and cosine distance in float for each.
            Lower score represents more similarity.
        """
        q_str = self._build_qstr(self._embeddings.embed_query(query), k, where_str)
        try:
            return [
                (
                    Document(
                        page_content=r[self.config.column_map["text"]],
                        metadata=r[self.config.column_map["metadata"]],
                    ),
                    r["dist"],
                )
                for r in self.client.query(q_str).named_results()
            ]
        except Exception as e:
            logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
            return []
[docs]    def drop(self) -> None:
        """
        Helper function: Drop data
        """
        self.client.command(
            f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}"
        )
    @property
    def metadata_column(self) -> str:
        return self.config.column_map["metadata"]