LightRAG: A More Efficient Solution than GraphRAG for RAG Systems?

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In this video, I introduce LightRAG, a new, cost-effective retrieval augmented generation (RAG) method that combines knowledge graphs and embedding-based retrieval. Compared to GraphRAG from Microsoft Research, LightRAG offers better performance and lower API costs. I’ll show you how to set up LightRAG on your local machine, explain how entity and relationship extraction works, and compare dual-level retrieval (low, high, and hybrid) with traditional methods.

You’ll learn about LightRAG’s step-by-step setup, why it’s more affordable than GraphRAG, and how to start using it for your own document retrieval tasks.

LINKS:
https://github.com/HKUDS/LightRAG
https://lightrag.github.io/
https://arxiv.org/pdf/2410.05779
https://microsoft.github.io/graphrag/
https://youtu.be/vX3A96_F3FU

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TIMESTAMP
00:00 What is LightRAG
01:57 How does it compare to GraphRAG?
03:05 How does LightRAG work?
08:10 How does it compare to other techniques?
10:57 How to setup LightRAG locally
13:52 Indexing your data
16:37 Understanding the Indexing process

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