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wikiindexquery.py

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+indexpath= "./wiki-index/knn.index"
+wiki_sentence_path="wikipedia-en-sentences.parquet"
+#wiki_fulltext_path="wikipedia-en.parquet"
+
+import faiss
+import glob
+import numpy as np
+import pandas as pd
+pd.set_option("display.max_colwidth", 1000)
+
+import nltk.data
+import numpy as np
+import time
+
+import os
+
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained('facebook/contriever-msmarco')
+contriever = AutoModel.from_pretrained('facebook/contriever-msmarco')
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+contriever.to(device)
+
+
+
+
+def cos_sim_2d(x, y):
+    norm_x = x / np.linalg.norm(x, axis=1, keepdims=True)
+    norm_y = y / np.linalg.norm(y, axis=1, keepdims=True)
+    return np.matmul(norm_x, norm_y.T)
+
+
+
+print(device)
+
+
+# Mean pooling
+def mean_pooling(token_embeddings, mask):
+    token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)
+    sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]
+    return sentence_embeddings
+print("loading df")
+
+df_sententces = pd.read_parquet( wiki_sentence_path , engine='fastparquet')
+#df_fulltext = pd.read_parquet( wiki_fulltext_path , engine='fastparquet')
+
+
+my_index = faiss.read_index(indexpath, faiss.IO_FLAG_MMAP | faiss.IO_FLAG_READ_ONLY)
+
+query =""
+
+while query != "q":
+
+   query=input("Type in your query: ")
+   print("Query Text: ", query)
+   inputs = tokenizer([query], padding=True, truncation=True, return_tensors="pt").to(device)
+   
+   outputs = contriever(**inputs)
+   embeddings = mean_pooling(outputs[0], inputs['attention_mask'])
+
+   query_vector = np.asarray(embeddings .cpu().detach().numpy()).reshape(1, 768)
+
+   #print(query_vector.shape)
+
+   k = 5
+   distances, indices = my_index.search(query_vector, k)
+
+   print(f"Top {k} elements in the dataset for max inner product search:")
+   for i, (dist, indice) in enumerate(zip(distances[0], indices[0])):
+     print(f"{i+1}: Vector number {indice:4} with distance {dist}")
+
+     text = str( df_sententces.iloc[[indice]]['text_snippet'] )
+     # get embedding of neighboring 3-sentence segments
+     try:
+       inputs = tokenizer([str( df_sententces.iloc[[indice-1]]['text_snippet'] ), str( df_sententces.iloc[[indice]]['text_snippet']), str( df_sententces.iloc[[indice+1]]['text_snippet'] ) ], padding=True, truncation=True, return_tensors="pt").to(device)
+       outputs = contriever(**inputs)
+       embeddings = mean_pooling(outputs[0], inputs['attention_mask'])
+       embeddings  = np.asarray(embeddings .cpu().detach().numpy())
+       #print(embeddings.shape )
+       #print(cos_sim_2d(embeddings[0].reshape(1, 768), embeddings[1].reshape(1, 768)))
+       if cos_sim_2d(embeddings[0].reshape(1, 768), embeddings[1].reshape(1, 768)) > 0.7:
+           text = str( df_sententces.iloc[[indice-1]]['text_snippet'] ) +" "+ str( df_sententces.iloc[[indice]]['text_snippet'] )
+
+       #print(cos_sim_2d(embeddings[1].reshape(1, 768), embeddings[2].reshape(1, 768)))
+       if cos_sim_2d(embeddings[0].reshape(1, 768), embeddings[1].reshape(1, 768)) > 0.7:
+           text += str( df_sententces.iloc[[indice+1]]['text_snippet'] )
+       
+     except:
+       pass
+
+     print(text)
+
+
+