hnsw

Package hnsw implements Hierarchical Navigable Small World graphs in Go. You can read up about how they work here. In essence, they allow for fast approximate nearest neighbor searches with high-dimensional vector data.

This package can be thought of as an in-memory alternative to your favorite vector database (e.g. Pinecone, Weaviate). It implements just the essential operations:

Operation	Complexity	Description
Insert	$O(log(n))$	Insert a vector into the graph
Delete	$O(M^2 \cdot log(n))$	Delete a vector from the graph
Search	$O(log(n))$	Search for the nearest neighbors of a vector
Lookup	$O(1)$	Retrieve a vector by ID

Note

Complexities are approximate where $n$ is the number of vectors in the graph and $M$ is the maximum number of neighbors each node can have. This paper is a good resource for understanding the effect of the various construction parameters.

Usage

go get github.com/coder/hnsw@main

g := hnsw.NewGraph[hnsw.Vector]()
g.Add(
    hnsw.MakeVector("1", []float32{1, 1, 1}),
    hnsw.MakeVector("2", []float32{1, -1, 0.999}),
    hnsw.MakeVector("3", []float32{1, 0, -0.5}),
)

neighbors := g.Search(
    []float32{0.5, 0.5, 0.5},
    1,
)
fmt.Printf("best friend: %v\n", neighbors[0].Embedding())
// Output: best friend: [1 1 1]

Persistence

While all graph operations are in-memory, hnsw provides facilities for loading/saving from persistent storage.

For an io.Reader/io.Writer interface, use Graph.Export and Graph.Import.

If you're using a single file as the backend, hnsw provides a convenient SavedGraph type instead:

path := "some.graph"
g1, err := LoadSavedGraph[hnsw.Vector](path)
if err != nil {
    panic(err)
}
// Insert some vectors
for i := 0; i < 128; i++ {
    g1.Add(MakeVector(strconv.Itoa(i), []float32{float32(i)}))
}

// Save to disk
err = g1.Save()
if err != nil {
    panic(err)
}

// Later...
// g2 is a copy of g1
g2, err := LoadSavedGraph[Vector](path)
if err != nil {
    panic(err)
}

See more:

• Export
• Import
• SavedGraph

We use a fast binary encoding for the graph, so you can expect to save/load nearly at disk speed. On my M3 Macbook I get these benchmark results:

goos: darwin
goarch: arm64
pkg: github.com/coder/hnsw
BenchmarkGraph_Import-16            2733            369803 ns/op         228.65 MB/s      352041 B/op       9880 allocs/op
BenchmarkGraph_Export-16            6046            194441 ns/op        1076.65 MB/s      261854 B/op       3760 allocs/op
PASS
ok      github.com/coder/hnsw   2.530s

when saving/loading a graph of 100 vectors with 256 dimensions.

Performance

By and large the greatest effect you can have on the performance of the graph is reducing the dimensionality of your data. At 1536 dimensions (OpenAI default), 70% of the query process under default parameters is spent in the distance function.

If you're struggling with slowness / latency, consider:

• Reducing dimensionality
• Increasing $M$

And, if you're struggling with excess memory usage, consider:

• Reducing $M$ a.k.a Graph.M (the maximum number of neighbors each node can have)
• Reducing $m_L$ a.k.a Graph.Ml (the level generation parameter)

Memory Overhead

The memory overhead of a graph looks like:

$$ \displaylines{ mem_{graph} = n \cdot \log(n) \cdot \text{size(id)} \cdot M \\ mem_{base} = n \cdot d \cdot 4 \\ mem_{total} = mem_{graph} + mem_{base} } $$

where:

• $n$ is the number of vectors in the graph
• $\text{size(id)}$ is the average size of the ID in bytes
• $M$ is the maximum number of neighbors each node can have
• $d$ is the dimensionality of the vectors
• $mem_{graph}$ is the memory used by the graph structure across all layers
• $mem_{base}$ is the memory used by the vectors themselves in the base or 0th layer

You can infer that:

• Connectivity ($M$) is very expensive if IDs are large
• If $d \cdot 4$ is far larger than $M \cdot \text{size(id)}$, you should expect linear memory usage spent on representing vector data
• If $d \cdot 4$ is far smaller than $M \cdot \text{size(id)}$, you should expect $n \cdot \log(n)$ memory usage spent on representing graph structure

In the example of a graph with 256 dimensions, and $M = 16$, with 8 byte IDs, you would see that each vector takes:

• $256 \cdot 4 = 1024$ data bytes
• $16 \cdot 8 = 128$ metadata bytes

and memory growth is mostly linear.