From PDF text to retrievable Markdown
The converter extracts embedded PDF text in the browser and formats it into Markdown that is easier to inspect before loading into a retrieval pipeline.
Local PDF to Markdown workflow
PDF to Markdown for RAG
RAG systems need clean, traceable text before embedding. This converter helps turn text-based PDFs into Markdown chunks with page markers and optional metadata for ingestion experiments.
Open the converterChunking choices for RAG
Use page chunks when the source document has strong page-level meaning. Use character chunks when you want more even embedding sizes across long reports.
Metadata and page references
YAML front matter and page headings make it easier to preserve file name, conversion date, and source page context alongside each chunk.
What this tool does not solve
This version does not generate embeddings, run OCR, classify tables, or host a vector database. It focuses on preparing clean Markdown input.
FAQ
Common questions
Is Markdown useful for RAG?
Yes. Markdown keeps headings and paragraphs visible, which makes preprocessing and debugging easier before embedding.
What chunk size should I use?
Start around 2,000 to 4,000 characters for retrieval experiments, then tune based on your model, embedding strategy, and evaluation set.
Does this create embeddings?
No. It prepares Markdown text that you can pass to your own embedding and retrieval pipeline.