Transformation

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.

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

API Reference:

• TransformChain from langchain.chains
• OpenAI from langchain.llms
• PromptTemplate from langchain.prompts

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

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)

    ' 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.'