LongRAG is a robust dual-perspective, retrieval-augmented generation system paradigm based on large language models (LLM), designed to enhance the understanding and retrieval capabilities of complex long-text knowledge. This model is particularly suited for Long-Context Question Answering (LCQA), as it effectively handles global information and factual details. Background information indicates that LongRAG improves performance on long-text question-answering tasks by integrating retrieval and generation techniques, especially in scenarios requiring multi-hop reasoning. The model is open-source and freely available, primarily targeting researchers and developers.