Files
zhenxun_bot/zhenxun/services/ai/tools/providers/skills/manager.py
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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

403 lines
14 KiB
Python

from abc import ABC, abstractmethod
import asyncio
from collections.abc import Sequence
import json
from pathlib import Path
import re
from typing import Any, cast
from typing_extensions import Self
import aiofiles
import yaml
from zhenxun.configs.path_config import DATA_PATH
from zhenxun.services.ai.utils.logger import log_tool as logger
from zhenxun.utils.pydantic_compat import model_dump, model_validate
from zhenxun.utils.utils import infer_plugin_namespace
from .models import (
INSTRUCTIONS,
METADATA,
RESOURCES,
Skill,
SkillBlueprintBuilder,
SkillEnvConfig,
SkillFrontmatter,
)
class SkillConfigManager:
"""基于 Skill ID 隔离的持久化配置金库管理器"""
def __init__(self):
self.config_path = DATA_PATH / "ai" / "skill_envs.json"
self.config = SkillEnvConfig()
self._load_sync()
def _load_sync(self):
if self.config_path.exists():
try:
with open(self.config_path, encoding="utf-8") as f:
self.config = model_validate(SkillEnvConfig, json.load(f))
except Exception as e:
logger.error(f"加载 skill_envs.json 失败: {e}")
async def save(self):
try:
self.config_path.parent.mkdir(parents=True, exist_ok=True)
async with aiofiles.open(self.config_path, "w", encoding="utf-8") as f:
content = json.dumps(
model_dump(self.config), ensure_ascii=False, indent=4
)
await f.write(content)
except Exception as e:
logger.error(f"保存 skill_envs.json 失败: {e}")
def get_envs_for_skill(self, namespace: str, skill_id: str) -> dict[str, str]:
return self.config.envs.get(namespace, {}).get(skill_id, {})
async def ensure_template(
self, namespace: str, skill_id: str, required_envs: list[str]
):
"""确保配置文件中存在所需的模板,缺失则补充为空字符串"""
if not required_envs:
return
changed = False
skill_envs = self.config.envs.setdefault(namespace, {}).setdefault(skill_id, {})
for env_key in required_envs:
if env_key not in skill_envs:
skill_envs[env_key] = ""
changed = True
if changed:
await self.save()
skill_env_manager = SkillConfigManager()
class SkillManager:
"""全局技能注册与发现中心 (本地文件系统模式)"""
_instance: "SkillManager | None" = None
def __new__(cls) -> Self:
if cls._instance is None:
cls._instance = super().__new__(cls)
return cast(Self, cls._instance)
def __init__(self):
if getattr(self, "_initialized", False):
return
self._skills: dict[str, Skill] = {}
self._scan_dirs: dict[Path, str] = {}
self._initialized = True
self._discovery_lock = asyncio.Lock()
default_skill_dir = DATA_PATH / "ai" / "skills"
default_skill_dir.mkdir(parents=True, exist_ok=True)
self._scan_dirs[default_skill_dir] = "global"
def add_scan_dir(self, path: str | Path, namespace: str | None = None):
p = Path(path).resolve()
if namespace is None:
namespace = infer_plugin_namespace()
if p not in self._scan_dirs:
self._scan_dirs[p] = namespace
def _get_valid_skill_dir(self, path: str | Path) -> Path | None:
"""统一目录校验:判断是否为合法的技能目录"""
p = Path(path).resolve()
if p.exists() and p.is_dir() and not p.name.startswith("."):
return p
return None
def _iter_valid_skill_dirs(self, target_dir: str | Path):
"""统一扫描器:迭代目标父目录下的所有合法技能子目录"""
p = Path(target_dir).resolve()
if not p.exists() or not p.is_dir():
return
for child in p.iterdir():
if valid_child := self._get_valid_skill_dir(child):
yield valid_child
async def load_local_skill(
self, path: str | Path, namespace: str | None = None
) -> Skill:
"""
[无痕加载] 读取指定路径的技能并返回独立的 Skill 实例。
该技能不会被注册到全局 manager 中,专门用于局部按需挂载。
"""
if namespace is None:
namespace = infer_plugin_namespace()
skill = await self._parse_skill_from_path(path, namespace)
