diff --git a/src/backend/langflow/services/monitor/utils.py b/src/backend/langflow/services/monitor/utils.py
index e4ea16d09..0b5a3d259 100644
--- a/src/backend/langflow/services/monitor/utils.py
+++ b/src/backend/langflow/services/monitor/utils.py
@@ -1,10 +1,15 @@
-from typing import Any, Dict, Optional, Type
+from typing import TYPE_CHECKING, Any, Dict, Optional, Type
 
 import duckdb
+from langflow.services.deps import get_monitor_service
 from loguru import logger
 from pydantic import BaseModel
 
-from langflow.services.deps import get_monitor_service
+if TYPE_CHECKING:
+    from langflow.api.v1.schemas import ResultDict
+
+
+INDEX_KEY = "index"
 
 
 def get_table_schema_as_dict(conn: duckdb.DuckDBPyConnection, table_name: str) -> dict:
@@ -14,8 +19,12 @@ def get_table_schema_as_dict(conn: duckdb.DuckDBPyConnection, table_name: str) -
 
 def model_to_sql_column_definitions(model: Type[BaseModel]) -> dict:
     columns = {}
-    for field_name, field_type in model.__fields__.items():
-        field_info = field_type.type_
+    for field_name, field_type in model.model_fields.items():
+        if hasattr(field_type.annotation, "__args__"):
+            field_args = field_type.annotation.__args__
+        else:
+            field_args = []
+        field_info = field_args[0] if field_args else field_type.annotation
         if field_info.__name__ == "int":
             sql_type = "INTEGER"
         elif field_info.__name__ == "str":
@@ -26,6 +35,8 @@ def model_to_sql_column_definitions(model: Type[BaseModel]) -> dict:
             sql_type = "BOOLEAN"
         elif field_info.__name__ == "dict":
             sql_type = "JSON"
+        elif field_info.__name__ == "Any":
+            sql_type = "VARCHAR"
         else:
             continue  # Skip types we don't handle
         columns[field_name] = sql_type
@@ -52,7 +63,7 @@ def drop_and_create_table_if_schema_mismatch(db_path: str, table_name: str, mode
                     conn.execute(f"CREATE SEQUENCE seq_{table_name} START 1;")
                 except duckdb.CatalogException:
                     pass
-                desired_schema["id"] = f"INTEGER PRIMARY KEY DEFAULT NEXTVAL('seq_{table_name}')"
+                desired_schema[INDEX_KEY] = f"INTEGER PRIMARY KEY DEFAULT NEXTVAL('seq_{table_name}')"
             columns_sql = ", ".join(f"{name} {data_type}" for name, data_type in desired_schema.items())
             create_table_sql = f"CREATE TABLE {table_name} ({columns_sql})"
             conn.execute(create_table_sql)
@@ -69,7 +80,7 @@ def add_row_to_table(
 
     # Extract data for the insert statement
     validated_dict = validated_data.model_dump(exclude_unset=True)
-    keys = [key for key in validated_dict.keys() if key != "id"]
+    keys = [key for key in validated_dict.keys() if key != INDEX_KEY]
     columns = ", ".join(keys)
 
     values_placeholders = ", ".join(["?" for _ in keys])
@@ -107,3 +118,27 @@ async def log_message(
         monitor_service.add_row(table_name="messages", data=row)
     except Exception as e:
         logger.error(f"Error logging message: {e}")
+
+
+async def log_vertex_build(
+    flow_id: str,
+    vertex_id: str,
+    valid: bool,
+    params: Any,
+    data: "ResultDict",
+    artifacts: Optional[dict] = None,
+):
+    try:
+        monitor_service = get_monitor_service()
+        row = {
+            "flow_id": flow_id,
+            "id": vertex_id,
+            "valid": valid,
+            "params": params,
+            "data": data.model_dump(),
+            "artifacts": artifacts or {},
+            # "timestamp": monitor_service.get_timestamp(),
+        }
+        monitor_service.add_row(table_name="vertex_builds", data=row)
+    except Exception as e:
+        logger.error(f"Error logging vertex build: {e}")