BBQvec is an open-source, embedded vector database index for Go and Rust, providing approximate K-nearest-neighbors (aKNN).
Read more about the algorithm on our blog!
package main
import (
"fmt"
bbq "github.com/daxe-ai/bbqvec"
)
func main() {
// Declare store parameters
dimensions := 200
nBasis := 10
// Initialize the store
backend := bbq.NewMemoryBackend(dimensions)
datastore, _ := bbq.NewVectorStore(backend, nBasis)
// Create some test data, 100K random vectors
vecs := bbq.NewRandVectorSet(100_000, dimensions, nil)
datastore.AddVectorsWithOffset(0, vecs)
/*
Equivalent to:
for i, v := range vecs {
datastore.AddVector(bbq.ID(i), v)
}
*/
// Run a query
targetVec := bbq.NewRandVector(dimensions, nil)
results, _ := datastore.FindNearest(targetVec, 10, 1000, 1)
// Inspect the results
top := results.ToSlice()[0]
vec, _ := backend.GetVector(top.ID)
fmt.Println(top.ID, vec, top.Similarity)
}
use bbqvec::IndexIDIterator;
fn main() -> Result<()> {
// Declare store parameters
let dimensions = 200;
let n_basis = 10;
// Initialize the store
let mem = bbqvec::MemoryBackend::new(dimensions, n_basis)?;
let mut store = bbqvec::VectorStore::new(mem)?;
// Create some test data, 100K random vectors
let vecs = bbqvec::create_vector_set(dimensions, 100000);
store.add_vector_iter(vecs.enumerate_ids())?;
// Run a query
let target = bbqvec::create_random_vector(dimensions);
let results = store.find_nearest(&target, 10, 1000, 1)?;
// Inspect the results
for res in results.iter_results() {
println!("{} {}", res.id, res.similarity)
}
}
We're still early; Go is the more tried-and-true and suits the beta use-case, but Rust is a good deal faster. We welcome contributions.
- More benchmarks
- New Quantizations
- Hamming Distance (single-bit vectors)
- Novel quantizations
- Finish disk backend to match Go (in progress, shortly)
- New Quantizations
Thank you to MariaLetta for the free-gophers-pack and to rustacean.net for the CC0 logo characters.