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♻️ refactor(core): 重构 AI 编排框架与记忆及 RAG 子系统 (#2149)
* ♻️ refactor(core): 重构 AI 编排框架与记忆及 RAG 子系统 - 【重构】重构 `BaseRunnable` 并引入统一的 `RunIntent` 意图载体，规范 Agent、Team 和 Workflow 的执行流 - 【解耦】将中期记忆槽和长期向量记忆从 `MemoryConfig` 中解耦，转为独立的能力组件与工具箱进行管理 - 【记忆】移除 `MemoryReader` 和 `MemoryWriter`，统一封装为 `SessionMemoryContext` 会话记忆门面 - 【RAG】重构检索器与存储后端接口，统一采用 `QueryRequest` 进行多维度联合检索，并引入 `InMemoryScorer` 提升打分性能 - 【事件】优化 `EventBus` 异步事件分发机制，引入队列机制确保事件按序处理，避免并发竞态问题 - 【依赖注入】移除 `memory` 注入项，优化 `DependencyInjector` 的签名解析缓存以提升性能 * 🚨 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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parent
922d092650
commit
52f7dbdedf
@@ -0,0 +1,17 @@
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from .base import BaseRunnable
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from .models import (
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BaseRuntimeConfig,
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ConcurrencyPolicy,
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ConcurrencyScope,
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InterventionPolicy,
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)
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from .runner import FlowRunner
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__all__ = [
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"BaseRunnable",
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"BaseRuntimeConfig",
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"ConcurrencyPolicy",
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"ConcurrencyScope",
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"FlowRunner",
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"InterventionPolicy",
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]
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@@ -0,0 +1,214 @@
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from __future__ import annotations
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from abc import ABC, abstractmethod
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import asyncio
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from collections.abc import AsyncIterator
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import contextlib
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from typing import TYPE_CHECKING, Any, Generic, TypeVar, cast
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from nonebot.params import Depends
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from zhenxun.services.ai.core.exceptions import (
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ConcurrencyInterruptException,
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ControlFlowExit,
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)
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from zhenxun.services.ai.core.models import CancellationToken
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from zhenxun.services.ai.core.stream_events import AgentStreamEvent, EventBus
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from zhenxun.services.ai.run.context import RunContext
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from zhenxun.services.ai.run.models import AgentRunError, RunIntent, StreamedRunResult
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from zhenxun.services.ai.run.subscribers import DefaultUISubscriber, TelemetrySubscriber
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from zhenxun.services.ai.run.ui import UIController
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from zhenxun.services.ai.utils import ContextUtils
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from zhenxun.services.ai.utils.logger import log_flow as logger
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if TYPE_CHECKING:
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from zhenxun.services.ai.flow.agent.models import Persona
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from zhenxun.services.ai.core.messages import PromptInput
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from .models import (
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BaseRuntimeConfig,
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ConcurrencyPolicy,
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)
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T_RunResult = TypeVar("T_RunResult")
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class BaseRunnable(ABC, Generic[T_RunResult]):
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"""
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所有可执行 AI 编排实体的统一基类
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统一了 Agent, Team, Workflow 的核心契约，支持物理上的任意嵌套。
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"""
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name: str
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"""可执行实体的名称标识"""
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description: str
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"""可执行实体的详细描述。用于外部路由(Router)或上层智能体(DelegateTool)决定是否调用它"""
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persona: "Persona | None" = None
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"""(可选) 实体的角色设定 (Persona)。包含 role 和 goal，
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在多智能体路由移交时优先级最高"""
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runtime_config: BaseRuntimeConfig
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"""运行时配置，如是否无状态、UI输出模式等"""
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@property
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def profile_summary(self) -> str:
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"""获取该实体的标准化简要画像/描述，供上层路由和规划决策使用"""
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if self.persona:
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return f"角色：{self.persona.role}，目标：{self.persona.goal}"
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return self.description or "处理节点"
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def bind(self, **kwargs: Any) -> Any:
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"""DI 注入语法糖：返回 Depends，自动绑定当前上下文"""
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from .runner import FlowRunner
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async def _dependency() -> FlowRunner[Any]:
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return FlowRunner[Any](self, **kwargs)
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return Depends(_dependency)
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async def reply(
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self,
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prompt: PromptInput | None = None,
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reply_to: bool = False,
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*,
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context: RunContext | None = None,
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**kwargs: Any,
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) -> T_RunResult:
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"""交互执行语法糖，自动渲染流式进度并最终将结果回复给终端用户"""
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from .runner import FlowRunner
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runner = FlowRunner(self, context=context, **kwargs)
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return cast(T_RunResult, await runner.reply(prompt=prompt, reply_to=reply_to))
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async def run(
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self,
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prompt: PromptInput | None = None,
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*,
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context: RunContext | None = None,
