* feat(recipes): associe ingredients, quantites et ustensiles aux techniques detectees
Etend le pipeline de detection de techniques (tech-step-matcher.ts) pour
resoudre, par clause, les metadonnees qui accompagnent une technique
detectee :
- Ingredients : nouvelle fonction findIngredientMentions (ingredient-matcher.ts)
qui scanne le texte d'une clause contre le catalogue Ingredient existant
(reutilise INGREDIENT_LABELS_FR/EN deja utilise par matchIngredientName),
avec extraction best-effort de la quantite+unite immediatement avant la
mention.
- Ustensiles : nouveau catalogue Utensil (Prisma) + second PhraseMatcher
cote service Python (intent_service/utensil_vocabulary.py), independant
du textcat des techniques (pas d'interpretation necessaire pour un
ustensile). POST /v1/process distingue desormais chaque entite via un
champ kind (technique|utensil).
- Persistance : deux nouvelles tables StepTechStepIngredient/
StepTechStepUtensil, liees a StepTechStep par sa cle composite
(stepId, order), peuplees au moment du matching (recipe.service.ts) et
exposees via StepTechStepView (packages/shared).
Aucune analyse syntaxique ajoutee (le parser spaCy reste exclu du
pipeline) : l'association se fait par appartenance a la clause deja
calculee par splitIntoClauses.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* fix(recipes): corrige les tests casses par les nouveaux champs ingredients/utensils
recipe-tech-step-correction.test.ts asserte StepTechStepView en dur sans
les nouveaux champs ingredients/utensils (toujours [] pour une correction
manuelle, qui ne repasse jamais par le scan de metadonnees).
Retire aussi le nouveau cas de tech-step-matcher.test.ts qui inventait une
phrase jamais vue par le corpus reel : verifie en CI que le textcat la
classe avec confiance comme caramelize plutot que melt, un artefact du
petit corpus BOW plutot qu'un bug du code de matching. L'extraction
quantite+unite reste couverte integralement et de facon deterministe par
ingredient-matcher.test.ts.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* feat(recipes): equilibre le corpus d'entrainement du textcat a 20 phrases par technique
Chaque technique n'avait que 3 a 7 utterances par locale (moyenne ~3.8),
un desequilibre reel entre classes qui contribue directement a des
classifications confiantes mais fausses sur une formulation jamais vue
(constate concretement dans la PR precedente : une phrase inedite pour
melt classee comme caramelize avec une confiance elevee).
Porte chaque technique a exactement 20 utterances par locale (fr et en) :
- Les utterances existantes sont conservees telles quelles, jamais
reecrites.
- Le complement vient d'augment_utterances.py (nouveau script maintainer,
reutilisable pour une future technique sous-alimentee) : enveloppe
chaque utterance deja a l'imperatif/infinitif dans une tournure modale
grammaticalement valide (il faut/veillez a/make sure to...) plutot que
de dupliquer ou d'inventer du texte generique - vraie diversite de
surface, vocabulaire distinctif de la technique intact.
- tests/test_training_data_balance.py fait respecter l'invariant en CI
(20 minimum, meme nombre fr/en) pour toute future modification.
_TRAINING_ITERATIONS recalibre de 25 a 10 (locale_pipeline.py) pour
compenser les ~2.6x d'exemples par epoque : temps d'entrainement mesure
quasi identique a avant (~687s fr+en combines contre ~670s), confiance
egale ou meilleure sur les cas deja suivis (simmer 0.31 -> 0.48).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* fix(recipes): remonte _TRAINING_ITERATIONS a 20, la gate F1 de CI etait sous 0.8 a 10
Le premier passage CI de l'equilibrage du corpus (20 utterances/technique)
a fait chuter le F1 agrege (tech-step-eval.test.ts) a 0.7999... avec
_TRAINING_ITERATIONS=10 : le pari qu'un corpus plus large convergerait en
moins d'epoques relatives etait faux a ce niveau de reduction. Remonte a
20 (mesure : ~699s pour la seule locale fr, previsiblement ~1360s pour
fr+en combines) - confiance nettement retablie sur les techniques
auparavant en echec au spot-check manuel (sweat ~0.99).
