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import enum
from pydantic import BaseModel, Field
from autogpt.core.ability.schema import AbilityResult
from autogpt.core.resource.model_providers.schema import (
LanguageModelFunction,
LanguageModelMessage,
LanguageModelProviderModelResponse,
)
class LanguageModelClassification(str, enum.Enum):
"""The LanguageModelClassification is a functional description of the model.
This is used to determine what kind of model to use for a given prompt.
Sometimes we prefer a faster or cheaper model to accomplish a task when
possible.
"""
FAST_MODEL: str = "fast_model"
SMART_MODEL: str = "smart_model"
class LanguageModelPrompt(BaseModel):
messages: list[LanguageModelMessage]
functions: list[LanguageModelFunction] = Field(default_factory=list)
def __str__(self):
return "\n\n".join([f"{m.role.value}: {m.content}" for m in self.messages])
class LanguageModelResponse(LanguageModelProviderModelResponse):
"""Standard response struct for a response from a language model."""
class TaskType(str, enum.Enum):
RESEARCH: str = "research"
WRITE: str = "write"
EDIT: str = "edit"
CODE: str = "code"
DESIGN: str = "design"
TEST: str = "test"
PLAN: str = "plan"
class TaskStatus(str, enum.Enum):
BACKLOG: str = "backlog"
READY: str = "ready"
IN_PROGRESS: str = "in_progress"
DONE: str = "done"
class TaskContext(BaseModel):
cycle_count: int = 0
status: TaskStatus = TaskStatus.BACKLOG
parent: "Task" = None
prior_actions: list[AbilityResult] = Field(default_factory=list)
memories: list = Field(default_factory=list)
user_input: list[str] = Field(default_factory=list)
supplementary_info: list[str] = Field(default_factory=list)
enough_info: bool = False
class Task(BaseModel):
objective: str
type: str # TaskType FIXME: gpt does not obey the enum parameter in its schema
priority: int
ready_criteria: list[str]
acceptance_criteria: list[str]
context: TaskContext = Field(default_factory=TaskContext)
# Need to resolve the circular dependency between Task and TaskContext once both models are defined.
TaskContext.update_forward_refs()
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