Update main.py

main.py

@@ -28,12 +28,12 @@ data.reset_index(inplace=True)
 # Create a FAISS index for fast similarity search
 index = faiss.IndexFlatL2(len(data["embedding"][0]))
 index.metric_type = faiss.METRIC_INNER_PRODUCT
-index.add(numpy.stack(data["embedding"].to_list(), axis=0))
+index.add(numpy.stack(faiss.normalize_L2(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)
+    query = numpy.expand_dims(faiss.normalize_L2(model.encode(query)), axis=0)
     D, I = index.search(query, k)
     top_five = data.loc[I[0]]
     search_results = ""