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ThearchyHelios
2e7d6103ea
- 配置：database_url、checkpoint_url（启动时校验写法，只认 PostgreSQL）、连接池四项；启动日志写用的是哪种库（主机、库名、用户，不写密码） - 引擎（db/base.make_engine）：SQLite 照旧 WAL、外键、忙等；PostgreSQL 用连接池、探活、会话时区 UTC，装上方言调整（db/postgres.py）：建表时 String(n) 是不限长的 VARCHAR（SQLite 从不查长度，导过来的存量也可能超长）、ORDER BY 里可空列的 NULL 位置照 SQLite 补写（升序在前、降序在后，窗口函数里同样）；Integer 列在 PostgreSQL 上是 BIGINT（列挂到表上时换成 with_variant，绑定参数跟着列按 BIGINT 转换）；JSON 列两边都存原文（PostgreSQL 的 json，不用 JSONB：JSONB 重排对象里的键，快照的列序、表的先后读回来就变了）；JSON 里的 NaN 写成 null、NUL 去掉（PostgreSQL 不收） - 迁移按方言：已有的列和索引用 inspector 认；PostgreSQL 上补列的类型取模型里这一列的、缺省值取迁移表里的（布尔 0/1 换成 false/true），ADD COLUMN IF NOT EXISTS；去重语句里的布尔按方言写；部分唯一索引补 postgresql_where；init_db 在 PostgreSQL 上拿咨询锁（几个进程同时启动不撞） - 应用库上的 SQLite 专用写法改成按方言编译的表达式（db/portable.py）：json_extract 的十几处（口径召回、出具、对账、个人应用、幂等键、访问控制、数据源变化通知、剖析任务、待办超时）换成 json_eq / json_flag / json_text，json_type 换成 json_is_string（两种库同一套带类型的比较）；能力登记的 sqlite insert 换成按会话方言的 insert_ignore；能力使用次数的 json_each 按方言分支；附件「没绑记录」的条件不靠 json 的等号；按报错文字认唯一约束的地方换成 unique_violation（按表和列认，两种库的报错都认得） - 断点（engine/checkpoints.py）：PostgreSQL 时开 psycopg 连接池、在咨询锁里 setup；runner 只认 BaseCheckpointSaver，读断点仍只走 saver 的公开接口 - 依赖：新 extra postgres（asyncpg、psycopg[binary] 3、psycopg-pool、langgraph-checkpoint-postgres 3.1.2）；langgraph-checkpoint 卡在 <4.3（断点格式就在这个包里，postgres 包新版要 4.3，不动已装的 4.2.0）；锁文件补上新包 - 运行收尾：记 action.unknown、安排核实做完才把运行摘掉（以前先摘掉，等「不在跑了」再读事件的一方在库慢时读不到这一条） - 测试：AGENTLAB_TEST_DATABASE_URL 时跑在 PostgreSQL 上（每个 xdist 进程建自己的库、按事件循环分的连接池 tests/pgpool.py），不设时盖掉环境里的数据库地址照旧用 SQLite；scripts/test-postgres.sh 起临时的 postgres:16 容器跑全量；sqlite_only 标记；另开连接改库的几个竞态测试、触发器测试改成两种库都能跑（tests/appdb.py）；新增 test_db_portable 守着方言调整、JSON 键序、超长字符串、大整数、NULL 排序、时区、PostgreSQL 老库迁移
475 lines
25 KiB
Python
475 lines
25 KiB
Python
from __future__ import annotations
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import json
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import logging
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import math
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import re
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import uuid
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from datetime import datetime, timezone
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from typing import Any, AsyncIterator
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from sqlalchemy import JSON, BigInteger, Column, DateTime, Integer, MetaData, event
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from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, async_sessionmaker, create_async_engine
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from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
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from sqlalchemy.pool import NullPool
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from sqlalchemy.types import TypeDecorator
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from app.core.config import settings
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logger = logging.getLogger(__name__)
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NAMING_CONVENTION = {
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"ix": "ix_%(column_0_label)s",
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"uq": "uq_%(table_name)s_%(column_0_name)s",
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"fk": "fk_%(table_name)s_%(column_0_name)s",
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"pk": "pk_%(table_name)s",
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}
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def utcnow() -> datetime:
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return datetime.now(timezone.utc)
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def new_id() -> str:
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return uuid.uuid4().hex
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class UTCDateTime(TypeDecorator):
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"""存 UTC、读出来也是带时区的 UTC。
