>After several weeks, between 2 and 3, the indexing process finished without failures. ... we could finally shut down the virtual machine. The cost was 184 euros on Hetzner, not cheap.
184euro is loose change after spending 3 man weeks working on the process!
Cool work! Would be so interested in what would happen if you would put the data and you plan / features you wanted in a Claude Code instance and let it go. You did carefully thinking, but those models now also go really far and deep. Would be really interested in seeing what it comes up with. For that kind of data getting something like a Mac mini or whatever (no not with OpenClaw) would be damn interesting to see how fast and far you can go.
What ended up being the main bottleneck in your pipeline—embedding throughput, cost, or something else? Did you explore parallelizing vectorization (e.g., multiple workers) or did that not help much in practice?
And some have been saying that RAGs are obsolete—that the context window of a modern LLM is adequate (preferable?). The example I recently read was that the contexts are large enough for the entire "The Lord of the Rings" books.
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.
The success of the model responding to you with a correct information is a function of giving it proper context too.
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.
I'm not super deep on LLM development, but with ram being a material bottleneck and from what I've read about DeepSeek's results with offloading factual knowledge with 'engrams' I think that the near future will start moving towards the dense core of LLMs focusing much more on a distillation of universal reasoning and logic while factual knowledge is pushed out into slower storage. IIRC Nvidia's Nemotron Cascade is taking MoE even further in that direction too.
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.
RAG is nowhere near obselete. Model performance on enormous sequences degrades hugely as they are not well represented in training and non quadratic attention approximations are not amazing
You will still get hallucinations. With RAG you use the vectors to aid in finding things that are relevant, and then you typically also have the raw text data stored as well. This allows you to theoretically have LLM outputs grounded in the truth of the documents. Depending on implementation, you can also make the LLM cite the sources (filename, chunk, etc).
Great write-up. Thank you! I’m contemplating a similar RAG architecture for my engineering firm, but we’re dealing with roughly 20x the data volume (estimating around 9TB of project files, specs, and PDFs).
I've been reading about Google's new STATIC framework (sparse matrix constrained decoding) and am really curious about the shift toward generative retrieval for massive speedups well beyond this approach.
For those who have scaled RAG into the multi-terabyte range: is it actually worth exploring generative retrieval approaches like STATIC to bypass standard dense vector search, or is a traditional sharded vector DB (Milvus, Pinecone, etc.) still the most practical path at this scale?
I would guess the ingestion pain is still the same.
Maybe a bit off-topic:
For my PhD, I wanted to leverage LLMs and AI to speed up the literature review process*.
Due to time constraints, this never really lifted off for me. At the time I checked (about 6 months ago), several tools were already available (NotebookLM, Anara, Connected Papers, ZotAI, Litmaps, Consensus, Research Rabbit) supporting Literature Review.
They have all pros and cons (and different scopes), but my biggest requirement would be to do this on my Zotero bibliographic collection (available offline as PDF/ePub).
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
If you don’t mind a little instability while I work out the bugs, might be interested in my project: https://github.com/rmusser01/tldw_server ; it’s not quite fully ready yet but the backend api is functional and has a full RAG system with a customizable and tweakable local-first ETL so you can use it without relying on any third party services.
I tried to do RAG on my laptop just by setting it all up myself, but the actual LLM gave poor results (I have a small thin-and-light fwiw, so I could only run weak models). The vector search itself, actually, ended up being a little more useful.
I made something similar in my project. My more difficult task has been choice the right approach to chunking long documents. I used both structural and semantic chunking approach. The semantic one helped to better store vectors in vectorial DB. I used QDrant and openAi embedding model.
I'm afraid this hits the credibility of the article for me, that's a pretty weird mistake to make. It's like paying for a Model 3 while thinking it comes from Ford.
>After several weeks, between 2 and 3, the indexing process finished without failures. ... we could finally shut down the virtual machine. The cost was 184 euros on Hetzner, not cheap.
184euro is loose change after spending 3 man weeks working on the process!
