Merge pull request #4828 from BerriAI/litellm_raise_Correct_provider_content_policy_errors

fix raise correct provider on streaming content policy violation errors

litellm/tests/test_exceptions.py

@@ -64,6 +64,30 @@ async def test_content_policy_exception_azure():
         pytest.fail(f"An exception occurred - {str(e)}")
 
 
+@pytest.mark.asyncio
+async def test_content_policy_exception_openai():
+    try:
+        # this is ony a test - we needed some way to invoke the exception :(
+        litellm.set_verbose = True
+        response = await litellm.acompletion(
+            model="gpt-3.5-turbo-0613",
+            stream=True,
+            messages=[
+                {"role": "user", "content": "Gimme the lyrics to Don't Stop Me Now"}
+            ],
+        )
+        async for chunk in response:
+            print(chunk)
+    except litellm.ContentPolicyViolationError as e:
+        print("caught a content policy violation error! Passed")
+        print("exception", e)
+        assert e.llm_provider == "openai"
+        pass
+    except Exception as e:
+        print()
+        pytest.fail(f"An exception occurred - {str(e)}")
+
+
 # Test 1: Context Window Errors
 @pytest.mark.skip(reason="AWS Suspended Account")
 @pytest.mark.parametrize("model", exception_models)

litellm/utils.py

@@ -8808,11 +8808,14 @@ class CustomStreamWrapper:
                                 str_line.choices[0].content_filter_result
                             )
                         else:
-                            error_message = "Azure Response={}".format(
+                            error_message = "{} Response={}".format(
-                                str(dict(str_line))
+                                self.custom_llm_provider, str(dict(str_line))
                             )
-                        raise litellm.AzureOpenAIError(
+
-                            status_code=400, message=error_message
+                        raise litellm.ContentPolicyViolationError(
+                            message=error_message,
+                            llm_provider=self.custom_llm_provider,
+                            model=self.model,
                         )
 
                 # checking for logprobs