fixing serve-llm stuff.

ray_serve/serve_llm.py

@@ -120,7 +120,7 @@ class LLMDeployment:
         except ImportError:
             self._mlflow = None
 
-    async def __call__(self, request: dict[str, Any]) -> dict[str, Any]:
+    async def __call__(self, request) -> dict[str, Any]:
         """
         Handle OpenAI-compatible chat completion requests.
 
@@ -134,14 +134,23 @@ class LLMDeployment:
             "stream": false
         }
         """
-        messages = request.get("messages", [])
-        temperature = request.get("temperature", 0.7)
+        # Ray Serve passes a Starlette Request for HTTP calls —
+        # parse the JSON body so we actually read the user's payload.
+        from starlette.requests import Request
+
+        if isinstance(request, Request):
+            body = await request.json()
+        else:
+            body = request
+
+        messages = body.get("messages", [])
+        temperature = body.get("temperature", 0.7)
         max_tokens = min(
-            request.get("max_tokens", self.default_max_tokens),
+            body.get("max_tokens", self.default_max_tokens),
             self.max_model_len,
         )
-        top_p = request.get("top_p", 1.0)
-        stop = request.get("stop")
+        top_p = body.get("top_p", 1.0)
+        stop = body.get("stop")
 
         # Convert messages to prompt
         prompt = self._format_messages(messages)