下面是计算一组文档之间成对余弦相似度的最小示例(假设您已成功地从数据库中检索到标题和文本)。在from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.metrics.pairwise import cosine_similarity

# Assume thats the data we have (4 short documents)

data = [

'I like beer and pizza',

'I love pizza and pasta',

'I prefer wine over beer',

'Thou shalt not pass'

]

# Vectorise the data

vec = TfidfVectorizer()

X = vec.fit_transform(data) # `X` will now be a TF-IDF representation of the data, the first row of `X` corresponds to the first sentence in `data`

# Calculate the pairwise cosine similarities (depending on the amount of data that you are going to have this could take a while)

S = cosine_similarity(X)

'''

S looks as follows:

array([[ 1. , 0.4078538 , 0.19297924, 0. ],

[ 0.4078538 , 1. , 0. , 0. ],

[ 0.19297924, 0. , 1. , 0. ],

[ 0. , 0. , 0. , 1. ]])

The first row of `S` contains the cosine similarities to every other element in `X`.

For example the cosine similarity of the first sentence to the third sentence is ~0.193.

Obviously the similarity of every sentence/document to itself is 1 (hence the diagonal of the sim matrix will be all ones).

Given that all indices are consistent it is straightforward to extract the corresponding sentences to the similarities.

'''