Automatic Data Extraction offers AI-driven web data extraction through a simple API, allowing for structured data retrieval from various content types. With Relevance AI, you can elevate this process by leveraging AI Agents to automate and optimize your data-driven decisions.
Automatic Data Extraction provides powerful web data extraction capabilities, while Relevance AI amplifies this by enabling AI Agents to analyze and act on the extracted data intelligently.
Intelligent Document Orchestration
The agent gains the ability to autonomously process, route, and organize complex document workflows with precision.
Data Pattern Recognition Mastery
Enhanced capability to identify and extract meaningful patterns across diverse document types and formats.
Real-time Processing Intelligence
Dynamic ability to extract and analyze document data instantly for immediate response and action.
Relevance AI seamlessly integrates with Automatic Data Extraction to enhance your data workflows with intelligent insights.
What you’ll need
You don't need to be a developer to set up this integration. Follow this simple guide to get started:
- A Relevance AI account
- An Airtable account with access to the base and table you'd like to use
- Authorization (you'll connect securely using OAuth—no sensitive info stored manually)
Security & Reliability
The Automatic Data Extraction integration leverages AI-powered web data extraction capabilities through a straightforward API interface, allowing developers to seamlessly extract structured data from various web content types such as articles, products, and job postings. This integration ensures instant access to structured web data, enhanced extraction accuracy, and supports multiple content types—all through a simple REST API.
To get started, you will need an Automatic Data Extraction account with API access, OAuth credentials with the necessary permissions, and valid API authentication tokens. Ensure your environment supports HTTPS, REST API calls, and JSON parsing, with a minimum processing capacity of 4KB for metadata.
Authentication is configured using OAuth, and the base URL for API calls is set to https://autoextract.scrapinghub.com. API headers must include your authorization token and content type.
For basic data extraction, you can send a POST request with the required parameters, such as the URL of the page you want to extract data from and the page type. Customized extraction requests can also be made by specifying additional parameters tailored to your needs.
The expected response format includes the extracted data, metadata, and a timestamp, ensuring you receive structured information in a consistent manner.
Supported page types include articles, product listings, job postings, and more, allowing for versatile data extraction across different domains.
In case of issues, common errors such as authentication failures, URL processing errors, and content length issues can be resolved by verifying credentials, ensuring proper URL formatting, and adhering to content length limits. Implementing best practices like caching results, rate limiting, and monitoring API usage will enhance your integration experience.
For further assistance, refer to the API documentation, submit support tickets, and check your account dashboard for rate limits and quotas. This guide provides a foundational understanding of the Automatic Data Extraction API, with advanced features available in the full documentation.
No training on your data
Your data remains private and is never utilized for model training purposes.
Security first
We never store anything we don’t need to. The inputs or outputs of your tools are never stored.
To get the most out of the 0CodeKit + Relevance AI integration without writing code:
- Start with a clear setup: Ensure your 0CodeKit account is properly configured with the necessary OAuth credentials and permissions.
- Utilize example code: Leverage the provided code snippets for PDF compression and barcode reading to jumpstart your integration.
- Validate inputs: Always check your input parameters for correctness before making API calls to avoid unnecessary errors.
- Test with sample data: Run your automations using test PDFs and images to ensure everything works smoothly before going live.
- Monitor API usage: Keep an eye on your API calls to avoid hitting rate limits, and implement caching where appropriate.
