PDF RAG FAQs

Question 1

How does PDF RAG handle challenging PDF layouts?

Accepted Answer

It uses automatic OCR fallback for scanned or image-based PDFs, PyMuPDF for layout-preserving text extraction in multi-column documents, and pdfplumber to detect and extract tables into clean markdown format.

Question 2

What is PDF RAG?

Accepted Answer

PDF RAG is an MCP server that enables Retrieval-Augmented Generation (RAG) by processing complex PDF documents. It extracts, chunks, embeds, and allows semantic searching of information from scanned, multi-column, and table-heavy PDFs for AI assistants.

Question 3

What kind of search capabilities does PDF RAG offer?

Accepted Answer

PDF RAG provides local semantic search across all ingested documents. This means you can find information by meaning and context, rather than just keywords, with results ranked by similarity scores and including page numbers.

Question 4

Does PDF RAG process documents locally to ensure privacy?

Accepted Answer

Absolutely. All document processing, including embedding generation and data storage, occurs entirely on your local machine. No PDF content or embeddings are ever sent to external APIs or services, guaranteeing your data privacy.

Question 5

Is PDF RAG compatible with popular AI clients?

Accepted Answer

Yes, PDF RAG is built as a Model Context Protocol (MCP) server, ensuring full compatibility with MCP clients like Claude Desktop, Claude Code, and Cursor, allowing your AI to interact directly with your PDF data.

PDF RAG

PDF RAG

主要功能

使用案例

主要功能

使用案例