fix(sap): null values handling

litellm/llms/sap/chat/transformation.py

@@ -419,25 +419,31 @@ class GenAIHubOrchestrationConfig(OpenAIGPTConfig):
         (already typed for this shape) and set reasoning_content to the concatenated
         text so callers that read the string field still get something useful.
         """
+        new_choices = []
         for choice in raw.get("choices", []):  # mutable-ok: sentinel default, never mutated
-            msg = choice.get("message", {})  # mutable-ok: sentinel default, never mutated
+            msg = choice.get("message", {})
             rc = msg.get("reasoning_content")
             if not isinstance(rc, list):
+                new_choices.append(choice)
                 continue
             thinking_blocks = [  # mutable-ok: local accumulator built once and assigned
                 {  # mutable-ok: each block dict constructed fresh per item
                     "type": "thinking",
-                    "thinking": item.get("content", ""),
+                    "thinking": item.get("content") or "",
                     "signature": item.get("signature"),
                 }
                 for item in rc
                 if isinstance(item, dict)
             ]
-            msg["thinking_blocks"] = thinking_blocks
-            msg["reasoning_content"] = (
-                "\n".join(b["thinking"] for b in thinking_blocks if b["thinking"]) or None
-            )
-        return raw
+            new_msg = {
+                **msg,
+                "thinking_blocks": thinking_blocks,
+                "reasoning_content": (
+                    "\n".join(b["thinking"] for b in thinking_blocks if b["thinking"]) or None
+                ),
+            }
+            new_choices.append({**choice, "message": new_msg})
+        return {**raw, "choices": new_choices}
 
     def _strip_markdown_json(self, response: ModelResponse) -> ModelResponse:
         """Strip markdown code block wrapper from JSON content if present.

tests/test_litellm/llms/sap/chat/test_sap_transformation.py

@@ -712,3 +712,75 @@ class TestNormalizeReasoningContent:
         out = self._normalize(raw)
         assert out["choices"][0]["message"]["reasoning_content"] == "thought A"
         assert out["choices"][1]["message"]["reasoning_content"] == "thought B"
+
+
+    def test_null_content_in_block_uses_empty_string(self):
+        """Explicit null content value must not leak None into thinking field."""
+        raw = {
+            "choices": [{
+                "message": {
+                    "reasoning_content": [{"content": None, "signature": "s"}],
+                }
+            }]
+        }
+        out = self._normalize(raw)
+        block = out["choices"][0]["message"]["thinking_blocks"][0]
+        assert block["thinking"] == ""
+        assert out["choices"][0]["message"]["reasoning_content"] is None
+
+    def test_transform_response_normalizes_list_reasoning_content(self):
+        """Production path: transform_response must produce a ModelResponse
+        with thinking_blocks populated when the raw payload carries a
+        list-shaped reasoning_content.
+        """
+        import json
+        from unittest.mock import MagicMock
+
+        from litellm.llms.sap.chat.transformation import GenAIHubOrchestrationConfig
+
+        config = GenAIHubOrchestrationConfig()
+
+        final_result = {
+            "id": "chatcmpl-test",
+            "object": "chat.completion",
+            "created": 1700000000,
+            "model": "gemini-test",
+            "choices": [{
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": "The answer is 42.",
+                    "reasoning_content": [
+                        {"content": "Let me think.", "signature": "sig1"},
+                        {"content": "Yes, 42.", "signature": "sig2"},
+                    ],
+                },
+                "finish_reason": "stop",
+            }],
+            "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+        }
+
+        raw_response = MagicMock()
+        raw_response.text = json.dumps({"final_result": final_result})
+        raw_response.json.return_value = {"final_result": final_result}
+
+        response = config.transform_response(
+            model="gemini-test",
+            raw_response=raw_response,
+            model_response=MagicMock(),
+            logging_obj=MagicMock(),
+            api_key="test",
+            request_data={},
+            messages=[],
+            optional_params={},
+            litellm_params={},
+            encoding=None,
+        )
+
+        choice = response.choices[0]
+        assert hasattr(choice.message, "thinking_blocks"), "thinking_blocks missing from message"
+        assert choice.message.thinking_blocks == [
+            {"type": "thinking", "thinking": "Let me think.", "signature": "sig1"},
+            {"type": "thinking", "thinking": "Yes, 42.", "signature": "sig2"},
+        ]
+        assert choice.message.reasoning_content == "Let me think.\nYes, 42."