Transformation

Often we want to transform inputs as they are passed from one component to another.

As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into a chain to summarize those.

from langchain.prompts import PromptTemplate

prompt = PromptTemplate.from_template(
    """Summarize this text:

{output_text}

Summary:"""
)

with open("../../state_of_the_union.txt") as f:
    state_of_the_union = f.read()

Using LCEL

With LCEL this is trivial, since we can add functions in any RunnableSequence.

from langchain.chat_models import ChatOpenAI
from langchain.schema import StrOutputParser

runnable = (
    {"output_text": lambda text: "\n\n".join(text.split("\n\n")[:3])}
    | prompt
    | ChatOpenAI()
    | StrOutputParser()
)
runnable.invoke(state_of_the_union)

'The speaker acknowledges the presence of important figures in the government and addresses the audience as fellow Americans. They highlight the impact of COVID-19 on keeping people apart in the previous year but express joy in being able to come together again. The speaker emphasizes the unity of Democrats, Republicans, and Independents as Americans.'

[Legacy] TransformationChain

This is a legacy class, using LCEL as shown above is preferred.

This notebook showcases using a generic transformation chain.

from langchain.chains import LLMChain, SimpleSequentialChain, TransformChain
from langchain.llms import OpenAI

def transform_func(inputs: dict) -> dict:
    text = inputs["text"]
    shortened_text = "\n\n".join(text.split("\n\n")[:3])
    return {"output_text": shortened_text}


transform_chain = TransformChain(
    input_variables=["text"], output_variables=["output_text"], transform=transform_func
)

template = """Summarize this text:

{output_text}

Summary:"""
prompt = PromptTemplate(input_variables=["output_text"], template=template)
llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)

sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])

sequential_chain.run(state_of_the_union)

' In an address to the nation, the speaker acknowledges the hardships of the past year due to the COVID-19 pandemic, but emphasizes that regardless of political affiliation, all Americans can come together.'