bi_etl.config.notifiers_config module

class bi_etl.config.notifiers_config.JiraNotifier(*, user_id: str | None = None, password_source: PasswordSource, <function _check_password_source at 0x7f47c416fcc0>] | None=None, raw_password: str | None = None, keyring_section: str | None = None, keepass_config: str | None = 'keepass', keepass: KeepassConfig | None = None, keepass_group: str | None = None, keepass_title: str | None = None, validate_password_on_load: bool = True, notifier_class: str = 'bi_etl.notifiers.jira.Jira', include_sensitive: bool = True, server: str, project: str, component: str | None = None, comment_on_each_instance: bool = True, exclude_statuses: List[str] = <factory>, issue_type: str = 'Bug', labels: List[str] | None = None, priority: str | None = None, team_name: str | None = None, epic_id: str | None = None, parent_issue: str | None = None, custom_field_default_values: dict[str, ~typing.Any] | None=None, subject_prefix: str = '', update_description: bool = True, add_date_to_message: bool = True, track_incident_count: bool = True, incident_count_prefix: str = 'Count of incidents: ', keep_first_comment: bool = True, recent_comments_to_keep: int = 1, keep_first_attachment: bool = True, recent_attachments_to_keep: int = 1, auto_header_color: str = 'C1C7D0', auto_header_begin_text: str = '— Begin Automatic Update Section —', auto_header_end_text: str = '— End Automatic Update Section —')

Bases: NotifierConfigBase, Credentials

__init__(**data: Any) → None

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

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

Uses something other than self the first arg to allow “self” as a settable attribute

add_child(name: str, child_object: ConfigHierarchy)

Set this configuration as a child in the hierarchy of another config. For any programmatically created config objects this is required so that the new object ‘knows’ where it lives in the hierarchy – most importantly so that it can find the hierarchies root object.

add_date_to_message: bool

Add the current date/time to the issue description.

auto_header_begin_text: str

auto_header_color: str

Color to use for automatic headers and footers (html hex format).

auto_header_end_text: str

check_model()

comment_on_each_instance: bool

Add a comment on each new instance of the same issue subject.

component: str | None

Component to tag on the issues created.

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• 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.

custom_field_default_values: dict[str, Any] | None

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

epic_id: str | None

exclude_statuses: List[str], FieldInfo(annotation=NoneType, required=False, default_factory=<lambda>)]

When searching for existing instances, exclude issues with these statuses.

classmethod from_orm(obj: Any) → Self

full_item_name(item_name: str = None, delimiter: str = ' -> ')

The fully qualified name of this config item in the config hierarchy.

get(section, item, fallback=Ellipsis)

Used as a drop in replacement for ConfigParser.get() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

get_copy(copied_by: str = 'get_copy') → ConfigHierarchy

Copy this configuration. Useful when you need to programmatically modify a configuration without modifying the original base configuration.

get_list(section, item, fallback=Ellipsis) → list

Used as a drop in replacement for ConfigParser.get() + list parsing with dynamic config field names (using a string variable for the section and item names instead of python code attribute access) that is then parsed as a list.

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

get_password() → str

Get the password for this resource. password_source controls where it looks for the password. If that is None, then the root level passwords container is checked for password_source value.

getboolean(section, item, fallback=Ellipsis) → bool

Used as a drop in replacement for ConfigParser.getboolean() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

incident_count_prefix: str

The line prefix for the count of incidents

include_sensitive: bool

Include the sensitive_message send() parameter value in this notifiers output?

This allows some notifiers to include only the non-sensitive message parts while others include the sensitive details.

issue_type: str

Type of issues to create.

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | 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

keep_first_attachment: bool

Keep the first attachment saved to the issue. Note: Only impacts attachments made by the bot user id.

keep_first_comment: bool

Keep the first comment made on the issue. Note: Only impacts comments made by the bot user id.

keepass

If the password_source is KEEPASS, then load a sub-section with the config_wrangler.config_templates.keepass_config.KeepassConfig) settings

keepass_config

If the password_source is KEEPASS, then which root level config item contains the settings for Keepass (must be an instance of config_wrangler.config_templates.keepass_config.KeepassConfig)

keepass_group

If the password_source is KEEPASS, which group in the Keepass database should be searched for an entry with a matching entry.

