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✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架 (#2146)
* ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架 - 【重构】将原 services/llm 重构并迁移至全新的 services/ai 架构，提供向下兼容垫片 - 【新增】引入 Agent、Team、Workflow 三大智能体与工作流编排范式 - 【新增】引入基于 RAG 的长期向量记忆与中期槽位记忆系统 - 【新增】引入基于 Docker 的安全代码执行沙箱环境 - 【新增】支持 MCP 协议，允许动态管理和调用 MCP 服务 - 【新增】引入输入输出安全合规护栏与自愈反思机制 - 【优化】重构并优化多厂商 API 适配器 (Gemini, OpenAI, DeepSeek, GLM 等) - 【优化】优化日志脱敏与 Token 预估机制 - 【移除】移除旧版 llm default 和 llm reset-key 命令，新增 llm mcp 管理命令 * 🔧 chore(deps): 更新项目依赖与配置 - 添加 mcp、jieba 和 aiodocker 依赖到配置文件及 requirements.txt - 在 pyright 配置中设置 reportMissingImports 为 none - 调整 .gitignore 中 resources 目录的忽略规则 * ♻️ refactor(tools): 重构工具终止机制并清理知识库日志输出 - 统一使用 `context.state["__end_run__"]` 替代 `EndRunResult` 控制任务结束 - 移除文件系统和向量知识库检索工具中 `ToolResult` 的 `.with_log` 调用 - 调整指令处理器（Directive）的返回值为 `tool_res.output` - 修复部分类型检查警告并优化联合类型判断语法 * ♻️ refactor(tools): 重构工具副作用指令与控制流熔断机制 - 引入 `DirectivePayload` 及 `ToolResult` 的子类以结构化表达工具副作用 - 移除通过 `context.state` 传递魔术变量的隐式控制流设计 - 重构 `DirectiveManager` 处理器接口，直接在处理器中修改 `AgentState` 并构建 `AgentRunResult` - 在 `StandardAgentExecutor` 中统一通过 `directive_manager` 调度工具返回的副作用指令 - 补全 `MessageBuilder` 中部分核心方法的文档注释 * 🐛 fix(sandbox): 修复 Docker 沙箱容器状态检测与会话清理逻辑 -【修复】修正 `is_alive` 中直接读取私有属性的问题，改用 `show()` 返回值 -【修复】解决 `execute_code` 中缓存的执行器与当前会话不一致的问题 -【优化】在清理工作区前增加容器存活检测，避免向已死容器发送请求 -【优化】创建容器时增加运行状态校验，若已停止则自动从缓存中移除并重建 -【优化】优化容器销毁和清理逻辑，静默处理容器不存在 (404) 的异常 * 📝 docs(core): 补充核心模块初始化方法的文档注释 * 🚨 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>
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webjoin111
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parent
bdc1374848
commit
80fc5b86a7
@@ -0,0 +1,181 @@
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import asyncio
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from collections.abc import Awaitable, Callable
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import heapq
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import time
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from typing import Any
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from nonebot.utils import is_coroutine_callable
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from zhenxun.services.log import logger
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class LifespanManager:
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"""
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高精度资源生命周期调度器
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"""
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def __init__(self):
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self._resources: dict[
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Any,
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tuple[
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float,
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float,
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Callable[[Any], Awaitable[Any] | Any],
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Callable[[], Awaitable[bool] | bool] | None,
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],
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] = {}
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self._heap: list[tuple[float, Any]] = []
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self._lock = asyncio.Lock()
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self._wakeup_event = asyncio.Event()
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self._watchdog_task: asyncio.Task | None = None
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def _ensure_watchdog(self):
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"""确保后台看门狗任务正在运行"""
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if self._watchdog_task is None or self._watchdog_task.done():
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self._watchdog_task = asyncio.create_task(self._watchdog_loop())
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async def register(
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self,
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resource_id: Any,
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ttl: float,
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cleanup_callback: Callable,
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is_busy_callback: Callable[[], Awaitable[bool] | bool] | None = None,
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):
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"""
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将资源注册到生命周期管理器中。
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参数:
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resource_id: 资源的唯一标识符 (可以是字符串、数字或其他 Hashable 对象)。
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ttl: 资源的存活时间 (秒)。
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cleanup_callback: 资源过期时触发的回调函数，接收 resource_id 作为唯一参数。
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is_busy_callback: (可选) 延迟存活探针。在触发清理前调用，若返回 True 则放弃清理并自动续期。
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""" # noqa: E501
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if ttl <= 0:
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await self.unregister(resource_id)
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return
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async with self._lock:
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expire_time = time.time() + ttl
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self._resources[resource_id] = (
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expire_time,
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ttl,
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cleanup_callback,
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is_busy_callback,
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)
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heapq.heappush(self._heap, (expire_time, resource_id))
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self._wakeup_event.set()
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self._ensure_watchdog()
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async def touch(self, resource_id: Any, ttl: float):
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"""刷新资源的存活时间，为其续命"""
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if ttl <= 0:
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await self.unregister(resource_id)
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return
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async with self._lock:
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if resource_id in self._resources:
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_, original_ttl, cb, is_busy = self._resources[resource_id]
