legal-ai/mcp-server/tests/test_panel_extraction.py

"""Unit tests for the tri-model panel extraction core (#152, Phase A).

Pure logic only — classify (the chair's approval rule), _coerce_list (judge-reply
normalisation), and cluster_candidates (cross-model matching/voting) with injected
embeddings. No LLM, no Voyage, no DB.
"""
from __future__ import annotations

import pytest

from legal_mcp import config
from legal_mcp.services import panel_extraction as pe


# ── classify — chaim's rule ────────────────────────────────────────

def test_classify_three_votes_approves_regardless_of_score():
    assert pe.classify(3, 0.10) == "approved"
    assert pe.classify(3, 0.99) == "approved"


def test_classify_two_votes_gated_by_floor():
    floor = config.HALACHA_PANEL_SCORE_FLOOR
    assert pe.classify(2, floor) == "approved"
    assert pe.classify(2, floor + 0.05) == "approved"
    assert pe.classify(2, floor - 0.01) == "pending_review"


def test_classify_one_or_zero_votes_rejected():
    assert pe.classify(1, 0.99) == "rejected"
    assert pe.classify(0, 0.99) == "rejected"


# ── _coerce_list — judge reply normalisation ───────────────────────

def test_coerce_list_accepts_bare_list():
    raw = [{"rule_statement": "כלל", "supporting_quote": "ציטוט", "score": 0.9}]
    out = pe._coerce_list(raw)
    assert len(out) == 1 and out[0]["rule_type"] == "interpretive"


def test_coerce_list_unwraps_dict_wrapper_and_drops_incomplete():
    raw = {"principles": [
        {"rule_statement": "כלל", "supporting_quote": "ציטוט", "rule_type": "holding", "score": 1.5},
        {"rule_statement": "", "supporting_quote": "ציטוט"},          # no rule → drop
        {"rule_statement": "כלל2", "supporting_quote": ""},            # no quote → drop
    ]}
    out = pe._coerce_list(raw)
    assert len(out) == 1
    assert out[0]["rule_type"] == "holding"
    assert out[0]["score"] == 1.0           # clamped to [0,1]


def test_coerce_list_bad_rule_type_falls_back():
    out = pe._coerce_list([{"rule_statement": "כלל", "supporting_quote": "צ", "rule_type": "obiter", "score": 0.5}])
    assert out[0]["rule_type"] == "interpretive"


def test_coerce_list_junk_returns_empty():
    assert pe._coerce_list("nonsense") == []
    assert pe._coerce_list(None) == []


# ── cluster_candidates — cross-model matching & voting ─────────────

def _c(rule, score):
    return {"rule_statement": rule, "supporting_quote": "q", "reasoning_summary": "",
            "rule_type": "interpretive", "score": score}


def test_cluster_merges_across_models_counts_votes_and_means_score():
    # same principle proposed by all three (identical embedding) → 1 cluster, 3 votes
    a, b, c = _c("X", 0.9), _c("X", 0.8), _c("X", 0.7)
    per_model = {"claude": [a], "deepseek": [b], "gemini": [c]}
    embs = {id(a): [1.0, 0.0], id(b): [1.0, 0.0], id(c): [1.0, 0.0]}
    out = pe.cluster_candidates(per_model, embs)
    assert len(out) == 1
    cl = out[0]
    assert cl["votes"] == 3
    assert cl["score"] == pytest.approx((0.9 + 0.8 + 0.7) / 3, abs=1e-3)
    assert cl["verdict"] == "approved"
    assert cl["voters"] == ["claude", "deepseek", "gemini"]


def test_cluster_separates_distinct_principles():
    a, b = _c("X", 0.9), _c("Y", 0.9)
    per_model = {"claude": [a, b]}
    embs = {id(a): [1.0, 0.0], id(b): [0.0, 1.0]}  # orthogonal → 2 clusters
    out = pe.cluster_candidates(per_model, embs)
    assert len(out) == 2
    assert all(cl["votes"] == 1 and cl["verdict"] == "rejected" for cl in out)


def test_cluster_same_model_twice_counts_one_vote_keeps_best_score():
    # one model proposes two near-dupes; another proposes the same → 2 votes, not 3
    a1, a2 = _c("X", 0.6), _c("X", 0.95)
    b = _c("X", 0.88)
    per_model = {"claude": [a1, a2], "deepseek": [b]}
    embs = {id(a1): [1.0, 0.0], id(a2): [1.0, 0.0], id(b): [1.0, 0.0]}
    out = pe.cluster_candidates(per_model, embs)
    assert len(out) == 1
    cl = out[0]
    assert cl["votes"] == 2                       # claude counts once
    # claude's best (0.95) and deepseek (0.88) → mean
    assert cl["score"] == pytest.approx((0.95 + 0.88) / 2, abs=1e-3)
    assert cl["rule_statement"] == "X"


def test_apply_cap_downgrades_over_cap_survivors_by_score():
    judged = [
        {"verdict": "approved", "score": 0.9},
        {"verdict": "approved", "score": 0.7},
        {"verdict": "pending_review", "score": 0.8},
        {"verdict": "rejected", "score": 0.95},   # already rejected stays
    ]
    out = pe.apply_cap(judged, max_new=2)
    fv = [j["final_verdict"] for j in out]
    # top-2 survivors by score = 0.9(approved) + 0.8(pending); 0.7 → over cap → rejected
    assert fv == ["approved", "rejected", "pending_review", "rejected"]


def test_apply_cap_keeps_all_when_under_cap():
    judged = [{"verdict": "approved", "score": 0.9}, {"verdict": "pending_review", "score": 0.5}]
    out = pe.apply_cap(judged, max_new=5)
    assert [j["final_verdict"] for j in out] == ["approved", "pending_review"]


def test_cluster_sorted_strongest_first():
    a = _c("X", 0.9)   # 1 vote
    b, c = _c("Y", 0.9), _c("Y", 0.9)  # 2 votes
    per_model = {"claude": [a, b], "deepseek": [c]}
    embs = {id(a): [1.0, 0.0], id(b): [0.0, 1.0], id(c): [0.0, 1.0]}
    out = pe.cluster_candidates(per_model, embs)
    assert out[0]["rule_statement"] == "Y" and out[0]["votes"] == 2
    assert out[1]["rule_statement"] == "X" and out[1]["votes"] == 1