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Web scraping is the process of using bots to extract content and data from websites. Unlike screen scraping, which simply captures the pixels displayed on a screen, web scraping captures the underlying HTML code along with the data stored in the corresponding database. This approach is among the most efficient and effective methods for data extraction from websites. It is an important tool for businesses and individuals who need to rapidly and efficiently collect information from the web. Web scraping involves creating custom scripts that interact directly with the Document Object Model (DOM) structure of web pages. This method can sometimes be complex and requires a solid understanding of HTML, CSS, and JavaScript. Even minor changes to a website’s structure can disrupt these scrapers, leading to frequent and time-consuming maintenance.
Various tools have been developed for web scraping. Some of the most commonly used libraries by developers are BeautifulSoup, Scrapy, and Selenium. These tools offer powerful functionalities for navigating and extracting data from websites, but they still demand a detailed understanding of page structures; hence, this approach can be resource-heavy. It also lacks built-in support for large language models (LLMs) that could improve adaptability to web layout changes.
To overcome these limitations, a new tool called Parsera has been developed. It is a lightweight Python library that leverages the power of LLMs to make web scraping more straightforward. It does not require manual interaction with the DOM; it allows users to specify the data they want to extract using simple language descriptions. The LLM then interprets the web page and extracts the required information. Parsera has been designed to focus on being lightweight and minimizing token usage, which helps increase processing speed and reduces the cost associated with using LLMs.
The primary advantage of parsera lies in its efficient use of tokens. By minimizing the number of tokens processed, scraping operations can be carried out more quickly than the other methods, which rely heavily on DOM parsing. Parsera’s ability to adapt to different web layouts without requiring manual updates to the scraping logic reduces ongoing maintenance efforts. The library also supports asynchronous methods, making it an excellent choice for real-time data extraction in various scenarios.
Overall, Parsera is a fresh approach to web scraping that utilizes LLMs to extract data from websites. As the demand for efficient web scraping tools grows, solutions like Parsera, simplifying the process and improving performance, will likely become essential for developers and businesses.
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“}]] [[{“value”:”Web scraping is the process of using bots to extract content and data from websites. Unlike screen scraping, which simply captures the pixels displayed on a screen, web scraping captures the underlying HTML code along with the data stored in the corresponding database. This approach is among the most efficient and effective methods for data
The post Parsera: Lightweight Python Library for Scraping with LLMs appeared first on MarkTechPost.”}]] Read More AI Shorts, AI Tool, Artificial Intelligence, Editors Pick, Staff, Tech News, Technology