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* ♻️ 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>
257 lines
9.3 KiB
Python
257 lines
9.3 KiB
Python
from abc import ABC, abstractmethod
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from collections.abc import Awaitable, Callable, Mapping, Sequence
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import inspect
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import re
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from typing import Any, cast
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from nonebot.utils import is_coroutine_callable
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from zhenxun.services.ai.core.messages import AgentMessage
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from zhenxun.services.ai.core.templates import PromptTemplate
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from zhenxun.services.ai.run import AgentTask, RunContext
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from zhenxun.services.ai.run.di import DependencyInjector
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from zhenxun.services.ai.utils.logger import log_team as logger
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from .models import RouteDecision, Transition
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class BaseRouter(ABC):
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"""团队多智能体路由器基类"""
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@abstractmethod
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async def route(
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self,
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context: RunContext,
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history: Sequence[AgentMessage],
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prompt: str | AgentTask | None = None,
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) -> RouteDecision | None:
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"""核心路由方法"""
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pass
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class FunctionRouter(BaseRouter):
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"""基于纯函数的极速路由器"""
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def __init__(self, selector_func: Callable[..., Any], target: str | None = None):
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"""
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初始化基于函数的极速路由器。
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参数:
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selector_func: 用于进行路由判断的选择函数,返回布尔值或字符串目标名。
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target: 当选择函数返回 True 时,默认路由到的目标成员名称。
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"""
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self.selector_func = selector_func
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self.target = target
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async def route(
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self,
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context: RunContext,
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history: Sequence[AgentMessage],
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prompt: str | AgentTask | None = None,
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) -> RouteDecision | None:
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sig = inspect.signature(self.selector_func)
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call_kwargs = {"prompt": prompt, "context": context, "history": history}
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if isinstance(prompt, AgentTask):
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call_kwargs["agent_task"] = prompt
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call_kwargs["task"] = prompt
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kwargs_resolved = await DependencyInjector.resolve_all(
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sig, call_kwargs, context
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)
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filtered_kwargs = {
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k: v for k, v in kwargs_resolved.items() if k in sig.parameters
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}
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if is_coroutine_callable(self.selector_func):
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_async_func = cast(Callable[..., Awaitable[Any]], self.selector_func)
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selected_target = await _async_func(**filtered_kwargs)
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else:
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_sync_func = cast(Callable[..., Any], self.selector_func)
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selected_target = _sync_func(**filtered_kwargs)
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if isinstance(selected_target, bool):
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if selected_target and self.target:
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logger.debug(f"命中函数极速路由 -> {self.target}")
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return RouteDecision(target_name=self.target, reason="")
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elif selected_target is not None and isinstance(selected_target, str):
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logger.debug(f"命中函数动态路由 -> {selected_target}")
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return RouteDecision(target_name=selected_target, reason="")
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return None
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class RegexRouter(BaseRouter):
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"""基于正则表达式的极速路由器"""
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def __init__(self, pattern: str, target: str):
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"""
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初始化基于正则表达式的极速路由器。
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参数:
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pattern: 正则表达式匹配规则。
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target: 当正则表达式成功匹配用户输入时路由到的目标成员名称。
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"""
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self.pattern = re.compile(pattern)
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self.target = target
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async def route(
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self,
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context: RunContext,
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history: Sequence[AgentMessage],
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prompt: str | AgentTask | None = None,
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) -> RouteDecision | None:
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text_to_match = (
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prompt.description
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if isinstance(prompt, AgentTask)
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else (prompt or context.run.user_input or "")
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)
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if self.pattern.search(text_to_match):
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logger.debug(f"命中正则极速路由 -> {self.target}")
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return RouteDecision(target_name=self.target, reason="")
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return None
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class ChainRouter(BaseRouter):
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"""责任链路由器:按顺序执行,直到其中一个命中"""
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def __init__(self, routers: list[BaseRouter]):
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"""
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初始化责任链路由器。
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参数:
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routers: 路由器实例列表,按顺序链式匹配,遇到首个命中的路由器即返回。
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"""
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self.routers = routers
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async def route(
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self,
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context: RunContext,
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history: Sequence[AgentMessage],
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prompt: str | AgentTask | None = None,
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) -> RouteDecision | None:
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for router in self.routers:
