LightRAG: A More Efficient Solution than GraphRAG for RAG Systems?
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Top Comments (10)
Thanks so much! You are the best channel when it comes to RAG. Please keep informing us about the latest advancements in this field. I have played with GraphRag. It can be expensive if you have tons of data, but considering how cheap GPT-4o and GTP-4o-mini have become, the price is not the biggest concern at least for my use case. I processes more than a thousand page document with GPT-4o and it cost me cents. The biggest problem with MS GraphRag is the inference latency. It is not very practical if you want to build a chatbot based on this. Also, it is less customizable in my recent experience. Hope LightRag is better in terms of accuracy, customizability and inference speed.
I started using a recursive variant of this for a bit, which evolved to a multi-LLM approach due to the need to optimize cost-performance efficiencies, but still leverage external inference time optimizations and multi-step sequencing and solving. I think most of these RAG and TAG mechanisms (light, long, standard GR, and the various fine and related tuning methods) will all continue to be superseded at an accelerating rate. The biggest problems I see in the industry from startups to universities and research groups is that the choices made and implementations used are often too brittle and subject to rip and replace requirements to be anywhere near cost-performance optimal in the long term, which for AI means even 1-2 years. So, better design patterns, tooling and implementation architectures are needed.
Pretty cool - if you do more with this, I'd love to see some experiements combining this with late chunking
such a great channel, thanks for this guide, i was just about to implement a knowledge base!
Thanks for this. I was looking for a way to add to the KG without having to rebuild it. Crossing my fingers that this is it. And cheaper too 🙂
absolutely fantastic explanation, legend.
Definitely want to learn more about lite rag. Cost was a hindrance with regular rag. 😊
Thanks for the really great video, by the way.
brilliant tutorial, regards from hong kong city, china... ^___^
Thank you heaps for the diagrams and explanations!
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Top Comments (10)
Thanks so much! You are the best channel when it comes to RAG. Please keep informing us about the latest advancements in this field. I have played with GraphRag. It can be expensive if you have tons of data, but considering how cheap GPT-4o and GTP-4o-mini have become, the price is not the biggest concern at least for my use case. I processes more than a thousand page document with GPT-4o and it cost me cents. The biggest problem with MS GraphRag is the inference latency. It is not very practical if you want to build a chatbot based on this. Also, it is less customizable in my recent experience. Hope LightRag is better in terms of accuracy, customizability and inference speed.
I started using a recursive variant of this for a bit, which evolved to a multi-LLM approach due to the need to optimize cost-performance efficiencies, but still leverage external inference time optimizations and multi-step sequencing and solving. I think most of these RAG and TAG mechanisms (light, long, standard GR, and the various fine and related tuning methods) will all continue to be superseded at an accelerating rate. The biggest problems I see in the industry from startups to universities and research groups is that the choices made and implementations used are often too brittle and subject to rip and replace requirements to be anywhere near cost-performance optimal in the long term, which for AI means even 1-2 years. So, better design patterns, tooling and implementation architectures are needed.
Pretty cool - if you do more with this, I'd love to see some experiements combining this with late chunking
such a great channel, thanks for this guide, i was just about to implement a knowledge base!
Thanks for this. I was looking for a way to add to the KG without having to rebuild it. Crossing my fingers that this is it. And cheaper too 🙂
absolutely fantastic explanation, legend.
Definitely want to learn more about lite rag. Cost was a hindrance with regular rag. 😊
Thanks for the really great video, by the way.
brilliant tutorial, regards from hong kong city, china... ^___^
Thank you heaps for the diagrams and explanations!