Implementing Semantic Search With Sequel and Pgvector - Custom AI Solutions

In my previous post, An LLM-based AI Assistant for the FastRuby.io Newsletter opens a new window, I introduced an AI-powered assistant we built with Sinatra to help our marketing team write summaries of blog posts for our newsletter. In this post, I’ll go over how we implemented semantic search using pgvector and Sequel to fetch examples of previous summaries based on article content. Semantic search allows our AI assistant to find the most relevant past examples, given meaning and context, when generating new summaries. This helps ensure consistency in tone and style while providing context-aware results that will serve as better examples for the large language modal (LLM) to generate new summaries, improving the quality of the generated output.