Basic TF-idf model using Scikit-learn

tfidf_basic.py

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

search_terms = 'fruit and vegetables'
documents = ['cars drive on the road', 'tomatoes are actually fruit']

doc_vectors = TfidfVectorizer().fit_transform([search_terms] + documents)

cosine_similarities = linear_kernel(doc_vectors[0:1], doc_vectors).flatten()
document_scores = [item.item() for item in cosine_similarities[1:]]
# [0.0, 0.190]