Consequence directe : le temps de demarrage du service passe d'environ
11 a environ 23 minutes. start_period (docker-compose.yml) et le timeout
d'attente /health (ci.yml) releves de 900s a 1800s en consequence.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* fix(recipes): reequilibre le corpus via substitution de synonyme plutot que du remplissage generique
Deux tentatives precedentes de porter chaque technique a 20 utterances
ont mesurablement degrade le F1 agrege (tech-step-eval.test.ts, 0.80 ->
0.79/0.791) au lieu de l'ameliorer : le generateur reposait surtout sur
des tournures modales generiques ("il faut ...", "make sure to ..."),
partagees identiquement par les 74 classes - un textcat bag-of-words lit
ca comme une separabilite reduite entre classes, pas un padding neutre.
augment_utterances.py revu : priorite a la substitution de synonyme
(l'un des synonyms propres a la technique en tete d'une utterance
existante, remplace par un autre - vocabulaire genuinement distinctif),
les tournures modales ne servant plus qu'de complement limite (5 par
locale, pas 12). Resultat : 13 a 20 utterances par technique/locale
(moyenne ~19.7), contre un forcage uniforme a 20 qui necessitait un
remplissage generique disproportionne pour les techniques au vocabulaire
propre pauvre (julienne, sweat, bainMarie - precisement celles qui
echouaient). Confiance mesuree nettement retablie sur ces techniques
(sweat ~0.99, bainMarie ~0.98, julienne ~0.88).
tests/test_training_data_balance.py : plancher abaisse a 12 (vise 20,
garanti seulement si le vocabulaire propre de la technique le permet
sans repasser par le piege ci-dessus) ; suppression de l'exigence
fr/en egaux, plus vraie avec cette strategie (le potentiel de
substitution differe naturellement entre les deux langues).
Suite complete locale : 35/35 verts (22m26s).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* revert(recipes): annule le reequilibrage du corpus d'entrainement du textcat
Trois strategies de generation differentes (tournures modales generiques,
tournures reduites + substitution de synonyme, substitution de synonyme
en priorite) ont ete tentees pour porter chaque technique a 20 utterances
par locale. Les trois degradent mesurablement le F1 agrege contre
TECH_STEP_EVAL_DATASET (tech-step-eval.test.ts) en dessous du seuil 0.8 :
0.7999 -> 0.791 -> 0.744 (chaque tentative pire que la precedente).
tech-step-eval-runner.ts documente explicitement ce seuil comme calibre
avec une marge deja tres etroite (0.8 pour un score mesure a 0.815) et
previent contre le fait de l'assouplir pour accommoder un classifieur
plus faible plutot que de corriger le probleme de fond - assouplir le
seuil ou le jeu d'evaluation pour faire passer cette PR irait a l'encontre
de cette convention documentee du projet.
Revient a l'etat d'avant tout reequilibrage (corpus a 3-7 utterances/
technique, _TRAINING_ITERATIONS=25, timeouts a 900s) - le dernier etat
confirme vert en CI sur cette branche. Ameliorer reellement l'equilibre
du corpus necessite du contenu redige a la main et verifie technique par
technique contre ce meme F1, pas une generation programmatique en bloc.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* fix(recipes): reequilibre le corpus via substitution de synonyme plutot que du remplissage generique
Trois tentatives precedentes d'egaliser chaque technique a 20 utterances
ont toutes degrade le F1 agrege sous 0.8 (voir le commit revert
precedent). Nouvelle strategie, beaucoup plus conservatrice : egalise
chaque technique vers le maximum DEJA present dans le corpus (7 en fr,
5 en en, portes par cook/preheat), pas vers un nombre choisi dans
l'absolu - +3-4 utterances en moyenne par technique au lieu de +13-17.
