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
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338
backend/app/scripts/clean_translations.py
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"""批量清洗历史译文里的 markdown 星号标记(`**` / `*` / `***`)。
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背景:
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- 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
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- 已发现的脏模式(LLM 输出不严谨):
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**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。
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- 期望:
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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。
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清洗规则(详见 app.services.translation.text_clean):
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***text*** -> text (粗+斜)
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**text** -> text (粗) — 循环直到稳定,处理连续多对
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*text* -> text (斜)
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兜底:所有零散 `*` 一律删除(应对 LLM 输出错位)
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字段清洗范围:
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- title_zh 直接清洗
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- body_zh_text 直接清洗
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- body_zh_html 用清洗后的 body_zh_text 重新 wrap(保证一致)
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- body_zh_formatted BeautifulSoup 解析,只清洗文本节点(保留 diary-para class + style)
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- summary_zh 直接清洗
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不动:
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- commentary / commentary_meituan(评论是另外的 LLM 产物,不在"翻译"范畴)
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- translation_status / translation_engine(状态/引擎标记本身没错)
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工作流程:
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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 `**`)
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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
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用法(在 worker 容器里):
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# 1) 先 dry-run 看看会改多少 / 样本对比
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docker compose exec worker python -m app.scripts.clean_translations --dry-run --show-samples 5
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# 2) 全量清洗
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docker compose exec worker python -m app.scripts.clean_translations
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# 3) 分批:每次 200 篇,避免一次性锁太久
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docker compose exec worker python -m app.scripts.clean_translations --batch-size 200
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# 4) 只清洗某个源
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docker compose exec worker python -m app.scripts.clean_translations \\
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--source-slug bbc-world
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# 5) 只清洗某个字段(排错用)
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docker compose exec worker python -m app.scripts.clean_translations \\
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--field body_zh_text --limit 10 --dry-run
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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 logging
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import sys
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from typing import Iterable
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from sqlalchemy import or_, select
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from app.database import AsyncSessionLocal
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from app.models.article import Article
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from app.models.source import Source
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from app.services.translation.text_clean import (
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clean_html_inner_text,
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clean_markdown_asterisks,
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wrap_html,
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)
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logger = logging.getLogger("news.scripts.clean_translations")
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# === SQL 过滤:任一翻译字段含 * 就捞出来 ===
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# LIKE 里的 `*` 是字面字符(只有 `%` 和 `_` 才是 wildcard),无需 escape
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_ASTERISK_COLUMNS = (
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Article.title_zh,
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Article.body_zh_text,
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Article.body_zh_html,
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Article.body_zh_formatted,
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Article.summary_zh,
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)
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def _scan_sql(*, source_slug: str | None):
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"""构造扫描 SQL。"""
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stmt = select(Article).where(
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or_(*(col.like("%*%") for col in _ASTERISK_COLUMNS))
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).order_by(Article.id.asc())
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if source_slug:
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# 注意:Source filter 在 main() 里手动处理(下面 async 块),
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# 因为这里不能 await source 查询 — 简单做法是先查 source id 再拼 where
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return stmt, source_slug
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return stmt, None
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async def fetch_candidates(
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*, source_slug: str | None, batch_size: int | None
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) -> list[Article]:
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"""从 DB 捞出含 * 的文章列表(轻量 — 不读全文,后面再按需读)。"""
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async with AsyncSessionLocal() as session:
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stmt, slug = _scan_sql(source_slug=source_slug)
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if slug:
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src = (
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await session.execute(select(Source).where(Source.slug == slug))
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).scalar_one_or_none()
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if not src:
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print(f"!! source_slug '{slug}' 不存在", file=sys.stderr)
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return []
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stmt = stmt.where(Article.source_id == src.id)
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if batch_size is not None:
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stmt = stmt.limit(batch_size)
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rows = (await session.execute(stmt)).scalars().all()
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return list(rows)
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# === 清洗单篇文章 ===
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def clean_one(
