fix: improve note interactions and markdown LaTeX support
## Bug Fixes ### Note Card Actions - Fix broken size change functionality (missing state declaration) - Implement React 19 useOptimistic for instant UI feedback - Add startTransition for non-blocking updates - Ensure smooth animations without page refresh - All note actions now work: pin, archive, color, size, checklist ### Markdown LaTeX Rendering - Add remark-math and rehype-katex plugins - Support inline equations with dollar sign syntax - Support block equations with double dollar sign syntax - Import KaTeX CSS for proper styling - Equations now render correctly instead of showing raw LaTeX ## Technical Details - Replace undefined currentNote references with optimistic state - Add optimistic updates before server actions for instant feedback - Use router.refresh() in transitions for smart cache invalidation - Install remark-math, rehype-katex, and katex packages ## Testing - Build passes successfully with no TypeScript errors - Dev server hot-reloads changes correctly
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# Story 3.1: Indexation Vectorielle Automatique
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Status: ready-for-dev
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## Story
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As a system,
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I want to generate and store vector embeddings for every note change,
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So that the notes are searchable by meaning later.
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## Acceptance Criteria
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1. **Given** a Prisma schema.
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2. **When** I run the migration.
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3. **Then** the `Note` table has a field to store vectors (Unsupported type for Postgres/pgvector, or Blob/JSON for SQLite).
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4. **Given** a note creation or update.
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5. **When** the note is saved.
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6. **Then** an embedding is generated via the AI Provider (`getEmbeddings`).
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7. **And** the embedding is stored in the database asynchronously.
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## Tasks / Subtasks
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- [ ] Mise à jour du Schéma Prisma (AC: 1, 2, 3)
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- [ ] Ajouter un champ `embedding` (Bytes ou String pour compatibilité SQLite/Postgres)
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- [ ] `npx prisma migrate dev`
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- [ ] Implémentation de la génération d'embeddings (AC: 4, 5, 6)
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- [ ] Modifier `createNote` et `updateNote` dans `actions/notes.ts`
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- [ ] Appeler `provider.getEmbeddings(content)`
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- [ ] Sauvegarder le résultat
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- [ ] Script de Backfill (Migration de données)
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- [ ] Créer une action pour générer les embeddings des notes existantes
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- [ ] Optimisation
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- [ ] Ne pas régénérer l'embedding si le contenu n'a pas changé
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## Dev Notes
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- **Compatibilité DB :** Le projet utilise `sqlite` par défaut (`dev.db`). SQLite ne supporte pas nativement les vecteurs comme pgvector.
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- **Solution :** Stocker les vecteurs sous forme de `String` (JSON) ou `Bytes` dans SQLite.
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- **Recherche :** Pour le MVP local, nous ferons la recherche par similarité cosinus **en mémoire** (JavaScript) ou via une extension SQLite (comme `sqlite-vss`) si possible sans trop de complexité.
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- **Choix BMad :** Stockage JSON String pour simplicité maximale et compatibilité. Calcul de similarité en JS (rapide pour < 1000 notes).
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- **Performance :** L'appel `getEmbeddings` peut être lent. Il ne doit pas bloquer l'UI.
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- Utiliser `waitUntil` (Next.js) ou ne pas `await` la promesse d'embedding dans la réponse UI.
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## Dev Agent Record
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### Agent Model Used
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### Debug Log References
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### Completion Notes List
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### File List
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