augment_utterances.py (nouveau, reutilisable) genere le complement en
priorite par substitution de synonyme (un des synonyms propres a la
technique, en tete d'une utterance existante, remplace par un autre) -
avec un garde-fou supplementaire par rapport aux tentatives precedentes :
le synonyme de remplacement doit lui aussi etre a l'imperatif/infinitif,
pas juste le synonyme d'origine, pour eviter de substituer un groupe
nominal/adjectif ("a petit feu", "gros bouillons") a la place d'un
verbe et produire une phrase grammaticalement cassee. Tournures modales
uniquement en dernier recours pour les techniques dont le vocabulaire
n'apparait qu'en milieu de phrase (julienne, brunoise...).
Resultat : chaque technique a exactement 7 utterances en fr et 5 en en,
sans exception (tests/test_training_data_balance.py fait respecter cet
invariant). _TRAINING_ITERATIONS reste a 25 (inchange). start_period/
timeout d'attente /health releves de 900s a 1200s (temps d'entrainement
mesure ~930s contre ~670s avant, la marge de securite existante etait
devenue trop juste).
Suite complete locale : 35/35 verts (14m41s).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* chore: retrigger CI (aucun run genere pour c7116d4, probable incident GitHub Actions)
---------
Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
459 lines
19 KiB
TypeScript
459 lines
19 KiB
TypeScript
import { INGREDIENT_LABEL_SYNONYMS_EN, INGREDIENT_LABEL_SYNONYMS_FR } from "@batch-cooking/shared";
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import { expect } from "chai";
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import { prisma } from "../../src/db/prisma.js";
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import {
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extractQuantity,
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findIngredientMentions,
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type IngredientMatchEntry,
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loadIngredientCatalog,
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loadUnitCatalog,
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matchIngredientName,
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matchUnit,
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type UnitMatchEntry,
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} from "../../src/lib/recipe-matching/ingredient-matcher.js";
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import { resetDatabase } from "../../test-support/reset-db.js";
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describe("ingredient-matcher", () => {
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describe("matchIngredientName", () => {
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const tomato: IngredientMatchEntry = { ingredientId: 1, label: "Tomato" };
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const chicken: IngredientMatchEntry = { ingredientId: 2, label: "Chicken" };
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const chickenBreast: IngredientMatchEntry = { ingredientId: 3, label: "Chicken breast" };
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const onion: IngredientMatchEntry = { ingredientId: 4, label: "Onion" };
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const allPurposeFlour: IngredientMatchEntry = { ingredientId: 5, label: "All-purpose flour" };
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const catalog = [tomato, chicken, chickenBreast, onion, allPurposeFlour];
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it("matches an exact single-word label", () => {
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expect(matchIngredientName("tomato", catalog)).to.equal(tomato.ingredientId);
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});
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it("is case- and accent-insensitive", () => {
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expect(matchIngredientName("TOMATO", catalog)).to.equal(tomato.ingredientId);
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expect(matchIngredientName("Tömato", catalog)).to.equal(tomato.ingredientId);
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});
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it("tolerates a regular plural", () => {
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expect(matchIngredientName("tomatoes", catalog)).to.equal(tomato.ingredientId);
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expect(matchIngredientName("onions", catalog)).to.equal(onion.ingredientId);
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});
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it("tolerates extra descriptive words around the match", () => {
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expect(matchIngredientName("2 large diced yellow onions", catalog)).to.equal(
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onion.ingredientId,
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);
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});
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it("prefers the more specific multi-word label over a shorter one it contains", () => {
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expect(matchIngredientName("boneless skinless chicken breasts", catalog)).to.equal(
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chickenBreast.ingredientId,
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);
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});
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it("still matches the shorter label when the more specific one isn't mentioned", () => {
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expect(matchIngredientName("diced chicken thighs", catalog)).to.equal(chicken.ingredientId);
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});
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it("matches a hyphenated multi-word label", () => {
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expect(matchIngredientName("2 cups all-purpose flour", catalog)).to.equal(
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allPurposeFlour.ingredientId,
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);
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});
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it("doesn't false-positive a short label inside an unrelated longer word", () => {
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// "egg" must not match inside "eggplant" — whole-token comparison, not substring.
