Skip to main content

👄 SmartScraperGraph Module

The SmartScraperGraph module defines a class for creating and executing a graph that automates the process of extracting information from web pages using a natural language model to interpret and answer prompts.

Classes​

SmartScraperGraph​

SmartScraperGraph is a scraping pipeline that automates the process of extracting information from web pages using a natural language model to interpret and answer prompts.

Attributes​

  • prompt (str): The prompt for the graph.
  • source (str): The source of the graph.
  • config (dict): Configuration parameters for the graph.
  • schema (str): The schema for the graph output.
  • llm_model: An instance of a language model client, configured for generating answers.
  • embedder_model: An instance of an embedding model client, configured for generating embeddings.
  • verbose (bool): A flag indicating whether to show print statements during execution.
  • headless (bool): A flag indicating whether to run the graph in headless mode.

Methods​

  • __init__(self, prompt: str, source: str, config: dict, schema: Optional[str] = None)

    • Initializes the SmartScraperGraph with a prompt, source, configuration, and schema.
    • Args:
      • prompt (str): The prompt for the graph.
      • source (str): The source of the graph.
      • config (dict): Configuration parameters for the graph.
      • schema (Optional[str]): The schema for the graph output.

  • _create_graph(self) -> BaseGraph

    • Creates the graph of nodes representing the workflow for web scraping.
    • Returns: An instance of BaseGraph.

  • run(self) -> str

    • Executes the scraping process and returns the answer to the prompt.
    • Returns: The answer to the prompt.

Example Usage​

Here is an example of how to use the SmartScraperGraph class:

from smart_scraper_graph import SmartScraperGraph

# Define the prompt, source, and configuration
prompt = "List me all the attractions in Chioggia."
source = "https://en.wikipedia.org/wiki/Chioggia"
config = {
    "llm": {"model": "gpt-3.5-turbo"}
}

# Create the smart scraper graph
smart_scraper = SmartScraperGraph(prompt, source, config)

# Run the smart scraper graph
result = smart_scraper.run()

print(result)