robots_node.py

"""
RobotsNode Module
"""

from typing import List, Optional
from urllib.parse import urlparse

from langchain.output_parsers import CommaSeparatedListOutputParser
from langchain.prompts import PromptTemplate
from langchain_community.document_loaders import AsyncChromiumLoader

from ..helpers import robots_dictionary
from ..prompts import TEMPLATE_ROBOT
from .base_node import BaseNode


class RobotsNode(BaseNode):
    """
    A node responsible for checking if a website is scrapeable or not based on the robots.txt file.
    It uses a language model to determine if the website allows scraping of the provided path.

    This node acts as a starting point in many scraping workflows, preparing the state
    with the necessary HTML content for further processing by subsequent nodes in the graph.

    Attributes:
        llm_model: An instance of the language model client used for checking scrapeability.
        force_scraping (bool): A flag indicating whether scraping should be enforced even
                               if disallowed by robots.txt.
        verbose (bool): A flag indicating whether to show print statements during execution.

    Args:
        input (str): Boolean expression defining the input keys needed from the state.
        output (List[str]): List of output keys to be updated in the state.
        node_config (dict): Additional configuration for the node.
        force_scraping (bool): A flag indicating whether scraping should be enforced even
                                 if disallowed by robots.txt. Defaults to True.
        node_name (str): The unique identifier name for the node, defaulting to "Robots".
    """

    def __init__(
        self,
        input: str,
        output: List[str],
        node_config: Optional[dict] = None,
        node_name: str = "RobotNode",
    ):
        super().__init__(node_name, "node", input, output, 1)

        self.llm_model = node_config["llm_model"]

        self.force_scraping = (
            False if node_config is None else node_config.get("force_scraping", False)
        )
        self.verbose = (
            True if node_config is None else node_config.get("verbose", False)
        )

    def execute(self, state: dict) -> dict:
        """
        Checks if a website is scrapeable based on the robots.txt file and updates the state
        with the scrapeability status. The method constructs a prompt for the language model,
        submits it, and parses the output to determine if scraping is allowed.

        Args:
            state (dict): The current state of the graph. The input keys will be used to fetch the

        Returns:
            dict: The updated state with the output key containing the scrapeability status.

        Raises:
            KeyError: If the input keys are not found in the state, indicating that the
                        necessary information for checking scrapeability is missing.
            KeyError: If the large language model is not found in the robots_dictionary.
            ValueError: If the website is not scrapeable based on the robots.txt file and
                        scraping is not enforced.
        """

        self.logger.info(f"--- Executing {self.node_name} Node ---")

        input_keys = self.get_input_keys(state)

        input_data = [state[key] for key in input_keys]

        source = input_data[0]
        output_parser = CommaSeparatedListOutputParser()

        if not source.startswith("http"):
            raise ValueError("Operation not allowed")

        else:
            parsed_url = urlparse(source)
            base_url = f"{parsed_url.scheme}://{parsed_url.netloc}"
            loader = AsyncChromiumLoader(f"{base_url}/robots.txt")
            document = loader.load()
            if "ollama" in self.llm_model.model:
                self.llm_model.model = self.llm_model.model.split("/")[-1]
                model = self.llm_model.model.split("/")[-1]
            else:
                model = self.llm_model.model
            try:
                agent = robots_dictionary[model]

            except KeyError:
                agent = model

            prompt = PromptTemplate(
                template=TEMPLATE_ROBOT,
                input_variables=["path"],
                partial_variables={"context": document, "agent": agent},
            )

            chain = prompt | self.llm_model | output_parser
            is_scrapable = chain.invoke({"path": source})[0]

            if "no" in is_scrapable:
                self.logger.warning(
                    "\033[31m(Scraping this website is not allowed)\033[0m"
                )

                if not self.force_scraping:
                    raise ValueError("The website you selected is not scrapable")
                else:
                    self.logger.warning(
                        """\033[33m(WARNING: Scraping this website is
                        not allowed but you decided to force it)\033[0m"""
                    )
            else:
                self.logger.warning("\033[32m(Scraping this website is allowed)\033[0m")

        state.update({self.output[0]: is_scrapable})
        return state