In a dynamic session hosted by OpenAI Academy, developers and AI enthusiasts from around the world gathered to explore a rapidly evolving frontier in artificial intelligence: GraphRAG, or Retrieval-Augmented Generation powered by knowledge graphs. The event, developed in collaboration with GPTech Day, went far beyond theoretical discussions, offering practical blueprints for building more reliable, auditable, and scalable AI applications.
At the heart of the conversation was one key insight: while traditional vector-based RAG excels in handling unstructured data, it struggles with precision, explainability, and complex relationships. That’s where knowledge graphs come in—and GraphRAG is proving to be a game-changer.
Why GraphRAG, and Why Now?
As presenter Gianni Crivello, a senior AI/ML engineer, explained, knowledge graphs aren’t new—but combining them with large language models unlocks a new era of structured reasoning and auditable AI workflows. The shift comes amid growing frustration with AI hallucinations in high-stakes domains like healthcare, finance, and enterprise search.
“My RAG apps were hallucinating when they needed to understand relationships or count things,” Crivello said. “GraphRAG changed that.”
By automatically generating Cypher queries (the query language used in Neo4j and other graph databases) through OpenAI’s function-calling API, GraphRAG allows models to run real-time, verifiable logic rather than relying on fuzzy similarity matching.
Expanding the GraphRAG Ecosystem: Tools, Techniques & Real-World Integration
The OpenAI Academy session didn’t stop at concepts—it showcased working tools and pipelines. Below are some of the highlights:
1. Neo4j’s GraphRAG Python Package
Neo4j’s GraphRAG Python package stood out as a robust tool to automate and scale knowledge graph creation.
- End-to-End Pipeline: Converts unstructured content like medical records into structured entities, relationships, and vector embeddings.
- Hybrid Retrieval: Supports graph traversal + vector search for better recall and relevance.
- Auditability: Logs every Cypher query for transparency—ideal for regulated industries.
retriever = HybridRetriever(
vector_retriever=VectorRetriever(...),
graph_retriever=GraphRetriever(...)
)
results = retriever.search("Patients with Lupus and kidney involvement")2. LLM-Powered Automation with OpenAI Function Calling
Using GPT-4o, developers can turn natural language into Cypher—removing the barrier of needing to know graph query languages.
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Count patients vaccinated in Q1 2024"}],
functions=[{
"name": "execute_cypher",
"description": "Generates Cypher for graph queries",
"parameters": {"type": "object", "properties": {...}}
}]
)Result: Up to 63% reduction in hallucinations compared to traditional RAG.
3. Healthcare Case Study: RAG vs GraphRAG
During the live demo using synthetic patient data:
- Vector RAG failed to answer questions involving relationships (e.g., “Which doctor prescribed Drug X?”).
- GraphRAG returned exact, citation-backed results, powered by structured traversal.
This isn’t just about accuracy—it’s about trust and traceability, which are paramount in domains like medicine and law.
Ethical & Governance Considerations
- Bias Reduction: Graphs enforce ontologies, reducing implicit bias common in vector search.
- Real-Time Updates: Tools like Apache Kafka allow dynamic graph updates for live systems (e.g., Electronic Health Records).
- Regulatory Compliance: With query logging and structured output, GraphRAG aligns with HIPAA, GDPR, and other strict standards.
“GraphRAG turns LLMs from storytellers into accountants—systems that show their work,” Crivello emphasized.
When to Use GraphRAG
GraphRAG shines in scenarios where:
| Use Case | Why GraphRAG Works |
|---|---|
| Relational Queries | Preserves structure (e.g., Doctor A prescribed Drug B) |
| Temporal Reasoning | Tracks sequences and time-based relationships |
| Compliance Needs | All outputs are auditable, traceable |
| Enterprise Data | Structured systems like EHRs or ERPs |
| Global Stats | Accurately answers “how many?” and “who did what?” |
For general summarization or basic doc search, vector-based RAG still performs well. But when your app needs to reason over structured data, GraphRAG is often essential.
Getting Started
- GitHub Repo: Neo4j’s GraphRAG library with healthcare examples.
- Code Samples & Slides: From the OpenAI Academy Session.
- Community: GPTech Day offers workshops and in-person support chapters worldwide.
Toward Safer, Smarter LLMs
As the AI field matures, GraphRAG represents a pivotal evolution—moving beyond chatbot-style output to transparent, dependable AI systems.
The OpenAI Academy session made one thing clear: GraphRAG isn’t just another RAG flavor—it’s a governance framework, and it might be what finally bridges the gap between AI exploration and enterprise-scale adoption.
For developers, product teams, and tech leaders alike, now is the time to ask: Are your LLMs guessing—or do they know?
