Scalable Vector Search (SVS) is a performance library for vector similarity search. Thanks to the use of Locally-adaptive Vector Quantization [ABHT23] and its highly optimized indexing and search algorithms, SVS provides vector similarity search:
* on billions of high-dimensional vectors, * at high accuracy * and state-of-the-art speed, * while enabling the use of less memory than its alternatives.
The compression is the main selling point - default LVQ4x4 gives 4x memory reduction compared to float32. Has other options like LVQ8 (8-bit quantization) and LVQ4 (4-bit for max savings). LeanVec variants also do dimensionality reduction.
Learn more in the official documentation: https://redis.io/docs/latest/develop/ai/search-and-query/vectors/#svs-vamana-index