Building upon the foundation of Approximate Nearest Neighbor algorithms from the previous chapter, we now focus on the practical necessity of optimizing vector search performance and resource usage. Low search latency, high query throughput, and efficient memory management are critical for deploying effective LLM applications.

This chapter introduces methods to achieve these goals. We will cover vector compression techniques such as Scalar Quantization ($SQ$) and Product Quantization ($PQ$), including its Optimized ($OPQ$) variant. You will study strategies for implementing efficient metadata filtering, contrasting pre-filtering and post-filtering mechanisms. Additionally, the chapter addresses hardware acceleration options like CPU SIMD instructions and GPUs, along with memory management and caching approaches. Completing this chapter will equip you with practical techniques to tune vector search operations for improved speed and efficiency.