dasini.net – Diary of a MySQL expert

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.

This new AI tech, called generative AI (or GenAI), can dive deep into what people are saying and tell us if they’re feeling positive, negative, or neutral.
Let’s see how HeatWave GenAI, can help you to enhance your understanding of customer sentiment, improve decision-making, and drive business success.

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.

This article guides you through setting up replication between a HeatWave MySQL instance (source) and an on-premise standalone MySQL instance (replica).
It highlights key steps like creating a replication user, securing the connection, and using MySQL Shell utilities for data transfer.