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**kwargs: Any,
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) -> T_RunResult:
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"""阻塞式核心运行入口，安全捕获内部抛出的静默退出信号"""
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try:
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async with self.run_stream(
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prompt=prompt, context=context, **kwargs
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) as stream_result:
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return cast(T_RunResult, await stream_result.get_run_result())
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except ControlFlowExit as e:
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logger.info(f"[{self.name}] 触发底层控制流，已安全退出: {e}")
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await UIController.handle_control_flow_exit_display(e, context)
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raise asyncio.CancelledError()
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@contextlib.asynccontextmanager
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async def run_stream(
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self,
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prompt: PromptInput | None = None,
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*,
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context: RunContext | None = None,
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deps: Any = None,
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event_bus: EventBus | None = None,
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**kwargs: Any,
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) -> AsyncIterator[StreamedRunResult[Any]]:
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"""统一的流式运行入口，负责生命周期调度、并发锁管理和事件总线挂载。"""
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from .concurrency import apply_concurrency_policy
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intent = RunIntent.from_input(prompt)
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bus = event_bus or EventBus()
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if context is None:
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safe_context = RunContext(session_id=kwargs.get("session_id"))
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if deps is not None:
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safe_context.deps = deps
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else:
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safe_context = context
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if deps is not None and safe_context.deps is None:
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safe_context.deps = deps
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is_root = not safe_context.state.get("__is_root_run_executed__", False)
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if is_root:
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safe_context.state["__is_root_run_executed__"] = True
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TelemetrySubscriber().attach(bus)
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if (
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safe_context.get_bot()
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and safe_context.get_event()
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and safe_context.run.delegate_depth == 0
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):
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config_obj = getattr(self, "config", self.runtime_config)
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verbose_ui = getattr(config_obj, "verbose_ui", False)
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DefaultUISubscriber(safe_context, verbose=verbose_ui).attach(bus)
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policy = getattr(self.runtime_config, "concurrency_policy", None)
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if policy is None:
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policy = (
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ConcurrencyPolicy.ALLOW
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if getattr(self.runtime_config, "stateless", True)
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else ConcurrencyPolicy.QUEUE
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)
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intervention_policy = getattr(self.runtime_config, "intervention_policy", None)
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lock_id = ContextUtils.extract_concurrency_lock_id(
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safe_context,
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getattr(self.runtime_config, "concurrency_scope", None),
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safe_context.session_id or "default_session",
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)
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async def _execution_task():
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cancel_token = safe_context.run.cancellation_token or CancellationToken()
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safe_context.run.cancellation_token = cancel_token
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try:
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async with apply_concurrency_policy(
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session_id=safe_context.session_id or "default_session",
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lock_id=lock_id,
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policy=policy,
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cancel_token=cancel_token,
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intervention_policy=intervention_policy,
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intent=intent,
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):
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async for event in self._execute_stream(
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intent=intent,
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context=safe_context,
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cancel_token=cancel_token,
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event_bus=bus,
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**kwargs,
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):
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await bus.emit(event)
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except ControlFlowExit as e:
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await bus.emit(AgentRunError(error=e))
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except asyncio.CancelledError:
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logger.debug(f"[{self.name}] 执行被并发策略中断取消。")
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await bus.emit(
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AgentRunError(
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error=ConcurrencyInterruptException("任务已被新请求打断并接管")
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)
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)
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except Exception as e:
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await bus.emit(AgentRunError(error=e))
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finally:
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await bus.end()
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task = asyncio.create_task(_execution_task())
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result_obj = StreamedRunResult[Any](bus)
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try:
