feat(translate): 增加译文清洗 — pipeline 接入源头防御 + 批量清洗历史脚本
- 新增 app/services/translation/text_clean.py clean_markdown_asterisks / clean_html_inner_text / wrap_html 共用工具, 清洗 LLM 输出残留的 ** / * / *** markdown 标记 - 改 pipeline.translate_article: 写库前清洗 tr_title/tr_body, 新翻译不再带 **;同时把私有 _wrap_html 替换为公开 wrap_html - 新增 app/scripts/clean_translations.py 批量清洗历史脏数据 — 5 字段(title_zh/body_zh_text/body_zh_html/ body_zh_formatted/summary_zh),支持 dry-run/limit/source-slug/field
This commit is contained in:
xiaji
338
backend/app/scripts/clean_translations.py
Normal file
338
backend/app/scripts/clean_translations.py
Normal file
@@ -0,0 +1,338 @@
|
||||
"""批量清洗历史译文里的 markdown 星号标记(`**` / `*` / `***`)。
|
||||
|
||||
背景:
|
||||
- LLM 翻译时偶尔把 markdown 加粗标记原样带进译文,前端展示就成了 `**FBI**局长` 这种脏数据
|
||||
- 已发现的脏模式(LLM 输出不严谨):
|
||||
**FBI**局长**卡什·帕特尔**表示,该机构挫败了一起针对周日发生在**白宫南草坪**的**UFC**活动的阴谋。
|
||||
- 期望:
|
||||
FBI局长卡什·帕特尔表示,该机构挫败了一起针对周日发生在白宫南草坪的UFC活动的阴谋。
|
||||
|
||||
清洗规则(详见 app.services.translation.text_clean):
|
||||
***text*** -> text (粗+斜)
|
||||
**text** -> text (粗) — 循环直到稳定,处理连续多对
|
||||
*text* -> text (斜)
|
||||
兜底:所有零散 `*` 一律删除(应对 LLM 输出错位)
|
||||
|
||||
字段清洗范围:
|
||||
- title_zh 直接清洗
|
||||
- body_zh_text 直接清洗
|
||||
- body_zh_html 用清洗后的 body_zh_text 重新 wrap(保证一致)
|
||||
- body_zh_formatted BeautifulSoup 解析,只清洗文本节点(保留 diary-para class + style)
|
||||
- summary_zh 直接清洗
|
||||
|
||||
不动:
|
||||
- commentary / commentary_meituan(评论是另外的 LLM 产物,不在"翻译"范畴)
|
||||
- translation_status / translation_engine(状态/引擎标记本身没错)
|
||||
|
||||
工作流程:
|
||||
1. 在 pipeline.py 的 translate_article() 写库前已接入防御性清洗(commit 后的 worker 不会再产生 `**`)
|
||||
2. 这个脚本专门清理历史脏数据 — 跑一次即可,后续靠 pipeline 端防御
|
||||
|
||||
用法(在 worker 容器里):
|
||||
|
||||
# 1) 先 dry-run 看看会改多少 / 样本对比
|
||||
docker compose exec worker python -m app.scripts.clean_translations --dry-run --show-samples 5
|
||||
|
||||
# 2) 全量清洗
|
||||
docker compose exec worker python -m app.scripts.clean_translations
|
||||
|
||||
# 3) 分批:每次 200 篇,避免一次性锁太久
|
||||
docker compose exec worker python -m app.scripts.clean_translations --batch-size 200
|
||||
|
||||
# 4) 只清洗某个源
|
||||
docker compose exec worker python -m app.scripts.clean_translations \\
|
||||
--source-slug bbc-world
|
||||
|
||||
# 5) 只清洗某个字段(排错用)
|
||||
docker compose exec worker python -m app.scripts.clean_translations \\
|
||||
--field body_zh_text --limit 10 --dry-run
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import logging
|
||||
import sys
|
||||
from typing import Iterable
|
||||
|
||||
from sqlalchemy import or_, select
|
||||
|
||||
from app.database import AsyncSessionLocal
|
||||
from app.models.article import Article
|
||||
from app.models.source import Source
|
||||
from app.services.translation.text_clean import (
|
||||
clean_html_inner_text,
|
||||
