Prepare course structrure

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

@@ -19,6 +19,7 @@ Dive into multi-vector text representations and discover how ColBERT changes the
 2. MaxSim Distance Metric
 3. Use Cases for Multi-Vector Search
 4. Problems of Multi-Vector Search
+5. Multi-Vector Embeddings in Qdrant
 
 You'll understand when multi-vector representations outperform traditional single-vector embeddings, and what kind of
 problems to expect when you start working with multi-vector search at scale.

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

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+---
+title: "Late Interaction Basics"
+description: Understand the late interaction paradigm and how it differs from traditional dense embeddings for text search.
+weight: 1
+---
+
+{{< date >}} Module 1 {{< /date >}}
+
+# Late Interaction Basics
+
+Traditional dense embedding models compress entire documents into single vectors. The late interaction paradigm takes a different approach: it represents documents as sets of token-level vectors and delays the interaction computation until search time.
+
+This fundamental shift enables more nuanced matching between queries and documents, capturing fine-grained semantic relationships that single vectors might miss.
+
+---
+
+TBD
+
+---
+
+Next, you'll learn about MaxSim, the distance metric that powers late interaction search.

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

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+---
+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
+---
+
+{{< date >}} Module 1 {{< /date >}}
+
+# MaxSim Distance Metric
+
+MaxSim (Maximum Similarity) is the core distance metric for late interaction models. Unlike traditional vector similarity metrics that operate on pairs of single vectors, MaxSim computes similarity between sets of vectors.
+
+Understanding MaxSim is crucial for working with multi-vector search effectively and understanding its performance characteristics.
+
+---
+
+TBD
+
+---
+
+Now that you understand how multi-vector search works technically, let's explore when it excels compared to traditional approaches.

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

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+---
+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: 4
+---
+
+{{< date >}} Module 2 {{< /date >}}
+
+# Multi-Vector Embeddings in Qdrant
+
+Qdrant provides first-class support for multi-vector embeddings through its multi-vector configuration. This lesson covers creating collections, indexing documents, and querying with MaxSim distance.
+
+By the end, you'll have a working multi-modal search system powered by ColPali and Qdrant.
+
+---
+
+TBD
+
+---
+
+You now have the tools to build multi-modal search systems. In Module 3, we'll tackle the scalability challenges and optimize for production deployment.

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

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+---
+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
+---
+
+{{< date >}} Module 1 {{< /date >}}
+
+# Problems of Multi-Vector Search
+
+Multi-vector search delivers impressive retrieval quality, but it comes with significant challenges. Before deploying multi-vector search in production, you need to understand these limitations and plan accordingly.
+
+The good news: Module 3 covers optimization techniques that address many of these challenges.
+
+---
+
+TBD
+
+---
+
+Understanding these challenges is crucial. In Module 2, we'll extend multi-vector search to multi-modal data, and in Module 3, we'll tackle these scalability issues head-on with optimization techniques.

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

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+---
+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
+---
+
+{{< date >}} Module 1 {{< /date >}}
+
+# Use Cases for Multi-Vector Search
+
+Multi-vector search isn't always the right choice, but in certain scenarios it significantly outperforms traditional single-vector embeddings. Understanding these use cases helps you decide when the added complexity and cost are worth it.
+
+Let's explore situations where multi-vector representations shine.
+
+---
+
+TBD
+
+---
+
+While multi-vector search has clear advantages, it also comes with challenges. Let's explore them next.