Basic Example Does not work

[Dakantz]

Basically, what the title says... when I run the example from the README:

from annoy import AnnoyIndex
import random

f = 40  # Length of item vector that will be indexed

t = AnnoyIndex(f, 'angular')
for i in range(1000):
    v = [random.gauss(0, 1) for z in range(f)]
    t.add_item(i, v)

t.build(10) # 10 trees
t.save('test.ann')

# ...

u = AnnoyIndex(f, 'angular')
u.load('test.ann') # super fast, will just mmap the file
print(u.get_nns_by_item(0, 1000)) # will find the 1000 nearest neighbors

I only get the first vector:

[0]

And when I use multiple orthogonal vectors:

import numpy as np
import annoy

N = 200
annoy_index = annoy.AnnoyIndex(N, metric="angular")

for i in range(N):
    vec = np.zeros(N, dtype=np.float64) + 0.01 * np.random.rand(N)
    vec[i] = 1.0
    annoy_index.add_item(i, vec)

print("Items:", annoy_index.get_n_items())

annoy_index.build(10)

query_vec = np.zeros(N, dtype=np.float64)
query_vec[5] = 1.0

ids, distances = annoy_index.get_nns_by_vector(query_vec, 20, search_k=200, include_distances=True)
print("IDs:", ids)
print("Distances:", distances)

I also only get a single vector:

Items: 200
IDs: [0]
Distances: [1.4107773303985596]

Can anyone explain what I am doing wrong?
My platform is:

System: M2 Mac
Python 3.14.0

[Dakantz]

Probably already fixed by #690