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import abc
import enum
from typing import Callable, ClassVar
from pydantic import BaseModel, Field, SecretStr, validator
from autogpt.core.configuration import UserConfigurable
from autogpt.core.resource.schema import (
Embedding,
ProviderBudget,
ProviderCredentials,
ProviderSettings,
ProviderUsage,
ResourceType,
)
class ModelProviderService(str, enum.Enum):
"""A ModelService describes what kind of service the model provides."""
EMBEDDING: str = "embedding"
LANGUAGE: str = "language"
TEXT: str = "text"
class ModelProviderName(str, enum.Enum):
OPENAI: str = "openai"
class MessageRole(str, enum.Enum):
USER = "user"
SYSTEM = "system"
ASSISTANT = "assistant"
class LanguageModelMessage(BaseModel):
role: MessageRole
content: str
class LanguageModelFunction(BaseModel):
json_schema: dict
class ModelProviderModelInfo(BaseModel):
"""Struct for model information.
Would be lovely to eventually get this directly from APIs, but needs to be
scraped from websites for now.
"""
name: str
service: ModelProviderService
provider_name: ModelProviderName
prompt_token_cost: float = 0.0
completion_token_cost: float = 0.0
class ModelProviderModelResponse(BaseModel):
"""Standard response struct for a response from a model."""
prompt_tokens_used: int
completion_tokens_used: int
model_info: ModelProviderModelInfo
class ModelProviderCredentials(ProviderCredentials):
"""Credentials for a model provider."""
api_key: SecretStr | None = UserConfigurable(default=None)
api_type: SecretStr | None = UserConfigurable(default=None)
api_base: SecretStr | None = UserConfigurable(default=None)
api_version: SecretStr | None = UserConfigurable(default=None)
deployment_id: SecretStr | None = UserConfigurable(default=None)
def unmasked(self) -> dict:
return unmask(self)
class Config:
extra = "ignore"
def unmask(model: BaseModel):
unmasked_fields = {}
for field_name, field in model.__fields__.items():
value = getattr(model, field_name)
if isinstance(value, SecretStr):
unmasked_fields[field_name] = value.get_secret_value()
else:
unmasked_fields[field_name] = value
return unmasked_fields
class ModelProviderUsage(ProviderUsage):
"""Usage for a particular model from a model provider."""
completion_tokens: int = 0
prompt_tokens: int = 0
total_tokens: int = 0
def update_usage(
self,
model_response: ModelProviderModelResponse,
) -> None:
self.completion_tokens += model_response.completion_tokens_used
self.prompt_tokens += model_response.prompt_tokens_used
self.total_tokens += (
model_response.completion_tokens_used + model_response.prompt_tokens_used
)
class ModelProviderBudget(ProviderBudget):
total_budget: float = UserConfigurable()
total_cost: float
remaining_budget: float
usage: ModelProviderUsage
def update_usage_and_cost(
self,
model_response: ModelProviderModelResponse,
) -> None:
"""Update the usage and cost of the provider."""
model_info = model_response.model_info
self.usage.update_usage(model_response)
incremental_cost = (
model_response.completion_tokens_used * model_info.completion_token_cost
+ model_response.prompt_tokens_used * model_info.prompt_token_cost
) / 1000.0
self.total_cost += incremental_cost
self.remaining_budget -= incremental_cost
class ModelProviderSettings(ProviderSettings):
resource_type = ResourceType.MODEL
credentials: ModelProviderCredentials
budget: ModelProviderBudget
class ModelProvider(abc.ABC):
"""A ModelProvider abstracts the details of a particular provider of models."""
defaults: ClassVar[ModelProviderSettings]
@abc.abstractmethod
def get_token_limit(self, model_name: str) -> int:
...
@abc.abstractmethod
def get_remaining_budget(self) -> float:
...
####################
# Embedding Models #
####################
class EmbeddingModelProviderModelInfo(ModelProviderModelInfo):
"""Struct for embedding model information."""
model_service = ModelProviderService.EMBEDDING
embedding_dimensions: int
class EmbeddingModelProviderModelResponse(ModelProviderModelResponse):
"""Standard response struct for a response from an embedding model."""
embedding: Embedding = Field(default_factory=list)
@classmethod
@validator("completion_tokens_used")
def _verify_no_completion_tokens_used(cls, v):
if v > 0:
raise ValueError("Embeddings should not have completion tokens used.")
return v
class EmbeddingModelProvider(ModelProvider):
@abc.abstractmethod
async def create_embedding(
self,
text: str,
model_name: str,
embedding_parser: Callable[[Embedding], Embedding],
**kwargs,
) -> EmbeddingModelProviderModelResponse:
...
###################
# Language Models #
###################
class LanguageModelProviderModelInfo(ModelProviderModelInfo):
"""Struct for language model information."""
model_service = ModelProviderService.LANGUAGE
max_tokens: int
class LanguageModelProviderModelResponse(ModelProviderModelResponse):
"""Standard response struct for a response from a language model."""
content: dict = None
class LanguageModelProvider(ModelProvider):
@abc.abstractmethod
async def create_language_completion(
self,
model_prompt: list[LanguageModelMessage],
functions: list[LanguageModelFunction],
model_name: str,
completion_parser: Callable[[dict], dict],
**kwargs,
) -> LanguageModelProviderModelResponse:
...
|
