Update app.py

app.py

@@ -112,8 +112,11 @@ def get_top_chunks(query, chunk_embeddings, text_chunks):
   query_embedding_normalized = query_embedding / query_embedding.norm()
 
   # Normalize all chunk embeddings to unit length for consistent comparison
-  chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
-
+  # chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
+    if chunk_embeddings.ndim == 1:
+        chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm()
+    else:
+        chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
   # Calculate cosine similarity between query and all chunks using matrix multiplication
   similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)