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Fix precommit check after moving to ruff (#927)

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Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
This commit is contained in:
Yuan Tang 2025-02-02 09:46:45 -05:00 • committed by GitHub
parent 4773092dd1
commit 34ab7a3b6c
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GPG key ID: B5690EEEBB952194
217 changed files with 981 additions and 2681 deletions
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36
llama_stack/providers/remote/inference/sambanova/sambanova.py
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@ -137,9 +137,7 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
else:
return await self._nonstream_chat_completion(request_sambanova)
async def _nonstream_chat_completion(
self, request: ChatCompletionRequest
) -> ChatCompletionResponse:
async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
response = self._get_client().chat.completions.create(**request)
choice = response.choices[0]
@ -147,30 +145,22 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
result = ChatCompletionResponse(
completion_message=CompletionMessage(
content=choice.message.content or "",
stop_reason=self.convert_to_sambanova_finish_reason(
choice.finish_reason
),
tool_calls=self.convert_to_sambanova_tool_calls(
choice.message.tool_calls
),
stop_reason=self.convert_to_sambanova_finish_reason(choice.finish_reason),
tool_calls=self.convert_to_sambanova_tool_calls(choice.message.tool_calls),
),
logprobs=None,
)
return result
async def _stream_chat_completion(
self, request: ChatCompletionRequest
) -> AsyncGenerator:
async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
async def _to_async_generator():
streaming = self._get_client().chat.completions.create(**request)
for chunk in streaming:
yield chunk
stream = _to_async_generator()
async for chunk in process_chat_completion_stream_response(
stream, self.formatter
):
async for chunk in process_chat_completion_stream_response(stream, self.formatter):
yield chunk
async def embeddings(
@ -180,14 +170,10 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
) -> EmbeddingsResponse:
raise NotImplementedError()
async def convert_chat_completion_request(
self, request: ChatCompletionRequest
) -> dict:
async def convert_chat_completion_request(self, request: ChatCompletionRequest) -> dict:
compatible_request = self.convert_sampling_params(request.sampling_params)
compatible_request["model"] = request.model
compatible_request["messages"] = await self.convert_to_sambanova_messages(
request.messages
)
compatible_request["messages"] = await self.convert_to_sambanova_messages(request.messages)
compatible_request["stream"] = request.stream
compatible_request["logprobs"] = False
compatible_request["extra_headers"] = {
@ -196,9 +182,7 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
compatible_request["tools"] = self.convert_to_sambanova_tool(request.tools)
return compatible_request
def convert_sampling_params(
self, sampling_params: SamplingParams, legacy: bool = False
) -> dict:
def convert_sampling_params(self, sampling_params: SamplingParams, legacy: bool = False) -> dict:
params = {}
if sampling_params:
@ -219,9 +203,7 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
return params
async def convert_to_sambanova_messages(
self, messages: List[Message]
) -> List[dict]:
async def convert_to_sambanova_messages(self, messages: List[Message]) -> List[dict]:
conversation = []
for message in messages:
content = {}