feat(harness): SQL formula evidence -- concept->formula units + retrieval (D14b)

New evidence/formula_store.py: ConceptFormula (concept, columns, sql, status,
sources) as a frontmatter-YAML + SQL-body unit, stored one-file-per-formula under
<formulas>/<slug>-<n>.sql.md. save_formula is append-only (competing drafts and
reviewed versions coexist); retrieve_formula(concept) returns all of them so the
gate can surface candidates and let the reviewer choose.

concept_formula_approved / concept_formula_rejected added to DecisionType
(records the reviewer's choice; approved formulas travel with schema-linking).

L1: test_formula (7 tests) -- retrieval by concept, save/reload roundtrip (SQL
body preserved, frontmatter well-formed), multiple formulas per concept, empty
on no-match / missing dir, decision-type existence, default draft status.

Deferred: --kind formula on nsp search (needs search_cmd porting) wires
retrieve_formula into the CLI; lands with the search command.
This commit is contained in:
2026-06-26 23:03:29 +02:00
parent c0285e55a0
commit f104c015a1
3 changed files with 190 additions and 0 deletions
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@@ -39,6 +39,11 @@ DecisionType = Literal[
# "phase:4", detail = il valore (es. "ablazione"), rationale = la/e colonna/e scelta/e
# dal reviewer (aggregate_lsh_multi le espone tutte senza collassare al miglior match).
"value_grounded",
# D14b: formula di concetto approvata/rifiutata dal reviewer. subject = "phase:4",
# detail = il concetto (es. "fascia pediatrica"), rationale = la/e colonna/e o il motivo.
# retrieve_formula restituisce i candidati; queste decisioni registrano la scelta.
"concept_formula_approved",
"concept_formula_rejected",
]
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@@ -0,0 +1,100 @@
"""SQL concept->formula evidence store (spec D14b, §4.7).
A concept (e.g. 'fascia pediatrica', 'ablazione') maps to a reusable SQL formula
(a CASE WHEN ...) that derives it from physical columns. These are reviewable
units: the gate surfaces a candidate formula, the reviewer approves or rejects it
(decision types concept_formula_approved / concept_formula_rejected), and approved
formulas travel with the schema-linking artifact.
Storage: one file per formula, frontmatter YAML + SQL body (same shape as
EvidenceDoc.parse). Directory layout: <root>/formulas/<slug>-<n>.sql.md.
Retrieve is by concept (may return several, e.g. competing drafts vs reviewed).
"""
from __future__ import annotations
import re
from pathlib import Path
from typing import Literal
import yaml
from pydantic import BaseModel
FORMULAS_SUBDIR = "formulas"
_SUFFIX_RE = re.compile(r"^(.*?)-(\d+)\.sql\.md$")
class ConceptFormula(BaseModel):
concept: str
columns: list[str] = []
sql: str
status: Literal["draft", "reviewed"] = "draft"
sources: list[str] = []
@property
def _slug(self) -> str:
"""ASCII slug for the filename (matches textutil.slugify shape)."""
import unicodedata
text = unicodedata.normalize("NFKD", self.concept).encode("ascii", "ignore").decode()
return re.sub(r"[^a-z0-9_]+", "-", text.lower()).strip("-") or "formula"
def dump(self) -> str:
meta = self.model_dump(exclude={"sql"}, mode="json")
fm = yaml.safe_dump(meta, sort_keys=False, allow_unicode=True)
return f"---\n{fm}---\n{self.sql}\n"
@classmethod
def parse(cls, text: str) -> "ConceptFormula":
if not text.startswith("---\n"):
raise ValueError("frontmatter mancante (atteso '---\\n' iniziale)")
try:
_, fm, body = text.split("---\n", 2)
except ValueError as e:
raise ValueError("frontmatter malformato") from e
meta = yaml.safe_load(fm)
if not isinstance(meta, dict):
raise ValueError("frontmatter non valido")
return cls.model_validate({**meta, "sql": body.strip("\n")})
def _next_path(root: Path, slug: str) -> Path:
"""First free <slug>-<n>.sql.md path under root (n starts at 1)."""
root.mkdir(parents=True, exist_ok=True)
existing = sorted(root.glob(f"{slug}-*.sql.md"))
n = 0
for p in existing:
m = _SUFFIX_RE.match(p.name)
if m:
n = max(n, int(m.group(2)))
return root / f"{slug}-{n + 1}.sql.md"
