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♻️ refactor(core): 重构 AI 能力与定时任务调度系统 (#2148)
* ♻️ 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>
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parent
0b32d69c9c
commit
922d092650
@@ -1,27 +1,42 @@
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import asyncio
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from collections.abc import Callable, Mapping, Sequence
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import contextlib
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from pathlib import Path
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from typing import Any
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from typing_extensions import Self
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from pydantic import BaseModel
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from zhenxun.services.ai.capabilities import AbstractCapability, DynamicCapability
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from zhenxun.services.ai.capabilities import (
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AbstractCapability,
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CapabilitySource,
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DynamicCapability,
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)
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from zhenxun.services.ai.core.exceptions import ConcurrencyInterruptException
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from zhenxun.services.ai.core.messages import PromptInput
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from zhenxun.services.ai.core.models import CancellationToken
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from zhenxun.services.ai.core.stream_events import EventBus
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from zhenxun.services.ai.flow.agent.agent import CapabilitySource, ToolSource
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from zhenxun.services.ai.flow.agent.agent import ToolSource
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from zhenxun.services.ai.flow.agent.models import Persona
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from zhenxun.services.ai.flow.base import BaseRunnable
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from zhenxun.services.ai.flow.team.models import TeamRuntimeConfig, Transition
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from zhenxun.services.ai.flow.team.router import BaseRouter
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from zhenxun.services.ai.flow.team.strategy import (
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from zhenxun.services.ai.flow.base import BaseRunnable, ConcurrencyPolicy
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from zhenxun.services.ai.flow.concurrency import apply_concurrency_policy
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from zhenxun.services.ai.run import (
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AgentRunResult,
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AgentTask,
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RunContext,
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StreamedRunResult,
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)
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from zhenxun.services.ai.run.models import AgentRunError
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from zhenxun.services.ai.tools.providers.skills.capabilities import SkillCapability
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from zhenxun.services.ai.tools.providers.skills.models import Skill, SkillSource
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from zhenxun.services.ai.utils import ContextUtils
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from zhenxun.utils.utils import infer_plugin_namespace
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from .models import TeamRuntimeConfig, Transition
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from .router import BaseRouter
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from .strategy import (
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BaseTeamStrategy,
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)
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from zhenxun.services.ai.run import AgentRunResult, AgentTask, RunContext
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from zhenxun.services.ai.tools.providers.skills.models import Skill, SkillSource
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from zhenxun.utils.utils import infer_plugin_namespace
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class Team(BaseRunnable[AgentRunResult[Any]]):
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@@ -115,6 +130,7 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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leader_model: str | None = None,
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leader_tools: list[ToolSource] | None = None,
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custom_prompt: str | None = None,
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max_handoffs: int = 3,
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) -> Self:
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"""
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应用路由策略，基于挂载的 Router 进行最合适的专家动态分发。
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@@ -128,8 +144,9 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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leader_model: 路由节点 (Leader) 使用的大模型名称，若为空则默认继承全局。
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leader_tools: 挂载给路由节点 (Leader) 的专属工具列表。
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custom_prompt: 自定义系统提示词，用于覆盖默认的路由系统提示词。
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max_handoffs: 同一会话中允许连续移交的最大次数，防止无限踢皮球。
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"""
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from zhenxun.services.ai.flow.team.strategy import RouteStrategy
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from .strategy import RouteStrategy
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self.strategy = RouteStrategy(
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state_flow=state_flow,
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@@ -138,6 +155,7 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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leader_model=leader_model,
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leader_tools=leader_tools,
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custom_prompt=custom_prompt,
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max_handoffs=max_handoffs,
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)
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self.selector_func = selector_func
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return self
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@@ -147,6 +165,7 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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leader_model: str | None = None,
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leader_tools: list[ToolSource] | None = None,
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custom_prompt: str | None = None,
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max_delegations: int = 3,
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) -> Self:
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"""
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应用协作策略，Leader 自主规划并主动将子任务委派给 Sub-Agents，最后汇总结果。
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@@ -158,13 +177,15 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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leader_model: 协调节点 (Leader) 使用的大模型名称，若为空则默认继承全局。
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leader_tools: 挂载给协调节点 (Leader) 的专属附加工具列表。
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custom_prompt: 自定义系统提示词，用于覆盖默认的协调系统提示词。
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max_delegations: 允许向同一个专员连续委派失败的最大重试次数。
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"""
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from zhenxun.services.ai.flow.team.strategy import CoordinateStrategy
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from .strategy import CoordinateStrategy
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self.strategy = CoordinateStrategy(
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leader_model=leader_model,
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leader_tools=leader_tools,
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custom_prompt=custom_prompt,
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max_delegations=max_delegations,
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)
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return self
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@@ -184,7 +205,7 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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leader_tools: 挂载给总结节点 (Leader) 的专属附加工具列表。
