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Diffstat (limited to 'autogpt/core/planning/strategies/initial_plan.py')
| -rw-r--r-- | autogpt/core/planning/strategies/initial_plan.py | 190 |
1 files changed, 0 insertions, 190 deletions
diff --git a/autogpt/core/planning/strategies/initial_plan.py b/autogpt/core/planning/strategies/initial_plan.py deleted file mode 100644 index 2f8d99b83..000000000 --- a/autogpt/core/planning/strategies/initial_plan.py +++ /dev/null @@ -1,190 +0,0 @@ -from autogpt.core.configuration import SystemConfiguration, UserConfigurable -from autogpt.core.planning.base import PromptStrategy -from autogpt.core.planning.schema import ( - LanguageModelClassification, - LanguageModelPrompt, - Task, - TaskType, -) -from autogpt.core.planning.strategies.utils import json_loads, to_numbered_list -from autogpt.core.resource.model_providers import ( - LanguageModelFunction, - LanguageModelMessage, - MessageRole, -) - - -class InitialPlanConfiguration(SystemConfiguration): - model_classification: LanguageModelClassification = UserConfigurable() - system_prompt_template: str = UserConfigurable() - system_info: list[str] = UserConfigurable() - user_prompt_template: str = UserConfigurable() - create_plan_function: dict = UserConfigurable() - - -class InitialPlan(PromptStrategy): - DEFAULT_SYSTEM_PROMPT_TEMPLATE = ( - "You are an expert project planner. You're responsibility is to create work plans for autonomous agents. " - "You will be given a name, a role, set of goals for the agent to accomplish. Your job is to " - "break down those goals into a set of tasks that the agent can accomplish to achieve those goals. " - "Agents are resourceful, but require clear instructions. Each task you create should have clearly defined " - "`ready_criteria` that the agent can check to see if the task is ready to be started. Each task should " - "also have clearly defined `acceptance_criteria` that the agent can check to evaluate if the task is complete. " - "You should create as many tasks as you think is necessary to accomplish the goals.\n\n" - "System Info:\n{system_info}" - ) - - DEFAULT_SYSTEM_INFO = [ - "The OS you are running on is: {os_info}", - "It takes money to let you run. Your API budget is ${api_budget:.3f}", - "The current time and date is {current_time}", - ] - - DEFAULT_USER_PROMPT_TEMPLATE = ( - "You are {agent_name}, {agent_role}\n" "Your goals are:\n" "{agent_goals}" - ) - - DEFAULT_CREATE_PLAN_FUNCTION = { - "name": "create_initial_agent_plan", - "description": "Creates a set of tasks that forms the initial plan for an autonomous agent.", - "parameters": { - "type": "object", - "properties": { - "task_list": { - "type": "array", - "items": { - "type": "object", - "properties": { - "objective": { - "type": "string", - "description": "An imperative verb phrase that succinctly describes the task.", - }, - "type": { - "type": "string", - "description": "A categorization for the task. ", - "enum": [t.value for t in TaskType], - }, - "acceptance_criteria": { - "type": "array", - "items": { - "type": "string", - "description": "A list of measurable and testable criteria that must be met for the task to be considered complete.", - }, - }, - "priority": { - "type": "integer", - "description": "A number between 1 and 10 indicating the priority of the task relative to other generated tasks.", - "minimum": 1, - "maximum": 10, - }, - "ready_criteria": { - "type": "array", - "items": { - "type": "string", - "description": "A list of measurable and testable criteria that must be met before the task can be started.", - }, - }, - }, - "required": [ - "objective", - "type", - "acceptance_criteria", - "priority", - "ready_criteria", - ], - }, - }, - }, - }, - } - - default_configuration = InitialPlanConfiguration( - model_classification=LanguageModelClassification.SMART_MODEL, - system_prompt_template=DEFAULT_SYSTEM_PROMPT_TEMPLATE, - system_info=DEFAULT_SYSTEM_INFO, - user_prompt_template=DEFAULT_USER_PROMPT_TEMPLATE, - create_plan_function=DEFAULT_CREATE_PLAN_FUNCTION, - ) - - def __init__( - self, - model_classification: LanguageModelClassification, - system_prompt_template: str, - system_info: list[str], - user_prompt_template: str, - create_plan_function: dict, - ): - self._model_classification = model_classification - self._system_prompt_template = system_prompt_template - self._system_info = system_info - self._user_prompt_template = user_prompt_template - self._create_plan_function = create_plan_function - - @property - def model_classification(self) -> LanguageModelClassification: - return self._model_classification - - def build_prompt( - self, - agent_name: str, - agent_role: str, - agent_goals: list[str], - abilities: list[str], - os_info: str, - api_budget: float, - current_time: str, - **kwargs, - ) -> LanguageModelPrompt: - template_kwargs = { - "agent_name": agent_name, - "agent_role": agent_role, - "os_info": os_info, - "api_budget": api_budget, - "current_time": current_time, - **kwargs, - } - template_kwargs["agent_goals"] = to_numbered_list( - agent_goals, **template_kwargs - ) - template_kwargs["abilities"] = to_numbered_list(abilities, **template_kwargs) - template_kwargs["system_info"] = to_numbered_list( - self._system_info, **template_kwargs - ) - - system_prompt = LanguageModelMessage( - role=MessageRole.SYSTEM, - content=self._system_prompt_template.format(**template_kwargs), - ) - user_prompt = LanguageModelMessage( - role=MessageRole.USER, - content=self._user_prompt_template.format(**template_kwargs), - ) - create_plan_function = LanguageModelFunction( - json_schema=self._create_plan_function, - ) - - return LanguageModelPrompt( - messages=[system_prompt, user_prompt], - functions=[create_plan_function], - # TODO: - tokens_used=0, - ) - - def parse_response_content( - self, - response_content: dict, - ) -> dict: - """Parse the actual text response from the objective model. - - Args: - response_content: The raw response content from the objective model. - - Returns: - The parsed response. - - """ - parsed_response = json_loads(response_content["function_call"]["arguments"]) - parsed_response["task_list"] = [ - Task.parse_obj(task) for task in parsed_response["task_list"] - ] - return parsed_response |