Posted by tmaly 1/14/2026
Ask HN: How are you doing RAG locally?
Are you using a vector database, some type of semantic search, a knowledge graph, a hypergraph?
The problems with datasheets is tables which span multiple pages, embedded images for diagrams and plots, they're generally PDFs, and only sometimes are they 2-column layout.
Converting from PDF to markdown while retaining tables correctly seems to work well for me with Mistral's latest OCR model, but this isn't an open model. Using docling with different models has produced much worse results.
I’ve optimized https://markdownconverter.pro/pdf-to-markdown to handle complex PDFs, including those tricky tables that span multiple pages and 2-column formats that usually trip up tools like Docling. It also extracts embedded diagrams/images and links them properly in the output.
Full disclosure: I'm the developer behind it. I’d love to see if it handles your specific datasheets better than the models you've tried. Feel free to give it a spin!
Question being: WHY would I be doing RAG locally?
TL;DR: - chunk files, index chunks - vector/hybrid search over the index - node app to handle requests (was the quickest to implement, LLMs understand OpenAPI well)
I wrote about it here: https://laurentcazanove.com/blog/obsidian-rag-api