kreuzberg

name: kreuzberg description: >- Extract text, tables, metadata, and images from 91+ document formats (PDF, Office, images, HTML, email, archives, academic) using Kreuzberg CLI. license: Elastic-2.0 metadata: author: kreuzberg-dev version: "1.0" repository: https://github.com/kreuzberg-dev/kreuzberg

Kreuzberg Document Extraction

Kreuzberg extracts text, tables, metadata, and images from 91+ file formats. Use it for document processing, OCR, batch extraction, structured LLM extraction, and embeddings.

Run kreuzberg --help for all commands, kreuzberg <command> --help for full flag reference.

Core Usage

# Single file → stdout (text) or JSON
kreuzberg extract document.pdf
kreuzberg extract document.pdf --content-format markdown --format json

# Batch
kreuzberg batch *.pdf --content-format markdown

# Detect MIME type
kreuzberg detect unknown-file

LLM Structured Extraction

Extracts typed JSON from a document using a schema + LLM model. API key falls back to OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.

kreuzberg extract-structured invoice.pdf \
  --schema schema.json \
  --model openai/gpt-4o \
  --strict

Embeddings

Local ONNX (no API key) or provider-hosted. Not available in Homebrew — use Docker.

kreuzberg embed --text "hello world" --preset balanced
echo "some text" | kreuzberg embed --provider llm --model openai/text-embedding-3-small

Chunking

kreuzberg chunk --text "..." --chunk-size 500 --chunk-overlap 50
kreuzberg chunk --chunker-type markdown --text "# Heading\n\nParagraph..."
cat file.txt | kreuzberg chunk --chunker-type semantic --topic-threshold 0.8

Configuration

Only kreuzberg.toml is auto-discovered. YAML/JSON require --config <path>.

kreuzberg extract doc.pdf                          # finds kreuzberg.toml automatically
kreuzberg extract doc.pdf --config my.yaml         # explicit for non-TOML
kreuzberg extract doc.pdf --config-json '{"ocr":{"language":"deu"}}'

Config file skeleton (field names are snake_case — max_chars not max_characters):

use_cache = true
enable_quality_processing = true
output_format = "markdown" # content format for file output

[ocr]
backend = "tesseract" # tesseract | paddle-ocr | easyocr
language = "eng" # ISO 639-3 for tesseract; short codes for paddle/easyocr

[chunking]
max_chars = 1000 # NOT max_characters
max_overlap = 200 # NOT overlap

[pdf_options]
extract_images = true

[server] # for `kreuzberg serve`
host = "127.0.0.1"
port = 8000

Extracting Images from PDFs

Images are not written to disk — they come back as byte arrays in JSON output. Two-step process required:

# Step 1: capture JSON
kreuzberg extract doc.pdf --pdf-extract-images true --format json > out.json

# Step 2: save images
python3 -c "
import json, pathlib
d = json.load(open('out.json'))
pathlib.Path('images').mkdir(exist_ok=True)
for img in d.get('images', []):
    pathlib.Path(f'images/image_{img[\"image_index\"]}.{img[\"format\"]}').write_bytes(bytes(img['data']))
"

Key Flags (Non-Obvious)

Common Pitfalls

1. --format ≠ --content-format: one controls the serialization envelope, the other the text inside it.
2. Config auto-discovery only finds kreuzberg.toml — not YAML or JSON.
3. PDF images land in result.images[] as byte arrays; nothing is written to disk automatically.
4. embed is excluded from the Homebrew build; use Docker or cargo install.
5. For large docs with extract-structured, use --token-reduction to stay within LLM context limits.

References

• Supported formats: grep references/supported-formats.md instead of reading it whole e.g. grep '.mdoc' references/supported-formats.md