refactor(aibox): 工具基类 Tool/ToolRegistry 抽离至 tools 包
延续 common 包的拆分模式，形成 common <- tools <- aibox 单向依赖， 为后续工具子类逐步迁移铺路；aibox.py 瘦身约 230 行，工具子类与注册逻辑零改动。
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@@ -41,6 +41,9 @@ from llm_client import create_llm_client
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# 基础设施（多语言 / 配置 / 常量 / 日志）已抽离至 common 模块
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from common import i18n, I18n, ConfigManager, Constants, constants, Logger
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# 工具基类与注册器已抽离至 tools 模块
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from tools import Tool, ToolRegistry
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# ==================== 提示词管理器 ====================
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class PromptManager:
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@@ -2959,238 +2962,6 @@ class SubprocessCommandExecutor(CommandExecutor):
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with self._process_lock:
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self._current_process = None
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# ==================== 工具基类 ====================
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class Tool(ABC):
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"""工具基类"""
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def __init__(self, logger: Optional[Logger] = None):
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self.logger = logger or Logger(self.__class__.__name__)
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self.config = ConfigManager()
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self.streaming_output = False # 标记是否正在流式输出
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self.output_queue = None # 添加输出队列属性
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@abstractmethod
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def get_name(self) -> str:
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"""返回工具名称"""
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pass
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@abstractmethod
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def get_description(self) -> str:
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"""返回工具描述"""
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pass
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@abstractmethod
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def get_parameters(self) -> Dict:
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"""返回工具参数定义（JSON Schema格式）"""
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pass
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@abstractmethod
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def execute(self, **kwargs) -> str:
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"""执行工具并返回结果"""
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pass
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def execute_streaming(self, output_queue: queue.Queue, **kwargs):
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"""流式执行工具，通过队列输出结果"""
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self.output_queue = output_queue # 保存队列引用
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self.streaming_output = True
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try:
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# 执行具体的工具逻辑，子类可以重写这个方法
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result = self.execute(**kwargs)
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# 如果结果不为空，发送到队列
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if result:
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# 对于非流式输出，直接发送完整结果
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output_queue.put(("complete", result))
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finally:
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self.streaming_output = False
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self.output_queue = None
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def validate_params(self, required: List[str], **kwargs) -> Optional[str]:
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"""验证必需参数"""
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missing = [p for p in required if p not in kwargs or kwargs[p] is None]
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if missing:
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return f"❌ 错误：缺少必需参数: {', '.join(missing)}"
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return None
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def to_function_call_format(self) -> Dict:
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"""转换为OpenAI函数调用格式"""
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return {
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"type": "function",
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"function": {
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"name": self.get_name(),
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"description": self.get_description(),
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"parameters": self.get_parameters()
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}
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}
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def format_result(self, success: bool, message: str, data: Dict = None) -> str:
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"""统一结果格式化"""
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result = []
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emoji = "✅" if success else "❌"
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result.append(f"{emoji} {message}")
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if data:
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for key, value in data.items():
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if isinstance(value, str) and "\n" in value:
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result.append(f" {key}:")
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for line in value.split("\n"):
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result.append(f" {line}")
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else:
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result.append(f" {key}: {value}")
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return "\n".join(result)
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def check_path_safety(self, filepath: str) -> Tuple[bool, str, str]:
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"""检查路径安全性（简化版）"""
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try:
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abs_path = os.path.abspath(os.path.expanduser(filepath))
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# 检查文件大小
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if os.path.exists(abs_path) and os.path.isfile(abs_path):
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file_size = os.path.getsize(abs_path)
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max_size = constants.MAX_FILE_SIZE
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if file_size > max_size:
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return False, abs_path, f"❌ 文件太大 ({file_size/1024/1024:.1f}MB)"
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return True, abs_path, ""
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except Exception as e:
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return False, filepath, f"❌ 路径检查错误: {str(e)}"
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def send_line(self, line: str):
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"""发送一行输出到队列（如果存在）"""
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if self.output_queue:
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self.output_queue.put(("line", line))
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def send_complete(self, result: str):
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"""发送完成结果到队列（如果存在）"""
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if self.output_queue:
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self.output_queue.put(("complete", result))
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class ToolRegistry:
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"""工具注册器 - 支持技能热重载"""
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def __init__(self, logger: Optional[Logger] = None):
