| | |
- builtins.object
-
- social_sync.bluesky.BlueskyClient
- social_sync.config.Config
- social_sync.mastodon.MastodonClient
- social_sync.sync.SyncManager
- builtins.str(builtins.object)
-
- social_sync.models.MediaType(builtins.str, enum.Enum)
- enum.Enum(builtins.object)
-
- social_sync.models.MediaType(builtins.str, enum.Enum)
- pydantic.main.BaseModel(builtins.object)
-
- social_sync.models.Link
- social_sync.models.MediaAttachment
- social_sync.models.SocialPost
-
- social_sync.models.BlueskyPost
- social_sync.models.MastodonPost
- social_sync.models.SyncRecord
class BlueskyClient(builtins.object) |
| |
BlueskyClient(config: social_sync.config.BlueskyConfig)
Client for interacting with the Bluesky API.
|
| |
Methods defined here:
- __init__(self, config: social_sync.config.BlueskyConfig)
- Initialize the Bluesky client.
Args:
config: Bluesky configuration with credentials
- authenticate(self) -> bool
- Authenticate with the Bluesky API.
Returns:
True if authentication was successful, False otherwise
- ensure_authenticated(self) -> bool
- Check authentication and re-authenticate if needed.
Returns:
True if authenticated, False otherwise
- get_recent_posts(self, hours_back: int = 24, limit: int = 20) -> List[social_sync.models.BlueskyPost]
- Get recent posts from the authenticated user.
Args:
hours_back: How many hours to look back
limit: Maximum number of posts to return
Returns:
List of BlueskyPost objects
Raises:
ValueError: If not authenticated
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
|
class BlueskyPost(SocialPost) |
| |
BlueskyPost(
*,
id: str,
content: str,
created_at: datetime.datetime,
platform: str = 'bluesky',
author_id: str,
author_handle: str,
author_display_name: Optional[str] = None,
media_attachments: List[social_sync.models.MediaAttachment] = <factory>,
links: List[social_sync.models.Link] = <factory>,
is_reply: bool = False,
is_repost: bool = False,
in_reply_to_id: Optional[str] = None,
repost_of_id: Optional[str] = None,
language: Optional[str] = None,
visibility: Optional[str] = None,
uri: str,
cid: str,
reply_root: Optional[str] = None,
reply_parent: Optional[str] = None,
like_count: Optional[int] = None,
repost_count: Optional[int] = None
) -> None
Bluesky-specific post model.
|
| |
- Method resolution order:
- BlueskyPost
- SocialPost
- pydantic.main.BaseModel
- builtins.object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'cid': <class 'str'>, 'like_count': typing.Optional[int], 'platform': <class 'str'>, 'reply_parent': typing.Optional[str], 'reply_root': typing.Optional[str], 'repost_count': typing.Optional[int], 'uri': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'social_sync.models.BlueskyPost'>, 'config': {'title': 'BlueskyPost'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'social_sync.models.BlueskyPost'>>]}, 'ref': 'social_sync.models.BlueskyPost:4685924240', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'author_display_name': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'author_handle': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'author_id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'cid': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'content': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'created_at': {'metadata': {}, 'schema': {'microseconds_precision': 'truncate', 'type': 'datetime'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'in_reply_to_id': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'is_reply': {'metadata': {}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'is_repost': {'metadata': {}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, ...}, 'model_name': 'BlueskyPost', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_fields__ = {'author_display_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'author_handle': FieldInfo(annotation=str, required=True), 'author_id': FieldInfo(annotation=str, required=True), 'cid': FieldInfo(annotation=str, required=True), 'content': FieldInfo(annotation=str, required=True), 'created_at': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=str, required=True), 'in_reply_to_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'is_reply': FieldInfo(annotation=bool, required=False, default=False), 'is_repost': FieldInfo(annotation=bool, required=False, default=False), ...}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri... name: "BlueskyPost",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="BlueskyPost", validator=M...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, content: str, created_at...one, repost_count: Optional[int] = None) -> None>
- model_config = {}
Data descriptors inherited from SocialPost:
- __weakref__
- list of weak references to the object
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- Helper for pickle.
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]' from pydantic._internal._repr.Representation
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str' from pydantic._internal._repr.Representation
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str' from pydantic._internal._repr.Representation
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str' from pydantic._internal._repr.Representation
- __rich_repr__(self) -> 'RichReprResult' from pydantic._internal._repr.Representation
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(
self,
*,
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)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'Dict[str, Any]'
- json(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
- model_copy(
self,
*,
update: 'Mapping[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/serialization.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]).
Args:
update: Values to change/add in the new model. Note: the data is not validated
before creating the new model. You should trust this data.
deep: Set to `True` to make a deep copy of the model.
Returns:
New model instance.
