Knowledge Graphs for RAG
by DeepLearning.AI × Neo4j
Use graph structure — not just vectors — to retrieve richer, more connected context for RAG.
Overview
Vector-only retrieval misses relationships between facts; knowledge graphs capture them. Built with Neo4j, this course teaches you to construct and query knowledge graphs with Cypher, combine graph traversal with vector search, and feed structurally-connected context into an LLM. It's the foundation of 'GraphRAG' approaches that are increasingly used for complex, multi-hop questions.
At a Glance
- Topic
- RAG
- Level
- Intermediate
- Format
- Course
- Cost
- Free
- Duration
- ~1-2 hours
- Provider
- DeepLearning.AI × Neo4j
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Modeling data as a knowledge graph
- ✓Querying graphs with Cypher
- ✓Combining graph and vector retrieval (GraphRAG)
- ✓Feeding connected context to an LLM
Highlights
- •Built with Neo4j
- •Introduces GraphRAG for multi-hop questions
Who It’s For
Best For
- ✓Developers with highly connected or relational data
Prerequisites
- •Basic Python
- •Familiarity with RAG basics
FAQ
What is Knowledge Graphs for RAG?
A course on combining knowledge graphs with vector search to improve retrieval, using Neo4j and Cypher.
Is Knowledge Graphs for RAG free?
Knowledge Graphs for RAG is free to access.
What level is Knowledge Graphs for RAG for?
Knowledge Graphs for RAG is aimed at a intermediate audience. Recommended background: Basic Python, Familiarity with RAG basics.
How long does Knowledge Graphs for RAG take?
Expect roughly ~1-2 hours. Most learners work through it at their own pace.
What will I learn from Knowledge Graphs for RAG?
You'll learn: Modeling data as a knowledge graph; Querying graphs with Cypher; Combining graph and vector retrieval (GraphRAG); Feeding connected context to an LLM.