diff --git a/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py b/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py index cfdd09baa..4dd92b293 100644 --- a/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py +++ b/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py @@ -44,8 +44,11 @@ from llama_stack.apis.agents.openai_responses import ( OpenAIResponseObjectStreamResponseRefusalDone, OpenAIResponseOutput, OpenAIResponseOutputMessageContentOutputText, + OpenAIResponseOutputMessageFileSearchToolCall, OpenAIResponseOutputMessageFunctionToolCall, + OpenAIResponseOutputMessageMCPCall, OpenAIResponseOutputMessageMCPListTools, + OpenAIResponseOutputMessageWebSearchToolCall, OpenAIResponseText, OpenAIResponseUsage, OpenAIResponseUsageInputTokensDetails, @@ -177,6 +180,7 @@ class StreamingResponseOrchestrator: # (some providers don't support non-empty response_format when tools are present) response_format = None if self.ctx.response_format.type == "text" else self.ctx.response_format logger.debug(f"calling openai_chat_completion with tools: {self.ctx.chat_tools}") + params = OpenAIChatCompletionRequestWithExtraBody( model=self.ctx.model, messages=messages, @@ -613,19 +617,22 @@ class StreamingResponseOrchestrator: # Emit output_item.added event for the new function call self.sequence_number += 1 - function_call_item = OpenAIResponseOutputMessageFunctionToolCall( - arguments="", # Will be filled incrementally via delta events - call_id=tool_call.id or "", - name=tool_call.function.name if tool_call.function else "", - id=tool_call_item_id, - status="in_progress", - ) - yield OpenAIResponseObjectStreamResponseOutputItemAdded( - response_id=self.response_id, - item=function_call_item, - output_index=len(output_messages), - sequence_number=self.sequence_number, - ) + is_mcp_tool = tool_call.function.name and tool_call.function.name in self.mcp_tool_to_server + if not is_mcp_tool and tool_call.function.name not in ["web_search", "knowledge_search"]: + # for MCP tools (and even other non-function tools) we emit an output message item later + function_call_item = OpenAIResponseOutputMessageFunctionToolCall( + arguments="", # Will be filled incrementally via delta events + call_id=tool_call.id or "", + name=tool_call.function.name if tool_call.function else "", + id=tool_call_item_id, + status="in_progress", + ) + yield OpenAIResponseObjectStreamResponseOutputItemAdded( + response_id=self.response_id, + item=function_call_item, + output_index=len(output_messages), + sequence_number=self.sequence_number, + ) # Stream tool call arguments as they arrive (differentiate between MCP and function calls) if tool_call.function and tool_call.function.arguments: @@ -806,6 +813,35 @@ class StreamingResponseOrchestrator: if not matching_item_id: matching_item_id = f"tc_{uuid.uuid4()}" + self.sequence_number += 1 + if tool_call.function.name and tool_call.function.name in self.mcp_tool_to_server: + item = OpenAIResponseOutputMessageMCPCall( + arguments="", + name=tool_call.function.name, + id=matching_item_id, + server_label=self.mcp_tool_to_server[tool_call.function.name].server_label, + status="in_progress", + ) + elif tool_call.function.name == "web_search": + item = OpenAIResponseOutputMessageWebSearchToolCall( + id=matching_item_id, + status="in_progress", + ) + elif tool_call.function.name == "knowledge_search": + item = OpenAIResponseOutputMessageFileSearchToolCall( + id=matching_item_id, + status="in_progress", + ) + else: + raise ValueError(f"Unsupported tool call: {tool_call.function.name}") + + yield OpenAIResponseObjectStreamResponseOutputItemAdded( + response_id=self.response_id, + item=item, + output_index=len(output_messages), + sequence_number=self.sequence_number, + ) + # Execute tool call with streaming tool_call_log = None tool_response_message = None @@ -1064,7 +1100,11 @@ class StreamingResponseOrchestrator: self.sequence_number += 1 yield OpenAIResponseObjectStreamResponseOutputItemAdded( response_id=self.response_id, - item=mcp_list_message, + item=OpenAIResponseOutputMessageMCPListTools( + id=mcp_list_message.id, + server_label=mcp_list_message.server_label, + tools=[], + ), output_index=len(output_messages) - 