> but my use case is in creating an offline-first local document storage system (data never reaches a cloud). Makes sense -- this is why we OSS'd DocQuery :)

> but my use case is in creating an offline-first local document storage system (data never reaches a cloud). Makes sense -- this is why we OSS'd DocQuery :) > The templates sound very cool - are they essentially just using a preset list of (natural language) queries tied to a particular document class? It seems like you're using a version of donut for your document classification? Yes that's the plan. We've done extensive testing with other approaches (e.g. NER) and realized that the benefits of using use-case specific queries (customizability, accuracy, flexibility for many use cases) outweigh the tradeoffs (NER only needs one execution for all fields). Currently, we support pre-trained Donut models for both querying and classification. You can play with it by adding the --classify flag to `docquery scan`. We're releasing some new stuff soon that should be faster and more accurate.