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art: Article, *, fields: set[str]
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) -> dict[str, tuple[int, int]]:
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"""清洗一篇文章的指定字段,返回 {field: (before_len, after_len)}。
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fields 是允许清洗的字段集合(由 --field 参数控制)。
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"""
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changes: dict[str, tuple[int, int]] = {}
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# --- title_zh ---
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if "title_zh" in fields and art.title_zh:
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cleaned = clean_markdown_asterisks(art.title_zh)
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if cleaned != art.title_zh:
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changes["title_zh"] = (len(art.title_zh), len(cleaned or ""))
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art.title_zh = cleaned or None
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# --- body_zh_text ---
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body_text_changed = False
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if "body_zh_text" in fields and art.body_zh_text:
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cleaned = clean_markdown_asterisks(art.body_zh_text)
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if cleaned != art.body_zh_text:
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changes["body_zh_text"] = (len(art.body_zh_text), len(cleaned or ""))
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art.body_zh_text = cleaned or None
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body_text_changed = True
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# --- body_zh_html ---
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# 策略:用清洗后的 body_zh_text 重新 wrap(保证两个字段一致)
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# 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)
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if "body_zh_html" in fields:
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if body_text_changed:
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new_html = wrap_html(art.body_zh_text)
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old_html = art.body_zh_html or ""
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if (new_html or "") != old_html:
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changes["body_zh_html"] = (len(old_html), len(new_html or ""))
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art.body_zh_html = new_html
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elif art.body_zh_html and "*" in art.body_zh_html:
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# body_zh_text 没改但 body_zh_html 仍含 *:BeautifulSoup 兜底清洗
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cleaned_html = clean_html_inner_text(art.body_zh_html)
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if cleaned_html and cleaned_html != art.body_zh_html:
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changes["body_zh_html"] = (
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len(art.body_zh_html), len(cleaned_html),
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)
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art.body_zh_html = cleaned_html
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# --- body_zh_formatted ---
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# 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
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if (
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"body_zh_formatted" in fields
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and art.body_zh_formatted
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and "*" in art.body_zh_formatted
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):
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cleaned = clean_html_inner_text(art.body_zh_formatted)
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if cleaned and cleaned != art.body_zh_formatted:
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changes["body_zh_formatted"] = (
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len(art.body_zh_formatted), len(cleaned),
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)
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art.body_zh_formatted = cleaned
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# --- summary_zh ---
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if "summary_zh" in fields and art.summary_zh:
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cleaned = clean_markdown_asterisks(art.summary_zh)
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if cleaned != art.summary_zh:
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changes["summary_zh"] = (len(art.summary_zh), len(cleaned or ""))
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art.summary_zh = cleaned or None
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return changes
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# === 主流程 ===
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ALL_FIELDS = ("title_zh", "body_zh_text", "body_zh_html", "body_zh_formatted", "summary_zh")
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async def commit_batch(arts: list[Article]) -> None:
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"""批量提交(避免每条都 round-trip)。"""
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if not arts:
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return
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async with AsyncSessionLocal() as session:
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for art in arts:
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await session.merge(art)
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await session.commit()
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def _preview_diff(art: Article, fields: set[str]) -> str:
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"""打印一条样本的清洗前后对比(dry-run 用)。"""
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lines: list[str] = []
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art_copy_title = clean_markdown_asterisks(art.title_zh) if (
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"title_zh" in fields and art.title_zh
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) else art.title_zh
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art_copy_body = clean_markdown_asterisks(art.body_zh_text) if (
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"body_zh_text" in fields and art.body_zh_text
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) else art.body_zh_text
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if art.title_zh and art_copy_title != art.title_zh:
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lines.append(f" TITLE before: {art.title_zh!r}")
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lines.append(f" TITLE after : {art_copy_title!r}")
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if art.body_zh_text and art_copy_body != art.body_zh_text:
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body_before = art.body_zh_text
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body_after = art_copy_body
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# 只截前 200 字符(样本,避免刷屏)
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if len(body_before) > 200 or len(body_after) > 200:
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lines.append(f" BODY before: {body_before[:200]!r}...")
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lines.append(f" BODY after : {body_after[:200]!r}...")
