

Scrapegraph Ai
Overview :
ScrapeGraphAI is a Python web crawling library that uses Large Language Models (LLM) and direct graph logic to create scraping pipelines for websites, documents, and XML files. Users simply specify the information they wish to extract, and the library will automatically complete this task. The main advantage of this library is the simplification of the web data scraping process, improving the efficiency and accuracy of data extraction. It is suitable for data exploration and research purposes, but should not be used abusively.
Target Users :
["Data Scientists: Rapidly extract required data from web pages for analysis.","Researchers: Collected network information on specific topics to support research work.","Developers: Integrated into applications to achieve automated data scraping functionalities.","Educators: Used as a teaching tool to demonstrate how to use AI for web data extraction."]
Use Cases
Extract all article titles and descriptions from a technical blog.
Collect market reports and analysis data for a specific industry.
Automatically scrape the latest news titles from news websites.
Features
Extract website information using the SmartScraper class by providing prompts
Supports Ollama model for information extraction
Supports Docker container deployment
Supports OpenAI model for information extraction
Supports Gemini model for information extraction
Provides direct graph implementation using common web scraping pipeline nodes
Outputs in a dictionary format containing extracted information
How to Use
Step 1: Install the Scrapegraph-ai library.
Step 2: Set up the required models and configurations, such as Ollama or OpenAI models.
Step 3: Create a SmartScraperGraph instance, providing prompts and source web pages for the information to be extracted.
Step 4: Call the run() method to perform information extraction.
Step 5: Print or process the extracted results, typically in dictionary format.
Step 6: Conduct further analysis or processing of the extracted information as needed.
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