if not skill:
raise ValueError(
f"无法从路径加载技能,请检查目录与 SKILL.md 是否合法: {path}"
)
return skill
async def discover_skills(self) -> dict[str, Skill]:
"""遍历所有扫描目录,发现并合并所有合法技能"""
async with self._discovery_lock:
if self._skills:
return self._skills
for scan_dir, namespace in self._scan_dirs.items():
for child in self._iter_valid_skill_dirs(scan_dir):
try:
skill = await self._parse_skill_metadata(child, namespace)
if skill:
self._skills[f"{namespace}::{skill.id}"] = skill
except Exception as e:
logger.error(f"解析技能 {child.name} 失败: {e}", e=e)
logger.info(f"技能扫描完成,共加载 {len(self._skills)} 个技能。")
return self._skills
async def get_skill_details(
self, name: str, namespace: str = "global"
) -> Skill | None:
"""获取技能详细信息,并在内存中缓存补全后的资源"""
skills = await self.discover_skills()
skill = skills.get(f"{namespace}::{name}") or skills.get(f"global::{name}")
if not skill:
return None
if skill.disclosure_level < RESOURCES:
skill = self._load_skill_resources(skill)
async with self._discovery_lock:
self._skills[f"{skill.namespace}::{skill.id}"] = skill
return skill
async def resolve_mixed_skills(
self, skills: Sequence[Any], namespace: str | None = None
) -> list[Skill]:
"""统一解析混合类型的技能列表(str, Path, Skill, SkillSource)"""
caller_namespace = namespace or infer_plugin_namespace()
resolved = []
resolvers = [self._normalize_to_resolver(s) for s in skills]
for r in resolvers:
resolved.extend(await r.resolve(self, caller_namespace))
return resolved
def _normalize_to_resolver(self, item: Any) -> "BaseSkillResolver":
if isinstance(item, Skill):
return SkillInstanceResolver(item)
elif isinstance(item, str):
return StringSkillResolver(item)
elif isinstance(item, Path):
return PathSkillResolver(item)
elif type(item).__name__ == "SkillSource":
return SourceSkillResolver(item)
raise TypeError(f"无法将对象 {type(item)} 解析为技能源。")
async def read_skill_resource(self, skill: Skill, file_path: str) -> str | None:
"""提供统一的资源文件(如 markdown 参考、脚本等)物理读取接口"""
target_file = (skill.path / file_path).resolve()
if not target_file.is_file():
fallback_file = (skill.path / "scripts" / file_path).resolve()
if fallback_file.is_file():
target_file = fallback_file
if target_file.is_file() and target_file.is_relative_to(skill.path.resolve()):
return target_file.read_text(encoding="utf-8")
return None
async def clear_cache(self):
"""清空缓存以重新扫描(方便测试热更)"""
async with self._discovery_lock:
self._skills.clear()
async def _parse_skill_from_path(
self, path: str | Path, namespace: str
) -> Skill | None:
"""按径解析技能,返回提权到最高级别的孤立技能实例,不污染全局状态"""
p = self._get_valid_skill_dir(path)
if not p:
logger.warning(
f"[SkillManager] 忽略无效技能目录 (不存在、非目录或为隐藏目录): {path}"
)
return None
try:
skill = await self._parse_skill_metadata(p, namespace)
if skill:
skill = self._load_skill_resources(skill)
return skill
except Exception as e:
logger.error(f"解析孤立技能 {p.name} 失败: {e}", e=e)
return None
async def _parse_frontmatter_and_body(
self, skill_dir: Path
) -> tuple[dict | None, str]:
skill_md_path = skill_dir / "SKILL.md"
if not skill_md_path.exists():
return None, ""
async with aiofiles.open(skill_md_path, encoding="utf-8") as f:
content = await f.read()
if not content.startswith("---"):
return None, ""
match = re.search(r"^---\s*\n(.*?)\n---\s*\n?(.*)$", content, re.DOTALL)
if not match:
return None, ""
yaml_content, body = match.groups()
try:
return yaml.safe_load(yaml_content), body.strip()
except yaml.YAMLError as e:
logger.warning(f"Skill {skill_dir.name} YAML 解析失败: {e}")
return None, ""
async def _parse_skill_metadata(
self, skill_dir: Path, namespace: str
) -> Skill | None:
fm_dict, _ = await self._parse_frontmatter_and_body(skill_dir)
