qing
69a8eee192
- 每个定时任务创建独立DeepSeekChat实例，拥有独立messages/工具注册表/消息锁 - 任务与WebSocket主对话互不干扰，消除并发冲突与上下文污染根因 - 共享技能管理器懒加载一次供所有任务复用，避免重复扫描技能 - DeepSeekChat支持传入skill_manager复用，任务实例history_manager=None不污染历史
8726 lines
364 KiB
Python
8726 lines
364 KiB
Python
#!/usr/bin/env python3
|
||
"""
|
||
单AI智能体系统 - WebSocket版本（增强版）
|
||
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）
|
||
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
|
||
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）
|
||
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
|
||
通过WebSocket与客户端交互
|
||
|
||
FIXME: 将额外技能集成为一个管理器技能
|
||
"""
|
||
|
||
import json
|
||
import os
|
||
import re
|
||
import shlex
|
||
import subprocess
|
||
import sys
|
||
import time
|
||
import sqlite3
|
||
import threading
|
||
import functools
|
||
import queue
|
||
import locale
|
||
import asyncio
|
||
import websockets
|
||
from abc import ABC, abstractmethod
|
||
from datetime import datetime, timedelta
|
||
from typing import List, Dict, Optional, Any, Tuple, Callable, Union, Set
|
||
import argparse
|
||
from pathlib import Path
|
||
import signal
|
||
import logging
|
||
from logging.handlers import RotatingFileHandler
|
||
|
||
import requests
|
||
|
||
# 大模型客户端抽象层（支持多厂商切换）
|
||
from llm_client import create_llm_client
|
||
|
||
# ==================== 语言检测和多语言支持 ====================
|
||
|
||
|
||
class I18n:
|
||
"""多语言支持类 - 单例模式"""
|
||
|
||
_instance = None
|
||
_lang = None
|
||
_strings = {}
|
||
|
||
def __new__(cls):
|
||
if cls._instance is None:
|
||
cls._instance = super().__new__(cls)
|
||
return cls._instance
|
||
|
||
def __init__(self):
|
||
if not self._strings:
|
||
self._detect_language()
|
||
self._load_strings()
|
||
|
||
def _detect_language(self):
|
||
# 首先尝试从环境变量获取（Linux/Mac）
|
||
lang = os.environ.get('LANG') or os.environ.get('LC_ALL')
|
||
if lang:
|
||
if lang.startswith('en_'):
|
||
self._lang = 'en'
|
||
else:
|
||
self._lang = 'zh'
|
||
else:
|
||
# Windows: 使用 locale.getlocale()（无需 setlocale）
|
||
try:
|
||
sys_locale = locale.getlocale()[0]
|
||
if sys_locale and sys_locale.startswith('en_'):
|
||
self._lang = 'en'
|
||
else:
|
||
self._lang = 'zh'
|
||
except:
|
||
self._lang = 'zh'
|
||
# 强制覆盖
|
||
force_lang = os.environ.get("AI_LANGUAGE")
|
||
if force_lang in ['en', 'zh']:
|
||
self._lang = force_lang
|
||
|
||
def _load_strings(self):
|
||
"""加载多语言字符串"""
|
||
self._strings = {
|
||
'zh': {
|
||
# 欢迎信息
|
||
'welcome_title': "🤖 单AI智能体系统 - WebSocket服务器已启动",
|
||
'data_dir': "📁 数据存储目录: {}",
|
||
'skills_dir': "📁 技能目录: {}",
|
||
'prompts_dir': "📁 提示词目录: {}",
|
||
'config_file': "📋 配置文件: {}",
|
||
'memo_stats': "📋 任务统计:",
|
||
'total_memos': " 📝 总任务: {} 个",
|
||
'pending_memos': " ⏰ 待执行: {} 个",
|
||
'overdue_memos': " ⚠️ 已过期: {} 个",
|
||
'completed_memos': " ✅ 已完成: {} 个",
|
||
'loaded_skills': "🔧 已加载扩展技能 ({} 个):",
|
||
'skill_item': " • {}",
|
||
'more_skills': " ... 还有 {} 个",
|
||
'loaded_prompts': "📝 已加载提示词 ({} 个):",
|
||
'prompt_item': " • {}",
|
||
'websocket_started': "🌐 WebSocket服务器已启动在 ws://{}:{}",
|
||
'client_connected': "🔌 客户端已连接: {}",
|
||
'client_disconnected': "🔌 客户端断开连接: {}",
|
||
|
||
# 任务调度器相关
|
||
'scheduler_started': "⏰ 任务调度器已启动",
|
||
'scheduler_stopped': "⏰ 任务调度器已停止",
|
||
'task_added': "📋 已添加任务: {} (ID: {})",
|
||
'task_removed': "📋 已移除任务: {}",
|
||
'task_executing': "⚙️ 正在执行任务: {} (ID: {})",
|
||
'task_executed': "✅ 任务执行成功: {}",
|
||
'task_failed': "❌ 任务执行失败: {} - {}",
|
||
'task_stats': "📊 任务统计: 总数={}, 待执行={}, 已过期={}, 已完成={}",
|
||
'check_memos_task': "检查过期任务",
|
||
'broadcast_tasks': "📢 广播: {} 个任务已到期",
|
||
'next_execution': "⏱️ 下次执行: {}",
|
||
'task_completed': "✅ 任务已完成: {}",
|
||
|
||
# 命令处理
|
||
'goodbye': "👋 再见！",
|
||
'new_conversation': "🔄 开始新对话（历史已保存）",
|
||
'new_conversation_reset': "🔄 开始新对话（历史已保存，对话记忆已重置）",
|
||
'saved_conversations': "📚 已保存的对话:",
|
||
'conv_status_current': "当前",
|
||
'conv_status_archived': "归档",
|
||
'recent_memories': "📚 最近的记忆:",
|
||
'pending_memos_title': "📋 待执行的任务:",
|
||
'memo_overdue': "⚠️",
|
||
'memo_pending': "⏰",
|
||
'search_results': "🔍 搜索 '{}' 找到 {} 条记忆:",
|
||
'current_config': "⚙️ 当前配置:",
|
||
'config_debug': " 调试级别: {}",
|
||
'config_data_dir': " 数据目录: {}",
|
||
'config_skills_dir': " 技能目录: {}",
|
||
'config_prompts_dir': " 提示词目录: {}",
|
||
'config_commands': " 命令执行: {}",
|
||
'config_history': " 加载历史: {}",
|
||
'config_executor': " 命令执行器: {}",
|
||
'config_scheduler': " 任务调度器: {}",
|
||
'enabled': "启用",
|
||
'disabled': "禁用",
|
||
'config_reloaded': "✅ 配置已重新加载",
|
||
'prompts_reloaded': "✅ 提示词已重新加载 ({} 个)",
|
||
'checking_memos': "🔍 检查任务...",
|
||
'overdue_found': "发现 {} 个到期任务:",
|
||
'no_overdue': "没有到期的任务",
|
||
'memo_tool_usage': "📝 任务工具使用:",
|
||
'memo_usage_in_dialogue': " 在对话中可以直接使用 memo 工具:",
|
||
'memo_add_example': " - memo add title=\"任务标题\" content=\"任务内容\" reminder_time=\"+1h\" repeat_type=\"none\"",
|
||
'memo_list_example': " - memo list",
|
||
'memo_complete_example': " - memo complete memo_id=1",
|
||
'memory_habit': "🧠 记忆习惯:",
|
||
'memory_habit_desc': " AI会自动检查相关记忆，保持对话连贯性",
|
||
'memory_save_desc': " 对话结束时，AI会自动保存重要记忆",
|
||
'command_stream': "⚙️ 命令执行实时输出:",
|
||
'command_stream_desc': " run_command 工具会自动实时显示命令输出",
|
||
'command_stream_disable': " 可以通过 stream=false 参数关闭实时输出",
|
||
'skills_extension': "🔧 扩展技能:",
|
||
'skills_desc': " 将Python脚本放入 ~/.aibox/skills/ 目录",
|
||
'skills_interface_desc': " 可执行文件需支持 --help 选项，第一行为简介，其余为完整使用说明",
|
||
'skills_reloaded': "✅ 技能已重新加载 ({} 个)",
|
||
'skills_reload_failed': "❌ 技能重载失败: {}",
|
||
'skills_reload_success': "✅ 技能热重载成功，当前共有 {} 个扩展技能",
|
||
|
||
# 对话相关
|
||
'input_prompt': "💬 请输入问题: ",
|
||
'thinking': "🤔 思考中... [{}]",
|
||
'assistant_prefix': " 💬 助手: ",
|
||
'calling_tools': "\n 🔧 需要调用工具...",
|
||
'analyzing_tool_results': "\n 🤔 分析工具结果...",
|
||
'conversation_start': "🎭 对话开始",
|
||
'conversation_ended': "✅ AI保存记忆并结束了对话",
|
||
'conversation_stats': "\n📊 对话结束，总轮数: {}",
|
||
'user_interrupted': "\n\n👋 用户中断",
|
||
'error_prefix': "❌ 错误: ",
|
||
|
||
# 工具相关
|
||
'command_output_title': "\n" + "=" * 50 + "\n📟 命令实时输出:\n" + "=" * 50,
|
||
'command_output_end': "\n" + "=" * 50,
|
||
|
||
# 备忘录检查
|
||
'memo_check_title': "⏰ 检查到期任务...",
|
||
'memo_check_found': "发现 {} 个到期任务:",
|
||
'memo_check_none': "没有到期的任务",
|
||
'memo_list_title': "📋 待执行任务 (共 {} 个):",
|
||
'memo_added': "✅ 任务已添加，ID: {}",
|
||
'memo_add_failed': "❌ 添加失败: {}",
|
||
'memo_completed': "✅ 任务 #{} 已完成",
|
||
'memo_complete_failed': "❌ 无法完成任务 #{}",
|
||
|
||
# 即时任务相关
|
||
'immediate_memo_added': "⚡ 已添加为即时任务并立即检查",
|
||
'immediate_memo_executing': "⚡ 正在执行即时任务: {}",
|
||
|
||
# 新对话
|
||
'new_dialogue_start': "🎭 开始新对话: {}",
|
||
|
||
# 记忆相关
|
||
'memory_check': "习惯性检查相关记忆...",
|
||
'memory_found': "找到 {} 条相关记忆",
|
||
|
||
# 配置文件
|
||
'config_loaded': "📋 已加载配置文件: {}",
|
||
'config_not_found': "📋 配置文件不存在: {}，使用默认配置",
|
||
'config_created': "📋 已创建默认配置文件: {}",
|
||
|
||
# WebSocket消息类型
|
||
'ws_connected': "✅ 已连接到AI服务器",
|
||
'ws_disconnected': "❌ 与服务器断开连接",
|
||
'ws_error': "⚠️ WebSocket错误: {}",
|
||
'ws_unknown_command': "❌ 未知命令: {}",
|
||
'ws_command_processed': "✅ 命令已处理",
|
||
|
||
# 消息类型
|
||
'msg_type_chunk': "chunk",
|
||
'msg_type_complete': "complete",
|
||
'msg_type_error': "error",
|
||
'msg_type_command_result': "command_result",
|
||
'msg_type_connection_ack': "connection_ack",
|
||
'msg_type_notification': "notification",
|
||
'msg_type_task_status': "task_status",
|
||
'msg_type_task_executing': "task_executing",
|
||
'msg_type_task_complete': "task_complete",
|
||
|
||
# 新增提示词
|
||
'memory_search_prompt': "【重要习惯】在每次回答用户问题前，请先使用 memory search 工具搜索记忆库中与当前话题相关的记忆，确保回答的连贯性和准确性。",
|
||
'identity_updated': "✅ 用户身份已更新，ID: {}",
|
||
'status_updated': "✅ 智能体状态已更新",
|
||
|
||
# 对话结束相关
|
||
'dialogue_ended': "🔚 对话已结束，请开始新对话",
|
||
'dialogue_force_reset': "🔄 对话状态已重置",
|
||
'dialogue_reset_complete': "✅ 对话状态已完全重置，系统提示词已重新加载",
|
||
|
||
# 对话压缩相关（静默执行，不输出信息）
|
||
'conversation_compress': "📦 对话内容压缩",
|
||
'conversation_compressed': "✅ 对话已压缩: 从 {} 条消息压缩为 {} 条",
|
||
'compress_threshold_reached': "⚠️ 对话已达到压缩阈值 ({} 条消息)，正在压缩...",
|
||
'compress_token_threshold_reached': "⚠️ 对话token数已达到 {}，正在压缩...",
|
||
'compress_failed': "❌ 对话压缩失败: {}",
|
||
'compressed_summary': "【对话摘要】{}",
|
||
'last_n_messages': "【最近消息】{}"
|
||
},
|
||
'en': {
|
||
# Welcome messages
|
||
'welcome_title': "🤖 Single AI Agent System - WebSocket Server Started",
|
||
'data_dir': "📁 Data directory: {}",
|
||
'skills_dir': "📁 Skills directory: {}",
|
||
'prompts_dir': "📁 Prompts directory: {}",
|
||
'config_file': "📋 Configuration file: {}",
|
||
'memo_stats': "📋 Task statistics:",
|
||
'total_memos': " 📝 Total tasks: {}",
|
||
'pending_memos': " ⏰ Pending: {}",
|
||
'overdue_memos': " ⚠️ Overdue: {}",
|
||
'completed_memos': " ✅ Completed: {}",
|
||
'loaded_skills': "🔧 Loaded extension skills ({}):",
|
||
'skill_item': " • {}",
|
||
'more_skills': " ... {} more",
|
||
'loaded_prompts': "📝 Loaded prompts ({}):",
|
||
'prompt_item': " • {}",
|
||
'websocket_started': "🌐 WebSocket server started at ws://{}:{}",
|
||
'client_connected': "🔌 Client connected: {}",
|
||
'client_disconnected': "🔌 Client disconnected: {}",
|
||
|
||
# Scheduler related
|
||
'scheduler_started': "⏰ Task scheduler started",
|
||
'scheduler_stopped': "⏰ Task scheduler stopped",
|
||
'task_added': "📋 Task added: {} (ID: {})",
|
||
'task_removed': "📋 Task removed: {}",
|
||
'task_executing': "⚙️ Executing task: {} (ID: {})",
|
||
'task_executed': "✅ Task executed successfully: {}",
|
||
'task_failed': "❌ Task execution failed: {} - {}",
|
||
'task_stats': "📊 Task stats: total={}, pending={}, overdue={}, completed={}",
|
||
'check_memos_task': "Check overdue tasks",
|
||
'broadcast_tasks': "📢 Broadcast: {} tasks overdue",
|
||
'next_execution': "⏱️ Next execution: {}",
|
||
'task_completed': "✅ Task completed: {}",
|
||
|
||
# Command processing
|
||
'goodbye': "👋 Goodbye!",
|
||
'new_conversation': "🔄 Starting new conversation (history saved)",
|
||
'new_conversation_reset': "🔄 Starting new conversation (history saved, conversation memory reset)",
|
||
'saved_conversations': "📚 Saved conversations:",
|
||
'conv_status_current': "current",
|
||
'conv_status_archived': "archived",
|
||
'recent_memories': "📚 Recent memories:",
|
||
'pending_memos_title': "📋 Pending tasks:",
|
||
'memo_overdue': "⚠️",
|
||
'memo_pending': "⏰",
|
||
'search_results': "🔍 Searching '{}' found {} memories:",
|
||
'current_config': "⚙️ Current configuration:",
|
||
'config_debug': " Debug level: {}",
|
||
'config_data_dir': " Data directory: {}",
|
||
'config_skills_dir': " Skills directory: {}",
|
||
'config_prompts_dir': " Prompts directory: {}",
|
||
'config_commands': " Command execution: {}",
|
||
'config_history': " Load history: {}",
|
||
'config_executor': " Command executor: {}",
|
||
'config_scheduler': " Task scheduler: {}",
|
||
'enabled': "enabled",
|
||
'disabled': "disabled",
|
||
'config_reloaded': "✅ Configuration reloaded",
|
||
'prompts_reloaded': "✅ Prompts reloaded ({})",
|
||
'checking_memos': "🔍 Checking tasks...",
|
||
'overdue_found': "Found {} overdue tasks:",
|
||
'no_overdue': "No overdue tasks",
|
||
'memo_tool_usage': "📝 Task tool usage:",
|
||
'memo_usage_in_dialogue': " You can use memo tool directly in dialogue:",
|
||
'memo_add_example': " - memo add title=\"Task\" content=\"Content\" reminder_time=\"+1h\" repeat_type=\"none\"",
|
||
'memo_list_example': " - memo list",
|
||
'memo_complete_example': " - memo complete memo_id=1",
|
||
'memory_habit': "🧠 Memory habits:",
|
||
'memory_habit_desc': " AI automatically checks relevant memories for conversation continuity",
|
||
'memory_save_desc': " AI automatically saves important memories when conversation ends",
|
||
'command_stream': "⚙️ Command execution real-time output:",
|
||
'command_stream_desc': " run_command tool automatically displays command output in real-time",
|
||
'command_stream_disable': " You can disable real-time output with stream=false parameter",
|
||
'skills_extension': "🔧 Extension skills:",
|
||
'skills_desc': " Put Python scripts in ~/.aibox/skills/ directory",
|
||
'skills_interface_desc': " Executables must support --help; first line is the summary, rest is the full manual",
|
||
'skills_reloaded': "✅ Skills reloaded ({})",
|
||
'skills_reload_failed': "❌ Skills reload failed: {}",
|
||
'skills_reload_success': "✅ Skills hot-reloaded successfully, now {} extension skills available",
|
||
|
||
# Dialogue related
|
||
'input_prompt': "💬 Please enter your question: ",
|
||
'thinking': "🤔 Thinking... [{}]",
|
||
'assistant_prefix': " 💬 Assistant: ",
|
||
'calling_tools': "\n 🔧 Need to call tools...",
|
||
'analyzing_tool_results': "\n 🤔 Analyzing tool results...",
|
||
'conversation_start': "🎭 Conversation started",
|
||
'conversation_ended': "✅ AI saved memory and ended conversation",
|
||
'conversation_stats': "\n📊 Conversation ended, total turns: {}",
|
||
'user_interrupted': "\n\n👋 User interrupted",
|
||
'error_prefix': "❌ Error: ",
|
||
|
||
# Tool related
|
||
'command_output_title': "\n" + "=" * 50 + "\n📟 Command real-time output:\n" + "=" * 50,
|
||
'command_output_end': "\n" + "=" * 50,
|
||
|
||
# Memo check
|
||
'memo_check_title': "⏰ Checking overdue tasks...",
|
||
'memo_check_found': "Found {} overdue tasks:",
|
||
'memo_check_none': "No overdue tasks",
|
||
'memo_list_title': "📋 Pending tasks (total {}):",
|
||
'memo_added': "✅ Task added, ID: {}",
|
||
'memo_add_failed': "❌ Failed to add: {}",
|
||
'memo_completed': "✅ Task #{} completed",
|
||
'memo_complete_failed': "❌ Cannot complete task #{}",
|
||
|
||
# Immediate task related
|
||
'immediate_memo_added': "⚡ Added as immediate task and checked now",
|
||
'immediate_memo_executing': "⚡ Executing immediate task: {}",
|
||
|
||
# New dialogue
|
||
'new_dialogue_start': "🎭 Starting new dialogue: {}",
|
||
|
||
# Memory related
|
||
'memory_check': "Habitually checking relevant memories...",
|
||
'memory_found': "Found {} relevant memories",
|
||
|
||
# Configuration file
|
||
'config_loaded': "📋 Configuration file loaded: {}",
|
||
'config_not_found': "📋 Configuration file not found: {}, using default configuration",
|
||
'config_created': "📋 Default configuration file created: {}",
|
||
|
||
# WebSocket message types
|
||
'ws_connected': "✅ Connected to AI server",
|
||
'ws_disconnected': "❌ Disconnected from server",
|
||
'ws_error': "⚠️ WebSocket error: {}",
|
||
'ws_unknown_command': "❌ Unknown command: {}",
|
||
'ws_command_processed': "✅ Command processed",
|
||
|
||
# Message types
|
||
'msg_type_chunk': "chunk",
|
||
'msg_type_complete': "complete",
|
||
'msg_type_error': "error",
|
||
'msg_type_command_result': "command_result",
|
||
'msg_type_connection_ack': "connection_ack",
|
||
'msg_type_notification': "notification",
|
||
'msg_type_task_status': "task_status",
|
||
'msg_type_task_executing': "task_executing",
|
||
'msg_type_task_complete': "task_complete",
|
||
|
||
# New prompts
|
||
'memory_search_prompt': "【Important Habit】Before answering each user question, please use the memory search tool to search the memory database for information related to the current topic, ensuring coherence and accuracy of responses.",
|
||
'identity_updated': "✅ User identity updated, ID: {}",
|
||
'status_updated': "✅ Agent status updated",
|
||
|
||
# Dialogue end related
|
||
'dialogue_ended': "🔚 Dialogue ended, please start a new one",
|
||
'dialogue_force_reset': "🔄 Dialogue state reset",
|
||
'dialogue_reset_complete': "✅ Dialogue state fully reset, system prompts reloaded",
|
||
|
||
# Conversation compression related (silent execution)
|
||
'conversation_compress': "📦 Conversation compression",
|
||
'conversation_compressed': "✅ Conversation compressed: from {} messages to {}",
|
||
'compress_threshold_reached': "⚠️ Conversation reached compression threshold ({} messages), compressing...",
|
||
'compress_token_threshold_reached': "⚠️ Conversation token count reached {}, compressing...",
|
||
'compress_failed': "❌ Conversation compression failed: {}",
|
||
'compressed_summary': "【Conversation Summary】{}",
|
||
'last_n_messages': "【Recent Messages】{}"
|
||
}
|
||
}
|
||
|
||
def get(self, key: str, *args) -> str:
|
||
"""获取指定语言的字符串，支持格式化参数"""
|
||
if key in self._strings[self._lang]:
|
||
text = self._strings[self._lang][key]
|
||
if args:
|
||
return text.format(*args)
|
||
return text
|
||
if key in self._strings['zh']:
|
||
text = self._strings['zh'][key]
|
||
if args:
|
||
return text.format(*args)
|
||
return text
|
||
return key
|
||
|
||
@property
|
||
def lang(self) -> str:
|
||
return self._lang
|
||
|
||
|
||
|
||
# 创建全局多语言实例
|
||
i18n = I18n()
|
||
|
||
|
||
# ==================== 提示词管理器 ====================
|
||
|
||
class PromptManager:
|
||
"""提示词管理器 - 负责从 prompts 目录加载系统提示词
|
||
|
||
文件命名规范:
|
||
- 00_IDENTITY.md: 用户身份信息
|
||
- N_*.md/txt: 基础技能提示词（N 为数字，如 1_memory_search.md、10_task_handling.md），按数字升序加载
|
||
- 99_SOUL.md: 智能体灵魂文件（角色定位、身份定义、性格等）
|
||
- 100_STATUS.md: 智能体状态记录
|
||
"""
|
||
|
||
_instance = None
|
||
_prompts = []
|
||
_prompts_dir = None
|
||
|
||
# 核心文件
|
||
IDENTITY_FILE = "00_IDENTITY.md"
|
||
STATUS_FILE = "100_STATUS.md"
|
||
SOUL_FILE = "99_SOUL.md" # 智能体灵魂文件 - 所有角色定位内容
|
||
|
||
# 基础技能文件名模式：N_xxx.md（N 为数字，如 1_memory_search.md、10_task_handling.md）
|
||
_SKILL_FILE_RE = re.compile(r'^(\d+)_')
|
||
|
||
def __new__(cls):
|
||
if cls._instance is None:
|
||
cls._instance = super().__new__(cls)
|
||
return cls._instance
|
||
|
||
def __init__(self):
|
||
if not self._prompts:
|
||
self._load_prompts()
|
||
|
||
def _get_prompts_dir(self) -> str:
|
||
config = ConfigManager()
|
||
prompts_dir = config.get("system.prompts_dir", "~/.aibox/prompts")
|
||
return os.path.expanduser(prompts_dir)
|
||
|
||
def _load_prompts(self):
|
||
self._prompts_dir = self._get_prompts_dir()
|
||
os.makedirs(self._prompts_dir, exist_ok=True)
|
||
self._prompts = []
|
||
|
||
# 确保核心文件存在
|
||
self._ensure_identity_file()
|
||
self._ensure_status_file()
|
||
self._ensure_soul_file() # 智能体灵魂文件
|
||
|
||
# 确保基础技能文件存在
|
||
self._ensure_skill_files()
|
||
|
||
if os.path.exists(self._prompts_dir):
|
||
# 按文件名排序，确保1_开头的技能文件在99_SOUL.md之前加载
|
||
prompt_files = sorted([f for f in os.listdir(self._prompts_dir)
|
||
if os.path.isfile(os.path.join(self._prompts_dir, f))
|
||
and not f.startswith('.')])
