Structured Types in @tool Arguments

In addition to the basic primitive types supported in Archytas tools (dict, list, int, float, bool, str None), more structured data types are supported in limited form. Currently supported structured types include:

• dataclass
• BaseModel from pydantic

For example:

from archytas.tool_utils import tool
from dataclasses import dataclass, field

@dataclass
class A:
    val1: int
    val2: str = 'hello'

@dataclass
class B:
    val3: list[int]
    val4: dict[str, int] = field(default_factory=dict)

@dataclass
class C:
    b: B
    val5: bool

@tool
def some_tool(a: A, c: C) -> str:
    """
    description of some tool
    
    Args:
        a (A): description of what `a` is for.
        c (C): description of what `c` is for.
    
    Returns:
        (str): ...
    """
    # implementation of tool ...

Or with pydantic models

from archytas.tool_utils import tool
from pydantic import BaseModel, Field

class A(BaseModel):
    val1: int = Field(..., description="what is val1 for")
    val2: str = Field(default='hello', description="what is val2 for")

class B(BaseModel):
    val3: list[int] = Field(..., description="what is val3 for")
    val4: dict[str, int] = Field(default_factory=dict, description="what is val4 for")

class C(BaseModel):
    b: B = Field(..., description="what is b for")
    val5: bool = Field(..., description="what is val5 for")

@tool
def some_tool(a: A, c: C) -> str:
    """
    description of some tool
    
    Args:
        a (A): description of what `a` is for.
        c (C): description of what `c` is for.
    
    Returns:
        (str): ...
    """
    # implementation of tool ...