Knowledge Graph Builder

Practical GraphRAG: Making LLMs smarter with Knowledge Graphs — Michael, Jesus, and Stephen, Neo4j

Jul 22, 2025 · 19:46

Michael Hunger and Stephen Chin of Neo4j present GraphRAG as a method to enhance LLMs by integrating knowledge graphs, achieving more accurate, contextual, and explainable answers than standard RAG. They highlight limitations of vector search, showing GraphRAG improves relevance and reduces hallucinations. They detail a three-step construction process: lexical graphs, entity extraction, graph enrichment with algorithms. They demonstrate open-source tools like Neo4j Knowledge Graph Builder and GraphRAG Python package, and show an agentic approach using domain-specific retrievals. They cite studies showing three times improvement in accuracy.

GraphRAG: The Marriage of Knowledge Graphs and RAG: Emil Eifrem

Aug 28, 2024 · 19:15

Emil Eifrem, Neo4j co-founder and CEO, argues that GraphRAG—combining knowledge graphs with vector search—significantly improves RAG application accuracy, ease of development, and explainability. Citing studies, he reports accuracy gains of 3x (Data.org), 75-77% (LinkedIn), and Microsoft's finding that GraphRAG enables answering entirely new question types. He demonstrates Neo4j's Knowledge Graph Builder, which auto-generates graphs from PDFs, Wikipedia, and YouTube, making graph creation accessible. Eifrem frames this as the next evolution in search after PageRank and Google's knowledge graph, urging developers to adopt GraphRAG for richer context and better AI outcomes.