if you have small data requirements (less than 100mb) I just run FAISS for embeddings and store the rows of data and vectors in JSON. I then have a small pytho

if you have small data requirements (less than 100mb) I just run FAISS for embeddings and store the rows of data and vectors in JSON. I then have a small python script to run a vector similarity algo like cosine similarity or whatever. It's not the fanciest or most efficient, but it works surprisingly well. I use it to search/rank my own blog posts (~300+) for relevancy. So the entire thing is only like 10mb. Probably will get super slow for really large dataasets