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Caching, Quantization, and Low-Level Optimizations in AI Systems
This chapter explores the role of caching in rag and compound AI systems, focusing on caching intermediate states, frequently accessed documents, and its impact on response times. It also delves into binary quantization in systems like Weaviate, discussing optimization methods, low-level optimizations, and comparing client performance in Python, Go, and Rust.