docling.rs

# docling.rs

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**docling.rs** converts documents — PDF, Office, HTML, scans, audio — into
Markdown, docling JSON and the other shapes an LLM or a RAG pipeline can
read. It is a Rust port of IBM's
[docling](https://github.com/docling-project/docling): the same output,
byte-for-byte, from one static binary. No Python, no PyTorch.

Pulling text out of a PDF is easy. Keeping its *structure* — headings in
order, tables as tables, scanned pages read — takes layout analysis, table
recognition and OCR. docling has the models for that, and a Python stack to
match. docling.rs runs the same models from a single binary: PDF 4.3× faster
than Python docling at under half the RAM, Office and HTML 20–60× faster, and
tables that survive — cell recall 0.94 where MarkItDown gets 0.31
([numbers](#conformance-with-microsoft-markitdown),
[method](./docs/PDF_CONFORMANCE.md#performance--review--profiling-notes)).

**Coming from [MarkItDown](https://github.com/microsoft/markitdown)?**
`pip install docling-rs`, swap one import —
`from docling_rs.markitdown import MarkItDown` — and keep its API, every
format it converts included, with real layout analysis, tables and OCR
behind it: [guide](./docs/MARKITDOWN.md),
[measured side by side](#conformance-with-microsoft-markitdown).


## Install

```bash
# CLI + models (Linux x64/arm64 prebuilt; builds from source elsewhere)
curl -fsSL https://raw.githubusercontent.com/docling-project/docling.rs/master/scripts/install/install.sh | bash
docling-rs example.pdf          # Markdown on stdout

# or from crates.io; --cli puts the models where the installed binary looks
cargo install docling-cli
curl -fsSL https://raw.githubusercontent.com/docling-project/docling.rs/master/scripts/install/download_dependencies.sh | sh -s -- --cli

pip install docling-rs          # Python — drop-in for docling's and MarkItDown's APIs
npm install docling.rs          # Node.js / Bun
cargo add docling               # Rust library

# containers, models baked in (-cuda variants for NVIDIA)
docker run --rm -v "$PWD:/data" ghcr.io/docling-project/docling-rs paper.pdf
docker run -p 127.0.0.1:5001:5001 ghcr.io/docling-project/docling-rs-serve
```

Office, HTML, Markdown and e-mail need no models. PDF, images and audio need
the ONNX models (layout, OCR, TableFormer, Whisper; under 1 GB): `install.sh`,
`--cli` and the containers bring them along; with `pip`, run
`python -c "import docling_rs; docling_rs.download_models()"` once; with `npm`
or a checkout, run `download_dependencies.sh` where you convert
([details](#getting-the-ml-models)). Windows: [`docs/WINDOWS.md`](./docs/WINDOWS.md).

**▶ [Try it in your browser](https://docling-project.github.io/docling.rs/)**
first — the converter as wasm, OCR included; nothing leaves your device.

## Use

```bash
docling-rs report.pdf > report.md               # Markdown (streamed page by page)
docling-rs report.pdf --to json                 # docling JSON; also html, text, latex, dclx, chunks, images
docling-rs --input ./docs --output ./out        # a directory tree; --jobs N in parallel
docling-rs https://example.com/paper.pdf        # URLs, data: URIs and stdin (`-`) too
docling-rs https://www.youtube.com/watch?v=ID   # a YouTube video: title, description, caption transcript (--to vtt keeps the timing)
docling-rs --help                               # every flag: OCR, page ranges, images, enrichment, redaction …
```

```python
from docling_rs import DocumentConverter       # was: from docling.document_converter import …
doc = DocumentConverter().convert("report.pdf").document
print(doc.export_to_markdown())
```

```js
import { convertFileAsync } from 'docling.rs'
const { content } = await convertFileAsync('report.pdf', { to: 'markdown' })
```

```rust
use docling::{DocumentConverter, SourceDocument};
let result = DocumentConverter::new().convert(SourceDocument::from_file("report.pdf")?)?;
println!("{}", result.document.export_to_markdown());
```

```bash
curl -F file=@report.pdf 'localhost:5001/v1/convert?to=md'   # the server; OpenAPI at /openapi.yaml
```

Same options, same outputs on every surface — every option in
[`docs/OPTIONS.md`](./docs/OPTIONS.md).

