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!

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.

This tutorial explores HeatWave GenAI, a cloud service that simplifies interacting with unstructured data using natural language. It combines large language models, vector stores, and SQL queries to enable tasks like content generation, chatbot, and retrieval-augmented generation (RAG). The focus is on RAG and how HeatWave GenAI’s architecture helps users gain insights from their data.

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.

Generative artificial intelligence (GenAI) is reshaping the content creation landscape. By training on vast datasets, these “intelligent” systems can produce new, human-quality content across a multitude of domains.

Oracle’s HeatWave GenAI (starting with version 9.0.1) is at the forefront of this revolution, offering an integrated platform that combines in-database large language models (LLMs), vector stores, and scale-out vector processing to streamline content generation.
This article explores how HeatWave GenAI is empowering businesses to produce high-quality content rapidly and effectively, making it an indispensable tool for industries demanding speed, accuracy, and security.