108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
"""Step 3 tests — non-tautological top-K retrieval + REAL-SessionContext two-arg injection.
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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 text but differ structurally. The injection test
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uses a REAL ``agent_framework.SessionContext`` (not a single-arg fake), exercising the
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genuine two-arg ``extend_instructions(source_id, instructions)`` GA signature — retiring the
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Critical Fase 1 risk. Pattern: tests/spikes/test_d_verdictstore.py + real SessionContext.
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"""
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import pytest
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from agent_framework import SessionContext
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from portfolio_optimiser.verdicts 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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capture_verdict,
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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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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,
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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,
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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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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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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_capture_verdict_mints_stable_id() -> None:
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a = capture_verdict(_QUERY, "approved", "ok")
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b = capture_verdict(
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ProposalFeatures(
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affected_codes=frozenset({"03.1", "05.2"}), # same set, different order
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measure_type="scope_reduction",
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claimed_saving_nok=220_000,
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description="DIFFERENT surface wording entirely", # text excluded from the id
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),
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"approved",
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"ok",
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)
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assert a.id == b.id # structurally identical -> stable id
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assert len(a.id) == 16
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async def test_before_run_populates_real_sessioncontext_two_arg() -> 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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# A REAL SessionContext (not a single-arg fake) — exercises the genuine GA two-arg
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# extend_instructions(source_id, instructions) signature.
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ctx = SessionContext(input_messages=[], instructions=[])
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await provider.before_run(agent=None, session=None, context=ctx, state={})
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assert any(true_id in instr for instr in ctx.instructions)
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