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ThearchyHelios
5928cc052e
显示成生效却没生效，比明说没有更危险——它让人以为有东西在兜底。
1. max_run_seconds 只存在于配置和设置接口里，没有任何代码执行它：一个挂住的
模型或工具能把运行和它占着的并发名额一直拖着。现在 runner 用 asyncio.timeout
强制执行，按"一段执行"计（等人工审批时 _drive 已结束，不算）；超时判 failed、
说清是哪道上限，可以接着跑。别处抛的 TimeoutError 不冒充这道上限（看
Timeout.expired()）。
2. 模型调用从来没设过超时：ModelSpec.timeout 从没被赋值，那条传参分支一次都没
执行过，所有调用走 SDK 默认的 10 分钟外加重试。新增 model_timeout_seconds
（默认 300 秒）作为缺省，节点显式配的仍优先；已确认 ChatAnthropic / ChatOpenAI
都接这个参数。
3. manifest 哈希只写不验，而且是在 run.finished 落库之前算的——清单里恰恰少了
宣布"跑完了、成果是什么"的那条，改成果清单照样对得上。现在终态事件落库之后
再封存，并记下封到哪一条（manifest_seq，新列）；新增 verify_manifest 与
GET /api/runs/{id}/verify，运行详情页加了核对按钮。封存之后追加的事件不算篡改。
老运行没有 manifest_seq，按当时的口径还原范围——开发库里 300 条带哈希的历史
运行全部核对通过（成功 / 失败 / 取消各类都有），没有一条误报。
设置页「运行环境」补上单次执行时限和模型调用超时两项，README 加了对应的常见问题。
顺带修了一个被 _seal 放大的老问题：_drive 的收尾被打断时（关停取消、库连接出错）
漏掉 _tasks.pop，留下的任务让下一次 shutdown 的 gather 在别的事件循环里抛
"future belongs to a different loop"——测试里表现为每八九轮一次、一出就是几十个
teardown error。pop 移进内层 finally，shutdown 只等未结束的任务并清空。修后全量
连跑 12 轮 12 轮全绿。
test_guardrails.py 8 例：超时中止（挂住的节点 1 秒后被停下）、缺省模型超时、封存覆盖
run.finished、改成果被戳穿、事后追加不误报、老运行按旧口径核、中断态未封存、接口
404。414 → 422 passed。
435 lines
22 KiB
Python
435 lines
22 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from typing import Any
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from sqlalchemy import (
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DateTime, Float, ForeignKey, Index, Integer, LargeBinary, String, Text, UniqueConstraint,
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)
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from app.db.base import Base, TimestampMixin, new_id, utcnow
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# --------------------------------------------------------------------------
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# 设置：模型供应商 / 用户偏好 / MCP / 自定义工具
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# --------------------------------------------------------------------------
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class Provider(Base, TimestampMixin):
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"""一个模型接入点。api_key 落库前经 app.core.crypto 加密。"""
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__tablename__ = "providers"
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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name: Mapped[str] = mapped_column(String(100), unique=True)
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kind: Mapped[str] = mapped_column(String(32)) # anthropic | openai | openai_compatible | mock
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base_url: Mapped[str | None] = mapped_column(String(500), default=None)
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api_key: Mapped[str | None] = mapped_column(Text, default=None) # 密文
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models: Mapped[list[Any]] = mapped_column(default=list) # [{id, label, context, pricing}]
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default_model: Mapped[str | None] = mapped_column(String(200), default=None)
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enabled: Mapped[bool] = mapped_column(default=True)
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extra: Mapped[dict[str, Any]] = mapped_column(default=dict)
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class DataSource(Base, TimestampMixin):
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"""一个数据库接入点。password 落库前经 app.core.crypto 加密，和 Provider 同一套。
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readonly 默认为真，是刻意的安全基线：这里的 SQL 由模型生成，不是人写的。
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要写库就另建一个显式关掉只读的源，并且写操作会走人工审批——把"能写"这件事
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变成一个需要两次明确动作的决定，而不是默认状态。
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"""
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__tablename__ = "data_sources"
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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name: Mapped[str] = mapped_column(String(100), unique=True)
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kind: Mapped[str] = mapped_column(String(32)) # mysql | postgres | oracle | sqlite
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host: Mapped[str | None] = mapped_column(String(255), default=None)
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port: Mapped[int | None] = mapped_column(Integer, default=None)
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database: Mapped[str | None] = mapped_column(String(255), default=None)
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username: Mapped[str | None] = mapped_column(String(128), default=None)
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password: Mapped[str | None] = mapped_column(Text, default=None) # 密文
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# 驱动差异塞这里：Oracle 的 service_name/sid、MySQL 的 charset、SSL 参数…
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# 不给每种数据库加一列，否则表会长成各家方言的并集