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self._tools: Dict[str, Tool] = {}
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self._builtin_tools: Dict[str, Tool] = {} # 内置工具单独存储
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self.logger = logger or Logger("ToolRegistry")
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self.current_output_queue = None # 用于流式输出的队列
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self.skill_manager: Optional['SkillManager'] = None # 技能管理器引用
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def register(self, tool: Tool, is_builtin: bool = False):
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"""注册工具，可以标记是否为内置工具"""
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self._tools[tool.get_name()] = tool
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if is_builtin:
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self._builtin_tools[tool.get_name()] = tool
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self.logger.info(f"工具已注册: {tool.get_name()} {'(内置)' if is_builtin else ''}")
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def register_many(self, tools: List[Tool], is_builtin: bool = False):
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"""批量注册工具"""
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for tool in tools:
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self.register(tool, is_builtin)
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def get_tool(self, name: str) -> Optional[Tool]:
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"""获取工具"""
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return self._tools.get(name)
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def get_all_tools(self) -> List[Tool]:
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"""获取所有工具"""
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return list(self._tools.values())
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def get_tools_schemas(self) -> List[Dict]:
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"""获取所有工具的OpenAI函数调用格式"""
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return [tool.to_function_call_format() for tool in self._tools.values()]
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def set_output_queue(self, queue: queue.Queue):
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"""设置输出队列"""
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self.current_output_queue = queue
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# 同时设置所有工具的队列
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for tool in self._tools.values():
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tool.output_queue = queue
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def execute_tool(self, name: str, **kwargs) -> str:
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"""执行工具"""
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tool = self.get_tool(name)
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if not tool:
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return f"❌ 错误：找不到工具 '{name}'"
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self.logger.debug(f"工具调用: {name}({kwargs})")
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start_time = time.time()
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try:
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# 检查是否有队列可用，并且工具支持流式输出
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if self.current_output_queue and hasattr(tool, 'execute_streaming'):
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# 设置工具的流式输出属性
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tool.streaming_output = True
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tool.output_queue = self.current_output_queue
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# 创建队列来收集流式输出
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output_queue = queue.Queue()
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# 执行流式输出
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thread = threading.Thread(
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target=tool.execute_streaming,
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args=(output_queue,),
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kwargs=kwargs
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)
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thread.daemon = True
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thread.start()
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# 从队列收集输出并发送到当前队列
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final_result = None
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while thread.is_alive() or not output_queue.empty():
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try:
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msg_type, content = output_queue.get(timeout=0.1)
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if msg_type == "line":
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# 发送到当前队列（会通过WebSocket发送）
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if self.current_output_queue:
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self.current_output_queue.put(("line", content))
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elif msg_type == "complete":
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final_result = content
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# 也发送完整结果
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if self.current_output_queue:
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self.current_output_queue.put(("complete", content))
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except queue.Empty:
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continue
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thread.join(timeout=5)
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if thread.is_alive():
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return "❌ 工具执行超时"
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result = final_result or ""
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tool.streaming_output = False
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tool.output_queue = None
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else:
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# 普通执行
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result = tool.execute(**kwargs)
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elapsed = time.time() - start_time
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self.logger.debug(f"工具结果 ({elapsed:.2f}s): {result[:100]}...")
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return result
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except TypeError as e:
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return f"❌ 工具执行错误: 参数错误 - {str(e)}"
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except Exception as e:
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return f"❌ 工具执行错误: {str(e)}"
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def reload_skills(self, skills_dir: str = None) -> int:
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"""
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重新加载所有扩展技能（热重载）。
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委托给 SkillManager 重新扫描目录并执行 --help 收集简介。
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"""
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self.logger.info("开始重新加载扩展技能...")
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if self.skill_manager is not None:
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count = self.skill_manager.reload()
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self.logger.info(i18n.get('skills_reloaded', count))
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return count
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self.logger.warning("skill_manager 未初始化，无法重载技能")
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return 0
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def get_skills_count(self) -> Tuple[int, int]:
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"""获取内置工具和扩展技能的数量"""
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builtin_count = len(self._builtin_tools)
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skill_count = len(self._tools) - builtin_count
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return builtin_count, skill_count
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# ==================== 扩展技能管理器（支持 Markdown 格式） ====================
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