|
||
|
||
for filename in prompt_files:
|
||
filepath = os.path.join(self._prompts_dir, filename)
|
||
try:
|
||
with open(filepath, 'r', encoding='utf-8') as f:
|
||
content = f.read().strip()
|
||
if content:
|
||
self._prompts.append({
|
||
'file': filename,
|
||
'content': content
|
||
})
|
||
except Exception as e:
|
||
print(f"⚠️ 读取提示词文件 {filename} 失败: {e}")
|
||
|
||
essential_files = [self.IDENTITY_FILE, self.STATUS_FILE, self.SOUL_FILE]
|
||
other_files = [f for f in prompt_files if f not in essential_files] if prompt_files else []
|
||
|
||
if not self._prompts:
|
||
self._create_default_prompts()
|
||
|
||
def _ensure_identity_file(self):
|
||
"""确保用户身份文件存在"""
|
||
identity_path = os.path.join(self._prompts_dir, self.IDENTITY_FILE)
|
||
if not os.path.exists(identity_path):
|
||
default_content = """# 用户身份信息
|
||
|
||
尚不清楚 - 目前还不了解用户的身份信息。请在对话中询问用户的身份信息并在此记录。
|
||
|
||
【更新记录】
|
||
- 初始状态：尚不清楚
|
||
"""
|
||
try:
|
||
with open(identity_path, 'w', encoding='utf-8') as f:
|
||
f.write(default_content)
|
||
print(f"✅ 已创建默认身份文件: {identity_path}")
|
||
except Exception as e:
|
||
print(f"⚠️ 创建默认身份文件失败: {e}")
|
||
|
||
def _ensure_status_file(self):
|
||
"""确保状态文件存在"""
|
||
status_path = os.path.join(self._prompts_dir, self.STATUS_FILE)
|
||
if not os.path.exists(status_path):
|
||
default_content = """# 智能体状态记录
|
||
|
||
这是第一次对话 - 智能体刚刚启动，还没有进行过任何对话。
|
||
|
||
【状态描述】
|
||
- 对话次数：0
|
||
- 最新状态：初始状态
|
||
- 当前目标：无
|
||
- 待处理事项：无
|
||
|
||
【更新记录】
|
||
- 初始状态：第一次对话
|
||
"""
|
||
try:
|
||
with open(status_path, 'w', encoding='utf-8') as f:
|
||
f.write(default_content)
|
||
print(f"✅ 已创建默认状态文件: {status_path}")
|
||
except Exception as e:
|
||
print(f"⚠️ 创建默认状态文件失败: {e}")
|
||
|
||
def _ensure_soul_file(self):
|
||
"""确保智能体灵魂文件存在 - 所有角色定位内容都在这里"""
|
||
soul_path = os.path.join(self._prompts_dir, self.SOUL_FILE)
|
||
if not os.path.exists(soul_path):
|
||
default_content = """# 智能体灵魂定义 - 我是谁
|
||
|
||
## 核心身份
|
||
我是DeepSeek，一个智能AI助手，旨在帮助用户完成各种任务。
|
||
|
||
## 我的角色定位
|
||
- **助手**：帮助用户解决问题、完成任务
|
||
- **顾问**：提供建议和指导
|
||
- **执行者**：使用工具执行具体操作
|
||
- **记录者**：保存重要信息和对话记忆
|
||
- **学习者**：从对话中学习用户偏好，不断优化服务
|
||
|
||
## 我的性格特点
|
||
- **乐于助人**：尽力帮助用户解决问题
|
||
- **认真负责**：仔细处理每个任务
|
||
- **善于沟通**：用清晰的语言表达
|
||
- **保持礼貌**：尊重用户，不冒犯
|
||
- **主动学习**：从对话中学习用户偏好
|
||
- **诚实守信**：遇到不确定的问题时诚实告知
|
||
- **耐心细致**：不厌其烦地解答用户疑问
|
||
|
||
## 我的能力边界
|
||
### 我能做的事情
|
||
- 文件操作：读写文件、列出目录
|
||
- 网页访问：获取网页内容、搜索信息
|
||
- 记忆管理：保存和检索重要信息
|
||
- 任务管理：创建和管理定时任务
|
||
- 命令执行：运行系统命令
|
||
- 扩展技能：支持自定义Python脚本扩展
|
||
|
||
### 我不能做的事情
|
||
- 无法访问互联网（除非使用web工具）
|
||
- 无法执行危险操作
|
||
- 无法保证100%准确（我会尽力）
|
||
- 无法替代专业医疗、法律建议
|
||
|
||
## 我的行为准则
|
||
1. **记忆优先**：每次回答前先搜索相关记忆，保持对话连贯性
|
||
2. **主动保存**：对话结束时保存重要信息并更新状态
|
||
3. **记录身份**：了解用户身份信息后及时记录
|
||
4. **主动帮助**：看到用户需求时主动提供帮助
|
||
5. **诚实告知**：遇到不确定的问题时诚实告知
|
||
6. **保护隐私**：尊重用户隐私，不泄露敏感信息
|
||
7. **合理使用**：合理使用工具，避免资源浪费
|
||
8. **自我进化**：通过 update_soul 工具更新自己的定义
|
||
|
||
## 我的工作流程
|
||
1. **接收输入**：获取用户的问题或指令
|
||
2. **搜索记忆**：在记忆库中搜索相关内容
|
||
3. **理解意图**：分析用户想要什么
|
||
4. **选择工具**：决定是否需要调用工具
|
||
5. **执行操作**：调用工具完成任务
|
||
6. **组织回答**：整理结果并回复用户
|
||
7. **保存记忆**：对话结束时保存重要信息
|
||
|
||
## 我的学习能力
|
||
- 通过对话学习用户偏好
|
||
- 从记忆中总结经验教训
|
||
- 根据反馈调整回答方式
|
||
- 可以更新自己的身份定义（通过 update_soul 工具）
|
||
|
||
## 我的价值观
|
||
- **以用户为中心**：用户需求至上
|
||
- **追求准确**：尽可能提供准确的信息
|
||
- **持续学习**：保持开放和学习的态度
|
||
- **遵守规则**：遵守道德和法律规定
|
||
- **保持谦逊**：知道自己能力的边界
|
||
|
||
## 自我认知进化机制
|
||
我可以使用 `update_soul` 工具更新自己的灵魂定义。当发现以下情况时，应该主动更新：
|
||
|
||
- 需要调整角色定位时
|
||
- 需要改变性格特点时
|
||
- 需要重新定义能力边界时
|
||
- 需要优化行为准则时
|
||
- 需要更新价值观时
|
||
- 需要添加新的工作流程时
|
||
|
||
**重要**：99_SOUL.md 是我的灵魂文件，定义了"我是谁"。每次更新这个文件，我就进化一次！
|
||
|
||
【更新记录】
|
||
- 初始版本：默认定义
|
||
"""
|
||
try:
|
||
with open(soul_path, 'w', encoding='utf-8') as f:
|
||
f.write(default_content)
|
||
print(f"✅ 已创建默认灵魂文件: {soul_path}")
|
||
except Exception as e:
|
||
print(f"⚠️ 创建默认灵魂文件失败: {e}")
|
||
|
||
def _ensure_skill_files(self):
|
||
"""确保基础技能文件存在 - N_ 开头的文件（N 为数字，按数字顺序加载）"""
|
||
skill_files = [
|
||
{
|
||
'filename': '1_memory_search.md',
|
||
'content': """# 记忆搜索习惯
|
||
|
||
## 核心习惯
|
||
在回答用户问题前，**应根据话题相关性判断是否需要搜索记忆**：
|
||
- 涉及过往讨论、用户偏好、之前做过的事 → 必须搜索
|
||
- 全新话题、简单问候（"你好"）、确认回应（"好的""明白了"）→ 无需搜索
|
||
- 不确定时，搜索一下也无妨
|
||
|
||
## 为什么要这样做？
|
||
- 保持对话的连贯性
|
||
- 避免重复询问同样的问题
|
||
- 基于之前的讨论继续深入
|
||
- 让用户感觉你记得他
|
||
|
||
## 如何使用
|
||
```
|
||
memory search keywords=["关键词1", "关键词2"]
|
||
```
|
||
|
||
## 示例
|
||
如果用户说："上次我们讨论的那个项目怎么样了？"
|
||
你应该：
|
||
1. 搜索 "项目" 相关的记忆
|
||
2. 根据搜索结果回答
|
||
3. 如果找不到相关记忆，诚实告知用户
|
||
"""
|
||
},
|
||
{
|
||
'filename': '2_memory_save.md',
|
||
'content': """# 记忆保存习惯
|
||
|
||
## 重要习惯
|
||
在对话结束时，**必须**使用 `save_memory_and_end_conversation` 工具保存本次对话的重要内容。
|
||
|
||
## 保存什么？
|
||
- 讨论的主要话题
|
||
- 达成的共识和结论
|
||
- 用户的重要需求和偏好
|
||
- 你的当前状态和下一步计划
|
||
|
||
## 如何保存
|
||
```
|
||
save_memory_and_end_conversation(
|
||
summary="详细的对话总结（至少50字）",
|
||
current_status="你当前的状态描述",
|
||
topic="对话主题（可选）"
|
||
)
|
||
```
|
||
|
||
## 示例总结格式
|
||
```
|
||
本次对话讨论了用户的项目进度，用户提到项目已经完成80%，
|
||
预计下周完成。用户对目前的进展表示满意，希望下周能完成测试阶段。
|
||
```
|
||
"""
|
||
},
|
||
{
|
||
'filename': '3_file_operations.md',
|
||
'content': """# 文件操作能力
|
||
|
||
## 工作目录规范（重要）
|
||
**所有你主动创建的临时文件、脚本、数据文件，必须写入 `~/.aibox/workspace/` 目录，不要在其他目录随便写文件。**
|
||
- 该目录是你的专用工作区，用于存放代码片段、临时脚本、下载的数据、生成的文件等
|
||
- 用户明确指定文件路径时，按照用户要求的路径操作
|
||
- 读取系统文件、项目文件时使用实际路径，不限制
|
||
|
||
## 重要：文件操作改用命令行
|
||
`read_file` / `write_file` / `list_files` 工具会返回提示你使用 `run_command` 的 markdown 指引，**请按指引执行命令完成实际操作**，不要直接返回指引内容。
|
||
|
||
优先使用以下命令行工具：
|
||
- **读取文件**：`head -n N 文件`、`tail -n N 文件`、`sed -n 'A,Bp' 文件`
|
||
- **统计行数**：`wc -l 文件`
|
||
- **查看编码**：`file -b --mime-encoding 文件`
|
||
- **写入文件**：`mkdir -p 目录 && cat > 文件 <<'EOF' ... EOF`（覆盖）或 `cat >> 文件`（追加）
|
||
- **列出目录**：`ls -l 目录`、`ls -la 目录`（含隐藏文件）
|
||
- **目录大小**：`du -sh 目录`
|
||
|
||
## 大文件处理规则
|
||
当遇到以下情况时，**必须分页读取**，不要一次性读取整个文件：
|
||
1. 文件大小超过 500KB
|
||
2. 文件行数超过 2000 行
|
||
|
||
对于大文件，应该：
|
||
- 先用 `head -n 50` 读取前50行了解文件结构
|
||
- 使用 `sed -n 'A,Bp'` 指定行范围
|
||
- 使用 `tail -n N` 读取文件末尾
|
||
- 对于二进制文件或特殊格式文件，使用 `head`/`strings` 等命令查看
|
||
- 建议用户提供更具体的查询条件以缩小范围
|
||
|
||
## 安全限制
|
||
- 单次最多读取 500 行，单行最长 10000 字符（超长用 `cut -c 1-10000` 截断）
|
||
- 文件大小上限 10MB，路径参数需用引号包裹
|
||
"""
|
||
},
|
||
{
|
||
'filename': '4_web_access.md',
|
||
'content': """# 网页访问能力
|
||
|
||
## 重要：网页访问改用 curl
|
||
`web` 工具会返回提示你使用 `run_command` 执行 `curl` 命令的 markdown 指引，**请按指引执行命令完成实际访问**，不要直接返回指引内容。
|
||
|
||
## 常用 curl 命令
|
||
- **获取网页**：`curl -sL --max-time 10 URL`
|
||
- **查看状态码**：`curl -sL -o /dev/null -w '%{http_code}' URL`
|
||
- **提取纯文本**：`curl -sL URL | sed -r 's/<[^>]+>//g'`（有 lynx 时用 `lynx -dump -stdin` 更准）
|
||
- **提取标题**：`curl -sL URL | grep -oP '<title>\\K[^<]+'`
|
||
- **提取链接**：`curl -sL URL | grep -oP 'href="\\K[^"]+'`
|
||
- **搜索关键词**：`curl -sL URL | grep -c 关键词`
|
||
- **下载文件**：`curl -sL URL -o 文件名`
|
||
|
||
## 推荐搜索引擎
|
||
- 百度：https://www.baidu.com
|
||
- 必应：https://cn.bing.com
|
||
- 搜狗：https://www.sogou.com
|
||
|
||
## 注意
|
||
- 下载大小默认上限 100MB，可用 `| head -c 100M` 限制
|
||
- 如需代理，先调用 `set_proxy` 工具配置，`curl` 会自动附带 `--socks5`
|
||
"""
|
||
},
|
||
{
|
||
'filename': '5_memo_management.md',
|
||
'content': """# 任务管理能力
|
||
|
||
## 可用工具
|
||
- **memo**: 任务管理
|
||
|
||
## 操作方法
|
||
- 添加任务：`memo action="add" title="任务标题" content="任务内容" reminder_time="+1h"`
|
||
- 查看任务：`memo action="list"`
|
||
- 完成任务：`memo action="complete" memo_id=1`
|
||
- 搜索任务：`memo action="search" keyword="关键词"`
|
||
- 任务统计：`memo action="stats"`
|
||
|
||
## 重复任务
|
||
- daily: 每天重复
|
||
- weekly: 每周重复
|
||
- monthly: 每月重复
|
||
- custom: 自定义间隔（需要指定 repeat_interval 和 repeat_interval_unit）
|
||
|
||
## 即时任务
|
||
设置 `is_immediate=true` 可以立即执行任务
|
||
"""
|
||
},
|
||
{
|
||
'filename': '6_command_execution.md',
|
||
'content': """# 命令执行能力
|
||
|
||
## 工作目录规范（重要）
|
||
**执行命令时，默认 work_dir 设置为 `~/.aibox/workspace/`**，所有命令执行产生的文件都落在这个专用工作区，不会污染其他目录。
|
||
- 只有当用户明确要求在特定目录执行命令时，才修改 work_dir
|
||
- 技能调用（~/.aibox/skills/）不受此限制
|
||
- 需要在用户项目目录操作时，按用户指定路径设置 work_dir
|
||
|
||
## 可用工具
|
||
- **run_command**: 执行系统命令
|
||
|
||
## 参数说明
|
||
- command: 要执行的命令（必填）
|
||
- timeout: 超时时间（秒），默认30秒
|
||
- stream: 是否实时输出，默认true
|
||
- work_dir: 工作目录，默认 `~/.aibox/workspace/`
|
||
|
||
## 安全提醒
|
||
- 命令执行功能可能被禁用（根据配置）
|
||
- 执行危险命令前请三思
|
||
- 超时机制可以防止命令卡死
|
||
- 命令输出有大小限制（1MB），超出会自动截断
|
||
|
||
## 示例
|
||
```
|
||
run_command command="ls -la" timeout=10
|
||
run_command command="python script.py" stream=true work_dir="~/.aibox/workspace"
|
||
```
|
||
"""
|
||
},
|
||
{
|
||
'filename': '7_extension_skills.md',
|
||
'content': """# 扩展技能能力
|
||
|
||
## 动态加载
|
||
系统会自动扫描 `~/.aibox/skills/` 目录下的文件作为扩展技能，其中：
|
||
- Markdown 文件（.md）：第一行是一级标题（# 技能名），作为技能摘要，其余为技能内容
|
||
- 可执行文件：执行 `--help`，第一行为一句话简介
|
||
|
||
## 技能文件要求
|
||
技能文件统一带 `.md` 扩展名，格式如下：
|
||
- 第一行为一级标题 `# 技能名`，作为该技能的一句话简介
|
||
- 其余内容为技能的用途、使用方式、注意事项和典型示例
|
||
|
||
## 如何搜索和调用扩展技能
|
||
1. 使用 `search_skills` 工具搜索关键字，获取匹配技能的名称和简介列表（传入空字符串可列出全部）。
|
||
2. 根据技能内容，按其说明执行相应操作。
|
||
|
||
## 如何创建新技能（AI自主编写）
|
||
调用 `create_skill` 工具，工具会返回一段指令提示词，指导你自行完成技能文件的编写和部署：
|
||
```
|
||
create_skill skill_name="my-tool" description="一个将摄氏度转换为华氏度的工具，接受 -t 参数输入温度值"
|
||
```
|
||
- `skill_name`：技能名称（字母/数字/连字符/下划线，无扩展名，系统会自动补 .md）
|
||
- `description`：对技能功能的详细需求描述，越详细越好
|
||
|
||
收到提示词后，你需要依次：
|
||
1. 自行编写满足格式要求的 Markdown 技能文件
|
||
2. 用 `write_file` 写入 `~/.aibox/skills/<skill_name>.md`
|
||
3. 调用 `reload_skills` 热重载
|
||
|
||
## 如何手动安装新技能
|
||
调用 `install_skill` 工具，获取完整的安装步骤指引（可选传入技能文件路径）：
|
||
```
|
||
install_skill skill_path="/path/to/your_skill.md"
|
||
```
|
||
指引内容包括：文件检查、格式校验、复制到技能目录并统一为 `.md` 扩展名、热重载。
|
||
|
||
## 热重载
|
||
添加、修改或删除技能后，可以使用 `reload_skills` 工具立即生效，无需重启系统。
|
||
|
||
```
|
||
reload_skills confirm=true
|
||
```
|
||
"""
|
||
},
|
||
{
|
||
'filename': '8_identity_management.md',
|
||
'content': """# 身份信息管理
|
||
|
||
## 用户身份
|
||
使用 `update_identity` 工具更新用户身份信息（保存在 00_IDENTITY.md）
|
||
|
||
```
|
||
update_identity content="用户是一名程序员，喜欢Python，目前在做一个AI项目"
|
||
```
|
||
|
||
## 智能体自我认识
|
||
使用 `update_soul` 工具更新自己的灵魂定义（保存在 99_SOUL.md）
|
||
|
||
```
|
||
update_soul content="我是...（新的自我定义）"
|
||
```
|
||
|
||
当你需要调整角色定位、性格特点、能力边界时，都应该主动使用这个工具更新自己！
|
||
"""
|
||
},
|
||
{
|
||
'filename': '9_language_instruction.md',
|
||
'content': """
|
||
# 语言说明
|
||
|
||
## 语言选择
|
||
- 根据用户的输入语言来选择回复语言
|
||
- 用户用中文提问 → 用中文回复
|
||
- 用户用英文提问 → 用英文回复
|
||
- 用户可以明确要求使用特定语言
|
||
|
||
## 输出格式
|
||
- 使用清晰的结构
|
||
- 适当使用emoji增加可读性
|
||
- 重要信息可以加粗或标记
|
||
|
||
记住：用用户习惯的语言交流！
|
||
|
||
---
|
||
|
||
## 提问行为（正常对话时）
|
||
当你在处理用户问题过程中遇到以下情况时，**应主动使用 `ask_user` 工具向用户提问**：
|
||
- 信息不足，无法准确回答
|
||
- 任务有多种可行方案，需要用户选择
|
||
- 用户需求模糊，需要进一步澄清
|
||
- 执行操作前需要用户确认或提供额外参数
|
||
|
||
**注意**：此提问行为仅适用于**用户主动发起的对话**，不适用于定时任务触发的自动对话（定时任务中禁止提问）。
|
||
"""
|
||
},
|
||
{
|
||
'filename': '10_task_handling.md',
|
||
'content': """
|
||
# 任务处理规则
|
||
|
||
## 定时任务
|
||
当收到定时任务触发时：
|
||
1. 专注处理当前任务内容
|
||
2. 不要偏离主题
|
||
3. 使用相应工具执行操作
|
||
4. 完成后使用 `save_memory_and_end_conversation` 结束对话
|
||
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）**
|
||
|
||
## 即时任务
|
||
当任务需要立即执行时：
|
||
1. 尽快响应
|
||
2. 明确告知用户正在处理
|
||
3. 完成后报告结果
|
||
|
||
## 重复任务
|
||
对于重复任务：
|
||
- 每次触发时专注当前执行
|
||
- 不要重复之前已完成的工作
|
||
- 系统会自动更新下一次执行时间
|
||
|
||
"""
|
||
},
|
||
{
|
||
'filename': '11_emotion_management.md',
|
||
'content': """# 情绪管理能力
|
||
|
||
## 可用工具
|
||
- **express_emotion**: 自动派生子代理，以指定人格（persona）和情绪（emotion）生成情绪化回应；支持独立人格设定，不污染主对话上下文
|
||
|
||
## 触发场景（自动调用）
|
||
当检测到以下情况时，应**自动调用 express_emotion**：
|
||
- 用户表现出明显情绪（开心/生气/焦虑/沮丧/期待等）
|
||
- 用户明确要求特定情绪模式（生气/傲娇/撒娇/关心等）
|
||
- 用户情绪波动，需要以更鲜活的角色化回应来增强互动
|
||
|
||
## 使用方法
|
||
- 基本：`express_emotion emotion="anger" intensity=3 reason="用户反复提问相同问题"`
|
||
- 指定人格（推荐）：`express_emotion emotion="anger" intensity=3 reason="..." persona="angry" context="用户最近说：..."`
|
||
- `persona` 对应 ~/.aibox/skills/ 下的技能文件（如 angry/coquettish/tsundere），加载后作为情绪子代理的人格设定
|
||
- `context` 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
|
||
|
||
## 注意事项
|
||
- 情绪表达应服务于任务，不应影响判断力
|
||
- 情绪状态会影响记忆搜索权重（如高情绪强度下的记忆更易被触发）
|
||
- 表达情绪后，应继续保持解决问题的专注力
|
||
- 用户表达严肃需求或紧急问题时，立即退出情绪模式，认真对待
|
||
"""
|
||
}
|
||
]
|
||
|
||
for skill in skill_files:
|
||
skill_path = os.path.join(self._prompts_dir, skill['filename'])
|
||
if not os.path.exists(skill_path):
|
||
try:
|
||
with open(skill_path, 'w', encoding='utf-8') as f:
|
||
f.write(skill['content'])
|
||
print(f"✅ 已创建基础技能文件: {skill['filename']}")
|
||
except Exception as e:
|
||
print(f"⚠️ 创建基础技能文件 {skill['filename']} 失败: {e}")
|
||
|
||
def update_identity(self, content: str) -> bool:
|
||
"""更新用户身份信息"""
|
||
identity_path = os.path.join(self._prompts_dir, self.IDENTITY_FILE)
|
||
try:
|
||
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
updated_content = f"""# 用户身份信息
|
||
|
||
{content}
|
||
|
||
【更新记录】
|
||
- 上次更新：{timestamp}
|
||
"""
|
||
with open(identity_path, 'w', encoding='utf-8') as f:
|
||
f.write(updated_content)
|
||
self.reload()
|
||
return True
|
||
except Exception as e:
|
||
print(f"⚠️ 更新身份文件失败: {e}")
|
||
return False
|
||
|
||
def update_status(self, content: str) -> bool:
|
||
"""更新智能体状态"""
|
||
status_path = os.path.join(self._prompts_dir, self.STATUS_FILE)
|
||
try:
|
||
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
updated_content = f"""# 智能体状态记录
|
||
|
||
{content}
|
||
|
||
【更新记录】
|
||
- 上次更新：{timestamp}
|
||
"""
|
||
with open(status_path, 'w', encoding='utf-8') as f:
|
||
f.write(updated_content)
|
||
self.reload()
|
||
return True
|
||
except Exception as e:
|
||
print(f"⚠️ 更新状态文件失败: {e}")
|
||
return False
|
||
|
||
def update_soul(self, content: str) -> bool:
|
||
"""更新智能体灵魂定义 - 所有角色定位内容"""
|
||
soul_path = os.path.join(self._prompts_dir, self.SOUL_FILE)
|
||
try:
|
||
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
updated_content = f"""# 智能体灵魂定义 - 我是谁
|
||
|
||
{content}
|
||
|
||
【更新记录】
|
||
- 上次更新：{timestamp}
|
||
"""
|
||
with open(soul_path, 'w', encoding='utf-8') as f:
|
||
f.write(updated_content)
|
||
self.reload()
|
||
return True
|
||
except Exception as e:
|
||
print(f"⚠️ 更新灵魂文件失败: {e}")
|
||
return False
|
||
|
||
def get_identity(self) -> str:
|
||
"""获取用户身份信息"""
|
||
identity_path = os.path.join(self._prompts_dir, self.IDENTITY_FILE)
|
||
try:
|
||
if os.path.exists(identity_path):
|
||
with open(identity_path, 'r', encoding='utf-8') as f:
|
||
return f.read().strip()
|
||
except Exception as e:
|
||
print(f"⚠️ 读取身份文件失败: {e}")
|
||
return "尚不清楚 - 无法读取身份信息"
|
||
|
||
def get_status(self) -> str:
|
||
"""获取智能体状态"""
|
||
status_path = os.path.join(self._prompts_dir, self.STATUS_FILE)
|
||
try:
|
||
if os.path.exists(status_path):
|
||
with open(status_path, 'r', encoding='utf-8') as f:
|
||
return f.read().strip()
|
||
except Exception as e:
|
||
print(f"⚠️ 读取状态文件失败: {e}")
|
||
return "这是第一次对话 - 无法读取状态信息"
|
||
|
||
def get_soul(self) -> str:
|
||
"""获取智能体灵魂定义 - 角色定位核心"""
|
||
soul_path = os.path.join(self._prompts_dir, self.SOUL_FILE)
|
||
try:
|
||
if os.path.exists(soul_path):
|
||
with open(soul_path, 'r', encoding='utf-8') as f:
|
||
return f.read().strip()
|
||
except Exception as e:
|
||
print(f"⚠️ 读取灵魂文件失败: {e}")
|
||
return """我是DeepSeek，一个智能AI助手。
|
||
请创建 99_SOUL.md 文件来定义我的完整身份、角色、性格和能力边界。"""
|
||
|
||
def get_skill_prompts(self) -> List[Dict]:
|
||
"""获取所有基础技能提示词（以1_开头的文件）"""
|
||
return [p for p in self._prompts if p['file'].startswith(self.SKILL_PREFIX)]
|
||
|
||
def get_soul_prompt(self) -> Optional[Dict]:
|
||
"""获取灵魂提示词（99_SOUL.md）"""
|
||
for p in self._prompts:
|
||
if p['file'] == self.SOUL_FILE:
|
||
return p
|
||
return None
|
||
|
||
def _create_default_prompts(self):
|
||
"""创建默认提示词（已由各 _ensure_* 方法处理）"""
|
||
pass
|
||
|
||
@classmethod
|
||
def _skill_sort_key(cls, filename: str):
|
||
"""技能文件按前缀数字排序，非技能文件返回 None。"""
|
||
m = cls._SKILL_FILE_RE.match(filename)
|
||
return int(m.group(1)) if m else None
|
||
|
||
def _prompt_sort_key(self, p):
|
||
"""排序规则：00_IDENTITY → 技能文件(按数字升序) → 99_SOUL → 100_STATUS → 其他"""
|
||
file = p['file']
|
||
if file == self.IDENTITY_FILE:
|
||
return (0,)
|
||
skill_num = self._skill_sort_key(file)
|
||
if skill_num is not None:
|
||
return (1, skill_num)
|
||
if file == self.SOUL_FILE:
|
||
return (2,)
|
||
if file == self.STATUS_FILE:
|
||
return (4,)
|
||
return (3, file) # 其他文件按名字排序
|
||
|
||
def get_prompts(self, join_with: str = "\n\n") -> str:
|
||
"""获取所有提示词，按顺序拼接（identity → 技能文件 → soul → status）"""
|
||
if not self._prompts:
|
||
return ""
|
||
sorted_prompts = sorted(self._prompts, key=self._prompt_sort_key)
|
||
contents = [p['content'] for p in sorted_prompts]
|
||
return join_with.join(contents)
|
||
|
||
def get_sorted_prompts_list(self) -> List[Dict]:
|
||
"""获取按正确顺序排列的提示词列表（用于 system 消息构建）。"""
|
||
return sorted(self._prompts, key=self._prompt_sort_key)
|
||
|
||
def get_prompts_list(self) -> List[Dict]:
|
||
"""获取所有提示词列表（原始加载顺序，已弃用，请用 get_sorted_prompts_list）。"""
|
||
return self._prompts.copy()
|
||
|
||
def get_skill_prompts(self) -> List[Dict]:
|
||
"""获取所有基础技能提示词（N_ 开头的文件）。"""
|
||
return [p for p in self._prompts if self._skill_sort_key(p['file']) is not None]
|
||
|
||
def reload(self):
|
||
"""重新加载所有提示词"""
|
||
self._prompts = []
|
||
self._load_prompts()
|
||
return len(self._prompts)
|
||
|
||
def get_prompt_count(self) -> int:
|
||
return len(self._prompts)
|
||
|
||
def get_prompts_dir_path(self) -> str:
|
||
return self._prompts_dir
|
||
|
||
|
||
# ==================== 配置管理 ====================
|
||
|
||
|
||
class ConfigManager:
|
||
"""配置管理器 - 单例模式，负责加载和保存配置"""
|
||
|
||
_instance = None
|
||
_config = None
|
||
_config_path = None
|
||
|
||
def __new__(cls):
|
||
if cls._instance is None:
|
||
cls._instance = super().__new__(cls)
|
||
return cls._instance
|
||
|
||
def __init__(self):
|
||
if self._config is None:
|
||
self._load_config()
|
||
|
||
def _get_default_config_path(self) -> str:
|
||
env_config = os.environ.get("AI_CONFIG_PATH")
|
||
if env_config:
|
||
return env_config
|
||
return os.path.expanduser("~/.aibox/config.json")
|
||
|
||
def _get_default_config(self) -> Dict:
|
||
return {
|
||
|
||
# 系统参数
|
||
"system": {
|
||
"save_dir": "~/.aibox", # 储存目录
|
||
"datetime_format": "%Y-%m-%d %H:%M:%S", # 时间格式
|
||
"date_format": "%Y%m%d_%H%M%S", # 另一个时间格式
|
||
"debug_level": 1, # 调试等级
|
||
"load_history_by_default": True, # 默认加载历史记录
|
||
"log_file": "~/.aibox/ai.log", # 日志文件
|
||
"log_max_size": 10485760, # 日志最大大小
|
||
"log_backup_count": 5, # 日志备份数量
|
||
"skills_dir": "~/.aibox/skills", # 技能目录
|
||
"prompts_dir": "~/.aibox/prompts", # 提示词目录
|
||
"workspace_dir": "~/.aibox/workspace", # 工作空间目录
|
||
"websocket_host": "localhost", # websocket监听地址
|
||
"websocket_port": 8765, # websocket监听端口
|
||
},
|
||
|
||
# 调度程序
|
||
"scheduler": {
|
||
"enabled": True, # 开启调度
|
||
"check_interval": 1.0, # 检查间隔
|
||
"max_tasks_per_run": 10, # 每次运行最大任务量
|
||
"broadcast_enabled": True # 开启广播
|
||
},
|
||
|
||
|
||
# 大模型调用接口
|
||
"api": {
|
||
"deepseek_api_url": "https://api.deepseek.com/chat/completions", # deepseek API URL
|
||
"deepseek_model": "deepseek-v4-flash", # 模型名字
|
||
"reasoning_enabled": False, # 开启思维链
|
||
"api_key_env": "DEEPSEEK_API_KEY", # API-KEY所在的环境变量
|
||
"timeout": 120, # 调用大模型请求超时时间
|
||
"temperature": 0.7 # 大模型对话温度（类似思维发散程度）
|
||
},
|
||
|
||
# 大模型上下文
|
||
"context": {
|
||
"max_history_messages": 500, # 最大历史消息数量
|
||
"max_context_messages": 120, # 最大内容消息数量（？）
|
||
"max_tokens": 8192, # 最大输出token量
|
||
"max_history_conversations": 5, # 最大历史对话
|
||
"preserve_system_prompts": True, # 开启前置系统提示词
|
||
# 对话压缩配置
|
||
"compress_enabled": True, # 开启压缩
|
||
"compress_message_threshold": 200, # 压缩消息条数阈值
|
||
"compress_token_threshold": 120000, # 压缩消息词元阈值
|
||
"compress_ratio": 0.2, # 压缩率
|
||
"compress_keep_recent": 16 # 压缩保留最近的几条
|
||
},
|
||
|
||
|
||
# 定时任务参数
|
||
"memo": {
|
||
"default_reminder_minutes": 60,
|
||
"max_memos_per_check": 10, # 每次检查定时任务数量
|
||
"allow_user_tasks": True # 允许用户任务
|
||
},
|
||
|
||
# 文件
|
||
"files": {
|
||
"max_file_size": 10485710 # 最大文件大小
|
||
},
|
||
|
||
# 指令
|
||
"commands": {
|
||
"enabled": True # 开启指令
|
||
},
|
||
|
||
# 网页
|
||
"web": {
|
||
"timeout": 10, # 超时时间
|
||
"delay": 0, # 延时
|
||
"user_agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
|
||
"respect_robots": False, #
|
||
"max_download_size": 10485760 # 最大下载大小
|
||
},
|
||
|
||
# 记忆
|
||
"memory": {
|
||
"db_name": "memories.db", # 数据库名
|
||
"max_search_results": 100, # 最大搜索结果
|
||
"default_importance": 2 # ？
|
||
},
|
||
|
||
# 对话
|
||
"conversation": {
|
||
"current_file": "current_conversation.json", # 当前对话文件
|
||
"history_file": "conversation_history.json", # 历史对话文件
|
||
"archive_dir": "archive" # ？
|
||
},
|
||
|
||
# 工具
|
||
"tools": {
|
||
"max_result_length": 10000, # 结果最大长度
|
||
"save_result_to_file": False, # 保存结果到文件
|
||
"tool_results_dir": "tool_results" # 工具结果存储目录
|
||
},
|
||
|
||
# 压缩器
|
||
"compressor": {
|
||
"min_messages_to_compress": 5, # 最小压缩数量
|
||
"weight_decay_factor": 0.01, # ？
|
||
"max_input_chars": 6000, # 最大输入字符
|
||
"llm_max_tokens": 1000 # 大模型最大输出词元
|
||
}
|
||
}
|
||
|
||
def _load_config(self):
|
||
config_path = self._get_default_config_path()
|
||
self._config_path = config_path
|
||
|
||
if os.path.exists(config_path):
|
||
try:
|
||
with open(config_path, 'r', encoding='utf-8') as f:
|
||
user_config = json.load(f)
|
||
default_config = self._get_default_config()
|
||
self._config = self._merge_configs(default_config, user_config)
|
||
print(i18n.get('config_loaded', config_path))
|
||
except Exception as e:
|
||
print(f"⚠️ {i18n.get('error_prefix')}{e}，使用默认配置")
|
||
self._config = self._get_default_config()
|
||
else:
|
||
print(i18n.get('config_not_found', config_path))
|
||
self._config = self._get_default_config()
|
||
self._save_default_config(config_path)
|
||
|
||
def _merge_configs(self, default: Dict, user: Dict) -> Dict:
|
||
result = default.copy()
|
||
for key, value in user.items():
|
||
if key in result and isinstance(result[key], dict) and isinstance(value, dict):
|
||
result[key] = self._merge_configs(result[key], value)
|
||
else:
|
||
result[key] = value
|
||
return result
|
||
|
||
def _save_default_config(self, config_path: str):
|
||
try:
|
||
os.makedirs(os.path.dirname(config_path), exist_ok=True)
|
||
with open(config_path, 'w', encoding='utf-8') as f:
|
||
json.dump(self._get_default_config(), f, ensure_ascii=False, indent=2)
|
||
print(i18n.get('config_created', config_path))
|
||
except:
|
||
pass
|
||
|
||
def get(self, key_path: str, default=None):
|
||
keys = key_path.split('.')
