> 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

<div class="date">
  <img class="date-icon" src="/icons/outline/date-blue.svg" alt="Calendar" />  Module 1 
</div>


# Further Reading

- [Distance Metrics](https://qdrant.tech/course/essentials/day-1/distance-metrics/index.md) Learn more about cosine similarity, dot product, and Euclidean distance.
- [Vector Embeddings Explained](https://qdrant.tech/articles/what-are-embeddings/index.md) A deeper introduction to how embedding models turn data into vectors.
- [FastEmbed](https://qdrant.tech/articles/fastembed/index.md) Learn more about the library used to generate embeddings in this module.

## What's Next: Module 2

In the next module, we'll break down:

- What is a vector, and why does it have hundreds to thousands of dimensions?
- How do dimensions actually represent meaning?
- How similarity really works under the hood, and when it fails.
- Your first Qdrant collection: points, payloads, and your first query.