Update app.py

app.py

@@ -78,7 +78,7 @@ def generate(query, api_key):
 with gr.Blocks() as demo:
     gr.Markdown(
         """
-  # Retrieval Augmented Generation with GPT 3.5 Turbo and MongoDB Atlas Vector Search: Question Answering demo
+  # Retrieval Augmented Generation with GPT 3.5 Turbo, MongoDB Atlas Vector Search, and LlamaIndex: Question Answering demo
   ### This demo uses the GPT 3.5 Turbo LLM and MongoDB Atlas Vector Search for fast and performant Retrieval Augmented Generation (RAG).
   ### The context is the new Oppenheimer movie's entire wikipedia page. The movie came out very recently in July, 2023, so the GPT 3.5 turbo model is not aware of it.
   Retrieval Augmented Generation (RAG) enables us to retrieve just the few small chunks of the document that are relevant to the our query and inject it into our prompt.