mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
synced 2026-06-06 21:13:22 +08:00
105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
"""
|
|
DocumentScraperMultiGraph Module
|
|
"""
|
|
|
|
from copy import deepcopy
|
|
from typing import List, Optional, Type
|
|
|
|
from pydantic import BaseModel
|
|
|
|
from ..nodes import GraphIteratorNode, MergeAnswersNode
|
|
from ..utils.copy import safe_deepcopy
|
|
from .abstract_graph import AbstractGraph
|
|
from .base_graph import BaseGraph
|
|
from .document_scraper_graph import DocumentScraperGraph
|
|
|
|
|
|
class DocumentScraperMultiGraph(AbstractGraph):
|
|
"""
|
|
DocumentScraperMultiGraph is a scraping pipeline that scrapes a list of URLs and
|
|
generates answers to a given prompt. It only requires a user prompt and a list of URLs.
|
|
|
|
Attributes:
|
|
prompt (str): The user prompt to search the internet.
|
|
llm_model (dict): The configuration for the language model.
|
|
embedder_model (dict): The configuration for the embedder model.
|
|
headless (bool): A flag to run the browser in headless mode.
|
|
verbose (bool): A flag to display the execution information.
|
|
model_token (int): The token limit for the language model.
|
|
|
|
Args:
|
|
prompt (str): The user prompt to search the internet.
|
|
source (List[str]): The list of URLs to scrape.
|
|
config (dict): Configuration parameters for the graph.
|
|
schema (Optional[BaseModel]): The schema for the graph output.
|
|
|
|
Example:
|
|
>>> search_graph = DocumentScraperMultiGraph(
|
|
... "What is Chioggia famous for?",
|
|
... ["http://example.com/page1", "http://example.com/page2"],
|
|
... {"llm_model": {"model": "openai/gpt-3.5-turbo"}}
|
|
... )
|
|
>>> result = search_graph.run()
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
prompt: str,
|
|
source: List[str],
|
|
config: dict,
|
|
schema: Optional[Type[BaseModel]] = None,
|
|
):
|
|
self.copy_config = safe_deepcopy(config)
|
|
self.copy_schema = deepcopy(schema)
|
|
|
|
super().__init__(prompt, config, source, schema)
|
|
|
|
def _create_graph(self) -> BaseGraph:
|
|
"""
|
|
Creates the graph of nodes representing the workflow for web scraping and searching.
|
|
|
|
Returns:
|
|
BaseGraph: A graph instance representing the web scraping and searching workflow.
|
|
"""
|
|
|
|
graph_iterator_node = GraphIteratorNode(
|
|
input="user_prompt & jsons",
|
|
output=["results"],
|
|
node_config={
|
|
"graph_instance": DocumentScraperGraph,
|
|
"scraper_config": self.copy_config,
|
|
},
|
|
schema=self.copy_schema,
|
|
)
|
|
|
|
merge_answers_node = MergeAnswersNode(
|
|
input="user_prompt & results",
|
|
output=["answer"],
|
|
node_config={"llm_model": self.llm_model, "schema": self.copy_schema},
|
|
)
|
|
|
|
return BaseGraph(
|
|
nodes=[
|
|
graph_iterator_node,
|
|
merge_answers_node,
|
|
],
|
|
edges=[
|
|
(graph_iterator_node, merge_answers_node),
|
|
],
|
|
entry_point=graph_iterator_node,
|
|
graph_name=self.__class__.__name__,
|
|
)
|
|
|
|
def run(self) -> str:
|
|
"""
|
|
Executes the web scraping and searching process.
|
|
|
|
Returns:
|
|
str: The answer to the prompt.
|
|
"""
|
|
|
|
inputs = {"user_prompt": self.prompt, "xmls": self.source}
|
|
self.final_state, self.execution_info = self.graph.execute(inputs)
|
|
|
|
return self.final_state.get("answer", "No answer found.")
|