Enterprise knowledge base queries often fail not because the model cannot reason, but because critical evidence is not retrieved into the context. The SAG approach addresses this by distilling document fragments into events, building an extensible index with entities, and using SQL to dynamically construct local relation chains at query time. Instead of replacing vector retrieval with a global knowledge graph, SAG assigns clear responsibilities to vectors, relational tables, and re-rankers. This hybrid architecture significantly improves recall and precision in complex multi-hop queries. For developers building production RAG systems, SAG offers a practical blueprint that combines the strengths of structured and unstructured retrieval without the overhead of a full graph database. The approach is particularly valuable for domains where relationships between entities are sparse or dynamic, such as customer support, legal document analysis, and medical records.
SAG retrieval mechanism uses event extraction and SQL dynamic hyperedges to improve evidence recall in enterprise RAG systems.