dasini.net – Diary of a MySQL expert

In this latest post, the final part of my series on building an AI-powered search engine with HeatWave GenAI, I dive into enhancing AI-powered search by embedding full article content into HeatWave.

By cleaning HTML, chunking content, generating embeddings, and running semantic similarity searches directly within HeatWave, we unlock highly relevant, context-rich search results.
If you’re building smart search engines or working with RAG pipelines, you’ll find practical tips and SQL-powered techniques you can apply right away.

This series demonstrates the powerful capabilities of HeatWave GenAI for building sophisticated, in-database AI-driven search solutions. Check out the article to level up your understanding of semantic search!

AI-powered applications require robust and scalable database solutions to manage and process large amounts of data efficiently. HeatWave is an excellent choice for such applications, providing high-performance OLTP, analytics, machine learning and generative artificial intelligence capabilities.

In this article, we will explore a Python 3 script that connects to an HeatWave instance and enables users to interact with different large language models (LLMs) dynamically.