Set all leaf pages to isLesson: true for multi-vector course

qdrant-landing/content/course/multi-vector-search/_index.md

@@ -12,6 +12,7 @@ content:
   title: "Multi-Vector Search"
   description: Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines.
 partition: course
+isLesson: true
 ---
 
 # Multi-Vector Search

qdrant-landing/content/course/multi-vector-search/module-0/installing-dependencies.md

@@ -2,6 +2,7 @@
 title: "Installing Dependencies"
 description: Install Python dependencies including FastEmbed and Qdrant client.
 weight: 2
+isLesson: true
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 {{< date >}} Module 0 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-0/qdrant-setup.md

@@ -2,6 +2,7 @@
 title: "Qdrant Setup"
 description: Set up Qdrant for multi-vector search. Learn how to create a collection and configure it for multi-vector embeddings.
 weight: 1
+isLesson: true
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 {{< date >}} Module 0 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md

@@ -2,6 +2,7 @@
 title: "Late Interaction Basics"
 description: Understand the late interaction paradigm and how it differs from traditional dense embeddings for text search.
 weight: 1
+isLesson: true
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 {{< date >}} Module 1 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-1/maxsim-distance.md

@@ -2,6 +2,7 @@
 title: "MaxSim Distance Metric"
 description: Learn about the MaxSim distance metric used in multi-vector search and how it computes similarity between multi-vector representations.
 weight: 2
+isLesson: true
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 {{< date >}} Module 1 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-1/multi-vector-in-qdrant.md

@@ -2,6 +2,7 @@
 title: "Multi-Vector Embeddings in Qdrant"
 description: Configure Qdrant collections for multi-vector embeddings and learn how to index and query multi-vector data.
 weight: 5
+isLesson: true
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 {{< date >}} Module 1 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-1/problems-multi-vector.md

@@ -2,6 +2,7 @@
 title: "Problems of Multi-Vector Search"
 description: Understand the challenges and limitations of multi-vector search at scale, including memory and performance considerations.
 weight: 4
+isLesson: true
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 {{< date >}} Module 1 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-1/use-cases-multi-vector.md

@@ -2,6 +2,7 @@
 title: "Use Cases for Multi-Vector Search"
 description: Discover scenarios where multi-vector search outperforms single-vector embeddings and provides better retrieval quality.
 weight: 3
+isLesson: true
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 {{< date >}} Module 1 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-2/colpali-family.md

@@ -2,6 +2,7 @@
 title: "ColPali Family Overview"
 description: Explore the ColPali model family and their capabilities for multi-modal document understanding and retrieval.
 weight: 2
+isLesson: true
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 {{< date >}} Module 2 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-2/how-colpali-works.md

@@ -2,6 +2,7 @@
 title: "How ColPali Models Work"
 description: Understand the inner workings of ColPali models and how they generate multi-vector representations for images and documents.
 weight: 1
+isLesson: true
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 {{< date >}} Module 2 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-2/visual-interpretability.md

@@ -2,6 +2,7 @@
 title: "Visual Interpretability of ColPali"
 description: Learn how to visualize and interpret ColPali embeddings to understand what the model focuses on in images.
 weight: 3
+isLesson: true
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 {{< date >}} Module 2 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md

@@ -2,6 +2,7 @@
 title: "Evaluating Search Pipelines"
 description: Learn how to evaluate different search configurations in terms of cost, latency, and retrieval quality using ground truth datasets and standardized metrics.
 weight: 5
+isLesson: true
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 {{< date >}} Module 3 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-3/final-project.md

@@ -2,6 +2,7 @@
 title: "Final Project: Build Your Own Multi-Vector Search System"
 description: Apply everything you've learned to build a multi-vector search system that solves a real problem of your choosing.
 weight: 7
+isLesson: true
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 {{< date >}} Module 3 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-3/multi-stage-retrieval.md

@@ -2,6 +2,7 @@
 title: "Multi-Stage Retrieval with Universal Query API"
 description: Combine multiple optimization techniques in multi-stage retrieval pipelines using Qdrant's Universal Query API.
 weight: 1
+isLesson: true
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 {{< date >}} Module 3 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-3/muvera.md

@@ -2,6 +2,7 @@
 title: "MUVERA"
 description: Understand MUVERA and how it enables HNSW indexing for multi-vector search despite MaxSim asymmetry.
 weight: 4
+isLesson: true
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 {{< date >}} Module 3 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md

@@ -2,6 +2,7 @@
 title: "Pooling Techniques"
 description: Reduce the number of vectors per document using row/column pooling and hierarchical token pooling strategies.
 weight: 3
+isLesson: true
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 {{< date >}} Module 3 {{< /date >}}

qdrant-landing/content/course/multi-vector-search/module-3/quantization-techniques.md

@@ -2,6 +2,7 @@
 title: "Vector Quantization Techniques"
 description: Learn how to reduce memory usage with scalar quantization, binary quantization, and other compression methods.
 weight: 2
+isLesson: true
 ---
 
 {{< date >}} Module 3 {{< /date >}}