Changement d'architecture demande par l'utilisateur : le dataset d'entrainement (TECH_STEP_TRAINING_DATA) quitte apps/api pour vivre entierement dans services/tech-step-intent-service (intent_service/training_data.py). Ce service est desormais autonome : il s'entraine lui-meme une seule fois, a son propre demarrage (PipelineRegistry.initialize, dans le lifespan FastAPI), sans plus dependre d'un POST /v1/train pousse par apps/api (route supprimee). apps/api ne connait plus aucune technique/synonyme, uniquement le resultat de POST /v1/process. Corpus enrichi avec les 48 techniques du lexique fourni (Arroser, Appertiser, Braiser, Caraméliser, Confire, Julienne/Brunoise/Mirepoix/ Paysanne, Cuire à blanc/au bain-marie/à l'étouffée, Déglacer variantes, Emulsionner, Glacer, Pocher, Réduire, Suer, Zester, etc.), soit 74 techniques au total (26 + 48). Integration complete bout en bout : - reference-seed-data.ts : 48 nouvelles entrees TECH_STEPS - apps/web/locales/fr/translation.json : libelles francais correspondants - "Mitonner" fondu comme synonyme de simmer (pas une technique distincte, sa propre definition le dit) - "Blanchir un oeuf" (whiskPale) distingue de "Blanchir un legume" (blanch, existant) via des synonymes en phrase complete plutot qu'au mot nu — filter_spans (deja en place) resout la collision par specificite Impact performance mesure : le corpus elargi (74 classes vs 26) rend l'entrainement bien plus lent a nombre d'iterations egal (150 iterations depassait 17 minutes par run de test) — reduit a 40 iterations apres mesures repetees en local (~200s/locale, ~400s pour fr+en combines). docker-compose.yml (healthcheck start_period 600s), CI (timeout curl 600s) et le README du service documentent ce nouveau temps de demarrage. CONFIDENCE_THRESHOLD recalibre a 0.2 par verification manuelle (0.75 puis 0.45 ne tenaient plus compte tenu du nombre de classes) — marque explicitement comme placeholder en attendant une vraie repasse de calibrate-tech-step-threshold.ts (necessite Postgres, indisponible dans cet environnement). Verifie : 28/28 tests pytest du service (suite complete re-ecrite pour s'entrainer une seule fois par session sur le vrai corpus, fixture partagee dans conftest.py), lint + build complets du monorepo. La suite Mocha d'apps/api reste a confirmer via CI (le root hook mocha n'attend plus l'entrainement, seulement CI's propre attente sur /health). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
350 lines
15 KiB
TypeScript
350 lines
15 KiB
TypeScript
import { expect } from "chai";
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import { prisma } from "../../src/db/prisma.js";
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import {
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normalizeText,
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splitIntoClauses,
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type TechniqueCandidate,
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techStepClassifier,
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} from "../../src/lib/recipe-matching/tech-step-matcher.js";
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import { resetDatabase } from "../../test-support/reset-db.js";
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describe("tech-step-matcher", () => {
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describe("normalizeText", () => {
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it("lowercases and strips accents", () => {
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expect(normalizeText("Déglacer AU FOUR")).to.equal("deglacer au four");
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});
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it("strips a variety of diacritics, including cedilla", () => {
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expect(normalizeText("Façon Œuf à l'Étouffée")).to.equal("facon œuf a l'etouffee");
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});
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it("leaves already-plain text unchanged, aside from casing", () => {
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expect(normalizeText("Mix everything")).to.equal("mix everything");
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});
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it("returns an empty string for an empty input", () => {
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expect(normalizeText("")).to.equal("");
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});
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});
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describe("splitIntoClauses", () => {
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// A candidate's own `uid` doesn't matter to the splitting logic itself
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// (it's opaque, carried through as `anchor`) — kept short and
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// arbitrary across these fixtures.
