feat: Memory Echo chunk-level — détecte connexions au niveau section

Quand la similarité whole-note ne passe pas le seuil, vérifie les chunks.
Si une section spécifique de la note A résonne avec une section de la note B,
la connexion est créée avec le snippet précis qui a matché.

SQL: cross-join LATERAL sur NoteEmbeddingChunk avec pgvector <=>.
Fallback gracieux si la table chunks est vide ou erreur.

memento-note/lib/ai/services/memory-echo.service.ts

@@ -259,15 +259,43 @@ export class MemoryEchoService {
           continue
         }
 
-        // Calculate cosine similarity
+        // Calculate cosine similarity — whole note level
         const similarity = cosineSimilarity(note1.embedding!, note2.embedding!)
 
-        // Similarity threshold for meaningful connections (adjusted by feedback)
-        const baseThreshold = this.pairSimilarityThreshold(note1, note2, demoMode)
-        const adjustedThreshold = baseThreshold
-          + (notePenalty.get(note1.id) || 0)
-          + (notePenalty.get(note2.id) || 0)
-        if (similarity >= adjustedThreshold) {
+        // Also check chunk-level similarity for more precise connections
+        // This catches cases where two notes share a similar SECTION even if overall different
+        let bestChunkSimilarity = similarity
+        let chunkSnippet: string | undefined
+        if (similarity < adjustedThreshold) {
+          // Only check chunks if whole-note similarity didn't pass — saves DB queries
+          try {
+            const chunkRows: Array<{ content: string; embedding: string }> = await prisma.$queryRawUnsafe(
+              `SELECT a.content AS content,
+                      1 - (a."embedding"::vector <=> b."embedding"::vector) AS chunk_sim
+               FROM "NoteEmbeddingChunk" a
+               CROSS JOIN LATERAL (
+                 SELECT "embedding"
+                 FROM "NoteEmbeddingChunk"
+                 WHERE "noteId" = $2 AND "embedding" IS NOT NULL
+                 ORDER BY "embedding"::vector <=> a."embedding"::vector ASC
+                 LIMIT 1
+               ) b
+               WHERE a."noteId" = $1 AND a."embedding" IS NOT NULL
+               ORDER BY chunk_sim DESC
+               LIMIT 1`,
+              note1.id, note2.id
+            )
+            if (chunkRows.length > 0 && chunkRows[0]) {
+              const row = chunkRows[0] as any
+              if (row.chunk_sim && row.chunk_sim > bestChunkSimilarity) {
+                bestChunkSimilarity = row.chunk_sim
+                chunkSnippet = row.content?.slice(0, 200)
+              }
+            }
+          } catch {}
+        }
+
+        if (bestChunkSimilarity >= adjustedThreshold) {
           connections.push({
             note1: {
               id: note1.id,
@@ -281,9 +309,10 @@ export class MemoryEchoService {
               content: this.connectionPlainText(note2.title, note2.content || ''),
               createdAt: note2.createdAt
             },
-            similarityScore: similarity,
+            similarityScore: bestChunkSimilarity,
             insight: '', // Will be generated by AI
-            daysApart
+            daysApart,
+            ...(chunkSnippet ? { contextSnippet: chunkSnippet } : {})
           })
         }
       }