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♻️ 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>
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webjoin111
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parent
0b32d69c9c
commit
922d092650
@@ -3,12 +3,14 @@
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"""
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from dataclasses import dataclass, field
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import fnmatch
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from typing import TYPE_CHECKING, Any, Literal
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from pydantic import BaseModel, ConfigDict, Field
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from zhenxun.services.ai.core.messages import ToolCallPart, UsageInfo
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from zhenxun.services.ai.run.context import RunContext
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from zhenxun.services.ai.utils.logger import log_tool as logger
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from zhenxun.utils.pydantic_compat import model_dump, model_validate
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if TYPE_CHECKING:
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@@ -210,12 +212,12 @@ class ToolOverride(BaseModel):
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async def resolve(self, context: RunContext | None = None) -> "ResolvedToolPayload":
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from zhenxun.services.ai.tools.engine.registry import tool_provider_manager
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from zhenxun.services.ai.tools.models import ResolvedToolPayload
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from zhenxun.services.log import logger
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found_tools = await tool_provider_manager.resolve_specific_tools([self.name])
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if found_tools:
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base_tool = found_tools[0]
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payload = await tool_provider_manager.resolve_tools(
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[self.name], context=context
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)
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if payload and payload.tools:
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base_tool = payload.tools[0]
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if hasattr(base_tool, "clone_with_options"):
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cloned_tool = base_tool.clone_with_options(self)
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if hasattr(cloned_tool, "resolve"):
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@@ -231,17 +233,8 @@ class ToolOverride(BaseModel):
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return ResolvedToolPayload()
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class GlobalToolFilter(BaseModel):
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"""全局宏观工具过滤器"""
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allowed_servers: list[str] | None = None
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"""仅允许的服务端名称列表"""
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excluded_servers: list[str] | None = None
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"""需要排除的服务端名称列表"""
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class ValidatedToolCall(BaseModel):
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"""工具调用验证结果载体（解耦验证与执行）"""
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"""工具调用验证结果载体"""
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model_config = ConfigDict(arbitrary_types_allowed=True)
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@@ -265,22 +258,68 @@ class Query(BaseModel):
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用于在 Agent 中精确或批量筛选加载特定命名空间、特定标签的工具。
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"""
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name: str | None = Field(default=None)
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"""如果提供，则必须与工具的名称完全一致。"""
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name: str | list[str] | None = Field(default=None)
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"""如果提供，则工具的最终解析名称必须等于该字符串或在列表中。"""
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toolkit: str | list[str] | None = Field(default=None)
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"""如果提供，则工具必须属于指定的 Toolkit (支持字符串或列表)。"""
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tags: list[str] | None = Field(default=None)
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"""如果提供，则工具必须包含这里列出的所有标签 (交集/AND匹配)。"""
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exclude_tags: list[str] | None = Field(default=None)
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"""如果提供，则工具不能包含这里列出的任何标签 (排斥过滤)。"""
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namespace: str | None = Field(default=None)
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"""必填(由系统补充或显式声明)。限制搜索的插件命名空间，'global' 将跨全插件搜索。"""
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"""限制搜索的插件命名空间。如果不指定，将自动推导为调用者所在的插件；
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'global' 将跨全插件搜索。"""
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metadata_filter: dict[str, Any] | None = Field(default=None)
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"""如果提供，则工具的 metadata 必须包含这里列出的所有键值对。"""
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def match(self, tool: "BaseTool") -> bool:
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"""判断某个工具或工具箱是否符合当前 Query 的筛选条件"""
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def _match_pattern(val: str, pattern: str | list[str]) -> bool:
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patterns = [pattern] if isinstance(pattern, str) else pattern
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for p in patterns:
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if "*" in p or "?" in p:
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if fnmatch.fnmatch(val, p):
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return True
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elif val == p:
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return True
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return False
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is_toolkit_itself = hasattr(tool, "get_tools") and not hasattr(tool, "execute")
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if self.toolkit:
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if is_toolkit_itself:
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tk_name = getattr(
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tool, "name", getattr(tool, "__class__", type).__name__
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)
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else:
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parent_tk = getattr(tool, "parent_toolkit", None)
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tk_name = (
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getattr(
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parent_tk,
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"name",
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getattr(parent_tk, "__class__", type).__name__,
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)
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if parent_tk
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else None
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)
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if not tk_name:
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return False
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if not _match_pattern(tk_name, self.toolkit):
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return False
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if is_toolkit_itself and (self.name or self.tags or self.exclude_tags):
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return True
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if self.name:
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tool_name = getattr(tool, "name", getattr(tool, "__class__", type).__name__)
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if tool_name != self.name:
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if not _match_pattern(tool_name, self.name):
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return False
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if self.tags:
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tool_tags = []
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if self.tags or self.exclude_tags:
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tool_config = getattr(tool, "config", None)
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if (
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tool_config
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@@ -291,9 +330,15 @@ class Query(BaseModel):
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else:
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tool_settings = getattr(tool, "settings", None)
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tool_tags = getattr(tool_settings, "tags", []) if tool_settings else []
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if self.tags:
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if not all(tag in tool_tags for tag in self.tags):
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return False
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if self.exclude_tags:
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if any(tag in tool_tags for tag in self.exclude_tags):
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return False
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if self.metadata_filter:
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tool_settings = getattr(tool, "settings", None)
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tool_metadata = (
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@@ -315,9 +360,15 @@ class ResolvedToolPayload:
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injected_prompts: list[str] = field(default_factory=list)
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toolkits: list[Any] = field(default_factory=list)
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def merge(self, other: "ResolvedToolPayload") -> "ResolvedToolPayload":
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"""将另一个解析载荷合并到当前载荷中"""
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self.tools.extend(other.tools)
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self.injected_prompts.extend(other.injected_prompts)
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self.toolkits.extend(other.toolkits)
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return self
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__all__ = [
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"GlobalToolFilter",
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"Query",
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"ResolvedToolPayload",
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"ToolOptions",
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