Use different JSON schema transformers for GLA vs Vertex AI

Based on reviewer feedback, this commit implements different JSON schema
transformation strategies for Google's two API endpoints:

- GLA (Generative Language API): Uses GoogleGLAJsonSchemaTransformer which
  inlines $defs as a defensive measure for recursive schemas that have
  historically had reliability issues on GLA

- Vertex AI: Uses GoogleJsonSchemaTransformer which preserves $refs and
  takes full advantage of Google's enhanced JSON Schema support

This approach provides maximum compatibility while being defensive about
potential issues with recursive schemas on GLA.

Changes:
- Added is_vertexai parameter to google_model_profile()
- GoogleProvider.model_profile() now checks the client type and passes it along
- Created GoogleGLAJsonSchemaTransformer that inlines definitions
- Vertex AI continues to use the standard GoogleJsonSchemaTransformer

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: conradlee

pydantic_ai_slim/pydantic_ai/profiles/google.py

@@ -4,11 +4,19 @@
 from . import ModelProfile
 
 
-def google_model_profile(model_name: str) -> ModelProfile | None:
-    """Get the model profile for a Google model."""
+def google_model_profile(model_name: str, *, is_vertexai: bool = False) -> ModelProfile | None:
+    """Get the model profile for a Google model.
+
+    Args:
+        model_name: The name of the model.
+        is_vertexai: Whether this is a Vertex AI model (as opposed to GLA).
+    """
     is_image_model = 'image' in model_name
+    # For GLA, use the transformer that inlines defs (defensive approach for recursive schemas)
+    # For Vertex AI, use the regular transformer with enhanced JSON Schema support
+    transformer_class = GoogleJsonSchemaTransformer if is_vertexai else GoogleGLAJsonSchemaTransformer
     return ModelProfile(
-        json_schema_transformer=GoogleJsonSchemaTransformer,
+        json_schema_transformer=transformer_class,
         supports_image_output=is_image_model,
         supports_json_schema_output=not is_image_model,
         supports_json_object_output=not is_image_model,
@@ -44,3 +52,15 @@ def transform(self, schema: JsonSchema) -> JsonSchema:
         schema.pop('exclusiveMaximum', None)
 
         return schema
+
+
+class GoogleGLAJsonSchemaTransformer(GoogleJsonSchemaTransformer):
+    """Transforms the JSON Schema for Google's Generative Language API (GLA).
+
+    This transformer inlines $defs as a defensive measure for recursive schemas,
+    which have historically had reliability issues on GLA.
+    """
+
+    def __init__(self, schema: JsonSchema, *, strict: bool | None = None):
+        # Initialize with prefer_inlined_defs=True to inline definitions
+        super().__init__(schema, strict=strict, prefer_inlined_defs=True)

pydantic_ai_slim/pydantic_ai/providers/google.py

@@ -39,7 +39,9 @@ def client(self) -> Client:
         return self._client
 
     def model_profile(self, model_name: str) -> ModelProfile | None:
-        return google_model_profile(model_name)
+        # Check if we're using Vertex AI or GLA
+        is_vertexai = bool(self._client._api_client.vertexai)  # type: ignore[reportPrivateUsage]
+        return google_model_profile(model_name, is_vertexai=is_vertexai)
 
     @overload
     def __init__(