Args:
            embedding (List[float]): The embedding vector being searched.
            k (int, optional): The amount of results to return. Defaults to 4.
            param (dict): The search params for the specified index.
                Defaults to None.
            expr (str, optional): Filtering expression. Defaults to None.
            timeout (int, optional): How long to wait before timeout error.
                Defaults to None.
            kwargs: Collection.search() keyword arguments.
        Returns:
            List[Tuple[Document, float]]: Result doc and score.
        """
        if self.col is None:
            logger.debug("No existing collection to search.")
            return []
        if param is None:
            param = self.search_params
        # Determine result metadata fields.
        output_fields = self.fields[:]
        output_fields.remove(self._vector_field)
        # Perform the search.
        res = self.col.search(
            data=[embedding],
            anns_field=self._vector_field,
            param=param,
            limit=k,
            expr=expr,
            output_fields=output_fields,
            timeout=timeout,
            **kwargs,
        )
        # Organize results.
        ret = []
        for result in res[0]:
            meta = {x: result.entity.get(x) for x in output_fields}
            doc = Document(page_content=meta.pop(self._text_field), metadata=meta)
            pair = (doc, result.score)
            ret.append(pair)
        return ret
[docs]    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,