解开图式RAG的结——事实证明向量RAG几乎足够
UnWeaving the knots of GraphRAG -- turns out VectorRAG is almost enough
AI总结：
本文提出UnWeaver框架，通过LLM解构文档内容为跨chunk的实体，提升检索和生成的准确性与效率，实验表明向量RAG在成本上优于图式RAG。
摘要：
检索增强生成（RAG）系统中的关键问题在于基于片段的检索流程将源片段视为原子对象，将其中信息混合成单一向量。这些向量被视为孤立、独立且自足，没有尝试表示它们之间的可能关系。此类方法缺乏处理多跳问题的专用机制。图式RAG系统通过将信息建模为知识图谱来缓解这一问题，实体由节点表示，通过稳健的关系连接并形成层次化社区。然而，这种方法自身也存在一些问题，包括为创建图式索引而增加数量级的组件复杂性，以及依赖启发式方法进行检索。我们提出UnWeaver，一种新颖的RAG框架，简化了图式RAG的理念。UnWeaver利用LLM将文档内容解构为可以在多个片段中出现的实体。在检索过程中，实体被用作恢复原始文本片段的中间方式，从而保持对源材料的忠实度。我们主张基于实体的分解能提供更浓缩的原始信息表示，同时还能减少索引和生成过程中的噪声。此外，我们实验表明，在端到端QA评估中，向量RAG的表现优于标准图式RAG，并且几乎与当前最先进的图式解决方案相当，但成本仅为其分数。
英文摘要：
One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retrieval pipelines represent the source chunks as atomic objects, mixing the information contained within such a chunk into a single vector. These vector representations are then fundamentally treated as isolated, independent and self-sufficient, with no attempt to represent possible relations between them. Such an approach has no dedicated mechanisms for handling multi-hop questions. Graph-based RAG systems aimed to ameliorate this problem by modeling information as knowledge-graphs, with entities represented by nodes being connected by robust relations, and forming hierarchical communities. This approach however suffers from its own issues with some of them being: orders of magnitude increased componential complexity in order to create graph-based indices, and reliance on heuristics for performing retrieval. We propose UnWeaver, a novel RAG framework simplifying the idea of GraphRAG. UnWeaver disentangles the contents of the documents into entities which can occur across multiple chunks using an LLM. In the retrieval process entities are used as an intermediate way of recovering original text chunks hence preserving fidelity to the source material. We argue that entity-based decomposition yields a more distilled representation of original information, and additionally serves to reduce noise in the indexing, and generation process. Furthermore we experimentally show that on end to end QA evaluation VectorRAG performs better than standard GraphRAG and almost as good as current SOTA graph-based solutions, for a fraction of the cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
