When building a search for an application, you typically face two broad approaches:
- Traditional keyword-based search — match words exactly or with simple variants.
- Semantic (or vector) search — match meaning or context using AI embeddings.
There’s also a hybrid approach, but I will leave that for a future article. Instead, in this post, I’ll walk you through how the two broad approaches work in Python using MariaDB and an AI embedding model, highlight where they differ, and show code that you can adapt.