text: numpy as npquery_numpy = np.array(query_result)for doc_res, doc in zip(document_result, docs):    document_numpy = np.array(doc_res)    similarity = np.dot(query_numpy, document_numpy) / (        np.linalg.norm(query_numpy) * np.linalg.norm(document_numpy)    )    print(f'Cosine similarity between "{doc}" and query: {similarity}')    Cosine similarity between "Dog is not a cat" and query: 0.7489097144129355    Cosine similarity between "Beta is the second letter of Greek alphabet" and query: 0.9519380640702013PreviousDashScopeNextElasticsearchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.