26794aab09
* feat(recall): add proof_count boost to combined scoring Observations with more supporting evidence now rank slightly higher in recall results. proof_count is threaded through the retrieval pipeline and applied as a multiplicative boost in reranking: - types.py: add proof_count field to RetrievalResult - retrieval.py: include proof_count in SELECT columns - reranking.py: add log1p-normalized proof_count boost (alpha=0.1) The boost uses the same multiplicative pattern as recency and temporal signals. proof_count=1 is neutral, proof_count=50 gives ~+5% boost. Non-observation fact types are unaffected (neutral 0.5). * fix(retrieval): Apply proof_count boost to graph and temporal retrieval, normalize scaling * fix(retrieval): correct proof_norm math to zero-center at count 1 * fix(retrieval): Apply proof_count boost to link_expansion retrieval * fix: remove BFS zombie, clamp proof_norm to [0,1], fix test comment (log1p->math.log)
96 lines
3.6 KiB
Python
96 lines
3.6 KiB
Python
"""
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Unit tests for proof_count boost in reranking.
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"""
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from datetime import datetime, timezone
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import pytest
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from uuid import uuid4
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from hindsight_api.engine.search.types import RetrievalResult, MergedCandidate, ScoredResult
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from hindsight_api.engine.search.reranking import apply_combined_scoring
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UTC = timezone.utc
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def create_mock_scored_result(proof_count: int | None = None, ce_score: float = 0.8) -> ScoredResult:
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"""Helper to create a minimal ScoredResult suitable for scoring tests."""
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retrieval = RetrievalResult(
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id=uuid4(),
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text="Test mock fact",
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fact_type="observation" if proof_count is not None else "world",
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document_id=uuid4(),
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chunk_id=uuid4(),
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embedding=[0.1]*384,
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similarity=0.9,
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proof_count=proof_count,
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# Default neutral dates for testing so only proof_count changes score
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occurred_start=datetime.now(UTC),
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occurred_end=datetime.now(UTC)
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)
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candidate = MergedCandidate(
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id=retrieval.id,
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retrieval=retrieval,
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semantic_rank=1,
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bm25_rank=1,
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rrf_score=0.1
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)
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return ScoredResult(
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candidate=candidate,
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cross_encoder_score=ce_score,
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cross_encoder_score_normalized=ce_score,
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weight=ce_score,
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)
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def test_proof_count_neutral_when_none():
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"""Test that when proof_count is None (e.g. non-observation), it gets neutral 0.5 norm."""
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sr = create_mock_scored_result(proof_count=None, ce_score=0.8)
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now = datetime.now(UTC)
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apply_combined_scoring([sr], now, proof_count_alpha=0.1)
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# Neutral multiplier means score shouldn't be boosted by proof_count
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# Since recency is neutral (just created) and temporal is neutral, score should remain unchanged
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assert sr.combined_score == pytest.approx(0.8, rel=1e-3)
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def test_proof_count_neutral_at_one():
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"""Test that proof_count=1 gives neutral multiplier."""
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sr = create_mock_scored_result(proof_count=1, ce_score=0.8)
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now = datetime.now(UTC)
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apply_combined_scoring([sr], now, proof_count_alpha=0.1)
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# proof_count=1 -> math.log(1) = 0 -> 0.5 + 0/10 = 0.5 (neutral) -> multiplier 1.0
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assert sr.combined_score == pytest.approx(0.8, rel=1e-3)
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def test_proof_count_increases_with_higher_counts():
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"""Test that higher proof counts yield strictly higher scores."""
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now = datetime.now(UTC)
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# Create results with increasing proof counts
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sr_5 = create_mock_scored_result(proof_count=5, ce_score=0.8)
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sr_50 = create_mock_scored_result(proof_count=50, ce_score=0.8)
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sr_100 = create_mock_scored_result(proof_count=100, ce_score=0.8)
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# Process them
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apply_combined_scoring([sr_5, sr_50, sr_100], now, proof_count_alpha=0.1)
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# Assure scores strictly increase
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assert sr_5.combined_score > 0.8
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assert sr_50.combined_score > sr_5.combined_score
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assert sr_100.combined_score > sr_50.combined_score
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def test_proof_count_no_hardcoded_cap_at_100():
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"""Test that proof_count continues to scale within the clamped [0, 1] range."""
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now = datetime.now(UTC)
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# Use values that stay below the clamp ceiling (proof_norm < 1.0)
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# log(5)/10=0.16, log(20)/10=0.30, log(100)/10=0.46 → all below 0.5 headroom
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sr_5 = create_mock_scored_result(proof_count=5, ce_score=0.8)
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sr_20 = create_mock_scored_result(proof_count=20, ce_score=0.8)
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sr_100 = create_mock_scored_result(proof_count=100, ce_score=0.8)
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apply_combined_scoring([sr_5, sr_20, sr_100], now, proof_count_alpha=0.1)
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# Must strictly increase within the valid range
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assert sr_20.combined_score > sr_5.combined_score
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assert sr_100.combined_score > sr_20.combined_score
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