If is None, then the KeepassConfig.default_group value will be checked. If that is also None, then a ValueError will be raised.

keepass_title

If the password_source is KEEPASS, this is an optional filter on the title of the keepass entries in the group.

keyring_section

If the password_source is KEYRING, then which section (AKA system) should this module look for the password in.

See https://pypi.org/project/keyring/ or https://github.com/jaraco/keyring

labels: List[str] | None

model_computed_fields = {}

model_config = {'validate_assignment': True, 'validate_credentials': True, 'validate_default': True}

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

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• 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.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → dict[str, Any]

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

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

Parameters:

• 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.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• 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][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → str

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

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

Parameters:

• indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

• ensure_ascii – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.

• 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.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• 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][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A JSON string representation of the model.

model_dump_non_private(*, mode: Literal['json', 'python'] | str = 'python', exclude: Set[str] = None) → dict[str, Any]

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'add_date_to_message': FieldInfo(annotation=bool, required=False, default=True), 'auto_header_begin_text': FieldInfo(annotation=str, required=False, default='— Begin Automatic Update Section —'), 'auto_header_color': FieldInfo(annotation=str, required=False, default='C1C7D0'), 'auto_header_end_text': FieldInfo(annotation=str, required=False, default='— End Automatic Update Section —'), 'comment_on_each_instance': FieldInfo(annotation=bool, required=False, default=True), 'component': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'custom_field_default_values': FieldInfo(annotation=Union[dict[str, Any], NoneType], required=False, default=None), 'epic_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'exclude_statuses': FieldInfo(annotation=List[str], required=False, default_factory=<lambda>), 'incident_count_prefix': FieldInfo(annotation=str, required=False, default='Count of incidents: '), 'include_sensitive': FieldInfo(annotation=bool, required=False, default=True), 'issue_type': FieldInfo(annotation=str, required=False, default='Bug'), 'keep_first_attachment': FieldInfo(annotation=bool, required=False, default=True), 'keep_first_comment': FieldInfo(annotation=bool, required=False, default=True), 'keepass': FieldInfo(annotation=Union[KeepassConfig, NoneType], required=False, default=None), 'keepass_config': FieldInfo(annotation=Union[str, NoneType], required=False, default='keepass'), 'keepass_group': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'keepass_title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'keyring_section': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'labels': FieldInfo(annotation=Union[List[str], NoneType], required=False, default=None), 'notifier_class': FieldInfo(annotation=str, required=False, default='bi_etl.notifiers.jira.Jira'), 'parent_issue': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'password_source': FieldInfo(annotation=Union[Annotated[PasswordSource, _check_password_source], NoneType], required=False, default=None), 'priority': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'project': FieldInfo(annotation=str, required=True), 'raw_password': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, repr=False), 'recent_attachments_to_keep': FieldInfo(annotation=int, required=False, default=1), 'recent_comments_to_keep': FieldInfo(annotation=int, required=False, default=1), 'server': FieldInfo(annotation=str, required=True), 'subject_prefix': FieldInfo(annotation=str, required=False, default=''), 'team_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'track_incident_count': FieldInfo(annotation=bool, required=False, default=True), 'update_description': FieldInfo(annotation=bool, required=False, default=True), 'user_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'validate_password_on_load': FieldInfo(annotation=bool, required=False, default=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: Literal['validation', 'serialization']='validation', *, union_format: Literal['any_of', 'primitive_type_array']='any_of') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

• by_alias – Whether to use attribute aliases or not.

• ref_template – The reference template.

• union_format –

  The format to use when combining schemas from unions together. Can be one of:

  • ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)

  keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

• schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self – The BaseModel instance.

