GraphRAG Patterns — PDF Bundle

## The Problem You built a vector RAG pipeline. It solved the basic hallucination problem — but only halfway. Vector search retrieves semantically similar chunks, and that's exactly the limit: it cannot model relationships between entities, follow multi-hop reasoning paths, or answer questions that require joining across documents. Your users are asking questions your pipeline should handle, and you're stuck returning "I don't know" or — worse — wrong answers. ## What This Guide Does For You After reading this guide, you'll be able to layer a knowledge graph on top of your vector store, turning retrieval from fuzzy similarity search into structured, queryable context. You'll ship a GraphRAG system that retrieves not just text chunks but the *relationships* between them — answering the questions that pure vector RAG can't touch. ## What You'll Be Able To Ship - **Hybrid retrieval architecture** — combine vector similarity with graph traversal in a single pipeline - **Neo4j schema design** — entity models, relationship types, and index strategies for RAG workloads - **Graph traversal for context** — Cypher queries that extract multi-hop paths as LLM context windows - **Query decomposition** — split complex questions into subqueries that route to vector or graph - **Entity extraction pipeline** — from raw documents to structured graph nodes via LLM - **Evaluation framework** — measure retrieval quality with graph-aware metrics - **Caching and performance** — hot-path optimization for real-time GraphRAG queries - **Migration guide** — extend your existing vector RAG pipeline with graph capabilities ## Who Will Benefit Most - Developers who built a vector RAG pipeline and hit the ceiling on complex questions - Data scientists adding structured knowledge to LLM workflows - Architects evaluating graph-enhanced retrieval for enterprise search ## What Success Looks Like You'll walk away from this guide with a GraphRAG pipeline that handles questions your old vector-only system couldn't — multi-hop queries, relationship-driven answers, structured context. Your team will finally have a retrieval system that knows how your data connects, not just what it looks like. ## Use Cases | Domain | Problem | GraphRAG Solution | |--------|---------|-------------------| | Legal | "Which clauses reference each other?" | Traverse contract entity graph | | Healthcare | "Drug interactions for patient with condition X" | Multi-hop drug-disease path | | Support | "Does this ticket match a known resolution pattern?" | Graph-based similarity | | Research | "Which papers cite and build on this result?" | Citation graph traversal | ## Format & Delivery **Format:** PDF, approximately 40 pages, with executable Cypher examples.