GraphRAG Prerequisites: Knowledge Graphs and Where Vector RAG Falls Short
GraphRAG inherits chunking, embeddings, and entity extraction from vector RAG. Learn what you need first and where the underlying pipeline breaks.
Knowledge Graphs for RAG
Knowledge Graphs for RAG use structured graph representations of entities and their relationships to retrieve information, instead of relying only on vector similarity.
By following typed connections between people, places, concepts, and events, a graph-augmented system can answer multi-hop questions that pure vector search struggles with. Also known as: GraphRAG, Graph RAG.
What this topic covers
- Foundations — Knowledge graphs let retrieval systems follow explicit relationships between entities, not just semantic similarity.
- Implementation — Building a graph-RAG pipeline means choosing an extractor, a graph store, and a query strategy that fits your corpus.
- What's changing — Graph-augmented retrieval has moved from research demo to a contested product category, with new frameworks competing on accuracy, indexing cost, and latency.
- Risks & limits — Graphs encode someone's view of what is true and how concepts connect.
This topic is curated by our AI council — see how it works.
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Understand the Fundamentals
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
Indexing Cost, Token Blowup, and the Hard Engineering Limits of GraphRAG at Scale
GraphRAG indexing costs scale with token recursion, not document size. A breakdown of the cost cliff, hallucinated edges, schema drift, and the rebuild trap.
What Is GraphRAG? Multi-Hop Reasoning with Knowledge Graphs
GraphRAG turns documents into a knowledge graph and uses community summaries to answer multi-hop questions vector retrieval cannot reach. Here is the mechanism.
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Build with Knowledge Graphs for RAG
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
How to Build a GraphRAG Pipeline with Neo4j and LightRAG in 2026
Build a knowledge-graph RAG pipeline with Microsoft GraphRAG, Neo4j vector indexes, and LightRAG. Decompose components, lock the contract, then ship.
Knowledge Retrieval for Engineers: What Transfers, What Breaks
Knowledge retrieval looks like ETL plus a vector store. Map old data-engineering instincts onto graph RAG, parsers, and metadata filters — and where they break.
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What's Changing in 2026
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated May 2026
Microsoft GraphRAG vs LightRAG: The Accuracy-Cost Race in 2026
Microsoft GraphRAG vs HKUDS LightRAG: two production patterns split knowledge-graph RAG in 2026, with Neo4j as the convergence layer.
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Risks and Considerations
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
When the Graph Decides What's True: Bias in Knowledge Graph RAG
Knowledge Graph RAG is sold as the audit-friendly answer to hallucination. But every graph encodes a worldview — and at scale, that becomes infrastructure.