/xberg

name: xberg
description: >-
  Extract text, tables, metadata, and images from 107 document formats
  (PDF, Office, images, HTML, email, archives, academic) using Xberg.
  Use when writing code that calls Xberg APIs in Python, Node.js/TypeScript,
  Rust, or CLI. Covers installation, extraction (sync/async), configuration
  (OCR, chunking, output format), batch processing, error handling, and plugins.
license: Elastic-2.0
metadata:
  author: xberg-io
  version: "0.1.0"
  repository: https://github.com/xberg-io/xberg

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Xberg Document Extraction

Xberg is a document intelligence library with a Rust core and bindings for Python, TypeScript/Node.js, Ruby, PHP, Go, Java, C#, Elixir, WebAssembly, Dart, Kotlin Android, Swift, Zig, and C. It extracts text, tables, metadata, and images from 107 formats across 140 unique file extensions and accepts 53 compatibility MIME aliases, including PDF, Office documents, images, HTML, email, archives, and academic formats.

Use this skill when writing code that:

• Extracts text or metadata from documents
• Performs OCR on scanned documents or images
• Batch-processes multiple files
• Configures extraction options (output format, chunking, OCR, language detection)
• Implements custom plugins (post-processors, validators, OCR backends)

> If the `xberg` MCP server is registered in this session, prefer its tools over shelling out to the CLI — they expose the same extraction surface with structured arguments and results.

Installation

Python

pip install xberg

Node.js

npm install @xberg-io/xberg

Rust

cargo add xberg

# Cargo.toml
[dependencies]
xberg = { version = "1.1.0", features = ["full"] }
tokio = { version = "1", features = ["full"] }
# feature flags: pdf, ocr, chunking, embeddings, language-detection, keywords, api, mcp
#                (or "formats" / "full" aggregates); tokio-runtime is on by default

CLI

brew install xberg-io/tap/xberg
# or run without a persistent install (the CLI proxy package self-installs the binary):
npx @xberg-io/xberg-cli --help
uvx --from xberg-cli xberg --help
# or download a prebuilt binary from the latest GitHub release:
#   https://github.com/xberg-io/xberg/releases/latest
# or build from source:
cargo install xberg-cli

Quick Start

The library entry points are `extract(input, config)` and `extract_batch(inputs, config)`. Both return an `ExtractionResult` **envelope** — the extracted document(s) live in `result.results`, and per-document data (`content`, `tables`, `metadata`, …) is on each `result.results[i]`. Python and Node are async-only.

Python

import asyncio
from xberg import ExtractInput, extract, ExtractionConfig

async def main() -> None:
    result = await extract(ExtractInput(uri="document.pdf"), ExtractionConfig())
    doc = result.results[0]
    print(doc.content)    # extracted text
    print(doc.metadata)   # document metadata
    print(doc.tables)     # extracted tables

asyncio.run(main())

Node.js

import { extract } from "@xberg-io/xberg";

const output = await extract({ kind: "uri", uri: "document.pdf" });
const doc = output.results[0];
console.log(doc.content);
console.log(doc.metadata);
console.log(doc.tables);

Rust

use xberg::{extract, ExtractInput, ExtractionConfig};

#[tokio::main]
async fn main() -> xberg::Result<()> {
    let output = extract(ExtractInput::from_uri("document.pdf"), &ExtractionConfig::default()).await?;
    println!("{}", output.results[0].content);
    Ok(())
}

CLI

xberg extract document.pdf
xberg extract document.pdf --format json
xberg extract document.pdf --content-format markdown

Configuration

All languages use the same configuration structure with language-appropriate naming conventions.

Python (snake_case)

from xberg import (
    ExtractInput, extract,
    ExtractionConfig, OcrConfig, TesseractConfig, PdfConfig, ChunkingConfig, OutputFormat,
)

config = ExtractionConfig(
    ocr=OcrConfig(
        backend="tesseract",
        language=["eng"],
        tesseract_config=TesseractConfig(psm=6, enable_table_detection=True),
    ),
    pdf_options=PdfConfig(passwords=["secret123"]),
    chunking=ChunkingConfig(max_characters=1000, overlap=200),
    output_format=OutputFormat("markdown"),
)

result = await extract(ExtractInput(uri="document.pdf"), config)

Node.js (camelCase)

import { extract, type ExtractionConfig } from "@xberg-io/xberg";

const config: ExtractionConfig = {
  ocr: { backend: "tesseract", language: ["eng"] },
  pdfOptions: { passwords: ["secret123"] },
  chunking: { maxCharacters: 1000, overlap: 200 },
  outputFormat: "markdown",
};

const output = await extract({ kind: "uri", uri: "document.pdf" }, config);

Rust (snake_case)

use xberg::{extract, ExtractInput, ExtractionConfig, OcrConfig, ChunkingConfig, OutputFormat};

let config = ExtractionConfig {
    ocr: Some(OcrConfig {
        backend: "tesseract".into(),
        language: vec!["eng".to_string()],
        ..Default::default()
    }),
    chunking: Some(ChunkingConfig {
        max_characters: 1000,
        overlap: 200,
        ..Default::default()
    }),
    output_format: OutputFormat::Markdown,
    ..Default::default()
};

let output = extract(ExtractInput::from_uri("document.pdf"), &config).await?;

Config File (TOML)

output_format = "markdown"

[ocr]
backend = "tesseract"
language = "eng"

[chunking]
max_characters = 1000
overlap = 200

[pdf_options]
passwords = ["secret123"]

# CLI: auto-discovers xberg.toml in current/parent directories
xberg extract doc.pdf
# or explicit:
xberg extract doc.pdf --con