Go_API.rst

Go code example

package main

import (
    "n2"
    "fmt"
    "math/rand"
)

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.AddData(item)
     }
     t.Build(5, 10, 4, 10, 3.5, "heuristic", "skip")
     t.PrintConfigs()
     t.SaveModel("test.ann")
     var result []int
     var distance []float32
     t.SearchByVector([]float32{2, 1, 0}, 1000, -1, &result, &distance)
     fmt.Println(result)
     fmt.Println(distance)
}

Go API

Note that if a user passes a negative value it, will be set to a default value when the metric has the default value.

• HnswIndex(dim, metric): returns a new Hnsw index
  • dim (int): dimension of vectors
  • metric (string): an optional parameter for choosing a metric of distance. (‘L2’|'euclidean'|‘angular’)
• index.AddData(v): 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 (recommend: 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 of data(over 10M))
      • "merge_level0": build an another graph in reverse order. then merge edges of level0 (recommended for under 10M scale data)
• index.SaveModel(fname): 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 value is true. If this parameter is set, N2 loads model through mmap
• index.UnloadModel(): unloads (unmap)
• index.SearchByVector(item_id, k, ef_serach=-1, vectors, distances): returns k nearest items.
  • ef_search (int): default value = 50 * k
• index.SearchById(v, k, ef_serach=-1, vectors, distances): returns k nearest items.
  • v (list of float): a query vector
  • k (int)
  • ef_search (int): default value = 50 * k