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https://github.com/ThearchyHelios/AgentLab.git
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ThearchyHelios
221 lines
10 KiB
Python
221 lines
10 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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]
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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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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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