text: PromptTemplate(template=template, input_variables=["question"])llm = Anyscale()llm_chain = LLMChain(prompt=prompt, llm=llm)question = "When was George Washington president?"llm_chain.run(question)With Ray, we can distribute the queries without asyncrhonized implementation. This not only applies to Anyscale LLM model, but to any other Langchain LLM models which do not have _acall or _agenerate implementedprompt_list = [    "When was George Washington president?",    "Explain to me the difference between nuclear fission and fusion.",    "Give me a list of 5 science fiction books I should read next.",    "Explain the difference between Spark and Ray.",    "Suggest some fun holiday ideas.",    "Tell a joke.",    "What is 2+2?",    "Explain what is machine learning like I am five years old.",    "Explain what is artifical intelligence.",]import ray@ray.remotedef send_query(llm, prompt):    resp = llm(prompt)    return respfutures = [send_query.remote(llm, prompt) for prompt in prompt_list]results = ray.get(futures)PreviousAmazon API GatewayNextAzure OpenAICommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.