### Install Davia (Bash)

Source: https://github.com/davialabs/davia/blob/main/README.md

Installs the Davia package using pip. For LangGraph specific features, optional dependencies including langgraph and langgraph-api are also installed.

```bash
pip install davia

pip install davia langgraph "langgraph-api==0.0.38"
```

--------------------------------

### Run a Basic Davia App (Python)

Source: https://github.com/davialabs/davia/blob/main/README.md

Demonstrates how to create a simple Davia application instance and define a basic task function. The application can then be run using the Davia CLI.

```python
from davia import Davia

app = Davia()

# Define your tasks and AI agents here
# For example:
@app.task
def my_python_function(name: str) -> str:
  return f"Hello, {name}!"

# To run this app, save it as my_app.py and execute:
# davia run my_app.py
```

--------------------------------

### Define Tasks with Pydantic Models (Python)

Source: https://github.com/davialabs/davia/blob/main/README.md

Shows how to define complex input and output types for Davia tasks using Pydantic models. This enhances code clarity and ensures smooth integration with the generated frontend.

```python
from pydantic import BaseModel
from typing import List
from davia import Davia

app = Davia()

class UserInput(BaseModel):
    name: str
    age: int
    preferences: List[str]

@app.task
def calculate_user_score(input_data: UserInput) -> int:
    """
    Calculate a user's score based on their profile information.

    The score is computed using the following formula:
    - Base score: 10 points per character in the name
    - Age bonus: 2 points per year of age
    - Preferences bonus: 5 points per preference

    Args:
        input_data (UserInput): User profile containing name, age, and preferences

    Returns:
        int: The calculated user score
    """
    # Example calculation based on user data
    base_score = len(input_data.name) * 10
    age_bonus = input_data.age * 2
    preferences_bonus = len(input_data.preferences) * 5
    return base_score + age_bonus + preferences_bonus
```

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