Chaim
0e35060d3d
The chair wanted an independent recommendation beside each tag, to reconsider his own judgments. Adds a NON-ground-truth AI second-opinion: - schema: halacha_goldset.ai_is_holding / ai_correct_type / ai_rationale / ai_generated_at (additive). - db.goldset_set_ai_recommendation + goldset_list now returns the ai_* fields. - scripts/goldset_ai_recommend.py — local claude_session judges is_holding + type + a one-line rationale per item, INDEPENDENTLY (own legal rubric). Independent of the rule-based validators #81.8 measures → no circularity. Never auto-applied; QA aid only. - web-ui: each card shows "🤖 המלצת AI: הלכה/לא · type" + rationale and an agreement/disagreement chip vs the human tag (amber on disagree); a "⚠ אי-הסכמות AI (N)" filter to review only the conflicts. Methodology note kept explicit: the human stays the ground truth; the AI is a prompt to reconsider, not to copy. Verified: tsc --noEmit 0; generator stores recs and flags disagreements with existing human tags. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
101 lines
4.6 KiB
Python
101 lines
4.6 KiB
Python
#!/usr/bin/env python3
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"""Generate the AI second-opinion for gold-set items (#81.7 QA aid).
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For each gold-set halacha, an INDEPENDENT local-LLM (claude_session, zero cost)
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judges: is it a real generalizable holding, what is its correct rule_type, and a
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one-line rationale. Stored in halacha_goldset.ai_* and shown beside the human
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tag so the chair can spot disagreements and reconsider.
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This is a QA aid, NOT ground truth and NOT auto-applied. It is also independent
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of the rule-based validators that #81.8 measures, so it doesn't bias that score.
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Must run locally (claude_session needs the local CLI — not the container):
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cd ~/legal-ai/mcp-server
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.venv/bin/python ../scripts/goldset_ai_recommend.py # missing only
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.venv/bin/python ../scripts/goldset_ai_recommend.py --force # regenerate all
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.venv/bin/python ../scripts/goldset_ai_recommend.py --limit 10 # smoke
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"""
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from __future__ import annotations
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import argparse
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import asyncio
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import sys
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from uuid import UUID
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from legal_mcp.services import claude_session, db
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VALID_TYPES = {"binding", "interpretive", "obiter", "application", "procedural", "persuasive"}
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SYSTEM = (
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"אתה בוחן-איכות משפטי המסווג 'הלכות' שחולצו מהחלטות ועדת-ערר ומפסקי-דין. "
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"לכל פריט הכרע שתי שאלות, באופן עצמאי ולפי המהות:\n"
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"1) is_holding — האם זו הלכה אמיתית בת-הכללה ובת-הסתמכות (true), או שזו יישום "
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"תלוי-עובדות / אמרת-אגב / ציטוט-עובדה ולא כלל בר-הכללה (false).\n"
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"2) type — הסוג הנכון: 'binding' (עיקרון הכרחי להכרעה), 'interpretive' (פרשנות "
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"חוק/מונח/תכנית), 'procedural' (סדר-דין: מועדים/סמכות/מיצוי/נטל), 'persuasive' "
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"(אסמכתה לא-מחייבת), 'application' (החלה על עובדות התיק — לרוב לא-הלכה), "
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"'obiter' (אמרת-אגב שלא הוכרעה — לא-הלכה).\n"
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"עקביות: is_holding=true → binding/interpretive/procedural/persuasive; "
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"is_holding=false → application/obiter.\n"
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'החזר JSON בלבד: {"is_holding": true/false, "type": "<אחד מהשישה>", '
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'"rationale": "<משפט אחד קצר בעברית>"}. ללא markdown.'
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)
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def _prompt(item: dict) -> str:
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src = "פסק-דין" if item.get("source_type") == "court_ruling" else "החלטת ועדת-ערר"
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return (
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f"מקור: {src} ({item.get('case_number') or ''}).\n"
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f"סוג שהמכונה נתנה: {item.get('rule_type')}.\n\n"
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f"ניסוח הכלל:\n{item.get('rule_statement') or ''}\n\n"
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f"ציטוט תומך:\n{item.get('supporting_quote') or ''}"
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)
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async def main(args: argparse.Namespace) -> int:
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items = await db.goldset_list(args.batch)
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todo = [it for it in items if args.force or not it.get("ai_generated_at")]
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if args.limit:
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todo = todo[: args.limit]
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print(f"gold-set {args.batch}: {len(items)} items, {len(todo)} to recommend", flush=True)
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ok, fail, disagree = 0, 0, 0
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for i, it in enumerate(todo, 1):
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try:
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v = await claude_session.query_json(_prompt(it), system=SYSTEM, effort="low")
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except Exception as e: # noqa: BLE001
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fail += 1
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print(f"[{i}/{len(todo)}] {it['case_number']}: FAIL {e}", flush=True)
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continue
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if not isinstance(v, dict):
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fail += 1
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continue
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ai_hold = bool(v.get("is_holding"))
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ai_type = str(v.get("type") or "").strip()
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if ai_type not in VALID_TYPES:
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ai_type = ""
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await db.goldset_set_ai_recommendation(
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UUID(str(it["id"])), ai_is_holding=ai_hold, ai_correct_type=ai_type,
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ai_rationale=str(v.get("rationale") or "")[:300],
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)
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ok += 1
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# note disagreements with the human tag (if tagged)
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flag = ""
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if it.get("is_holding") is not None and it["is_holding"] != ai_hold:
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disagree += 1
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flag = " ⚠ DISAGREE is_holding"
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print(f"[{i}/{len(todo)}] {it['case_number']}: ai={ai_hold}/{ai_type}{flag}", flush=True)
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print(f"\nDONE — {ok} stored, {fail} failed, {disagree} disagree with existing human tag",
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flush=True)
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return 0
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if __name__ == "__main__":
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ap = argparse.ArgumentParser()
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ap.add_argument("--batch", default="default")
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ap.add_argument("--force", action="store_true", help="regenerate even if present")
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ap.add_argument("--limit", type=int, default=None)
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sys.exit(asyncio.run(main(ap.parse_args())))
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