langchain/vectorstores/milvus.py/Milvus/similarity_search_with_score
class Milvus:    def similarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        param: Optional[dict] = None,
        expr: Optional[str] = None,
        timeout: Optional[int] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Perform a search on a query string and return results with score.

        For more information about the search parameters, take a look at the pymilvus
        documentation found here:
        https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md

        Args:
            query (str): The text being searched.
            k (int, optional): The amount of results ot 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[float], List[Tuple[Document, any, any]]:
        """
        if self.col is None:
            logger.debug("No existing collection to search.")
            return []

        # Embed the query text.
        embedding = self.embedding_func.embed_query(query)

        res = self.similarity_search_with_score_by_vector(
            embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs
        )
        return res