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yield result_obj
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finally:
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if not task.done():
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task.cancel()
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@abstractmethod
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async def _execute_stream(
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self,
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intent: RunIntent,
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context: RunContext,
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cancel_token: CancellationToken,
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event_bus: EventBus,
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**kwargs: Any,
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) -> AsyncIterator[AgentStreamEvent]:
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"""核心执行流（由子类实现），通过 yield 返回执行事件。"""
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if False:
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yield cast(Any, None)
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@@ -0,0 +1,114 @@
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import asyncio
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from contextlib import asynccontextmanager
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from zhenxun.services.ai.core.exceptions import (
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ConcurrencyRejectException,
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InterventionHandledException,
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)
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from zhenxun.services.ai.core.models import CancellationToken
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from zhenxun.services.ai.run.models import RunIntent
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from zhenxun.services.ai.run.session import LockContext, session_manager
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from zhenxun.services.ai.utils.logger import log_flow as logger
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from .models import ConcurrencyPolicy, InterventionPolicy
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@asynccontextmanager
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async def apply_concurrency_policy(
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session_id: str,
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lock_id: str,
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policy: ConcurrencyPolicy,
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cancel_token: CancellationToken,
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intervention_policy: InterventionPolicy | None = None,
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intent: RunIntent | None = None,
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):
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"""
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异步上下文管理器：对大模型执行流应用特定的并发及消息干预调度策略。
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负责请求互斥锁竞争、任务中断/拒绝处理，以及运行时用户实时指令的插队控制。
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参数：
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session_id: 当前会话的唯一标识，用于在会话管理器中隔离上下文。
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lock_id: 当前锁域标识，决定了哪些 Agent 或任务使用同一套互斥锁竞争机制。
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policy: 当发生并发锁占用时执行的策略（允许、拒绝、中断、排队）。
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cancel_token: 运行时用于监听取消请求的取消令牌实例。
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intervention_policy: 用户消息干预策略（转向、追加）。
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message: 并发竞争发生时新入站的用户请求消息或 AgentTask 载荷。
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返回：
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AsyncGenerator: 返回异步生成器，供 async with 消费，包裹大模型的整个执行环节。
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"""
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current_task = asyncio.current_task()
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task_tuple = (cancel_token, current_task)
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if session_id not in session_manager.live_tasks:
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session_manager.live_tasks[session_id] = []
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session_manager.live_tasks[session_id].append(task_tuple)
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try:
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exec_lock = session_manager.get_exec_lock(lock_id)
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lock_ctx = session_manager.lock_contexts.setdefault(lock_id, LockContext())
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if exec_lock.locked():
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if intervention_policy in (
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InterventionPolicy.STEER,
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InterventionPolicy.FOLLOW_UP,
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):
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session = await session_manager.get_or_create(session_id)
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if intervention_policy == InterventionPolicy.STEER:
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session.steer_queue.enqueue(intent.text if intent else "")
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raise InterventionHandledException(
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"Steer successful",
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display_content="💬 已将您的补充信息传递给正在思考的 AI...",
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)
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elif intervention_policy == InterventionPolicy.FOLLOW_UP:
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session.follow_up_queue.enqueue(intent.text if intent else "")
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raise InterventionHandledException(
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"Follow-up successful",
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display_content="📝 已记录，AI 处理完当前任务后即刻执行...",
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)
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if policy == ConcurrencyPolicy.ALLOW:
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yield
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return
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if policy == ConcurrencyPolicy.REJECT:
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if exec_lock.locked():
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raise ConcurrencyRejectException(
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f"并发域 {lock_id} 正忙，新请求被拒绝。"
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)
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elif policy == ConcurrencyPolicy.INTERRUPT:
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if exec_lock.locked():
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if lock_ctx.cancel_token:
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lock_ctx.cancel_token.cancel()
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if lock_ctx.active_task and not lock_ctx.active_task.done():
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lock_ctx.active_task.cancel()
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elif policy == ConcurrencyPolicy.QUEUE:
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if exec_lock.locked():
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logger.info(
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f"⏳ [并发控制] 锁域 {lock_id} 被占用，"
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"新请求已进入后台等待队列 (QUEUE)..."
