李修然回到皇宫的第一件事，就是处理婚约。他颤抖着手打开那张写满名字的帖子，嘴角不自觉地抽搐了一下。
“太子薨了，由太子爷接替。”
手指无意识地摩挲着衣角，他的瞳孔微微收缩——帖上那个人的名字赫然在列。
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
他的手指顿了顿，指尖有些发凉。
“多谢太子爷体谅。”
他强忍着内心的挣扎，将婚约贴撕下扔进了火盆。
第二天，他便找到了真相。
那日早朝上，由太子爷主持， everyone was looking at him expectantly.
李修然是唯一一个没有说话的人。
当他开口时，声音低沉而沙哑：
“父皇，此子与某 Lady 有不正当关系。”
会议室里一片死寂。
seconds later, theroom burst into chaos as noisy voices filled the air.
“The Prince’s father is dead! The Prince has been engaged to……”
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True)
top3_data = [item[1] for item in ranked_data[:3]]
print(top3_data)
” datasets = data.split(‘\n’)
similarities = []
for doc in documents:
similarities.append(cosine_similarity(doc, dataset))
result = similar(similarities)
data = ‘回到古代当皇子\n男朋友出轨之后\n宫斗\n成长\n反转’
split_data = data.split(‘\n’)
similarity_scores = []
for split in split_data:
similarity = cosine_similarity(split, ‘回到古代当皇子’)
similarity_scores.append(similarity)
ranked_data = sorted(zip(similarity_scores, split_data), key=lambda x: x[0], reverse=True
