langchain/docs/modules/chains/generic/transformation.ipynb

Transformation Chain

This notebook showcases using a generic transformation chain.

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 an LLMChain to summarize those.

In [1]:

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

In [3]:

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

In [4]:

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)

In [5]:

template = """Summarize this text:

{output_text}

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

In [6]:

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

In [7]:

sequential_chain.run(state_of_the_union)

Out [7]:

' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'

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