fil/packages/swift

Swift

Extract text, tables, images, and metadata from 90+ file formats and 300+ programming languages including PDF, Office documents, and images. Swift bindings via swift-bridge for macOS, iOS, and Linux, with native Swift types and async/await support.

What This Package Provides

• Document intelligence core — extract text, tables, images, metadata, entities, keywords, and code intelligence from one API.
• Format coverage — PDF, Office, images, HTML/XML, email, archives, notebooks, citations, scientific formats, and plain text.
• OCR choices — Tesseract, PaddleOCR, EasyOCR where supported, VLM OCR through liter-llm, and plugin hooks for custom backends.
• Same engine as every binding — Rust, Python, Node.js, Go, Java, PHP, Ruby, .NET, Elixir, R, WASM, Kotlin Android, Swift, Dart, Zig, and C FFI share the same Rust implementation.
• SwiftPM package — Swift Concurrency API for Apple targets.

Installation

Package Installation

Add to your Package.swift dependencies:

.package(url: "https://github.com/kreuzberg-dev/kreuzberg.git", from: "5.0.0-rc.3"),

Then add the product to the relevant target:

.target(
    name: "YourTarget",
    dependencies: [
        .product(name: "Kreuzberg", package: "kreuzberg"),
    ]
),

System Requirements

• Swift 6.0+ (swift-tools-version: 6.0) on macOS 13+ or iOS 16+
• Native runtime delivered through the C FFI surface from kreuzberg-ffi; published artifacts ship as a binary target
• Optional: ONNX Runtime version 1.22.x for embeddings support
• Optional: Tesseract OCR for OCR functionality

Quick Start

Basic Extraction

Extract text, metadata, and structure from any supported document format:

import Foundation
import Kreuzberg
import RustBridge

let config = try extractionConfigFromJson("{}")
let result = try extractFileSync("document.pdf", nil, config)

print(result.content().toString())
print("MIME type: \(result.mime_type().toString())")
print("Tables: \(result.tables().count)")

Common Use Cases

Extract with Custom Configuration

Most use cases benefit from configuration to control extraction behavior:

With OCR (for scanned documents):

import Foundation
import Kreuzberg
import RustBridge

let configJson = """
{
    "ocr": {
        "backend": "tesseract",
        "language": "eng"
    }
}
"""

let config = try extractionConfigFromJson(configJson)
let result = try extractFileSync("scanned.pdf", nil, config)

print(result.content().toString())

Table Extraction

See Configuration Guide for table extraction options.

Processing Multiple Files

import Foundation
import Kreuzberg
import RustBridge

// `BatchFileItem` is an opaque swift-bridge class with no public Swift
// constructor — build items from JSON via `batchFileItemFromJson`.
let items = RustVec<BatchFileItem>()
for path in ["doc1.pdf", "doc2.docx", "report.pdf"] {
    let json = "{\"path\": \"\(path)\"}"
    items.push(value: try batchFileItemFromJson(json))
}

let config = try extractionConfigFromJson("{}")
let results = try batchExtractFilesSync(items, config)

for (index, result) in results.enumerated() {
    print("File \(index): \(result.content().toString().count) chars")
}

Async Processing

For non-blocking document processing:

import Foundation
import Kreuzberg
import RustBridge

@main
struct App {
    static func main() async throws {
        let config = try extractionConfigFromJson("{}")
        // The Swift binding exposes async-compatible entrypoints; even though
        // the bridge calls are synchronous internally, callers may `await` them
        // to integrate with Swift Concurrency.
        let result = try await extractFile("document.pdf", nil, config)

        print(result.content().toString())
        print("MIME type: \(result.mime_type().toString())")
        print("Tables: \(result.tables().count)")
    }
}

Next Steps

• Installation Guide - Platform-specific setup
• API Documentation - Complete API reference
• Examples & Guides - Full code examples and usage guides
• Configuration Guide - Advanced configuration options

Features

Supported File Formats (90+)

90+ file formats across 8 major categories with intelligent format detection and comprehensive metadata extraction.

Office Documents

Category	Formats	Capabilities
Word Processing	.docx, .docm, .dotx, .dotm, .dot, .odt	Full text, tables, images, metadata, styles
Spreadsheets	.xlsx, .xlsm, .xlsb, .xls, .xla, .xlam, .xltm, .xltx, .xlt, .ods	Sheet data, formulas, cell metadata, charts
Presentations	.pptx, .pptm, .ppsx, .potx, .potm, .pot, .ppt	Slides, speaker notes, images, metadata
PDF	.pdf	Text, tables, images, metadata, OCR support
eBooks	.epub, .fb2	Chapters, metadata, embedded resources
Database	.dbf	Table data extraction, field type support
Hangul	.hwp, .hwpx	Korean document format, text extraction

Images (OCR-Enabled)

Category	Formats	Features
Raster	.png, .jpg, .jpeg, .gif, .webp, .bmp, .tiff, .tif	OCR, table detection, EXIF metadata, dimensions, color space
Advanced	.jp2, .jpx, .jpm, .mj2, .jbig2, .jb2, .pnm, .pbm, .pgm, .ppm	OCR via hayro-jpeg2000 (pure Rust decoder), JBIG2 support, table detection, format-specific metadata
Vector	.svg	DOM parsing, embedded text, graphics metadata

Web & Data

Category	Formats	Features
Markup	.html, .htm, .xhtml, .xml, .svg	DOM parsing, metadata (Open Graph, Twitter Card), link extraction
Structured Data	.json, .yaml, .yml, .toml, .csv, .tsv	Schema detection, nested structures, validation
Text & Markdown	.txt, .md, .markdown, .djot, .rst, .org, .rtf	CommonMark, GFM, Djot, reStructuredText, Org Mode

