GraphRAG : From Zero to Hero

Source: Dev.to

Introduction

GraphRAG is a Retrieval‑Augmented Generation (RAG) system that combines knowledge graphs with vector search to deliver more accurate, context‑aware AI responses. Unlike traditional RAG, which relies solely on document embeddings, GraphRAG leverages the structured relationships and semantic connections in knowledge graphs to understand context and retrieve the most relevant information.

In this hands‑on tutorial you will learn how to build a sophisticated GraphRAG pipeline that integrates:

• Bidirectional Neo4j Integration – flexible access to graph data with seamless extraction and writing capabilities to Protégé, enabling your knowledge base to evolve over time.
• Protégé Ontology Creation & Modification – standardize and structure data for improved semantic understanding and query precision; create or modify ontologies directly from an LLM.
• Vector Database Storage – optimize retrieval of relevant information for accurate RAG responses; store and retrieve Neo4j or Protégé ontologies in a RAG store.
• Semantic Search Capabilities – deliver more meaningful, context‑aware results compared to traditional keyword‑based search.
• NLP‑Powered Querying – simplify interactions through SPARQL and Cypher, making knowledge graphs accessible to users of any technical level.
• LLM‑Driven Dynamic Ontology Creation – quickly adapt to changing data needs and build evolving, complex knowledge graphs.

This implementation provides a complete workflow for managing and querying knowledge graphs, suitable for fraud detection systems, recommendation engines, intelligent search platforms, and more.

What You Will Need

• Protégé – GitHub
• Neo4j AuraDB – Console
• GraphRAG Plugin – GitHub
• VidyaAstra Plugin – GitHub
• Neo4j‑Protégé Plugin – GitHub

The ontology used in the examples is available here:
fraud‑detection‑ontology.owl

Note: These plugins are actively maintained. Package names may be refactored and new features added. If you encounter issues, feel free to reach out to the author.

Once the three plugins are installed they appear in Protégé’s toolbar:

Connecting to Neo4j

You can connect to a Neo4j AuraDB instance using the Neo4j‑Protégé plugin. The connection dialog is accessible from the toolbar:

With this plugin you can:

• Import/export ontologies between Neo4j and OWL/RDF formats.
• Retrieve the ontology locally and run NLP‑driven queries.
• Import subsets of the graph for use in a local Protégé project.

You can also execute plain‑English queries that are automatically translated to SPARQL/Cypher:

Import to Protégé

After exporting an ontology from Neo4j, import it into Protégé:

Build the RAG Store

Once the ontology is loaded in Protégé, generate vector embeddings and store them in a local RAG store using the GraphRAG plugin:

You can now query the RAG store just like any standard RAG system:

Fraud Detection Use Case

The fraud‑detection ontology is the same file linked above. After importing it into Protégé, store it in the RAG store via the GraphRAG plugin:

Combining with VidyaAstra Plugin

The VidyaAstra plugin enriches the graph with additional information and enables deeper sub‑graph exploration:

Plugin Features

Both the GraphRAG and VidyaAstra plugins provide:

• Explain Graph – visual explanations of how entities are connected.
• NLP Query – natural‑language queries that are translated to SPARQL/Cypher.
• Dynamic Ontology Updates – on‑the‑fly modifications driven by LLM suggestions.
• Semantic Search – context‑aware retrieval across the combined graph‑vector store.