---
title: "Multi-Vector Search Course"
page_title: "Qdrant Multi-Vector Search Course"
short_description: "Master multi-vector search with ColBERT and ColPali: late interaction, MaxSim scoring, multi-stage retrieval, MUVERA indexing, and evaluation."
description: Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines.
content:
  sidebarTitle: "Multi-Vector Search Course"
  menuTitle:
    text: Course Overview
    url: /course/multi-vector-search/
  nextButton: Continue to Next Step
  nextDay: Complete
  title: "Multi-Vector Search"
  description: Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines.
partition: course
isLesson: true
---

# Multi-Vector Search

**Build production-ready multi-vector search pipelines**

Go beyond single-vector embeddings with late interaction models like ColBERT and ColPali. Learn the MaxSim distance metric, optimize for billion-scale search, and evaluate your retrieval pipelines with industry-standard metrics.

<div class="video">
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  src="https://www.youtube-nocookie.com/embed/WXEf6K6DBq8?rel=0"
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<br/>

{{< cards-list >}}
- icon: /icons/outline/play-white.svg
  title: 4 modules
  content: Focused lessons building from fundamentals to production
- icon: /icons/outline/cloud-check-blue.svg
  title: Shareable certificate
  content: Earn a digital certificate upon completion
- icon: /icons/outline/time-blue.svg
  title: Flexible schedule
  content: Learn at your own pace (1–2 hours/module)
- icon: /icons/outline/plan.svg
  title: Advanced level
  content: Assumes familiarity with vector search basics

{{< /cards-list >}}

<br/>

## What you'll learn
{{< course-card
 title="Skills you'll gain:"
 image="/icons/outline/training-white.svg"
 type="wide-list">}}

- Late interaction paradigm and MaxSim distance metric
- ColBERT for text and ColPali for visual documents
- Multi-stage retrieval with prefetch and reranking
- Quantization and pooling techniques for memory optimization
- MUVERA indexing for billion-scale search
- Evaluation metrics: Recall@k, NDCG, MRR

{{< /course-card >}}

### The Path

**Module 0**: Setup. Configure Qdrant Cloud or local instance and install Python dependencies.

**Module 1**: Text multi-vectors. Understand the late interaction paradigm, learn the MaxSim distance metric, explore use cases and challenges, and implement ColBERT with Qdrant.

**Module 2**: Multi-modal search. Apply multi-vector representations to images and PDFs with ColPali. Explore model variants and leverage visual interpretability for debugging.

**Module 3**: Optimization and evaluation. Master quantization, pooling, and MUVERA for memory-efficient search. Build multi-stage retrieval pipelines and evaluate with standard metrics.

## How the course works

{{< cards-list >}}

- icon: /icons/outline/training-purple.svg
  title: Video-first lessons
  content: Clear, concise modules by the Qdrant team
- icon: /icons/outline/hacker-purple.svg
  title: Final project
  content: Build a production-ready multi-modal search system
- icon: /icons/outline/similarity-blue.svg
  title: Hands-on notebooks
  content: Practice each concept with Colab notebooks
- icon: /icons/outline/copy.svg
  title: Progressive learning
  content: Build from fundamentals to advanced optimization
  {{< /cards-list >}}

<br/>

## Syllabus

{{< accordion >}}
- title: "Module 0: Setting Up Dependencies"
  content: |
    - Qdrant Setup
    - Installing Dependencies
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/multi-vector-search/module-0/">→ Start Module 0</a></p>

- title: "Module 1: Multi-Vector Representations for Textual Data"
  content: |
    - Late Interaction Basics
    - MaxSim Distance Metric
    - Use Cases for Multi-Vector Search
    - Problems of Multi-Vector Search
    - Multi-Vector Embeddings in Qdrant
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/multi-vector-search/module-1/">→ Start Module 1</a></p>

- title: "Module 2: Multi-Vector Representations for Multi-Modal Data"
  content: |
    - How ColPali Models Work
    - ColPali Family Overview
    - Visual Interpretability of ColPali
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/multi-vector-search/module-2/">→ Start Module 2</a></p>

- title: "Module 3: Scalability and Optimization"
  content: |
    - Multi-Stage Retrieval with Universal Query API
    - Vector Quantization Techniques
    - Pooling Techniques
    - MUVERA Indexing
    - Evaluating Search Pipelines
    - Final Project
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/multi-vector-search/module-3/">→ Start Module 3</a></p>
{{< /accordion >}}


## Who it's for

ML, backend, and search engineers who want to go beyond single-vector embeddings. Requires intermediate Python, basic familiarity with vector search concepts (embeddings, similarity metrics), and comfort with APIs.

## Time commitment

- Duration: 4 modules at 1 hour/module
- Video learning: 1.5 hours
- Hands-on notebooks: 1.5 hours
- Final project: 1-3 hours
- Total: 4-6 hours


{{< course-card
 title="Ready to master multi-vector search?"
 image="/icons/outline/rocket-white-light.svg"
 link="/course/multi-vector-search/module-0/">}}
**What you'll get**
- Build production-ready multi-vector pipelines
- Practice with real Colab notebooks
- Learn optimization techniques for scale
- Portfolio project and community support
{{< /course-card >}}