Update app.py

app.py

@@ -92,6 +92,20 @@ def gradio_fn(message, history, api_key):
     return expert_response
 
 with gr.Blocks() as demo:
+    read_me = gr.Markdown(
+        """
+        # Simple Dual LLM Chatbot
+        
+        This is a playground for testing out Standford's 'Meta-Prompting' logic ([paper link](https://arxiv.org/abs/2401.12954)), in whcih for every user request, it first passes the request to a 'meta' bot, the 'meta' bot will then generate a system prompt of a field-related 'Expert' bot for answering user's request.  
+        That is, for each round, the LLM should accordingly assigns the best expert for answering user's specific request.  
+        Standford claimed that this simple implementation result in a 60%+ better accuracy compared to a standard 'syst_prompt + chat_history' logic.  
+        Hence, one can't be too curious in checking it out, here is a simple implemnetaion for everybody to play around.  
+
+        Something to keep in mind:
+        1. Currently it requires an api key from chatglm (get one here if you don't have one: [link](https://open.bigmodel.cn/usercenter/apikeys))
+        2. To balance contextual-understanding and token-saving, the meta bot's logic is modified to have access to only the last round of chat and the current user request when 'generating' an expert.
+        """
+    )
     api_key = gr.Textbox()
     main_interface = gr.ChatInterface(fn=gradio_fn, additional_inputs=api_key)