qing
eb88a77238
- config.set_proxy gains password_env (aligned with api_key_env convention): config file persists only the env var name; plaintext password lives in memory for the current session only - _save_config deep-copies and nulls proxy.socks5.password before json.dump, so every save path is sanitized (defense in depth) - get_proxy resolves password as in-memory plaintext first, then via password_env from os.environ; downstream consumers (llm_client, web tool curl args) unchanged - legacy migration on load: plaintext found on disk is kept in memory for the session, file is immediately rewritten sanitized - SetProxyTool exposes password_env and reports password source - registry debug logs mask password/api_key/token/secret/authorization kwargs (N11) Tests: new tests/test_config.py (3 cases: no plaintext on save, password_env resolution, legacy migration); full suite 267 passed. Audit report updated with Batch F progress.
162 lines
6.6 KiB
Python
162 lines
6.6 KiB
Python
#!/usr/bin/env python3
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"""
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tools.registry - 工具注册器
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抽离自 aibox.py 的 ToolRegistry 类。仅依赖 common 与标准库，
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不反向依赖 aibox 任何符号（skill_manager 为前向引用，运行期注入）。
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"""
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import queue
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import threading
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import time
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from typing import Dict, List, Optional, Tuple
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from common import Logger, i18n
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from .base import Tool
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# 工具参数名命中这些关键词时，debug 日志里打码（防密码/密钥进日志文件）
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_SENSITIVE_PARAM_KEYS = {"password", "api_key", "token", "secret", "authorization"}
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def _sanitize_kwargs(kwargs: Dict) -> Dict:
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"""复制一份参数并把敏感键的值替换为 ***（仅用于日志输出）"""
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return {
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k: ("***" if str(k).lower() in _SENSITIVE_PARAM_KEYS else v)
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for k, v in kwargs.items()
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}
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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}({_sanitize_kwargs(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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# M2 修复（审计 2026-08-17）：转发循环加总超时。
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# 此前 while thread.is_alive() 在工具内部卡死（如子进程
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# 等待 stdin）时永不退出；超时后放弃等待，避免本调用
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# 线程被连带挂起。
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tool_timeout = 3600 # 单工具总上限 1 小时（工具自身的秒级超时之外的兜底）
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deadline = time.time() + tool_timeout
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final_result = None
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while thread.is_alive() or not output_queue.empty():
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if time.time() > deadline:
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self.logger.warning(
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f"工具 {name} 流式执行超过 {tool_timeout}s 总上限，放弃等待")
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return f"❌ 工具执行超时（>{tool_timeout}s，已放弃等待）"
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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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