From 3d5fa5da35378f678a0dac661cbf02e3a3ac0415 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kacper=20=C5=81ukawski?= Date: Thu, 22 Jan 2026 14:20:40 +0100 Subject: [PATCH] Remove course plan --- .../course/multi-vector-search/plan.md | 40 ------------------- 1 file changed, 40 deletions(-) delete mode 100644 qdrant-landing/content/course/multi-vector-search/plan.md diff --git a/qdrant-landing/content/course/multi-vector-search/plan.md b/qdrant-landing/content/course/multi-vector-search/plan.md deleted file mode 100644 index c8a405f2b..000000000 --- a/qdrant-landing/content/course/multi-vector-search/plan.md +++ /dev/null @@ -1,40 +0,0 @@ -# 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.