What are the limitations of text-only RAG systems, and how do graph-based RAG systems address these limitations?

Text-only RAG systems retrieve based heavily on textual similarity, so they struggle to capture latent or complex relationships that require multi-hop reasoning across datasets or external sources like ontologies and knowledge graphs. This leads to lower retrieval accuracy and can pull in irrelevant documents, limiting the generative model's effectiveness. Graph-based RAG systems address these limitations by representing the knowledge base as a graph of entities and relationships, letting the retriever traverse connections and uncover relationships between disparate pieces of information for richer and more relevant retrieval.

Standard text-only RAG integrates an external document corpus with a generative model, retrieving relevant passages by textual similarity and synthesizing an answer from them. However, the retriever may miss underlying relationships that are not directly reflected in text overlap, especially when answering complex queries that need multi-hop reasoning over multiple datasets or structured sources such as ontologies and knowledge graphs. As a result, retrieval accuracy drops and irrelevant documents are often included, which reduces the quality of the generated response. Graph-based RAG overcomes this by representing knowledge in a graph format with nodes as entities or concepts and edges as relationships. Because the retriever can traverse the graph database, it is able to expose connections between seemingly unrelated pieces of information, something linear text formats usually hide. This produces more contextually adequate retrieval output, and graph-specific indexing and search mechanisms further improve query processing efficiency.