feat: 读标可以走完一份文件；审计待确认区，修规则缺陷、提示词出新版本
读标操作（FR-M2-07、08） - 后端：清单项详情（ETag）、修改（If-Match 选填，记 edited_fields 和 AI 原值）、 采纳（补出处或声明「人工确认、无出处」，可带修改）、删除 / 丢弃与恢复、人工新增、 替换出处、人工建出处（选同一页的句子）、修改记录（来自审计）、确认清单（不可变版本， 内容没变复用上一版）、版本列表；正式清单有变化时已确认的清单回到草稿；项目已递交只读 - 重新解析不推翻人工决定：丢弃的不再冒出来，采纳的留在正式清单，人工补过的出处不被覆盖 - 前端：待确认区采纳 / 补上出处后采纳 / 修改后采纳 / 丢弃，正式清单修改、补出处、删除 （可撤销）、修改记录、新增条目、确认清单；pdf.js 原文查看器（点出处跳页、按坐标框高亮、 选句补出处），整屏对照布局，J / K 上下条；跨页的一句话合成一处出处显示 待确认审计（架构 §13 SK11） - 规则只修真正的缺陷：表格框线不算下划线（文字层 v3）；句号后的右括号并进同一句 （切句 s3-v3）；中文编号小节和冒号编号条目的上级条款，依据沿上级链核对；取值认中文 数字、同一行单元格和评分因素标题；补几种无效写法；比对引文前去掉我们加进输入的标记 - 不放宽：引文最短 8 个字、编号必须对、证据全部通过 - 提示词新版本：substantive v3，qualification / scoring / schedule / tech_param v2； 引文复制整句、并列多项不拆、跨页只引上一行，除实质性条款外输入改成纯文本 - BD_AI_MODELS 按提示词声明的能力选模型；离线评测工具 evals/run_extract.py - 待确认：国动 46 → 4，S02 17 → 13，S06 174 → 57 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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YANG JIANKUAN
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evals/run_extract.py
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evals/run_extract.py
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"""离线抽取评测：对一份招标文件跑全部抽取任务，缓存模型原始输出，统计结果和模型输出里的不合理模式。
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用法（在 server/ 下，模型配置读根目录 .env）：
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../.venv/bin/python ../evals/run_extract.py <文件> ../evals/out/<名字> [--v extract.tech_param=2 ...] [--only extract.substantive]
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BD_AI_MODELS='{"reasoning": "deepseek-v4-pro"}' 换模型对比。
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同一模型 + 同一提示词版本 + 同一输入只调用一次模型（缓存在 <输出目录>/../cache/），只改规则时重放不花钱。
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输出：report.md（结果统计 + 模型输出里不合理模式的审计）、items.json（每条结果和模型原始输出）、normalized.pdf。
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真实招标文件和输出都不进 git（evals/out/ 已忽略）。
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"""
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from __future__ import annotations
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import argparse
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import asyncio
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import dataclasses
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import hashlib
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import json
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import pathlib
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import re
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import sys
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from collections import Counter, defaultdict
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from typing import Any
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from uuid import UUID
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from bid_doctor.ai import prompts
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from bid_doctor.ai.service import provider
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from bid_doctor.checklist import extract
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from bid_doctor.core.db import TenantContext
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from bid_doctor.document import formats
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from bid_doctor.document.pipeline import convert, structure, text_layer
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ap = argparse.ArgumentParser()
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ap.add_argument("file")
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ap.add_argument("out")
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ap.add_argument("--v", action="append", default=[], help="task=version")
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ap.add_argument("--only", action="append", default=[])
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args = ap.parse_args()
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out = pathlib.Path(args.out)
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out.mkdir(parents=True, exist_ok=True)
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cache_dir = out.parent / "cache"
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cache_dir.mkdir(exist_ok=True)
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versions = dict(v.split("=") for v in args.v)
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extract.TASKS[:] = [
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dataclasses.replace(t, prompt_version=int(versions.get(t.name, t.prompt_version)))
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for t in extract.TASKS
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if not args.only or t.name in args.only
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]
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calls: list[dict[str, Any]] = []
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async def cached_run_json(ctx: Any, *, job_type: str, job_id: Any, task: str, prompt_id: str, version: int, user: str) -> dict[str, Any]:
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del ctx, job_type, job_id
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p = prompts.get(prompt_id, version)
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from bid_doctor.core.config import get_settings
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s = get_settings()
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model = s.ai_models.get(p.capability) or s.ai_model
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tag = "" if model == "deepseek-chat" else f"{model}\n" # 旧缓存没带模型名：deepseek-chat 保持原来的键
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key = hashlib.sha256(f"{tag}{prompt_id}\n{version}\n{p.body}\n{user}".encode()).hexdigest()[:32]
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f = cache_dir / f"{key}.json"
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if f.exists():
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data = json.loads(f.read_text())
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else:
