featu: support passing "extra body" throught to providers

# What does this PR do?
Allows passing through extra_body parameters to inference providers.


closes #2720

## Test Plan
CI and added new test

tests/unit/providers/inference/test_remote_vllm.py

@@ -13,11 +13,16 @@ import pytest
 from llama_stack.apis.inference import (
     OpenAIAssistantMessageParam,
     OpenAIChatCompletion,
-    OpenAIChatCompletionRequest,
+    OpenAIChatCompletionRequestWithExtraBody,
     OpenAIChoice,
+    OpenAICompletion,
+    OpenAICompletionChoice,
+    OpenAICompletionRequestWithExtraBody,
     ToolChoice,
 )
 from llama_stack.apis.models import Model
+from llama_stack.core.routers.inference import InferenceRouter
+from llama_stack.core.routing_tables.models import ModelsRoutingTable
 from llama_stack.providers.datatypes import HealthStatus
 from llama_stack.providers.remote.inference.vllm.config import VLLMInferenceAdapterConfig
 from llama_stack.providers.remote.inference.vllm.vllm import VLLMInferenceAdapter
@@ -57,7 +62,7 @@ async def test_old_vllm_tool_choice(vllm_inference_adapter):
         mock_client_property.return_value = mock_client
 
         # No tools but auto tool choice
-        params = OpenAIChatCompletionRequest(
+        params = OpenAIChatCompletionRequestWithExtraBody(
             model="mock-model",
             messages=[{"role": "user", "content": "test"}],
             stream=False,
@@ -173,7 +178,7 @@ async def test_openai_chat_completion_is_async(vllm_inference_adapter):
         )
 
     async def do_inference():
-        params = OpenAIChatCompletionRequest(
+        params = OpenAIChatCompletionRequestWithExtraBody(
             model="mock-model",
             messages=[{"role": "user", "content": "one fish two fish"}],
             stream=False,
@@ -191,3 +196,148 @@ async def test_openai_chat_completion_is_async(vllm_inference_adapter):
 