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const eggplant: IngredientMatchEntry = { ingredientId: 6, label: "Eggplant" };
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const egg: IngredientMatchEntry = { ingredientId: 7, label: "Egg" };
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expect(matchIngredientName("eggplant", [egg, eggplant])).to.equal(eggplant.ingredientId);
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});
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it("returns null when nothing matches", () => {
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expect(matchIngredientName("mango", catalog)).to.equal(null);
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});
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it("returns null for an empty catalog", () => {
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expect(matchIngredientName("tomato", [])).to.equal(null);
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});
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it("returns null for an empty name", () => {
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expect(matchIngredientName("", catalog)).to.equal(null);
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});
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it("matches an alternate wording of the same ingredient via a second catalog entry sharing its ingredientId (issue #54)", () => {
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const vanillaBean: IngredientMatchEntry = { ingredientId: 8, label: "Vanilla bean" };
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const vanillaBeanSynonym: IngredientMatchEntry = { ingredientId: 8, label: "Vanilla pod" };
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const synonymCatalog = [vanillaBean, vanillaBeanSynonym];
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expect(matchIngredientName("1 vanilla pod", synonymCatalog)).to.equal(8);
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expect(matchIngredientName("1 vanilla bean", synonymCatalog)).to.equal(8);
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});
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it("breaks a same-specificity tie by the lowest ingredientId", () => {
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const onionA: IngredientMatchEntry = { ingredientId: 20, label: "Onion" };
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const onionB: IngredientMatchEntry = { ingredientId: 21, label: "Onion" };
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expect(matchIngredientName("onion", [onionB, onionA])).to.equal(20);
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});
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describe("locale: fr", () => {
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const carotte: IngredientMatchEntry = { ingredientId: 30, label: "Carotte" };
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const poulet: IngredientMatchEntry = { ingredientId: 31, label: "Poulet" };
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const blancDePoulet: IngredientMatchEntry = { ingredientId: 32, label: "Blanc de poulet" };
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const frCatalog = [carotte, poulet, blancDePoulet];
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it("tolerates a regular French plural (a bare 's', unlike English's several suffix patterns)", () => {
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// Regression case: French plurals like "carottes" end in "es", which
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// the English stemmer's own "es" rule would wrongly strip down to
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// "carott" (losing the "e" that's part of the singular "carotte")
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// — see stemWordFr's own doc comment. Locale "fr" must use the
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// French stemmer instead, or this never matches.
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expect(matchIngredientName("carottes", frCatalog, "fr")).to.equal(carotte.ingredientId);
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});
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it("is accent-insensitive the same way the English path is", () => {
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expect(matchIngredientName("CAROTTES", frCatalog, "fr")).to.equal(carotte.ingredientId);
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});
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it("tolerates extra descriptive words around the match", () => {
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expect(matchIngredientName("2 carottes râpées", frCatalog, "fr")).to.equal(
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carotte.ingredientId,
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);
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});
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it("prefers the more specific multi-word label over a shorter one it contains", () => {
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expect(matchIngredientName("blancs de poulet fermier", frCatalog, "fr")).to.equal(
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blancDePoulet.ingredientId,
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);
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});
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it("defaults to the English stemmer when no locale is passed — 'fr' text needs to opt in explicitly", () => {
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// Without locale: "fr", "carottes" stems via the English rules
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// (endsWith("es") -> strip 2 chars) into "carott", which doesn't
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// equal the catalog's own (also English-stemmed) "carotte" — no
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// match. This is the exact bug locale-aware stemming fixes; this
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// test pins down that the *default* stays exactly as it was for
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// every pre-existing English-only caller.
