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zhenxun_bot/zhenxun/services/ai/llm/engine/router.py
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80fc5b86a7 ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架 (#2146)
* ✨ 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>
2026-07-03 08:53:56 +08:00

163 lines
5.9 KiB
Python

from abc import ABC, abstractmethod
from typing import Any
from zhenxun.services.ai.core.exceptions import (
ConfigurationException,
LLMException,
UpstreamServerException,
)
from zhenxun.services.ai.core.options import GenerationConfig
from zhenxun.services.ai.llm.manager import (
_get_group_name,
_resolve_model_group,
get_default_model,
get_model_instance,
list_available_models,
)
from zhenxun.services.ai.llm.system.capabilities import get_model_capabilities
from zhenxun.services.ai.llm.system.network import health_manager
from zhenxun.services.log import logger
class BaseModelRouter(ABC):
@abstractmethod
async def route(
self,
request: Any,
model_names: list[str],
task: str,
override_config: GenerationConfig | dict | None,
cancellation_token: Any | None,
) -> Any:
pass
class FallbackRouter(BaseModelRouter):
"""主备故障转移路由器"""
async def route(
self,
request: Any,
model_names: list[str],
task: str,
override_config: GenerationConfig | dict | None,
cancellation_token: Any | None,
) -> Any:
errors = []
all_nodes_bypassed = True
is_routed_call = len(model_names) > 1
request.extra["_is_routed_call"] = is_routed_call
start_idx = request.extra.get("_working_route_index", 0)
indices_to_try = list(range(start_idx, len(model_names))) + list(
range(0, start_idx)
)
for idx in indices_to_try:
m_name = model_names[idx]
if not health_manager.is_route_healthy(m_name, strict_mode=is_routed_call):
logger.debug(f"👉 [Orchestrator] 节点 '{m_name}' 熔断中,已跳过")
errors.append(f"{m_name}(熔断中)")
continue
caps = get_model_capabilities(m_name)
if not caps.supports_task(task):
errors.append(f"{m_name}(Unsupported Task: {task})")
continue
all_nodes_bypassed = False
try:
if len(model_names) > 1:
if idx != start_idx:
logger.debug(f"🔄 [Orchestrator] 切换至备用节点: '{m_name}'...")
async with await get_model_instance(
m_name, override_config, task=task
) as instance:
response = await instance.invoke(request, cancellation_token)
request.extra["_working_route_index"] = idx
if run_ctx := request.extra.get("run_context"):
run_ctx.state["_working_route_index"] = idx
return response
except LLMException as e:
if not e.should_failover:
logger.warning(
f"🚫 [Orchestrator] 节点 '{m_name}' "
f"返回不可恢复错误 ({e.__class__.__name__}),停止故障转移。"
)
raise e
logger.warning(
f"⚠️ [Orchestrator] 节点 '{m_name}' "
f"错误 ({e.__class__.__name__}),触发故障转移..."
)
errors.append(f"{m_name}({e.__class__.__name__})")
except Exception as e:
logger.warning(
f"⚠️ [Orchestrator] 节点 '{m_name}' 发生未知异常,触发故障转移: {e}"
)
errors.append(f"{m_name}(Error)")
if all_nodes_bypassed and len(model_names) > 1:
fallback_model = health_manager.get_best_fallback_route(model_names)
logger.warning(
f"⚠️ [Orchestrator] 路由组所有节点均已宕机!"
f"强制放行 '{fallback_model}' 探活..."
)
try:
async with await get_model_instance(
fallback_model, override_config, task=task
) as instance:
return await instance.invoke(request, cancellation_token)
except Exception as e:
errors.append(f"{fallback_model}(保底探活彻底失败:{e})")
err_msg = f"所有路由尝试均已失败: {', '.join(errors)}"
raise UpstreamServerException(err_msg)
class BaseOrchestrator:
"""顶层大模型请求编排器,负责组解析与路由策略委派。"""
def __init__(self, router: BaseModelRouter | None = None):
self.router = router or FallbackRouter()
async def invoke(
self,
request: Any,
model_name: str | None = None,
task: str = "chat",
override_config: GenerationConfig | dict | None = None,
cancellation_token: Any | None = None,
) -> Any:
resolved_model_name = model_name
if resolved_model_name is None:
resolved_model_name = get_default_model(task)
if resolved_model_name is None:
available_models = list_available_models()
if not available_models:
raise ConfigurationException("未配置任何AI模型")
resolved_model_name = available_models[0]["full_name"]
logger.warning(f"未指定模型,使用第一个可用模型: {resolved_model_name}")
group_name = _get_group_name(resolved_model_name)
if group_name is not None:
model_names = _resolve_model_group(group_name)
if not model_names:
raise ConfigurationException(
f"模型路由组 '{group_name}' 解析失败或为空,请检查配置。"
)
else:
model_names = [resolved_model_name]
return await self.router.route(
request=request,
model_names=model_names,
task=task,
override_config=override_config,
cancellation_token=cancellation_token,
)
LLMOrchestrator = BaseOrchestrator()