Update app.py

app.py

@@ -24,6 +24,30 @@ sample_filenames = ["Attention Is All You Need.pdf",
                    "Parameter-Efficient Transfer Learning for NLP.pdf",
                   ]
 
+sample_desc = """
+### 1. Attention Is All You Need (Vaswani et al., 2017)
+This groundbreaking paper introduced the **Transformer** architecture. It revolutionized natural language processing by enabling parallelization and significantly improving performance on tasks like translation, leading to models like *BERT* and *GPT*.
+
+### 2. Generative Adversarial Nets (Goodfellow et al., 2014)
+This paper proposed **GANs**, a novel framework for generative modeling using two neural networks—a generator and a discriminator—that compete in a zero-sum game.  
+
+### 3. Parameter-Efficient Transfer Learning for NLP (Houlsby et al., 2019)
+This paper introduces **adapter modules**, a method for fine-tuning large pre-trained language models with significantly fewer parameters.
+
+It could take several minutes to load and index the files.
+"""
+
+rag_desc = """
+### This is a Demo of Retrieval-Augmented Generation (RAG)
+
+**RAG** is an approach that combines retrieval-based and generative LLM models to improve the accuracy and relevance of generated text.  
+It works by first retrieving relevant documents from an external knowledge source (like PDF files) and then using a LLM model to produce responses based on both the input query and the retrieved content.  
+This method enhances factual correctness and allows the model to access up-to-date or domain-specific information without retraining.
+
+
+""")
+
+
 examples_questions = [["What is Transformer?"],
             ["What is Attention?"],
             ["What is Scaled Dot-Product Attention?"],
@@ -131,21 +155,13 @@ additional_inputs = [
 # Create the Gradio interface
 with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
     with gr.Tab("Indexing"):
+        gr.Markdown(rag_desc)
         # pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
         # pdf_input = gr.Textbox(label="PDF File")
         # index_button = gr.Button("Index PDF")
         # load_sample = gr.Button("Alternatively, Load and Index [Attention Is All You Need.pdf] as a Sample")
         load_sample = gr.Button("Load and Index the following three papers as a RAG Demo")
-        sample_description = gr.Markdown("""
-# 1. Attention Is All You Need (Vaswani et al., 2017)
-This groundbreaking paper introduced the **Transformer** architecture. It revolutionized natural language processing by enabling parallelization and significantly improving performance on tasks like translation, leading to models like *BERT* and *GPT*.
-# 2. Generative Adversarial Nets (Goodfellow et al., 2014)
-This paper proposed **GANs**, a novel framework for generative modeling using two neural networks—a generator and a discriminator—that compete in a zero-sum game.  
-# 3. Parameter-Efficient Transfer Learning for NLP (Houlsby et al., 2019)
-This paper introduces **adapter modules**, a method for fine-tuning large pre-trained language models with significantly fewer parameters.
-
-It could take several minutes to load and index the files.
-""")
+        sample_description = gr.Markdown(sample_desc)
         index_output = gr.Textbox(label="Indexing Status")
         # index_button.click(index_pdf, inputs=pdf_input, outputs=index_output)
         load_sample.click(load_sample_pdf, inputs=None, outputs=index_output)