fix(ai): Handle Pydantic model classes in _normalize_data

Previously, _normalize_data would attempt to call model_dump() on Pydantic
model classes (types) when they were passed as arguments, which would fail.
This commonly occurs when model classes are used in schema definitions.

This change adds an inspect.isclass() check to detect when a class type is
passed instead of an instance. For classes, we now return a string
representation "<ClassType: ClassName>" instead of attempting to call
model_dump().

Author: skalinchuk

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}