clean_markdown_asterisks,
|
||||
wrap_html,
|
||||
)
|
||||
|
||||
logger = logging.getLogger("news.scripts.clean_translations")
|
||||
|
||||
|
||||
# === SQL 过滤:任一翻译字段含 * 就捞出来 ===
|
||||
# LIKE 里的 `*` 是字面字符(只有 `%` 和 `_` 才是 wildcard),无需 escape
|
||||
_ASTERISK_COLUMNS = (
|
||||
Article.title_zh,
|
||||
Article.body_zh_text,
|
||||
Article.body_zh_html,
|
||||
Article.body_zh_formatted,
|
||||
Article.summary_zh,
|
||||
)
|
||||
|
||||
|
||||
def _scan_sql(*, source_slug: str | None):
|
||||
"""构造扫描 SQL。"""
|
||||
stmt = select(Article).where(
|
||||
or_(*(col.like("%*%") for col in _ASTERISK_COLUMNS))
|
||||
).order_by(Article.id.asc())
|
||||
if source_slug:
|
||||
# 注意:Source filter 在 main() 里手动处理(下面 async 块),
|
||||
# 因为这里不能 await source 查询 — 简单做法是先查 source id 再拼 where
|
||||
return stmt, source_slug
|
||||
return stmt, None
|
||||
|
||||
|
||||
async def fetch_candidates(
|
||||
*, source_slug: str | None, batch_size: int | None
|
||||
) -> list[Article]:
|
||||
"""从 DB 捞出含 * 的文章列表(轻量 — 不读全文,后面再按需读)。"""
|
||||
async with AsyncSessionLocal() as session:
|
||||
stmt, slug = _scan_sql(source_slug=source_slug)
|
||||
if slug:
|
||||
src = (
|
||||
await session.execute(select(Source).where(Source.slug == slug))
|
||||
).scalar_one_or_none()
|
||||
if not src:
|
||||
print(f"!! source_slug '{slug}' 不存在", file=sys.stderr)
|
||||
return []
|
||||
stmt = stmt.where(Article.source_id == src.id)
|
||||
if batch_size is not None:
|
||||
stmt = stmt.limit(batch_size)
|
||||
rows = (await session.execute(stmt)).scalars().all()
|
||||
return list(rows)
|
||||
|
||||
|
||||
# === 清洗单篇文章 ===
|
||||
def clean_one(
|
||||
art: Article, *, fields: set[str]
|
||||
) -> dict[str, tuple[int, int]]:
|
||||
"""清洗一篇文章的指定字段,返回 {field: (before_len, after_len)}。
|
||||
|
||||
fields 是允许清洗的字段集合(由 --field 参数控制)。
|
||||
"""
|
||||
changes: dict[str, tuple[int, int]] = {}
|
||||
|
||||
# --- title_zh ---
|
||||
if "title_zh" in fields and art.title_zh:
|
||||
cleaned = clean_markdown_asterisks(art.title_zh)
|
||||
if cleaned != art.title_zh:
|
||||
changes["title_zh"] = (len(art.title_zh), len(cleaned or ""))
|
||||
art.title_zh = cleaned or None
|
||||
|
||||
# --- body_zh_text ---
|
||||
body_text_changed = False
|
||||
if "body_zh_text" in fields and art.body_zh_text:
|
||||
cleaned = clean_markdown_asterisks(art.body_zh_text)
|
||||
if cleaned != art.body_zh_text:
|
||||
changes["body_zh_text"] = (len(art.body_zh_text), len(cleaned or ""))
|
||||
art.body_zh_text = cleaned or None
|
||||
body_text_changed = True
|
||||
|
||||
# --- body_zh_html ---
|
||||
# 策略:用清洗后的 body_zh_text 重新 wrap(保证两个字段一致)
|
||||
# 触发条件:body_zh_text 被改 / 或原 body_zh_html 本身含 * 但 body_zh_text 不含(老数据不一致)
|
||||
if "body_zh_html" in fields:
|
||||
if body_text_changed:
|
||||
