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

---

## 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.