An inner product is a bilinear operation on a vector space that combines two vectors to produce a scalar, generalising the geometric notion of a dot product to abstract vector spaces. It defines notions of length and angle, and its normalised form underlies cosine similarity, a standard measure of semantic closeness between embeddings. Inner products are computed extensively in semantic search systems to rank candidate vectors against a query vector.