61–70 of 147 posts
I don't see this scaling: https://deepwiki.com/search/how-is-the-tree-formed-and-tra_9... I'd do some large scale benchmarks before doubling down on this approach.
> It moves RAG away from approximate "semantic vibes" and toward explicit reasoning about where information lives. That clarity can help teams trust outputs and debug workflows more effectively. Wasn't this a feature of RAGs, though? That they could match semantics instead of structure, while us mere balls of flesh need to rely on indexes. I'd be interested in benchmarks of this versus traditional vector-based RAGs,…
Not sure if I fully understand it, but this seems highly inefficient? Instead of using embeddings which are easy to make a cheap to compare, you use summarized sections of documents and process them with an LLM? LLM's are slower and more expensive to run.
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.
Strongly agree, I also found txtai is super interesting! Thank you for your open-source effort!
The original documents are in HTML format and although I don't have access to them I can obtain them if I want. Is it better to just use these HTML documents instead? Previously I tried converting HTML to markdown and then use these for RAG. I wasn't too happy with the result although I fear I might be doing something wrong.
Very cool. These days I’m building RAG over a large website, and when I look at the results being fed into the LLM, most of them are so silly it’s surprising the LLM even manages to extract something meaningful. Always makes me wonder if it’s just using prior knowledge even though it’s instructed not to do so (which is hacky). I like your approach because it seems like a very natural search process, like a human woul…
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.
"Human-like Retrieval: Simulates how human experts navigate and extract knowledge from complex documents." - pretty sure I use control-f when I look for stuff
The folks who are using RAG, what's the SOTA for extracting text from pdf documents? I have been following discussions on HN and I have seen a few promising solutions that involve converting pdf to png and then doing extraction. However, for my application this looks a bit risky because my pdfs have tons of tables and I can't afford to get in return incorrect of made up numbers. The original documents are in HTML for…
The folks who are using RAG, what's the SOTA for extracting text from pdf documents? I have been following discussions on HN and I have seen a few promising solutions that involve converting pdf to png and then doing extraction. However, for my application this looks a bit risky because my pdfs have tons of tables and I can't afford to get in return incorrect of made up numbers. The original documents are in HTML for…