The author identifies 'Semantic Clobbering' as a limitation in pure vector search-based RAG, which corrupts retrieval logic. They propose Graph-RAG as a paradigm shift, utilizing knowledge graphs for structured information retrieval and enabling multi-hop reasoning.
This article details the evolution of 2asy.ai's retrieval system, transitioning from basic vector search to a cross-domain ontology Graph RAG. This rebuild aims to overcome the limitations of similarity-based retrieval and better understand complex causal relationships within tariff news.
The real challenge in Graph RAG is not data extraction but normalization to ensure graph navigability. The author discovered this when building a knowledge graph for 2asy.ai, where inconsistent naming of entities and relation types rendered the graph unusable despite successful extraction.