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198 | class Yi(OpenAI):
"""
Yi LLM.
Examples:
`pip install llama-index-llms-yi`
```python
from llama_index.llms.yi import Yi
# get api key from: https://platform.01.ai/
llm = Yi(model="yi-large", api_key="YOUR_API_KEY")
response = llm.complete("Hi, who are you?")
print(response)
```
"""
model: str = Field(default=DEFAULT_YI_MODEL, description="The Yi model to use.")
context_window: int = Field(
default=yi_modelname_to_context_size(DEFAULT_YI_MODEL),
description=LLMMetadata.model_fields["context_window"].description,
)
is_chat_model: bool = Field(
default=True,
description=LLMMetadata.model_fields["is_chat_model"].description,
)
is_function_calling_model: bool = Field(
default=False,
description=LLMMetadata.model_fields["is_function_calling_model"].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_YI_MODEL,
api_key: Optional[str] = None,
api_base: Optional[str] = DEFAULT_YI_ENDPOINT,
**kwargs: Any,
) -> None:
api_key = api_key or os.environ.get("YI_API_KEY", None)
super().__init__(
model=model,
api_key=api_key,
api_base=api_base,
**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 "Yi_LLM"
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Complete the prompt."""
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:
"""Stream complete the prompt."""
if not formatted:
prompt = self.completion_to_prompt(prompt)
return super().stream_complete(prompt, **kwargs)
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""Chat with the model."""
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:
"""Complete the prompt."""
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:
"""Stream complete the prompt."""
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:
"""Chat with the model."""
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)
|
