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Usage: python <data file> [<search k>]
(works like ./distance in word2vec)
* Data file can be
1. A .bin file that is compatible with word2vec binary formats.
There are pre-trained vectors to download at
2. A .gz file with the GloVe format (item then a list of floats in plaintext)
3. A plain text file with the same format as above
* Dependencies: lmdb (pip install lmdb)
* Will be (very) slow first time because it creates big data structures, fast subsequent times
An additional feature is that each word in input can be preceded with a plus or a minus.
This will add vectors (eg. the canonical king +woman -man example).
import annoy
import lmdb
import os
import struct
import sys
import numpy
from util import get_vectors
if len(sys.argv) < 2:
print __doc__
fn = sys.argv[1]
search_k = 100000
if len(sys.argv) > 2:
search_k = int(sys.argv[2])
fn_annoy = fn + '.annoy'
fn_lmdb = fn + '.lmdb' # stores word <-> id mapping
word, vec = get_vectors(fn).next()
size = len(vec)
env =, map_size=int(1e9))
if not os.path.exists(fn_annoy) or not os.path.exists(fn_lmdb):
i = 0
a = annoy.AnnoyIndex(size)
with env.begin(write=True) as txn:
for word, vec in get_vectors(sys.argv[1]):
a.add_item(i, vec)
id = 'i%d' % i
word = 'w' + word
txn.put(id, word)
txn.put(word, id)
i += 1
if i % 1000 == 0:
print i, '...'
a = annoy.AnnoyIndex(size)
with env.begin() as txn:
for line in sys.stdin:
if line.strip() == 'EXIT':
ids = []
for word in line.strip().split():
if word[0] in ['+', '-']:
word, f = word[1:], int(word[0] + '1')
id = int(txn.get('w' + word)[1:])
words.append((f, id))
vs = [(f, a.get_item_vector(id)) for f, id in ids]
vs = [f * v /, v)**0.5 for f, v in vs]
v = numpy.sum(vs, axis=0)
for id, dist in zip(*a.get_nns_by_vector(v, 20, search_k, True)):
if id not in ids:
word = txn.get('i%d' % id)[1:]
print '%50s\t%f' % (word, dist)