def _default_search(
        self,
        query: VectorStoreQuery,
        string_expr: str,
        output_fields: List[str],
        **kwargs,
    ) -> Tuple[List[BaseNode], List[float], List[str]]:
        """
        Perform default search: dense embedding search.
        """
        res = self.client.search(
            collection_name=self.collection_name,
            data=[query.query_embedding],
            filter=string_expr,
            limit=query.similarity_top_k,
            output_fields=output_fields,
            search_params=kwargs.get("milvus_search_config", self.search_config),
            anns_field=self.embedding_field,
        )
        logger.debug(
            f"Successfully searched embedding in collection: {self.collection_name}"
            f" Num Results: {len(res[0])}"
        )
        nodes, similarities, ids = self._parse_from_milvus_results(res)
        return nodes, similarities, ids

        Perform default search: dense embedding search.
        
llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py