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KAG – Knowledge Graph RAG Framework

(github.com)
230 points taikon | 1 comments | 30 Dec 24 02:55 UTC | HN request time: 0s | source
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dcreater ◴[30 Dec 24 06:01 UTC] No.42546890[source]▶
Yet another RAG/knowledge graph implementation.

At this point, the onus is on the developer to prove it's value through AB comparisons versus traditional RAG. No person/team has the bandwidth to try out this (n + 1) solution.

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ertdfgcvb ◴[30 Dec 24 06:19 UTC] No.42546979[source]▶
I enjoy the explosion of tools. Only time will tell which ones stand the test of time. But this is my day job so I never get tired of new tools but I can see how non-industry folks can find it overwhelming
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trees101 ◴[30 Dec 24 06:33 UTC] No.42547052[source]▶
Can you expand on that? Where do big enterprise orgs products fit in, eg Microsoft, Google? What are the leading providers as you see them? As an outsider it is bewildering. First I hear that llama_index is good, then I hear that its overcomplicating slop. What sources or resources are reliable on this? How can we develop anything that will still stand in 12 months time?
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1. ertdfgcvb ◴[30 Dec 24 07:53 UTC] No.42547418{3}[source]▶
>What sources or resources are reliable on this?

imo, none. Unfortunately, the landscape is changing too fast. May be things will stabilize, but for now I find experimentation a time-consuming but essential part of maintaining any ML stack.

But it's okay not to experiment with every new tool (it can be overwhelming to do this). The key is in understanding one's own stack and filtering out anything that doesn't fit into it.