> ## Documentation Index > Fetch the complete documentation index at: https://docs.pdf4llm.com/llms.txt > Use this file to discover all available pages before exploring further. # FAQ > Common questions about the `pymupdf4llm` Python library.
## How do I install pymupdf4llm? Install from PyPI with a single command: ```bash theme={null} pip install pymupdf4llm ``` PyMuPDF is installed automatically as a dependency. Python 3.8 or later is required. To verify the installation worked, run: ```python theme={null} import pymupdf4llm print(pymupdf4llm.version) ``` ## 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. ```python theme={null} import pymupdf4llm md_text = pymupdf4llm.to_markdown("my-document.pdf") print(md_text) ``` To save the output to a file, use Python's `pathlib`: ```python theme={null} from pathlib import Path Path("output.md").write_text(md_text) ``` ## What output formats are supported? There are three extraction functions, all sharing a consistent interface: | Function | Output | Best for | | --------------- | ----------------------------------------------------- | ---------------------------------------- | | `to_markdown()` | Markdown string or per-page chunk dicts | LLM ingestion and RAG pipelines | | `to_json()` | Structured JSON with bounding boxes and font metadata | Custom pipelines needing positional data | | `to_text()` | Plain text, stripped of all Markdown syntax | Search indexing and NLP preprocessing | ## 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. ```python theme={null} 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. ## 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. ```python theme={null} 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. ## 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](/python/getting-started/supported-formats) for a full list of supported input and output formats. ## 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. ```python theme={null} # 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. ## 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. ```python theme={null} 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: ```python theme={null} md_text = pymupdf4llm.to_markdown("document.pdf", pages=[2, 3], force_ocr=True) ``` ## 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. ```python theme={null} md_text = pymupdf4llm.to_markdown("document.pdf", use_ocr=False) ``` ## 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 `+`: ```python theme={null} 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: ```bash theme={null} sudo apt install tesseract-ocr-deu tesseract-ocr-fra ``` See [Tesseract Language Packs](/python/guides/OCR/tesseract-language-packs/) for more installation instructions. ## 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. ## 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](/python/reference/JSON-schema) reference for full details. ## 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.