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286 | class Xinference(CustomLLM):
"""Xinference LLM.
Examples:
`pip install llama-index-llms-xinference`
```python
from llama_index.llms.xinference import Xinference
# Set up Xinference with required parameters
llm = Xinference(
model_name="xinference-1.0",
app_id="ml",
user_id="xinference",
api_key="<YOUR XINFERENCE API KEY>"
temperature=0.5,
max_tokens=256,
)
# Call the complete function
response = llm.complete("Hello World!")
print(response)
```
"""
model_uid: str = Field(description="The Xinference model to use.")
endpoint: str = Field(description="The Xinference endpoint URL to use.")
temperature: float = Field(
description="The temperature to use for sampling.", gte=0.0, lte=1.0
)
max_tokens: int = Field(
description="The maximum new tokens to generate as answer.", gt=0
)
context_window: int = Field(
description="The maximum number of context tokens for the model.", gt=0
)
model_description: Dict[str, Any] = Field(
description="The model description from Xinference."
)
_generator: Any = PrivateAttr()
def __init__(
self,
model_uid: str,
endpoint: str,
temperature: float = DEFAULT_XINFERENCE_TEMP,
max_tokens: Optional[int] = 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:
generator, context_window, model_description = self.load_model(
model_uid, endpoint
)
self._generator = generator
if max_tokens is None:
max_tokens = context_window // 4
elif max_tokens > context_window:
raise ValueError(
f"received max_tokens {max_tokens} with context window {context_window}"
"max_tokens can not exceed the context window of the model"
)
super().__init__(
model_uid=model_uid,
endpoint=endpoint,
temperature=temperature,
context_window=context_window,
max_tokens=max_tokens,
model_description=model_description,
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,
)
def load_model(self, model_uid: str, endpoint: str) -> Tuple[Any, int, dict]:
try:
from xinference.client import RESTfulClient
except ImportError:
raise ImportError(
"Could not import Xinference library."
'Please install Xinference with `pip install "xinference[all]"`'
)
client = RESTfulClient(endpoint)
try:
assert isinstance(client, RESTfulClient)
except AssertionError:
raise RuntimeError(
"Could not create RESTfulClient instance."
"Please make sure Xinference endpoint is running at the correct port."
)
generator = client.get_model(model_uid)
model_description = client.list_models()[model_uid]
try:
assert generator is not None
assert model_description is not None
except AssertionError:
raise RuntimeError(
"Could not get model from endpoint."
"Please make sure Xinference endpoint is running at the correct port."
)
model = model_description["model_name"]
if "context_length" in model_description:
context_window = model_description["context_length"]
else:
warnings.warn(
"""
Parameter `context_length` not found in model description,
using `xinference_modelname_to_contextsize` that is no longer maintained.
Please update Xinference to the newest version.
"""
)
context_window = xinference_modelname_to_contextsize(model)
return generator, context_window, model_description
@classmethod
def class_name(cls) -> str:
return "Xinference_llm"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
assert isinstance(self.context_window, int)
return LLMMetadata(
context_window=int(self.context_window // TOKEN_RATIO),
num_output=self.max_tokens,
model_name=self.model_uid,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
assert self.context_window is not None
base_kwargs = {
"temperature": self.temperature,
"max_length": self.context_window,
}
return {
**base_kwargs,
**self.model_description,
}
def _get_input_dict(self, prompt: str, **kwargs: Any) -> Dict[str, Any]:
return {"prompt": prompt, **self._model_kwargs, **kwargs}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
assert self._generator is not None
prompt = messages[-1].content if len(messages) > 0 else ""
history = [xinference_message_to_history(message) for message in messages[:-1]]
response_text = self._generator.chat(
prompt=prompt,
chat_history=history,
generate_config={
"stream": False,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
},
)["choices"][0]["message"]["content"]
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT,
content=response_text,
),
delta=None,
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
assert self._generator is not None
prompt = messages[-1].content if len(messages) > 0 else ""
history = [xinference_message_to_history(message) for message in messages[:-1]]
response_iter = self._generator.chat(
prompt=prompt,
chat_history=history,
generate_config={
"stream": True,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
},
)
def gen() -> ChatResponseGen:
text = ""
for c in response_iter:
delta = c["choices"][0]["delta"].get("content", "")
text += delta
yield ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT,
content=text,
),
delta=delta,
)
return gen()
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
assert self._generator is not None
response_text = self._generator.chat(
prompt=prompt,
chat_history=None,
generate_config={
"stream": False,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
},
)["choices"][0]["message"]["content"]
return CompletionResponse(
delta=None,
text=response_text,
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
assert self._generator is not None
response_iter = self._generator.chat(
prompt=prompt,
chat_history=None,
generate_config={
"stream": True,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
},
)
def gen() -> CompletionResponseGen:
text = ""
for c in response_iter:
delta = c["choices"][0]["delta"].get("content", "")
text += delta
yield CompletionResponse(
delta=delta,
text=text,
)
return gen()
|