Knowledge Graphs for AI 2026: GraphRAG, Entity Extraction and Real Use Cases
Vector search returns similar chunks, but it cannot answer what connects these companies. Knowledge graphs add relationships - and GraphRAG made them fashionable again. What is the real stack?
💡 What You Will Learn
Vector search returns similar chunks, but it cannot answer what connects these companies. Knowledge graphs add relationships - and GraphRAG made them fashionable again. What is the real stack?
📜 Table of Contents
Why Graphs, Why Now
Vector RAG retrieves chunks by similarity. It fails on multi-hop questions: which products are affected by supplier X recall? requires joining facts across documents. A knowledge graph stores entities and relations explicitly, so multi-hop joins become graph queries.
GraphRAG - The 2026 Revival
Microsoft GraphRAG (35,386 stars) made the concept practical: build a graph from your documents, then answer questions by walking the graph instead of pure vector search. The pipeline: chunk documents, extract entities and relations with an LLM, build a knowledge graph, index it with community summaries, answer via graph traversal.
The Practical Stack (2026)
| Layer | Tool |
|---|---|
| Entity/relation extraction | LLM (any strong model) with structured output |
| Graph storage | Neo4j (or Kuzu for local/small) |
| Graph + vector hybrid | Neo4j vector index, or Microsoft GraphRAG on top of any storage |
| Orchestration | LangChain / LlamaIndex graph modules |
Real Use Cases That Pay
- Fraud detection: link accounts, devices, addresses across records. Graph queries surface rings that per-row ML misses.
- Supply chain: trace component to product to customer impact of a recall.
- Life sciences: drug-target-disease relationships; multi-hop literature search.
- Enterprise knowledge: org charts, dependencies, who owns what - questions that similarity search structurally cannot answer.
Cost Reality Check
Graph construction is LLM-heavy: extracting entities from 100k documents is a serious token bill, and quality varies with extraction prompts. 2026 best practice: build the graph for the 5-10% of questions that are genuinely multi-hop, and keep vector RAG for everything else. Hybrid, not either/or.
Getting Started in One Evening
Take 50 documents, extract (entity, relation, entity) triples with structured output, load into Neo4j, and run 10 multi-hop questions. You will immediately see which questions the graph answers that vector search cannot - and which ones it over-engineers.
