extraction_methods.core package

Submodules

extraction_methods.core.extraction_method module

Extraction Method Models

class extraction_methods.core.extraction_method.Backend(*args: Any, **kwargs: Any)

Bases: SetInput, ABC

Class to act as a base for Backends. Defines the basic method signature and ensure compliance by all subclasses.

abstract run(body: dict[str, Any]) → Iterator[dict[str, Any]]

Run the backend.

Parameters:

body (dict) – current generated properties

Returns:

updated body dict

Return type:

dict

class extraction_methods.core.extraction_method.ExtractionMethod(*args: Any, **kwargs: Any)

Bases: SetInput, ABC

Class to act as a base for all extracion methods. Defines the basic method signature and ensure compliance by all subclasses.

abstract run(body: dict[str, Any]) → dict[str, Any]

Run the method.

Parameters:

body (dict) – current generated properties

Returns:

updated body dict

Return type:

dict

class extraction_methods.core.extraction_method.SetEntryPointsMixin(*args: Any, **kwargs: Any)

Bases: object

Mixin to set entry_points attribute.

entry_point_group: str

entry_points: dict[str, EntryPoint] = {}

class extraction_methods.core.extraction_method.SetInput(*args: Any, **kwargs: Any)

Bases: object

Class to set input attribute from kwargs.

dummy_input_class

alias of DummyInput

input_class

alias of Input

extraction_methods.core.extraction_method.update_input(func: Callable[[Any, dict[str, Any]], Any]) → Callable[[Any, dict[str, Any]], Any]

Wrapper to update inputs with body values before run.

Parameters:

func (Callable) – function that wrapper is to be run on

Returns:

function that wrapper is to be run on

Return type:

Callable

extraction_methods.core.types module

Extraction Method Types

class extraction_methods.core.types.Backend(*, name: str, inputs: dict[str, Any] = {})

Bases: BaseModel

Model for Backend configuration.

inputs: dict[str, Any]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'inputs': FieldInfo(annotation=dict[str, Any], required=False, default={}, description='Inputs for backend.'), 'name': FieldInfo(annotation=str, required=True, description='Name of backend.')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

name: str

class extraction_methods.core.types.DummyInput(*, exists_key: str = '$', exists_delimiter: str = '.', **extra_data: Any)

Bases: Input

Dummy input used before attributes are updated.

model_config: ClassVar[ConfigDict] = {'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'exists_delimiter': FieldInfo(annotation=str, required=False, default='.', description='Delimiter for nested exists terms.'), 'exists_key': FieldInfo(annotation=str, required=False, default='$', description='Key to signify a previously extracted terms.')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

update_attr(value: Any, body: dict[str, Any]) → Any

Update attribute using the exists key.

Parameters:

• value (Any) – current attribute value

• body (dict) – current generated properties

Returns:

updated attribute

Return type:

Any

update_attrs(body: dict[str, Any]) → None

Update instance attributes with body.

Parameters:

body (dict) – current generated properties

update_dict_attr(input_dict: dict[str, Any], body: dict[str, Any]) → dict[str, Any]

Update nested dictionary attributes.

Parameters:

• input_dict (dict) – attribute dictionary to update

• body (dict) – current generated properties

Returns:

updated dictionary attribute

Return type:

dict

update_list_attr(input_list: list[Any], body: dict[str, Any]) → list[Any]

Update list of attributes.

Parameters:

• input_list (list) – attribute list to update

• body (list) – current generated properties

Returns:

updated list attribute

Return type:

list

class extraction_methods.core.types.Input(*, exists_key: str = '$', exists_delimiter: str = '.')

Bases: BaseModel

Model for method input.

exists_delimiter: str

exists_key: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'exists_delimiter': FieldInfo(annotation=str, required=False, default='.', description='Delimiter for nested exists terms.'), 'exists_key': FieldInfo(annotation=str, required=False, default='$', description='Key to signify a previously extracted terms.')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class extraction_methods.core.types.KeyOutputKey(*, key: str, output_key: str = '')

Bases: BaseModel

Model for key and output key pairs.

key: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'key': FieldInfo(annotation=str, required=True), 'output_key': FieldInfo(annotation=str, required=False, default='')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

output_key: str

set_default_key() → Self

Set the default for key

Module contents