marcopan
c0285e55a0
Ports search/__init__.py (combined_search/RRF/aggregate) renamed psdwp3->nsp. New aggregate_lsh_multi (the D14a deviation): groups LSH hits by table keeping EVERY column where a value appears -- NOT collapsed to a single best column. The old _aggregate_lsh hid alternative groundings (e.g. 'ablazione' matching both a boolean flag and a free-text patologia field). aggregate_lsh_multi exposes all columns so the value-grounding widget lets the reviewer choose the anchor(s). Within one (table, column) the best-scored value is kept; columns ordered by score. value_grounded added to DecisionType (records the reviewer's anchor choice). L1: test_value_grounding (6 tests) -- multi-column exposure, grouping, within-column best-value, ordering, empty, and the value_grounded decision-type existence. Deferred: lshindex/ (needs vendor/thoth_lsh) and the L0 test_rrf.py land with the nsp lsh build command + index-building path; not needed for the pure L1 core here.
90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
from datetime import UTC, datetime
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from pathlib import Path
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from typing import Literal
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from pydantic import BaseModel
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DECISIONS_FILE = "review_decisions.jsonl"
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# 22 tipi di ChironeWp3 (verified leggendo session/decisions.py) + 1 nuovo (D15):
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# `decision_retracted` per il rollback a granularità step (ritira una decisione
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# senza cancellarne la riga dal log di audit; effective_decisions la onora).
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DecisionType = Literal[
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"concept_clarified",
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"question_rewritten",
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"table_promoted",
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"table_excluded",
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"column_corrected",
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"join_modified",
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"evidence_accepted",
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"evidence_rejected",
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"ambiguity_open",
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"memory_rejected",
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"cte_approved",
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"cte_corrected",
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"cte_rejected",
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"sql_revised",
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"sql_approved",
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"sql_rejected",
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"phase_approved",
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"phase_auto_approved",
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"phase_reopened",
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"phase_skipped",
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"datamart_requested",
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"datamart_declined",
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# D15: marker di ritrazione. subject = "phase:N", retracts = decision_seq ritirata.
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# Resta nel log di audit (append-only); effective_decisions() la esclude dalla vista.
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"decision_retracted",
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# D14a: valore citato nella domanda ancorato a una o piu' colonne. subject =
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# "phase:4", detail = il valore (es. "ablazione"), rationale = la/e colonna/e scelta/e
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# dal reviewer (aggregate_lsh_multi le espone tutte senza collassare al miglior match).
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"value_grounded",
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]
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class DecisionRecord(BaseModel):
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seq: int
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ts: datetime
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type: DecisionType
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subject: str
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detail: str = ""
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rationale: str = ""
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# D15: se type == "decision_retracted", indica quale seq viene ritirata.
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retracts: int | None = None
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def list_decisions(session_dir: Path) -> list[DecisionRecord]:
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path = session_dir / DECISIONS_FILE
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if not path.exists():
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return []
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return [
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DecisionRecord.model_validate_json(line)
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for line in path.read_text().splitlines()
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if line.strip()
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]
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def append_decision(
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session_dir: Path,
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*,
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type: str,
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subject: str,
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detail: str = "",
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rationale: str = "",
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retracts: int | None = None,
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) -> DecisionRecord:
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record = DecisionRecord(
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seq=len(list_decisions(session_dir)) + 1,
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ts=datetime.now(UTC),
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type=type,
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subject=subject,
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detail=detail,
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rationale=rationale,
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retracts=retracts,
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)
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path = session_dir / DECISIONS_FILE
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("a") as f:
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f.write(record.model_dump_json() + "\n")
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return record
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