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>
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@@ -725,19 +725,43 @@ async def _build_precedents_context(
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style_parts: list[str] = []
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caselaw_parts: list[str] = []
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case_law_ids: list[str] = []
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# block → golden-ratio section, for targeted exemplar retrieval (T2)
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_BLOCK_SECTION = {
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"block-vav": "background", "block-zayin": "claims",
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"block-yod": "discussion", "block-yod-alef": "summary",
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}
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try:
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case = await db.get_case(case_id)
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case_number = case.get("case_number", "") if case else ""
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subject = case.get("subject", "") if case else ""
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practice_area = case.get("practice_area", "") if case else ""
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decision = await db.get_decision_by_case(case_id)
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outcome = (decision or {}).get("outcome", "")
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query = f"דיון משפטי בנושא {subject}" if subject else "דיון משפטי ועדת ערר"
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query_emb = await embeddings.embed_query(query)
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section = _BLOCK_SECTION.get(block_id)
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# Stream 1: paragraph_embeddings — Dafna's own prose (STYLE exemplars, not content)
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# Stream 1a (PRIMARY): Dafna's own block-level prose from her corpus
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# (style_exemplars) — matched by section + outcome + practice_area (T2/T3).
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if section:
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exemplars = await db.search_style_exemplars(
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query_embedding=query_emb, section=section,
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outcome=outcome or None, practice_area=practice_area or None, limit=6,
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)
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exemplars = [e for e in exemplars if e.get("decision_number", "") != case_number]
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for e in exemplars[:4]:
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style_parts.append(
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f"[דוגמת-סגנון (מבנה/קול בלבד — התאם, אל תעתיק תוכן) — "
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f"{e.get('decision_number', '?')}, {section}, "
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f"outcome={e.get('outcome') or '—'}]\n{e['paragraph_text'][:1100]}"
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)
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# Stream 1b: paragraphs from pipeline cases (legacy path; may be empty)
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para_results = await db.search_similar_paragraphs(
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query_embedding=query_emb, limit=10, block_type="block-yod",
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)
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para_results = [r for r in para_results if r.get("case_number", "") != case_number]
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for r in para_results[:4]:
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for r in para_results[:2]:
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style_parts.append(
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f"[דוגמת-סגנון — החלטת {r.get('case_number', '?')} "
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f"{r.get('case_title', '')}, בלוק {r.get('block_type', '')}]\n{r['content'][:500]}"
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@@ -1204,6 +1204,28 @@ CREATE INDEX IF NOT EXISTS idx_draft_final_pairs_case ON draft_final_pairs(case_
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CREATE INDEX IF NOT EXISTS idx_draft_final_pairs_status ON draft_final_pairs(status);
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"""
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SCHEMA_V27_SQL = """
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-- style_exemplars (T1-T3): block-level paragraphs from Dafna's OWN decisions
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-- (style_corpus + internal_committee finals), embedded for retrieval as
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-- style exemplars at write-time. Purpose-built so we DON'T fabricate synthetic
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-- cases just to reuse decision_paragraphs. INV-LRN5: style material only — the
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-- writer is told to adapt structure/voice, copy only boilerplate, never substance.
