shenleiandClaude Opus 5
6b42b7abc3
## 独立词库 理解词原先只能跟着课程单元走，学完 A0 十课词汇量只增加约 22 个实词， 不足以解决"记不住单词"。新增一份独立词库 assets/words/wordbank.json （2748 词，A1–B1），挂进 receptiveWordRegistry 的合成单元 bank-A1/A2/B1， 完全复用理解词已有的状态机，不依赖课程进度，第一天就能用。 数据来源、许可与合成规则记在 tool/words/DATA-NOTE.md：CEFR-J 定等级、 公开词书提供音标、AI 重写全部释义并生成例句、OpenSubtitles 提供口语词频。 词书部分为 CC BY-NC-SA 4.0 且上游权利不明，仅供个人非商用； 若要分发或上架，须替换音标那一列。 ## 背单词机制 - 间隔阶梯 1/3/7/15/30/60/120 天，连续答对上一级，答错回第一级。 原先首次答对后要等 7 天才复习，正是"第二天就忘"的成因。 - 每日新词上限（10 分钟 8 个 / 20 分钟 15 个 / 30 分钟 20 个）。 阶梯第一级是次日，今天引入的新词就是明天的工作量。 - 新词按口语频率发放，不再按字母序 —— A1 从 a.m./ability 变成 no/not/know/just。 - 三个方向按层级轮转：看词（英→中）→ 听词（音→中）→ 想词（中→英）。 想词题仍是选择题，不要求产出，理解词定位不变，不进升级分母。 - 单词页独立成 tab，首页今日任务卡下方给一张认词入口卡。 ## 用法对照 课程 JSON 增加 usage 字段（when/reply/swap/confuse）：一个句型用在什么场合、 对方通常怎么答、还能怎么说、跟哪个学过的句型容易混。 知道 How are you? 的意思，不等于知道它不是用来问名字的。 ## 复习流 - 当日快闪（recap）独立成队列，不占复习预算，也不计入积压。 - 只发放当日预算内的量，其余保持到期状态等下次，不悄悄丢弃或改期。 - 答错的项隔几题后回来，而不是立刻重问。 测试 296 通过，flutter analyze 干净。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
"""Merges the AI rewrite back into assets/words/wordbank.json.
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Anything the model returned is checked before it lands: a gloss that is empty,
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parenthesised or sentence-long is worse than the word book's version, so the
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old value is kept and reported instead of being written over.
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"""
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import json
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import os
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import re
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import sys
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import tempfile
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ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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BANK = os.path.join(ROOT, 'assets/words/wordbank.json')
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BAD_GLOSS = re.compile(r'[()（）\[\]【】<>]')
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def usable_gloss(text):
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text = (text or '').strip()
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return bool(text) and len(text) <= 10 and not BAD_GLOSS.search(text)
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def mentions(word, sentence):
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"""Whether the sentence visibly uses the word.
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This cannot be a hard check: `teach` shows up as "taught" and `bus stop`
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as two words, so a prefix match has no way to confirm them. It only flags
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a sentence as worth a human glance -- nothing is dropped over it.
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"""
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parts = [re.sub(r'[^a-z]', '', part) for part in word.lower().split()]
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stem = max(parts, key=len)[:3]
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return bool(stem) and stem in sentence.lower()
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def main():
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cache_path = os.environ.get(
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'ENRICH_CACHE', os.path.join(tempfile.gettempdir(), 'enrich_cache.jsonl')
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)
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rows = {}
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with open(cache_path) as handle:
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for line in handle:
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row = json.loads(line)
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rows[row['id']] = row
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with open(BANK) as handle:
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bank = json.load(handle)
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kept = {'gloss': 0, 'example': 0, 'missing': 0, 'bad_gloss': 0, 'no_example': 0}
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unclear = []
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for word in bank['words']:
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row = rows.get(word['id'])
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if row is None:
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kept['missing'] += 1
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continue
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if usable_gloss(row.get('zh')):
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word['zh'] = row['zh'].strip()
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more = (row.get('more') or '').strip()
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senses = [s for s in more.split('；') if usable_gloss(s) and s != word['zh']]
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if senses:
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word['more'] = '；'.join(senses[:2])
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else:
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word.pop('more', None)
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kept['gloss'] += 1
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else:
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kept['bad_gloss'] += 1
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sentence = (row.get('ex') or '').strip()
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if not sentence:
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kept['no_example'] += 1
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continue
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word['ex'] = sentence
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word['exZh'] = (row.get('ex_zh') or '').strip()
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kept['example'] += 1
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if not mentions(word['en'], sentence):
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unclear.append('%s | %s' % (word['en'], sentence))
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print(json.dumps(kept, indent=2))
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if unclear:
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print('%d sentences to eyeball:' % len(unclear))
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for line in unclear:
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print(' ' + line)
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if '--dry-run' in sys.argv:
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return
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bank['source'] = (
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'CEFR-J Vocabulary Profile 1.5 (levels) + 公开词书 (音标) + AI 重写 (释义与例句)'
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)
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with open(BANK, 'w') as handle:
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json.dump(bank, handle, ensure_ascii=False, separators=(',', ':'))
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handle.write('\n')
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if __name__ == '__main__':
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main()
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