@Karthik_Karanm 

This is a known issue pattern when using multiple Unity Catalog (UC) vector search indexes in
Databricks Model Serving — especially under MLflow model serving endpoints with RAG architecture.

Your model serving environment (i.e., the model inference cluster running the MLflow model)
does not inherit the same permissions that your interactive environment (like a notebook) does. This leads to:
- 403 PERMISSION_DENIED errors from Unity Catalog
- Even though you can query and use those vector search tables during development, the model serving endpoint runs in a separate,
tightly scoped environment, and likely lacks direct access to the underlying Unity Catalog assets (like schema.table_vs)

To resolve this, you'll need to explicitly grant access to the Unity Catalog entities (vector search tables) for the model serving principal.