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SQLite 没有时区类型：DateTime(timezone=True) 写进去的只是一串不带偏移的
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字面时间，读回来是 naive 的。API 于是吐出 '2026-09-26T01:11:19'，浏览器把
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不带偏移的 ISO 串当本地时间解析，运行列表整体慢了 8 小时。在这一层补上 UTC，
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pydantic 序列化自然带 +00:00，所有接口一次修好。
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写入前先换算成 UTC 再去掉时区：SQLite 方言格式化时直接丢弃 tzinfo，
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一个 +08:00 的时间原样写进去，读回来就差出 8 小时。
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"""
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impl = DateTime
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cache_ok = True
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def process_bind_param(self, value: datetime | None, dialect: Any) -> datetime | None:
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if value is None:
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return None
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if value.tzinfo is not None:
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value = value.astimezone(timezone.utc).replace(tzinfo=None)
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return value
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def process_result_value(self, value: datetime | None, dialect: Any) -> datetime | None:
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if value is None:
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return None
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if value.tzinfo is None:
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return value.replace(tzinfo=timezone.utc)
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return value.astimezone(timezone.utc)
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#: 应用库的 JSON 列：两种库上都是存原文的 JSON（PostgreSQL 的 json，不是 JSONB）。JSONB 会把对象里的键重新排序：
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#: 应用里不少地方按存进去的键的先后出东西（快照的列、表的先后、复核结论各段的先后），换成 JSONB 读回来就变了样，
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#: 测试当场抓到好几处。json 的代价是没有相等运算符：== 比较、DISTINCT、GROUP BY、ORDER BY 不能直接作用在整列上
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#: （应用里没有这种写法，test_db_portable 守着）；按里面某个键筛选走 db/portable 的 json_eq 等，只把取出来的那一小段
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#: 转成 JSONB 比较
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PortableJSON = JSON()
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class Base(DeclarativeBase):
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metadata = MetaData(naming_convention=NAMING_CONVENTION)
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# 图定义、节点配置、事件负载都是任意形状的 JSON，统一映射到 JSON 列（见 PortableJSON）
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type_annotation_map = {dict[str, Any]: PortableJSON, list[Any]: PortableJSON, datetime: UTCDateTime}
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@event.listens_for(Column, "after_parent_attach")
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def _portable_integer(column: Column, table: Any) -> None:
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"""应用库的 Integer 列在 PostgreSQL 上换成 BIGINT（SQLite 照旧是 INTEGER，主键照旧是 rowid）。
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SQLite 的 INTEGER 是 64 位，PostgreSQL 的只有 32 位：行数估计、字节数、累计的计数都可能过 21 亿。在列挂到表上
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的那一刻换（定义模型时，任何查询之前）：建表是 BIGINT（自增主键是 BIGSERIAL），和这一列比较、写进这一列的绑定
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参数也按 BIGINT 转换。只管挂在应用 metadata 上、类型正好是 Integer 的列；业务数据源那边的表不受影响。"""
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if getattr(table, "metadata", None) is Base.metadata and type(column.type) is Integer \
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and not column.type._variant_mapping:
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column.type = Integer().with_variant(BigInteger(), "postgresql")
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class TimestampMixin:
