ComponentsLLMsAnyscaleAnyscaleAnyscale is a fully-managed Ray platform, on which you can build, deploy, and manage scalable AI and Python applicationsThis example goes over how to use LangChain to interact with Anyscale service. It will send the requests to Anyscale Service endpoint, which is concatenate ANYSCALE_SERVICE_URL and ANYSCALE_SERVICE_ROUTE, with a token defined in ANYSCALE_SERVICE_TOKENimport osos.environ["ANYSCALE_SERVICE_URL"] = ANYSCALE_SERVICE_URLos.environ["ANYSCALE_SERVICE_ROUTE"] = ANYSCALE_SERVICE_ROUTEos.environ["ANYSCALE_SERVICE_TOKEN"] = ANYSCALE_SERVICE_TOKENfrom langchain.llms import Anyscalefrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChaintemplate = """Question: {question}Answer: Let's think step by step."""prompt = 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 GatewayNextArcee