Precisely parse various types of unstructured data
recognize the layout of documents, identifying areas such as tables, images, formulas, and text. And revert them to json or markdown format
- Intelligent table detection & extraction
- Multi-format document parsing
- Seamless integration with APIs
from undatasio.undatasio import UnDatasIO
token = 'Your API token'
task_name = 'your task name'
# 1. Initialize the UnDatasIO client
client = UnDatasIO(token=token, task_name=task_name)}
return go(f, seed, [])
}
# 2. Upload files
upload_response = client.upload(file_dir_path='./example_files')
# 3. View all uploaded files
upload_filename_response = client.show_upload()
# 4. Parse files
parse_response = client.parser(file_name_list=['example_file1.pdf', 'example_file2.pdf']
# 5. View historical parsing results
parse_filename_response = client.show_version()
Seamless Integration with Your Workflow
APIs enable different platforms and applications to collaborate seamlessly, facilitating data sharing and the integration of business processes.
- Real-time analytics
- Cross-platform integration
- Automated workflow
Choose your best plan
Select the plan that suits your needs and benefit from our analytics tools.
Pro
Unlock more features and elevate your data analysis.
- A total of 50000 credit per month
- Permanently save uploaded files
- Permanently save parsed results
- Priority technical support, quick response to your questions
- API interface access permission, easily integrate UnDatasIO into your workflow
- Support background running
Latest Insights
Stay updated with the latest trends and insights in UnDatasIO
Retrieval-Augmented Generation (RAG) Explained: Bridging the Gap Between LLMs and Real-Time Data (RAG vs LLM)
Learn how Retrieval-Augmented Generation (RAG) enhances LLMs with real-time data, improving accuracy and reducing hallucinations. Discover RAG applications, frameworks, and implementation best practices. Also learn how UndatasIO transforms unstructured data into AI-ready assets for optimal RAG performance. (RAG vs LLM)
Revolutionizing Content with RAG Applications: Mastering Retrieval Augmented Generation for Next-Gen Content
Discover how Retrieval Augmented Generation (RAG) is transforming AI content creation. Learn about RAG models, implementation strategies, and real-world applications to enhance accuracy, personalization, and efficiency. Explore how **UndatasIO** helps transform unstructured data into AI-ready assets for RAG pipelines.
Unlock the Power of RAG Applications A Comprehensive Guide
Learn how Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) for improved accuracy and reduced hallucinations. Discover RAG applications, frameworks, and best practices. Optimize your RAG pipeline with **UndatasIO**, the leading solution for transforming unstructured data into AI-ready assets.
Have any questions?
Frequently Asked Questions
What is UnDatasIO?
UnDatasIO is a powerful online data parsing tool designed to help users easily extract and process data from various format files.
What file formats does UnDatasIO support?
UnDatasIO supports multiple common file formats, such as PDF, MP4, MP3, M4A,DOCX,PPTX,PNG,JPG,HTML, and so on. We continue to add support for more formats.
What is the security of UnDatasIO? Is my data secure?
We attach great importance to data security. All uploaded files and parsing results are encrypted and stored, and are protected by strict security measures.
How credit works in UnDatasIO?
UndataslO operates on a credit-based system for its parsing services. Here's how credits are used: Document Parsing: 1 credit = Parsing of 1 page (PDF, DOCX, JPG, PNG, HTML, MD) Audio/Video Transcription: 1 credit = 10 seconds of transcription Video Segmentation: 1 credit = 10 seconds of segmentation Your UndataslO credits are shared across all services, allowing you to combine document parsing, transcription, and video segmentation as needed.
How can I view my remaining credit?
After logging into your UnDatasIO account, you can view the remaining number of credit for the current plan on the account information page.