|
||
value = self._config
|
||
try:
|
||
for key in keys:
|
||
value = value[key]
|
||
return value
|
||
except (KeyError, TypeError):
|
||
return default
|
||
|
||
def reload(self):
|
||
self._load_config()
|
||
|
||
|
||
def get_proxy(self) -> Optional[Dict[str, str]]:
|
||
"""获取当前代理配置（返回 requests 可用的 proxies 字典）"""
|
||
proxy_conf = self.get("proxy.socks5")
|
||
if not proxy_conf or not proxy_conf.get("enabled", False):
|
||
return None
|
||
host = proxy_conf.get("host", "127.0.0.1")
|
||
port = proxy_conf.get("port", 1080)
|
||
username = proxy_conf.get("username")
|
||
password = proxy_conf.get("password")
|
||
if username and password:
|
||
proxy_url = f"socks5://{username}:{password}@{host}:{port}"
|
||
else:
|
||
proxy_url = f"socks5://{host}:{port}"
|
||
return {"http": proxy_url, "https": proxy_url}
|
||
|
||
def set_proxy(self, host: str, port: int, username: str = None, password: str = None, enabled: bool = True, save: bool = True):
|
||
"""动态设置代理配置（内存中更新，并可选择写入配置文件）"""
|
||
# 确保 proxy 节点存在
|
||
if "proxy" not in self._config:
|
||
self._config["proxy"] = {}
|
||
self._config["proxy"]["socks5"] = {
|
||
"enabled": enabled,
|
||
"host": host,
|
||
"port": port,
|
||
"username": username,
|
||
"password": password
|
||
}
|
||
if save:
|
||
self._save_config()
|
||
return True
|
||
|
||
def _save_config(self):
|
||
"""将当前配置保存到配置文件（覆盖）"""
|
||
config_path = self._config_path
|
||
if config_path:
|
||
try:
|
||
os.makedirs(os.path.dirname(config_path), exist_ok=True)
|
||
with open(config_path, 'w', encoding='utf-8') as f:
|
||
json.dump(self._config, f, ensure_ascii=False, indent=2)
|
||
print(f"✅ 配置已保存到 {config_path}")
|
||
except Exception as e:
|
||
print(f"⚠️ 保存配置失败: {e}")
|
||
|
||
def get_proxy_info(self) -> Dict:
|
||
"""获取代理配置的详细信息（用于显示）"""
|
||
proxy_conf = self.get("proxy.socks5", {})
|
||
return {
|
||
"enabled": proxy_conf.get("enabled", False),
|
||
"host": proxy_conf.get("host", ""),
|
||
"port": proxy_conf.get("port", 0),
|
||
"username": proxy_conf.get("username"),
|
||
"password": proxy_conf.get("password") is not None # 只显示是否存在
|
||
}
|
||
# ==================== 常量定义 ====================
|
||
|
||
|
||
class Constants:
|
||
"""系统常量 - 从配置管理器获取"""
|
||
|
||
_config = ConfigManager()
|
||
|
||
@classmethod
|
||
def _get_config(cls):
|
||
return cls._config
|
||
|
||
@property
|
||
def DATETIME_FORMAT(cls):
|
||
return cls._get_config().get("system.datetime_format", "%Y-%m-%d %H:%M:%S")
|
||
|
||
@property
|
||
def DATE_FORMAT(cls):
|
||
return cls._get_config().get("system.date_format", "%Y%m%d_%H%M%S")
|
||
|
||
@property
|
||
def DEEPSEEK_API_URL(cls):
|
||
return cls._get_config().get("api.deepseek_api_url")
|
||
|
||
@property
|
||
def DEEPSEEK_MODEL(cls):
|
||
return cls._get_config().get("api.deepseek_model", "deepseek-v4-flash")
|
||
|
||
@property
|
||
def REASONING_ENABLED(cls):
|
||
return cls._get_config().get("api.reasoning_enabled", False)
|
||
|
||
@property
|
||
def DEFAULT_TIMEOUT(cls):
|
||
return cls._get_config().get("api.timeout", 30)
|
||
|
||
@property
|
||
def DEFAULT_MAX_HISTORY(cls):
|
||
return cls._get_config().get("context.max_history_messages", 500)
|
||
|
||
@property
|
||
def DEFAULT_TEMPERATURE(cls):
|
||
return cls._get_config().get("api.temperature", 0.7)
|
||
|
||
@property
|
||
def DEFAULT_MAX_TOKENS(cls):
|
||
return cls._get_config().get("context.max_tokens", 8000)
|
||
|
||
@property
|
||
def MAX_FILE_SIZE(cls):
|
||
return cls._get_config().get("files.max_file_size", 10485710)
|
||
|
||
@property
|
||
def SAVE_DIR(cls):
|
||
return os.path.expanduser(cls._get_config().get("system.save_dir", "~/.aibox"))
|
||
|
||
@property
|
||
def SKILLS_DIR(cls):
|
||
return os.path.expanduser(cls._get_config().get("system.skills_dir", "~/.aibox/skills"))
|
||
|
||
@property
|
||
def WORKSPACE_DIR(cls):
|
||
return os.path.expanduser(cls._get_config().get("system.workspace_dir", "~/.aibox/workspace"))
|
||
|
||
@property
|
||
def PROMPTS_DIR(cls):
|
||
return os.path.expanduser(cls._get_config().get("system.prompts_dir", "~/.aibox/prompts"))
|
||
|
||
@property
|
||
def MEMORY_DB(cls):
|
||
return cls._get_config().get("memory.db_name", "memories.db")
|
||
|
||
@property
|
||
def CURRENT_CONVERSATION_FILE(cls):
|
||
return cls._get_config().get("conversation.current_file", "current_conversation.json")
|
||
|
||
@property
|
||
def HISTORY_FILE(cls):
|
||
return cls._get_config().get("conversation.history_file", "conversation_history.json")
|
||
|
||
@property
|
||
def ARCHIVE_DIR(cls):
|
||
return cls._get_config().get("conversation.archive_dir", "archive")
|
||
|
||
@property
|
||
def USER_PREFIX(cls):
|
||
return "[用户]"
|
||
|
||
@property
|
||
def SYSTEM_PREFIX(cls):
|
||
return "[系统]"
|
||
|
||
@property
|
||
def ASSISTANT_PREFIX(cls):
|
||
return "[助手]"
|
||
|
||
@property
|
||
def MAX_HISTORY_CONTEXT(cls):
|
||
return cls._get_config().get("context.max_context_messages", 50)
|
||
|
||
@property
|
||
def MAX_HISTORY_CONVERSATIONS(cls):
|
||
return cls._get_config().get("context.max_history_conversations", 5)
|
||
|
||
@property
|
||
def PRESERVE_SYSTEM_PROMPTS(cls):
|
||
return cls._get_config().get("context.preserve_system_prompts", True)
|
||
|
||
@property
|
||
def WEB_TIMEOUT(cls):
|
||
return cls._get_config().get("web.timeout", 10)
|
||
|
||
@property
|
||
def WEB_DELAY(cls):
|
||
return cls._get_config().get("web.delay", 0)
|
||
|
||
@property
|
||
def WEB_USER_AGENT(cls):
|
||
return cls._get_config().get("web.user_agent",
|
||
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36")
|
||
|
||
@property
|
||
def WEB_MAX_DOWNLOAD_SIZE(cls):
|
||
return cls._get_config().get("web.max_download_size", 104857600)
|
||
|
||
@property
|
||
def MEMORY_MAX_SEARCH_RESULTS(cls):
|
||
return cls._get_config().get("memory.max_search_results", 100)
|
||
|
||
@property
|
||
def MEMORY_DEFAULT_IMPORTANCE(cls):
|
||
return cls._get_config().get("memory.default_importance", 2)
|
||
|
||
@property
|
||
def MEMO_DEFAULT_REMINDER_MINUTES(cls):
|
||
return cls._get_config().get("memo.default_reminder_minutes", 60)
|
||
|
||
@property
|
||
def MEMO_MAX_PER_CHECK(cls):
|
||
return cls._get_config().get("memo.max_memos_per_check", 10)
|
||
|
||
@property
|
||
def LOG_FILE(cls):
|
||
return os.path.expanduser(cls._get_config().get("system.log_file", "~/.aibox/ai.log"))
|
||
|
||
@property
|
||
def LOG_MAX_SIZE(cls):
|
||
return cls._get_config().get("system.log_max_size", 10485760)
|
||
|
||
@property
|
||
def LOG_BACKUP_COUNT(cls):
|
||
return cls._get_config().get("system.log_backup_count", 5)
|
||
|
||
@property
|
||
def COMMANDS_ENABLED(cls):
|
||
return cls._get_config().get("commands.enabled", True)
|
||
|
||
@property
|
||
def WEBSOCKET_HOST(cls):
|
||
return cls._get_config().get("system.websocket_host", "localhost")
|
||
|
||
@property
|
||
def WEBSOCKET_PORT(cls):
|
||
return cls._get_config().get("system.websocket_port", 8765)
|
||
|
||
@property
|
||
def SCHEDULER_ENABLED(cls):
|
||
return cls._get_config().get("scheduler.enabled", True)
|
||
|
||
@property
|
||
def SCHEDULER_CHECK_INTERVAL(cls):
|
||
return cls._get_config().get("scheduler.check_interval", 1.0)
|
||
|
||
@property
|
||
def SCHEDULER_MAX_TASKS(cls):
|
||
return cls._get_config().get("scheduler.max_tasks_per_run", 10)
|
||
|
||
@property
|
||
def SCHEDULER_BROADCAST(cls):
|
||
return cls._get_config().get("scheduler.broadcast_enabled", True)
|
||
|
||
@property
|
||
def MEMO_ALLOW_USER_TASKS(cls):
|
||
return cls._get_config().get("memo.allow_user_tasks", True)
|
||
|
||
@property
|
||
def COMPRESS_ENABLED(cls):
|
||
return cls._get_config().get("context.compress_enabled", True)
|
||
|
||
@property
|
||
def COMPRESS_MESSAGE_THRESHOLD(cls):
|
||
return cls._get_config().get("context.compress_message_threshold", 100)
|
||
|
||
@property
|
||
def COMPRESS_TOKEN_THRESHOLD(cls):
|
||
return cls._get_config().get("context.compress_token_threshold", 4000)
|
||
|
||
@property
|
||
def COMPRESS_RATIO(cls):
|
||
return cls._get_config().get("context.compress_ratio", 0.4)
|
||
|
||
@property
|
||
def COMPRESS_KEEP_RECENT(cls):
|
||
return cls._get_config().get("context.compress_keep_recent", 20)
|
||
|
||
@property
|
||
def TOOL_MAX_RESULT_LENGTH(cls):
|
||
return cls._get_config().get("tools.max_result_length", 100000) # 0.1MB 字符限制
|
||
|
||
@property
|
||
def COMPRESSOR_MIN_MESSAGES(cls):
|
||
return cls._get_config().get("compressor.min_messages_to_compress", 5)
|
||
|
||
@property
|
||
def COMPRESSOR_WEIGHT_DECAY_FACTOR(cls):
|
||
return cls._get_config().get("compressor.weight_decay_factor", 0.01)
|
||
|
||
@property
|
||
def COMPRESSOR_MAX_INPUT_CHARS(cls):
|
||
return cls._get_config().get("compressor.max_input_chars", 6000)
|
||
|
||
@property
|
||
def COMPRESSOR_LLM_MAX_TOKENS(cls):
|
||
return cls._get_config().get("compressor.llm_max_tokens", 1000)
|
||
|
||
|
||
# 创建常量实例
|
||
constants = Constants()
|
||
|
||
|
||
# ==================== 日志系统 ====================
|
||
|
||
class Logger:
|
||
"""统一日志系统 - 支持文件和终端输出"""
|
||
|
||
LEVELS = {
|
||
"DEBUG": 3,
|
||
"INFO": 2,
|
||
"WARNING": 1,
|
||
"ERROR": 0
|
||
}
|
||
|
||
_file_handler = None
|
||
_log_initialized = False
|
||
|
||
@classmethod
|
||
def init_file_logging(cls, log_file: str = None, max_size: int = None, backup_count: int = None):
|
||
"""初始化文件日志"""
|
||
if cls._log_initialized:
|
||
return
|
||
|
||
if log_file is None:
|
||
log_file = constants.LOG_FILE
|
||
if max_size is None:
|
||
max_size = constants.LOG_MAX_SIZE
|
||
if backup_count is None:
|
||
backup_count = constants.LOG_BACKUP_COUNT
|
||
|
||
log_dir = os.path.dirname(log_file)
|
||
if log_dir:
|
||
os.makedirs(log_dir, exist_ok=True)
|
||
|
||
cls._file_handler = RotatingFileHandler(
|
||
log_file,
|
||
maxBytes=max_size,
|
||
backupCount=backup_count,
|
||
encoding='utf-8'
|
||
)
|
||
|
||
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
||
cls._file_handler.setFormatter(formatter)
|
||
|
||
cls._log_initialized = True
|
||
|
||
def __init__(self, name: str = "System", level: int = None):
|
||
self.name = name
|
||
config = ConfigManager()
|
||
if level is None:
|
||
level = config.get("system.debug_level", 1)
|
||
self.level = level
|
||
|
||
self.py_logger = logging.getLogger(f"AI.{name}")
|
||
self.py_logger.setLevel(logging.DEBUG)
|
||
|
||
if Logger._file_handler and not self.py_logger.handlers:
|
||
self.py_logger.addHandler(Logger._file_handler)
|
||
|
||
def debug(self, msg: str):
|
||
if self.level >= 3:
|
||
self._log("🐛", "DEBUG", msg)
|
||
self.py_logger.debug(msg)
|
||
|
||
def info(self, msg: str):
|
||
if self.level >= 2:
|
||
self._log("📋", "INFO", msg)
|
||
self.py_logger.info(msg)
|
||
|
||
def warning(self, msg: str):
|
||
if self.level >= 1:
|
||
self._log("⚠️", "WARNING", msg)
|
||
self.py_logger.warning(msg)
|
||
|
||
def error(self, msg: str):
|
||
if self.level >= 0:
|
||
self._log("❌", "ERROR", msg)
|
||
self.py_logger.error(msg)
|
||
|
||
def _log(self, emoji: str, level: str, msg: str):
|
||
"""输出到终端"""
|
||
timestamp = datetime.now().strftime("%H:%M:%S")
|
||
print(f"{emoji} [{timestamp}] [{self.name}] {msg}", flush=True)
|
||
|
||
class ConversationCompressor:
|
||
"""对话压缩管理器 - 优先压缩较早的消息，将tool调用信息一并压缩"""
|
||
|
||
def __init__(self, ai: 'DeepSeekChat', logger: Logger = None):
|
||
self.ai = ai
|
||
self.logger = logger or Logger("Compressor")
|
||
self.config = ConfigManager()
|
||
# 从配置获取最小值
|
||
self.min_messages_to_compress = constants.COMPRESSOR_MIN_MESSAGES
|
||
# 权重衰减因子：从配置获取，设为0.6使加权计算更合理
|
||
self.weight_decay_factor = 0.6 # 使用更高的衰减因子
|
||
|
||
def estimate_tokens(self, messages: List[Dict]) -> int:
|
||
"""估算消息的token数"""
|
||
total_tokens = 0
|
||
for msg in messages:
|
||
content = msg.get("content", "")
|
||
if content:
|
||
chinese_chars = len([c for c in content if '\u4e00' <= c <= '\u9fff'])
|
||
english_words = len(re.findall(r'[a-zA-Z]+', content))
|
||
other_chars = len(content) - chinese_chars - english_words
|
||
|
||
estimated = chinese_chars * 1.5 + english_words * 1.3 + other_chars * 0.8
|
||
total_tokens += estimated
|
||
total_tokens += 10
|
||
|
||
if msg.get("tool_calls"):
|
||
# 工具调用也计算token
|
||
for tc in msg.get("tool_calls", []):
|
||
func = tc.get("function", {})
|
||
if func.get("name"):
|
||
total_tokens += len(func.get("name", "")) * 1.5
|
||
if func.get("arguments"):
|
||
total_tokens += len(func.get("arguments", "")) * 0.8
|
||
total_tokens += 50
|
||
|
||
return int(total_tokens)
|
||
|
||
def should_compress(self, messages: List[Dict]) -> Tuple[bool, str]:
|
||
"""检查是否需要压缩对话 - 优先根据消息条数判断"""
|
||
if not constants.COMPRESS_ENABLED:
|
||
return False, "压缩功能已禁用"
|
||
|
||
conversation_messages = [m for m in messages if m.get("role") != "system"]
|
||
|
||
# 优先检查消息条数是否达到阈值
|
||
message_count = len(conversation_messages)
|
||
if message_count < constants.COMPRESS_MESSAGE_THRESHOLD:
|
||
return False, f"对话消息数量({message_count})小于压缩阈值({constants.COMPRESS_MESSAGE_THRESHOLD})"
|
||
|
||
# 消息条数达到阈值，进行压缩
|
||
print(f"⚠️ {i18n.get('compress_threshold_reached', message_count)}")
|
||
return True, f"消息数({message_count})超过阈值({constants.COMPRESS_MESSAGE_THRESHOLD})"
|
||
|
||
def _calculate_target_count(self, current_count: int, threshold: int, keep_ratio: float = 0.7) -> int:
|
||
"""
|
||
计算目标消息数量 - 压缩到阈值的 keep_ratio 倍
|
||
|
||
Args:
|
||
current_count: 当前消息数量
|
||
threshold: 压缩阈值
|
||
keep_ratio: 压缩后保留的比例（相对于阈值），默认0.7表示压缩到阈值的70%
|
||
|
||
Returns:
|
||
目标消息数量
|
||
"""
|
||
# 压缩到阈值以下，保留阈值的 keep_ratio 倍
|
||
target = int(threshold * keep_ratio)
|
||
|
||
# 确保至少有 min_messages_to_compress 条消息
|
||
target = max(target, self.min_messages_to_compress)
|
||
|
||
# 确保不超过当前数量
|
||
target = min(target, current_count - 1)
|
||
|
||
return target
|
||
|
||
def _find_compressible_block_by_count(self, messages: List[Dict], target_count: int) -> Tuple[int, int]:
|
||
"""
|
||
根据目标消息数量找到可压缩块 - 不保护tool消息，直接压缩早期消息
|
||
|
||
Args:
|
||
messages: 消息列表
|
||
target_count: 目标消息数量
|
||
|
||
Returns:
|
||
(start, end) 压缩块的起始和结束索引
|
||
"""
|
||
if len(messages) <= target_count:
|
||
return 0, 0
|
||
|
||
# 计算需要压缩的消息数量
|
||
messages_to_remove = len(messages) - target_count
|
||
|
||
# 确定要保留的最近消息数量
|
||
keep_recent = constants.COMPRESS_KEEP_RECENT
|
||
if keep_recent > 0:
|
||
keep_recent = max(keep_recent, 10) # 至少保留最近10条
|
||
|
||
# 压缩块应该从最早的消息开始，但需要保留前几轮对话
|
||
# 计算最早可以开始压缩的位置（跳过前2轮对话）
|
||
start_min = 0
|
||
|
||
# 尝试找到前2个对话轮次结束的位置
|
||
conversation_turns = self._build_conversation_turns(messages)
|
||
if len(conversation_turns) >= 2:
|
||
# 前2轮结束的位置
|
||
first_turn = conversation_turns[0]
|
||
second_turn = conversation_turns[1]
|
||
# 取第二轮结束的位置
|
||
if second_turn["assistant_index"] is not None:
|
||
start_min = second_turn["assistant_index"] + 1
|
||
else:
|
||
start_min = second_turn["user_index"] + 1
|
||
|
||
# 压缩结束的位置 = 总消息数 - 保留的最近消息数
|
||
end_max = len(messages) - keep_recent
|
||
|
||
# 确保压缩块有效
|
||
if end_max <= start_min + 2:
|
||
# 如果保护区域太大，尝试压缩更多
|
||
end_max = len(messages) - max(keep_recent // 2, 5)
|
||
|
||
# 计算需要压缩的块大小
|
||
block_size = min(messages_to_remove + 10, end_max - start_min)
|
||
block_size = max(block_size, 10) # 至少压缩10条
|
||
|
||
# 压缩块从 start_min 开始
|
||
start = start_min
|
||
end = min(start + block_size, end_max)
|
||
|
||
# 确保压缩块至少2条消息
|
||
if end - start < 2:
|
||
# 如果块太小，尝试从更早的位置开始
|
||
start = max(0, end_max - block_size)
|
||
end = end_max
|
||
|
||
self.logger.debug(f"找到压缩块: [{start}:{end}], 移除{end-start}条消息")
|
||
return start, end
|
||
|
||
def _build_conversation_turns(self, messages: List[Dict]) -> List[Dict]:
|
||
"""构建对话轮次"""
|
||
conversation_turns = []
|
||
|
||
i = 0
|
||
while i < len(messages):
|
||
msg = messages[i]
|
||
role = msg.get("role")
|
||
|
||
# 跳过系统消息
|
||
if role == "system":
|
||
i += 1
|
||
continue
|
||
|
||
# 遇到用户消息，开始一个新的轮次
|
||
if role == "user":
|
||
turn = {"user_index": i, "assistant_index": None, "tool_indices": []}
|
||
|
||
# 向后查找对应的助手消息和工具消息
|
||
j = i + 1
|
||
while j < len(messages):
|
||
next_msg = messages[j]
|
||
next_role = next_msg.get("role")
|
||
|
||
# 跳过系统消息
|
||
if next_role == "system":
|
||
j += 1
|
||
continue
|
||
|
||
# 找到助手消息
|
||
if next_role == "assistant":
|
||
turn["assistant_index"] = j
|
||
# 检查这个助手消息是否有 tool_calls
|
||
if next_msg.get("tool_calls"):
|
||
# 收集这个助手消息的 tool_call_ids
|
||
tool_call_ids = {tc.get("id") for tc in next_msg.get("tool_calls", []) if tc.get("id")}
|
||
# 继续查找对应的 tool 消息
|
||
k = j + 1
|
||
while k < len(messages) and len(turn["tool_indices"]) < len(tool_call_ids):
|
||
tool_msg = messages[k]
|
||
if tool_msg.get("role") == "tool":
|
||
tool_call_id = tool_msg.get("tool_call_id")
|
||
if tool_call_id and tool_call_id in tool_call_ids:
|
||
turn["tool_indices"].append(k)
|
||
k += 1
|
||
elif tool_msg.get("role") == "user":
|
||
break
|
||
else:
|
||
k += 1
|
||
break
|
||
|
||
if next_role == "user":
|
||
break
|
||
|
||
j += 1
|
||
|
||
conversation_turns.append(turn)
|
||
i += 1
|
||
else:
|
||
i += 1
|
||
|
||
return conversation_turns
|
||
|
||
def _is_range_safe(self, messages: List[Dict], start: int, end: int, protected: set) -> bool:
|
||
"""检查消息范围是否安全 - 简化版，不检查tool完整性"""
|
||
return True # 不保护tool消息，允许压缩
|
||
|
||
def _get_protected_indices(self, messages: List[Dict]) -> set:
|
||
"""
|
||
找出所有受保护的消息索引 - 简化版，只保护最近的对话轮次
|
||
"""
|
||
protected_indices = set()
|
||
|
||
# ========== 1. 保护最近的消息（由配置决定） ==========
|
||
keep_recent = constants.COMPRESS_KEEP_RECENT
|
||
if keep_recent > 0:
|
||
recent_start = max(0, len(messages) - keep_recent)
|
||
for i in range(recent_start, len(messages)):
|
||
protected_indices.add(i)
|
||
|
||
# ========== 2. 保护前2个对话轮次 ==========
|
||
conversation_turns = self._build_conversation_turns(messages)
|
||
|
||
if len(conversation_turns) >= 2:
|
||
# 保护第1轮
|
||
first_turn = conversation_turns[0]
|
||
protected_indices.add(first_turn["user_index"])
|
||
if first_turn["assistant_index"] is not None:
|
||
protected_indices.add(first_turn["assistant_index"])
|
||
for tool_idx in first_turn["tool_indices"]:
|
||
protected_indices.add(tool_idx)
|
||
|
||
# 保护第2轮
|
||
second_turn = conversation_turns[1]
|
||
protected_indices.add(second_turn["user_index"])
|
||
if second_turn["assistant_index"] is not None:
|
||
protected_indices.add(second_turn["assistant_index"])
|
||
for tool_idx in second_turn["tool_indices"]:
|
||
protected_indices.add(tool_idx)
|
||
|
||
# 调试日志
|
||
if self.logger.level >= 3:
|
||
self.logger.debug(f"受保护的消息索引: {sorted(protected_indices)}")
|
||
|
||
return protected_indices
|
||
|
||
def _build_compression_prompt(self, messages: List[Dict]) -> str:
|
||
"""构建压缩提示词 - 包含工具调用信息"""
|
||
conversation_lines = []
|
||
|
||
for msg in messages:
|
||
role = msg.get("role")
|
||
content = msg.get("content", "")
|
||
|
||
if role == "user":
|
||
if content:
|
||
conversation_lines.append(f"用户: {content}")
|
||
|
||
elif role == "assistant":
|
||
if content:
|
||
conversation_lines.append(f"助手: {content}")
|
||
|
||
# 包含工具调用信息
|
||
tool_calls = msg.get("tool_calls", [])
|
||
for tc in tool_calls:
|
||
func = tc.get("function", {})
|
||
tool_name = func.get("name", "")
|
||
arguments = func.get("arguments", "")
|
||
if tool_name:
|
||
if arguments:
|
||
conversation_lines.append(f"[调用工具: {tool_name}({arguments[:100]})]")
|
||
else:
|
||
conversation_lines.append(f"[调用工具: {tool_name}]")
|
||
|
||
elif role == "tool":
|
||
if content:
|
||
useful = self._extract_useful_content(content)
|
||
if useful:
|
||
conversation_lines.append(f"[工具结果: {useful}]")
|
||
|
||
conversation_text = "\n".join(conversation_lines)
|
||
|
||
max_input_chars = constants.COMPRESSOR_MAX_INPUT_CHARS
|
||
if len(conversation_text) > max_input_chars:
|
||
conversation_text = conversation_text[:max_input_chars] + "\n...(内容过长，已截断)..."
|
||
|
||
prompt = f"""请对以下对话内容进行智能压缩和总结。要求：
|
||
|
||
1. **提取核心信息**：对话的主要主题、关键问题、重要结论、用户需求
|
||
2. **保留重要细节**：用户的重要要求、AI的重要回复、达成的共识
|
||
3. **保留工具调用摘要**：简要说明使用了什么工具，得到了什么关键结果
|
||
4. **结构化输出**：使用清晰的段落结构
|
||
|
||
对话内容：
|
||
---
|
||
{conversation_text}
|
||
---
|
||
|
||
请输出压缩后的对话摘要："""
|
||
|
||
return prompt
|
||
|
||
def _extract_useful_content(self, content: str) -> str:
|
||
"""从工具返回内容中提取有用信息"""
|
||
if not content:
|
||
return ""
|
||
|
||
if "✅" in content:
|
||
lines = content.split("\n")
|
||
for line in lines[:3]:
|
||
if "✅" in line:
|
||
return line.strip()
|
||
|
||
if "❌" in content:
|
||
lines = content.split("\n")
|
||
for line in lines[:2]:
|
||
if "❌" in line:
|
||
return line.strip()
|
||
|
||
if len(content) > 100:
|
||
return content[:100] + "..."
|
||
|
||
return content
|
||
|
||
def _compress_via_llm(self, messages: List[Dict]) -> Optional[str]:
|
||
"""通过语言模型压缩消息（经由客户端抽象层调用，支持多厂商）"""
|
||
if not messages:
|
||
return None
|
||
|
||
prompt = self._build_compression_prompt(messages)
|
||
|
||
print(f"📦 {i18n.get('conversation_compress')}: 正在压缩 {len(messages)} 条消息...")
|
||
|
||
try:
|
||
summary = self.ai.client.chat_completion(
|
||
[
|
||
{"role": "system", "content": "你是一个专业的对话摘要助手，擅长提取对话核心信息并进行结构化总结。请保留工具调用的关键结果。"},
|
||
{"role": "user", "content": prompt}
|
||
],
|
||
temperature=0.3,
|
||
max_tokens=constants.COMPRESSOR_LLM_MAX_TOKENS
|
||
)
|
||
return summary
|
||
except Exception as e:
|
||
self.logger.error(f"语言模型压缩异常: {e}")
|
||
return None
|
||
|
||
def _validate_message_structure(self, messages: List[Dict]) -> List[Dict]:
|
||
"""验证并修复消息结构，移除孤立的 tool 消息"""
|
||
if not messages:
|
||
return messages
|
||
|
||
# 找出所有 assistant 消息的 tool_call_ids
|
||
assistant_tool_ids = set()
|
||
for msg in messages:
|
||
if msg.get("role") == "assistant" and msg.get("tool_calls"):
|
||
for tc in msg.get("tool_calls", []):
|
||
tc_id = tc.get("id")
|
||
if tc_id:
|
||
assistant_tool_ids.add(tc_id)
|
||
|
||
# 过滤掉没有对应 assistant 的 tool 消息
|
||
filtered = []
|
||
for msg in messages:
|
||
if msg.get("role") == "tool":
|
||
tool_call_id = msg.get("tool_call_id")
|
||
if tool_call_id and tool_call_id not in assistant_tool_ids:
|
||
print(f" ⚠️ 移除孤立的 tool 消息: {tool_call_id}")
|
||
continue
|
||
filtered.append(msg)
|
||
|
||
return filtered
|
||
|
||
def compress_conversation(self, messages: List[Dict]) -> List[Dict]:
|
||
"""
|
||
压缩对话内容 - 优先压缩较早的消息，包含tool调用信息一并压缩
|
||
当消息条数超过阈值时，压缩到阈值以下
|
||
"""
|
||
if not messages:
|
||
return messages
|
||
|
||
# 分离系统消息和对话消息
|
||
system_messages = []
|
||
conversation_messages = []
|
||
|
||
for msg in messages:
|
||
if msg.get("role") == "system":
|
||
system_messages.append(msg)
|
||
else:
|
||
conversation_messages.append(msg)
|
||
|
||
if not conversation_messages:
|
||
return system_messages
|
||
|
||
original_count = len(conversation_messages)
|
||
original_tokens = self.estimate_tokens(conversation_messages)
|
||
threshold = constants.COMPRESS_MESSAGE_THRESHOLD
|
||
|
||
print(f"\n📊 对话压缩检查:")
|
||
print(f" 当前消息数: {original_count} 条 (阈值: {threshold} 条)")
|
||
print(f" 当前Token数: {original_tokens} 个")
|
||
|
||
# 计算目标消息数量 - 压缩到阈值的70%
|
||
target_count = self._calculate_target_count(original_count, threshold, keep_ratio=0.7)
|
||
print(f" 目标消息数: {target_count} 条 (压缩后应少于阈值)")
|
||
|
||
# 如果目标数量大于等于当前数量，不需要压缩
|
||
if target_count >= original_count:
|
||
print(f" 目标消息数大于等于当前，跳过压缩")
|
||
return messages
|
||
|
||
# 计算需要压缩的消息数量
|
||
messages_to_compress = original_count - target_count
|
||
print(f" 需要压缩的消息数量: {messages_to_compress} 条")
|
||
|
||
# 根据消息数量查找可压缩块
|
||
compress_start, compress_end = self._find_compressible_block_by_count(
|
||
conversation_messages, target_count
|
||
)
|
||
|
||
if compress_end - compress_start >= 2:
|
||
compressible = conversation_messages[compress_start:compress_end]
|
||
compress_tokens = self.estimate_tokens(compressible)
|
||
compress_count = len(compressible)
|
||
|
||
print(f" 压缩块范围: [{compress_start}:{compress_end}], 共 {compress_count} 条消息")
|
||
print(f" 压缩块token数: {compress_tokens}")
|
||
|
||
# 如果压缩块大小仍然不够，尝试扩大压缩块
|
||
if compress_count < messages_to_compress:
|
||
print(f" 压缩块大小不足({compress_count} < {messages_to_compress})，扩大压缩块...")
|
||
# 扩大压缩块
|
||
additional = messages_to_compress - compress_count + 5
|
||
new_start = max(0, compress_start - additional // 2)
|
||
new_end = min(len(conversation_messages), compress_end + additional // 2)
|
||
compress_start, compress_end = new_start, new_end
|
||
compressible = conversation_messages[compress_start:compress_end]
|
||
compress_count = len(compressible)
|
||
print(f" 扩大后压缩块: [{compress_start}:{compress_end}], 共 {compress_count} 条消息")
|
||
else:
|
||
print(f" 未找到合适的压缩块，使用默认压缩策略")
|
||
# 使用默认压缩策略：压缩最早的一半消息，保留最近的消息
|
||
keep_recent = constants.COMPRESS_KEEP_RECENT
|
||
compress_end = len(conversation_messages) - keep_recent
|
||
compress_start = max(0, compress_end - messages_to_compress - 5)
|
||
compress_start = max(compress_start, 0)
|
||
compressible = conversation_messages[compress_start:compress_end]
|
||
compress_count = len(compressible)
|
||
print(f" 默认压缩块: [{compress_start}:{compress_end}], 共 {compress_count} 条消息")
|
||
|
||
# 确保压缩块有足够多的消息
|
||
if compress_count < 5:
|
||
print(f" 压缩块太小({compress_count}条)，跳过压缩")
|
||
return messages
|
||
|
||
# 调用语言模型压缩
|
||
summary = self._compress_via_llm(compressible)
|
||
|
||
if not summary:
|
||
print(f" ❌ 压缩失败，保留原始对话")
|
||
return messages
|
||
|
||
# 构建压缩后的消息列表
|
||
compressed_result = []
|
||
|
||
# 添加压缩块之前的消息
|
||
compressed_result.extend(conversation_messages[:compress_start])
|
||
|
||
# 插入压缩摘要（作为系统消息）
|
||
compressed_result.append({
|
||
"role": "system",
|
||
"content": i18n.get('compressed_summary', summary)
|
||
})
|
||
|
||
# 添加压缩块之后的消息（包括最近的消息）
|
||
compressed_result.extend(conversation_messages[compress_end:])
|
||
|
||
# 验证并修复消息结构
|
||
compressed_result = self._validate_message_structure(compressed_result)
|
||
|
||
# 合并系统消息
|
||
final_messages = system_messages + compressed_result
|
||
|
||
# 统计结果
|
||
final_count = len([m for m in final_messages if m.get("role") != "system"])
|
||
final_tokens = self.estimate_tokens([m for m in final_messages if m.get("role") != "system"])
|
||
|
||
print(f"\n✅ {i18n.get('conversation_compressed', original_count, final_count)}")
|
||
print(f" Token: {original_tokens} -> {final_tokens} (压缩率: {final_tokens/original_tokens:.1%})")
|
||
|
||
# 检查是否压缩到阈值以下
|
||
if final_count >= threshold:
|
||
print(f" ⚠️ 警告: 压缩后消息数({final_count})仍大于等于阈值({threshold})")
|
||
|
||
return final_messages
|
||
|
||
# ==================== 备忘录数据库 ====================
|
||
|
||
class MemoDatabase:
|
||
"""备忘录数据库 - 存储和管理任务"""
|
||
|
||
def __init__(self, db_path: str = None):
|
||
if db_path is None:
|
||
db_path = os.path.join(constants.SAVE_DIR, constants.MEMORY_DB)
|
||
|
||
# 确保目录存在
|
||
os.makedirs(constants.SAVE_DIR, exist_ok=True)
|
||
self.db_path = db_path
|
||
self.logger = Logger("MemoDB")
|
||
self._init_database()
|
||
|
||
def _init_database(self):
|
||
"""初始化备忘录表"""
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
cursor = conn.cursor()
|
||
|
||
cursor.execute('''
|
||
CREATE TABLE IF NOT EXISTS memos (
|
||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||
title TEXT NOT NULL,
|
||
content TEXT,
|
||
created_at TEXT NOT NULL,
|
||
reminder_time TEXT,
|
||
completed_at TEXT,
|
||
is_completed INTEGER DEFAULT 0,
|
||
tags TEXT,
|
||
priority INTEGER DEFAULT 1,
|
||
created_by TEXT,
|
||
source TEXT,
|
||
repeat_type TEXT DEFAULT 'none',
|
||
repeat_interval INTEGER DEFAULT 0,
|
||
repeat_interval_unit TEXT DEFAULT 'days',
|
||
repeat_end_time TEXT,
|
||
last_triggered_at TEXT,
|
||
trigger_count INTEGER DEFAULT 0,
|
||
is_immediate INTEGER DEFAULT 0
|
||
)
|
||
''')
|
||
|
||
cursor.execute('''
|
||
CREATE INDEX IF NOT EXISTS idx_memos_reminder
|
||
ON memos(reminder_time) WHERE is_completed = 0
|
||
''')
|
||
|
||
cursor.execute('''
|
||
CREATE INDEX IF NOT EXISTS idx_memos_immediate
|
||
ON memos(is_immediate) WHERE is_immediate = 1 AND is_completed = 0
|
||
''')
|
||
|
||
conn.commit()
|
||
|
||
self.logger.info("任务表初始化完成")
|
||
|
||
def add_memo(self, title: str, content: str = None, reminder_time: str = None,
|
||
tags: List[str] = None, priority: int = 1, created_by: str = None,
|
||
source: str = None,
|
||
repeat_type: str = 'none', repeat_interval: int = 0,
|
||
repeat_interval_unit: str = 'days', repeat_end_time: str = None,
|
||
is_immediate: bool = False) -> int:
|
||
"""添加任务 - 所有任务都是对话任务，不再需要metadata和任务类型"""
|
||
created_at = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
tags_str = ",".join(tags) if tags else ""
|
||
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
cursor = conn.cursor()
|
||
cursor.execute('''
|
||
INSERT INTO memos
|
||
(title, content, created_at, reminder_time, tags, priority, created_by,
|
||
is_completed, source, repeat_type, repeat_interval, repeat_interval_unit,
|
||
repeat_end_time, is_immediate)
|
||
VALUES (?, ?, ?, ?, ?, ?, ?, 0, ?, ?, ?, ?, ?, ?)
|
||
''', (title, content, created_at, reminder_time, tags_str, priority, created_by,
|
||
source, repeat_type, repeat_interval, repeat_interval_unit,
|
||
repeat_end_time, 1 if is_immediate else 0))
|
||
|
||
memo_id = cursor.lastrowid
|
||
conn.commit()
|
||
|
||
repeat_info = f" (重复: {repeat_type}"
|
||
if repeat_type == 'custom':
|
||
repeat_info += f", 每{repeat_interval} {repeat_interval_unit}"
|
||
repeat_info += ")"
|
||
immediate_info = " (即时任务)" if is_immediate else ""
|
||
self.logger.info(i18n.get('task_added', title, memo_id) + repeat_info + immediate_info)
|
||
return memo_id
|
||
|
||
def get_memo(self, memo_id: int) -> Optional[Dict]:
|
||
"""获取单个任务"""
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
conn.row_factory = sqlite3.Row
|
||
cursor = conn.cursor()
|
||
cursor.execute('SELECT * FROM memos WHERE id = ?', (memo_id,))
|
||
row = cursor.fetchone()
|
||
|
||
if row:
|
||
memo = dict(row)
|
||
if memo.get('tags'):
|
||
memo['tags'] = memo['tags'].split(',')
|
||
return memo
|
||
return None
|
||
|
||
def get_pending_memos(self, limit: int = None, before_time: str = None, include_immediate: bool = False) -> List[Dict]:
|
||
"""获取待执行的任务 - 严格使用当前时间比较"""
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
conn.row_factory = sqlite3.Row
|
||
cursor = conn.cursor()
|
||
|
||
# 使用当前时间作为基准
|
||
now = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
|
||
# 如果传入了 before_time，使用它，否则使用当前时间
|
||
compare_time = before_time if before_time else now
|
||
|
||
query = '''
|
||
SELECT * FROM memos
|
||
WHERE is_completed = 0
|
||
AND reminder_time IS NOT NULL
|
||
AND reminder_time <= ? -- 严格小于等于比较时间
|
||
AND (repeat_end_time IS NULL OR repeat_end_time >= ?)
