README.md

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]

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)

Roadmap

• #3 Persistence / serialization