fix(ai): Handle Pydantic model classes in _normalize_data

sentry_sdk/ai/utils.py

@@ -1,3 +1,4 @@
+import inspect 
 import json
 from collections import deque
 from typing import TYPE_CHECKING
@@ -38,6 +39,11 @@ def _normalize_data(data, unpack=True):
     # type: (Any, bool) -> Any
     # convert pydantic data (e.g. OpenAI v1+) to json compatible format
     if hasattr(data, "model_dump"):
+        # Check if it's a class (type) rather than an instance
+        # Model classes can be passed as arguments (e.g., for schema definitions)
+        if inspect.isclass(data):
+            return f"<ClassType: {data.__name__}>"
+
         try:
             return _normalize_data(data.model_dump(), unpack=unpack)
         except Exception as e:

tests/utils/test_general.py

@@ -2,6 +2,7 @@
 import os
 
 import pytest
+from sentry_sdk.ai.utils import _normalize_data
 
 
 from sentry_sdk.utils import (
@@ -621,3 +622,28 @@ def test_failed_base64_conversion(input):
 )
 def test_strip_string(input, max_length, result):
     assert strip_string(input, max_length) == result
+
+
+def test_normalize_data_with_pydantic_class():
+    """Test that _normalize_data handles Pydantic model classes"""
+    class TestClass:
+        name: str = None
+
+        def __init__(self, name: str):
+            self.name = name
+
+        def model_dump(self):
+            return {"name": self.name}
+
+    # Test with class (should NOT call model_dump())
+    result = _normalize_data(TestClass)
+    assert result == "<ClassType: TestClass>"
+
+    # Test with instance (should call model_dump())
+    instance = TestClass(name="test")
+    result = _normalize_data(instance)
+    assert result == {"name": "test"}
+
+    # Test with dict containing class
+    result = _normalize_data({"schema": TestClass, "count": 5})
+    assert result == {"schema": "<ClassType: TestClass>", "count": 5}