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for attempt in range(3):
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try:
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r = await provider().complete_json(p.body, user, p.capability, task)
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break
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except Exception as e: # noqa: BLE001
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if attempt == 2:
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raise
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print("retry", task, e, file=sys.stderr)
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await asyncio.sleep(3)
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data = {"data": r.data, "model": r.model, "tokens_in": r.tokens_in, "tokens_out": r.tokens_out}
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f.write_text(json.dumps(data, ensure_ascii=False))
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calls.append({"task": task, "version": version, "user": user, **data})
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return data["data"]
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extract.ai.run_json = cached_run_json # type: ignore[attr-defined]
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async def main() -> None:
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raw = pathlib.Path(args.file).read_bytes()
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det = formats.detect(raw, pathlib.Path(args.file).name)
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pdf = raw if det.format == "pdf" else await convert.to_pdf(raw, det.format)
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(out / "normalized.pdf").write_bytes(pdf)
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pages = text_layer.extract("D", pdf)
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body = text_layer.body_sentences(pages)
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notes = extract.marking_notes(body)
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doc = extract.Doc(
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sentences=body,
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parents=structure.parents(body),
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continuations=text_layer.continuations(body),
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notes=notes,
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declared={m for n in notes for m in n.markers},
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)
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ctx = TenantContext(tenant_id=UUID(int=1), user_id=UUID(int=1))
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items, info = await extract.run(ctx, UUID(int=2), doc)
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by_sid = {f"p{s.page_no}s{s.seq}": s for s in body}
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# ---------- 结果 ----------
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zone = Counter((i.zone, i.pending_reason) for i in items)
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cats = Counter((c, i.zone) for i in items for c in i.categories)
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res = [
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{
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"task": i.task,
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"zone": i.zone,
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"reason": i.pending_reason,
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"categories": i.categories,
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"title": i.summary,
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"importance": i.importance,
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"sentences": [f"p{s.page_no}s{s.seq}" for s in i.sentences],
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"quote_pages": sorted({s.page_no for s in i.sentences}),
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"raw": i.ai_values,
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}
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for i in items
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]
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(out / "items.json").write_text(json.dumps(res, ensure_ascii=False, indent=1))
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# ---------- 模型原始输出的审计 ----------
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audit: dict[str, Counter[str]] = defaultdict(Counter)
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samples: dict[str, list[str]] = defaultdict(list)
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per_sentence: dict[str, Counter[str]] = defaultdict(Counter)
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def note(task: str, kind: str, example: str) -> None:
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audit[task][kind] += 1
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if len(samples[f"{task}|{kind}"]) < 4:
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samples[f"{task}|{kind}"].append(example)
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for c in calls:
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task = c["task"]
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chunk_ids = set(re.findall(r"^\[(p\d+s\d+)\]", c["user"], re.M))
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for it in (c["data"] or {}).get("items") or []:
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if not isinstance(it, dict):
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continue
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audit[task]["items"] += 1
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evs = [e for e in it.get("evidence") or [] if isinstance(e, dict)]
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if not evs:
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note(task, "没有证据", json.dumps(it, ensure_ascii=False)[:120])
