Integration: Kreuzberg

Locally convert 91+ document formats into Haystack Documents using Kreuzberg's Rust-core engine

Authors

deepset

Table of Contents

• Overview
• Installation
• Usage
• Additional Features
• License

Overview

Kreuzberg is a document intelligence framework with a Rust core that extracts text, tables, and metadata from 91+ file formats — entirely locally with no external API calls.

This integration provides KreuzbergConverter, a Haystack component that converts files into Haystack Document objects with rich metadata. It supports parallel batch extraction using Rust’s rayon thread pool for high throughput.

Supported format categories:

• Documents: PDF, DOCX, DOC, PPTX, PPT, XLSX, XLS, ODT, ODS, ODP, RTF, Pages, Keynote, Numbers, and more
• Images (via OCR): PNG, JPEG, TIFF, GIF, BMP, WebP, JPEG 2000, SVG
• Text/Markup: Markdown, HTML, XML, LaTeX, Typst, JSON, YAML, reStructuredText, Jupyter notebooks
• Email: EML, MSG (with attachment extraction)
• Archives: ZIP, TAR, GZIP, 7Z (extracts and processes contents recursively)
• eBooks & Academic: EPUB, BibTeX, DocBook, JATS

Installation

pip install kreuzberg-haystack

Usage

Components

This integration introduces one component:

• KreuzbergConverter: Converts files and directories into Haystack Document objects. Accepts file paths, directory paths, and ByteStream objects as input.

Basic Usage

from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter

converter = KreuzbergConverter()
result = converter.run(sources=["report.pdf", "notes.docx"])
documents = result["documents"]

Markdown Output with OCR

Use ExtractionConfig to customize output format, OCR backend, and other extraction settings:

from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter
from kreuzberg import ExtractionConfig, OcrConfig

converter = KreuzbergConverter(
    config=ExtractionConfig(
        output_format="markdown",
        ocr=OcrConfig(backend="tesseract", language="eng"),
    ),
)
result = converter.run(sources=["scanned_document.pdf"])
documents = result["documents"]

In a Pipeline

from haystack import Pipeline
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter

document_store = InMemoryDocumentStore()

pipeline = Pipeline()
pipeline.add_component("converter", KreuzbergConverter())
pipeline.add_component("splitter", DocumentSplitter(split_by="sentence", split_length=5))
pipeline.add_component("writer", DocumentWriter(document_store=document_store))

pipeline.connect("converter", "splitter")
pipeline.connect("splitter", "writer")

pipeline.run({"converter": {"sources": ["report.pdf", "presentation.pptx"]}})

Additional Features

• Per-page extraction: Create one Document per page using PageConfig(extract_pages=True)
• Chunking: Split documents by token count with configurable overlap via ChunkingConfig
• Token reduction: Reduce token count with modes from "light" to "maximum" via TokenReductionConfig
• Rich metadata: Quality scores, detected languages, extracted keywords, table data, and PDF annotations
• Batch processing: Parallel extraction enabled by default; set batch=False for sequential mode
• Config from file: Load extraction settings from a TOML, YAML, or JSON file via config_path

For the full configuration reference and format support matrix, see the Kreuzberg documentation.

License

kreuzberg-haystack is distributed under the terms of the Apache-2.0 license.