base¶

Base classes for prompt decorators.

This module provides the base classes and utilities for creating and using prompt decorators.

Classes¶

`BaseDecorator`¶

Base class for all prompt decorators.

This class defines the common interface and behavior for all decorators. Subclasses should implement the apply_to_prompt and transform_response methods.

Attributes¶

conflicts_with: set = set()
description: str = 'Base decorator class'
from_dict: classmethod = <classmethod(<function DecoratorBase.from_dict at 0x105acf4c0>)>
name: str = 'DecoratorBase'
parameters: dict = {}
transformation_template: dict = {'instruction': '', 'parameterMapping': {}, 'placement': 'prepend', 'compositionBehavior': 'accumulate'}

Methods¶

`init`¶

Initialize a decorator with parameter values.

Args: **kwargs: Parameter values for the decorator

Raises: ValidationError: If any parameter values are invalid

Signature: __init__(self, **kwargs)

Parameters:

kwargs:

`apply_to_prompt`¶

Apply the decorator to a prompt.

This method uses the transformation_template to transform the prompt according to the decorator's intended behavior.

Args: prompt: The prompt to decorate

Returns: The decorated prompt

Signature: apply_to_prompt(self, prompt: str) -> str

Parameters:

prompt: str

Returns: str

`transform_response`¶

Transform the LLM response according to the decorator's behavior.

The base implementation returns the response unchanged. Subclasses should override this method if they need to modify the response.

Args: response: The LLM response to transform

Returns: The transformed response

Signature: transform_response(self, response: str) -> str

Parameters:

response: str

Returns: str

`DecoratorBase`¶

Base class for all prompt decorators.

This class defines the common interface and behavior for all decorators. Subclasses should implement the apply_to_prompt and transform_response methods.

Attributes¶

conflicts_with: set = set()
description: str = 'Base decorator class'
from_dict: classmethod = <classmethod(<function DecoratorBase.from_dict at 0x105acf4c0>)>
name: str = 'DecoratorBase'
parameters: dict = {}
transformation_template: dict = {'instruction': '', 'parameterMapping': {}, 'placement': 'prepend', 'compositionBehavior': 'accumulate'}

Methods¶

`init`¶

Initialize a decorator with parameter values.

Args: **kwargs: Parameter values for the decorator

Raises: ValidationError: If any parameter values are invalid

Signature: __init__(self, **kwargs)

Parameters:

kwargs:

`apply_to_prompt`¶

Apply the decorator to a prompt.

This method uses the transformation_template to transform the prompt according to the decorator's intended behavior.

Args: prompt: The prompt to decorate

Returns: The decorated prompt

Signature: apply_to_prompt(self, prompt: str) -> str

Parameters:

prompt: str

Returns: str

`transform_response`¶

Transform the LLM response according to the decorator's behavior.

The base implementation returns the response unchanged. Subclasses should override this method if they need to modify the response.

Args: response: The LLM response to transform

Returns: The transformed response

Signature: transform_response(self, response: str) -> str

Parameters:

response: str

Returns: str

`DecoratorParameter`¶

Represents a parameter for a decorator.

This class is used by dynamic decorators to define parameters and validate values.

Attributes¶

from_dict: classmethod = <classmethod(<function DecoratorParameter.from_dict at 0x105acf380>)>

Methods¶

`init`¶

Initialize a decorator parameter.

Args: name: The name of the parameter description: A description of the parameter type_: The type of the parameter (string, integer, float, boolean, enum) required: Whether the parameter is required default: Default value for the parameter enum_values: Possible values for enum type min_value: Minimum value for numeric types max_value: Maximum value for numeric types min_length: Minimum length for string or array types max_length: Maximum length for string or array types pattern: Regex pattern for string validation

Signature: __init__(self, name: str, description: str, type_: str = 'string', required: bool = False, default: Any = None, enum_values: Optional[List[str]] = None, min_value: Union[int, float, NoneType] = None, max_value: Union[int, float, NoneType] = None, min_length: Optional[int] = None, max_length: Optional[int] = None, pattern: Optional[str] = None)

Parameters:

name: str
description: str
type_: str (default: string)
required: bool (default: False)
default: Any (default: None)
enum_values: Optional (default: None)
min_value: Union (default: None)
max_value: Union (default: None)
min_length: Optional (default: None)
max_length: Optional (default: None)
pattern: Optional (default: None)

`to_dict`¶

Convert the parameter to a dictionary representation.

Signature: to_dict(self) -> Dict[str, Any]

Parameters:

Returns: Dict

`validate`¶

Validate a parameter value against constraints.

Args: value: The value to validate

Returns: The validated value (possibly converted to the correct type)

Raises: ValidationError: If the value is invalid

Signature: validate(self, value: Any) -> Any

Parameters:

value: Any

Returns: Any

`Parameter`¶

Represents a parameter for a decorator.

This class defines the metadata for a parameter, including its name, type, description, default value, and constraints.

Bases: pydantic.main.BaseModel

Attributes¶

model_config: dict = {}

Methods¶

`init`¶

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Signature: __init__(self, /, **data: 'Any') -> 'None'

Parameters:

data: Any

Returns: None

`copy`¶

Returns a copy of the model.

Deprecated

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: include: Optional set or mapping specifying which fields to include in the copied model. exclude: Optional set or mapping specifying which fields to exclude in the copied model. update: Optional dictionary of field-value pairs to override field values in the copied model. deep: If True, the values of fields that are Pydantic models will be deep-copied.

Returns: A copy of the model with included, excluded and updated fields as specified.

