler.register(websocket)
|
||
async for message in websocket:
|
||
# 【关键修复】用create_task异步处理消息，不await阻塞接收循环
|
||
# 这样当AI在后台生成响应时，interrupt消息仍能被立即接收和处理
|
||
task = asyncio.create_task(
|
||
self.ws_handler.handle_message(websocket, message)
|
||
)
|
||
# N14：按连接登记任务集合。此前按 ws id 覆盖式登记，同连接并发
|
||
# 多任务时先完成任务的 done_callback 会误 pop 掉后续任务的登记
|
||
tasks = self.ws_handler._processing_tasks.setdefault(websocket, set())
|
||
tasks.add(task)
|
||
|
||
def _cleanup_task(t, ws=websocket):
|
||
pending = self.ws_handler._processing_tasks.get(ws)
|
||
if pending is not None:
|
||
pending.discard(t)
|
||
task.add_done_callback(_cleanup_task)
|
||
except websockets.exceptions.ConnectionClosed as e:
|
||
self.logger.info(f"连接关闭: {e}")
|
||
except Exception as e:
|
||
self.logger.error(f"处理错误: {e}")
|
||
finally:
|
||
# 连接关闭时取消该连接所有仍在进行的处理任务（N14：此前仅最后一个）
|
||
pending = self.ws_handler._processing_tasks.pop(websocket, set())
|
||
for t in pending:
|
||
if not t.done():
|
||
t.cancel()
|
||
await self.ws_handler.unregister(websocket)
|
||
|
||
async def websocket_server(self):
|
||
"""启动WebSocket服务器并集成任务调度器"""
|
||
host = constants.WEBSOCKET_HOST
|
||
port = constants.WEBSOCKET_PORT
|
||
|
||
self.ws_handler = WebSocketHandler(self)
|
||
# M2（审计 2026-08-18）：技能扫描对每个可执行技能串行跑 --help
|
||
# 子进程（各 ≤10s），懒加载于首条消息/首个任务时在事件循环线程
|
||
# 执行会冻结整个服务。启动前预热，把扫描成本移到连接建立之前。
|
||
self.ws_handler._get_shared_skill_manager()
|
||
|
||
# 如果启用了任务调度器，创建并启动
|
||
if constants.SCHEDULER_ENABLED:
|
||
self.scheduler = TaskScheduler(asyncio.get_running_loop(), self.logger)
|
||
self.scheduler.set_websocket_handler(self.ws_handler)
|
||
self.scheduler.set_ai_manager(self) # 设置AI管理器引用
|
||
self.scheduler._get_shared_skill_manager()
|
||
|
||
async def handler(websocket):
|
||
await self.handle_connection(websocket, None)
|
||
|
||
# ping_interval=None：禁用服务端协议层主动 Ping。
|
||
# websockets 库默认 ping_interval=20s / ping_timeout=20s，服务端每 20s 发协议层
|
||
# Ping 帧，20s 内收不到 Pong 即强制断连。经 WAF→gateway 两层代理转发后 Pong 无法
|
||
# 可靠回传，导致连接约 20s 被服务端关闭。前端已通过 25s 应用层心跳（{type:'ping'}）
|
||
# 保活，故此处完全禁用服务端协议层 ping。
|
||
# T1（审计 2026-08-21，产品决策：仅告警不加鉴权）：绑定非回环地址时
|
||
# WebSocket 端点（经工具链可达任意命令执行）即暴露给网络内任意
|
||
# 客户端，启动时显著告警提醒确认网关/WAF 接入与网络边界
|
||
_h = (host or "").strip().lower()
|
||
if _h not in ("", "localhost", "127.0.0.1", "::1", "[::1]"):
|
||
for _ in range(3):
|
||
self.logger.warning(
|
||
f"⚠️ WebSocket 绑定在 {host}:{port}（非回环地址）：任何能建立 "
|
||
f"TCP 连接的客户端都可使用本智能体（含命令执行能力），"
|
||
f"请确认已置于可信网络或有网关/WAF 认证保护")
|
||
server = await websockets.serve(handler, host, port, ping_interval=None)
|
||
self.logger.info(i18n.get('websocket_started', host, port))
|
||
|
||
try:
|
||
if self.scheduler:
|
||
# 同时运行服务器和调度器
|
||
await asyncio.gather(
|
||
server.wait_closed(),
|
||
self.scheduler.run(self.memo_db)
|
||
)
|
||
else:
|
||
# 只运行服务器
|
||
await server.wait_closed()
|
||
finally:
|
||
if self.scheduler:
|
||
self.scheduler.stop()
|
||
# I 批：释放命令专用线程池
|
||
self.ws_handler.shutdown()
|
||
|
||
def run(self):
|
||
"""运行WebSocket服务器"""
|
||
self.add_ai()
|
||
