Add application file

inference_script.py

@@ -1,41 +1,41 @@
 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
+    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 
+    # ALL_final_ans_list_ALL = []
+    # batch_size = 10 
 
-    sentence_pairs = [[question, j] for j in corpus_list]
+    # sentence_pairs = [[question, j] for j in corpus_list]
 
-    listofscore = []
-    compute_Score = range(0, len(sentence_pairs), batch_size)
+    # 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) 
+    # 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 = []
+    # score_ALL = []
 
 
-    for score_dict in listofscore:
+    # for score_dict in listofscore:
 
-        score_ALL.extend(score_dict['colbert+sparse+dense'])
+    #     score_ALL.extend(score_dict['colbert+sparse+dense'])
 
-    ALL_final_ans_list_ALL.append(score_ALL)
+    # 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:]
+    # 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]]