fix: guard against closed streams in promo and missing vector tables (#579, #607)

- Wrap isatty() in _is_interactive_session() with try/except ValueError
  so MCP stdio transport shutdown no longer produces noisy tracebacks
- Check both search_vector_chunks AND search_vector_embeddings exist
  before running JOIN queries in get_embedding_status(), fixing
  OperationalError when only the chunks table is present
- Add test for closed-stream scenario

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>

src/basic_memory/cli/promo.py

@@ -24,7 +24,13 @@ def _promos_disabled_by_env() -> bool:
 
 def _is_interactive_session() -> bool:
     """Return whether stdin/stdout are interactive terminals."""
-    return sys.stdin.isatty() and sys.stdout.isatty()
+    try:
+        return sys.stdin.isatty() and sys.stdout.isatty()
+    except ValueError:
+        # Trigger: stdin/stdout already closed (e.g., MCP stdio transport shutdown)
+        # Why: isatty() raises ValueError on closed file descriptors
+        # Outcome: treat as non-interactive, suppressing promo output
+        return False
 
 
 def _build_cloud_promo_message() -> str:

src/basic_memory/services/project_service.py

@@ -942,19 +942,21 @@ class ProjectService:
         is_postgres = config.database_backend == DatabaseBackend.POSTGRES
 
         # --- Check vector table existence ---
+        # Both search_vector_chunks and search_vector_embeddings must exist
+        # for the detailed stats queries (JOINs between them) to work.
         if is_postgres:
             table_check_sql = text(
                 "SELECT COUNT(*) FROM information_schema.tables "
-                "WHERE table_name = 'search_vector_chunks'"
+                "WHERE table_name IN ('search_vector_chunks', 'search_vector_embeddings')"
             )
         else:
             table_check_sql = text(
                 "SELECT COUNT(*) FROM sqlite_master "
-                "WHERE type = 'table' AND name = 'search_vector_chunks'"
+                "WHERE type = 'table' AND name IN ('search_vector_chunks', 'search_vector_embeddings')"
             )
 
         table_result = await self.repository.execute_query(table_check_sql, {})
-        vector_tables_exist = (table_result.scalar() or 0) > 0
+        vector_tables_exist = (table_result.scalar() or 0) == 2
 
         if not vector_tables_exist:
             # Count distinct entities in search index for the recommendation message