RAG et connaissances
opendataloader-pdf
Turn PDFs into AI-ready data: extract Markdown, JSON and tables — and auto-tag for accessibility
★ 29,5 k étoilesApache-2.0
Voir le guide →xberg est un projet de RAG et connaissances open source, créé par xberg-io (xberg-io/xberg sur GitHub). Il compte 9 388 étoiles sur GitHub et 590 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.
Polyglot document intelligence in Rust — extract text, tables and structured data from 106 formats.
Pick the binding for your stack (Python, Node, CLI, Docker or MCP) and the document types you need to extract, including whether OCR is required.
Clone xberg-io/xberg from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
Install the package for your language — for Python that is a single pip install — or pull the Docker image for the server modes.
Extract one real document and inspect the structured output (text, tables and metadata), then wire it into your pipeline or MCP client.
Extract text, metadata, images, tables and structured data from 106 formats across roughly 140 file extensions and 56 MIME aliases.
Use it from fifteen language bindings, or as a CLI, REST API or MCP server for agent tooling.
Beyond documents, xberg understands code across 371 languages for mixed code-and-docs corpora.
Choose Tesseract (native or WASM), PaddleOCR via ONNX, the pure-Rust Candle backend, or vision LLMs for scanned pages.
Pull schema-shaped structured data out of documents with LLM-backed extraction when rules are not enough.
Entity recognition, PII redaction (phone, email, SSN, cards, addresses), summarisation, translation and page classification are included.
Get the source from the official GitHub repository for xberg.
git clone https://github.com/xberg-io/xberg.git
cd xbergInstall the Python binding exactly as the README's installation table shows.
pip install xbergRun the published container image for the server/CLI deployment modes.
docker run ghcr.io/xberg-io/xberg:latest # see README for the exact mode argumentsxberg-io, through the xberg-io/xberg repository, with documentation at docs.xberg.io.
Yes — the project is open source under the MIT license as recorded for this listing. You can use, modify and distribute it under the MIT terms.
Core extraction and OCR run locally with no key. Optional vision-LLM OCR and LLM extraction/enrichment use a provider you configure, with your own key.
Hosted parsers charge per page and require uploading documents. xberg is a local Rust engine you embed in your own stack across fifteen languages — you operate it yourself, and only optional LLM features need a provider.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — extract one real document and inspect the structured output (text, tables and metadata), then wire it into your pipeline or MCP client.
Turn PDFs into AI-ready data: extract Markdown, JSON and tables — and auto-tag for accessibility
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