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expect(matchIngredientName("carottes", frCatalog)).to.equal(null);
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});
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});
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});
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describe("matchUnit", () => {
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const gram: UnitMatchEntry = { unitId: 1, synonyms: ["g", "gram", "grams"] };
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const tablespoon: UnitMatchEntry = {
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unitId: 2,
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synonyms: ["tbsp", "tbs", "tablespoon", "tablespoons"],
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};
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const cup: UnitMatchEntry = { unitId: 3, synonyms: ["cup", "cups"] };
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const catalog = [gram, tablespoon, cup];
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it("matches a full word synonym", () => {
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expect(matchUnit("tablespoon", catalog)).to.equal(tablespoon.unitId);
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});
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it("matches an abbreviation synonym", () => {
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expect(matchUnit("tbsp", catalog)).to.equal(tablespoon.unitId);
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});
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it("matches a plural synonym via the same stemming as ingredients", () => {
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expect(matchUnit("cups", catalog)).to.equal(cup.unitId);
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});
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it("is case-insensitive", () => {
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expect(matchUnit("TBSP", catalog)).to.equal(tablespoon.unitId);
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});
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it("ignores trailing text after the unit word", () => {
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expect(matchUnit("cup flour", catalog)).to.equal(cup.unitId);
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});
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it("also finds the unit word when it isn't first — unlike before French support existed, this is no longer only a first-word check (see the function's own doc comment)", () => {
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expect(matchUnit("a heaped tablespoon of sugar", catalog)).to.equal(tablespoon.unitId);
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});
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it("doesn't match a short abbreviation inside an unrelated word", () => {
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// "g" alone must not match "grated" — whole-token comparison.
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expect(matchUnit("grated", catalog)).to.equal(null);
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});
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it("returns null when nothing matches", () => {
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expect(matchUnit("pound", catalog)).to.equal(null);
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});
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it("returns null for an empty catalog", () => {
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expect(matchUnit("cup", [])).to.equal(null);
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});
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it("returns null for an empty string", () => {
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expect(matchUnit("", catalog)).to.equal(null);
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});
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describe("locale: fr", () => {
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const gramme: UnitMatchEntry = { unitId: 40, synonyms: ["g", "gr", "gramme", "grammes"] };
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const cuillereASoupe: UnitMatchEntry = {
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unitId: 41,
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synonyms: ["cuillère à soupe", "cuillères à soupe", "càs"],
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};
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const frCatalog = [gramme, cuillereASoupe];
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it("matches a genuinely multi-word synonym — the bug this locale support fixes: the old single-first-token check could never equal a whole multi-word phrase", () => {
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expect(matchUnit("cuillères à soupe de farine", frCatalog, "fr")).to.equal(
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cuillereASoupe.unitId,
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);
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});
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it("matches a single-word abbreviation the same way English units do", () => {
