Working with ANN Indexes in Rust

• Building and Searching an index

Building and Searching an index

use cuvs::cagra::{Index, IndexParams};
use cuvs::{Resources, Result};

use ndarray_rand::rand_distr::Uniform;
use ndarray_rand::RandomExt;

/// Example showing how to index and search data with CAGRA
fn cagra_example() -> Result<()> {
    let res = Resources::new()?;

    // Create a new random dataset to index
    let n_datapoints = 65536;
    let n_features = 512;
    let dataset =
        ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));

    // build the cagra index
    let build_params = IndexParams::new()?;
    let index = Index::build(&res, &build_params, &dataset)?;

    // use the first 4 points from the dataset as queries : will test that we get them back
    // as their own nearest neighbor
    let n_queries = 4;
    let queries = dataset.slice(s![0..n_queries, ..]);

    let k = 10;

    // CAGRA search API requires queries and outputs to be on device memory
    // copy query data over, and allocate new device memory for the distances/ neighbors
    // outputs
    let queries = ManagedTensor::from(&queries).to_device(&res)?;
    let mut neighbors_host = ndarray::Array::<u32, _>::zeros((n_queries, k));
    let neighbors = ManagedTensor::from(&neighbors_host).to_device(&res)?;

    let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
    let distances = ManagedTensor::from(&distances_host).to_device(&res)?;

    let search_params = SearchParams::new()?;

    index.search(&res, &search_params, &queries, &neighbors, &distances)?;

    // Copy back to host memory
    distances.to_host(&res, &mut distances_host)?;
    neighbors.to_host(&res, &mut neighbors_host)?;

    // nearest neighbors should be themselves, since queries are from the
    // dataset
    println!("Neighbors {:?}", neighbors_host);
    println!("Distances {:?}", distances_host);

    Ok(())
}