feat: 独立词库、三向认词与当日快闪复习

## 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>
2026-09-20 23:54:17 +09:00
parent 1a10ca88e0
commit 6b42b7abc3
52 changed files with 4044 additions and 82 deletions
@@ -0,0 +1,93 @@
+"""Merges the AI rewrite back into assets/words/wordbank.json.
+
+Anything the model returned is checked before it lands: a gloss that is empty,
+parenthesised or sentence-long is worse than the word book's version, so the
+old value is kept and reported instead of being written over.
+"""
+
+import json
+import os
+import re
+import sys
+import tempfile
+
+ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+BANK = os.path.join(ROOT, 'assets/words/wordbank.json')
+
+BAD_GLOSS = re.compile(r'[()（）\[\]【】<>]')
+
+
+def usable_gloss(text):
+    text = (text or '').strip()
+    return bool(text) and len(text) <= 10 and not BAD_GLOSS.search(text)
+
+
+def mentions(word, sentence):
+    """Whether the sentence visibly uses the word.
+
+    This cannot be a hard check: `teach` shows up as "taught" and `bus stop`
+    as two words, so a prefix match has no way to confirm them. It only flags
+    a sentence as worth a human glance -- nothing is dropped over it.
+    """
+    parts = [re.sub(r'[^a-z]', '', part) for part in word.lower().split()]
+    stem = max(parts, key=len)[:3]
+    return bool(stem) and stem in sentence.lower()
+
+
+def main():
+    cache_path = os.environ.get(
+        'ENRICH_CACHE', os.path.join(tempfile.gettempdir(), 'enrich_cache.jsonl')
+    )
+    rows = {}
+    with open(cache_path) as handle:
+        for line in handle:
+            row = json.loads(line)
+            rows[row['id']] = row
+    with open(BANK) as handle:
+        bank = json.load(handle)
+
+    kept = {'gloss': 0, 'example': 0, 'missing': 0, 'bad_gloss': 0, 'no_example': 0}
+    unclear = []
+    for word in bank['words']:
+        row = rows.get(word['id'])
+        if row is None:
+            kept['missing'] += 1
+            continue
+        if usable_gloss(row.get('zh')):
+            word['zh'] = row['zh'].strip()
+            more = (row.get('more') or '').strip()
+            senses = [s for s in more.split('；') if usable_gloss(s) and s != word['zh']]
+            if senses:
+                word['more'] = '；'.join(senses[:2])
+            else:
+                word.pop('more', None)
+            kept['gloss'] += 1
+        else:
+            kept['bad_gloss'] += 1
+        sentence = (row.get('ex') or '').strip()
+        if not sentence:
+            kept['no_example'] += 1
+            continue
+        word['ex'] = sentence
+        word['exZh'] = (row.get('ex_zh') or '').strip()
+        kept['example'] += 1
+        if not mentions(word['en'], sentence):
+            unclear.append('%s | %s' % (word['en'], sentence))
+
+    print(json.dumps(kept, indent=2))
+    if unclear:
+        print('%d sentences to eyeball:' % len(unclear))
+        for line in unclear:
+            print('  ' + line)
+    if '--dry-run' in sys.argv:
+        return
+    bank['source'] = (
+        'CEFR-J Vocabulary Profile 1.5 (levels) + 公开词书 (音标) + AI 重写 (释义与例句)'
+    )
+    with open(BANK, 'w') as handle:
+        json.dump(bank, handle, ensure_ascii=False, separators=(',', ':'))
+        handle.write('\n')
+
+
+if __name__ == '__main__':
+    main()