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/miner.py

@@ -102,31 +102,65 @@ for _lvl, _pos_map in CEFR_LEXICON.items():
                 _WORD_INDEX[_w_low] = []
             _WORD_INDEX[_w_low].append((_lvl, _pos))
 
-# Common abbreviations to prevent false splits
-_HONORIFICS = ["Mr.", "Mrs.", "Ms.", "Dr.", "Prof.", "St.", "vs.", "etc.", "e.g.", "i.e.", "Jr.", "Sr."]
+def robust_sentence_split(text: str) -> List[str]:
+    """
+    Splits English narrative prose into clean, complete sentences.
+    Protects decimals, acronyms, single-letter initials, honorifics, titles, and abbreviations.
+    """
+    if not text:
+        return []
+
+    # Step 1: Protect decimal numbers (e.g. 3.14, 1.5)
+    s = re.sub(r"(\d+)\.(\d+)", r"\1§DOT§\2", text)
+
+    # Step 2: Protect acronyms with dots like U.S., U.K., e.g., i.e., a.m., p.m., Ph.D.
+    s = re.sub(r"\b((?:[A-Za-z]\.){2,})", lambda m: m.group(1).replace(".", "§DOT§"), s)
+
+    # Step 3: Protect single-letter initials like 'John F. Kennedy', 'J. K. Rowling'
+    s = re.sub(r'\b([A-Za-z])\.(?=\s+[A-Za-z0-9"“\'‘«—])', r"\1§DOT§", s)
+
+    # Step 4: Protect common honorifics, titles, and abbreviations
+    abbr_pattern = (
+        r"\b("
+        r"Mr|Mrs|Ms|Miss|Dr|Prof|Rev|Hon|Capt|Col|Gen|Lt|Cmdr|Sgt|Cpl|Pvt|Maj|Adm|"
+        r"Gov|Sen|Rep|Pres|Sec|Treas|Insp|Det|Supt|Messrs|Mmes|St|Mt|Ft|"
+        r"etc|vs|v|Jr|Sr|approx|app|apt|dept|univ|assn|assoc|bldg|co|corp|inc|ltd|"
+        r"est|misc|al|ibid|op|cit|ed|eds|trans|vol|vols|no|nos|sec|secs|ch|chs|"
+        r"fig|figs|p|pp|ave|blvd|rd|st|ct|ln|dr|hwy|"
+        r"jan|feb|mar|apr|jun|jul|aug|sep|sept|oct|nov|dec"
+        r")\."
+    )
+    s = re.sub(abbr_pattern, r"\1§DOT§", s, flags=re.IGNORECASE)
+
+    # Step 5: Protect ellipsis (... or ....)
+    s = re.sub(r"\.{2,}", lambda m: "§ELLIPSIS§" + ("§DOT§" * len(m.group(0))), s)
+
+    # Step 6: Protect any period followed by lowercase letter
+    s = re.sub(r"\.([\'\"”’»)]*\s+[a-zа-яё])", r"§DOT§\1", s)
+
+    # Step 7: Split on sentence terminators [.!?] followed by whitespace and uppercase/number/quote
+    marked = re.sub(r'([.!?][\'\"”’»)]*)(\s+)(?=[A-Z0-9"“\'‘«—]|$)', r"\1§SEP§", s)
+
+    raw_sentences = [
+        re.sub(r"\s+", " ", part.replace("§DOT§", ".").replace("§ELLIPSIS§", "")).strip()
+        for part in marked.split("§SEP§")
+    ]
+    return [sent for sent in raw_sentences if sent]
 
 
 def split_into_clean_sentences(text: str) -> List[str]:
     """
     Safely splits narrative prose into clean, grammatically complete sentences.
-    Protects honorifics and abbreviations from being split incorrectly.
+    Protects honorifics, initials, and abbreviations from being split incorrectly.
     """
     if not text:
         return []
 
-    # Protect common abbreviations and titles
-    protected = text
-    for abbr in _HONORIFICS:
-        protected = protected.replace(abbr, abbr.replace(".", "§DOT§"))
-
-    # Split on sentence-ending punctuation (.!?) followed by space or newline
-    raw_splits = re.split(r"(?<=[.!?])\s+", protected)
+    raw_splits = robust_sentence_split(text)
 
     clean_sentences = []
     for s in raw_splits:
-        restored = s.replace("§DOT§", ".").strip()
-        # Flatten multiple spaces and line breaks
-        flattened = re.sub(r"\s+", " ", restored)
+        flattened = re.sub(r"\s+", " ", s).strip()
 
         # Basic validity filters:
         # 1. Skip dialogue lines starting with quotation marks or dashes