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ThearchyHelios
315 lines
16 KiB
Python
315 lines
16 KiB
Python
from __future__ import annotations
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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, DateTime, MetaData, event
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from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
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from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
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from sqlalchemy.types import TypeDecorator
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from app.core.config import settings
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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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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 SQLite 的 JSON 列
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type_annotation_map = {dict[str, Any]: JSON, list[Any]: JSON, datetime: UTCDateTime}
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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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_stop_aiosqlite_once()
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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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engine = create_async_engine(settings.db_url, echo=False, future=True, pool_pre_ping=True)
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@event.listens_for(engine.sync_engine, "connect")
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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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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。SQLite 的 ADD COLUMN
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# 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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_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"),
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# 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，
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# 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
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("data_sources", "current_recipe_id", "VARCHAR(32)"),
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("table_imports", "recipe_id", "VARCHAR(32)"),
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("table_imports", "period_start", "VARCHAR(10)"),
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("table_imports", "period_end", "VARCHAR(10)"),
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("table_imports", "context", "JSON"),
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("table_imports", "checks", "JSON"),
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("table_imports", "overrides", "JSON"),
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("table_imports", "waivers", "JSON"),
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("table_imports", "confirmations", "JSON"),
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("table_imports", "manifest_artifact", "VARCHAR(64)"),
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("table_imports", "signed_by", "VARCHAR(100)"),
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("table_imports", "staging_id", "VARCHAR(32)"),
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# 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，
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# 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
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("source_snapshots", "mode", "VARCHAR(20)"),
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("source_snapshots", "recipe_id", "VARCHAR(32)"),
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("source_snapshots", "activated_at", "DATETIME"),
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("source_snapshots", "manifest_artifact", "VARCHAR(64)"),
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("table_imports", "revoked", "JSON"),
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("import_stagings", "edits", "JSON"),
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("import_stagings", "answers_dropped", "JSON"),
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("import_stagings", "redraft_sha256", "VARCHAR(64)"),
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# 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：
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# 没有选项，也不是定时任务发起的
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("runs", "options", "JSON DEFAULT '{}'"),
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("runs", "schedule_id", "VARCHAR(32)"),
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# 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。
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# 存量任务回填 0 和 NULL：升级前没记这些
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("catalog_draft_jobs", "timeouts", "INTEGER DEFAULT 0"),
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("catalog_draft_jobs", "failed_calls", "INTEGER DEFAULT 0"),
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("catalog_draft_jobs", "part_columns", "INTEGER"),
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("catalog_draft_jobs", "notice", "TEXT"),
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("catalog_draft_tasks", "blank_columns", "INTEGER DEFAULT 0"),
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("catalog_draft_tasks", "blank_sample", "JSON"),
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# 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
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("catalog_draft_tasks", "parts_written", "INTEGER DEFAULT 0"),
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("catalog_draft_tasks", "chunks_total", "INTEGER"),
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("catalog_draft_tasks", "chunks_done", "INTEGER"),
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# 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
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("catalog_draft_tasks", "log", "JSON"),
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# 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：
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# 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
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("runs", "origin_kind", "VARCHAR(20)"),
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("runs", "origin", "JSON"),
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("runs", "app_id", "VARCHAR(32)"),
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("runs", "app_version", "INTEGER"),
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("workflow_versions", "published_at", "DATETIME"),
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("workflow_versions", "published_by", "VARCHAR(100)"),
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("workflow_versions", "retired_at", "DATETIME"),
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("workflow_versions", "retired_by", "VARCHAR(100)"),
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("workflow_versions", "retire_note", "TEXT"),
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("workflow_versions", "capability_versions", "JSON"),
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("workflows", "origin", "JSON"),
