kreuzberg 4.9.1

High-performance document intelligence library for Python. Extract text, metadata, and structured data from PDFs, Office documents, images, and 88+ formats. Powered by Rust core for 10-50x speed improvements.

Python

Extract text, tables, images, and metadata from 91+ file formats and 248 programming languages including PDF, Office documents, and images. Native Python bindings with async/await support, multiple OCR backends (Tesseract, EasyOCR, PaddleOCR), and extensible plugin system.

Installation

Package Installation

Install via pip:

pip install kreuzberg

For async support and additional features:

pip install kreuzberg[async]

System Requirements

• Python 3.10+ required
• 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 asyncio
from kreuzberg import extract_file, ExtractionConfig

async def main() -> None:
    config = ExtractionConfig(
        use_cache=True,
        enable_quality_processing=True
    )
    result = await extract_file("document.pdf", config=config)
    print(result.content)

asyncio.run(main())

Common Use Cases

Extract with Custom Configuration

Most use cases benefit from configuration to control extraction behavior:

With OCR (for scanned documents):

import asyncio
from kreuzberg import extract_file

async def main() -> None:
    result = await extract_file("document.pdf")
    print(result.content)

asyncio.run(main())

Table Extraction

import asyncio
from kreuzberg import extract_file

async def main() -> None:
    result = await extract_file("document.pdf")

    content: str = result.content
    tables: int = len(result.tables)
    format_type: str | None = result.metadata.format_type

    print(f"Content length: {len(content)} characters")
    print(f"Tables found: {tables}")
    print(f"Format: {format_type}")

asyncio.run(main())

Processing Multiple Files

import asyncio
from kreuzberg import extract_file, ExtractionConfig, OcrConfig, TesseractConfig

async def main() -> None:
    config = ExtractionConfig(
        force_ocr=True,
        ocr=OcrConfig(
            backend="tesseract",
            language="eng",
            tesseract_config=TesseractConfig(psm=3)
        )
    )
    result = await extract_file("scanned.pdf", config=config)
    print(result.content)
    print(f"Detected Languages: {result.detected_languages}")

asyncio.run(main())

Async Processing

For non-blocking document processing:

import asyncio
from pathlib import Path
from kreuzberg import extract_file

async def main() -> None:
    file_path: Path = Path("document.pdf")

    result = await extract_file(file_path)

    print(f"Content: {result.content}")
    print(f"MIME Type: {result.metadata.format_type}")
    print(f"Tables: {len(result.tables)}")

asyncio.run(main())

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 (91+)

91+ 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 (248 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 248 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

• Easyocr

• Paddleocr

OCR Configuration Example

import asyncio
from kreuzberg import extract_file

async def main() -> None:
    result = await extract_file("document.pdf")
    print(result.content)

asyncio.run(main())

Async Support

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

import asyncio
from pathlib import Path
from kreuzberg import extract_file

async def main() -> None:
    file_path: Path = Path("document.pdf")

    result = await extract_file(file_path)

    print(f"Content: {result.content}")
    print(f"MIME Type: {result.metadata.format_type}")
    print(f"Tables: {len(result.tables)}")

asyncio.run(main())

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 asyncio
from kreuzberg import extract_file, ExtractionConfig, OcrConfig, TesseractConfig

async def main() -> None:
    config = ExtractionConfig(
        force_ocr=True,
        ocr=OcrConfig(
            backend="tesseract",
            language="eng",
            tesseract_config=TesseractConfig(psm=3)
        )
    )
    result = await extract_file("scanned.pdf", config=config)
    print(result.content)
    print(f"Detected Languages: {result.detected_languages}")

asyncio.run(main())

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.

License

MIT License - see LICENSE file for details.

Support

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