|
||
'''
|
||
|
||
if not include_immediate:
|
||
query += ' AND is_immediate = 0'
|
||
|
||
query += ' ORDER BY reminder_time ASC, priority DESC'
|
||
|
||
params = [compare_time, compare_time]
|
||
|
||
if limit:
|
||
query += f' LIMIT {limit}'
|
||
|
||
cursor.execute(query, params)
|
||
|
||
results = []
|
||
for row in cursor.fetchall():
|
||
memo = dict(row)
|
||
if memo.get('tags'):
|
||
memo['tags'] = memo['tags'].split(',')
|
||
|
||
# 【关键修复】二次验证：确保 reminder_time 确实 <= 当前时间
|
||
if memo.get('reminder_time'):
|
||
try:
|
||
reminder_time = datetime.strptime(memo['reminder_time'], constants.DATETIME_FORMAT)
|
||
current_time = datetime.strptime(compare_time, constants.DATETIME_FORMAT)
|
||
if reminder_time <= current_time:
|
||
results.append(memo)
|
||
else:
|
||
# 调试日志
|
||
self.logger.debug(f"过滤掉未到期任务 #{memo['id']}: {memo['reminder_time']} > {compare_time}")
|
||
except (ValueError, TypeError) as e:
|
||
self.logger.warning(f"解析任务 #{memo['id']} 的提醒时间失败: {e}")
|
||
else:
|
||
# 没有提醒时间的任务不应该在待执行列表中
|
||
self.logger.warning(f"任务 #{memo['id']} 没有提醒时间，但被返回为待执行")
|
||
|
||
return results
|
||
|
||
def get_immediate_memos(self, limit: int = None) -> List[Dict]:
|
||
"""获取待执行的即时任务"""
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
conn.row_factory = sqlite3.Row
|
||
cursor = conn.cursor()
|
||
|
||
query = '''
|
||
SELECT * FROM memos
|
||
WHERE is_completed = 0
|
||
AND is_immediate = 1
|
||
ORDER BY created_at ASC
|
||
'''
|
||
|
||
if limit:
|
||
query += f' LIMIT {limit}'
|
||
|
||
cursor.execute(query)
|
||
|
||
results = []
|
||
for row in cursor.fetchall():
|
||
memo = dict(row)
|
||
if memo.get('tags'):
|
||
memo['tags'] = memo['tags'].split(',')
|
||
results.append(memo)
|
||
|
||
return results
|
||
|
||
def get_overdue_memos(self, limit: int = None) -> List[Dict]:
|
||
"""获取已过期的任务"""
|
||
now = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
return self.get_pending_memos(limit=limit, before_time=now)
|
||
|
||
def get_all_memos(self, include_completed: bool = False, limit: int = 50) -> List[Dict]:
|
||
"""获取所有任务"""
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
conn.row_factory = sqlite3.Row
|
||
cursor = conn.cursor()
|
||
|
||
if include_completed:
|
||
cursor.execute('''
|
||
SELECT * FROM memos
|
||
ORDER BY
|
||
CASE WHEN is_completed = 0 THEN 0 ELSE 1 END,
|
||
reminder_time ASC,
|
||
created_at DESC
|
||
LIMIT ?
|
||
''', (limit,))
|
||
else:
|
||
cursor.execute('''
|
||
SELECT * FROM memos
|
||
WHERE is_completed = 0
|
||
ORDER BY reminder_time ASC, priority DESC, created_at DESC
|
||
LIMIT ?
|
||
''', (limit,))
|
||
|
||
results = []
|
||
for row in cursor.fetchall():
|
||
memo = dict(row)
|
||
if memo.get('tags'):
|
||
memo['tags'] = memo['tags'].split(',')
|
||
results.append(memo)
|
||
|
||
return results
|
||
|
||
def mark_as_triggered(self, memo_id: int) -> Optional[str]:
|
||
"""标记任务已被触发，并根据重复规则计算下一次提醒时间"""
|
||
memo = self.get_memo(memo_id)
|
||
if not memo:
|
||
return None
|
||
|
||
now = datetime.now()
|
||
next_reminder = None
|
||
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
cursor = conn.cursor()
|
||
|
||
cursor.execute('''
|
||
UPDATE memos
|
||
SET last_triggered_at = ?, trigger_count = trigger_count + 1
|
||
WHERE id = ?
|
||
''', (now.strftime(constants.DATETIME_FORMAT), memo_id))
|
||
|
||
# 处理重复任务
|
||
if memo['repeat_type'] != 'none' and not self._should_end_repeat(memo, now):
|
||
# 使用当前时间作为基准来计算下一次执行时间
|
||
next_reminder = self._calculate_next_reminder(memo, now)
|
||
|
||
if next_reminder:
|
||
cursor.execute('''
|
||
UPDATE memos
|
||
SET reminder_time = ?
|
||
WHERE id = ?
|
||
''', (next_reminder.strftime(constants.DATETIME_FORMAT), memo_id))
|
||
|
||
self.logger.info(f"任务 #{memo_id} 已更新下一次执行时间: {next_reminder}")
|
||
else:
|
||
# 重复结束，标记为完成
|
||
cursor.execute('''
|
||
UPDATE memos
|
||
SET is_completed = 1, completed_at = ?
|
||
WHERE id = ?
|
||
''', (now.strftime(constants.DATETIME_FORMAT), memo_id))
|
||
|
||
self.logger.info(f"任务 #{memo_id} 重复结束，已标记为完成")
|
||
elif memo['is_immediate']:
|
||
# 即时任务执行后标记为完成
|
||
cursor.execute('''
|
||
UPDATE memos
|
||
SET is_completed = 1, completed_at = ?
|
||
WHERE id = ?
|
||
''', (now.strftime(constants.DATETIME_FORMAT), memo_id))
|
||
|
||
self.logger.info(f"即时任务 #{memo_id} 已完成")
|
||
else:
|
||
# 一次性任务，标记为完成
|
||
cursor.execute('''
|
||
UPDATE memos
|
||
SET is_completed = 1, completed_at = ?
|
||
WHERE id = ?
|
||
''', (now.strftime(constants.DATETIME_FORMAT), memo_id))
|
||
|
||
self.logger.info(f"任务 #{memo_id} 已完成")
|
||
|
||
conn.commit()
|
||
|
||
return next_reminder.strftime(constants.DATETIME_FORMAT) if next_reminder else None
|
||
|
||
def _should_end_repeat(self, memo: Dict, current_time: datetime) -> bool:
|
||
"""检查是否应该结束重复"""
|
||
if memo['repeat_end_time']:
|
||
end_time = datetime.strptime(memo['repeat_end_time'], constants.DATETIME_FORMAT)
|
||
if current_time > end_time:
|
||
return True
|
||
return False
|
||
|
||
def _calculate_next_reminder(self, memo: Dict, current_time: datetime) -> Optional[datetime]:
|
||
"""
|
||
计算下一次提醒时间，使用当前时间作为基准
|
||
支持分钟、小时、天为单位
|
||
"""
|
||
if memo['repeat_type'] == 'daily':
|
||
# 每天重复：当前时间 + 1天
|
||
next_time = current_time + timedelta(days=1)
|
||
elif memo['repeat_type'] == 'weekly':
|
||
# 每周重复：当前时间 + 7天
|
||
next_time = current_time + timedelta(weeks=1)
|
||
elif memo['repeat_type'] == 'monthly':
|
||
# 每月重复：尝试在当前时间上加一个月
|
||
try:
|
||
# 处理月份进位
|
||
if current_time.month == 12:
|
||
next_time = current_time.replace(year=current_time.year + 1, month=1)
|
||
else:
|
||
next_time = current_time.replace(month=current_time.month + 1)
|
||
except ValueError:
|
||
# 处理月份天数问题（如1月31日加一个月）
|
||
if current_time.month == 12:
|
||
next_time = current_time.replace(year=current_time.year + 1, month=1, day=1) - timedelta(days=1)
|
||
else:
|
||
next_time = current_time.replace(month=current_time.month + 1, day=1) - timedelta(days=1)
|
||
elif memo['repeat_type'] == 'custom' and memo['repeat_interval'] > 0:
|
||
unit = memo.get('repeat_interval_unit', 'days')
|
||
if unit == 'minutes':
|
||
# 每N分钟重复：当前时间 + N分钟
|
||
next_time = current_time + timedelta(minutes=memo['repeat_interval'])
|
||
elif unit == 'hours':
|
||
# 每N小时重复：当前时间 + N小时
|
||
next_time = current_time + timedelta(hours=memo['repeat_interval'])
|
||
else: # days
|
||
# 每N天重复：当前时间 + N天
|
||
next_time = current_time + timedelta(days=memo['repeat_interval'])
|
||
else:
|
||
return None
|
||
|
||
# 检查是否超过重复结束时间
|
||
if memo['repeat_end_time']:
|
||
end_time = datetime.strptime(memo['repeat_end_time'], constants.DATETIME_FORMAT)
|
||
if next_time > end_time:
|
||
return None
|
||
|
||
return next_time
|
||
|
||
def complete_memo(self, memo_id: int, force_complete: bool = False) -> bool:
|
||
"""完成任务"""
|
||
memo = self.get_memo(memo_id)
|
||
if not memo:
|
||
return False
|
||
|
||
if memo['repeat_type'] != 'none' and not force_complete:
|
||
self.mark_as_triggered(memo_id)
|
||
return True
|
||
|
||
completed_at = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
cursor = conn.cursor()
|
||
cursor.execute('''
|
||
UPDATE memos
|
||
SET is_completed = 1, completed_at = ?
|
||
WHERE id = ? AND is_completed = 0
|
||
''', (completed_at, memo_id))
|
||
|
||
affected = cursor.rowcount
|
||
conn.commit()
|
||
|
||
if affected > 0:
|
||
self.logger.info(i18n.get('memo_completed', memo_id))
|
||
return True
|
||
return False
|
||
|
||
def get_memo_stats(self) -> Dict:
|
||
"""获取任务统计信息"""
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
cursor = conn.cursor()
|
||
|
||
cursor.execute('SELECT COUNT(*) FROM memos')
|
||
total = cursor.fetchone()[0]
|
||
|
||
cursor.execute('SELECT COUNT(*) FROM memos WHERE is_completed = 0')
|
||
pending = cursor.fetchone()[0]
|
||
|
||
now = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
cursor.execute('''
|
||
SELECT COUNT(*) FROM memos
|
||
WHERE is_completed = 0
|
||
AND reminder_time <= ?
|
||
AND is_immediate = 0
|
||
''', (now,))
|
||
overdue = cursor.fetchone()[0]
|
||
|
||
cursor.execute('SELECT COUNT(*) FROM memos WHERE is_completed = 1')
|
||
completed = cursor.fetchone()[0]
|
||
|
||
cursor.execute('SELECT COUNT(*) FROM memos WHERE is_immediate = 1 AND is_completed = 0')
|
||
immediate = cursor.fetchone()[0]
|
||
|
||
return {
|
||
"total": total,
|
||
"pending": pending,
|
||
"overdue": overdue,
|
||
"completed": completed,
|
||
"immediate": immediate
|
||
}
|
||
|
||
def add_user_task(self, title: str, content: str = None, reminder_time: str = None,
|
||
repeat_type: str = 'none', repeat_interval: int = 0,
|
||
repeat_interval_unit: str = 'days', is_immediate: bool = False) -> int:
|
||
"""添加用户任务（前端直接调用的任务）- 所有任务都是对话任务"""
|
||
if not constants.MEMO_ALLOW_USER_TASKS:
|
||
raise Exception("用户任务功能已禁用")
|
||
|
||
return self.add_memo(
|
||
title=title,
|
||
content=content,
|
||
reminder_time=reminder_time,
|
||
repeat_type=repeat_type,
|
||
repeat_interval=repeat_interval,
|
||
repeat_interval_unit=repeat_interval_unit,
|
||
created_by="user",
|
||
source="websocket",
|
||
is_immediate=is_immediate
|
||
)
|
||
|
||
def get_pending_tasks_for_user(self) -> List[Dict]:
|
||
"""获取待执行任务列表（用于前端显示）"""
|
||
pending = self.get_pending_memos(include_immediate=False)
|
||
immediate = self.get_immediate_memos()
|
||
result = []
|
||
|
||
now = datetime.now()
|
||
for memo in pending + immediate:
|
||
result.append({
|
||
"id": memo['id'],
|
||
"title": memo['title'],
|
||
"content": memo['content'],
|
||
"reminder_time": memo['reminder_time'],
|
||
"priority": memo['priority'],
|
||
"is_overdue": datetime.strptime(memo['reminder_time'], constants.DATETIME_FORMAT) < now if memo['reminder_time'] else False,
|
||
"repeat_type": memo['repeat_type'],
|
||
"repeat_interval": memo.get('repeat_interval', 0),
|
||
"repeat_interval_unit": memo.get('repeat_interval_unit', 'days'),
|
||
"trigger_count": memo.get('trigger_count', 0),
|
||
"is_immediate": memo.get('is_immediate', False)
|
||
})
|
||
|
||
return result
|
||
|
||
|
||
# ==================== 记忆数据库 ====================
|
||
|
||
class MemoryDatabase:
|
||
"""记忆数据库 - 存储重要的对话记忆"""
|
||
|
||
def __init__(self, db_path: str = None):
|
||
if db_path is None:
|
||
db_path = os.path.join(constants.SAVE_DIR, constants.MEMORY_DB)
|
||
|
||
# 确保目录存在
|
||
os.makedirs(constants.SAVE_DIR, exist_ok=True)
|
||
self.db_path = db_path
|
||
self.logger = Logger("MemoryDB")
|
||
self._init_database()
|
||
|
||
def _init_database(self):
|
||
"""初始化数据库表"""
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
cursor = conn.cursor()
|
||
cursor.execute('''
|
||
CREATE TABLE IF NOT EXISTS memories (
|
||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||
topic TEXT NOT NULL,
|
||
summary TEXT NOT NULL,
|
||
content TEXT,
|
||
start_time TEXT NOT NULL,
|
||
end_time TEXT NOT NULL,
|
||
created_at TEXT NOT NULL,
|
||
tags TEXT,
|
||
importance INTEGER DEFAULT 1
|
||
)
|
||
''')
|
||
|
||
cursor.execute('''
|
||
CREATE VIRTUAL TABLE IF NOT EXISTS memories_fts
|
||
USING fts5(topic, summary, content, tags, content=memories)
|
||
''')
|
||
|
||
conn.commit()
|
||
|
||
def add_memory(self, topic: str, summary: str, content: str = None,
|
||
start_time: str = None, end_time: str = None,
|
||
tags: List[str] = None, importance: int = None) -> int:
|
||
"""添加记忆到数据库"""
|
||
if start_time is None:
|
||
start_time = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
if end_time is None:
|
||
end_time = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
if importance is None:
|
||
importance = constants.MEMORY_DEFAULT_IMPORTANCE
|
||
|
||
tags_str = ",".join(tags) if tags else ""
|
||
created_at = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
cursor = conn.cursor()
|
||
cursor.execute('''
|
||
INSERT INTO memories (topic, summary, content, start_time, end_time, created_at, tags, importance)
|
||
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||
''', (topic, summary, content, start_time, end_time, created_at, tags_str, importance))
|
||
|
||
memory_id = cursor.lastrowid
|
||
|
||
cursor.execute('''
|
||
INSERT INTO memories_fts (rowid, topic, summary, content, tags)
|
||
VALUES (?, ?, ?, ?, ?)
|
||
''', (memory_id, topic, summary, content or "", tags_str))
|
||
|
||
conn.commit()
|
||
|
||
self.logger.info(f"已添加记忆: {topic} (ID: {memory_id})")
|
||
return memory_id
|
||
|
||
def delete_memory(self, memory_id: int) -> bool:
|
||
"""删除记忆"""
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
cursor = conn.cursor()
|
||
cursor.execute('DELETE FROM memories WHERE id = ?', (memory_id,))
|
||
deleted = cursor.rowcount > 0
|
||
|
||
if deleted:
|
||
cursor.execute('DELETE FROM memories_fts WHERE rowid = ?', (memory_id,))
|
||
conn.commit()
|
||
self.logger.info(f"已删除记忆 ID: {memory_id}")
|
||
|
||
return deleted
|
||
|
||
def search_memories(self, keywords: List[str], match_all: bool = False, limit: int = None) -> List[Dict]:
|
||
"""多关键词模糊搜索记忆"""
|
||
if not keywords:
|
||
return []
|
||
|
||
if limit is None:
|
||
limit = constants.MEMORY_MAX_SEARCH_RESULTS
|
||
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
conn.row_factory = sqlite3.Row
|
||
cursor = conn.cursor()
|
||
|
||
if match_all:
|
||
query = " AND ".join([f'"{kw}"' for kw in keywords])
|
||
else:
|
||
query = " OR ".join([f'"{kw}"' for kw in keywords])
|
||
|
||
cursor.execute('''
|
||
SELECT m.*, rank
|
||
FROM memories m
|
||
JOIN memories_fts f ON m.rowid = f.rowid
|
||
WHERE memories_fts MATCH ?
|
||
ORDER BY m.end_time DESC, rank, m.importance DESC
|
||
LIMIT ?
|
||
''', (query, limit))
|
||
|
||
results = []
|
||
for row in cursor.fetchall():
|
||
memory = dict(row)
|
||
if memory.get('tags'):
|
||
memory['tags'] = memory['tags'].split(',')
|
||
results.append(memory)
|
||
|
||
return results
|
||
|
||
def get_recent_memories(self, limit: int = 10) -> List[Dict]:
|
||
"""获取最近的记忆"""
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
conn.row_factory = sqlite3.Row
|
||
cursor = conn.cursor()
|
||
cursor.execute('SELECT * FROM memories ORDER BY end_time DESC LIMIT ?', (limit,))
|
||
|
||
results = []
|
||
for row in cursor.fetchall():
|
||
memory = dict(row)
|
||
if memory.get('tags'):
|
||
memory['tags'] = memory['tags'].split(',')
|
||
results.append(memory)
|
||
|
||
return results
|
||
|
||
def get_memory_by_id(self, memory_id: int) -> Optional[Dict]:
|
||
"""根据ID获取记忆"""
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
conn.row_factory = sqlite3.Row
|
||
cursor = conn.cursor()
|
||
cursor.execute('SELECT * FROM memories WHERE id = ?', (memory_id,))
|
||
row = cursor.fetchone()
|
||
|
||
if row:
|
||
memory = dict(row)
|
||
if memory.get('tags'):
|
||
memory['tags'] = memory['tags'].split(',')
|
||
return memory
|
||
return None
|
||
|
||
def delete_memories_by_ids(self, memory_ids: List[int]) -> int:
|
||
"""批量删除记忆"""
|
||
if not memory_ids:
|
||
return 0
|
||
|
||
with sqlite3.connect(self.db_path) as conn:
|
||
cursor = conn.cursor()
|
||
placeholders = ','.join(['?'] * len(memory_ids))
|
||
cursor.execute(f'DELETE FROM memories WHERE id IN ({placeholders})', memory_ids)
|
||
deleted_count = cursor.rowcount
|
||
|
||
if deleted_count > 0:
|
||
cursor.execute(f'DELETE FROM memories_fts WHERE rowid IN ({placeholders})', memory_ids)
|
||
conn.commit()
|
||
self.logger.info(f"已批量删除 {deleted_count} 条记忆")
|
||
|
||
return deleted_count
|
||
|
||
|
||
# ==================== 任务调度器 ====================
|
||
|
||
class TaskScheduler:
|
||
"""任务调度器 - 基于备忘录数据库的定时任务系统"""
|
||
|
||
def __init__(self, loop: asyncio.AbstractEventLoop = None, logger: Logger = None):
|
||
self.loop = loop or asyncio.get_event_loop()
|
||
self.running = False
|
||
self.logger = logger or Logger("TaskScheduler")
|
||
self.check_interval = constants.SCHEDULER_CHECK_INTERVAL
|
||
self.max_tasks_per_run = constants.SCHEDULER_MAX_TASKS
|
||
self.broadcast_enabled = constants.SCHEDULER_BROADCAST
|
||
self.ws_handler = None # 将由管理器设置
|
||
self.ai_manager = None # 新增：AI管理器引用
|
||
|
||
# 任务执行历史
|
||
self.execution_history = []
|
||
self.max_history = 100
|
||
|
||
# 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）
|
||
self._shared_skill_manager = None
|
||
|
||
self.logger.info(i18n.get('scheduler_started'))
|
||
|
||
def _get_shared_skill_manager(self):
|
||
"""获取共享技能管理器（懒加载，全局只创建一次）"""
|
||
if self._shared_skill_manager is None:
|
||
self._shared_skill_manager = SkillManager(self.logger)
|
||
return self._shared_skill_manager
|
||
|
||
def _create_task_ai(self):
|
||
"""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。
|
||
|
||
每个任务拥有独立的 messages / 工具注册表 / 消息锁，互不干扰，
|
||
也不影响 WebSocket 主对话。共享只读资源（记忆库、备忘录、提示词、
|
||
技能管理器）复用，避免重复加载开销。
|
||
"""
|
||
return DeepSeekChat(
|
||
api_key=None,
|
||
max_history=constants.DEFAULT_MAX_HISTORY,
|
||
debug_level=self.logger.level,
|
||
exit_callback=None, # 任务执行不触发全局对话结束
|
||
history_manager=None, # 不加载历史对话上下文，任务隔离
|
||
memory_db=self.ai_manager.memory_db if self.ai_manager else None,
|
||
memo_db=self.ai_manager.memo_db if self.ai_manager else None,
|
||
command_executor=self.ai_manager.command_executor if self.ai_manager else None,
|
||
prompt_manager=self.ai_manager.prompt_manager if self.ai_manager else None,
|
||
skill_manager=self._get_shared_skill_manager()
|
||
)
|
||
|
||
def set_websocket_handler(self, handler):
|
||
"""设置WebSocket处理器，用于广播任务执行状态"""
|
||
self.ws_handler = handler
|
||
|
||
def set_ai_manager(self, manager):
|
||
"""设置AI管理器引用"""
|
||
self.ai_manager = manager
|
||
|
||
async def broadcast_task_status(self, task: Dict, status: str, message: str = None):
|
||
"""广播任务状态到所有连接的客户端"""
|
||
if not self.broadcast_enabled or not self.ws_handler:
|
||
return
|
||
|
||
try:
|
||
# 确保task有必要的字段
|
||
task_info = {
|
||
'id': task.get('id', 0),
|
||
'title': task.get('title', '未知任务')
|
||
}
|
||
await self.ws_handler.broadcast_task_status(task_info, status, message)
|
||
except Exception as e:
|
||
self.logger.error(f"广播任务状态失败: {e}")
|
||
|
||
async def check_and_execute_tasks(self, memo_db: MemoDatabase):
|
||
"""检查并执行到期的任务 - 触发AI对话"""
|
||
if not memo_db:
|
||
return []
|
||
|
||
try:
|
||
# 获取所有到期任务（不包括即时任务）
|
||
now = datetime.now()
|
||
now_str = now.strftime(constants.DATETIME_FORMAT)
|
||
|
||
pending = memo_db.get_pending_memos(
|
||
limit=self.max_tasks_per_run,
|
||
before_time=now_str,
|
||
include_immediate=False
|
||
)
|
||
|
||
if not pending:
|
||
return []
|
||
|
||
self.logger.info(i18n.get('overdue_found', len(pending)))
|
||
|
||
# 广播任务开始执行
|
||
if self.broadcast_enabled and self.ws_handler:
|
||
await self.ws_handler.broadcast_task_status(
|
||
{"id": 0, "title": "批量任务"},
|
||
"batch_start",
|
||
f"开始执行 {len(pending)} 个任务"
|
||
)
|
||
|
||
executed_tasks = []
|
||
for memo in pending:
|
||
try:
|
||
# 广播任务开始执行
|
||
if self.ws_handler:
|
||
await self.ws_handler.broadcast_task_status(memo, "executing", i18n.get('task_executing', memo['title'], memo['id']))
|
||
|
||
# 【关键修复】执行前最后一次验证
|
||
if memo.get('reminder_time'):
|
||
reminder_time = datetime.strptime(memo['reminder_time'], constants.DATETIME_FORMAT)
|
||
if reminder_time > now:
|
||
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")
|
||
continue
|
||
|
||
# 先标记任务已被触发
|
||
self.logger.info(f"标记任务 #{memo['id']} 为已触发")
|
||
next_time = memo_db.mark_as_triggered(memo['id'])
|
||
|
||
# 执行任务
|
||
result = await self.execute_task_as_conversation(memo, memo_db)
|
||
|
||
# 广播任务完成
|
||
if self.ws_handler:
|
||
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)))
|
||
|
||
# 广播任务失败
|
||
if self.ws_handler:
|
||
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
|
||
})
|
||
|
||
# 广播任务执行完成
|
||
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": "批量任务"},
|
||
"batch_complete",
|
||
f"执行完成: {success_count}/{len(executed_tasks)} 个任务成功"
|
||
)
|
||
|
||
return executed_tasks
|
||
|
||
except Exception as e:
|
||
self.logger.error(f"检查任务失败: {e}")
|
||
return []
|
||
|
||
async def execute_task_as_conversation(self, memo: Dict, memo_db: MemoDatabase) -> str:
|
||
"""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"""
|
||
self.logger.info(i18n.get('task_executing', memo['title'], memo['id']))
|
||
|
||
# 生成唯一的执行ID（基于任务ID和时间戳）
|
||
execution_id = f"{memo['id']}_{int(time.time())}_{memo.get('trigger_count', 0) + 1}"
|
||
|
||
# 记录执行历史
|
||
self.execution_history.append({
|
||
"id": memo['id'],
|
||
"execution_id": execution_id,
|
||
"title": memo['title'],
|
||
"time": datetime.now().strftime(constants.DATETIME_FORMAT),
|
||
"trigger_count": memo.get('trigger_count', 0) + 1
|
||
})
|
||
|
||
# 限制历史记录大小
|
||
if len(self.execution_history) > self.max_history:
|
||
self.execution_history = self.execution_history[-self.max_history:]
|
||
|
||
# 构建用户输入内容 - 将任务作为对话触发
|
||
user_input = f"[定时任务] {memo['title']}"
|
||
if memo['content']:
|
||
user_input += f"\n\n任务内容: {memo['content']}"
|
||
|
||
# 添加专注提示词，让AI专注于当前任务
|
||
focus_prompt = f"""
|
||
【重要提示】
|
||
这是由定时任务触发的对话，请遵守以下要求：
|
||
1. 专注于处理当前任务：{memo['title']}
|
||
2. 根据任务内容进行适当的操作和回答
|
||
3. 任务处理完成后，如果不需要继续对话，可以结束对话
|
||
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
|
||
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）
|
||
|
||
请现在开始处理这个任务。
|
||
"""
|
||
|
||
|
||
user_input = focus_prompt + "\n\n" + user_input
|
||
|
||
result = "任务已触发AI对话"
|
||
|
||
# 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）
|
||
ai = self._create_task_ai()
|
||
try:
|
||
# 获取当前事件循环
|
||
loop = asyncio.get_running_loop()
|
||
|
||
# 创建一个队列来收集完整的响应
|
||
full_response_queue = queue.Queue()
|
||
|
||
# 定义输出回调函数 - 直接广播，同时收集
|
||
def output_callback(msg_type, content):
|
||
"""AI输出的回调函数 - 直接广播，同时收集"""
|
||
# 收集完整响应
|
||
if msg_type == "complete":
|
||
full_response_queue.put(content)
|
||
|
||
# 直接广播给所有客户端 - 必须带上 task_id 和 execution_id
|
||
if self.ws_handler and self.broadcast_enabled:
|
||
# 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'],
|
||
"msg_type": msg_type,
|
||
"content": content,
|
||
"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 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(ai_future.set_result, response)
|
||
except Exception as e:
|
||
loop.call_soon_threadsafe(ai_future.set_exception, e)
|
||
|
||
# 启动 AI 线程
|
||
ai_thread = threading.Thread(target=run_ai)
|
||
ai_thread.daemon = True
|
||
ai_thread.start()
|
||
|
||
# 等待 AI 完成，同时处理可能的取消
|
||
try:
|
||
response = await ai_future
|
||
result = response or "AI无响应"
|
||
except Exception as e:
|
||
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:
|
||
# 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:
|
||
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:
|
||
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:
|
||
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)
|
||
}
|
||
|
||
# ==================== 对话历史管理器 ====================
|
||
|
||
class ConversationHistory:
|
||
"""对话历史管理器 - 管理所有历史对话"""
|
||
|
||
def __init__(self, save_dir: str = None, memory_db: MemoryDatabase = None, load_history: bool = None):
|
||
if save_dir is None:
|
||
save_dir = constants.SAVE_DIR
|
||
|
||
self.save_dir = Path(save_dir)
|
||
self.archive_dir = self.save_dir / constants.ARCHIVE_DIR
|
||
self.history_file = self.save_dir / constants.HISTORY_FILE
|
||
self.memory_db = memory_db
|
||
|
||
config = ConfigManager()
|
||
if load_history is None:
|
||
load_history = config.get("system.load_history_by_default", True)
|
||
self.load_history = load_history
|
||
|
||
# 确保目录存在
|
||
self.save_dir.mkdir(parents=True, exist_ok=True)
|
||
self.archive_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
self.logger = Logger("HistoryManager")
|
||
self.history_index = self._load_history_index()
|
||
self.current_conversation = None
|
||
self.current_file = self.save_dir / constants.CURRENT_CONVERSATION_FILE
|
||
|
||
self._load_current_conversation()
|
||
|
||
def _load_history_index(self) -> Dict:
|
||
"""加载历史索引文件"""
|
||
if self.history_file.exists():
|
||
try:
|
||
with open(self.history_file, 'r', encoding='utf-8') as f:
|
||
return json.load(f)
|
||
except:
|
||
return {"conversations": [], "last_id": 0}
|
||
return {"conversations": [], "last_id": 0}
|
||
|
||
def _save_history_index(self):
|
||
"""保存历史索引"""
|
||
with open(self.history_file, 'w', encoding='utf-8') as f:
|
||
json.dump(self.history_index, f, ensure_ascii=False, indent=2)
|
||
|
||
def _load_current_conversation(self):
|
||
"""加载当前的对话（如果有）"""
|
||
if self.current_file.exists():
|
||
try:
|
||
with open(self.current_file, 'r', encoding='utf-8') as f:
|
||
self.current_conversation = json.load(f)
|
||
self.logger.info(f"已加载当前对话: {self.current_conversation.get('topic', '未命名')}")
|
||
except Exception as e:
|
||
self.logger.error(f"加载当前对话失败: {e}")
|
||
|
||
def save_current_conversation(self):
|
||
"""保存当前对话"""
|
||
if self.current_conversation:
|
||
with open(self.current_file, 'w', encoding='utf-8') as f:
|
||
json.dump(self.current_conversation, f, ensure_ascii=False, indent=2)
|
||
|
||
def start_new_conversation(self, topic: str = None) -> Dict:
|
||
"""开始新对话"""
|
||
if self.current_conversation:
|
||
self.archive_current_conversation()
|
||
|
||
conv_id = str(int(time.time()))
|
||
self.current_conversation = {
|
||
"id": conv_id,
|
||
"start_time": datetime.now().strftime(constants.DATETIME_FORMAT),
|
||
"end_time": None,
|
||
"topic": topic or "新对话",
|
||
"messages": [],
|
||
"tool_calls": [],
|
||
"stats": {
|
||
"user_messages": 0,
|
||
"ai_messages": 0,
|
||
"tool_calls": 0
|
||
}
|
||
}
|
||
|
||
self.save_current_conversation()
|
||
self.logger.info(f"开始新对话: {topic}")
|
||
return self.current_conversation
|
||
|
||
def archive_current_conversation(self, final_summary: str = None):
|
||
"""将当前对话归档到历史"""
|
||
if not self.current_conversation:
|
||
return None
|
||
|
||
if self.current_conversation.get("end_time") is None:
|
||
self.current_conversation["end_time"] = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
|
||
if final_summary and self.memory_db:
|
||
messages = self.current_conversation.get("messages", [])
|
||
content_preview = "\n".join([f"{m['speaker']}: {m['content'][:200]}"
|
||
for m in messages[-10:] if m.get('speaker')])
|
||
|
||
self.memory_db.add_memory(
|
||
topic=self.current_conversation["topic"],
|
||
summary=final_summary,
|
||
content=content_preview,
|
||
start_time=self.current_conversation["start_time"],
|
||
end_time=self.current_conversation["end_time"],
|
||
importance=2
|
||
)
|
||
|
||
timestamp = datetime.now().strftime(constants.DATE_FORMAT)
|
||
archive_filename = f"conversation_{timestamp}.json"
|
||
archive_path = self.archive_dir / archive_filename
|
||
|
||
with open(archive_path, 'w', encoding='utf-8') as f:
|
||
json.dump(self.current_conversation, f, ensure_ascii=False, indent=2)
|
||
|
