Update main.py

main.py

@@ -34,12 +34,13 @@ index.add(numpy.stack(data["embedding"].to_list(), axis=0))
 # Define the search function
 def search(query: str, k: int):
     query = numpy.expand_dims(model.encode(query), axis=0)
-    _, I = index.search(query, k)
+    D, I = index.search(query, k)
     top_five = data.loc[I[0]]
     search_results = ""
 
     for i in range(k):
         search_results += "### " + top_five["bib_dict"].values[i]["title"] + "\n\n"
+        search_results += str(D[i]) + "\n\n"
         if top_five["pub_url"].values[i] is not None:
             search_results += "[Full Text](" + top_five["pub_url"].values[i] + ") "
         if top_five["citedby_url"].values[i] is not None: