Refine Learn navigation, guide presentation, and authoring

qdrant-landing/content/articles/agentic-builders-guide.md

@@ -38,7 +38,7 @@ Reliable agentic workflows require a clear plan executed with precise tools. A v
 
 * [**Real-time memory layer:**](https://qdrant.tech/blog/case-study-fieldy/) fast access to prior steps, actions, and knowledge  
 * [**Multimodal support**](https://qdrant.tech/blog/case-study-mixpeek/): text, image, videos, audio, and code  
-* [**Hybrid search**](https://qdrant.tech/articles/hybrid-search/)**:** combining dense \+ sparse vectors  
+* [**Hybrid search**](https://qdrant.tech/documentation/search-tuning/hybrid-search/)**:** combining dense \+ sparse vectors
 * [**Advanced filtering**](https://qdrant.tech/documentation/search/filtering/)**:** semantic \+ metadata \+ keyword constraints  
 * [**Millisecond vector retrieval**](https://qdrant.tech/articles/vector-search-production/)**:** Fast retrieval at \>billion vector scale
 

qdrant-landing/content/documentation/frameworks/langgraph.md

@@ -35,7 +35,7 @@ from langchain_community.embeddings import FastEmbedEmbeddings
 from langchain_qdrant import FastEmbedSparse, QdrantVectorStore, RetrievalMode
 
 # We'll set up Qdrant to retrieve documents using Hybrid search.
-# Learn more at https://qdrant.tech/articles/hybrid-search/
+# Learn more at https://qdrant.tech/documentation/search-tuning/hybrid-search/
 retriever = QdrantVectorStore.from_texts(
     url="http://localhost:6333/",
     collection_name="langgraph-collection",

qdrant-landing/content/documentation/production-patterns/_index.md

@@ -4,6 +4,9 @@ short_description: Plan multitenancy, bulk uploads, and memory placement as your
 description: Plan multitenancy, bulk uploads, and memory placement as your Qdrant application and vector collection grow.
 partition: learn
 learning_kind: guides
+cascade:
+  partition: learn
+  learning_kind: guides
 layout: guides
 weight: 150
 hideTOC: true

qdrant-landing/content/documentation/search-patterns/_index.md

@@ -4,6 +4,9 @@ short_description: Choose embedding models, plan query decomposition, and apply
 description: Design Qdrant search around your application by choosing embedding models, decomposing multi-hop queries, and filtering results with payload indexes.
 partition: learn
 learning_kind: guides
+cascade:
+  partition: learn
+  learning_kind: guides
 layout: guides
 weight: 115
 hideTOC: true

qdrant-landing/content/documentation/search-quality/_index.md

@@ -4,6 +4,9 @@ short_description: Measure retrieval relevance and pipeline output quality to es
 description: Evaluate Qdrant retrieval relevance and pipeline output quality with labeled queries, repeatable measurements, and checks for meaningful improvements.
 partition: learn
 learning_kind: guides
+cascade:
+  partition: learn
+  learning_kind: guides
 layout: guides
 weight: 100
 hideTOC: true

qdrant-landing/content/documentation/search-tuning/_index.md

@@ -4,6 +4,9 @@ short_description: Tune candidate depth, fusion, reranking, and memory against y
 description: "Tune Qdrant retrieval with measured changes to candidate depth, hybrid fusion, reranking, and memory use. Follow the six-part series in order."
 partition: learn
 learning_kind: guides
+cascade:
+  partition: learn
+  learning_kind: guides
 layout: guides
 weight: 125
 hideTOC: true