Update app.py

app.py

@@ -49,6 +49,9 @@ def search(query, top_k=5):
     # Encode the query
     query_embedding = model.encode([query])[0]
     
+    # Debug: Print query embedding
+    print(f"Query embedding: {query_embedding}")
+    
     # Ensure the query embedding has the same dimension as the index
     if query_embedding.shape[0] != index.d:
         print(f"Query embedding dimension ({query_embedding.shape[0]}) does not match index dimension ({index.d})")
@@ -61,6 +64,10 @@ def search(query, top_k=5):
     # Perform similarity search
     distances, indices = index.search(np.array([query_embedding]), top_k)
     
+    # Debug: Print distances and indices
+    print(f"Distances: {distances}")
+    print(f"Indices: {indices}")
+    
     results = []
     for i, idx in enumerate(indices[0]):
         patent_number = patent_numbers[idx]