metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
            kwargs: vectorstore specific parameters
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        if self.awadb_client is None:
            raise ValueError("AwaDB client is None!!!")
        embeddings = None
        if self.embedding_model is not None:
            embeddings = self.embedding_model.embed_documents(list(texts))
        added_results: List[str] = []
        doc_no = 0
        for text in texts:
            doc: List[Any] = []
            if embeddings is not None:
                doc.append(text)
                doc.append(embeddings[doc_no])
            else:
                dict_tmp = {}
                dict_tmp["embedding_text"] = text
                doc.append(dict_tmp)
            if metadatas is not None:
                if doc_no < metadatas.__len__():
                    doc.append(metadatas[doc_no])
            self.awadb_client.Add(doc)
            added_results.append(str(self.added_doc_count))
            doc_no = doc_no + 1
            self.added_doc_count = self.added_doc_count + 1
        return added_results
[docs]    def load_local(
        self,
        table_name: str = _DEFAULT_TABLE_NAME,
        **kwargs: Any,
    ) -> bool:
        if self.awadb_client is None:
            raise ValueError("AwaDB client is None!!!")
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/awadb.html