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* ♻️ 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>
161 lines
6.2 KiB
Python
161 lines
6.2 KiB
Python
from __future__ import annotations
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import json
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from typing import Any
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from pydantic import BaseModel, Field
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from zhenxun.services.ai.core.exceptions import AbortException, ControlFlowExit
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from zhenxun.services.ai.core.stream_events import ToolStreamChunkEvent
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from zhenxun.services.ai.flow.core.base import BaseRunnable
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from zhenxun.services.ai.run import RunContext
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from zhenxun.services.ai.tools.core.tool import BaseTool
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from zhenxun.services.ai.tools.models import ToolResult
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from zhenxun.services.ai.utils.logger import log_tool as logger
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from zhenxun.utils.pydantic_compat import model_dump
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STRUCTURED_INPUT_PREAMBLE = (
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"\n\n### 🛠️ [嵌套调用前置语境]\n"
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"你现在正在作为一个『工具/子节点』被外部主智能体调用。\n"
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"以下是外部系统传递给你的结构化输入数据:\n"
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"```json\n{payload}\n```\n"
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"请严格将上述内容视为你的核心数据和约束条件,专注于解决该子任务,并直接返回结果,不要说多余的废话。\n"
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)
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class DelegateArgs(BaseModel):
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task: str = Field(
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..., description="指派给该实体的具体任务描述、指令或需要回答的问题"
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)
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class DelegateTool(BaseTool):
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"""将可运行实体 (Agent/Team/Workflow) 包装为子例程委派工具。"""
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def __init__(
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self,
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runnable: BaseRunnable[Any],
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name: str | None = None,
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description: str | None = None,
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max_delegations: int = 3,
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):
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"""初始化委派工具。
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参数:
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runnable: 被包装的可运行实体。
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name: 自定义工具名称。
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description: 工具描述信息。
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max_delegations: 允许向同一个实体连续委派且未获成功的最大次数限制。
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"""
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resolved_name = name or getattr(runnable, "name", "SubRunnable")
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resolved_desc = description or getattr(
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runnable,
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"profile_summary",
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getattr(runnable, "description", f"将子任务委派给 {resolved_name} 执行"),
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)
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final_name = (
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f"delegate_to_{resolved_name}"
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if not resolved_name.startswith("delegate_")
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else resolved_name
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)
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super().__init__(name=final_name, description=resolved_desc)
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self.runnable = runnable
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self.args_schema = DelegateArgs
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self.max_delegations = max_delegations
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async def execute(
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self, context: RunContext | None = None, **kwargs: Any
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) -> ToolResult:
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task = kwargs.get("task", "")
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context = context or RunContext()
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counts = context.session.shared_state.setdefault("__delegate_counts__", {})
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counts[self.name] = counts.get(self.name, 0) + 1
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if counts[self.name] > self.max_delegations:
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return ToolResult(
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output=(
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f"❌ 系统拦截:委派重试次数已达上限。\n"
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f"你已经连续 {counts[self.name]} 次将子任务委派给下级实体 "
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f"{self.name} 且未获最终成功"
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f"(超出最大允许次数 {self.max_delegations})。\n"
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"请立即停止委派,"
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"改变你的思考方向或直接向用户汇报失败结论!"
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)
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).as_error()
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depth = context.run.delegate_depth
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if depth >= 3:
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logger.warning(
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f"⚠️ [DelegateTool] 委派深度超限 ({depth}),强制阻断: {self.name}"
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)
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raise AbortException(
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reason="嵌套层级过深,系统已强制拒绝执行委派",
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display=f"⚠️ {self.name} 嵌套层级过深",
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)
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logger.debug(
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f"🔄 [DelegateTool] 正在委派下级实体 {self.name} (Task: {task[:30]}...)"
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)
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sub_context = context.clone_for_member(self.name)
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sub_context.run.delegate_depth = depth + 1
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payload_str = json.dumps(kwargs, ensure_ascii=False, indent=2)
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preamble = STRUCTURED_INPUT_PREAMBLE.format(payload=payload_str)
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sub_context.run.add_system_prompt(preamble)
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try:
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event_bus = context.run.event_bus
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async with self.runnable.run_stream(
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prompt=task,
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context=sub_context,
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) as stream_result:
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response = await stream_result.forward_to(event_bus, self.name)
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if response is None:
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raise RuntimeError(f"Sub-agent {self.name} did not return a response.")
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if isinstance(response.output, BaseModel):
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final_output = model_dump(response.output)
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else:
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final_output = response.output
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if response.handoff:
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final_output = (
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f"⚠️ 子任务未完成。下级实体主动发起了工作流移交 (Handoff)。\n"
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f"移交目标: {response.handoff.target}\n"
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f"移交原因: {response.handoff.reason}\n"
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f"附带数据: {response.handoff.context_data}"
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)
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is_fatal = isinstance(final_output, str) and (
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"DepthLimitExceeded" in final_output or "嵌套层级过深" in final_output
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)
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if is_fatal:
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raise AbortException(
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reason="下级实体遇到深度限制异常",
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display=f"⚠️ 实体 {self.name} 委派失败",
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)
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usage = getattr(response, "usage", None)
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if context:
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await context.run.emit(
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ToolStreamChunkEvent(
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tool_name=self.name, content=f"🧠 实体 {self.name} 执行完毕"
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)
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)
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return ToolResult(
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output=final_output,
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usage=usage,
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)
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except ControlFlowExit as e:
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raise e
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except Exception as e:
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logger.error(f"委派实体 {self.name} 执行失败: {e}", e=e)
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raise AbortException(
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reason=f"Delegate Execution Error: {e}",
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display=f"❌ 实体 {self.name} 执行异常",
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)
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