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decision = await router.route(context, history, prompt)
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if decision is not None:
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return decision
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return None
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class LLMRouter(BaseRouter):
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"""基于大模型的意图路由器"""
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def __init__(
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self,
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team_name: str,
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members: list[Any],
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leader_model: str | None = None,
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leader_tools: list[Any] | None = None,
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state_flow: Mapping[str, Sequence[Transition | str]] | Callable | None = None,
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runtime_config: Any = None,
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custom_prompt: str | None = None,
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allowed_transitions: list[Transition] | None = None,
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max_handoffs: int = 3,
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):
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"""
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初始化基于大模型的意图路由器。
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参数:
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team_name: 当前团队的名称标识。
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members: 团队的成员列表,包含 Agent, Team 或 Workflow。
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leader_model: 用于进行意图决策的路由器大模型名称,若为空则默认继承全局配置。
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leader_tools: 挂载给意图决策路由器的额外可用工具列表。
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state_flow: 状态流转规则字典或动态流转函数,定义智能体成员之间的转接路径。
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runtime_config: 团队级别的运行时全局配置。
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custom_prompt: 自定义的系统提示词模板,用以覆盖默认的路由系统指令。
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allowed_transitions: 允许的状态移交规则与前置条件列表。
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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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self.team_name = team_name
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self.members = members
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self.leader_model = leader_model
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self.leader_tools = leader_tools or []
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self.state_flow = state_flow
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self.runtime_config = runtime_config
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self.custom_prompt = custom_prompt
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self.allowed_transitions = allowed_transitions
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self.max_handoffs = max_handoffs
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async def route(
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self,
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context: RunContext,
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history: Sequence[AgentMessage],
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prompt: str | AgentTask | None = None,
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) -> RouteDecision | None:
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from zhenxun.services.ai.flow.agent.agent import Agent
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from zhenxun.services.ai.flow.agent.models import AgentConfig
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from .capabilities import TeamRoutingCapability
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default_system_prompt = """## 角色与目标
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你是一个高级任务路由器 (所在团队: {{ team_name }})。
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请根据用户的输入意图,立刻调用相应的移交工具 (transfer_to_...)
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将对话物理转移给合适的专员处理。
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你必须且只能选择移交,不能自己作答。"""
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if self.allowed_transitions:
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transitions_desc = "\n## 可用的移交目标及条件:\n"
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for t in self.allowed_transitions:
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desc = getattr(t, "description", "") or "无特定条件"
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transitions_desc += (
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f"- 移交至 [{getattr(t, 'target', 'unknown')}]:{desc}\n"
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)
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default_system_prompt += transitions_desc
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template = self.custom_prompt or default_system_prompt
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route_prompt = PromptTemplate(template).render(team_name=self.team_name)
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routing_cap = TeamRoutingCapability(
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team_name=self.team_name,
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members=self.members,
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state_flow=self.state_flow,
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max_handoffs=self.max_handoffs,
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)
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leader_config = AgentConfig(
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stateless=self.runtime_config.stateless if self.runtime_config else True,
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enable_hitl=getattr(self.runtime_config, "leader_enable_hitl", False),
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)
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target_model = self.leader_model
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if not target_model:
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for m in self.members:
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if m_model := getattr(m, "model_name", None) or getattr(
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m, "model", None
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):
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target_model = m_model
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break
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router_agent = Agent(
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name=f"{self.team_name}_Router",
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instruction=route_prompt,
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model=target_model,
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tools=self.leader_tools,
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config=leader_config,
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)
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sub_context = context.clone_for_member(router_agent.name)
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sub_context.capabilities = list(sub_context.capabilities)
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sub_context.capabilities.append(routing_cap)
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logger.debug("🤖 [LLMRouter] 启动 LLM 思考路由决策...")
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res = await router_agent.run(
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prompt=prompt,
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context=sub_context,
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config=AgentConfig(message_history=history),
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)
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if res.handoff:
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logger.debug(f"🤖 [LLMRouter] 决策完毕: 移交给 -> {res.handoff.target}")
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return RouteDecision(
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target_name=res.handoff.target,
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reason=res.handoff.reason,
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context_data=res.handoff.context_data,
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)
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logger.warning("🤖 [LLMRouter] LLM 没有调用移交工具,放弃路由。")
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return None
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