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https://github.com/VinciGit00/Scrapegraph-ai.git
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115 lines
4.5 KiB
Python
115 lines
4.5 KiB
Python
"""
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MergeAnswersNode 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, StrOutputParser
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from tqdm import tqdm
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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 MergeGeneratedScriptsNode(BaseNode):
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"""
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A node responsible for merging scripts generated.
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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 = "MergeGeneratedScripts",
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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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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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Executes the node's logic to merge the answers from multiple graph instances into a
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single answer.
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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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# Interpret input keys based on the provided input expression
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input_keys = self.get_input_keys(state)
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# Fetching data from the state based on the input keys
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input_data = [state[key] for key in input_keys]
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user_prompt = input_data[0]
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scripts = input_data[1]
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# merge the scripts in one string
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scripts_str = ""
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for i, script in enumerate(scripts):
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scripts_str += "-----------------------------------\n"
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scripts_str += f"SCRIPT URL {i+1}\n"
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scripts_str += "-----------------------------------\n"
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scripts_str += script
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# TODO: should we pass the schema to the output parser even if the scripts already have it implemented?
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# schema to be used for output parsing
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# if self.node_config.get("schema", None) is not None:
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# output_schema = JsonOutputParser(pydantic_object=self.node_config["schema"])
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# else:
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# output_schema = JsonOutputParser()
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# format_instructions = output_schema.get_format_instructions()
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template_merge = """
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You are a python expert in web scraping and you have just generated multiple scripts to scrape different URLs.\n
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The scripts are generated based on a user question and the content of the websites.\n
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You need to create one single script that merges the scripts generated for each URL.\n
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The scraped contents are in a JSON format and you need to merge them based on the context and providing a correct JSON structure.\n
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The output should be just in python code without any comment and should implement the main function.\n
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The python script, when executed, should format the extracted information sticking to the user question and scripts output format.\n
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USER PROMPT: {user_prompt}\n
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SCRIPTS:\n
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{scripts}
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"""
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prompt_template = PromptTemplate(
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template=template_merge,
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input_variables=["user_prompt"],
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partial_variables={
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"scripts": scripts_str,
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},
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)
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merge_chain = prompt_template | self.llm_model | StrOutputParser()
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answer = merge_chain.invoke({"user_prompt": user_prompt})
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# Update the state with the generated answer
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state.update({self.output[0]: answer})
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return state |