Neo4j + LLM Integration Guide

## The Problem You've built a vector RAG pipeline. It handles semantic similarity beautifully — but when a user asks "Which customers ordered product X and also complained about shipping?" or "Find all papers that cite this author's work AND were published after 2024," it falls apart. Vector search can't traverse relationships, follow multi-hop paths, or answer questions that require joining across documents. You're hitting the ceiling on complex questions, and you know there has to be a better way. ## What This Guide Does For You After reading this guide, you'll be able to ship a hybrid RAG system that combines Neo4j knowledge graphs with vector embeddings — giving your LLM both fuzzy semantic matching and exact relational querying. Your team will finally have a retrieval pipeline that handles the hard questions without duct-taping multiple services together. ## What You'll Be Able To Build - **Hybrid retrieval architecture** — combine Neo4j vector indexes with Cypher graph traversal in a single retriever - **Cypher + vector fusion** — write queries that filter by embedding similarity AND graph relationships simultaneously - **Entity resolution** — deduplicate LLM-extracted entities before inserting into Neo4j, keeping your graph clean - **Query decomposition** — split compound questions into sub-queries routed to vector or graph retrieval - **Context window formatting** — serialize traversal paths as structured LLM context that preserves relationship information - **Incremental updates** — add new entities and relationships without full rebuilds of your graph - **Performance tuning** — connection pooling, index strategies, and query profiling for LLM workloads - **Evaluation** — measure retrieval precision, recall, and hallucination reduction vs. pure vector ## Who Will Benefit Most - Engineers building production RAG pipelines that need structured reasoning - Teams using LangChain or LlamaIndex who want to add graph-backed retrievers - Neo4j developers integrating their existing graph data with LLM applications ## What Success Looks Like You'll walk away with a complete hybrid retrieval system — deployed, tested, and handling questions your old vector-only pipeline couldn't touch. Your team will ship complex query answers with confidence, backed by both semantic similarity and graph-validated relationships. ## Sample Architecture ``` User Query -> Question Classifier -> [Vector Retriever | Graph Traverser] -> Context Fuser -> LLM | | Embedding Store Neo4j KG ``` ## Format & Delivery **Format:** PDF, approximately 45 pages, with runnable Cypher queries and Python integration code.