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103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
"""
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SearchLinkGraph Module
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"""
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from typing import Optional
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import logging
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from pydantic import BaseModel
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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 (FetchNode,
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SearchLinkNode,
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SearchLinksWithContext)
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class SearchLinkGraph(AbstractGraph):
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"""
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SearchLinkGraph is a scraping pipeline that automates the process of
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extracting information from web pages using a natural language model
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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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Args:
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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, optional): The schema for the graph output. Defaults to None.
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"""
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def __init__(self, source: str, config: dict, schema: Optional[BaseModel] = None):
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super().__init__("", 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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"force": self.config.get("force", False),
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"cut": self.config.get("cut", True),
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"loader_kwargs": self.config.get("loader_kwargs", {}),
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}
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)
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if self.config.get("llm_style") == (True, None):
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search_link_node = SearchLinksWithContext(
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input="doc",
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output=["parsed_doc"],
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node_config={
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"llm_model": self.llm_model,
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"chunk_size": self.model_token,
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}
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)
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else:
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search_link_node = SearchLinkNode(
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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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return BaseGraph(
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nodes=[
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fetch_node,
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search_link_node
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],
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edges=[
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(fetch_node, search_link_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("parsed_doc", "No answer found.")
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