feat: Add dynamic authentication token forwarding support for vLLM (#3388)

# What does this PR do?


*Add dynamic authentication token forwarding support for vLLM provider*

This enables per-request authentication tokens for vLLM providers,
supporting use cases like RAG operations where different requests may
need different authentication tokens. The implementation follows the
same pattern as other providers like Together AI, Fireworks, and
Passthrough.

- Add LiteLLMOpenAIMixin that manages the vllm_api_token properly

Usage:

- Static: VLLM_API_TOKEN env var or config.api_token
- Dynamic: X-LlamaStack-Provider-Data header with vllm_api_token
All existing functionality is preserved while adding new dynamic
capabilities.


<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->

## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->

```
curl -X POST "http://localhost:8000/v1/chat/completions" -H "Authorization: Bearer my-dynamic-token" \
  -H "X-LlamaStack-Provider-Data: {\"vllm_api_token\": \"Bearer my-dynamic-token\", \"vllm_url\": \"http://dynamic-server:8000\"}" \
  -H "Content-Type: application/json" \
  -d '{"model": "llama-3.1-8b", "messages": [{"role": "user", "content": "Hello!"}]}'
  
```

---------

Signed-off-by: Akram Ben Aissi <akram.benaissi@gmail.com>

llama_stack/providers/registry/inference.py

@@ -78,6 +78,7 @@ def available_providers() -> list[ProviderSpec]:
                 pip_packages=[],
                 module="llama_stack.providers.remote.inference.vllm",
                 config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig",
+                provider_data_validator="llama_stack.providers.remote.inference.vllm.VLLMProviderDataValidator",
                 description="Remote vLLM inference provider for connecting to vLLM servers.",
             ),
         ),

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

@@ -4,9 +4,15 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from pydantic import BaseModel
+
 from .config import VLLMInferenceAdapterConfig
 
 
+class VLLMProviderDataValidator(BaseModel):
+    vllm_api_token: str | None = None
+
+
 async def get_adapter_impl(config: VLLMInferenceAdapterConfig, _deps):
     from .vllm import VLLMInferenceAdapter
 

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

@@ -4,8 +4,9 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 import json
-from collections.abc import AsyncGenerator
+from collections.abc import AsyncGenerator, AsyncIterator
 from typing import Any
+from urllib.parse import urljoin
 
 import httpx
 from openai import APIConnectionError, AsyncOpenAI
@@ -55,6 +56,7 @@ from llama_stack.providers.datatypes import (
     HealthStatus,
     ModelsProtocolPrivate,
 )
+from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.model_registry import (
     ModelRegistryHelper,
     build_hf_repo_model_entry,
@@ -62,6 +64,7 @@ from llama_stack.providers.utils.inference.model_registry import (
 from llama_stack.providers.utils.inference.openai_compat import (
     UnparseableToolCall,
     convert_message_to_openai_dict,
+    convert_openai_chat_completion_stream,
     convert_tool_call,
     get_sampling_options,
     process_chat_completion_stream_response,
@@ -281,15 +284,31 @@ async def _process_vllm_chat_completion_stream_response(
         yield c
 
 
-class VLLMInferenceAdapter(OpenAIMixin, Inference, ModelsProtocolPrivate):
+class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsProtocolPrivate):
     # automatically set by the resolver when instantiating the provider
     __provider_id__: str
     model_store: ModelStore | None = None
 
     def __init__(self, config: VLLMInferenceAdapterConfig) -> None:
+        LiteLLMOpenAIMixin.__init__(
+            self,
+            build_hf_repo_model_entries(),
+            litellm_provider_name="vllm",
+            api_key_from_config=config.api_token,
+            provider_data_api_key_field="vllm_api_token",
+            openai_compat_api_base=config.url,
+        )
         self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries())
         self.config = config
 
+    get_api_key = LiteLLMOpenAIMixin.get_api_key
+
+    def get_base_url(self) -> str:
+        """Get the base URL from config."""
+        if not self.config.url:
+            raise ValueError("No base URL configured")
+        return self.config.url
+
     async def initialize(self) -> None:
         if not self.config.url:
             raise ValueError(
@@ -297,6 +316,7 @@ class VLLMInferenceAdapter(OpenAIMixin, Inference, ModelsProtocolPrivate):
             )
 
     async def should_refresh_models(self) -> bool:
+        # Strictly respecting the refresh_models directive
         return self.config.refresh_models
 
     async def list_models(self) -> list[Model] | None:
@@ -325,12 +345,18 @@ class VLLMInferenceAdapter(OpenAIMixin, Inference, ModelsProtocolPrivate):
         Performs a health check by verifying connectivity to the remote vLLM server.
         This method is used by the Provider API to verify
         that the service is running correctly.
+        Uses the unauthenticated /health endpoint.
         Returns:
 
