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 basicsWhat 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 optimizationSyllabus
Module 0: Setting Up Dependencies
- Qdrant Setup
- Installing Dependencies
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
Module 2: Multi-Vector Representations for Multi-Modal Data
- How ColPali Models Work
- ColPali Family Overview
- Visual Interpretability of ColPali
Module 3: Scalability and Optimization
- Multi-Stage Retrieval with Universal Query API
- Vector Quantization Techniques
- Pooling Techniques
- MUVERA Indexing
- Evaluating Search Pipelines
- Final Project
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 2-3 hours/module
- Video learning: ~4 hours
- Hands-on notebooks: ~4 hours
- Final project: 2-4 hours
- Total: 8-12 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
