def similarity_search(query_embed, target_embeddings, content_list,                       k=5, threshold=0.05, temperature=0.5):    """       Perform similarity search over embeddings and return top k results.    """    # Calculate similarities    similarities = torch.matmul(query_embed, target_embeddings.T)    # Rescale similarities via softmax    scores = torch.nn.functional.softmax(similarities/temperature, dim=1)    # Get sorted indices and scores    sorted_indices = scores.argsort(descending=True)[0]    sorted_scores = scores[0][sorted_indices]    # Filter by threshold and get top k    filtered_indices = [        idx.item() for idx, score in zip(sorted_indices, sorted_scores)         if score.item() >= threshold    ][:k]    # Get corresponding content items and scores    top_results = [content_list[i] for i in filtered_indices]    result_scores = [scores[0][i].item() for i in filtered_indices]    return top_results, result_scores