Dot Product

The dot product of two vectors is the sum of the element-wise products of their components. When both vectors are unit-normalized, the dot product equals the cosine similarity. Many approximate nearest neighbor algorithms and neural retrieval systems prefer dot product because it is fast to compute and naturally integrates with inner-product-optimized hardware. Some embedding models are trained with a dot-product objective rather than cosine normalization, so it is important to match the metric to the training protocol.