diff options
Diffstat (limited to 'autogpt/core/planning/strategies/next_ability.py')
| -rw-r--r-- | autogpt/core/planning/strategies/next_ability.py | 183 |
1 files changed, 0 insertions, 183 deletions
diff --git a/autogpt/core/planning/strategies/next_ability.py b/autogpt/core/planning/strategies/next_ability.py deleted file mode 100644 index dff310c37..000000000 --- a/autogpt/core/planning/strategies/next_ability.py +++ /dev/null @@ -1,183 +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, -) -from autogpt.core.planning.strategies.utils import json_loads, to_numbered_list -from autogpt.core.resource.model_providers import ( - LanguageModelFunction, - LanguageModelMessage, - MessageRole, -) - - -class NextAbilityConfiguration(SystemConfiguration): - model_classification: LanguageModelClassification = UserConfigurable() - system_prompt_template: str = UserConfigurable() - system_info: list[str] = UserConfigurable() - user_prompt_template: str = UserConfigurable() - additional_ability_arguments: dict = UserConfigurable() - - -class NextAbility(PromptStrategy): - DEFAULT_SYSTEM_PROMPT_TEMPLATE = "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 = ( - "Your current task is is {task_objective}.\n" - "You have taken {cycle_count} actions on this task already. " - "Here is the actions you have taken and their results:\n" - "{action_history}\n\n" - "Here is additional information that may be useful to you:\n" - "{additional_info}\n\n" - "Additionally, you should consider the following:\n" - "{user_input}\n\n" - "Your task of {task_objective} is complete when the following acceptance criteria have been met:\n" - "{acceptance_criteria}\n\n" - "Please choose one of the provided functions to accomplish this task. " - "Some tasks may require multiple functions to accomplish. If that is the case, choose the function that " - "you think is most appropriate for the current situation given your progress so far." - ) - - DEFAULT_ADDITIONAL_ABILITY_ARGUMENTS = { - "motivation": { - "type": "string", - "description": "Your justification for choosing choosing this function instead of a different one.", - }, - "self_criticism": { - "type": "string", - "description": "Thoughtful self-criticism that explains why this function may not be the best choice.", - }, - "reasoning": { - "type": "string", - "description": "Your reasoning for choosing this function taking into account the `motivation` and weighing the `self_criticism`.", - }, - } - - default_configuration = NextAbilityConfiguration( - model_classification=LanguageModelClassification.SMART_MODEL, - system_prompt_template=DEFAULT_SYSTEM_PROMPT_TEMPLATE, - system_info=DEFAULT_SYSTEM_INFO, - user_prompt_template=DEFAULT_USER_PROMPT_TEMPLATE, - additional_ability_arguments=DEFAULT_ADDITIONAL_ABILITY_ARGUMENTS, - ) - - def __init__( - self, - model_classification: LanguageModelClassification, - system_prompt_template: str, - system_info: list[str], - user_prompt_template: str, - additional_ability_arguments: 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._additional_ability_arguments = additional_ability_arguments - - @property - def model_classification(self) -> LanguageModelClassification: - return self._model_classification - - def build_prompt( - self, - task: Task, - ability_schema: list[dict], - os_info: str, - api_budget: float, - current_time: str, - **kwargs, - ) -> LanguageModelPrompt: - template_kwargs = { - "os_info": os_info, - "api_budget": api_budget, - "current_time": current_time, - **kwargs, - } - - for ability in ability_schema: - ability["parameters"]["properties"].update( - self._additional_ability_arguments - ) - ability["parameters"]["required"] += list( - self._additional_ability_arguments.keys() - ) - - template_kwargs["task_objective"] = task.objective - template_kwargs["cycle_count"] = task.context.cycle_count - template_kwargs["action_history"] = to_numbered_list( - [action.summary() for action in task.context.prior_actions], - no_items_response="You have not taken any actions yet.", - **template_kwargs, - ) - template_kwargs["additional_info"] = to_numbered_list( - [memory.summary() for memory in task.context.memories] - + [info for info in task.context.supplementary_info], - no_items_response="There is no additional information available at this time.", - **template_kwargs, - ) - template_kwargs["user_input"] = to_numbered_list( - [user_input for user_input in task.context.user_input], - no_items_response="There are no additional considerations at this time.", - **template_kwargs, - ) - template_kwargs["acceptance_criteria"] = to_numbered_list( - [acceptance_criteria for acceptance_criteria in task.acceptance_criteria], - **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), - ) - functions = [ - LanguageModelFunction(json_schema=ability) for ability in ability_schema - ] - - return LanguageModelPrompt( - messages=[system_prompt, user_prompt], - functions=functions, - # 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. - - """ - function_name = response_content["function_call"]["name"] - function_arguments = json_loads(response_content["function_call"]["arguments"]) - parsed_response = { - "motivation": function_arguments.pop("motivation"), - "self_criticism": function_arguments.pop("self_criticism"), - "reasoning": function_arguments.pop("reasoning"), - "next_ability": function_name, - "ability_arguments": function_arguments, - } - return parsed_response |