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("mcp_servers", "headers", "JSON DEFAULT '{}'"),
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("mcp_servers", "endpoint_id", "VARCHAR(32)"),
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("conversations", "app_id", "VARCHAR(32)"),
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("conversations", "created_by", "VARCHAR(100)"),
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# 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
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("apps", "scope", "VARCHAR(20) DEFAULT 'shared'"),
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("apps", "owner", "VARCHAR(100)"),
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("apps", "blueprint_id", "VARCHAR(32)"),
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# 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；
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# 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）
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("runs", "end_reason", "VARCHAR(20)"),
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("schedules", "target_kind", "VARCHAR(20) DEFAULT 'workflow'"),
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("schedules", "target_id", "VARCHAR(32)"),
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("schedule_fires", "eval_run_id", "VARCHAR(32)"),
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# 第 2 波：服务账号（users.kind）；剖析上传表格源时的快照
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("users", "kind", "VARCHAR(20) DEFAULT 'person'"),
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("catalog_profile_tasks", "snapshot_id", "VARCHAR(32)"),
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# 第 3 波：发布时这一版引用的表的结构版本
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("workflow_versions", "schema_versions", "JSON"),
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]
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#: 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。
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#: 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，
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#: IF NOT EXISTS 才不会重复建
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_INDEX_MIGRATIONS: list[tuple[str, str, str | tuple[str, ...]]] = [
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# 定时任务的触发历史按它找这个任务发起的运行
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("ix_runs_schedule_id", "runs", "schedule_id"),
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# 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
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("ix_runs_origin_kind", "runs", "origin_kind"),
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("ix_runs_app_id", "runs", "app_id"),
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("ix_runs_wf_ver_status", "runs", ("workflow_id", "version", "status")),
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("ix_conversations_app_id", "conversations", "app_id"),
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("ix_conversations_created_by", "conversations", "created_by"),
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# 口径账本按种类反查工件（kind=caliber_match，next-round 02 做法 2）
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("ix_artifacts_kind", "artifacts", "kind"),
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]
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#: 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
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#: 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
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#: 能用），多出的助手对话归档（不删，历史还在）
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_UNIQUE_INDEX_MIGRATIONS: list[tuple[str, str, tuple[str, ...], str | None, str]] = [
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("ux_app_hooks_app_name", "app_hooks", ("app_id", "name"), None,
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"DELETE FROM app_hooks WHERE id NOT IN (SELECT id FROM (SELECT id, ROW_NUMBER() OVER "
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"(PARTITION BY app_id, name ORDER BY created_at, id) AS rn FROM app_hooks) WHERE rn = 1)"),
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("ux_conversations_app_owner", "conversations", ("app_id", "created_by"), "kind = 'app' AND archived = 0",
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"UPDATE conversations SET archived = 1 WHERE kind = 'app' AND archived = 0 AND created_by IS NOT NULL "
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"AND id NOT IN (SELECT id FROM (SELECT id, ROW_NUMBER() OVER (PARTITION BY app_id, created_by "
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"ORDER BY last_active_at DESC, id) AS rn FROM conversations WHERE kind = 'app' AND archived = 0 "
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"AND created_by IS NOT NULL) WHERE rn = 1)"),
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]
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def _migrate(conn) -> None: # pragma: no cover - 同步回调
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from sqlalchemy import text
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for table, column, ddl in _COLUMN_MIGRATIONS:
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cols = {row[1] for row in conn.execute(text(f"PRAGMA table_info({table})"))}
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if cols and column not in cols:
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conn.execute(text(f"ALTER TABLE {table} ADD COLUMN {column} {ddl}"))
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for name, table, columns in _INDEX_MIGRATIONS:
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wanted = (columns,) if isinstance(columns, str) else columns
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cols = {row[1] for row in conn.execute(text(f"PRAGMA table_info({table})"))}
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if all(c in cols for c in wanted):
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conn.execute(text(f"CREATE INDEX IF NOT EXISTS {name} ON {table} ({', '.join(wanted)})"))
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for name, table, columns, where, dedupe in _UNIQUE_INDEX_MIGRATIONS:
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cols = {row[1] for row in conn.execute(text(f"PRAGMA table_info({table})"))}
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exists = conn.execute(text("SELECT 1 FROM sqlite_master WHERE type = 'index' AND name = :n"), {"n": name}).first()
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if exists or not all(c in cols for c in columns):
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continue
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conn.execute(text(dedupe))
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conn.execute(text(f"CREATE UNIQUE INDEX {name} ON {table} ({', '.join(columns)})"
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+ (f" WHERE {where}" if where else "")))
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async def init_db() -> None:
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from app.db import models # noqa: F401 确保模型已注册到 metadata
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async with engine.begin() as conn:
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await conn.run_sync(Base.metadata.create_all)
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await conn.run_sync(_migrate)
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