Understanding distance and relationships between data points is crucial in generative AI, machine learning, and analytics. Large amounts of unstructured data can be stored in vector databases represented in Euclidean space, where distances are calculated using Cartesian coordinates. Different methods exist to calculate distance between vectors, including Manhattan distance, Euclidean distance, Cosine distance, and dot product. These metrics capture similarity or dissimilarity between vectors, essential for tasks like recommendation systems, clustering, and information retrieval. When dealing with vectors of different lengths, padding the shorter vector with zeros ensures accurate calculations. The choice of distance metric depends on the application, and using efficient algorithms can improve performance. A tutorial is provided to calculate distances between two pets using various techniques, highlighting the importance of selecting the right distance metric for a specific use case.