import annoy
import numpy as np
import scipy.spatial.distance as ssd

dim = 20
index = annoy.AnnoyIndex(dim, 'angular')

vectors = []
for i in range(1000):
    vec = np.random.normal(size=dim)
    vec /= np.linalg.norm(vec)

    index.add_item(i, vec)
    vectors.append(vec)

index.build(10)

query = np.random.normal(size=dim)
query /= np.linalg.norm(query)
nns, distances = index.get_nns_by_vector(query, 10, include_distances=True)
for nn_idx, dist in zip(nns, distances):
    print('annoy_dist = {}\teuclidean = {}\tdot = {}'.format(dist, ssd.euclidean(vectors[nn_idx], query), np.dot(vectors[nn_idx], query)))