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created_at: Mapped[datetime] = mapped_column(UTCDateTime, default=utcnow)
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updated_at: Mapped[datetime] = mapped_column(UTCDateTime, default=utcnow, onupdate=utcnow)
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def _stop_aiosqlite_once() -> None:
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"""aiosqlite 连接的工作线程只停一次；之后再要求停的，拿到的是同一个结果。
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aiosqlite 0.22 的 stop() 每调一次都往工作线程队列里放一个"关闭并退出线程"
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的请求，而线程处理完第一个就退出了——之后放进去的请求没人处理，等它的一方
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永远挂着。close() 的 finally 里会调 stop()，SQLAlchemy 的强制 terminate 也会
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直接调；两个 close 并发、或者 close 撞上强制 terminate，就会卡死。实测同一
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连接并发 close 两次，10 次卡 10 次。
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在这里触发它的路径：并行的两条支路一条失败，LangGraph 取消另一条，而那一条
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正在写工件。取消打进进行中的数据库操作，SQLAlchemy 在两条路径上各 terminate
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一次同一条连接——节点任务取消不完，整个运行卡在 running，占着的并发名额也不
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还。大约五次里有一次。连接池和 checkpointer 都走 aiosqlite，补在这一层都管得到。
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"""
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import weakref
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import aiosqlite
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original = aiosqlite.Connection.stop
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if getattr(original, "_agentlab_once", False):
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return
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stopping: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
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def stop(self): # type: ignore[no-untyped-def]
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if self not in stopping:
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stopping[self] = original(self)
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return stopping[self]
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stop._agentlab_once = True # type: ignore[attr-defined]
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aiosqlite.Connection.stop = stop # type: ignore[method-assign]
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# 应用库是 PostgreSQL 时也照样打：上传表格、手工登记的 SQLite 业务库（data/engine）还是走 aiosqlite 的连接池
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_stop_aiosqlite_once()
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def _set_sqlite_pragma(dbapi_conn, _record): # pragma: no cover - 驱动回调
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"""WAL + 外键约束。WAL 让读写不互相阻塞，事件写入和前端轮询能并行。"""
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cur = dbapi_conn.cursor()
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cur.execute("PRAGMA journal_mode=WAL")
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cur.execute("PRAGMA foreign_keys=ON")
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cur.execute("PRAGMA busy_timeout=5000")
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cur.close()
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def _finite(value: Any) -> Any:
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if isinstance(value, float):
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return value if math.isfinite(value) else None
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if isinstance(value, dict):
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return {k: _finite(v) for k, v in value.items()}
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if isinstance(value, (list, tuple)):
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return [_finite(v) for v in value]
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return value
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def _no_nul(value: Any) -> Any:
