我会这样做：
1.获取每个句子的余弦相似度得分，并将它们存储在一个数组中。
1.根据updated_date对数组进行排序
1.从排序数组中获取前5个索引
1.使用索引从数据库表中获取相应的句子
1.这将为您提供最新的updated_date或索引顺序的前5个匹配项。

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Create a CountVectorizer object to transform the sentences into vectors
vectorizer = CountVectorizer()

# Transform the sentences into vectors using the CountVectorizer object
vectors = vectorizer.fit_transform(sentences)

# Calculate the cosine similarity scores using the cosine_similarity function
cosine_scores = cosine_similarity(vectors)

# Convert the cosine similarity scores to a 1-dimensional numpy array
cosine_scores = cosine_scores.flatten()

# Sort the array of cosine similarity scores in ascending order
sorted_indices = cosine_scores.argsort()

# Get the top 5 indices from the sorted array
top_5_indices = sorted_indices[-5:]

# Get the corresponding sentences from the database table using the indices
top_5_sentences = [sentences[i] for i in top_5_indices]