/ai

DocJev launches with a claimed 6x speedup over gpt-5.6-luna

The launch announcement describes an open-source library that uses natural-language category rules to classify documents or find boundaries between sub-documents, claiming equivalent accuracy to gpt-5.6-luna.

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JL

1 Source, 6h ago, first seen 6h ago

TLDR

DocJev’s announcement says the library uses Jev to predict document categories or boundaries between sub-documents from natural-language category rules. It claims 6x faster performance than gpt-5.6-luna with equivalent accuracy, including liteparse processing time. Users can choose liteparse or LlamaParse for preprocessing. The announcement says LlamaParse adds latency but is an option for complex documents and for caching document representations for downstream tasks.

Today's Rank

#18

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33K

1 Source, first seen 6h ago

1 Source

@jerryjliu0Introducing 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/

Today's Rank

#18

Combined views

33K

1 Source, first seen 6h ago

581 likes

25 comments

809 saves

48 reposts

Sentiment

Positive——Negative

Summary

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TLDR

DocJev’s announcement says the library uses Jev to predict document categories or boundaries between sub-documents from natural-language category rules. It claims 6x faster performance than gpt-5.6-luna with equivalent accuracy, including liteparse processing time. Users can choose liteparse or LlamaParse for preprocessing. The announcement says LlamaParse adds latency but is an option for complex documents and for caching document representations for downstream tasks.

25 comments

809 saves

48 reposts

Sentiment

Positive——Negative

Summary

Not enough discussion yet.

No sentiment analysis available yet.

Sentiment

Positive——Negative

Summary

Not enough discussion yet.

No sentiment analysis available yet.

1 Source

@jerryjliu0Introducing 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/

Sentiment

Positive——Negative

Summary

Not enough discussion yet.

No sentiment analysis available yet.

Today's Rank

#18

Today's Rank

#18