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!

Discover how to build an AI-powered search engine for your applications using HeatWave GenAI. This approach leverages large language models (LLMs) for semantic search, offering a smarter alternative to traditional SQL and full-text search methods. By using embeddings—vector representations of words—the search engine understands context and intent, delivering more relevant results.

In this article, I’ll guide you through building an AI-powered search for a WordPress blog using HeatWave GenAI, focusing on its in-database LLMs and vector store capabilities. We’ll create embeddings for post titles and excerpts to enable semantic search, ensuring users find the most relevant content quickly.

In the previous episode we’ve seen how to use an OCI Bastion session in order to provide a restricted and time-limited access to administer our MySQL instance.
One more step to our Discovering MySQL Database Service journey.

In this episode, we’ll learn how to connect to our MySQL Database Service instance using the fantastic MySQL Shell – an interactive Javascript, Python, or SQL interface supporting development and administration for the MySQL Server.