batchCooking/packages/shared/src/schemas/admin.ts
Nicolas 16f593d5d0
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feat(recipes): permet d'ajouter des ingredients hors-catalogue
Quand le catalogue seede ne couvre pas un ingredient, l'utilisateur pouvait
etre bloque (creation manuelle) ou perdre silencieusement la ligne (import).
Une ligne de recette accepte desormais `placeholderName` (texte libre) au
lieu de `ingredientId` : l'API cree une ligne `Ingredient` `isPlaceholder`
(cle `placeholder:<uuid>`, `displayName`, `createdById`) dans la transaction
de la recette, et emet `ingredient.placeholder_created`. Ces lignes sont
exclues de `GET /reference/ingredients` et de `ingredient-matcher`.

Front : bouton "Ajouter << ... >>" dans l'etat vide de `IngredientPicker`
(formulaire + import), badge "a completer" sur la ligne, helper
`ingredientLabel` applique partout ou un libelle d'ingredient est rendu.

Admin : `/admin/catalog/*` (+ page `apps/admin-web`) liste les placeholders
regroupes par nom normalise, "marquer traite" (`reviewedAt`) et purge des
orphelins. La promotion en vraie entree catalogue reste manuelle.

Migration `ingredient_placeholder` ecrite a la main (Postgres indisponible).
Suites Mocha DB-backed ecrites, non executees en session ; test pur
`normalizePlaceholderName` + Cypress admin-web/web verts.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2026-08-28 23:36:42 +02:00