## Highlights

- **From Python docling:** `from docling_rs import DocumentConverter`;
  `result.document` is a genuine `DoclingDocument`
  ([migration notes](./crates/docling-py/README.md#migrating-from-python-docling)).
- **Every docling format, and a few it lacks** — the table under
  [Status](#status), each validated against live docling
  ([Conformance](#conformance-with-python-docling)).
- **Every output docling has:** Markdown (docling's, or a stricter dialect),
  docling JSON, HTML, plain text, DocLang, LaTeX, Pandoc AST (→ DOCX, EPUB,
  Typst… through pandoc), WebVTT subtitles, RAG chunks and page images
  ([output formats](./crates/docling/README.md#output-formats)).
- **From any language:** Rust, Python, Node.js / Bun, the browser (wasm,
  fully client-side), C, C++, C#, Go, Java and Swift through one C header,
  any HTTP client, the shell
  ([Integration](#integration--using-doclingrs-from-your-language)).
- **A drop-in server for docling-serve:** Open WebUI uses it as its Docling
  content-extraction engine — point *Docling Server URL* at
  `http://<host>:5001` — and n8n, Dify and LangChain clients work unchanged
  ([`docling-rs serve`](#http-conversion-api--docling-rs-serve),
  [Open WebUI setup](./crates/docling-serve/README.md#open-webui)).
- **AI agents (MCP):** `docling-rs mcp` gives Claude Desktop, Claude Code,
  Cursor or VS Code MarkItDown's `convert_to_markdown` tool, plus `convert`
  for JSON, chunks and the rest — `uvx docling-rs-mcp`, or
  `claude mcp add docling -- docling-rs mcp` ([MCP server](#mcp-server--docling-rs-mcp)).
- **LangChain:** `pip install "docling-rs[langchain]"` — langchain-docling's
  `DoclingLoader` on the Rust engine, one import to swap
  ([details](./crates/docling-py/README.md#langchain)).
- **AutoGen:** `pip install "docling-rs[autogen]"` — `DoclingIndexer` fills
  any AutoGen memory with chunks that carry their pages, `DoclingConvertTool`
  lets an agent read documents ([details](./crates/docling-py/README.md#autogen)).
- **Your own formats:** register a backend for a format docling.rs does not
  read — or to read one of its formats your way — from Rust, Python
  (`@docling_rs.register_backend(extensions=["rtf2"])`, and MarkItDown's
  `register_converter` and plugins) or Node.js, ahead of the built-in formats
  ([pluggable backends](./crates/docling/README.md#pluggable-backends)).
- **RAG:** `docling-rag` — chunk, embed and search with a REST API and a web
  UI ([RAG subsystem](#rag-subsystem)).
- **Runs where you are:** Linux x64 / arm64 / s390x and Windows prebuilt (CLI,
  wheels, npm), macOS from source, containers for amd64 and arm64; NVIDIA
  CUDA and TensorRT, DirectML, CoreML and WebGPU in the browser as opt-in
  accelerators ([GPU](./docs/MODELS.md#gpu-execution-providers-optional-off-by-default),
  [IBM Z](./docs/IBMZ.md)).

Developed with **Claude Code** and _[TENET](https://github.com/artiz/tenet/tree/master)_ (minimalistic AI-driven development framework).

## Status

Every format in docling's pipeline converts — plus a few docling lacks. A
file with no or an unknown extension, stdin, an upload or a `data:` URL is
typed by its content (`InputFormat::detect`, `docling_rs.detect_format`,
`detectFormat`: the packages, the OLE files, PDF, images, audio and video,
iWork, XML, JSON, notebooks, messages, …), and a file that fails under its
extension is checked by content and converted as what it really is, with a
warning.

| Category | Formats |
|---|---|
| Text & markup | Markdown, AsciiDoc, HTML, MHTML, LaTeX (multi-file arXiv projects, math, tables); plain text and source code (`.py`, `.yaml`, `.log`, … — any text file, as a code block or paragraphs), generic JSON / JSON Lines and XML (kept as code blocks); RSS / Atom / RDF feeds (`.rss`, `.atom`, `.rdf` or a bare `.xml`: the feed's title, each item's heading, date and body); Jupyter notebooks (`.ipynb`: markdown cells, code cells in the kernel's language, outputs on request) |
| Word processing | DOCX, Word 97–2004 `.doc` (and Word 6/95, 1.x/2.0), OD