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Indeed they seem to actually know/show how the sausage is made... but still, no fire and forget approach for any random dataset. check out what you need to do if the default isnt working for you (scroll down to eg. entity_extraction settings). there is so much complexity there to deal with that i'd just roll my own extraction pipeline from the start, rather than learning someone elses complex setup (that you have to tweak for each new usecase)

https://microsoft.github.io/graphrag/config/yaml/



IMO like with most other out-of-the-box LLM frameworks, the value is in looking at their prompts and then doing it yourself.

[1] https://github.com/microsoft/graphrag/tree/main/graphrag/pro...


> i'd just roll my own extraction pipeline from the start, rather than learning someone elses complex setup

I have to agree. It’s actually quite a good summary of hacking with AI-related libraries these days. A lot of them get complex fast once you get slightly out of the intended path. I hope it’ll get better, but unfortunately it is where we are.




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