Knowledge Graph RAG
Learn everything you need to know to combine Generative AI and knowledge graphs to produce highly accurate responses, with rich context and deep explainability.
1Knowledge graph fundamentals
Knowledge graphs provide the foundation for representing complex relationships that traditional databases can't effectively handle.
2Building knowledge graphs
Learn how to combine structured and unstructured data to create a knowledge graph.
Vectors & Relationships
Enhance your semantic search with vector embeddings with additional context from your knowledge graph.
Domain Graphs
Build a domain graph to represent the relationships between entities that make sense to you and your use case.
Knowledge Enrichment
Use out-of-the-box Graph Data Science algorithms to enrich your knowledge graph.
3Applied GraphRAG projects
Apply everything you've learned to build production-ready GraphRAG application that deliver more accurate, contextual, and explainable AI results.
Frameworks
Agents
4Evaluating GraphRAG
Learn how to evaluate the performance of your GraphRAG applications and understand the metrics that matter.
Yes, but what is GraphRAG?
GraphRAG (Graph Retrieval-Augmented Generation) is the union of Generative AI (GenAI) and Knowledge Graphs. In GraphRAG, a knowledge graph provides contextual facts and relationships that ground a Large Language Model (LLM), helping to prevent hallucinations and produce more accurate answers.
On this page you'll learn what is GraphRAG and why it matters, and discover how to build knowledge graphs from unstructured data and assemble end-to-end GraphRAG pipelines using Neo4j's GenAI tools. Our step-by-step learning path covers everything from LLM basics to advanced RAG workflows, with pointers to free GraphAcademy courses that teach each topic.
What is GraphRAG?
GraphRAG (Graph Retrieval-Augmented Generation) is the union of Generative AI (GenAI) and Knowledge Graphs. In GraphRAG, a knowledge graph provides contextual facts and relationships that ground a Large Language Model (LLM), helping to prevent hallucinations and produce more accurate answers.
Why context matters
Traditional RAG often misses relationships between entities. GraphRAG leverages the connected nature of knowledge graphs to provide more contextual and accurate answers.
More accurate responses to complex queries that require understanding relationships
Why relationships are key
Flat data representations miss complex relationships between entities. GraphRAG enables sophisticated reasoning by understanding how entities relate to each other.
Build applications that understand how people, concepts, and data interconnect in meaningful ways
Why explainability is important
Black-box AI responses lack transparency. GraphRAG lets you trace reasoning through knowledge graphs, providing clear evidence for AI-generated responses.
Build trust in AI systems by providing clear reasoning paths for every response