84 lines
3.6 KiB
Python
84 lines
3.6 KiB
Python
"""Principle-level gold matching (importance layer #153, component 1).
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Flags the SPECIFIC principle a chair relied on — not the whole precedent. A chair
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cites a precedent for ONE holding; we embed the citation's ``match_context`` (or a
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digest's ``headline_holding``) and cosine-match it to the principles extracted from
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that precedent. The best match ≥ threshold gets the flag:
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• OUR_CHAIR (דפנה) cited it → ``gold_chair`` (tier-1, protective in the cull)
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• another committee chair cited it → ``chair_cited`` (tier-2, weight not protection)
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• a digest highlights it → ``gold_digest`` (tier-1)
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Embedding-only — NO LLM, so it is cheap and re-runnable (the periodic refresh job).
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Tuned toward recall (HALACHA_GOLD_MATCH_THRESHOLD): missing a chair-relied principle
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(buried by the cull) is worse than over-protecting one.
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"""
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from __future__ import annotations
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import logging
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from uuid import UUID
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from legal_mcp import config
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from legal_mcp.services import db, embeddings
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logger = logging.getLogger(__name__)
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async def _embed_all(texts: list[str]) -> list[list[float]]:
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"""Embed in Voyage-sized batches (embed_texts already chunks at 128)."""
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return await embeddings.embed_texts(texts, input_type="query") if texts else []
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async def match_all(threshold: float | None = None, apply: bool = False) -> dict:
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"""Run gold matching over all chair citations + digests.
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Returns a stats dict; when ``apply`` writes the flags (idempotent — clears
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prior flags first). Dry-run computes everything but writes nothing.
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"""
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threshold = config.HALACHA_GOLD_MATCH_THRESHOLD if threshold is None else threshold
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if apply:
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cleared = await db.reset_principle_gold()
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logger.info("principle_gold: cleared %d prior flags before re-match", cleared)
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citations = await db.gold_chair_citations()
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digests = await db.gold_digest_holdings()
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cit_vecs = await _embed_all([c["match_context"] for c in citations])
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dig_vecs = await _embed_all([d["holding_text"] for d in digests])
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stats = {"threshold": threshold, "applied": apply,
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"chair_citations": len(citations), "digests": len(digests),
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"gold_chair": 0, "chair_cited": 0, "gold_digest": 0,
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"chair_no_match": 0, "digest_no_match": 0, "samples": []}
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async def _flag(case_law_id, vec, *, gold_chair=False, gold_digest=False,
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chair_cited=False, label=""):
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match = await db.nearest_original_principle(case_law_id, list(vec))
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if match is None or match[1] < threshold:
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return None
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hid, sim = match
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if apply:
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await db.set_principle_gold(
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UUID(hid), gold_chair=gold_chair, gold_digest=gold_digest,
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chair_cited=chair_cited, score=sim)
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if len(stats["samples"]) < 12:
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stats["samples"].append({"label": label, "halacha_id": hid, "sim": round(sim, 3)})
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return hid
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for c, vec in zip(citations, cit_vecs):
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cid = c["cited_case_law_id"]
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if c["is_our_chair"]:
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hid = await _flag(cid, vec, gold_chair=True, label="chair:דפנה")
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stats["gold_chair" if hid else "chair_no_match"] += 1
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else:
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hid = await _flag(cid, vec, chair_cited=True, label=f"chair:{c['citing_chair']}")
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stats["chair_cited" if hid else "chair_no_match"] += 1
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for d, vec in zip(digests, dig_vecs):
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hid = await _flag(d["linked_case_law_id"], vec, gold_digest=True, label="digest")
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stats["gold_digest" if hid else "digest_no_match"] += 1
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if apply:
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stats["coverage"] = await db.gold_coverage_stats()
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return stats
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