Your vector-only RAG hits the same wall every time a question spans more than one chunk: “which upstream services did the customers affected by the March auth outage also churn from?” returns three plausible-sounding paragraphs and zero correct entities. Cosine similarity has no concept of a join. Meanwhile, half the GraphRAG tutorials you find are demo notebooks that ingest one clean PDF, never re-run, and quietly break the moment your document set changes — leaving you with duplicated nodes, orphaned relationships, and an index you have to nuke and rebuild every Monday.
This is written for developers who already ship Python and have run a vector store in production. You should be comfortable with async calls, Docker, and reading a stack trace; no Neo4j or graph theory background is assumed — Cypher is taught from the ground up, but at engineer pace, not tourist pace. Out of scope: fine-tuning, training your own embedding models, Kubernetes operator design, and any framework other than the Neo4j and LlamaIndex stack. This is not a survey of the field. It is one opinionated path, built end to end.
Honest framing: LLMs are genuinely good at schema-guided entity extraction from messy text, and they are good at drafting Cypher when you inject the live schema. They are bad at deciding what your ontology should be, bad at entity resolution across near-duplicate names, and dangerously confident when they generate a traversal that runs but answers the wrong question. Schema design, resolution thresholds, and any generated query touching production data need a human in the loop — the guide is explicit about which sandboxing and review gates are non-negotiable before you let generated Cypher near a live database.
What This Guide Covers
- A clear diagnostic for when GraphRAG actually beats chunked vector search — and when it is expensive overkill
- Cypher and APOC taught specifically for retrieval work, skipping the parts RAG engineers never touch
- Three working environment paths — AuraDB free tier, self-hosted Docker, and the Python toolchain — with the tradeoffs of each
- Property-graph schema design principles that survive re-ingestion instead of collapsing on the second load
- Extraction strategy for genuinely messy sources: scanned PDFs, support tickets, inconsistent formatting
- Idempotent loading patterns so re-running your pipeline updates the graph rather than duplicating it
- Entity resolution approaches that catch “Acme Corp” and “ACME, Inc.” without over-merging distinct records
- A complete LlamaIndex PropertyGraphIndex build, from empty database to answering queries
- Hybrid retrieval design that combines semantic search with multi-hop traversal in a single pass
- Text2Cypher prompt patterns, schema injection, and the sandboxing layer that keeps generated queries safe
- Community detection and query routing so global “what are the themes” questions stop being answered locally
- Citation tracking and incremental re-indexing for document sets that change weekly
- A reproducible evaluation harness and golden-set methodology so you can prove the graph earned its cost
- Cost, latency, and scaling budgets comparing managed tiers against self-hosting at realistic volumes
- A troubleshooting appendix covering the failure modes that cost the most debugging hours
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