130 lines
6.2 KiB
TypeScript

import { z } from "zod";
// Shared between apps/api (server-side validation, source of truth) and
// apps/admin-web (client-side validation for instant feedback). Same
// rationale as schemas/auth.ts — one set of rules, French messages surfaced
// as-is in the admin login form.
/**
* Payload accepted by `POST /admin/auth/login`. Deliberately its own schema
* (not a re-export of `loginSchema`): the admin surface is a separate
* contract from the end-user one even though the shape currently matches.
*/
export const adminLoginSchema = z.object({
email: z.string().trim().toLowerCase().email("Email invalide"),
password: z.string().min(1, "Le mot de passe est requis"),
});
/** Inferred TS type for {@link adminLoginSchema}'s validated output. */
export type AdminLoginInput = z.infer<typeof adminLoginSchema>;
/**
* Query params for `GET /admin/metrics` — `?days=` bounds how far back the
* time-series go (and how many daily buckets they carry). Coerced from the
* query string; clamped to a sane window so a huge value can't make the
* dashboard scan the whole history.
*/
export const getMetricsSchema = z.object({
days: z.coerce.number().int().min(7).max(365).default(30),
});
/** Inferred TS type for {@link getMetricsSchema}'s validated output. */
export type GetMetricsInput = z.infer<typeof getMetricsSchema>;
/**
* Payload of `POST /internal/tech-steps/heartbeat` — `services/tech-step-llm-worker`
* (which has no inbound HTTP of its own) reporting that it's alive. Sent on
* `boot`, on every scheduler `tick`, and after each `job` (with that job's
* name, outcome and counts). The worker key is fixed server-side (only one
* worker exists), so it isn't in the payload.
*/
export const workerHeartbeatSchema = z.object({
event: z.enum(["boot", "tick", "job"]),
/** The job that just ran — present only when `event === "job"`. */
job: z.string().max(100).optional(),
/** Whether that job succeeded — present only when `event === "job"`. */
ok: z.boolean().optional(),
/** Small `{ label: number }` summary of that job (e.g. `{ suggestions: 3 }`). */
counts: z.record(z.string(), z.number()).optional(),
});
/** Inferred TS type for {@link workerHeartbeatSchema}'s validated output. */
export type WorkerHeartbeatInput = z.infer<typeof workerHeartbeatSchema>;
/** Statuses a `TechStepTrainingSuggestion` can be filtered by / set to. */
export const TRAINING_SUGGESTION_STATUSES = ["pending", "applied", "rejected"] as const;
/** One of {@link TRAINING_SUGGESTION_STATUSES}. */
export type TrainingSuggestionStatus = (typeof TRAINING_SUGGESTION_STATUSES)[number];
/** Query params for `GET /admin/tech-steps/suggestions` — every filter optional. */
export const listSuggestionsQuerySchema = z.object({
status: z.enum(TRAINING_SUGGESTION_STATUSES).optional(),
sourceType: z.enum(["correction", "llm_audit"]).optional(),
techStepKey: z.string().min(1).optional(),
locale: z.string().min(1).optional(),
});
/** Inferred TS type for {@link listSuggestionsQuerySchema}. */
export type ListSuggestionsQuery = z.infer<typeof listSuggestionsQuerySchema>;
/** Query params for `GET /admin/tech-steps/corrections`. `consumed`/`hasCorrectedTechStep` are tri-state (omitted = no filter). */
export const listCorrectionsQuerySchema = z.object({
consumed: z.enum(["true", "false"]).optional(),
hasCorrectedTechStep: z.enum(["true", "false"]).optional(),
});
/** Inferred TS type for {@link listCorrectionsQuerySchema}. */
export type ListCorrectionsQuery = z.infer<typeof listCorrectionsQuerySchema>;
/**
* Body of `PATCH /admin/tech-steps/suggestions/:id` — curate a suggestion
* before it feeds a `training_data.py` edit. Every field optional; at least
* one must be present (enforced service-side).
*/
export const updateTrainingSuggestionSchema = z.object({
status: z.enum(TRAINING_SUGGESTION_STATUSES).optional(),
suggestedSynonyms: z.array(z.string().min(1)).max(200).optional(),
suggestedUtterances: z.array(z.string().min(1)).max(200).optional(),
});
/** Inferred TS type for {@link updateTrainingSuggestionSchema}. */
export type UpdateTrainingSuggestionInput = z.infer<typeof updateTrainingSuggestionSchema>;
/**
* Body of `POST /admin/tech-steps/retrain` — runs the F1 regression gate
* then, if it passes, backfills every step and marks the given suggestion
* ids. Both id lists optional (an empty run just re-gates + backfills).
*/
export const retrainRequestSchema = z.object({
appliedIds: z.array(z.number().int().positive()).max(500).optional(),
rejectedIds: z.array(z.number().int().positive()).max(500).optional(),
});
/** Inferred TS type for {@link retrainRequestSchema}. */
export type RetrainRequestInput = z.infer<typeof retrainRequestSchema>;
/** Query params for `GET /admin/tech-steps/training-data-snippet`. */
export const trainingDataSnippetQuerySchema = z.object({
techStepKey: z.string().min(1),
locale: z.string().min(1).default("fr"),
status: z.enum(TRAINING_SUGGESTION_STATUSES).default("applied"),
});
/** Inferred TS type for {@link trainingDataSnippetQuerySchema}. */
export type TrainingDataSnippetQuery = z.infer<typeof trainingDataSnippetQuerySchema>;
/**
* Query params for `GET /admin/catalog/placeholders` — the off-catalog
* ingredient review. `reviewed` is tri-state: omitted / `"false"` hides
* groups every row of which has already been triaged (the default working
* view), `"true"` shows only fully-triaged groups.
*/
export const listPlaceholdersQuerySchema = z.object({
reviewed: z.enum(["true", "false"]).optional(),
});
/** Inferred TS type for {@link listPlaceholdersQuerySchema}. */
export type ListPlaceholdersQuery = z.infer<typeof listPlaceholdersQuerySchema>;
/**
* Body of `PATCH /admin/catalog/placeholders/mark-reviewed` — stamps
* `reviewedAt` on the given placeholder `Ingredient` ids (a maintainer has
* seen this catalog gap and, if warranted, added the real entry by hand).
* Bounded so one call can't sweep an unbounded set.
*/
export const markPlaceholdersReviewedSchema = z.object({
ingredientIds: z.array(z.number().int().positive()).min(1).max(500),
});
/** Inferred TS type for {@link markPlaceholdersReviewedSchema}. */
export type MarkPlaceholdersReviewedInput = z.infer<typeof markPlaceholdersReviewedSchema>;