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if isinstance(value, str):
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return value.replace("\x00", "")
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if isinstance(value, dict):
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return {_no_nul(k): _no_nul(v) for k, v in value.items()}
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if isinstance(value, list):
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return [_no_nul(v) for v in value]
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return value
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def pg_json_dumps(value: Any) -> str:
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"""PostgreSQL 上写 JSON 列。它不收 NaN / Infinity（SQLite 照存）；字符串里的 \\u0000 json 列收，但一转成 JSONB
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（按键筛选时，db/portable）、一取成文本就报错，那一整条查询跟着失败。
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非有限的浮点写成 null（同浏览器的 JSON.stringify），字符串里的 NUL 字符去掉；平常的值一次 dumps 就过，
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只有碰上了才多走一遍。"""
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try:
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text = json.dumps(value, allow_nan=False)
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except ValueError:
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text = json.dumps(_finite(value), allow_nan=False)
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if "\\u0000" in text:
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text = json.dumps(_no_nul(json.loads(text)), allow_nan=False)
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return text
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#: 应用连 PostgreSQL 时报的 application_name。copy-to-postgres 按它认目标库上还有没有服务连着（它自己报的是 agentlab-copy）
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APPLICATION_NAME = "agentlab"
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def make_engine(url: str | None = None, *, application_name: str = APPLICATION_NAME) -> AsyncEngine:
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"""应用库的引擎。SQLite：WAL、外键、忙等，单个文件；PostgreSQL：连接池、方言调整（db/postgres.py）。
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copy-to-postgres 建目标库的引擎也走这里，两边的列类型、排序规则是同一套。
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"""
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url = url or settings.db_url
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if url.startswith("sqlite"):
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# pool_pre_ping：取连接前先探一下活。取消异常打进 SQLAlchemy 的清理流程时
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# （_close_connection 记完日志会把 CancelledError 原样抛出，跳过了把连接记录
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# 清空的那一行），已经关掉的连接会留在池里，下一个取到它的人拿到的是
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# "no active connection"。探活失败会被当成断开，换一条新的——SQLite 本地一次
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# SELECT 1 的代价，换池子不被污染。
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made = create_async_engine(url, echo=False, future=True, pool_pre_ping=True)
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event.listen(made.sync_engine, "connect", _set_sqlite_pragma)
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return made
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from app.db import postgres
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pool: dict[str, Any] = (
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{"poolclass": NullPool} if settings.db_pool_size <= 0 else {
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"pool_size": settings.db_pool_size, "max_overflow": max(settings.db_max_overflow, 0),
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"pool_timeout": settings.db_pool_timeout, "pool_recycle": settings.db_pool_recycle})
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made = create_async_engine(
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url, echo=False, future=True, pool_pre_ping=True, json_serializer=pg_json_dumps,
