This article explains how Google integrated vector search into BigQuery, simplifying the process of using embeddings for data analysis. It details the challenges faced before this integration and highlights the benefits of the new serverless architecture, including easier index management and immediate data accessibility.

This article explains how flattening structured JSON data into natural language improves vector search performance. It details the challenges of tokenization and attention mechanisms in raw JSON, demonstrating that a simple preprocessing step can enhance retrieval metrics significantly.

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.