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.

翻译：检索增强生成（RAG）系统中的关键问题之一在于：基于分块的检索流程将源文档块视为原子对象，将此类块中的信息混合为单个向量。这些向量表示在本质上被当作孤立、独立且自足的单元，未尝试表示它们之间可能存在的关联。这种方法缺乏处理多跳问题的专用机制。基于图的RAG系统旨在通过将信息建模为知识图谱来改善此问题——实体由节点表示，节点之间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若干问题，例如为构建图索引而增加的成数量级的组件复杂性，以及检索过程对启发式方法的依赖。我们提出UnWeaver，一种简化GraphRAG理念的新型RAG框架。UnWeaver利用大语言模型将文档内容解耦为可跨多个分块出现的实体。在检索过程中，实体被用作恢复原始文本块的中间媒介，从而保持对源材料保真度。我们认为，基于实体的分解能生成原始信息更精炼的表示，并有助于降低索引和生成过程中的噪声。