mirror of
https://github.com/zhenxun-org/zhenxun_bot.git
synced 2026-10-05 03:39:59 +08:00
* ♻️ 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>
160 lines
5.8 KiB
Python
160 lines
5.8 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.ai.utils.logger import log_llm as 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"👉 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"🔄 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"🚫 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"⚠️ 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"⚠️ 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"⚠️ 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()
|