from sentence_transformers import SentenceTransformer
import numpy as np

def generate_embeddings(texts):
    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
    embeddings = model.encode(texts, convert_to_tensor=True)
    return embeddings

def calculate_similarity(embedding1, embedding2):
    cosine_similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
    return cosine_similarity.item()