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|
import json
import logging
from autogpt.config import AIDirectives, AIProfile, Config
from autogpt.core.configuration import SystemConfiguration, UserConfigurable
from autogpt.core.prompting import (
ChatPrompt,
LanguageModelClassification,
PromptStrategy,
)
from autogpt.core.prompting.utils import json_loads
from autogpt.core.resource.model_providers.schema import (
AssistantChatMessage,
ChatMessage,
ChatModelProvider,
CompletionModelFunction,
)
from autogpt.core.utils.json_schema import JSONSchema
logger = logging.getLogger(__name__)
class AgentProfileGeneratorConfiguration(SystemConfiguration):
model_classification: LanguageModelClassification = UserConfigurable(
default=LanguageModelClassification.SMART_MODEL
)
_example_call: object = [
{
"type": "function",
"function": {
"name": "create_agent",
"arguments": {
"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."
),
"directives": {
"best_practices": [
(
"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."
),
],
"constraints": [
"Do not suggest illegal or unethical plans or strategies.",
"Take reasonable budgetary limits into account.",
],
},
},
},
}
]
system_prompt: str = UserConfigurable(
default=(
"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 (_GPT), "
"an informative description for what the agent does, and 1 to 5 directives "
"in each of the categories Best Practices and Constraints, "
"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 Call:\n"
"```\n"
f"{json.dumps(_example_call, indent=4)}"
"\n```"
)
)
user_prompt_template: str = UserConfigurable(default='"""{user_objective}"""')
create_agent_function: dict = UserConfigurable(
default=CompletionModelFunction(
name="create_agent",
description="Create a new autonomous AI agent to complete a given task.",
parameters={
"name": JSONSchema(
type=JSONSchema.Type.STRING,
description="A short role-based name for an autonomous agent.",
required=True,
),
"description": JSONSchema(
type=JSONSchema.Type.STRING,
description=(
"An informative one sentence description "
"of what the AI agent does"
),
required=True,
),
"directives": JSONSchema(
type=JSONSchema.Type.OBJECT,
properties={
"best_practices": JSONSchema(
type=JSONSchema.Type.ARRAY,
minItems=1,
maxItems=5,
items=JSONSchema(
type=JSONSchema.Type.STRING,
),
description=(
"One to five highly effective best practices "
"that are optimally aligned with the completion "
"of the given task"
),
required=True,
),
"constraints": JSONSchema(
type=JSONSchema.Type.ARRAY,
minItems=1,
maxItems=5,
items=JSONSchema(
type=JSONSchema.Type.STRING,
),
description=(
"One to five reasonable and efficacious constraints "
"that are optimally aligned with the completion "
"of the given task"
),
required=True,
),
},
required=True,
),
},
).schema
)
class AgentProfileGenerator(PromptStrategy):
default_configuration: AgentProfileGeneratorConfiguration = (
AgentProfileGeneratorConfiguration()
)
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],
)
return prompt
def parse_response_content(
self,
response_content: AssistantChatMessage,
) -> tuple[AIProfile, AIDirectives]:
"""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.name} function; "
"agent profile creation failed"
)
arguments: object = json_loads(
response_content.tool_calls[0].function.arguments
)
ai_profile = AIProfile(
ai_name=arguments.get("name"),
ai_role=arguments.get("description"),
)
ai_directives = AIDirectives(
best_practices=arguments.get("directives", {}).get("best_practices"),
constraints=arguments.get("directives", {}).get("constraints"),
resources=[],
)
except KeyError:
logger.debug(f"Failed to parse this response content: {response_content}")
raise
return ai_profile, ai_directives
async def generate_agent_profile_for_task(
task: str,
app_config: Config,
llm_provider: ChatModelProvider,
) -> tuple[AIProfile, AIDirectives]:
"""Generates an AIConfig object from the given string.
Returns:
AIConfig: The AIConfig object tailored to the user's input
"""
agent_profile_generator = AgentProfileGenerator(
**AgentProfileGenerator.default_configuration.dict() # HACK
)
prompt = agent_profile_generator.build_prompt(task)
# Call LLM with the string as user input
output = (
await llm_provider.create_chat_completion(
prompt.messages,
model_name=app_config.smart_llm,
functions=prompt.functions,
)
).response
# Debug LLM Output
logger.debug(f"AI Config Generator Raw Output: {output}")
# Parse the output
ai_profile, ai_directives = agent_profile_generator.parse_response_content(output)
return ai_profile, ai_directives
|