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# 会话时区定成 UTC：时间列是不带时区的 UTC（UTCDateTime），库里 now() 之类的也按 UTC 算
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connect_args={"server_settings": {"application_name": application_name, "timezone": "UTC"}},
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**pool)
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postgres.install(made.sync_engine.dialect)
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return made
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def describe_database() -> str:
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"""启动日志里的一句：用的是哪种库、在哪（不写密码）。"""
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from app.db.urls import describe
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if settings.checkpoint_url:
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points = f"断点在 PostgreSQL {describe(settings.checkpoint_url)}"
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elif settings.db_dialect == "sqlite":
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points = f"断点 {settings.checkpoint_path}"
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else:
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points = "断点在同一个库"
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if settings.db_dialect == "sqlite":
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return f"SQLite（{settings.db_path}，{points}）"
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return f"PostgreSQL（{describe(settings.db_url)}，{points}）"
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engine = make_engine()
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SessionLocal = async_sessionmaker(engine, expire_on_commit=False, class_=AsyncSession)
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async def get_session() -> AsyncIterator[AsyncSession]:
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"""FastAPI 依赖注入用的会话。"""
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async with SessionLocal() as session:
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yield session
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# 轻量迁移：create_all 只建新表，不给已有表加列。ADD COLUMN 便宜且带默认值回填，够用；等真需要改列类型再考虑 alembic。
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# 第三项照 SQLite 写（类型和缺省值的字面量）。PostgreSQL 上类型取模型里这一列的（按应用库的方言调整编译：String(n)
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# 是 VARCHAR、Integer 是 BIGINT、JSON 是 json、时间是 TIMESTAMP），缺省值取这里写的，布尔的 0 / 1 换成 false / true
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# （_column_ddl）。所以这里加的每一列模型里都得有：test_db_portable 守着
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_COLUMN_MIGRATIONS: list[tuple[str, str, str]] = [
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("runs", "run_class", "TEXT DEFAULT 'exploratory'"),
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("runs", "version", "INTEGER"),
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("runs", "version_hash", "VARCHAR(64)"),
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("runs", "manifest_hash", "VARCHAR(64)"),
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("runs", "manifest_seq", "INTEGER"),
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("runs", "started_by", "VARCHAR(100)"),
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# 续跑、恢复要沿用第一次发起时的记忆域、知识库和审批默认值；以前只在第一段
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# _drive 里传过一次，恢复后回落成 "default"，同一次运行前后两段查的不是同一个库
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("runs", "memory_scope", "VARCHAR(100)"),
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("runs", "collection", "VARCHAR(100)"),
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("runs", "approval_default", "VARCHAR(20)"),
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("runs", "tool_trust", "JSON"),
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("runs", "agent_limits", "JSON"),
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("runs", "error_node_id", "VARCHAR(64)"),
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("workflows", "status", "TEXT DEFAULT 'draft'"),
