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119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
"""
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XMLScraperGraph Module
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"""
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from .base_graph import BaseGraph
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from ..nodes import (
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FetchNode,
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ParseNode,
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RAGNode,
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GenerateAnswerNode
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)
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from .abstract_graph import AbstractGraph
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class XMLScraperGraph(AbstractGraph):
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"""
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XMLScraperGraph is a scraping pipeline that extracts information from XML files using a natural
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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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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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model_token (int): The token limit for the language model.
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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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Example:
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>>> xml_scraper = XMLScraperGraph(
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... "List me all the attractions in Chioggia.",
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... "data/chioggia.xml",
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... {"llm": {"model": "gpt-3.5-turbo"}}
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... )
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>>> result = xml_scraper.run()
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"""
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def __init__(self, prompt: str, source: str, config: dict):
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super().__init__(prompt, config, source)
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self.input_key = "xml" if source.endswith("xml") else "xml_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="xml_dir",
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output=["doc"],
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node_config={
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"headless": self.headless,
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"verbose": self.verbose
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}
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)
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parse_node = ParseNode(
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input="doc",
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output=["parsed_doc"],
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node_config={
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"chunk_size": self.model_token,
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"verbose": self.verbose
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}
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)
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rag_node = RAGNode(
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input="user_prompt & (parsed_doc | doc)",
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output=["relevant_chunks"],
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node_config={
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"llm": self.llm_model,
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"embedder_model": self.embedder_model,
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"verbose": self.verbose
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}
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)
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generate_answer_node = GenerateAnswerNode(
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input="user_prompt & (relevant_chunks | parsed_doc | doc)",
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output=["answer"],
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node_config={
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"llm": self.llm_model,
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"verbose": self.verbose
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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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rag_node,
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generate_answer_node,
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],
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edges=[
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(fetch_node, parse_node),
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(parse_node, rag_node),
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(rag_node, generate_answer_node)
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],
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entry_point=fetch_node
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)
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def run(self) -> str:
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"""
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Executes the web 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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