Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload
English summary
The tutorial shows how to parse PDFs locally using the Docling tool, preserving table cells, OCR text, captions, and headings. The output matches cloud-grade document structure without any cloud upload, API keys, or per-page billing. This approach enables privacy-preserving document intelligence for RAG pipelines by converting PDFs into richly structured data ready for ingestion.
Chinese summary
本教程演示如何使用Docling工具在本地解析PDF，保留表格单元、OCR文本、标题和说明文字，实现云端级文档结构化而无需上传、API密钥或按页付费。该方法将PDF转换为丰富结构数据，用于RAG流水线，确保数据隐私。
Key points
Parses PDFs entirely locally using the Docling library, no cloud upload required.
使用Docling库完全本地解析PDF，无需上传云端。
Extracts rich document structure: table cells, OCR text, captions, and headings.
提取丰富的文档结构：表格单元、OCR文本、标题和说明文字。
Delivers cloud-grade output without API keys or per-page costs, preserving privacy.
提供云端级输出，无需API密钥或按页计费，保护数据隐私。
Output is directly usable for retrieval-augmented generation (RAG) systems.
输出可直接用于检索增强生成（RAG）系统。