Remote vLLM client is now initialized directly prior to first use instead of VLLMInferenceAdapter.initialize.

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

@@ -231,12 +231,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         self.client = None
 
     async def initialize(self) -> None:
-        log.info(f"Initializing VLLM client with base_url={self.config.url}")
+        pass
-        self.client = AsyncOpenAI(
-            base_url=self.config.url,
-            api_key=self.config.api_token,
-            http_client=None if self.config.tls_verify else httpx.AsyncClient(verify=False),
-        )
 
     async def shutdown(self) -> None:
         pass
@@ -249,6 +244,20 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
             raise ValueError("Model store not set")
         return await self.model_store.get_model(model_id)
 
+    def _lazy_initialize_client(self):
+        if self.client is not None:
+            return
+
+        log.info(f"Initializing VLLM client with base_url={self.config.url}")
+        self.client = self._create_client()
+
+    def _create_client(self):
+        return AsyncOpenAI(
+            base_url=self.config.url,
+            api_key=self.config.api_token,
+            http_client=None if self.config.tls_verify else httpx.AsyncClient(verify=False),
+        )
+
     async def completion(
         self,
         model_id: str,
@@ -258,6 +267,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]:
+        self._lazy_initialize_client()
         if sampling_params is None:
             sampling_params = SamplingParams()
         model = await self._get_model(model_id)
@@ -287,6 +297,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         logprobs: Optional[LogProbConfig] = None,
         tool_config: Optional[ToolConfig] = None,
     ) -> ChatCompletionResponse | AsyncGenerator[ChatCompletionResponseStreamChunk, None]:
+        self._lazy_initialize_client()
         if sampling_params is None:
             sampling_params = SamplingParams()
         model = await self._get_model(model_id)
@@ -357,9 +368,9 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
             yield chunk
 
     async def register_model(self, model: Model) -> Model:
-        assert self.client is not None
+        client = self._create_client() if self.client is None else self.client
         model = await self.register_helper.register_model(model)
-        res = await self.client.models.list()
+        res = await client.models.list()
         available_models = [m.id async for m in res]
         if model.provider_resource_id not in available_models:
             raise ValueError(
@@ -410,6 +421,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         output_dimension: Optional[int] = None,
         task_type: Optional[EmbeddingTaskType] = None,
     ) -> EmbeddingsResponse:
+        self._lazy_initialize_client()
         assert self.client is not None
         model = await self._get_model(model_id)
 
@@ -449,6 +461,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         guided_choice: Optional[List[str]] = None,
         prompt_logprobs: Optional[int] = None,
     ) -> OpenAICompletion:
+        self._lazy_initialize_client()
         model_obj = await self._get_model(model)
 
         extra_body: Dict[str, Any] = {}
@@ -505,6 +518,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         top_p: Optional[float] = None,
         user: Optional[str] = None,
     ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
+        self._lazy_initialize_client()
         model_obj = await self._get_model(model)
         params = await prepare_openai_completion_params(
             model=model_obj.provider_resource_id,