What I learned using Ollama on a real Paperless archive: model choice was not the main problem

I maintain Tagvico, an open-source companion for Paperless-ngx. I added Ollama because document text is exactly the kind of data many people do not want to send to a hosted model. The surprising failure was not that a small local model could not classify documents. It was that an unconstrained classifier produced reasonable-sounding metadata too eagerly. My first real archive test ended up with 73 tags. Many were synonyms or categories that were individually defensible but useless as a filing system. The changes that helped more than swapping models were: • cap assigned tags at four by default • optionally restrict output to the existing Paperless vocabulary • separate “suggest a new tag” from ordinary classification • show exactly which tags a cleanup would merge and into which destination • treat short or poor OCR as a retry/failed state instead of forcing a confident answer • keep the original metadata so every write can be restored For chat over the archive, the harder problem is different: reliable tool selection and showing the user which Paperless searches and documents informed the answer. The project supports live Ollama model discovery rather than a hardcoded picker: https://github.com/arturict/tagvico-ai For people doing local document classification, which Ollama models have been most consistent for structured metadata? I am especially interested in the tradeoff between small classification models and models that also behave well with tool calling. submitted by /u/its_artur1 [link] [comments]

Source: r/ollama | 2026-07-24

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