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* ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架 - 【重构】将原 services/llm 重构并迁移至全新的 services/ai 架构,提供向下兼容垫片 - 【新增】引入 Agent、Team、Workflow 三大智能体与工作流编排范式 - 【新增】引入基于 RAG 的长期向量记忆与中期槽位记忆系统 - 【新增】引入基于 Docker 的安全代码执行沙箱环境 - 【新增】支持 MCP 协议,允许动态管理和调用 MCP 服务 - 【新增】引入输入输出安全合规护栏与自愈反思机制 - 【优化】重构并优化多厂商 API 适配器 (Gemini, OpenAI, DeepSeek, GLM 等) - 【优化】优化日志脱敏与 Token 预估机制 - 【移除】移除旧版 llm default 和 llm reset-key 命令,新增 llm mcp 管理命令 * 🔧 chore(deps): 更新项目依赖与配置 - 添加 mcp、jieba 和 aiodocker 依赖到配置文件及 requirements.txt - 在 pyright 配置中设置 reportMissingImports 为 none - 调整 .gitignore 中 resources 目录的忽略规则 * ♻️ refactor(tools): 重构工具终止机制并清理知识库日志输出 - 统一使用 `context.state["__end_run__"]` 替代 `EndRunResult` 控制任务结束 - 移除文件系统和向量知识库检索工具中 `ToolResult` 的 `.with_log` 调用 - 调整指令处理器(Directive)的返回值为 `tool_res.output` - 修复部分类型检查警告并优化联合类型判断语法 * ♻️ refactor(tools): 重构工具副作用指令与控制流熔断机制 - 引入 `DirectivePayload` 及 `ToolResult` 的子类以结构化表达工具副作用 - 移除通过 `context.state` 传递魔术变量的隐式控制流设计 - 重构 `DirectiveManager` 处理器接口,直接在处理器中修改 `AgentState` 并构建 `AgentRunResult` - 在 `StandardAgentExecutor` 中统一通过 `directive_manager` 调度工具返回的副作用指令 - 补全 `MessageBuilder` 中部分核心方法的文档注释 * 🐛 fix(sandbox): 修复 Docker 沙箱容器状态检测与会话清理逻辑 -【修复】修正 `is_alive` 中直接读取私有属性的问题,改用 `show()` 返回值 -【修复】解决 `execute_code` 中缓存的执行器与当前会话不一致的问题 -【优化】在清理工作区前增加容器存活检测,避免向已死容器发送请求 -【优化】创建容器时增加运行状态校验,若已停止则自动从缓存中移除并重建 -【优化】优化容器销毁和清理逻辑,静默处理容器不存在 (404) 的异常 * 📝 docs(core): 补充核心模块初始化方法的文档注释 * 🚨 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>
248 lines
9.1 KiB
Python
248 lines
9.1 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.flow.team.models import RouteDecision, Transition
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from zhenxun.services.ai.run import RunContext, Task
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from zhenxun.services.ai.run.di import DependencyInjector
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from zhenxun.services.log import logger
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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 | Task | 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 | Task | 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, Task):
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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 | Task | 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, Task)
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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 | Task | 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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):
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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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"""
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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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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 | Task | 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 zhenxun.services.ai.flow.team.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, members=self.members, state_flow=self.state_flow
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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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