LlamaParse 推出 Conversational Extract，通过对话定义文档提取
LlamaParse 出了新功能，跟聊天一样定义要提取什么字段，不用再手写 JSON schema 了，还能一键跑百万级文档，带引用和置信度。
LlamaParse 发布 Conversational Extract 功能，用户可通过自然语言对话定义文档提取字段，无需手写 JSON Schema。用户上传参考文档后，AI agent 自动推断 schema，支持对话交互调整。系统可在规模达 100 万以上的文档上运行提取，为每个字段返回 bounding boxes、引用和置信度分数。
Extracting information from millions of documents at scale used to take an insane number of human hours. Even with recent OCR + document AI tech, humans would still have to spend a lot of time carefully tuning the schema and precisely defining the fields they want to extract. We've launched a Conversational Extract feature within LlamaParse that now lets you define and run complex document extraction through a conversation. 1️⃣ Describe the fields you want to extract, and upload a reference document 2️⃣ Refine the document schema directly, or through additional conversations. Select the right level of agentic reasoning needed 3️⃣ Run extraction at scale over 1M+ docs. Get bounding boxes, citations, and confidence scores for every single extracted field. Check it out today: cloud.llamaindex.ai Your browser does not support the video tag. 🔗 View on Twitter LlamaIndex 🦙 @llama_index Your extraction schema is now a conversation away. 📄 Upload a doc — the agent drafts the schema from it 💬 Want changes? Just ask 📚 Or grab a template and go Writing JSON Schema by hand? That's over. Conversational Extract is live in LlamaParse. Try it now cloud.llamaindex.ai/?utm_medium=so… ip6 Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 4 🔄 1 ❤️ 10 👀 1225 📊 5 ⚡
