test bedrock guardrails

litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py

@@ -727,14 +727,21 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
             )
             return
 
-        outputs: List[BedrockGuardrailOutput] = (
-            response.get("outputs", []) or []
-        )
-        if not any(output.get("text") for output in outputs):
-            verbose_proxy_logger.warning(
-                "Bedrock AI: not running guardrail. No output text in response"
-            )
-            return
+        # Check if the ModelResponse has text content in its choices
+        # to avoid sending empty content to Bedrock (e.g., during tool calls)
+        if isinstance(response, litellm.ModelResponse):
+            has_text_content = False
+            for choice in response.choices:
+                if isinstance(choice, litellm.Choices):
+                    if choice.message.content and isinstance(choice.message.content, str):
+                        has_text_content = True
+                        break
+            
+            if not has_text_content:
+                verbose_proxy_logger.warning(
+                    "Bedrock AI: not running guardrail. No output text in response"
+                )
+                return
 
         #########################################################
         ########## 1. Make parallel Bedrock API requests ##########

tests/guardrails_tests/test_bedrock_guardrails.py

@@ -1384,28 +1384,34 @@ async def test_bedrock_guardrail_post_call_success_hook_no_output_text():
         guardrailVersion="DRAFT"
     )
     
-    # Mock Bedrock API with no output text
-    mock_bedrock_response = MagicMock()
-    mock_bedrock_response.status_code = 200
-    mock_bedrock_response.json.return_value = {
-        "output": {
-            "message": {
-                "role": "assistant",
-                "content": [
-                    {
-                        "toolUse": {
-                            "toolUseId": "tooluse_kZJMlvQmRJ6eAyJE5GIl7Q",
-                            "name": "top_song",
-                            "input": {
-                                "sign": "WZPZ"
-                            }
-                        }
-                    }
-                ]
-            }
-        },
-        "stopReason": "tool_use"
-    }
+    # Create a ModelResponse with tool calls (no text content)
+    # This simulates a response where the LLM is making a tool call
+    mock_response = litellm.ModelResponse(
+        id="test-id",
+        choices=[
+            litellm.Choices(
+                index=0,
+                message=litellm.Message(
+                    role="assistant",
+                    content=None,  # No text content
+                    tool_calls=[
+                        litellm.utils.ChatCompletionMessageToolCall(
+                            id="tooluse_kZJMlvQmRJ6eAyJE5GIl7Q",
+                            function=litellm.utils.Function(
+                                name="top_song",
+                                arguments='{"sign": "WZPZ"}'
+                            ),
+                            type="function"
+                        )
+                    ]
+                ),
+                finish_reason="tool_calls"
+            )
+        ],
+        created=1234567890,
+        model="gpt-4o",
+        object="chat.completion"
+    )
         
     data = {
         "model": "gpt-4o",
@@ -1415,10 +1421,11 @@ async def test_bedrock_guardrail_post_call_success_hook_no_output_text():
     } 
     mock_user_api_key_dict = UserAPIKeyAuth()
 
-    return await guardrail.async_post_call_success_hook(
+    result = await guardrail.async_post_call_success_hook(
         data=data,
-        response=mock_bedrock_response, 
+        response=mock_response, 
         user_api_key_dict=mock_user_api_key_dict,
     )
-    # If no error is raised, then the test passes
+    # If no error is raised and result is None, then the test passes
+    assert result is None
     print("✅ No output text in response test passed")