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

title, short_description, description, isLesson, weight

title	short_description	description	isLesson	weight
Module 1: Multi-Vector Representations for Textual Data	Module 1: late interaction with ColBERT, MaxSim scoring, multi-vector use cases, and the practical challenges of running them in Qdrant.	Learn about multi-vector representations for text with ColBERT. Understand how they differ from single vector embeddings and when to use them.	true	20

{{< date >}} Module 1 {{< /date >}}

Multi-Vector Representations for Textual Data

Dive into multi-vector text representations and discover how ColBERT changes the vector search landscape.

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Today's path

1. Late Interaction Basics
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.