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ThearchyHelios
fcdcc30d4e
一、可点击证据第二期
取数链路
- 智能体、工具节点、代码节点、知识检索取数时都记进证据台账；报告目录按台账编出 Q1… / K1…
- 报告可以引用快照里的一格（[[v:Q1.r0.gmv]]），也可以由系统从快照生成整张表（[[table:Q1 cols=… rows=0-4]]），
表里每一格都是可点的片段；取值一律以快照为准，取回快照时复验哈希
- 口径卡表达式新增 cell(x, row, column)，lineage 精确到单元格
- 智能体开 cite_fields（配合 output_schema）后，循环结束做一次结构化抽取，每个字段标出取自哪次查询的哪一格，
再到快照里核对：一致记 verified；不一致以快照为准并告警；查不到的记为空值，永远不兜底成 0
- 代码节点新增 evidence_role（source / compute）和 code_sha；沙箱算出来的数不能被报告直接引用，要进口径卡
- 查询快照记下列类型，文本列按文本渲染（年份 "2026" 不再显示成「2,026」）
- 升级前发起的运行（停在审批上的）一律走旧逻辑：新的 task 只追加在循环之后，恢复时不会错号
证据接口与面板
- 片段链接上查询步骤：SQL、被引用的行加前后各 2 行、高亮到格；数据源可设 mask_columns 遮罩列，
遮罩在查询时记进快照，数据源事后改名或删除也照样遮罩（在有身份体系之前，遮罩只减少暴露，不是安全边界）
- 输入来源写明核对结果（与快照一致 / 模型报的值和快照不一致 / 没查到记为空）；口径卡缺输入时也给出完整的链
- 受管出具要在契约里声明 cells 才能引用单元格；受管与否记在所钉版本上，改画布不再让受管版本的正式运行降级
- 口径卡可以用 caliber_from 钉住另一个已发布工作流里的口径卡，上游有新版本时按 upgrade_policy 处置，
和子工作流同一套规则；子工作流节点也补上了升版处置的配置项
Copilot、校验与报错
- 问数据的答案里有数字时，搭 input → 智能体 → 报告 → 出口，报告直接引用查过的单元格
- 写 SQL 前核对数据源的表结构，SQL 里出现表结构里没有的列会被打回
- 不再搭「智能体文字 → 转换节点解析 JSON」这种图；转换节点的 JSON 报错分清是上游模型写坏了、
模板里裸插了模型文字（给出 | json 的改法），还是模板本身写错了
- 工具节点遇到「查询失败」「SQL 被拒绝」判为失败；发起运行前检查绑定的工具是否存在，不存在时直接拒绝并给出入口
二、发布前自动修复
- 问题带上机器编号（code）和修复 id；新增只读接口 POST /api/workflows/{id}/publish-check 和 /autofix
- 修复分三类：答案唯一、不降低要求的一键修复；要人拿主意的给选项（不替人选）；结构性问题交给 Copilot
- 一律先预览，不自动保存、不自动发布；删节点、删契约、放宽审批、关掉 strict、改级别等降低要求的改动一律拒绝，
Copilot 的方案也要过这一关；应用后重跑检查，错误必须变少且不能冒出新的
- 发布弹窗打开就先检查；问题面板新增「发布前检查」，平时就能看到、就地修掉
- 门禁修正：契约有 report_from 时不再要求 narrative（模板⑨可以发成受管）；全图默认审批为「全部放行」时同样拦下
- 修掉画布保存会抹掉全图默认（defaults）的老问题：以前任何一次保存都会把它清空，审批 always 因此被悄悄放宽
验证（沙箱）：tsc 干净，生产构建通过，pytest 2268 passed（两遍），check-all 17 项检查、3915 项全过
（新增 check-publish）；关键行为做了变异验证、全部抓到。
446 lines
23 KiB
Python
446 lines
23 KiB
Python
from __future__ import annotations
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import logging
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import os
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from typing import Any
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from sqlalchemy import func, select
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from app.core.crypto import encrypt
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from app.db.base import SessionLocal
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from app.db.models import Provider, Skill, Workflow, WorkflowVersion
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from app.engine.schema import GraphSpec
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from app.providers import catalog
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logger = logging.getLogger(__name__)
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def _n(node_id: str, node_type: str, label: str, **config: Any) -> dict[str, Any]:
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return {
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"id": node_id,
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"type": node_type,
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"position": {"x": 0, "y": 0},
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"data": {"label": label, "config": config},
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}
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def _e(source: str, target: str, handle: str | None = None) -> dict[str, Any]:
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edge: dict[str, Any] = {"source": source, "target": target}
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if handle:
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edge["sourceHandle"] = handle
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return edge
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# --------------------------------------------------------------------------
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# 内置模板：打开就有得玩，也是各种节点用法的活文档
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# --------------------------------------------------------------------------
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TEMPLATES: list[dict[str, Any]] = [
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{
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"name": "① 最小问答",
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"description": "一个模型节点跑通全流程。先从这个开始改。",
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"tags": ["入门"],
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"nodes": [
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_n("start", "input", "提问", fields=[{"name": "question", "required": True,
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"description": "你想问什么"}]),
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_n("answer", "llm", "回答", system="你是一个耐心、准确的助手。",
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prompt="{{ input.question }}", assign_to="answer"),
