Initial structure + plan

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

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+---
+title: "Multi-Vector Search Course"
+todo: Prepare all the other attributes of the course
+---
+
+# Multi-Vector Search
+
+TBD

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

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+# Qdrant Multi-Vector Search Course
+
+This document is a proposal for another Qdrant course. I suggest covering both text and multi-modal multi-vector representations, focusing on making them usable in Qdrant.
+
+# Outline
+
+1. **Module 1:** Multi-vector representations for textual data  
+   * ColBERT basics  
+   * Comparison to regular dense embedding models  
+   * Examples when multi-vectors may work better than single vectors  
+   * MaxSim distance  
+2. **Module 2:** Multi-vector representations for multi-modal data (image \+ text)  
+   * ColPali family  
+   * Inner workings of the ColPali models  
+   * Visual interpretability of ColPali representations  
+   * Setting up Qdrant for multi-vector embeddings  
+3. **Module 3:** Scalability issues caused by multi-vector representations  
+   * The implications of high memory usage and ways to solve it  
+     * Vector quantization: scalar, binary, \+1.5/2-bit  
+     * Pooling techniques: row/column pooling, hierarchical token pooling  
+   * Incompatibility with HNSW due to MaxSim asymmetry  
+     * MUVERA  
+   * Combining multiple optimizations in multi-stage retrieval via Universal Query API  
+   * Evaluating different search pipelines in terms of cost and latency
+
+## Project
+
+I suggest having just one project. It should start with a set of PDFs or even scanned documents for which the participants are supposed to create a two-stage ColPali retrieval system using both MUVERA for fast retrieval and some memory optimization (or multiple ones, like SQ \+ token pooling) for reduced memory usage.
+
+## Supplementary content
+
+To keep the momentum, I suggest publishing a few related blog posts in the upcoming weeks:
+
+1. **The asymmetry of MaxSim**  
+   We suggest disabling HNSW for MaxSim, but it is never discussed why it doesn’t make sense. It’s not worth an article, but a short explanation with some examples would make things clearer.  
+2. **A review of modern multi-vector retrievers for visual data**  
+   There’s been a lot of excitement around ModernVBERT, so it would make sense to present how to use it with Qdrant, even though we don’t support it in FastEmbed.  
+3. **FastEmbed: implementation of different pooling techniques for multi-vectors**  
+   E.g., row/column pooling (if possible), hierarchical token pooling. Techniques might be implemented in the library itself, but we should also create a blog post about it.