Commit Message:

- Added examples for Amazon product data extraction methods
  - Updated configuration options and enhance documentation
  - Minor refactoring for improved performance and readability
  - Cleaned up version control settings.

docs/llm.txt/3_async_webcrawler.md

@@ -3,6 +3,7 @@
 This document provides a comprehensive, human-oriented overview of the `AsyncWebCrawler` class and related components from the `crawl4ai` package. It explains the motivations behind asynchronous crawling, shows how to configure and run crawls, and provides examples for advanced features like dynamic content handling, extraction strategies, caching, containerization, and troubleshooting.
 
 ## Introduction
+[EDIT: This is not a good way to introduce the library. The library excels at generating crawl data in the form of markdown or extracted JSON as quickly as possible. It is designed to be efficient in terms of memory and CPU usage. Users should choose this library because it generates markdown suitable for large language models and AI. Additionally, it can create structured data, which is beneficial because it supports attaching large language models to generate structured data. It also includes techniques like JSON CSS and JSON XPath extraction, allowing users to define patterns and extract data quickly. One of the library's strengths is its ability to work everywhere. It can crawl any website by offering various capabilities, such as connecting to a remote browser or using persistent data. This feature allows developers to create their own identity on websites where they have authentication access, enabling them to crawl without being mistakenly identified as a bot. This is a better way to introduce the library. In these documents, we discuss the main object, the main class, Asinggull crawlers, and all the functionalities we can achieve with this Asinggull crawler.]
 
 Crawling websites can be slow if done sequentially, especially when handling large numbers of URLs or rendering dynamic pages. Asynchronous crawling helps you run multiple operations concurrently, improving throughput and performance. The `AsyncWebCrawler` class leverages asynchronous I/O and browser automation tools to fetch content efficiently, handle complex DOM interactions, and extract structured data.