feat: migrate semantic search to pgvector + full-text search

Replace JSON-string embeddings with native pgvector(1536) storage and
add PostgreSQL full-text search (tsvector/GIN) with Reciprocal Rank Fusion
for hybrid keyword + semantic ranking.

Changes:
- NoteEmbedding.embedding: String → vector(1536) via pgvector
- NoteEmbedding: added updatedAt for reindex tracking
- Note: added tsv (tsvector) with auto-update trigger for FTS
- semantic-search.service: hybrid FTS + vector search with RRF fusion
- embedding.service: toVectorString() for pgvector SQL literals
- Removed JS-side cosine similarity loops (now DB-side via <=>)
- Added HNSW index on NoteEmbedding.embedding (cosine distance)
- Added GIN index on Note.tsv for FTS queries

Schema migration in: prisma/migrations/20260512120000_pgvector_and_fts_search/

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

memento-note/prisma/schema.prisma

@@ -155,6 +155,7 @@ model Note {
   languageConfidence  Float?
   lastAiAnalysis      DateTime?
   trashedAt           DateTime?
+  tsv                 Unsupported("tsvector")?
   aiFeedback          AiFeedback[]
   memoryEchoAsNote1   MemoryEchoInsight[] @relation("EchoNote1")
   memoryEchoAsNote2   MemoryEchoInsight[] @relation("EchoNote2")
@@ -299,8 +300,9 @@ model UserAISettings {
 model NoteEmbedding {
   id        String   @id @default(cuid())
   noteId    String   @unique
-  embedding String
+  embedding Unsupported("vector(1536)")
   createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
   note      Note     @relation(fields: [noteId], references: [id], onDelete: Cascade)
 
   @@index([noteId])