fixed links and image file reference

Author: HeidiSteen

articles/search/media/tutorial-rag-solution/side-by-side-comparison.png

@@ -328,7 +328,7 @@ As a final step, switch to the Azure portal to compare the vector storage requir
 
 :::image type="content" source="media/tutorial-rag-solution/side-by-side-comparison.png" lightbox="media/tutorial-rag-solution/side-by-side-comparison.png" alt-text="Screenshot of the original vector index with the index created using the schema in this tutorial.":::
 
-The index created in this tutorial uses half-precision floating-point numbers (float16) for the text vectors. This reduces the storage requirements for the vectors by half compared to the previous index that used single-precision floating-point numbers (float32). Scalar compression and the omission of one set of the vectors account for the remaining storage savings. For more information about reducing vector size, see [Choose an approach for optimizing vector storage and processing](https://learn.microsoft.com/azure/search/vector-search-how-to-configure-compression-storage).
+The index created in this tutorial uses half-precision floating-point numbers (float16) for the text vectors. This reduces the storage requirements for the vectors by half compared to the previous index that used single-precision floating-point numbers (float32). Scalar compression and the omission of one set of the vectors account for the remaining storage savings. For more information about reducing vector size, see [Choose an approach for optimizing vector storage and processing](vector-search-how-to-configure-compression-storage.md).
 
 Consider revisiting the queries from the previous tutorials so that you can compare query speed and utility. You should expect some variation in LLM output whenever you repeat a query, but in general the storage-saving techniques you implemented shouldn't degrade the quality of your search results.