Update app.py

app.py

@@ -44,7 +44,7 @@ def retrieve_relevant_chunks(query, chunks, chunk_embeddings):
     query_embedding = get_embeddings(query)
     similarities = cosine_similarity([query_embedding], chunk_embeddings)[0]
     top_indices = np.argsort(similarities)[::-1][:3]  # Get top 3 relevant chunks
-    return [chunks[i] for i in top_indices]
+    return [chunks[i] for i in top_indices]   #we use the chunks list to retrieve the actual text of those relevant chunks. These text chunks will form the context.
 
 # Function to generate a response
 def generate_response(context, query):