Migrating from LLMChain

LLMChain combined a prompt template, LLM, and output parser into a class.

Some advantages of switching to the LCEL implementation are:

• Clarity around contents and parameters. The legacy LLMChain contains a default output parser and other options.
• Easier streaming. LLMChain only supports streaming via callbacks.
• Easier access to raw message outputs if desired. LLMChain only exposes these via a parameter or via callback.

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

import os
from getpass import getpass

os.environ["OPENAI_API_KEY"] = getpass()

Legacy

from langchain.chains import LLMChain
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_messages(
    [("user", "Tell me a {adjective} joke")],
)

chain = LLMChain(llm=ChatOpenAI(), prompt=prompt)

chain({"adjective": "funny"})

API Reference:LLMChain | ChatPromptTemplate | ChatOpenAI

{'adjective': 'funny',
 'text': "Why couldn't the bicycle stand up by itself?\n\nBecause it was two tired!"}

LCEL

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

prompt = ChatPromptTemplate.from_messages(
    [("user", "Tell me a {adjective} joke")],
)

chain = prompt | ChatOpenAI() | StrOutputParser()

chain.invoke({"adjective": "funny"})

API Reference:StrOutputParser | ChatPromptTemplate | ChatOpenAI

'Why was the math book sad?\n\nBecause it had too many problems.'

Note that LLMChain by default returns a dict containing both the input and the output. If this behavior is desired, we can replicate it using another LCEL primitive, RunnablePassthrough:

from langchain_core.runnables import RunnablePassthrough

outer_chain = RunnablePassthrough().assign(text=chain)

outer_chain.invoke({"adjective": "funny"})

API Reference:RunnablePassthrough

{'adjective': 'funny',
 'text': 'Why did the scarecrow win an award? Because he was outstanding in his field!'}

Next steps

See this tutorial for more detail on building with prompt templates, LLMs, and output parsers.

Check out the LCEL conceptual docs for more background information.