把 21 个内置 agent 的 system_prompt 从 specs.py 的 Python 常量外置为 prompts/<spec.name>.md,import 期由 load_prompt 确定性加载;建立 SPECS 名册 + SCHEMA_CATALOG(Pydantic 类型留 Python)+ 统一只读解析入口 SpecResolver。 纯重构、零功能/schema 变更,缓存断点前块字节级不变(不变量 #9)。 @llm packages/agents(步骤1-3) - spec_model.py:抽出 AgentSpec(frozen,字段不变) - prompt_loader.py:load_prompt = utf-8-sig 去BOM → LF 归一 → NFC → rstrip尾LF, 内存缓存 + fail-fast(PromptNotFoundError),import 期确定性 - schema_catalog.py:SCHEMA_CATALOG[name]→output type 唯一真相源(refiner=None) - prompts/*.md ×21:取常量「运行时值」程序化外迁(反斜杠折行已塌缩, 物理换行≡运行时换行);文件名按 spec.name 连字符(style.md/character-gen.md 等) - specs.py:删 21 常量 + AgentSpec 类;system_prompt=load_prompt(name)、 output_schema=SCHEMA_CATALOG[name];建 SPECS + REVIEW_RESERVED_NAMES; *_spec 兼容期保留且 SPECS[name] is *_spec(同一实例)。804→337 行 - __init__.py:显式 __all__ 重导出(避 F401) @backend packages/skills(步骤4-5) - SpecResolver:内置 SPECS(纯内存、零 DB)+ 用户 SkillRegistry 统一 get; 内置 name 永不触发 DB;output_schema_for 精确匹配 - skill_registry:保留命名空间守卫前移至入库校验,拒同名内置 → VALIDATION - toolbox_registry:GeneratorTool.spec 改走 SPECS[...],删 12 个 *_spec 直接 import @devops repo-root - .gitattributes:prompts/*.md text eol=lf(修正:须用完整嵌套路径才匹配) - packages/agents/pyproject:hatchling artifacts 纳入 prompts/*.md 随 wheel/sdist 分发 - ci.yml:新增 build wheel → 裸装 → import ww_agents.SPECS 冒烟 TDD 全程 mock 网关;门禁绿:ruff/format clean · mypy 209 files · pytest 744 passed (含金标准 sha256 回归 / md↔spec↔catalog 一一对应 / fail-fast / BOM+NFC / 内置守卫 / 同一实例 / 编排器无回归 / apps/api import-smoke / 打包冒烟)
163 lines
6.1 KiB
Python
163 lines
6.1 KiB
Python
"""Skill registry loader(ARCH §5.6 加载段 / PRODUCT_SPEC §5.5)。
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从 `SkillRepo`(`skills` 表的读侧抽象)加载声明式 skill 行 → 转 frozen `AgentSpec`
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(内置 builtin / 用户 custom / 社区 community 同构)。加载后按 `tier` 经 LLM 网关执行
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(复用 §5.1 的 agent 机制,本模块**不**重实现网关)。
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加载时对每条声明跑 `validate_declaration`:越权(reads/writes 指向未知表)的 skill
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直接拒绝(不入册),守 §5.6 表权限契约(不可信用户输入的第一道闸)。
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不变量 #2:行里只带 `tier`(writer/analyst/light),registry 不解析具体 model
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(model 解析在网关)。registry 加载后不可变(specs 字典只读)。
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"""
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from __future__ import annotations
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from typing import Protocol, cast, get_args
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from pydantic import BaseModel
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from ww_agents import REVIEW_RESERVED_NAMES, SPECS, AgentSpec
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from ww_db.models import Skill
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from ww_llm_gateway.types import Tier
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from ww_shared import AppError, ErrorCode
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from ww_skills.skill_permissions import validate_declaration
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_VALID_TIERS: frozenset[str] = frozenset(get_args(Tier))
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# 内置保留命名空间:四审受信名(REVIEW_RESERVED_NAMES)∪ 全部内置 SPECS name。
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# 用户 skill 不可冒用这些 name(守卫前移在入库期,呼应不变量 #3:四审 prompt 不被偷换)。
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_RESERVED_NAMES: frozenset[str] = REVIEW_RESERVED_NAMES | frozenset(SPECS)
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def _reject_reserved_name(spec: AgentSpec) -> None:
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"""用户 skill 冒用内置/保留名 → AppError(VALIDATION)(不可信声明的第一道闸)。"""
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if spec.name in _RESERVED_NAMES:
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raise AppError(
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ErrorCode.VALIDATION,
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f"Skill「{spec.name}」与内置保留名冲突,不可覆盖",
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{"skill": spec.name},
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)
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class SkillRecord(BaseModel):
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"""`skills` 表一行的声明式快照(frozen;snake_case)。
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`input_schema`/`output_schema` 在 DB 里是 JSON Schema dict(用户自定义无 Python 类型),
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registry 暂以纯声明(prompt + 表权限 + tier)执行;结构化 schema 的运行期绑定属后续,
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故这里不携带 type[BaseModel](内置 8 agent 仍用各自硬编码的 spec,见 ww_agents)。
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"""
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model_config = {"frozen": True}
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name: str
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scope: str # builtin / custom / community
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tier: Tier
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system_prompt: str
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# 不可变序列:frozen 只防整字段重绑,不防 list 原地变异(record.reads.append 可旁路);
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# tuple 无 append/clear,对齐 AgentSpec 同字段语义。Pydantic v2 coerce list→tuple。
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reads: tuple[str, ...] = ()
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writes: tuple[str, ...] = ()
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genre: str | None = None
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def _to_spec(record: SkillRecord) -> AgentSpec:
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return AgentSpec(
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name=record.name,
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tier=record.tier,
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system_prompt=record.system_prompt,
