async def delete_index(
            table_name: VectorTableName = Path(
                default=...,
                description="The table of vector embeddings to delete (e.g. `vectors`, `entity`, `document_collections`)",
            ),
            index_name: str = Path(
                ..., description="The name of the index to delete"
            ),
            # concurrently: bool = Body(
            #     default=True,
            #     description="Whether to delete the index concurrently (recommended for large indices)",
            # ),
            # run_with_orchestration: Optional[bool] = Body(True),
            auth_user=Depends(self.providers.auth.auth_wrapper()),
        ) -> WrappedGenericMessageResponse:
            """Delete an existing vector similarity search index.

            This endpoint removes the specified index from the database. Important considerations:

            - Deletion is permanent and cannot be undone
            - Underlying vector data remains intact
            - Queries will fall back to sequential scan
            - Running queries during deletion may be slower
            - Use run_with_orchestration=True for large indices to prevent timeouts
            - Consider index dependencies before deletion

            The operation returns immediately but cleanup may continue in background.
            """
            logger.info(
                f"Deleting vector index {index_name} from table {table_name}"
            )

            return await self.providers.orchestration.run_workflow(  # type: ignore
                "delete-vector-index",
                {
                    "request": {
                        "index_name": index_name,
                        "table_name": table_name,
                        "concurrently": True,
                    },
                },
                options={
                    "additional_metadata": {},
                },
            )
Delete an existing vector similarity search index.

            This endpoint removes the specified index from the database. Important considerations:

            - Deletion is permanent and cannot be undone
            - Underlying vector data remains intact
            - Queries will fall back to sequential scan
            - Running queries during deletion may be slower
            - Use run_with_orchestration=True for large indices to prevent timeouts
            - Consider index dependencies before deletion

            The operation returns immediately but cleanup may continue in background.
            
py/core/main/api/v3/indices_router.py