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225 | class OctoAI(LLM):
model: str = Field(
default=DEFAULT_OCTOAI_MODEL, description="The model to use with OctoAI"
)
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description="The temperature to use during generation.",
ge=0.0,
le=1.0,
)
max_tokens: Optional[int] = Field(
description="The maximum number of tokens to generate.",
gt=0,
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the OctoAI SDK."
)
def __init__(
self,
model: str = DEFAULT_OCTOAI_MODEL,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: Optional[int] = None,
timeout: int = 120,
token: Optional[str] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
# base class
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[MessagesToPromptCallable] = None,
completion_to_prompt: Optional[CompletionToPromptCallable] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
super().__init__(
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
model=model,
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs,
)
token = get_from_param_or_env("token", token, "OCTOAI_TOKEN", "")
if not token:
raise ValueError(
"You must provide an API token to use OctoAI. "
"You can either pass it in as an argument or set it `OCTOAI_TOKEN`."
"To generate a token in your OctoAI account settings: https://octoai.cloud/settings`."
)
try:
self._client = Client(api_key=token, timeout=timeout)
except ImportError as err:
raise ImportError(
"Could not import OctoAI python package. "
"Please install it with `pip install octoai-sdk`."
) from err
@property
def metadata(self) -> LLMMetadata:
"""Get LLM metadata."""
return LLMMetadata(
context_window=octoai_modelname_to_contextsize(self.model),
num_output=self.max_tokens or -1,
is_chat_model=True,
model_name=self.model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"temperature": self.temperature,
}
if self.max_tokens:
base_kwargs["max_tokens"] = self.max_tokens
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
response = self._client.text_gen.create_chat_completion(
messages=to_octoai_messages(messages), **self._get_all_kwargs(**kwargs)
)
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT, content=response.choices[0].message.content
),
raw=dict(response),
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
streaming_response = self._client.text_gen.create_chat_completion_stream(
messages=to_octoai_messages(messages),
**self._get_all_kwargs(**kwargs),
)
def gen() -> ChatResponseGen:
content = ""
role = MessageRole.ASSISTANT
for completion in streaming_response:
content_delta = completion.choices[0].delta.content
if content_delta is None:
continue
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=completion,
)
return gen()
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
complete_fn = chat_to_completion_decorator(self.chat)
return complete_fn(prompt, **kwargs)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
stream_complete_fn = stream_chat_to_completion_decorator(self.stream_chat)
return stream_complete_fn(prompt, **kwargs)
# ===== Async Endpoints =====
@llm_chat_callback()
async def achat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponse:
raise NotImplementedError
@llm_chat_callback()
async def astream_chat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponseAsyncGen:
raise NotImplementedError
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
raise NotImplementedError
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError
|
