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/main.py

@@ -190,6 +190,7 @@ class RfcFetchRequest(BaseModel):
 class SentenceTranslateRequest(BaseModel):
     sentence: str = Field(..., min_length=1)
     target_word: Optional[str] = ""
+    context: Optional[str] = ""
 
 
 class CefrAnswerRequest(BaseModel):
@@ -446,7 +447,7 @@ async def api_translate_sentence(
     clean_s = req.sentence.strip()
     if not clean_s:
         raise HTTPException(status_code=400, detail="Sentence cannot be empty")
-    return await translate_sentence_ai(clean_s, req.target_word or "")
+    return await translate_sentence_ai(clean_s, req.target_word or "", req.context or "")
 
 
 @app.get("/api/ai/status")