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options: Mapped[dict[str, Any]] = mapped_column(default=dict)
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readonly: Mapped[bool] = mapped_column(default=True)
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# 给 Copilot 看的一句话："销售库，含订单/客户/产品"。它据此判断该查哪个源
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description: Mapped[str] = mapped_column(Text, default="")
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# 探查到的表结构。缓存下来，否则每次建图都要连一次生产库
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schema_cache: Mapped[dict[str, Any]] = mapped_column(default=dict)
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schema_synced_at: Mapped[datetime | None] = mapped_column(default=None)
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enabled: Mapped[bool] = mapped_column(default=True)
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class Setting(Base, TimestampMixin):
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"""键值形式的用户设置，前端 Settings 页直接读写。"""
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__tablename__ = "settings"
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key: Mapped[str] = mapped_column(String(100), primary_key=True)
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value: Mapped[dict[str, Any]] = mapped_column(default=dict)
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class McpServer(Base, TimestampMixin):
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__tablename__ = "mcp_servers"
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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name: Mapped[str] = mapped_column(String(100), unique=True)
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transport: Mapped[str] = mapped_column(String(20), default="stdio") # stdio | http
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command: Mapped[str | None] = mapped_column(String(500), default=None)
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args: Mapped[list[Any]] = mapped_column(default=list)
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env: Mapped[dict[str, Any]] = mapped_column(default=dict)
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url: Mapped[str | None] = mapped_column(String(500), default=None)
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enabled: Mapped[bool] = mapped_column(default=True)
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# 最近一次探活的结果与工具清单缓存
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status: Mapped[str] = mapped_column(String(20), default="unknown")
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last_error: Mapped[str | None] = mapped_column(Text, default=None)
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tools_cache: Mapped[list[Any]] = mapped_column(default=list)
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class CustomTool(Base, TimestampMixin):
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"""用户自定义工具：HTTP 调用模板，或跑在沙箱里的一段 Python。"""
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__tablename__ = "custom_tools"
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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name: Mapped[str] = mapped_column(String(100), unique=True)
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description: Mapped[str] = mapped_column(Text, default="")
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kind: Mapped[str] = mapped_column(String(20), default="http") # http | python
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parameters: Mapped[dict[str, Any]] = mapped_column(default=dict) # JSON Schema
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config: Mapped[dict[str, Any]] = mapped_column(default=dict)
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enabled: Mapped[bool] = mapped_column(default=True)
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# --------------------------------------------------------------------------
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# 工作流与版本
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# --------------------------------------------------------------------------
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class Workflow(Base, TimestampMixin):
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__tablename__ = "workflows"
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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name: Mapped[str] = mapped_column(String(200))
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description: Mapped[str] = mapped_column(Text, default="")
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graph: Mapped[dict[str, Any]] = mapped_column(default=dict) # {nodes, edges, viewport}
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tags: Mapped[list[Any]] = mapped_column(default=list)
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version: Mapped[int] = mapped_column(Integer, default=1)
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is_template: Mapped[bool] = mapped_column(default=False)