Email & Archives

Category	Formats	Features
Email	.eml, .msg	Headers, body (HTML/plain), attachments, threading
Archives	.zip, .tar, .tgz, .gz, .7z	File listing, nested archives, metadata

Academic & Scientific

Category	Formats	Features
Citations	.bib, .biblatex, .ris, .nbib, .enw, .csl	Structured parsing: RIS (structured), PubMed/MEDLINE, EndNote XML (structured), BibTeX, CSL JSON
Scientific	.tex, .latex, .typst, .jats, .ipynb, .docbook	LaTeX, Jupyter notebooks, PubMed JATS
Documentation	.opml, .pod, .mdoc, .troff	Technical documentation formats

Code Intelligence (300+ Languages)

Feature	Description
Structure Extraction	Functions, classes, methods, structs, interfaces, enums
Import/Export Analysis	Module dependencies, re-exports, wildcard imports
Symbol Extraction	Variables, constants, type aliases, properties
Docstring Parsing	Google, NumPy, Sphinx, JSDoc, RustDoc, and 10+ formats
Diagnostics	Parse errors with line/column positions
Syntax-Aware Chunking	Split code by semantic boundaries, not arbitrary byte offsets

Powered by tree-sitter-language-pack — documentation.

Complete Format Reference

Key Capabilities

• Text Extraction - Extract all text content with position and formatting information
• Metadata Extraction - Retrieve document properties, creation date, author, etc.
• Table Extraction - Parse tables with structure and cell content preservation
• Image Extraction - Extract embedded images and render page previews
• OCR Support - Integrate multiple OCR backends for scanned documents
• Async/Await - Non-blocking document processing with concurrent operations
• Plugin System - Extensible post-processing for custom text transformation
• Embeddings - Generate vector embeddings using ONNX Runtime models
• Batch Processing - Efficiently process multiple documents in parallel
• Memory Efficient - Stream large files without loading entirely into memory
• Language Detection - Detect and support multiple languages in documents
• Code Intelligence - Extract structure, imports, exports, symbols, and docstrings from 300+ programming languages via tree-sitter
• Configuration - Fine-grained control over extraction behavior

Performance Characteristics

Format	Speed	Memory	Notes
PDF (text)	10-100 MB/s	~50MB per doc	Fastest extraction
Office docs	20-200 MB/s	~100MB per doc	DOCX, XLSX, PPTX
Images (OCR)	1-5 MB/s	Variable	Depends on OCR backend
Archives	5-50 MB/s	~200MB per doc	ZIP, TAR, etc.
Web formats	50-200 MB/s	Streaming	HTML, XML, JSON

OCR Support

Kreuzberg supports multiple OCR backends for extracting text from scanned documents and images:

• Tesseract

• Paddleocr

OCR Configuration Example

import Foundation
import Kreuzberg
import RustBridge

let configJson = """
{
    "ocr": {
        "backend": "tesseract",
        "language": "eng"
    }
}
"""

let config = try extractionConfigFromJson(configJson)
let result = try extractFileSync("scanned.pdf", nil, config)

print(result.content().toString())

Async Support

This binding provides full async/await support for non-blocking document processing:

import Foundation
import Kreuzberg
import RustBridge

@main
struct App {
    static func main() async throws {
        let config = try extractionConfigFromJson("{}")
        // The Swift binding exposes async-compatible entrypoints; even though
        // the bridge calls are synchronous internally, callers may `await` them
        // to integrate with Swift Concurrency.
        let result = try await extractFile("document.pdf", nil, config)

        print(result.content().toString())
        print("MIME type: \(result.mime_type().toString())")
        print("Tables: \(result.tables().count)")
    }
}

Plugin System

Kreuzberg supports extensible post-processing plugins for custom text transformation and filtering.

For detailed plugin documentation, visit Plugin System Guide.

Embeddings Support

Generate vector embeddings for extracted text using the built-in ONNX Runtime support. Requires ONNX Runtime installation.

Embeddings Guide

Batch Processing

Process multiple documents efficiently:

import Foundation
import Kreuzberg
import RustBridge

// `BatchFileItem` is an opaque swift-bridge class with no public Swift
// constructor — build items from JSON via `batchFileItemFromJson`.
let items = RustVec<BatchFileItem>()
for path in ["doc1.pdf", "doc2.docx", "report.pdf"] {
    let json = "{\"path\": \"\(path)\"}"
    items.push(value: try batchFileItemFromJson(json))
}

let config = try extractionConfigFromJson("{}")
let results = try batchExtractFilesSync(items, config)

for (index, result) in results.enumerated() {
    print("File \(index): \(result.content().toString().count) chars")
}

Configuration

For advanced configuration options including language detection, table extraction, OCR settings, and more:

Configuration Guide

Documentation

• Official Documentation
• API Reference
• Examples & Guides

Contributing

Contributions are welcome! See Contributing Guide.

Part of Kreuzberg.dev

• Kreuzberg Cloud — managed extraction API with SDKs, dashboards, and observability.
• kreuzcrawl — web crawling and scraping with HTML→Markdown and headless-Chrome fallback.
• html-to-markdown — fast, lossless HTML→Markdown engine.
• liter-llm — universal LLM API client with native bindings for 14 languages and 143 providers.
• tree-sitter-language-pack — tree-sitter grammars and code-intelligence primitives.
• alef — the polyglot binding generator that produces this README and all per-language bindings.
• Discord — community, roadmap, announcements.

License

Elastic-2.0 License — see LICENSE for details.

Support

• Discord Community: Join our Discord
• GitHub Issues: Report bugs
• Discussions: Ask questions