112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
"""Spike D tests — non-tautological top-K retrieval (B2).
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The true match shares the *structured* similarity fields with the query but uses different
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description text; the decoys share surface description text but differ structurally. A
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text-matching retriever would be fooled by the decoys — a structural one is not.
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Pattern: tests/test_reference_domain.py.
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"""
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import pytest
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from spikes.d_verdictstore import (
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ExpeLContextProvider,
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ProposalFeatures,
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Verdict,
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VerdictStore,
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seed_store,
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)
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_QUERY = ProposalFeatures(
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affected_codes=frozenset({"05.2", "03.1"}),
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measure_type="scope_reduction",
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claimed_saving_nok=220_000, # bucket [100k, 500k)
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description="asphalt base course reduction near school",
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)
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def _store_with_true_match_and_decoys() -> tuple[VerdictStore, str]:
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true_match = Verdict(
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id="TRUE",
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proposal_features=ProposalFeatures(
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affected_codes=frozenset({"05.2", "03.1"}), # same codes
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measure_type="scope_reduction", # same measure type
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claimed_saving_nok=200_000, # same magnitude bucket
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description="zzz totally unrelated wording alpha beta", # DIFFERENT text
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),
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decision="approved",
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rationale="prior scope reduction on the same codes was approved",
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)
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# Decoys share the query's SURFACE text but differ structurally (disjoint codes,
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# different measure type, different magnitude bucket).
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decoy_low = Verdict(
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id="DECOY-LOW",
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proposal_features=ProposalFeatures(
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affected_codes=frozenset({"09.1"}),
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measure_type="rate_renegotiation",
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claimed_saving_nok=50_000, # bucket [0, 100k)
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description="asphalt base course reduction near school", # same words as query
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),
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decision="rejected",
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rationale="surface-text decoy",
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)
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decoy_high = Verdict(
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id="DECOY-HIGH",
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proposal_features=ProposalFeatures(
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affected_codes=frozenset({"21.2"}),
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measure_type="material_substitution",
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claimed_saving_nok=700_000, # bucket [500k, 1M)
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description="asphalt base course reduction extra words",
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),
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decision="rejected",
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rationale="surface-text decoy",
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)
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# Order deliberately not putting the true match first.
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return VerdictStore(verdicts=[decoy_low, true_match, decoy_high]), "TRUE"
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def test_retrieve_finds_structural_match_over_text_decoys() -> None:
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store, true_id = _store_with_true_match_and_decoys()
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hits = store.retrieve(_QUERY, k=3)
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assert hits[0].id == true_id # structural match ranks #1 despite different wording
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assert true_id in {h.id for h in hits[:1]} # within top-K (top-1 here)
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def test_retrieve_is_deterministic() -> None:
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store, _ = _store_with_true_match_and_decoys()
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assert [h.id for h in store.retrieve(_QUERY, k=3)] == [
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h.id for h in store.retrieve(_QUERY, k=3)
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]
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def test_retrieve_rejects_non_positive_k() -> None:
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store, _ = _store_with_true_match_and_decoys()
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with pytest.raises(ValueError):
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store.retrieve(_QUERY, k=0)
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def test_seed_store_has_10_to_20_verdicts() -> None:
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store = seed_store()
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assert 10 <= len(store.verdicts) <= 20
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class _RecordingContext:
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"""Duck-typed stand-in for SessionContext — records injected instructions without
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depending on the (private) SessionContext internals."""
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def __init__(self) -> None:
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self.instructions: list[str] = []
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def extend_instructions(self, items: list[str]) -> None:
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self.instructions.extend(items)
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async def test_expel_provider_injects_retrieved_fewshot() -> None:
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store, true_id = _store_with_true_match_and_decoys()
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provider = ExpeLContextProvider(store, _QUERY, k=2)
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# The deterministic deliverable: the few-shot text carries the retrieved true match.
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assert true_id in provider.format_fewshot()
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# The injection hook (real ContextProvider.before_run signature) extends instructions.
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ctx = _RecordingContext()
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await provider.before_run(agent=None, session=None, context=ctx, state={})
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assert len(ctx.instructions) == 1
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assert true_id in ctx.instructions[0]
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