Hi all! I'm Marko, CTO at Memgraph, and the author of this post.
The post argues that the GraphRAG pipeline can be expressed as a single database query rather than a chain of application-layer steps. The idea is to keep components such as retrieval, expansion, ranking, and final context assembly within the database query plan.
I go through:
* what I mean by GraphRAG in practice;
* why "single-query" execution can reduce moving parts;
* why can that help with latency/cost by returning only the final payload;
* and how it can make tracing/debugging easier by returning the context plus the path used to assemble it.
The post also contrasts this with Python-orchestrated pipelines and touches on agentic pipeline selection (called Agentic GraphRAG). Happy to answer technical questions or discuss where this breaks down.
The post argues that the GraphRAG pipeline can be expressed as a single database query rather than a chain of application-layer steps. The idea is to keep components such as retrieval, expansion, ranking, and final context assembly within the database query plan.
I go through: * what I mean by GraphRAG in practice; * why "single-query" execution can reduce moving parts; * why can that help with latency/cost by returning only the final payload; * and how it can make tracing/debugging easier by returning the context plus the path used to assemble it.
The post also contrasts this with Python-orchestrated pipelines and touches on agentic pipeline selection (called Agentic GraphRAG). Happy to answer technical questions or discuss where this breaks down.