Vector quantization (VQ) in embeddings is a technique that compresses high-dimensional vectors into a smaller set of representative prototypes, reducing storage and computational costs. It works by grouping similar vectors into clusters and replacing each original vector with the index of its closest cluster centroid. These centroids are stored in a “codebook,” a predefined set of reference vectors. For example, if you have 1 million 512-dimensional embeddings, VQ might map them to 256 centroids (an 8-bit index), drastically cutting memory usage while preserving approximate similarity relationships.
The process typically involves two steps: training and encoding. During training, algorithms like k-means cluster the original vectors to create the codebook. Each centroid becomes a prototype representing its cluster. In encoding, every input vector is replaced by the index of its nearest centroid. For instance, in natural language processing, word embeddings might be quantized so that synonyms like “car” and “automobile” map to the same centroid. When searching for similar items, you compare queries to centroids instead of all vectors, speeding up operations. However, this introduces a trade-off: smaller codebooks save more space but lose fine-grained details, potentially reducing accuracy.
Developers often use VQ in retrieval systems or recommendation engines where speed matters. A practical example is approximate nearest neighbor search: instead of comparing a query vector to all 1 million embeddings, you precompute distances to 256 centroids and only search within the closest clusters. Extensions like product quantization improve efficiency by splitting vectors into subvectors and quantizing each separately. For instance, a 128D vector could be split into four 32D subvectors, each quantized to 256 centroids, resulting in a compact 4-byte representation. While VQ introduces some error, its balance of speed, memory, and accuracy makes it a staple in large-scale machine learning pipelines.