• context – The context.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors – Whether to raise errors, defaults to True.

• _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

• _types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate a pydantic model instance.

Parameters:

• obj – The object to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• from_attributes – Whether to extract data from object attributes.

• context – Additional context to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data – The JSON data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate the given object with string data against the Pydantic model.

Parameters:

• obj – The object containing string data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

notifier_class: str

parent_issue: str | None

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

password_source

The source to use when getting a password for the user. See PasswordSource for valid values.

priority: str | None

Priority to create issues with. None for project default.

project: str

Project to add issues to.

raw_password

This is only used for the extremely non-secure CONFIG_FILE password source. The password is stored directly in the config file next to the user_id with the setting name raw_password

recent_attachments_to_keep: int

Only used if comment_on_each_instance = True. The number of attachments comments to keep. It will always keep the newest attachment, so this has no impact until it is 2 or greater. Note: Only impacts attachments made by the bot user id.

recent_comments_to_keep: int

Only used if comment_on_each_instance = True. The number of recent comments to keep. It will always keep the newest comment, so this has no impact until it is 2 or greater. Note: Only impacts comments made by the bot user id.

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

server: str

//jira.example.net)

Type:

HTTP prefix for the Jira server to connect to (e.g. https

set_as_child(name: str, other_config_item: ConfigHierarchy)

subject_prefix: str

Prefix to add to issue subjects.

team_name: str | None

track_incident_count: bool

Add a count of the number of occurrences of an issue. Only valid if update_description is True.

static translate_config_data(config_data: MutableMapping)

Children classes can provide translation logic to allow older config files to be used with newer config class definitions.

update_description: bool

Update the issue description on each incident if an issue.

classmethod update_forward_refs(**localns: Any) → None

user_id

The user ID to use

classmethod validate(value: Any) → Self

validate_password_on_load

Should config_wrangler query the password source for this password at load (startup) time? If so, it will raise an error if the password is None or an empty string. It does not actually connect or authenticate the user_id & password combination.

class bi_etl.config.notifiers_config.LogNotifierConfig(*, notifier_class: str = 'bi_etl.notifiers.log_notifier.LogNotifier', include_sensitive: bool = True)

Bases: NotifierConfigBase

__init__(**data: Any) → None

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

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

Uses something other than self the first arg to allow “self” as a settable attribute

add_child(name: str, child_object: ConfigHierarchy)

Set this configuration as a child in the hierarchy of another config. For any programmatically created config objects this is required so that the new object ‘knows’ where it lives in the hierarchy – most importantly so that it can find the hierarchies root object.

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• 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.

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

classmethod from_orm(obj: Any) → Self

full_item_name(item_name: str = None, delimiter: str = ' -> ')

The fully qualified name of this config item in the config hierarchy.

get(section, item, fallback=Ellipsis)

Used as a drop in replacement for ConfigParser.get() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

get_copy(copied_by: str = 'get_copy') → ConfigHierarchy

Copy this configuration. Useful when you need to programmatically modify a configuration without modifying the original base configuration.

get_list(section, item, fallback=Ellipsis) → list

Used as a drop in replacement for ConfigParser.get() + list parsing with dynamic config field names (using a string variable for the section and item names instead of python code attribute access) that is then parsed as a list.

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

getboolean(section, item, fallback=Ellipsis) → bool

Used as a drop in replacement for ConfigParser.getboolean() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

include_sensitive: bool

Include the sensitive_message send() parameter value in this notifiers output?

This allows some notifiers to include only the non-sensitive message parts while others include the sensitive details.

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | 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

model_computed_fields = {}

model_config = {'validate_assignment': True, 'validate_credentials': True, 'validate_default': True}

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

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• 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.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → dict[str, Any]

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

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

Parameters:

• 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.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• 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][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → str

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

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

Parameters:

• indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

• ensure_ascii – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.

