An AI-agent-tutored curriculum for learning graph and vector databases by building a CS/AI/ML knowledge base in Neo4j.
12 progressive lessons that take you from graph fundamentals to GraphRAG:
| # | Topic |
|---|---|
| 1 | Graph Databases 101 — nodes, edges, labels, the graph data model |
| 2 | Neo4j Setup + Your First Nodes — CREATE, properties, labels |
| 3 | Relationships — directed edges, relationship types, topology |
| 4 | Querying with Cypher — MATCH, WHERE, filtering, projection |
| 5 | Building the Knowledge Graph — MERGE, UNWIND, schema design |
| 6 | Pattern Matching and Traversal — variable-length paths, subgraphs |
| 7 | Graph Algorithms — centrality, PageRank, community detection |
| 8 | Vector Search Fundamentals — embeddings, semantic meaning, distance |
| 9 | Neo4j Vector Indexes — HNSW, index configuration |
| 10 | Similarity Search — KNN queries, semantic retrieval |
| 11 | Hybrid Graph + Vector Search — combining structure and semantics |
| 12 | GraphRAG — retrieval-augmented generation with graph context |
An AI tutor agent guides you through each lesson, explaining concepts before code, checking your understanding, and building a running knowledge base that grows across all 12 lessons. No skipping fundamentals — everything is explained from first principles.
- Database: Neo4j 5.x+ (run via Docker)
- Query Language: Cypher
- Plugins: APOC, GDS
- Interface: Neo4j Browser (
localhost:7474) orcypher-shell
- Docker (for running Neo4j)
- Neo4j 5.x+ with APOC plugin
- No prior graph database experience needed
# Start Neo4j
docker compose up -d
# Open the browser
open http://localhost:7474Then follow the lessons in TUTOR.md with the AI tutor agent.
- TUTOR.md — The 12-lesson curriculum and agent system prompt
- AGENT.md — Cypher conventions, Neo4j setup, and teaching rules
- CS/AI/ML knowledge base as the running example — you're building a graph about the domain you're studying
- Neo4j native vectors over external vector DB — one tool to learn, demonstrates graph+vector integration
- Pre-computed embeddings over live API calls — focus stays on database concepts, not ML plumbing
- GraphRAG as finale — shows the convergence of graph and vector search in modern AI applications