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🐰 XMLScraperGraph Module

The XMLScraperGraph module defines a class for creating and executing a graph that extracts information from XML files using a natural language model to interpret and answer prompts.

Classes​

XMLScraperGraph​

XMLScraperGraph is a scraping pipeline that extracts information from XML files 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.
  • model_token (int): The token limit for the language model.

Methods​

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

    • Initializes the XMLScraperGraph with a prompt, source (XML file or directory), configuration, and schema.
    • Args:
      • prompt (str): The prompt for the graph.
      • source (str): The source of the graph (XML file or directory).
      • 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 web 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 XMLScraperGraph class:

from xml_scraper_graph import XMLScraperGraph

# Define the prompt, source (XML file or directory), and configuration
prompt = "List me all the attractions in Chioggia."
source = "data/chioggia.xml"
config = {
    "llm": {"model": "gpt-3.5-turbo"}
}

# Create the XML scraper graph
xml_scraper = XMLScraperGraph(prompt, source, config)

# Run the XML scraper graph
result = xml_scraper.run()

print(result)