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

title, page_title, short_description, description, weight, content, partition, isLesson

title	page_title	short_description	description	weight	content	partition	isLesson
Multi-Vector Search Course	Qdrant Multi-Vector Search Course	Master multi-vector search with ColBERT and ColPali: late interaction, MaxSim scoring, multi-stage retrieval, MUVERA indexing, and evaluation.	Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines.	20	sidebarTitle menuTitle nextButton nextDay title description Multi-Vector Search Course text url Course Overview /course/multi-vector-search/ Continue to Next Step Complete Multi-Vector Search Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines.	course	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.

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• 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

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What you'll learn

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• 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

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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

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• 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 >}}

Syllabus

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• title: "Module 0: Setting Up Dependencies" content: |

  • Qdrant Setup
  • Installing Dependencies
    
    

  → Start Module 0

• 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
    
    

  → Start Module 1

• title: "Module 2: Multi-Vector Representations for Multi-Modal Data" content: |

  • How ColPali Models Work
  • ColPali Family Overview
  • Visual Interpretability of ColPali
    
    

  → Start Module 2

• 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
    
    

  → Start Module 3

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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 >}}