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A vector similarity metric that calculates the dot product of two vectors, combining both magnitude and direction. Equivalent to cosine similarity when vectors are normalized, and commonly used for Maximum Inner Product Search (MIPS).

A search problem focused on finding vectors that maximize the inner product with a query vector. Common in recommendation systems and neural search where magnitude carries semantic meaning, requiring specialized algorithms like those in ScaNN.