This article explains how to use vector embeddings to quantify the similarity between SQL queries. It covers techniques for generating embeddings, storing queries, and analyzing their relationships through clustering and distance measurements. The approach enhances understanding of user behavior and query efficiency in data lakes.

Complete the intermediate course on implementing multimodal vector search with BigQuery, which takes 1 hour and 45 minutes. Participants will learn to use Gemini for SQL generation, conduct sentiment analysis, summarize text, generate embeddings, create a Retrieval Augmented Generation (RAG) pipeline, and perform multimodal vector searches.