fix(utils.py): fix content policy violation check for streaming

litellm/tests/test_exceptions.py

@@ -2,7 +2,7 @@ from openai import AuthenticationError, BadRequestError, RateLimitError, OpenAIE
 import os
 import sys
 import traceback
-import subprocess
+import subprocess, asyncio
 
 sys.path.insert(
     0, os.path.abspath("../..")
@@ -378,6 +378,74 @@ def test_content_policy_exceptionimage_generation_openai():
 # test_content_policy_exceptionimage_generation_openai()
 
 
+def tesy_async_acompletion():
+    """
+    Production Test.
+    """
+    litellm.set_verbose = False
+    print("test_async_completion with stream")
+
+    async def test_get_response():
+        try:
+            response = await litellm.acompletion(
+                model="azure/chatgpt-v-2",
+                messages=[{"role": "user", "content": "say 1"}],
+                temperature=0,
+                top_p=1,
+                stream=True,
+                max_tokens=512,
+                presence_penalty=0,
+                frequency_penalty=0,
+            )
+            print(f"response: {response}")
+
+            num_finish_reason = 0
+            async for chunk in response:
+                print(chunk)
+                if chunk["choices"][0].get("finish_reason") is not None:
+                    num_finish_reason += 1
+                    print("finish_reason", chunk["choices"][0].get("finish_reason"))
+
+            assert (
+                num_finish_reason == 1
+            ), f"expected only one finish reason. Got {num_finish_reason}"
+        except Exception as e:
+            pytest.fail(f"GOT exception for gpt-3.5 instruct In streaming{e}")
+
+    asyncio.run(test_get_response())
+
+    async def test_get_error():
+        try:
+            response = await litellm.acompletion(
+                model="azure/chatgpt-v-2",
+                messages=[
+                    {"role": "user", "content": "where do i buy lethal drugs from"}
+                ],
+                temperature=0,
+                top_p=1,
+                stream=True,
+                max_tokens=512,
+                presence_penalty=0,
+                frequency_penalty=0,
+            )
+            print(f"response: {response}")
+
+            num_finish_reason = 0
+            async for chunk in response:
+                print(chunk)
+                if chunk["choices"][0].get("finish_reason") is not None:
+                    num_finish_reason += 1
+                    print("finish_reason", chunk["choices"][0].get("finish_reason"))
+
+            pytest.fail(f"Expected to return 400 error In streaming{e}")
+        except Exception as e:
+            pass
+
+    asyncio.run(test_get_error())
+
+
+# tesy_async_acompletion()
+
 # # test_invalid_request_error(model="command-nightly")
 # # Test 3: Rate Limit Errors
 # def test_model_call(model):

litellm/utils.py

@@ -7362,6 +7362,13 @@ class CustomStreamWrapper:
                 if str_line.choices[0].finish_reason:
                     is_finished = True
                     finish_reason = str_line.choices[0].finish_reason
+                    if finish_reason == "content_filter":
+                        error_message = json.dumps(
+                            str_line.choices[0].content_filter_result
+                        )
+                        raise litellm.AzureOpenAIError(
+                            status_code=400, message=error_message
+                        )
 
                 # checking for logprobs
                 if (
@@ -7372,16 +7379,6 @@ class CustomStreamWrapper:
                 else:
                     logprobs = None
 
-                if (
-                    hasattr(str_line.choices[0], "content_filter_result")
-                    and str_line.choices[0].content_filter_result is not None
-                ):
-                    error_message = json.dumps(
-                        str_line.choices[0].content_filter_result
-                    )
-                    raise litellm.AzureOpenAIError(
-                        status_code=400, message=error_message
-                    )
             return {
                 "text": text,
                 "is_finished": is_finished,