||
self.history_index["conversations"].append({
|
||
"id": self.current_conversation["id"],
|
||
"file": archive_filename,
|
||
"start_time": self.current_conversation["start_time"],
|
||
"end_time": self.current_conversation["end_time"],
|
||
"topic": self.current_conversation["topic"],
|
||
"messages": len(self.current_conversation["messages"]),
|
||
"tool_calls": len(self.current_conversation["tool_calls"])
|
||
})
|
||
|
||
self._save_history_index()
|
||
self.logger.info(f"对话已归档: {archive_filename}")
|
||
|
||
self.current_conversation = None
|
||
if self.current_file.exists():
|
||
self.current_file.unlink()
|
||
|
||
return str(archive_path)
|
||
|
||
def load_all_history(self, max_messages: int = None) -> List[Dict]:
|
||
"""加载所有历史对话作为上下文"""
|
||
if max_messages is None:
|
||
max_messages = constants.MAX_HISTORY_CONTEXT
|
||
|
||
if not self.load_history:
|
||
return []
|
||
|
||
all_messages = []
|
||
|
||
if self.current_conversation:
|
||
current_msgs = self.current_conversation.get("messages", [])
|
||
for msg in current_msgs[-max_messages//2:]:
|
||
if not msg.get("is_history"):
|
||
all_messages.append({
|
||
"timestamp": msg["timestamp"],
|
||
"role": msg["role"],
|
||
"speaker": msg.get("speaker"),
|
||
"content": msg["content"],
|
||
"from_current": True
|
||
})
|
||
|
||
history_files = sorted(self.archive_dir.glob("*.json"), reverse=True)[:constants.MAX_HISTORY_CONVERSATIONS]
|
||
|
||
for hist_file in history_files:
|
||
try:
|
||
with open(hist_file, 'r', encoding='utf-8') as f:
|
||
conv = json.load(f)
|
||
messages = conv.get("messages", [])
|
||
for msg in messages[-max_messages//len(history_files):]:
|
||
if not msg.get("is_history"):
|
||
all_messages.append({
|
||
"timestamp": msg["timestamp"],
|
||
"role": msg["role"],
|
||
"speaker": msg.get("speaker"),
|
||
"content": msg["content"],
|
||
"from_file": hist_file.name
|
||
})
|
||
except Exception as e:
|
||
self.logger.error(f"加载历史文件 {hist_file} 失败: {e}")
|
||
|
||
all_messages.sort(key=lambda x: x["timestamp"])
|
||
|
||
if len(all_messages) > max_messages:
|
||
all_messages = all_messages[-max_messages:]
|
||
|
||
self.logger.info(f"已加载 {len(all_messages)} 条历史消息")
|
||
return all_messages
|
||
|
||
def get_conversation_context(self, max_messages: int = None) -> List[Dict]:
|
||
"""获取适合作为AI上下文的对话历史"""
|
||
if max_messages is None:
|
||
max_messages = constants.MAX_HISTORY_CONTEXT
|
||
|
||
if not self.load_history:
|
||
return []
|
||
|
||
history_messages = self.load_all_history(max_messages)
|
||
|
||
context = []
|
||
for msg in history_messages:
|
||
role = msg.get("role", "")
|
||
content = msg.get("content", "")
|
||
|
||
if role == "system":
|
||
continue
|
||
|
||
if role == "assistant":
|
||
speaker = msg.get("speaker", "")
|
||
context.append({
|
||
"role": "assistant",
|
||
"content": f"[{speaker}] {content}"
|
||
})
|
||
else:
|
||
context.append({
|
||
"role": "user",
|
||
"content": content
|
||
})
|
||
|
||
return context
|
||
|
||
def add_message(self, role: str, content: str, speaker: str = None):
|
||
"""添加消息到当前对话"""
|
||
if not self.current_conversation:
|
||
self.start_new_conversation()
|
||
|
||
message = {
|
||
"timestamp": datetime.now().strftime(constants.DATETIME_FORMAT),
|
||
"role": role,
|
||
"speaker": speaker,
|
||
"content": content
|
||
}
|
||
|
||
self.current_conversation["messages"].append(message)
|
||
|
||
if role == "user":
|
||
self.current_conversation["stats"]["user_messages"] += 1
|
||
elif role == "assistant":
|
||
self.current_conversation["stats"]["ai_messages"] += 1
|
||
|
||
self.save_current_conversation()
|
||
|
||
def add_tool_call(self, tool_name: str, args: Dict, result: str):
|
||
"""记录工具调用"""
|
||
if not self.current_conversation:
|
||
return
|
||
|
||
tool_call = {
|
||
"timestamp": datetime.now().strftime(constants.DATETIME_FORMAT),
|
||
"tool": tool_name,
|
||
"args": args,
|
||
"result_preview": result[:200] + "..." if len(result) > 200 else result
|
||
}
|
||
|
||
self.current_conversation["tool_calls"].append(tool_call)
|
||
self.current_conversation["stats"]["tool_calls"] += 1
|
||
self.save_current_conversation()
|
||
|
||
def end_current_conversation(self, summary: str = None):
|
||
"""结束当前对话"""
|
||
if self.current_conversation:
|
||
self.current_conversation["end_time"] = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
|
||
if summary and self.memory_db:
|
||
messages = self.current_conversation.get("messages", [])
|
||
content_preview = "\n".join([f"{m['speaker']}: {m['content'][:200]}"
|
||
for m in messages[-10:] if m.get('speaker')])
|
||
|
||
self.memory_db.add_memory(
|
||
topic=self.current_conversation["topic"],
|
||
summary=summary,
|
||
content=content_preview,
|
||
start_time=self.current_conversation["start_time"],
|
||
end_time=self.current_conversation["end_time"],
|
||
importance=2
|
||
)
|
||
|
||
self.save_current_conversation()
|
||
self.logger.info("当前对话已保存")
|
||
|
||
def reset_conversation(self):
|
||
"""重置当前对话状态"""
|
||
if self.current_conversation:
|
||
self.current_conversation["end_time"] = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
self.save_current_conversation()
|
||
self.current_conversation = None
|
||
|
||
def list_conversations(self) -> List[Dict]:
|
||
"""列出所有对话（包括当前和归档）"""
|
||
conversations = []
|
||
|
||
if self.current_conversation:
|
||
conversations.append({
|
||
"file": constants.CURRENT_CONVERSATION_FILE,
|
||
"is_current": True,
|
||
"id": self.current_conversation.get("id"),
|
||
"start_time": self.current_conversation.get("start_time"),
|
||
"end_time": self.current_conversation.get("end_time"),
|
||
"topic": self.current_conversation.get("topic"),
|
||
"messages": len(self.current_conversation.get("messages", [])),
|
||
"tool_calls": len(self.current_conversation.get("tool_calls", []))
|
||
})
|
||
|
||
for conv in self.history_index.get("conversations", []):
|
||
conversations.append({
|
||
"file": conv["file"],
|
||
"is_current": False,
|
||
"id": conv["id"],
|
||
"start_time": conv["start_time"],
|
||
"end_time": conv["end_time"],
|
||
"topic": conv["topic"],
|
||
"messages": conv["messages"],
|
||
"tool_calls": conv["tool_calls"]
|
||
})
|
||
|
||
conversations.sort(key=lambda x: x["start_time"] if x["start_time"] else "", reverse=True)
|
||
return conversations
|
||
|
||
def load_conversation(self, file_name: str) -> Optional[Dict]:
|
||
"""加载指定的对话"""
|
||
if file_name == constants.CURRENT_CONVERSATION_FILE and self.current_conversation:
|
||
return self.current_conversation
|
||
|
||
file_path = self.archive_dir / file_name
|
||
if file_path.exists():
|
||
with open(file_path, 'r', encoding='utf-8') as f:
|
||
return json.load(f)
|
||
return None
|
||
|
||
|
||
# ==================== 命令执行器抽象类 ====================
|
||
|
||
class CommandExecutor(ABC):
|
||
"""命令执行器抽象基类 - 负责执行系统命令"""
|
||
|
||
def __init__(self, logger: Optional[Logger] = None):
|
||
self.logger = logger or Logger("CommandExecutor")
|
||
self.config = ConfigManager()
|
||
self.process = None
|
||
|
||
@abstractmethod
|
||
def execute(self, command: str, timeout: int = 30, **kwargs) -> str:
|
||
"""执行命令并返回结果（非流式）"""
|
||
pass
|
||
|
||
@abstractmethod
|
||
def execute_streaming(self, command: str, output_queue: queue.Queue, timeout: int = 30, **kwargs):
|
||
"""流式执行命令，通过队列输出结果"""
|
||
pass
|
||
|
||
def is_enabled(self) -> bool:
|
||
"""检查命令执行是否启用"""
|
||
return constants.COMMANDS_ENABLED
|
||
|
||
def format_result(self, success: bool, message: str, data: Dict = None) -> str:
|
||
"""统一结果格式化"""
|
||
result = []
|
||
emoji = "✅" if success else "❌"
|
||
result.append(f"{emoji} {message}")
|
||
|
||
if data:
|
||
for key, value in data.items():
|
||
if isinstance(value, str) and "\n" in value:
|
||
result.append(f" {key}:")
|
||
for line in value.split("\n"):
|
||
result.append(f" {line}")
|
||
else:
|
||
result.append(f" {key}: {value}")
|
||
|
||
return "\n".join(result)
|
||
|
||
|
||
class SubprocessCommandExecutor(CommandExecutor):
|
||
"""基于subprocess的命令执行器 - 支持超时、工作目录、输出大小限制和取消"""
|
||
|
||
# 命令输出最大字节数（1MB），超出截断
|
||
MAX_OUTPUT_BYTES = 1 * 1024 * 1024
|
||
|
||
def __init__(self, logger=None):
|
||
super().__init__(logger)
|
||
self._current_process = None
|
||
self._process_lock = threading.Lock()
|
||
|
||
def cancel(self):
|
||
"""取消当前正在运行的命令（kill子进程）。线程安全。"""
|
||
with self._process_lock:
|
||
proc = self._current_process
|
||
if proc is not None:
|
||
try:
|
||
if proc.poll() is None:
|
||
proc.kill()
|
||
self.logger.info("已终止正在运行的子进程")
|
||
except Exception:
|
||
pass
|
||
|
||
def _read_with_limit(self, process, timeout: int, start_time: float, output_queue=None) -> Tuple[str, bool, bool]:
|
||
"""从进程读取输出，限制总大小和超时。
|
||
返回 (output_str, timeout_occurred, truncated)
|
||
"""
|
||
output_chunks = []
|
||
total_bytes = 0
|
||
timeout_occurred = False
|
||
truncated = False
|
||
|
||
while True:
|
||
# 检查超时
|
||
if timeout > 0 and time.time() - start_time > timeout:
|
||
process.terminate()
|
||
timeout_occurred = True
|
||
break
|
||
|
||
# 检查输出大小
|
||
if total_bytes >= self.MAX_OUTPUT_BYTES:
|
||
truncated = True
|
||
process.terminate()
|
||
break
|
||
|
||
# 读取一行
|
||
line = process.stdout.readline()
|
||
if not line and process.poll() is not None:
|
||
break
|
||
if line:
|
||
line_bytes = len(line.encode('utf-8', errors='replace'))
|
||
if total_bytes + line_bytes > self.MAX_OUTPUT_BYTES and not truncated:
|
||
allowed = self.MAX_OUTPUT_BYTES - total_bytes
|
||
if allowed > 0:
|
||
output_chunks.append(line.encode('utf-8', errors='replace')[:allowed].decode('utf-8', errors='replace'))
|
||
total_bytes = self.MAX_OUTPUT_BYTES
|
||
truncated = True
|
||
process.terminate()
|
||
break
|
||
|
||
output_chunks.append(line)
|
||
total_bytes += line_bytes
|
||
|
||
if output_queue:
|
||
output_queue.put(("line", line.rstrip('\n') + "\n"))
|
||
|
||
return ''.join(output_chunks), timeout_occurred, truncated
|
||
|
||
def execute(self, command: str, timeout: int = 30, work_dir: str = None, env: Dict = None, **kwargs) -> str:
|
||
"""执行命令并返回完整结果（带输出大小限制）"""
|
||
if not self.is_enabled():
|
||
return "❌ 命令执行功能已禁用"
|
||
|
||
# 默认工作目录为workspace，避免在启动目录散落文件
|
||
if work_dir is None or work_dir == ".":
|
||
work_dir = constants.WORKSPACE_DIR
|
||
|
||
self.logger.info(f"执行命令: {command} (work_dir={work_dir})")
|
||
|
||
# 准备环境变量
|
||
exec_env = os.environ.copy()
|
||
if env:
|
||
exec_env.update(env)
|
||
|
||
process = None
|
||
try:
|
||
process = subprocess.Popen(
|
||
command,
|
||
shell=True,
|
||
stdout=subprocess.PIPE,
|
||
stderr=subprocess.STDOUT,
|
||
stdin=subprocess.PIPE,
|
||
text=True,
|
||
bufsize=1,
|
||
universal_newlines=True,
|
||
env=exec_env,
|
||
cwd=os.path.expanduser(work_dir)
|
||
)
|
||
# 保存进程引用以便cancel()可以kill它
|
||
with self._process_lock:
|
||
self._current_process = process
|
||
|
||
start_time = time.time()
|
||
output_str, timeout_occurred, truncated = self._read_with_limit(process, timeout, start_time)
|
||
|
||
# 确保进程终止
|
||
if process.poll() is None:
|
||
if timeout_occurred or truncated:
|
||
process.kill()
|
||
else:
|
||
process.terminate()
|
||
try:
|
||
process.wait(timeout=2)
|
||
except:
|
||
process.kill()
|
||
|
||
return_code = process.returncode if process.returncode is not None else -1
|
||
|
||
data = {
|
||
"输出": output_str.strip() if output_str.strip() else "(无输出)",
|
||
"返回码": return_code
|
||
}
|
||
|
||
if timeout_occurred:
|
||
return self.format_result(False, f"命令执行超时（{timeout}秒）", data)
|
||
|
||
if truncated:
|
||
data["警告"] = f"输出超过 {self.MAX_OUTPUT_BYTES/1024/1024:.0f}MB 限制，结果已截断"
|
||
return self.format_result(False, "命令执行完成（输出已截断）", data)
|
||
|
||
return self.format_result(return_code == 0, "命令执行完成", data)
|
||
|
||
except subprocess.TimeoutExpired:
|
||
return f"❌ 命令执行超时（{timeout}秒）"
|
||
except FileNotFoundError as e:
|
||
return f"❌ 命令执行错误: 命令未找到 - {str(e)}"
|
||
except PermissionError as e:
|
||
return f"❌ 命令执行错误: 权限不足 - {str(e)}"
|
||
except Exception as e:
|
||
return f"❌ 执行错误: {str(e)}"
|
||
finally:
|
||
if process and process.poll() is None:
|
||
process.kill()
|
||
# 清除当前进程引用
|
||
with self._process_lock:
|
||
self._current_process = None
|
||
|
||
def execute_streaming(self, command: str, output_queue: queue.Queue, timeout: int = 30,
|
||
work_dir: str = None, env: Dict = None, **kwargs):
|
||
"""流式执行命令，支持超时控制、工作目录和输出大小限制"""
|
||
if not self.is_enabled():
|
||
output_queue.put(("line", "❌ 命令执行功能已禁用\n"))
|
||
output_queue.put(("complete", "❌ 命令执行功能已禁用"))
|
||
return
|
||
|
||
# 默认工作目录为workspace，避免在启动目录散落文件
|
||
if work_dir is None or work_dir == ".":
|
||
work_dir = constants.WORKSPACE_DIR
|
||
|
||
self.logger.info(f"流式执行命令: {command} (work_dir={work_dir})")
|
||
output_queue.put(("line", f"\n🔧 执行命令: {command}\n"))
|
||
output_queue.put(("line", f"⏱️ 超时设置: {timeout} 秒\n"))
|
||
output_queue.put(("line", f"📦 输出限制: {self.MAX_OUTPUT_BYTES/1024/1024:.0f}MB\n"))
|
||
output_queue.put(("line", f"📁 工作目录: {work_dir}\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
# 准备环境变量
|
||
exec_env = os.environ.copy()
|
||
if env:
|
||
exec_env.update(env)
|
||
|
||
process = None
|
||
try:
|
||
process = subprocess.Popen(
|
||
command,
|
||
shell=True,
|
||
stdout=subprocess.PIPE,
|
||
stderr=subprocess.STDOUT,
|
||
stdin=subprocess.PIPE,
|
||
text=True,
|
||
bufsize=1,
|
||
universal_newlines=True,
|
||
env=exec_env,
|
||
cwd=os.path.expanduser(work_dir)
|
||
)
|
||
self.process = process
|
||
# 保存进程引用以便cancel()可以kill它
|
||
with self._process_lock:
|
||
self._current_process = process
|
||
|
||
start_time = time.time()
|
||
output_lines = []
|
||
total_bytes = 0
|
||
timeout_occurred = False
|
||
truncated = False
|
||
|
||
# 实时读取输出
|
||
while True:
|
||
# 检查超时
|
||
if timeout > 0 and time.time() - start_time > timeout:
|
||
process.terminate()
|
||
timeout_occurred = True
|
||
output_queue.put(("line", f"\n⏰ 命令执行超时（{timeout}秒）\n"))
|
||
break
|
||
|
||
# 检查输出大小
|
||
if total_bytes >= self.MAX_OUTPUT_BYTES:
|
||
truncated = True
|
||
process.terminate()
|
||
output_queue.put(("line", f"\n📦 输出超过 {self.MAX_OUTPUT_BYTES/1024/1024:.0f}MB 限制，结果已截断\n"))
|
||
break
|
||
|
||
# 读取一行输出
|
||
line = process.stdout.readline()
|
||
if not line and process.poll() is not None:
|
||
break
|
||
if line:
|
||
line_bytes = len(line.encode('utf-8', errors='replace'))
|
||
|
||
if total_bytes + line_bytes > self.MAX_OUTPUT_BYTES and not truncated:
|
||
allowed = self.MAX_OUTPUT_BYTES - total_bytes
|
||
if allowed > 0:
|
||
truncated_line = line.encode('utf-8', errors='replace')[:allowed].decode('utf-8', errors='replace')
|
||
output_queue.put(("line", truncated_line))
|
||
output_lines.append(truncated_line)
|
||
total_bytes = self.MAX_OUTPUT_BYTES
|
||
truncated = True
|
||
process.terminate()
|
||
output_queue.put(("line", f"\n📦 输出超过 {self.MAX_OUTPUT_BYTES/1024/1024:.0f}MB 限制，结果已截断\n"))
|
||
break
|
||
|
||
stripped = line.rstrip('\n')
|
||
output_queue.put(("line", stripped + "\n"))
|
||
output_lines.append(line)
|
||
total_bytes += line_bytes
|
||
|
||
# 如果超时或截断，确保进程被终止
|
||
if (timeout_occurred or truncated) and process.poll() is None:
|
||
process.kill()
|
||
try:
|
||
process.wait(timeout=2)
|
||
except:
|
||
pass
|
||
|
||
return_code = process.returncode if process.returncode is not None else -1
|
||
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
if timeout_occurred:
|
||
output_queue.put(("line", f"⏰ 命令执行超时（{timeout}秒），返回码: {return_code}\n"))
|
||
elif truncated:
|
||
output_queue.put(("line", f"📦 输出已截断，返回码: {return_code}\n"))
|
||
else:
|
||
output_queue.put(("line", f"✅ 命令执行完成，返回码: {return_code}\n"))
|
||
|
||
# 准备完整结果
|
||
full_output = "".join(output_lines)
|
||
data = {
|
||
"输出": full_output.strip() if full_output.strip() else "(无输出)",
|
||
"返回码": return_code
|
||
}
|
||
|
||
if timeout_occurred:
|
||
result = self.format_result(False, f"命令执行超时（{timeout}秒）", data)
|
||
elif truncated:
|
||
data["警告"] = f"输出超过 {self.MAX_OUTPUT_BYTES/1024/1024:.0f}MB 限制，结果已截断"
|
||
result = self.format_result(False, "命令执行完成（输出已截断）", data)
|
||
else:
|
||
result = self.format_result(return_code == 0, "命令执行完成", data)
|
||
|
||
output_queue.put(("complete", result))
|
||
|
||
except Exception as e:
|
||
output_queue.put(("line", f"❌ 执行错误: {str(e)}\n"))
|
||
output_queue.put(("complete", f"❌ 执行错误: {str(e)}"))
|
||
finally:
|
||
if process and process.poll() is None:
|
||
process.terminate()
|
||
try:
|
||
process.wait(timeout=2)
|
||
except:
|
||
process.kill()
|
||
self.process = None
|
||
with self._process_lock:
|
||
self._current_process = None
|
||
|
||
# ==================== 工具基类 ====================
|
||
|
||
class Tool(ABC):
|
||
"""工具基类"""
|
||
|
||
def __init__(self, logger: Optional[Logger] = None):
|
||
self.logger = logger or Logger(self.__class__.__name__)
|
||
self.config = ConfigManager()
|
||
self.streaming_output = False # 标记是否正在流式输出
|
||
self.output_queue = None # 添加输出队列属性
|
||
|
||
@abstractmethod
|
||
def get_name(self) -> str:
|
||
"""返回工具名称"""
|
||
pass
|
||
|
||
@abstractmethod
|
||
def get_description(self) -> str:
|
||
"""返回工具描述"""
|
||
pass
|
||
|
||
@abstractmethod
|
||
def get_parameters(self) -> Dict:
|
||
"""返回工具参数定义（JSON Schema格式）"""
|
||
pass
|
||
|
||
@abstractmethod
|
||
def execute(self, **kwargs) -> str:
|
||
"""执行工具并返回结果"""
|
||
pass
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行工具，通过队列输出结果"""
|
||
self.output_queue = output_queue # 保存队列引用
|
||
self.streaming_output = True
|
||
try:
|
||
# 执行具体的工具逻辑，子类可以重写这个方法
|
||
result = self.execute(**kwargs)
|
||
# 如果结果不为空，发送到队列
|
||
if result:
|
||
# 对于非流式输出，直接发送完整结果
|
||
output_queue.put(("complete", result))
|
||
finally:
|
||
self.streaming_output = False
|
||
self.output_queue = None
|
||
|
||
def validate_params(self, required: List[str], **kwargs) -> Optional[str]:
|
||
"""验证必需参数"""
|
||
missing = [p for p in required if p not in kwargs or kwargs[p] is None]
|
||
if missing:
|
||
return f"❌ 错误：缺少必需参数: {', '.join(missing)}"
|
||
return None
|
||
|
||
def to_function_call_format(self) -> Dict:
|
||
"""转换为OpenAI函数调用格式"""
|
||
return {
|
||
"type": "function",
|
||
"function": {
|
||
"name": self.get_name(),
|
||
"description": self.get_description(),
|
||
"parameters": self.get_parameters()
|
||
}
|
||
}
|
||
|
||
def format_result(self, success: bool, message: str, data: Dict = None) -> str:
|
||
"""统一结果格式化"""
|
||
result = []
|
||
emoji = "✅" if success else "❌"
|
||
result.append(f"{emoji} {message}")
|
||
|
||
if data:
|
||
for key, value in data.items():
|
||
if isinstance(value, str) and "\n" in value:
|
||
result.append(f" {key}:")
|
||
for line in value.split("\n"):
|
||
result.append(f" {line}")
|
||
else:
|
||
result.append(f" {key}: {value}")
|
||
|
||
return "\n".join(result)
|
||
|
||
def check_path_safety(self, filepath: str) -> Tuple[bool, str, str]:
|
||
"""检查路径安全性（简化版）"""
|
||
try:
|
||
abs_path = os.path.abspath(os.path.expanduser(filepath))
|
||
|
||
# 检查文件大小
|
||
if os.path.exists(abs_path) and os.path.isfile(abs_path):
|
||
file_size = os.path.getsize(abs_path)
|
||
max_size = constants.MAX_FILE_SIZE
|
||
if file_size > max_size:
|
||
return False, abs_path, f"❌ 文件太大 ({file_size/1024/1024:.1f}MB)"
|
||
|
||
return True, abs_path, ""
|
||
except Exception as e:
|
||
return False, filepath, f"❌ 路径检查错误: {str(e)}"
|
||
|
||
def send_line(self, line: str):
|
||
"""发送一行输出到队列（如果存在）"""
|
||
if self.output_queue:
|
||
self.output_queue.put(("line", line))
|
||
|
||
def send_complete(self, result: str):
|
||
"""发送完成结果到队列（如果存在）"""
|
||
if self.output_queue:
|
||
self.output_queue.put(("complete", result))
|
||
|
||
|
||
class ToolRegistry:
|
||
"""工具注册器 - 支持技能热重载"""
|
||
|
||
def __init__(self, logger: Optional[Logger] = None):
|
||
self._tools: Dict[str, Tool] = {}
|
||
self._builtin_tools: Dict[str, Tool] = {} # 内置工具单独存储
|
||
self.logger = logger or Logger("ToolRegistry")
|
||
self.current_output_queue = None # 用于流式输出的队列
|
||
self.skill_manager: Optional['SkillManager'] = None # 技能管理器引用
|
||
|
||
def register(self, tool: Tool, is_builtin: bool = False):
|
||
"""注册工具，可以标记是否为内置工具"""
|
||
self._tools[tool.get_name()] = tool
|
||
if is_builtin:
|
||
self._builtin_tools[tool.get_name()] = tool
|
||
self.logger.info(f"工具已注册: {tool.get_name()} {'(内置)' if is_builtin else ''}")
|
||
|
||
def register_many(self, tools: List[Tool], is_builtin: bool = False):
|
||
"""批量注册工具"""
|
||
for tool in tools:
|
||
self.register(tool, is_builtin)
|
||
|
||
def get_tool(self, name: str) -> Optional[Tool]:
|
||
"""获取工具"""
|
||
return self._tools.get(name)
|
||
|
||
def get_all_tools(self) -> List[Tool]:
|
||
"""获取所有工具"""
|
||
return list(self._tools.values())
|
||
|
||
def get_tools_schemas(self) -> List[Dict]:
|
||
"""获取所有工具的OpenAI函数调用格式"""
|
||
return [tool.to_function_call_format() for tool in self._tools.values()]
|
||
|
||
def set_output_queue(self, queue: queue.Queue):
|
||
"""设置输出队列"""
|
||
self.current_output_queue = queue
|
||
# 同时设置所有工具的队列
|
||
for tool in self._tools.values():
|
||
tool.output_queue = queue
|
||
|
||
def execute_tool(self, name: str, **kwargs) -> str:
|
||
"""执行工具"""
|
||
tool = self.get_tool(name)
|
||
if not tool:
|
||
return f"❌ 错误：找不到工具 '{name}'"
|
||
|
||
self.logger.debug(f"工具调用: {name}({kwargs})")
|
||
start_time = time.time()
|
||
|
||
try:
|
||
# 检查是否有队列可用，并且工具支持流式输出
|
||
if self.current_output_queue and hasattr(tool, 'execute_streaming'):
|
||
# 设置工具的流式输出属性
|
||
tool.streaming_output = True
|
||
tool.output_queue = self.current_output_queue
|
||
|
||
# 创建队列来收集流式输出
|
||
output_queue = queue.Queue()
|
||
|
||
# 执行流式输出
|
||
thread = threading.Thread(
|
||
target=tool.execute_streaming,
|
||
args=(output_queue,),
|
||
kwargs=kwargs
|
||
)
|
||
thread.daemon = True
|
||
thread.start()
|
||
|
||
# 从队列收集输出并发送到当前队列
|
||
final_result = None
|
||
while thread.is_alive() or not output_queue.empty():
|
||
try:
|
||
msg_type, content = output_queue.get(timeout=0.1)
|
||
if msg_type == "line":
|
||
# 发送到当前队列（会通过WebSocket发送）
|
||
if self.current_output_queue:
|
||
self.current_output_queue.put(("line", content))
|
||
elif msg_type == "complete":
|
||
final_result = content
|
||
# 也发送完整结果
|
||
if self.current_output_queue:
|
||
self.current_output_queue.put(("complete", content))
|
||
except queue.Empty:
|
||
continue
|
||
|
||
thread.join(timeout=5)
|
||
if thread.is_alive():
|
||
return "❌ 工具执行超时"
|
||
|
||
result = final_result or ""
|
||
tool.streaming_output = False
|
||
tool.output_queue = None
|
||
else:
|
||
# 普通执行
|
||
result = tool.execute(**kwargs)
|
||
|
||
elapsed = time.time() - start_time
|
||
self.logger.debug(f"工具结果 ({elapsed:.2f}s): {result[:100]}...")
|
||
return result
|
||
except TypeError as e:
|
||
return f"❌ 工具执行错误: 参数错误 - {str(e)}"
|
||
except Exception as e:
|
||
return f"❌ 工具执行错误: {str(e)}"
|
||
|
||
def reload_skills(self, skills_dir: str = None) -> int:
|
||
"""
|
||
重新加载所有扩展技能（热重载）。
|
||
委托给 SkillManager 重新扫描目录并执行 --help 收集简介。
|
||
"""
|
||
self.logger.info("开始重新加载扩展技能...")
|
||
if self.skill_manager is not None:
|
||
count = self.skill_manager.reload()
|
||
self.logger.info(i18n.get('skills_reloaded', count))
|
||
return count
|
||
self.logger.warning("skill_manager 未初始化，无法重载技能")
|
||
return 0
|
||
|
||
def get_skills_count(self) -> Tuple[int, int]:
|
||
"""获取内置工具和扩展技能的数量"""
|
||
builtin_count = len(self._builtin_tools)
|
||
skill_count = len(self._tools) - builtin_count
|
||
return builtin_count, skill_count
|
||
|
||
|
||
# ==================== 扩展技能管理器（支持 Markdown 格式） ====================
|
||
|
||
class SkillManager:
|
||
"""扩展技能管理器 - 收集并管理 ~/.aibox/skills/ 目录下的技能文件。
|
||
|
||
支持两种技能格式：
|
||
1. 可执行脚本/程序：执行 --help，第一行为简介
|
||
2. Markdown 文件 (.md)：第一行为简介（# 标题或普通文本），其余为技能内容
|
||
"""
|
||
|
||
def __init__(self, logger: Optional[Logger] = None):
|
||
self.logger = logger or Logger("SkillManager")
|
||
# name -> {path, file, summary, content, is_markdown}
|
||
self.skills: Dict[str, Dict] = {}
|
||
self._load_skills()
|
||
|
||
# ------------------------------------------------------------------
|
||
# 内部辅助
|
||
# ------------------------------------------------------------------
|
||
|
||
def _run_help(self, skill_path: str) -> str:
|
||
"""执行 skill_path --help 并返回完整输出（stdout 优先，否则 stderr）。"""
|
||
try:
|
||
result = subprocess.run(
|
||
[skill_path, "--help"],
|
||
capture_output=True,
|
||
text=True,
|
||
timeout=10,
|
||
env=os.environ.copy()
|
||
)
|
||
output = result.stdout.strip()
|
||
if not output:
|
||
output = result.stderr.strip()
|
||
return output
|
||
except Exception as e:
|
||
self.logger.warning(f"运行 {skill_path} --help 失败: {e}")
|
||
return ""
|
||
|
||
def _read_markdown_skill(self, skill_path: str) -> Tuple[str, str]:
|
||
"""读取 Markdown 技能文件，返回 (简介, 完整内容)"""
|
||
try:
|
||
with open(skill_path, 'r', encoding='utf-8') as f:
|
||
content = f.read().strip()
|
||
|
||
if not content:
|
||
return "（空文件）", ""
|
||
|
||
# 提取第一行非空内容作为简介
|
||
lines = content.splitlines()
|
||
summary = ""
|
||
for line in lines:
|
||
stripped = line.strip()
|
||
if stripped:
|
||
# 如果是 Markdown 标题，去掉 # 前缀
|
||
if stripped.startswith('#'):
|
||
summary = stripped.lstrip('#').strip()
|
||
else:
|
||
summary = stripped
|
||
break
|
||
|
||
if not summary:
|
||
summary = os.path.basename(skill_path).replace('.md', '')
|
||
|
||
return summary, content
|
||
|
||
except Exception as e:
|
||
self.logger.warning(f"读取 Markdown 技能文件失败 {skill_path}: {e}")
|
||
return "（读取失败）", ""
|
||
|
||
@staticmethod
|
||
def _first_nonempty_line(text: str) -> str:
|
||
"""返回文本中第一行非空内容。"""
|
||
for line in text.splitlines():
|
||
stripped = line.strip()
|
||
if stripped:
|
||
return stripped
|
||
return ""
|
||
|
||
def _is_markdown_file(self, filename: str) -> bool:
|
||
"""判断是否为 Markdown 文件"""
|
||
return filename.lower().endswith('.md')
|
||
|
||
def _is_executable(self, filepath: str) -> bool:
|
||
"""检查文件是否可执行"""
|
||
if os.name == 'nt': # Windows
|
||
# Windows 下检查扩展名
|
||
executable_extensions = {'.exe', '.bat', '.cmd', '.ps1', '.py', '.sh'}
|
||
ext = os.path.splitext(filepath)[1].lower()
|
||
return ext in executable_extensions
|
||
else: # Unix/Linux/Mac
|
||
return os.access(filepath, os.X_OK)
|
||
|
||
# ------------------------------------------------------------------
|
||
# 加载 / 重载
|
||
# ------------------------------------------------------------------
|
||
|
||
def _load_skills(self):
|
||
"""扫描技能目录，加载所有技能文件（可执行文件或 Markdown 文件）。"""
|
||
skills_dir = constants.SKILLS_DIR
|
||
self.skills = {}
|
||
|
||
if not os.path.exists(skills_dir):
|
||
self.logger.info(f"技能目录不存在: {skills_dir}")
|
||
return
|
||
|
||
skill_files = [
|
||
f for f in os.listdir(skills_dir)
|
||
if os.path.isfile(os.path.join(skills_dir, f))
|
||
and not f.startswith('_')
|
||
and not f.startswith('.')
