Retrieval-Augmented Generation (RAG) is one of the leading and most widely used techniques for enhancing LLM retrieval capabilities, but it still faces significant limitations in commercial use cases. RAG primarily relies on the query-chunk text-to-text similarity in the embedding space for retrieval and can fail to capture deeper semantic relationships across chunks, is highly sensitive to chunking strategies, and is prone to hallucinations. To address these challenges, we propose TOBUGraph, a graph-based retrieval framework that first constructs the knowledge graph from unstructured data dynamically and automatically. Using LLMs, TOBUGraph extracts structured knowledge and diverse relationships among data, going beyond RAG's text-to-text similarity. Retrieval is achieved through graph traversal, leveraging the extracted relationships and structures to enhance retrieval accuracy, eliminating the need for chunking configurations while reducing hallucination. We demonstrate TOBUGraph's effectiveness in TOBU, a real-world application in production for personal memory organization and retrieval. Our evaluation using real user data demonstrates that TOBUGraph outperforms multiple RAG implementations in both precision and recall, significantly improving user experience through improved retrieval accuracy.

翻译：检索增强生成（RAG）是提升大语言模型检索能力的主流且应用最广泛的技术之一，但在商业应用场景中仍存在显著局限。RAG主要依赖嵌入空间中查询-文本块间的文本相似度进行检索，难以捕捉跨文本块的深层语义关联，对文本分块策略高度敏感，且易产生幻觉。为应对这些挑战，我们提出TOBUGraph——一种基于图谱的检索框架，该框架首先从非结构化数据中动态、自动地构建知识图谱。TOBUGraph利用大语言模型提取结构化知识及数据间的多样化关联，突破了RAG仅依赖文本相似度的局限。通过图谱遍历实现检索，充分利用提取的关联与结构提升检索准确率，无需配置文本分块策略的同时降低了幻觉产生。我们在TOBU——一个用于个人记忆组织与检索的实际生产系统中验证了TOBUGraph的有效性。基于真实用户数据的评估表明，TOBUGraph在精确率与召回率上均优于多种RAG实现方案，通过提升检索准确率显著改善了用户体验。