Information extraction

Rocket Scraper uses advanced AI to automatically extract structured data from any webpage - no CSS selectors, XPath, or maintenance required. Our AI understands web content contextually, just like a human would, making your scraping resilient to website changes.

Key Benefits

• No Selectors Required: Unlike traditional scrapers, you don't need to specify CSS selectors or XPath. Simply describe what data you want in plain English.
• Change Resistant: Your scraping won't break when websites update their HTML structure or styling.
• Context Aware: Our AI understands content semantically, identifying correct data even when formatting or placement changes.
• Natural Language Processing: Extract and analyze text content with capabilities like summarization, sentiment analysis, and language translation.

How It Works

1. Define Your Schema: Specify the structure of data you want to extract using simple field names and data types.
2. Optional Task Description: Add natural language instructions for complex requirements.
3. AI Processing: Our system analyzes the webpage content, understanding context and relationships.
4. Structured Output: Receive clean, structured data matching your schema.

Example: Job Listings

Here's how easy it is to extract job listing details without any selectors:

Python

from rocketscraper import RocketClient

try:
    client = RocketClient('YOUR_API_KEY')
    
    schema = {
        "jobTitle": "string",
        "company": "string",
        "salary": {
            "min": "number",
            "max": "number",
            "currency": "string"
        },
        "location": "string",
        "jobDescription": "string",
        "companyRating": "number"
    }

    result = client.scrape(
        'https://example.com/jobs/software-engineer-123',
        schema
    )
    print(result)

except Exception as e:
    print(f"Error: {e}")

Node.js

import { createRocketClient } from 'rocketscraper';

try {
    const client = createRocketClient({
        apiKey: 'YOUR_API_KEY'
    });

    const schema = {
        jobTitle: "string",
        company: "string",
        salary: {
            min: "number",
            max: "number",
            currency: "string"
        },
        location: "string",
        jobDescription: "string",
        companyRating: "number"
    };

    const result = await client.scrape({
        url: 'https://example.com/jobs/software-engineer-123',
        schema
    });
    console.log(result);

} catch (error) {
    console.error('Error:', error.message);
}

Curl

curl -X POST \
  https://api.rocketscraper.com/scrape \
  -H 'x-api-key: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "url": "https://example.com/jobs/software-engineer-123",
    "schema": {
        "jobTitle": "string",
        "company": "string",
        "salary": {
            "min": "number",
            "max": "number",
            "currency": "string"
        },
        "location": "string",
        "jobDescription": "string",
        "companyRating": "number"
    }
  }'

Example Output

{
    "jobTitle": "Senior Software Engineer",
    "company": "TechCorp Solutions",
    "salary": {
        "min": 120000,
        "max": 180000,
        "currency": "USD"
    },
    "location": "San Francisco, CA (Hybrid)",
    "jobDescription": "We're seeking an experienced software engineer to join our growing team. You'll be responsible for designing and implementing scalable solutions for our cloud-based platform...",
    "companyRating": 4.2
}

Best Practices

1. Use Descriptive Field Names: Help the AI understand your requirements with clear field names (e.g., currentPriceUSD instead of just price).

2. Leverage Task Descriptions: For complex extractions, provide additional context in the task description.

3. Start Simple: Begin with basic schemas and add complexity as needed. The AI handles most cases without requiring complex instructions.

4. Test Different URLs: Your same schema will work across different product pages, categories, and even different websites in the same industry.

Common Use Cases

• E-commerce: Product details, pricing, inventory, and specifications
• News & Content: Article text, summaries, topics, and sentiment
• Real Estate: Property details, prices, amenities, and location data
• Job Listings: Job descriptions, requirements, and company information
• Reviews: User ratings, review text, and sentiment analysis

By using AI-powered information extraction, you can focus on using the data rather than maintaining brittle scraping code. Your scraping will continue to work even as websites evolve, saving you time and resources.

Try It Now

Ready to see it in action? Test out Rocket Scraper's AI-powered extraction in our interactive playground.