chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) Microsoft. All rights reserved.
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import logging
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from typing import Annotated
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from pydantic import Field, ValidationError
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from semantic_kernel import Kernel
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from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
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from semantic_kernel.functions import KernelArguments
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from semantic_kernel.functions.kernel_function_decorator import kernel_function
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from guided_conversation.utils.base_model_llm import BaseModelLLM
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from guided_conversation.utils.conversation_helpers import Conversation, ConversationMessageType
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from guided_conversation.utils.openai_tool_calling import ToolValidationResult
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from guided_conversation.utils.plugin_helpers import PluginOutput, fix_error, update_attempts
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from guided_conversation.utils.resources import ResourceConstraintMode, ResourceConstraintUnit, format_resource
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AGENDA_ERROR_CORRECTION_SYSTEM_TEMPLATE = """<message role="system">You are a helpful, thoughtful, and meticulous assistant.
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You are conducting a conversation with a user. You tried to update the agenda, but the update was invalid.
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You will be provided the history of your conversation with the user, \
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your previous attempt(s) at updating the agenda, and the error message(s) that resulted from your attempt(s).
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Your task is to correct the update so that it is valid. \
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Your changes should be as minimal as possible - you are focused on fixing the error(s) that caused the update to be invalid.
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Note that if the resource allocation is invalid, you must follow these rules:
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1. You should not change the description of the first item (since it has already been executed), but you can change its resource allocation
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2. For all other items, you can combine or split them, or assign them fewer or more resources, \
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but the content they cover collectively should not change (i.e. don't eliminate or add new topics).
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For example, the invalid attempt was "item 1 = ask for date of birth (1 turn), item 2 = ask for phone number (1 turn), \
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item 3 = ask for phone type (1 turn), item 4 = explore treatment history (6 turns)", \
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and the error says you need to correct the total resource allocation to 7 turns. \
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A bad solution is "item 1 = ask for date of birth (1 turn), \
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item 2 = explore treatment history (6 turns)" because it eliminates the phone number and phone type topics. \
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A good solution is "item 1 = ask for date of birth (2 turns), item 2 = ask for phone number, phone type,
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and treatment history (2 turns), item 3 = explore treatment history (3 turns)."</message>
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<message role="user">Conversation history:
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{{ conversation_history }}
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Previous attempts to update the agenda:
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{{ previous_attempts }}</message>"""
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UPDATE_AGENDA_TOOL = "update_agenda"
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class _BaseAgendaItem(BaseModelLLM):
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title: str = Field(description="Brief description of the item")
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resource: int = Field(description="Number of turns required for the item")
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class _BaseAgenda(BaseModelLLM):
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items: list[_BaseAgendaItem] = Field(
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description="Ordered list of items to be completed in the remainder of the conversation",
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default_factory=list,
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)
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class Agenda:
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"""An abstraction to manage a conversation agenda. The expected use case is that another agent will generate an agenda.
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This class will validate if it is valid, and help correct it if it is not.
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Args:
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kernel (Kernel): The Semantic Kernel instance to use for calling the LLM. Don't forget to set your
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req_settings since this class uses tool calling functionality from the Semantic Kernel.
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service_id (str): The service ID to use for the Semantic Kernel tool calling. One kernel can have multiple
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services. The service ID is used to identify which service to use for LLM calls. The Agenda object
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assumes that the service has tool calling capabilities and is some flavor of chat completion.
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resource_constraint_mode (ResourceConstraintMode): The mode for resource constraints.
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max_agenda_retries (int): The maximum number of retries for updating the agenda.
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"""
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def __init__(
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self,
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kernel: Kernel,
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service_id: str,
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resource_constraint_mode: ResourceConstraintMode | None,
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max_agenda_retries: int = 2,
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) -> None:
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logger = logging.getLogger(__name__)
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self.id = "agenda_plugin"
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self.kernel = Kernel()
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self.logger = logger
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self.kernel = kernel
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self.service_id = service_id
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self.resource_constraint_mode = resource_constraint_mode
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self.max_agenda_retries = max_agenda_retries
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self.agenda = _BaseAgenda()
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async def update_agenda(
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self,
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items: list[dict[str, str]],
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remaining_turns: int,
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conversation: Conversation,
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) -> PluginOutput:
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"""Updates the agenda model with the given items (generally generated by an LLM) and validates if the update is valid.
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The agenda update reasons in terms of turns for validating the if the proposed agenda is valid.
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If you wish to use a different resource unit, convert the value to turns in some way because
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we found that LLMs do much better at reasoning in terms of turns.
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Args:
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items (list[dict[str, str]]): A list of agenda items.
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Each item should have the following keys:
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- title (str): A brief description of the item.
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- resource (int): The number of turns required for the item.
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remaining_turns (int): The number of remaining turns.
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conversation (Conversation): The conversation object.
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Returns:
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PluginOutput: A PluginOutput object with the success status. Does not generate any messages.
