Imagine: you need a data structure which allows you to store vectors in memory and then say "here is a vector A, give me the 10 vectors which are closest to the

Imagine: you need a data structure which allows you to store vectors in memory and then say "here is a vector A, give me the 10 vectors which are closest to the same direction of this vector A, in n dimensional space". One can imagine there is some optimal way to lay out the data in memory such that it would be relatively quick to do that. One can imagine a naive way to do it which probably wouldn't be fast. A vector DB does the first thing - lays out the data in a way which then enables it to do that fast. And then it does all the other stuff a DB does - persisting to disk (which means data needs to be laid out in a sensible way on disk too), handling multiple queries, updates, and the 50 other complicated things databases tend to do. Users generally want to do other operations on vector as well so a vector DB does those too. For a small number of vectors you can build a vector DB yourself. Write a list of vectors to a file, load them into memory in no particular order, then for your "n closest" function, just iterate through the list calculating the difference in direction one by one and keeping the top n. Your simple system will work just fine for a toy demo.