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Multi-stage LLM Worfklow to Summarize and Translate an Article using LangChain
Latest   Machine Learning

Multi-stage LLM Worfklow to Summarize and Translate an Article using LangChain

Last Updated on May 9, 2024 by Editorial Team

Author(s): Steve George

Originally published on Towards AI.

“An LLMChain is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.

An LLMChain consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.”

In this article, we will create two LLMChain which perform summarization and translation, respectively. And later create a sequence using these two chains.

Below is the overall framework of the approach.

Source: Image by the author

Using google-t5/t5-small, we are building a summarization pipeline. For creating the pipeline, the first thing we require is setting a prompt template. Using the prompt_template package available in langchain, we can define the template.

As per the template defined below, we will summarize the input text. The user can enhance the prompt template by using various methods like including delimiters.

from langchain import PromptTemplatesummary_template = """Write a summarization of the below article in two sentencesArticle:" {article}"Summary: """summary_prompt_template = PromptTemplate(input_variables=['article'], template = summary_template)#input variable value should match the value mentioned in the template. #Multiple variables can be passed based on the use-casearticle="As… Read the full blog for free on Medium.

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Published via Towards AI

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