Enhancing RAG with Fast GraphRAG and InstructLab: A Scalable, Interpretable, and Efficient Framework
summary
Systematically compares plain RAG, Microsoft GraphRAG, HippoRAG and a new FastGraphRAG using a 100 Q&A gold set generated by Claude 3.7 and scored by a GPT‑4 evaluator. Baseline RAG scores 3.68 with ~4s indexing and ~2m generation; GraphRAG raises accuracy to ~4.42 but increases indexing to 2m20s, generation to ~24m30s and cost to ≈$1; HippoRAG cuts calls from 224→90 and generation to ~10m but scores ~3.79; FastGraphRAG combines community detection with Personalized PageRank, yields near‑Graph accuracy (single query 4.8) with lower latency. Also introduces InstructLab for synthetic fine‑tuning of LLMs and embeddings.
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chapters (8)
- 0:00Introduction
- 1:36Problem with Maya’s use case
- 6:16Baseline RAG solution
- 10:35Simple RAG limits and GraphRAG
- 14:13HippoRAG for scalable graphs
- 17:21Fast GraphRAG hybrid
- 20:09InstructLab for domain adaptation
- 21:58Q&A
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