content = item_detail[item_key]
                elif item_key == "Field@1":  # embedding field for the document
                    continue
                elif item_key == "score":  # L2 distance
                    if scores is not None:
                        score = item_detail[item_key]
                        scores.append(score)
                else:
                    meta_data[item_key] = item_detail[item_key]
            results.append(Document(page_content=content, metadata=meta_data))
        return results
[docs]    @classmethod
    def from_texts(
        cls: Type[AwaDB],
        texts: List[str],
        embedding: Optional[Embeddings] = None,
        metadatas: Optional[List[dict]] = None,
        table_name: str = _DEFAULT_TABLE_NAME,
        logging_and_data_dir: Optional[str] = None,
        client: Optional[awadb.Client] = None,
        **kwargs: Any,
    ) -> AwaDB:
        """Create an AwaDB vectorstore from a raw documents.
        Args:
            texts (List[str]): List of texts to add to the table.
            embedding (Optional[Embeddings]): Embedding function. Defaults to None.
            metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
            table_name (str): Name of the table to create.
            logging_and_data_dir (Optional[str]): Directory of logging and persistence.
            client (Optional[awadb.Client]): AwaDB client
        Returns:
            AwaDB: AwaDB vectorstore.
        """
        awadb_client = cls(
            table_name=table_name,
            embedding_model=embedding,
            log_and_data_dir=logging_and_data_dir,
            client=client,
        )
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/awadb.html