What is Normalization?
Normalization is the process of standardizing text or vectors into a consistent format to improve comparison, retrieval, and processing quality. In text processing, normalization includes operations like converting to lowercase, removing accents, standardizing whitespace, expanding contractions, or converting numbers to standard formats. In vector operations, normalization typically refers to scaling vectors to unit length, ensuring that similarity comparisons focus on direction rather than magnitude.
Text normalization is commonly applied during preprocessing before embedding or retrieval. It ensures that semantically equivalent text with minor formatting differences (like "AI" vs "ai" or "don't" vs "do not") is treated consistently. The specific normalization steps depend on the application: aggressive normalization improves recall by treating more variations as equivalent, while minimal normalization preserves distinctions that might be meaningful (like case in proper nouns or technical terms).
Vector normalization (scaling to unit length) is particularly important when using cosine similarity as a distance metric. For normalized vectors, cosine similarity becomes equivalent to dot product, which can be computed more efficiently. Many embedding models produce pre-normalized vectors, while others require explicit normalization. Understanding whether vectors are normalized and how similarity metrics interact with vector magnitude is important for correctly implementing and optimizing vector search systems.