Inner Product

Inner product, also called dot product when applied to vectors, is the most fundamental similarity computation in machine learning — the sum of element-wise products of two vectors. Inner product is the operation that defines linear projection, attention scoring inside transformer models, and the core comparison in most vector search engines. Maximum Inner Product Search, or MIPS, is the formal name for the retrieval problem of finding vectors with the highest inner product against a query, and ScaNN and HNSW both support MIPS modes directly. Inner product differs from cosine similarity in that it is sensitive to vector magnitude, which can encode additional signal like document length, popularity, or quality when embeddings are designed to use it. Modern best practice for text retrieval is to L2-normalize vectors at embedding time so that inner product and cosine similarity become equivalent, gaining the speed of inner product with the magnitude-insensitivity of cosine. AI governance teams document the choice between raw inner product and normalized cosine-equivalent inner product in their embedding pipeline lineage.

Inner product search through Centralpoint: Centralpoint supports inner product, cosine, Euclidean, and other similarity metrics across whatever vector backend you operate, under one model-agnostic governance layer. Tokens are metered, prompts stay local, and retrieval-augmented chatbots deploy across portals with one line of JavaScript.


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