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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
@@ -10,19 +10,26 @@ from zhenxun.services.ai.core.stream_events import ToolStreamChunkEvent
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from zhenxun.services.ai.core.templates import PromptTemplate
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from zhenxun.services.ai.flow.agent.agent import Agent, ToolSource
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from zhenxun.services.ai.flow.agent.models import AgentConfig
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from zhenxun.services.ai.flow.team.models import (
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from zhenxun.services.ai.run import AgentTask, RunContext
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from zhenxun.services.ai.run.blackboard import BlackboardManager
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from zhenxun.services.ai.tools.bridges.delegate import DelegateTool
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from zhenxun.services.ai.tools.providers.builtin.blackboard import BlackboardToolkit
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from zhenxun.services.ai.utils.logger import log_team as logger
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from .models import (
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CallAction,
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ConcurrentCallAction,
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FinishAction,
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TaskBoardState,
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TaskNodeStatus,
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TeamAction,
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Transition,
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)
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from zhenxun.services.ai.flow.team.router import BaseRouter
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from zhenxun.services.ai.run import AgentTask, RunContext
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from zhenxun.services.ai.tools.bridges.delegate import DelegateTool
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from zhenxun.services.log import logger
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from .router import BaseRouter
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from .task_tools import TaskPlanningToolkit
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if TYPE_CHECKING:
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from zhenxun.services.ai.flow.team.team import Team
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from .team import Team
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class BaseTeamStrategy(ABC):
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@@ -106,6 +113,7 @@ class RouteStrategy(BaseTeamStrategy):
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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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):
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"""
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路由策略初始化，基于挂载的 Router 进行最合适的专家分发。
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@@ -117,16 +125,16 @@ class RouteStrategy(BaseTeamStrategy):
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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。
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"""
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super().__init__(custom_prompt=custom_prompt)
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self.selector_func = selector_func
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self.router = router
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self.leader_model = leader_model
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self.leader_tools = leader_tools or []
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self.max_handoffs = max_handoffs
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if isinstance(state_flow, dict):
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from zhenxun.services.ai.flow.team.models import Transition
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normalized_flow = {}
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for k, targets in state_flow.items():
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normalized_targets = []
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@@ -163,6 +171,7 @@ class RouteStrategy(BaseTeamStrategy):
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state_flow=self.state_flow,
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runtime_config=getattr(team, "runtime_config", None),
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custom_prompt=self.custom_prompt,
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max_handoffs=self.max_handoffs,
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)
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)
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router = ChainRouter(routers)
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@@ -171,13 +180,11 @@ class RouteStrategy(BaseTeamStrategy):
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exec_config = kwargs.get("config")
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max_cycles = getattr(exec_config, "max_cycles", 15) if exec_config else 15
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logger.info(f"🛣️ [RouteStrategy] '{team.name}' 正在获取初始路由决策...")
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logger.debug(f"🛣️ '{team.name}' 正在获取初始路由决策...")
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decision = await router.route(context, [], prompt)
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if not decision:
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logger.warning(
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f"🚨 [RouteStrategy] Team '{team.name}' 的所有路由策略未能命中目标。"
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)
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logger.warning(f"🚨 Team '{team.name}' 的所有路由策略未能命中目标。")
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raise AbortException(
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reason=f"Team '{team.name}' 无法找到合适的路由节点处理该任务",
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display="🚨 团队协作失败，无法分配任务。",
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@@ -191,7 +198,7 @@ class RouteStrategy(BaseTeamStrategy):
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cycle_count += 1
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if cycle_count > max_cycles:
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logger.error(
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f"🚨 [RouteStrategy] Team '{team.name}' 路由陷入死循环！"
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f"🚨 Team '{team.name}' 路由陷入死循环！"
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f"已达到最大限制 {max_cycles} 次。"
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)
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raise AbortException(
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@@ -229,7 +236,7 @@ class RouteStrategy(BaseTeamStrategy):
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run_result = yield CallAction(
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agent=current_target,
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task=prompt,
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task=prompt or "",
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history=handoff_history_messages,
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kwargs=kwargs,
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)
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@@ -268,7 +275,7 @@ class RouteStrategy(BaseTeamStrategy):
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pass
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if fast_routed:
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logger.info(
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logger.debug(
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f"🛣️ **路由决策**: 委派给专员 👨💼`{current_target}`"
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"(系统拦截：正则/函数状态流发生转移)"
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)
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@@ -291,18 +298,21 @@ class CoordinateStrategy(BaseTeamStrategy):
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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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):
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"""
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协作策略初始化，Leader 主动拆解任务，委派给 Sub-Agents 并汇总结果。
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参数:
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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) 的专属工具列表。
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leader_tools: 挂载给协调节点 (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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super().__init__(custom_prompt=custom_prompt)
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self.leader_model = leader_model
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self.leader_tools = leader_tools or []
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self.max_delegations = max_delegations
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async def generate_plan(
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self,
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@@ -323,6 +333,7 @@ class CoordinateStrategy(BaseTeamStrategy):
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runnable=m,
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name=f"delegate_to_{m.name}",
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description=f"将子任务委派给专员 [{m.name}] 处理。专长：{desc}",
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max_delegations=self.max_delegations,
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)
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)
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@@ -338,17 +349,16 @@ class CoordinateStrategy(BaseTeamStrategy):
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logger.debug(f"✨ **团队 [{team.name}] Leader** 正在汇总各方报告...")