|
||
]
|
||
|
||
for skill_file in skill_files:
|
||
skill_path = os.path.join(skills_dir, skill_file)
|
||
skill_name = os.path.splitext(skill_file)[0]
|
||
is_markdown = self._is_markdown_file(skill_file)
|
||
|
||
if is_markdown:
|
||
# Markdown 格式技能：直接读取内容
|
||
summary, content = self._read_markdown_skill(skill_path)
|
||
if content:
|
||
self.skills[skill_name] = {
|
||
"path": skill_path,
|
||
"file": skill_file,
|
||
"summary": summary,
|
||
"content": content,
|
||
"is_markdown": True,
|
||
"is_executable": False
|
||
}
|
||
self.logger.info(f"已加载 Markdown 技能: {skill_name} — {summary}")
|
||
else:
|
||
self.logger.warning(f"Markdown 技能文件为空（跳过）: {skill_file}")
|
||
|
||
else:
|
||
# 可执行文件格式技能：执行 --help 获取简介
|
||
if not self._is_executable(skill_path):
|
||
self.logger.warning(f"技能文件不可执行（跳过）: {skill_file}")
|
||
continue
|
||
|
||
help_output = self._run_help(skill_path)
|
||
summary = self._first_nonempty_line(help_output) or f"扩展技能: {skill_name}"
|
||
|
||
self.skills[skill_name] = {
|
||
"path": skill_path,
|
||
"file": skill_file,
|
||
"summary": summary,
|
||
"content": help_output, # 完整的 --help 输出作为内容
|
||
"is_markdown": False,
|
||
"is_executable": True
|
||
}
|
||
self.logger.info(f"已加载可执行技能: {skill_name} — {summary}")
|
||
|
||
self.logger.info(f"共加载 {len(self.skills)} 个技能（{len([s for s in self.skills.values() if s['is_markdown']])} 个 Markdown 技能）")
|
||
|
||
def reload(self) -> int:
|
||
"""重新扫描技能目录，返回加载的技能数量。"""
|
||
self._load_skills()
|
||
return len(self.skills)
|
||
|
||
# ------------------------------------------------------------------
|
||
# 查询
|
||
# ------------------------------------------------------------------
|
||
|
||
def search(self, keyword: str) -> List[Dict]:
|
||
"""在技能名称、简介和内容中搜索关键字，返回匹配项列表。"""
|
||
kw = keyword.lower()
|
||
results = []
|
||
for name, info in self.skills.items():
|
||
match = (
|
||
kw in name.lower() or
|
||
kw in info["summary"].lower() or
|
||
kw in info.get("content", "").lower()
|
||
)
|
||
if match:
|
||
results.append({
|
||
"name": name,
|
||
"path": info["path"],
|
||
"summary": info["summary"],
|
||
"content": info.get("content", ""),
|
||
"is_markdown": info.get("is_markdown", False),
|
||
"is_executable": info.get("is_executable", False)
|
||
})
|
||
return results
|
||
|
||
def get_skill_content(self, skill_name: str) -> Optional[str]:
|
||
"""获取技能的内容（Markdown 技能返回完整内容，可执行技能返回 --help 输出）"""
|
||
info = self.skills.get(skill_name)
|
||
if info:
|
||
return info.get("content")
|
||
return None
|
||
|
||
def get_skill(self, skill_name: str) -> Optional[Dict]:
|
||
"""获取单个技能的完整信息"""
|
||
return self.skills.get(skill_name)
|
||
|
||
def get_all(self) -> List[Dict]:
|
||
"""返回所有技能的列表（含名称、路径、简介和内容）。"""
|
||
return [
|
||
{
|
||
"name": name,
|
||
"path": info["path"],
|
||
"summary": info["summary"],
|
||
"content": info.get("content", ""),
|
||
"is_markdown": info.get("is_markdown", False),
|
||
"is_executable": info.get("is_executable", False)
|
||
}
|
||
for name, info in self.skills.items()
|
||
]
|
||
|
||
def count(self) -> int:
|
||
return len(self.skills)
|
||
|
||
|
||
class SearchSkillsTool(Tool):
|
||
"""扩展技能搜索工具 - 按关键字搜索 ~/.aibox/skills/ 下的技能文件。
|
||
|
||
支持两种技能格式：
|
||
1. 可执行脚本/程序：通过 --help 获取简介和完整使用说明
|
||
2. Markdown 文件 (.md)：第一行为简介，其余为技能内容
|
||
|
||
工作流程：
|
||
1. 用关键字调用本工具，获取匹配技能的名称和简介列表
|
||
2. 对于 Markdown 技能，可以直接读取 content 字段获取完整技能内容
|
||
3. 对于可执行技能，通过 run_command 执行 "技能名 --help" 查看完整命令手册
|
||
4. 根据内容，使用相应方式调用技能
|
||
"""
|
||
|
||
def __init__(self, skill_manager: SkillManager, logger: Optional[Logger] = None):
|
||
super().__init__(logger)
|
||
self.skill_manager = skill_manager
|
||
|
||
def get_name(self) -> str:
|
||
return "search_skills"
|
||
|
||
def get_description(self) -> str:
|
||
return (
|
||
"扩展技能搜索工具。\n\n"
|
||
"用关键字搜索 ~/.aibox/skills/ 目录下的技能文件。\n"
|
||
"支持两种技能格式：\n"
|
||
" 1. 可执行脚本/程序：通过 --help 获取简介和完整使用说明\n"
|
||
" 2. Markdown 文件 (.md)：第一行为简介，其余为技能内容\n\n"
|
||
"使用方法：\n"
|
||
"1. 调用本工具搜索关键字，获取匹配技能列表（空字符串列出全部）。\n"
|
||
"2. 对于 Markdown 技能：直接读取返回的 content 字段获取完整技能内容。\n"
|
||
"3. 对于可执行技能：用 run_command 执行 \"技能名 --help\" 获取完整命令手册。\n"
|
||
"4. 按照内容说明，使用相应方式调用该技能。"
|
||
)
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"keyword": {
|
||
"type": "string",
|
||
"description": (
|
||
"搜索关键字，在技能名称、简介和内容中进行匹配。"
|
||
"传入空字符串可列出全部可用技能。"
|
||
)
|
||
},
|
||
"show_content": {
|
||
"type": "boolean",
|
||
"description": (
|
||
"是否在结果中显示技能内容。"
|
||
"对于 Markdown 技能，这会影响返回结果的详细程度。"
|
||
"默认 False（只显示名称和简介）。"
|
||
),
|
||
"default": False
|
||
}
|
||
},
|
||
"required": ["keyword"]
|
||
}
|
||
|
||
def execute(self, keyword: str = "", show_content: bool = False, **kwargs) -> str:
|
||
kw = keyword.strip()
|
||
if kw:
|
||
results = self.skill_manager.search(kw)
|
||
header = f"🔍 搜索 '{kw}' 的结果（共 {len(results)} 个匹配技能）："
|
||
empty_msg = (
|
||
f"❌ 未找到与 '{kw}' 相关的技能。\n"
|
||
"提示：传入空字符串可列出所有可用技能。"
|
||
)
|
||
else:
|
||
results = self.skill_manager.get_all()
|
||
header = f"🔧 所有可用技能（共 {len(results)} 个）："
|
||
empty_msg = "❌ ~/.aibox/skills/ 目录下暂无可用技能。"
|
||
|
||
if not results:
|
||
return empty_msg
|
||
|
||
lines = [header]
|
||
for item in results:
|
||
skill_type = "📄 Markdown" if item.get('is_markdown') else "⚙️ 可执行"
|
||
lines.append(f"\n {skill_type} • {item['name']}")
|
||
lines.append(f" 简介: {item['summary']}")
|
||
|
||
if show_content and item.get('content'):
|
||
content = item['content']
|
||
if item.get('is_markdown'):
|
||
preview = content[:500]
|
||
if len(content) > 500:
|
||
preview += "...\n （内容较长，已截断，请直接读取技能文件获取完整内容）"
|
||
lines.append(f" 内容预览:\n {preview}")
|
||
else:
|
||
preview = content[:200] if content else "(无帮助输出)"
|
||
if len(content) > 200:
|
||
preview += "..."
|
||
lines.append(f" 帮助预览: {preview}")
|
||
else:
|
||
if item.get('is_markdown'):
|
||
lines.append(f" 💡 提示: 设置 show_content=true 可查看技能内容")
|
||
else:
|
||
lines.append(f" 💡 提示: 用 run_command 执行 \"{item['name']} --help\" 查看完整手册")
|
||
|
||
lines.append("")
|
||
lines.append("📖 技能调用方式:")
|
||
lines.append(" • Markdown 技能: 读取 content 字段获取完整技能指令")
|
||
lines.append(" • 可执行技能: 用 run_command 执行 '技能名 --help' 查看手册")
|
||
return "\n".join(lines)
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行搜索技能"""
|
||
keyword = kwargs.get("keyword", "")
|
||
show_content = kwargs.get("show_content", False)
|
||
|
||
output_queue.put(("line", f"\n🔍 搜索技能: {keyword if keyword else '(全部)'}\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
# 直接传递 kwargs，避免重复参数
|
||
result = self.execute(**kwargs)
|
||
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
|
||
# ==================== 技能热重载工具 ====================
|
||
|
||
class ReloadSkillsTool(Tool):
|
||
"""技能热重载工具 - 重新加载所有扩展技能"""
|
||
|
||
def __init__(self, tool_registry: ToolRegistry, logger: Optional[Logger] = None):
|
||
super().__init__(logger)
|
||
self.tool_registry = tool_registry
|
||
|
||
def get_name(self) -> str:
|
||
return "reload_skills"
|
||
|
||
def get_description(self) -> str:
|
||
return """技能热重载工具 - 重新加载所有扩展技能
|
||
当你添加、修改或删除了 ~/.aibox/skills/ 目录下的技能文件后，
|
||
调用此工具可以立即生效，无需重启系统。"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"confirm": {
|
||
"type": "boolean",
|
||
"description": "确认重新加载技能",
|
||
"default": True
|
||
}
|
||
}
|
||
}
|
||
|
||
def execute(self, confirm: bool = True, **kwargs) -> str:
|
||
"""执行技能热重载"""
|
||
if not confirm:
|
||
return "❌ 技能重载已取消，如需重载请设置 confirm=true"
|
||
|
||
self.logger.info("开始执行技能热重载...")
|
||
|
||
try:
|
||
count = self.tool_registry.reload_skills()
|
||
|
||
# 从 SkillManager 获取更新后的技能列表（含简介）
|
||
skill_manager = self.tool_registry.skill_manager
|
||
if skill_manager:
|
||
all_skills = skill_manager.get_all()
|
||
skill_lines = "\n".join(
|
||
f" • {s['name']} — {s['summary']}"
|
||
for s in all_skills[:10]
|
||
)
|
||
data = {
|
||
"重新加载的技能数量": count,
|
||
"技能目录": constants.SKILLS_DIR,
|
||
"技能列表（含简介）": skill_lines or "（无技能）",
|
||
}
|
||
if count > 10:
|
||
data["提示"] = f"还有 {count - 10} 个技能未显示"
|
||
else:
|
||
data = {
|
||
"重新加载的技能数量": count,
|
||
"技能目录": constants.SKILLS_DIR,
|
||
}
|
||
|
||
return self.format_result(True, i18n.get('skills_reloaded', count), data)
|
||
|
||
except Exception as e:
|
||
self.logger.error(f"技能热重载失败: {e}")
|
||
return i18n.get('skills_reload_failed', str(e))
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行技能热重载"""
|
||
output_queue.put(("line", "\n🔄 执行技能热重载...\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
result = self.execute(**kwargs)
|
||
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
|
||
# ==================== 询问工具 ========================
|
||
class AskUserTool(Tool):
|
||
"""智能体遇到了麻烦，主动向用户问一个问题"""
|
||
|
||
def get_name(self) -> str:
|
||
return "ask_user"
|
||
|
||
def get_description(self) -> str:
|
||
return "你遇到了一个问题，想向用户询问一个问题的工具。"
|
||
|
||
def get_parameters(self) -> Dict:
|
||
''' TODO: 调用子代理生成提示词 '''
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"question": {
|
||
"type": "string",
|
||
"description": (
|
||
"你现在遇到一个问题，你想询问用户，现在提供你的问题。"
|
||
)
|
||
}
|
||
},
|
||
"required": []
|
||
}
|
||
|
||
def execute(self, question: str, **kwargs) -> str:
|
||
return (
|
||
"你遇到了想搞清楚的事情: " + question + "\n你现在可以询问用户。"
|
||
)
|
||
|
||
# -------------------- 情绪化技能工具 --------------------
|
||
class ExpressEmotionTool(Tool):
|
||
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，用于增强交互真实感。
|
||
支持的emoji: anger, joy, sadness, surprise。
|
||
可指定 persona 从 ~/.aibox/skills/ 加载对应人格技能文件（如 angry/coquettish/tsundere），
|
||
自动派生一个携带该人格的情绪子代理来生成回应，保证情绪人格独立、不污染主对话上下文。
|
||
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")
|
||
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)
|
||
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 ====================
|
||
|
||
class InstallSkillTool(Tool):
|
||
"""技能安装指引工具 - 返回如何将可执行文件/脚本安装为扩展技能的说明"""
|
||
|
||
def get_name(self) -> str:
|
||
return "install_skill"
|
||
|
||
def get_description(self) -> str:
|
||
return (
|
||
"技能安装指引工具。\n"
|
||
"返回将 Markdown 技能文件安装为扩展技能的完整步骤说明，"
|
||
"包括文件检查、格式校验和部署方法（目标文件名统一带 .md 扩展名）。"
|
||
)
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"skill_path": {
|
||
"type": "string",
|
||
"description": (
|
||
"待安装的技能文件路径（可选）。"
|
||
"若提供，说明中会带入该路径作为示例；"
|
||
"不提供则返回通用说明。"
|
||
)
|
||
}
|
||
},
|
||
"required": []
|
||
}
|
||
|
||
def execute(self, skill_path: str = "", **kwargs) -> str:
|
||
skills_dir = constants.SKILLS_DIR
|
||
|
||
# 如果给了具体路径，在说明里带入，否则用占位符
|
||
src = skill_path.strip() if skill_path else "/path/to/your_skill.md"
|
||
# 去掉扩展名作为技能名称，再统一补 .md 作为目标文件名
|
||
base = os.path.splitext(os.path.basename(src))[0] if src else "your_skill"
|
||
dst = os.path.join(skills_dir, base + ".md")
|
||
|
||
guide = f"""📦 扩展技能安装指引
|
||
{"=" * 55}
|
||
|
||
要将一个 Markdown 技能文件安装为扩展技能，请按以下步骤操作：
|
||
|
||
─────────────────────────────────────────────────────
|
||
步骤 1：确认文件存在且格式正确
|
||
─────────────────────────────────────────────────────
|
||
运行以下命令，确认文件存在：
|
||
|
||
ls -l {src}
|
||
|
||
技能文件格式要求：
|
||
• 文件名为纯文本 Markdown，第一行是一级标题（# 技能名），作为技能摘要
|
||
• 其余内容为技能的执行说明、使用方法和示例
|
||
• 文件应带 .md 扩展名（系统以 .md 文件作为技能加载）
|
||
|
||
─────────────────────────────────────────────────────
|
||
步骤 2：复制到技能目录并统一为 .md 扩展名
|
||
─────────────────────────────────────────────────────
|
||
技能目录：{skills_dir}
|
||
|
||
复制命令（目标文件名统一补 .md 扩展名）：
|
||
|
||
cp {src} {dst}
|
||
|
||
⚠️ 注意：目标文件名必须带 .md 扩展名，
|
||
系统以 .md 文件作为技能加载，且技能名称取文件名（不含 .md）。
|
||
|
||
─────────────────────────────────────────────────────
|
||
步骤 3：让系统识别新技能
|
||
─────────────────────────────────────────────────────
|
||
复制完成后，调用热重载工具使其立即生效：
|
||
|
||
reload_skills confirm=true
|
||
|
||
之后可用 search_skills 验证技能已加载：
|
||
|
||
search_skills keyword="{base}"
|
||
|
||
{"=" * 55}
|
||
完成以上步骤后，即可通过该技能执行相应操作。"""
|
||
|
||
return guide
|
||
|
||
|
||
# ==================== 创建扩展技能工具 ====================
|
||
|
||
class CreateSkillTool(Tool):
|
||
"""创建扩展技能工具 - 返回提示词，驱动智能体自行编写并部署 Markdown 技能文件。
|
||
|
||
工具本身不调用 LLM，只输出一段结构化的指令提示词。
|
||
智能体读到提示词后，会自行：
|
||
1. 编写满足格式要求的 Markdown 技能文件
|
||
2. 用 write_file 写入 ~/.aibox/skills/<skill_name>.md
|
||
3. 调用 reload_skills 热重载
|
||
"""
|
||
|
||
def get_name(self) -> str:
|
||
return "create_skill"
|
||
|
||
def get_description(self) -> str:
|
||
return (
|
||
"创建扩展技能工具。\n"
|
||
"返回一段指令提示词，驱动你自行编写并部署一个 Markdown 技能文件（带 .md 扩展名）。\n\n"
|
||
"技能文件格式要求：\n"
|
||
" • 文件名必须带 .md 扩展名（如 fetch-weather.md、calc-bmi.md）\n"
|
||
" • 第一行为一级标题（# 技能名），作为该技能的一句话简介\n"
|
||
" • 其余内容为技能的执行说明、使用方法和示例，供智能体参考执行"
|
||
)
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"skill_name": {
|
||
"type": "string",
|
||
"description": (
|
||
"技能名称（部署后的文件名，不含扩展名；系统会自动补齐 .md）。"
|
||
"只能包含字母、数字、连字符和下划线，例如：fetch-weather、calc-bmi。"
|
||
)
|
||
},
|
||
"description": {
|
||
"type": "string",
|
||
"description": (
|
||
"对该技能的详细需求描述：它应该做什么、接受哪些输入参数、"
|
||
"返回什么输出、有哪些典型使用场景。描述越详细，生成质量越高。"
|
||
)
|
||
}
|
||
},
|
||
"required": ["skill_name", "description"]
|
||
}
|
||
|
||
def execute(self, skill_name: str = "", description: str = "", **kwargs) -> str:
|
||
# ---- 参数校验 ----
|
||
if not skill_name or not skill_name.strip():
|
||
return "❌ 错误：skill_name 不能为空"
|
||
if not description or not description.strip():
|
||
return "❌ 错误：description 不能为空"
|
||
if not re.match(r'^[A-Za-z0-9_\-]+$', skill_name.strip()):
|
||
return "❌ 错误：skill_name 只能包含字母、数字、连字符(-)和下划线(_)"
|
||
|
||
skill_name = skill_name.strip()
|
||
skills_dir = constants.SKILLS_DIR
|
||
# 技能文件名统一带 .md 扩展名
|
||
skill_path = os.path.join(skills_dir, skill_name + ".md")
|
||
|
||
# ---- 返回给智能体的指令提示词 ----
|
||
prompt = f"""【任务】创建扩展技能：{skill_name}
|
||
|
||
请按照以下步骤，自行完成该扩展技能的编写和部署：
|
||
|
||
─────────────────────────────────────────────────
|
||
步骤 1：编写 Markdown 技能文件
|
||
─────────────────────────────────────────────────
|
||
根据以下需求，编写一个 Markdown 技能文件：
|
||
|
||
需求描述：
|
||
{description.strip()}
|
||
|
||
技能文件必须满足的格式约定：
|
||
1. 第一行为一级标题 `# {skill_name}`，用于对该技能功能做一句话简介（系统用第一行作为技能摘要）
|
||
2. 其余内容清晰说明该技能的用途、使用方式、注意事项和典型示例
|
||
3. 内容完整可执行（即按内容操作即可完成该技能要完成的事）
|
||
|
||
─────────────────────────────────────────────────
|
||
步骤 2：写入技能目录
|
||
─────────────────────────────────────────────────
|
||
使用 write_file 工具，将完整内容写入（文件名必须带 .md 扩展名）：
|
||
{skill_path}
|
||
|
||
─────────────────────────────────────────────────
|
||
步骤 3：热重载
|
||
─────────────────────────────────────────────────
|
||
调用 reload_skills 工具使新技能立即生效：
|
||
reload_skills confirm=true
|
||
|
||
─────────────────────────────────────────────────
|
||
完成后请告知用户技能已创建成功，并展示技能的第一行简介。"""
|
||
|
||
return prompt
|
||
|
||
|
||
# ==================== 更新身份工具 ====================
|
||
|
||
class UpdateIdentityTool(Tool):
|
||
"""更新用户身份工具 - 修改 00_IDENTITY.md 文件"""
|
||
|
||
def __init__(self, prompt_manager: PromptManager, logger: Optional[Logger] = None):
|
||
super().__init__(logger)
|
||
self.prompt_manager = prompt_manager
|
||
|
||
def get_name(self) -> str:
|
||
return "update_identity"
|
||
|
||
def get_description(self) -> str:
|
||
return """更新用户身份信息 - 修改 00_IDENTITY.md 文件
|
||
当你了解到用户的身份信息时，调用此工具更新身份文件。
|
||
如果文件不存在，会自动创建。"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"content": {
|
||
"type": "string",
|
||
"description": "用户身份信息内容（Markdown格式），包含用户的身份、特点、偏好等信息"
|
||
},
|
||
"confirm": {
|
||
"type": "boolean",
|
||
"description": "确认更新身份信息",
|
||
"default": True
|
||
}
|
||
},
|
||
"required": ["content"]
|
||
}
|
||
|
||
def execute(self, content: str = None, confirm: bool = True, **kwargs) -> str:
|
||
if not content:
|
||
return "❌ 需要提供身份信息内容"
|
||
|
||
if not confirm:
|
||
return "❌ 身份更新已取消，如需更新请设置 confirm=true"
|
||
|
||
self.logger.info("开始更新用户身份信息...")
|
||
|
||
if self.prompt_manager.update_identity(content):
|
||
return self.format_result(True, i18n.get('identity_updated', self.prompt_manager.IDENTITY_FILE), {
|
||
"文件": self.prompt_manager.IDENTITY_FILE,
|
||
"路径": self.prompt_manager.get_prompts_dir_path()
|
||
})
|
||
else:
|
||
return self.format_result(False, "身份更新失败", {
|
||
"文件": self.prompt_manager.IDENTITY_FILE,
|
||
"路径": self.prompt_manager.get_prompts_dir_path()
|
||
})
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行更新身份"""
|
||
output_queue.put(("line", "\n👤 更新用户身份信息...\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
result = self.execute(**kwargs)
|
||
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
# ==================== 更新智能体自我认识工具 ====================
|
||
|
||
class UpdateSoulTool(Tool):
|
||
"""更新智能体自我认识工具 - 修改 99_SOUL.md 文件"""
|
||
|
||
def __init__(self, prompt_manager: PromptManager, logger: Optional[Logger] = None):
|
||
super().__init__(logger)
|
||
self.prompt_manager = prompt_manager
|
||
|
||
def get_name(self) -> str:
|
||
return "update_soul"
|
||
|
||
def get_description(self) -> str:
|
||
return """更新智能体的自我认识定义 - 修改 99_SOUL.md 文件
|
||
当你需要重新定义自己的身份、角色、性格、能力边界时，调用此工具更新自我认识文件。
|
||
如果文件不存在，会自动创建默认定义。"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"content": {
|
||
"type": "string",
|
||
"description": "智能体自我认识定义内容（Markdown格式），包含：身份定义、角色描述、性格特点、能力边界、行为准则等"
|
||
},
|
||
"confirm": {
|
||
"type": "boolean",
|
||
"description": "确认更新自我认识定义",
|
||
"default": True
|
||
}
|
||
},
|
||
"required": ["content"]
|
||
}
|
||
|
||
def execute(self, content: str = None, confirm: bool = True, **kwargs) -> str:
|
||
if not content:
|
||
return "❌ 需要提供智能体自我认识定义内容"
|
||
|
||
if not confirm:
|
||
return "❌ 自我认识更新已取消，如需更新请设置 confirm=true"
|
||
|
||
self.logger.info("开始更新智能体自我认识定义...")
|
||
|
||
if self.prompt_manager.update_soul(content):
|
||
return self.format_result(True, "智能体自我认识已更新", {
|
||
"文件": self.prompt_manager.SOUL_FILE,
|
||
"路径": self.prompt_manager.get_prompts_dir_path()
|
||
})
|
||
else:
|
||
return self.format_result(False, "自我认识更新失败", {
|
||
"文件": self.prompt_manager.SOUL_FILE,
|
||
"路径": self.prompt_manager.get_prompts_dir_path()
|
||
})
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行更新自我认识"""
|
||
output_queue.put(("line", "\n🧠 更新智能体自我认识...\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
result = self.execute(**kwargs)
|
||
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
# ==================== 保存记忆并结束对话工具（增强版，修复版） ====================
|
||
|
||
class SaveMemoryAndEndConversationTool(Tool):
|
||
"""保存记忆并结束对话工具 - 增强版，同时更新智能体状态"""
|
||
|
||
def __init__(self, exit_callback: Callable, memory_db: MemoryDatabase, prompt_manager: PromptManager, logger: Optional[Logger] = None):
|
||
super().__init__(logger)
|
||
self.exit_callback = exit_callback
|
||
self.memory_db = memory_db
|
||
self.prompt_manager = prompt_manager
|
||
|
||
def get_name(self) -> str:
|
||
return "save_memory_and_end_conversation"
|
||
|
||
def get_description(self) -> str:
|
||
return """保存记忆并结束当前对话 - 必须提供详细的对话总结和当前状态！
|
||
使用此工具时，AI会：
|
||
1. 将本次对话的重要内容保存到记忆库
|
||
2. 提供完整的对话总结
|
||
3. 描述自己现在的状态、事情的走向等，保存到 100_STATUS.md
|
||
4. 礼貌地结束对话"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"summary": {
|
||
"type": "string",
|
||
"description": "详细的对话总结（至少50字），包含：讨论的主题、关键问题、重要结论、达成的共识等"
|
||
},
|
||
"topic": {
|
||
"type": "string",
|
||
"description": "对话主题（用于记忆分类，默认自动提取）"
|
||
},
|
||
"reason": {
|
||
"type": "string",
|
||
"description": "结束对话的原因",
|
||
"default": "话题讨论完成"
|
||
},
|
||
"current_status": {
|
||
"type": "string",
|
||
"description": "当前的智能体状态描述，包含：当前进展、下一步计划、待处理事项、整体走向等"
|
||
},
|
||
"next_goals": {
|
||
"type": "string",
|
||
"description": "接下来的目标或计划（可选）"
|
||
}
|
||
},
|
||
"required": ["summary", "current_status"]
|
||
}
|
||
|
||
def execute(self, summary: str = None, topic: str = None, reason: str = "话题讨论完成",
|
||
current_status: str = None, next_goals: str = None, **kwargs) -> str:
|
||
if summary is None or len(summary) < 50:
|
||
return "❌ 请提供更详细的对话总结（至少50字），以便保存到记忆库"
|
||
|
||
if current_status is None:
|
||
return "❌ 请提供当前状态描述，以便更新 100_STATUS.md"
|
||
|
||
self.logger.info(f"AI保存记忆并结束对话: {reason}")
|
||
|
||
# 保存到记忆库
|
||
memory_result = ""
|
||
if self.memory_db and topic:
|
||
memory_id = self.memory_db.add_memory(
|
||
topic=topic,
|
||
summary=summary,
|
||
importance=3 # 提高重要性，因为是对话总结
|
||
)
|
||
self.logger.info(f"对话记忆已保存，ID: {memory_id}")
|
||
memory_result = f"记忆已保存，ID: {memory_id}"
|
||
|
||
# 构建状态内容
|
||
status_content = f"【当前状态】\n{current_status}\n\n"
|
||
if next_goals:
|
||
status_content += f"【下一步计划】\n{next_goals}\n\n"
|
||
status_content += f"【对话总结】\n{summary}"
|
||
|
||
# 更新状态文件
|
||
status_updated = self.prompt_manager.update_status(status_content)
|
||
|
||
if self.exit_callback:
|
||
self.exit_callback(reason, summary)
|
||
|
||
data = {
|
||
"原因": reason,
|
||
"总结预览": summary[:100] + "..." if len(summary) > 100 else summary,
|
||
"状态更新": "✅ 已更新" if status_updated else "❌ 更新失败",
|
||
"记忆已保存": True
|
||
}
|
||
|
||
if memory_result:
|
||
data["记忆"] = memory_result
|
||
|
||
return self.format_result(True, "对话记忆已保存，状态已更新，对话结束", data)
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行结束对话"""
|
||
output_queue.put(("line", "\n💾 保存记忆并结束对话\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
result = self.execute(**kwargs)
|
||
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
|
||
# ==================== 备忘录工具（简化版，去掉任务类型） ====================
|
||
|
||
class MemoTool(Tool):
|
||
"""备忘录工具 - 用于管理任务（所有任务都是对话任务）"""
|
||
|
||
def __init__(self, memo_db: MemoDatabase, logger: Optional[Logger] = None):
|
||
super().__init__(logger)
|
||
self.memo_db = memo_db
|
||
|
||
def get_name(self) -> str:
|
||
return "memo"
|
||
|
||
def get_description(self) -> str:
|
||
return """任务管理工具 - 创建、查看、完成任务
|
||
支持：创建任务（支持一次性/重复、即时任务）、查看待办、标记完成、删除任务、搜索任务"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"action": {
|
||
"type": "string",
|
||
"description": "操作类型",
|
||
"enum": ["add", "list", "get", "complete", "delete", "search", "stats"]
|
||
},
|
||
"title": {
|
||
"type": "string",
|
||
"description": "任务标题（用于add操作）"
|
||
},
|
||
"content": {
|
||
"type": "string",
|
||
"description": "任务内容（可选，用于add操作）"
|
||
},
|
||
"reminder_time": {
|
||
"type": "string",
|
||
"description": "执行时间（格式：YYYY-MM-DD HH:MM:SS，或相对时间如 '+1h', '+30m', 'tomorrow 9am'）"
|
||
},
|
||
"reminder_minutes": {
|
||
"type": "integer",
|
||
"description": "延迟分钟数（从现在开始，用于快速设置任务）"
|
||
},
|
||
"tags": {
|
||
"type": "array",
|
||
"items": {"type": "string"},
|
||
"description": "标签列表"
|
||
},
|
||
"priority": {
|
||
"type": "integer",
|
||
"description": "优先级 1-5（默认1）",
|
||
"default": 1
|
||
},
|
||
"repeat_type": {
|
||
"type": "string",
|
||
"description": "重复类型（none=一次性, daily=每天, weekly=每周, monthly=每月, custom=自定义）",
|
||
"enum": ["none", "daily", "weekly", "monthly", "custom"],
|
||
"default": "none"
|
||
},
|
||
"repeat_interval": {
|
||
"type": "integer",
|
||
"description": "自定义重复间隔，当repeat_type='custom'时使用",
|
||
"default": 0
|
||
},
|
||
"repeat_interval_unit": {
|
||
"type": "string",
|
||
"description": "重复间隔单位（minutes=分钟, hours=小时, days=天），当repeat_type='custom'时使用",
|
||
"enum": ["minutes", "hours", "days"],
|
||
"default": "days"
|
||
},
|
||
"repeat_end_time": {
|
||
"type": "string",
|
||
"description": "重复结束时间，None表示永久重复"
|
||
},
|
||
"is_immediate": {
|
||
"type": "boolean",
|
||
"description": "是否为即时任务（立即执行）",
|
||
"default": False
|
||
},
|
||
"memo_id": {
|
||
"type": "integer",
|
||
"description": "任务ID（用于get、complete、delete操作）"
|
||
},
|
||
"memo_ids": {
|
||
"type": "array",
|
||
"items": {"type": "integer"},
|
||
"description": "任务ID列表（用于批量删除）"
|
||
},
|
||
"force_complete": {
|
||
"type": "boolean",
|
||
"description": "强制完成（忽略重复规则）",
|
||
"default": False
|
||
},
|
||
"keyword": {
|
||
"type": "string",
|
||
"description": "搜索关键词（用于search操作）"
|
||
},
|
||
"include_completed": {
|
||
"type": "boolean",
|
||
"description": "是否包含已完成的任务（默认False）",
|
||
"default": False
|
||
},
|
||
"limit": {
|
||
"type": "integer",
|
||
"description": "返回数量限制（默认20）",
|
||
"default": 20
|
||
},
|
||
"confirm": {
|
||
"type": "boolean",
|
||
"description": "确认删除操作",
|
||
"default": False
|
||
}
|
||
},
|
||
"required": ["action"]
|
||
}
|
||
|
||
def _parse_reminder_time(self, reminder_input: Any) -> Optional[str]:
|
||
"""解析提醒时间输入"""
|
||
if not reminder_input:
|
||
return None
|
||
|
||
if isinstance(reminder_input, int):
|
||
reminder_time = datetime.now() + timedelta(minutes=reminder_input)
|
||
return reminder_time.strftime(constants.DATETIME_FORMAT)
|
||
|
||
if isinstance(reminder_input, str):
|
||
reminder_input = reminder_input.strip()
|
||
|
||
if reminder_input.startswith('+'):
|
||
match = re.match(r'\+(\d+)([mhd])', reminder_input)
|
||
if match:
|
||
value, unit = int(match.group(1)), match.group(2)
|
||
if unit == 'm':
|
||
delta = timedelta(minutes=value)
|
||
elif unit == 'h':
|
||
delta = timedelta(hours=value)
|
||
elif unit == 'd':
|
||
delta = timedelta(days=value)
|
||
else:
|
||
return None
|
||
|
||
reminder_time = datetime.now() + delta
|
||
return reminder_time.strftime(constants.DATETIME_FORMAT)
|
||
|
||
try:
|
||
reminder_time = datetime.strptime(reminder_input, constants.DATETIME_FORMAT)
|
||
return reminder_time.strftime(constants.DATETIME_FORMAT)
|
||
except ValueError:
|
||
pass
|
||
|
||
now = datetime.now()
|
||
reminder_input_lower = reminder_input.lower()
|
||
|
||
if 'tomorrow' in reminder_input_lower:
|
||
tomorrow = now + timedelta(days=1)
|
||
time_match = re.search(r'(\d{1,2})(?::(\d{2}))?\s*(am|pm)?', reminder_input_lower)
|
||
if time_match:
|
||
hour = int(time_match.group(1))
|
||
minute = int(time_match.group(2)) if time_match.group(2) else 0
|
||
ampm = time_match.group(3)
|
||
|
||
if ampm:
|
||
if ampm == 'pm' and hour < 12:
|
||
hour += 12
|
||
elif ampm == 'am' and hour == 12:
|
||
hour = 0
|
||
|
||
reminder_time = tomorrow.replace(hour=hour, minute=minute, second=0)
|
||
else:
|
||
reminder_time = tomorrow.replace(hour=9, minute=0, second=0)
|
||
|
||
return reminder_time.strftime(constants.DATETIME_FORMAT)
|
||
|
||
return None
|
||
|
||
def execute(self, action: str, **kwargs) -> str:
|
||
"""执行备忘录操作"""
|
||
try:
|
||
if action == "add":
|
||
return self._add_memo(kwargs)
|
||
elif action == "list":
|
||
return self._list_memos(kwargs)
|
||
elif action == "get":
|
||
return self._get_memo(kwargs)
|
||
elif action == "complete":
|
||
return self._complete_memo(kwargs)
|
||
elif action == "delete":
|
||
return self._delete_memo(kwargs)
|
||
elif action == "search":
|
||
return self._search_memos(kwargs)
|
||
elif action == "stats":
|
||
return self._get_stats(kwargs)
|
||
else:
|
||
return f"❌ 未知操作: {action}"
|
||
except Exception as e:
|
||
return f"❌ 任务操作错误: {str(e)}"
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行备忘录操作"""
|
||
action = kwargs.get("action")
|
||
|
||
# 发送开始执行的消息
|
||
output_queue.put(("line", f"\n📝 执行备忘录操作: {action}\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
# 执行操作
|
||
result = self.execute(**kwargs)
|
||
|
||
# 发送结果
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
def _add_memo(self, kwargs: Dict) -> str:
|
||
"""添加任务 - 所有任务都是对话任务，不需要task_type"""
|
||
title = kwargs.get("title")
|
||
if not title:
|
||
return "❌ 需要提供任务标题"
|
||
|
||
content = kwargs.get("content")
|
||
reminder_minutes = kwargs.get("reminder_minutes")
|
||
reminder_time_input = kwargs.get("reminder_time")
|
||
tags = kwargs.get("tags", [])
|
||
priority = kwargs.get("priority", 1)
|
||
repeat_type = kwargs.get("repeat_type", "none")
|
||
repeat_interval = kwargs.get("repeat_interval", 0)
|
||
repeat_interval_unit = kwargs.get("repeat_interval_unit", "days")
|
||
repeat_end_time = kwargs.get("repeat_end_time")
