buy off the shelf: https://archil.com/
We replaced RAG with a virtual filesystem for our AI documentation assistant
181–190 of 190 posts
Re: We replaced RAG with a virtual filesystem for our AI documentation assistant
#182Earlier quoted context omitted.
You seem like someone who knows what they're doing, and I understand the theoretical underpinnings of LLMs (math background), but I have little kids that were born in 2016 and so the entire AI thing has left me in the dust. Never any time to even experiment. I am active in fandoms and want to create a search where someone can ask "what was that fanfic where XYZ happened?" and get an answer back in the form of links t…
Honestly, just from this question, I think you know enough that I’d go spend $20/month for a subscription to Codex, Claude Code, or Cursor, and ask them to teach you all this. I bet if you put in your comment verbatim with Opus 4.6 and went back and forth a bit, it could help you figure out exactly what you need and build a first version in a couple hours. Seriously, if you know the fundamentals and can poke and prod…
You don't need to pay an external crowd for that.
You can run Claude Code using a local instance of ~recent Ollama fine, and it'll do the teaching job perfectly well using (say) Qwen 3.5.
Doesn't even need to be one of the large models, one of the mid-size ones that fit in ~16GB of ram when given 128k+ context size should be fine.
Re: We replaced RAG with a virtual filesystem for our AI documentation assistant
#183Earlier quoted context omitted.
Hm. I think a dedicated 16-core box with 64 ram can be had for under $1000/year. It being dedicated there are no limits on session lifetime and it'd run 16 those sessions no problem, so the real price should be around ~$70/year for that load.
It looks like, to me, that someone spent a long back-and-forth with an LLM refining a design - everything they wrote screams "over-engineered, lots of moving parts, creating tiny little sub-problems that need to then be solved". I find it very hard to believe that a human designed their process around a "Daytona Sandbox" (whatever the fuck that is) at 100x markup over simply renting a VPS (a DO droplet is what, $6/m?…
> What sort of human engineer comes up with this sort of approach?
I don't know. There is that "just-bash" thing in typescript which they call "a reimplementation of bash that supports cat and cd".
The problem they solve I think is translating one query language (of find and ripgrep) into one of their existing "db". The approach is hilarious of course.
It's "beyond engineering" :)
Re: We replaced RAG with a virtual filesystem for our AI documentation assistant
#184The real thing I think people are rediscovering with file system based search is that there’s a type of semantic search that’s not embedding based retrieval. One that looks more like how a librarian organizes files into shelves based on the domain. We’re rediscovering forms of in search we’ve known about for decades. And it turns out they’re more interpretable to agents. https://softwaredoug.com/blog/2026/01/08/seman…
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I built tilth (https://github.com/jahala/tilth) much for this reason. Couldn't bother with RAG, but the agents kept using too many tokens - and too many turns - for finding what it needed. So I combined ripgrep and tree-sitter and some fiddly bits, and now agents find things faster and with ~40% less token use (benchmarked).
Re: We replaced RAG with a virtual filesystem for our AI documentation assistant
#185Earlier quoted context omitted.
SQLite + GPT5.4 works very well for me. My biggest success is a Roslyn method that takes a .NET solution and converts it into a SQLite database with Files, Lines, Symbols, and References tables. I've found this approach to perform substantially better than a flat, file-based setup (i.e., like what Copilot provides in Visual Studio). Especially, for very large projects. 100+ megs of source is no problem. The relationa…
Is this something you can share in more detail? Did you document a skill for the LLM to use? And with what tasks do you see most improvement?
The point of this is to reduce a complex tool surface to a single sql query tool without losing the richness of the underlying representation.
In practice this allows for me to combine multiple, complex data sources with a constant number of tools. I can add a whole new database and not add a new tool. My prompts are effectively empty aside from metadata around the handful of tools it has access to.
This only seems to perform well with powerful models right now. I've only seen it work with GPT5.x. But, when it does work it works at least as well as a human given access to the exact same tools. The bootstrapping behavior is extremely compelling. The way the LLM probes system tables, etc.
The tasks this provides the most uplift for are the hardest ones. Being able to make targeted queries over tables like references and symbols dramatically reduces the number of tokens we need to handle throughout. Fewer tokens means fewer opportunities for error.
Re: We replaced RAG with a virtual filesystem for our AI documentation assistant
#186cool, but where're the benchmarks?
Re: We replaced RAG with a virtual filesystem for our AI documentation assistant
#187Earlier quoted context omitted.
> They said our docs were too big and for some reason their chunking process was failing. Why would the size of your docs have any bearing on whether or not the chunking process works? That makes no sense. Unless of course they're operating on the document entirely in memory which seems not very bright unless you're very confident of the maximum size of document you're going to be dealing with. (I implemented a RAG p…
I agree it makes no sense. The whole point of chunking is to handle large documents. If your chunking system fails because a document is too big, that seems like a pretty glaring omission. I just chalked it up to the tech being new and novel and therefore having more bugs/people not fully understanding how it worked/etc. It was a vendor and they never gave us more details. Not all problems have to be solved. We just…
That's good. I think if you can get the result you need with a technology that's already familiar to you then, in cases where that tech is still supported, that's going to be a win.
RAG worked well for us in this recent case but, in 3+ years of developing LLM backed solutions, it's the first time I've had to reach for it.
Re: We replaced RAG with a virtual filesystem for our AI documentation assistant
#188Re: We replaced RAG with a virtual filesystem for our AI documentation assistant
#189Earlier quoted context omitted.
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Parent may or may not be AI generated or AI edited. As such it MAY breach one of the HN commenting guidelines Your comment however definitely breaches several of them.
Understood. I’m willing to defy guidelines and take the consequences. I still think it’s worth pointing out slop so people don’t waste their time talking to a machine.
Re: We replaced RAG with a virtual filesystem for our AI documentation assistant
#190Let's say I want a free, local or free-tier-llm, simple solution to search information mostly from my emails and a little bit from text, doc and pdf files. Are there any tool I should try to have ollamma or gemini able to reply with my own knowledge base?
https://onyx.app/ This could be useful.
If you are still ready comments, have you heard of this? https://docs.kiln.tech/docs/documents-and-search-rag