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103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
"""
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HtmlAnalyzerNode Module
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"""
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from typing import List, Optional
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from langchain.prompts import PromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.runnables import RunnableParallel
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from langchain_core.utils.pydantic import is_basemodel_subclass
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from langchain_community.chat_models import ChatOllama
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from tqdm import tqdm
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from .base_node import BaseNode
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from ..utils import reduce_html
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from ..prompts import (
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TEMPLATE_HTML_ANALYSIS, TEMPLATE_HTML_ANALYSIS_WITH_CONTEXT
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)
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class HtmlAnalyzerNode(BaseNode):
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"""
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A node that generates an analysis of the provided HTML code based on the wanted infromations to be extracted.
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Attributes:
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llm_model: An instance of a language model client, configured 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 = "HtmlAnalyzer",
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):
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super().__init__(node_name, "node", input, output, 2, node_config)
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self.llm_model = node_config["llm_model"]
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if isinstance(node_config["llm_model"], ChatOllama):
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self.llm_model.format="json"
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self.verbose = (
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True if node_config is None else node_config.get("verbose", False)
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)
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self.force = (
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False if node_config is None else node_config.get("force", False)
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)
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self.script_creator = (
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False if node_config is None else node_config.get("script_creator", False)
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)
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self.is_md_scraper = (
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False if node_config is None else node_config.get("is_md_scraper", False)
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)
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self.additional_info = node_config.get("additional_info")
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def execute(self, state: dict) -> dict:
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"""
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Generates an analysis of the provided HTML code based on the wanted infromations to be extracted.
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Args:
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state (dict): The current state of the graph. The input keys will be used
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to fetch the correct data from the state.
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Returns:
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dict: The updated state with the output key containing the generated answer.
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Raises:
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KeyError: If the input keys are not found in the state, indicating
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that the necessary information for generating an answer is missing.
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"""
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self.logger.info(f"--- Executing {self.node_name} Node ---")
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input_keys = self.get_input_keys(state)
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input_data = [state[key] for key in input_keys]
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refined_prompt = input_data[0]
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html = input_data[1]
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reduced_html = reduce_html(html[0].page_content, self.node_config.get("reduction", 0))
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if self.additional_info is not None:
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prompt = PromptTemplate(
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template=TEMPLATE_HTML_ANALYSIS_WITH_CONTEXT,
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partial_variables={"initial_analysis": refined_prompt,
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"html_code": reduced_html,
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"additional_context": self.additional_info})
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else:
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prompt = PromptTemplate(
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template=TEMPLATE_HTML_ANALYSIS,
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partial_variables={"initial_analysis": refined_prompt,
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"html_code": reduced_html})
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output_parser = StrOutputParser()
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chain = prompt | self.llm_model | output_parser
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html_analysis = chain.invoke({})
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state.update({self.output[0]: html_analysis, self.output[1]: reduced_html})
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return state
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