• 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.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• 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][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A JSON string representation of the model.

model_dump_non_private(*, mode: Literal['json', 'python'] | str = 'python', exclude: Set[str] = None) → dict[str, Any]

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'include_sensitive': FieldInfo(annotation=bool, required=False, default=True), 'notifier_class': FieldInfo(annotation=str, required=False, default='bi_etl.notifiers.log_notifier.LogNotifier')}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: Literal['validation', 'serialization']='validation', *, union_format: Literal['any_of', 'primitive_type_array']='any_of') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

• by_alias – Whether to use attribute aliases or not.

• ref_template – The reference template.

• union_format –

  The format to use when combining schemas from unions together. Can be one of:

  • ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)

  keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

• schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self – The BaseModel instance.

• context – The context.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors – Whether to raise errors, defaults to True.

• _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

• _types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate a pydantic model instance.

Parameters:

• obj – The object to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• from_attributes – Whether to extract data from object attributes.

• context – Additional context to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data – The JSON data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate the given object with string data against the Pydantic model.

Parameters:

• obj – The object containing string data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

notifier_class: str

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

set_as_child(name: str, other_config_item: ConfigHierarchy)

static translate_config_data(config_data: MutableMapping)

Children classes can provide translation logic to allow older config files to be used with newer config class definitions.

classmethod update_forward_refs(**localns: Any) → None

classmethod validate(value: Any) → Self

class bi_etl.config.notifiers_config.NotifierConfigBase(*, notifier_class: str, include_sensitive: bool = False)

Bases: ConfigHierarchy

__init__(**data: Any) → None

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

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

Uses something other than self the first arg to allow “self” as a settable attribute

add_child(name: str, child_object: ConfigHierarchy)

Set this configuration as a child in the hierarchy of another config. For any programmatically created config objects this is required so that the new object ‘knows’ where it lives in the hierarchy – most importantly so that it can find the hierarchies root object.

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• 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.

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

classmethod from_orm(obj: Any) → Self

full_item_name(item_name: str = None, delimiter: str = ' -> ')

The fully qualified name of this config item in the config hierarchy.

get(section, item, fallback=Ellipsis)

Used as a drop in replacement for ConfigParser.get() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

get_copy(copied_by: str = 'get_copy') → ConfigHierarchy

Copy this configuration. Useful when you need to programmatically modify a configuration without modifying the original base configuration.

get_list(section, item, fallback=Ellipsis) → list

Used as a drop in replacement for ConfigParser.get() + list parsing with dynamic config field names (using a string variable for the section and item names instead of python code attribute access) that is then parsed as a list.

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

getboolean(section, item, fallback=Ellipsis) → bool

Used as a drop in replacement for ConfigParser.getboolean() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

include_sensitive: bool

Include the sensitive_message send() parameter value in this notifiers output?

This allows some notifiers to include only the non-sensitive message parts while others include the sensitive details.

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | 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

model_computed_fields = {}

model_config = {'validate_assignment': True, 'validate_credentials': True, 'validate_default': True}

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

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• 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.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → dict[str, Any]

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

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

Parameters:

• 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.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• 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][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → str

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

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

Parameters:

• indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

• ensure_ascii – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.

• 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.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• 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][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A JSON string representation of the model.

model_dump_non_private(*, mode: Literal['json', 'python'] | str = 'python', exclude: Set[str] = None) → dict[str, Any]

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'include_sensitive': FieldInfo(annotation=bool, required=False, default=False), 'notifier_class': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: Literal['validation', 'serialization']='validation', *, union_format: Literal['any_of', 'primitive_type_array']='any_of') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

• by_alias – Whether to use attribute aliases or not.

• ref_template – The reference template.

• union_format –

  The format to use when combining schemas from unions together. Can be one of:

  • ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)

  keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

• schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self – The BaseModel instance.

• context – The context.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors – Whether to raise errors, defaults to True.

• _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

• _types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate a pydantic model instance.

Parameters:

• obj – The object to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• from_attributes – Whether to extract data from object attributes.

