HNSWIndex

Perform KNN Query for millions of data points fast and with great accuracy.

HNSWIndex is a .NET library for constructing approximate nearest-neighbor (ANN) indices based on the Hierarchical Navigable Small World (HNSW) graph. This data structure provides efficient similarity searches for large, high-dimensional datasets.

Key Features

• High Performance: Implements the HNSW algorithm for fast approximate k-NN search.
• Flexible Distance Metric: Pass any Func<TVector, TVector, TDistance> for custom distance calculation.
• Flexible Heuristic: Pass heuristic function for nodes linking.
• Concurrency Support: Thread safe graph building API
• Configurable Parameters: Fine-tune the indexing performance and memory trade-offs with parameters
• Save and Load: Save resulting structure on file system and restore later

Installation

Install via NuGet:

dotnet add package HNSWIndex

Or inside your .csproj:

<PackageReference Include="HNSWIndex" Version="x.x.x" />

Getting Started

1. Optionally configure parameters

var parameters = new HNSWParameters
{ 
    RandomSeed = 123,
    DistributionRate = 1.0,
    MaxEdges = 16,
    CollectionSize = 1024,
    // ... other parameters
};

2. Create empty graph structure ()

var index = new HNSWIndex<float[], float>(Metrics.SquaredEuclideanMetric.Compute, parameters);

3. Build the graph

var vectors = RandomVectors();
foreach (var vector in vectors)
{
	index.Add(vector);
}

Or multi-threaded

var vectors = RandomVectors();
Parallel.For(0, vectors.Count, i => {
    index.Add(vectors[i]);
});

4. Query the structure

var k = 5;
var results = index.KnnQuery(queryPoint, k);

5. Save and Load graph from file system