This tutorial will explore building dashboards using Large Language Models (LLMs) and LangChain. We will use LangChain chains to extract insights from BigQuery by first generating SQL and then pushing the generated SQL to BigQuery as views, forming the basis for interactive dashboards.
Extracting insights from customer data can be a time-consuming process, especially for those who lack advanced SQL skills. Many companies struggle to gain a clear understanding of important acquisition and retention metrics such as repeat revenue, customer lifetime value (CLV), churn rates, or product bundling opportunities. Such a knowledge gap can impede business growth.
Fortunately, large language models (LLMs) have fundamentally changed how we approach data analysis. By leveraging LLMs, we can extract meaningful information from databases with remarkable efficiency, effectively reducing the time required by a factor of ten. This opens new business possibilities to uncover actionable insights and make informed strategic decisions quickly.
In this tutorial, I will explore how to harness the capabilities of LLMs and LangChain to build interactive dashboards that provide a comprehensive view of your business data. I will use LangChain chains to extract insights from BigQuery and build the dashboards in Looker Studio. The process involves generating SQL queries using LLMs and then pushing these queries to BigQuery as views. While I will use BigQuery and Looker Studio as example platforms, the concepts and techniques covered in this tutorial can be easily extended to other data warehouses and dashboard tools.
In this tutorial, you will learn to:
Set up the necessary environment and dependencies
Fetch BigQuery schemas to understand the structure of your data
Connect to BigQuery using the appropriate credentials
Configure and utilize LLMs, such as Claude-3, to generate SQL queries
Create BigQuery views based on the generated SQL queries
Load and process insights from a file to automate view creation
Generate meaningful view names using LLMs
Handle query execution failures and retries
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By the end of this tutorial, you will have a solid foundation for building dashboards using LLMs and LangChain, enabling you to unlock valuable insights from your business data efficiently.
The Colab Notebook
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