Abstract: The increasing application of large language models(LLMs) in natural language processing tasks has established retrieval-augmented generation(RAG) as a critical technique for enhancing factual accuracy.However,the distributed storage of cross-domain data presents significant challenges,including barriers to data aggregation,insufficient scalability,and the lack of native distributed coordination.These challenges render traditional centralized RAG frameworks inadequate for cross-domain scenarios.To address this limitation,a distributed RAG framework named Multi-RAG is proposed for handling cross-domain data.This framework enables individual nodes to maintain independent embedding models and vector indexes tailored to their local data characteristics.Queries are routed in parallel to relevant nodes through a query distribution module.Each node retrieves and returns locally high-scoring document segments.A global re-ranking module then performs global semantic reranking on these segments.The optimally re-ranked context is subsequently fed into the LLM for answer generation.Experiments conducted within a synthetically constructed distributed environment using the MultiHop-RAG dataset demonstrate Multi-RAG's effectiveness.The framework achieves a Hits@10 score of 0.765 9,representing a 72% improvement over single-node retrieval(0.445 2) and maintaining performance within 3.1% of a centralized approach.Answer generation accuracy using the DeepSeek-R1 model marks a 48% increase compared to the single-node baseline.The study indicates that through its streamlined distributed coordination mechanism and global information fusion strategy,Multi-RAG effectively enhances retrieval and generation performance in cross-domain settings without requiring raw data consolidation.This framework provides a practical and efficient solution for collaborative knowledge utilization across institutions and domains.

Key words: Distributed retrieval-augmented generation, Cross-domain data collaboration, Global re-ranking, Large language mo-del, Information fusion