adk-python/contributing/samples/pydantic_argument

Pydantic Argument Sample Agent

This sample demonstrates the automatic Pydantic model conversion feature in ADK FunctionTool.

What This Demonstrates

This sample shows two key features of the Pydantic argument conversion:

1. Optional Type Handling

The create_full_user_account function demonstrates Optional[PydanticModel] conversion:

Before the fix, Optional parameters required manual conversion:

def create_full_user_account(
    profile: UserProfile, 
    preferences: Optional[UserPreferences] = None
) -> dict:
    # Manual conversion needed:
    if not isinstance(profile, UserProfile):
        profile = UserProfile.model_validate(profile)
    
    if preferences is not None and not isinstance(preferences, UserPreferences):
        preferences = UserPreferences.model_validate(preferences)
    
    # Your function logic here...

After the fix, Union/Optional Pydantic models are handled automatically:

def create_full_user_account(
    profile: UserProfile, 
    preferences: Optional[UserPreferences] = None
) -> dict:
    # Both profile and preferences are guaranteed to be proper instances!
    # profile: UserProfile instance (converted from JSON)
    # preferences: UserPreferences instance OR None (converted from JSON or kept as None)
    return {"profile": profile.name, "theme": preferences.theme if preferences else "default"}

2. Union Type Handling

The create_entity_profile function demonstrates Union[PydanticModel1, PydanticModel2] conversion:

Before the fix, Union types required complex manual type checking:

def create_entity_profile(entity: Union[UserProfile, CompanyProfile]) -> dict:
    # Manual conversion needed:
    if isinstance(entity, dict):
        # Try to determine which model to use and convert manually
        if 'company_name' in entity:
            entity = CompanyProfile.model_validate(entity)
        elif 'name' in entity:
            entity = UserProfile.model_validate(entity)
        else:
            raise ValueError("Cannot determine entity type")
    # Your function logic here...

After the fix, Union Pydantic models are handled automatically:

def create_entity_profile(entity: Union[UserProfile, CompanyProfile]) -> dict:
    # entity is guaranteed to be either UserProfile or CompanyProfile instance!
    # The LLM sends appropriate JSON structure, and it gets converted
    # to the correct Pydantic model based on JSON schema matching
    if isinstance(entity, UserProfile):
        return {"type": "user", "name": entity.name}
    else:  # CompanyProfile
        return {"type": "company", "name": entity.company_name}

How to Run

1. Set up API credentials (choose one):

   Option A: Google AI API

   export GOOGLE_GENAI_API_KEY="your-api-key"
   

   Option B: Vertex AI (requires Google Cloud project)

   export GOOGLE_CLOUD_PROJECT="your-project-id"
   export GOOGLE_CLOUD_LOCATION="us-central1"
   

2. Run the sample:

   cd contributing/samples
   python -m pydantic_argument.main
   

Expected Output

The agent will be prompted to create user profiles and accounts, demonstrating automatic Pydantic model conversion.

Test Scenarios:

1. Full Account with Preferences (Optional Type):

   • Input: "Create an account for Alice, 25 years old, with dark theme and Spanish language preferences"
   • Tool Called: create_full_user_account(profile=UserProfile(...), preferences=UserPreferences(...))
   • Conversion: Two JSON dicts → UserProfile + UserPreferences instances

2. Account with Different Preferences (Optional Type):

   • Input: "Create a user account for Bob, age 30, with light theme, French language, and notifications disabled"
   • Tool Called: create_full_user_account(profile=UserProfile(...), preferences=UserPreferences(...))
   • Conversion: Two JSON dicts → UserProfile + UserPreferences instances

3. Account with Default Preferences (Optional Type):

   • Input: "Make an account for Charlie, 28 years old, but use default preferences"
   • Tool Called: create_full_user_account(profile=UserProfile(...), preferences=None)
   • Conversion: JSON dict → UserProfile, None → None (Optional handling)

4. Company Profile Creation (Union Type):

   • Input: "Create a profile for Tech Corp company, software industry, with 150 employees"
   • Tool Called: create_entity_profile(entity=CompanyProfile(...))
   • Conversion: JSON dict → CompanyProfile instance (Union type resolution)

5. User Profile Creation (Union Type):

   • Input: "Create an entity profile for Diana, 32 years old"
   • Tool Called: create_entity_profile(entity=UserProfile(...))
   • Conversion: JSON dict → UserProfile instance (Union type resolution)