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function candidate(uid: string, start: number, end: number): TechniqueCandidate {
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return { uid, start, end };
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}
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it("returns the whole description as one anchor-less clause when there are no candidates", () => {
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const text = "Servir immédiatement";
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const result = splitIntoClauses(text, []);
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expect(result).to.deep.equal([{ start: 0, end: text.length, anchor: null }]);
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});
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it("returns the whole description as one clause anchored on the single candidate", () => {
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const melt = candidate("melt", 6, 13);
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const text = "Faire fondre le beurre";
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const result = splitIntoClauses(text, [melt]);
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expect(result).to.deep.equal([{ start: 0, end: text.length, anchor: melt }]);
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});
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it("splits into two clauses at the whitespace nearest the gap's midpoint between two candidates", () => {
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// "Préchauffer la poêle, puis faire fondre le beurre"
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// 0 1 2 3 4
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// 0123456789012345678901234567890123456789012345678901
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const preheat = candidate("preheat", 0, 11); // "Préchauffer"
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const melt = candidate("melt", 27, 39); // "faire fondre"
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const text = "Préchauffer la poêle, puis faire fondre le beurre";
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const result = splitIntoClauses(text, [preheat, melt]);
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expect(result).to.have.length(2);
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// The gap between the two candidates is [11, 27) — its raw midpoint
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// (19) falls inside "poêle" (see findGapSplitPoint's doc comment for
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// why that's specifically what this snaps away from); the nearest
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// actual whitespace to that midpoint is the space at 21, right after
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// the comma.
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expect(result[0]).to.deep.equal({ start: 0, end: 21, anchor: preheat });
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expect(result[1]).to.deep.equal({ start: 21, end: text.length, anchor: melt });
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// The two clauses are contiguous and cover the whole text.
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expect(
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text.slice(result[0].start, result[0].end) + text.slice(result[1].start, result[1].end),
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).to.equal(text);
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});
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it("sorts out-of-order candidates before splitting, and anchors each clause on the matching one", () => {
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const preheat = candidate("preheat", 0, 11);
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const melt = candidate("melt", 27, 39);
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// Passed in reverse — the function must still produce clauses in
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// reading order, each anchored on the right candidate.
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const result = splitIntoClauses("Préchauffer la poêle, puis faire fondre le beurre", [
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melt,
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preheat,
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]);
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expect(result.map((clause) => clause.anchor?.uid)).to.deep.equal(["preheat", "melt"]);
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});
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it("produces N contiguous clauses for N candidates, each anchored on its own", () => {
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const a = candidate("a", 0, 3);
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const b = candidate("b", 10, 13);
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const c = candidate("c", 20, 23);
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const text = "x".repeat(30);
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const result = splitIntoClauses(text, [a, b, c]);
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expect(result).to.have.length(3);
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expect(result.map((clause) => clause.anchor?.uid)).to.deep.equal(["a", "b", "c"]);
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// Contiguous: each clause's end is the next one's start.
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expect(result[0].start).to.equal(0);
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expect(result[0].end).to.equal(result[1].start);
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expect(result[1].end).to.equal(result[2].start);
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expect(result[2].end).to.equal(text.length);
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});
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it("clamps the split point to the earlier candidate's own end when two candidates are adjacent/overlapping", () => {
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// Gap midpoint would fall *before* `a`'s own end here — must not
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// produce a clause that cuts into `a`'s own anchor span.
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const a = candidate("a", 0, 10);
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const b = candidate("b", 8, 15);
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const result = splitIntoClauses("x".repeat(20), [a, b]);
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expect(result[0].end).to.be.at.least(a.end);
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expect(result[1].start).to.equal(result[0].end);
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});
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});
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describe("techStepClassifier", () => {
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// `techStepClassifier` is the one shared singleton (see
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// tech-step-matcher.ts's own doc comment on why) — these tests
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// exercise it against the real training corpus
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// (`services/tech-step-intent-service`'s `training_data.py`) and the
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// real seeded `TechStep` catalog, rather than synthetic injectable
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// fixtures the old regex-based `matchTechStepSpans(description,
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// mappings)` allowed. Every call round-trips over HTTP to a real,
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// locally running `services/tech-step-intent-service` (see that
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// service's own README and `apps/api/.env.test`) — that service trains
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// itself once at its own startup (`test-support/mocha-root-hooks.ts`'s
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// root hook doesn't wait on it, CI's own "wait for /health" step
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// already does), so calls here are just a normal HTTP round-trip,
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// comfortably inside this suite's default 10s timeout (.mocharc.json).