1, sequence_number=self.sequence_number, ) diff --git a/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py b/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py index 9ba04e63f..659dc599e 100644 --- a/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py +++ b/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py @@ -93,7 +93,7 @@ class ToolExecutor: # Build result messages from tool execution output_message, input_message = await self._build_result_messages( - function, tool_call_id, tool_kwargs, ctx, error_exc, result, has_error, mcp_tool_to_server + function, tool_call_id, item_id, tool_kwargs, ctx, error_exc, result, has_error, mcp_tool_to_server ) # Yield the final result @@ -356,6 +356,7 @@ class ToolExecutor: self, function, tool_call_id: str, + item_id: str, tool_kwargs: dict, ctx: ChatCompletionContext, error_exc: Exception | None, @@ -375,7 +376,7 @@ class ToolExecutor: ) message = OpenAIResponseOutputMessageMCPCall( - id=tool_call_id, + id=item_id, arguments=function.arguments, name=function.name, server_label=mcp_tool_to_server[function.name].server_label, @@ -389,14 +390,14 @@ class ToolExecutor: else: if function.name == "web_search": message = OpenAIResponseOutputMessageWebSearchToolCall( - id=tool_call_id, + id=item_id, status="completed", ) if has_error: message.status = "failed" elif function.name == "knowledge_search": message = OpenAIResponseOutputMessageFileSearchToolCall( - id=tool_call_id, + id=item_id, queries=[tool_kwargs.get("query", "")], status="completed", ) diff --git a/tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-6a62c598.json b/tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-6a62c598.json new file mode 100644 index 000000000..8bd649eb8 --- /dev/null +++ b/tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-6a62c598.json @@ -0,0 +1,881 @@ +{ + "test_id": null, + "request": { + "method": "POST", + "url": "https://api.openai.com/v1/v1/models", + "headers": {}, + "body": {}, + "endpoint": "/v1/models", + "model": "" + }, + "response": { + "body": [ + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "gpt-4-0613", + "created": 1686588896, + "object": "model", + "owned_by": "openai" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "gpt-4", + "created": 1687882411, + "object": "model", + "owned_by": "openai" + } + }, + { + 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f"Bearer {AUTH_TOKEN}", + }, } ] - agent = Agent( client=llama_stack_client, model=text_model_id, @@ -111,47 +112,22 @@ def test_mcp_invocation(llama_stack_client, text_model_id, mcp_server): extra_headers=auth_headers, ) ) - events = [chunk.event for chunk in chunks] + final_response = next((chunk.response for chunk in reversed(chunks) if chunk.response), None) assert final_response is not None issued_calls = [ event for event in events if isinstance(event, StepProgress) and isinstance(event.delta, ToolCallIssuedDelta) ] - assert issued_calls and issued_calls[0].delta.tool_name == "greet_everyone" + assert issued_calls + + assert issued_calls[-1].delta.tool_name == "greet_everyone" tool_events = [ event for event in events if isinstance(event, StepCompleted) and event.step_type == "tool_execution" ] - assert tool_events and tool_events[0].result.tool_calls[0].tool_name == "greet_everyone" + assert tool_events + assert tool_events[-1].result.tool_calls[0].tool_name == "greet_everyone" assert "hello" in final_response.output_text.lower() - - # when streaming, we currently don't check auth headers upfront and fail the request - # early. but we should at least be generating a 401 later in the process. - response_stream = agent.create_turn( - session_id=session_id, - messages=[ - { - "type": "message", - "role": "user", - "content": [ - { - "type": "input_text", - "text": "What is the boiling point of polyjuice? Use tools to answer.", - } - ], - } - ], - stream=True, - ) - if isinstance(llama_stack_client, LlamaStackAsLibraryClient): - with pytest.raises(AuthenticationRequiredError): - for _ in response_stream: - pass - else: - error_chunks = [chunk for chunk in response_stream if "error" in chunk.model_dump()] - assert len(error_chunks) == 1 - chunk = error_chunks[0].model_dump() - assert "Unauthorized" in chunk["error"]["message"]