arXiv AI By Chandan Rajah

post-graph-rag: A PostgreSQL-Native Bi-Temporal Graph RAG Engine with Temporal Grounding at Synthesis

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post-graph-rag is an open‑source PostgreSQL‑native engine that unifies chunks, embeddings, a canonical entity graph, and community summaries in a single database, using pgvector for search and edge tables for traversal. It validates extraction output—rejecting vague predicates, normalising predicates, resolving entities to unique vertices, and flagging negations—before writing, and employs a bi‑temporal layer to record when a relation held and when the system believed it, superseding incompatible earlier assertions. In benchmarks against LightRAG, it builds denser, more queryable graphs and achieves higher scores on LongMemEval, largely due to its temporal grounding in prompts.

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