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How do I install pymupdf4llm?

Install from PyPI with a single command: 
pip install pymupdf4llm
PyMuPDF is installed automatically as a dependency. Python 3.8 or later is required. To verify the installation worked, run: 
import pymupdf4llm
print(pymupdf4llm.version)

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How do I convert a PDF to Markdown?

Call to_markdown() with a file path. It returns a single Markdown string with reading order preserved, tables intact, and images handled. 
import pymupdf4llm

md_text = pymupdf4llm.to_markdown("my-document.pdf")
print(md_text)
To save the output to a file, use Python’s pathlib: 
from pathlib import Path
Path("output.md").write_text(md_text)

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What output formats are supported?

There are three extraction functions, all sharing a consistent interface:
FunctionOutputBest for
to_markdown()Markdown string or per-page chunk dictsLLM ingestion and RAG pipelines
to_json()Structured JSON with bounding boxes and font metadataCustom pipelines needing positional data
to_text()Plain text, stripped of all Markdown syntaxSearch indexing and NLP preprocessing

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How do I extract only specific pages?

Pass a list of zero-based page numbers to the pages parameter. This works on all three extraction functions. 
md_text = pymupdf4llm.to_markdown("my-document.pdf", pages=[0, 1, 2])
Page numbers are zero-indexed, so page 1 of the document is 0, page 2 is 1, and so on. This is especially useful for speeding up OCR-heavy documents by limiting which pages are processed.

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How do I get per-page chunks for a RAG pipeline?

Set page_chunks=True on to_markdown(). This returns a list of dictionaries — one per page — each containing the text and rich metadata. 
chunks = pymupdf4llm.to_markdown("my-document.pdf", page_chunks=True)

for chunk in chunks:
    print(chunk["metadata"]["page"])  # page number
    print(chunk["text"])              # Markdown content
Each chunk includes bounding box data, page dimensions, TOC entries, and document metadata — everything a downstream pipeline needs.

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What document formats are supported as input?

Standard formats — PDF, XPS, EPUB, MOBI, and more — are supported out of the box with no extra configuration. Office formats such as DOCX, PPTX, and XLSX require PyMuPDF Pro, which unlocks them via the same consistent API. See the Supported Formats guide for a full list of supported input and output formats.

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Does it handle scanned or image-based PDFs?

Yes. OCR runs automatically when a page contains no selectable text. Pages with native digital text skip OCR entirely, keeping processing fast. The resulting output is seamless — OCR’d pages and native pages are combined with no distinction. 
# OCR triggers automatically where needed
md_text = pymupdf4llm.to_markdown("scanned-document.pdf")
Tesseract is the default OCR engine, included by default. RapidOCR and PaddleOCR are also available as optional engines.

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How do I force OCR on every page?

Use force_ocr=True to bypass auto-detection. This is useful when the native text layer is corrupt or misaligned with the visual content. 
md_text = pymupdf4llm.to_markdown("document.pdf", force_ocr=True)
Note: Forcing OCR on clean, text-based PDFs will slow processing significantly and may reduce output quality. Only use it when you have reason to distrust the native text layer.
You can also target specific pages: 
md_text = pymupdf4llm.to_markdown("document.pdf", pages=[2, 3], force_ocr=True)

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How do I disable OCR entirely?

Set use_ocr=False. Pages with no selectable text will return empty strings. This is useful when you know your documents are always text-based, or when you want to handle OCR yourself in a downstream step. 
md_text = pymupdf4llm.to_markdown("document.pdf", use_ocr=False)

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How do I use OCR with a non-English language?

Pass a Tesseract language code to ocr_language. The default is "eng". Combine multiple languages with a +: 
md_text = pymupdf4llm.to_markdown("multilingual.pdf", ocr_language="eng+deu")
The corresponding Tesseract language packs must be installed on your system first. On Ubuntu: 
sudo apt install tesseract-ocr-deu tesseract-ocr-fra
See Tesseract Language Packs for more installation instructions.

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Does it integrate with LangChain or LlamaIndex?

Yes. There are native loaders for both frameworks. The LlamaMarkdownReader class implements a LlamaIndex BaseReader that loads documents as Document objects for use in pipelines and vector stores. For LangChain, a dedicated integration is also documented. Both plug into existing pipelines with no glue code.

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What does to_json() return and when should I use it?

to_json() returns a list of dictionaries with bounding boxes, font metadata, and layout data for every block on every page. Use it when your pipeline needs precise positional data — for example, building redaction tools, ML pipelines, or custom rendering logic. It accepts the same pages and margins parameters as the other extraction functions. See the JSON schema reference for full details.

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How do I detect and strip repeating headers and footers?

Use the IdentifyHeaders class. It detects repeating page headers and footers and returns bounding boxes, plus a get_margins() helper that produces a tuple you can pass directly to any extraction function to exclude those regions.