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import numpy as np
def answer_question(question , model , rerankmodel , corpus_embed , corpus_list,llm_chain):
# embeddings_1 = model.encode(question, batch_size=16, max_length=8192 ,)['dense_vecs']
# embeddings_2 = corpus_embed
# BGM3similarity = embeddings_1 @ embeddings_2.T
#==========================================================
ALL_final_ans_list_ALL = []
batch_size = 10
sentence_pairs = [[question, j] for j in corpus_list]
listofscore = []
compute_Score = range(0, len(sentence_pairs), batch_size)
for i in compute_Score:
batch_pairs = sentence_pairs[i:i+batch_size]
allscore = model.compute_score(batch_pairs,
max_passage_length=512,
weights_for_different_modes=[0.4, 0.2, 0.4]) # sum: w[0]*dense_score + w[1]*sparse_score + w[2]*colbert_score
listofscore.append(allscore)
score_ALL = []
for score_dict in listofscore:
score_ALL.extend(score_dict['colbert+sparse+dense'])
ALL_final_ans_list_ALL.append(score_ALL)
#==========================================================
topkindex = 15
topk15scoresimilar_BGM3 = np.argsort(ALL_final_ans_list_ALL)[:,-topkindex:]
# topk15scoresimilar_BGM3 = np.argsort(BGM3similarity)[-topkindex:]
BGM3_1_retrieval = [corpus_list[i] for i in topk15scoresimilar_BGM3[0]]
scores = []
for passage in BGM3_1_retrieval:
passage = str(passage)
score = rerankmodel.compute_score([question, passage], normalize=True)
scores.append(score)
# print(passage[:20])
highest_scoring_index = scores.index(max(scores))
result_passage = BGM3_1_retrieval[highest_scoring_index]
# print(f"Retrieval{result_passage[:20]}")
# print(f"Question{question}")
inputs = {"section": result_passage, "question": question}
response = llm_chain.run(inputs)
print(response)
return response
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