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>

architectural-grid10/README.md

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+<div align="center">
+<img width="1200" height="475" alt="GHBanner" src="https://github.com/user-attachments/assets/0aa67016-6eaf-458a-adb2-6e31a0763ed6" />
+</div>
+
+# Run and deploy your AI Studio app
+
+This contains everything you need to run your app locally.
+
+View your app in AI Studio: https://ai.studio/apps/b7b577c6-4d9f-44ac-8fe1-85bc3c6d6e66
+
+## Run Locally
+
+**Prerequisites:**  Node.js
+
+
+1. Install dependencies:
+   `npm install`
+2. Set the `GEMINI_API_KEY` in [.env.local](.env.local) to your Gemini API key
+3. Run the app:
+   `npm run dev`