Ask HN: What is the state of art for doing RAG in Enterprise LLM agents?

I am trying to understand and build RAG in few of the LLM Agents I am working on.

I have noted a small collection of things that are currently being done in the field at: https://pankajpipada.com/posts/2024-12-17-rag-resources/

From all the reading and experimenting it looks like Graph + vectors + optional pagerank for certain usecases gives best results.

Question:

For enterprise unstructured data (docs/logs/scripts/etc), what is the current state of art technology wrt establishing a knowledge base to be used in either search or context population for some code generation usecases?

Thanks in advance

Don't underrate just having your agent understand how your traditional search engine works and generating well-understood keyword matches + filters. Agents are good at generating structured queries into specific fields, etc (this taxonomy category, a title that looks like this, etc)

https://softwaredoug.com/blog/2024/09/11/generativeai-augmen...

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