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CREATE TABLE IF NOT EXISTS style_exemplars (
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id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
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decision_number TEXT DEFAULT '',
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source TEXT DEFAULT '', -- style_corpus | internal_committee
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practice_area TEXT DEFAULT '',
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outcome TEXT DEFAULT '', -- rejection | partial_acceptance | full_acceptance | ''
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section TEXT DEFAULT 'other', -- background | claims | discussion | summary | other
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paragraph_text TEXT NOT NULL,
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word_count INTEGER DEFAULT 0,
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embedding vector(1024),
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created_at TIMESTAMPTZ DEFAULT now()
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);
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CREATE INDEX IF NOT EXISTS idx_style_exemplars_section ON style_exemplars(section);
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CREATE INDEX IF NOT EXISTS idx_style_exemplars_decision ON style_exemplars(decision_number, source);
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"""
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async def _run_schema_migrations(pool: asyncpg.Pool) -> None:
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async with pool.acquire() as conn:
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@@ -1234,7 +1256,8 @@ async def _run_schema_migrations(pool: asyncpg.Pool) -> None:
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await conn.execute(SCHEMA_V24_SQL)
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await conn.execute(SCHEMA_V25_SQL)
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await conn.execute(SCHEMA_V26_SQL)
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logger.info("Database schema initialized (v1-v26)")
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await conn.execute(SCHEMA_V27_SQL)
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logger.info("Database schema initialized (v1-v27)")
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async def init_schema() -> None:
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@@ -2329,6 +2352,85 @@ async def list_draft_final_pairs(status: str | None = None, limit: int = 200) ->
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return [dict(r) for r in rows]
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async def insert_style_exemplar(
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decision_number: str, source: str, practice_area: str, outcome: str,
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section: str, paragraph_text: str, word_count: int, embedding: list[float],
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) -> None:
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"""Insert one block-level style exemplar (T1 backfill)."""
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pool = await get_pool()
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async with pool.acquire() as conn:
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await conn.execute(
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"""INSERT INTO style_exemplars
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(decision_number, source, practice_area, outcome, section,
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paragraph_text, word_count, embedding)
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VALUES ($1, $2, $3, $4, $5, $6, $7, $8)""",
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decision_number, source, practice_area, outcome, section,
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paragraph_text, word_count, str(embedding),
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)
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async def delete_style_exemplars(decision_number: str, source: str) -> int:
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"""Idempotent backfill: clear a decision's exemplars before re-inserting."""
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pool = await get_pool()
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async with pool.acquire() as conn:
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res = await conn.execute(
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"DELETE FROM style_exemplars WHERE decision_number = $1 AND source = $2",
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decision_number, source,
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)
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try:
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return int(res.split()[-1])
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except (ValueError, IndexError):
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return 0
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async def search_style_exemplars(
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query_embedding: list[float],
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section: str | None = None,
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outcome: str | None = None,
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practice_area: str | None = None,
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limit: int = 6,
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) -> list[dict]:
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"""Retrieve Dafna's own block-level paragraphs as STYLE exemplars (T2).
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Filters by section (block) + optionally outcome/practice_area for the closest
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match to the block being written. Soft filters: outcome/practice_area narrow but
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never zero-out — section is the hard filter."""
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pool = await get_pool()
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conditions, params, idx = [], [query_embedding, limit], 3
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if section:
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conditions.append(f"section = ${idx}"); params.append(section); idx += 1
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if outcome:
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conditions.append(f"(outcome = ${idx} OR outcome = '')"); params.append(outcome); idx += 1
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if practice_area:
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conditions.append(f"(practice_area = ${idx} OR practice_area = '')"); params.append(practice_area); idx += 1
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where = f"WHERE {' AND '.join(conditions)}" if conditions else ""
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sql = f"""
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SELECT decision_number, source, section, outcome, practice_area,
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paragraph_text, word_count,
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1 - (embedding <=> $1) AS score
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FROM style_exemplars
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{where}
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ORDER BY embedding <=> $1
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LIMIT $2
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"""
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async with pool.acquire() as conn:
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rows = await conn.fetch(sql, *params)
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return [dict(r) for r in rows]
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async def count_style_exemplars() -> dict:
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"""Coverage check for the backfill."""
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pool = await get_pool()
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async with pool.acquire() as conn:
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total = await conn.fetchval("SELECT count(*) FROM style_exemplars")
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by_section = await conn.fetch(
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"SELECT section, count(*) AS n FROM style_exemplars GROUP BY section ORDER BY n DESC"
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)
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decisions = await conn.fetchval(
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"SELECT count(DISTINCT decision_number) FROM style_exemplars"
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)
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return {"total": total, "decisions": decisions, "by_section": [dict(r) for r in by_section]}
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async def upsert_style_pattern(
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pattern_type: str,
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pattern_text: str,
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