self.show_welcome_message()
|
||
|
||
# 检查过期任务
|
||
overdue = self.memo_db.get_overdue_memos()
|
||
if overdue:
|
||
print(f"\n⏰ {i18n.get('overdue_found', len(overdue))}")
|
||
for memo in overdue[:5]:
|
||
immediate_tag = "⚡" if memo.get('is_immediate') else ""
|
||
repeat_info = ""
|
||
if memo['repeat_type'] != 'none':
|
||
if memo['repeat_type'] == 'custom':
|
||
unit = memo.get('repeat_interval_unit', 'days')
|
||
repeat_info = f" (每{memo['repeat_interval']} {unit})"
|
||
else:
|
||
repeat_info = f" ({memo['repeat_type']})"
|
||
print(f" • {immediate_tag}[ID: {memo['id']}] {memo['title']}{repeat_info} - {memo['reminder_time']}")
|
||
if len(overdue) > 5:
|
||
print(f" ... {i18n.get('more_skills', len(overdue)-5)}")
|
||
|
||
# 运行WebSocket服务器
|
||
try:
|
||
# 使用 asyncio.run() 运行服务器
|
||
asyncio.run(self.websocket_server())
|
||
except KeyboardInterrupt:
|
||
print(f"\n\n{i18n.get('user_interrupted')}")
|
||
self.history_manager.end_current_conversation()
|
||
except Exception as e:
|
||
print(f"\n{i18n.get('error_prefix')}{e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
# W6（审计 2026-08-20）：启动/运行失败此前打印堆栈后以退出码 0
|
||
# 收场，supervisor/脚本误判成功
|
||
sys.exit(1)
|
||
|
||
|
||
# ==================== 命令行参数处理 ====================
|
||
|
||
def _port_number(value):
|
||
"""W6（审计 2026-08-20）：--port argparse 类型校验，非法值启动前即报错"""
|
||
try:
|
||
iv = int(value)
|
||
except ValueError:
|
||
raise argparse.ArgumentTypeError(f"端口必须是整数: {value!r}")
|
||
if not (1 <= iv <= 65535):
|
||
raise argparse.ArgumentTypeError(f"端口必须在 1~65535 之间: {iv}")
|
||
return iv
|
||
|
||
|
||
def parse_args():
|
||
"""解析命令行参数"""
|
||
parser = argparse.ArgumentParser(description='单AI智能体系统 - WebSocket版本（所有任务都是对话任务）')
|
||
|
||
parser.add_argument('--verbose', '-v', action='store_true', help='显示详细日志')
|
||
|
||
parser.add_argument('--check-memo', action='store_true', help='立即检查到期任务并退出')
|
||
|
||
parser.add_argument('--memo-add', type=str, help='添加任务，格式: "标题|内容|提醒时间|重复类型|重复间隔|单位"')
|
||
parser.add_argument('--memo-list', action='store_true', help='列出任务')
|
||
parser.add_argument('--memo-complete', type=int, help='完成指定ID的任务')
|
||
|
||
parser.add_argument('-d', '--debug', type=int, choices=[0, 1, 2, 3], default=None, help='调试级别')
|
||
parser.add_argument('-c', '--config', type=str, help='指定配置文件路径')
|
||
|
||
# 添加语言选项
|
||
parser.add_argument('--lang', type=str, choices=['zh', 'en'], help='强制指定语言 (zh/en)')
|
||
|
||
# WebSocket选项
|
||
parser.add_argument('--host', type=str, help='WebSocket服务器主机')
|
||
parser.add_argument('--port', type=_port_number, help='WebSocket服务器端口')
|
||
|
||
# 调度器选项
|
||
parser.add_argument('--no-scheduler', action='store_true', help='禁用任务调度器')
|
||
parser.add_argument('--scheduler-interval', type=float, default=1.0, help='调度器检查间隔（秒）')
|
||
|
||
# 一次性对话模式（CLI 单次调用：执行 QUERY 后退出，不启动 WebSocket）
|
||
parser.add_argument('--once', type=str, metavar='QUERY', help='一次性对话：执行 QUERY 并输出结果后退出')
|
||
parser.add_argument('--quiet', action='store_true', help='静默模式：仅 stdout 输出回答正文，过程行/思考全静默（适合管道）')
|
||