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("workflows", "published_version", "INTEGER"),
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("workflows", "published_by", "VARCHAR(100)"),
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("workflow_versions", "graph_hash", "VARCHAR(64)"),
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# 存量版本回填成 NULL：不知道当时按哪一档发布的，正式运行照旧看工作流的 status
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("workflow_versions", "level", "VARCHAR(20)"),
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# 发布时的数据目录版本（阶段 4A）。存量版本回填成 NULL：不知道当时按哪一版目录发布的，正式运行不提醒
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("workflow_versions", "catalog_versions", "JSON"),
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("approvals", "resolved_by", "VARCHAR(100)"),
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# 存量向量没有这两列，回填成空串 / 0 —— 正好表示"不知道是谁建的"，
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# 检索侧会把它们当成对不上当前 embedder 处理
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("chunks", "embed_model", "VARCHAR(100) DEFAULT ''"),
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("chunks", "embed_dim", "INTEGER DEFAULT 0"),
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("memories", "embed_model", "VARCHAR(100) DEFAULT ''"),
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("memories", "embed_dim", "INTEGER DEFAULT 0"),
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("chunks", "token_len", "INTEGER DEFAULT 0"),
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("documents", "status", "TEXT DEFAULT 'ready'"),
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("documents", "error", "TEXT DEFAULT ''"),
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# 存量轮次没有复核结论。NULL 正好表示"这一轮没复核过"，和"复核过、没问题"
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# （存 verdict=ok）是两回事，前端要分得开
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("conversation_turns", "review", "JSON"),
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("conversation_turns", "meta", "JSON"),
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("data_sources", "last_check", "JSON"),
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("mcp_servers", "last_check", "JSON"),
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# 存量的源一律先记成 manual；老的上传源由 table_versions.startup 按路径认出来，
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# 补上 v0 版本后再改成 upload
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("data_sources", "origin", "TEXT DEFAULT 'manual'"),
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("data_sources", "current_snapshot_id", "VARCHAR(64)"),
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# 存量运行没有钉过版本，NULL 正好表示「升级前发起的」
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("runs", "data_versions", "JSON"),
|
||
# 按配方导入（期 2）。新表 table_recipes、import_stagings 由 create_all 建；这些是已有表的新列，
|
||
# 存量行一律 NULL：存量的源都是简单导入或手工源，存量的导入记录都不是按配方导入的
|
||
("data_sources", "current_recipe_id", "VARCHAR(32)"),
|
||
("table_imports", "recipe_id", "VARCHAR(32)"),
|
||
("table_imports", "period_start", "VARCHAR(10)"),
|
||
("table_imports", "period_end", "VARCHAR(10)"),
|
||
("table_imports", "context", "JSON"),
|
||
("table_imports", "checks", "JSON"),
|
||
("table_imports", "overrides", "JSON"),
|
||
("table_imports", "waivers", "JSON"),
|
||
("table_imports", "confirmations", "JSON"),
|
||
("table_imports", "manifest_artifact", "VARCHAR(64)"),
|
||
("table_imports", "signed_by", "VARCHAR(100)"),
|
||
("table_imports", "staging_id", "VARCHAR(32)"),
|
||
# 期 3：按期累积、版本页（P3-SPEC 9.6）。新表 snapshot_activations 由 create_all 建；存量行一律 NULL，
|
||
# 含义是「期 3 之前的」：快照的 mode 按 replace、recipe_id 取最后一期导入的、activated_at 按 created_at
|
||
("source_snapshots", "mode", "VARCHAR(20)"),
|
||
("source_snapshots", "recipe_id", "VARCHAR(32)"),
|
||
("source_snapshots", "activated_at", "DATETIME"),
|
||
("source_snapshots", "manifest_artifact", "VARCHAR(64)"),
|
||
("table_imports", "revoked", "JSON"),
|
||
("import_stagings", "edits", "JSON"),
|
||
("import_stagings", "answers_dropped", "JSON"),
|
||
