resp = self.embed_documents([text])
        return resp[0]
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """
        Embeds a list of text documents using the AutoVOT algorithm.
        Args:
            texts (List[str]): A list of text documents to embed.
        Returns:
            List[List[float]]: A list of embeddings for each document in the input list.
                            Each embedding is represented as a list of float values.
        """
        text_in_chunks = [
            texts[i : i + self.chunk_size]
            for i in range(0, len(texts), self.chunk_size)
        ]
        lst = []
        for chunk in text_in_chunks:
            resp = self.client.do(texts=chunk)
            lst.extend([res["embedding"] for res in resp["data"]])
        return lst
[docs]    async def aembed_query(self, text: str) -> List[float]:
        embeddings = await self.aembed_documents([text])
        return embeddings[0]
[docs]    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
        text_in_chunks = [
            texts[i : i + self.chunk_size]
            for i in range(0, len(texts), self.chunk_size)
        ]
        lst = []
        for chunk in text_in_chunks:
            resp = await self.client.ado(texts=chunk)
            for res in resp["data"]:
                lst.extend([res["embedding"]])
        return lst