text: me a joke")    CPU times: user 5.8 ms, sys: 2.71 ms, total: 8.51 ms    Wall time: 745 ms    '\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'llm("Tell me a joke")    CPU times: user 4.91 ms, sys: 2.64 ms, total: 7.55 ms    Wall time: 623 ms    '\n\nTwo guys stole a calendar. They got six months each.'Optional Caching in Chainsâ€‹You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, its often easier to construct the chain first, and then edit the LLM afterwards.As an example, we will load a summarizer map-reduce chain. We will cache results for the map-step, but then not freeze it for the combine step.llm = OpenAI(model_name="text-davinci-002")no_cache_llm = OpenAI(model_name="text-davinci-002", cache=False)from langchain.text_splitter import CharacterTextSplitterfrom langchain.chains.mapreduce import MapReduceChaintext_splitter = CharacterTextSplitter()with open("../../../state_of_the_union.txt") as f:    state_of_the_union = f.read()texts = text_splitter.split_text(state_of_the_union)from langchain.docstore.document import Documentdocs = [Document(page_content=t) for t in texts[:3]]from langchain.chains.summarize import load_summarize_chainchain = load_summarize_chain(llm, chain_type="map_reduce", reduce_llm=no_cache_llm)chain.run(docs)    CPU times: user 452 ms, sys: 60.3 ms, total: 512 ms    Wall