Introduction
"Graph RAG typically pre-builds a global graph offline, then uses PageRank for query-time scoring and expansion — but PageRank has decay: the longer the chain, the lower the score. SAG uses SQL JOINs to expand at query time instead. No decay, no pre-built global graph."
This is article #116 in the Open Source Project of the Day series. Today's project is SAG (Structured Agentic Graph) — Zleap-AI's open-source next-generation multi-hop RAG framework, backed by an arXiv paper (2606.15971).
Multi-hop retrieval's core challenge: some questions need to chain information from multiple separate document passages. Standard vector retrieval finds semantically similar chunks — but "semantically similar" doesn't mean "can answer questions requiring multi-step reasoning." GraphRAG and HippoRAG solve this with knowledge graphs, but at two costs: expensive offline global graph construction, and PageRank score decay over longer chains.
SAG takes a different path: no global graph pre-built, SQL JOINs for graph-structure expansion at query time. Each chunk produces one "event" (semantic summary) and a set of entities. Relationships between events and entities are expressed through relational database foreign keys. Multi-hop expansion is just a JOIN operation — deterministic, traceable, no decay.
