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* ♻️ refactor(core): 重构 AI 能力与定时任务调度系统 - 【AI 能力与工具】重构 Capability 注册与管理机制，引入 CapabilityManager 统一管理 - 移除全局能力注册表，改用声明式装饰器 `@capability` 进行解耦注册 - 重构工具解析器链，使用统一的 BaseToolResolver 代替原有的多个特定解析器 - 增强工具查询过滤，支持通配符匹配、工具箱过滤和排除标签 - 【定时任务调度】重构定时任务管理器，引入 SchedulerRegistry 统一管理任务元数据 - 引入 JobConfig 聚合定时任务配置，支持用户维度的定时任务调度 - 重构执行分发器，支持并发限制、串行间隔和随机延迟打散 - 【运行上下文】引入 ScheduledDeps 以支持后台和定时任务环境下的依赖注入 - 优化 RunContext，支持从定时任务上下文快速构造，并提供 emit 辅助方法 - 【日志与监控】引入 AILoggerProxy，实现 AI 各模块的专属日志输出 - 将各模块的全局 logger 替换为对应的模块专属日志代理 - 【其他优化】修复 Pydantic V1 兼容层中 model_validator 的装饰器兼容性问题 - 在非交互式环境（如定时任务）中自动隐藏 HITL 交互工具以节省 Token * ♻️ refactor(core): 优化内部导入路径并提升 Pydantic 兼容性 - 【重构】将 `services/ai` 模块内的绝对导入重构为相对导入，优化包结构 - 【重构】移除不必要的 `if TYPE_CHECKING` 保护，通过 `from __future__ import annotations` 直接导入类型 - 【清理】清理 `core/messages/types.py` 中未使用的 `AssistantContentUnion` 等联合类型定义 - 【优化】在 `utils/pydantic_compat.py` 中新增 `model_rebuild` 兼容函数，统一 Pydantic V1/V2 的模型重建逻辑 - 【优化】将部分函数内部的延迟导入提升至模块顶部，规范代码结构 * ♻️ refactor(imports): 优化导入路径为相对导入并清理冗余导入 - 【重构】将 AI 服务相关模块中的绝对导入路径修改为相对导入，提升模块内聚性与可移植性 - 【清理】移除多处函数内部或类方法中未使用的冗余导入，避免循环引用和资源浪费 - 【格式化】微调部分工具装饰器和返回语句的格式与尾随逗号 * 🚨 auto fix by pre-commit hooks --------- Co-authored-by: webjoin111 <455457521@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
from collections.abc import AsyncGenerator
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from contextlib import asynccontextmanager
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import json
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from typing import Any
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from anyio import create_memory_object_stream, create_task_group
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from mcp.shared.message import SessionMessage
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from mcp.types import JSONRPCMessage
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from zhenxun.services.ai.sandbox.protocols import SupportsStreamExecution
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from zhenxun.services.ai.sandbox.registry import SandboxRegistry
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from zhenxun.services.ai.utils.logger import log_sandbox as logger
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from zhenxun.utils.pydantic_compat import model_dump_json, model_validate
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from .base import BaseMcpProxyExtension
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class UniversalMcpExtension(BaseMcpProxyExtension):
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"""通用 MCP 代理扩展类，用于在沙箱内连接 MCP 服务"""
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@property
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def extension_name(self) -> str:
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"""获取 MCP 代理扩展的唯一名称"""
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return "universal_mcp"
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@asynccontextmanager
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async def connect_mcp(
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self, command: str, args: list[str], env: dict[str, str] | None = None
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) -> AsyncGenerator[tuple[Any, Any], None]:
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"""启动沙箱内的 MCP 服务器，并建立与之进行 JSON-RPC 通信的双向内存流管道"""
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if not isinstance(self.session, SupportsStreamExecution):
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raise RuntimeError(
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"当前沙箱驱动不支持流式后台进程执行 (SupportsStreamExecution)，"
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"无法启动原生 MCP 代理。"
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)
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logger.info(
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"[UniversalMcpExtension] 正在沙箱内原生启动 MCP 服务器: "
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f"{command} {' '.join(args)}"
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)
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cmd_list = [command, *args]
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async with self.session.create_stream_process(
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command=cmd_list, cwd=self.session.workspace_path, env=env
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) as process_stream:
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read_prod, read_cons = create_memory_object_stream(10)
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write_prod, write_cons = create_memory_object_stream(10)
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async def stream_reader():
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buffer = b""
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try:
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while True:
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msg = await process_stream.read()
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if msg is None:
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break
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if msg.stream_type == 1:
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buffer += msg.data
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while b"\n" in buffer:
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line, buffer = buffer.split(b"\n", 1)
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if not line.strip():
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continue
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try:
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msg = model_validate(
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JSONRPCMessage, json.loads(line)
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)
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await read_prod.send(SessionMessage(message=msg))
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except Exception as exc:
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await read_prod.send(exc)
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except Exception:
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pass
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finally:
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await read_prod.aclose()
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async def stream_writer():
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try:
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async for msg in write_cons:
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data = (
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model_dump_json(
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msg.message, by_alias=True, exclude_none=True
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).encode("utf-8")
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+ b"\n"
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)
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await process_stream.write(data)
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except Exception:
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pass
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async with create_task_group() as tg:
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tg.start_soon(stream_reader)
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tg.start_soon(stream_writer)
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yield read_cons, write_prod
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tg.cancel_scope.cancel()
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SandboxRegistry.register_extension(UniversalMcpExtension)
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__all__ = [
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"UniversalMcpExtension",
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]
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