feat(ai): pgvector embedding infrastructure for semantic search

Semantic-search slice 1 (infra). Adds the pgvector plumbing the backfill worker and
hybrid search will use:
- swap the Postgres image to pgvector/pgvector:pg16 (drop-in for pg16 data)
- Pgvector + Pgvector.EntityFrameworkCore (0.2.0, EF8-compatible); UseVector() on the
  runtime + design-time contexts
- Email.Embedding vector(768) column (nomic-embed-text dims), nullable, with an HNSW
  cosine index; ignored under the InMemory test provider
- migration: CREATE EXTENSION vector + column + HNSW index

Verified against a real pgvector container: extension, HNSW, and cosine search work, and
the full EF round-trip (store a Pgvector.Vector, CosineDistance operator, nearest-first
ordering) applies all migrations and passes. No vulnerable packages. Build + all 41 tests
pass. Column stays null until Ollama generates embeddings (search falls back to lexical).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

docker-compose.yml

@@ -1,6 +1,8 @@
 services:
   postgres:
-    image: postgres:16-alpine
+    # pgvector-enabled Postgres 16 (semantic search). Drop-in for postgres:16 data;
+    # the 'vector' extension is created by the AddEmbeddingColumn migration.
+    image: pgvector/pgvector:pg16
     environment:
       POSTGRES_DB: inboxintel
       POSTGRES_USER: inboxintel