A Generative AI Engineer is using the following code to test setting up a vector store:

from databricks.vector_search.client import VectorSearchClient

vsc = VectorSearchClient()
vs_index = vsc.create_delta_sync_index(
  endpoint_name="...",
  index_name="my_vector_index",
  source_table_name="my_catalog.my_schema.my_chunked_docs",
  pipeline_type="TRIGGERED",
  primary_key="id",
  embedding_source_column="text"
)

from databricks.vector_search.client import VectorSearchClient

vsc = VectorSearchClient()
vs_index = vsc.create_delta_sync_index(
  endpoint_name="...",
  index_name="my_vector_index",
  source_table_name="my_catalog.my_schema.my_chunked_docs",
  pipeline_type="TRIGGERED",
  primary_key="id",
  embedding_source_column="text"
)

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?_