Dimensionality is the number of numeric values in an embedding vector. A 768-dimensional embedding contains 768 values. The dimensions are learned features, not usually human-readable categories.
More dimensions can represent finer distinctions, but they increase storage, memory bandwidth, network transfer, and distance-computation cost. Higher dimension does not automatically mean better retrieval because model training quality matters more.
Some models support shortened vectors trained to retain useful information. Compare available dimensions on your relevance set. Estimate index and replica memory before choosing. Use the smallest dimension that meets quality goals, and never compare vectors created with different dimensions or incompatible models.
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