Thank you! To answer your questions; - There is no direct integration with langchain, but I think either langchain or microsoft will develop that part. That said, I think it's possible to use it within langchain since it's a separate library. - Yes indeed. The amount of tokens consumed by this approach is ridiculously high and that's one of the main reasons that I think make it not practical to use in production. - Regarding cost, what I would do is to avoid using LLM as much as possible for KG construction phase. There are many other BERT models you can use for entity/relation extraction. Which make is very cheap to build the KG. - Building KG is a semi-automatic process. If you want to make it completely automatic, you either have to use LLM which makes it costly or end up with a graph that is not completely reliable. I mean, there could be noise in there. The best way is to define a schema manually for your KG and then populate it with info with/without LLM. - Which RAG system workbook? Do you mean the book that I authored a few months ago?

@@MehdiAllahyari Appreciate the response. I'm indeed referring to the "A practical approach to RAG" pdf (great read btw! but It could be nice to have continuous updates seeing how fast this industry moves ). Some more thoughts/Questions : - if we consider graphRAG not (yet) production ready do we consider Hippo-RAG under the same umbrella ? - for technical documentation QA (pdf) is there a specific RAG set-up you would recommend learning more about ? Cheers !

@@awakenwithoutcoffee I'd love to update the book as there are so many new techniques and methods have been proposed. I need to find the time. :) - Yes graphRAG and hippoRAG both use KGs as the main source of knowledge. But the way they represent and query KG is different. I'd personally like hippoRag better as it's simple to implement and maintain. - For QA over technical documentations, there is no universal solution. I would go with a very custom one depending on the nature of the documentation and domain. The general RAG process is the same however, the implementation details will be totally different. You need to do several experimentations to figure the right end-to-end solution.

Potentially, you can prototype with this library. We also shared pros and cons in this video about this approach. Hope it helps!

It's not mentioned in the paper, nevertheless they mention it on their website. They are using openAI embedding. but you can change it to other embedding models via configuration. Here's more details: microsoft.github.io/graphrag/posts/config/env_vars/

You can replace openAI models with local ones. But usually the quality is lower and latency is a lot compare to more classical methods.