Retrieval-augmented generation (RAG) systems rely on large-scale embedding search. However, the growth of corpora and the demand for real-time queries expose the memory capacity and bandwidth limitations of DRAM-centric architectures. To address this challenge, we adopt CXL memory in conjunction with cluster-scale RAG deployments and optimize the system in terms of performance, latency and cost efficiency. Our design leverages state-of-the-art CXL memory devices and platforms with memory management policies such as weighted interleaving and tiering between DRAM and CXL memory. In comparison with a DRAM baseline, our system archives 20% higher vector search performance and 43% lower LLM time-to-first token (TTFT), demonstrating clear end-to-end performance benefits. Moreover, in the context of overall system efficiency, we observed up to 20% and 42% improvement in power efficiency (QPS/Watt) and cost efficiency (QPS/$), respectively. These results demonstrate that, despite the relatively high latency of CXL memory, memory management policy-level optimization enable CXL memory to serve as a first-class memory resource in cluster-based RAG systems.