Remove course plan

2026-10-04 10:28:29 +02:00 · 2026-01-22 14:20:40 +01:00
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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 0:** Setting up dependencies
-2. **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  
-3. **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  
-4. **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.