Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Show HN: PageIndex – Vectorless RAG

github.com

Post reply on HN
Print view View original

31–40 of 147 posts

Re: Show HN: PageIndex – Vectorless RAG

#31

Post by rafaelmn »

mosselman wrote: post #4

So if I understand this correctly it goes over every possible document with an LLM each time someone performs a search? I might have misunderstood of course. If so, then the use cases for this would be fairly limited since you'd have to deal with lots of latency and costs. In some cases (legal documents, medical records, etc) it might be worth it though. An interesting alternative I've been meaning to try out is inve…

I didn't look at the implementation but sounds similar to something I two years ago recursively summarize the documentation based on structure (domain/page/section) and then ask the model to walk the hierarchy based on summaries.

My motivation back then I had 8k context length to work with so I had to be very conservative about what I include. I still used vectors to narrow down the entry points and then use LLM to drill down or pick the most relevant ones and the search threads were separate, would summarize the response based on the tree path they took and then main thread would combine it.

Re: Show HN: PageIndex – Vectorless RAG

#35

Post by huqedato »

I have a RAG built on 10000+ docs knowledge base. On vector store, of course (Qdrant - hybrid search). It work smoothly and quite reliable.

I wonder how this "vectorless" engine would deal with this. Simply, I can't see this tech scalable.

Re: Show HN: PageIndex – Vectorless RAG

#36

Post by rco8786 »

This seems really interesting but I can't quite figure out if this is like a SaaS product or an OSS library? The code sample seems to indicate that it uses some sort of "client" to send the document somewhere and then wait to retrieve it later.

But the home page doesn't indicate any sort of sign up or pricing.

So I'm a little confused.

edit Ok I found a sign up flow, but the verification email never came :(

Re: Show HN: PageIndex – Vectorless RAG

#37

Post by malnourish »

jdthedisciple wrote: post #28
mosselman wrote: post #4

So if I understand this correctly it goes over every possible document with an LLM each time someone performs a search? I might have misunderstood of course. If so, then the use cases for this would be fairly limited since you'd have to deal with lots of latency and costs. In some cases (legal documents, medical records, etc) it might be worth it though. An interesting alternative I've been meaning to try out is inve…

> let an LLM note all of the possible questions that you can answer What does this even mean? At what point do you know you have all of them? Humans are quite ingenious coming up with new, unique questions in my observation, whereas LLMs have a hard time replicating those efficiently.

Cantors diagonalization is trivial to show for questions. There are uncountably many.

Re: Show HN: PageIndex – Vectorless RAG

#38

Post by dmezzetti »

Context and prompt engineering is the most important of AI, hands down.

There are plenty of lightweight retrieval options that don't require a separate vector database (I'm the author of txtai [https://github.com/neuml/txtai], which is one of them).

It can be as simple this in Python: you pass an index operation a data generator and save the index to a local folder. Then use that for RAG.

Re: Show HN: PageIndex – Vectorless RAG

#39

Post by dcre »

My approach in "LLM-only RAG for small corpora" [0] was to mechanically make an outline version of all the documents _without_ an LLM, feed that to an LLM with the prompt to tell which docs are likely relevant, and then feed the entirety of those relevant docs to a second LLM call to answer the prompt. It only works with markdown and asciidoc files, but it's surprisingly solid for, for example, searching a local copy of the jj or helix docs. And if the corpus is small enough and your model is on the cheap side (like Gemini 2.5 Flash), you can of course skip the retrieval step and just send the entire thing every time.

[0]: https://crespo.business/posts/llm-only-rag/

Re: Show HN: PageIndex – Vectorless RAG

#40

Post by bjornsing »

agentcoops wrote: post #21
mosselman wrote: post #4

So if I understand this correctly it goes over every possible document with an LLM each time someone performs a search? I might have misunderstood of course. If so, then the use cases for this would be fairly limited since you'd have to deal with lots of latency and costs. In some cases (legal documents, medical records, etc) it might be worth it though. An interesting alternative I've been meaning to try out is inve…

I’ve been working on RAG systems a lot this year and I think one thing people miss is that often for internal RAG efficiency/latency is not the main concern. You want predictable, linear pricing of course, but sometimes you want to simply be able to get a predictably better response by throwing a bit more money/compute time at it. It’s really hard to get to such a place with standard vector-based systems, even GraphR…

> Moreover, GraphRag preprocessing is insanely expensive and precisely does not scale linearly with your dataset.

Sounds interesting. What exactly is the expensive computation?

On a separate note: I have a feeling RAG could benefit from a kind of ”simultaneous vector search” across several different embedding spaces, sort of like AND in an SQL database. Do you agree?

Post reply on HN