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)
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async with exec_lock:
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session = await session_manager.get_or_create(session_id)
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session.active_task = asyncio.current_task()
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session.cancel_token = cancel_token
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lock_ctx.active_task = asyncio.current_task()
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lock_ctx.cancel_token = cancel_token
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try:
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yield
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finally:
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session.active_task = None
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session.cancel_token = None
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lock_ctx.active_task = None
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lock_ctx.cancel_token = None
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finally:
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if session_id in session_manager.live_tasks:
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if task_tuple in session_manager.live_tasks[session_id]:
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session_manager.live_tasks[session_id].remove(task_tuple)
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if not session_manager.live_tasks[session_id]:
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del session_manager.live_tasks[session_id]
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@@ -0,0 +1,53 @@
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from enum import Enum
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from pydantic import BaseModel, Field
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class ConcurrencyPolicy(str, Enum):
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"""并发执行策略枚举"""
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ALLOW = "allow"
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"""允许并发：不做任何限制（适用于无状态或独立任务）"""
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REJECT = "reject"
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"""拒绝新请求：当前有任务在执行时，直接丢弃新任务并提醒"""
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QUEUE = "queue"
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"""排队等待：当前有任务在执行时，新任务排队等待（先进先出）"""
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INTERRUPT = "interrupt"
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"""中断旧任务：新任务到达时，立即强制取消并覆盖正在执行的旧任务"""
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class ConcurrencyScope(str, Enum):
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"""并发作用域枚举（决定锁的粒度，解耦于会话隔离）"""
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GLOBAL = "global"
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"""全局互斥：整个系统同一时间只能执行一个该任务"""
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GROUP = "group"
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"""群组互斥：同一群组内串行排队（私聊退化为用户级），防止抢话刷屏"""
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USER = "user"
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"""用户互斥：同一用户发起的任务串行排队（允许同群不同人并行）"""
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SESSION = "session"
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"""会话互斥：跟随记忆 SessionID 进行物理锁隔离"""
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class InterventionPolicy(str, Enum):
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"""运行时消息干预策略枚举"""
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IGNORE = "ignore"
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"""忽略干预：丢弃在任务执行期间收到的额外消息（默认）"""
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STEER = "steer"
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"""动态转向：将额外消息立即注入到下一轮大模型推理历史中，影响其思考方向"""
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FOLLOW_UP = "follow_up"
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"""追加执行：将额外消息放入队列，在当前大模型意图（所有工具等）执行完毕后追加推理"""
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class BaseRuntimeConfig(BaseModel):
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"""所有可执行实体（Agent/Team/Workflow）的通用基础运行时配置"""
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stateless: bool = Field(default=True)
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"""是否使用临时会话，不持久化历史记录"""
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concurrency_policy: ConcurrencyPolicy | None = Field(default=None)
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"""并发执行策略。如果未显式指定，无状态(stateless=True)默认为ALLOW，有状态(stateless=False)默认为QUEUE。"""
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concurrency_scope: ConcurrencyScope | None = Field(default=None)
|
||||
"""并发作用域，决定锁的粒度。如果未显式指定，默认为 GROUP 级排队。"""
|
||||
intervention_policy: InterventionPolicy | None = Field(default=None)
|
||||
"""运行时干预策略，决定在大模型执行期间接收到新消息时该如何处理数据流合并。"""
|
||||
@@ -0,0 +1,154 @@
|
||||
import asyncio
|
||||
from typing import Any, Generic, cast
|
||||
