gradient

GradientContext and Gradient

Classes

Gradient(*, from_response, to_pred[, id, ...])	It will handle gradients and feedbacks.
GradientContext(variable_desc, input_output, ...)	GradientContext is used to describe the component's function and trace its input and output.

class GradientContext(variable_desc: str, input_output: str, response_desc: str)

Bases: DataClass

GradientContext is used to describe the component’s function and trace its input and output.

To get the component’s function desc, use GradientContext.to_yaml_signature() To get the data: use instance.to_yaml()

variable_desc: str

input_output: str

response_desc: str

class Gradient(*, from_response: Parameter, to_pred: Parameter, id: str | None = None, score: float | None = None, data_id: str | None = None, data: Any = None)

Bases: DataClass

It will handle gradients and feedbacks.

It tracks the d_from_response_id / d_to_pred_id and the score of the whole response.

if two gradients have the same data_id, different from_response_id, and same from_response_component_id, this is a cycle component structure.

context: GradientContext = None

prompt: str | None = None

is_default_copy: bool = False

from_response_component_id: str = None

from_response_id: str = None

to_pred_id: str = None

score: float | None = None

data_id: str | None = None

data: Any = None

order: int | None = None

add_context(context: GradientContext)

add_data(data: Any)

update_from_to(from_response: Parameter, to_pred: Parameter)

add_prompt(prompt: str)