core¶

Core components of the prompt decorators system.

This package contains the core components and functionality that power the prompt decorators system, including the base decorator classes, validation logic, request handling, and model-specific adaptations.

Public API¶

This module exports the following components:

DecoratorBase: Class - Base class for all prompt decorators
BaseDecorator: Class - Base class for all prompt decorators
DecoratorParameter: Class - Represents a parameter for a decorator
Parameter: Class - Represents a parameter for a decorator
ValidationError: Class - Exception raised when decorator validation fails
ParameterType: Class - Types of parameters supported in decorators
DecoratorParser: Class - Parser for extracting decorator annotations from prompts
DecoratorRegistry: Class - Registry class for managing prompt decorators

Classes¶

`BaseDecorator`¶

Imported from prompt_decorators.core.base

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`¶

Imported from prompt_decorators.core.base

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`¶

Imported from prompt_decorators.core.base

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

`DecoratorParser`¶

Imported from prompt_decorators.core.parser

Parser for extracting decorator annotations from prompts.

This class handles the parsing of decorator annotations in the format: +++DecoratorName(param1=value1, param2=value2)

Attributes¶

DECORATOR_PATTERN: str = '\\+\\+\\+([A-Za-z0-9_]+)(?:\\(([^)]*)\\))?'

Methods¶

`init`¶

Initialize the decorator parser.

Args: registry: Optional decorator registry to use for creating decorators. If not provided, the global registry will be used.

Signature: __init__(self, registry: Optional[prompt_decorators.core.registry.DecoratorRegistry] = None)

Parameters:

registry: Optional (default: None)

`extract_decorators`¶

Extract decorator annotations from a prompt.

This method extracts all decorator annotations from the prompt text, creates decorator instances for each annotation, and returns both the list of decorators and the cleaned prompt text.

Args: prompt: The prompt text to parse

Returns: A tuple containing: - A list of decorator instances - The prompt text with decorator annotations removed

Signature: extract_decorators(self, prompt: str) -> Tuple[List[prompt_decorators.core.base.DecoratorBase], str]

Parameters:

prompt: str

Returns: Tuple

`DecoratorRegistry`¶

Imported from prompt_decorators.core.registry

Registry class for managing prompt decorators.

This class provides an object-oriented interface to the decorator registry, allowing for easier management and access to registered decorators.

Attributes¶

categories: property = <property object at 0x105ac72e0>
decorators: property = <property object at 0x105ac7100>

Methods¶

`init`¶

Initialize the decorator registry.

Signature: __init__(self) -> None

Parameters:

`clear`¶

Clear the registry.

Signature: clear(self) -> None

Parameters:

`get_by_category`¶

Get all decorators in a category.

Args: category: The category to get decorators for

Returns: List of decorator classes in the category

Signature: get_by_category(self, category: str) -> List[Type[prompt_decorators.core.base.DecoratorBase]]

Parameters:

category: str

Returns: List

`get_decorator`¶

Get a decorator class by name.

Args: name: The name of the decorator to get

Returns: The decorator class, or None if not found

Signature: get_decorator(self, name: str) -> Optional[Type[prompt_decorators.core.base.DecoratorBase]]

Parameters:

name: str

Returns: Optional

`register`¶

Register a decorator class.

Args: decorator_class: The decorator class to register category: The category to register the decorator under

Returns: None

Signature: register(self, decorator_class: Type[prompt_decorators.core.base.DecoratorBase], category: str = 'unknown') -> None

Parameters:

decorator_class: Type
category: str (default: unknown)

`Parameter`¶

Imported from prompt_decorators.core.base

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`¶

Imported from prompt_decorators.core.base

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`¶

Imported from prompt_decorators.core.base

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)

core¶

Public API¶

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¶

DecoratorParser¶

Attributes¶

Methods¶

__init__¶

extract_decorators¶

DecoratorRegistry¶

Attributes¶

Methods¶

__init__¶

clear¶

get_by_category¶

get_decorator¶

register¶

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`¶

`DecoratorParser`¶

`init`¶

`extract_decorators`¶

`DecoratorRegistry`¶

`init`¶

`clear`¶

`get_by_category`¶

`get_decorator`¶

`register`¶

`Parameter`¶

`init`¶

`copy`¶

`dict`¶

`json`¶

`model_copy`¶

`model_dump`¶

`model_dump_json`¶

`model_post_init`¶

`validate_value`¶

`ParameterType`¶

`init`¶

`ValidationError`¶

`init`¶