fix

qdrant-landing/content/advanced-search/advanced-search-features.md

@@ -19,7 +19,7 @@ features:
   description: By combining dense vector embeddings with sparse vectors e.g. BM25, Qdrant powers semantic search to deliver context-aware results, transcending traditional keyword search by understanding the deeper meaning of data.
   link:
     text: Learn More
-    url: /documentation/tutorials/hybrid-search-fastembed/
+    url: /documentation/beginner-tutorials/hybrid-search-fastembed/
 - id: 2
   icon:
     src: /icons/outline/selection-blue.svg

qdrant-landing/content/documentation/beginner-tutorials/neural-search.md

@@ -18,7 +18,7 @@ A neural search service uses artificial neural networks to improve the accuracy
 
 <aside role="status">
 There is a version of this tutorial that uses <a href="https://github.com/qdrant/fastembed">Fastembed</a> model inference engine instead of Sentence Transformers.
-Check it out <a href="/documentation/tutorials/hybrid-search-fastembed/">here</a>.
+Check it out <a href="/documentation/beginner-tutorials/hybrid-search-fastembed/">here</a>.
 </aside>
 
 

qdrant-landing/content/documentation/build-tab.md

@@ -24,7 +24,7 @@ content:
       title: Search
       description: Build a simple neural search service with Qdrant and FastEmbed. Learn how to upload data, create indexes, and run search queries.
       link:
-        url: /documentation/tutorials/hybrid-search-fastembed/
+        url: /documentation/beginner-tutorials/hybrid-search-fastembed/
         text: Read More
     - id: 2
       image: