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import logging
from autogpt.core.configuration import SystemConfiguration, UserConfigurable
from autogpt.core.planning.schema import Task
from autogpt.core.prompting import PromptStrategy
from autogpt.core.prompting.schema import ChatPrompt, LanguageModelClassification
from autogpt.core.prompting.utils import to_numbered_list
from autogpt.core.resource.model_providers import (
AssistantChatMessage,
ChatMessage,
CompletionModelFunction,
)
from autogpt.core.utils.json_schema import JSONSchema
from autogpt.core.utils.json_utils import json_loads
logger = logging.getLogger(__name__)
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": JSONSchema(
type=JSONSchema.Type.STRING,
description=(
"Your justification for choosing choosing this function instead of a "
"different one."
),
),
"self_criticism": JSONSchema(
type=JSONSchema.Type.STRING,
description=(
"Thoughtful self-criticism that explains why this function may not be "
"the best choice."
),
),
"reasoning": JSONSchema(
type=JSONSchema.Type.STRING,
description=(
"Your reasoning for choosing this function taking into account the "
"`motivation` and weighing the `self_criticism`."
),
),
}
default_configuration: NextAbilityConfiguration = 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={
k: v.to_dict() for k, v in DEFAULT_ADDITIONAL_ABILITY_ARGUMENTS.items()
},
)
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 = JSONSchema.parse_properties(
additional_ability_arguments
)
for p in self._additional_ability_arguments.values():
p.required = True
@property
def model_classification(self) -> LanguageModelClassification:
return self._model_classification
def build_prompt(
self,
task: Task,
ability_specs: list[CompletionModelFunction],
os_info: str,
api_budget: float,
current_time: str,
**kwargs,
) -> ChatPrompt:
template_kwargs = {
"os_info": os_info,
"api_budget": api_budget,
"current_time": current_time,
**kwargs,
}
for ability in ability_specs:
ability.parameters.update(self._additional_ability_arguments)
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 = ChatMessage.system(
self._system_prompt_template.format(**template_kwargs)
)
user_prompt = ChatMessage.user(
self._user_prompt_template.format(**template_kwargs)
)
return ChatPrompt(
messages=[system_prompt, user_prompt],
functions=ability_specs,
# TODO:
tokens_used=0,
)
def parse_response_content(
self,
response_content: AssistantChatMessage,
) -> 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.
"""
try:
if not response_content.tool_calls:
raise ValueError("LLM did not call any function")
function_name = response_content.tool_calls[0].function.name
function_arguments = json_loads(
response_content.tool_calls[0].function.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,
}
except KeyError:
logger.debug(f"Failed to parse this response content: {response_content}")
raise
return parsed_response
|
