E.g. `{"max_price" : 15.66, "min_price": 4.20}`
            `price` is the metadata field, means range filter(4.20<'price'<15.66).
            E.g. `{"maxe_price" : 15.66, "mine_price": 4.20}`
            `price` is the metadata field, means range filter(4.20<='price'<=15.66).
            kwargs: Any possible extend parameters in the future.
        Returns:
            Returns the k most similar documents to the specified text query.
        """
        if self.awadb_client is None:
            raise ValueError("AwaDB client is None!!!")
        embedding = None
        if self.using_table_name in self.table2embeddings:
            embedding = self.table2embeddings[self.using_table_name].embed_query(query)
        else:
            from awadb import AwaEmbedding
            embedding = AwaEmbedding().Embedding(query)
        not_include_fields: Set[str] = {"text_embedding", "_id", "score"}
        return self.similarity_search_by_vector(
            embedding,
            k,
            text_in_page_content=text_in_page_content,
            meta_filter=meta_filter,
            not_include_fields_in_metadata=not_include_fields,
        )
[docs]    def similarity_search_with_score(
        self,
        query: str,
        k: int = DEFAULT_TOPN,
        text_in_page_content: Optional[str] = None,
        meta_filter: Optional[dict] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """The most k similar documents and scores of the specified query.
        Args:
            query: Text query.