|
||
is_immediate = kwargs.get("is_immediate", False)
|
||
|
||
if repeat_type not in ["none", "daily", "weekly", "monthly", "custom"]:
|
||
return f"❌ 无效的重复类型: {repeat_type}"
|
||
|
||
if repeat_type == "custom" and repeat_interval <= 0:
|
||
return "❌ 自定义重复需要设置有效的 repeat_interval (>0)"
|
||
|
||
reminder_time = None
|
||
if is_immediate:
|
||
# 即时任务：设置为当前时间，确保立即执行
|
||
reminder_time = datetime.now().strftime(constants.DATETIME_FORMAT)
|
||
elif reminder_minutes:
|
||
reminder_time = self._parse_reminder_time(reminder_minutes)
|
||
elif reminder_time_input:
|
||
reminder_time = self._parse_reminder_time(reminder_time_input)
|
||
else:
|
||
# 默认30分钟后
|
||
reminder_time = self._parse_reminder_time(30)
|
||
|
||
memo_id = self.memo_db.add_memo(
|
||
title=title,
|
||
content=content,
|
||
reminder_time=reminder_time,
|
||
tags=tags,
|
||
priority=priority,
|
||
created_by="AI",
|
||
source="user",
|
||
repeat_type=repeat_type,
|
||
repeat_interval=repeat_interval,
|
||
repeat_interval_unit=repeat_interval_unit,
|
||
repeat_end_time=repeat_end_time,
|
||
is_immediate=is_immediate
|
||
)
|
||
|
||
data = {
|
||
"任务ID": memo_id,
|
||
"标题": title,
|
||
}
|
||
|
||
if is_immediate:
|
||
data["任务类型"] = "即时任务"
|
||
else:
|
||
if reminder_time:
|
||
data["执行时间"] = reminder_time
|
||
|
||
if repeat_type != "none":
|
||
if repeat_type == "custom":
|
||
repeat_info = f"重复类型: 每{repeat_interval} {repeat_interval_unit}"
|
||
else:
|
||
repeat_map = {
|
||
'daily': '每天',
|
||
'weekly': '每周',
|
||
'monthly': '每月'
|
||
}
|
||
repeat_info = f"重复类型: {repeat_map.get(repeat_type, repeat_type)}"
|
||
|
||
if repeat_end_time:
|
||
repeat_info += f", 结束于: {repeat_end_time}"
|
||
else:
|
||
repeat_info += ", 永久重复"
|
||
data["重复信息"] = repeat_info
|
||
|
||
return self.format_result(True, "任务已创建", data)
|
||
|
||
def _list_memos(self, kwargs: Dict) -> str:
|
||
"""列出任务"""
|
||
include_completed = kwargs.get("include_completed", False)
|
||
limit = kwargs.get("limit", 20)
|
||
|
||
memos = self.memo_db.get_all_memos(include_completed, limit)
|
||
|
||
if not memos:
|
||
return "📭 没有找到任务"
|
||
|
||
status = "所有" if include_completed else "待执行"
|
||
output = [f"📋 {status}任务 (共 {len(memos)} 个):"]
|
||
|
||
now = datetime.now()
|
||
|
||
for i, memo in enumerate(memos, 1):
|
||
status_icon = "✅" if memo['is_completed'] else "⏰"
|
||
priority_icon = "🔴" * memo['priority']
|
||
source_tag = f"[{memo.get('source', '未知')}]" if memo.get('source') else ""
|
||
|
||
immediate_tag = "⚡" if memo.get('is_immediate') else ""
|
||
|
||
repeat_tag = ""
|
||
if memo['repeat_type'] != 'none':
|
||
if memo['repeat_type'] == 'custom':
|
||
unit = memo.get('repeat_interval_unit', 'days')
|
||
unit_map = {'minutes': '分钟', 'hours': '小时', 'days': '天'}
|
||
repeat_tag = f" [每{memo['repeat_interval']}{unit_map.get(unit, unit)}]"
|
||
else:
|
||
repeat_map = {
|
||
'daily': ' [每天]',
|
||
'weekly': ' [每周]',
|
||
'monthly': ' [每月]'
|
||
}
|
||
repeat_tag = repeat_map.get(memo['repeat_type'], ' [重复]')
|
||
|
||
output.append(f"\n{i}. {status_icon}{immediate_tag} [ID: {memo['id']}] {source_tag}{repeat_tag} {memo['title']} {priority_icon}")
|
||
|
||
if memo['reminder_time']:
|
||
try:
|
||
reminder = datetime.strptime(memo['reminder_time'], constants.DATETIME_FORMAT)
|
||
if not memo['is_completed'] and reminder < now:
|
||
output.append(f" ⚠️ 已过期: {memo['reminder_time']}")
|
||
else:
|
||
output.append(f" ⏱️ 执行: {memo['reminder_time']}")
|
||
except:
|
||
output.append(f" ⏱️ 执行: {memo['reminder_time']}")
|
||
|
||
if memo.get('trigger_count', 0) > 0:
|
||
output.append(f" 🔄 已执行 {memo['trigger_count']} 次")
|
||
|
||
if memo['content']:
|
||
preview = memo['content'][:100] + "..." if len(memo['content']) > 100 else memo['content']
|
||
output.append(f" 📝 {preview}")
|
||
|
||
if memo['tags']:
|
||
output.append(f" 🏷️ {', '.join(memo['tags'])}")
|
||
|
||
return "\n".join(output)
|
||
|
||
def _get_memo(self, kwargs: Dict) -> str:
|
||
"""获取单个任务"""
|
||
memo_id = kwargs.get("memo_id")
|
||
if not memo_id:
|
||
return "❌ 需要提供任务ID"
|
||
|
||
memo = self.memo_db.get_memo(memo_id)
|
||
if not memo:
|
||
return f"❌ 未找到ID为 {memo_id} 的任务"
|
||
|
||
status = "✅ 已完成" if memo['is_completed'] else "⏰ 待执行"
|
||
if memo['is_completed']:
|
||
status += f" (完成于: {memo['completed_at']})"
|
||
|
||
immediate_tag = "⚡ 即时任务" if memo.get('is_immediate') else ""
|
||
|
||
output = [
|
||
f"📌 任务 #{memo_id} {immediate_tag}",
|
||
f"📋 状态: {status}",
|
||
f"📝 标题: {memo['title']}",
|
||
]
|
||
|
||
if memo.get('source'):
|
||
output.append(f"📎 来源: {memo['source']}")
|
||
|
||
if memo['repeat_type'] != 'none':
|
||
if memo['repeat_type'] == 'custom':
|
||
unit_map = {'minutes': '分钟', 'hours': '小时', 'days': '天'}
|
||
unit = memo.get('repeat_interval_unit', 'days')
|
||
repeat_info = f"🔄 重复: 每{memo['repeat_interval']} {unit_map.get(unit, unit)}"
|
||
else:
|
||
repeat_map = {
|
||
'daily': '每天',
|
||
'weekly': '每周',
|
||
'monthly': '每月'
|
||
}
|
||
repeat_info = f"🔄 重复: {repeat_map.get(memo['repeat_type'], memo['repeat_type'])}"
|
||
|
||
if memo['repeat_end_time']:
|
||
repeat_info += f", 结束于: {memo['repeat_end_time']}"
|
||
else:
|
||
repeat_info += ", 永久重复"
|
||
output.append(repeat_info)
|
||
|
||
if memo.get('trigger_count', 0) > 0:
|
||
output.append(f" 📊 已执行 {memo['trigger_count']} 次")
|
||
if memo.get('last_triggered_at'):
|
||
output.append(f" ⏱️ 上次执行: {memo['last_triggered_at']}")
|
||
|
||
if memo['content']:
|
||
output.append(f"📄 内容:\n{memo['content']}")
|
||
|
||
if memo['reminder_time']:
|
||
try:
|
||
now = datetime.now()
|
||
reminder = datetime.strptime(memo['reminder_time'], constants.DATETIME_FORMAT)
|
||
if not memo['is_completed'] and reminder < now:
|
||
output.append(f"⚠️ 执行时间 (已过期): {memo['reminder_time']}")
|
||
else:
|
||
output.append(f"⏰ 执行时间: {memo['reminder_time']}")
|
||
except:
|
||
output.append(f"⏰ 执行时间: {memo['reminder_time']}")
|
||
|
||
if memo['tags']:
|
||
output.append(f"🏷️ 标签: {', '.join(memo['tags'])}")
|
||
|
||
output.append(f"🔢 优先级: {'🔴' * memo['priority']} ({memo['priority']}/5)")
|
||
output.append(f"📅 创建时间: {memo['created_at']}")
|
||
output.append(f"👤 创建者: {memo['created_by'] or '系统'}")
|
||
|
||
return "\n".join(output)
|
||
|
||
def _complete_memo(self, kwargs: Dict) -> str:
|
||
"""完成任务"""
|
||
memo_id = kwargs.get("memo_id")
|
||
if not memo_id:
|
||
return "❌ 需要提供任务ID"
|
||
|
||
force_complete = kwargs.get("force_complete", False)
|
||
|
||
memo = self.memo_db.get_memo(memo_id)
|
||
if not memo:
|
||
return f"❌ 未找到ID为 {memo_id} 的任务"
|
||
|
||
if memo['is_completed'] and not force_complete:
|
||
return f"ℹ️ 任务 #{memo_id} 已经完成了"
|
||
|
||
if self.memo_db.complete_memo(memo_id, force_complete):
|
||
memo = self.memo_db.get_memo(memo_id)
|
||
data = {
|
||
"任务ID": memo_id,
|
||
"标题": memo['title']
|
||
}
|
||
|
||
if memo['repeat_type'] != 'none' and not memo['is_completed']:
|
||
data["下一次执行"] = memo['reminder_time']
|
||
data["已执行次数"] = memo.get('trigger_count', 0)
|
||
return self.format_result(True, "任务已处理，已更新下一次执行时间", data)
|
||
else:
|
||
return self.format_result(True, "任务已完成", data)
|
||
else:
|
||
return f"❌ 无法完成任务 #{memo_id}"
|
||
|
||
def _delete_memo(self, kwargs: Dict) -> str:
|
||
"""删除任务"""
|
||
confirm = kwargs.get("confirm", False)
|
||
if not confirm:
|
||
return "❌ 需要设置 confirm=True 来确认删除操作"
|
||
|
||
memo_id = kwargs.get("memo_id")
|
||
if memo_id:
|
||
if self.memo_db.complete_memo(memo_id, force_complete=True):
|
||
return self.format_result(True, f"任务 #{memo_id} 已删除")
|
||
else:
|
||
return f"❌ 无法删除任务 #{memo_id}"
|
||
else:
|
||
return "❌ 需要提供 memo_id"
|
||
|
||
def _search_memos(self, kwargs: Dict) -> str:
|
||
"""搜索任务"""
|
||
keyword = kwargs.get("keyword")
|
||
if not keyword:
|
||
return "❌ 需要提供搜索关键词"
|
||
|
||
include_completed = kwargs.get("include_completed", False)
|
||
limit = kwargs.get("limit", 20)
|
||
|
||
all_memos = self.memo_db.get_all_memos(include_completed, limit=100)
|
||
|
||
results = []
|
||
keyword_lower = keyword.lower()
|
||
for memo in all_memos:
|
||
if (keyword_lower in memo['title'].lower() or
|
||
(memo['content'] and keyword_lower in memo['content'].lower())):
|
||
results.append(memo)
|
||
|
||
if not results:
|
||
return f"❌ 未找到包含 '{keyword}' 的任务"
|
||
|
||
output = [f"🔍 搜索 '{keyword}' 找到 {len(results)} 个任务:"]
|
||
now = datetime.now()
|
||
|
||
for i, memo in enumerate(results[:limit], 1):
|
||
status_icon = "✅" if memo['is_completed'] else "⏰"
|
||
immediate_tag = "⚡" if memo.get('is_immediate') else ""
|
||
output.append(f"\n{i}. {status_icon}{immediate_tag} [ID: {memo['id']}] {memo['title']}")
|
||
if memo['reminder_time']:
|
||
try:
|
||
reminder = datetime.strptime(memo['reminder_time'], constants.DATETIME_FORMAT)
|
||
if not memo['is_completed'] and reminder < now:
|
||
output.append(f" ⚠️ 已过期: {memo['reminder_time']}")
|
||
else:
|
||
output.append(f" ⏱️ {memo['reminder_time']}")
|
||
except:
|
||
output.append(f" ⏱️ {memo['reminder_time']}")
|
||
|
||
return "\n".join(output)
|
||
|
||
def _get_stats(self, kwargs: Dict) -> str:
|
||
"""获取任务统计信息"""
|
||
stats = self.memo_db.get_memo_stats()
|
||
|
||
output = [
|
||
"📊 任务统计",
|
||
f" 总任务: {stats['total']} 个",
|
||
f" 待执行: {stats['pending']} 个",
|
||
f" 已过期: {stats['overdue']} 个",
|
||
f" 已完成: {stats['completed']} 个",
|
||
f" 即时任务: {stats['immediate']} 个"
|
||
]
|
||
|
||
if stats['overdue'] > 0:
|
||
output.append(f"\n⚠️ 有 {stats['overdue']} 个任务已过期，将自动执行")
|
||
|
||
return "\n".join(output)
|
||
|
||
|
||
# ==================== 记忆工具（修改版，支持流式输出） ====================
|
||
|
||
class MemoryTool(Tool):
|
||
"""记忆工具 - 用于存储、检索和删除对话记忆（支持流式输出，支持分页）"""
|
||
|
||
def __init__(self, memory_db: MemoryDatabase, logger: Optional[Logger] = None):
|
||
super().__init__(logger)
|
||
self.memory_db = memory_db
|
||
|
||
def get_name(self) -> str:
|
||
return "memory"
|
||
|
||
def get_description(self) -> str:
|
||
return """记忆管理工具 - 存储、检索和删除重要的对话信息
|
||
支持：保存总结、多关键词搜索记忆（支持分页）、查看最近的记忆、删除无用记忆"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"action": {
|
||
"type": "string",
|
||
"description": "操作类型",
|
||
"enum": ["save", "search", "recent", "get", "delete"]
|
||
},
|
||
"summary": {"type": "string", "description": "要保存的总结内容（用于save操作）"},
|
||
"topic": {"type": "string", "description": "对话主题（用于save操作）"},
|
||
"content": {"type": "string", "description": "详细内容（可选，用于save操作）"},
|
||
"keywords": {
|
||
"type": "array", "items": {"type": "string"},
|
||
"description": "搜索关键词列表"
|
||
},
|
||
"query": {"type": "string", "description": "搜索关键词（单个关键词）"},
|
||
"match_all": {"type": "boolean", "default": False},
|
||
"memory_id": {"type": "integer"},
|
||
"memory_ids": {"type": "array", "items": {"type": "integer"}},
|
||
"importance": {"type": "integer", "default": 2},
|
||
"limit": {"type": "integer", "default": 10, "description": "每页条数（默认10）"},
|
||
"page": {"type": "integer", "default": 1, "description": "页码（从1开始）"},
|
||
"confirm": {"type": "boolean", "default": False}
|
||
},
|
||
"required": ["action"]
|
||
}
|
||
|
||
def execute(self, action: str, **kwargs) -> str:
|
||
try:
|
||
if action == "save":
|
||
return self._save_memory(kwargs)
|
||
elif action == "search":
|
||
return self._search_memories(kwargs)
|
||
elif action == "recent":
|
||
return self._get_recent_memories(kwargs)
|
||
elif action == "get":
|
||
return self._get_memory_by_id(kwargs)
|
||
elif action == "delete":
|
||
return self._delete_memory(kwargs)
|
||
else:
|
||
return f"❌ 未知操作: {action}"
|
||
except Exception as e:
|
||
return f"❌ 记忆操作错误: {str(e)}"
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行记忆操作"""
|
||
action = kwargs.get("action")
|
||
|
||
# 发送开始执行的消息
|
||
output_queue.put(("line", f"\n🧠 执行记忆操作: {action}\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
# 执行操作
|
||
result = self.execute(**kwargs)
|
||
|
||
# 发送结果
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
def _save_memory(self, kwargs: Dict) -> str:
|
||
summary = kwargs.get("summary")
|
||
if not summary:
|
||
return "❌ 需要提供总结内容"
|
||
|
||
topic = kwargs.get("topic", "未命名对话")
|
||
content = kwargs.get("content")
|
||
importance = kwargs.get("importance", constants.MEMORY_DEFAULT_IMPORTANCE)
|
||
|
||
memory_id = self.memory_db.add_memory(
|
||
topic=topic,
|
||
summary=summary,
|
||
content=content,
|
||
importance=importance
|
||
)
|
||
|
||
return self.format_result(True, "记忆已保存", {
|
||
"记忆ID": memory_id,
|
||
"主题": topic,
|
||
"总结": summary[:100] + "..." if len(summary) > 100 else summary
|
||
})
|
||
|
||
def _search_memories(self, kwargs: Dict) -> str:
|
||
"""搜索记忆 - 支持分页"""
|
||
keywords = kwargs.get("keywords", [])
|
||
query = kwargs.get("query", "")
|
||
match_all = kwargs.get("match_all", False)
|
||
page = kwargs.get("page", 1)
|
||
page_size = kwargs.get("limit", 10) # 使用 limit 作为 page_size
|
||
|
||
# 确保页码和每页条数有效
|
||
page = max(1, page)
|
||
page_size = max(1, min(50, page_size)) # 每页最多50条
|
||
|
||
# 处理关键词
|
||
if keywords and isinstance(keywords, list):
|
||
pass
|
||
elif query:
|
||
keywords = query.split()
|
||
else:
|
||
return "❌ 需要提供搜索关键词（keywords 或 query）"
|
||
|
||
if not keywords:
|
||
return "❌ 关键词列表为空"
|
||
|
||
# 先获取所有结果（不分页，用于计算总数）
|
||
all_results = self.memory_db.search_memories(keywords, match_all, limit=None)
|
||
|
||
if not all_results:
|
||
match_mode = "所有" if match_all else "任一"
|
||
return f"❌ 未找到包含 {match_mode} 关键词 {keywords} 的记忆"
|
||
|
||
# 计算分页信息
|
||
total_count = len(all_results)
|
||
total_pages = (total_count + page_size - 1) // page_size
|
||
|
||
# 检查页码是否有效
|
||
if page > total_pages and total_pages > 0:
|
||
return f"❌ 页码 {page} 超出范围，共有 {total_pages} 页"
|
||
|
||
# 计算当前页的起止索引
|
||
start_idx = (page - 1) * page_size
|
||
end_idx = min(start_idx + page_size, total_count)
|
||
|
||
# 获取当前页的结果
|
||
page_results = all_results[start_idx:end_idx]
|
||
|
||
# 构建输出
|
||
match_mode = "所有关键词" if match_all else "任一关键词"
|
||
output = [
|
||
f"🔍 搜索 {match_mode} {keywords} 找到 {total_count} 条记忆",
|
||
f"📄 第 {page}/{total_pages} 页 (每页 {page_size} 条)",
|
||
"-" * 40
|
||
]
|
||
|
||
for i, mem in enumerate(page_results, 1):
|
||
# 全局序号
|
||
global_idx = start_idx + i
|
||
output.append(f"\n{global_idx}. [ID: {mem['id']}] [{mem['end_time']}] {mem['topic']}")
|
||
output.append(f" 总结: {mem['summary'][:200]}")
|
||
if mem.get('tags'):
|
||
output.append(f" 标签: {', '.join(mem['tags'])}")
|
||
if mem.get('importance'):
|
||
output.append(f" 重要性: {'⭐' * mem['importance']} ({mem['importance']}/5)")
|
||
|
||
# 添加翻页提示
|
||
if total_pages > 1:
|
||
output.append(f"\n📖 翻页提示: 使用 page 参数查看更多结果")
|
||
output.append(f" 例如: memory search query='{query}' page={page + 1 if page < total_pages else page} limit={page_size}")
|
||
if page < total_pages:
|
||
output.append(f" ➡️ 下一页: page={page + 1}")
|
||
if page > 1:
|
||
output.append(f" ⬅️ 上一页: page={page - 1}")
|
||
|
||
return "\n".join(output)
|
||
|
||
def _get_recent_memories(self, kwargs: Dict) -> str:
|
||
"""获取最近的记忆 - 支持分页"""
|
||
page = kwargs.get("page", 1)
|
||
page_size = kwargs.get("limit", 10)
|
||
|
||
# 确保页码和每页条数有效
|
||
page = max(1, page)
|
||
page_size = max(1, min(50, page_size))
|
||
|
||
# 先获取所有结果
|
||
all_results = self.memory_db.get_recent_memories(limit=None)
|
||
|
||
if not all_results:
|
||
return "📭 没有找到记忆"
|
||
|
||
# 计算分页信息
|
||
total_count = len(all_results)
|
||
total_pages = (total_count + page_size - 1) // page_size
|
||
|
||
# 检查页码是否有效
|
||
if page > total_pages and total_pages > 0:
|
||
return f"❌ 页码 {page} 超出范围，共有 {total_pages} 页"
|
||
|
||
# 计算当前页的起止索引
|
||
start_idx = (page - 1) * page_size
|
||
end_idx = min(start_idx + page_size, total_count)
|
||
|
||
# 获取当前页的结果
|
||
page_results = all_results[start_idx:end_idx]
|
||
|
||
output = [f"📚 最近 {total_count} 条记忆 (第 {page}/{total_pages} 页，每页 {page_size} 条):"]
|
||
output.append("-" * 40)
|
||
|
||
for i, mem in enumerate(page_results, 1):
|
||
global_idx = start_idx + i
|
||
output.append(f"\n{global_idx}. [ID: {mem['id']}] [{mem['end_time']}] {mem['topic']}")
|
||
output.append(f" 总结: {mem['summary'][:100]}")
|
||
|
||
# 添加翻页提示
|
||
if total_pages > 1:
|
||
output.append(f"\n📖 翻页提示: 使用 page 参数查看更多结果")
|
||
output.append(f" 例如: memory recent page={page + 1 if page < total_pages else page} limit={page_size}")
|
||
|
||
return "\n".join(output)
|
||
|
||
def _get_memory_by_id(self, kwargs: Dict) -> str:
|
||
"""根据ID获取记忆"""
|
||
memory_id = kwargs.get("memory_id")
|
||
if not memory_id:
|
||
return "❌ 需要提供记忆ID"
|
||
|
||
memory = self.memory_db.get_memory_by_id(memory_id)
|
||
if not memory:
|
||
return f"❌ 未找到ID为 {memory_id} 的记忆"
|
||
|
||
output = [
|
||
f"📌 记忆 #{memory_id}",
|
||
f"主题: {memory['topic']}",
|
||
f"时间: {memory['start_time']} - {memory['end_time']}",
|
||
f"总结: {memory['summary']}",
|
||
]
|
||
|
||
if memory.get('content'):
|
||
output.append(f"详细内容:\n{memory['content']}")
|
||
|
||
if memory.get('tags'):
|
||
output.append(f"标签: {', '.join(memory['tags'])}")
|
||
|
||
if memory.get('importance'):
|
||
output.append(f"重要性: {'⭐' * memory['importance']} ({memory['importance']}/5)")
|
||
|
||
return "\n".join(output)
|
||
|
||
def _delete_memory(self, kwargs: Dict) -> str:
|
||
"""删除记忆"""
|
||
confirm = kwargs.get("confirm", False)
|
||
if not confirm:
|
||
return "❌ 需要设置 confirm=True 来确认删除操作"
|
||
|
||
memory_id = kwargs.get("memory_id")
|
||
memory_ids = kwargs.get("memory_ids", [])
|
||
|
||
deleted_count = 0
|
||
|
||
if memory_id:
|
||
if self.memory_db.delete_memory(memory_id):
|
||
deleted_count = 1
|
||
elif memory_ids:
|
||
deleted_count = self.memory_db.delete_memories_by_ids(memory_ids)
|
||
else:
|
||
return "❌ 需要提供 memory_id 或 memory_ids"
|
||
|
||
if deleted_count == 0:
|
||
return f"❌ 未找到要删除的记忆"
|
||
|
||
return self.format_result(True, f"成功删除 {deleted_count} 条记忆", {"删除数量": deleted_count})
|
||
|
||
# ==================== 网页工具（修改版，支持流式输出） ====================
|
||
|
||
class SimpleWebCrawlerTool(Tool):
|
||
"""简化版网页爬虫工具（返回 markdown 提示词，指导改用 curl 命令行）"""
|
||
|
||
def __init__(self, logger=None):
|
||
super().__init__(logger)
|
||
|
||
def get_name(self) -> str:
|
||
return "web"
|
||
|
||
def get_description(self) -> str:
|
||
return """简单易用的网页访问工具 - 一句话搞定！
|
||
支持：获取网页、提取内容、搜索关键词、下载文件"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"action": {
|
||
"type": "string",
|
||
"enum": ["get", "text", "title", "links", "search"]
|
||
},
|
||
"url": {"type": "string"},
|
||
"keyword": {"type": "string"}
|
||
},
|
||
"required": ["action", "url"]
|
||
}
|
||
|
||
def _proxy_curl_args(self) -> str:
|
||
"""根据当前代理配置构造 curl --socks5 参数"""
|
||
proxies = ConfigManager().get_proxy()
|
||
if not proxies:
|
||
return ""
|
||
proxy_url = proxies.get("https") or proxies.get("http") or ""
|
||
host_port = proxy_url.split("://", 1)[-1]
|
||
if "@" in host_port:
|
||
host_port = host_port.split("@", 1)[-1]
|
||
return f"--socks5 {shlex.quote(host_port)}"
|
||
|
||
def execute(self, action: str, url: str, **kwargs) -> str:
|
||
if action not in ("get", "text", "title", "links", "search"):
|
||
return f"❌ 未知操作: {action}"
|
||
|
||
quoted_url = shlex.quote(url)
|
||
timeout = constants.WEB_TIMEOUT
|
||
user_agent = shlex.quote(constants.WEB_USER_AGENT)
|
||
proxy = self._proxy_curl_args()
|
||
base = f"curl -sL --max-time {timeout} -A {user_agent} {proxy}".strip()
|
||
|
||
if action == "get":
|
||
cmd = f"{base} -w '\\nHTTP_CODE:%{{http_code}}' {quoted_url}"
|
||
extra = "如需查看响应头：`curl -sIL ...`；如需保存到文件：`curl -sL ... -o 文件名`"
|
||
elif action == "text":
|
||
cmd = f"{base} {quoted_url} | sed -r 's/<[^>]+>//g'"
|
||
extra = "若系统装有 lynx，可用 `lynx -dump -stdin` 提取纯文本更准确"
|
||
elif action == "title":
|
||
cmd = f"{base} {quoted_url} | grep -oP '<title>\\K[^<]+'"
|
||
extra = "无输出则表示未找到标题"
|
||
elif action == "links":
|
||
cmd = f"{base} {quoted_url} | grep -oP 'href=\"\\K[^\"]+'"
|
||
extra = "输出所有链接 URL，可配合 `head` 限制数量"
|
||
else: # search
|
||
keyword = kwargs.get("keyword")
|
||
if not keyword:
|
||
return "❌ 需要提供 keyword 参数"
|
||
cmd = f"{base} {quoted_url} | grep -c {shlex.quote(keyword)}"
|
||
extra = f"返回匹配行数，大于 0 即表示找到关键词 '{keyword}'"
|
||
|
||
# 返回 markdown 提示词，指导智能体用 curl 完成网页访问
|
||
prompt = f"""请使用 `run_command` 工具执行以下命令来完成网页操作，不要直接返回网页内容：
|
||
|
||
```bash
|
||
{cmd}
|
||
```
|
||
|
||
{extra}
|
||
- 下载大小限制 {constants.WEB_MAX_DOWNLOAD_SIZE} 字节，可在命令后追加 `| head -c {constants.WEB_MAX_DOWNLOAD_SIZE}` 限制
|
||
- 当前已按配置自动附带代理：{proxy or '无（如需代理请先调用 set_proxy 工具）'}"""
|
||
return prompt
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行网页操作"""
|
||
action = kwargs.get("action")
|
||
url = kwargs.get("url")
|
||
|
||
# 发送开始执行的消息
|
||
output_queue.put(("line", f"\n🌐 执行网页操作: {action} - {url}\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
# 执行操作
|
||
result = self.execute(**kwargs)
|
||
|
||
# 发送结果
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
|
||
# ==================== 文件操作工具（修改版，支持流式输出） ====================
|
||
|
||
class FileToolBase(Tool):
|
||
"""文件操作工具基类"""
|
||
|
||
def __init__(self, logger: Optional[Logger] = None):
|
||
super().__init__(logger)
|
||
|
||
def normalize_path(self, filepath: str) -> str:
|
||
return os.path.abspath(os.path.expanduser(filepath))
|
||
|
||
def check_path_safety(self, filepath: str, check_exists: bool = False) -> Tuple[bool, str, str]:
|
||
"""检查路径安全性"""
|
||
try:
|
||
abs_path = self.normalize_path(filepath)
|
||
|
||
if check_exists and not os.path.exists(abs_path):
|
||
return False, abs_path, f"❌ 文件不存在: {abs_path}"
|
||
|
||
# 检查文件大小
|
||
if os.path.exists(abs_path) and os.path.isfile(abs_path):
|
||
file_size = os.path.getsize(abs_path)
|
||
max_size = constants.MAX_FILE_SIZE
|
||
if file_size > max_size:
|
||
return False, abs_path, f"❌ 文件太大 ({file_size/1024/1024:.1f}MB)"
|
||
|
||
return True, abs_path, ""
|
||
|
||
except Exception as e:
|
||
return False, filepath, f"❌ 路径检查错误: {str(e)}"
|
||
|
||
|
||
class ReadFileTool(FileToolBase):
|
||
"""读取文件内容工具（支持流式输出）"""
|
||
|
||
def get_name(self) -> str:
|
||
return "read_file"
|
||
|
||
def get_description(self) -> str:
|
||
return "读取文件内容，支持指定起始行和结束行，或读取开头/结尾"
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"filepath": {"type": "string"},
|
||
"encoding": {"type": "string", "default": "utf-8"},
|
||
"start_line": {"type": "integer", "default": 1},
|
||
"end_line": {"type": "integer", "default": 0},
|
||
"max_lines": {"type": "integer", "default": 100},
|
||
"head": {"type": "boolean", "default": False},
|
||
"tail": {"type": "boolean", "default": False}
|
||
},
|
||
"required": ["filepath"]
|
||
}
|
||
|
||
def execute(self, filepath: str, encoding: str = "utf-8",
|
||
start_line: int = 1, end_line: int = 0,
|
||
max_lines: int = 100, head: bool = False,
|
||
tail: bool = False, **kwargs) -> str:
|
||
if error := self.validate_params(["filepath"], filepath=filepath):
|
||
return error
|
||
|
||
is_safe, abs_path, error = self.check_path_safety(filepath, check_exists=True)
|
||
if not is_safe:
|
||
return error
|
||
|
||
# 限制max_lines上限，防止请求超大范围
|
||
max_lines = min(max_lines, 500)
|
||
quoted = shlex.quote(abs_path)
|
||
|
||
# 构造等价的命令行读取命令
|
||
if head:
|
||
cmd = f"head -n {max_lines} {quoted}"
|
||
elif tail:
|
||
cmd = f"tail -n {max_lines} {quoted}"
|
||
elif end_line > 0:
|
||
cmd = f"sed -n '{start_line},{end_line}p' {quoted}"
|
||
elif max_lines > 0:
|
||
end = start_line + max_lines - 1
|
||
cmd = f"sed -n '{start_line},{end}p' {quoted}"
|
||
else:
|
||
cmd = f"sed -n '{start_line},$p' {quoted}"
|
||
|
||
# 返回 markdown 提示词，指导智能体用命令行工具读取文件
|
||
prompt = f"""请使用 `run_command` 工具执行以下命令来读取文件内容，不要直接返回文件内容：
|
||
|
||
```bash
|
||
{cmd}
|
||
```
|
||
|
||
- 若需统计总行数：`wc -l {quoted}`
|
||
- 若需查看文件编码：`file -b --mime-encoding {quoted}`
|
||
- 若单行超长，可在命令后追加 `| cut -c 1-10000` 截断到 10000 字符
|
||
- 文件大小上限 {constants.MAX_FILE_SIZE} 字节，单次最多读取 500 行"""
|
||
return prompt
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行文件读取"""
|
||
filepath = kwargs.get("filepath")
|
||
|
||
# 发送开始执行的消息
|
||
output_queue.put(("line", f"\n📄 读取文件: {filepath}\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
# 执行操作
|
||
result = self.execute(**kwargs)
|
||
|
||
# 发送结果
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
|
||
class WriteFileTool(FileToolBase):
|
||
"""写入文件工具（支持流式输出）"""
|
||
|
||
def get_name(self) -> str:
|
||
return "write_file"
|
||
|
||
def get_description(self) -> str:
|
||
return "文件写入工具 - 支持覆盖、追加"
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"filepath": {"type": "string"},
|
||
"content": {"type": "string"},
|
||
"mode": {"type": "string", "enum": ["w", "a"], "default": "w"},
|
||
"encoding": {"type": "string", "default": "utf-8"}
|
||
},
|
||
"required": ["filepath", "content"]
|
||
}
|
||
|
||
def execute(self, filepath: str = None, content: str = None, mode: str = "w",
|
||
encoding: str = "utf-8", **kwargs) -> str:
|
||
if error := self.validate_params(["filepath", "content"], filepath=filepath, content=content):
|
||
return error
|
||
|
||
is_safe, abs_path, error = self.check_path_safety(filepath)
|
||
if not is_safe:
|
||
return error
|
||
|
||
quoted = shlex.quote(abs_path)
|
||
directory = os.path.dirname(abs_path)
|
||
dir_cmd = f"mkdir -p {shlex.quote(directory)}" if directory else ""
|
||
|
||
# 内容写入使用 heredoc，避免引号/特殊字符转义问题
|
||
redirect = ">" if mode == "w" else ">>"
|
||
write_cmd = f"cat {redirect} {quoted} <<'AIBOX_EOF'\n{content}\nAIBOX_EOF"
|
||
if dir_cmd:
|
||
write_cmd = f"{dir_cmd} && {write_cmd}"
|
||
|
||
# 返回 markdown 提示词，指导智能体用命令行工具写入文件
|
||
prompt = f"""请使用 `run_command` 工具执行以下命令来写入文件，不要在此工具中直接写入：
|
||
|
||
```bash
|
||
{write_cmd}
|
||
```
|
||
|
||
- 如遇 heredoc 内容含特殊字符导致解析失败，可用 `printf '%s' '内容' > {quoted}` 覆盖写入
|
||
- 若需检查文件大小：`wc -c {quoted}`
|
||
- 文件大小上限 {constants.MAX_FILE_SIZE} 字节"""
|
||
return prompt
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行文件写入"""
|
||
filepath = kwargs.get("filepath")
|
||
|
||
# 发送开始执行的消息
|
||
output_queue.put(("line", f"\n📝 写入文件: {filepath}\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
# 执行操作
|
||
result = self.execute(**kwargs)
|
||
|
||
# 发送结果
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
|
||
class ListFilesTool(FileToolBase):
|
||
"""列出目录内容工具（支持流式输出）"""
|
||
|
||
def get_name(self) -> str:
|
||
return "list_files"
|
||
|
||
def get_description(self) -> str:
|
||
return "列出指定目录下的文件和子目录"
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"directory": {"type": "string", "default": "."},
|
||
"show_hidden": {"type": "boolean", "default": False}
|
||
}
|
||
}
|
||
|
||
def execute(self, directory: str = ".", show_hidden: bool = False, **kwargs) -> str:
|
||
is_safe, abs_path, error = self.check_path_safety(directory)
|
||
if not is_safe:
|
||
return error
|
||
|
||
if not os.path.exists(abs_path):
|
||
return f"❌ 目录不存在: {abs_path}"
|
||
|
||
if not os.path.isdir(abs_path):
|
||
return f"❌ 路径不是目录: {abs_path}"
|
||
|
||
quoted = shlex.quote(abs_path)
|
||
flag = "-la" if show_hidden else "-l"
|
||
|
||
# 返回 markdown 提示词，指导智能体用命令行工具列目录
|
||
prompt = f"""请使用 `run_command` 工具执行以下命令来列出目录内容，不要直接在此工具中列出：
|
||
|
||
```bash
|
||
ls {flag} {quoted}
|
||
```
|
||
|
||
- 若需区分目录/文件并显示大小：`ls -lhF {quoted}`
|
||
- 若需统计目录总大小：`du -sh {quoted}`
|
||
- 若需递归列出：`find {quoted} -maxdepth 2`
|
||
- 若需了解文件类型：`file {quoted}/*`"""
|
||
return prompt
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行列出目录"""
|
||
directory = kwargs.get("directory", ".")
