fix(genai): Add ProviderStrategy response_format support

- Add handling for response_format parameter from ProviderStrategy

Author: RohanDisa

libs/genai/langchain_google_genai/chat_models.py

@@ -2220,14 +2220,23 @@ def _prepare_params(
             gen_config = {**gen_config, **generation_config}
 
         response_mime_type = kwargs.get("response_mime_type", self.response_mime_type)
-        if response_mime_type is not None:
-            gen_config["response_mime_type"] = response_mime_type
-
         response_schema = kwargs.get("response_schema", self.response_schema)
 
         # In case passed in as a direct kwarg
         response_json_schema = kwargs.get("response_json_schema")
 
+        # Handle response_format from ProviderStrategy (OpenAI-style format)
+        # Format: {'type': 'json_schema', 'json_schema': {'schema': {...}}}
+        # Only extract from response_format if response_json_schema is not already set
+        if response_json_schema is None:
+            response_format = kwargs.get("response_format")
+            if response_format is not None and isinstance(response_format, dict):
+                if response_format.get("type") == "json_schema":
+                    json_schema_obj = response_format.get("json_schema", {})
+                    if isinstance(json_schema_obj, dict) and "schema" in json_schema_obj:
+                        # Extract the actual schema from the nested structure
+                        response_json_schema = json_schema_obj["schema"]
+
         # Handle both response_schema and response_json_schema
         # (Regardless, we use `response_json_schema` in the request)
         schema_to_use = (
@@ -2236,6 +2245,15 @@ def _prepare_params(
             else response_schema
         )
 
+        # Automatically set response_mime_type to "application/json" when response_schema
+        # is provided but response_mime_type is not set. This enables seamless support
+        # for structured output strategies like ProviderStrategy.
+        if schema_to_use is not None and response_mime_type is None:
+            response_mime_type = "application/json"
+
+        if response_mime_type is not None:
+            gen_config["response_mime_type"] = response_mime_type
+
         if schema_to_use is not None:
             if response_mime_type != "application/json":
                 param_name = (

libs/genai/tests/unit_tests/test_chat_models.py

@@ -10,6 +10,7 @@
 from unittest.mock import ANY, AsyncMock, Mock, patch
 
 import google.ai.generativelanguage as glm
+import proto  # type: ignore[import-untyped]
 import pytest
 from google.ai.generativelanguage_v1beta.types import (
     Candidate,
@@ -2957,6 +2958,110 @@ def test_response_schema_mime_type_validation() -> None:
     assert llm_with_json_schema is not None
 
 
+def _convert_proto_to_dict(obj: Any) -> Any:
+    """Recursively convert proto objects to dicts for comparison."""
+    if isinstance(obj, dict):
+        return {k: _convert_proto_to_dict(v) for k, v in obj.items()}
+    elif isinstance(obj, (list, tuple)):
+        return [_convert_proto_to_dict(item) for item in obj]
+    elif hasattr(obj, "__class__") and "proto" in str(type(obj)):
+        # Try to convert proto object to dict
+        try:
+            if hasattr(obj, "__iter__") and not isinstance(obj, str):
+                converted = dict(obj)
+                # Recursively convert nested proto objects
+                return {k: _convert_proto_to_dict(v) for k, v in converted.items()}
+            else:
+                return obj
+        except (TypeError, ValueError):
+            return obj
+    return obj
+
+
+def test_response_format_provider_strategy() -> None:
+    """Test that `response_format` from ProviderStrategy is correctly handled."""
+    llm = ChatGoogleGenerativeAI(
+        model=MODEL_NAME, google_api_key=SecretStr(FAKE_API_KEY)
+    )
+
+    schema_dict = {
+        "type": "object",
+        "properties": {
+            "sentiment": {
+                "type": "string",
+                "enum": ["positive", "negative", "neutral"],
+            },
+            "confidence": {"type": "number", "minimum": 0, "maximum": 1},
+        },
+        "required": ["sentiment", "confidence"],
+        "additionalProperties": False,
+    }
+
+    # Test response_format with ProviderStrategy format (OpenAI-style)
+    response_format = {
+        "type": "json_schema",
+        "json_schema": {
+            "name": "response_format_test",
+            "schema": schema_dict,
+        },
+    }
+
+    gen_config = llm._prepare_params(stop=None, response_format=response_format)
+
+    # response_json_schema may be converted to proto object, so convert to dict for comparison
+    schema = _convert_proto_to_dict(gen_config.response_json_schema)
+    assert schema == schema_dict
+    assert gen_config.response_mime_type == "application/json"
+
+    # Test that response_json_schema takes precedence over response_format
+    different_schema = {
+        "type": "object",
+        "properties": {"age": {"type": "integer"}},
+        "required": ["age"],
+    }
+
+    gen_config_2 = llm._prepare_params(
+        stop=None,
+        response_format=response_format,
+        response_json_schema=different_schema,
+    )
+    
+    # response_json_schema may be converted to proto object, so convert to dict for comparison
+    schema_2 = _convert_proto_to_dict(gen_config_2.response_json_schema)
+    assert schema_2 == different_schema
+    assert gen_config_2.response_mime_type == "application/json"
+
+    
+    old_schema = {
+        "type": "object",
+        "properties": {"old_field": {"type": "string"}},
+        "required": ["old_field"],
+    }
+
+    gen_config_3 = llm._prepare_params(
+        stop=None,
+        response_schema=old_schema,
+        response_format=response_format,
+    )
+
+    # response_json_schema may be converted to proto object, so convert to dict for comparison
+    schema_3 = _convert_proto_to_dict(gen_config_3.response_json_schema)
+    assert schema_3 == schema_dict
+    assert gen_config_3.response_mime_type == "application/json"
+
+    invalid_response_format = {"type": "invalid_type"}
+    gen_config_4 = llm._prepare_params(
+        stop=None,
+        response_format=invalid_response_format,
+        response_schema=schema_dict,
+    )
+    # Should fall back to response_schema
+    # response_json_schema may be converted to proto object, so convert to dict for comparison
+    schema_4 = _convert_proto_to_dict(gen_config_4.response_json_schema)
+    assert schema_4 == schema_dict
+    assert gen_config_4.response_mime_type == "application/json"
+
+
 def test_is_new_gemini_model() -> None:
     assert _is_gemini_3_or_later("gemini-3.0-pro") is True
     assert _is_gemini_3_or_later("gemini-2.5-pro") is False