The 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…
> Our documentation was already indexed, chunked, and stored in a Chroma database to power our search, so we built ChromaFs
It's obvious by that sentence that these guys neither understand RAG nor realized that the solution to their agentic problem didn't need any of this further abstractions including vector or grep
It’s something of a historical accident We started with LLMs when everyone in search was building question answering systems. Those architectures look like the vector DB + chunking we associate with RAG. Agents ability to call tools, using any retrieval backend, call that into question. We really shouldn’t start RAG with the assumption we need that. I’ll be speaking about the subject in a few weeks https://maven.com/…
Right. R in RAG stands for retrieval , and for a brief moment initially, it meant just that: any kind of tool call that retrieves information based on query, whether that was web search, or RDBMS query, or grep call, or asking someone to look up an address in a phone book. Nothing in RAG implies vector search and text embeddings (beyond those in the LLM itself), yet somehow people married the acronym to one very part…
> yet somehow people married the acronym to one very particular implementation of the idea.
Likely due to the rise in popularity of semantic search via LLM embeddings, which for some reason became the main selling point for RAG. Meanwhile keyword search has existed for decades.
since the solution here doesnt appear to be open source, I think you can get something similar by asking your agents to take AgentFS and replacing the DB with ChromaDB
It’s something of a historical accident We started with LLMs when everyone in search was building question answering systems. Those architectures look like the vector DB + chunking we associate with RAG. Agents ability to call tools, using any retrieval backend, call that into question. We really shouldn’t start RAG with the assumption we need that. I’ll be speaking about the subject in a few weeks https://maven.com/…
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, these tools are amazing for helping expand your knowledge base. And constraints like how much you want to pay are excellent for steering the models. Seriously, just try it!
The 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…
Someone simply assumed at some point that RAG must be based on vector search, and everyone followed.
RAG is like when you want someone to know something they're not quite getting so you yell a bit louder. For a workflow that's mainly search based, it's useful to keep things grounded.
Less useful in other contexts, unless you move away from traditional chunked embeddings and into things like graphs where the relationships provide constraints as much as additional grounding
The 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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There a lot of methods in IR/RAG that maintain structure as metadata used in a hybrid fusion to augment search. Graph databases is an extreme form but some RAG pipelines pull out and embed the metadata with the chunk together. In the specific case of code, other layered approaches like ColGrep (late interaction) show promise.... the point is most search most of the time will benefit from a combination approach not a silver bullet
The 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…
Someone simply assumed at some point that RAG must be based on vector search, and everyone followed.
We were given a demo of a vector based approach, and it didn't work. They said our docs were too big and for some reason their chunking process was failing. So we ended up using a good old fashioned Elastic backend because that's what we know, and simply forwarding a few of these giant documents to the LLM verbatim along with the user's question. The results have been great, not a single complaint about accuracy, results are fast and cheap using OpenAI's micro models, Elastic is mature tech everyone understands so it's easy to maintain.
I think this turned out to be one of those lessons about premature optimization. It didn't need to be as complex as what people initially assumed. Perhaps with older models it would have been a different story.
Someone simply assumed at some point that RAG must be based on vector search, and everyone followed.
We were given a demo of a vector based approach, and it didn't work. They said our docs were too big and for some reason their chunking process was failing. So we ended up using a good old fashioned Elastic backend because that's what we know, and simply forwarding a few of these giant documents to the LLM verbatim along with the user's question. The results have been great, not a single complaint about accuracy, res…
> 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 process from scratch a few weeks ago, having never done so before. For our use case it's actually not that hard. Not trivial, but not that hard. I realise there are now SaaS RAG solutions but we have almost no budget and, in any case, data residence is a huge concern for us, and to get control of that you generally have to go for the expensive Enterprise tier.)