Practical Gen AI Use Cases - Q&A on Table SQL LangChain & BigQuery - Google Cloud LLM - DIY#7
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Introduction
In this article, we will delve into how to use LangChain on top of BigQuery for natural language processing and running queries programmatically. We will be utilizing Google Cloud's BigQuery with the Iowa liquor sales table dataset. This tutorial will guide you through creating prompts, utilizing SQL Alchemy, and implementing Gen AI for Q&A on BigQuery data.
To begin with, the article covers the essential steps of setting up the environment, authenticating, and initiating the LangChain Vertex platform. It demonstrates how to create a dataset in BigQuery, copy data from the public dataset, create a schema, and establish an SQL Alchemy engine to interact with the data effectively.
The article then moves on to building prompt templates for querying the BigQuery data, providing examples such as counting total invoices, calculating total sales for a specific county and year, identifying stores with the highest sales, and extracting monthly sales data by month.
By following the provided script and examples, users can learn how to effectively utilize LangChain on top of BigQuery for natural language querying and gain insights into generating SQL prompts for their data analysis needs.
Keywords: Gen AI, LangChain, BigQuery, Google Cloud, LLM, SQL Alchemy, Dataset, Prompt Template, Data Analysis, Natural Language Processing, Querying
FAQ:
1. What is LangChain? LangChain is a text-based AI system integrated with BigQuery on Google Cloud that allows for natural language processing and running queries on datasets programmatically.
2. How can I create prompts for querying BigQuery data? You can create prompt templates that define the structure of your queries using input variables such as questions, table names, and query parameters, enabling the generation of SQL queries based on user prompts.
3. What are some practical applications of using LangChain on BigQuery? Some practical applications include counting invoices, calculating total sales for specific criteria, identifying top-performing stores, and extracting specific data points from a dataset using natural language prompts.