![](https://crypto4nerd.com/wp-content/uploads/2023/12/1k5zLSE-vDbbjn0PIXF-kQ@2x.jpeg)
Deconstructing the complexity behind vector data’s use in machine learning algorithms.
When working with vector databases and embedding models, the range and typical dimensions used, as well as the types of distance measures, are key considerations.
But before we dive in, let’s revisit the concept of vectors and what they represent. Think of a vector like a backpack you’re packing for a trip. Each item in the backpack (like clothes, toothbrush, snacks) is a dimension. Each item adds more detail about what your trip will be like.
In the vector world, each dimension is like one of these items. It adds a piece of information about whatever the vector is describing. So, if a vector has three dimensions, it’s like a backpack with three types of items, each giving more detail about your trip. The more dimensions (or items) you have, the clearer the picture of what you’re preparing for.