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https://github.com/VinciGit00/Scrapegraph-ai.git
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325 lines
13 KiB
Python
325 lines
13 KiB
Python
""""
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FetchNode Module
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"""
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import json
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from typing import List, Optional
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from langchain_openai import ChatOpenAI, AzureChatOpenAI
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import pandas as pd
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import requests
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from langchain_community.document_loaders import PyPDFLoader
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from langchain_core.documents import Document
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from ..utils.cleanup_html import cleanup_html
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from ..docloaders import ChromiumLoader
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from ..utils.convert_to_md import convert_to_md
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from ..utils.logging import get_logger
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from .base_node import BaseNode
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class FetchNode(BaseNode):
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"""
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A node responsible for fetching the HTML content of a specified URL and updating
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the graph's state with this content. It uses ChromiumLoader to fetch
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the content from a web page asynchronously (with proxy protection).
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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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headless (bool): A flag indicating whether the browser should run in headless mode.
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verbose (bool): A flag indicating whether to print verbose output 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 (Optional[dict]): Additional configuration for the node.
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node_name (str): The unique identifier name for the node, defaulting to "Fetch".
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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 = "Fetch",
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):
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super().__init__(node_name, "node", input, output, 1, node_config)
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self.headless = (
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True if node_config is None else node_config.get("headless", True)
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)
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self.verbose = (
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False if node_config is None else node_config.get("verbose", False)
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)
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self.use_soup = (
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False if node_config is None else node_config.get("use_soup", False)
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)
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self.loader_kwargs = (
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{} if node_config is None else node_config.get("loader_kwargs", {})
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)
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self.llm_model = (
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{} if node_config is None else node_config.get("llm_model", {})
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)
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self.force = (
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False if node_config is None else node_config.get("force", False)
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)
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self.script_creator = (
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False if node_config is None else node_config.get("script_creator", False)
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)
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self.openai_md_enabled = (
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False if node_config is None else node_config.get("openai_md_enabled", False)
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)
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self.cut = (
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False if node_config is None else node_config.get("cut", True)
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)
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self.browser_base = (
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None if node_config is None else node_config.get("browser_base", None)
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)
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self.scrape_do = (
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None if node_config is None else node_config.get("scrape_do", None)
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)
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def execute(self, state):
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"""
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Executes the node's logic to fetch HTML content from a specified URL and
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update the state with this content.
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Args:
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state (dict): The current state of the graph. The input keys will be used
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to fetch the correct data types from the state.
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Returns:
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dict: The updated state with a new output key containing the fetched HTML content.
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Raises:
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KeyError: If the input key is not found in the state, indicating that the
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necessary information to perform the operation is missing.
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"""
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self.logger.info(f"--- Executing {self.node_name} Node ---")
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# Interpret input keys based on the provided input expression
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input_keys = self.get_input_keys(state)
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# Fetching data from the state based on the input keys
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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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input_type = input_keys[0]
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handlers = {
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"json_dir": self.handle_directory,
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"xml_dir": self.handle_directory,
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"csv_dir": self.handle_directory,
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"pdf_dir": self.handle_directory,
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"md_dir": self.handle_directory,
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"pdf": self.handle_file,
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"csv": self.handle_file,
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"json": self.handle_file,
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"xml": self.handle_file,
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"md": self.handle_file,
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}
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if input_type in handlers:
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return handlers[input_type](state, input_type, source)
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elif self.input == "pdf_dir":
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return state
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elif not source.startswith("http") and not source.startswith("www"):
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return self.handle_local_source(state, source)
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else:
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return self.handle_web_source(state, source)
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def handle_directory(self, state, input_type, source):
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"""
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Handles the directory by compressing the source document and updating the state.
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Parameters:
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state (dict): The current state of the graph.
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input_type (str): The type of input being processed.
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source (str): The source document to be compressed.
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Returns:
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dict: The updated state with the compressed document.
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"""
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compressed_document = [
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source
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]
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state.update({self.output[0]: compressed_document})
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return state
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def handle_file(self, state, input_type, source):
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"""
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Loads the content of a file based on its input type.
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Parameters:
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state (dict): The current state of the graph.
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input_type (str): The type of the input file (e.g., "pdf", "csv", "json", "xml", "md").
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source (str): The path to the source file.
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Returns:
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dict: The updated state with the compressed document.
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The function supports the following input types:
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- "pdf": Uses PyPDFLoader to load the content of a PDF file.
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- "csv": Reads the content of a CSV file using pandas and converts it to a string.
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- "json": Loads the content of a JSON file.
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- "xml": Reads the content of an XML file as a string.
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- "md": Reads the content of a Markdown file as a string.
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"""
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compressed_document = self.load_file_content(source, input_type)
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return self.update_state(state, compressed_document)
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def load_file_content(self, source, input_type):
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"""
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Loads the content of a file based on its input type.
