Merge pull request #2053 from qdrant/course/multi-vector-search

Course: multi vector search

qdrant-landing/content/course/essentials/day-1/chunking-strategies.md

@@ -2,6 +2,7 @@
 title: "Text Chunking Strategies"
 description: Learn how to split text into meaningful chunks for vector search. Compare six chunking strategies and discover how metadata improves retrieval precision in Qdrant.
 weight: 4
+isLesson: true
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 {{< date >}} Day 1 {{< /date >}}

qdrant-landing/content/course/essentials/day-1/distance-metrics.md

@@ -2,6 +2,7 @@
 title: "Distance Metrics"
 description: Learn how distance metrics like cosine, Euclidean, Manhattan, and dot product shape vector similarity in Qdrant. Discover which metric fits your data and use case. 
 weight: 3
+isLesson: true
 ---
 
 {{< date >}} Day 1 {{< /date >}}

qdrant-landing/content/course/essentials/day-1/embedding-models.md

@@ -2,6 +2,7 @@
 title: "Points, Vectors and Payloads"
 description: Learn Qdrant’s core data model with points, vectors, payloads, and named vectors. Compare dense, sparse, and multivectors, understand dimensionality trade-offs, and master filtering with payload indexes for precise retrieval. 
 weight: 2
+isLesson: true
 ---
 
 {{< date >}} Day 1 {{< /date >}}

qdrant-landing/content/course/essentials/day-1/movie-search-system.md

@@ -2,6 +2,7 @@
 title: "Demo: Semantic Movie Search"
 description: Build a semantic movie search with Qdrant. Compare chunking strategies, embed descriptions, and combine cosine similarity with metadata filters and grouping for accurate, theme-aware recommendations. 
 weight: 5
+isLesson: true
 ---
 
 {{< date >}} Day 1 {{< /date >}}

qdrant-landing/content/course/essentials/day-1/pitstop-project.md

@@ -2,6 +2,7 @@
 title: "Project: Building a Semantic Search Engine"
 description: Build a semantic search engine with Qdrant. Compare chunking strategies, index embeddings, and query by meaning to discover what works best for your domain. 
 weight: 6
+isLesson: true
 ---
 
 {{< date >}} Day 1 {{< /date >}}