mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
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102 lines
3.4 KiB
Python
102 lines
3.4 KiB
Python
"""
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DocumentScraperMultiGraph Module
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"""
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from copy import deepcopy
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from typing import List, 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 .document_scraper_graph import DocumentScraperGraph
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from ..nodes import (
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GraphIteratorNode,
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MergeAnswersNode
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)
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from ..utils.copy import safe_deepcopy
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class DocumentScraperMultiGraph(AbstractGraph):
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"""
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DocumentScraperMultiGraph is a scraping pipeline that scrapes a list of URLs and
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generates answers to a given prompt. It only requires a user prompt and a list of URLs.
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Attributes:
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prompt (str): The user prompt to search the internet.
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llm_model (dict): The configuration for the language model.
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embedder_model (dict): The configuration for the embedder model.
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headless (bool): A flag to run the browser in headless mode.
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verbose (bool): A flag to display the execution information.
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model_token (int): The token limit for the language model.
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Args:
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prompt (str): The user prompt to search the internet.
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source (List[str]): The list of URLs to scrape.
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config (dict): Configuration parameters for the graph.
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schema (Optional[BaseModel]): The schema for the graph output.
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Example:
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>>> search_graph = DocumentScraperMultiGraph(
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... "What is Chioggia famous for?",
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... ["http://example.com/page1", "http://example.com/page2"],
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... {"llm_model": {"model": "openai/gpt-3.5-turbo"}}
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... )
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>>> result = search_graph.run()
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"""
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def __init__(self, prompt: str, source: List[str],
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config: dict, schema: Optional[BaseModel] = None):
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self.copy_config = safe_deepcopy(config)
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self.copy_schema = deepcopy(schema)
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super().__init__(prompt, config, source, schema)
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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 and searching.
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Returns:
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BaseGraph: A graph instance representing the web scraping and searching workflow.
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"""
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graph_iterator_node = GraphIteratorNode(
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input="user_prompt & jsons",
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output=["results"],
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node_config={
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"graph_instance": DocumentScraperGraph,
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"scraper_config": self.copy_config,
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},
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schema=self.copy_schema
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)
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merge_answers_node = MergeAnswersNode(
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input="user_prompt & results",
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output=["answer"],
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node_config={
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"llm_model": self.llm_model,
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"schema": self.copy_schema
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}
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)
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return BaseGraph(
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nodes=[
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graph_iterator_node,
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merge_answers_node,
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],
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edges=[
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(graph_iterator_node, merge_answers_node),
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],
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entry_point=graph_iterator_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 web scraping and searching process.
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Returns:
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str: The answer to the prompt.
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"""
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inputs = {"user_prompt": self.prompt, "xmls": 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("answer", "No answer found.")
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