Source code for autogen_core.components.tools._base
import json
from abc import ABC, abstractmethod
from collections.abc import Sequence
from typing import Any, Dict, Generic, Mapping, Protocol, Type, TypedDict, TypeVar, runtime_checkable
from pydantic import BaseModel
from typing_extensions import NotRequired
from ...base import CancellationToken
from .._function_utils import normalize_annotated_type
T = TypeVar("T", bound=BaseModel, contravariant=True)
[docs]
class ParametersSchema(TypedDict):
type: str
properties: Dict[str, Any]
required: NotRequired[Sequence[str]]
[docs]
class ToolSchema(TypedDict):
parameters: NotRequired[ParametersSchema]
name: str
description: NotRequired[str]
[docs]
@runtime_checkable
class Tool(Protocol):
@property
def name(self) -> str: ...
@property
def description(self) -> str: ...
@property
def schema(self) -> ToolSchema: ...
[docs]
async def run_json(self, args: Mapping[str, Any], cancellation_token: CancellationToken) -> Any: ...
ArgsT = TypeVar("ArgsT", bound=BaseModel, contravariant=True)
ReturnT = TypeVar("ReturnT", bound=BaseModel, covariant=True)
StateT = TypeVar("StateT", bound=BaseModel)
[docs]
class BaseTool(ABC, Tool, Generic[ArgsT, ReturnT]):
def __init__(
self,
args_type: Type[ArgsT],
return_type: Type[ReturnT],
name: str,
description: str,
) -> None:
self._args_type = args_type
# Normalize Annotated to the base type.
self._return_type = normalize_annotated_type(return_type)
self._name = name
self._description = description
@property
def schema(self) -> ToolSchema:
model_schema = self._args_type.model_json_schema()
tool_schema = ToolSchema(
name=self._name,
description=self._description,
parameters=ParametersSchema(
type="object",
properties=model_schema["properties"],
),
)
if "required" in model_schema:
assert "parameters" in tool_schema
tool_schema["parameters"]["required"] = model_schema["required"]
return tool_schema
@property
def name(self) -> str:
return self._name
@property
def description(self) -> str:
return self._description
[docs]
def return_value_as_string(self, value: Any) -> str:
if isinstance(value, BaseModel):
dumped = value.model_dump()
if isinstance(dumped, dict):
return json.dumps(dumped)
return str(dumped)
return str(value)
[docs]
@abstractmethod
async def run(self, args: ArgsT, cancellation_token: CancellationToken) -> ReturnT: ...
[docs]
async def run_json(self, args: Mapping[str, Any], cancellation_token: CancellationToken) -> Any:
return_value = await self.run(self._args_type.model_validate(args), cancellation_token)
return return_value
[docs]
class BaseToolWithState(BaseTool[ArgsT, ReturnT], ABC, Generic[ArgsT, ReturnT, StateT]):
def __init__(
self,
args_type: Type[ArgsT],
return_type: Type[ReturnT],
state_type: Type[StateT],
name: str,
description: str,
) -> None:
super().__init__(args_type, return_type, name, description)
self._state_type = state_type
[docs]
def load_state_json(self, state: Mapping[str, Any]) -> None:
self.load_state(self._state_type.model_validate(state))