fix: remote vLLM tool execution now works when the last chunk contains the call arguments (#2112)

# What does this PR do?
Closes #2111.
Fixes an error causing Llama Stack to just return `<tool_call>` and
complete the turn without actually executing the tool. See the issue
description for more detail.

## Test Plan
1) Ran existing unit tests
2) Added a dedicated test verifying correct behavior in this edge case
3) Ran the code snapshot from #2111

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

@@ -168,6 +168,12 @@ async def _process_vllm_chat_completion_stream_response(
             log.warning("vLLM failed to generation any completions - check the vLLM server logs for an error.")
             continue
         choice = chunk.choices[0]
+        if choice.delta.tool_calls:
+            tool_call = convert_tool_call(choice.delta.tool_calls[0])
+            tool_call_buf.tool_name += str(tool_call.tool_name)
+            tool_call_buf.call_id += tool_call.call_id
+            # TODO: remove str() when dict type for 'arguments' is no longer allowed
+            tool_call_buf.arguments += str(tool_call.arguments)
         if choice.finish_reason:
             args_str = tool_call_buf.arguments
             args = None
@@ -208,13 +214,7 @@ async def _process_vllm_chat_completion_stream_response(
                     stop_reason=_convert_to_vllm_finish_reason(choice.finish_reason),
                 )
             )
-        elif choice.delta.tool_calls:
-            tool_call = convert_tool_call(choice.delta.tool_calls[0])
-            tool_call_buf.tool_name += str(tool_call.tool_name)
-            tool_call_buf.call_id += tool_call.call_id
-            # TODO: remove str() when dict type for 'arguments' is no longer allowed
-            tool_call_buf.arguments += str(tool_call.arguments)
-        else:
+        elif not choice.delta.tool_calls:
             yield ChatCompletionResponseStreamChunk(
                 event=ChatCompletionResponseEvent(
                     event_type=event_type,

tests/unit/providers/inference/test_remote_vllm.py

@@ -28,6 +28,7 @@ from openai.types.model import Model as OpenAIModel
 
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
+    ChatCompletionResponseEventType,
     CompletionMessage,
     SystemMessage,
     ToolChoice,
@@ -294,3 +295,82 @@ async def test_get_params_empty_tools(vllm_inference_adapter):
     )
     params = await vllm_inference_adapter._get_params(request)
     assert "tools" not in params
+
+
+@pytest.mark.asyncio
+async def test_process_vllm_chat_completion_stream_response_tool_call_args_last_chunk():
+    """
+    Tests the edge case where the model returns the arguments for the tool call in the same chunk that
+    contains the finish reason (i.e., the last one).
+    We want to make sure the tool call is executed in this case, and the parameters are passed correctly.
+    """
+
+    mock_tool_name = "mock_tool"
+    mock_tool_arguments = {"arg1": 0, "arg2": 100}
+    mock_tool_arguments_str = json.dumps(mock_tool_arguments)
+
+    async def mock_stream():
+        mock_chunks = [
+            OpenAIChatCompletionChunk(
+                id="chunk-1",
+                created=1,
+                model="foo",
+                object="chat.completion.chunk",
+                choices=[
+                    {
+                        "delta": {
+                            "content": None,
+                            "tool_calls": [
+                                {
+                                    "index": 0,
+                                    "id": "mock_id",
+                                    "type": "function",
+                                    "function": {
+                                        "name": mock_tool_name,
+                                        "arguments": None,
+                                    },
+                                }
+                            ],
+                        },
+                        "finish_reason": None,
+                        "logprobs": None,
+                        "index": 0,
+                    }
+                ],
+            ),
+            OpenAIChatCompletionChunk(
+                id="chunk-1",
+                created=1,
+                model="foo",
+                object="chat.completion.chunk",
+                choices=[
+                    {
+                        "delta": {
+                            "content": None,
+                            "tool_calls": [
+                                {
+                                    "index": 0,
+                                    "id": None,
+                                    "function": {
+                                        "name": None,
+                                        "arguments": mock_tool_arguments_str,
+                                    },
+                                }
+                            ],
+                        },
+                        "finish_reason": "tool_calls",
+                        "logprobs": None,
+                        "index": 0,
+                    }
+                ],
+            ),
+        ]
+        for chunk in mock_chunks:
+            yield chunk
+
+    chunks = [chunk async for chunk in _process_vllm_chat_completion_stream_response(mock_stream())]
+    assert len(chunks) == 2
+    assert chunks[-1].event.event_type == ChatCompletionResponseEventType.complete
+    assert chunks[-2].event.delta.type == "tool_call"
+    assert chunks[-2].event.delta.tool_call.tool_name == mock_tool_name
+    assert chunks[-2].event.delta.tool_call.arguments == mock_tool_arguments