xberg

name	xberg
description	Extract text, tables, metadata, and images from 97+ document formats (PDF, Office, images, HTML, email, archives, academic) using Xberg CLI.
license	MIT
metadata	{"author":"xberg-io","version":"1.0.14","repository":"https://github.com/xberg-io/xberg"}

Xberg Document Extraction

Xberg extracts text, tables, metadata, and images from 97+ file formats. Use it for document processing, OCR, batch extraction, and embeddings.

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

Core Usage

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

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

# Detect MIME type
xberg detect unknown-file

Embeddings

Embeddings require ONNX Runtime. Use a local preset or pipe text through standard input.

xberg embed --text "hello world" --preset balanced
echo "some text" | xberg embed --preset fast

Chunking

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

Configuration

Xberg auto-discovers xberg.toml, xberg.yaml, xberg.yml, or xberg.json in the current or parent directories. Pass any configuration file explicitly with --config <path>.

xberg extract doc.pdf                          # finds xberg.toml automatically
xberg extract doc.pdf --config my.yaml         # explicit configuration file
xberg 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
force_ocr = false

[ocr]
backend = "tesseract"
language = "eng"

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

[pdf_options]
extract_images = true

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
xberg 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)

Flag	Note
--format	Wire format for CLI output: text (default for extract) or json
--content-format	Format of extracted text: plain, markdown, djot, html. --output-format is a deprecated alias.
--token-reduction	off/light/moderate/aggressive/maximum — reduce tokens before LLM consumption
--acceleration	ONNX provider: auto, cpu, coreml (macOS), cuda, tensorrt
--pdf-extract-images	Embeds image bytes in JSON result (see above)

Common Pitfalls

1. --format ≠ --content-format: one controls the serialization envelope, the other the text inside it.
2. Config auto-discovery searches xberg.toml, xberg.yaml, xberg.yml, and xberg.json.
3. PDF images land in result.images[] as byte arrays; nothing is written to disk automatically.
4. embed requires ONNX Runtime; install it for your platform before using embeddings.
5. For large documents, use --token-reduction to reduce LLM context usage.

References

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