LangChain Expression LanguageHow toRun arbitrary functionsOn this pageRun arbitrary functionsYou can use arbitrary functions in the pipelineNote that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single input and unpacks it into multiple argument.from langchain.schema.runnable import RunnableLambdafrom langchain.prompts import ChatPromptTemplatefrom langchain.chat_models import ChatOpenAIfrom operator import itemgetterdef length_function(text):    return len(text)def _multiple_length_function(text1, text2):    return len(text1) * len(text2)def multiple_length_function(_dict):    return _multiple_length_function(_dict["text1"], _dict["text2"])prompt = ChatPromptTemplate.from_template("what is {a} + {b}")model = ChatOpenAI()chain1 = prompt | modelchain = {    "a": itemgetter("foo") | RunnableLambda(length_function),    "b": {"text1": itemgetter("foo"), "text2": itemgetter("bar")} | RunnableLambda(multiple_length_function)} | prompt | modelchain.invoke({"foo": "bar", "bar": "gah"})    AIMessage(content='3 + 9 equals 12.', additional_kwargs={}, example=False)Accepting a Runnable Config​Runnable lambdas can optionally accept a RunnableConfig, which they can use to pass callbacks, tags, and other configuration information to nested runs.from langchain.schema.runnable import RunnableConfigfrom langchain.schema.output_parser import StrOutputParserimport jsondef parse_or_fix(text: str, config: RunnableConfig):    fixing_chain = (        ChatPromptTemplate.from_template(            "Fix the following text:\n\n```text\n{input}\n```\nError: {error}"            " Don't narrate, just respond with the fixed data."        )        | ChatOpenAI()        | StrOutputParser()    )    for _ in range(3):        try:            return json.loads(text)        except Exception as e:            text = fixing_chain.invoke({"input": text, "error": e}, config)    return "Failed to parse"from langchain.callbacks import get_openai_callbackwith get_openai_callback() as cb:    RunnableLambda(parse_or_fix).invoke("{foo: bar}", {"tags": ["my-tag"], "callbacks": [cb]})    print(cb)    Tokens Used: 65        Prompt Tokens: 56        Completion Tokens: 9    Successful Requests: 1    Total Cost (USD): $0.00010200000000000001PreviousAdd fallbacksNextUse RunnableParallel/RunnableMapAccepting a Runnable Config