fix: populate required OpenResponses fields with non-null defaults

The OpenResponses conformance spec requires several response fields to
always be present as concrete non-null values. llama-stack was returning
null for these fields when not explicitly set, causing Zod schema
validation to fail on every conformance test.

For each affected field, the fix keeps the Python/OpenAPI schema
definition unchanged (preserving OpenAI spec conformance scores) while
ensuring the actual HTTP response body always carries a concrete value.

Fields fixed:

- background: always False for non-background responses (was None)
- tool_choice: defaults to "auto" when not specified (was None)
- truncation: defaults to "disabled" when not specified (was None)
- service_tier: defaults to "default" when not specified (was None)
- tools: always an array; available_tools() already returns [] not None
- temperature: defaults to 1.0 when not specified (was None)
- top_p: defaults to 1.0 when not specified (was None)
- logprobs: always an empty array when not requested (was None)

Changes are applied in _snapshot_response() and _create_refusal_response()
in the streaming orchestrator, and in convert_chat_choice_to_response_message()
for the non-streaming path.

Closes #4987
Closes #4988
Closes #4990

Signed-off-by: Charlie Doern <cdoern@redhat.com>

Author: cdoern

src/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py

@@ -215,16 +215,21 @@ async def _create_refusal_response(self, violation_message: str) -> OpenAIRespon
 
         # Create a completed refusal response
         refusal_response = OpenAIResponseObject(
+            background=False,
             id=self.response_id,
             created_at=self.created_at,
             model=self.ctx.model,
             status="completed",
             output=[OpenAIResponseMessage(role="assistant", content=[refusal_content], type="message")],
+            temperature=self.ctx.temperature if self.ctx.temperature is not None else 1.0,
+            top_p=self.ctx.top_p if self.ctx.top_p is not None else 1.0,
+            tools=self.ctx.available_tools(),
+            tool_choice=self.ctx.tool_choice or OpenAIResponseInputToolChoiceMode.auto,
+            truncation=self.truncation or "disabled",
             max_output_tokens=self.max_output_tokens,
             safety_identifier=self.safety_identifier,
-            service_tier=self.service_tier,
+            service_tier=self.service_tier or "default",
             metadata=self.metadata,
-            truncation=self.truncation,
             store=self.store,
             prompt_cache_key=self.prompt_cache_key,
         )
@@ -250,6 +255,7 @@ def _snapshot_response(
     ) -> OpenAIResponseObject:
         completed_at = int(time.time()) if status == "completed" else None
         return OpenAIResponseObject(
+            background=False,
             created_at=self.created_at,
             completed_at=completed_at,
             id=self.response_id,
@@ -258,9 +264,10 @@ def _snapshot_response(
             status=status,
             output=self._clone_outputs(outputs),
             text=self.text,
-            top_p=self.ctx.top_p,
+            temperature=self.ctx.temperature if self.ctx.temperature is not None else 1.0,
+            top_p=self.ctx.top_p if self.ctx.top_p is not None else 1.0,
             tools=self.ctx.available_tools(),
-            tool_choice=self.ctx.tool_choice,
+            tool_choice=self.ctx.tool_choice or OpenAIResponseInputToolChoiceMode.auto,
             error=error,
             incomplete_details=incomplete_details,
             usage=self.accumulated_usage,
@@ -271,9 +278,9 @@ def _snapshot_response(
             reasoning=self.reasoning,
             max_output_tokens=self.max_output_tokens,
             safety_identifier=self.safety_identifier,
-            service_tier=self.service_tier,
+            service_tier=self.service_tier or "default",
             metadata=self.metadata,
-            truncation=self.truncation,
+            truncation=self.truncation or "disabled",
             store=self.store,
             prompt_cache_key=self.prompt_cache_key,
         )
@@ -1050,7 +1057,7 @@ async def _process_streaming_chunks(
                     OpenAIResponseOutputMessageContentOutputText(
                         text=final_text,
                         annotations=[],
-                        logprobs=chat_response_logprobs if chat_response_logprobs else None,
+                        logprobs=chat_response_logprobs if chat_response_logprobs else [],
                     )
                 )
 

src/llama_stack/providers/inline/agents/meta_reference/responses/utils.py

@@ -110,7 +110,7 @@ async def convert_chat_choice_to_response_message(
     output_content = choice.message.content or ""
 
     annotations, clean_text = _extract_citations_from_text(output_content, citation_files or {})
-    logprobs = choice.logprobs.content if choice.logprobs and choice.logprobs.content else None
+    logprobs = choice.logprobs.content if choice.logprobs and choice.logprobs.content else []
 
     return OpenAIResponseMessage(
         id=message_id or f"msg_{uuid.uuid4()}",

src/llama_stack_api/openai_responses.py

@@ -712,7 +712,7 @@ class OpenAIResponseIncompleteDetails(BaseModel):
 class OpenAIResponseObject(BaseModel):
     """Complete OpenAI response object containing generation results and metadata.
 
-    :param background: Whether this response was run in background mode
+    :param background: Whether this response was run in background mode (default: False)
     :param created_at: Unix timestamp when the response was created
     :param completed_at: (Optional) Unix timestamp when the response was completed
     :param error: (Optional) Error details if the response generation failed

tests/integration/responses/test_basic_responses.py

@@ -243,7 +243,7 @@ def test_include_logprobs_non_streaming(client_with_models, text_model_id):
     assert len(response_w_o_logprobs.output) == 1
     message_outputs = [output for output in response_w_o_logprobs.output if output.type == "message"]
     assert len(message_outputs) == 1, f"Expected one message output, got {len(message_outputs)}"
-    assert message_outputs[0].content[0].logprobs is None, "Expected no logprobs in the returned response"
+    assert message_outputs[0].content[0].logprobs == [], "Expected no logprobs in the returned response"
 
     # Create a response with include["message.output_text.logprobs"]
     response_with_logprobs = client_with_models.responses.create(