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/api/endpoints/ai_sessions.py

@@ -493,6 +493,32 @@ async def search_sessions(
     ]
 
 
+# ── Similar Sessions ──
+
+@router.get("/{session_id}/similar")
+@limiter.limit("15/minute")
+async def get_similar_sessions(
+    request: Request,
+    session_id: UUID,
+    current_user: Annotated[User, Depends(get_current_active_user)],
+    db: Annotated[AsyncSession, Depends(get_db)],
+    limit: int = Query(5, ge=1, le=20),
+):
+    """Find sessions semantically similar to this one using vector embeddings."""
+    from app.services.session_embedding_service import find_similar_sessions
+
+    if not current_user.account_id:
+        raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail="No account")
+
+    results = await find_similar_sessions(
+        session_id=session_id,
+        account_id=current_user.account_id,
+        db=db,
+        limit=limit,
+    )
+    return results
+
+
 # ── List sessions ──
 
 @router.get("", response_model=list[AISessionSummary])