fix(ai,reader): robust sentence segmentation without abbreviation cutoffs & context-aware literary translation

- Implement robustSentenceSplit across reader.js and backend (miner.py, cefr.py) to prevent false cutoffs on honorifics (Mr., Mrs., Dr.), single-letter initials (John F. Kennedy), decimals, acronyms (U.S., U.K.), and abbreviations (No. 4, etc.)
- Upgrade translation prompt to professional literary Russian, eliminating literal word-for-word translation and preserving titles/honorifics
- Inject surrounding paragraph context into translation API to avoid out-of-context translations
- Inject offline dictionary hints into LLM prompt for accurate contextual word meaning
- Prefer qwen2.5:3b for higher translation fluency and fidelity
- Purge truncated cache entries from sentence_translations and restore Card 30 (briefcase) with authentic full sentence
- Bump Service Worker cache to notes-v6.0
- Add unit tests for robust sentence splitting, prompt formatting, and translation API

app/database.py

@@ -386,7 +386,8 @@ def _migrate(conn: sqlite3.Connection):
 def repair_mismatched_cefr_contexts(conn: sqlite3.Connection):
     """
     Repairs legacy vocabulary cards that were imported with placeholder blanks ('___')
-    or with mismatched contexts from CEFR test questions.
+    or with truncated/mismatched contexts from CEFR test questions and reader.
+    Also purges truncated sentence cache entries.
     """
     try:
         from app.cefr import resolve_context_sentence_for_term
@@ -396,11 +397,25 @@ def repair_mismatched_cefr_contexts(conn: sqlite3.Connection):
         if not tbl:
             return
 
+        # Purge truncated sentence cache entries
+        trans_tbl = conn.execute("SELECT 1 FROM sqlite_master WHERE type='table' AND name='sentence_translations'").fetchone()
+        if trans_tbl:
+            conn.execute(
+                """
+                DELETE FROM sentence_translations
+                WHERE original_text LIKE '%pecked Mrs.%'
+                   OR original_text LIKE '%the John F.'
+                   OR instr(original_text, '___') > 0
+                """
+            )
+
         rows = conn.execute(
             """
             SELECT id, term, context_sentence
             FROM vocabulary_cards
             WHERE instr(context_sentence, '___') > 0
+               OR context_sentence LIKE '%pecked Mrs.%'
+               OR context_sentence LIKE '%the John F.'
             """
         ).fetchall()