Update app.py

app.py

@@ -81,11 +81,11 @@ def invoke(openai_api_key, use_rag, prompt):
         result = chain.run({"question": prompt})
     return result
 
-description = """<strong>Overview:</strong> The app demonstrates how to use a Large Language Model (LLM) with Retrieval Augmented Generation (RAG) on external data 
-                 (private/public & structured/unstructured).\n\n
+description = """<strong>Overview:</strong> The app demonstrates how to use a <strong>Large Language Model (LLM)</strong> with <strong>Retrieval Augmented Generation (RAG)</strong> 
+                 on <strong>external data<strong> (private/public & structured/unstructured).\n\n
                  <strong>Instructions:</strong> Enter an OpenAI API key and perform LLM use cases (semantic search, summarization, translation, etc.) on 
-                 <a href='""" + YOUTUBE_URL_1 + """'>YouTube</a>, <a href='""" + PDF_URL + """'>PDF</a>, and <a href='""" + WEB_URL + """'>Web</a> data about GPT-4, 
-                 created after training cutoff.
+                 <a href='""" + YOUTUBE_URL_1 + """'>YouTube</a>, <a href='""" + PDF_URL + """'>PDF</a>, and <a href='""" + WEB_URL + """'>Web</a> 
+                 <strong>data about GPT-4</strong> (created after training cutoff).
                  <ul style="list-style-type:square;">
                  <li>Set "Retrieval Augmented Generation" to "<strong>False</strong>" and submit prompt "What is GPT-4?" The LLM <strong>without</strong> RAG does not know the answer.</li>
                  <li>Set "Retrieval Augmented Generation" to "<strong>True</strong>" and submit prompt "What is GPT-4?" The LLM <strong>with</strong> RAG knows the answer.</li>