Build an AI-Powered Search Engine with HeatWave GenAI (part 1)

Traditional SQL search relies on structured queries (SELECT, WHERE, JOIN, …) and exact or partial matches based on conditions (e.g., WHERE name = 'Olivier' / WHERE name LIKE '%Olivier%').

A typical query may look like:

SELECT title 
FROM articles 
WHERE Category = 'HeatWave' OR tag LIKE "%AI%";        

While efficient for structured data, it has limited flexibility for search variations and fails to grasp context or intent.

An alternative is SQL Full-Text Search (FTS), which enables efficient keyword-based searches across large text datasets. For example, MySQL implements FTS using MATCH and AGAINST (e.g. MATCH(name) AGAINST(‘Olivier’)). This feature indexes text content within database columns, allowing for advanced search capabilities such as phrase matching, proximity searches, and relevance scoring.

A typical query may look like:

SELECT * FROM articles
WHERE MATCH (title, body)
AGAINST ('HeatWave' IN NATURAL LANGUAGE MODE);        

FTS is usually faster and more relevant than basic SQL searches, efficiently handling large text fields. However, it remains keyword-based rather than semantic, meaning it may overlook context-based variations.

Another option is AI-powered search using large language models (LLMs), also known as semantic search. Unlike keyword-based methods, it leverages embeddings — vector representations of words or sentences — to understand meaning. This enables it to handle synonyms, paraphrasing, and contextual relationships (e.g., searching for ‘AI’ may also return articles on ‘machine learning’). Additionally, it often integrates retrieval-augmented generation (RAG) to enhance responses with external knowledge.

In this article, we’ll dive deeper into AI-powered search using an LLM with the help of HeatWave GenAI…

https://dasini.net/blog/2025/03/13/build-an-ai-powered-search-engine-with-heatwave-genai-part-1/