wren
40 lines
2.0 KiB
Python
40 lines
2.0 KiB
Python
"""Govdoc 规则结果模型 —— govdoc_rule_results 表。"""
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from __future__ import annotations
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from sqlalchemy import BigInteger, Integer, Numeric, String, Text
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from sqlalchemy.orm import Mapped, mapped_column
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from fastapi_common.fastapi_common_web.models import BaseModel
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class GovdocRuleResult(BaseModel):
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"""公文规则执行结果明细表。"""
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__tablename__ = "govdoc_rule_results"
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Id: Mapped[int] = mapped_column("id", BigInteger, primary_key=True, autoincrement=True)
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runId: Mapped[int] = mapped_column("run_id", BigInteger, comment="关联 govdoc_runs.id")
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# 规则标识
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ruleId: Mapped[str] = mapped_column("rule_id", String(128), comment="规则标识")
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ruleName: Mapped[str | None] = mapped_column("rule_name", String(256), comment="规则名称")
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severity: Mapped[str | None] = mapped_column("severity", String(32), comment="严重等级：error/warning/info")
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category: Mapped[str | None] = mapped_column("category", String(128), comment="规则分类")
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# 结果内容
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message: Mapped[str | None] = mapped_column("message", Text, comment="结果描述")
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suggestion: Mapped[str | None] = mapped_column("suggestion", Text, comment="修改建议")
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actual: Mapped[str | None] = mapped_column("actual", Text, comment="实际值")
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expected: Mapped[str | None] = mapped_column("expected", Text, comment="期望值")
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evidence: Mapped[str | None] = mapped_column("evidence", Text, comment="证据文本")
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# 文档定位
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paragraphIndex: Mapped[int | None] = mapped_column("paragraph_index", Integer, comment="段落索引")
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paragraphText: Mapped[str | None] = mapped_column("paragraph_text", Text, comment="段落原文")
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locationPath: Mapped[str | None] = mapped_column("location_path", String(512), comment="文档结构位置路径")
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# 判定
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result: Mapped[str] = mapped_column("result", String(32), default="pass", comment="执行结果：pass/fail/skipped/error")
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score: Mapped[float | None] = mapped_column("score", Numeric(10, 2), comment="本条得分")
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