return list(
            map(
                lambda item: Document(
                    page_content=item["text"],
                    metadata={
                        key: item[key] for key in item if key not in exclude_keys
                    },
                ),
                response["result"],
            )
        )
[docs]    @classmethod
    def from_texts(
        cls: Type[Epsilla],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        client: Any = None,
        db_path: Optional[str] = _LANGCHAIN_DEFAULT_DB_PATH,
        db_name: Optional[str] = _LANGCHAIN_DEFAULT_DB_NAME,
        collection_name: Optional[str] = _LANGCHAIN_DEFAULT_TABLE_NAME,
        drop_old: Optional[bool] = False,
        **kwargs: Any,
    ) -> Epsilla:
        """Create an Epsilla vectorstore from raw documents.
        Args:
            texts (List[str]): List of text data to be inserted.
            embeddings (Embeddings): Embedding function.
            client (pyepsilla.vectordb.Client): Epsilla client to connect to.
            metadatas (Optional[List[dict]]): Metadata for each text.
                    Defaults to None.
            db_path (Optional[str]): The path where the database will be persisted.
                    Defaults to "/tmp/langchain-epsilla".
            db_name (Optional[str]): Give a name to the loaded database.
                    Defaults to "langchain_store".
            collection_name (Optional[str]): Which collection to use.
                    Defaults to "langchain_collection".
                    If provided, default collection name will be set as well.
            drop_old (Optional[bool]): Whether to drop the previous collection