We employed LightRAG approach instead of maintaining a full GraphRAG. This reduces the cost significantly and sits does retain lot of information that vector RAG alone misses out.
GraphRAG builds knowledge graphs from text using LLMs, but ontology-guided approaches can achieve similar reasoning power at far lower cost by reducing repeated extraction and enforcing structured semantics.
The "derived and perishable" framing is the part most teams underestimate. GraphRAG's edge on global queries is real, but re-running extraction and clustering every time the corpus shifts turns it into a standing infra commitment, not a one-time index.
Your disclaimer mentioned references. I don't see them.
We employed LightRAG approach instead of maintaining a full GraphRAG. This reduces the cost significantly and sits does retain lot of information that vector RAG alone misses out.
https://substack.com/@ktanvikreddy/note/p-211908824?r=5zi3jh&utm_medium=ios&utm_source=notes-share-action
GraphRAG builds knowledge graphs from text using LLMs, but ontology-guided approaches can achieve similar reasoning power at far lower cost by reducing repeated extraction and enforcing structured semantics.
The "derived and perishable" framing is the part most teams underestimate. GraphRAG's edge on global queries is real, but re-running extraction and clustering every time the corpus shifts turns it into a standing infra commitment, not a one-time index.