---
title: "ANNy - Very fast HNSW"
description: "Fast semantic search indices"
image: "https://docs.flowercomputer.com/og/base.png"
---

> Documentation Index
> Fetch the complete documentation index at: https://docs.flowercomputer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# ANNy - Very fast HNSW

### Approximate Nearest Neighbors... yeah (ANNy)
This is a very fast crate for creating and searching HNSWs.

Let's take a look at an example paired with ESE:

```rust
use anny::hnsw::Hnsw;
use anny::metric::Cosine;

const DIM: usize = ese::DIMENSIONS;

// Hnsw is parameterized entirely at compile time:
//   Dtype, Metric, DIM, M_0, K, EF_SEARCH, EF_BUILD, MAX_LEVEL
// here: ese embedding dim, cosine distance, return top-2 neighbors.
type Index = Hnsw<f32, Cosine, DIM, 32, 2, 64, 128, 16>;

fn main() {
// every index starts empty
let mut ix: Index = Hnsw::new(Cosine, 1);

let docs = ["potato", "root vegetable", "spaceship"];
for doc in docs {
    // insert returns a stable u32 id for the vector
    ix.insert(ese::encode_single(doc));
}

// search returns up to K nearest (distance, id), ascending by distance
// (cosine distance: smaller = closer)
let hits = ix.search(&ese::encode_single("tuber"));
for (dist, id) in hits {
    println!("{dist:.3}  {}", docs[id as usize]);
}
// potato and root vegetable beat spaceship
}
```

Source: https://docs.flowercomputer.com/bogkit/anny/index.md