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_n("done", "output", "成果", fields=[{"name": "回答", "value": "{{ vars.answer }}"}]),
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],
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"edges": [_e("start", "answer"), _e("answer", "done")],
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},
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{
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"name": "② 研究助手（Agent + 工具）",
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"description": "Agent 自主决定搜什么、抓哪篇，再汇总成带出处的简报。",
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"tags": ["agent", "工具"],
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"nodes": [
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_n("start", "input", "研究主题", fields=[{"name": "topic", "required": True}]),
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_n("research", "agent", "调研 Agent",
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system="你是严谨的研究员。先搜索再抓取原文，只写有依据的内容。",
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prompt="围绕这个主题做调研：{{ input.topic }}",
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tools=["web_search", "web_fetch", "current_time"],
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max_steps=8, approval="never", assign_to="findings"),
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_n("brief", "llm", "写成简报",
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system="你是一位把复杂信息讲清楚的编辑。",
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prompt="把下面的调研结果整理成中文简报，分「结论 / 关键发现 / 待确认」三段，"
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"每条结论后面标注来源链接：\n\n{{ vars.findings }}",
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assign_to="brief"),
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_n("done", "output", "简报", fields=[
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{"name": "简报", "value": "{{ vars.brief }}"},
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{"name": "调研过程", "value": "{{ nodes.research.tool_calls | json }}"}]),
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],
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"edges": [_e("start", "research"), _e("research", "brief"), _e("brief", "done")],
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},
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{
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"name": "③ 带人工审批的发布流",
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"description": "起草 → 人工把关 → 通过就发布、驳回就改写。演示中断与断点恢复。",
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"tags": ["人工审批", "分支"],
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"nodes": [
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_n("start", "input", "主题", fields=[{"name": "topic", "required": True}]),
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_n("draft", "llm", "起草", system="你是品牌文案。",
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prompt="为「{{ input.topic }}」写一条 100 字以内的对外公告。",
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assign_to="draft"),
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_n("review", "human", "人工把关", mode="approve",
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title="这条公告可以发吗？",
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message="草稿：\n\n{{ vars.draft }}"),
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_n("publish", "transform", "标记发布", mode="template",
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template="✅ 已批准发布\n\n{{ vars.draft }}", assign_to="final"),
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_n("rewrite", "llm", "按意见改写",
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prompt="下面这条公告被驳回了，理由是「{{ nodes.review.note }}」。请据此改写：\n\n{{ vars.draft }}",
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assign_to="final"),
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_n("done", "output", "成果", fields=[
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{"name": "结果", "value": "{{ vars.final }}"},
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{"name": "是否通过", "value": "{{ nodes.review.approved }}"}]),
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],
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"edges": [
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_e("start", "draft"), _e("draft", "review"),
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_e("review", "publish", "approved"), _e("review", "rewrite", "rejected"),
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_e("publish", "done"), _e("rewrite", "done"),
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],
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},
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{
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"name": "④ 知识库问答（RAG）",
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"description": "先检索再回答，检索不到就明说。需要先在知识库页上传文档。",