         assert mock_create_client.call_count == 4  # no cheating
         assert total_time < (sleep_time * 2), f"Total time taken: {total_time}s exceeded expected max"
+
+
+async def test_vllm_completion_extra_body():
+    """
+    Test that vLLM-specific guided_choice and prompt_logprobs parameters are correctly forwarded
+    via extra_body to the underlying OpenAI client through the InferenceRouter.
+    """
+    # Set up the vLLM adapter
+    config = VLLMInferenceAdapterConfig(url="http://mocked.localhost:12345")
+    vllm_adapter = VLLMInferenceAdapter(config=config)
+    vllm_adapter.__provider_id__ = "vllm"
+    await vllm_adapter.initialize()
+
+    # Create a mock model store
+    mock_model_store = AsyncMock()
+    mock_model = Model(identifier="mock-model", provider_resource_id="mock-model", provider_id="vllm")
+    mock_model_store.get_model.return_value = mock_model
+    mock_model_store.has_model.return_value = True
+
+    # Create a mock dist_registry
+    mock_dist_registry = MagicMock()
+    mock_dist_registry.get = AsyncMock(return_value=mock_model)
+    mock_dist_registry.set = AsyncMock()
+
+    # Set up the routing table
+    routing_table = ModelsRoutingTable(
+        impls_by_provider_id={"vllm": vllm_adapter},
+        dist_registry=mock_dist_registry,
+        policy=[],
+    )
+    # Inject the model store into the adapter
+    vllm_adapter.model_store = routing_table
+
+    # Create the InferenceRouter
+    router = InferenceRouter(routing_table=routing_table)
+
+    # Patch the OpenAI client
+    with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_client_property:
+        mock_client = MagicMock()
+        mock_client.completions.create = AsyncMock(
+            return_value=OpenAICompletion(
+                id="cmpl-abc123",
+                created=1,
+                model="mock-model",
+                choices=[
+                    OpenAICompletionChoice(
+                        text="joy",
+                        finish_reason="stop",
+                        index=0,
+                    )
+                ],
+            )
+        )
+        mock_client_property.return_value = mock_client
+
+        # Test with guided_choice and prompt_logprobs as extra fields
+        params = OpenAICompletionRequestWithExtraBody(
+            model="mock-model",
+            prompt="I am feeling happy",
+            stream=False,
+            guided_choice=["joy", "sadness"],
+            prompt_logprobs=5,
+        )
+        await router.openai_completion(params)
+
+        # Verify that the client was called with extra_body containing both parameters
+        mock_client.completions.create.assert_called_once()
+        call_kwargs = mock_client.completions.create.call_args.kwargs
+        assert "extra_body" in call_kwargs
+        assert "guided_choice" in call_kwargs["extra_body"]
+        assert call_kwargs["extra_body"]["guided_choice"] == ["joy", "sadness"]
+        assert "prompt_logprobs" in call_kwargs["extra_body"]
+        assert call_kwargs["extra_body"]["prompt_logprobs"] == 5
+
+
+async def test_vllm_chat_completion_extra_body():
+    """
+    Test that vLLM-specific parameters (e.g., chat_template_kwargs) are correctly forwarded
+    via extra_body to the underlying OpenAI client through the InferenceRouter for chat completion.
+    """
+    # Set up the vLLM adapter
+    config = VLLMInferenceAdapterConfig(url="http://mocked.localhost:12345")
+    vllm_adapter = VLLMInferenceAdapter(config=config)
+    vllm_adapter.__provider_id__ = "vllm"
+    await vllm_adapter.initialize()
+
+    # Create a mock model store
+    mock_model_store = AsyncMock()
+    mock_model = Model(identifier="mock-model", provider_resource_id="mock-model", provider_id="vllm")
+    mock_model_store.get_model.return_value = mock_model
+    mock_model_store.has_model.return_value = True
+
+    # Create a mock dist_registry
+    mock_dist_registry = MagicMock()
+    mock_dist_registry.get = AsyncMock(return_value=mock_model)
+    mock_dist_registry.set = AsyncMock()
+
+    # Set up the routing table
+    routing_table = ModelsRoutingTable(
+        impls_by_provider_id={"vllm": vllm_adapter},
+        dist_registry=mock_dist_registry,
+        policy=[],
+    )
+    # Inject the model store into the adapter
+    vllm_adapter.model_store = routing_table
+
+    # Create the InferenceRouter
+    router = InferenceRouter(routing_table=routing_table)
+
+    # Patch the OpenAI client
+    with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_client_property:
+        mock_client = MagicMock()
+        mock_client.chat.completions.create = AsyncMock(
+            return_value=OpenAIChatCompletion(
+                id="chatcmpl-abc123",
+                created=1,
+                model="mock-model",
+                choices=[
+                    OpenAIChoice(
+                        message=OpenAIAssistantMessageParam(
+                            content="test response",
+                        ),
+                        finish_reason="stop",
+                        index=0,
+                    )
+                ],
+            )
+        )
+        mock_client_property.return_value = mock_client
+
+        # Test with chat_template_kwargs as extra field
+        params = OpenAIChatCompletionRequestWithExtraBody(
+            model="mock-model",
+            messages=[{"role": "user", "content": "test"}],
+            stream=False,
+            chat_template_kwargs={"thinking": True},
+        )
+        await router.openai_chat_completion(params)
+
+        # Verify that the client was called with extra_body containing chat_template_kwargs
+        mock_client.chat.completions.create.assert_called_once()
+        call_kwargs = mock_client.chat.completions.create.call_args.kwargs
+        assert "extra_body" in call_kwargs
+        assert "chat_template_kwargs" in call_kwargs["extra_body"]
+        assert call_kwargs["extra_body"]["chat_template_kwargs"] == {"thinking": True}