• context – Additional context to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data – The JSON data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate the given object with string data against the Pydantic model.

Parameters:

• obj – The object containing string data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

notifier_class: str

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

set_as_child(name: str, other_config_item: ConfigHierarchy)

static translate_config_data(config_data: MutableMapping)

Children classes can provide translation logic to allow older config files to be used with newer config class definitions.

classmethod update_forward_refs(**localns: Any) → None

classmethod validate(value: Any) → Self

class bi_etl.config.notifiers_config.SMTP_Notifier(*, user_id: str | None = None, password_source: ~typing.Annotated[~config_wrangler.config_templates.password_source.PasswordSource, <function _check_password_source at 0x7f47c416fcc0>] | None = None, raw_password: str | None = None, keyring_section: str | None = None, keepass_config: str | None = 'keepass', keepass: ~config_wrangler.config_templates.keepass_config.KeepassConfig | None = None, keepass_group: str | None = None, keepass_title: str | None = None, validate_password_on_load: bool = True, notifier_class: str = 'bi_etl.notifiers.email.Email', include_sensitive: bool = False, email_from: str, gateway_host: str | None = None, gateway_port: int = 0, use_ssl: bool = False, debug: bool = False, distro_list: ~typing.List[str])

Bases: NotifierConfigBase, Credentials

__init__(**data: Any) → None

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

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

Uses something other than self the first arg to allow “self” as a settable attribute

add_child(name: str, child_object: ConfigHierarchy)

Set this configuration as a child in the hierarchy of another config. For any programmatically created config objects this is required so that the new object ‘knows’ where it lives in the hierarchy – most importantly so that it can find the hierarchies root object.

check_model()

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• 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.

debug: bool

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

distro_list: List[str]

email_from: str

classmethod from_orm(obj: Any) → Self

full_item_name(item_name: str = None, delimiter: str = ' -> ')

The fully qualified name of this config item in the config hierarchy.

gateway_host: str | None

gateway_port: int

get(section, item, fallback=Ellipsis)

Used as a drop in replacement for ConfigParser.get() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

get_copy(copied_by: str = 'get_copy') → ConfigHierarchy

Copy this configuration. Useful when you need to programmatically modify a configuration without modifying the original base configuration.

get_list(section, item, fallback=Ellipsis) → list

Used as a drop in replacement for ConfigParser.get() + list parsing with dynamic config field names (using a string variable for the section and item names instead of python code attribute access) that is then parsed as a list.

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

get_password() → str

Get the password for this resource. password_source controls where it looks for the password. If that is None, then the root level passwords container is checked for password_source value.

getboolean(section, item, fallback=Ellipsis) → bool

Used as a drop in replacement for ConfigParser.getboolean() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

include_sensitive

Include the sensitive_message send() parameter value in this notifiers output?

This allows some notifiers to include only the non-sensitive message parts while others include the sensitive details.

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | 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

keepass

If the password_source is KEEPASS, then load a sub-section with the config_wrangler.config_templates.keepass_config.KeepassConfig) settings

keepass_config

If the password_source is KEEPASS, then which root level config item contains the settings for Keepass (must be an instance of config_wrangler.config_templates.keepass_config.KeepassConfig)

keepass_group

If the password_source is KEEPASS, which group in the Keepass database should be searched for an entry with a matching entry.

If is None, then the KeepassConfig.default_group value will be checked. If that is also None, then a ValueError will be raised.

keepass_title

If the password_source is KEEPASS, this is an optional filter on the title of the keepass entries in the group.

keyring_section

If the password_source is KEYRING, then which section (AKA system) should this module look for the password in.

See https://pypi.org/project/keyring/ or https://github.com/jaraco/keyring

model_computed_fields = {}

model_config = {'validate_assignment': True, 'validate_credentials': True, 'validate_default': True}

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

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• 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.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → dict[str, Any]

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

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

Parameters:

• 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.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• 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][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → str

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

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

Parameters:

• indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

• ensure_ascii – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.

