fix: Convert argument to pydantic model when tool declare to accept pydantic model as argument

PiperOrigin-RevId: 814273005

src/google/adk/tools/function_tool.py

@@ -18,16 +18,18 @@ import inspect
 import logging
 from typing import Any
 from typing import Callable
+from typing import get_args
+from typing import get_origin
 from typing import Optional
 from typing import Union
 
 from google.genai import types
+import pydantic
 from typing_extensions import override
 
 from ..utils.context_utils import Aclosing
 from ._automatic_function_calling_util import build_function_declaration
 from .base_tool import BaseTool
-from .tool_confirmation import ToolConfirmation
 from .tool_context import ToolContext
 
 logger = logging.getLogger('google_adk.' + __name__)
@@ -95,11 +97,69 @@ class FunctionTool(BaseTool):
 
     return function_decl
 
+  def _preprocess_args(self, args: dict[str, Any]) -> dict[str, Any]:
+    """Preprocess and convert function arguments before invocation.
+
+    Currently handles:
+    - Converting JSON dictionaries to Pydantic model instances where expected
+
+    Future extensions could include:
+    - Type coercion for other complex types
+    - Validation and sanitization
+    - Custom conversion logic
+
+    Args:
+      args: Raw arguments from the LLM tool call
+
+    Returns:
+      Processed arguments ready for function invocation
+    """
+    signature = inspect.signature(self.func)
+    converted_args = args.copy()
+
+    for param_name, param in signature.parameters.items():
+      if param_name in args and param.annotation != inspect.Parameter.empty:
+        target_type = param.annotation
+
+        # Handle Optional[PydanticModel] types
+        if get_origin(param.annotation) is Union:
+          union_args = get_args(param.annotation)
+          # Find the non-None type in Optional[T] (which is Union[T, None])
+          non_none_types = [arg for arg in union_args if arg is not type(None)]
+          if len(non_none_types) == 1:
+            target_type = non_none_types[0]
+
+        # Check if the target type is a Pydantic model
+        if inspect.isclass(target_type) and issubclass(
+            target_type, pydantic.BaseModel
+        ):
+          # Skip conversion if the value is None and the parameter is Optional
+          if args[param_name] is None:
+            continue
+
+          # Convert to Pydantic model if it's not already the correct type
+          if not isinstance(args[param_name], target_type):
+            try:
+              converted_args[param_name] = target_type.model_validate(
+                  args[param_name]
+              )
+            except Exception as e:
+              logger.warning(
+                  f"Failed to convert argument '{param_name}' to Pydantic model"
+                  f' {target_type.__name__}: {e}'
+              )
+              # Keep the original value if conversion fails
+              pass
+
+    return converted_args
+
   @override
   async def run_async(
       self, *, args: dict[str, Any], tool_context: ToolContext
   ) -> Any:
-    args_to_call = args.copy()
+    # Preprocess arguments (includes Pydantic model conversion)
+    args_to_call = self._preprocess_args(args)
+
     signature = inspect.signature(self.func)
     valid_params = {param for param in signature.parameters}
     if 'tool_context' in valid_params:

