Vector quantization is a technique used to compress vector data by reducing the number of unique vectors. This is achieved by grouping similar vectors into clusters and representing each cluster with a single prototype vector. By doing so, vector quantization reduces the size of the dataset, making it easier to store and process.
In the context of vector search, vector quantization optimizes the process by decreasing the computational burden during similarity search. With fewer vectors to compare, the search process becomes faster and more efficient. This is particularly beneficial when dealing with large datasets, where computational resources can be a limiting factor.
Moreover, vector quantization can improve the accuracy of search results by enhancing the representation of data in the embedding space. By ensuring that similar items are grouped together, the search algorithm can more effectively identify semantically similar data points, leading to more accurate and relevant results.
Overall, vector quantization is a valuable tool for optimizing vector search, offering a balance between storage efficiency and search accuracy.