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https://github.com/VinciGit00/Scrapegraph-ai.git
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120 lines
3.7 KiB
Python
120 lines
3.7 KiB
Python
"""
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CSVScraperMultiGraph Module
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"""
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from copy import copy, 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 .csv_scraper_graph import CSVScraperGraph
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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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class CSVScraperMultiGraph(AbstractGraph):
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"""
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CSVScraperMultiGraph is a scraping pipeline that scrapes a list of URLs and generates answers to a given prompt.
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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 source of the graph.
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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 = MultipleSearchGraph(
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... "What is Chioggia famous for?",
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... {"llm": {"model": "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], config: dict, schema: Optional[BaseModel] = None):
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self.max_results = config.get("max_results", 3)
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if all(isinstance(value, str) for value in config.values()):
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self.copy_config = copy(config)
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else:
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self.copy_config = deepcopy(config)
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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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# ************************************************
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# Create a SmartScraperGraph instance
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# ************************************************
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smart_scraper_instance = CSVScraperGraph(
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prompt="",
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source="",
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config=self.copy_config,
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)
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# ************************************************
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# Define the graph nodes
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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": smart_scraper_instance,
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}
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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.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, "jsons": 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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