• 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.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• 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][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A JSON string representation of the model.

model_dump_non_private(*, mode: Literal['json', 'python'] | str = 'python', exclude: Set[str] = None) → dict[str, Any]

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'debug': FieldInfo(annotation=bool, required=False, default=False), 'distro_list': FieldInfo(annotation=List[str], required=True), 'email_from': FieldInfo(annotation=str, required=True), 'gateway_host': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'gateway_port': FieldInfo(annotation=int, required=False, default=0), 'include_sensitive': FieldInfo(annotation=bool, required=False, default=False), 'keepass': FieldInfo(annotation=Union[KeepassConfig, NoneType], required=False, default=None), 'keepass_config': FieldInfo(annotation=Union[str, NoneType], required=False, default='keepass'), 'keepass_group': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'keepass_title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'keyring_section': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'notifier_class': FieldInfo(annotation=str, required=False, default='bi_etl.notifiers.email.Email'), 'password_source': FieldInfo(annotation=Union[Annotated[PasswordSource, _check_password_source], NoneType], required=False, default=None), 'raw_password': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, repr=False), 'use_ssl': FieldInfo(annotation=bool, required=False, default=False), 'user_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'validate_password_on_load': FieldInfo(annotation=bool, required=False, default=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: Literal['validation', 'serialization']='validation', *, union_format: Literal['any_of', 'primitive_type_array']='any_of') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

• by_alias – Whether to use attribute aliases or not.

• ref_template – The reference template.

• union_format –

  The format to use when combining schemas from unions together. Can be one of:

  • ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)

  keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

• schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self – The BaseModel instance.

• context – The context.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors – Whether to raise errors, defaults to True.

• _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

• _types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate a pydantic model instance.

Parameters:

• obj – The object to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• from_attributes – Whether to extract data from object attributes.

• context – Additional context to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data – The JSON data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate the given object with string data against the Pydantic model.

Parameters:

• obj – The object containing string data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

notifier_class: str

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

password_source

The source to use when getting a password for the user. See PasswordSource for valid values.

raw_password

This is only used for the extremely non-secure CONFIG_FILE password source. The password is stored directly in the config file next to the user_id with the setting name raw_password

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

set_as_child(name: str, other_config_item: ConfigHierarchy)

static translate_config_data(config_data: MutableMapping)

Children classes can provide translation logic to allow older config files to be used with newer config class definitions.

classmethod update_forward_refs(**localns: Any) → None

use_ssl: bool

user_id

The user ID to use

classmethod validate(value: Any) → Self

validate_password_on_load

Should config_wrangler query the password source for this password at load (startup) time? If so, it will raise an error if the password is None or an empty string. It does not actually connect or authenticate the user_id & password combination.

class bi_etl.config.notifiers_config.SlackNotifier(*, notifier_class: str = 'bi_etl.notifiers.slack.Slack', include_sensitive: bool = False, channel: str, token: str | None = None, mention: str | None = None, token_source: PasswordSource | None = None, keyring_section: str | None = None, keepass_config: str = 'keepass', keepass: KeepassConfig | None = None, keepass_group: str | None = None, keepass_title: str | None = None)

Bases: NotifierConfigBase

__init__(**data: Any) → None

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

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

Uses something other than self the first arg to allow “self” as a settable attribute

add_child(name: str, child_object: ConfigHierarchy)

Set this configuration as a child in the hierarchy of another config. For any programmatically created config objects this is required so that the new object ‘knows’ where it lives in the hierarchy – most importantly so that it can find the hierarchies root object.

channel: str

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• 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.