new_html = wrap_html(art.body_zh_text)
|
||||
old_html = art.body_zh_html or ""
|
||||
if (new_html or "") != old_html:
|
||||
changes["body_zh_html"] = (len(old_html), len(new_html or ""))
|
||||
art.body_zh_html = new_html
|
||||
elif art.body_zh_html and "*" in art.body_zh_html:
|
||||
# body_zh_text 没改但 body_zh_html 仍含 *:BeautifulSoup 兜底清洗
|
||||
cleaned_html = clean_html_inner_text(art.body_zh_html)
|
||||
if cleaned_html and cleaned_html != art.body_zh_html:
|
||||
changes["body_zh_html"] = (
|
||||
len(art.body_zh_html), len(cleaned_html),
|
||||
)
|
||||
art.body_zh_html = cleaned_html
|
||||
|
||||
# --- body_zh_formatted ---
|
||||
# 不能直接重新生成(会丢 diary-para class + 内联 style),只能用 BS 清洗文本节点
|
||||
if (
|
||||
"body_zh_formatted" in fields
|
||||
and art.body_zh_formatted
|
||||
and "*" in art.body_zh_formatted
|
||||
):
|
||||
cleaned = clean_html_inner_text(art.body_zh_formatted)
|
||||
if cleaned and cleaned != art.body_zh_formatted:
|
||||
changes["body_zh_formatted"] = (
|
||||
len(art.body_zh_formatted), len(cleaned),
|
||||
)
|
||||
art.body_zh_formatted = cleaned
|
||||
|
||||
# --- summary_zh ---
|
||||
if "summary_zh" in fields and art.summary_zh:
|
||||
cleaned = clean_markdown_asterisks(art.summary_zh)
|
||||
if cleaned != art.summary_zh:
|
||||
changes["summary_zh"] = (len(art.summary_zh), len(cleaned or ""))
|
||||
art.summary_zh = cleaned or None
|
||||
|
||||
return changes
|
||||
|
||||
|
||||
# === 主流程 ===
|
||||
ALL_FIELDS = ("title_zh", "body_zh_text", "body_zh_html", "body_zh_formatted", "summary_zh")
|
||||
|
||||
|
||||
async def commit_batch(arts: list[Article]) -> None:
|
||||
"""批量提交(避免每条都 round-trip)。"""
|
||||
if not arts:
|
||||
return
|
||||
async with AsyncSessionLocal() as session:
|
||||
for art in arts:
|
||||
await session.merge(art)
|
||||
await session.commit()
|
||||
|
||||
|
||||
def _preview_diff(art: Article, fields: set[str]) -> str:
|
||||
"""打印一条样本的清洗前后对比(dry-run 用)。"""
|
||||
lines: list[str] = []
|
||||
art_copy_title = clean_markdown_asterisks(art.title_zh) if (
|
||||
"title_zh" in fields and art.title_zh
|
||||
) else art.title_zh
|
||||
art_copy_body = clean_markdown_asterisks(art.body_zh_text) if (
|
||||
"body_zh_text" in fields and art.body_zh_text
|
||||
) else art.body_zh_text
|
||||
if art.title_zh and art_copy_title != art.title_zh:
|
||||
lines.append(f" TITLE before: {art.title_zh!r}")
|
||||
lines.append(f" TITLE after : {art_copy_title!r}")
|
||||
if art.body_zh_text and art_copy_body != art.body_zh_text:
|
||||
body_before = art.body_zh_text
|
||||
body_after = art_copy_body
|
||||
# 只截前 200 字符(样本,避免刷屏)
|
||||
if len(body_before) > 200 or len(body_after) > 200:
|
||||
lines.append(f" BODY before: {body_before[:200]!r}...")
|
||||
lines.append(f" BODY after : {body_after[:200]!r}...")
