distillpdf 0.0.3

Pure-Rust PDF extraction that distills documents into clean, LLM-ready HTML — for LLMs and RAG, built on lopdf

distillPDF

Turn any PDF into clean, LLM-ready HTML — structure-aware, pure-Rust, MIT-licensed.

distillpdf reads a PDF and reconstructs its structure — reading order, headings, paragraphs, lists, tables, and figures — then emits compact, semantic HTML (or plain text) ready to feed to an LLM or a RAG pipeline. No styling noise, no layout junk: just the content a model needs.

It's built on lopdf and shipped to Python via PyO3 + maturin as a small, self-contained wheel — a lightweight, permissively licensed alternative to AGPL/heavyweight extractors (PyMuPDF, pdfminer, Unstructured), with no system dependencies and no Python runtime deps.

🧪 Early release (0.0.3) — testers wanted. The API is small and may still change. If you have PDFs that come out wrong, please open an issue with the file (or a description) — real-world documents are exactly what this needs to get better.

Install

pip install distillpdf

Prebuilt wheels; no compiler or system libraries required. Installing also puts a distillpdf command on your PATH.

Command line

Convert a PDF to clean HTML in one command:

distillpdf paper.pdf                  # HTML to stdout
distillpdf paper.pdf -o paper.html    # ...or to a file
distillpdf *.pdf -o out/              # batch: out/<name>.html per input

distillpdf paper.pdf --no-images      # <image N> placeholders, no base64 bytes
distillpdf paper.pdf --no-toc         # omit the table-of-contents nav
distillpdf paper.pdf --text           # plain text instead of HTML
distillpdf paper.pdf --toc            # print the table of contents
distillpdf paper.pdf --section abstract

(Also available as python -m distillpdf.)

Quickstart

import distillpdf

doc = distillpdf.open("paper.pdf")        # or distillpdf.from_bytes(data)

html     = doc.to_html()                  # clean, semantic HTML for an LLM
text     = doc.extract_text()             # plain text, in reading order
toc      = doc.toc()                      # [(level, title, page, anchor_id), ...]
abstract = doc.section("abstract")        # targeted section extraction

Want compact, text-only output? Drop the inline image bytes — each embedded image becomes a lightweight <image N> placeholder (captions and figure anchors are kept):

doc = distillpdf.open("paper.pdf", images=False)
doc.to_html()    # <figure id="fig-1"><image 1><figcaption>…</figcaption></figure>

Pass toc=False to skip the auto table-of-contents <nav> (heading anchors are still emitted, so #section links and doc.section(...) keep working):

distillpdf.open("paper.pdf", toc=False).to_html()

open() / from_bytes() options

Option	Default	Effect on to_html()
images=	True	False swaps inline base64 images for <image N> placeholders (captions + #fig-N anchors kept)
toc=	True	False omits the <nav> table of contents (heading anchors still emitted)

Both flags only change to_html() output — toc(), section(), and the raw extractors below are unaffected.

Raw pieces

Need the structured data instead of HTML?

doc.extract_tables()   # cell grids (handles multi-level / colspan headers)
doc.extract_images()   # embedded images, with raw bytes
doc.extract_links()    # hyperlinks with targets
doc.extract_fonts()    # font inventory
doc.page_count()       # number of pages

Why distillPDF

• Structure, not just text. Two-column reading order, multi-level table headers mapped onto a single grid (colspan), vector figures transcoded to inline SVG (including rotated axis labels), an auto-generated table of contents, and named section extraction (doc.section("methods")).
• LLM-ready output. Lean, class-free HTML — semantic markup a model can read directly, with anchor ids so toc() entries link straight into the document.
• Small & permissive. Pure Rust on lopdf, MIT-licensed, no system dependencies, no Python runtime dependencies. Drops into any pipeline without license headaches.
• Fast. Native Rust extraction with a release build tuned for speed (LTO, single codegen unit).

Scope

In scope: text, table, image, and font extraction, plus an HTML/markdown output layer for RAG and LLM ingestion.

Out of scope (for now): page rendering, PDF generation, OCR.

Comparison

	distillPDF	PyMuPDF	pdfminer.six	Unstructured
License	MIT	AGPL / commercial	MIT	Apache (heavy deps)
Structure-aware HTML	✅	partial	❌	✅
System deps	none	none	none	many
Implementation	Rust	C	Python	Python

Contributing & feedback

This is a young project and feedback is the fastest way to improve it. The most useful things you can do:

1. Try it on your PDFs and tell me where the output is wrong — open an issue.
2. Star the repo if it's useful, so others can find it.
3. PRs welcome — see the development notes below.

Development

The test suite lives in tests/ (pytest) and runs on CI. It needs only distillpdf installed. CI runs entirely on data we own — a self-contained demo PDF (tests/demo/, end-to-end structure check) and a synthetic table corpus (tests/corpus_tables/). The third-party PDF corpora (tests/corpus*/) are gitignored, so their tests self-skip on a fresh clone and run only when the corpora are present locally for deeper coverage.

Build from source with maturin:

git clone https://github.com/kkollsga/distillpdf
cd distillpdf
maturin develop --release    # build + install into the current venv
bash tests/run.sh            # build distillpdf + run pytest
pytest tests/ -q             # or just run the tests against an installed build

License

MIT — see LICENSE. Use it anywhere, including commercial and closed-source projects.