DocJev: an open-source library for document classification and splitting with Jev
DocJev takes natural-language category rules and has Jev predict document categories or sub-document boundaries, claiming 6x the speed of gpt-5.6-luna at equivalent accuracy, with optional liteparse or LlamaParse OCR backends.
Introducing DocJev - a lightning-fast OSS library for document classification and splitting with jev ⚡️ Give a document alongside some natural language category rules. Jev will predict the document category (classify) or the boundaries between sub-documents (split). It is 6x faster than gpt-5.6-luna with equivalent accuracy. You can choose between different OCR backends: liteparse: the fastest/most accurate free+OSS text parser out there. Perfect for digitalized documents. The speedups vs. luna above are inclusive of liteparse times. LlamaParse: the most advanced VLM-based solution for complex docs. Using this will of course add latency on document preprocessing. But it’s a good option if you want to cache the digitalized representations of documents for a variety of downstream tasks. Check it out: https://github.com/jerryjliu/docjev If you want to dive deeper into liteparse or LlamaParse check the links below: liteparse: https://github.com/run-llama/liteparse LlamaParse: https://cloud.llamaindex.ai/