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else:
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lines.append(f" BODY before: {body_before!r}")
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lines.append(f" BODY after : {body_after!r}")
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return "\n".join(lines)
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async def main() -> int:
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p = argparse.ArgumentParser(
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description="批量清洗历史译文里的 markdown 星号标记(** / * / ***)",
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)
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p.add_argument(
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"--dry-run",
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action="store_true",
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help="只打印待改列表 + 样本对比,不动数据库",
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)
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p.add_argument(
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"--limit",
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type=int,
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default=None,
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help="最多处理多少条文章(分批用,避免一次性锁太久)",
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)
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p.add_argument(
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"--batch-size",
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type=int,
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default=None,
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help="从 DB 一次捞多少条(默认 = limit, 即一次全部)",
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)
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p.add_argument(
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"--source-slug",
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default=None,
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help="限定某个采集源(按 slug)",
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)
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p.add_argument(
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"--field",
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choices=ALL_FIELDS + ("all",),
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default="all",
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help="只清洗指定字段(排错用;默认 all = 清洗全部 5 个字段)",
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)
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p.add_argument(
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"--show-samples",
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type=int,
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default=0,
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metavar="N",
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help="dry-run 时额外打印前 N 条样本的清洗前后对比",
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)
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p.add_argument(
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"--show-stats",
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action="store_true",
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help="按字段分组统计改动次数 + 总字符差",
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)
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args = p.parse_args()
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# --- 字段白名单 ---
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if args.field == "all":
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fields = set(ALL_FIELDS)
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else:
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fields = {args.field}
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# --- 扫描 ---
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fetch_limit = args.batch_size if args.batch_size is not None else args.limit
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candidates = await fetch_candidates(
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source_slug=args.source_slug, batch_size=fetch_limit,
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)
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if not candidates:
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print("✅ 没找到含 `*` 标记的文章,无需清洗")
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return 0
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print(
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f"找到 {len(candidates)} 条含 `*` 标记的文章"
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f"(mode={'DRY-RUN' if args.dry_run else 'EXECUTE'}, "
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f"fields={sorted(fields)}, source={args.source_slug or 'ALL'})"
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)
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# --- 处理 ---
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field_stats: dict[str, int] = {f: 0 for f in ALL_FIELDS}
