Update app.py

app.py

@@ -31,9 +31,9 @@ It works by first retrieving relevant documents from an external knowledge sourc
 This method enhances factual correctness and allows the model to access up-to-date or domain-specific information without retraining.
 
 Click the button below to load a **User Guide** and an **Installation Guide** for a smoke alarm device into the vector database. It could take a couple of minutes to process.
-Once you see the message *"PDF indexed successfully!"*, go to the **Chatbot** tab to ask any relevant questions about the device.
+Once you see the message *"PDF(s) indexed successfully!"*, go to the **Chatbot** tab to ask any relevant questions about the device.
 
-You can change the LLM models in the **Additional Inputs** at the bottom of the **Chatbot** tab, you can also adjust the LLM parameters there.
+You can change the LLM models in the **Additional Inputs** at the bottom of the **Chatbot** tab, in case of certain model is out of date. You can also adjust the LLM parameters there.
 
 """
 
@@ -103,7 +103,7 @@ def load_sample_pdf():
     # Store the embeddings in the vector store
     vector_store = FAISS.from_documents(texts, embeddings)
 
-    return "PDFs indexed successfully!"
+    return "PDF(s) indexed successfully!"
 
 
 def format_docs(docs):