test(gemini): add parity test between native and chat schema normalization

litellm/llms/gemini/google_genai/transformation.py

@@ -2,6 +2,7 @@
 Transformation for Calling Google models in their native format.
 """
 
+from copy import deepcopy
 from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union, cast
 
 import httpx
@@ -11,6 +12,10 @@
 from litellm.llms.base_llm.google_genai.transformation import (
     BaseGoogleGenAIGenerateContentConfig,
 )
+from litellm.llms.vertex_ai.common_utils import (
+    _build_vertex_schema,
+    supports_response_json_schema,
+)
 from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
 from litellm.types.router import GenericLiteLLMParams
 
@@ -302,6 +307,47 @@ async def get_auth_token_and_url(
             litellm_params=litellm_params,
         )
 
+    @staticmethod
+    def _normalize_response_schema(
+        generate_content_config_dict: Dict, model: str
+    ) -> None:
+        schema_key = next(
+            (
+                k
+                for k in ("responseSchema", "response_schema")
+                if k in generate_content_config_dict
+            ),
+            None,
+        )
+        json_schema_key = next(
+            (
+                k
+                for k in ("responseJsonSchema", "response_json_schema")
+                if k in generate_content_config_dict
+            ),
+            None,
+        )
+
+        if schema_key is None:
+            return
+
+        value = generate_content_config_dict[schema_key]
+        if not isinstance(value, dict):
+            return
+
+        if supports_response_json_schema(model):
+            if json_schema_key is not None:
+                generate_content_config_dict.pop(schema_key)
+                return
+            generate_content_config_dict.pop(schema_key)
+            generate_content_config_dict["responseJsonSchema"] = value
+        else:
+            if json_schema_key is not None:
+                generate_content_config_dict.pop(json_schema_key)
+            generate_content_config_dict[schema_key] = _build_vertex_schema(
+                parameters=deepcopy(value), add_property_ordering=True
+            )
+
     def transform_generate_content_request(
         self,
         model: str,
@@ -315,6 +361,8 @@ def transform_generate_content_request(
             GenerateContentRequestDict,
         )
 
+        self._normalize_response_schema(generate_content_config_dict, model)
+
         typed_generate_content_request = GenerateContentRequestDict(
             model=model,
             contents=contents,

litellm/llms/vertex_ai/google_genai/transformation.py

@@ -79,6 +79,9 @@ def transform_generate_content_request(
         Transform the generate content request for Vertex AI.
         Since Vertex AI natively supports Google GenAI format, we can pass most fields directly.
         """
+        if generate_content_config_dict:
+            self._normalize_response_schema(generate_content_config_dict, model)
+
         # Build the request in Google GenAI format that Vertex AI expects
         result = {
             "model": model,

tests/test_litellm/google_genai/test_google_genai_transformation.py

@@ -335,6 +335,233 @@ def test_transform_generate_content_request_system_instruction_with_tools():
     assert result["model"] == "gemini-3-flash-preview"
 