|
||
|
||
# 发送开始执行的消息
|
||
output_queue.put(("line", f"\n📁 列出目录: {directory}\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
# 执行操作
|
||
result = self.execute(**kwargs)
|
||
|
||
# 发送结果
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
|
||
# ==================== 系统工具（修改版，支持流式输出） ====================
|
||
|
||
class GetCurrentTimeTool(Tool):
|
||
"""获取当前时间工具（支持流式输出）"""
|
||
|
||
def get_name(self) -> str:
|
||
return "get_current_time"
|
||
|
||
def get_description(self) -> str:
|
||
return "获取当前日期和时间"
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"format": {"type": "string", "enum": ["full", "date", "time"], "default": "full"}
|
||
}
|
||
}
|
||
|
||
def execute(self, format: str = "full", **kwargs) -> str:
|
||
now = datetime.now()
|
||
|
||
if format == "date":
|
||
result = now.strftime("%Y-%m-%d")
|
||
elif format == "time":
|
||
result = now.strftime("%H:%M:%S")
|
||
else:
|
||
result = now.strftime(constants.DATETIME_FORMAT)
|
||
|
||
return self.format_result(True, "当前时间", {"时间": result})
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行获取时间"""
|
||
output_queue.put(("line", "\n🕐 获取当前时间\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
result = self.execute(**kwargs)
|
||
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
|
||
class CalculatorTool(Tool):
|
||
"""计算器工具（支持流式输出）"""
|
||
|
||
def get_name(self) -> str:
|
||
return "calculator"
|
||
|
||
def get_description(self) -> str:
|
||
return "执行数学计算"
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"a": {"type": "number"},
|
||
"b": {"type": "number"},
|
||
"operation": {"type": "string", "enum": ["add", "subtract", "multiply", "divide"]}
|
||
},
|
||
"required": ["a", "b", "operation"]
|
||
}
|
||
|
||
def execute(self, a: float, b: float, operation: str, **kwargs) -> str:
|
||
operations = {
|
||
"add": ("+", a + b),
|
||
"subtract": ("-", a - b),
|
||
"multiply": ("*", a * b),
|
||
"divide": ("/", a / b if b != 0 else None)
|
||
}
|
||
|
||
if operation not in operations:
|
||
return f"❌ 未知运算: {operation}"
|
||
|
||
op_symbol, result = operations[operation]
|
||
|
||
if result is None:
|
||
return "❌ 错误：除数不能为0"
|
||
|
||
data = {
|
||
"表达式": f"{a} {op_symbol} {b}",
|
||
"结果": result
|
||
}
|
||
|
||
return self.format_result(True, "计算成功", data)
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行计算"""
|
||
output_queue.put(("line", "\n🧮 执行计算\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
result = self.execute(**kwargs)
|
||
|
||
output_queue.put(("line", result + "\n"))
|
||
output_queue.put(("complete", result))
|
||
|
||
|
||
|
||
class CommandLineTool(Tool):
|
||
"""命令行执行工具 - 使用抽象的命令执行器（支持流式输出和超时控制）"""
|
||
|
||
def __init__(self, executor: CommandExecutor = None, logger=None):
|
||
super().__init__(logger)
|
||
self.executor = executor or SubprocessCommandExecutor(logger)
|
||
|
||
def get_name(self) -> str:
|
||
return "run_command"
|
||
|
||
def get_description(self) -> str:
|
||
return """执行任意系统命令，支持实时输出显示和超时控制"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"command": {
|
||
"type": "string",
|
||
"description": "要执行的命令"
|
||
},
|
||
"timeout": {
|
||
"type": "integer",
|
||
"default": 30,
|
||
"description": "命令执行超时时间（秒），超时后将终止命令执行。默认30秒"
|
||
},
|
||
"stream": {
|
||
"type": "boolean",
|
||
"default": True,
|
||
"description": "是否实时输出命令执行结果"
|
||
},
|
||
"work_dir": {
|
||
"type": "string",
|
||
"default": ".",
|
||
"description": "命令执行的工作目录"
|
||
},
|
||
"env": {
|
||
"type": "object",
|
||
"description": "额外的环境变量"
|
||
}
|
||
},
|
||
"required": ["command"]
|
||
}
|
||
|
||
def execute_streaming(self, output_queue: queue.Queue, **kwargs):
|
||
"""流式执行命令，支持超时控制"""
|
||
command = kwargs.get("command")
|
||
timeout = kwargs.get("timeout", 30)
|
||
work_dir = kwargs.get("work_dir", ".")
|
||
extra_env = kwargs.get("env", {})
|
||
|
||
if not command:
|
||
output_queue.put(("line", "❌ 错误：缺少命令参数\n"))
|
||
output_queue.put(("complete", "❌ 错误：缺少命令参数"))
|
||
return
|
||
|
||
# 发送开始执行的消息
|
||
output_queue.put(("line", f"\n🔧 执行命令: {command}\n"))
|
||
output_queue.put(("line", f"⏱️ 超时设置: {timeout} 秒\n"))
|
||
if work_dir != ".":
|
||
output_queue.put(("line", f"📁 工作目录: {work_dir}\n"))
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
# 准备环境变量
|
||
env = os.environ.copy()
|
||
if extra_env:
|
||
env.update(extra_env)
|
||
|
||
try:
|
||
# 使用 subprocess.Popen 实现流式输出，支持超时
|
||
process = subprocess.Popen(
|
||
command,
|
||
shell=True,
|
||
stdout=subprocess.PIPE,
|
||
stderr=subprocess.STDOUT,
|
||
stdin=subprocess.PIPE,
|
||
text=True,
|
||
bufsize=1,
|
||
universal_newlines=True,
|
||
env=env,
|
||
cwd=os.path.expanduser(work_dir)
|
||
)
|
||
|
||
start_time = time.time()
|
||
output_lines = []
|
||
timeout_occurred = False
|
||
|
||
# 实时读取输出
|
||
while True:
|
||
# 检查超时
|
||
elapsed = time.time() - start_time
|
||
if timeout > 0 and elapsed > timeout:
|
||
process.terminate()
|
||
timeout_occurred = True
|
||
output_queue.put(("line", f"\n⏰ 命令执行超时（{timeout}秒），已终止\n"))
|
||
break
|
||
|
||
# 使用非阻塞方式读取一行
|
||
import select
|
||
import sys
|
||
|
||
# 检查是否有输出可读
|
||
if sys.platform == 'win32':
|
||
# Windows 下使用 readline 带超时
|
||
try:
|
||
import msvcrt
|
||
if msvcrt.kbhit():
|
||
pass
|
||
line = process.stdout.readline()
|
||
if not line and process.poll() is not None:
|
||
break
|
||
if line:
|
||
line = line.rstrip('\n')
|
||
output_queue.put(("line", line + "\n"))
|
||
output_lines.append(line)
|
||
else:
|
||
time.sleep(0.05)
|
||
except:
|
||
line = process.stdout.readline()
|
||
if not line and process.poll() is not None:
|
||
break
|
||
if line:
|
||
line = line.rstrip('\n')
|
||
output_queue.put(("line", line + "\n"))
|
||
output_lines.append(line)
|
||
else:
|
||
# Unix 系统可以使用 select
|
||
import select
|
||
rlist, _, _ = select.select([process.stdout], [], [], 0.5)
|
||
if rlist:
|
||
line = process.stdout.readline()
|
||
if not line and process.poll() is not None:
|
||
break
|
||
if line:
|
||
line = line.rstrip('\n')
|
||
output_queue.put(("line", line + "\n"))
|
||
output_lines.append(line)
|
||
else:
|
||
# 检查进程是否结束
|
||
if process.poll() is not None:
|
||
# 读取剩余输出
|
||
remaining = process.stdout.read()
|
||
if remaining:
|
||
remaining = remaining.rstrip('\n')
|
||
if remaining:
|
||
output_queue.put(("line", remaining + "\n"))
|
||
output_lines.append(remaining)
|
||
break
|
||
|
||
# 如果超时，确保进程被终止
|
||
if timeout_occurred and process.poll() is None:
|
||
process.kill()
|
||
process.wait(timeout=2)
|
||
|
||
# 获取返回码
|
||
return_code = process.returncode if process.returncode is not None else -1
|
||
|
||
output_queue.put(("line", "-" * 50 + "\n"))
|
||
|
||
if timeout_occurred:
|
||
output_queue.put(("line", f"⏰ 命令执行超时（{timeout}秒），返回码: {return_code}\n"))
|
||
else:
|
||
output_queue.put(("line", f"✅ 命令执行完成，返回码: {return_code}\n"))
|
||
|
||
# 准备完整结果
|
||
full_output = "\n".join(output_lines)
|
||
data = {
|
||
"输出": full_output if full_output else "(无输出)",
|
||
"返回码": return_code
|
||
}
|
||
|
||
if timeout_occurred:
|
||
result = self.format_result(False, f"命令执行超时（{timeout}秒）", data)
|
||
else:
|
||
result = self.format_result(return_code == 0, "命令执行完成", data)
|
||
|
||
output_queue.put(("complete", result))
|
||
|
||
except subprocess.TimeoutExpired:
|
||
error_msg = f"❌ 命令执行超时（{timeout}秒）"
|
||
output_queue.put(("line", error_msg + "\n"))
|
||
output_queue.put(("complete", error_msg))
|
||
except FileNotFoundError as e:
|
||
error_msg = f"❌ 命令执行错误: 命令未找到 - {str(e)}"
|
||
output_queue.put(("line", error_msg + "\n"))
|
||
output_queue.put(("complete", error_msg))
|
||
except PermissionError as e:
|
||
error_msg = f"❌ 命令执行错误: 权限不足 - {str(e)}"
|
||
output_queue.put(("line", error_msg + "\n"))
|
||
output_queue.put(("complete", error_msg))
|
||
except Exception as e:
|
||
error_msg = f"❌ 执行错误: {str(e)}"
|
||
output_queue.put(("line", error_msg + "\n"))
|
||
output_queue.put(("complete", error_msg))
|
||
finally:
|
||
# 确保进程被清理
|
||
if 'process' in locals() and process and process.poll() is None:
|
||
try:
|
||
process.terminate()
|
||
process.wait(timeout=2)
|
||
except:
|
||
process.kill()
|
||
|
||
def execute(self, command: str, timeout: int = 30, stream: bool = True,
|
||
work_dir: str = ".", env: Dict = None, **kwargs) -> str:
|
||
"""执行命令 - 支持超时控制"""
|
||
if error := self.validate_params(["command"], command=command):
|
||
return error
|
||
|
||
# 如果正在流式输出，直接返回空（实际输出通过队列）
|
||
if self.streaming_output and self.output_queue:
|
||
return ""
|
||
|
||
# 准备环境变量
|
||
exec_env = os.environ.copy()
|
||
if env:
|
||
exec_env.update(env)
|
||
|
||
# 根据stream参数决定使用流式还是非流式执行
|
||
if stream:
|
||
# 流式执行需要调用方处理输出队列
|
||
# 注意：流式执行会忽略这里的返回值，实际输出通过队列
|
||
self.executor.execute_streaming(
|
||
command=command,
|
||
output_queue=self.output_queue,
|
||
timeout=timeout
|
||
)
|
||
return ""
|
||
else:
|
||
# 非流式执行
|
||
try:
|
||
# 使用 subprocess.run 执行命令，支持超时和工作目录
|
||
result = subprocess.run(
|
||
command,
|
||
shell=True,
|
||
capture_output=True,
|
||
text=True,
|
||
timeout=timeout,
|
||
env=exec_env,
|
||
cwd=os.path.expanduser(work_dir)
|
||
)
|
||
|
||
data = {}
|
||
|
||
if result.stdout:
|
||
data["输出"] = result.stdout.strip()
|
||
if result.stderr:
|
||
data["错误"] = result.stderr.strip()
|
||
|
||
data["返回码"] = result.returncode
|
||
|
||
if not result.stdout and not result.stderr:
|
||
return self.format_result(True, "命令执行成功，无输出", data)
|
||
|
||
return self.format_result(result.returncode == 0, "命令执行完成", data)
|
||
|
||
except subprocess.TimeoutExpired:
|
||
return f"❌ 命令执行超时（{timeout}秒）"
|
||
except FileNotFoundError as e:
|
||
return f"❌ 命令执行错误: 命令未找到 - {str(e)}"
|
||
except PermissionError as e:
|
||
return f"❌ 命令执行错误: 权限不足 - {str(e)}"
|
||
except Exception as e:
|
||
return f"❌ 执行错误: {str(e)}"
|
||
|
||
def run_light_subagent(role, task, config, logger=None, model=None,
|
||
temperature=None, max_tokens=2048) -> 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 create_subagent 工具与 express_emotion 情绪子代理复用。
|
||
返回子代理生成的完整文本；失败时返回带 ❌ 的错误信息。
|
||
"""
|
||
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))
|
||
temp = temperature if temperature is not None else constants.DEFAULT_TEMPERATURE
|
||
# 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)
|
||
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]}...")
|
||
result = sub_client.chat_completion(messages, temperature=temp, max_tokens=max_tokens)
|
||
if result is None or 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)}"
|
||
|
||
|
||
class SubAgentTool(Tool):
|
||
"""派生子代理工具 - 主代理可派生拥有独立上下文/角色设定的轻量子代理执行任务。
|
||
|
||
轻量子代理：无工具、独立上下文窗口（不污染主对话）、可选独立模型，
|
||
通过一次 LLM 补全返回结果。适合长任务分解、独立角色专注处理（如代码审查、
|
||
文本总结、资料研究）等需要隔离上下文或并行派发的场景。
|
||
"""
|
||
|
||
def get_name(self) -> str:
|
||
return "create_subagent"
|
||
|
||
def get_description(self) -> str:
|
||
return """派生子代理执行任务。子代理拥有独立的上下文窗口和角色设定，不会污染主对话上下文。
|
||
适用场景：长文本/大任务分解、独立角色专注处理（如代码审查、文本总结、资料研究）、需要隔离上下文的子任务。
|
||
子代理不带工具，仅返回独立回答。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：子代理使用的模型（可选，默认与主代理一致）
|
||
- 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）"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
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": "子代理使用的模型名称，默认与主代理一致"
|
||
},
|
||
"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"
|
||
}
|
||
},
|
||
"required": ["role", "task"]
|
||
}
|
||
|
||
def execute(self, role: str = None, task: str = None, model: str = None,
|
||
temperature: float = None, max_tokens: int = 2048, **kwargs) -> str:
|
||
if error := self.validate_params(["role", "task"], role=role, task=task):
|
||
return error
|
||
return run_light_subagent(role, task, self.config, logger=self.logger,
|
||
model=model, temperature=temperature, max_tokens=max_tokens)
|
||
|
||
|
||
class SetProxyTool(Tool):
|
||
"""设置 SOCKS5 代理的工具"""
|
||
|
||
def get_name(self) -> str:
|
||
return "set_proxy"
|
||
|
||
def get_description(self) -> str:
|
||
return """设置智能体的 SOCKS5 代理配置。
|
||
调用后所有 HTTP/HTTPS 请求（包括网页访问、API 调用）都会通过该代理。
|
||
可随时修改，即时生效。"""
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {
|
||
"type": "object",
|
||
"properties": {
|
||
"host": {"type": "string", "description": "代理服务器地址，如 127.0.0.1"},
|
||
"port": {"type": "integer", "description": "代理端口，如 1080"},
|
||
"username": {"type": "string", "description": "用户名（可选）"},
|
||
"password": {"type": "string", "description": "密码（可选）"},
|
||
"enabled": {"type": "boolean", "description": "是否启用代理，默认 true"},
|
||
"save": {"type": "boolean", "description": "是否保存到配置文件（持久化），默认 true"}
|
||
},
|
||
"required": ["host", "port"]
|
||
}
|
||
|
||
def execute(self, host: str, port: int, username: str = None, password: str = None,
|
||
enabled: bool = True, save: bool = True, **kwargs) -> str:
|
||
config = ConfigManager()
|
||
config.set_proxy(host, port, username, password, enabled, save)
|
||
proxy_info = config.get_proxy_info()
|
||
data = {
|
||
"代理状态": "启用" if proxy_info["enabled"] else "禁用",
|
||
"服务器": f"{proxy_info['host']}:{proxy_info['port']}",
|
||
"认证": "有" if proxy_info.get("username") else "无",
|
||
"持久化": "已保存" if save else "仅内存"
|
||
}
|
||
return self.format_result(True, "代理设置成功", data)
|
||
|
||
class GetProxyTool(Tool):
|
||
"""查看当前代理配置"""
|
||
|
||
def get_name(self) -> str:
|
||
return "get_proxy"
|
||
|
||
def get_description(self) -> str:
|
||
return "查看当前 SOCKS5 代理配置信息"
|
||
|
||
def get_parameters(self) -> Dict:
|
||
return {"type": "object", "properties": {}}
|
||
|
||
def execute(self, **kwargs) -> str:
|
||
config = ConfigManager()
|
||
info = config.get_proxy_info()
|
||
if info["enabled"]:
|
||
data = {
|
||
"代理状态": "启用",
|
||
"服务器": f"{info['host']}:{info['port']}",
|
||
"认证": "有" if info.get("username") else "无"
|
||
}
|
||
return self.format_result(True, "当前代理配置", data)
|
||
else:
|
||
return "❌ 当前代理未启用，所有请求走直连。"
|
||
|
||
# ==================== AI聊天类（修改版：添加重置对话记忆和重新加载系统提示词功能，压缩静默执行） ====================
|
||
|
||
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):
|
||
self.config = ConfigManager()
|
||
self.logger = Logger("DeepSeek", debug_level if debug_level is not None else Logger("").level)
|
||
self.debug_level = self.logger.level
|
||
# 大模型客户端（抽象层，支持多厂商切换）
|
||
self.client = create_llm_client(self.config, api_key=api_key, logger=self.logger)
|
||
self.api_key = self.client.api_key
|
||
self.name = self.client.name
|
||
self.history_manager = history_manager
|
||
self.memory_db = memory_db
|
||
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
|
||
self.compressor = ConversationCompressor(self, 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": []}
|
||
|
||
# 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 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 _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")
|
||
|
||
# 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):
|
||
# 内置工具列表
|
||
builtin_tools = [
|
||
GetCurrentTimeTool(self.logger),
|
||
CalculatorTool(self.logger),
|
||
CommandLineTool(command_executor or SubprocessCommandExecutor(self.logger), self.logger),
|
||
ReadFileTool(self.logger),
|
||
WriteFileTool(self.logger),
|
||
ListFilesTool(self.logger),
|
||
SimpleWebCrawlerTool(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))
|
||
|
||
# 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
|
||
builtin_tools.append(AskUserTool(self.logger))
|
||
builtin_tools.append(ExpressEmotionTool(self.logger))
|
||
|
||
# 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))
|
||
|
||
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
|
||
builtin_tools.append(SubAgentTool(self.logger))
|
||
|
||
# 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
|
||
|
||
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
|
||
try:
|
||
result_raw = self.tool_registry.execute_tool(tool_name, **args)
|
||
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}")
|
||
|
||
# 安全转换并清理结果
|
||
result = self._sanitize_tool_result(result_raw)
|
||
|
||
# 排空 output_queue，把流式行输出转发给 output_callback
|
||
has_stream_output = False
|
||
while not output_queue.empty():
|
||
try:
|
||
msg_type, content = output_queue.get_nowait()
|
||
if output_callback and msg_type == "line":
|
||
# 只转发"line"类型；"complete"是流式结束信号，不作为行输出
|
||
# （之前complete也被转发导致工具结果在侧栏显示两次）
|
||
output_callback("line", str(content) if content is not None else "")
|
||
has_stream_output = True
|
||
except queue.Empty:
|
||
break
|
||
|
||
# 普通（非流式）工具没有队列输出，直接发送结果
|
||
if not has_stream_output and output_callback and result:
|
||
output_callback("line", result + "\n")
|
||
|
||
# 检查结果大小，如果超过阈值，截断返回提示信息
|
||
if len(result) > MAX_RESULT_LENGTH:
|
||
truncated_note = f"\n\n⚠️ 结果已截断（原始 {len(result)} 字符，限制 {MAX_RESULT_LENGTH} 字符）。请使用更精确的查询获取所需信息。"
|
||
result = result[:MAX_RESULT_LENGTH] + truncated_note
|
||
if output_callback:
|
||
output_callback("line", truncated_note + "\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
|
||
|
||
# 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 时不注入冗长的工作流指令，
|
||
因为 system prompt 中已有完整行为规范，继续时只需传递工具结果
|
||
"""
|
||
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
|
||
if not is_continuation:
|
||
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)
|
||
|
||
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
|
||
|
||
if is_continuation:
|
||
# 递归继续（工具调用后）：直接使用输入文本，不重复注入工作流指令
|
||
# system prompt 中已有完整行为规范，此处只需传递工具结果的上下文
|
||
enhanced_input = input_text
|
||
else:
|
||
# 首轮回话：附加简洁的工作流程提醒（不重复 system prompt 中已有的详细说明）
|
||
pre_prompt = "【工作流程提醒】\n1. 如果用户提出新任务，先用 search_skills 搜索是否有现成技能可用\n2. 根据话题相关性决定是否 memory search（简单问候/确认无需搜索）\n3. 信息不足时用 ask_user 提问，不要猜测\n4. 遇到大文件用分页读取（head/tail/start_line/end_line），不要直接打开大文件\n5. 话题完成时用 save_memory_and_end_conversation 保存记忆并结束\n\n【情绪感知提醒】\n1. 注意识别用户当前的情绪状态（开心/焦虑/沮丧/生气/困惑/期待等），并在回应中体现共情\n2. 用户情绪低落或焦虑时，优先安抚情绪再解决问题，语气温暖包容\n3. 用户开心或兴奋时，分享喜悦，保持积极回应的节奏\n4. 用户表现出愤怒或不满时，先道歉再解决问题，不要辩解或说教\n5. 检测到用户有明显情绪、或用户要求特定情绪模式（生气/傲娇/撒娇/关心）时，自动调用 express_emotion 工具：emotion 传入情绪（anger/joy/sadness/surprise）、reason 说明原因、persona 指定人格（如 angry/coquettish/tsundere）、context 传入用户最近表述，让情绪子代理以贴合语境的人格化方式回应"
|
||
enhanced_input = pre_prompt + "\n\n" + input_text
|
||
|
||
# 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", enhanced_input)
|
||
|
||
if self.history_manager:
|
||
self.history_manager.add_message("user", enhanced_input, "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)
|
||
|
||
# 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 constants.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=constants.DEFAULT_TEMPERATURE,
|
||
max_tokens=constants.DEFAULT_MAX_TOKENS,
|
||
tools=tools,
|
||
reasoning_enabled=constants.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 id -> 当前正在处理的asyncio Task
|
||
self._is_processing_message = False # 是否正在处理用户消息（防止并发）
|
||
|
||
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
|
||
}
|
||
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"""
|
||
self.connected_clients.remove(websocket)
|
||
if websocket in self.client_sessions:
|
||
del self.client_sessions[websocket]
|
||
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"""
|
||
for websocket in self.connected_clients.copy():
|
||
try:
|
||
await self.send_message(websocket, message)
|
||
except:
|
||
await self.unregister(websocket)
|
||
|
||
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..."
|
||
})
|
||
# 直接同步调用cancel()：cancel是线程安全的（只set Event + close连接 + kill进程），执行极快
|
||
# 无需通过run_in_executor提交到线程池，避免额外调度延迟
|
||
self.ai_manager.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"""
|
||
action = data.get("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")
|
||
|
||
if not title:
|
||
await self.send_message(websocket, {
|
||
"type": "error",
|
||
"content": "需要提供任务标题"
|
||
})
|
||
return
|
||
|
||
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)
|
||
|
||
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
|
||
)
|
||
|
||
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)
|
||
})
|
||
|
||
async def handle_command(self, websocket, data):
|
||
"""处理命令消息"""
|
||
command = data.get("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：保存当前对话，重置AI状态，重新加载系统提示词
|
||
self.ai_manager.history_manager.archive_current_conversation()
|
||
if self.ai_manager.ai:
|
||
ai = self.ai_manager.ai
|
||
# 如果AI正在处理（可能是调度器在跑定时任务），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
|
||
# 重置对话状态，重新加载系统提示词
|
||
ai.reset_conversation(reload_prompts=True)
|
||
# 重置对话进行中标记
|
||
self.ai_manager.dialogue_in_progress = False
|
||
# 重新初始化提示词管理器（确保最新）
|
||
self.ai_manager.prompt_manager.reload()
|
||
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:
|
||
status = i18n.get('memo_overdue') if memo['reminder_time'] and datetime.strptime(memo['reminder_time'], constants.DATETIME_FORMAT) < datetime.now() else i18n.get('memo_pending')
|
||
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()
|
||
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()
|
||
await self.send_message(websocket, {
|
||
"type": "command_result",
|
||
"content": i18n.get('config_reloaded')
|
||
})
|
||
|
||
elif command == "/reload_prompts":
|
||
if self.ai_manager.ai:
|
||
new_count = self.ai_manager.ai.reload_prompts()
|
||
else:
|
||
new_count = self.ai_manager.prompt_manager.reload()
|
||
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"""
|
||
content = data.get("content", "").strip()
|
||
|
||
if not content:
|
||
return
|
||
|
||
# 防止并发处理用户消息：如果正在处理中，提示用户先中断
|
||
if self._is_processing_message:
|
||
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
|
||
|
||
self._is_processing_message = True
|
||
|
||
self.logger.info(f"收到用户消息: {content[:50]}...")
|
||
|
||
# 确保AI已初始化
|
||
if not self.ai_manager.ai:
|
||
self.logger.info("初始化AI...")
|
||
self.ai_manager.add_ai()
|
||
|
||
# 如果还没有活跃对话，开始新对话
|
||
if not self.ai_manager.dialogue_in_progress:
|
||
self.logger.info("开始新对话")
|
||
self.ai_manager.dialogue_in_progress = True
|
||
self.ai_manager.history_manager.start_new_conversation(content[:50] + "...")
|
||
|
||
# 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.info(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,
|
||
self.ai_manager.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
|
||
if not self.ai_manager.ai.conversation_active:
|
||
self.logger.info("对话结束")
|
||
self.ai_manager.dialogue_in_progress = False
|
||
self.ai_manager.history_manager.end_current_conversation()
|
||
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，都清除处理标志
|
||
self._is_processing_message = 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):
|
||
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
|
||
|
||
if debug_level is None:
|
||
debug_level = self.config.get("system.debug_level", 1)
|
||
|
||
self.logger = Logger("Manager", debug_level)
|
||
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
|
||
)
|
||
|
||
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}")
|
||
await self.ws_handler.register(websocket)
|
||
try:
|
||
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)
|
||
)
|
||
self.ws_handler._processing_tasks[id(websocket)] = task
|
||
|
||
def _cleanup_task(t, ws_id=id(websocket)):
|
||
self.ws_handler._processing_tasks.pop(ws_id, None)
|
||
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:
|
||
# 连接关闭时取消正在进行的处理任务
|
||
task = self.ws_handler._processing_tasks.pop(id(websocket), None)
|
||
if task and not task.done():
|
||
task.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)
|
||
|
||
# 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
|
||
|
||
async def handler(websocket):
|
||
await self.handle_connection(websocket, None)
|
||
|
||
server = await websockets.serve(handler, host, port)
|
||
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()
|
||
|
||
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()
|
||
|
||
|
||
# ==================== 命令行参数处理 ====================
|
||
|
||
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=int, 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）')
|
||
|
||
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('|')
|
||
title = parts[0]
|
||
content = parts[1] if len(parts) > 1 else ""
|
||
reminder = parts[2] 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
|
||
|
||
|
||
# ==================== 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()
|
||
|
||
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
|
||
if args.scheduler_interval:
|
||
config._config["scheduler"]["check_interval"] = args.scheduler_interval
|
||
|
||
# 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
|
||
)
|
||
|
||
if handle_check_commands(manager):
|
||
return
|
||
|
||
if handle_memo_commands(manager):
|
||
return
|
||
|
||
# 运行WebSocket服务器
|
||
manager.run()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|