**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 ids is None:
            ids = [str(uuid.uuid1()) for _ in texts]
        embeddings = self.embedding_function.embed_documents(list(texts))
        if not metadatas:
            metadatas = [{} for _ in texts]
        self.add_embeddings(texts, embeddings, metadatas, ids, **kwargs)
        return ids
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        filter: Optional[dict] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Run similarity search with Hologres with distance.
        Args:
            query (str): Query text to search for.
            k (int): Number of results to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
        Returns:
            List of Documents most similar to the query.
        """
        embedding = self.embedding_function.embed_query(text=query)
        return self.similarity_search_by_vector(
            embedding=embedding,
            k=k,
            filter=filter,
        )
[docs]    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[dict] = None,