added the 'S3_to_MongoDB_Workflow_using_Unstructured_API.ipynb' card (#650)

Co-authored-by: Maria Khalusova <[email protected]>

Author: vannestn

examplecode/notebooks.mdx

@@ -6,6 +6,13 @@ description: "Notebooks contain complete working sample code for end-to-end solu
 ---
 
 <CardGroup cols={2}>
+    <Card title="Create a S3 to MongoDB Pipeline using the Unstructured API" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/S3_to_MongoDB_Workflow_using_Unstructured_API.ipynb">
+    <br/>
+       Learn how to build an end-to-end document processing pipeline that processes PDFs from S3 and stores structured results in MongoDB. Features VLM-powered partitioning, semantic chunking, and vector embeddings using the Unstructured Workflows API.
+    <br/>
+       ``Unstructured API`` ``Workflows`` ``S3`` ``MongoDB`` ``VLM`` ``Embeddings``
+    <br/>
+    </Card>
     <Card title="Getting Started with Unstructured API and IBM watsonx.data" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Azure_to_IBM_WatsonX.ipynb">
     <br/>
        Learn how to create data processing workflows with Unstructured API and its Python SDK to preprocess all of your unstructured data from your Azure Blob Storage into your IBM watsonx.data instance.