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Snowflake for DevelopersGuidesBuilding an LLM (Large Language Model) Application

Building an LLM (Large Language Model) Application

Chanin Nantasenamat
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Overview

This solution architecture covers how to build an LLM application that uses different tool stack. It covers several use-cases such as:

  • Text summarization with LLMs using Streamlit and Langchain
  • Data exploration with LLM powered chatbot
  • Run sentiment analysis and Text-to-SQL conversion using Snowflake and OpenAI

Solution Architecture: Text Summarization with LLMs

Architecture Diagram
  • The user submits an input text to be summarized into the Streamlit app frontend.
  • The app pre-processes the text by splitting it into several chunks, creating documents for each chunk, and applying the summarization chain with the help of the LLM model.
  • After a few moments, the summarized text is displayed in the app.

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Updated 2026-04-29

This content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances