wehub-resource-sync
136 lines
4.5 KiB
Python
136 lines
4.5 KiB
Python
"""
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This module implements the Omni Scraper Graph for the ScrapeGraphAI application.
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"""
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from typing import Optional, Type
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from pydantic import BaseModel
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from ..models import OpenAIImageToText
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from ..nodes import FetchNode, GenerateAnswerOmniNode, ImageToTextNode, ParseNode
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from .abstract_graph import AbstractGraph
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from .base_graph import BaseGraph
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class OmniScraperGraph(AbstractGraph):
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"""
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OmniScraper is a scraping pipeline that automates the process of
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extracting information from web pages
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using a natural language model to interpret and answer prompts.
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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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llm_model: An instance of a language model client, configured for generating answers.
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embedder_model: An instance of an embedding model client,
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configured for generating embeddings.
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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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max_images (int): The maximum number of images to process.
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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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>>> omni_scraper = OmniScraperGraph(
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... "List me all the attractions in Chioggia and describe their pictures.",
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... "https://en.wikipedia.org/wiki/Chioggia",
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... {"llm": {"model": "openai/gpt-4o"}}
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... )
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>>> result = omni_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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prompt: str,
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source: str,
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config: dict,
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schema: Optional[Type[BaseModel]] = None,
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):
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self.max_images = 5 if config is None else config.get("max_images", 5)
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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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"loader_kwargs": self.config.get("loader_kwargs", {}),
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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 & (url | local_dir)",
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output=["parsed_doc", "link_urls", "img_urls"],
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node_config={
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"chunk_size": self.model_token,
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"parse_urls": True,
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"llm_model": self.llm_model,
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},
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)
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image_to_text_node = ImageToTextNode(
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input="img_urls",
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output=["img_desc"],
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node_config={
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"llm_model": OpenAIImageToText(self.config["llm"]),
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"max_images": self.max_images,
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},
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)
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generate_answer_omni_node = GenerateAnswerOmniNode(
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input="user_prompt & (relevant_chunks | parsed_doc | doc) & img_desc",
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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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"additional_info": self.config.get("additional_info"),
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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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fetch_node,
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parse_node,
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image_to_text_node,
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generate_answer_omni_node,
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],
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edges=[
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(fetch_node, parse_node),
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(parse_node, image_to_text_node),
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(image_to_text_node, generate_answer_omni_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 answer to the prompt.
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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, 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("answer", "No answer found.")
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