# bfes

## Brute force embedding search

Given a set of embeddings, and a query embedding, this algorithm searches for the
nearest embeddings for each query. As it is a brute force search, you don't
need to regenerate the index and can add new embeddings to the index at any
time. The algorithm is O(n) in time and space.

## Performance

Time to brute force search 100,000 512-dimensional embeddings:

Windows 11, AMD Ryzen 9 5950x @ 3.4 GHz

test tests::bench_cosine_similarity ... bench:  20,018,120 ns/iter (+/- 2,042,521)

Mac OS X, M1 Max MacbookPro18,4

test tests::bench_cosine_similarity ... bench:  11,302,216 ns/iter (+/- 185,505)

Mac OS X, M1 Macmini9,1

test tests::bench_cosine_similarity ... bench:  9,559,170 ns/iter (+/- 592,620)

Ubuntu 18.04, AWS c6i.large

test tests::bench_cosine_similarity ... bench:  25,452,715 ns/iter (+/- 848,001)

Ubuntu 22.04, AWS c6a.large

test tests::bench_cosine_similarity ... bench:  17,306,132 ns/iter (+/- 78,319)

Ubuntu 22.04, AWS c6g.medium

test tests::bench_cosine_similarity ... bench:  30,262,326 ns/iter (+/- 64,509)