LlamaParse与LiteParse覆盖文档解析精度、成本、延迟全场景
LlamaIndex这次把文档解析的三种场景都做了：高精度的、低成本大批量的、还有低延迟实时的。有LlamaParse和开源LiteParse，不同需求直接用对应的就行。
LlamaIndex推出LlamaParse和LiteParse，分别针对文档解析的三个关键场景：高精度解析要求99%+准确率，面向金融和保险等监管行业，价格不敏感；低成本高吞吐解析支持代理批量处理大量文档；低延迟低成本解析适配用户临时上传大量文件（如1000个PDF给Claude Cowork）的代理循环。LlamaParse覆盖成本-精度模式，LiteParse为开源项目，可路由至VLM深度解析模式。所有测试结果可在ParseBench查看。
Fully solving document parsing includes covering every point on the Pareto curve of accuracy, cost, and latency: 1️⃣ High-accuracy parsing - requires 99%+ accuracy, price insensitive. Especially relevant in regulated industries like financial service and insurance. 2️⃣ Low cost, high volume parsing - requires inhaling a massive volume of documents as context for agents. Can run offline in a batch setting. 3️⃣Low latency and low cost parsing - these are use cases where the user is uploading a massive volume of files ad-hoc and in the agent loop (e.g. uploading 1k pdfs to claude cowork). Requires an extremely fast pass to make sense of the docs before a deeper dive LlamaParse covers the cost-accuracy modes for document OCR with our document agent harness. LiteParse, our OSS project, is designed to be in the agent loop, and can route to deeper VLM-enabled modes. I talked about this and other topics during the @aiDotEngineer talk today. Stay tuned for the slides! In the meantime, check out our full set of parsing results on ParseBench: parsebench.ai LlamaParse: cloud.llamaindex.ai LiteParse: github.com/run-llama/lite… 💬 0 🔄 0 ❤️ 0 👀 51 ⚡