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let simmerId: number;
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let cookId: number;
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let bakeId: number;
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let preheatId: number;
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let meltId: number;
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let boilId: number;
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let chopId: number;
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beforeEach(async () => {
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await resetDatabase();
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const [simmer, cook, bake, preheat, melt, boil, chop] = await Promise.all([
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prisma.techStep.findFirstOrThrow({ where: { key: "simmer" } }),
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prisma.techStep.findFirstOrThrow({ where: { key: "cook" } }),
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prisma.techStep.findFirstOrThrow({ where: { key: "bake" } }),
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prisma.techStep.findFirstOrThrow({ where: { key: "preheat" } }),
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prisma.techStep.findFirstOrThrow({ where: { key: "melt" } }),
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prisma.techStep.findFirstOrThrow({ where: { key: "boil" } }),
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prisma.techStep.findFirstOrThrow({ where: { key: "chop" } }),
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]);
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simmerId = simmer.id;
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cookId = cook.id;
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bakeId = bake.id;
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preheatId = preheat.id;
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meltId = melt.id;
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boilId = boil.id;
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chopId = chop.id;
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});
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after(async () => {
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await prisma.$disconnect();
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});
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describe("matchTechSteps", () => {
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it("matches an exact expression", async () => {
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expect(
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await techStepClassifier.matchTechSteps("Faire mijoter à feu doux", "fr"),
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).to.deep.equal([simmerId]);
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});
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it("is case- and accent-insensitive", async () => {
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expect(await techStepClassifier.matchTechSteps("FAIRE MIJOTER", "fr")).to.deep.equal([
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simmerId,
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]);
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});
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it("returns an empty sequence when nothing matches", async () => {
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expect(
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await techStepClassifier.matchTechSteps("Ranger les couverts dans le tiroir", "fr"),
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).to.deep.equal([]);
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});
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it("returns an empty sequence for an empty description", async () => {
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expect(await techStepClassifier.matchTechSteps("", "fr")).to.deep.equal([]);
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});
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it("returns an empty sequence for a locale nothing was trained on", async () => {
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expect(
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await techStepClassifier.matchTechSteps("Faire mijoter à feu doux", "de"),
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).to.deep.equal([]);
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});
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it("detects several distinct techniques in one step, in reading order", async () => {
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expect(
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await techStepClassifier.matchTechSteps(
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"Préchauffer la poêle, puis faire fondre le beurre",
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"fr",
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),
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).to.deep.equal([preheatId, meltId]);
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});
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it("reverses the sequence when the techniques are mentioned in the opposite order", async () => {
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expect(
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await techStepClassifier.matchTechSteps(
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"Faire fondre le beurre puis préchauffer le four",
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"fr",
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),
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).to.deep.equal([meltId, preheatId]);
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});
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it("still matches the generic technique on its own when the more specific one isn't implied", async () => {
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expect(
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await techStepClassifier.matchTechSteps("Faire cuire à feu moyen", "fr"),
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).to.deep.equal([cookId]);
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});
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it("resolves the more specific technique when a generic one's own vocabulary is embedded in it", async () => {
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// "Cuire au four" literally contains "cuire" (the generic `cook`
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// verb) but means the more specific `bake` — the classifier (not
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// a weight table) is what has to get this right now.
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expect(
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await techStepClassifier.matchTechSteps("Cuire au four pendant 30 minutes", "fr"),
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).to.deep.equal([bakeId]);
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});
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it("understands a technique described without ever naming it — the whole point of moving off pure keyword matching", async () => {
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// No literal "fondre"/"fondu" anywhere in this sentence, yet it
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// unambiguously means `melt` — this is the exact motivating case
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// (see this module's own doc comment) a regex could never catch.
