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[–] 3 points 7 months ago (2 children)

Looks interesting. Will give it a whirl on my home server.

In this article, they talk about bringing up a local RAG system to let people run an LLM off a large document corpus: https://en.andros.dev/blog/aa31d744/from-zero-to-a-rag-system-successes-and-failures/

Wonder if this, connected to something like that, and wrapped in an easy end-user friendly script or UI could be a good combination for a local, domain-specific, grounded knowledge-base?

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  • [+] [S] 4 points 7 months ago* (last edited 6 months ago) (1 child)
  • [–] 2 points 7 months ago

    The problem with CAG is not just that it hogs memory, but to keep it fresh you have to keep re-indexing. If the corpus is large and dynamic, it can easily fall out of date and, at runtime, blow out the context window.

    GraphRAG has some promise. NVidia has a playbook for converting text into a knowledge graph: https://build.nvidia.com/spark/txt2kg

    It'll probably have the same issues with reindexing, but that will be a common problem, until someone comes up with better incremental training/indexing.

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