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"tags": ["RAG", "知识库"],
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"nodes": [
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_n("start", "input", "提问", fields=[{"name": "question", "required": True},
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{"name": "collection", "default": "default"}]),
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_n("search", "retrieve", "检索知识库", query="{{ input.question }}",
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collection="{{ input.collection }}", limit=5, alpha=0.5, assign_to="context"),
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_n("gate", "branch", "检索到东西了吗", mode="expression",
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cases=[{"key": "hit", "condition": "nodes.search.count > 0", "label": "有命中"}]),
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_n("answer", "llm", "基于资料回答",
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system="只根据提供的资料回答，每句话后面用 [编号] 标注依据。资料不足就直接说不确定。",
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prompt="资料：\n{{ vars.context }}\n\n问题：{{ input.question }}",
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assign_to="answer"),
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_n("miss", "transform", "没有资料", mode="template",
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template="知识库里没有找到与「{{ input.question }}」相关的内容，请先上传相关文档。",
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assign_to="answer"),
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_n("done", "output", "答案", fields=[
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{"name": "答案", "value": "{{ vars.answer }}"},
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{"name": "引用片段", "value": "{{ nodes.search.hits | json }}"}]),
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],
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"edges": [
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_e("start", "search"), _e("search", "gate"),
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_e("gate", "answer", "hit"), _e("gate", "miss", "default"),
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_e("answer", "done"), _e("miss", "done"),
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],
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},
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{
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"name": "⑤ 写代码跑数据（沙箱 + 校验）",
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"description": "模型写 Python，沙箱里真跑，输出还要过 JSON Schema 校验，不合格自动返工。",
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"tags": ["沙箱", "结构化"],
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"nodes": [
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_n("start", "input", "分析需求", fields=[
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{"name": "task", "required": True, "default": "统计 1 到 100 里所有质数的个数与总和"}]),
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_n("write", "llm", "写代码",
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system="你是数据工程师。只输出可直接运行的 Python 代码，不要解释、不要 markdown 围栏。"
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"结果必须用 print(json.dumps(...)) 输出一个 JSON 对象。",
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prompt="任务：{{ input.task }}", assign_to="code"),
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_n("run", "code", "沙箱执行", language="python", code="{{ vars.code }}",
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timeout=30, network=False, fail_fast=False, assign_to="result"),
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_n("check", "validate", "校验结果", source="{{ nodes.run.stdout }}",
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max_retries=2, repair_with_llm=True, fail_fast=False,
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schema={"type": "object", "minProperties": 1}),
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_n("done", "output", "成果", fields=[
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{"name": "结果", "value": "{{ nodes.check.data | json }}"},
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{"name": "代码", "value": "{{ vars.code }}"},
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{"name": "校验通过", "value": "{{ nodes.check.valid }}"}]),
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],
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"edges": [_e("start", "write"), _e("write", "run"), _e("run", "check"), _e("check", "done")],
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},
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{
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"name": "⑥ 多 Agent 协作",
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"description": "调度者按进展分派任务给研究员、批评家和写手，直到目标达成。",
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"tags": ["多agent"],
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"nodes": [
