GraphRAG practitioner's guide: 6 architectural patterns from Text-to-Cypher to sparse graphs
nilukush · reddit · 2026-10-03
A systematic walkthrough of GraphRAG pipelines and six architecture patterns.
Four pipeline components: LLM-based entity/relation extraction → graph database (Neo4j, NebulaGraph, Memgraph; Cypher queries) → retrieval → context injection and generation.
The six patterns
- Text-to-Cypher: LLM translates natural language directly into graph queries
- Parallel Hybrid RAG: vector index and knowledge graph queried simultaneously, each handling what it's best at
- Sequential Hybrid (Graph-First): graph traversal filters vector search for tighter context and lower token cost
- Sparse Graph Architecture: replace expensive dense LLM extraction with a skeletal graph built by SpaCy or small models, letting downstream vector search fill in nuance — a direct cost fix
- Sequential Hybrid (Vector-First): wide semantic recall first, graph sharpens context afterward
- Adaptive Router Agent: a front decision layer routes queries by intent, entity density, and relational complexity
Each pattern comes with trade-off analysis, making this a practical selection guide for GraphRAG adoption.
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