post-graph-rag

post-graph-rag is an open-source Python library developed by Chandan Rajah that brings Graph RAG (Retrieval-Augmented Generation) natively to PostgreSQL, using pgvector for HNSW similarity search and a property graph stored as two tables. It is available on PyPI under the Apache 2.0 license and installable with a single pip install post-graph-rag command.

What It Is

post-graph-rag extracts entities and relations from documents using an LLM, stores them as a property graph alongside pgvector embeddings, and answers questions by fusing vector similarity, graph traversal, and full-text search. Its defining architectural bet is that no separate vector store or graph engine is needed — PostgreSQL handles all three, giving a single consistency model, one backup, and transactions that span the knowledge graph and application tables simultaneously. The underlying graph storage layer, post-graph, is also available as a standalone library.

The Two Core Problems It Solves

Most Graph RAG systems treat extracted relations as timeless assertions. When one document says two people are allies and a later document says they became rivals, both edges land in the graph as co-equal current facts. post-graph-rag addresses two structural gaps that recur across existing systems:

• Supersession from document order: A later assertion closes an earlier one rather than coexisting with it. The project page reports that indexing the d'Artagnan trilogy in publication order closed 13 relationships that later books contradicted — resolved from document order alone, with no dates extracted from the prose. This is described as a capability no comparable system (GraphRAG, LightRAG, Graphiti) currently offers.
• Controlled predicate vocabularies: Free-text LLM-extracted edge labels are unqueryable. The project page reports that an unconstrained extraction run produced 395 distinct predicates across 705 relations, with 74% used exactly once. A configured vocabulary reduced this to 44 distinct predicates with 94% adherence, enabling WHERE relation_type = 'worked_with' queries to return meaningful results.

Architecture and Retrieval Design

The retrieval pipeline fuses three channels using Reciprocal Rank Fusion (RRF):

• Entity vector search followed by configurable multi-hop graph traversal (default 2 hops, configurable per query)
• Relation embedding search — relations carry their own embeddings so they can be found by similarity rather than only by traversal
• Lexical/BM25 search over relations

Community summarisation follows Microsoft GraphRAG's pattern: the entity graph is clustered using Leiden (with deterministic label-propagation fallback), each cluster is summarised by an LLM, and each report is stored as a vertex with its own embedding. This enables corpus-level "what are the main themes?" queries via vector similarity rather than enumeration.

Temporal grounding is carried through to the synthesis prompt — each relation's validity period is rendered inline — which the project page identifies as the single largest performance lever, reporting a +38.4 point improvement on temporal-reasoning questions in an ablation study.

Benchmark Results

The project page reports benchmark results against Zep's published Graphiti numbers on the full 500-question LongMemEval set. According to the project's own evaluation, post-graph-rag with gemini-3.6-flash scored 94.0% overall against Zep/Graphiti's 71.2% with gpt-4o, with the widest margins on temporal-reasoning (96.2% vs 62.4%) and multi-session (90.2% vs 57.9%). The project page notes qualifications: different judge panels, different model generations, and one excluded question. On the ECT-QA earnings-call benchmark, the project reports 0.807 Correct under element-wise scoring against published figures of 0.599 for TG-RAG, 0.405 for GraphRAG, and 0.406 for LightRAG.

Setup Path

The dependency list is minimal: PostgreSQL with the pgvector extension, any OpenAI-compatible endpoint (including local ones), and the Python package. The library supports multi-tenant realms with optional schema-per-tenant isolation, configurable chunking, gleaning passes for improved recall, and seven runnable example scripts covering quickstart, supersession, bi-temporal audit, multi-tenant spaces, incremental indexing, community exploration, and retrieval modes.

Update: Version 1.10.0 and Recent Activity

The GitHub repository was last pushed on 2026-09-10. Version 1.10.0 added exploration support through three engine calls: a hierarchical topic tree via recursive supergraph clustering (community_levels), opt-in retrieval telemetry for coverage tracking (least_explored_communities, dark_entities), and a changes_since delta API using database-clock watermarks for exactly-once polling. Version 1.8.0 fixed a document key bug where using only source collapsed entire corpora onto one key. Version 1.13.0 added per-document stats and graph views. The architecture paper is available at arXiv:2608.24921, with a revision covering LongMemEval and ECT-QA evaluations described as in preparation.