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103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
"""
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MergeAnswersNode 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 JsonOutputParser
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from langchain_openai import ChatOpenAI
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from langchain_mistralai import ChatMistralAI
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from .base_node import BaseNode
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from ..prompts import TEMPLATE_COMBINED
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from ..utils.output_parser import get_structured_output_parser, get_pydantic_output_parser
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class MergeAnswersNode(BaseNode):
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"""
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A node responsible for merging the answers from multiple graph instances into a single answer.
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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 = "MergeAnswers",
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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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input_keys = self.get_input_keys(state)
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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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answers = input_data[1]
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answers_str = ""
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for i, answer in enumerate(answers):
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answers_str += f"CONTENT WEBSITE {i+1}: {answer}\n"
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if self.node_config.get("schema", None) is not None:
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if isinstance(self.llm_model, (ChatOpenAI, ChatMistralAI)):
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self.llm_model = self.llm_model.with_structured_output(
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schema = self.node_config["schema"]) # json schema works only on specific models
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output_parser = get_structured_output_parser(self.node_config["schema"])
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format_instructions = "NA"
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else:
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output_parser = get_pydantic_output_parser(self.node_config["schema"])
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format_instructions = output_parser.get_format_instructions()
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else:
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output_parser = JsonOutputParser()
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format_instructions = output_parser.get_format_instructions()
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prompt_template = PromptTemplate(
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template=TEMPLATE_COMBINED,
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input_variables=["user_prompt"],
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partial_variables={
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"format_instructions": format_instructions,
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"website_content": answers_str,
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},
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)
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merge_chain = prompt_template | self.llm_model | output_parser
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answer = merge_chain.invoke({"user_prompt": user_prompt})
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answer["sources"] = state.get("urls", [])
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state.update({self.output[0]: answer})
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return state
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