fix(decisions): return guardrail_information on OpenAI-format decisions when requested

litellm/proxy/decisions_endpoints/endpoints.py

@@ -8,7 +8,11 @@ from pydantic import TypeAdapter, ValidationError
 from litellm.decisions.openai_transformation import to_openai_response, to_systemone_request
 from litellm.exceptions import BadRequestError
 from litellm.proxy.auth.user_api_key_auth import UserAPIKeyAuth, user_api_key_auth
-from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
+from litellm.proxy.common_request_processing import (
+    ProxyBaseLLMRequestProcessing,
+    attach_guardrail_information,
+    include_guardrail_response_requested,
+)
 from litellm.types.decisions import DecisionsRequestBody, DecisionsResponse, OpenAIDecisionRequestBody
 
 router: Final = APIRouter()
@@ -104,11 +108,14 @@ async def _process_systemone(
         )
         if openai_body is None or isinstance(result, Response):
             return result
-        return to_openai_response(
+        openai_response: Final = to_openai_response(
             _DECISIONS_RESPONSE_ADAPTER.validate_python(result),
             openai_body.questions,
             str(data.get("model", "")),
         )
+        if include_guardrail_response_requested(processor.data):
+            return attach_guardrail_information(response=openai_response, request_data=processor.data)
+        return openai_response
     except ValidationError as error:
         raise await _invalid_request(raw_data=data, error=error, user_api_key_dict=user_api_key_dict)
     except Exception as error:

litellm/types/decisions.py

@@ -294,4 +294,4 @@ class OpenAIDecisionResponse(LiteLLMPydanticObjectBase):
     answers: tuple[OpenAIDecisionAnswer, ...]
     usage: OpenAIDecisionUsage
 
-    model_config = ConfigDict(frozen=True)
+    model_config = ConfigDict(extra="allow", frozen=True)

tests/unit/proxy/decisions_endpoints/test_endpoints.py

@@ -385,6 +385,29 @@ def test_openai_format_decisions_translate_through_systemone(
     assert float(response.headers["x-litellm-response-cost"]) > 0
 
 
+@pytest.mark.parametrize(
+    ("endpoint", "request_body", "upstream_response"),
+    (
+        ("/v1/systemone", _REQUEST, _RESPONSE),
+        ("/v1/decisions", _OPENAI_FORMAT_REQUEST, _SYSTEMONE_ANSWERS_FOR_OPENAI_REQUEST),
+    ),
+    ids=("systemone", "openai_format"),
+)
+def test_decisions_return_guardrail_information_when_requested(
+    client: TestClient,
+    respx_mock: respx.MockRouter,
+    endpoint: str,
+    request_body: Mapping[str, object],
+    upstream_response: Mapping[str, object],
+) -> None:
+    respx_mock.post("https://api.perplexity.ai/v1/decisions").respond(json=upstream_response)
+
+    response: Final = client.post(endpoint, json={**request_body, "include_guardrail_response": True})
+
+    assert response.status_code == 200, response.text
+    assert response.json()["guardrail_information"] == []
+
+
 @pytest.mark.parametrize(
     "request_body",
     (
@@ -402,7 +425,9 @@ def test_openai_format_decisions_translate_through_systemone(
         },
         {
             "model": "decider",
-            "input": [{"role": "user", "content": [{"type": "input_image", "image_url": "data:image/png;base64,AA=="}]}],
+            "input": [
+                {"role": "user", "content": [{"type": "input_image", "image_url": "data:image/png;base64,AA=="}]}
+            ],
             "questions": [{"type": "predicate", "instructions": "Is this a defect?"}],
         },
     ),