parser.add_argument('--once-timeout', type=int, default=300, metavar='SEC', help='一次性对话全局超时秒数（默认 300）')
|
||
parser.add_argument('--no-save-memory', action='store_true', help='一次性对话不注册记忆工具，禁止写入记忆数据库')
|
||
parser.add_argument('--show-reasoning', action='store_true', help='一次性对话在 stderr 显示思考过程流（默认不显示）')
|
||
parser.add_argument('--worker-only', action='store_true', help='一次性对话以 worker 纯执行面模式运行（7 种执行工具，无记忆/人格/派生）')
|
||
parser.add_argument('--model', type=str, metavar='NAME', help='覆盖配置中的模型：档案名（models 节，如 kimi）或裸模型名（同端点换模型）；歧义时可用 model:NAME 强制裸名 / profile:NAME 强制档案。服务模式与 --once 均生效（进程级覆盖）')
|
||
parser.add_argument('--temperature', type=float, metavar='T', help='覆盖默认采样温度（0.0~2.0，越低越确定）')
|
||
|
||
return parser.parse_args()
|
||
|
||
|
||
def create_command_executor(args):
|
||
"""根据命令行参数创建命令执行器"""
|
||
# 现在只使用本地执行器
|
||
return SubprocessCommandExecutor()
|
||
|
||
|
||
def handle_check_commands(manager):
|
||
"""处理检查命令"""
|
||
args = parse_args()
|
||
|
||
if args.check_memo:
|
||
print(i18n.get('memo_check_title'))
|
||
overdue = manager.memo_db.get_overdue_memos()
|
||
if overdue:
|
||
print(i18n.get('memo_check_found', len(overdue)))
|
||
for memo in overdue:
|
||
immediate_tag = "⚡" if memo.get('is_immediate') else ""
|
||
repeat_info = ""
|
||
if memo['repeat_type'] != 'none':
|
||
if memo['repeat_type'] == 'custom':
|
||
unit = memo.get('repeat_interval_unit', 'days')
|
||
repeat_info = f" (每{memo['repeat_interval']} {unit})"
|
||
else:
|
||
repeat_info = f" ({memo['repeat_type']})"
|
||
print(f" • {immediate_tag}[ID: {memo['id']}] {memo['title']}{repeat_info} (提醒: {memo['reminder_time']})")
|
||
else:
|
||
print(i18n.get('memo_check_none'))
|
||
return True
|
||
|
||
return False
|
||
|
||
|
||
def handle_memo_commands(manager):
|
||
"""处理备忘录命令"""
|
||
args = parse_args()
|
||
|
||
if args.memo_list:
|
||
memos = manager.memo_db.get_all_memos(include_completed=False)
|
||
if not memos:
|
||
print("📭 " + i18n.get('memo_check_none'))
|
||
else:
|
||
print(i18n.get('memo_list_title', len(memos)))
|
||
for memo in memos:
|
||
overdue = ""
|
||
if memo['reminder_time']:
|
||
try:
|
||
reminder = datetime.strptime(memo['reminder_time'], constants.DATETIME_FORMAT)
|
||
if reminder < datetime.now():
|
||
overdue = " ⚠️"
|
||
except:
|
||
pass
|
||
immediate_tag = "⚡" if memo.get('is_immediate') else ""
|
||
repeat_info = ""
|
||
if memo['repeat_type'] != 'none':
|
||
if memo['repeat_type'] == 'custom':
|
||
unit = memo.get('repeat_interval_unit', 'days')
|
||
repeat_info = f" [每{memo['repeat_interval']} {unit}]"
|
||
else:
|
||
repeat_info = f" [{memo['repeat_type']}]"
|
||
print(f" {immediate_tag}[ID: {memo['id']}] {memo['title']}{repeat_info}{overdue}")
|
||
return True
|
||
|
||
if args.memo_add:
|
||
try:
|
||
parts = args.memo_add.split('|')
|
||
# W12（审计 2026-08-20）："|x" 空标题此前直接入库；"a|b|" 尾部空段
|
||
# 以 "" 传入绕过 J1 时间校验——空串规整为 None，空标题由 db 层拦
|
||
title = parts[0].strip()
|
||
content = parts[1] if len(parts) > 1 else ""
|
||
reminder = (parts[2].strip() or None) if len(parts) > 2 else None
|
||
|
||
# 解析重复参数
|