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Parameters:
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source (str): The path to the source file.
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input_type (str): The type of the input file (e.g., "pdf", "csv", "json", "xml", "md").
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Returns:
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list: A list containing a Document object with the loaded content and metadata.
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"""
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if input_type == "pdf":
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loader = PyPDFLoader(source)
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return loader.load()
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elif input_type == "csv":
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return [Document(page_content=str(pd.read_csv(source)), metadata={"source": "csv"})]
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elif input_type == "json":
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with open(source, encoding="utf-8") as f:
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return [Document(page_content=str(json.load(f)), metadata={"source": "json"})]
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elif input_type == "xml" or input_type == "md":
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with open(source, "r", encoding="utf-8") as f:
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data = f.read()
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return [Document(page_content=data, metadata={"source": input_type})]
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def handle_local_source(self, state, source):
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"""
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Handles the local source by fetching HTML content, optionally converting it to Markdown,
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and updating the state.
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Parameters:
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state (dict): The current state of the graph.
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source (str): The HTML content from the local source.
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Returns:
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dict: The updated state with the processed content.
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Raises:
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ValueError: If the source is empty or contains only whitespace.
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"""
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self.logger.info(f"--- (Fetching HTML from: {source}) ---")
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if not source.strip():
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raise ValueError("No HTML body content found in the local source.")
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parsed_content = source
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if (isinstance(self.llm_model, ChatOpenAI) or \
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isinstance(self.llm_model, AzureChatOpenAI)) \
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and not self.script_creator or self.force and not self.script_creator:
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parsed_content = convert_to_md(source)
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else:
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parsed_content = source
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compressed_document = [
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Document(page_content=parsed_content, metadata={"source": "local_dir"})
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]
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return self.update_state(state, compressed_document)
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def handle_web_source(self, state, source):
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"""
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Handles the web source by fetching HTML content from a URL,
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optionally converting it to Markdown, and updating the state.
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Parameters:
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state (dict): The current state of the graph.
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source (str): The URL of the web source to fetch HTML content from.
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Returns:
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dict: The updated state with the processed content.
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Raises:
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ValueError: If the fetched HTML content is empty or contains only whitespace.
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"""
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self.logger.info(f"--- (Fetching HTML from: {source}) ---")
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if self.use_soup:
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response = requests.get(source)
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if response.status_code == 200:
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if not response.text.strip():
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raise ValueError("No HTML body content found in the response.")
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if not self.cut:
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parsed_content = cleanup_html(response, source)
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if isinstance(self.llm_model, (ChatOpenAI, AzureChatOpenAI)) \
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and not self.script_creator or (self.force and not self.script_creator):
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parsed_content = convert_to_md(source, parsed_content)
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compressed_document = [Document(page_content=parsed_content)]
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else:
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self.logger.warning(
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f"Failed to retrieve contents from the webpage at url: {source}"
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)
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else:
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loader_kwargs = {}
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if self.node_config:
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loader_kwargs = self.node_config.get("loader_kwargs", {})
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if self.browser_base:
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try:
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from ..docloaders.browser_base import browser_base_fetch
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except ImportError:
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raise ImportError("""The browserbase module is not installed.
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Please install it using `pip install browserbase`.""")
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data = browser_base_fetch(self.browser_base.get("api_key"),
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self.browser_base.get("project_id"), [source])
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document = [Document(page_content=content,
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metadata={"source": source}) for content in data]
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elif self.scrape_do:
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from ..docloaders.scrape_do import scrape_do_fetch
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if (self.scrape_do.get("use_proxy") is None) or \
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self.scrape_do.get("geoCode") is None or \
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self.scrape_do.get("super_proxy") is None:
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data = scrape_do_fetch(self.scrape_do.get("api_key"),
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source)
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else:
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data = scrape_do_fetch(self.scrape_do.get("api_key"),
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source, self.scrape_do.get("use_proxy"),
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self.scrape_do.get("geoCode"),
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self.scrape_do.get("super_proxy"))
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document = [Document(page_content=data,
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metadata={"source": source})]
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else:
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loader = ChromiumLoader([source], headless=self.headless, **loader_kwargs)
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document = loader.load()
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if not document or not document[0].page_content.strip():
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raise ValueError("""No HTML body content found in
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the document fetched by ChromiumLoader.""")
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parsed_content = document[0].page_content
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if (isinstance(self.llm_model, ChatOpenAI) \
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or isinstance(self.llm_model, AzureChatOpenAI)) \
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and not self.script_creator or self.force \
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and not self.script_creator and not self.openai_md_enabled:
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parsed_content = convert_to_md(document[0].page_content, parsed_content)
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compressed_document = [
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Document(page_content=parsed_content, metadata={"source": "html file"})
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]
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state["original_html"] = document
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state.update({self.output[0]: compressed_document,})
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return state
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