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245 | class LMStudio(CustomLLM):
base_url: str = Field(
default="http://localhost:1234/v1",
description="Base url the model is hosted under.",
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model.",
gt=0,
)
model_name: str = Field(description="The model to use.")
request_timeout: float = Field(
default=DEFAULT_REQUEST_TIMEOUT,
description="The timeout for making http request in seconds to LM Studio API server.",
)
num_output: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description=LLMMetadata.model_fields["num_output"].description,
)
is_chat_model: bool = Field(
default=True,
description=(
"LM Studio API supports chat."
+ LLMMetadata.model_fields["is_chat_model"].description
),
)
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description=("The temperature to use for sampling."),
gte=0.0,
lte=1.0,
)
timeout: float = Field(
default=120, description=("The timeout to use in seconds."), gte=0
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description=("Additional kwargs to pass to the model.")
)
def _create_payload_from_messages(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> Dict[str, Any]:
return {
"model": self.model_name,
"messages": [
{
"role": message.role.value,
"content": message.content,
**(
message.additional_kwargs
if message.additional_kwargs is not None
else {}
),
}
for message in messages
],
"options": self._model_kwargs,
"stream": False,
**kwargs,
}
def _create_chat_response_from_http_response(
self, response: httpx.Response
) -> ChatResponse:
raw = response.json()
message = raw["choices"][0]["message"]
return ChatResponse(
message=ChatMessage(
content=message.get("content"),
role=MessageRole(message.get("role")),
additional_kwargs=get_additional_kwargs(message, ("content", "role")),
),
raw=raw,
additional_kwargs=get_additional_kwargs(raw, ("choices",)),
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
payload = self._create_payload_from_messages(messages, **kwargs)
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
response = client.post(
url=f"{self.base_url}/chat/completions",
json=payload,
)
response.raise_for_status()
return self._create_chat_response_from_http_response(response)
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
payload = self._create_payload_from_messages(messages, **kwargs)
async with httpx.AsyncClient(timeout=Timeout(self.request_timeout)) as client:
response = await client.post(
url=f"{self.base_url}/chat/completions",
json=payload,
)
response.raise_for_status()
return self._create_chat_response_from_http_response(response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
complete_fn = chat_to_completion_decorator(self.chat)
return complete_fn(prompt, **kwargs)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
acomplete_fn = achat_to_completion_decorator(self.achat)
return await acomplete_fn(prompt, **kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
payload = self._create_payload_from_messages(messages, stream=True, **kwargs)
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
with client.stream(
method="POST",
url=f"{self.base_url}/chat/completions",
json=payload,
) as response:
response.raise_for_status()
text = ""
for line in response.iter_lines():
if line:
line = line.strip()
if isinstance(line, bytes):
line = line.decode("utf-8")
if line.startswith("data: [DONE]"):
break
# Slice the line to remove the "data: " prefix
chunk = json.loads(line[5:])
delta = chunk["choices"][0].get("delta")
role = delta.get("role") or MessageRole.ASSISTANT
content_delta = delta.get("content") or ""
text += content_delta
yield ChatResponse(
message=ChatMessage(
content=text,
role=MessageRole(role),
additional_kwargs=get_additional_kwargs(
chunk, ("choices",)
),
),
delta=content_delta,
raw=chunk,
additional_kwargs=get_additional_kwargs(
chunk, ("choices",)
),
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
stream_complete_fn = stream_chat_to_completion_decorator(self.stream_chat)
return stream_complete_fn(prompt, **kwargs)
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
astream_complete_fn = astream_chat_to_completion_decorator(self.astream_chat)
return await astream_complete_fn(prompt, **kwargs)
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=self.num_output,
model_name=self.model_name,
is_chat_model=self.is_chat_model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
"num_ctx": self.context_window,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
|