Module 1: Multi-Vector Representations for Textual Data on Qdrant - Vector Search Engine

Late Interaction Basics

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Module 1

Late Interaction Basics

When building a search system, one fundamental question emerges: when should a query and document interact? The answer to this question may affect both the quality of search results and the system’s scalability.

This lesson introduces the late interaction paradigm - the foundation of multi-vector search - and explores how it compares to other approaches.

Follow along in Colab:

MaxSim Distance Metric

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Module 1

MaxSim Distance Metric

MaxSim (Maximum Similarity) is the core distance metric for late interaction models. Unlike traditional vector similarity metrics that operate on pairs of single vectors, MaxSim computes similarity between sequences of vectors.

Understanding MaxSim is important for working with multi-vector search effectively and understanding its performance characteristics.

Follow along in Colab:

The MaxSim Formula

In late interaction, we represent documents and queries as sequences of token vectors. But how do we measure similarity between two sets of vectors?

Use Cases for Multi-Vector Search

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Module 1

Use Cases for Multi-Vector Search

When is the added complexity of multi-vector search actually worth it? Multi-vector representations require more storage, more computation, and more careful implementation than simple single-vector embeddings. So why bother?

The answer comes down to one core capability: fine-grained matching. In the previous lessons, you learned how late interaction preserves token-level representations and how MaxSim computes similarity through independent token matching. Now you’ll see when this precision actually matters - and when it doesn’t.

Problems of Multi-Vector Search

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Module 1

Problems of Multi-Vector Search

Multi-vector search delivers impressive retrieval quality, but it comes with significant challenges. Before deploying multi-vector search in production, you need to understand these limitations and plan accordingly.

The good news: Module 3 covers optimization techniques that address many of these challenges.

The Indexing Challenge: Why HNSW Doesn’t Work

One of the fundamental challenges with multi-vector search stems from HNSW indexing incompatibility. As you learned in the MaxSim lesson, traditional vector search relies on HNSW (Hierarchical Navigable Small World) graphs to enable fast approximate nearest neighbor search. HNSW works by building static proximity graphs that connect similar documents, allowing efficient traversal during queries.

Multi-Vector Embeddings in Qdrant

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Module 1

Multi-Vector Embeddings in Qdrant

You’ve learned how MaxSim enables fine-grained token-level matching and explored both the benefits and challenges of multi-vector search. Now it’s time to put that knowledge into practice.

Qdrant provides first-class support for multi-vector embeddings, making it straightforward to build search systems that leverage late interaction. In this lesson, you’ll learn how to configure Qdrant collections for multi-vector search, index documents with token-level embeddings, and execute queries using MaxSim distance.