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

classmethod from_orm(obj: Any) → Self

full_item_name(item_name: str = None, delimiter: str = ' -> ')

The fully qualified name of this config item in the config hierarchy.

get(section, item, fallback=Ellipsis)

Used as a drop in replacement for ConfigParser.get() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

get_copy(copied_by: str = 'get_copy') → ConfigHierarchy

Copy this configuration. Useful when you need to programmatically modify a configuration without modifying the original base configuration.

get_list(section, item, fallback=Ellipsis) → list

Used as a drop in replacement for ConfigParser.get() + list parsing with dynamic config field names (using a string variable for the section and item names instead of python code attribute access) that is then parsed as a list.

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

getboolean(section, item, fallback=Ellipsis) → bool

Used as a drop in replacement for ConfigParser.getboolean() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

include_sensitive

Include the sensitive_message send() parameter value in this notifiers output?

This allows some notifiers to include only the non-sensitive message parts while others include the sensitive details.

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | 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

keepass: KeepassConfig | None

If the password_source is KEEPASS, then load a sub-section with the config_wrangler.config_templates.keepass_config.KeepassConfig) settings

keepass_config: str

If the password_source is KEEPASS, then which root level config item contains the settings for Keepass (must be an instance of config_wrangler.config_templates.keepass_config.KeepassConfig)

keepass_group: str | None

If the password_source is KEEPASS, which group in the Keepass database should be searched for an entry with a matching entry.

If is None, then the KeepassConfig.default_group value will be checked. If that is also None, then a ValueError will be raised.

keepass_title: str | None

If the password_source is KEEPASS, this is an optional filter on the title of the keepass entries in the group.

keyring_section: str | None

If the password_source is KEYRING, then which section (AKA system) should this module look for the password in.

See https://pypi.org/project/keyring/ or https://github.com/jaraco/keyring

mention: str | None

Channel notification tags to use. For example @channel for all members.

model_computed_fields = {}

model_config = {'validate_assignment': True, 'validate_credentials': True, 'validate_default': True}

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

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• 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.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → dict[str, Any]

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

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

Parameters:

• 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.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• 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][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → str

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

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

Parameters:

• indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

• ensure_ascii – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.

• 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.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• 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][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A JSON string representation of the model.

model_dump_non_private(*, mode: Literal['json', 'python'] | str = 'python', exclude: Set[str] = None) → dict[str, Any]

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'channel': FieldInfo(annotation=str, required=True), 'include_sensitive': FieldInfo(annotation=bool, required=False, default=False), 'keepass': FieldInfo(annotation=Union[KeepassConfig, NoneType], required=False, default=None), 'keepass_config': FieldInfo(annotation=str, required=False, default='keepass'), 'keepass_group': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'keepass_title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'keyring_section': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'mention': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'notifier_class': FieldInfo(annotation=str, required=False, default='bi_etl.notifiers.slack.Slack'), 'token': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'token_source': FieldInfo(annotation=Union[PasswordSource, NoneType], required=False, default=None)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: Literal['validation', 'serialization']='validation', *, union_format: Literal['any_of', 'primitive_type_array']='any_of') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

• by_alias – Whether to use attribute aliases or not.

• ref_template – The reference template.

• union_format –

  The format to use when combining schemas from unions together. Can be one of:

  • ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)

  keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

• schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self – The BaseModel instance.

• context – The context.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors – Whether to raise errors, defaults to True.

• _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

• _types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate a pydantic model instance.

Parameters:

• obj – The object to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• from_attributes – Whether to extract data from object attributes.

• context – Additional context to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data – The JSON data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate the given object with string data against the Pydantic model.

Parameters:

• obj – The object containing string data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

notifier_class: str

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

set_as_child(name: str, other_config_item: ConfigHierarchy)

token: str | None

This is only used for the extremely non-secure CONFIG_FILE token source valid values defined using PasswordSource. The token is stored directly in the config file with the setting name token

token_source: PasswordSource | None

The source to use when getting a token for slack. See PasswordSource for valid values.

static translate_config_data(config_data: MutableMapping)

Children classes can provide translation logic to allow older config files to be used with newer config class definitions.

classmethod update_forward_refs(**localns: Any) → None

classmethod validate(value: Any) → Self