Building a content-based music recommendation system
If the features are identical, good start would be to use n-neighbors approach.

It would be something like that

from sklearn.neighbors import NearestNeighbors
all_songs_features = [[0, 0, 2], [1, 0, 0], [0, 0, 1], [100, 100, 100], [0, 0, 1.5]]
neigh = NearestNeighbors()
neigh.fit(all_songs_features)

my_song_features = [[0, 0, 1.3], [1.1, 0, 0]]
print(neigh.kneighbors(my_song_features, 2, return_distance=False))


These code returns the indexes of similar songs from the bigger dataset.

Note, if the features are with different range (i.e. one with values 0-1 and another 10000-1000000), then you need to scale them, for example with StandardScaler