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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

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass()

Legacy​

Details
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")],
)

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

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

Note that LLMChain by default returned a dict containing both the input and the output from StrOutputParser, so to extract the output, you need to access the "text" key.

legacy_result["text"]
"Why couldn't the bicycle stand up by itself?\n\nBecause it was two tired!"

LCEL​

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

If you'd like to mimic the dict packaging of input and output in LLMChain, you can use a RunnablePassthrough.assign like:

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.

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