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Showing content from https://python.langchain.com/docs/versions/migrating_chains/llm_chain/ below:

Migrating from LLMChain | 🦜️🔗 LangChain

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()

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.

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

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"})

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

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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