Files
zhenxun_bot/zhenxun/services/ai/llm/adapters/factory.py
922d092650 ♻️ 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>
2026-07-10 09:14:06 +08:00

169 lines
5.3 KiB
Python

"""
LLM 适配器工厂类
"""
from __future__ import annotations
import fnmatch
from typing import Any, ClassVar
import httpx
from zhenxun.services.ai.core.exceptions import ConfigurationException
from zhenxun.services.ai.core.models import ModelIdentity
from .base import BaseAdapter, RequestData
class LLMAdapterFactory:
"""适配器注册与按 API 类型分发的统一入口。"""
_adapters: ClassVar[dict[str, BaseAdapter]] = {}
_api_type_mapping: ClassVar[dict[str, str]] = {}
@classmethod
def initialize(cls) -> None:
"""初始化默认适配器"""
if cls._adapters:
return
from .deepseek import DeepSeekAdapter
from .doubao import DoubaoAdapter
from .gemini import GeminiAdapter
from .glm import GLMAdapter
from .jina import JinaAdapter
from .mimo import MiMoAdapter
from .minimax import MiniMaxAdapter
from .openai import OpenAIAdapter, OpenAIResponsesAdapter
from .openrouter import OpenRouterAdapter
cls.register_adapter(OpenAIAdapter())
cls.register_adapter(OpenAIResponsesAdapter())
cls.register_adapter(OpenRouterAdapter())
cls.register_adapter(DeepSeekAdapter())
cls.register_adapter(JinaAdapter())
cls.register_adapter(GeminiAdapter())
cls.register_adapter(GLMAdapter())
cls.register_adapter(SmartAdapter())
cls.register_adapter(MiMoAdapter())
cls.register_adapter(MiniMaxAdapter())
cls.register_adapter(DoubaoAdapter())
@classmethod
def register_adapter(cls, adapter: BaseAdapter) -> None:
"""注册适配器"""
adapter_key = adapter.api_type
cls._adapters[adapter_key] = adapter
for api_type in adapter.supported_api_types:
cls._api_type_mapping[api_type] = adapter_key
@classmethod
def get_adapter(cls, api_type: str) -> BaseAdapter:
"""获取适配器"""
cls.initialize()
adapter_key = cls._api_type_mapping.get(api_type)
if not adapter_key:
raise ConfigurationException(
f"不支持的API类型: {api_type}",
details={
"api_type": api_type,
"supported_types": list(cls._api_type_mapping.keys()),
},
)
return cls._adapters[adapter_key]
@classmethod
def list_supported_types(cls) -> list[str]:
"""列出所有支持的API类型"""
cls.initialize()
return list(cls._api_type_mapping.keys())
@classmethod
def list_adapters(cls) -> dict[str, BaseAdapter]:
"""列出所有注册的适配器"""
cls.initialize()
return cls._adapters.copy()
def get_adapter_for_api_type(api_type: str) -> BaseAdapter:
"""按 API 类型获取适配器实例。"""
return LLMAdapterFactory.get_adapter(api_type)
def register_adapter(adapter: BaseAdapter) -> None:
"""向工厂注册新的适配器实例。"""
LLMAdapterFactory.register_adapter(adapter)
class SmartAdapter(BaseAdapter):
"""
智能路由适配器。
本身不处理序列化,而是根据规则委托给 OpenAIAdapter 或 GeminiAdapter。
"""
@property
def log_sanitization_context(self) -> str:
"""返回智能路由适配器的默认日志清洗上下文。"""
return "openai_request"
_ROUTING_RULES: ClassVar[list[tuple[str, str]]] = [
("*nano-banana*", "gemini"),
("*gemini*", "gemini"),
("*deepseek*", "deepseek"),
("*minimax*", "minimax"),
("*gpt*", "openai_responses"),
]
_DEFAULT_API_TYPE: ClassVar[str] = "openai"
def __init__(self):
"""初始化模型名到目标适配器的路由缓存。"""
self._adapter_cache: dict[str, BaseAdapter] = {}
@property
def api_type(self) -> str:
"""适配器主类型标识。"""
return "smart"
@property
def supported_api_types(self) -> list[str]:
"""当前适配器支持的 API 类型列表。"""
return ["smart"]
def _get_delegate_adapter(self, identity: ModelIdentity) -> BaseAdapter:
"""
核心路由逻辑:决定使用哪个适配器 (带缓存)
"""
if identity.api_type and identity.api_type != "smart":
return get_adapter_for_api_type(identity.api_type)
model_name = identity.model_name
if model_name in self._adapter_cache:
return self._adapter_cache[model_name]
target_api_type = self._DEFAULT_API_TYPE
model_name_lower = model_name.lower()
for pattern, api_type in self._ROUTING_RULES:
if fnmatch.fnmatch(model_name_lower, pattern):
target_api_type = api_type
break
adapter = get_adapter_for_api_type(target_api_type)
self._adapter_cache[model_name] = adapter
return adapter
async def prepare_payload(
self, identity: ModelIdentity, api_key: str, request: Any
) -> RequestData:
adapter = self._get_delegate_adapter(identity)
return await adapter.prepare_payload(identity, api_key, request)
async def parse_payload(
self, identity: ModelIdentity, request: Any, raw_response: httpx.Response
) -> Any:
adapter = self._get_delegate_adapter(identity)
return await adapter.parse_payload(identity, request, raw_response)