 
+def test_transform_generate_content_request_normalizes_response_schema_2_5():
+    """For Gemini 2.0+, ``response_schema`` with ``$defs``/``$ref`` should be
+    promoted to ``responseJsonSchema`` (which Gemini 2.0+ accepts natively),
+    not forwarded as ``responseSchema`` (which rejects ``$defs``)."""
+    config = GoogleGenAIConfig()
+
+    schema = {
+        "$defs": {
+            "Highlight": {
+                "type": "object",
+                "properties": {
+                    "title": {"type": "string"},
+                    "detail": {"type": "string"},
+                },
+                "required": ["title"],
+            }
+        },
+        "type": "object",
+        "properties": {
+            "park_name": {"type": "string"},
+            "highlights": {
+                "type": "array",
+                "items": {"$ref": "#/$defs/Highlight"},
+            },
+        },
+        "required": ["park_name", "highlights"],
+    }
+
+    result = config.transform_generate_content_request(
+        model="gemini-2.5-flash-lite",
+        contents=[{"role": "user", "parts": [{"text": "hi"}]}],
+        tools=None,
+        generate_content_config_dict={
+            "responseMimeType": "application/json",
+            "responseSchema": schema,
+        },
+        system_instruction=None,
+    )
+
+    gen_config = result["generationConfig"]
+    assert "responseSchema" not in gen_config
+    assert "responseJsonSchema" in gen_config
+    normalized = gen_config["responseJsonSchema"]
+    assert "$defs" in normalized
+    assert normalized["properties"]["highlights"]["items"] == {
+        "$ref": "#/$defs/Highlight"
+    }
+
+
+def test_transform_generate_content_request_flattens_response_schema_1_5():
+    """For Gemini 1.5, ``responseSchema`` is kept but flattened via
+    ``_build_vertex_schema`` so ``$defs``/``$ref`` are unpacked."""
+    config = GoogleGenAIConfig()
+
+    schema = {
+        "$defs": {
+            "Highlight": {
+                "type": "object",
+                "properties": {"title": {"type": "string"}},
+                "required": ["title"],
+            }
+        },
+        "type": "object",
+        "properties": {
+            "highlights": {
+                "type": "array",
+                "items": {"$ref": "#/$defs/Highlight"},
+            }
+        },
+        "required": ["highlights"],
+    }
+
+    result = config.transform_generate_content_request(
+        model="gemini-1.5-pro",
+        contents=[{"role": "user", "parts": [{"text": "hi"}]}],
+        tools=None,
+        generate_content_config_dict={"responseSchema": schema},
+        system_instruction=None,
+    )
+
+    gen_config = result["generationConfig"]
+    assert "responseSchema" in gen_config
+    assert "responseJsonSchema" not in gen_config
+    normalized = gen_config["responseSchema"]
+    assert "$defs" not in normalized
+    items = normalized["properties"]["highlights"]["items"]
+    assert "$ref" not in items
+    assert "title" in items["properties"]
+    assert items["properties"]["title"]["type"].lower() == "string"
+
+
+def test_transform_generate_content_request_passes_through_response_json_schema():
+    """If the caller already used ``responseJsonSchema``, it should be
+    preserved (Gemini 2.0+ accepts standard JSON Schema as-is)."""
+    config = GoogleGenAIConfig()
+
+    schema = {
+        "type": "object",
+        "properties": {"name": {"type": "string"}},
+        "required": ["name"],
+    }
+
+    result = config.transform_generate_content_request(
+        model="gemini-2.5-flash-lite",
+        contents=[{"role": "user", "parts": [{"text": "hi"}]}],
+        tools=None,
+        generate_content_config_dict={"responseJsonSchema": schema},
+        system_instruction=None,
+    )
+
+    gen_config = result["generationConfig"]
+    assert gen_config["responseJsonSchema"] == schema
+    assert "responseSchema" not in gen_config
+
+
+def test_transform_generate_content_request_preserves_response_json_schema_when_response_schema_co_present():
+    """When both ``responseJsonSchema`` and ``responseSchema`` are supplied on
+    Gemini 2.0+, the caller's ``responseJsonSchema`` must win — the
+    ``responseSchema`` value must not clobber it."""
+    config = GoogleGenAIConfig()
+
+    caller_json_schema = {
+        "type": "object",
+        "properties": {"answer": {"type": "string"}},
+        "required": ["answer"],
+    }
+    redundant_response_schema = {
+        "type": "object",
+        "properties": {"other": {"type": "string"}},
+    }
+
+    result = config.transform_generate_content_request(
+        model="gemini-2.5-flash-lite",
+        contents=[{"role": "user", "parts": [{"text": "hi"}]}],
+        tools=None,
+        generate_content_config_dict={
+            "responseJsonSchema": caller_json_schema,
+            "responseSchema": redundant_response_schema,
+        },
+        system_instruction=None,
+    )
+
+    gen_config = result["generationConfig"]
+    assert "responseSchema" not in gen_config
+    assert gen_config["responseJsonSchema"] == caller_json_schema
+
+
+def test_response_schema_normalization_parity_across_chat_and_native_paths():
+    """Parity guard between the two Gemini schema-normalization paths.
+
+    The native ``generateContent`` path (``_normalize_response_schema``) must
+    produce the same normalized schema as the ``/chat/completions`` path
+    (``apply_response_schema_transformation``) for the same input schema, on
+    both Gemini 2.0+ and Gemini 1.5. If either implementation drifts, this
+    test fails — forcing both paths to be updated together.
+    """
+    from copy import deepcopy
+
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        VertexGeminiConfig,
+    )
+
+    schema = {
+        "$defs": {
+            "Highlight": {
+                "type": "object",
+                "properties": {
+                    "title": {"type": "string"},
+                    "detail": {"type": "string"},
+                },
+                "required": ["title"],
+            }
+        },
+        "type": "object",
+        "properties": {
+            "park_name": {"type": "string"},
+            "highlights": {
+                "type": "array",
+                "items": {"$ref": "#/$defs/Highlight"},
+            },
+            "rating": {"anyOf": [{"type": "number"}, {"type": "null"}]},
+        },
+        "required": ["park_name", "highlights"],
+    }
+
+    native_config = GoogleGenAIConfig()
+    chat_config = VertexGeminiConfig()
+
+    for model, native_out_key, chat_out_key in (
+        ("gemini-2.5-flash-lite", "responseJsonSchema", "response_json_schema"),
+        ("gemini-1.5-pro", "responseSchema", "response_schema"),
+    ):
+        native_dict = {"responseSchema": deepcopy(schema)}
+        native_config._normalize_response_schema(native_dict, model)
+
+        chat_optional_params: dict = {}
+        chat_config.apply_response_schema_transformation(
+            value={"type": "json_schema", "response_schema": deepcopy(schema)},
+            optional_params=chat_optional_params,
+            model=model,
+        )
+
+        assert native_dict[native_out_key] == chat_optional_params[chat_out_key], (
+            f"Schema normalization drifted between native generateContent and "
+            f"chat/completions paths for {model}. Update both "
+            f"GoogleGenAIConfig._normalize_response_schema and "
+            f"VertexGeminiConfig.apply_response_schema_transformation together."
+        )
+
+
+def test_transform_generate_content_request_without_schema_unchanged():
+    """No schema in config → no normalization side effects."""
+    config = GoogleGenAIConfig()
+
+    result = config.transform_generate_content_request(
+        model="gemini-2.5-flash-lite",
+        contents=[{"role": "user", "parts": [{"text": "hi"}]}],
+        tools=None,
+        generate_content_config_dict={"temperature": 0.7},
+        system_instruction=None,
+    )
+
+    assert result["generationConfig"]["temperature"] == 0.7
+    assert "responseSchema" not in result["generationConfig"]
+    assert "responseJsonSchema" not in result["generationConfig"]
+
+
 def test_validate_environment_with_dict_api_key():
     """
     Test that validate_environment correctly handles api_key as a dict.