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103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
"""
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SmartScraperGraph Module
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"""
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from typing import Optional
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from pydantic import BaseModel
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from .base_graph import BaseGraph
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from .abstract_graph import AbstractGraph
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from ..nodes import (
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FetchNode,
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ParseNode,
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)
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class SmartScraperLiteGraph(AbstractGraph):
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"""
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SmartScraperLiteGraph is a scraping pipeline that automates the process of
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extracting information from web pages.
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Attributes:
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prompt (str): The prompt for the graph.
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source (str): The source of the graph.
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config (dict): Configuration parameters for the graph.
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schema (BaseModel): The schema for the graph output.
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verbose (bool): A flag indicating whether to show print statements during execution.
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headless (bool): A flag indicating whether to run the graph in headless mode.
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Args:
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prompt (str): The prompt for the graph.
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source (str): The source of the graph.
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config (dict): Configuration parameters for the graph.
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schema (BaseModel): The schema for the graph output.
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Example:
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>>> scraper = SmartScraperLiteGraph(
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... "https://en.wikipedia.org/wiki/Chioggia",
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... {"llm": {"model": "openai/gpt-3.5-turbo"}}
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... )
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>>> result = smart_scraper.run()
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)
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"""
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def __init__(
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self,
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source: str,
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config: dict,
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prompt: str = "",
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schema: Optional[BaseModel] = None,
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):
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super().__init__(prompt, config, source, schema)
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self.input_key = "url" if source.startswith("http") else "local_dir"
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def _create_graph(self) -> BaseGraph:
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"""
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Creates the graph of nodes representing the workflow for web scraping.
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Returns:
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BaseGraph: A graph instance representing the web scraping workflow.
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"""
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fetch_node = FetchNode(
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input="url| local_dir",
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output=["doc"],
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node_config={
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"llm_model": self.llm_model,
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"force": self.config.get("force", False),
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"cut": self.config.get("cut", True),
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"loader_kwargs": self.config.get("loader_kwargs", {}),
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"browser_base": self.config.get("browser_base"),
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"scrape_do": self.config.get("scrape_do"),
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"storage_state": self.config.get("storage_state"),
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},
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)
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parse_node = ParseNode(
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input="doc",
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output=["parsed_doc"],
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node_config={"llm_model": self.llm_model, "chunk_size": self.model_token},
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)
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return BaseGraph(
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nodes=[
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fetch_node,
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parse_node,
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],
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edges=[
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(fetch_node, parse_node),
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],
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entry_point=fetch_node,
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graph_name=self.__class__.__name__,
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)
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def run(self) -> str:
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"""
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Executes the scraping process and returns the scraping content.
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Returns:
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str: The scraping content.
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"""
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inputs = {"user_prompt": self.prompt, self.input_key: self.source}
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self.final_state, self.execution_info = self.graph.execute(inputs)
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return self.final_state.get("parsed_doc", "No document found.")
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