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custom_prompt: 自定义系统提示词，用于覆盖默认的广播总结系统提示词。
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"""
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from zhenxun.services.ai.flow.team.strategy import BroadcastStrategy
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from .strategy import BroadcastStrategy
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self.strategy = BroadcastStrategy(
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leader_model=leader_model,
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@@ -198,8 +219,7 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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leader_model: str | None = None,
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leader_tools: list[ToolSource] | None = None,
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max_iterations: int = 15,
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blackboard_schema: type[BaseModel] | None = None,
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initial_blackboard_state: BaseModel | None = None,
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blackboard: type[BaseModel] | BaseModel | None = None,
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custom_prompt: str | None = None,
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) -> Self:
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"""
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@@ -213,18 +233,16 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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leader_model: 规划节点 (Leader) 使用的大模型名称，若为空则默认继承全局。
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leader_tools: 挂载给规划节点 (Leader) 的专属附加工具列表。
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max_iterations: 引擎驱动的状态机最大迭代/循环次数，防止死循环。
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blackboard_schema: 团队共享黑板的数据结构类型 (Pydantic Model 类)。
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initial_blackboard_state: 共享黑板的初始数据状态实例。
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blackboard: (可选) 团队共享黑板。可传入 Schema 类型类，或直接传入带有初始数据的 Schema 实例对象。
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custom_prompt: 自定义系统提示词，用于覆盖默认的规划系统提示词。
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"""
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from zhenxun.services.ai.flow.team.strategy import TaskStrategy
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""" # noqa: E501
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from .strategy import TaskStrategy
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self.strategy = TaskStrategy(
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leader_model=leader_model,
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leader_tools=leader_tools,
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max_iterations=max_iterations,
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blackboard_schema=blackboard_schema,
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initial_blackboard_state=initial_blackboard_state,
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blackboard=blackboard,
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custom_prompt=custom_prompt,
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)
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return self
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@@ -260,10 +278,6 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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"""
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self._ensure_strategy()
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if skills:
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from zhenxun.services.ai.tools.providers.skills.capabilities import (
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SkillCapability,
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)
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capabilities = list(capabilities) if capabilities else []
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capabilities.append(
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SkillCapability(skills=skills, namespace=self.namespace)
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@@ -273,8 +287,6 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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prompt=prompt, context=context, capabilities=capabilities, **kwargs
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)
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import contextlib
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@contextlib.asynccontextmanager
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async def run_stream(
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self,
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@@ -297,10 +309,6 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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context.capabilities.extend(self.capabilities)
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if skills:
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from zhenxun.services.ai.tools.providers.skills.capabilities import (
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SkillCapability,
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)
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capabilities = list(capabilities) if capabilities else []
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capabilities.append(
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SkillCapability(skills=skills, namespace=self.namespace)
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@@ -313,8 +321,7 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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elif callable(cap):
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context.capabilities.append(DynamicCapability(cap))
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from zhenxun.services.ai.flow.team.runner import TeamRunner
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from zhenxun.services.ai.run import StreamedRunResult
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from .runner import TeamRunner
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event_bus = EventBus()
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context.run.event_bus = event_bus
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@@ -323,8 +330,6 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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policy = getattr(self.runtime_config, "concurrency_policy", None)
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if policy is None:
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from zhenxun.services.ai.flow.base import ConcurrencyPolicy
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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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@@ -333,8 +338,6 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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intervention_policy = getattr(self.runtime_config, "intervention_policy", None)
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from zhenxun.services.ai.utils import ContextUtils
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lock_id = ContextUtils.extract_concurrency_lock_id(
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context,
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getattr(self.runtime_config, "concurrency_scope", None),
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@@ -342,8 +345,6 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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)
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async def _execution_task():
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from zhenxun.services.ai.flow.concurrency import apply_concurrency_policy
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cancel_token = context.run.cancellation_token or CancellationToken()
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context.run.cancellation_token = cancel_token
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@@ -359,8 +360,6 @@ class Team(BaseRunnable[AgentRunResult[Any]]):
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async for event in runner.run_stream(prompt, context, **kwargs):
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await event_bus.emit(event)
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except BaseException as e:
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from zhenxun.services.ai.run.models import AgentRunError
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if isinstance(e, asyncio.CancelledError):
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e = ConcurrencyInterruptException("团队执行已被新请求打断并接管")
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await event_bus.emit(AgentRunError(error=e))
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