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if context.run.event_bus:
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await context.run.event_bus.emit(
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ToolStreamChunkEvent(
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tool_name="Team Leader",
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content="✨ 团队 Leader 正在汇总各方报告...",
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)
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await context.run.emit(
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ToolStreamChunkEvent(
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tool_name="Team Leader",
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content="✨ 团队 Leader 正在汇总各方报告...",
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)
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)
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logger.debug(f"👨💼 [CoordinateStrategy] '{team.name}' 正在启动协调推理循环...")
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leader_res = yield CallAction(agent=leader_agent, task=prompt)
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leader_res = yield CallAction(agent=leader_agent, task=prompt or "")
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yield FinishAction(result=leader_res)
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@@ -389,24 +399,22 @@ class BroadcastStrategy(BaseTeamStrategy):
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prompt.description if isinstance(prompt, AgentTask) else (prompt or "")
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)
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if context.run.event_bus:
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await context.run.event_bus.emit(
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ToolStreamChunkEvent(
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tool_name="Team Broadcaster",
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content=f"🚀 正在并发广播任务给 {len(team.members)} 位专家...",
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)
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await context.run.emit(
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ToolStreamChunkEvent(
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tool_name="Team Broadcaster",
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content=f"🚀 正在并发广播任务给 {len(team.members)} 位专家...",
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)
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)
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actions = [CallAction(agent=m.name, task=task_desc_str) for m in team.members]
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results = yield ConcurrentCallAction(actions=actions)
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if context.run.event_bus:
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await context.run.event_bus.emit(
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ToolStreamChunkEvent(
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tool_name="Team Leader",
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content="✨ 所有专家汇报完毕，Leader 正在融合各方观点...",
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)
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await context.run.emit(
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ToolStreamChunkEvent(
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tool_name="Team Leader",
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content="✨ 所有专家汇报完毕，Leader 正在融合各方观点...",
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)
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)
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logger.debug(f"✨ **团队 [{team.name}] Leader** 正在汇总各方报告...")
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@@ -451,8 +459,7 @@ class TaskStrategy(BaseTeamStrategy):
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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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):
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"""
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@@ -463,10 +470,9 @@ class TaskStrategy(BaseTeamStrategy):
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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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""" # noqa: E501
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super().__init__(custom_prompt=custom_prompt)
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self.leader_model = leader_model
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self.leader_tools = leader_tools or []
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@@ -474,14 +480,21 @@ class TaskStrategy(BaseTeamStrategy):
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self.blackboard = None
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self.bb_toolkit = None
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if blackboard_schema is not None:
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from zhenxun.services.ai.run.blackboard import BlackboardManager
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from zhenxun.services.ai.tools.providers.builtin.blackboard import (
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BlackboardToolkit,
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)
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if blackboard is not None:
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schema = None
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initial_state = None
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if isinstance(blackboard, type) and issubclass(blackboard, BaseModel):
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schema = blackboard
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elif isinstance(blackboard, BaseModel):
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schema = type(blackboard)
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initial_state = blackboard
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else:
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raise ValueError(
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"blackboard 参数必须是 Pydantic BaseModel 的子类(类型)或其实例"
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)
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self.blackboard = BlackboardManager(
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schema=blackboard_schema, initial_state=initial_blackboard_state
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schema=schema, initial_state=initial_state
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)
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self.bb_toolkit = BlackboardToolkit(self.blackboard)
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self.leader_tools.append(self.bb_toolkit)
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@@ -493,9 +506,6 @@ class TaskStrategy(BaseTeamStrategy):
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context: RunContext,
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**kwargs,
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) -> AsyncGenerator[TeamAction, Any]:
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from zhenxun.services.ai.flow.team.models import TaskBoardState, TaskNodeStatus
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from zhenxun.services.ai.flow.team.task_tools import TaskPlanningToolkit
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if self.blackboard is not None:
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context.session.blackboard = self.blackboard
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@@ -541,9 +551,7 @@ class TaskStrategy(BaseTeamStrategy):
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)
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if "__task_board__" not in context.session.shared_state:
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context.session.shared_state["__task_board__"] = (
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self.blackboard._state if self.blackboard else TaskBoardState()
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)
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context.session.shared_state["__task_board__"] = TaskBoardState()
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board = cast(TaskBoardState, context.session.shared_state["__task_board__"])
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max_iterations = self.max_iterations
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@@ -576,15 +584,17 @@ class TaskStrategy(BaseTeamStrategy):
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请检查是否有 failed 的任务需要修复重新指派？或者如果所有任务均已 completed，
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请立刻调用 `mark_all_complete` 汇报总结。"""
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logger.info(f"🧠 [TaskStrategy] 唤醒 Planner (Iter: {iteration})")
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leader_res = yield CallAction(agent=leader_agent, task=planner_prompt)
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logger.debug(f"🧠 [TaskStrategy] 唤醒 Planner (Iter: {iteration})")
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leader_res = yield CallAction(
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agent=leader_agent, task=planner_prompt or ""
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)
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if board.is_goal_complete:
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yield FinishAction(result=board.final_summary or leader_res.output)
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return
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continue
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logger.info(
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logger.debug(
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f"🚀 [TaskStrategy] 引擎接管：并发执行 {len(available_tasks)} 个任务..."
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)
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actions = []
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