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_n("start", "input", "目标", fields=[{"name": "goal", "required": True}]),
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_n("team", "supervisor", "协作团队", goal="{{ input.goal }}", max_rounds=6,
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agents=[
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{"name": "researcher", "description": "查资料、找事实",
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"system": "你是研究员，负责收集事实和数据，给出来源。",
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"tools": ["web_search", "web_fetch"], "max_steps": 4},
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{"name": "critic", "description": "挑毛病、找漏洞",
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"system": "你是批评家。指出前面工作里的事实错误、逻辑漏洞和遗漏，要具体。",
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"tools": [], "max_steps": 2},
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{"name": "writer", "description": "整合成最终文稿",
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"system": "你是写手，把团队成果整合成结构清晰的中文文稿。",
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"tools": [], "max_steps": 2},
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], assign_to="result"),
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_n("done", "output", "成果", fields=[
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{"name": "结果", "value": "{{ vars.result }}"},
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{"name": "协作记录", "value": "{{ nodes.team.transcript | json }}"}]),
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],
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"edges": [_e("start", "team"), _e("team", "done")],
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},
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{
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"name": "⑧ 周报（口径卡 + 三档出具）",
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"description": "模板定骨架、agent 填关节的完整示范：取数固定、口径卡算数、"
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"叙述只许引用清单里的数、出具时逐数回指——现编的数字会被点名并降档。",
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"tags": ["出具", "口径卡", "周报"],
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"nodes": [
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_n("start", "input", "周期", fields=[
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{"name": "week", "default": "2026-W37", "description": "报告周期"}]),
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# 取数节点：产出本身就是源数据（evidence_role=source），口径卡读它记得下出处
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_n("fetch", "code", "取数（数据底座）", language="python", network=False,
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assign_to="agg", evidence_role="source",
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code=(
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"# 固定层：取数与汇总。真实场景换成对数据库/API 的查询，\n"
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"# 结果会作为快照落工件库，出具溯源指回这里。\n"
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"import json\n"
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"print(json.dumps({'orders': 1234, 'amount': 45678.5,\n"
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" 'amount_prev': 42010.0, 'returns': 29}))"
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)),
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_n("caliber", "metrics", "周报口径卡",
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caliber="周报口径", caliber_version="v1", assign_to="m",
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metrics=[
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{"id": "orders", "name": "订单数", "unit": "单",
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"expression": "vars.agg.orders"},
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{"id": "amount", "name": "销售额", "unit": "元",
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"expression": "vars.agg.amount"},
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{"id": "wow", "name": "环比增幅", "unit": "%",
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"expression": "round((vars.agg.amount - vars.agg.amount_prev) / vars.agg.amount_prev * 100, 1)"},
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{"id": "return_rate", "name": "退货率", "unit": "%",
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"expression": "round(vars.agg.returns / vars.agg.orders * 100, 2)"},
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]),
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_n("narrate", "llm", "叙述（关节层）",
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system="你是周报撰写人。只允许引用下面指标清单里出现的数值，"
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"一个清单外的数字都不要写——包括字数统计、序号补充说明等元内容。"
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"只输出周报正文本身，不要任何撰写说明。"
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"你的自由度在于：挑哪些变化值得说、怎么归因。",
