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273 | class Friendli(LLM):
"""友好的LLM。"""
model: str = Field(description="The friendli model to use.")
max_tokens: int = Field(description="The maximum number of tokens to generate.")
temperature: Optional[float] = Field(
description="The temperature to use for sampling."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the Friendli API."
)
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
def __init__(
self,
model: str = "mixtral-8x7b-instruct-v0-1",
friendli_token: Optional[str] = None,
max_tokens: int = 256,
temperature: Optional[float] = 0.1,
additional_kwargs: Optional[Dict[str, Any]] = 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([])
self._client = friendli.Friendli(token=friendli_token)
self._aclient = friendli.AsyncFriendli(token=friendli_token)
super().__init__(
model=model,
max_tokens=max_tokens,
temperature=temperature,
additional_kwargs=additional_kwargs,
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:
"""获取类名。"""
return "Friendli_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=friendli_modelname_to_contextsize(self.model),
num_output=self.max_tokens,
is_chat_model=True,
model_name=self.model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
}
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:
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.chat.completions.create(
stream=False,
**get_chat_request(messages),
**all_kwargs,
)
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT, content=response.choices[0].message.content
),
raw=response.__dict__,
additional_kwargs={"usage": response.usage.__dict__},
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.completions.create(
prompt=prompt,
stream=False,
**all_kwargs,
)
return CompletionResponse(
text=response.choices[0].text,
additional_kwargs={"usage": response.usage.__dict__},
raw=response.__dict__,
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs)
stream = self._client.chat.completions.create(
stream=True,
**get_chat_request(messages),
**all_kwargs,
)
def gen() -> ChatResponseGen:
content = ""
for chunk in stream:
content_delta = chunk.choices[0].delta.content or ""
content += content_delta
yield ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=content),
delta=content_delta,
raw=chunk.__dict__,
)
return gen()
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs)
stream = self._client.completions.create(
prompt=prompt,
stream=True,
**all_kwargs,
)
def gen() -> CompletionResponseGen:
content = ""
for chunk in stream:
content_delta = chunk.text
content += content_delta
yield CompletionResponse(
text=content,
delta=content_delta,
raw=chunk.__dict__,
)
return gen()
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
response = await self._aclient.chat.completions.create(
stream=False,
**get_chat_request(messages),
**all_kwargs,
)
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT, content=response.choices[0].message.content
),
raw=response.__dict__,
additional_kwargs={"usage": response.usage.__dict__},
)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
response = await self._aclient.completions.create(
prompt=prompt,
stream=False,
**all_kwargs,
)
return CompletionResponse(
text=response.choices[0].text,
additional_kwargs={"usage": response.usage.__dict__},
raw=response.__dict__,
)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
all_kwargs = self._get_all_kwargs(**kwargs)
stream = await self._aclient.chat.completions.create(
stream=True,
**get_chat_request(messages),
**all_kwargs,
)
async def gen() -> ChatResponseAsyncGen:
content = ""
async for chunk in stream:
content_delta = chunk.choices[0].delta.content or ""
content += content_delta
yield ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=content),
delta=content_delta,
raw=chunk.__dict__,
)
return gen()
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
all_kwargs = self._get_all_kwargs(**kwargs)
stream = await self._aclient.completions.create(
prompt=prompt,
stream=True,
**all_kwargs,
)
async def gen() -> CompletionResponseAsyncGen:
content = ""
async for chunk in stream:
content_delta = chunk.text
content += content_delta
yield CompletionResponse(
text=content,
delta=content_delta,
raw=chunk.__dict__,
)
return gen()
|