> Explore Qdrant's agent skills catalog at https://skills.qdrant.tech/
> Search the documentation at https://skills.qdrant.tech/search?query=your+query+here
> Use this file to discover all available pages: https://qdrant.tech/llms.txt

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  <img class="date-icon" src="/icons/outline/date-blue.svg" alt="Calendar" />  Module 2 
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# First Principles of Vector Search

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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](https://github.com/qdrant/examples/blob/master/course/beginners/Module2.ipynb)

#### 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](https://qdrant.tech/course/beginners/module-2/from-idea-to-system/index.md)
2. [Core Data Model](https://qdrant.tech/course/beginners/module-2/core-data-model/index.md)
3. [Distance Metrics](https://qdrant.tech/course/beginners/module-2/distance-metrics/index.md)
4. [Top-K Retrieval](https://qdrant.tech/course/beginners/module-2/top-k-retrieval/index.md)
5. [Fast Approximate Search: HNSW](https://qdrant.tech/course/beginners/module-2/hnsw/index.md)
6. [Payload Filtering](https://qdrant.tech/course/beginners/module-2/payload-filtering/index.md)
7. [Chunking Strategies](https://qdrant.tech/course/beginners/module-2/chunking-strategies/index.md)
8. [Ingestion Pipeline: End-to-End](https://qdrant.tech/course/beginners/module-2/ingestion-pipeline/index.md)
9. [Further Reading](https://qdrant.tech/course/beginners/module-2/further-reading/index.md)