from typing_extensions import TypeVar
|
||||
import uuid
|
||||
|
||||
from nonebot.adapters import Bot, Event
|
||||
from nonebot_plugin_alconna.uniseg import UniMessage
|
||||
|
||||
from zhenxun.services.ai.core.exceptions import (
|
||||
ConcurrencyInterruptException,
|
||||
ConcurrencyRejectException,
|
||||
ControlFlowExit,
|
||||
InterventionHandledException,
|
||||
)
|
||||
from zhenxun.services.ai.core.messages import PromptInput, UsageInfo
|
||||
from zhenxun.services.ai.flow.core.base import BaseRunnable
|
||||
from zhenxun.services.ai.run import AgentRunResult, RunContext
|
||||
from zhenxun.services.ai.run.models import AgentRunEnd, AgentRunError, AgentTask
|
||||
from zhenxun.services.ai.run.ui import UIController
|
||||
from zhenxun.services.ai.utils.logger import log_flow as logger
|
||||
from zhenxun.utils.message import MessageUtils
|
||||
from zhenxun.utils.platform import PlatformUtils
|
||||
|
||||
T_Deps = TypeVar("T_Deps", default=Any)
|
||||
T_Out = TypeVar("T_Out", default=str)
|
||||
|
||||
|
||||
class FlowRunner(Generic[T_Out]):
|
||||
"""
|
||||
执行流交互运行器。
|
||||
负责将大模型的纯净数据流包装为平台交互动作（发消息、UI渲染）。
|
||||
自带 ContextVars 隐式上下文提取魔法。
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
runnable: BaseRunnable,
|
||||
context: RunContext | None = None,
|
||||
**kwargs: Any,
|
||||
):
|
||||
self.runnable = runnable
|
||||
self.context = context or RunContext(**kwargs)
|
||||
|
||||
is_stateless = (
|
||||
getattr(self.runnable.runtime_config, "stateless", True)
|
||||
if hasattr(self.runnable, "runtime_config")
|
||||
else True
|
||||
)
|
||||
if is_stateless and self.context.session_id:
|
||||
if not self.context.session_id.startswith("stateless_"):
|
||||
self.context.session_id = (
|
||||
f"stateless_{self.context.session_id}_{uuid.uuid4().hex[:8]}"
|
||||
)
|
||||
if self.context.session:
|
||||
self.context.session.session_id = self.context.session_id
|
||||
|
||||
@property
|
||||
def _bot(self) -> Bot | None:
|
||||
return self.context.get_bot()
|
||||
|
||||
@property
|
||||
def _event(self) -> Event | None:
|
||||
return self.context.get_event()
|
||||
|
||||
async def reply(
|
||||
self,
|
||||
prompt: PromptInput | AgentTask | None = None,
|
||||
reply_to: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> AgentRunResult[T_Out]:
|
||||
"""交互式执行：将 Agent 运行过程中的工具调用状态和最终结果自动发送给用户。"""
|
||||
final_result = None
|
||||
|
||||
profile = kwargs.pop("profile", None)
|
||||
|
||||
try:
|
||||
async with self.runnable.run_stream(
|
||||
prompt=prompt,
|
||||
context=self.context,
|
||||
profile=profile,
|
||||
**kwargs,
|
||||
) as stream_result:
|
||||
async for stream_event in stream_result.stream_events():
|
||||
if isinstance(stream_event, AgentRunEnd):
|
||||
final_result = stream_event.result
|
||||
|
||||
elif isinstance(stream_event, AgentRunError):
|
||||
raise stream_event.error
|
||||
|
||||
except ControlFlowExit as e:
|
||||
if isinstance(e, InterventionHandledException):
|
||||
logger.info(f"✨ {self.runnable.name} 触发运行时干预: {e.message}")
|
||||
if e.display_content and self._bot and self._event:
|
||||
await MessageUtils.build_message(str(e.display_content)).send(
|
||||
reply_to=reply_to
|
||||
)
|
||||
return cast(
|
||||
AgentRunResult[T_Out], AgentRunResult(output="", usage=UsageInfo())
|
||||
)
|
||||
|
||||
if isinstance(e, ConcurrencyRejectException):
|
||||
logger.warning(
|
||||
f"⏳ {self.runnable.name} 触发并发拒绝 (REJECT): {e.message}"
|
||||
)
|
||||
return cast(
|
||||
AgentRunResult[T_Out], AgentRunResult(output="", usage=UsageInfo())
|
||||
)
|
||||
|
||||
if isinstance(e, ConcurrencyInterruptException):
|
||||
logger.warning(
|
||||
f"🛑 {self.runnable.name} 触发并发中断 (INTERRUPT): {e.message}"
|
||||
)
|
||||
return cast(
|
||||
AgentRunResult[T_Out], AgentRunResult(output="", usage=UsageInfo())
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
f"{self.runnable.name} 控制流正常中断: {type(e).__name__} - {e}"
|
||||
)
|
||||
await UIController.handle_control_flow_exit_display(
|
||||
e, self.context, reply_to
|
||||
)
|
||||
|
||||
raise asyncio.CancelledError()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.runnable.name} 运行失败: {e}", e=e)
|
||||
if self._bot and self._event:
|
||||
await MessageUtils.build_message(f"❌ 运行发生错误: {e}").send()
|
||||
raise e
|
||||
|
||||
if final_result and final_result.output and self._bot:
|
||||
msg_to_send = (
|
||||
final_result.output
|
||||
if isinstance(final_result.output, UniMessage)
|
||||
else MessageUtils.build_message(str(final_result.output))
|
||||
)
|
||||
if self._event:
|
||||
await msg_to_send.send(self._event, bot=self._bot, reply_to=reply_to)
|
||||
else:
|
||||
target = PlatformUtils.get_target(
|
||||
user_id=self.context.get_user_id(),
|
||||
group_id=self.context.get_group_id(),
|
||||
)
|
||||
if target:
|
||||
await msg_to_send.send(target=target, bot=self._bot)
|
||||
|
||||
if isinstance(final_result.output, UniMessage):
|
||||
final_result.output = final_result.output.extract_plain_text()
|
||||
|
||||
if final_result is None:
|
||||
raise RuntimeError("智能体运行流异常结束：未返回最终结果。")
|
||||
|
||||
return cast(AgentRunResult[T_Out], final_result)
|
||||
Reference in New Issue
Block a user