dasini.net – Diary of a MySQL expert

In this part 2 we focused on enhancing search relevance. We introduced reranking techniques using weighted distances of titles and excerpts to refine initial search results. Then we delved into leveraging article summaries for more effective semantic search, utilizing HeatWave’s capability to execute JavaScript stored procedures for sanitizing HTML content and generating these summaries. Finally, we demonstrated how to create embeddings from these summaries and perform similarity searches, showcasing HeatWave GenAI’s power for advanced information retrieval directly within the database.

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.

While HeatWave GenAI excels at generating English text, its capabilities extend to translation as well. Let’s explore how we can effectively translate English content into French using this powerful tool.