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/config.py

@@ -42,5 +42,5 @@ DB_PATH = Path(os.getenv("DB_PATH", str(BASE_DIR / "data" / "notes.db")))
 DB_PATH.parent.mkdir(parents=True, exist_ok=True)
 
 OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://127.0.0.1:11434").rstrip("/")
-OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "qwen2.5:1.5b")
+OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "qwen2.5:3b")