Local Ai
Xberg v1: a local, CPU-only document extraction engine for feeding an Ollama RAG (101 formats, OCR)
I maintain xberg, an open-source (MIT) document extraction engine, and v1 is out. Sharing here because a common piece of a local Ollama RAG setup is 'get clean text out of my PDFs/Office files/images,
I maintain xberg, an open-source (MIT) document extraction engine, and v1 is out. Sharing here because a common piece of a local Ollama RAG setup is "get clean text out of my PDFs/Office files/images," and xberg does exactly that, fully local and CPU-only (no GPU). It extracts 101 file formats (PDF, DOCX, PPTX, XLSX, images, and more) to clean Markdown/text, with OCR built in (Tesseract + PaddleOCR), layout-aware reading order, tables, and optional chunking for your vector store. Rust core, so it's fast on modest hardware. Nothing leaves the box. Run it as a local API (xberg serve) and point ingestion at it, use one of the 15 language bindings directly, or the CLI. It also drops into Open WebUI as a Docling-compatible backend if you run that in front of Ollama. pip install xberg import asyncio from xberg import ExtractInput, extract async def main(): output = await extract(ExtractInput(kind="uri", uri="report.pdf")) print(output.results[0].content) # markdown, ready to chunk + embed asyncio.run(main()) Benchmarks (native PDF #1 on quality; image OCR currently #2, improving): https://xberg.io/benchmarks Repo: https://github.com/xberg-io/xberg submitted by /u/Goldziher [link] [comments]
Source: r/ollama | 2026-08-03