if (
not isinstance(fm_dict, dict)
or "name" not in fm_dict
or "description" not in fm_dict
):
return None
blueprint, required_envs = SkillBlueprintBuilder.build(fm_dict)
fm_dict["blueprint"] = blueprint
fm_dict["required_envs"] = required_envs
skill = Skill(
frontmatter=SkillFrontmatter(**fm_dict),
path=skill_dir,
disclosure_level=METADATA,
source="local",
namespace=namespace,
)
if skill.frontmatter.required_envs:
await skill_env_manager.ensure_template(
namespace, skill.id, skill.frontmatter.required_envs
)
return skill
def _load_skill_instructions(self, skill: Skill) -> Skill:
if skill.disclosure_level >= INSTRUCTIONS:
return skill
_, body = skill_manager._parse_frontmatter_and_body_sync(skill.path)
return skill.with_disclosure_level(level=INSTRUCTIONS, instructions=body)
def _parse_frontmatter_and_body_sync(
self, skill_dir: Path
) -> tuple[dict | None, str]:
"""同步版本的回退方法"""
skill_md_path = skill_dir / "SKILL.md"
if not skill_md_path.exists():
return None, ""
content = skill_md_path.read_text(encoding="utf-8")
if not content.startswith("---"):
return None, ""
match = re.search(r"^---\s*\n(.*?)\n---\s*\n?(.*)$", content, re.DOTALL)
if not match:
return None, ""
try:
return yaml.safe_load(match.group(1)), match.group(2).strip()
except yaml.YAMLError:
return None, ""
def _load_skill_resources(self, skill: Skill) -> Skill:
if skill.disclosure_level >= RESOURCES:
return skill
if skill.disclosure_level < INSTRUCTIONS:
skill = self._load_skill_instructions(skill)
scripts = (
[
f.name
for f in (skill.path / "scripts").iterdir()
if f.is_file() and not f.name.startswith(".")
]
if (skill.path / "scripts").is_dir()
else []
)
refs = (
[
f.name
for f in (skill.path / "references").iterdir()
if f.is_file() and not f.name.startswith(".")
]
if (skill.path / "references").is_dir()
else []
)
return skill.with_disclosure_level(
level=RESOURCES, scripts=scripts, references=refs
)
skill_manager = SkillManager()
class BaseSkillResolver(ABC):
"""技能解析器基类,用于统一不同类型的技能加载逻辑"""
@abstractmethod
async def resolve(self, manager: SkillManager, namespace: str) -> list[Skill]: ...
class SkillInstanceResolver(BaseSkillResolver):
"""直接解析已有的 Skill 实例解析器"""
def __init__(self, skill: Skill):
self.skill = skill
async def resolve(self, manager: SkillManager, namespace: str) -> list[Skill]:
return [self.skill]
class StringSkillResolver(BaseSkillResolver):
"""根据技能 ID 解析技能的解析器"""
def __init__(self, skill_id: str):
self.skill_id = skill_id
async def resolve(self, manager: SkillManager, namespace: str) -> list[Skill]:
skill = await manager.get_skill_details(self.skill_id, namespace)
return [skill] if skill else []
class PathSkillResolver(BaseSkillResolver):
"""根据本地路径解析技能的解析器"""
def __init__(self, path: Path):
self.path = path
async def resolve(self, manager: SkillManager, namespace: str) -> list[Skill]:
skill = await manager.load_local_skill(self.path, namespace)
return [skill] if skill else []
class SourceSkillResolver(BaseSkillResolver):
"""根据 SkillSource 描述符动态扫描解析技能的解析器"""
def __init__(self, source: Any):
self.source = source
async def resolve(self, manager: SkillManager, namespace: str) -> list[Skill]:
candidates = []
if self.source.fetch_all:
candidates = list((await manager.discover_skills()).values())
elif self.source.scan_dir:
for child in manager._iter_valid_skill_dirs(self.source.scan_dir):
sk = await manager._parse_skill_from_path(child, namespace)
if sk:
candidates.append(sk)
resolved = []
for cand in candidates:
if (
not self.source.exclude_skills
or cand.id not in self.source.exclude_skills
):
resolved.append(cand)
return resolved