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ThearchyHelios
123 lines
5.0 KiB
Python
123 lines
5.0 KiB
Python
"""查看的接口与形状（L2、L3），以及生成过程的注入接口（L1）。前端解码（run/decode.ts）按这里的形状认 look 事件。
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look 行（模型输出）：
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{"op":"look","need":"…","targets":[{"kind":"table","source":"库 B","name":"refunds"}],"sources":["库 B"]}
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旧写法 tables:[{source, table}] 也认，读进来换成 targets（kind=table）。
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look 事件（发给前端、存进 meta.looks）：
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{"op":"look","id":"L1","by":"model"|"system","need":"…","status":"ok"|"index"|"failed","reason"?,
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"elapsed_ms":…,"objects":[{"kind":"table","source":…,"source_id":…,"named":[…],"picked":[…],"joined":[…],
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"missing":[…],"total":…,"explored"?:…,"catalog_tables":…,"schema_synced_at":…,"catalog_versions":{…}},
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{"kind":"capability","named":[…],"missing":[…],"versions":{…}},
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{"kind":"workflow_version","named":[…],"missing":[…]}]}
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Protocol, TypedDict
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#: 实现的对象类型。口径（caliber）是已发布口径卡上的指标，蓝图助手复用口径时看（look/calibers.py，normal-user-apps N2）
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LOOK_KINDS = ("table", "capability", "workflow_version", "caliber")
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LOOK_LIMIT = 3 # 起草阶段每轮最多查看几次
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REPAIR_LOOK_LIMIT = 1 # 自查修正阶段另给几次
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LOOK_RESULT_CHARS = 12_000 # 一次查看结果的字数上限
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PICK_LIMIT = 12 # 挑选最多挑几个；候选不超过这个数时不挑、全给（沿用）
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CONTEXT_TABLE_LIMIT = 16 # 一次查看最多给几张表的详情（含中间表，沿用）
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#: 挑选时限。经 OpenAI 兼容接入的推理模型关不掉思考（look/pick.PICK_MAX_TOKENS），先想再写：原来的 20 秒常常想到一半就
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#: 掐了，交回目录让主模型自己再看一遍反而更慢
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PICK_TIMEOUT_S = 45
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class LookTarget(TypedDict, total=False):
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kind: str # LOOK_KINDS 之一
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source: str # 表：库名；能力：所属系统；工作流版本：工作流名
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name: str # 表名 / 能力名 / "v3"
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@dataclass
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class LookResult:
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"""一次查看交回给模型的文字，和发给前端、存进 meta.looks 的记录。
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记录为 None 的是没真正查看的那几种回话（need 没写、次数用完、重复查看），不发事件。counted：占不占这一轮的次数。
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"""
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text: str
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record: dict[str, Any] | None
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counted: bool = True
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class LookKind(Protocol):
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"""每种可查看的对象都实现同一组方法（L3）。遮罩按类型各自处理。
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detail 交回的记录块是 objects[] 里的若干项（表按库各一项，能力、工作流版本各一项）；missing 是 resolve 认不出的
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点名，由 detail 写进文字和记录。key 是一个对象在这一轮里的身份，重复查看靠它短路。
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"""
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kind: str
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#: 写给模型的类别名：表、能力、工作流版本
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label: str
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async def overview(self) -> list[str]:
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"""概览：进 system，每个来源一行。"""
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...
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async def index(self, sources: list[str] | None) -> str:
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"""目录：挑选失败时交回，每个对象一行。"""
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...
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async def resolve(self, targets: list[LookTarget]) -> tuple[list[Any], list[LookTarget]]:
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"""按点名解析：(认出的对象, 没找到的点名)。"""
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...
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async def pick(self, need: str, sources: list[str] | None) -> tuple[list[Any], str | None]:
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"""按需求挑选：(挑出的对象, 失败原因)。返回空列表且原因为 None 表示「按这个需求没有相关的」。"""
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...
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async def detail(self, objects: list[Any], budget: int, *,
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missing: list[LookTarget] = ()) -> tuple[str, list[dict[str, Any]]]:
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"""详情文字（受字数上限约束）和这一类的记录块（objects[] 里的项）。"""
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...
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def key(self, obj: Any) -> str:
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...
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# ---- 生成过程的注入接口（L1）：循环只管「读到 look 停下、执行、接着写、计次数」，落地和自查由调用方注入 ----
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@dataclass
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class Applied:
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changed: bool
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forward: bool = True
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problem: str | None = None
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class Applier(Protocol):
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def apply(self, op: dict[str, Any]) -> Applied: ...
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def landed(self) -> list[str]:
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"""已经落地的对象 id（接着写的提示里列出来，提醒模型不要重复）。"""
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...
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def continuing(self) -> None:
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"""进入「接着写」：之后和已落地完全相同的操作忽略、不转发（look §2.5.5）。"""
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...
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def repairing(self) -> None:
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"""进入自查修正：plan、done 由循环吃掉；回答、目录修改提案这类只在起草时有意义的不再收。"""
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...
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class Checker(Protocol):
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async def issues(self) -> list[dict[str, Any]]: ...
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def repair_request(self, issues: list[dict[str, Any]], looks_text: str) -> str: ...
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@dataclass
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class LookBudget:
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limit: int = LOOK_LIMIT
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used: int = 0
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seen: set[str] = field(default_factory=set)
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