fix: solve ruff B008 warnings (#1444)

# What does this PR do?

The commit addresses the Ruff warning B008 by refactoring the code to
avoid calling SamplingParams() directly in function argument defaults.
Instead, it either uses Field(default_factory=SamplingParams) for
Pydantic models or sets the default to None and instantiates
SamplingParams inside the function body when the argument is None.

Signed-off-by: Sébastien Han <seb@redhat.com>

llama_stack/apis/agents/agents.py

@@ -199,7 +199,7 @@ AgentToolGroup = register_schema(
 
 
 class AgentConfigCommon(BaseModel):
-    sampling_params: Optional[SamplingParams] = SamplingParams()
+    sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams)
 
     input_shields: Optional[List[str]] = Field(default_factory=list)
     output_shields: Optional[List[str]] = Field(default_factory=list)

llama_stack/apis/batch_inference/batch_inference.py

@@ -40,7 +40,7 @@ class BatchInference(Protocol):
         self,
         model: str,
         content_batch: List[InterleavedContent],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         logprobs: Optional[LogProbConfig] = None,
     ) -> BatchCompletionResponse: ...
@@ -50,7 +50,7 @@ class BatchInference(Protocol):
         self,
         model: str,
         messages_batch: List[List[Message]],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         # zero-shot tool definitions as input to the model
         tools: Optional[List[ToolDefinition]] = list,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,

llama_stack/apis/inference/inference.py

@@ -278,7 +278,7 @@ ResponseFormat = register_schema(
 class CompletionRequest(BaseModel):
     model: str
     content: InterleavedContent
-    sampling_params: Optional[SamplingParams] = SamplingParams()
+    sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams)
     response_format: Optional[ResponseFormat] = None
     stream: Optional[bool] = False
     logprobs: Optional[LogProbConfig] = None
@@ -357,7 +357,7 @@ class ToolConfig(BaseModel):
 class ChatCompletionRequest(BaseModel):
     model: str
     messages: List[Message]
-    sampling_params: Optional[SamplingParams] = SamplingParams()
+    sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams)
 
     tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
     tool_config: Optional[ToolConfig] = Field(default_factory=ToolConfig)
@@ -444,7 +444,7 @@ class Inference(Protocol):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
@@ -467,7 +467,7 @@ class Inference(Protocol):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
         tool_prompt_format: Optional[ToolPromptFormat] = None,

llama_stack/distribution/routers/routers.py

@@ -217,7 +217,7 @@ class InferenceRouter(Inference):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = None,
@@ -230,6 +230,8 @@ class InferenceRouter(Inference):
             "core",
             f"InferenceRouter.chat_completion: {model_id=}, {stream=}, {messages=}, {tools=}, {tool_config=}, {response_format=}",
         )
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.routing_table.get_model(model_id)
         if model is None:
             raise ValueError(f"Model '{model_id}' not found")
@@ -320,11 +322,13 @@ class InferenceRouter(Inference):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         logcat.debug(
             "core",
             f"InferenceRouter.completion: {model_id=}, {stream=}, {content=}, {sampling_params=}, {response_format=}",

llama_stack/providers/inline/inference/meta_reference/inference.py

@@ -136,11 +136,13 @@ class MetaReferenceInferenceImpl(
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> Union[CompletionResponse, CompletionResponseStreamChunk]:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         if logprobs:
             assert logprobs.top_k == 1, f"Unexpected top_k={logprobs.top_k}"
 
@@ -244,7 +246,7 @@ class MetaReferenceInferenceImpl(
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
@@ -253,6 +255,8 @@ class MetaReferenceInferenceImpl(
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         if logprobs:
             assert logprobs.top_k == 1, f"Unexpected top_k={logprobs.top_k}"
 

llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py

@@ -53,7 +53,7 @@ class SentenceTransformersInferenceImpl(
         self,
         model_id: str,
         content: str,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
@@ -64,7 +64,7 @@ class SentenceTransformersInferenceImpl(
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,

llama_stack/providers/inline/inference/vllm/vllm.py

@@ -143,7 +143,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
@@ -154,7 +154,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
         tool_prompt_format: Optional[ToolPromptFormat] = None,
@@ -163,6 +163,8 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> ChatCompletionResponse | ChatCompletionResponseStreamChunk:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         assert self.engine is not None
 
         request = ChatCompletionRequest(

llama_stack/providers/remote/inference/bedrock/bedrock.py

@@ -72,7 +72,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
@@ -83,7 +83,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
@@ -92,6 +92,8 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = ChatCompletionRequest(
             model=model.provider_resource_id,

