docling.rs-wasm

docling.rs compiled to WebAssembly: convert DOCX, HTML, XLSX, PPTX, CSV, AsciiDoc, EPUB, ODF, Markdown, WebVTT, Email, MHTML, JATS, USPTO, XBRL, LaTeX, JSON, DocLang — and the embedded text layer of PDFs — to Markdown / docling JSON / DocLang XML entirely in the browser. No server; the file never leaves the page.

~1.9 MB gzipped, no models needed for any of the above. Scanned PDFs and images additionally work through the in-browser ML pipeline (RT-DETR layout + PP-OCRv3 + TableFormer via ONNX Runtime Web) once you provide the models — see the full docs and the live demo.

Bundlers (Vite, webpack, …)

import { convert, supported_extensions } from "docling.rs-wasm";

const file = input.files[0];
const bytes = new Uint8Array(await file.arrayBuffer());
const markdown = convert(bytes, file.name, "md");
const json     = convert(bytes, file.name, "json");
const withPics = convert(bytes, file.name, "md", "embedded"); // data: URIs

(The bundler target loads the wasm as a module import — Vite needs no config; webpack 5 needs experiments.asyncWebAssembly = true.)

No bundler (plain <script type="module">)

import init, { convert } from "docling.rs-wasm/web";
await init(); // fetches docling_wasm_bg.wasm next to the JS

const markdown = convert(bytes, file.name, "md");

API

convert(
  bytes: Uint8Array,
  filename: string,                        // extension drives format detection
  to?: "md" | "json" | "doclang",          // default "md"
  images?: "placeholder" | "embedded",     // default "placeholder", Markdown only
  max_pages?: number,                      // convert only the first N PDF pages
): string
supported_extensions(): string             // JSON array, e.g. for <input accept=…>
version(): string

Structured digital-PDF conversion (DigitalConverter: headings, lists, tables, pictures via the layout model), scanned-document OCR (ScannedConverter) and TableFormer table structure are exported too — they need ONNX Runtime Web sessions on the JS side; the crate README has the complete wiring, and the demo page source is a working reference.

Tauri / Electron

The package runs in any webview as-is — but in a Tauri app you usually want the native pipeline in the Rust backend instead (full OCR, TableFormer, GPU, no wasm limits). See “Tauri and other desktop shells” in the crate README.

Related packages

• docling.rs — native Node.js / Bun bindings with the full ML pipeline (use this on servers).
• docling.rs-cuda — the same with CUDA execution providers.
• docling-rs — Python wheel.