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expect(matchUnit("càs de farine", frCatalog, "fr")).to.equal(cuillereASoupe.unitId);
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});
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it("is accent-insensitive", () => {
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expect(matchUnit("2 CUILLÈRES À SOUPE de farine", frCatalog, "fr")).to.equal(
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cuillereASoupe.unitId,
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);
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});
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it("doesn't match a multi-word phrase against unrelated text mentioning the same first word alone", () => {
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expect(matchUnit("cuillère de bois", frCatalog, "fr")).to.equal(null);
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});
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});
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});
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describe("extractQuantity", () => {
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it("extracts a plain integer", () => {
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expect(extractQuantity("2 onions")).to.deep.equal({ quantity: 2, remainder: "onions" });
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});
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it("extracts a decimal using a dot", () => {
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expect(extractQuantity("1.5 cups flour")).to.deep.equal({
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quantity: 1.5,
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remainder: "cups flour",
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});
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});
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it("extracts a decimal using a comma", () => {
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expect(extractQuantity("1,5 cups flour")).to.deep.equal({
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quantity: 1.5,
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remainder: "cups flour",
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});
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});
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it("extracts a simple fraction", () => {
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expect(extractQuantity("1/2 cup sugar")).to.deep.equal({
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quantity: 0.5,
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remainder: "cup sugar",
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});
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});
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it("extracts a mixed number", () => {
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expect(extractQuantity("1 1/2 cups sugar")).to.deep.equal({
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quantity: 1.5,
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remainder: "cups sugar",
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});
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});
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it("returns null quantity and the trimmed original text when there's no leading number", () => {
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expect(extractQuantity("salt to taste")).to.deep.equal({
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quantity: null,
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remainder: "salt to taste",
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});
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});
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it("trims surrounding whitespace", () => {
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expect(extractQuantity(" 2 eggs ")).to.deep.equal({ quantity: 2, remainder: "eggs" });
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});
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it("only takes the first number of a hyphenated range", () => {
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expect(extractQuantity("2-3 carrots")).to.deep.equal({
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quantity: 2,
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remainder: "-3 carrots",
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});
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});
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});
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describe("findIngredientMentions", () => {
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const butter: IngredientMatchEntry = { ingredientId: 1, label: "Butter" };
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const flour: IngredientMatchEntry = { ingredientId: 2, label: "Flour" };
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const egg: IngredientMatchEntry = { ingredientId: 3, label: "Egg" };
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const catalog = [butter, flour, egg];