             HealthResponse: A dictionary containing the health status.
         """
         try:
-            _ = [m async for m in self.client.models.list()]  # Ensure the client is initialized
+            base_url = self.get_base_url()
+            health_url = urljoin(base_url, "health")
+
+            async with httpx.AsyncClient() as client:
+                response = await client.get(health_url)
+                response.raise_for_status()
                 return HealthResponse(status=HealthStatus.OK)
         except Exception as e:
             return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}")
@@ -340,16 +366,10 @@ class VLLMInferenceAdapter(OpenAIMixin, Inference, ModelsProtocolPrivate):
             raise ValueError("Model store not set")
         return await self.model_store.get_model(model_id)
 
-    def get_api_key(self):
-        return self.config.api_token
-
-    def get_base_url(self):
-        return self.config.url
-
     def get_extra_client_params(self):
         return {"http_client": httpx.AsyncClient(verify=self.config.tls_verify)}
 
-    async def completion(
+    async def completion(  # type: ignore[override]  # Return type more specific than base class  which is allows for both streaming and non-streaming responses.
         self,
         model_id: str,
         content: InterleavedContent,
@@ -411,13 +431,14 @@ class VLLMInferenceAdapter(OpenAIMixin, Inference, ModelsProtocolPrivate):
             tool_config=tool_config,
         )
         if stream:
-            return self._stream_chat_completion(request, self.client)
+            return self._stream_chat_completion_with_client(request, self.client)
         else:
             return await self._nonstream_chat_completion(request, self.client)
 
     async def _nonstream_chat_completion(
         self, request: ChatCompletionRequest, client: AsyncOpenAI
     ) -> ChatCompletionResponse:
+        assert self.client is not None
         params = await self._get_params(request)
         r = await client.chat.completions.create(**params)
         choice = r.choices[0]
@@ -431,9 +452,24 @@ class VLLMInferenceAdapter(OpenAIMixin, Inference, ModelsProtocolPrivate):
         )
         return result
 
-    async def _stream_chat_completion(
+    async def _stream_chat_completion(self, response: Any) -> AsyncIterator[ChatCompletionResponseStreamChunk]:
+        # This method is called from LiteLLMOpenAIMixin.chat_completion
+        # The response parameter contains the litellm response
+        # We need to convert it to our format
+        async def _stream_generator():
+            async for chunk in response:
+                yield chunk
+
+        async for chunk in convert_openai_chat_completion_stream(
+            _stream_generator(), enable_incremental_tool_calls=True
+        ):
+            yield chunk
+
+    async def _stream_chat_completion_with_client(
         self, request: ChatCompletionRequest, client: AsyncOpenAI
     ) -> AsyncGenerator[ChatCompletionResponseStreamChunk, None]:
+        """Helper method for streaming with explicit client parameter."""
+        assert self.client is not None
         params = await self._get_params(request)
 
         stream = await client.chat.completions.create(**params)
@@ -445,7 +481,8 @@ class VLLMInferenceAdapter(OpenAIMixin, Inference, ModelsProtocolPrivate):
             yield chunk
 
     async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse:
-        assert self.client is not None
+        if self.client is None:
+            raise RuntimeError("Client is not initialized")
         params = await self._get_params(request)
         r = await self.client.completions.create(**params)
         return process_completion_response(r)
@@ -453,7 +490,8 @@ class VLLMInferenceAdapter(OpenAIMixin, Inference, ModelsProtocolPrivate):
     async def _stream_completion(
         self, request: CompletionRequest
     ) -> AsyncGenerator[CompletionResponseStreamChunk, None]:
-        assert self.client is not None
+        if self.client is None:
+            raise RuntimeError("Client is not initialized")
         params = await self._get_params(request)
 
         stream = await self.client.completions.create(**params)

tests/unit/providers/inference/test_remote_vllm.py

@@ -66,11 +66,15 @@ def mock_openai_models_list():
         yield mock_list
 
 
-@pytest.fixture(scope="module")
+@pytest.fixture(scope="function")
 async def vllm_inference_adapter():
     config = VLLMInferenceAdapterConfig(url="http://mocked.localhost:12345")
     inference_adapter = VLLMInferenceAdapter(config)
     inference_adapter.model_store = AsyncMock()
+    # Mock the __provider_spec__ attribute that would normally be set by the resolver
+    inference_adapter.__provider_spec__ = MagicMock()
+    inference_adapter.__provider_spec__.provider_type = "vllm-inference"
+    inference_adapter.__provider_spec__.provider_data_validator = MagicMock()
     await inference_adapter.initialize()
     return inference_adapter
 
