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120 lines
3.8 KiB
Python
120 lines
3.8 KiB
Python
"""
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SmartScraperGraph Module
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"""
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from typing import Optional
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from .base_graph import BaseGraph
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from .abstract_graph import AbstractGraph
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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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class SmartScraperGraph(AbstractGraph):
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"""
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SmartScraper 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 (str): 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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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 (str): The schema for the graph output.
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Example:
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>>> smart_scraper = SmartScraperGraph(
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... "List me all the attractions in Chioggia.",
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... "https://en.wikipedia.org/wiki/Chioggia",
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... {"llm": {"model": "gpt-3.5-turbo"}}
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... )
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>>> result = smart_scraper.run()
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)
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"""
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def __init__(self, prompt: str, source: str, config: dict, schema: Optional[str] = None):
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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", "link_urls", "img_urls"],
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node_config={
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"loader_kwargs": self.config.get("loader_kwargs", {}),
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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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}
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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_model": self.llm_model,
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"embedder_model": self.embedder_model
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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_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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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 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.") |