@simon: Simon Willison:Embeddings: What they are and why they matter <– All that we see is but a dream within a dream of an array of 1536 dimensional vectors LOL 🙂 -> QUOTE:I currently have 472 articles on my site. I calculated the 1,536 dimensional embedding vector (array of floating point numbers) for each of those articles, and stored those vectors in my site’s SQLite database....Now, if I want to find related articles for a given article, I can calculate the cosine similarity between the embedding vector for that article and every other article in the database, then return the 10 closest matches by distance.