Retrieval-augmented generation (RAG) has emerged as a critical paradigm for deploying reliable and knowledge-intensive large language model (LLM) applications. Its unique execution pipeline introduces large overhead in system retrieval, such as massive context loading and document dependencies. To reduce this overhead, existing work on RAG serving systems often relies on generic LLM inference traces or synthetic RAG datasets. However, these approaches fail to capture authentic temporal reuse patterns of RAG workloads, resulting in a disconnect between theoretical optimization and real-world deployment efficiency.

To bridge this gap, this paper introduces RAGPulse, an open-source RAG workload trace dataset that preserves authentic temporal information missing in existing traces. Collected from a production-grade university Q A system serving over 40,000 students and faculty members since April 2024, the dataset comprises 134K requests over 389 days. It unveils key RAG dynamics including time-sensitive document reuse, skewed access patterns over long time scales, and bursty temporal locality. By capturing these temporal reuse patterns, RAGPulse empowers the systems community to develop and validate next-generation optimization techniques, offering practical guidance for efficient RAG serving. The trace is available at https://github.com/flashserve/RAGPulse.