From c958463a93bfcb354917eb4af08482cf5b5d510b Mon Sep 17 00:00:00 2001
From: vannestn <Nicholas.vannest@gmail.com>
Date: Thu, 25 Sep 2025 10:28:54 -0400
Subject: [PATCH] Update notebooks.mdx

---
 examplecode/notebooks.mdx | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/examplecode/notebooks.mdx b/examplecode/notebooks.mdx
index 1f06f3cf..b76004d7 100644
--- a/examplecode/notebooks.mdx
+++ b/examplecode/notebooks.mdx
@@ -6,6 +6,13 @@ description: "Notebooks contain complete working sample code for end-to-end solu
 ---
 
 <CardGroup cols={2}>
+    <Card title="Agentic RAG with Visually Grounded Answers and Visual Citations" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Agentic-RAG-with-Visually-Grounded-Answers-and-Visual-Citations.ipynb">
+    <br/>
+       Learn how to build an AI-powered document processing system that extracts both text and images from PDFs in S3, generates intelligent descriptions for visual elements, and enables a searchable knowledge base that can answer questions about charts, diagrams, and product visuals.
+    <br/>
+       ``Unstructured API`` ``Workflows`` ``S3`` ``Image Processing`` ``Visual RAG`` ``Enterprise AI``
+    <br/>
+    </Card>
     <Card title="Building a Hybrid RAG System: From Fragmented Data to Unified Intelligence" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Unstructured_Hybrid_RAG_Pipeline_with_ElasticSearch.ipynb">
         <br/>
            Learn how to build a comprehensive hybrid RAG system that processes multiple data sources simultaneously - combining S3 PDFs and Elasticsearch records into a unified knowledge base for enterprise AI applications.