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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. On
by softwaredoug 7mo ago
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/semantic-search-without-embeddings https://softwaredoug.com/blog/2026/01/08/semantic-search-wit...
- neuzhou 6mo ago[flagged]
- huflungdung 6mo ago[dead]
- siva7 6mo ago> Switched to just letting the agent browse the directory tree and read files on demand -- it figured out the module structure in about 30 seconds You guess what's the difference between code and loosely structured text...
- jimmySixDOF 6mo agoThere 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
- jimbokun 6mo agoJust like the approach in the article. Everything is based on the metadata stored with chunks, just allowing the agent to navigate that metadata through ls, cd, find and grep.
- pertymcpert 6mo ago[flagged]
- holoduke 6mo agoindeed. moltbook vibes
- phs318u 6mo agoParent 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.
- pertymcpert 6mo agoUnderstood. 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.
- mikkupikku 6mo agoI'd rather read a hundred comments like that than one more like yours.
- pertymcpert 6mo agoEach to their own. I appreciate you writing that comment yourself.
- deleted 6mo ago[deleted]
- jahala 6mo agoI built tilth (https://github.com/jahala/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).
- whattheheckheck 7mo agoTurns out the millions of people in knowledge work arent librarians and they wing shit everywhere
- khalic 7mo agoThis kind of circles back to ontological NLP, that was using knowledge representation as a primitive for language processing. There is _a ton_ of work in that direction.
- softwaredoug 7mo agoExactly. And LLMs supervised by domain experts unlock a lot of capabilities to help with these types of knowledge organization problems.
- wielebny 7mo agoSomeone simply assumed at some point that RAG must be based on vector search, and everyone followed.
- morkalork 7mo agoDoesn't have to be tho, I've had great success letting an agent loose on an Apache Lucene instance. Turns out LLMs are great at building queries.
- softwaredoug 7mo agoIt’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/p/7105dc/rag-is-the-what-agentic-search-is-the-how https://maven.com/p/7105dc/rag-is-the-what-agentic-search-is...
- TeMPOraL 7mo agoRight. 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 particular implementation of the idea.
- UltraSane 7mo agoInverted indexes have the major advantages of supporting Boolean operators.
- deleted 7mo ago[deleted]
- skeptrune 7mo agoI think it's cool that LLMs can effectively do this kind of categorization on the fly at relatively large scale. When you give the LLM tools beyond just "search", it really is effectively cheating.
- czhu12 7mo agoSimilar effort with PageIndex [1], which basically creates a table of contents like tree. Then an LLM traverses the tree to figure out which chunks are relevant for the context in the prompt. 1: https://github.com/VectifyAI/PageIndex https://github.com/VectifyAI/PageIndex
- woah 6mo agoMy intuition is that since AI assistants are fictional characters in a story being autocompleted by an LLM, mechanisms that are interpretable as human interactions with language and appear in the pretraining data have a surprising advantage over mechanisms that are more like speculation about how the brain works or abstract concepts.
- reactordev 6mo agoThis is also why LLMs get 80% of the way there and crap out on logic. They were trained on all the open source abandonware on GitHub.
- _boffin_ 6mo agoAnd next, we’ll get to tag based file systems
- rao-v 6mo agoI got to say people also seem to be missing really simple tricks with RAG that help. Using longer chunks and appending the file path to the chunk makes a big difference. Having said that, generally agree that keyword searching via rg and using the folder structure is easier and better.
- 3abiton 6mo ago> I got to say people also seem to be missing really simple tricks with RAG that help. Using longer chunks and appending the file path to the chunk makes a big difference. > > Having said that, generally agree that keyword searching via rg and using the folder structure is easier and better. It depends on the task no? Codebase RAG for example has arguably a different setup than text search. I wonder how much the FS "native" embedding would help.
- danelliot 6mo ago[dead]
- baby 6mo agoYep, I was using RAG for all sorts of stuff and now moved everything to just rg+fd+cd+ls, much faster, easier, etc.
- siva7 6mo ago> 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
- risyachka 6mo agomore and more often you see "new discoveries" that are very old concepts. the only discovery that usually happens there is that the author discovers for himself this concept. but it is essential nowadays to post it like if you discovered something new
- andai 6mo agoI spent a while working on a retrieval system for LLMs and ended up reinventing a concordance (which is like an index). It's basically the same thing as Google's inverted index, which is how Google search works. Nothing new under the sun :)
- stingraycharles 6mo agoAren’t most successful RAGs using a combination of embedding similarity + BM25 + reranking? I thought there were very few RAGs that only did pure embedding similarity, but I may be mistaken.
- manunamz 6mo agoExactly. Traditional library science truly captured deep patterns of information architecture. https://x.com/wibomd/status/1818305066303910006 https://x.com/wibomd/status/1818305066303910006 Pixar got this right in Ralph Wrecks The Internet. https://x.com/wibomd/status/1827067434794127648 https://x.com/wibomd/status/1827067434794127648