mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
synced 2026-06-25 21:11:11 +08:00
117 lines
3.7 KiB
Python
117 lines
3.7 KiB
Python
"""
|
|
DeepScraperGraph Module
|
|
"""
|
|
|
|
from .base_graph import BaseGraph
|
|
from ..nodes import (
|
|
FetchNode,
|
|
SearchLinkNode,
|
|
ParseNode,
|
|
RAGNode,
|
|
GenerateAnswerNode
|
|
)
|
|
from .abstract_graph import AbstractGraph
|
|
|
|
|
|
class DeepScraperGraph(AbstractGraph):
|
|
"""
|
|
[WIP]
|
|
|
|
DeepScraper is a scraping pipeline that automates the process of
|
|
extracting information from web pages
|
|
using a natural language model to interpret and answer prompts.
|
|
|
|
Unlike SmartScraper, DeepScraper can navigate to the links within the input webpage,
|
|
to fuflfil the task within the prompt.
|
|
|
|
|
|
Attributes:
|
|
prompt (str): The prompt for the graph.
|
|
source (str): The source of the graph.
|
|
config (dict): Configuration parameters for the graph.
|
|
llm_model: An instance of a language model client, configured for generating answers.
|
|
embedder_model: An instance of an embedding model client,
|
|
configured for generating embeddings.
|
|
verbose (bool): A flag indicating whether to show print statements during execution.
|
|
headless (bool): A flag indicating whether to run the graph in headless mode.
|
|
Args:
|
|
prompt (str): The prompt for the graph.
|
|
source (str): The source of the graph.
|
|
config (dict): Configuration parameters for the graph.
|
|
Example:
|
|
>>> deep_scraper = DeepScraperGraph(
|
|
... "List me all the job titles and detailed job description.",
|
|
... "https://www.google.com/about/careers/applications/jobs/results/?location=Bangalore%20India",
|
|
... {"llm": {"model": "gpt-3.5-turbo"}}
|
|
... )
|
|
>>> result = deep_scraper.run()
|
|
)
|
|
"""
|
|
|
|
def __init__(self, prompt: str, source: str, config: dict):
|
|
super().__init__(prompt, config, source)
|
|
|
|
self.input_key = "url" if source.startswith("http") else "local_dir"
|
|
|
|
def _create_graph(self) -> BaseGraph:
|
|
"""
|
|
Creates the graph of nodes representing the workflow for web scraping.
|
|
Returns:
|
|
BaseGraph: A graph instance representing the web scraping workflow.
|
|
"""
|
|
fetch_node = FetchNode(
|
|
input="url | local_dir",
|
|
output=["doc", "link_urls", "img_urls"]
|
|
)
|
|
parse_node = ParseNode(
|
|
input="doc",
|
|
output=["parsed_doc"],
|
|
node_config={
|
|
"chunk_size": self.model_token
|
|
}
|
|
)
|
|
rag_node = RAGNode(
|
|
input="user_prompt & (parsed_doc | doc)",
|
|
output=["relevant_chunks"],
|
|
node_config={
|
|
"llm_model": self.llm_model,
|
|
"embedder_model": self.embedder_model
|
|
}
|
|
)
|
|
search_node = SearchLinkNode(
|
|
input="user_prompt & relevant_chunks",
|
|
output=["relevant_links"],
|
|
node_config={
|
|
"llm_model": self.llm_model,
|
|
"embedder_model": self.embedder_model
|
|
}
|
|
)
|
|
|
|
return BaseGraph(
|
|
nodes=[
|
|
fetch_node,
|
|
parse_node,
|
|
rag_node,
|
|
search_node
|
|
],
|
|
edges=[
|
|
(fetch_node, parse_node),
|
|
(parse_node, rag_node),
|
|
(rag_node, search_node)
|
|
|
|
],
|
|
entry_point=fetch_node
|
|
)
|
|
|
|
def run(self) -> str:
|
|
"""
|
|
Executes the scraping process and returns the answer to the prompt.
|
|
Returns:
|
|
str: The answer to the prompt.
|
|
"""
|
|
|
|
inputs = {"user_prompt": self.prompt, self.input_key: self.source}
|
|
self.final_state, self.execution_info = self.graph.execute(inputs)
|
|
|
|
return self.final_state.get("answer", "No answer found.")
|