How do you convert PDFs to RAG-ready data?
To convert PDFs to RAG-ready data, extract the text into clean, structured Markdown using a document parser, then split it into chunks for embedding in a vector store. The extraction step is the hardest part: rule-based parsers work for machine-generated PDFs with consistent layouts but fail on scanned documents, complex tables, or PDFs where multi-column layouts produce garbled reading order.
| Factor | PyMuPDF / pdfplumber | LLM-based extraction | Firecrawl document parsing |
|---|---|---|---|
| Scanned PDF support | No | Yes | Yes (auto or OCR mode) |
| Table structure | Partial | Semantically understood | Preserved in Markdown |
| Reading order | Can garble multi-column | Correct | Correct |
| Output format | Raw text strings | Schema-defined JSON | Structured Markdown |
| Setup | Local install | API + prompt engineering | Single API call |
Use rule-based parsers for structured, machine-generated PDFs where extraction is deterministic. For mixed corpora (research papers, filings, contracts, reports) where formats vary, LLM-based extraction or a managed parsing API produces more consistent chunks.
Firecrawl's document parsing converts any PDF URL into clean Markdown in one API call. The output preserves headings, tables, and lists in a format that chunking libraries like LangChain's RecursiveCharacterTextSplitter can split directly. For scanned sources, set mode: "ocr" to force OCR on every page before chunking. No local dependencies, no layout configuration needed. See the PDF parser v2 release for the v2 feature set, or Fire-PDF for Firecrawl's current Rust-based PDF parsing engine—which routes text-based pages to native extraction and only scanned content through GPU, averaging under 400ms per page on mixed corpora.
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