| output |
|---|
github_document |
{rsimsimd} is a light R wrapper around the rust crate simsimd.
It allows efficient calculation of similarity metrics using SIMD operations
You can install the package like so:
# dev version
# install.packages("remotes")
# remotes::install_github("DavZim/rsimsimd")
# CRAN version
install.packages("rsimsimd")
This is a basic example which shows you how to solve a common problem:
library(rsimsimd)
# a simple cosine similarity calculation
dist_cosine(c(1, 2, 3),
c(4, 5, 6))
#> [1] 0.9746317
# more realistic embedding use case
# with 1536 (OpenAI) embedding dimensions
n_dimensions <- 1536
set.seed(123)
vec1 <- rnorm(n_dimensions)
vec2 <- rnorm(n_dimensions)
dist_cosine(vec1, vec2)
#> [1] -0.01064177
# if you have a list (or a vector) of embeddings (eg a database)
# and you want to compare a list of vectors, you can achieve this like so
# simulate a DB of 1000 embedding vectors
db <- lapply(seq(1000), function(x) rnorm(n_dimensions))
# simulate a lookup of 3 embedding vectors
lookup <- lapply(seq(3), function(x) rnorm(n_dimensions))
res <- dist_cosine(lookup, db)
# one row for each lookup, one column for each DB entry
dim(res)
#> [1] 3 1000
-
[
dist_cosine()] Cosine Similarity Matrix [ ] dist_dot [ ] dist_sqeuclidean [ ] div_jensenshannon [ ] div_kullbackleibler -
[
get_capabilities()] reports current hardware capabilities
×ばつ 6 #> expression min median `itr/sec` mem_alloc `gc/sec` #> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> #> 1 lsa_cosine(v1, v2) 6.79μs 7.24μs 118470. 0B 0 #> 2 dist_cosine(v1, v2) 6.72μs 8.5μs 79961. 0B 0 # compare 1 embedding to 1'000 embeddings ll_1k <- lapply(seq(1000), function(i) rnorm(n_dimensions)) bench::mark( sapply(ll_1k, function(ll) lsa_cosine(v1, ll)), dist_cosine(v1, ll_1k), check = FALSE # rounding errors ) #> # A tibble: 2 ×ばつ 6 #> expression min median `itr/sec` mem_alloc `gc/sec` #> <bch:expr> <bch:> <bch:> <dbl> <bch:byt> <dbl> #> 1 sapply(ll_1k, function(ll) lsa_cos... 7.94ms 8.52ms 112. 31.7KB 2.07 #> 2 dist_cosine(v1, ll_1k) 1.25ms 1.78ms 521. 35.6KB 0 # compare 1 embedding to 100'000 embeddings ll_100k <- lapply(seq(100000), function(i) rnorm(n_dimensions)) bench::mark( sapply(ll_100k, function(ll) lsa_cosine(v1, ll)), dist_cosine(v1, ll_100k), check = FALSE # rounding errors ) #> Warning: Some expressions had a GC in every iteration; so filtering is #> disabled. #> # A tibble: 2 ×ばつ 6 #> expression min median `itr/sec` mem_alloc `gc/sec` #> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> #> 1 sapply(ll_100k, function(ll) l... 1.07s 1.07s 0.932 3.29MB 0.932 #> 2 dist_cosine(v1, ll_100k) 170.37ms 178.28ms 5.67 5.96MB 0 # 1k x 1k comparisons => 1mln comparisons bench::mark( dist_cosine(ll_1k) ) #> # A tibble: 1 ×ばつ 6 #> expression min median `itr/sec` mem_alloc `gc/sec` #> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> #> 1 dist_cosine(ll_1k) 299ms 307ms 3.26 7.66MB 0">
# alternative implementation of cosine, taken from lsa
# see also https://github.com/cran/lsa/blob/master/R/cosine.R
lsa_cosine <- function(x, y) {
as.vector(crossprod(x, y) / sqrt(crossprod(x) * crossprod(y)))
}
set.seed(123)
n_dimensions <- 1536
v1 <- rnorm(N)
v2 <- rnorm(N)
bench::mark(
lsa_cosine(v1, v2),
dist_cosine(v1, v2)
)
#> # A tibble: 2 ×ばつ 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 lsa_cosine(v1, v2) 6.79μs 7.24μs 118470. 0B 0
#> 2 dist_cosine(v1, v2) 6.72μs 8.5μs 79961. 0B 0
# compare 1 embedding to 1'000 embeddings
ll_1k <- lapply(seq(1000), function(i) rnorm(n_dimensions))
bench::mark(
sapply(ll_1k, function(ll) lsa_cosine(v1, ll)),
dist_cosine(v1, ll_1k),
check = FALSE # rounding errors
)
#> # A tibble: 2 ×ばつ 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:> <bch:> <dbl> <bch:byt> <dbl>
#> 1 sapply(ll_1k, function(ll) lsa_cos... 7.94ms 8.52ms 112. 31.7KB 2.07
#> 2 dist_cosine(v1, ll_1k) 1.25ms 1.78ms 521. 35.6KB 0
# compare 1 embedding to 100'000 embeddings
ll_100k <- lapply(seq(100000), function(i) rnorm(n_dimensions))
bench::mark(
sapply(ll_100k, function(ll) lsa_cosine(v1, ll)),
dist_cosine(v1, ll_100k),
check = FALSE # rounding errors
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 ×ばつ 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 sapply(ll_100k, function(ll) l... 1.07s 1.07s 0.932 3.29MB 0.932
#> 2 dist_cosine(v1, ll_100k) 170.37ms 178.28ms 5.67 5.96MB 0
# 1k x 1k comparisons => 1mln comparisons
bench::mark(
dist_cosine(ll_1k)
)
#> # A tibble: 1 ×ばつ 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 dist_cosine(ll_1k) 299ms 307ms 3.26 7.66MB 0