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field_char_delta: dict[str, int] = {f: 0 for f in ALL_FIELDS}
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dirty: list[Article] = [] # 有改动的文章(dry-run 用)
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modified_arts: list[Article] = [] # 真正要 commit 的
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for art in candidates:
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changes = clean_one(art, fields=fields)
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if not changes:
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# scan SQL 命中 LIKE '%*%' 但清洗函数判定无变化(极少见,
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# 比如 * 在 escaped 位置) — 跳过
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continue
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for field, (before_len, after_len) in changes.items():
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field_stats[field] += 1
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field_char_delta[field] += before_len - after_len # 正数 = 字符减少
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dirty.append(art)
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if not args.dry_run:
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modified_arts.append(art)
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if args.show_samples and len(dirty) <= args.show_samples:
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print(f"\n[样本 #{len(dirty)}] id={art.id} source_id={art.source_id}")
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print(_preview_diff(art, fields))
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# --- 提交 ---
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if not args.dry_run:
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await commit_batch(modified_arts)
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# --- 统计输出 ---
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changed_n = len(dirty)
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print(f"\n实际改动: {changed_n} 条 (mode={'DRY-RUN' if args.dry_run else 'EXECUTE'})")
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if args.show_stats:
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print("\n按字段统计:")
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for field in ALL_FIELDS:
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n = field_stats[field]
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delta = field_char_delta[field]
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if n == 0:
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continue
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print(f" {field:<22} {n:>5} 条 字符差 {delta:+d}")
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if args.dry_run:
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print(f"\n[DRY-RUN] 共 {changed_n} 条待改,实际未改动。去掉 --dry-run 真正执行。")
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else:
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print(f"\n✅ 已清洗 {changed_n} 条,提交完毕。")
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return 0
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
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sys.exit(asyncio.run(main()))
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121
backend/app/services/translation/text_clean.py
Normal file
121
backend/app/services/translation/text_clean.py
Normal file
@@ -0,0 +1,121 @@
|
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"""译文文本清洗工具。
|
||||
|
||||
应用场景:
|
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- LLM 翻译时偶尔把 markdown 标记(加粗 `**`、`*`、`***`)原样带进译文里,
|
||||
前端展示出来就成了 `**FBI**局长` 这种带星号的脏数据。
|
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- enrichment 阶段也会把 `**` 带到 `body_zh_formatted` / `summary_zh`。
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提供两个核心函数:
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- `clean_markdown_asterisks(text)`:清洗字符串里的 `*` / `**` / `***` 标记
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- `clean_html_inner_text(html)`:BeautifulSoup 解析 HTML,只清洗文本节点,
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保留标签结构和内联 style
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另附 `wrap_html(text)`:把清洗后的 body_zh_text 包成简单 `<p>` HTML,
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之前在 pipeline.py 是私有 `_wrap_html`,提到此处供复用。
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设计原则:
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- 不引入额外依赖(只用 `re` + 项目已有的 `beautifulsoup4`)
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- 对 None / 空串安全返回 None / 空串
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- 处理顺序从长到短,避免 `**` 被 `*` 先吃掉
|
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- 反复循环直到稳定,应对 `**a****b**` 这种连续多对
|
||||
- 兜底删除所有残留 `*`,保守但符合"去掉星号"的用户意图
|
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"""
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from __future__ import annotations
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|