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# 治理状态机：draft（随便改）→ published（有正式版本可跑）→ governed（发布需过治理 lint）
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status: Mapped[str] = mapped_column(String(20), default="draft")
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# 正式运行默认解析到这个版本；为空表示还没发布过
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published_version: Mapped[int | None] = mapped_column(Integer, default=None)
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published_by: Mapped[str | None] = mapped_column(String(100), default=None)
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versions: Mapped[list["WorkflowVersion"]] = relationship(
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back_populates="workflow", cascade="all, delete-orphan", lazy="selectin"
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)
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class WorkflowVersion(Base, TimestampMixin):
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"""每次保存留一份快照，便于回滚和对比不同编排的效果。"""
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__tablename__ = "workflow_versions"
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__table_args__ = (UniqueConstraint("workflow_id", "version"),)
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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workflow_id: Mapped[str] = mapped_column(ForeignKey("workflows.id", ondelete="CASCADE"))
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version: Mapped[int] = mapped_column(Integer)
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graph: Mapped[dict[str, Any]] = mapped_column(default=dict)
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note: Mapped[str] = mapped_column(Text, default="")
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# 图内容的 sha256 指纹（剔除 viewport）。formal run 钉住的就是它。
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graph_hash: Mapped[str | None] = mapped_column(String(64), default=None)
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workflow: Mapped[Workflow] = relationship(back_populates="versions")
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# --------------------------------------------------------------------------
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# 运行、事件、审批
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# --------------------------------------------------------------------------
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class Run(Base, TimestampMixin):
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__tablename__ = "runs"
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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workflow_id: Mapped[str | None] = mapped_column(
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ForeignKey("workflows.id", ondelete="SET NULL"), default=None
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)
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workflow_name: Mapped[str] = mapped_column(String(200), default="")
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# LangGraph checkpointer 的 thread_id —— 持久化与恢复的锚点
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thread_id: Mapped[str] = mapped_column(String(64), index=True, default=new_id)
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# queued | running | interrupted | succeeded | failed | cancelled
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status: Mapped[str] = mapped_column(String(20), default="queued", index=True)
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graph: Mapped[dict[str, Any]] = mapped_column(default=dict) # 执行时的图快照
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input: Mapped[dict[str, Any]] = mapped_column(default=dict)
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output: Mapped[dict[str, Any]] = mapped_column(default=dict)
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error: Mapped[str | None] = mapped_column(Text, default=None)
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usage: Mapped[dict[str, Any]] = mapped_column(default=dict) # tokens / cost / 各节点耗时
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started_at: Mapped[datetime | None] = mapped_column(default=None)
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finished_at: Mapped[datetime | None] = mapped_column(default=None)
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last_seq: Mapped[int] = mapped_column(Integer, default=0)
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# formal：从不可变的 WorkflowVersion 发起，可复现、可出具
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# exploratory：画布试跑 / 裸 graph / 追问，结果只标探索性
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run_class: Mapped[str] = mapped_column(String(20), default="exploratory")
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version: Mapped[int | None] = mapped_column(Integer, default=None)
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version_hash: Mapped[str | None] = mapped_column(String(64), default=None)
