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⚾️ csv scraper graph

The CSVScraperGraph module provides a class for creating and executing a graph that automates the process of extracting information from web pages using a natural language model.

Classes​

CSVScraperGraph​

CSVScraperGraph is a comprehensive web scraping tool 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 (Optional[str]): The schema for the graph output.
  • input_key (str): The key for the input source (either csv or csv_dir).
  • 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.
  • loader_kwargs: Additional keyword arguments for the loader.
  • graph: The graph of nodes representing the workflow for web scraping.
  • final_state: The final state of the graph after execution.
  • execution_info: Information about the execution of the graph.

Methods​

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

    • Initializes the CSVScraperGraph with a prompt, source, and configuration.
    • 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)

    • 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 CSVScraperGraph class:

from csv_scraper_graph import CSVScraperGraph

# Define the prompt, source, and configuration
prompt = "Extract information from this CSV."
source = "data.csv"
config = {
    "llm": {"model": "gpt-3.5-turbo"},
    "verbose": True,
    "headless": False,
}

# Create the scraper graph
scraper_graph = CSVScraperGraph(prompt, source, config)

# Run the scraper graph
answer = scraper_graph.run()

print(answer)