Update app.py

app.py

@@ -144,7 +144,6 @@ with gr.Blocks(theme=Monochrome()) as demo:
         with gr.Column():  
            with gr.Accordion("What This Demo Does", open = True):
             gr.Markdown("""
-          This AI Chatbot application uses Retrieval-Augmented Generation (RAG) to provide intelligent responses based on the content of uploaded PDF documents. It allows users to:
             1. Upload multiple PDF documents
             2. Process and index the content of these documents
             3. Ask questions about the content
@@ -153,13 +152,10 @@ with gr.Blocks(theme=Monochrome()) as demo:
         with gr.Column():  
           with gr.Accordion("How does it work?", open = True):
             gr.Markdown("""
-          **Question Answering:**
            - When a user asks a question, the system searches for the most relevant chunks of text from the uploaded documents.
            - It then uses these relevant chunks as context for a large language model (LLM) to generate an answer.
            - The LLM (in this case, GPT-4) formulates a response based on the provided context and the user's question.
-          **Pixeltable Integration:**
-           - Pixeltable is used to manage the document data, chunks, and embeddings efficiently.
-           - It provides a declarative interface for complex data operations, making it easier to build and maintain this RAG system.
+           - Pixeltable is used to manage the document data, chunks, and embeddings while also to retrieve context.
           """)
 
     with gr.Row():