DocJev OSS Library Classifies and Splits Document Packets
The non-obvious angle is that DocJev combines small-model prediction with pluggable OCR backends, letting teams trade latency for accuracy per document type rather than defaulting to a heavy VLM pipeline. Developers building RAG ingestion or document-processing workflows can wire it into existing pipelines via the GitHub repo, with the default path fully free. Compared with gpt-5.6-luna, it targets teams where throughput on large document packets matters more than marginal accuracy gains.
Jerry Liu, founder of LlamaIndex, released DocJev, an open-source library for document classification and splitting. Users provide a document plus natural language category rules, and Jev predicts the category or the boundaries between sub-documents. The library claims 6x faster processing than gpt-5.6-luna with equivalent accuracy, with speedups inclusive of the liteparse OCR stage. It offers two OCR backends: liteparse, a free OSS parser for digitalized documents, and LlamaParse, a VLM-based option for complex documents.
DocJev is the fastest way to classify and split complex document packets ⚡️. (and the default is fully free and OSS!) I made a sick teaser video below. TY Opus 5.5 🙏 check it out: github.com/jerryjliu/docj… I Your browser does not support the video tag. 🔗 View on Twitter Jerry Liu @jerryjliu0 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: github.com/jerryjliu/docj… If you want to dive deeper into liteparse or LlamaParse check the links below: liteparse: github.com/run-llama/lite… LlamaParse: cloud.llamaindex.ai Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 4 🔄 4 ❤️ 45 👀 2978 📊 14 ⚡
