145 lines
6.1 KiB
Python
145 lines
6.1 KiB
Python
"""Spike D — VerdictStore + ExpeL retrieval (B2).
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De-risks the assumption that retrieval surfaces a relevant *prior* verdict for a similar
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new proposal — the substrate for the framework's learning loop.
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**Similarity is structural, not textual (reviewer refinement #2):** a weighted score over
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*structured* fields — Jaccard on the affected cost-code set + a `measure_type` match + a
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magnitude-bucket match on the claimed saving — **never** raw description text. This makes
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the retrieval non-tautological: a true match with different wording beats decoys that
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merely share surface text.
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`retrieve()` is the guaranteed SC-D deliverable (always tested). A thin
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`ExpeLContextProvider` wraps it for few-shot injection via the real `ContextProvider`
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hook. The embedding-based similarity path is intentionally **out of scope** for a
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throwaway spike (a Fase 2 option — see findings-d).
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from agent_framework import ContextProvider
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# Weights: the affected cost-code overlap dominates, then measure type, then magnitude.
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_W_CODES, _W_MEASURE, _W_MAGNITUDE = 0.60, 0.25, 0.15
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_MAGNITUDE_BUCKETS = [(0.0, 1e5), (1e5, 5e5), (5e5, 1e6), (1e6, float("inf"))]
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@dataclass(frozen=True)
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class ProposalFeatures:
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"""The *structured* features retrieval ranks on. ``description`` is surface text and
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is deliberately NOT part of the similarity score."""
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affected_codes: frozenset[str]
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measure_type: str
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claimed_saving_nok: float
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description: str = ""
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@dataclass(frozen=True)
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class Verdict:
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"""One historical expert verdict in the store."""
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id: str
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proposal_features: ProposalFeatures
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decision: str # "approved" | "rejected"
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rationale: str
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def _magnitude_bucket(value: float) -> int:
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for i, (low, high) in enumerate(_MAGNITUDE_BUCKETS):
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if low <= value < high:
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return i
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return len(_MAGNITUDE_BUCKETS) - 1
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def _jaccard(a: frozenset[str], b: frozenset[str]) -> float:
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union = a | b
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return len(a & b) / len(union) if union else 1.0
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def similarity(query: ProposalFeatures, candidate: ProposalFeatures) -> float:
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"""Weighted structural similarity in [0, 1] — text is ignored by design."""
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codes = _jaccard(query.affected_codes, candidate.affected_codes)
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measure = 1.0 if query.measure_type == candidate.measure_type else 0.0
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magnitude = 1.0 if _magnitude_bucket(query.claimed_saving_nok) == _magnitude_bucket(
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candidate.claimed_saving_nok
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) else 0.0
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return _W_CODES * codes + _W_MEASURE * measure + _W_MAGNITUDE * magnitude
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@dataclass
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class VerdictStore:
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"""Minimal in-memory store of historical verdicts."""
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verdicts: list[Verdict]
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def retrieve(self, query: ProposalFeatures, k: int) -> list[Verdict]:
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"""Return the top-``k`` verdicts by structural similarity. Deterministic: ties
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break by verdict id, so ordering is stable across runs."""
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if k <= 0:
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raise ValueError(f"k must be positive, got {k}")
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ranked = sorted(
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self.verdicts,
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key=lambda v: (-similarity(query, v.proposal_features), v.id),
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)
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return ranked[:k]
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class ExpeLContextProvider(ContextProvider):
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"""Wraps `VerdictStore.retrieve` for ExpeL few-shot injection.
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Asserted only against the introspected `ContextProvider` interface
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(`before_run` + `SessionContext.extend_instructions`); the `retrieve` ranking remains
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the guaranteed SC-D deliverable regardless of the MAF session surface.
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"""
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def __init__(self, store: VerdictStore, query: ProposalFeatures, *, k: int = 3) -> None:
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super().__init__(source_id="expel-verdictstore")
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self._store = store
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self._query = query
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self._k = k
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def format_fewshot(self) -> str:
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hits = self._store.retrieve(self._query, self._k)
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body = "\n".join(f"- [{v.id}] {v.decision}: {v.rationale}" for v in hits)
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return f"Relevant prior verdicts (ExpeL few-shot):\n{body}"
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async def before_run(self, *, agent: object, session: object, context: object, state: dict) -> None:
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context.extend_instructions([self.format_fewshot()]) # type: ignore[attr-defined]
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def seed_store() -> VerdictStore:
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"""Seed 12 synthetic verdicts spanning the reference domain's cost codes, measure
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types, magnitudes, and decisions (B2 store of 10–20)."""
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rows = [
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("V01", {"05.2", "03.1"}, "scope_reduction", 180_000, "approved", "asphalt + base course trimmed within feasible range"),
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("V02", {"05.2"}, "rate_renegotiation", 60_000, "approved", "renegotiated asphalt unit rate"),
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("V03", {"07.4"}, "material_substitution", 120_000, "rejected", "granite kerb substitution unsafe"),
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("V04", {"09.1"}, "scope_reduction", 240_000, "approved", "fewer LED masts on low-traffic stretch"),
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("V05", {"02.3", "03.1"}, "scope_reduction", 350_000, "rejected", "soil replacement is load-bearing, cannot cut"),
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("V06", {"21.2"}, "rate_renegotiation", 90_000, "approved", "blasting rate renegotiated"),
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("V07", {"22.4"}, "material_substitution", 700_000, "rejected", "fiber shotcrete spec is mandated"),
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("V08", {"88.2"}, "scope_reduction", 150_000, "approved", "concrete repair area re-measured smaller"),
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("V09", {"87.3"}, "material_substitution", 130_000, "approved", "alternative membrane qualified"),
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("V10", {"05.2", "03.1"}, "rate_renegotiation", 200_000, "approved", "combined paving rate discount"),
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("V11", {"01.1"}, "scope_reduction", 95_000, "rejected", "rigging is fixed cost, no scope to cut"),
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("V12", {"31.3"}, "scope_reduction", 110_000, "approved", "drainage length reduced after survey"),
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]
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return VerdictStore(
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verdicts=[
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Verdict(
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id=vid,
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proposal_features=ProposalFeatures(
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affected_codes=frozenset(codes),
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measure_type=mtype,
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claimed_saving_nok=saving,
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description=desc,
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),
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decision=decision,
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rationale=desc,
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)
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for vid, codes, mtype, saving, decision, desc in rows
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]
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)
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