ai-scraper-py

AI Scraper is a cutting-edge tool designed for data extraction from various web sources. It automates complex scraping tasks, enabling users to create efficient workflows for scalable AI scraping operations.

[{"step":"Install and configure ai-scraper-py","action":"Run `pip install ai-scraper-py` and set up environment variables for proxies (if needed) and headers to mimic a real browser. Configure the tool to handle JavaScript-rendered content by enabling a headless browser (e.g., Playwright).","tip":"Use `--headless-mode` for faster scraping or `--browser-mode` if the target site requires JavaScript execution."},{"step":"Define the scraping target and data fields","action":"Specify the target URL (e.g., `https://example.com/products`) and the data fields to extract (e.g., `[product_name, price, availability]`). Use the tool’s `--selectors` flag to map HTML elements to fields via CSS selectors or XPath.","tip":"Inspect the target page using browser dev tools to identify stable selectors (e.g., `#product-title`, `.price`). Avoid selectors with dynamic IDs like `data-testid-12345`."},{"step":"Handle pagination and anti-scraping measures","action":"Configure pagination by setting `--max-pages 5` and use `--delay 2` to avoid rate-limiting. Enable `--proxy-rotation` if the site blocks your IP. For CAPTCHAs, integrate a solver service (e.g., 2Captcha) via `--captcha-solver`.","tip":"Test the scraper on a single page first to validate selectors and error handling before scaling up."},{"step":"Output and process the extracted data","action":"Save the output to a file using `--output-format json --output-file products.json`. Validate the data with `--validate` to ensure all required fields are present. For large datasets, stream the output to a database (e.g., PostgreSQL) using `--db-connection-string`.","tip":"Use `--log-level debug` to troubleshoot issues like failed requests or missing data."},{"step":"Automate and scale the scraping workflow","action":"Schedule the scraper using cron (Linux/macOS) or Task Scheduler (Windows) to run at set intervals. For enterprise use, deploy the scraper on a cloud platform (e.g., AWS Lambda) with `--cloud-mode` to handle high volumes.","tip":"Monitor scraping performance with `--metrics` to track success rates, errors, and execution time."}]