Web scraping involves using bots to extract content and data from websites. Unlike screen scraping, which captures pixels displayed on a screen, web scraping captures underlying HTML code along with data stored in corresponding databases. This approach is among the most efficient methods for data extraction from websites.
Parsera is a new tool that has been developed to overcome limitations of traditional web scraping methods. It’s a lightweight Python library that leverages the power of large language models (LLMs) to make web scraping more straightforward. Users can specify the data they want to extract using simple language descriptions, and the LLM interprets the web page and extracts the required information.
Parsera’s primary advantage lies in its efficient use of tokens, minimizing processing speed and reducing costs associated with using LLMs. The library also supports asynchronous methods, making it an excellent choice for real-time data extraction.
As the demand for efficient web scraping tools grows, solutions like Parsera that simplify the process and improve performance will become essential for developers and businesses.
Source: https://www.marktechpost.com/2024/08/16/parsera-lightweight-python-library-for-scraping-with-llms/