Merge branch 'main' into watsonx_health_check

llama_stack/providers/inline/agents/meta_reference/openai_responses.py

@@ -292,12 +292,12 @@ class OpenAIResponsesImpl:
     async def _store_response(
         self,
         response: OpenAIResponseObject,
-        original_input: str | list[OpenAIResponseInput],
+        input: str | list[OpenAIResponseInput],
     ) -> None:
         new_input_id = f"msg_{uuid.uuid4()}"
-        if isinstance(original_input, str):
+        if isinstance(input, str):
             # synthesize a message from the input string
-            input_content = OpenAIResponseInputMessageContentText(text=original_input)
+            input_content = OpenAIResponseInputMessageContentText(text=input)
             input_content_item = OpenAIResponseMessage(
                 role="user",
                 content=[input_content],
@@ -307,7 +307,7 @@ class OpenAIResponsesImpl:
         else:
             # we already have a list of messages
             input_items_data = []
-            for input_item in original_input:
+            for input_item in input:
                 if isinstance(input_item, OpenAIResponseMessage):
                     # These may or may not already have an id, so dump to dict, check for id, and add if missing
                     input_item_dict = input_item.model_dump()
@@ -334,7 +334,6 @@ class OpenAIResponsesImpl:
         tools: list[OpenAIResponseInputTool] | None = None,
     ):
         stream = False if stream is None else stream
-        original_input = input  # Keep reference for storage
 
         output_messages: list[OpenAIResponseOutput] = []
 
@@ -372,7 +371,7 @@ class OpenAIResponsesImpl:
                 inference_result=inference_result,
                 ctx=ctx,
                 output_messages=output_messages,
-                original_input=original_input,
+                input=input,
                 model=model,
                 store=store,
                 tools=tools,
@@ -382,7 +381,7 @@ class OpenAIResponsesImpl:
                 inference_result=inference_result,
                 ctx=ctx,
                 output_messages=output_messages,
-                original_input=original_input,
+                input=input,
                 model=model,
                 store=store,
                 tools=tools,
@@ -393,7 +392,7 @@ class OpenAIResponsesImpl:
         inference_result: Any,
         ctx: ChatCompletionContext,
         output_messages: list[OpenAIResponseOutput],
-        original_input: str | list[OpenAIResponseInput],
+        input: str | list[OpenAIResponseInput],
         model: str,
         store: bool | None,
         tools: list[OpenAIResponseInputTool] | None,
@@ -423,7 +422,7 @@ class OpenAIResponsesImpl:
         if store:
             await self._store_response(
                 response=response,
-                original_input=original_input,
+                input=input,
             )
 
         return response
@@ -433,7 +432,7 @@ class OpenAIResponsesImpl:
         inference_result: Any,
         ctx: ChatCompletionContext,
         output_messages: list[OpenAIResponseOutput],
-        original_input: str | list[OpenAIResponseInput],
+        input: str | list[OpenAIResponseInput],
         model: str,
         store: bool | None,
         tools: list[OpenAIResponseInputTool] | None,
@@ -544,7 +543,7 @@ class OpenAIResponsesImpl:
         if store:
             await self._store_response(
                 response=final_response,
-                original_input=original_input,
+                input=input,
             )
 
         # Emit response.completed

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

@@ -218,7 +218,7 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin):
                 "json_schema": {
                     "name": name,
                     "schema": fmt,
-                    "strict": True,
+                    "strict": False,
                 },
             }
         if request.tools:

scripts/distro_codegen.py

@@ -107,6 +107,13 @@ def collect_template_dependencies(template_dir: Path) -> tuple[str | None, list[
     return None, []
 
 
+def pre_import_templates(template_dirs: list[Path]) -> None:
+    # Pre-import all template modules to avoid deadlocks.
+    for template_dir in template_dirs:
+        module_name = f"llama_stack.templates.{template_dir.name}"
+        importlib.import_module(module_name)
+
+
 def main():
     templates_dir = REPO_ROOT / "llama_stack" / "templates"
     change_tracker = ChangedPathTracker()
@@ -118,6 +125,8 @@ def main():
         template_dirs = list(find_template_dirs(templates_dir))
         task = progress.add_task("Processing distribution templates...", total=len(template_dirs))
 
+        pre_import_templates(template_dirs)
+
         # Create a partial function with the progress bar
         process_func = partial(process_template, progress=progress, change_tracker=change_tracker)
 

tests/unit/providers/agents/meta_reference/test_openai_responses.py

@@ -628,3 +628,69 @@ async def test_responses_store_list_input_items_logic():
     result = await responses_store.list_response_input_items("resp_123", limit=0, order=Order.asc)
     assert result.object == "list"
     assert len(result.data) == 0  # Should return no items
+
+
+@pytest.mark.asyncio
+async def test_store_response_uses_rehydrated_input_with_previous_response(
+    openai_responses_impl, mock_responses_store, mock_inference_api
+):
+    """Test that _store_response uses the full re-hydrated input (including previous responses)
+    rather than just the original input when previous_response_id is provided."""
+
+    # Setup - Create a previous response that should be included in the stored input
+    previous_response = OpenAIResponseObjectWithInput(
+        id="resp-previous-123",
+        object="response",
+        created_at=1234567890,
+        model="meta-llama/Llama-3.1-8B-Instruct",
+        status="completed",
+        input=[
+            OpenAIResponseMessage(
+                id="msg-prev-user", role="user", content=[OpenAIResponseInputMessageContentText(text="What is 2+2?")]
+            )
+        ],
+        output=[
+            OpenAIResponseMessage(
+                id="msg-prev-assistant",
+                role="assistant",
+                content=[OpenAIResponseOutputMessageContentOutputText(text="2+2 equals 4.")],
+            )
+        ],
+    )
+
+    mock_responses_store.get_response_object.return_value = previous_response
+
+    current_input = "Now what is 3+3?"
+    model = "meta-llama/Llama-3.1-8B-Instruct"
+    mock_chat_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
+    mock_inference_api.openai_chat_completion.return_value = mock_chat_completion
+
+    # Execute - Create response with previous_response_id
+    result = await openai_responses_impl.create_openai_response(
+        input=current_input,
+        model=model,
+        previous_response_id="resp-previous-123",
+        store=True,
+    )
+
+    store_call_args = mock_responses_store.store_response_object.call_args
+    stored_input = store_call_args.kwargs["input"]
+
+    # Verify that the stored input contains the full re-hydrated conversation:
+    # 1. Previous user message
+    # 2. Previous assistant response
+    # 3. Current user message
+    assert len(stored_input) == 3
+
+    assert stored_input[0].role == "user"
+    assert stored_input[0].content[0].text == "What is 2+2?"
+
+    assert stored_input[1].role == "assistant"
+    assert stored_input[1].content[0].text == "2+2 equals 4."
+
+    assert stored_input[2].role == "user"
+    assert stored_input[2].content == "Now what is 3+3?"
+
+    # Verify the response itself is correct
+    assert result.model == model
+    assert result.status == "completed"