Chaim
338a8a947f
Grounded (INV-AH) multi-instance synthesis with drift guard + chair gate (pending_review, G10). Single path used by backfill, MCP tool, nightly drain. HELD from production run pending the principles-redesign (rename+cull, #152). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
117 lines
4.6 KiB
Python
117 lines
4.6 KiB
Python
"""Unit tests for the tri-model panel extraction core (#152, Phase A).
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Pure logic only — classify (the chair's approval rule), _coerce_list (judge-reply
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normalisation), and cluster_candidates (cross-model matching/voting) with injected
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embeddings. No LLM, no Voyage, no DB.
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"""
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from __future__ import annotations
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import pytest
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from legal_mcp import config
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from legal_mcp.services import panel_extraction as pe
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# ── classify — chaim's rule ────────────────────────────────────────
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def test_classify_three_votes_approves_regardless_of_score():
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assert pe.classify(3, 0.10) == "approved"
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assert pe.classify(3, 0.99) == "approved"
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def test_classify_two_votes_gated_by_floor():
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floor = config.HALACHA_PANEL_SCORE_FLOOR
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assert pe.classify(2, floor) == "approved"
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assert pe.classify(2, floor + 0.05) == "approved"
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assert pe.classify(2, floor - 0.01) == "pending_review"
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def test_classify_one_or_zero_votes_rejected():
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assert pe.classify(1, 0.99) == "rejected"
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assert pe.classify(0, 0.99) == "rejected"
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# ── _coerce_list — judge reply normalisation ───────────────────────
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def test_coerce_list_accepts_bare_list():
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raw = [{"rule_statement": "כלל", "supporting_quote": "ציטוט", "score": 0.9}]
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out = pe._coerce_list(raw)
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assert len(out) == 1 and out[0]["rule_type"] == "interpretive"
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def test_coerce_list_unwraps_dict_wrapper_and_drops_incomplete():
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raw = {"principles": [
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{"rule_statement": "כלל", "supporting_quote": "ציטוט", "rule_type": "holding", "score": 1.5},
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{"rule_statement": "", "supporting_quote": "ציטוט"}, # no rule → drop
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{"rule_statement": "כלל2", "supporting_quote": ""}, # no quote → drop
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]}
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out = pe._coerce_list(raw)
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assert len(out) == 1
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assert out[0]["rule_type"] == "holding"
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assert out[0]["score"] == 1.0 # clamped to [0,1]
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def test_coerce_list_bad_rule_type_falls_back():
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out = pe._coerce_list([{"rule_statement": "כלל", "supporting_quote": "צ", "rule_type": "obiter", "score": 0.5}])
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assert out[0]["rule_type"] == "interpretive"
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def test_coerce_list_junk_returns_empty():
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assert pe._coerce_list("nonsense") == []
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assert pe._coerce_list(None) == []
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# ── cluster_candidates — cross-model matching & voting ─────────────
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def _c(rule, score):
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return {"rule_statement": rule, "supporting_quote": "q", "reasoning_summary": "",
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"rule_type": "interpretive", "score": score}
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def test_cluster_merges_across_models_counts_votes_and_means_score():
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# same principle proposed by all three (identical embedding) → 1 cluster, 3 votes
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a, b, c = _c("X", 0.9), _c("X", 0.8), _c("X", 0.7)
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per_model = {"claude": [a], "deepseek": [b], "gemini": [c]}
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embs = {id(a): [1.0, 0.0], id(b): [1.0, 0.0], id(c): [1.0, 0.0]}
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out = pe.cluster_candidates(per_model, embs)
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assert len(out) == 1
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cl = out[0]
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assert cl["votes"] == 3
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assert cl["score"] == pytest.approx((0.9 + 0.8 + 0.7) / 3, abs=1e-3)
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assert cl["verdict"] == "approved"
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assert cl["voters"] == ["claude", "deepseek", "gemini"]
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def test_cluster_separates_distinct_principles():
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a, b = _c("X", 0.9), _c("Y", 0.9)
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per_model = {"claude": [a, b]}
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embs = {id(a): [1.0, 0.0], id(b): [0.0, 1.0]} # orthogonal → 2 clusters
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out = pe.cluster_candidates(per_model, embs)
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assert len(out) == 2
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assert all(cl["votes"] == 1 and cl["verdict"] == "rejected" for cl in out)
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def test_cluster_same_model_twice_counts_one_vote_keeps_best_score():
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# one model proposes two near-dupes; another proposes the same → 2 votes, not 3
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a1, a2 = _c("X", 0.6), _c("X", 0.95)
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b = _c("X", 0.88)
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per_model = {"claude": [a1, a2], "deepseek": [b]}
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embs = {id(a1): [1.0, 0.0], id(a2): [1.0, 0.0], id(b): [1.0, 0.0]}
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out = pe.cluster_candidates(per_model, embs)
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assert len(out) == 1
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cl = out[0]
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assert cl["votes"] == 2 # claude counts once
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# claude's best (0.95) and deepseek (0.88) → mean
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assert cl["score"] == pytest.approx((0.95 + 0.88) / 2, abs=1e-3)
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assert cl["rule_statement"] == "X"
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def test_cluster_sorted_strongest_first():
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a = _c("X", 0.9) # 1 vote
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b, c = _c("Y", 0.9), _c("Y", 0.9) # 2 votes
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per_model = {"claude": [a, b], "deepseek": [c]}
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embs = {id(a): [1.0, 0.0], id(b): [0.0, 1.0], id(c): [0.0, 1.0]}
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out = pe.cluster_candidates(per_model, embs)
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assert out[0]["rule_statement"] == "Y" and out[0]["votes"] == 2
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assert out[1]["rule_statement"] == "X" and out[1]["votes"] == 1
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