How can I handle pagination in LLM-based scraping for sites using JavaScript without URL changes?

Below are some strategies you might consider when dealing with pagination where the URL does not change between pages:

1. Headless Browser Automation with Heuristics

Even if you cannot hard-code a “click next” selector for every site, you can use browser automation tools (like Playwright or Puppeteer) to render JavaScript and capture dynamic content. Develop a set of heuristics such as:

• Identifying Pagination Controls: Use generic approaches (e.g., scanning for buttons with “next”, “load more”, or arrow icons) to trigger subsequent page loads.
• Waiting for DOM Changes: After simulating a click or scroll, wait for changes in the page content (using visual cues or network events) before scraping the new data.

By building a flexible abstraction over these tools, you can try a “best-guess” approach across different sites.

2. Intercepting Network Requests

Many modern websites load subsequent pages by making AJAX calls rather than a full page refresh. You can:

• Monitor AJAX/XHR Calls: Use browser devtools or automation code to capture network requests triggered by pagination.
• Mimic API Requests: Once you detect the API endpoints and the payload required (including headers, cookies, and tokens), you can replicate these requests outside of a browser context, bypassing UI interactions.

This approach can be more efficient than interacting with UI elements directly, though it still requires some site-specific discovery or heuristics.

3. Hybrid Approaches with Dynamic Analysis

Since you are using a language model (LLM) to help scrape useful information, consider an iterative approach:

• Initial Analysis: Use an LLM to analyze the JavaScript code or the rendered HTML to try to determine how pagination is handled.
• Adaptation: Based on the analysis, programmatically decide whether to rely on UI automation for a “click next” event or to mimic AJAX calls.

This could involve a framework where the scraping logic is partially learned from patterns recognized by the LLM and then refined based on observed differences.

4. Event Simulation Beyond Simple Button Clicks

If pagination relies on session state or dynamic JavaScript triggers, simulate more complete user interactions:

• Scroll Events: For infinite scroll pages, simulate scrolling to trigger lazy loading.
• Navigation Events: Simulate any auxiliary user behavior (e.g., mouse movements) if needed to force the site to load the next batch of data.

Key Considerations

• Generic vs. Site-Specific Logic: Even with these strategies, many websites will require some tailored logic. Creating a modular architecture that allows you to plug in custom “handlers” for particular types of pagination can be very beneficial.
• Rate Limiting and Detection: Ensure you handle session cookies, tokens, and potential anti-scraping mechanisms gracefully when simulating user interactions or mimicking AJAX requests.
• Scalability: A hybrid system that first inspects the site (either through heuristics or using the LLM) and then decides on the best strategy (UI simulation vs. network calls) can help in automating scraping across diverse websites.

Example: Using Playwright with Heuristics

Below is a simplified code snippet demonstrating how you might try clicking on a “next” button if one exists:

PYTHONCopied
from playwright.sync_api import sync_playwright

def scrape_page(page):
    # Your scraping logic here
    content = page.content()
    print("Scraped content length:", len(content))
    return content

def load_next_page(page):
    try:
        # Try to find a pagination element by button text or aria-label
        next_btn = page.query_selector("button:has-text('Next')")
        if next_btn is None:
            next_btn = page.query_selector("button[aria-label='next']")
        if next_btn:
            next_btn.click()
            # Wait for content to update by waiting for network or DOM changes
            page.wait_for_timeout(1000)  # adjust based on page load speed
            return True
    except Exception as e:
        print("Error clicking next:", e)
    return False

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()
    page.goto("https://example.com")
    
    # Initial scrape
    scrape_page(page)
    
    # Loop to handle pagination
    while load_next_page(page):
        scrape_page(page)
    
    browser.close()

This example is a starting point. You may need to extend it with smarter detection logic (e.g., checking for infinite scroll, or dynamically identifying AJAX endpoints).

By combining headless browser automation, network request interception, and dynamic analysis (potentially aided by LLMs), you can build a more robust, scalable solution for scraping sites with varying pagination implementations.