# Multi-Vector Search Course # 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.

4 modules

Focused lessons building from fundamentals to production

Shareable certificate

Earn a digital certificate upon completion

Flexible schedule

Learn at your own pace (1–2 hours/module)

Advanced level

Assumes familiarity with vector search basics

## What you'll learn

Skills you'll gain:

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

### 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

Video-first lessons

Clear, concise modules by the Qdrant team

Final project

Build a production-ready multi-modal search system

Hands-on notebooks

Practice each concept with Colab notebooks

Progressive learning

Build from fundamentals to advanced optimization

## Syllabus

Module 0: Setting Up Dependencies

Qdrant Setup
Installing Dependencies

→ Start Module 0

Module 1: Multi-Vector Representations for Textual Data

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

Module 2: Multi-Vector Representations for Multi-Modal Data

How ColPali Models Work
ColPali Family Overview
Visual Interpretability of ColPali

→ Start Module 2

Module 3: Scalability and Optimization

Multi-Stage Retrieval with Universal Query API
Vector Quantization Techniques
Pooling Techniques
MUVERA Indexing
Evaluating Search Pipelines
Final Project

→ Start Module 3

## 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

Ready to master multi-vector search?

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

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