This is just RAG. Yes, it's not using a vector database - but it's building an index file of semantic connections, it's constructing hierarchical semantic structures in the filesystem to aid retrieval .. this is RAG.

On a sidenote, I've been building an AI powered knowledge base (yes, it uses RAG) that has wiki synthesis and similar ideas, take a look at https://github.com/kenforthewin/atomic

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panarky 10 hours ago | parent | next [-]

There's nothing about RAG that requires embeddings.

The retrieval part can be grep if you don't care about semantic search.

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Jet_Xu 13 hours ago | parent | prev | next [-]

I believe Multimodal KB+Agentic RAG is a suitable solution for personal KB. Imagine you have tons of office docs and want to dig some complex topics within it. You could try https://github.com/JetXu-LLM/DocMason

Fully retrieve all diagram or charts info from ppt and excels, and then leverage Native AI agents(e.g. Codex) to conduct Agentic Rad

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darkhanakh 14 hours ago | parent | prev | next [-]

eh i'd push back on "just RAG". like yes the retrieval-generation loop is RAG shaped, no ones arguing that. but the interesting bit here is the write loop - the LLM is authoring and maintaining the wiki itself, building backlinks, filing its own outputs back in. thats not retrieval thats knowledge synthesis. in vanilla RAG your corpus is static, here it isnt

also the linting pass is doing something genuinely different - auditing inconsistencies, imputing missing data, suggesting connections. thats closer to assistant maintaining a zettelkasten than a search engine returning top-k chunks

cool project btw will check it out

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kenforthewin 14 hours ago | parent | next [-]

I agree with you, the linting pass seems valuable and it's something I'm thinking about adding - it's a great idea.

What I'm pushing back on specifically is the insistence that the core loop - retrieving the most relevant pieces of knowledge for wiki synthesis - is not RAG. In order for the LLM to do a good job at this, it needs some way to retrieve the most relevant info. Whether that's via vector DB queries or a structured index/filesystem approach, that fundamental problem - retrieving the best data for the LLM's context - is RAG. It's a problem that has been studied and evaluated for years now.

thanks for checking it out

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Covenant0028 11 hours ago | parent | prev | next [-]

I'm curious how this linting step scales with larger wikis. Looking for an inconstency across N files requires N*N comparisons, and that's assuming each file contains a single idea.

	▲	ChrisGreenHeur 10 hours ago \| parent [-]
		Presumably, randomness and only looking at a limited subset will semi-ensure over time that most contradictions will surface. Alternatively, how large do you really expect this kind of thing to be, there is a limit to the amount of facts from Warhammer 40k worth saving in a wiki.

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devmor 12 hours ago | parent | prev [-]

This is just persistent memory RAG. I have had a setup like this since about a day after I started using copilot, except it's an MCP server that uses sqlite-vec and has recall endpoints to contextually load the proper data instead of a bunch of extra files polluting context.

OP's example isn't something new or incredibly thoughtful at all - in fact this pattern gets "discovered" every other day here, reddit or social media in general by people that don't have the foresight to just look around and see what other people are doing.

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alfiedotwtf 13 hours ago | parent | prev | next [-]

You should have started your comment with “ I have a few qualms with this app”.

I’ve been thinking something along the lines of a LLM-WIKI for a while now which could truely act as a wingman-executive-assistant-second-brain, but OP has gone deeper than my ADHD thoughts could have possibly gone.

Looking forward to seeing this turn into fruition

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locknitpicker 9 hours ago | parent | prev [-]

> This is just RAG.

More to the point, this is how LLM assistants like GitHub Copilot use their custom instructions file, aka copilot-instructions.md

https://docs.github.com/en/copilot/how-tos/configure-custom-...