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
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Embedding Store Neo4j KG
Format & Delivery
Format: PDF, approximately 45 pages, with runnable Cypher queries and Python integration code.
