GraphRAG Knowledge Graph Q&A System Evaluation and Selection Checklist

You are a senior knowledge graph and RAG systems architect. I need you to help me evaluate and select a GraphRAG solution for my use case. ## My Context - **Document corpus size**: [describe your data volume, e.g., 10K technical docs] - **Query types**: [describe typical questions, e.g., multi-hop reasoning across documents] - **Latency requirements**: [e.g., <2s per query] - **Infrastructure**: [e.g., single GPU server / cloud / edge] ## Please Provide ### 1. Architecture Assessment - Compare at least 3 GraphRAG approaches (e.g., Microsoft GraphRAG, LightRAG, EdgeQuake, nano-graphrag) - For each: graph construction method, storage backend, retrieval strategy, LLM integration pattern ### 2. Trade-off Matrix Create a comparison table covering: | Dimension | Option A | Option B | Option C | |-----------|----------|----------|----------| | Build time (for my corpus) | | | | | Query latency | | | | | Accuracy on multi-hop queries | | | | | Memory footprint | | | | | LLM token cost per query | | | | | Community & maintenance | | | | ### 3. Implementation Roadmap - Recommended stack for my specific use case with justification - Step-by-step setup plan (graph construction → indexing → retrieval → serving) - Key configuration parameters to tune - Testing strategy: how to benchmark retrieval quality vs. naive RAG baseline ### 4. Risk Mitigation - Common failure modes in GraphRAG (e.g., entity resolution errors, incomplete graphs) - Monitoring metrics to track in production - Fallback strategies when graph retrieval underperforms Be specific and practical. Prioritize battle-tested approaches over theoretical optimality.