Update app.py

app.py

@@ -40,38 +40,29 @@ def create_embeddings(text_chunks):
 chunk_embeddings = create_embeddings(cleaned_chunks)# Complete this line
 
 def get_top_chunks(query, chunk_embeddings, text_chunks):
-  # Convert the query text into a vector embedding
   query_embedding = model.encode(query, convert_to_tensor=True) # Complete this line
 
-  # Normalize the query embedding to unit length for accurate similarity comparison
   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)
 
-  # Calculate cosine similarity between query and all chunks using matrix multiplication
   similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized) # Complete this line
 
-  # Print the similarities
   print(similarities)
 
-  # Find the indices of the 3 chunks with highest similarity scores
   top_indices = torch.topk(similarities, k=3).indices
 
-  # Print the top indices
   print(top_indices)
 
-  # Create an empty list to store the most relevant chunks
   top_chunks = []
 
-  # Loop through the top indices and retrieve the corresponding text chunks
   for i in top_indices:
     chunk = text_chunks[i]
     top_chunks.append(chunk)
 
   return top_chunks
 
-top_results = get_top_chunks("How does water get into the sky?", chunk_embeddings, cleaned_chunks) # Complete this line
+top_results = get_top_chunks("Your account has been compromised", chunk_embeddings, cleaned_chunks) # Complete this line
 
 print(top_results)