@@ -120,6 +124,10 @@ async def test_tool_call_response(vllm_inference_adapter):
         mock_client.chat.completions.create = AsyncMock()
         mock_create_client.return_value = mock_client
 
+        # Mock the model to return a proper provider_resource_id
+        mock_model = Model(identifier="mock-model", provider_resource_id="mock-model", provider_id="vllm-inference")
+        vllm_inference_adapter.model_store.get_model.return_value = mock_model
+
         messages = [
             SystemMessage(content="You are a helpful assistant"),
             UserMessage(content="How many?"),
@@ -555,31 +563,29 @@ async def test_health_status_success(vllm_inference_adapter):
     """
     Test the health method of VLLM InferenceAdapter when the connection is successful.
 
-    This test verifies that the health method returns a HealthResponse with status OK, only
-    when the connection to the vLLM server is successful.
+    This test verifies that the health method returns a HealthResponse with status OK
+    when the /health endpoint responds successfully.
     """
-    with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client:
-        # Create mock client and models
-        mock_client = MagicMock()
-        mock_models = MagicMock()
+    with patch("httpx.AsyncClient") as mock_client_class:
+        # Create mock response
+        mock_response = MagicMock()
+        mock_response.raise_for_status.return_value = None
 
-        # Create a mock async iterator that yields a model when iterated
-        async def mock_list():
-            for model in [MagicMock()]:
-                yield model
-
-        # Set up the models.list to return our mock async iterator
-        mock_models.list.return_value = mock_list()
-        mock_client.models = mock_models
-        mock_create_client.return_value = mock_client
+        # Create mock client instance
+        mock_client_instance = MagicMock()
+        mock_client_instance.get = AsyncMock(return_value=mock_response)
+        mock_client_class.return_value.__aenter__.return_value = mock_client_instance
 
         # Call the health method
         health_response = await vllm_inference_adapter.health()
+
         # Verify the response
         assert health_response["status"] == HealthStatus.OK
 
-        # Verify that models.list was called
-        mock_models.list.assert_called_once()
+        # Verify that the health endpoint was called
+        mock_client_instance.get.assert_called_once()
+        call_args = mock_client_instance.get.call_args[0]
+        assert call_args[0].endswith("/health")
 
 
 async def test_health_status_failure(vllm_inference_adapter):
@@ -589,28 +595,42 @@ async def test_health_status_failure(vllm_inference_adapter):
     This test verifies that the health method returns a HealthResponse with status ERROR
     and an appropriate error message when the connection to the vLLM server fails.
     """
-    with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client:
-        # Create mock client and models
-        mock_client = MagicMock()
-        mock_models = MagicMock()
-
-        # Create a mock async iterator that raises an exception when iterated
-        async def mock_list():
-            raise Exception("Connection failed")
-            yield  # Unreachable code
-
-        # Set up the models.list to return our mock async iterator
-        mock_models.list.return_value = mock_list()
-        mock_client.models = mock_models
-        mock_create_client.return_value = mock_client
+    with patch("httpx.AsyncClient") as mock_client_class:
+        # Create mock client instance that raises an exception
+        mock_client_instance = MagicMock()
+        mock_client_instance.get.side_effect = Exception("Connection failed")
+        mock_client_class.return_value.__aenter__.return_value = mock_client_instance
 
         # Call the health method
         health_response = await vllm_inference_adapter.health()
+
         # Verify the response
         assert health_response["status"] == HealthStatus.ERROR
         assert "Health check failed: Connection failed" in health_response["message"]
 
-        mock_models.list.assert_called_once()
+
+async def test_health_status_no_static_api_key(vllm_inference_adapter):
+    """
+    Test the health method of VLLM InferenceAdapter when no static API key is provided.
+
+    This test verifies that the health method returns a HealthResponse with status OK
+    when the /health endpoint responds successfully, regardless of API token configuration.
+    """
+    with patch("httpx.AsyncClient") as mock_client_class:
+        # Create mock response
+        mock_response = MagicMock()
+        mock_response.raise_for_status.return_value = None
+
+        # Create mock client instance
+        mock_client_instance = MagicMock()
+        mock_client_instance.get = AsyncMock(return_value=mock_response)
+        mock_client_class.return_value.__aenter__.return_value = mock_client_instance
+
+        # Call the health method
+        health_response = await vllm_inference_adapter.health()
+
+        # Verify the response
+        assert health_response["status"] == HealthStatus.OK
 
