Go Interface

Basic Usage

package main

import (

func main() {
     f := 3
     t := n2.NewHnswIndex(f)
     for i := 0; i < 1000; i++ {
       item := make([]float32, 0, f)
       for x:= 0; x < f; x++ {
           item = append(item, rand.Float32())
     t.Build(5, 10, 4, 10, 3.5, "heuristic", "skip")
     var result []int
     var distance []float32
     t.SearchByVector([]float32{2, 1, 0}, 1000, -1, &result, &distance)

You can see more code examples at examples/go.

Main Interface

HnswIndex(dim, metric)

  • Returns a new Hnsw index.

  • dim (int): Dimension of vectors.

  • metric (string): An optional parameter to choose a distance metric ('angular' | 'L2' | 'dot').


  • Adds vector v.

  • v (list of float): A vector with dimension dim.

index.Build(M, Max_M0, ef_construction, n_threads, mult, neighbor_selecting, graph_merging)

  • Builds a hnsw graph with given configurations.

  • M (int): Max number of edges for nodes at level > 0 (default: 12).

  • Max_M0 (int): Max number of edges for nodes at level == 0 (default: 24).

  • ef_construction (int): efConstruction (see HNSW paper.) (default: 100).

  • n_threads (int): Number of threads for building index.

  • mult (float): Level multiplier (recommended: use default value) (default: 1/log(1.0*M)).

  • neighbor_selecting (string): Neighbor selecting policy.

    • Available values

      • "heuristic" (default): Select neighbors using algorithm4 on HNSW paper (recommended).

      • "naive": Select closest neighbors (not recommended).

  • graph_merging (string): Graph merging heuristic.

    • Available values

      • "skip" (default): Do not merge (recommended for large-scale data (over 10M)).

      • "merge_level0": Performs an additional graph build in reverse order, then merges edges at level 0. So, it takes twice the build time compared to "skip" but shows slightly higher accuracy. (recommended for data under 10M scale).


  • Saves the index to disk.

  • fname (string)

index.LoadModel(fname, use_mmap)

  • Loads an index from disk with mmap.

  • fname (string)

  • use_mmap (bool): An optional parameter (default: true). If this parameter is set, N2 loads model through mmap.


  • Unloads (unmap) the index.

index.SearchByVector(item_id, k, ef_search=-1, vectors, distances)

  • Returns k nearest items (as vectors) to a query item.

  • ef_search (int): (default: 50 * k).

index.SearchById(v, k, ef_search=-1, vectors, distances)

  • Returns k nearest items (as ids) to a query item.

  • v (list of float): A query vector.

  • k (int)

  • ef_search (int): (default: 50 * k).


Currently, batch search functions are not supported in Go binding.