184euro is loose change after spending 3 man weeks working on the process!
That may be, but then there's an entire law library, the entirety of Wikipedia (and the example in this article of 451 GB). Surely those are at least an order of magnitude larger than Tolkien's prose and might still benefit from a RAG.
That hasn't changed nor I think it will, even with the models having very large context windows (eg Gemini has 2M). It is observed that having a large context alone is not enough and that it is better to give the model sufficiently enough and quality information rather than filling it with virtually everything. Latter is also impossible and does not scale well with long and complicated tasks where reaching the context limit is inevitable. In that case you need to have the RAG which will be smart enough to extract the sufficient information from previous answers/context, and make it part of the new context, which in turn will make it possible for the model to keep its performance at satisfactory level.
When any given document can fit into context, and when we can generate highly mission-specific summarization and retrieval engines (for which large amounts of production data can be held in context as they are being implemented)... is the way we index and retrieve still going to be based on naive chunking, and off-the-shelf embedding models?
For instance, a system that reads every article and continuously updates a list of potential keywords with each document and the code assumptions that led to those documents being generated, then re-runs and tags each article with those keywords and weights, and does the same to explode a query into relevant keywords with weights... this is still RAG, but arguably a version where dimensionality is closer tied to your data.
(Such a system, for instance, might directly intuit the difference in vector space between "pet-friendly" and "pets considered," or between legal procedures that are treated differently in different jurisdictions. Naive RAG can throw dimensions at this, and your large-context post-processing may just be able to read all the candidates for relevance... but is this optimal?)
I'm very curious whether benchmarks have been done on this kind of approach.
For example there's evidence that typical use of AGENTS.md actually doesn't improve outcomes but just slows the LLMs down and confuses them.
In my personal testing and exploration I found that small (local) LLMs perform drastically better, both in accuracy and speed, with heavily pruned and focused context.
Just because you can fill in more context, doesn't mean that you should.
The worry I have is that common usage will lead to LLMs being trained and fined tuned in order to accommodate ways of using them that doesn't make a lot of sense (stuffing context, wasting tokens etc.), just because that's how most people use them.
I don't need a coding model to be able to give me an analysis of the declaration of independence in urdu from 'memory' and the price in ram for being able to do that, impressive as it is, is an inefficiency.
ZotAI can use LMStudio (for embeddings and LLM models), but at that time, ZotAI was super slow and buggy.
Instead of going through the valley of sorrows (as threatofrain shared in the blog post - thanks for that), is there a more or less out-of-the-box solution (paid or free) for the demand (RAG for local literature review support)?
*If I am honest, it was rather a procrastination exercise, but this is for sure relatable for readers of HN :-D
there are a few other local apps with simple knowledge base type things you can use with pdfs. cherry studio is nice, no reranking though.
I love those site features!
In a submission of a few days ago there was something similar.
I love it when a website gives a hint to the old web :)
Hardest part is always figuring out your company’s knowledge management has been dogsh!t for years so now you need to either throw most of it away or stick to the authoritative stuff somehow.
Elastic plus an agent with MCP may have worked as a prototype very quickly here, but hosting costs for 500GB worth of indexes sounds too expensive for this person’s use case if $185 is a lot.
I would guess the ingestion pain is still the same.
This new world is astounding.
you could probably use the hybrid search in llamaindex; or elasticsearch. there is an off the shelf discovery engine api on gcp. vertex rag engine is end to end for building your own. gcp is too expensive though. alibaba cloud have a similar solution.
Typically you would have a reindex process, and you keep track of hashes of chunks to check if you’ve already calculated this exact block before to avoid extra costs. And then run such a reindex process pretty frequently as it’s cheap / costs nothing when there are no changes.
I've already got the data mostly structured because I did some research on the fandom last year, charting trends and such, so I don't even need to massage the data. I've got authors, dates, chapters, reader comments, and full text already in a local SQLite db.
Did you look at Turbopuffer btw?