Cool work! Would be so interested in what would happen if you would put the data and you plan / features you wanted in a Claude Code instance and let it go. You did carefully thinking, but those models now also go really far and deep. Would be really interested in seeing what it comes up with. For that kind of data getting something like a Mac mini or whatever (no not with OpenClaw) would be damn interesting to see how fast and far you can go.
What ended up being the main bottleneck in your pipeline—embedding throughput, cost, or something else? Did you explore parallelizing vectorization (e.g., multiple workers) or did that not help much in practice?
And some have been saying that RAGs are obsolete—that the context window of a modern LLM is adequate (preferable?). The example I recently read was that the contexts are large enough for the entire "The Lord of the Rings" books.
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.
The success of the model responding to you with a correct information is a function of giving it proper context too.
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.
I'm not super deep on LLM development, but with ram being a material bottleneck and from what I've read about DeepSeek's results with offloading factual knowledge with 'engrams' I think that the near future will start moving towards the dense core of LLMs focusing much more on a distillation of universal reasoning and logic while factual knowledge is pushed out into slower storage. IIRC Nvidia's Nemotron Cascade is taking MoE even further in that direction too.
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.
RAG is nowhere near obselete. Model performance on enormous sequences degrades hugely as they are not well represented in training and non quadratic attention approximations are not amazing
I assume it’s not possible to get the same results by fine tuning a model with the documents instead?
You will still get hallucinations. With RAG you use the vectors to aid in finding things that are relevant, and then you typically also have the raw text data stored as well. This allows you to theoretically have LLM outputs grounded in the truth of the documents. Depending on implementation, you can also make the LLM cite the sources (filename, chunk, etc).
Great write-up. Thank you! I’m contemplating a similar RAG architecture for my engineering firm, but we’re dealing with roughly 20x the data volume (estimating around 9TB of project files, specs, and PDFs). I've been reading about Google's new STATIC framework (sparse matrix constrained decoding) and am really curious about the shift toward generative retrieval for massive speedups well beyond this approach. For those who have scaled RAG into the multi-terabyte range: is it actually worth exploring generative retrieval approaches like STATIC to bypass standard dense vector search, or is a traditional sharded vector DB (Milvus, Pinecone, etc.) still the most practical path at this scale?
I would guess the ingestion pain is still the same.
This new world is astounding.
Odd to me that Elasticsearch isn't finding a second breath in these new ecosystems. It basically is that now, a RAG engine with model integration.
Maybe a bit off-topic: For my PhD, I wanted to leverage LLMs and AI to speed up the literature review process*. Due to time constraints, this never really lifted off for me. At the time I checked (about 6 months ago), several tools were already available (NotebookLM, Anara, Connected Papers, ZotAI, Litmaps, Consensus, Research Rabbit) supporting Literature Review. They have all pros and cons (and different scopes), but my biggest requirement would be to do this on my Zotero bibliographic collection (available offline as PDF/ePub).
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
If you don’t mind a little instability while I work out the bugs, might be interested in my project: https://github.com/rmusser01/tldw_server ; it’s not quite fully ready yet but the backend api is functional and has a full RAG system with a customizable and tweakable local-first ETL so you can use it without relying on any third party services.
I tried to do RAG on my laptop just by setting it all up myself, but the actual LLM gave poor results (I have a small thin-and-light fwiw, so I could only run weak models). The vector search itself, actually, ended up being a little more useful.
51 visitors in real-time.
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 :)
Think that's the first time i've seen someone write about checkpointing, definitely worth doing for similar projects.
Cool to see Nomic embeddings mentioned. Though surpriser you didn't land on Voyage.
Did you look at Turbopuffer btw?
I made something similar in my project. My more difficult task has been choice the right approach to chunking long documents. I used both structural and semantic chunking approach. The semantic one helped to better store vectors in vectorial DB. I used QDrant and openAi embedding model.
Great writeup but ... pretty sure ChromaDB is open source and not "Google's database"?
I'm afraid this hits the credibility of the article for me, that's a pretty weird mistake to make. It's like paying for a Model 3 while thinking it comes from Ford.
Thank you for your feedback!
ChromaDB is open source with Apache-2.0 license.
https://github.com/chroma-core/chroma