fix: Handle tool calling in remote vLLM provider

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>

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

@@ -187,12 +187,51 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
             )
         return model
 
+    def convert_to_vllm_tools(self, tools: List[ToolDefinition]) -> List[dict]:
+        if tools is None:
+            return tools
+
+        compat_tools = []
+
+        for tool in tools:
+            properties = {}
+            compat_required = []
+            if tool.parameters:
+                for tool_key, tool_param in tool.parameters.items():
+                    properties[tool_key] = {"type": tool_param.param_type}
+                    if tool_param.description:
+                        properties[tool_key]["description"] = tool_param.description
+                    if tool_param.default:
+                        properties[tool_key]["default"] = tool_param.default
+                    if tool_param.required:
+                        compat_required.append(tool_key)
+
+            compat_tool = {
+                "type": "function",
+                "function": {
+                    "name": tool.tool_name,
+                    "description": tool.description,
+                    "parameters": {
+                        "type": "object",
+                        "properties": properties,
+                        "required": compat_required,
+                    },
+                },
+            }
+
+            compat_tools.append(compat_tool)
+
+        if len(compat_tools) > 0:
+            return compat_tools
+        return None
+
     async def _get_params(self, request: Union[ChatCompletionRequest, CompletionRequest]) -> dict:
         options = get_sampling_options(request.sampling_params)
         if "max_tokens" not in options:
             options["max_tokens"] = self.config.max_tokens
 
         input_dict = {}
+        input_dict["tools"] = self.convert_to_vllm_tools(request.tools)
 
         if isinstance(request, ChatCompletionRequest):
             input_dict["messages"] = [await convert_message_to_openai_dict(m, download=True) for m in request.messages]