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const gram: UnitMatchEntry = { unitId: 1, synonyms: ["g", "gram", "grams"] };
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const unitCatalog = [gram];
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it("finds a single mention with no quantity or unit", () => {
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const text = "melt the butter";
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const mentions = findIngredientMentions(text, catalog, unitCatalog);
|
|
expect(mentions).to.have.length(1);
|
|
const [mention] = mentions;
|
|
expect(mention?.ingredientId).to.equal(butter.ingredientId);
|
|
expect(text.slice(mention?.start, mention?.end)).to.equal("butter");
|
|
expect(mention?.quantity).to.equal(null);
|
|
expect(mention?.unitId).to.equal(null);
|
|
});
|
|
|
|
it('resolves a quantity and unit glued directly to the ingredient ("200g butter")', () => {
|
|
const text = "add 200g butter";
|
|
const [mention] = findIngredientMentions(text, catalog, unitCatalog);
|
|
expect(mention?.ingredientId).to.equal(butter.ingredientId);
|
|
expect(mention?.quantity).to.equal(200);
|
|
expect(mention?.unitId).to.equal(gram.unitId);
|
|
});
|
|
|
|
it("finds several mentions in reading order, non-overlapping", () => {
|
|
const text = "melt the butter then add the flour and an egg";
|
|
const mentions = findIngredientMentions(text, catalog, unitCatalog);
|
|
expect(mentions.map((mention) => mention.ingredientId)).to.deep.equal([
|
|
butter.ingredientId,
|
|
flour.ingredientId,
|
|
egg.ingredientId,
|
|
]);
|
|
});
|
|
|
|
it("is case- and accent-insensitive", () => {
|
|
const text = "MELT THE BUTTER";
|
|
const [mention] = findIngredientMentions(text, catalog, unitCatalog);
|
|
expect(mention?.ingredientId).to.equal(butter.ingredientId);
|
|
});
|
|
|
|
it("ignores an unrelated number earlier in the text (e.g. an oven temperature)", () => {
|
|
const text = "preheat to 180 degrees then add the egg";
|
|
const [mention] = findIngredientMentions(text, catalog, unitCatalog);
|
|
expect(mention?.ingredientId).to.equal(egg.ingredientId);
|
|
expect(mention?.quantity).to.equal(null);
|
|
});
|
|
|
|
it("returns an empty array when nothing in the catalog is mentioned", () => {
|
|
expect(findIngredientMentions("stir well", catalog, unitCatalog)).to.deep.equal([]);
|
|
});
|
|
|
|
it("returns an empty array for empty text", () => {
|
|
expect(findIngredientMentions("", catalog, unitCatalog)).to.deep.equal([]);
|
|
});
|
|
|
|
describe("locale: fr", () => {
|
|
const beurre: IngredientMatchEntry = { ingredientId: 10, label: "Beurre" };
|
|
const farine: IngredientMatchEntry = { ingredientId: 11, label: "Farine" };
|
|
const frCatalog = [beurre, farine];
|
|
const gramme: UnitMatchEntry = { unitId: 40, synonyms: ["g", "gr", "gramme", "grammes"] };
|
|
const cuillereASoupe: UnitMatchEntry = {
|
|
unitId: 41,
|
|
synonyms: ["cuillère à soupe", "cuillères à soupe", "càs"],
|
|
};
|
|
const frUnitCatalog = [gramme, cuillereASoupe];
|
|
|
|
it("resolves a quantity and unit before the ingredient, connected by 'de'", () => {
|
|
const text = "faire fondre 50g de beurre";
|
|
const [mention] = findIngredientMentions(text, frCatalog, frUnitCatalog, "fr");
|
|
expect(mention?.ingredientId).to.equal(beurre.ingredientId);
|
|
expect(mention?.quantity).to.equal(50);
|
|
expect(mention?.unitId).to.equal(gramme.unitId);
|
|
expect(text.slice(mention?.start, mention?.end)).to.equal("beurre");
|
|
});
|
|
|
|
it('resolves a multi-word unit connected by "d\'"', () => {
|
|
const text = "ajouter 2 cuillères à soupe de farine";
|
|
const [mention] = findIngredientMentions(text, frCatalog, frUnitCatalog, "fr");
|
|
expect(mention?.ingredientId).to.equal(farine.ingredientId);
|
|
expect(mention?.quantity).to.equal(2);
|
|
expect(mention?.unitId).to.equal(cuillereASoupe.unitId);
|
|
});
|
|
|
|
it("is accent-insensitive", () => {
|
|
const text = "FAIRE FONDRE LE BEURRE";
|
|
const [mention] = findIngredientMentions(text, frCatalog, frUnitCatalog, "fr");
|
|
expect(mention?.ingredientId).to.equal(beurre.ingredientId);
|
|
});
|
|
});
|
|
});
|
|
|
|
describe("loadIngredientCatalog / loadUnitCatalog", () => {
|
|
beforeEach(async () => {
|
|
await resetDatabase();
|
|
});
|
|
|
|
after(async () => {
|
|
await prisma.$disconnect();
|
|
});
|
|
|
|
it("loads one entry per Ingredient that has an English label, plus one per alternate wording (INGREDIENT_LABEL_SYNONYMS_EN), keyed by real ingredientId", async () => {
|
|
const tomato = await prisma.ingredient.findFirstOrThrow({ where: { key: "tomato" } });
|
|
const vanillaBean = await prisma.ingredient.findFirstOrThrow({
|
|
where: { key: "vanillaBean" },
|
|
});
|
|
const ingredientCount = await prisma.ingredient.count();
|
|
const synonymCount = Object.values(INGREDIENT_LABEL_SYNONYMS_EN).reduce(
|
|
(sum, synonyms) => sum + synonyms.length,
|
|
0,
|
|
);
|
|
|
|
const catalog = await loadIngredientCatalog();
|
|
|
|
// Every seeded ingredient has an authored English label (verified at
|
|
// generation time — see packages/shared/src/data/catalog-labels-en.ts),
|
|
// so nothing should be silently skipped — plus one extra entry per
|
|
// synonym (issue #54), sharing the same ingredientId as the primary
|
|
// label's entry.
