def from_param(
        cls,
        host: str,
        port: int,
        user: str,
        password: str,
        database: str,
        table_name: str,
        table_schema: Dict[str, str] = {"document": "text"},
        embedding_dimension: int = 1536,
        pre_delete_table: bool = False,
    ) -> "HologresVectorStore":
        """
        Create Hologres Vector Store from database configurations.

        Args:
            host: host
            port: port number
            user: hologres user
            password: hologres password
            database: hologres database
            table_name: hologres table name
            table_schema: table column schemam
            embedding_dimension: dimension size of embedding vector
            pre_delete_table: whether to erase data from table on creation

        """
        connection_string = HologresVector.connection_string_from_db_params(
            host, port, database, user, password
        )
        return cls.from_connection_string(
            connection_string=connection_string,
            table_name=table_name,
            embedding_dimension=embedding_dimension,
            table_schema=table_schema,
            pre_delete_table=pre_delete_table,
        )

        Create Hologres Vector Store from database configurations.

        Args:
            host: host
            port: port number
            user: hologres user
            password: hologres password
            database: hologres database
            table_name: hologres table name
            table_schema: table column schemam
            embedding_dimension: dimension size of embedding vector
            pre_delete_table: whether to erase data from table on creation

        
llama-index-integrations/vector_stores/llama-index-vector-stores-hologres/llama_index/vector_stores/hologres/base.py