("import_stagings", "redraft_sha256", "VARCHAR(64)"),
|
||
# 运行选项（发起时的快照，整次运行沿用）和发起它的定时任务。存量运行回填 {} 和 NULL：
|
||
# 没有选项，也不是定时任务发起的
|
||
("runs", "options", "JSON DEFAULT '{}'"),
|
||
("runs", "schedule_id", "VARCHAR(32)"),
|
||
# 数据目录起草任务（验收后修正）：超时、没写出来的调用次数，当前的列数上限，提醒；逐表的模型留空的列。
|
||
# 存量任务回填 0 和 NULL：升级前没记这些
|
||
("catalog_draft_jobs", "timeouts", "INTEGER DEFAULT 0"),
|
||
("catalog_draft_jobs", "failed_calls", "INTEGER DEFAULT 0"),
|
||
("catalog_draft_jobs", "part_columns", "INTEGER"),
|
||
("catalog_draft_jobs", "notice", "TEXT"),
|
||
("catalog_draft_tasks", "blank_columns", "INTEGER DEFAULT 0"),
|
||
("catalog_draft_tasks", "blank_sample", "JSON"),
|
||
# 数据目录闭环第一步：宽表逐段落盘的段数、块进度。存量任务回填 0 和 NULL：升级前没记这些
|
||
("catalog_draft_tasks", "parts_written", "INTEGER DEFAULT 0"),
|
||
("catalog_draft_tasks", "chunks_total", "INTEGER"),
|
||
("catalog_draft_tasks", "chunks_done", "INTEGER"),
|
||
# 复核后：逐表的日志（模型提议的有效记录条件没有收下的原因）。存量任务回填 NULL，接口给空列表
|
||
("catalog_draft_tasks", "log", "JSON"),
|
||
# 应用平台（docs/design/app-platform-and-look.md §3.1.2）。新表由 create_all 建；这些是已有表的新列，存量一律 NULL：
|
||
# 运行的来源未记录、版本的发布人和退役没记过、MCP 没有认证头、对话不属于任何应用
|
||
("runs", "origin_kind", "VARCHAR(20)"),
|
||
("runs", "origin", "JSON"),
|
||
("runs", "app_id", "VARCHAR(32)"),
|
||
("runs", "app_version", "INTEGER"),
|
||
("workflow_versions", "published_at", "DATETIME"),
|
||
("workflow_versions", "published_by", "VARCHAR(100)"),
|
||
("workflow_versions", "retired_at", "DATETIME"),
|
||
("workflow_versions", "retired_by", "VARCHAR(100)"),
|
||
("workflow_versions", "retire_note", "TEXT"),
|
||
("workflow_versions", "capability_versions", "JSON"),
|
||
("workflows", "origin", "JSON"),
|
||
("mcp_servers", "headers", "JSON DEFAULT '{}'"),
|
||
("mcp_servers", "endpoint_id", "VARCHAR(32)"),
|
||
("conversations", "app_id", "VARCHAR(32)"),
|
||
("conversations", "created_by", "VARCHAR(100)"),
|
||
# 普通用户建应用（docs/design/normal-user-apps.md §3.1）：个人应用（scope=personal、owner）和出自哪个蓝图
|
||
("apps", "scope", "VARCHAR(20) DEFAULT 'shared'"),
|
||
("apps", "owner", "VARCHAR(100)"),
|
||
("apps", "blueprint_id", "VARCHAR(32)"),
|
||
# 下一轮（docs/design/next-round/00-总方案.md X4、X9）：运行为什么结束在 cancelled；定时的目标种类和 id；
|
||
# 定时触发建的那次评测。存量行：end_reason 为空，target_kind 是 workflow（target_id 由 scheduler 按 workflow_id 读）
|
||
("runs", "end_reason", "VARCHAR(20)"),
|
||
("schedules", "target_kind", "VARCHAR(20) DEFAULT 'workflow'"),
|
||
("schedules", "target_id", "VARCHAR(32)"),
|
||
("schedule_fires", "eval_run_id", "VARCHAR(32)"),
|
||
# 编排完善（docs/design/orchestration-round/00-总方案.md §4）：定时任务的触发方式。存量行都是 cron
|
||
("schedules", "trigger_kind", "VARCHAR(20) DEFAULT 'cron'"),
|
||
("schedules", "trigger", "JSON"),
|
||
# 第 2 波：服务账号（users.kind）；剖析上传表格源时的快照
|
||
("users", "kind", "VARCHAR(20) DEFAULT 'person'"),
|
||
("catalog_profile_tasks", "snapshot_id", "VARCHAR(32)"),
|
||
# 第 3 波：发布时这一版引用的表的结构版本
|
||
("workflow_versions", "schema_versions", "JSON"),
|
||
]
|
||
|
||
#: 迁移补上的列要建的索引：(索引名, 表, 列或多列)。create_all 不碰已有的表，表上新列的索引也就不会建。
|
||
#: 索引名照 NAMING_CONVENTION 取（多列的照模型 __table_args__ 里写的名字），和新库 create_all 建出来的同名，
|
||
#: IF NOT EXISTS 才不会重复建
|
||
_INDEX_MIGRATIONS: list[tuple[str, str, str | tuple[str, ...]]] = [
|
||
# 定时任务的触发历史按它找这个任务发起的运行
|
||
("ix_runs_schedule_id", "runs", "schedule_id"),
|
||
# 应用平台：按来源、按应用筛运行；按工作流、版本、状态找钉住版本最近一次封存的运行
|
||
("ix_runs_origin_kind", "runs", "origin_kind"),
|
||
("ix_runs_app_id", "runs", "app_id"),
|
||
("ix_runs_wf_ver_status", "runs", ("workflow_id", "version", "status")),
|
||
("ix_conversations_app_id", "conversations", "app_id"),
|
||
("ix_conversations_created_by", "conversations", "created_by"),
|
||
# 口径账本按种类反查工件（kind=caliber_match，next-round 02 做法 2）
|
||
("ix_artifacts_kind", "artifacts", "kind"),
|
||
]
|
||
|
||
|
||
#: 迁移补建的唯一索引：(索引名, 表, 列, 部分索引的条件, 建之前的去重语句)。只在索引还不存在时先去重再建——存量库里可能
|
||
#: 已经有并发留下的重复行，直接建会失败。去重的规矩写在各自的语句里：多出的钩子行删掉（只留最早的那一行，它的密钥照旧
|
||
#: 能用），多出的助手对话归档（不删，历史还在）。条件和语句里的 {false} / {true} 按方言换成布尔的字面量（SQLite 是 0 / 1）
|
||
_UNIQUE_INDEX_MIGRATIONS: list[tuple[str, str, tuple[str, ...], str | None, str]] = [
|
||
