77 lines
2.1 KiB
Markdown
77 lines
2.1 KiB
Markdown
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# Instant Distance: fast HNSW indexing
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[](https://github.com/InstantDomain/instant-distance/actions?query=workflow%3ACI)
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[](LICENSE-MIT)
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[](LICENSE-APACHE)
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Instance Distance is a fast pure-Rust implementation of the [Hierarchical
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Navigable Small Worlds paper][paper] by Malkov and Yashunin for finding
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approximate nearest neighbors. This implementation powers the
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[InstantDomainSearch.com][ids] backend services used for word vector indexing.
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## What it does
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Instant Distance is an implementation of a fast approximate nearest neighbor
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search algorithm. The algorithm is used to find the closest point(s) to a given
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point in a set. As one example, it can be used to make [simple translations][translations].
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## Using the library
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### Rust
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```toml
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[dependencies]
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instant-distance = "0.5.0"
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```
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## Example
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```rust
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use instant_distance::{Builder, Search};
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fn main() {
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let points = vec![Point(255, 0, 0), Point(255, 0, 0), Point(255, 0, 0)];
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let values = vec!["red", "green", "blue"];
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let map = Builder::default().build(points, values);
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let mut search = Search::default();
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let cambridge_blue = Point(163, 193, 173);
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let closest_point = map.search(&cambridge_blue, &mut search).next().unwrap();
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println!("{:?}", closest_point.value);
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}
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#[derive(Clone, Copy, Debug)]
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struct Point(isize, isize, isize);
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impl instant_distance::Point for Point {
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fn distance(&self, other: &Self) -> f32 {
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// Euclidean distance metric
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(((self.0 - other.0).pow(2) + (self.1 - other.1).pow(2) + (self.2 - other.2).pow(2)) as f32)
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.sqrt()
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}
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}
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```
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## Testing
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Rust:
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```
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cargo t -p instant-distance --all-features
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```
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Python:
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```
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make test-python
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```
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[paper]: https://arxiv.org/abs/1603.09320
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[ids]: https://instantdomainsearch.com/
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[translations]: https://instantdomainsearch.com/engineering/how-to-use-fasttext-for-instant-translations
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