Create a runnable with the `@chain` decorator

Обновлено 24 мая 2024

You can also turn an arbitrary function into a chain by adding a @chain decorator. This is functionaly equivalent to wrapping in a RunnableLambda.

This will have the benefit of improved observability by tracing your chain correctly. Any calls to runnables inside this function will be traced as nested childen.

It will also allow you to use this as any other runnable, compose it in chain, etc.

Let's take a look at this in action!

%pip install --upgrade --quiet  langchain langchain-openai

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import chain
from langchain_openai import ChatOpenAI

prompt1 = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
prompt2 = ChatPromptTemplate.from_template("What is the subject of this joke: {joke}")

@chain
def custom_chain(text):
    prompt_val1 = prompt1.invoke({"topic": text})
    output1 = ChatOpenAI().invoke(prompt_val1)
    parsed_output1 = StrOutputParser().invoke(output1)
    chain2 = prompt2 | ChatOpenAI() | StrOutputParser()
    return chain2.invoke({"joke": parsed_output1})

custom_chain is now a runnable, meaning you will need to use invoke

custom_chain.invoke("bears")

    'The subject of this joke is bears.'

If you check out your LangSmith traces, you should see a custom_chain trace in there, with the calls to OpenAI nested underneath

Create a runnable with the @chain decorator

Create a runnable with the `@chain` decorator