♻️ 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>

zhenxun/services/ai/llm/adapters/handlers/gemini_handlers.py

@@ -56,7 +56,9 @@ from zhenxun.services.ai.llm.adapters.base import (
     ResponseData,
     process_image_data,
 )
-from zhenxun.services.ai.llm.adapters.handlers.base import (
+from zhenxun.services.ai.utils.logger import log_llm as logger
+
+from .base import (
     BaseAudioHandler,
     BaseEmbeddingHandler,
     BaseImageHandler,
@@ -66,7 +68,6 @@ from zhenxun.services.ai.llm.adapters.handlers.base import (
     ResponseParser,
     ToolSerializer,
 )
-from zhenxun.services.log import logger
 
 
 class GeminiConfigMapper(ConfigMapper):
@@ -121,22 +122,19 @@ class GeminiConfigMapper(ConfigMapper):
                     fc_config["allowedFunctionNames"] = user_funcs
             params["toolConfig"] = {"functionCallingConfig": fc_config}
 
-        has_effort = bool(
-            config.common.reasoning_effort
-            and str(config.common.reasoning_effort).lower() != "none"
-        )
-        if (
-            has_effort
-            and capabilities
-            and capabilities.reasoning_mode == ReasoningMode.LEVEL
-        ):
-            thinking_config = params.setdefault("thinkingConfig", {})
+        if config.common.reasoning_effort and capabilities:
             effort = str(config.common.reasoning_effort).lower()
-
             if capabilities.reasoning_effort_map:
                 effort = capabilities.reasoning_effort_map.get(effort, effort)
 
-            thinking_config["thinkingLevel"] = effort
+            if capabilities.reasoning_mode == ReasoningMode.LEVEL and effort != "none":
+                thinking_config = params.setdefault("thinkingConfig", {})
+                thinking_config["thinkingLevel"] = effort
+            elif (
+                capabilities.reasoning_mode == ReasoningMode.BUDGET and effort == "none"
+            ):
+                thinking_config = params.setdefault("thinkingConfig", {})
+                thinking_config["thinkingBudget"] = 0
 
         if config.gemini_options.include_thoughts is not None:
             thinking_config = params.setdefault("thinkingConfig", {})
@@ -829,10 +827,6 @@ class GeminiEmbeddingHandler(BaseEmbeddingHandler):
         url = f"{base_url}/v1beta/{api_model_name}:batchEmbedContents"
         headers = adapter.get_base_headers(api_key)
 
-        from zhenxun.services.ai.llm.adapters.handlers.gemini_handlers import (
-            GeminiMessageConverter,
-        )
-
         converter = GeminiMessageConverter()
 
         requests_payload = []

zhenxun/services/ai/llm/adapters/handlers/mimo_handlers.py

@@ -16,13 +16,13 @@ from zhenxun.services.ai.core.messages import (
 )
 from zhenxun.services.ai.core.models import (
     ModelCapabilities,
-    ModelDetail,
     ModelIdentity,
 )
-from zhenxun.services.ai.core.options import GenerationConfig, TTSConfig
+from zhenxun.services.ai.core.options import TTSConfig
 from zhenxun.services.ai.llm.adapters.base import BaseAdapter, RequestData
-from zhenxun.services.ai.llm.adapters.handlers.base import BaseAudioHandler
-from zhenxun.services.ai.llm.adapters.handlers.openai_handlers import (
+
+from .base import BaseAudioHandler
+from .openai_handlers import (
     OpenAIConfigMapper,
     OpenAIMessageConverter,
     OpenAITextHandler,
@@ -53,35 +53,6 @@ class MiMoToolSerializer(OpenAIToolSerializer):
         return res
 