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prompt="{{ nodes.caliber.text }}\n\n请为 {{ input.week }} 写一段简短的周报正文。",
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assign_to="report"),
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_n("done", "output", "出具",
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fields=[
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{"name": "周报", "value": "{{ vars.report }}"},
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{"name": "指标清单", "value": "{{ nodes.caliber.text }}"}],
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contract={
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"metrics_from": ["caliber"],
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"narrative": "{{ vars.report }}",
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"required": ["amount", "orders"],
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"expected": ["wow", "return_rate"],
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"allow_numbers": ["37", "120"],
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"strict": False,
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}),
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],
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"edges": [
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_e("start", "fetch"), _e("fetch", "caliber"),
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_e("caliber", "narrate"), _e("narrate", "done"),
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],
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},
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{
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"name": "⑨ 可追溯周报",
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"description": "报告里的每个数都点得开出处：tool 节点查库、口径卡登记指标、报告撰写节点只写引用"
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"标记，数字由系统从口径卡取出来渲染，出口按引用逐段核对后三档出具。取数节点查的是示例数据源 "
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"shop 的 orders 表（week、amount、refunded 三列）：先接入你的库（或者上传一张表格、命名为 "
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"shop），再把 SQL 换成你的口径。",
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"tags": ["出具", "口径卡", "周报", "证据"],
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"nodes": [
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_n("start", "input", "周期", fields=[
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{"name": "week", "default": "2026-W37", "description": "报告周期（ISO 周）"},
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{"name": "prev_week", "default": "2026-W36", "description": "对比的上一周期"}]),
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# 聚合放在 SQL 里做完，口径卡只做标量运算。列的顺序就是口径卡里 rows[0][n] 的 n
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_n("fetch", "tool", "取数（查库）", tool="db_query__shop", args={"sql": (
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"SELECT\n"
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" SUM(CASE WHEN week = '{{ input.week }}' THEN amount ELSE 0 END) AS gmv,\n"
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" SUM(CASE WHEN week = '{{ input.prev_week }}' THEN amount ELSE 0 END) AS gmv_prev,\n"
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" SUM(CASE WHEN week = '{{ input.week }}' THEN 1 ELSE 0 END) AS order_cnt,\n"
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" SUM(CASE WHEN week = '{{ input.week }}' AND refunded = 1 THEN 1 ELSE 0 END) AS refund_cnt\n"
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"FROM orders")}),
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# 查询工具交回来的是 JSON 文本 {columns, rows}，解析成对象口径卡才取得到里面的数
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_n("parse", "transform", "解析查询结果", mode="json", template="{{ nodes.fetch }}",
|
||
assign_to="q"),
|
||
_n("caliber", "metrics", "周报口径卡", caliber="周报口径", caliber_version="v1",
|
||
metrics=[
|
||
{"id": "gmv", "name": "销售额", "unit": "元", "decimals": 2, "format": "thousands",
|
||
"expression": "vars.q.rows[0][0]"},
|
||
{"id": "orders", "name": "订单数", "unit": "单", "format": "thousands",
|
||
"expression": "vars.q.rows[0][2]"},
|
||
# 分母为 0 时记为空值（缺输入），交给契约按 expected 降档，而不是整张卡失败
|
||
{"id": "wow", "name": "环比增幅", "unit": "%", "decimals": 1, "format": "plain",
|
||
"expression": "round((vars.q.rows[0][0] - vars.q.rows[0][1]) / vars.q.rows[0][1] * 100, 1)"
|
||
" if vars.q.rows[0][1] else None"},
|
||
{"id": "aov", "name": "客单价", "unit": "元", "decimals": 2, "format": "thousands",
|
||
"expression": "round(vars.q.rows[0][0] / vars.q.rows[0][2], 2) if vars.q.rows[0][2] else None"},
|
||
{"id": "refund_rate", "name": "退款率", "decimals": 4, "format": "percent_of_ratio",
|
||
"expression": "vars.q.rows[0][3] / vars.q.rows[0][2] if vars.q.rows[0][2] else None"},
|
||
]),
|
||
_n("write", "report", "报告撰写", metrics_from=["caliber"],
|
||
system="你是周报撰写人：结论先行，只写有数据支撑的判断。",
|
||
instructions="为 {{ input.week }} 写一份简短的周报：先用一两句话总结本周，"
|
||
"再分点说销售额、订单、客单价和退款的变化。"),