tests/unittests/tools/test_function_tool_pydantic.py

@@ -0,0 +1,284 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Pydantic model conversion tests
+
+from typing import Optional
+from unittest.mock import MagicMock
+
+from google.adk.agents.invocation_context import InvocationContext
+from google.adk.sessions.session import Session
+from google.adk.tools.function_tool import FunctionTool
+from google.adk.tools.tool_context import ToolContext
+import pydantic
+import pytest
+
+
+class UserModel(pydantic.BaseModel):
+  """Test Pydantic model for user data."""
+
+  name: str
+  age: int
+  email: Optional[str] = None
+
+
+class PreferencesModel(pydantic.BaseModel):
+  """Test Pydantic model for preferences."""
+
+  theme: str = "light"
+  notifications: bool = True
+
+
+def sync_function_with_pydantic_model(user: UserModel) -> dict:
+  """Sync function that takes a Pydantic model."""
+  return {
+      "name": user.name,
+      "age": user.age,
+      "email": user.email,
+      "type": str(type(user).__name__),
+  }
+
+
+async def async_function_with_pydantic_model(user: UserModel) -> dict:
+  """Async function that takes a Pydantic model."""
+  return {
+      "name": user.name,
+      "age": user.age,
+      "email": user.email,
+      "type": str(type(user).__name__),
+  }
+
+
+def function_with_optional_pydantic_model(
+    user: UserModel, preferences: Optional[PreferencesModel] = None
+) -> dict:
+  """Function with required and optional Pydantic models."""
+  result = {
+      "user_name": user.name,
+      "user_type": str(type(user).__name__),
+  }
+  if preferences:
+    result.update({
+        "theme": preferences.theme,
+        "notifications": preferences.notifications,
+        "preferences_type": str(type(preferences).__name__),
+    })
+  return result
+
+
+def function_with_mixed_args(
+    name: str, user: UserModel, count: int = 5
+) -> dict:
+  """Function with mixed argument types including Pydantic model."""
+  return {
+      "name": name,
+      "user_name": user.name,
+      "user_type": str(type(user).__name__),
+      "count": count,
+  }
+
+
+def test_preprocess_args_with_dict_to_pydantic_conversion():
+  """Test _preprocess_args converts dict to Pydantic model."""
+  tool = FunctionTool(sync_function_with_pydantic_model)
+
+  input_args = {
+      "user": {"name": "Alice", "age": 30, "email": "alice@example.com"}
+  }
+
+  processed_args = tool._preprocess_args(input_args)
+
+  # Check that the dict was converted to a Pydantic model
+  assert "user" in processed_args
+  user = processed_args["user"]
+  assert isinstance(user, UserModel)
+  assert user.name == "Alice"
+  assert user.age == 30
+  assert user.email == "alice@example.com"
+
+
+def test_preprocess_args_with_existing_pydantic_model():
+  """Test _preprocess_args leaves existing Pydantic model unchanged."""
+  tool = FunctionTool(sync_function_with_pydantic_model)
+
+  # Create an existing Pydantic model
+  existing_user = UserModel(name="Bob", age=25)
+  input_args = {"user": existing_user}
+
+  processed_args = tool._preprocess_args(input_args)
+
+  # Check that the existing model was not changed (same object)
+  assert "user" in processed_args
+  user = processed_args["user"]
+  assert user is existing_user
+  assert isinstance(user, UserModel)
+  assert user.name == "Bob"
+
+
+def test_preprocess_args_with_optional_pydantic_model_none():
+  """Test _preprocess_args handles None for optional Pydantic models."""
+  tool = FunctionTool(function_with_optional_pydantic_model)
+
+  input_args = {"user": {"name": "Charlie", "age": 35}, "preferences": None}
+
+  processed_args = tool._preprocess_args(input_args)
+
+  # Check user conversion
+  assert isinstance(processed_args["user"], UserModel)
+  assert processed_args["user"].name == "Charlie"
+
+  # Check preferences remains None
+  assert processed_args["preferences"] is None
+
+
+def test_preprocess_args_with_optional_pydantic_model_dict():
+  """Test _preprocess_args converts dict for optional Pydantic models."""
+  tool = FunctionTool(function_with_optional_pydantic_model)
+
+  input_args = {
+      "user": {"name": "Diana", "age": 28},
+      "preferences": {"theme": "dark", "notifications": False},
+  }
+
+  processed_args = tool._preprocess_args(input_args)
+
+  # Check both conversions
+  assert isinstance(processed_args["user"], UserModel)
+  assert processed_args["user"].name == "Diana"
+
+  assert isinstance(processed_args["preferences"], PreferencesModel)
+  assert processed_args["preferences"].theme == "dark"
