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Harrison ChaseandShreya Rajpal 985496f4be Docs refactor (#480 )

Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:

- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.

There is also a full reference section, as well as extra resources
(glossary, gallery, etc)

Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>

2023-01-02 08:24:09 -08:00

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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.'

In [ ]:

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Transformation Chain

3.2 KiB

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