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expire_time = time.time() + ttl
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self._resources[resource_id] = (expire_time, original_ttl, cb, is_busy)
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heapq.heappush(self._heap, (expire_time, resource_id))
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self._wakeup_event.set()
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async def unregister(self, resource_id: Any):
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"""主动从管理器中注销资源 (不再触发超时回收)"""
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async with self._lock:
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self._resources.pop(resource_id, None)
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async def _watchdog_loop(self):
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"""核心看门狗循环"""
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try:
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while True:
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await self._wakeup_event.wait()
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self._wakeup_event.clear()
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while True:
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async with self._lock:
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if not self._heap:
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break
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expire_time, res_id = self._heap[0]
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if (
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res_id not in self._resources
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or self._resources[res_id][0] != expire_time
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):
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heapq.heappop(self._heap)
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continue
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now = time.time()
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sleep_time = expire_time - now
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if sleep_time > 0:
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try:
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await asyncio.wait_for(
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self._wakeup_event.wait(), timeout=sleep_time
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)
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self._wakeup_event.clear()
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continue
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except asyncio.TimeoutError:
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pass
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async with self._lock:
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if (
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res_id not in self._resources
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or self._resources[res_id][0] != expire_time
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):
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continue
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_, original_ttl, cb, is_busy = self._resources[res_id]
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is_active = False
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if is_busy is not None:
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try:
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res = is_busy()
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if isinstance(res, Awaitable):
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is_active = await res
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else:
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is_active = res
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except Exception as e:
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logger.error(
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f"执行资源存活探针失败: {e}", command="LifespanManager"
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)
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if is_active:
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logger.debug(
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f"探针检测到资源 '{res_id}' 仍在忙碌，"
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f"已自动续期 ({original_ttl}s)。",
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command="LifespanManager",
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)
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await self.touch(res_id, original_ttl)
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continue
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async with self._lock:
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if (
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res_id in self._resources
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and self._resources[res_id][0] == expire_time
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):
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self._resources.pop(res_id)
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heapq.heappop(self._heap)
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else:
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continue
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logger.info(
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f"♻️ 资源 '{res_id}' 闲置超时，触发自动回收。",
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command="LifespanManager",
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)
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try:
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if is_coroutine_callable(cb):
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await cb(res_id)
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else:
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cb(res_id)
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except Exception as e:
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logger.error(
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f"回收资源 '{res_id}' 时发生业务异常: {e}",
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command="LifespanManager",
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)
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except asyncio.CancelledError:
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pass
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async def stop(self):
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"""停止生命周期管理器"""
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if self._watchdog_task:
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self._watchdog_task.cancel()
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