|
||
_n("done", "output", "出具",
|
||
fields=[{"name": "周报", "value": "{{ nodes.write.text }}"}],
|
||
contract={
|
||
"report_from": "write",
|
||
"metrics_from": ["caliber"],
|
||
"required": ["gmv", "orders"],
|
||
"expected": ["wow", "aov", "refund_rate"],
|
||
"strict": True,
|
||
}),
|
||
],
|
||
"edges": [
|
||
_e("start", "fetch"), _e("fetch", "parse"), _e("parse", "caliber"),
|
||
_e("caliber", "write"), _e("write", "done"),
|
||
],
|
||
},
|
||
{
|
||
"name": "⑦ 批量处理（循环）",
|
||
"description": "对列表逐项处理再汇总。演示 loop 节点的 body / done 两个出口怎么接。",
|
||
"tags": ["循环"],
|
||
"nodes": [
|
||
_n("start", "input", "条目列表", fields=[
|
||
{"name": "items", "required": True,
|
||
"default": ["降低获客成本", "提升续费率", "缩短上线周期"],
|
||
"description": "JSON 数组或每行一条"}]),
|
||
_n("each", "loop", "逐条循环", mode="foreach", items="{{ input.items }}",
|
||
item_var="item", max_iterations=10),
|
||
_n("handle", "llm", "处理单条",
|
||
prompt="针对「{{ vars.item }}」给出一条可执行的建议，不超过 40 字。",
|
||
assign_to="one"),
|
||
_n("collect", "transform", "累积结果", mode="expression",
|
||
expression="(vars.collected or '') + '· ' + str(vars.one) + '\\n'",
|
||
assign_to="collected"),
|
||
_n("done", "output", "汇总", fields=[
|
||
{"name": "逐条建议", "value": "{{ vars.collected }}"},
|
||
{"name": "处理条数", "value": "{{ nodes.each.iteration }}"}]),
|
||
],
|
||
"edges": [
|
||
_e("start", "each"),
|
||
_e("each", "handle", "body"), _e("handle", "collect"), _e("collect", "each"),
|
||
_e("each", "done", "done"),
|
||
],
|
||
},
|
||
]
|
||
|
||
|
||
SKILLS: list[dict[str, Any]] = [
|
||
{
|
||
"name": "结构化分析",
|
||
"description": "把开放问题拆成结论先行的分析",
|
||
"instructions": (
|
||
"分析任何问题时遵循：\n"
|
||
"1. 先给结论，再给理由，不要铺垫\n"
|
||
"2. 区分「事实」「推断」「假设」，并明确标注\n"
|
||
"3. 给出至少一个反面视角\n"
|
||
"4. 指出当前信息不足以判断的部分，不要硬答"
|
||
),
|
||
"tags": ["通用"],
|
||
},
|
||
{
|
||
"name": "批判性审阅",
|
||
"description": "挑毛病而不是夸奖",
|
||
"instructions": (
|
||
"审阅内容时：\n"
|
||
"1. 只指出问题，不要复述优点\n"
|
||
"2. 每条问题写清楚「问题是什么 / 为什么是问题 / 怎么改」\n"
|
||
"3. 按严重程度排序，最致命的放最前面\n"
|
||
"4. 没有问题就直说没有，不要为凑数硬挑"
|
||
),
|
||
"tags": ["审阅"],
|
||
},
|
||
{
|
||
"name": "中文写作",
|
||
"description": "干净、不翻译腔的中文",
|
||
"instructions": (
|
||
"写中文时：\n"
|
||
"1. 不用「进行」「作出」这类空动词，直接用实义动词\n"
|
||
"2. 少用被动句和长定语从句\n"
|
||
"3. 术语首次出现时用括号给出英文原文\n"
|
||
"4. 能用短句就不写长句"
|
||
),
|
||
"tags": ["写作"],
|
||
},
|
||
]
|
||
|
||
|
||
async def seed_defaults() -> None:
|
||
"""首次启动时灌入默认 provider、模板工作流和 Skill。
|
||
|
||
已经存在的不会被覆盖 —— 这个函数每次启动都会跑，必须是幂等的。
|
||
"""
|
||
async with SessionLocal() as session:
|
||
# --- provider ---
|
||
count = (await session.execute(select(func.count(Provider.id)))).scalar() or 0
|
||
if count == 0:
|
||
session.add(
|
||
Provider(
|
||
name="Mock（无需 Key）",
|
||
kind="mock",
|
||
models=catalog.MOCK_MODELS,
|
||
default_model="mock-fast",
|
||
enabled=True,
|
||
)
|
||
)
|
||
# 环境里已有 Anthropic 凭据就自动配好，省得再手填一遍
|
||
token = os.environ.get("ANTHROPIC_API_KEY") or os.environ.get("ANTHROPIC_AUTH_TOKEN")
|
||
if token:
|
||
base_url = os.environ.get("ANTHROPIC_BASE_URL")
|
||
is_gateway = bool(base_url) and not os.environ.get("ANTHROPIC_API_KEY")
|
||
session.add(
|
||
Provider(
|
||
name="Anthropic" + ("（网关）" if is_gateway else ""),
|
||
kind="anthropic",
|
||
base_url=base_url,
|
||
api_key=encrypt(token),
|
||
models=catalog.ANTHROPIC_MODELS,
|
||
default_model="claude-opus-5",
|
||
enabled=True,
|
||
# 中转网关通常认 Authorization: Bearer 而不是 x-api-key
|
||
extra={"auth_style": "bearer"} if is_gateway else {},
|
||
)
|
||
)
|
||
logger.info("已从环境变量自动创建 Anthropic provider")
|
||
if os.environ.get("OPENAI_API_KEY"):
|
||
session.add(
|
||
Provider(
|
||
name="OpenAI",
|
||
kind="openai",
|
||
api_key=encrypt(os.environ["OPENAI_API_KEY"]),
|
||
models=catalog.OPENAI_MODELS,
|
||
default_model="gpt-5.2",
|
||
enabled=True,
|
||
)
|
||
)
|
||
await session.commit()
|
||
|
||
# --- Skill ---
|
||
existing_skills = {
|
||
s for (s,) in await session.execute(select(Skill.name))
|
||
}
|
||
for skill in SKILLS:
|
||
if skill["name"] not in existing_skills:
|
||
session.add(Skill(**skill))
|
||
await session.commit()
|
||
|
||
# --- 模板工作流 ---
|
||
from app.engine.layout import auto_layout
|
||
|
||
existing = {n for (n,) in await session.execute(select(Workflow.name))}
|
||
added = 0
|
||
for template in TEMPLATES:
|
||
if template["name"] in existing:
|
||
continue
|
||
spec = auto_layout(
|
||
GraphSpec.model_validate(
|
||
{"nodes": template["nodes"], "edges": template["edges"]}
|
||
)
|
||
)
|
||
graph = spec.model_dump(mode="json")
|
||
workflow = Workflow(
|
||
name=template["name"],
|
||
description=template["description"],
|
||
graph=graph,
|
||
tags=template["tags"],
|
||
is_template=True,
|
||
)
|
||
session.add(workflow)
|
||
await session.flush()
|
||
session.add(
|
||
WorkflowVersion(workflow_id=workflow.id, version=1, graph=graph, note="内置模板")
|
||
)
|
||
added += 1
|
||
if added:
|
||
await session.commit()
|
||
logger.info("已导入 %d 个内置模板", added)
|