Xberg

Xberg Document Extraction

Xberg is a high-performance document intelligence library with a Rust core and native bindings for Python, Node.js/TypeScript, Ruby, Go, Java, C#, PHP, and Elixir. It extracts text, tables, metadata, and images from 91+ file formats including PDF, Office documents, images (with OCR), 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 — the crate is currently 1.0.0-rc.1 (a pre-release, so pin it explicitly)
[dependencies]
xberg = { version = "1.0.0-rc.1", 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.from_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,
)

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="markdown",
)

result = await extract(ExtractInput.from_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: { maxChars: 1000, maxOverlap: 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: "eng".into(),
        ..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 --config xberg.toml
xberg extract doc.pdf --config-json '{"ocr":{"backend":"tesseract","language":"deu"}}'

Batch Processing

extract_batch takes a list of ExtractInputs and returns one envelope whose results array holds a document per input (in input order); per-input failures are reported in result.errors.

Python

from xberg import ExtractInput, extract_batch

inputs = [
    ExtractInput.from_uri("doc1.pdf"),
    ExtractInput.from_uri("doc2.docx"),
    ExtractInput.from_uri("doc3.xlsx"),
]
output = await extract_batch(inputs)

for doc in output.results:
    print(f"{len(doc.content)} chars extracted")

Node.js

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

const output = await extractBatch([
  { kind: "uri", uri: "doc1.pdf" },
  { kind: "uri", uri: "doc2.docx" },
]);
for (const doc of output.results) {
  console.log(`${doc.content.length} chars`);
}

Rust

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

let config = ExtractionConfig::default();
let inputs = vec![ExtractInput::from_uri("doc1.pdf"), ExtractInput::from_uri("doc2.docx")];
let output = extract_batch(inputs, &config).await?;

CLI

xberg batch *.pdf --format json
xberg batch docs/*.docx --content-format markdown

OCR

OCR runs automatically for images and scanned PDFs. Tesseract is the default backend (native binding, no external install required).

Backends

Select with OcrConfig.backend:

• tesseract (default): built-in native binding. All Tesseract languages supported.
• paddleocr ("paddleocr" / "paddle-ocr"): ONNX-based PaddleOCR.
• vlm: Vision-Language-Model OCR (configure via OcrConfig.vlm_config).

Custom backends can be registered in Python/Node via register_ocr_backend (see Advanced Features).

Language Codes

config = ExtractionConfig(ocr=OcrConfig(language="eng"))        # English
config = ExtractionConfig(ocr=OcrConfig(language="eng+deu"))    # Multiple (string)
config = ExtractionConfig(ocr=OcrConfig(language=["eng", "deu"]))  # Multiple (list)

Force OCR

config = ExtractionConfig(force_ocr=True)  # OCR even if text is extractable

Result Envelope and Document Fields

extract / extract_batch return an ExtractionResult envelope: results (list of documents), errors (per-input failures), and summary (counts). Per-document fields live on each document in results — bind doc = result.results[0] (Python/Node) or &output.results[0] (Rust) first.

Error Handling

Python

Exceptions inherit from XbergError (note: the OCR exception is OcrError, not OCRError). Per-input failures during extract_batch are reported non-fatally in result.errors.

from xberg import (
    ExtractInput, extract, ExtractionConfig,
    XbergError, ParsingError, OcrError, ValidationError, MissingDependencyError,
)

try:
    result = await extract(ExtractInput.from_uri("file.pdf"), ExtractionConfig())
except ParsingError as e:
    print(f"Failed to parse: {e}")
except OcrError as e:
    print(f"OCR failed: {e}")
except ValidationError as e:
    print(f"Invalid input: {e}")
except MissingDependencyError as e:
    print(f"Missing dependency: {e}")
except XbergError as e:
    print(f"Extraction failed: {e}")

Node.js

The Node binding throws plain Error objects (it does not export typed error subclasses). Catch with instanceof Error, and inspect output.errors for non-fatal per-input failures.

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

try {
  const output = await extract({ kind: "uri", uri: "file.pdf" });
  if (output.errors.length > 0) {
    console.error("Per-input errors:", output.errors);
  }
} catch (e) {
  if (e instanceof Error) {
    console.error(`Extraction failed: ${e.message}`);
  }
}

Rust

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

let config = ExtractionConfig::default();
match extract(ExtractInput::from_uri("file.pdf"), &config).await {
    Ok(output) => println!("{}", output.results[0].content),
    Err(XbergError::Parsing { message, .. }) => eprintln!("Parse error: {message}"),
    Err(XbergError::Ocr { message, .. }) => eprintln!("OCR error: {message}"),
    Err(XbergError::UnsupportedFormat(mime)) => eprintln!("Unsupported: {mime}"),
    Err(e) => eprintln!("Error: {e}"),
}

Common Pitfalls

1. Result is an envelope: extract / extract_batch return ExtractionResult with results, errors, and summary. Per-document fields (content, tables, chunks, …) are on result.results[i], NOT on the top-level return.
2. Async-only: Python and Node have no sync variants — always await extract(...). Rust extract is async; use #[tokio::main] or an async context.
3. Build the input: pass an ExtractInput, not a bare path. Use ExtractInput.from_uri(...) / ExtractInput::from_uri(...) (Python/Rust) or { kind: "uri", uri: "..." } (Node); for bytes use kind="bytes" with bytes/mime_type.
4. Python ChunkingConfig fields: Use max_characters and overlap (defaults 1000 / 200). Node uses maxChars / maxOverlap; Rust uses max_characters / overlap.
5. Python OCR exception: it is OcrError, not OCRError. Node throws plain Error (no typed error subclasses).
6. Rust extract signature: extract(input, &config) — the config is a reference. Use &ExtractionConfig::default() for defaults.
7. CLI --format vs --content-format: --format controls CLI output (text/json). --content-format controls content format (plain/markdown/djot/html).
8. Config file field names: Use snake_case in TOML/YAML/JSON config files — [chunking] fields are max_characters and overlap; other fields use names like output_format, pdf_options.

Supported Formats (Summary)

See references/supported-formats.md for the complete format reference with MIME types.

Additional Resources

Detailed reference files for specific topics:

• Python API Reference — All functions, config classes, plugin protocols, exact signatures
• Node.js API Reference — All functions, TypeScript interfaces, worker pool APIs
• Rust API Reference — All functions with feature gates, structs, Cargo.toml examples
• CLI Reference — All commands, flags, config precedence, exit codes
• Configuration Reference — TOML/YAML/JSON formats, auto-discovery, env vars, full schema
• Supported Formats — All 91+ formats with file extensions and MIME types
• Advanced Features — Plugins, embeddings, MCP server, API server, security limits
• Other Language Bindings — Go, Ruby, Java, C#, PHP, Elixir, WASM, Docker

Related skills

Task-focused sibling skills go deeper than this overview:

• extracting-with-ocr — OCR backends, language packs, force-OCR, tuning.
• extracting-tables — layout-aware table detection and table models.
• chunking — chunk size/overlap, markdown/yaml/semantic chunkers, the chunk command.
• extracting-keywords — YAKE/RAKE keywords, language detection, the embed command.
• batch-extraction — the batch command, --file-configs, parallelism, error recovery.
• picking-a-format — choosing --format / --content-format per consumer.

Full documentation: https://docs.xberg.io GitHub: https://github.com/xberg-io/xberg