You deploy an LLM assistant, the demo goes well, and then a real customer asks: "What device is Sarah using, and what should we offer her?" The model answers confidently. And wrong—because it has no idea who Sarah is.
That's not a model problem. It's a data access problem. And standard RAG only gets you halfway—it can fetch relevant documents, but it can't follow relationships between them. That's where GraphRAG comes in: pairing a Knowledge Graph with an LLM so the AI reasons over your actual connected data, not a pile of text chunks.
Our stack for this: Neo4j for the graph, LangChain and LangGraph for orchestration, and LlamaIndex to translate plain English into graph queries.
The Four Problems It Solves
1. Hallucinations
LLMs fill gaps with plausible-sounding guesses. GraphRAG stops that by retrieving verified facts first—device model, purchase date, support history—and grounding the prompt in real data before the model writes a single word.
2. Disconnected Data
Vector search finds similar text. It can't answer "which customers in Region D, with Device B, contacted support this month"—that requires traversing relationships. Neo4j's Cypher language follows edges across your graph in a single query, making multi-hop reasoning straightforward.
3. Generic Responses
Two customers, one AI, completely different needs:
- Priya — three high-end devices, frequent traveller, early adopter. Her sub-graph surfaces the flagship recommendation with enthusiast-level detail.
- Marcus — one mid-range device, home user, two battery complaints. His sub-graph points to a practical upgrade pitched around longevity.
Same LLM, same prompt template, different retrieved context — different, accurate responses.
4. Needing a Database Expert to Ask a Question
LlamaIndex's NL2GraphQuery layer translates "What should we recommend to Priya?" into a Cypher query automatically. LangChain wraps the results into the LLM prompt. LangGraph handles the cases where answering requires chaining multiple lookups. Business users just type; the stack handles the rest.
How the Stack Fits Together
- Neo4j — stores entities (users, devices, locations) and their relationships as a native property graph, queried via Cypher. Available self-hosted or on AuraDB.
- LangChain — manages prompt templates and LLM calls. Model-agnostic: OpenAI, Anthropic, Mistral, or local models all plug in the same way.
- LangGraph — orchestrates multi-step reasoning as a stateful workflow, so complex questions don't get crammed into one overloaded prompt.
- LlamaIndex — handles NL-to-Cypher translation via
KnowledgeGraphIndexandNeo4jGraphStore, bridging natural language and the graph.
Query flow: question in → Cypher query out → Neo4j sub-graph returned → LangGraph loops if needed → LangChain generates the grounded response.
Standard RAG vs. GraphRAG
| Feature | Standard RAG (Vector) | GraphRAG (Neo4j) |
|---|---|---|
| Data Structure | Unstructured text chunks | Structured entities & relationships |
| Search Method | Semantic similarity | Relationship traversal (multi-hop) |
| Best For | Finding a relevant document | Finding connections between data points |
| Personalization | Segment-level at best | Individual sub-graph per user |
| Accuracy on Specific Facts | Drops as questions get specific | Grounded in verified graph data |
Where It Works
Any domain with relational data and a need for accurate, individual-level responses is a good fit:
- Retail: Customer 360 graphs power recommendations that reflect actual purchase history and preferences, not just category averages.
- Telecom: Network and subscriber graphs let support tools instantly surface the facts relevant to one specific customer's issue.
- Financial Services: Fraud detection and compliance queries require traversing entity relationships—vector search can't do this reliably.
- Healthcare: Patient graphs (diagnoses, medications, allergies, labs) give clinical tools the precise, interconnected context where accuracy is non-negotiable.
Getting Started
The tooling is mature. The harder work is the design upfront:
- Sketch your ontology. Define your entity types, relationships, and key properties before writing any code. A clear schema makes everything downstream easier.
- Run Neo4j. AuraDB Free is enough for a proof of concept. Scale to Enterprise or self-hosted when you need it.
- Connect an LLM via LangChain. Start with whatever fits your cost and latency targets. You can swap models later without touching the graph pipeline.
- Add LlamaIndex. The
Neo4jGraphStoreandKnowledgeGraphIndexintegrations wire up NL2GraphQuery in minimal configuration. - Use LangGraph for multi-step queries. Not every question needs it, but once you're chaining lookups, you want explicit state management rather than prompt hacks.
Worth Building?
If your AI keeps getting the specifics wrong, or your recommendations feel generic, the issue is almost always retrieval—not the model. GraphRAG is a practical fix: open-source tools, no cloud lock-in, and an architecture that scales from a small pilot to production without a rewrite.
Get in touch if you'd like to explore what this looks like for your data.