# define k and simiarlity thresholdk = 5threshold = 0.05# multimodal search over articlestext_similarities = matmul(query_embed, text_embeddings.T)# rescale similarities via softmaxtemp=0.25text_scores = softmax(text_similarities/temp, dim=1)# return top k filtered text resultsisorted_scores = argsort(text_scores, descending=True)[0]sorted_scores = text_scores[0][isorted_scores]itop_k_filtered = [idx.item()                     for idx, score in zip(isorted_scores, sorted_scores)                     if score.item() >= threshold][:k]top_k = [text_content_list[i] for i in itop_k_filtered]print(top_k)