|
|
expect(catalog).to.have.length(ingredientCount + synonymCount);
|
|
const tomatoEntry = catalog.find((entry) => entry.ingredientId === tomato.id);
|
|
expect(tomatoEntry?.label).to.equal("Tomato");
|
|
|
|
const vanillaBeanEntries = catalog.filter((entry) => entry.ingredientId === vanillaBean.id);
|
|
expect(vanillaBeanEntries.map((entry) => entry.label)).to.deep.equal([
|
|
"Vanilla bean",
|
|
"Vanilla pod",
|
|
]);
|
|
});
|
|
|
|
it("loads one entry per Unit that has English synonyms, keyed by real unitId", async () => {
|
|
const cup = await prisma.unit.findFirstOrThrow({ where: { key: "cup" } });
|
|
const unitCount = await prisma.unit.count();
|
|
|
|
const catalog = await loadUnitCatalog();
|
|
|
|
expect(catalog).to.have.length(unitCount);
|
|
const cupEntry = catalog.find((entry) => entry.unitId === cup.id);
|
|
expect(cupEntry?.synonyms).to.deep.equal(["cup", "cups"]);
|
|
});
|
|
|
|
it("loads one entry per Ingredient that has a French label, plus one per alternate wording (INGREDIENT_LABEL_SYNONYMS_FR), keyed by real ingredientId", async () => {
|
|
const carrot = await prisma.ingredient.findFirstOrThrow({ where: { key: "carrot" } });
|
|
const vanillaBean = await prisma.ingredient.findFirstOrThrow({
|
|
where: { key: "vanillaBean" },
|
|
});
|
|
const ingredientCount = await prisma.ingredient.count();
|
|
const synonymCount = Object.values(INGREDIENT_LABEL_SYNONYMS_FR).reduce(
|
|
(sum, synonyms) => sum + synonyms.length,
|
|
0,
|
|
);
|
|
|
|
const catalog = await loadIngredientCatalog("fr");
|
|
|
|
// Every seeded ingredient has an authored French label too (copied
|
|
// from apps/web's fr locale — see catalog-labels-fr.ts's own doc
|
|
// comment), so this mirrors the English test above 1:1.
|
|
expect(catalog).to.have.length(ingredientCount + synonymCount);
|
|
const carrotEntry = catalog.find((entry) => entry.ingredientId === carrot.id);
|
|
expect(carrotEntry?.label).to.equal("Carotte");
|
|
|
|
const vanillaBeanEntries = catalog.filter((entry) => entry.ingredientId === vanillaBean.id);
|
|
expect(vanillaBeanEntries.map((entry) => entry.label)).to.deep.equal([
|
|
"Vanille (gousse)",
|
|
"Gousse de vanille",
|
|
]);
|
|
});
|
|
|
|
it("loads one entry per Unit that has French synonyms, keyed by real unitId", async () => {
|
|
const cup = await prisma.unit.findFirstOrThrow({ where: { key: "cup" } });
|
|
const unitCount = await prisma.unit.count();
|
|
|
|
const catalog = await loadUnitCatalog("fr");
|
|
|
|
expect(catalog).to.have.length(unitCount);
|
|
const cupEntry = catalog.find((entry) => entry.unitId === cup.id);
|
|
expect(cupEntry?.synonyms).to.deep.equal(["tasse", "tasses"]);
|
|
});
|
|
|
|
it("returns an empty catalog for a locale with no label table at all — the DB is still queried, there's just nothing in either table to match a row against", async () => {
|
|
const ingredientCatalog = await loadIngredientCatalog("de");
|
|
const unitCatalog = await loadUnitCatalog("de");
|
|
|
|
expect(ingredientCatalog).to.deep.equal([]);
|
|
expect(unitCatalog).to.deep.equal([]);
|
|
});
|
|
});
|
|
});
|