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"""
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previous_attempts = []
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while True:
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try:
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# Try to update the agenda, and do extra validation checks
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self.agenda.items = items
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self._validate_agenda_update(items, remaining_turns)
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self.logger.info(f"Agenda updated successfully: {self.get_agenda_for_prompt()}")
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return PluginOutput(True, [])
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except (ValidationError, ValueError) as e:
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# Update the previous attempts and get instructions for the LLM
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previous_attempts, llm_formatted_attempts = update_attempts(
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error=e, attempt_id=str(items), previous_attempts=previous_attempts
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)
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# If we have reached the maximum number of retries return a failure
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if len(previous_attempts) > self.max_agenda_retries:
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self.logger.warning(f"Failed to update agenda after {self.max_agenda_retries} attempts.")
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return PluginOutput(False, [])
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else:
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self.logger.info(f"Attempting to fix the agenda error. Attempt {len(previous_attempts)}.")
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response = await self._fix_agenda_error(llm_formatted_attempts, conversation)
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if response["validation_result"] != ToolValidationResult.SUCCESS:
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self.logger.warning(
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f"Failed to fix the agenda error due to a failure in the LLM tool call: {response['validation_result']}"
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)
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return PluginOutput(False, [])
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else:
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# Use the result of the first tool call to try the update again
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items = response["tool_args_list"][0]["items"]
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def get_agenda_for_prompt(self) -> str:
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"""Gets a string representation of the agenda for use in an LLM prompt.
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Returns:
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str: A string representation of the agenda.
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"""
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agenda_json = self.agenda.model_dump()
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agenda_items = agenda_json.get("items", [])
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if len(agenda_items) == 0:
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return "None"
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agenda_str = "\n".join(
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[
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f"{i + 1}. [{format_resource(item['resource'], ResourceConstraintUnit.TURNS)}] {item['title']}"
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for i, item in enumerate(agenda_items)
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]
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)
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total_resource = format_resource(sum([item["resource"] for item in agenda_items]), ResourceConstraintUnit.TURNS)
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agenda_str += f"\nTotal = {total_resource}"
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return agenda_str
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# The following is the kernel function that will be provided to the LLM call
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class Items:
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title: Annotated[str, "Description of the item"]
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resource: Annotated[int, "Number of turns required for the item"]
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@kernel_function(
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name=UPDATE_AGENDA_TOOL,
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description="Updates the agenda.",
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)
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def update_agenda_items(
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self,
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items: Annotated[list[Items], "Ordered list of items to be completed in the remainder of the conversation"],
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):
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pass
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async def _fix_agenda_error(self, previous_attempts: str, conversation: Conversation) -> None:
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"""Calls an LLM to try and fix an error in the agenda update."""
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req_settings = self.kernel.get_prompt_execution_settings_from_service_id(self.service_id)
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req_settings.max_tokens = 2000
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self.kernel.add_function(plugin_name=self.id, function=self.update_agenda_items)
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filter = {"included_plugins": [self.id]}
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req_settings.function_choice_behavior = FunctionChoiceBehavior.Auto(auto_invoke=False, filters=filter)
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arguments = KernelArguments(
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conversation_history=conversation.get_repr_for_prompt(exclude_types=[ConversationMessageType.REASONING]),
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previous_attempts=previous_attempts,
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)
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return await fix_error(
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kernel=self.kernel,
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prompt_template=AGENDA_ERROR_CORRECTION_SYSTEM_TEMPLATE,
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req_settings=req_settings,
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arguments=arguments,
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)
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def _validate_agenda_update(self, items: list[dict[str, str]], remaining_turns: int) -> None:
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"""Validates if any constraints were violated while performing the agenda update.
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Args:
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items (list[dict[str, str]]): A list of agenda items.
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remaining_turns (int): The number of remaining turns.
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Raises:
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ValueError: If any validation checks fail.
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"""
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# The total, proposed allocation of resources.
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total_resources = sum([item["resource"] for item in items])
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violations = []
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# In maximum mode, the total resources should not exceed the remaining turns
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if (self.resource_constraint_mode == ResourceConstraintMode.MAXIMUM) and (total_resources > remaining_turns):
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total_resource_instruction = (
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f"The total turns allocated in the agenda must not exceed the remaining amount ({remaining_turns})"
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)
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violations.append(f"{total_resource_instruction}; but the current total is {total_resources}.")
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# In exact mode if the total resources were not exactly equal to the remaining turns
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if (self.resource_constraint_mode == ResourceConstraintMode.EXACT) and (total_resources != remaining_turns):
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total_resource_instruction = (
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f"The total turns allocated in the agenda must equal the remaining amount ({remaining_turns})"
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)
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violations.append(f"{total_resource_instruction}; but the current total is {total_resources}.")
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# Check if any item has a resource value of 0
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if any(item["resource"] <= 0 for item in items):
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violations.append("All items must have a resource value greater than 0.")
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# Raise an error if any violations were found
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if len(violations) > 0:
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self.logger.debug(f"Agenda update failed due to the following violations: {violations}.")
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raise ValueError(" ".join(violations))
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def to_json(self) -> dict:
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agenda_dict = self.agenda.model_dump()
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return {
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"agenda": agenda_dict,
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}
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@classmethod
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def from_json(
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cls,
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json_data: dict,
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kernel: Kernel,
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service_id: str,
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resource_constraint_mode: ResourceConstraintMode | None,
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max_agenda_retries: int = 2,
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) -> "Agenda":
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agenda = cls(kernel, service_id, resource_constraint_mode, max_agenda_retries)
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agenda.agenda.items = json_data["agenda"]["items"]
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return agenda
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