Parameters:

include: AbstractSetIntStr | MappingIntStrAny | None (default: None)
exclude: AbstractSetIntStr | MappingIntStrAny | None (default: None)
update: Dict[str, Any] | None (default: None)
deep: bool (default: False)

Returns: Self

`dict`¶

Signature: dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'

Parameters:

include: IncEx | None (default: None)
exclude: IncEx | None (default: None)
by_alias: bool (default: False)
exclude_unset: bool (default: False)
exclude_defaults: bool (default: False)
exclude_none: bool (default: False)

Returns: Dict[str, Any]

`json`¶

Signature: json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'

Parameters:

include: IncEx | None (default: None)
exclude: IncEx | None (default: None)
by_alias: bool (default: False)
exclude_unset: bool (default: False)
exclude_defaults: bool (default: False)
exclude_none: bool (default: False)
encoder: Callable[[Any], Any] | None (default: PydanticUndefined)
models_as_dict: bool (default: PydanticUndefined)
dumps_kwargs: Any

Returns: str

`model_copy`¶

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

Args: update: Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. deep: Set to True to make a deep copy of the model.

Returns: New model instance.

Signature: model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'

Parameters:

update: Mapping[str, Any] | None (default: None)
deep: bool (default: False)

Returns: Self

`model_dump`¶

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: The mode in which to_python should run. If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. include: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a PydanticSerializationError. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns: A dictionary representation of the model.

Signature: model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'

Parameters:

mode: Literal['json', 'python'] | str (default: python)
include: IncEx | None (default: None)
exclude: IncEx | None (default: None)
context: Any | None (default: None)
by_alias: bool (default: False)
exclude_unset: bool (default: False)
exclude_defaults: bool (default: False)
exclude_none: bool (default: False)
round_trip: bool (default: False)
warnings: bool | Literal['none', 'warn', 'error'] (default: True)
serialize_as_any: bool (default: False)

Returns: dict[str, Any]

`model_dump_json`¶

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic's to_json method.

Args: indent: Indentation to use in the JSON output. If None is passed, the output will be compact. include: Field(s) to include in the JSON output. exclude: Field(s) to exclude from the JSON output. context: Additional context to pass to the serializer. by_alias: Whether to serialize using field aliases. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a PydanticSerializationError. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns: A JSON string representation of the model.

Signature: model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, serialize_as_any: 'bool' = False) -> 'str'

Parameters:

indent: int | None (default: None)
include: IncEx | None (default: None)
exclude: IncEx | None (default: None)
context: Any | None (default: None)
by_alias: bool (default: False)
exclude_unset: bool (default: False)
exclude_defaults: bool (default: False)
exclude_none: bool (default: False)
round_trip: bool (default: False)
warnings: bool | Literal['none', 'warn', 'error'] (default: True)
serialize_as_any: bool (default: False)

Returns: str

`model_post_init`¶

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Signature: model_post_init(self, _BaseModel__context: 'Any') -> 'None'

Parameters:

_BaseModel__context: Any

Returns: None

`validate_value`¶

Validate a parameter value against the parameter's constraints.

Args: value: The value to validate

Returns: The validated value (possibly converted to the correct type)

Raises: ValidationError: If the value is invalid

Signature: validate_value(self, value: Any) -> Any

Parameters:

value: Any

Returns: Any

`ParameterType`¶

Types of parameters supported in decorators.

Bases: builtins.str, enum.Enum

Attributes¶

ARRAY: ParameterType = <ParameterType.ARRAY: 'array'>
BOOLEAN: ParameterType = <ParameterType.BOOLEAN: 'boolean'>
ENUM: ParameterType = <ParameterType.ENUM: 'enum'>
FLOAT: ParameterType = <ParameterType.FLOAT: 'float'>
INTEGER: ParameterType = <ParameterType.INTEGER: 'integer'>
OBJECT: ParameterType = <ParameterType.OBJECT: 'object'>
STRING: ParameterType = <ParameterType.STRING: 'string'>

Methods¶

`init`¶

Signature: __init__(self, *args, **kwds)

Parameters:

args:
kwds:

`ValidationError`¶

Exception raised when decorator validation fails.

Bases: builtins.Exception

Methods¶

`init`¶

Initialize ValidationError.

Args: message: The error message decorator_name: Optional name of the decorator where validation failed

Signature: __init__(self, message: str, decorator_name: Optional[str] = None)

Parameters:

message: str
decorator_name: Optional (default: None)

base¶

Classes¶

BaseDecorator¶

Attributes¶

Methods¶

__init__¶

apply_to_prompt¶

transform_response¶

DecoratorBase¶

Attributes¶

Methods¶

__init__¶

apply_to_prompt¶

transform_response¶

DecoratorParameter¶

Attributes¶

Methods¶

__init__¶

to_dict¶

validate¶

Parameter¶

Attributes¶

Methods¶

__init__¶

copy¶

dict¶

json¶

model_copy¶

model_dump¶

model_dump_json¶

model_post_init¶

validate_value¶

ParameterType¶

Attributes¶

Methods¶

__init__¶

ValidationError¶

Methods¶

__init__¶

`BaseDecorator`¶

`init`¶

`apply_to_prompt`¶

`transform_response`¶

`DecoratorBase`¶

`init`¶

`apply_to_prompt`¶

`transform_response`¶

`DecoratorParameter`¶

`init`¶

`to_dict`¶

`validate`¶

`Parameter`¶

`init`¶

`copy`¶

`dict`¶

`json`¶

`model_copy`¶

`model_dump`¶

`model_dump_json`¶

`model_post_init`¶

`validate_value`¶

`ParameterType`¶

`init`¶

`ValidationError`¶

`init`¶