Master Multi-Vector Search With Qdrant

Most vector search tutorials stop at single-vector embeddings: one document, one vector, one similarity score. That works for demos. It falls apart when your retrieval pipeline needs to capture fine-grained token-level interactions across text, images, and PDFs at production scale.

Until now, engineers who wanted to go deeper had to piece together scattered papers, blog posts, and half-documented repos. There was no structured, hands-on resource that connected the theory of late interaction models to real implementation in a production search engine.

We built one.

Introducing the Multi-Vector Search Course

Qdrant’s Multi-Vector Search Course is a free, advanced course created by Kacper Łukawski. Kacper designed this course to fill a real gap in the developer community: practical, production-focused education on multi-vector retrieval that goes well beyond “here’s how embeddings work.”

This is an advanced course! It’s built for ML engineers, backend engineers, and search engineers who already understand vector search fundamentals and want to master what comes next.

What You’ll Learn

The course is organized into four modules, each taking roughly one to two hours:

Module 0: Setup. Configure a 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 real use cases and challenges, and implement ColBERT-based search with Qdrant.

Module 2: Multi-Modal Search. Apply multi-vector representations to images and PDFs using ColPali. Explore model variants in the ColPali family and use visual interpretability for debugging retrieval results.

Module 3: Optimization and Evaluation. Master vector quantization, pooling techniques, and MUVERA indexing for memory-efficient search at billion scale. Build multi-stage retrieval pipelines with Qdrant’s Universal Query API and evaluate with industry-standard metrics (Recall@k, NDCG, MRR).

The course wraps up with a final project: build your own production-ready multi-modal search system from scratch.

How It Works

Every module combines video lessons from the Qdrant team with hands-on Google Colab notebooks. You watch, you build, you evaluate. The progressive structure means each module builds directly on the previous one, so you finish with a complete, working pipeline rather than a collection of disconnected concepts.

Earn a Qdrant Certification

Complete the course and pass the certification exam to earn a shareable Qdrant Multi-Vector Search certificate. It’s a concrete way to demonstrate that you can design, implement, and optimize multi-vector retrieval pipelines in production.

Certifications are available through Qdrant Academy.

Free Swag for the First 20 Certified

Dive in now: the first 20 people who complete their Multi-Vector Search certification and post on LinkedIn with the hashtag #QdrantCertified will receive free Qdrant swag. Share your certificate, tag us, and we’ll reach out.

Start Now

The course is free, self-paced, and available today. If you’ve been looking for a structured path from “I understand embeddings” to “I can build and evaluate multi-vector retrieval at scale,” this is it.

Start the Multi-Vector Search Course

Questions? Join our Discord community where Qdrant experts collaborate.