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LiteParse API Reference

skills/liteparse/references/api_reference.md

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LiteParse API Reference

Targets liteparse 2.0.0 (Python) and @llamaindex/liteparse (Node). Rust crate: liteparse = "2".

Python: LiteParse

python
from liteparse import LiteParse, ParseResult, ParsedPage, TextItem, ScreenshotResult, search_items

Constructor options

Python parameterTypeDefaultDescription
ocr_enabledboolTrueRun OCR on regions needing it
ocr_languagestr"eng"Tesseract language code
ocr_server_urlstr | NoneNoneHTTP OCR server (see ocr_and_formats.md)
tessdata_pathstr | NoneNonePath to tessdata directory
max_pagesint1000Maximum pages to parse
target_pagesstr | NoneNonee.g. "1-5,10,15-20"
dpifloat150Render DPI (OCR / screenshots)
output_formatstr"json""json" or "text" (affects native output mode)
preserve_very_small_textboolFalseKeep very small text runs
passwordstr | NoneNoneEncrypted PDF password
quietboolFalseSuppress progress output
num_workersintCPU−1Concurrent OCR workers

parse(file_data)

Input: file path (str / Path) or raw PDF bytes (bytes).

Returns: ParseResult

python
@dataclass
class ParseResult:
    pages: List[ParsedPage]
    text: str              # full document text (layout-preserved)

    @property
    def num_pages(self) -> int

    def get_page(self, page_num: int) -> Optional[ParsedPage]  # 1-indexed
python
@dataclass
class ParsedPage:
    page_num: int
    width: float
    height: float
    text: str
    text_items: List[TextItem]
python
@dataclass
class TextItem:
    text: str
    x: float
    y: float
    width: float
    height: float
    font_name: Optional[str]
    font_size: Optional[float]
    confidence: Optional[float]   # 0.0–1.0 when from OCR

Raises: FileNotFoundError, ParseError

screenshot(file_path, *, page_numbers=None)

Input: path to document (PDF or convertible format).

Returns: List[ScreenshotResult] with PNG bytes.

python
@dataclass
class ScreenshotResult:
    page_num: int
    width: int
    height: int
    image_bytes: bytes

Non-PDF formats are converted when LibreOffice/ImageMagick are installed.

get_config()

Returns resolved LiteParseConfig dataclass.

search_items(items, phrase, *, case_sensitive=False)

Search a list of TextItem for a phrase that may span multiple items. Returns merged TextItem objects with combined bounding boxes.

python
from liteparse import search_items

matches = search_items(page.text_items, "Figure 1", case_sensitive=False)

TypeScript / Node.js

typescript
import { LiteParse } from '@llamaindex/liteparse';

const parser = new LiteParse();
const result = await parser.parse('document.pdf');
console.log(result.text);

for (const page of result.pages) {
  console.log(`Page ${page.pageNum}: ${page.textItems.length} items`);
}

Constructor options (camelCase)

TypeScriptPython equivalent
ocrEnabledocr_enabled
ocrLanguageocr_language
ocrServerUrlocr_server_url
tessdataPathtessdata_path
maxPagesmax_pages
targetPagestarget_pages
dpidpi
preserveVerySmallTextpreserve_very_small_text
passwordpassword
quietquiet
numWorkersnum_workers

Parse from bytes

typescript
import { readFile } from 'fs/promises';

const pdfBytes = await readFile('document.pdf');
const result = await parser.parse(pdfBytes);

Screenshots

typescript
const screenshots = parser.screenshot('document.pdf', [1, 2, 3]);
for (const s of screenshots) {
  // s.pageNum, s.width, s.height, s.imageBuffer (PNG)
}

Install: npm i @llamaindex/liteparse (includes lit CLI).

Browser/edge: @llamaindex/liteparse-wasm — see upstream WASM README.

Rust (library)

rust
use liteparse::{LiteParse, LiteParseConfig};

let parser = LiteParse::new(LiteParseConfig::default());
let result = parser.parse("document.pdf").await?;

Custom OCR: implement OcrEngine trait and .with_ocr_engine(Arc::new(engine)).

CLI: cargo install liteparse