 
-class MiMoConfigMapper(OpenAIConfigMapper):
-    """MiMo 配置映射器，处理深度思考参数差异"""
-
-    def map_config(
-        self,
-        config: GenerationConfig,
-        model_detail: ModelDetail | None = None,
-        capabilities: ModelCapabilities | None = None,
-    ) -> dict[str, Any]:
-        params = super().map_config(config, model_detail, capabilities)
-
-        if config.common.reasoning_effort:
-            effort = str(config.common.reasoning_effort).lower()
-            if effort == "none":
-                params["thinking"] = {"type": "disabled"}
-            else:
-                params["thinking"] = {"type": "enabled"}
-        elif (
-            hasattr(config, "deepseek_options")
-            and config.deepseek_options.thinking is not None
-        ):
-            if config.deepseek_options.thinking is True:
-                params["thinking"] = {"type": "enabled"}
-            elif config.deepseek_options.thinking is False:
-                params["thinking"] = {"type": "disabled"}
-
-        return params
-
-
 class MiMoMessageConverter(OpenAIMessageConverter):
     """MiMo 消息转换器，拦截处理特有的音视频多模态结构"""
 
@@ -141,7 +112,7 @@ class MiMoTextHandler(OpenAITextHandler):
         super().__init__(api_type=api_type)
         self.converter = MiMoMessageConverter(api_type=api_type)
         self.serializer = MiMoToolSerializer(api_type=api_type)
-        self.mapper = MiMoConfigMapper(api_type=api_type)
+        self.mapper = OpenAIConfigMapper(api_type=api_type)
 
 
 class MiMoAudioHandler(BaseAudioHandler):

zhenxun/services/ai/llm/adapters/handlers/openai_handlers.py

@@ -53,7 +53,9 @@ from zhenxun.services.ai.llm.adapters.base import (
     ResponseData,
     process_image_data,
 )
-from zhenxun.services.ai.llm.adapters.handlers.base import (
+from zhenxun.services.ai.utils.logger import log_llm as logger
+
+from .base import (
     BaseAudioHandler,
     BaseEmbeddingHandler,
     BaseImageHandler,
@@ -64,7 +66,6 @@ from zhenxun.services.ai.llm.adapters.handlers.base import (
     ResponseParser,
     ToolSerializer,
 )
-from zhenxun.services.log import logger
 
 
 class OpenAIConfigMapper(ConfigMapper):
@@ -114,7 +115,13 @@ class OpenAIConfigMapper(ConfigMapper):
             if capabilities and capabilities.reasoning_effort_map:
                 effort = capabilities.reasoning_effort_map.get(effort, effort)
 
-            if effort != "none":
+            if (
+                effort == "none"
+                and capabilities
+                and capabilities.supports_thinking_toggle
+            ):
+                params["thinking"] = {"type": "disabled"}
+            else:
                 params["reasoning_effort"] = effort
 
         if isinstance(config.output.response_format, dict):
@@ -1165,45 +1172,3 @@ class OpenAIAudioHandler(BaseAudioHandler):
             usage=UsageInfo(),
             model_name=identity.model_name,
         )
-
-
-class CompositeOpenAITextHandler(BaseTextHandler):
-    """
-    OpenAI 复合文本对话处理器 (Composite Pattern)。
-    内部包装标准协议与 responses 协议 Handler，根据模型配置在请求时动态路由
-    """
-
-    def __init__(self, api_type: str = "openai"):
-        self.api_type = api_type
-        self._standard_handler = OpenAITextHandler(api_type=api_type)
-        self._responses_handler = OpenAIResponsesTextHandler(
-            api_type="openai_responses"
-        )
-
-    def _get_active_handler(self, identity: ModelIdentity) -> BaseTextHandler:
-        current_api_type = identity.api_type
-        if current_api_type == "openai_responses":
-            return self._responses_handler
-        return self._standard_handler
-
-    async def prepare_text_request(
-        self,
-        adapter: BaseAdapter,
-        identity: ModelIdentity,
-        api_key: str,
-        request: ChatRequest,
-    ) -> RequestData:
-        handler = self._get_active_handler(identity)
-        return await handler.prepare_text_request(adapter, identity, api_key, request)
-
-    def parse_text_response(
-        self,
-        adapter: BaseAdapter,
-        identity: ModelIdentity,
-        response_json: dict[str, Any],
-        is_advanced: bool = False,
-    ) -> ResponseData:
-        handler = self._get_active_handler(identity)
-        return handler.parse_text_response(
-            adapter, identity, response_json, is_advanced
-        )