@llamaindex/liteparse-wasm

Browser/WebAssembly build of LiteParse — a fast, lightweight PDF parser with spatial text extraction.

This package runs entirely in the browser. No server, no cloud calls.

Install

npm install @llamaindex/liteparse-wasm

Quick start

import init, { LiteParse } from "@llamaindex/liteparse-wasm";

// Load the wasm module (point at the file shipped with the package).
await init();

const parser = new LiteParse({
  ocrEnabled: false, // OCR requires a JS-side engine (see below)
  outputFormat: "json",
});

// `data` is a Uint8Array (e.g. from fetch / File / drag-drop).
const bytes = new Uint8Array(await file.arrayBuffer());
const result = await parser.parse(bytes);

console.log(result.text);          // full document text
console.log(result.pages[0]);      // per-page items with bboxes

Document complexity

Before committing to a full parse, check whether a document needs OCR or heavier processing. isComplex is a cheap, text-layer-only pass that returns one entry per page with a needsOcr verdict and the signals behind it — useful for routing documents or deciding whether the JS-side OCR engine is worth wiring up.

const parser = new LiteParse({ ocrEnabled: false });
const bytes = new Uint8Array(await file.arrayBuffer());
const pages = await parser.isComplex(bytes);

if (pages.some((p) => p.needsOcr)) {
  // This document would benefit from OCR — see "OCR in the browser" below
  for (const page of pages.filter((p) => p.needsOcr)) {
    console.log(`Page ${page.pageNumber}: ${page.reasons.join(", ")}`);
  }
}

reasons is one of "scanned", "no-text", "sparse-text", "embedded-images", "garbled", or "vector-text".

Config options

All optional, camelCase:

| Option | Type | Default | Description | |---|---|---|---| | ocrLanguage | string | "eng" | Language code passed to the OCR engine | | ocrEnabled | boolean | true | Run OCR on text-sparse pages | | maxPages | number | 1000 | Stop after this many pages | | targetPages | string | — | e.g. "1-5,10,15-20" | | dpi | number | 150 | Render DPI for OCR / screenshots | | outputFormat | "json" \| "text" \| "markdown" | "json" | Output format; "markdown" returns rendered Markdown on result.text | | imageMode | "off" \| "placeholder" \| "embed" | "placeholder" | How raster images are surfaced in markdown output | | extractLinks | boolean | true | Render hyperlink annotations as [text](url) in markdown output | | preserveVerySmallText | boolean | false | Keep tiny text that's normally filtered | | password | string | — | Password for protected PDFs | | quiet | boolean | false | Suppress progress logging | | ocrEngine | object | — | JS-side OCR engine (see below) |

OCR in the browser

The native HTTP-OCR and Tesseract backends are not available in the browser. To use OCR, pass an object with a recognize method:

const parser = new LiteParse({
  ocrEnabled: true,
  ocrLanguage: "eng",
  ocrEngine: {
    /**
     * @param imageData PNG-encoded image bytes
     * @param width  rendered page width  in pixels
     * @param height rendered page height in pixels
     * @param language e.g. "eng"
     * @returns array of { text, bbox: [x1,y1,x2,y2], confidence }
     */
    async recognize(imageData, width, height, language) {
      // e.g. call a worker that wraps tesseract.js, or a remote OCR service
      return [
        { text: "Hello", bbox: [10, 20, 80, 40], confidence: 0.98 },
      ];
    },
  },
});

Building from source

Requires Rust + wasm-pack:

# from packages/wasm
npm run build           # web target (default)
npm run build:bundler   # for webpack/rollup/vite
npm run build:nodejs    # for node.js

Output goes to pkg/.

License

Apache-2.0