wehub-resource-sync
106 lines
3.7 KiB
Python
106 lines
3.7 KiB
Python
"""
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SmartScraperMultiGraph Module
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"""
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from copy import deepcopy
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from typing import List, Optional, Type
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from pydantic import BaseModel
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from ..nodes import GraphIteratorNode, MergeAnswersNode
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from ..utils.copy import safe_deepcopy
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from .abstract_graph import AbstractGraph
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from .base_graph import BaseGraph
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from .smart_scraper_lite_graph import SmartScraperLiteGraph
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class SmartScraperMultiLiteGraph(AbstractGraph):
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"""
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SmartScraperMultiLiteGraph is a scraping pipeline that scrapes a
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list of URLs and merge the content first and finally generates answers to a given prompt.
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It only requires a user prompt and a list of URLs.
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The difference with the SmartScraperMultiGraph is that in this case the content is merged
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before to be passed to the llm.
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Attributes:
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prompt (str): The user prompt to search the internet.
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llm_model (dict): The configuration for the language model.
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embedder_model (dict): The configuration for the embedder model.
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headless (bool): A flag to run the browser in headless mode.
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verbose (bool): A flag to display the execution information.
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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 user prompt to search the internet.
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source (List[str]): The source of the graph.
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config (dict): Configuration parameters for the graph.
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schema (Optional[BaseModel]): The schema for the graph output.
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Example:
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>>> smart_scraper_multi_lite_graph = SmartScraperMultiLiteGraph(
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... prompt="Who is ?",
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... source= [
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... "https://perinim.github.io/",
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... "https://perinim.github.io/cv/"
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... ],
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... config={"llm": {"model": "openai/gpt-3.5-turbo"}}
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... )
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>>> result = smart_scraper_multi_lite_graph.run()
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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: List[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.copy_config = safe_deepcopy(config)
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self.copy_schema = deepcopy(schema)
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super().__init__(prompt, config, source, schema)
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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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and parsing and then merge the content and generates answers to a given prompt.
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"""
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graph_iterator_node = GraphIteratorNode(
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input="user_prompt & urls",
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output=["parsed_doc"],
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node_config={
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"graph_instance": SmartScraperLiteGraph,
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"scraper_config": self.copy_config,
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},
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schema=self.copy_schema,
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)
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merge_answers_node = MergeAnswersNode(
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input="user_prompt & parsed_doc",
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output=["answer"],
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node_config={"llm_model": self.llm_model, "schema": self.copy_schema},
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)
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return BaseGraph(
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nodes=[
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graph_iterator_node,
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merge_answers_node,
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],
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edges=[
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(graph_iterator_node, merge_answers_node),
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],
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entry_point=graph_iterator_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 web scraping and parsing process first and
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then concatenate the content and generates answers to a given 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, "urls": 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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