a tiny vectorstore implementation built with numpy. that's it.
numpy is all you need?
tired of hearing about all the available vectorstore libraries along with buzzwords thrown around, here is a tiny implementation of a "Vectorstore" built with numpy and sentence-transformers in python.
this implementation is only ~100 lines of python code but still works fast enough (on cpu).
lol, lmao even.
this is how the code to use this Vectostore will look like.
code:
docs = [
"Super mario is a nice video game.",
"The USA election are on the way!",
"A video game is fun to play with friends.",
"What if the earth was covered with plasma instead of water?"
]
vs = Vectorstore.from_docs(docs, embedder=model)
query = "which is a nice game you can think of?"
similar_docs, scores = vs.search(query, k=2)output:
Most similar documents: ['A video game is fun to play with friends.', 'Super mario is a nice video game.']
Scores w.r.t query (lower is better): [14.200933, 15.170744]