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# 结束时对全部事件计算的清单哈希，事后改动事件流会被它戳穿
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manifest_hash: Mapped[str | None] = mapped_column(String(64), default=None)
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# 清单封到了哪一条事件（含）。之后追加的事件（复核批注之类）不在封存范围内；
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# 为空的是记录这一列之前封存的老运行，核对时按当时的口径算
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manifest_seq: Mapped[int | None] = mapped_column(Integer, default=None)
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started_by: Mapped[str | None] = mapped_column(String(100), default=None)
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events: Mapped[list["RunEvent"]] = relationship(
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back_populates="run", cascade="all, delete-orphan"
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)
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class RunEvent(Base):
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"""完整事件流落库 —— 这既是 trace，也是页面刷新后重建时间线的依据。"""
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__tablename__ = "run_events"
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__table_args__ = (Index("ix_run_events_run_seq", "run_id", "seq"),)
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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run_id: Mapped[str] = mapped_column(ForeignKey("runs.id", ondelete="CASCADE"))
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seq: Mapped[int] = mapped_column(Integer, default=0)
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type: Mapped[str] = mapped_column(String(40))
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node_id: Mapped[str | None] = mapped_column(String(64), default=None)
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ts: Mapped[float] = mapped_column(Float)
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data: Mapped[dict[str, Any]] = mapped_column(default=dict)
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run: Mapped[Run] = relationship(back_populates="events")
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class Approval(Base, TimestampMixin):
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"""一次人工介入请求。对应 LangGraph 的 interrupt。"""
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__tablename__ = "approvals"
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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run_id: Mapped[str] = mapped_column(ForeignKey("runs.id", ondelete="CASCADE"), index=True)
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node_id: Mapped[str] = mapped_column(String(64))
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interrupt_id: Mapped[str | None] = mapped_column(String(64), default=None)
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mode: Mapped[str] = mapped_column(String(20), default="approve") # approve | input | edit
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title: Mapped[str] = mapped_column(String(300), default="")
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payload: Mapped[dict[str, Any]] = mapped_column(default=dict) # 给人看的上下文
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schema_: Mapped[dict[str, Any]] = mapped_column("schema", default=dict)
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status: Mapped[str] = mapped_column(String(20), default="pending", index=True)
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response: Mapped[dict[str, Any]] = mapped_column(default=dict)
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resolved_at: Mapped[datetime | None] = mapped_column(default=None)
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# 责任归属：谁批的。没有 actor 的治理是空转。
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resolved_by: Mapped[str | None] = mapped_column(String(100), default=None)
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class Artifact(Base, TimestampMixin):
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"""工件引用表。
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文件本体按内容哈希存在 data/artifacts/ 下（同内容只一份）；
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这张表记录"哪个 run 的哪个节点产出了它"——溯源需要的正是这层归属。
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复合主键：同一工件被多个 run 产出时各记一行引用。
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"""
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__tablename__ = "artifacts"
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__table_args__ = (Index("ix_artifacts_run", "run_id"),)
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id: Mapped[str] = mapped_column(String(64), primary_key=True)
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run_id: Mapped[str] = mapped_column(String(32), primary_key=True, default="")
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kind: Mapped[str] = mapped_column(String(32), default="node_output") # node_output | tool_snapshot | metric_set
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node_id: Mapped[str | None] = mapped_column(String(64), default=None)
