Files
zhenxun_bot/zhenxun/services/ai/tools/providers/skills/manager.py
T
80fc5b86a7 ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架 (#2146)
* ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架

- 【重构】将原 services/llm 重构并迁移至全新的 services/ai 架构,提供向下兼容垫片
- 【新增】引入 Agent、Team、Workflow 三大智能体与工作流编排范式
- 【新增】引入基于 RAG 的长期向量记忆与中期槽位记忆系统
- 【新增】引入基于 Docker 的安全代码执行沙箱环境
- 【新增】支持 MCP 协议,允许动态管理和调用 MCP 服务
- 【新增】引入输入输出安全合规护栏与自愈反思机制
- 【优化】重构并优化多厂商 API 适配器 (Gemini, OpenAI, DeepSeek, GLM 等)
- 【优化】优化日志脱敏与 Token 预估机制
- 【移除】移除旧版 llm default 和 llm reset-key 命令,新增 llm mcp 管理命令

* 🔧 chore(deps): 更新项目依赖与配置

- 添加 mcp、jieba 和 aiodocker 依赖到配置文件及 requirements.txt
- 在 pyright 配置中设置 reportMissingImports 为 none
- 调整 .gitignore 中 resources 目录的忽略规则

* ♻️ refactor(tools): 重构工具终止机制并清理知识库日志输出

- 统一使用 `context.state["__end_run__"]` 替代 `EndRunResult` 控制任务结束
- 移除文件系统和向量知识库检索工具中 `ToolResult` 的 `.with_log` 调用
- 调整指令处理器(Directive)的返回值为 `tool_res.output`
- 修复部分类型检查警告并优化联合类型判断语法

* ♻️ refactor(tools): 重构工具副作用指令与控制流熔断机制

- 引入 `DirectivePayload` 及 `ToolResult` 的子类以结构化表达工具副作用
- 移除通过 `context.state` 传递魔术变量的隐式控制流设计
- 重构 `DirectiveManager` 处理器接口,直接在处理器中修改 `AgentState` 并构建 `AgentRunResult`
- 在 `StandardAgentExecutor` 中统一通过 `directive_manager` 调度工具返回的副作用指令
- 补全 `MessageBuilder` 中部分核心方法的文档注释

* 🐛 fix(sandbox): 修复 Docker 沙箱容器状态检测与会话清理逻辑

-【修复】修正 `is_alive` 中直接读取私有属性的问题,改用 `show()` 返回值
-【修复】解决 `execute_code` 中缓存的执行器与当前会话不一致的问题
-【优化】在清理工作区前增加容器存活检测,避免向已死容器发送请求
-【优化】创建容器时增加运行状态校验,若已停止则自动从缓存中移除并重建
-【优化】优化容器销毁和清理逻辑,静默处理容器不存在 (404) 的异常

* 📝 docs(core): 补充核心模块初始化方法的文档注释

* 🚨 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>
2026-07-03 08:53:56 +08:00

402 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.tools.providers.skills.models import (
INSTRUCTIONS,
METADATA,
RESOURCES,
Skill,
SkillBlueprintBuilder,
SkillEnvConfig,
SkillFrontmatter,
)
from zhenxun.services.log import logger
from zhenxun.utils.pydantic_compat import model_dump, model_validate
from zhenxun.utils.utils import infer_plugin_namespace
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