Euclidean distance is straight-line distance — Pythagoras extended to as many dimensions as you like:
d = sqrt(Σ (a[i] - b[i])²)
Task: write euclidean_distance(point_a, point_b) returning the distance, rounded to 4 decimal places.
It's the default distance almost everywhere — k-NN, k-means, nearest-neighbour search — but it gets unhelpful in very high dimensions, where all pairwise distances drift toward each other and "nearest" stops meaning much. That's why embeddings are usually compared by cosine instead.