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89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
"""
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Module for creating the smart scraper
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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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GenerateAnswerCSVNode
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)
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from .abstract_graph import AbstractGraph
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class CSVScraperGraph(AbstractGraph):
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"""
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SmartScraper is a comprehensive web scraping tool that automates the process of extracting
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information from web pages using a natural language model to interpret and answer prompts.
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"""
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def __init__(self, prompt: str, source: str, config: dict):
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"""
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Initializes the CSVScraperGraph with a prompt, source, and configuration.
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"""
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super().__init__(prompt, config, source)
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self.input_key = "csv" if source.endswith("csv") else "csv_dir"
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def _create_graph(self):
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"""
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Creates the graph of nodes representing the workflow for web scraping.
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"""
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fetch_node = FetchNode(
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input="csv_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 = GenerateAnswerCSVNode(
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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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"""
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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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