FAISS Cosine similarity example

faiss_cosine.py

# Just adding example if noob like me came here to find how to calculate the Cosine similarity from scratch
# Source https://github.com/facebookresearch/faiss/issues/95

import numpy as np
import faiss

dataSetI = [.1, .2, .3]
dataSetII = [.4, .5, .6]
#dataSetII = [.1, .2, .3]

x = np.array([dataSetI]).astype(np.float32)
q = np.array([dataSetII]).astype(np.float32)
index = faiss.index_factory(3, "Flat", faiss.METRIC_INNER_PRODUCT)
index.ntotal
faiss.normalize_L2(x)
index.add(x)
faiss.normalize_L2(q)
distance, index = index.search(q, 5)
print('Distance by FAISS:{}'.format(distance))

# To Tally the results check the cosine similarity of the following example

from scipy import spatial

result = 1 - spatial.distance.cosine(dataSetI, dataSetII)
print('Distance by FAISS:{}'.format(result))

add import numpy as np