+  assert processed_args["preferences"].notifications is False
+
+
+def test_preprocess_args_with_mixed_types():
+  """Test _preprocess_args handles mixed argument types correctly."""
+  tool = FunctionTool(function_with_mixed_args)
+
+  input_args = {
+      "name": "test_name",
+      "user": {"name": "Eve", "age": 40},
+      "count": 10,
+  }
+
+  processed_args = tool._preprocess_args(input_args)
+
+  # Check that only Pydantic model was converted
+  assert processed_args["name"] == "test_name"  # string unchanged
+  assert processed_args["count"] == 10  # int unchanged
+
+  # Check Pydantic model conversion
+  assert isinstance(processed_args["user"], UserModel)
+  assert processed_args["user"].name == "Eve"
+  assert processed_args["user"].age == 40
+
+
+def test_preprocess_args_with_invalid_data_graceful_failure():
+  """Test _preprocess_args handles invalid data gracefully."""
+  tool = FunctionTool(sync_function_with_pydantic_model)
+
+  # Invalid data that can't be converted to UserModel
+  input_args = {"user": "invalid_string"}  # string instead of dict/model
+
+  processed_args = tool._preprocess_args(input_args)
+
+  # Should keep original value when conversion fails
+  assert processed_args["user"] == "invalid_string"
+
+
+def test_preprocess_args_with_non_pydantic_parameters():
+  """Test _preprocess_args ignores non-Pydantic parameters."""
+
+  def simple_function(name: str, age: int) -> dict:
+    return {"name": name, "age": age}
+
+  tool = FunctionTool(simple_function)
+
+  input_args = {"name": "test", "age": 25}
+  processed_args = tool._preprocess_args(input_args)
+
+  # Should remain unchanged (no Pydantic models to convert)
+  assert processed_args == input_args
+
+
+@pytest.mark.asyncio
+async def test_run_async_with_pydantic_model_conversion_sync_function():
+  """Test run_async with Pydantic model conversion for sync function."""
+  tool = FunctionTool(sync_function_with_pydantic_model)
+
+  tool_context_mock = MagicMock(spec=ToolContext)
+  invocation_context_mock = MagicMock(spec=InvocationContext)
+  session_mock = MagicMock(spec=Session)
+  invocation_context_mock.session = session_mock
+  tool_context_mock.invocation_context = invocation_context_mock
+
+  args = {"user": {"name": "Frank", "age": 45, "email": "frank@example.com"}}
+
+  result = await tool.run_async(args=args, tool_context=tool_context_mock)
+
+  # Verify the function received a proper Pydantic model
+  assert result["name"] == "Frank"
+  assert result["age"] == 45
+  assert result["email"] == "frank@example.com"
+  assert result["type"] == "UserModel"
+
+
+@pytest.mark.asyncio
+async def test_run_async_with_pydantic_model_conversion_async_function():
+  """Test run_async with Pydantic model conversion for async function."""
+  tool = FunctionTool(async_function_with_pydantic_model)
+
+  tool_context_mock = MagicMock(spec=ToolContext)
+  invocation_context_mock = MagicMock(spec=InvocationContext)
+  session_mock = MagicMock(spec=Session)
+  invocation_context_mock.session = session_mock
+  tool_context_mock.invocation_context = invocation_context_mock
+
+  args = {"user": {"name": "Grace", "age": 32}}
+
+  result = await tool.run_async(args=args, tool_context=tool_context_mock)
+
+  # Verify the function received a proper Pydantic model
+  assert result["name"] == "Grace"
+  assert result["age"] == 32
+  assert result["email"] is None  # default value
+  assert result["type"] == "UserModel"
+
+
+@pytest.mark.asyncio
+async def test_run_async_with_optional_pydantic_models():
+  """Test run_async with optional Pydantic models."""
+  tool = FunctionTool(function_with_optional_pydantic_model)
+
+  tool_context_mock = MagicMock(spec=ToolContext)
+  invocation_context_mock = MagicMock(spec=InvocationContext)
+  session_mock = MagicMock(spec=Session)
+  invocation_context_mock.session = session_mock
+  tool_context_mock.invocation_context = invocation_context_mock
+
+  # Test with both required and optional models
+  args = {
+      "user": {"name": "Henry", "age": 50},
+      "preferences": {"theme": "dark", "notifications": True},
+  }
+
+  result = await tool.run_async(args=args, tool_context=tool_context_mock)
+
+  assert result["user_name"] == "Henry"
+  assert result["user_type"] == "UserModel"
+  assert result["theme"] == "dark"
+  assert result["notifications"] is True
+  assert result["preferences_type"] == "PreferencesModel"
+  assert result["preferences_type"] == "PreferencesModel"
+  assert result["preferences_type"] == "PreferencesModel"