("ux_app_hooks_app_name", "app_hooks", ("app_id", "name"), None,
|
||
"DELETE FROM app_hooks WHERE id NOT IN (SELECT id FROM (SELECT id, ROW_NUMBER() OVER "
|
||
"(PARTITION BY app_id, name ORDER BY created_at, id) AS rn FROM app_hooks) AS ranked WHERE rn = 1)"),
|
||
("ux_conversations_app_owner", "conversations", ("app_id", "created_by"), "kind = 'app' AND archived = {false}",
|
||
"UPDATE conversations SET archived = {true} WHERE kind = 'app' AND archived = {false} AND created_by IS NOT NULL "
|
||
"AND id NOT IN (SELECT id FROM (SELECT id, ROW_NUMBER() OVER (PARTITION BY app_id, created_by "
|
||
"ORDER BY last_active_at DESC, id) AS rn FROM conversations WHERE kind = 'app' AND archived = {false} "
|
||
"AND created_by IS NOT NULL) AS ranked WHERE rn = 1)"),
|
||
]
|
||
|
||
#: 布尔字面量：SQLite 的布尔列存 0 / 1
|
||
_BOOL_LITERALS = {"sqlite": {"false": "0", "true": "1"}, "postgresql": {"false": "false", "true": "true"}}
|
||
|
||
_DEFAULT = re.compile(r"\s+DEFAULT\s+(.+)$", re.I | re.S)
|
||
|
||
|
||
def _column_ddl(dialect: Any, table: str, column: str, ddl: str) -> str:
|
||
"""ADD COLUMN 里列名后面那一段。SQLite 原样用迁移表里写的；PostgreSQL 类型取模型里这一列的，缺省值取迁移表里的。"""
|
||
if dialect.name == "sqlite":
|
||
return ddl
|
||
from sqlalchemy import Boolean
|
||
|
||
from app.db.postgres import AppTypeCompiler
|
||
|
||
col = Base.metadata.tables[table].c[column]
|
||
out = AppTypeCompiler(dialect).process(col.type, type_expression=col)
|
||
if match := _DEFAULT.search(ddl):
|
||
default = match.group(1).strip()
|
||
if isinstance(col.type, Boolean):
|
||
default = {"0": "false", "1": "true"}.get(default, default)
|
||
out += f" DEFAULT {default}"
|
||
return out
|
||
|
||
|
||
def _migrate(conn) -> None: # pragma: no cover - 同步回调
|
||
"""给老库补列、补索引。已有的列和索引按 inspector 认（两种库同一套），再跑一遍什么都不做。"""
|
||
from sqlalchemy import inspect, text
|
||
|
||
dialect = conn.dialect
|
||
literals = _BOOL_LITERALS.get(dialect.name, _BOOL_LITERALS["postgresql"])
|
||
inspector = inspect(conn)
|
||
tables = set(inspector.get_table_names())
|
||
columns = {t: {c["name"] for c in inspector.get_columns(t)} for t in tables}
|
||
# IF NOT EXISTS：PostgreSQL 上多个进程同时启动，各自的检查和补列之间别人可能已经补上了（init_db 另外拿着锁）
|
||
add = "ADD COLUMN IF NOT EXISTS" if dialect.name == "postgresql" else "ADD COLUMN"
|
||
for table, column, ddl in _COLUMN_MIGRATIONS:
|
||
cols = columns.get(table)
|
||
if cols and column not in cols:
|
||
conn.execute(text(f"ALTER TABLE {table} {add} {column} {_column_ddl(dialect, table, column, ddl)}"))
|
||
cols.add(column)
|
||
for name, table, wanted in _INDEX_MIGRATIONS:
|
||
wanted = (wanted,) if isinstance(wanted, str) else wanted
|
||
cols = columns.get(table) or set()
|
||
if all(c in cols for c in wanted):
|
||
conn.execute(text(f"CREATE INDEX IF NOT EXISTS {name} ON {table} ({', '.join(wanted)})"))
|
||
for name, table, wanted, where, dedupe in _UNIQUE_INDEX_MIGRATIONS:
|
||
cols = columns.get(table) or set()
|
||
if not all(c in cols for c in wanted):
|
||
continue
|
||
if any(ix["name"] == name for ix in inspect(conn).get_indexes(table)):
|
||
continue
|
||
conn.execute(text(dedupe.format(**literals)))
|
||
conn.execute(text(f"CREATE UNIQUE INDEX {name} ON {table} ({', '.join(wanted)})"
|
||
+ (f" WHERE {where.format(**literals)}" if where else "")))
|
||
|
||
|
||
#: init_db 在 PostgreSQL 上拿的会话级咨询锁的号：几个进程同时启动时，建表和迁移一个一个来（同一个号别处别用）
|
||
_INIT_LOCK = 0x4167656E744C6162 # "AgentLab"
|
||
|
||
|
||
async def create_schema(target: AsyncEngine) -> None:
|
||
"""建表、补列补索引（create_all 只建新表，老库的新列靠 _migrate）。两种库同一套；copy-to-postgres 建目标库也用它。"""
|
||
from app.db import models # noqa: F401 确保模型已注册到 metadata
|
||
|
||
async with target.begin() as conn:
|
||
if conn.dialect.name == "postgresql":
|
||
# 事务结束自动放锁。CREATE TABLE IF NOT EXISTS 在 PostgreSQL 上并发执行也会撞（系统表的唯一约束）
|
||
from sqlalchemy import text
|
||
|
||
await conn.execute(text("SELECT pg_advisory_xact_lock(:k)"), {"k": _INIT_LOCK})
|
||
await conn.run_sync(Base.metadata.create_all)
|
||
await conn.run_sync(_migrate)
|
||
|
||
|
||
async def init_db() -> None:
|
||
logger.info("应用库：%s", describe_database())
|
||
await create_schema(engine)
|