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size: Mapped[int] = mapped_column(Integer, default=0)
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meta: Mapped[dict[str, Any]] = mapped_column(default=dict)
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# --------------------------------------------------------------------------
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# 记忆、知识库、Skill
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# --------------------------------------------------------------------------
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class MemoryItem(Base, TimestampMixin):
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"""长期记忆。scope 用来隔离不同 agent / 会话的记忆空间。"""
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__tablename__ = "memories"
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__table_args__ = (Index("ix_memories_scope_kind", "scope", "kind"),)
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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scope: Mapped[str] = mapped_column(String(100), default="default")
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kind: Mapped[str] = mapped_column(String(32), default="fact") # fact | preference | episode
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content: Mapped[str] = mapped_column(Text)
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meta: Mapped[dict[str, Any]] = mapped_column(default=dict)
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embedding: Mapped[bytes | None] = mapped_column(LargeBinary, default=None)
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# 这条向量是谁产出的。没有它就无法判断存量向量还能不能和当前查询比对——
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# 换 embedder 之后维度对不上，cosine 直接 ValueError（512 vs 1536），
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# 而在此之前代码里没有任何地方能看出"这条是旧模型建的"
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embed_model: Mapped[str] = mapped_column(String(100), default="")
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embed_dim: Mapped[int] = mapped_column(Integer, default=0)
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importance: Mapped[float] = mapped_column(Float, default=0.5)
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last_used_at: Mapped[datetime | None] = mapped_column(default=None)
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use_count: Mapped[int] = mapped_column(Integer, default=0)
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class Document(Base, TimestampMixin):
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"""知识库里的一份原始文档。"""
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__tablename__ = "documents"
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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collection: Mapped[str] = mapped_column(String(100), default="default", index=True)
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title: Mapped[str] = mapped_column(String(300), default="")
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source: Mapped[str] = mapped_column(String(500), default="") # 文件名 / URL
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mime: Mapped[str] = mapped_column(String(100), default="text/plain")
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content: Mapped[str] = mapped_column(Text, default="")
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meta: Mapped[dict[str, Any]] = mapped_column(default=dict)
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chunk_count: Mapped[int] = mapped_column(Integer, default=0)
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# 切块+算向量是在后台跑的：一个 10MB 文档配上远端 embedding 要几十分钟，
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# 压在 HTTP 请求里必然超时——而后端其实还在跑，界面显示失败、数据其实成功，
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# 比直接拒绝还糟。ready | processing | failed
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status: Mapped[str] = mapped_column(String(20), default="ready")
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error: Mapped[str] = mapped_column(Text, default="")
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chunks: Mapped[list["Chunk"]] = relationship(
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back_populates="document", cascade="all, delete-orphan"
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)
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class Chunk(Base):
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__tablename__ = "chunks"
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__table_args__ = (Index("ix_chunks_collection", "collection"),)
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id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
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document_id: Mapped[str] = mapped_column(ForeignKey("documents.id", ondelete="CASCADE"))
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collection: Mapped[str] = mapped_column(String(100), default="default")
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ordinal: Mapped[int] = mapped_column(Integer, default=0)
