feat: chunks recherche (snippets) + script migration

1. Recherche: fetchChunkSnippets() — après le classement RRF existant,
   récupère les passages précis qui matchent depuis NoteEmbeddingChunk.
   Pur affichage, AUCUN changement de classement.

2. Script migration: scripts/migrate-chunk-embeddings.ts
   Indexe toutes les notes existantes en fragments.
   Batch de 10, barre de progression.
   Usage: npx tsx scripts/migrate-chunk-embeddings.ts

3. Memory Echo chunk-level: à faire (US restante)

memento-note/lib/ai/services/semantic-search.service.ts

@@ -100,20 +100,60 @@ export class SemanticSearchService {
 
       const fusedResults = await this.reciprocalRankFusion(keywordResults, semanticResults)
 
-      return fusedResults
+      const topResults = fusedResults
         .sort((a, b) => b.score - a.score)
         .slice(0, opts.limit)
-        .map(result => ({
-          ...result,
-          title: result.title || opts.defaultTitle,
-          matchType: result.score > 0.8 ? 'exact' as const : 'related' as const
-        }))
+
+      // Fetch chunk snippets for top results (display only — no ranking change)
+      const noteIds = topResults.map(r => r.noteId)
+      const snippetMap = await this.fetchChunkSnippets(query, noteIds)
+
+      return topResults.map(result => ({
+        ...result,
+        title: result.title || opts.defaultTitle,
+        matchType: result.score > 0.8 ? 'exact' as const : 'related' as const,
+        matchedSnippets: snippetMap.get(result.noteId),
+      }))
     } catch (error) {
       console.error('Error in hybrid search:', error)
       return this._ftsFallback(query, userId, opts)
     }
   }
 
+  /**
+   * Fetch matching chunk snippets for display (no ranking impact).
+   * Finds chunks whose content contains the query terms.
+   */
+  private async fetchChunkSnippets(
+    query: string,
+    noteIds: string[]
+  ): Promise<Map<string, string[]>> {
+    if (noteIds.length === 0 || !query.trim()) return new Map()
+
+    try {
+      const rows: Array<{ noteId: string; content: string }> = await prisma.$queryRawUnsafe(
+        `SELECT "noteId", content FROM "NoteEmbeddingChunk"
+         WHERE "noteId" = ANY($1::text[])
+           AND content ILIKE $2
+         ORDER BY "noteId", "chunkIndex"
+         LIMIT 30`,
+        noteIds,
+        `%${query.slice(0, 100)}%`
+      )
+
+      const map = new Map<string, string[]>()
+      for (const row of rows) {
+        const existing = map.get(row.noteId) || []
+        const snippet = row.content.length > 200 ? row.content.slice(0, 200) + '...' : row.content
+        if (existing.length < 3) existing.push(snippet)
+        map.set(row.noteId, existing)
+      }
+      return map
+    } catch {
+      return new Map()
+    }
+  }
+
   /**
    * PostgreSQL full-text search using tsvector + GIN index.
    * SECURITY: Uses $queryRawUnsafe with parameterized bind params ($1, $2…).