GraphRAG

A Practitioner's Guide to Graphs - Tim Ainge, Good Collective

Jul 18, 2026 · 14:18

Tim Ainge from Good Collective presents a practitioner's guide to graphs, covering extraction from unstructured text, schema-first design, and graph-native algorithms to make AI applications smarter, cheaper, and more reliable. He demonstrates that giving extractors a schema (e.g., recipe with ingredients and steps) yields more meaningful graphs, and using ontology instructions standardizes units and ingredient names. Embedding models solve the potato–potato problem by flexibly matching duplicate nodes. Personalized PageRank, inspired by Pinterest's Pixie paper and HIPORAG, finds authoritative nodes in dense graphs, e.g., identifying Miranda v. Arizona as a landmark case not directly cited. Shortest path algorithms reduce tool calls by 40% in code search by retrieving intermediate nodes missed by vector search. Subgraph matching detects software design patterns like the decorator by querying graph shape without specific node details.

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.

Intro to GraphRAG — Zach Blumenfeld

Jun 30, 2025 · 1:18:35

Zach Blumenfeld introduces GraphRAG using Neo4j, showing how to build knowledge graphs from employee and skill data, combine structured and unstructured data, and use graph traversal with vector search for retrieval. The workshop covers Cypher query patterns for multi-hop similarity, entity extraction from resumes via LLMs, and creating semantic similarity relationships. It demonstrates Leiden community detection for skill clustering and builds a LangGraph agent with four custom tools that balance exact skill matches with vector similarity. Blumenfeld argues that knowledge graphs provide controlled, explainable retrieval logic for agentic workflows, enabling developers to decompose data into graph models that expose domain logic for more accurate retrieval than pure vector search.

Why Your Agent’s Brain Needs a Playbook: Practical Wins from Using Ontologies - Jesús Barrasa, Neo4j

Jun 27, 2025 · 13:54

Jesús Barrasa, AI Field CTO at Neo4j, argues that ontologies are the secret weapon for building robust GraphRAG applications, providing both a formal, implementation-agnostic schema for knowledge graph creation and a dynamic data layer that drives retriever behavior. He explains how ontologies replace rigid code-orchestrated workflows and chaotic LLM-generated ones by guiding entity extraction from unstructured data and mapping structured sources into a property graph. During retrieval, storing the ontology in the graph enables dynamic Cypher queries that automatically navigate contextualizing relationships — such as "acted-in" rather than "directed" — based on annotated subproperties, without hard-coded logic. This allows developers to change retrieval behavior on the fly by modifying the ontology as a data artifact. The talk covers two practical wins: using ontologies as a shared domain model for graph construction, and leveraging them to build adaptive, schema-driven retrievers that improve result completeness and relevance.

GraphRAG methods to create optimized LLM context windows for Retrieval — Jonathan Larson, Microsoft

Jun 27, 2025 · 15:09

Jonathan Larson of Microsoft Research presents GraphRAG methods, arguing that LLM memory with structure is a key enabler for effective AI applications, and that agents paired with these structures amplify their power. He demonstrates GraphRAG for Code, which uses graph-based understanding to outperform regular RAG on code comprehension, translation from Python to Rust, and even multi-file feature addition like adding jumping to the original DOOM codebase. Larson announces Benchmark QED, an open-source evaluation framework for measuring local and global query quality across comprehensiveness, diversity, empowerment, and relevance. He introduces Lazy GraphRAG, which beats vector RAG on 92% of local and 90% of global queries at one-tenth the cost of million-token context windows, and is being integrated into Azure Local and Microsoft Discovery.