feat(style-acq T1-T3): קורפוס-דוגמאות של דפנה לכותב (style_exemplars)

ממלא את ערוץ-הדוגמאות (B) של מערכת רכישת-הסגנון: הכותב מאחזר פסקאות-בלוק
אמיתיות של דפנה בזמן כתיבה, ממוקדות section+outcome+practice_area.

T1 — תשתית + backfill:
- SCHEMA_V27: טבלת style_exemplars (purpose-built — בלי תיקים מזויפים בשרשרת
  decision_paragraphs). decision_number/source/section/outcome/practice_area+embedding.
- db: insert/delete/search_style_exemplars + count_style_exemplars.
- scripts/backfill_style_exemplars.py: מפצל קורפוס דפנה (style_corpus +
  internal_committee) לסעיפים→פסקאות, embed, שמירה. אידמפוטנטי, dry-run/apply.

T2 — אחזור ממוקד:
- search_style_exemplars(section, outcome, practice_area) — section=hard filter,
  outcome/practice_area=soft. block_writer._build_precedents_context ממפה
  block→section ומאחזר (ראשי), לצד הנתיב הישן (משלים).

T3 — contrastive/adapt:
- הדוגמאות מתויגות "מבנה/קול בלבד — התאם, אל תעתיק תוכן"; פסקה מלאה (1100 תווים).

INV-LRN5 (טוהר — סגנון בלבד). G11. הרצת backfill --apply בנפרד.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/backfill_style_exemplars.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+"""T1 — אכלוס style_exemplars מקורפוס דפנה (style_corpus + internal_committee).
+
+מפצל כל החלטה של דפנה לסעיפים (chunker._split_into_sections), ומכל סעיף לפסקאות,
+מטמיע (Voyage) ושומר ב-style_exemplars עם section/outcome/practice_area — כדי
+שהכותב יוכל לאחזר פסקאות-בלוק אמיתיות של דפנה בזמן כתיבה (T2/T3).
+
+מקור-סגנון בלבד (INV-LRN5) — לא מהות. אידמפוטנטי: מנקה לכל decision לפני הכנסה.
+
+שימוש:
+    python3 scripts/backfill_style_exemplars.py            # dry-run (סופר בלבד)
+    python3 scripts/backfill_style_exemplars.py --apply    # מטמיע ושומר
+
+דורש POSTGRES_URL + מפתח Voyage בסביבה (כמו שאר ה-MCP).
+"""
+from __future__ import annotations
+
+import argparse
+import asyncio
+import logging
+
+from legal_mcp.services import db, embeddings
+from legal_mcp.services.chunker import _split_into_sections
+
+logging.basicConfig(level=logging.INFO, format="%(message)s")
+log = logging.getLogger("backfill_exemplars")
+
+# chunker section_type → style_exemplars.section
+_SECTION_MAP = {
+    "facts": "background",
+    "appellant_claims": "claims",
+    "respondent_claims": "claims",
+    "legal_analysis": "discussion",
+    "conclusion": "summary",
+    "ruling": "summary",
+    "intro": "other",
+    "other": "other",
+}
+
+MIN_WORDS = 25      # skip tiny fragments
+MAX_WORDS = 450     # skip over-long blobs (likely un-split)
+MAX_PER_SECTION = 15
+
+
+def _paragraphs(section_text: str) -> list[str]:
+    """Split a section into paragraph units (blank-line separated; fall back to lines)."""
+    raw = [p.strip() for p in section_text.split("\n\n")]
+    if len(raw) <= 1:
+        raw = [p.strip() for p in section_text.split("\n")]
+    out = []
+    for p in raw:
+        wc = len(p.split())
+        if MIN_WORDS <= wc <= MAX_WORDS:
+            out.append(p)
+    return out[:MAX_PER_SECTION]
+
+
+async def _gather_sources() -> list[dict]:
+    """All of Dafna's decisions: style_corpus + internal_committee (chair דפנה)."""
+    pool = await db.get_pool()
+    rows: list[dict] = []
+    async with pool.acquire() as conn:
+        sc = await conn.fetch(
+            "SELECT decision_number, full_text, outcome, practice_area "
+            "FROM style_corpus WHERE full_text <> ''"
+        )
+        for r in sc:
+            rows.append({
+                "decision_number": r["decision_number"] or "",
+                "source": "style_corpus",
+                "full_text": r["full_text"],
+                "outcome": r["outcome"] or "",
+                "practice_area": r["practice_area"] or "",
+            })
+        ic = await conn.fetch(
+            "SELECT case_number, full_text, practice_area FROM case_law "
+            "WHERE source_kind = 'internal_committee' AND coalesce(chair_name,'') LIKE '%דפנה%' "
+            "AND coalesce(full_text,'') <> ''"
+        )
+        for r in ic:
+            rows.append({
+                "decision_number": r["case_number"] or "",
+                "source": "internal_committee",
+                "full_text": r["full_text"],
+                "outcome": "",
+                "practice_area": r["practice_area"] or "",
+            })
+    return rows
+
+
+async def main(apply: bool) -> None:
+    sources = await _gather_sources()
+    log.info("מקורות: %d החלטות של דפנה (style_corpus + internal_committee)", len(sources))
+
+    total_paras = 0
+    for src in sources:
+        units: list[tuple[str, str]] = []  # (section, paragraph)
+        for section_type, section_text in _split_into_sections(src["full_text"]):
+            section = _SECTION_MAP.get(section_type, "other")
+            for para in _paragraphs(section_text):
+                units.append((section, para))
+        if not units:
+            continue
+        total_paras += len(units)
+        log.info("  %-14s %-16s → %d פסקאות", src["source"], src["decision_number"], len(units))
+        if not apply:
+            continue
+
+        await db.delete_style_exemplars(src["decision_number"], src["source"])
+        texts = [u[1] for u in units]
+        vecs = await embeddings.embed_texts(texts, input_type="document")
+        for (section, para), vec in zip(units, vecs):
+            await db.insert_style_exemplar(
+                decision_number=src["decision_number"], source=src["source"],
+                practice_area=src["practice_area"], outcome=src["outcome"],
+                section=section, paragraph_text=para, word_count=len(para.split()),
+                embedding=vec,
+            )
+
+    if apply:
+        cov = await db.count_style_exemplars()
+        log.info("הושלם. style_exemplars: %s", cov)
+    else:
+        log.info("dry-run: %d פסקאות יוטמעו. הרץ --apply לביצוע.", total_paras)
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--apply", action="store_true", help="embed + insert (default: dry-run)")
+    args = ap.parse_args()
+    asyncio.run(main(args.apply))