Python_API.rst

Python code example

from n2 import HnswIndex
import random

f = 40
t = HnswIndex(f) # HnswIndex(f, "L2, euclidean, or angular")
for i in xrange(1000):
    v = [random.gauss(0, 1) for z in xrange(f)]
    t.add_data(v)

t.build(m=5, max_m0=10, n_threads=4)
t.save('test.hnsw')

u = HnswIndex(f)
u.load('test.hnsw')
print(u.search_by_id(0, 1000))

Python 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.add_data(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=150)
  • 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.save(fname): saves the index to a disk
  • fname (string)
• index.load(fname): loads an index from a disk.
  • fname (string)  - use_mmap (bool): An optional parameter, default value is true. If this parameter is set, N2 loads model through mmap.
• index.unload(): unloads (unmap)
• index.search_by_id(item_id, k, ef_serach=-1, include_distances=False): returns k nearest items.
  • item_id (int)
  • k (int)
  • ef_search (int): default value = 50 * k
  • include_distances (boolean): If you set this argument to True, it will return a list of tuples((item_id, distance)).
• index.search_by_vector(v, k, ef_serach=-1, include_distances=False): returns k nearest items.
  • v (list of float): a query vector
  • k (int)
  • ef_search (int): default value = 50 * k
  • include_distances (boolean): If you set this argument to True, it will return a list of tuples((item_id, distance)).