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if len(evs) > 3:
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note(task, "证据超过 3 段", f"{len(evs)} 段：{(it.get('summary') or it.get('requirement') or it.get('factor'))}")
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for e in evs:
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sid, q = str(e.get("sid")), extract.norm(str(e.get("quote", "")))
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per_sentence[task][sid] += 1
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if sid not in chunk_ids:
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note(task, "编号不在本分块", f"{sid} {q[:30]}")
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continue
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s = by_sid.get(sid)
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if s is None:
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continue
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t = extract.norm(s.text)
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if q not in t:
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joined = "".join(extract.norm(x.text) for x in body[body.index(s): body.index(s) + 3])
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if q and q in joined:
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note(task, "引文跨句", f"{sid} {q[:40]}")
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elif any(q and q in extract.norm(x.text) for x in body):
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note(task, "编号和引文不是同一句", f"{sid} 引文「{q[:30]}」原句「{t[:30]}」")
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else:
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note(task, "引文不是原文", f"{sid} 引文「{q[:30]}」原句「{t[:30]}」")
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elif len(q) < min(8, len(t)):
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note(task, "引文太短", f"{sid} 「{q}」← {t[:40]}")
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elif len(t) >= 20 and len(q) / len(t) < 0.3:
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note(task, "引文只摘了原句一小段（<30%）", f"{sid} 「{q[:30]}」← {t[:40]}")
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if s.location == "header_footer":
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note(task, "引了页眉页脚", sid)
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if s.key in doc.note_keys:
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note(task, "引了标注规则说明", sid)
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basis = it.get("basis")
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if basis in ("italic_underline", "star", "asterisk"):
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ok = any((s := by_sid.get(str(e.get("sid")))) and extract.is_marked(s, doc.declared) == basis for e in evs)
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if not ok:
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marked = "".join(by_sid[str(e.get("sid"))].marked[:50] for e in evs[:1] if str(e.get("sid")) in by_sid)
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note(task, f"声称 {basis} 但原文没有该标注", marked)
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for task, cnt in per_sentence.items():
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multi = {sid: n for sid, n in cnt.items() if n > 1}
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if multi:
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audit[task]["同一句被拆成多条"] = len(multi)
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top = sorted(multi.items(), key=lambda kv: -kv[1])[:3]
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samples[f"{task}|同一句被拆成多条"] = [f"{sid} ×{n}：{by_sid[sid].text[:40]}" for sid, n in top if sid in by_sid]
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tokens = sum((c.get("tokens_in") or 0) for c in calls), sum((c.get("tokens_out") or 0) for c in calls)
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lines = [
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f"# {pathlib.Path(args.file).name}",
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f"页数 {len(pages)}，正文句子 {len(body)}，分块 {len(extract.chunks(body, doc.continuations))}，模型调用 {len(calls)} 次，tokens {tokens[0]} / {tokens[1]}",
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f"版本：{ {t.name: t.prompt_version for t in extract.TASKS} }",
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"",
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"## 结果",
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f"条目 {len(items)}：" + "，".join(f"{z}/{r or '-'} {n}" for (z, r), n in zone.most_common()),
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"按类别：" + "，".join(f"{c}/{z}:{n}" for (c, z), n in sorted(cats.items())),
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"项目信息：" + "，".join(f"{k}={getattr(v, 'value', v)}" for k, v in info.fields.items())[:600],
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"",
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"## 模型输出审计",
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]
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for task, cnt in audit.items():
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lines.append(f"### {task}（{cnt['items']} 条）")
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for k, n in cnt.most_common():
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if k == "items":
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continue
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lines.append(f"- {k}：{n}")
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for ex in samples.get(f"{task}|{k}", []):
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lines.append(f" - {ex}")
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report = "\n".join(lines)
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(out / "report.md").write_text(report)
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print(report)
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asyncio.run(main())
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