 
 async def test_openai_chat_completion_is_async(vllm_inference_adapter):
@@ -656,3 +676,109 @@ 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_should_refresh_models():
+    """
+    Test the should_refresh_models method with different refresh_models configurations.
+
+    This test verifies that:
+    1. When refresh_models is True, should_refresh_models returns True regardless of api_token
+    2. When refresh_models is False, should_refresh_models returns False regardless of api_token
+    """
+
+    # Test case 1: refresh_models is True, api_token is None
+    config1 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token=None, refresh_models=True)
+    adapter1 = VLLMInferenceAdapter(config1)
+    result1 = await adapter1.should_refresh_models()
+    assert result1 is True, "should_refresh_models should return True when refresh_models is True"
+
+    # Test case 2: refresh_models is True, api_token is empty string
+    config2 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="", refresh_models=True)
+    adapter2 = VLLMInferenceAdapter(config2)
+    result2 = await adapter2.should_refresh_models()
+    assert result2 is True, "should_refresh_models should return True when refresh_models is True"
+
+    # Test case 3: refresh_models is True, api_token is "fake" (default)
+    config3 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="fake", refresh_models=True)
+    adapter3 = VLLMInferenceAdapter(config3)
+    result3 = await adapter3.should_refresh_models()
+    assert result3 is True, "should_refresh_models should return True when refresh_models is True"
+
+    # Test case 4: refresh_models is True, api_token is real token
+    config4 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="real-token-123", refresh_models=True)
+    adapter4 = VLLMInferenceAdapter(config4)
+    result4 = await adapter4.should_refresh_models()
+    assert result4 is True, "should_refresh_models should return True when refresh_models is True"
+
+    # Test case 5: refresh_models is False, api_token is real token
+    config5 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="real-token-456", refresh_models=False)
+    adapter5 = VLLMInferenceAdapter(config5)
+    result5 = await adapter5.should_refresh_models()
+    assert result5 is False, "should_refresh_models should return False when refresh_models is False"
+
+
+async def test_provider_data_var_context_propagation(vllm_inference_adapter):
+    """
+    Test that PROVIDER_DATA_VAR context is properly propagated through the vLLM inference adapter.
+    This ensures that dynamic provider data (like API tokens) can be passed through context.
+    Note: The base URL is always taken from config.url, not from provider data.
+    """
+    # Mock the AsyncOpenAI class to capture provider data
+    with (
+        patch("llama_stack.providers.utils.inference.openai_mixin.AsyncOpenAI") as mock_openai_class,
+        patch.object(vllm_inference_adapter, "get_request_provider_data") as mock_get_provider_data,
+    ):
+        mock_client = AsyncMock()
+        mock_client.chat.completions.create = AsyncMock()
+        mock_openai_class.return_value = mock_client
+
+        # Mock provider data to return test data
+        mock_provider_data = MagicMock()
+        mock_provider_data.vllm_api_token = "test-token-123"
+        mock_provider_data.vllm_url = "http://test-server:8000/v1"
+        mock_get_provider_data.return_value = mock_provider_data
+
+        # Mock the model
+        mock_model = Model(identifier="test-model", provider_resource_id="test-model", provider_id="vllm-inference")
+        vllm_inference_adapter.model_store.get_model.return_value = mock_model
+
+        try:
+            # Execute chat completion
+            await vllm_inference_adapter.chat_completion(
+                "test-model",
+                [UserMessage(content="Hello")],
+                stream=False,
+                tools=None,
+                tool_config=ToolConfig(tool_choice=ToolChoice.auto),
+            )
+
+            # Verify that ALL client calls were made with the correct parameters
+            calls = mock_openai_class.call_args_list
+            incorrect_calls = []
+
+            for i, call in enumerate(calls):
+                api_key = call[1]["api_key"]
+                base_url = call[1]["base_url"]
+
+                if api_key != "test-token-123" or base_url != "http://mocked.localhost:12345":
+                    incorrect_calls.append({"call_index": i, "api_key": api_key, "base_url": base_url})
+
+            if incorrect_calls:
+                error_msg = (
+                    f"Found {len(incorrect_calls)} calls with incorrect parameters out of {len(calls)} total calls:\n"
+                )
+                for incorrect_call in incorrect_calls:
+                    error_msg += f"  Call {incorrect_call['call_index']}: api_key='{incorrect_call['api_key']}', base_url='{incorrect_call['base_url']}'\n"
+                error_msg += "Expected: api_key='test-token-123', base_url='http://mocked.localhost:12345'"
+                raise AssertionError(error_msg)
+
+            # Ensure at least one call was made
+            assert len(calls) >= 1, "No AsyncOpenAI client calls were made"
+
+            # Verify that chat completion was called
+            mock_client.chat.completions.create.assert_called_once()
+
+        finally:
+            # Clean up context
+            pass