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input_schema=None,
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output_schema=None,
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reads=record.reads,
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writes=record.writes,
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genre=record.genre,
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scope=record.scope,
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)
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class SkillRepo(Protocol):
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"""`skills` 表读侧接口(registry 加载源;测试注内存 fake)。"""
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async def list_all(self) -> list[SkillRecord]: ...
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class SkillRegistryLike(Protocol):
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"""`SpecResolver` 消费的 registry 只读契约(结构子集;测试注 duck-type fake)。
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SpecResolver 只用 `.get` / `.names` / `.list_scope` 三个方法——以 Protocol 精确捕获该
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契约,避免依赖具体 `SkillRegistry` 类逼测试 fake 加 `# type: ignore`。
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"""
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def get(self, name: str) -> AgentSpec: ...
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def names(self) -> list[str]: ...
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def list_scope(self, scope: str) -> list[AgentSpec]: ...
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class SkillRegistry:
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"""加载后的只读 skill 注册表(name → AgentSpec)。"""
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def __init__(self, specs: dict[str, AgentSpec]) -> None:
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self._specs = dict(specs)
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@classmethod
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async def load(cls, repo: SkillRepo) -> SkillRegistry:
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"""从 repo 拉全部声明 → 校验表权限 → 建 frozen AgentSpec 字典。
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越权声明(reads/writes 指向未知表)→ AppError(VALIDATION),加载中断(不入册)。
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"""
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specs: dict[str, AgentSpec] = {}
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for record in await repo.list_all():
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spec = _to_spec(record)
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_reject_reserved_name(spec) # 冒用内置/保留名 → 抛 VALIDATION
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validate_declaration(spec) # 越权 → 抛 VALIDATION
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specs[spec.name] = spec
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return cls(specs)
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def get(self, name: str) -> AgentSpec:
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"""按名取 spec;不存在 → AppError(NOT_FOUND)。"""
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spec = self._specs.get(name)
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if spec is None:
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raise AppError(ErrorCode.NOT_FOUND, f"skill not found: {name}")
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return spec
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def names(self) -> list[str]:
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return sorted(self._specs)
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def list_scope(self, scope: str) -> list[AgentSpec]:
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"""按 scope(builtin/custom/community)过滤,按 name 排序。"""
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return [s for s in sorted(self._specs.values(), key=lambda s: s.name) if s.scope == scope]
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def _row_to_record(row: Skill) -> SkillRecord:
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# DB `tier` 是裸 str,非法档位 → VALIDATION(不可信用户输入第一道闸)。
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if row.tier not in _VALID_TIERS:
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raise AppError(
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ErrorCode.VALIDATION,
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f"Skill「{row.name}」声明了非法档位:{row.tier}",
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{"skill": row.name, "tier": row.tier},
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)
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return SkillRecord(
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name=row.name,
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scope=row.scope,
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tier=cast(Tier, row.tier),
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system_prompt=row.system_prompt,
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reads=tuple(str(t) for t in (row.reads or [])),
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writes=tuple(str(t) for t in (row.writes or [])),
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genre=row.genre,
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)
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class SqlSkillRepo:
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"""`skills` 表读侧 SQLAlchemy 实现(registry 加载源;只读)。"""
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def __init__(self, session: AsyncSession) -> None:
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self._s = session
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async def list_all(self) -> list[SkillRecord]:
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rows = (await self._s.execute(select(Skill))).scalars().all()
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return [_row_to_record(r) for r in rows]
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