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -72,11 +72,13 @@ class CerebrasInferenceAdapter(ModelRegistryHelper, Inference):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = CompletionRequest(
             model=model.provider_resource_id,
@@ -112,7 +114,7 @@ class CerebrasInferenceAdapter(ModelRegistryHelper, Inference):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
         tool_prompt_format: Optional[ToolPromptFormat] = None,
@@ -121,6 +123,8 @@ class CerebrasInferenceAdapter(ModelRegistryHelper, Inference):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = ChatCompletionRequest(
             model=model.provider_resource_id,

llama_stack/providers/remote/inference/databricks/databricks.py

@@ -71,7 +71,7 @@ class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
         self,
         model: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
@@ -82,7 +82,7 @@ class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
         self,
         model: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
@@ -91,6 +91,8 @@ class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         request = ChatCompletionRequest(
             model=model,
             messages=messages,

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -86,11 +86,13 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = CompletionRequest(
             model=model.provider_resource_id,
@@ -157,7 +159,7 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
         tool_prompt_format: Optional[ToolPromptFormat] = None,
@@ -166,6 +168,8 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = ChatCompletionRequest(
             model=model.provider_resource_id,

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -93,11 +93,13 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> Union[CompletionResponse, AsyncIterator[CompletionResponseStreamChunk]]:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         if content_has_media(content):
             raise NotImplementedError("Media is not supported")
 
@@ -188,7 +190,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
@@ -197,6 +199,8 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         if tool_prompt_format:
             warnings.warn("tool_prompt_format is not supported by NVIDIA NIM, ignoring", stacklevel=2)
 

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -90,11 +90,13 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = CompletionRequest(
             model=model.provider_resource_id,
@@ -145,7 +147,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
@@ -154,6 +156,8 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = ChatCompletionRequest(
             model=model.provider_resource_id,

llama_stack/providers/remote/inference/passthrough/passthrough.py

@@ -81,11 +81,13 @@ class PassthroughInferenceAdapter(Inference):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         client = self._get_client()
         model = await self.model_store.get_model(model_id)
 
@@ -107,7 +109,7 @@ class PassthroughInferenceAdapter(Inference):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
         tool_prompt_format: Optional[ToolPromptFormat] = None,
@@ -116,6 +118,8 @@ class PassthroughInferenceAdapter(Inference):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         client = self._get_client()
         model = await self.model_store.get_model(model_id)
 

llama_stack/providers/remote/inference/runpod/runpod.py

@@ -54,7 +54,7 @@ class RunpodInferenceAdapter(ModelRegistryHelper, Inference):
         self,
         model: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
@@ -65,7 +65,7 @@ class RunpodInferenceAdapter(ModelRegistryHelper, Inference):
         self,
         model: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
@@ -74,6 +74,8 @@ class RunpodInferenceAdapter(ModelRegistryHelper, Inference):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         request = ChatCompletionRequest(
             model=model,
             messages=messages,

llama_stack/providers/remote/inference/sambanova/sambanova.py

@@ -74,7 +74,7 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
@@ -85,7 +85,7 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
@@ -94,6 +94,8 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
         tool_config: Optional[ToolConfig] = None,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
 
         request = ChatCompletionRequest(

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -98,11 +98,13 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = CompletionRequest(
             model=model.provider_resource_id,
@@ -201,7 +203,7 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
         tool_prompt_format: Optional[ToolPromptFormat] = None,
@@ -210,6 +212,8 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = ChatCompletionRequest(
             model=model.provider_resource_id,

llama_stack/providers/remote/inference/together/together.py

@@ -70,11 +70,13 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = CompletionRequest(
             model=model.provider_resource_id,
@@ -151,7 +153,7 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
         tool_prompt_format: Optional[ToolPromptFormat] = None,
@@ -160,6 +162,8 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = ChatCompletionRequest(
             model=model.provider_resource_id,

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -241,11 +241,13 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> Union[CompletionResponse, CompletionResponseStreamChunk]:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = CompletionRequest(
             model=model.provider_resource_id,
@@ -264,7 +266,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
@@ -273,6 +275,8 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         # This is to be consistent with OpenAI API and support vLLM <= v0.6.3
         # References:

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -74,7 +74,7 @@ class LiteLLMOpenAIMixin(
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
@@ -85,7 +85,7 @@ class LiteLLMOpenAIMixin(
         self,
         model_id: str,
         messages: List[Message],
-        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        sampling_params: Optional[SamplingParams] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
         tool_prompt_format: Optional[ToolPromptFormat] = None,
@@ -94,6 +94,8 @@ class LiteLLMOpenAIMixin(
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
+        if sampling_params is None:
+            sampling_params = SamplingParams()
         model = await self.model_store.get_model(model_id)
         request = ChatCompletionRequest(
             model=model.provider_resource_id,

pyproject.toml

@@ -136,8 +136,6 @@ ignore = [
 
     # These are the additional ones we started ignoring after moving to ruff. We should look into each one of them later.
     "C901", # Complexity of the function is too high
-    # these ignores are from flake8-bugbear; please fix!
-    "B008",
 ]
 
 [tool.mypy]