alt Hacker NewsIt’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...
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Stuck it on my calendar, looking forward to it.
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 to fanfiction that are responsive.
This is a RAG system, right? I understand I need an actual model (that's something like ollama), the thing that trawls the fanfiction archive and inserts whatever it's supposed to insert into one of these vector DBs, and I need a front-facing thing I write, that takes a user query, sends it to ollama, which can then search the vector DB and return results.
Or something like that.
Is it a RAG system that solves my use case? And if so, what software might I go about using to provide this service to me and my friends? I'm assuming it's pretty low in resource usage since it's just text indexing (maybe indexing new stuff once a week).
The goal is self-hosting. I don't wanna be making monthly payments indefinitely for some silly little thing I'm doing for me and my friends.
I am just a stay at home dad these days and don't have anyone to ask. I'm totally out the tech game for a few years now. I hope that you could respond (or someone else could), and maybe it will help other people.
There's just so many moving parts these days that I can't even hope to keep up. (It's been rather annoying to be totally unable to ride this tech wave the way I've done in the past; watching it all blow by me is disheartening).
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 particular implementation of the idea.