Update app.py

app.py

@@ -155,7 +155,7 @@ def index_from_url(url: str) -> tuple[str, str]:
 # -----------------------------
 def search(query: str, k: int):
     """
-    Search the currently indexed PDF pages for the most relevant content and
+    Search the current database of PDF document pages for the most relevant content and
     generate an answer grounded ONLY in those pages.
 
     MCP tool description:
@@ -174,10 +174,6 @@ def search(query: str, k: int):
 
     Returns:
         ai_response (str): Answer grounded only in retrieved pages, with citations (page numbers).
-
-    Notes:
-        • Requires that a PDF has been indexed first.
-        • Citations reference 1-based page numbers as shown in the gallery captions.
     """
     global ds, images
 
@@ -243,12 +239,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# ColPali: Efficient Document Retrieval with Vision Language Models (ColQwen2) 📚")
     gr.Markdown(
         """Demo to test ColQwen2 (ColPali) on PDF documents.  
-ColPali is implemented from the [ColPali paper](https://arxiv.org/abs/2407.01449).
-
-This demo lets you **upload a PDF or load a sample**, then **search** for the most relevant pages and get a grounded answer.
-
-⚠️ The model was trained on A4 portrait English PDFs; performance may drop on other formats/languages.
-"""
+ColPali is implemented from the [ColPali paper](https://arxiv.org/abs/2407.01449)."""
     )
 
     with gr.Row():