Merge branch 'main' into pr1573

llama_stack/apis/agents/agents.py

@@ -370,7 +370,7 @@ class AgentTurnResumeRequest(BaseModel):
     agent_id: str
     session_id: str
     turn_id: str
-    tool_responses: Union[List[ToolResponse], List[ToolResponseMessage]]
+    tool_responses: List[ToolResponse]
     stream: Optional[bool] = False
 
 
@@ -449,7 +449,7 @@ class Agents(Protocol):
         agent_id: str,
         session_id: str,
         turn_id: str,
-        tool_responses: Union[List[ToolResponse], List[ToolResponseMessage]],
+        tool_responses: List[ToolResponse],
         stream: Optional[bool] = False,
     ) -> Union[Turn, AsyncIterator[AgentTurnResponseStreamChunk]]:
         """Resume an agent turn with executed tool call responses.
@@ -460,7 +460,6 @@ class Agents(Protocol):
         :param session_id: The ID of the session to resume.
         :param turn_id: The ID of the turn to resume.
         :param tool_responses: The tool call responses to resume the turn with.
-            NOTE: ToolResponseMessage will be deprecated. Use ToolResponse.
         :param stream: Whether to stream the response.
         :returns: A Turn object if stream is False, otherwise an AsyncIterator of AgentTurnResponseStreamChunk objects.
         """

llama_stack/apis/telemetry/telemetry.py

@@ -96,6 +96,13 @@ class MetricEvent(EventCommon):
     unit: str
 
 
+@json_schema_type
+class MetricInResponse(BaseModel):
+    metric: str
+    value: Union[int, float]
+    unit: Optional[str] = None
+
+
 # This is a short term solution to allow inference API to return metrics
 # The ideal way to do this is to have a way for all response types to include metrics
 # and all metric events logged to the telemetry API to be inlcuded with the response
@@ -117,7 +124,7 @@ class MetricEvent(EventCommon):
 
 
 class MetricResponseMixin(BaseModel):
-    metrics: Optional[List[MetricEvent]] = None
+    metrics: Optional[List[MetricInResponse]] = None
 
 
 @json_schema_type

llama_stack/distribution/routers/routers.py

@@ -48,7 +48,7 @@ from llama_stack.apis.scoring import (
     ScoringFnParams,
 )
 from llama_stack.apis.shields import Shield
-from llama_stack.apis.telemetry import MetricEvent, Telemetry
+from llama_stack.apis.telemetry import MetricEvent, MetricInResponse, Telemetry
 from llama_stack.apis.tools import (
     RAGDocument,
     RAGQueryConfig,
@@ -206,12 +206,12 @@ class InferenceRouter(Inference):
         completion_tokens: int,
         total_tokens: int,
         model: Model,
-    ) -> List[MetricEvent]:
+    ) -> List[MetricInResponse]:
         metrics = self._construct_metrics(prompt_tokens, completion_tokens, total_tokens, model)
         if self.telemetry:
             for metric in metrics:
                 await self.telemetry.log_event(metric)
-        return metrics
+        return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics]
 
     async def _count_tokens(
         self,
@@ -238,7 +238,6 @@ class InferenceRouter(Inference):
         tool_config: Optional[ToolConfig] = None,
     ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
         logger.debug(
-            "core",
             f"InferenceRouter.chat_completion: {model_id=}, {stream=}, {messages=}, {tools=}, {tool_config=}, {response_format=}",
         )
         if sampling_params is None:

llama_stack/distribution/utils/context.py

@@ -19,7 +19,7 @@ def preserve_contexts_async_generator(
     and we need to preserve the context across the event loop boundary.
     """
 
-    async def wrapper():
+    async def wrapper() -> AsyncGenerator[T, None]:
         while True:
             try:
                 item = await gen.__anext__()

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

@@ -218,18 +218,10 @@ class ChatAgent(ShieldRunnerMixin):
         steps = []
         messages = await self.get_messages_from_turns(turns)
         if is_resume:
-            if isinstance(request.tool_responses[0], ToolResponseMessage):
-                tool_response_messages = request.tool_responses
-                tool_responses = [
-                    ToolResponse(call_id=x.call_id, tool_name=x.tool_name, content=x.content)
-                    for x in request.tool_responses
-                ]
-            else:
-                tool_response_messages = [
-                    ToolResponseMessage(call_id=x.call_id, tool_name=x.tool_name, content=x.content)
-                    for x in request.tool_responses
-                ]
-                tool_responses = request.tool_responses
+            tool_response_messages = [
+                ToolResponseMessage(call_id=x.call_id, tool_name=x.tool_name, content=x.content)
+                for x in request.tool_responses
+            ]
             messages.extend(tool_response_messages)
             last_turn = turns[-1]
             last_turn_messages = self.turn_to_messages(last_turn)
@@ -252,7 +244,7 @@ class ChatAgent(ShieldRunnerMixin):
                 step_id=(in_progress_tool_call_step.step_id if in_progress_tool_call_step else str(uuid.uuid4())),
                 turn_id=request.turn_id,
                 tool_calls=(in_progress_tool_call_step.tool_calls if in_progress_tool_call_step else []),
-                tool_responses=tool_responses,
+                tool_responses=request.tool_responses,
                 completed_at=now,
                 started_at=(in_progress_tool_call_step.started_at if in_progress_tool_call_step else now),
             )

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

@@ -172,7 +172,7 @@ class MetaReferenceAgentsImpl(Agents):
         agent_id: str,
         session_id: str,
         turn_id: str,
-        tool_responses: Union[List[ToolResponse], List[ToolResponseMessage]],
+        tool_responses: List[ToolResponse],
         stream: Optional[bool] = False,
     ) -> AsyncGenerator:
         request = AgentTurnResumeRequest(