How We Built a Content Recommendation System With Pgai and Pgvectorscale
We at Pondhouse Data build customized AI applications as part of our professional services, from self-hosted chat applications to tailored AI model training, like content recommendation systems. All our projects have the following requirements:
That's where we fell in love with pgai and pgvectorscale, as both tools perfectly fit our requirements and were essentially a milestone for building AI projects in PostgreSQL.
In this blog post, we'll show you one of our more recent projects, provided as a hands-on guide. We'll walk you through our process of building a content recommendation system for SEO-related internal link building using pgai and pgvectorscale. A content recommendation system is an algorithmic tool used to suggest relevant content (such as articles, products, or media) to users based on their preferences, behavior, or characteristics.
At the end of this post, you'll have a sound understanding of how to use both pgai and pgvectorscale, and also why both tools are currently one of the best solutions for the problems they are solving.
