Module 2

First Principles of Vector Search

Understand collections, points, vectors, payloads, and the HNSW index, and move from theory to actual system design in Qdrant.

Follow-along code: Module 2 notebook

Overview

Module 1 explained why semantic search works. In this module, you’ll learn where your data lives and how Qdrant searches it. You’ll explore collections, points, vectors, payloads, and distance metrics, then see how Qdrant finds the top-k matches without scanning every vector. You’ll also learn how to filter results by metadata and split long documents into smaller chunks before embedding them. By the end, you’ll have created a collection, stored points, and run your first filtered query.

Today’s Path

1. From Idea to System
2. Core Data Model
3. Distance Metrics
4. Top-K Retrieval
5. Fast Approximate Search: HNSW
6. Payload Filtering
7. Chunking Strategies
8. Ingestion Pipeline: End-to-End
9. Further Reading