mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
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121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
"""
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SearchLinkNode Module
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"""
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# Imports from standard library
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from typing import List, Optional
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from tqdm import tqdm
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# Imports from Langchain
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from langchain.prompts import PromptTemplate
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from langchain_core.output_parsers import JsonOutputParser
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from langchain_core.runnables import RunnableParallel
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from ..utils.logging import get_logger
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# Imports from the library
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from .base_node import BaseNode
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class SearchLinkNode(BaseNode):
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"""
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A node that can filter out the relevant links in the webpage content for the user prompt.
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Node expects the aleready scrapped links on the webpage and hence it is expected
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that this node be used after the FetchNode.
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Attributes:
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llm_model: An instance of the language model client used for generating answers.
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verbose (bool): A flag indicating whether to show print statements during execution.
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Args:
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input (str): Boolean expression defining the input keys needed from the state.
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output (List[str]): List of output keys to be updated in the state.
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node_config (dict): Additional configuration for the node.
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node_name (str): The unique identifier name for the node, defaulting to "GenerateAnswer".
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"""
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def __init__(
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self,
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input: str,
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output: List[str],
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node_config: Optional[dict] = None,
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node_name: str = "GenerateLinks",
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):
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super().__init__(node_name, "node", input, output, 1, node_config)
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self.llm_model = node_config["llm_model"]
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self.verbose = (
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False if node_config is None else node_config.get("verbose", False)
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)
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def execute(self, state: dict) -> dict:
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"""
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Filter out relevant links from the webpage that are relavant to prompt. Out of the filtered links, also
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ensure that all links are navigable.
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Args:
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state (dict): The current state of the graph. The input keys will be used to fetch the
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correct data types from the state.
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Returns:
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dict: The updated state with the output key containing the list of links.
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Raises:
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KeyError: If the input keys are not found in the state, indicating that the
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necessary information for generating the answer is missing.
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"""
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self.logger.info(f"--- Executing {self.node_name} Node ---")
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# Interpret input keys based on the provided input expression
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input_keys = self.get_input_keys(state)
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user_prompt = state[input_keys[0]]
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parsed_content_chunks = state[input_keys[1]]
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output_parser = JsonOutputParser()
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prompt_relevant_links = """
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You are a website scraper and you have just scraped the following content from a website.
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Content: {content}
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You are now tasked with identifying all hyper links within the content that are potentially
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relevant to the user task: {user_prompt}
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Assume relevance broadly, including any links that might be related or potentially useful
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in relation to the task.
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Please list only valid URLs and make sure to err on the side of inclusion if it's uncertain
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whether the content at the link is directly relevant.
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Output only a list of relevant links in the format:
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[
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"link1",
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"link2",
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"link3",
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.
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.
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.
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]
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"""
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relevant_links = []
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for i, chunk in enumerate(
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tqdm(
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parsed_content_chunks,
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desc="Processing chunks",
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disable=not self.verbose,
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)
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):
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merge_prompt = PromptTemplate(
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template=prompt_relevant_links,
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input_variables=["content", "user_prompt"],
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)
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merge_chain = merge_prompt | self.llm_model | output_parser
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# merge_chain = merge_prompt | self.llm_model
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answer = merge_chain.invoke(
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{"content": chunk.page_content, "user_prompt": user_prompt}
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)
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relevant_links += answer
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state.update({self.output[0]: relevant_links})
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return state
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