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import logging
from autogpt.core.configuration import SystemConfiguration, UserConfigurable
from autogpt.core.prompting import PromptStrategy
from autogpt.core.prompting.schema import ChatPrompt, LanguageModelClassification
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 NameAndGoalsConfiguration(SystemConfiguration):
model_classification: LanguageModelClassification = UserConfigurable()
system_prompt: str = UserConfigurable()
user_prompt_template: str = UserConfigurable()
create_agent_function: dict = UserConfigurable()
class NameAndGoals(PromptStrategy):
DEFAULT_SYSTEM_PROMPT = (
"Your job is to respond to a user-defined task, given in triple quotes, by "
"invoking the `create_agent` function to generate an autonomous agent to "
"complete the task. "
"You should supply a role-based name for the agent, "
"an informative description for what the agent does, and "
"1 to 5 goals that are optimally aligned with the successful completion of "
"its assigned task.\n"
"\n"
"Example Input:\n"
'"""Help me with marketing my business"""\n\n'
"Example Function Call:\n"
"create_agent(name='CMOGPT', "
"description='A professional digital marketer AI that assists Solopreneurs in "
"growing their businesses by providing world-class expertise in solving "
"marketing problems for SaaS, content products, agencies, and more.', "
"goals=['Engage in effective problem-solving, prioritization, planning, and "
"supporting execution to address your marketing needs as your virtual Chief "
"Marketing Officer.', 'Provide specific, actionable, and concise advice to "
"help you make informed decisions without the use of platitudes or overly "
"wordy explanations.', 'Identify and prioritize quick wins and cost-effective "
"campaigns that maximize results with minimal time and budget investment.', "
"'Proactively take the lead in guiding you and offering suggestions when faced "
"with unclear information or uncertainty to ensure your marketing strategy "
"remains on track.'])"
)
DEFAULT_USER_PROMPT_TEMPLATE = '"""{user_objective}"""'
DEFAULT_CREATE_AGENT_FUNCTION = CompletionModelFunction(
name="create_agent",
description="Create a new autonomous AI agent to complete a given task.",
parameters={
"agent_name": JSONSchema(
type=JSONSchema.Type.STRING,
description="A short role-based name for an autonomous agent.",
),
"agent_role": JSONSchema(
type=JSONSchema.Type.STRING,
description=(
"An informative one sentence description of what the AI agent does"
),
),
"agent_goals": JSONSchema(
type=JSONSchema.Type.ARRAY,
minItems=1,
maxItems=5,
items=JSONSchema(
type=JSONSchema.Type.STRING,
),
description=(
"One to five highly effective goals that are optimally aligned "
"with the completion of a specific task. "
"The number and complexity of the goals should correspond to the "
"complexity of the agent's primary objective."
),
),
},
)
default_configuration: NameAndGoalsConfiguration = NameAndGoalsConfiguration(
model_classification=LanguageModelClassification.SMART_MODEL,
system_prompt=DEFAULT_SYSTEM_PROMPT,
user_prompt_template=DEFAULT_USER_PROMPT_TEMPLATE,
create_agent_function=DEFAULT_CREATE_AGENT_FUNCTION.schema,
)
def __init__(
self,
model_classification: LanguageModelClassification,
system_prompt: str,
user_prompt_template: str,
create_agent_function: dict,
):
self._model_classification = model_classification
self._system_prompt_message = system_prompt
self._user_prompt_template = user_prompt_template
self._create_agent_function = CompletionModelFunction.parse(
create_agent_function
)
@property
def model_classification(self) -> LanguageModelClassification:
return self._model_classification
def build_prompt(self, user_objective: str = "", **kwargs) -> ChatPrompt:
system_message = ChatMessage.system(self._system_prompt_message)
user_message = ChatMessage.user(
self._user_prompt_template.format(
user_objective=user_objective,
)
)
prompt = ChatPrompt(
messages=[system_message, user_message],
functions=[self._create_agent_function],
# TODO
tokens_used=0,
)
return prompt
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(
f"LLM did not call {self._create_agent_function} function; "
"agent profile creation failed"
)
parsed_response = json_loads(
response_content.tool_calls[0].function.arguments
)
except KeyError:
logger.debug(f"Failed to parse this response content: {response_content}")
raise
return parsed_response
|
