and embedding for you.
    Manually ingestion/deletion of the documents/texts is not supported for delta-sync
    index.
    If you want to use a delta-sync index with self-managed embeddings, you need to
    provide the embedding model and text column name to use for the embeddings.
    Example:
        .. code-block:: python
            from langchain_community.vectorstores import DatabricksVectorSearch
            from databricks.vector_search.client import VectorSearchClient
            from langchain_community.embeddings.openai import OpenAIEmbeddings
            vs_client = VectorSearchClient()
            vs_index = vs_client.get_index(
              endpoint_name="vs_endpoint",
              index_name="ml.llm.index"
            )
            vectorstore = DatabricksVectorSearch(
              index=vs_index,
              embedding=OpenAIEmbeddings(),
              text_column="document_content"
            )
    If you want to manage the documents ingestion/deletion yourself, you can use a
    direct-access index.
    Example:
        .. code-block:: python
            from langchain_community.vectorstores import DatabricksVectorSearch
            from databricks.vector_search.client import VectorSearchClient
            from langchain_community.embeddings.openai import OpenAIEmbeddings
            vs_client = VectorSearchClient()
            vs_index = vs_client.get_index(
              endpoint_name="vs_endpoint",
              index_name="ml.llm.index"
            )
            vectorstore = DatabricksVectorSearch(
              index=vs_index,
              embedding=OpenAIEmbeddings(),
              text_column="document_content"
            )
            vectorstore.add_texts(
              texts=["text1", "text2"]
            )
    For more information on Databricks Vector Search, see `Databricks Vector Search