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226 | class Solar(OpenAI):
api_key: str = Field(default=None, description="The SOLAR API key.")
api_base: str = Field(default="", description="The base URL for SOLAR API.")
model: str = Field(
default="solar-1-mini-chat", description="The SOLAR model to use."
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description=LLMMetadata.__fields__["context_window"].field_info.description,
)
is_chat_model: bool = Field(
default=False,
description=LLMMetadata.__fields__["is_chat_model"].field_info.description,
)
is_function_calling_model: bool = Field(
default=False,
description=LLMMetadata.__fields__[
"is_function_calling_model"
].field_info.description,
)
tokenizer: Union[Tokenizer, str, None] = Field(
default=None,
description=(
"An instance of a tokenizer object that has an encode method, or the name"
" of a tokenizer model from Hugging Face. If left as None, then this"
" disables inference of max_tokens."
),
)
def __init__(
self,
model: str = DEFAULT_SOLAR_MODEL,
temperature: float = 0.1,
max_tokens: Optional[int] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
max_retries: int = 3,
timeout: float = 60.0,
reuse_client: bool = True,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
default_headers: Optional[Dict[str, str]] = None,
http_client: Optional[httpx.Client] = None,
# base class
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
**kwargs: Any,
) -> None:
# add warning for this class is deprecated
warnings.warn(
"""Solar LLM is deprecated. Please use Upstage LLM instead.
Install the package using `pip install llama-index-llms-upstage`
""",
)
api_key, api_base = resolve_solar_credentials(
api_key=api_key,
api_base=api_base,
)
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs,
max_retries=max_retries,
callback_manager=callback_manager,
api_key=api_key,
api_version=api_version,
api_base=api_base,
timeout=timeout,
reuse_client=reuse_client,
default_headers=default_headers,
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,
**kwargs,
)
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_tokens or -1,
is_chat_model=self.is_chat_model,
is_function_calling_model=self.is_function_calling_model,
model_name=self.model,
)
@property
def _tokenizer(self) -> Optional[Tokenizer]:
if isinstance(self.tokenizer, str):
return AutoTokenizer.from_pretrained(self.tokenizer)
return self.tokenizer
@classmethod
def class_name(cls) -> str:
return "Solar"
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""完成提示。"""
if not formatted:
prompt = self.completion_to_prompt(prompt)
return super().complete(prompt, **kwargs)
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""完成了提示的翻译。"""
if not formatted:
prompt = self.completion_to_prompt(prompt)
return super().stream_complete(prompt, **kwargs)
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""与模型进行交流。"""
if not self.metadata.is_chat_model:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
return super().chat(messages, **kwargs)
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if not self.metadata.is_chat_model:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
return super().stream_chat(messages, **kwargs)
# -- Async methods --
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""完成提示。"""
if not formatted:
prompt = self.completion_to_prompt(prompt)
return await super().acomplete(prompt, **kwargs)
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
"""完成了提示的翻译。"""
if not formatted:
prompt = self.completion_to_prompt(prompt)
return await super().astream_complete(prompt, **kwargs)
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
"""与模型进行交流。"""
if not self.metadata.is_chat_model:
prompt = self.messages_to_prompt(messages)
completion_response = await self.acomplete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
return await super().achat(messages, **kwargs)
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
if not self.metadata.is_chat_model:
prompt = self.messages_to_prompt(messages)
completion_response = await self.astream_complete(
prompt, formatted=True, **kwargs
)
return async_stream_completion_response_to_chat_response(
completion_response
)
return await super().astream_chat(messages, **kwargs)
|