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Analyzing the computational bottleneck of Maximum Inner Product Search (MIPS) in RAG: when candidate vectors reach millions and dimensions reach thousands, exhaustive search is costly; approximate search and other acceleration methods can significantly improve retrieval efficiency with controlled error, enhancing RAG end-to-end latency and throughput.