class EverlyAI(OpenAI):
"""EverlyAI LLM.
Examples:
`pip install llama-index-llms-everlyai`
```python
from llama_index.llms.everlyai import EverlyAI
llm = EverlyAI(api_key="your-api-key")
response = llm.complete("Hello World!")
print(response)
```
"""
def __init__(
self,
model: str = DEFAULT_MODEL,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: int = DEFAULT_NUM_OUTPUTS,
additional_kwargs: Optional[Dict[str, Any]] = None,
max_retries: int = 10,
api_key: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
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,
) -> None:
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
api_key = get_from_param_or_env("api_key", api_key, "EverlyAI_API_KEY")
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
api_base=EVERLYAI_API_BASE,
api_key=api_key,
additional_kwargs=additional_kwargs,
max_retries=max_retries,
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,
)
@classmethod
def class_name(cls) -> str:
return "EverlyAI_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=everlyai_modelname_to_contextsize(self.model),
num_output=self.max_tokens,
is_chat_model=True,
model_name=self.model,
)
@property
def _is_chat_model(self) -> bool:
return True
