feat(search): add semantic similar session matching via Voyage AI embeddings

Adds vector-based similar session discovery using the existing Voyage AI
embedding infrastructure and pgvector cosine similarity search.

- New AISessionEmbedding model with vector(1024) column
- session_embedding_service: generate + upsert embeddings, find similar sessions
- Embeddings generated on session create (from problem_summary/domain) and
  updated on resolve (adds resolution_summary)
- GET /ai-sessions/{id}/similar endpoint returns top-N similar sessions
- Migration a7c9e3b1f402 creates ai_session_embeddings table

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/app/models/__init__.py

@@ -48,6 +48,7 @@ from .notification_log import NotificationLog
 from .notification import Notification
 from .psa_activity_log import PsaActivityLog
 from .file_upload import FileUpload
+from .ai_session_embedding import AISessionEmbedding
 
 __all__ = [
     "User",
@@ -110,4 +111,5 @@ __all__ = [
     "Notification",
     "PsaActivityLog",
     "FileUpload",
+    "AISessionEmbedding",
 ]