def save_formula(root: Path | str, formula: ConceptFormula) -> Path:
"""Persist a single concept->formula unit under <root>/formulas/. Returns the
written path. Append-only: each save writes a new file (so competing drafts and
reviewed versions coexist until a curator prunes)."""
root = Path(root)
formulas_dir = root / FORMULAS_SUBDIR
path = _next_path(formulas_dir, formula._slug)
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(formula.dump())
return path
def retrieve_formula(root: Path | str, concept: str) -> list[ConceptFormula]:
"""All formulas for `concept` under <root>/formulas/. Empty list if none (or if
the dir is absent). Multiple results mean competing drafts/versions for the same
concept -- the caller (gate) lets the reviewer pick."""
root = Path(root)
formulas_dir = root / FORMULAS_SUBDIR
if not formulas_dir.is_dir():
return []
out: list[ConceptFormula] = []
for f in sorted(formulas_dir.glob("*.sql.md")):
try:
formula = ConceptFormula.parse(f.read_text())
except ValueError:
continue # malformed file: skip, don't crash retrieval
if formula.concept == concept:
out.append(formula)
return out
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"""L1: SQL formula evidence -- concept->formula units (spec D14b).
A concept (e.g. 'fascia pediatrica') maps to a SQL formula (CASE WHEN ...) that
derives it from physical columns. These are reusable, reviewable units: the gate
surfaces a candidate formula, the reviewer approves or rejects it (recorded via
concept_formula_approved / concept_formula_rejected), and approved formulas are
part of the schema-linking artifact. The store is frontmatter-YAML + SQL body.
"""
from pathlib import Path
import pytest
from nsp.evidence.formula_store import ConceptFormula, retrieve_formula, save_formula
def test_formula_retrieval_by_concept(tmp_path):
f = ConceptFormula(
concept="fascia pediatrica",
columns=["data_nascita"],
sql="CASE WHEN age < 18 THEN 'pediatrica' ELSE 'adulto' END",
status="reviewed",
sources=["src1"],
)
save_formula(tmp_path, f)
results = retrieve_formula(tmp_path, "fascia pediatrica")
assert len(results) == 1
assert results[0].sql.startswith("CASE WHEN")
assert results[0].concept == "fascia pediatrica"
assert results[0].status == "reviewed"
assert "data_nascita" in results[0].columns
def test_save_and_reload_roundtrip_preserves_sql_body(tmp_path):
f = ConceptFormula(
concept="fascia pediatrica",
columns=["data_nascita"],
sql="CASE\n WHEN x THEN 1\nEND",
status="draft",
sources=[],
)
path = save_formula(tmp_path, f)
assert path.exists()
# the file is frontmatter YAML + SQL body
text = path.read_text()
assert text.startswith("---")
assert "concept: fascia pediatrica" in text
assert "CASE" in text # SQL body preserved
def test_retrieve_multiple_formulas_for_concept(tmp_path):
# two competing formulas for the same concept (different sources/status)
save_formula(tmp_path, ConceptFormula(concept="ablazione", columns=["flag"],
sql="SELECT 1", status="draft", sources=["a"]))
save_formula(tmp_path, ConceptFormula(concept="ablazione", columns=["testo"],
sql="SELECT 2", status="reviewed", sources=["b"]))
results = retrieve_formula(tmp_path, "ablazione")
assert len(results) == 2
statuses = {r.status for r in results}
assert statuses == {"draft", "reviewed"}
def test_retrieve_empty_when_no_match(tmp_path):
save_formula(tmp_path, ConceptFormula(concept="altro", columns=["c"],
sql="SELECT 1", status="reviewed", sources=[]))
assert retrieve_formula(tmp_path, "inesistente") == []
def test_retrieve_empty_on_missing_dir(tmp_path):
# no formulas dir at all -> empty list, not error
assert retrieve_formula(tmp_path / "nope", "anything") == []
def test_concept_formula_decision_types_exist():
import typing
from nsp.decisions import DecisionType
args = typing.get_args(DecisionType)
assert "concept_formula_approved" in args
assert "concept_formula_rejected" in args
def test_concept_formula_default_status(tmp_path):
# status has a sensible default so an author can write a draft quickly
f = ConceptFormula(concept="x", columns=["c"], sql="SELECT 1")
assert f.status == "draft" # not yet reviewed
assert f.sources == []