from pathlib import Path
from sentence_transformers.cross_encoder import CrossEncoder
from more_itertools import windowed
#model = CrossEncoder('cross-encoder/mmarco-mMiniLMv2-L12-H384-v1', max_length=512, device='cpu')
model = CrossEncoder('retailcrmservices/crossencoder-mMiniLMv2-L12-H384-v1-ru', max_length=512, device='cpu')

def rerank(sentence_combinations):
  similarity_scores = model.predict(sentence_combinations)
  scores = [(score_max,idx) for idx,score_max in enumerate(similarity_scores)]
  sim_scores_argsort = sorted(scores, key=lambda x: x[0], reverse=True)
  return sim_scores_argsort

def search(query, sentences):
  scores = rerank([[query, s] for s in sentences])
  return scores[0]