||
repeat_type = parts[3] if len(parts) > 3 else "none"
|
||
repeat_interval = int(parts[4]) if len(parts) > 4 else 0
|
||
repeat_interval_unit = parts[5] if len(parts) > 5 else "days"
|
||
|
||
is_immediate = reminder == "immediate" # 如果提醒时间是"immediate"，则创建即时任务
|
||
|
||
memo_id = manager.memo_db.add_memo(
|
||
title=title,
|
||
content=content,
|
||
reminder_time=None if is_immediate else reminder,
|
||
created_by="cli",
|
||
source="cli",
|
||
repeat_type=repeat_type,
|
||
repeat_interval=repeat_interval,
|
||
repeat_interval_unit=repeat_interval_unit,
|
||
is_immediate=is_immediate
|
||
)
|
||
|
||
repeat_msg = ""
|
||
if repeat_type != "none":
|
||
if repeat_type == "custom":
|
||
repeat_msg = f" (每{repeat_interval} {repeat_interval_unit})"
|
||
else:
|
||
repeat_msg = f" ({repeat_type})"
|
||
|
||
print(i18n.get('memo_added', memo_id) + repeat_msg + (" (即时任务)" if is_immediate else ""))
|
||
except Exception as e:
|
||
print(i18n.get('memo_add_failed', e))
|
||
return True
|
||
|
||
if args.memo_complete:
|
||
if manager.memo_db.complete_memo(args.memo_complete):
|
||
print(i18n.get('memo_completed', args.memo_complete))
|
||
else:
|
||
print(i18n.get('memo_complete_failed', args.memo_complete))
|
||
return True
|
||
|
||
return False
|
||
|
||
|
||
def handle_once_mode(manager, args) -> bool:
|
||
"""一次性对话模式：命中 --once 则执行单次 AI 对话并以退出码结束进程。
|
||
|
||
设计要点：
|
||
- stdout 仅输出 AI 回答正文（chunk 流式），line/reasoning 过程行走 stderr，管道安全
|
||
- 退出码：0 成功 / 2 API 或链路失败 / 3 用户中断(Ctrl+C) / 4 全局超时
|
||
- 复用三层中断机制（Event + HTTP 关闭 + 子进程 kill）：SIGINT 与超时 Timer 均走 cancel()
|
||
- 状态隔离：不加载对话历史、不挂全局退出回调（与 TaskScheduler 任务实例同款隔离）
|
||
"""
|
||
if not args.once:
|
||
return False
|
||
|
||
# worker 模式注入精简 system prompt：纯执行面，不加载人格/身份/技能提示词
|
||
# （与 run_full_subagent 的子代理语义对齐：执行专注、结果导向、产物落盘 workspace）
|
||
worker_system_prompt = None
|
||
if args.worker_only:
|
||
worker_system_prompt = (
|
||
"你是一个任务执行器。你只有执行工具（命令行/文件/网页/计算/时间），"
|
||
"没有记忆、人格、派生、提问能力。\n"
|
||
"规则：\n"
|
||
"1. 直接执行任务，不要提问、不要寒暄、不要情绪表达。\n"
|
||
"2. 任务完成后立即给出最终回答，不要继续调用工具。\n"
|
||
"3. 产物文件一律写入 ~/.aibox/workspace/ 并在回答中给出绝对路径。\n"
|
||
"4. 无法完成时，在最终回答中明确说明原因与已完成的部分。"
|
||
)
|
||
|
||
# 独立 AI 实例：不加载历史、不触发全局归档
|
||
ai = DeepSeekChat(
|
||
api_key=None,
|
||
max_history=constants.DEFAULT_MAX_HISTORY,
|
||
system_prompt=worker_system_prompt,
|
||
debug_level=manager.logger.level,
|
||
exit_callback=None, # AI 主动结束对话时不触发全局归档/重置
|
||
history_manager=None, # 单次模式不加载/不写对话历史
|
||
memory_db=manager.memory_db,
|
||
memo_db=manager.memo_db,
|
||
command_executor=manager.command_executor,
|
||
prompt_manager=manager.prompt_manager,
|
||
enable_memory=not args.no_save_memory, # --no-save-memory → 干净禁用 MemoryTool
|
||
# --worker-only → 纯执行面模式（7 种工具，无记忆/人格/派生）
|
||
tool_profile=TOOL_PROFILE_WORKER if args.worker_only else TOOL_PROFILE_ORCHESTRATOR,
|
||
model=args.model, # --model 覆盖配置模型
|
||
temperature=args.temperature, # --temperature 覆盖默认采样温度
|
||
)
|
||
manager.ai = ai # 挂到 manager 上，cancel() 可达
|
||
|
||
state = {"exit_code": 0, "interrupted": False, "timed_out": False}
