mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
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132 lines
5.0 KiB
Python
132 lines
5.0 KiB
Python
"""
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RobotsNode Module
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"""
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from typing import List, Optional
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from urllib.parse import urlparse
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from langchain.output_parsers import CommaSeparatedListOutputParser
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from langchain.prompts import PromptTemplate
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from langchain_community.document_loaders import AsyncChromiumLoader
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from ..helpers import robots_dictionary
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from ..prompts import TEMPLATE_ROBOT
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from .base_node import BaseNode
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class RobotsNode(BaseNode):
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"""
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A node responsible for checking if a website is scrapeable or not based on the robots.txt file.
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It uses a language model to determine if the website allows scraping of the provided path.
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This node acts as a starting point in many scraping workflows, preparing the state
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with the necessary HTML content for further processing by subsequent nodes in the graph.
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Attributes:
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llm_model: An instance of the language model client used for checking scrapeability.
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force_scraping (bool): A flag indicating whether scraping should be enforced even
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if disallowed by robots.txt.
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verbose (bool): A flag indicating whether to show print statements during execution.
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Args:
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input (str): Boolean expression defining the input keys needed from the state.
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output (List[str]): List of output keys to be updated in the state.
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node_config (dict): Additional configuration for the node.
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force_scraping (bool): A flag indicating whether scraping should be enforced even
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if disallowed by robots.txt. Defaults to True.
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node_name (str): The unique identifier name for the node, defaulting to "Robots".
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"""
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def __init__(
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self,
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input: str,
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output: List[str],
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node_config: Optional[dict] = None,
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node_name: str = "RobotNode",
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):
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super().__init__(node_name, "node", input, output, 1)
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self.llm_model = node_config["llm_model"]
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self.force_scraping = (
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False if node_config is None else node_config.get("force_scraping", False)
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)
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self.verbose = (
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True if node_config is None else node_config.get("verbose", False)
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)
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def execute(self, state: dict) -> dict:
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"""
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Checks if a website is scrapeable based on the robots.txt file and updates the state
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with the scrapeability status. The method constructs a prompt for the language model,
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submits it, and parses the output to determine if scraping is allowed.
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Args:
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state (dict): The current state of the graph. The input keys will be used to fetch the
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Returns:
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dict: The updated state with the output key containing the scrapeability status.
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Raises:
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KeyError: If the input keys are not found in the state, indicating that the
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necessary information for checking scrapeability is missing.
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KeyError: If the large language model is not found in the robots_dictionary.
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ValueError: If the website is not scrapeable based on the robots.txt file and
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scraping is not enforced.
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"""
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self.logger.info(f"--- Executing {self.node_name} Node ---")
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input_keys = self.get_input_keys(state)
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input_data = [state[key] for key in input_keys]
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source = input_data[0]
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output_parser = CommaSeparatedListOutputParser()
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if not source.startswith("http"):
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raise ValueError("Operation not allowed")
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else:
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parsed_url = urlparse(source)
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base_url = f"{parsed_url.scheme}://{parsed_url.netloc}"
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loader = AsyncChromiumLoader(f"{base_url}/robots.txt")
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document = loader.load()
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if "ollama" in self.llm_model.model:
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self.llm_model.model = self.llm_model.model.split("/")[-1]
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model = self.llm_model.model.split("/")[-1]
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else:
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model = self.llm_model.model
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try:
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agent = robots_dictionary[model]
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except KeyError:
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agent = model
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prompt = PromptTemplate(
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template=TEMPLATE_ROBOT,
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input_variables=["path"],
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partial_variables={"context": document, "agent": agent},
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)
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chain = prompt | self.llm_model | output_parser
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is_scrapable = chain.invoke({"path": source})[0]
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if "no" in is_scrapable:
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self.logger.warning(
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"\033[31m(Scraping this website is not allowed)\033[0m"
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)
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if not self.force_scraping:
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raise ValueError("The website you selected is not scrapable")
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else:
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self.logger.warning(
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"""\033[33m(WARNING: Scraping this website is
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not allowed but you decided to force it)\033[0m"""
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)
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else:
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self.logger.warning("\033[32m(Scraping this website is allowed)\033[0m")
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state.update({self.output[0]: is_scrapable})
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return state
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