LLMsWebScraper 1.0.1

A Python library to extract structured data from web pages using LLMs.

LLMs Web Scraper

LLM Web Scraper is a Python library that uses a generative AI model to extract structured data from web pages.

Features

• Fetch HTML content from web pages.
• Extract structured data using instructions and an LLM.
• Save extracted data to a JSON file.

How to Use

1. Install the pip Library: Use the pip install command.

   pip install LLMsWebScraper
   

2. Test the Installed Library After the library is installed, you can import and use it in your Python projects just like any other library.

   Create a Python File to Test It: Create a new Python file or open a Python REPL to use your library.

   For example:

   from LLMsWebScraper import LLMsWebScraper  
   import os
   from dotenv import load_dotenv
   import logging
   
   # Load environment variables
   load_dotenv()
   
   # Initialize the scraper
   scraper = LLMsWebScraper(api_key=os.getenv("Gemini_API_KEY"))
   
   # Define instructions
   instructions = """
   Extract the following information:
   1. Titles of all blog posts on the page.
   2. Author names for each blog post.
   3. Publication dates of each blog post.
   
   Please provide the extracted information in a structured JSON format.
   Expecting property name enclosed in double quotes and values in string format.
   Example:
   {
   "blog_posts": [
           {
               "title": "Blog Post 1",
               "author": "Author 1",
               "publication_date": "2022-01-01"
           },
           {
               "title": "Blog Post 2",
               "author": "Author 2",
               "publication_date": "2022-01-02"
           }
       ]
   }
   """
   
   # URL of the webpage to scrape
   url = "https://chirpy.cotes.page/"
   
   # Extract data
   blog_data = scraper.toJSON(url, instructions)
   
   # Print the data
   print(blog_data)
   
   # If need to save like as json file
   if blog_data:
       scraper.toFile(blog_data, "output/data.json")
   else:
       logging.warning("No blog data to save.")