.

README.md

@@ -1,5 +1,5 @@
 ---
-title: RAG with enrich answer \n MongoDB Atlas Vector Search + OpenAI
+title: RAG - MongoDB Atlas Vector Search & OpenAI
 emoji: 🦀
 colorFrom: purple
 colorTo: red

app.py

@@ -13,6 +13,11 @@ from gradio.themes.base import Base
 #import key_param
 import os
 
+DESCRIPTION = """\
+# RAG - MongoDB Atlas Vector Search & OpenAI
+This app is designed to provide a simple chat where to improve the answer from OpenAI with private information located in MongoDB Atlas cluster.
+"""
+
 def query_data(query,openai_api_key,mongo_uri):
     os.environ["OPENAI_API_KEY"] = openai_api_key
     os.environ["MONGO_URI"] = mongo_uri
@@ -64,10 +69,7 @@ def query_data(query,openai_api_key,mongo_uri):
 # Create a web interface for the app, using Gradio
 
 with gr.Blocks(theme=Base(), title="MongoDB Atlas Vector Search + RAG Architecture") as demo:
-    gr.Markdown(
-        """
-        # MongoDB Atlas Vector Search + RAG Architecture
-        """)
+    gr.Markdown(DESCRIPTION)
     openai_api_key = gr.Textbox(label = "OpenAI API Key (sk-...)", type = "password", lines = 1)
     mongo_uri = gr.Textbox(label = "Mongo Atlas URI (mongodb+srv://..)", type = "password", lines = 1)
     textbox = gr.Textbox(label="Enter your Question:")