Project Title

omniparse — Ingest, parse, and optimize any data format for enhanced compatibility with GenAI frameworks

Overview

OmniParse is a Python-based platform designed to ingest and parse unstructured data into structured, actionable data optimized for GenAI (LLM) applications. It supports a wide range of file types, including documents, multimedia, and web pages, and offers features like table extraction, image extraction/captioning, audio/video transcription, and web page crawling. OmniParse stands out for its local processing capabilities, support for various file formats, and ease of deployment using Docker and Skypilot.

Key Features

Completely local processing, no external APIs
Fits in a T4 GPU
Supports ~20 file types
Converts documents, multimedia, and web pages to high-quality structured markdown
Table extraction, image extraction/captioning, audio/video transcription, web page crawling
Easily deployable using Docker and Skypilot
Colab friendly
Interactive UI powered by Gradio

Use Cases

Data scientists and developers working with GenAI applications can use OmniParse to prepare clean, structured data for AI applications such as RAG and fine-tuning.
Businesses can leverage OmniParse to convert unstructured data from various sources into a structured format for analysis and decision-making.
Researchers can utilize OmniParse for data ingestion and parsing tasks in their multimedia and document-based projects.

Advantages

OmniParse is designed to handle a wide variety of data formats, making it a versatile tool for different use cases.
The platform's local processing capabilities eliminate the need for external APIs, enhancing data privacy and security.
Its compatibility with Docker and Skypilot simplifies deployment and scalability.

Limitations / Considerations

OmniParse is only compatible with Linux-based systems due to specific dependencies and system configurations.
The platform's performance may vary depending on the complexity and volume of the data being processed.

Apache Tika: A content analysis toolkit that can detect and extract metadata and structured text content from various file types. Unlike OmniParse, it does not focus on optimizing data for GenAI applications.
Parsr: A tool for turning PDFs into structured data. While Parsr is limited to PDFs, OmniParse supports a broader range of file formats.
Doccano: A text annotation tool for machine learning practitioners. While Doccano is focused on annotation, OmniParse provides a comprehensive solution for data ingestion and parsing.

Basic Information

GitHub: https://github.com/adithya-s-k/omniparse
Stars: 6,731
License: Unknown
Last Commit: 2025-11-15

📊 Project Information

Project Name: omniparse
GitHub URL: https://github.com/adithya-s-k/omniparse
Programming Language: Python
⭐ Stars: 6,731
🍴 Forks: 531
📅 Created: 2024-06-04
🔄 Last Updated: 2025-11-15

🏷️ Project Topics

Topics: [, ", i, n, g, e, s, t, i, o, n, -, a, p, i, ", ,, , ", o, c, r, ", ,, , ", o, m, n, i, p, a, r, s, e, r, ", ,, , ", p, a, r, s, e, -, s, e, r, v, e, r, ", ,, , ", p, a, r, s, e, r, -, l, i, b, r, a, r, y, ", ,, , ", v, i, s, i, o, n, -, t, r, a, n, s, f, o, r, m, e, r, ", ,, , ", w, e, b, -, c, r, a, w, l, e, r, ", ,, , ", w, h, i, s, p, e, r, -, a, p, i, ", ]