|
||
|
||
def cli_callback(msg_type, content):
|
||
"""AI 流式输出回调：chunk→stdout（权威内容），其余→stderr（过程元信息）。"""
|
||
try:
|
||
if msg_type == "chunk":
|
||
sys.stdout.write(content)
|
||
sys.stdout.flush()
|
||
elif msg_type == "complete":
|
||
sys.stdout.write("\n")
|
||
sys.stdout.flush()
|
||
elif msg_type == "error":
|
||
sys.stderr.write(str(content) + "\n")
|
||
sys.stderr.flush()
|
||
state["exit_code"] = 2
|
||
elif msg_type == "line":
|
||
if not args.quiet:
|
||
sys.stderr.write(str(content))
|
||
sys.stderr.flush()
|
||
elif msg_type in ("reasoning", "reasoning_start", "reasoning_end"):
|
||
# thinking 流默认静默，仅 --show-reasoning 显式开启（且非 --quiet）
|
||
if args.show_reasoning and not args.quiet:
|
||
sys.stderr.write(str(content))
|
||
sys.stderr.flush()
|
||
# ai_break / dialogue_ended：CLI 无 widget 概念，忽略
|
||
except (BrokenPipeError, OSError):
|
||
# 下游管道提前关闭（如 | head -1）：终止 AI，避免无效消耗
|
||
ai.cancel()
|
||
|
||
# Ctrl+C → 三层取消（与 WebSocket interrupt 路径等价），随后以退出码 3 结束
|
||
def _sigint_handler(sig, frame):
|
||
state["interrupted"] = True
|
||
manager.cancel()
|
||
signal.signal(signal.SIGINT, _sigint_handler)
|
||
|
||
# 全局超时：守护 Timer 到点 cancel，随后以退出码 4 结束
|
||
def _on_timeout():
|
||
state["timed_out"] = True
|
||
manager.cancel()
|
||
# I 批（F9，审计 2026-08-18）：软超时兜底——cancel 后若 AI 卡死在
|
||
# 不可中断路径（HTTP 请求空窗、忽略信号的子进程），think 可能永不
|
||
# 返回、进程挂起（退出码 4 永远到不了）。宽限一个超时周期后强杀进程，
|
||
# 保证 --once 有硬性结束语义
|
||
def _hard_exit():
|
||
print(f"\n⏰ 一次性对话强制超时（{args.once_timeout * 2} 秒），已终止",
|
||
file=sys.stderr)
|
||
sys.stderr.flush()
|
||
os._exit(4)
|
||
|
||
hard_timer = threading.Timer(max(1, args.once_timeout), _hard_exit)
|
||
hard_timer.daemon = True
|
||
hard_timer.start()
|
||
timer = threading.Timer(max(1, args.once_timeout), _on_timeout)
|
||
timer.daemon = True
|
||
timer.start()
|
||
|
||
try:
|
||
response = ai.think_and_respond(args.once, cli_callback)
|
||
# 未走 error 回调但仍无有效回答时，视为链路失败
|
||
if state["exit_code"] == 0 and not state["interrupted"] and not state["timed_out"]:
|
||
if response is None or (isinstance(response, str) and not response.strip()):
|
||
state["exit_code"] = 2
|
||
except Exception as e:
|
||
# J15（审计 2026-08-19）：except KeyboardInterrupt 为死代码——SIGINT
|
||
# 已由自定义 handler 接管（_sigint_handler），KeyboardInterrupt 永不抛出
|
||
print(f"{i18n.get('error_prefix')}{e}", file=sys.stderr)
|
||
state["exit_code"] = 2
|
||
finally:
|
||
timer.cancel()
|
||
try:
|
||
ai.cancel() # 兜底清理可能残留的子进程
|
||
except Exception:
|
||
pass
|
||
|
||
if state["timed_out"]:
|
||
print(f"\n⏰ 一次性对话超时（{args.once_timeout} 秒）", file=sys.stderr)
|
||
sys.exit(4)
|
||
if state["interrupted"]:
|
||
print(f"\n{i18n.get('user_interrupted')}", file=sys.stderr)
|
||
sys.exit(3)
|
||
# 工具执行链路失败（run_command 非零码或工具异常）→ 退出码 1
|
||
if state["exit_code"] == 0 and ai.stats.get("tool_failed"):
|
||
state["exit_code"] = 1
|
||
sys.exit(state["exit_code"])
|
||
|
||
|
||
# ==================== 主程序 ====================
|
||
|
||
def main():
|
||
"""主程序入口"""
|
||
args = parse_args()
|
||
|
||
# 如果命令行指定了语言，覆盖自动检测
|
