Graph‑RAG vs “Chunk‑RAG”

If your domain has real entities, relationships, provenance, and multi‑hop questions, a knowledge graph turns retrieval from “find similar paragraphs” into “reconstruct the context”.

When simple RAG breaks

Multi‑hop reasoning

“A relates to B which relates to C” rarely lives in one chunk.

Audit & provenance

You need to show the chain: sources → facts → decision.

Temporal ‘as‑of’ questions

“What was true at time T?” needs validity windows, not embeddings alone.

Evolving entities / aliases

Canonical entities + aliasing beats repeatedly chunking & re‑embedding.

What Graph‑RAG adds

Hybrid retrieval: use vector similarity to find candidates, then traverse the graph to pull the related facts, provenance, and connected entities — so the LLM sees the reconstructed context.

Entity grounding: canonical nodes for people, assets, contracts, molecules, etc.
Relationship-aware expansion: pull neighbors and paths relevant to the question.
Explainability: return the path(s) used as evidence, not just a blob of text.
Governance: explicit lineage and versioned artefacts (schemas, model packs, policies).

Vendor landscape (practical) and GraphRAG vs RAG

In 2025, Graph‑RAG is available as open‑source toolkits and patterns across cloud vendors and graph databases. Microsoft’s Project GraphRAG is a pipeline/library you run yourself; Neo4j, TigerGraph, Memgraph, ArangoDB and others publish end‑to‑end examples; AWS has Neptune GraphRAG tooling; Google promotes partner architectures. But neo4j is the only graph DB that is a fully managed cloud solution that supports semantic search. You can run neo4j on Azure as well. MIT says that 95 % of Gen AI solutions fail to reach production as for complex domains you need knowledge graphs to tie the words to the context of the entities and relationships in the domain, and to provide the provenance and audit trail for regulated domains. THIS IS THE FIX THAT AVOIDS THE HALLUCINATIONS PROBLEM!

Download vendor landscape (.docx)