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Comments (47)
- diarmuidc>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!
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- JKCalhounAnd 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.
- whakimI'd argue the author missed a trick here by using a fancy embedding model without any re-ranking. One of the benefits of a re-ranker (or even a series of re-rankers!) is that you can embed your documents using a really small and cheap model (this also often means smaller embeddings).
- z02dMaybe 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
- mettamage51 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 :)
- abd7894What 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?
- trgnOdd 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.
- civengGreat 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.
- lucfrankenCool 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.
- supermookaThanks for an interesting read! Are you monitoring usage, and what kind of user feedback have you received? Always curious if these projects end up used because, even with the perfect tech, if the data is low quality, nobody is going to bother
- alansaberThink that's the first time i've seen someone write about checkpointing, definitely worth doing for similar projects.
- aledevvI 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.
- Horatius77Great writeup but ... pretty sure ChromaDB is open source and not "Google's database"?
- smrtinsertWhat would it look like to regularly react to source data changes? Seems like a big missing piece. Event based? regular cadence? Curious what people choose. Great post though.
- KPGv2This article came just in the nick of time. I'm in fandoms that lean heavily into fanfiction, and there's a LOT out there on Ao3. Ao3 has the worst search (and yo can't even search your account's history!), so I've been wanting to create something like this as a tool for the fandom, where we can query "what was the fic about XYZ where ABC happened?" and get hopefully helpful responses. I'm very tired of not being able to do this, and it would be a fun learning experience.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.
- redwoodCool to see Nomic embeddings mentioned. Though surpriser you didn't land on Voyage.Did you look at Turbopuffer btw?
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