|
||||
else:
|
||||
lines.append(f" BODY before: {body_before!r}")
|
||||
lines.append(f" BODY after : {body_after!r}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
async def main() -> int:
|
||||
p = argparse.ArgumentParser(
|
||||
description="批量清洗历史译文里的 markdown 星号标记(** / * / ***)",
|
||||
)
|
||||
p.add_argument(
|
||||
"--dry-run",
|
||||
action="store_true",
|
||||
help="只打印待改列表 + 样本对比,不动数据库",
|
||||
)
|
||||
p.add_argument(
|
||||
"--limit",
|
||||
type=int,
|
||||
default=None,
|
||||
help="最多处理多少条文章(分批用,避免一次性锁太久)",
|
||||
)
|
||||
p.add_argument(
|
||||
"--batch-size",
|
||||
type=int,
|
||||
default=None,
|
||||
help="从 DB 一次捞多少条(默认 = limit, 即一次全部)",
|
||||
)
|
||||
p.add_argument(
|
||||
"--source-slug",
|
||||
default=None,
|
||||
help="限定某个采集源(按 slug)",
|
||||
)
|
||||
p.add_argument(
|
||||
"--field",
|
||||
choices=ALL_FIELDS + ("all",),
|
||||
default="all",
|
||||
help="只清洗指定字段(排错用;默认 all = 清洗全部 5 个字段)",
|
||||
)
|
||||
p.add_argument(
|
||||
"--show-samples",
|
||||
type=int,
|
||||
default=0,
|
||||
metavar="N",
|
||||
help="dry-run 时额外打印前 N 条样本的清洗前后对比",
|
||||
)
|
||||
p.add_argument(
|
||||
"--show-stats",
|
||||
action="store_true",
|
||||
help="按字段分组统计改动次数 + 总字符差",
|
||||
)
|
||||
args = p.parse_args()
|
||||
|
||||
# --- 字段白名单 ---
|
||||
if args.field == "all":
|
||||
fields = set(ALL_FIELDS)
|
||||
else:
|
||||
fields = {args.field}
|
||||
|
||||
# --- 扫描 ---
|
||||
fetch_limit = args.batch_size if args.batch_size is not None else args.limit
|
||||
candidates = await fetch_candidates(
|
||||
source_slug=args.source_slug, batch_size=fetch_limit,
|
||||
)
|
||||
if not candidates:
|
||||
print("✅ 没找到含 `*` 标记的文章,无需清洗")
|
||||
return 0
|
||||
|
||||
print(
|
||||
f"找到 {len(candidates)} 条含 `*` 标记的文章"
|
||||
f"(mode={'DRY-RUN' if args.dry_run else 'EXECUTE'}, "
|
||||
f"fields={sorted(fields)}, source={args.source_slug or 'ALL'})"
|
||||
)
|
||||
|
||||
# --- 处理 ---
|
||||
field_stats: dict[str, int] = {f: 0 for f in ALL_FIELDS}
|
||||
field_char_delta: dict[str, int] = {f: 0 for f in ALL_FIELDS}
|
||||
dirty: list[Article] = [] # 有改动的文章(dry-run 用)
|
||||
modified_arts: list[Article] = [] # 真正要 commit 的
|
||||
|
||||
for art in candidates:
|
||||
changes = clean_one(art, fields=fields)
|
||||
if not changes:
|
||||
# scan SQL 命中 LIKE '%*%' 但清洗函数判定无变化(极少见,
|
||||
# 比如 * 在 escaped 位置) — 跳过
|
||||
continue
|
||||
for field, (before_len, after_len) in changes.items():
|
||||
field_stats[field] += 1
|
||||
field_char_delta[field] += before_len - after_len # 正数 = 字符减少
|
||||
dirty.append(art)
|
||||
if not args.dry_run:
|
||||
modified_arts.append(art)
|
||||
if args.show_samples and len(dirty) <= args.show_samples:
|
||||
print(f"\n[样本 #{len(dirty)}] id={art.id} source_id={art.source_id}")
|
||||
print(_preview_diff(art, fields))
|
||||
|
||||
# --- 提交 ---
|
||||
if not args.dry_run:
|
||||
await commit_batch(modified_arts)
|
||||
|
||||
# --- 统计输出 ---
|
||||
changed_n = len(dirty)
|
||||
print(f"\n实际改动: {changed_n} 条 (mode={'DRY-RUN' if args.dry_run else 'EXECUTE'})")
|
||||
if args.show_stats:
|
||||
print("\n按字段统计:")
|
||||
for field in ALL_FIELDS:
|
||||
n = field_stats[field]
|
||||
delta = field_char_delta[field]
|
||||
if n == 0:
|
||||
continue
|
||||
print(f" {field:<22} {n:>5} 条 字符差 {delta:+d}")
|
||||
|
||||
if args.dry_run:
|
||||
print(f"\n[DRY-RUN] 共 {changed_n} 条待改,实际未改动。去掉 --dry-run 真正执行。")
|
||||
else:
|
||||
print(f"\n✅ 已清洗 {changed_n} 条,提交完毕。")
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
|
||||
sys.exit(asyncio.run(main()))
|
||||
Reference in New Issue
Block a user