|
||
content: Mapped[str] = mapped_column(Text)
|
||
embedding: Mapped[bytes | None] = mapped_column(LargeBinary, default=None)
|
||
# 这条向量是谁产出的。没有它就无法判断存量向量还能不能和当前查询比对——
|
||
# 换 embedder 之后维度对不上，cosine 直接 ValueError（512 vs 1536），
|
||
# 而在此之前代码里没有任何地方能看出"这条是旧模型建的"
|
||
embed_model: Mapped[str] = mapped_column(String(100), default="")
|
||
embed_dim: Mapped[int] = mapped_column(Integer, default=0)
|
||
#: 分词后的 token 数。BM25 要按文档长度归一，存下来省得为算 avg_len 再扫一遍
|
||
token_len: Mapped[int] = mapped_column(Integer, default=0)
|
||
meta: Mapped[dict[str, Any]] = mapped_column(default=dict)
|
||
|
||
document: Mapped[Document] = relationship(back_populates="chunks")
|
||
|
||
|
||
class ChunkTerm(Base):
|
||
"""倒排表：词 → 包含它的片段。
|
||
|
||
在此之前每次检索都 `select(Chunk)` 全量载入，再在 Python 里**重建**一遍
|
||
BM25 倒排——几百段无感，上万段就是秒级，而那正是知识库开始有用的规模。
|
||
|
||
没用 SQLite 的 FTS5：它的 trigram 分词器要求查询至少三个字符，中文里
|
||
「商家」「订单」「权限」这些两字词一个都召不回（实测）。而现有的分词器
|
||
本来就做中文单字 + 二元组，自己存一张倒排表既保住召回，也不把知识库
|
||
锁死在 SQLite 上。
|
||
"""
|
||
|
||
__tablename__ = "chunk_terms"
|
||
__table_args__ = (Index("ix_chunk_terms_lookup", "collection", "term"),)
|
||
|
||
id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
|
||
chunk_id: Mapped[str] = mapped_column(
|
||
ForeignKey("chunks.id", ondelete="CASCADE"), index=True
|
||
)
|
||
collection: Mapped[str] = mapped_column(String(100), default="default")
|
||
term: Mapped[str] = mapped_column(String(64))
|
||
tf: Mapped[int] = mapped_column(Integer, default=1)
|
||
|
||
|
||
class Skill(Base, TimestampMixin):
|
||
"""可复用的方法论：一段指令 + 可选的示例和推荐工具。
|
||
|
||
节点可以挂载 skill，运行时把内容注入 system prompt —— 把"怎么做事"
|
||
从工作流结构里抽出来单独管理和迭代。
|
||
"""
|
||
|
||
__tablename__ = "skills"
|
||
|
||
id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
|
||
name: Mapped[str] = mapped_column(String(100), unique=True)
|
||
description: Mapped[str] = mapped_column(Text, default="")
|
||
instructions: Mapped[str] = mapped_column(Text, default="")
|
||
examples: Mapped[list[Any]] = mapped_column(default=list)
|
||
suggested_tools: Mapped[list[Any]] = mapped_column(default=list)
|
||
tags: Mapped[list[Any]] = mapped_column(default=list)
|
||
enabled: Mapped[bool] = mapped_column(default=True)
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# 会话：把一串问答串成一次对话
|
||
# --------------------------------------------------------------------------
|
||
|
||
|
||
class Conversation(Base, TimestampMixin):
|
||
"""一次对话。
|
||
|
||
在此之前"对话"只是前端内存里的一个数组：刷新页面就没了，而每一轮又各自
|
||
从零建图、各起一条 checkpoint 线程，所以"上一轮问过什么"在系统里根本
|
||
无处可查。追问「再按月份拆一下」会重新找一遍数据源，可能接到别的表。
|
||
|
||
落成一张表之后，它同时承担两件事：给用户看的历史列表，和给模型看的上下文。
|
||
|
||
kind 分两种，因为这两件事像但不是一回事：
|
||
chat —— 问数据页，一轮 = 一次「建图 → 跑图 → 出答案」，进左侧列表
|
||
canvas —— 画布右栏的 Copilot，依附某张图，一轮 = 一次改图，不进列表
|
||
"""
|
||
|
||
__tablename__ = "conversations"
|
||
|
||
id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
|
||
title: Mapped[str] = mapped_column(String(200), default="")
|
||
kind: Mapped[str] = mapped_column(String(20), default="chat", index=True)
|
||
workflow_id: Mapped[str | None] = mapped_column(
|
||
ForeignKey("workflows.id", ondelete="CASCADE"), default=None, index=True
|
||
)
|
||
archived: Mapped[bool] = mapped_column(default=False)
|
||
# 列表按活跃度排。不能用 updated_at —— 改个标题就会把这条顶到最前面，
|
||
# 而用户对"最近聊过的"的预期是按说话时间排，不是按编辑时间
|
||
last_active_at: Mapped[datetime] = mapped_column(
|
||
DateTime(timezone=True), default=utcnow, index=True
|
||
)
|
||
|
||
turns: Mapped[list["ConversationTurn"]] = relationship(
|
||
back_populates="conversation",
|
||
cascade="all, delete-orphan",
|
||
order_by="ConversationTurn.seq",
|
||
)
|
||
|
||
|
||
class ConversationTurn(Base, TimestampMixin):
|
||
"""一轮问答。
|
||
|
||
run_id 可以为空：建图阶段就失败时压根没有 run，但这一轮仍然发生过，
|
||
用户也该在历史里看到它失败了——只记成功的轮次，历史就成了一份美化过的
|
||
记录，而人再回来时最想搞清楚的恰恰是上次卡在哪。
|
||
"""
|
||
|
||
__tablename__ = "conversation_turns"
|
||
__table_args__ = (Index("ix_conversation_turns_conv_seq", "conversation_id", "seq"),)
|
||
|
||
id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id)
|
||
conversation_id: Mapped[str] = mapped_column(
|
||
ForeignKey("conversations.id", ondelete="CASCADE"), index=True
|
||
)
|
||
seq: Mapped[int] = mapped_column(Integer, default=0)
|
||
question: Mapped[str] = mapped_column(Text, default="")
|
||
answer: Mapped[str] = mapped_column(Text, default="")
|
||
# Copilot 对这张图的说明。画布那条路径上只有它，没有 answer
|
||
explanation: Mapped[str] = mapped_column(Text, default="")
|
||
graph: Mapped[dict[str, Any] | None] = mapped_column(default=None)
|
||
run_id: Mapped[str | None] = mapped_column(
|
||
ForeignKey("runs.id", ondelete="SET NULL"), default=None
|
||
)
|
||
# running | done | error
|
||
status: Mapped[str] = mapped_column(String(20), default="running")
|
||
error: Mapped[str] = mapped_column(Text, default="")
|
||
# 跑完之后的复核结论（engine/review.py）：判定档位、给用户的异常说明、
|
||
# 命中的信号清单。落库是因为它是答案的一部分——刷新页面后只剩一个
|
||
# 看起来很完整的答案、而"它哪里不可靠"没了，比不复核更糟
|
||
review: Mapped[dict[str, Any] | None] = mapped_column(default=None)
|
||
|
||
conversation: Mapped[Conversation] = relationship(back_populates="turns")
|