||
if args.lang:
|
||
os.environ["AI_LANGUAGE"] = args.lang
|
||
# 重新初始化i18n
|
||
global i18n
|
||
i18n = I18n()
|
||
|
||
if args.config:
|
||
os.environ["AI_CONFIG_PATH"] = args.config
|
||
|
||
config = ConfigManager()
|
||
|
||
# W8（审计 2026-08-20）：--temperature 入口钳制，防 99/-1 直达 API 400
|
||
if args.temperature is not None:
|
||
args.temperature = min(2.0, max(0.0, args.temperature))
|
||
|
||
# W11（审计 2026-08-20）：config.json 的 system/scheduler 节被写成非 dict
|
||
# 时，下方 CLI 覆盖赋值 TypeError 崩启动——回退默认节
|
||
_section_defaults = config._get_default_config()
|
||
for _section in ("system", "scheduler"):
|
||
if not isinstance(config._config.get(_section), dict):
|
||
config._config[_section] = _section_defaults.get(_section, {})
|
||
|
||
if args.debug is not None:
|
||
config._config["system"]["debug_level"] = args.debug
|
||
|
||
if args.verbose:
|
||
config._config["system"]["debug_level"] = 3
|
||
|
||
# 覆盖WebSocket主机和端口
|
||
if args.host:
|
||
config._config["system"]["websocket_host"] = args.host
|
||
if args.port:
|
||
config._config["system"]["websocket_port"] = args.port
|
||
|
||
# 调度器设置
|
||
if args.no_scheduler:
|
||
config._config["scheduler"]["enabled"] = False
|
||
# W7（审计 2026-08-20）：负值直达 asyncio.sleep(负) 空转热循环，0 被 falsy
|
||
# 静默忽略——统一钳制下限 0.1s
|
||
config._config["scheduler"]["check_interval"] = max(0.1, args.scheduler_interval)
|
||
|
||
# W5（审计 2026-08-20）：暂存 CLI 进程级覆盖，WS /reload 后重放（否则
|
||
# config.reload() 重建 _config 会静默丢弃）
|
||
_cli_overrides = {"system": {}, "scheduler": {}}
|
||
if args.debug is not None:
|
||
_cli_overrides["system"]["debug_level"] = args.debug
|
||
if args.verbose:
|
||
_cli_overrides["system"]["debug_level"] = 3
|
||
if args.host:
|
||
_cli_overrides["system"]["websocket_host"] = args.host
|
||
if args.port:
|
||
_cli_overrides["system"]["websocket_port"] = args.port
|
||
if args.no_scheduler:
|
||
_cli_overrides["scheduler"]["enabled"] = False
|
||
_cli_overrides["scheduler"]["check_interval"] = config._config["scheduler"]["check_interval"]
|
||
config._cli_overrides = {k: v for k, v in _cli_overrides.items() if v}
|
||
|
||
# 确保 ~/.aibox 目录存在
|
||
os.makedirs(constants.SAVE_DIR, exist_ok=True)
|
||
|
||
# 确保 skills 目录存在
|
||
os.makedirs(constants.SKILLS_DIR, exist_ok=True)
|
||
|
||
# 确保 prompts 目录存在
|
||
os.makedirs(constants.PROMPTS_DIR, exist_ok=True)
|
||
|
||
# 确保 workspace 工作目录存在（智能体专用文件区）
|
||
os.makedirs(constants.WORKSPACE_DIR, exist_ok=True)
|
||
|
||
Logger.init_file_logging()
|
||
|
||
# 创建命令执行器 - 现在只使用本地执行器
|
||
command_executor = SubprocessCommandExecutor()
|
||
|
||
manager = EnhancedAIManager(
|
||
debug_level=args.debug,
|
||
command_executor=command_executor,
|
||
model=args.model # H3：--model 服务模式同样生效（进程级覆盖）
|
||
)
|
||
|
||
if handle_check_commands(manager):
|
||
return
|
||
|
||
if handle_memo_commands(manager):
|
||
return
|
||
|
||
# 一次性对话模式（--once）：执行后内部 sys.exit，不启动 WebSocket
|
||
if handle_once_mode(manager, args):
|
||
return
|
||
|
||
# 运行WebSocket服务器
|
||
manager.run()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|