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RAG Is Simpler Than You Think

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Re: RAG Is Simpler Than You Think

#101

Post by AshleyGrant »

brazukadev wrote: post #86
ninkendo wrote: post #53

Earlier quoted context omitted.

When I hear stuff like this I always imagine going to a restaurant and asking the waitress for a menu and them replying “lol just google it”. It’s not that I can’t or don’t know how, it’s rather that the expectation should be that a website should… link you to the information it believes to be relevant background. It’s why it’s called a “web”, linking is a core concept.

> When I hear stuff like this I always imagine going to a restaurant and asking the waitress for a menu and them replying “lol just google it”. in this case there was a menu in the next empty table and you saw it but in place of getting it you want the waitress to get it for you. Which is a normal behavior but you could save your time by just getting the menu yourself.

No. It isn't. With acronyms, there's often plenty of potential things it can stand for, and if the person doesn't know enough to know which one is the correct acronym, Googling it isn't going to help them.

As OP said, simply providing a link to a Wikipedia article, or a glossary, helps widen the audience beyond "IFYKYK."

The NWS knows this and automatically links to their glossary for both acronyms as well as jargon in their discussions. <-- See what I did there? What does NWS mean in this context? If only I had provided a link that would help you know. I very easily could have. I just didn't.

Re: RAG Is Simpler Than You Think

#102

Post by idontneedcoffee »

quijoteuniv wrote: post #90
usernametaken29 wrote: post #27

I worked on large scale RAG systems before and can say people vastly underestimate full text search and vastly overestimate embeddings. FTS is really easy, portable and scalable and gets you very far, the 80/20 rule applies. Embeddings appear to be nice and magic but when you really get into them you notice: semantic similarity isn’t as good as you think and certainly it won’t make everyone happy. You will inevitably…

On my last go at making my own rag i still got better results by collecting the data and uploading to a project in open(butclosed)ai. My own rag, used by an agent was giving poorer results, and even the agent prefered (derailed)to not use it and look for the info itself rather than using the rag

I would be really grateful if someone could battle-test my frankendb in a full-fledged RAG setup(lmdb + roaring bitmaps + to-be-removed lance with a bitmap-based virtual fs-like tree on top of your data) outside of its original narrow use-case (index for user data + workflows)

https://github.com/canvas-ui/canvas-synapsd

Re: RAG Is Simpler Than You Think

#103

Post by mdp2021 »

usernametaken29 wrote: post #27

I worked on large scale RAG systems before and can say people vastly underestimate full text search and vastly overestimate embeddings. FTS is really easy, portable and scalable and gets you very far, the 80/20 rule applies. Embeddings appear to be nice and magic but when you really get into them you notice: semantic similarity isn’t as good as you think and certainly it won’t make everyone happy. You will inevitably…

> people vastly underestimate full text search

It is not psychological, it is fully justified: substring search cannot find synonyms, periphrases and mistaken neighbours.

Re: RAG Is Simpler Than You Think

#105

Post by mdp2021 »

inigyou wrote: post #98
Lorean1 wrote: post #36

Earlier quoted context omitted.

Maybe if a person can't even google RAG they are not the intended audience of that article.

I thought this would be a useless search that brought up pictures of rags, but indeed, DDG delivers a full page of results about retrieval-augmented generation for the query "rag"

We can confirm, RAG has been a very big thing in the past few years. It's actually bewildering that it be new to some now - but we are also getting the vibe that some are living an ""AI"-nausea" that may be shielding them from some trends.

Re: RAG Is Simpler Than You Think

#106

Post by rglover »

petesergeant wrote: post #8
7734128 wrote: post #2

There have been many blogs like this over the last years. Yes, embeddings are computationally heavy, but they are not at all complicated and they provide a lot of benefit. 90% of "document" based RAG projects should view semantic search with embeddings as their primary method. It's very powerful and so easy to implement that you could try it out and discover whether performance would be an issue rather than trying to…

Embeddings are reasonably simple, but it’s a journey to get there, and I am very proud of the dog-heavy explainer I wrote on them: https://sgnt.ai/p/embeddings-explainer/

Started reading and will have to finish later but thank you for sharing. Very helpful post.

Re: RAG Is Simpler Than You Think

#107

Post by mdp2021 »

robrorcroptrer wrote: post #51
usernametaken29 wrote: post #29

Earlier quoted context omitted.

> embed everything the first time This assumes your text is small. Try embedding pdf reports - though luck. It surely won’t fit into most embeddings. I can think of many more examples: books, news articles, medical reports, insurance claims etc. they’re all too big to “index it all at once”

What about splitting bigger content into chunks before embedding?

What member freakynit said nearby about chunks and relations between chunks, plus the storage and information efficiency problem: make some calculations about storing vectors - for paragraphs and for collections of paragraphs -, then compare the needed space with the original data...

Because you could have clever ideas about vectors related to more paragraphs related in the document structure - but that would multiply the vectors. The index can become much bigger than the corpus.

Re: RAG Is Simpler Than You Think

#108

Post by mdp2021 »

TudorAndrei wrote: post #5
nilirl wrote: post #4

Maybe I'm old but where exactly are the "dragons"? How is RAG any different from the search systems we've been building before LLMs? Is it the sudden need for everyone to design a search API and engine that's driven this trend? If so, I'd like to see more design patterns around existing search problems: - Correcting or backtracking based on feedback. - Measuring relevance. - Comparison with task-based pre-written que…

It's just information retrieval packaged as something new.

It's just information retrieval through a new NN based technology that allows to map concepts and ideas as the compression of long text into points in a multidimensional space that manages to compress even more dimensions than the given ones, through non-transparent engines that give different mappings and results, and still (the information retrieval) requires many more clever tricks than the simple idea of vector distance ordering because things do not quite work as they should.

Let's say it's just "computation packaged as something new". "Trivial things".

Re: RAG Is Simpler Than You Think

#109

Post by esafak »

Foobar8568 wrote: post #59
ewidar wrote: post #24

Earlier quoted context omitted.

not really, vectorising text/books is old school ML by this point. at least to me that seems the same as https://en.wikipedia.org/wiki/Word2vec for e.g.

Well... Everything new is old "A vector space model for automatic indexing" 1975 - https://dl.acm.org/doi/10.1145/361219.361220

I wonder who was doing doing semantic search in the last century!

"The future is already here—It's just not very evenly distributed..."

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