LLamaIndex 推出“即时OCR”文档处理方案

“Just-in-Time” OCR The latest agent harnesses (e.g. Cowork, Codex, Grok) do 2 passes of ad-hoc docu...

“Just-in-Time” OCR The latest agent harnesses (e.g. Cowork, Codex, Grok) do 2 passes of ad-hoc document processing when executing over a user-uploaded ‘data room’ of documents: 1️⃣ A fast and light pass using a free/OSS doc parsing tool, can be cheaply run over 100+ docs 2️⃣ A “just-in-time” pass using a dedicated OCR tool (like LlamaParse) that does more accurate transcription over a subset of pages. This pattern is common enough that I wanted to give it a name: “Just-in-Time OCR”. It works surprisingly well over a small to medium batch of docs (e.g. 10-100 docs); it is accurate at the expense of cost and latency. It’s not a substitute for large-scale offline indexing and retrieval. The ‘default’ approach for any frontier agent harness is to use an OSS tool like pypdf for the first pass and its native VLM (e.g. Fable 5.1) as the second pass. This is both expensive and lacks accuracy and grounding. If you’re a builder looking to implement this type of harness of a document data room, you can try out our suite of tools: - Liteparse as the first pass (the best/fastest OSS text parser) - LlamaParse as the second pass (most Pareto-efficient, includes grounding) Check out the blog post below! llamaindex.ai/blog/just-in-t… LlamaIndex 🦙 @llama_index just-in-time OCR is all the rage. most pipelines parse every page before anyone asks a question. for an agent working through an ad-hoc data room, that's slow, expensive, and most of those pages never get read. the better pattern is just-in-time OCR in two passes: ✅️ LiteParse (free, OSS, Rust, 50+ formats) does a fast layout-aware first pass: spatial text, bounding boxes, headings, tables, and a per-page complexity flag. a full data room in 32 seconds. ✅️ LlamaParse zooms in on only the pages that need it, by page number, and returns cell-level tables, bounding boxes, and confidence scores. the rest fills in the background. pypdf and pdftotext can't do the first pass well. parsing everything up front can't do it cheaply. two passes gets you both. full breakdown with numbers: llamaindex.ai/blog/just-in-t… 🔗 View Quoted Tweet 💬 3 🔄 0 ❤️ 4 👀 426 📊 4 ⚡