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import re
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|
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from bs4 import BeautifulSoup
|
||||
|
||||
|
||||
# === 核心清洗函数 ===
|
||||
|
||||
# ***text*** → text(粗+斜体)
|
||||
_ASTERISK_TRIPLE_RE = re.compile(r"\*\*\*([^*]+?)\*\*\*")
|
||||
# **text** → text(粗体)
|
||||
_ASTERISK_DOUBLE_RE = re.compile(r"\*\*([^*]+?)\*\*")
|
||||
# *text* → text(斜体,要求 text 不为空且不与 * 相邻,避免误伤 `*2*3*` 这种)
|
||||
# 第一个负向回看 (?<!\*) 防止匹配 ** 中的 *
|
||||
# 第二个 (?!\\*) 同理
|
||||
# [^*\s] 强制开头非空白非 *,避免匹配孤立 " * "
|
||||
_ASTERISK_SINGLE_RE = re.compile(r"(?<!\*)\*([^*\s][^*]*?)\*(?!\*)")
|
||||
# 兜底:删除所有残留的 * (单/双/多)
|
||||
_ANY_ASTERISK_RE = re.compile(r"\*+")
|
||||
|
||||
|
||||
def clean_markdown_asterisks(text: str | None) -> str | None:
|
||||
"""清洗字符串里的 markdown 星号标记残留。
|
||||
|
||||
处理顺序:
|
||||
1. `***text***` -> `text`
|
||||
2. `**text**` -> `text`(循环直到稳定,处理 `**a****b**` 这种连续)
|
||||
3. `*text*` -> `text`
|
||||
4. 兜底:残留的 `*` / `**` / `***` 一律删除(LLM 输出不严谨的脏数据)
|
||||
|
||||
对 None / 空串安全返回原值。
|
||||
"""
|
||||
if not text:
|
||||
return text
|
||||
|
||||
# 1+2) 先把 *** / ** 多轮替换,直到稳定(处理嵌套/连续多对)
|
||||
prev: str | None = None
|
||||
while prev != text:
|
||||
prev = text
|
||||
text = _ASTERISK_TRIPLE_RE.sub(r"\1", text)
|
||||
text = _ASTERISK_DOUBLE_RE.sub(r"\1", text)
|
||||
|
||||
# 3) 单星号斜体
|
||||
text = _ASTERISK_SINGLE_RE.sub(r"\1", text)
|
||||
|
||||
# 4) 兜底:删掉所有零散 *
|
||||
text = _ANY_ASTERISK_RE.sub("", text)
|
||||
|
||||
return text
|
||||
|
||||
|
||||
def clean_html_inner_text(html: str | None) -> str | None:
|
||||
"""清洗 HTML 内的文本节点(保留标签结构和属性)。
|
||||
|
||||
用途:`body_zh_formatted` 这种由 LLM 排版产物 ——
|
||||
不能整个重新生成(会丢 `diary-para` class 和内联 style),
|
||||
只能用 BeautifulSoup 找到所有文本节点单独清洗。
|
||||
|
||||
对 None / 空串安全返回原值。
|
||||
"""
|
||||
if not html:
|
||||
return html
|
||||
soup = BeautifulSoup(html, "html.parser")
|
||||
changed = False
|
||||
for node in list(soup.find_all(string=True)):
|
||||
# 跳过纯空白文本节点
|
||||
original = str(node)
|
||||
if not original or not original.strip():
|
||||
continue
|
||||
cleaned = clean_markdown_asterisks(original)
|
||||
if cleaned != original:
|
||||
node.replace_with(cleaned)
|
||||
changed = True
|
||||
return str(soup) if changed else html
|
||||
|
||||
|
||||
# === 公开版 wrap_html(原 pipeline._wrap_html,提到此处供脚本复用)===
|
||||
|
||||
def wrap_html(text: str | None) -> str | None:
|
||||
"""把清洗后的译文纯文本包成简单的 `<p>` 段落 HTML。
|
||||
|
||||
内部会自动调用 `clean_markdown_asterisks` 清洗 `**` / `*` / `***`,
|
||||
调用方无需"先清洗再 wrap"——这是幂等的,即使输入已清洗也是 no-op。
|
||||
|
||||
行为:
|
||||
- 按 `\n\n` 切段,空段过滤
|
||||
- 每段包 `<p>...</p>`
|
||||
- 段落之间用 `\n` 拼接
|
||||
|
||||
对 None / 空串返回 None。
|
||||
"""
|
||||
if not text:
|
||||
return None
|
||||
cleaned = clean_markdown_asterisks(text)
|
||||
if not cleaned:
|
||||
return None
|
||||
parts = [f"<p>{p.strip()}</p>" for p in cleaned.split("\n\n") if p.strip()]
|
||||
return "\n".join(parts) if parts else None
|
||||
@@ -19,6 +19,7 @@ from app.models.source import Source, SourceKind
|
||||
from app.services.fetchers import get_fetcher
|
||||
from app.services.fetchers.base import FetchedItem, url_hash
|
||||
from app.services.translation.service import service as translation_service
|
||||
from app.services.translation.text_clean import clean_markdown_asterisks, wrap_html
|
||||
|
||||
logger = logging.getLogger("news.pipeline")
|
||||
|
||||
@@ -232,15 +233,20 @@ async def translate_article(article_id: int) -> None:
|
||||
await session.commit()
|
||||
return
|
||||
|
||||
# 写库前清洗:LLM 偶尔会把 markdown 加粗标记 ** / * 带进译文。
|
||||
# 源头控制比事后批量洗更稳 — 历史脏数据由 scripts/clean_translations.py 处理。
|
||||
tr_title_clean = clean_markdown_asterisks(tr_title.text)
|
||||
tr_body_clean = clean_markdown_asterisks(tr_body)
|
||||
|
||||
# 写回
|
||||
async with AsyncSessionLocal() as session:
|
||||
art = (
|
||||
await session.execute(select(Article).where(Article.id == article_id_ref))
|
||||
).scalar_one_or_none()
|
||||
if art:
|
||||
art.title_zh = tr_title.text if tr_title.text else None
|
||||
art.body_zh_text = tr_body or None
|
||||
art.body_zh_html = _wrap_html(tr_body) if tr_body else None
|
||||
art.title_zh = tr_title_clean or None
|
||||
art.body_zh_text = tr_body_clean or None
|
||||
art.body_zh_html = wrap_html(tr_body_clean) if tr_body_clean else None
|
||||
art.translation_status = status
|
||||
art.translation_engine = engine_label
|
||||
art.translation_chars = total_chars
|
||||
@@ -293,14 +299,6 @@ def _split_long_para(para: str, max_chars: int) -> list[str]:
|
||||
return parts
|
||||
|
||||
|
||||
def _wrap_html(text: str) -> str:
|
||||
"""把译文包成 HTML 段落。"""
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
parts = [f"<p>{p.strip()}</p>" for p in text.split("\n\n") if p.strip()]
|
||||
return "\n".join(parts) if parts else ""
|
||||
|
||||
|
||||
# === 全量跑(供测试 / 手动触发) ===
|
||||
async def run_once() -> None:
|
||||
async with AsyncSessionLocal() as session:
|
||||
|
||||
Reference in New Issue
Block a user