OmniParse: Turn Documents and Media into AI-Ready Data

Overview

OmniParse is a self-hosted ingestion and parsing service for converting unstructured files and web pages into structured, GenAI-friendly output, including Markdown. It brings document extraction, OCR, image understanding, audio and video transcription, and web crawling behind a local API, reducing the need to assemble separate ingestion tools for each format.

It is aimed at developers and teams preparing data for retrieval-augmented generation (RAG), fine-tuning, or other LLM applications. It makes sense when you need multiple input types processed locally; it may be a poor fit if you need Windows or macOS support, a lightweight CPU-only setup, or consistently precise document layout extraction.

Key Features

• Parses PDF, Word, and PowerPoint documents, plus common image, audio, and video formats.
• Converts content into structured Markdown for downstream AI workflows.
• Extracts tables and images, and supports image OCR, captions, and object detection.
• Transcribes audio and video using Whisper.
• Crawls and parses web pages, including dynamic pages.
• Offers HTTP API endpoints and an interactive Gradio UI.
• Supports local deployment with Docker, including GPU-enabled runs.

Use Cases

• RAG developers can ingest mixed document collections and prepare their contents for retrieval pipelines.
• Data and ML teams can turn scanned pages, images, and tables into text and structured content for model workflows.
• Teams building media search or summarization can transcribe audio and video before indexing or analysis.
• Developers collecting web content can crawl pages and add their output to an AI knowledge base.

Getting Started

The server requires Linux. Clone the repository and install its dependencies:

git clone https://github.com/adithya-s-k/omniparse
cd omniparse
conda create -n omniparse-venv python=3.10
conda activate omniparse-venv
poetry install

See the README for Docker instructions, model setup, server options, and API examples.

Alternatives

• xberg: Xberg focuses on document extraction across file types, while OmniParse also handles audio, video, and web pages.
• docling-api: docling-api is a self-hosted conversion service for documents and images, with CPU or GPU deployment; OmniParse covers more media types.
• marker: Marker converts documents to structured formats such as Markdown and JSON, rather than serving OmniParse’s broader media-parsing API.
• RAG-Anything: RAG-Anything combines multimodal document parsing with retrieval and querying, while OmniParse focuses on parsing into structured output.

Considerations

• The README states that the server only works on Linux-based systems.
• A GPU with at least 8–10 GB of VRAM is needed for the deep-learning models.
• Document parsing is not exact: tables, equations, whitespace, and line joining can be imperfect. The project notes that parsing works best on digital PDFs and may struggle with languages such as Chinese.
• The README identifies Marker and Surya model-weight licensing restrictions for some commercial uses. Review the project and model licenses before deployment.
• The repository is not archived, but its open-issue count is 75; check the issue tracker for current limitations and support status.