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expect(
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await techStepClassifier.matchTechSteps(
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"jusqu'à ce que le beurre ait disparu dans la poêle",
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"fr",
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),
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).to.deep.equal([meltId]);
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});
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it("understands preheating described without the verb 'préchauffer'", async () => {
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expect(
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await techStepClassifier.matchTechSteps("mettre la poêle sur feu vif", "fr"),
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).to.deep.equal([preheatId]);
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});
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it("matches English text against the English-trained vocabulary", async () => {
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expect(
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await techStepClassifier.matchTechSteps(
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"Bring a large saucepan of salted water to the boil",
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"en",
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),
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).to.deep.equal([boilId]);
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});
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});
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describe("matchTechStepSpans", () => {
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it("returns a tight keyword span, and a wider context span that's the whole description when there's only one candidate", async () => {
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const text = "Faire mijoter à feu doux";
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const result = await techStepClassifier.matchTechStepSpans(text, "fr");
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expect(result).to.deep.equal([
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{ techStepId: simmerId, start: 6, end: 13, contextStart: 0, contextEnd: text.length },
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]);
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expect(text.slice(6, 13).toLowerCase()).to.equal("mijoter");
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});
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it("returns an empty list when nothing matches", async () => {
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expect(
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await techStepClassifier.matchTechStepSpans("Ranger les couverts dans le tiroir", "fr"),
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).to.deep.equal([]);
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});
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it("returns each distinct technique's own tight keyword span and its own wider context span, in reading order", async () => {
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const text = "Préchauffer la poêle, puis faire fondre le beurre";
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const result = await techStepClassifier.matchTechStepSpans(text, "fr");
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expect(result).to.have.length(2);
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expect(result[0].techStepId).to.equal(preheatId);
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expect(result[1].techStepId).to.equal(meltId);
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// Each keyword span, sliced back out of the original text, is
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// exactly the word(s) that anchored that match — what the frontend
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// needs to highlight the exact right characters.
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expect(text.slice(result[0].start, result[0].end).toLowerCase()).to.equal("préchauffer");
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expect(text.slice(result[1].start, result[1].end).toLowerCase()).to.equal("faire fondre");
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// Each context span is the wider clause the keyword was found in —
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// the two are contiguous and cover the whole description between
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// them (see splitIntoClauses, which computed these).
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expect(text.slice(result[0].contextStart, result[0].contextEnd)).to.equal(
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"Préchauffer la poêle,",
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);
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expect(text.slice(result[1].contextStart, result[1].contextEnd)).to.equal(
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" puis faire fondre le beurre",
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);
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expect(result[0].contextEnd).to.equal(result[1].contextStart);
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});
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it("understands both techniques in the classic 'Dans une poêle chaude, faire chauffer une noix de beurre' example, each with its own keyword and context", async () => {
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// The motivating example for context spans in the first place:
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// `preheat`'s keyword is a noun phrase ("poêle chaude"), not a
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// verb — its context ("Dans une poêle chaude") is what actually
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// shows this is about preparing the pan, not (say) deglazing one.
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const text = "Dans une poêle chaude, faire chauffer une noix de beurre";
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const result = await techStepClassifier.matchTechStepSpans(text, "fr");
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expect(result).to.have.length(2);
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expect(result[0]).to.deep.equal({
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techStepId: preheatId,
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start: 9,
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end: 21,
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contextStart: 0,
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contextEnd: 22,
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});
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expect(result[1]).to.deep.equal({
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techStepId: meltId,
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start: 23,
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end: 37,
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contextStart: 22,
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contextEnd: text.length,
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});
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expect(text.slice(result[0].start, result[0].end)).to.equal("poêle chaude");
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expect(text.slice(result[0].contextStart, result[0].contextEnd)).to.equal(
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"Dans une poêle chaude,",
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);
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expect(text.slice(result[1].start, result[1].end)).to.equal("faire chauffer");
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expect(text.slice(result[1].contextStart, result[1].contextEnd)).to.equal(
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" faire chauffer une noix de beurre",
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);
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});
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it("falls back to highlighting the whole clause for both spans when a technique was found with no literal anchor word", async () => {
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const text = "jusqu'à ce que le beurre ait disparu dans la poêle";
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const result = await techStepClassifier.matchTechStepSpans(text, "fr");
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expect(result).to.deep.equal([
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{
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techStepId: meltId,
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start: 0,
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end: text.length,
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contextStart: 0,
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contextEnd: text.length,
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},
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]);
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});
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it("chop matches English text against the English-trained vocabulary, tight keyword span", async () => {
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const text = "Chop the onions finely";
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const result = await techStepClassifier.matchTechStepSpans(text, "en");
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expect(result).to.deep.equal([
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{ techStepId: chopId, start: 0, end: 4, contextStart: 0, contextEnd: text.length },
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]);
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expect(text.slice(0, 4)).to.equal("Chop");
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});
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});
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});
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});
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