Retrieval-Augmented Generation (RAG) enhances large language model (LLM) generation quality by incorporating relevant external knowledge. However, deploying RAG on consumer-grade platforms is challenging due to limited memory and the increasing scale of both models and knowledge bases. In this work, we introduce RAGDoll, a resource-efficient, self-adaptive RAG serving system integrated with LLMs, specifically designed for resource-constrained platforms. RAGDoll exploits the insight that RAG retrieval and LLM generation impose different computational and memory demands, which in a traditional serial workflow result in substantial idle times and poor resource utilization. Based on this insight, RAGDoll decouples retrieval and generation into parallel pipelines, incorporating joint memory placement and dynamic batch scheduling strategies to optimize resource usage across diverse hardware devices and workloads. Extensive experiments demonstrate that RAGDoll adapts effectively to various hardware configurations and LLM scales, achieving up to 3.6 times speedup in average latency compared to serial RAG systems based on vLLM.