Tech dive: Comprehensive compression leveraging quantization & dimensionality reduction

Redis, in collaboration with Intel, has integrated Quantization and Dimensionality Reduction techniques into its Query Engine to enhance vector search by leveraging Intel's SVS-VAMANA technology. This integration addresses the memory-intensive nature of high-dimensional embeddings, which are crucial for AI applications but can significantly increase infrastructure costs. By applying advanced compression methods such as Locally-adaptive Vector Quantization (LVQ) and LeanVec, Redis reduces the memory footprint by 26-37% without compromising search quality or performance. These methods ensure efficient use of memory bandwidth, crucial for handling datasets with billions of vectors, and maintain sub-millisecond search times while operating on compressed vectors. The use of a two-level vector compression strategy with LVQ and LeanVec allows for precise and performant searches even with reduced memory usage, providing significant latency and throughput improvements over traditional methods like HNSW. The solution is optimized for x86 platforms, particularly Intel, but also offers a fallback for AMD and ARM, though with varying efficiency. This advancement in vector search technology not only improves performance but also enables cost-efficient scaling of AI applications, with potential future enhancements to further optimize memory and computing efficiency across different hardware architectures.