Module 1

Further Reading

• Distance Metrics Learn more about cosine similarity, dot product, and Euclidean distance.
• Vector Embeddings Explained A deeper introduction to how embedding models turn data into vectors.
• FastEmbed 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.