- model_dump(
self,
*,
mode: "Literal['json', 'python'] | str" = 'python',
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A dictionary representation of the model.
- model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef'
- __get_pydantic_core_schema__(
source: 'type[BaseModel]',
handler: 'GetCoreSchemaHandler',
/
) -> 'CoreSchema'
- __get_pydantic_json_schema__(
core_schema: 'CoreSchema',
handler: 'GetJsonSchemaHandler',
/
) -> 'JsonSchemaValue'
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None'
- This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
- from_orm(obj: 'Any') -> 'Self'
- 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.
Args:
_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_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
- Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
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.
- 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.
Args:
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_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.
Args:
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`.
- model_validate(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | 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.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_strings(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
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.
- parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- parse_obj(obj: 'Any') -> 'Self'
- parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
- schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
- update_forward_refs(**localns: 'Any') -> 'None'
- validate(value: 'Any') -> 'Self'
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- 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.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'author_display_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'author_handle': FieldInfo(annotation=str, required=True), 'author_id': FieldInfo(annotation=str, required=True), 'cid': FieldInfo(annotation=str, required=True), 'content': FieldInfo(annotation=str, required=True), 'created_at': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=str, required=True), 'in_reply_to_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'is_reply': FieldInfo(annotation=bool, required=False, default=False), 'is_repost': FieldInfo(annotation=bool, required=False, default=False), ...}
|
class Config(builtins.object) |
| |
Config(
bluesky: social_sync.config.BlueskyConfig,
mastodon: social_sync.config.MastodonConfig,
lookback_hours: int = 24,
sync_interval_minutes: int = 60,
max_posts_per_run: int = 5,
include_media: bool = True,
include_links: bool = True
) -> None
Main configuration container.
|
| |
Methods defined here:
- __eq__(self, other)
- Return self==value.
- __init__(
self,
bluesky: social_sync.config.BlueskyConfig,
mastodon: social_sync.config.MastodonConfig,
lookback_hours: int = 24,
sync_interval_minutes: int = 60,
max_posts_per_run: int = 5,
include_media: bool = True,
include_links: bool = True
) -> None
- Initialize self. See help(type(self)) for accurate signature.
- __replace__ = _replace(self, /, **changes) from dataclasses
- __repr__(self)
- Return repr(self).
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
Data and other attributes defined here:
- __annotations__ = {'bluesky': <class 'social_sync.config.BlueskyConfig'>, 'include_links': <class 'bool'>, 'include_media': <class 'bool'>, 'lookback_hours': <class 'int'>, 'mastodon': <class 'social_sync.config.MastodonConfig'>, 'max_posts_per_run': <class 'int'>, 'sync_interval_minutes': <class 'int'>}
- __dataclass_fields__ = {'bluesky': Field(name='bluesky',type=<class 'social_sync.co...appingproxy({}),kw_only=False,_field_type=_FIELD), 'include_links': Field(name='include_links',type=<class 'bool'>,d...appingproxy({}),kw_only=False,_field_type=_FIELD), 'include_media': Field(name='include_media',type=<class 'bool'>,d...appingproxy({}),kw_only=False,_field_type=_FIELD), 'lookback_hours': Field(name='lookback_hours',type=<class 'int'>,d...appingproxy({}),kw_only=False,_field_type=_FIELD), 'mastodon': Field(name='mastodon',type=<class 'social_sync.c...appingproxy({}),kw_only=False,_field_type=_FIELD), 'max_posts_per_run': Field(name='max_posts_per_run',type=<class 'int'...appingproxy({}),kw_only=False,_field_type=_FIELD), 'sync_interval_minutes': Field(name='sync_interval_minutes',type=<class '...appingproxy({}),kw_only=False,_field_type=_FIELD)}
- __dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,ord...rue,kw_only=False,slots=False,weakref_slot=False)
- __hash__ = None
- __match_args__ = ('bluesky', 'mastodon', 'lookback_hours', 'sync_interval_minutes', 'max_posts_per_run', 'include_media', 'include_links')
- include_links = True
- include_media = True
- lookback_hours = 24
- max_posts_per_run = 5
- sync_interval_minutes = 60
|
class Link(pydantic.main.BaseModel) |
| |
Link(
*,
url: str,
title: Optional[str] = None,
description: Optional[str] = None,
image_url: Optional[str] = None
) -> None
Represents an external link in a post.
|
| |
- Method resolution order:
- Link
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'description': typing.Optional[str], 'image_url': typing.Optional[str], 'title': typing.Optional[str], 'url': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'social_sync.models.Link'>, 'config': {'title': 'Link'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'social_sync.models.Link'>>]}, 'ref': 'social_sync.models.Link:4685900704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'description': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'image_url': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'title': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'url': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'Link', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_fields__ = {'description': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'image_url': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'url': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri...,
name: "Link",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="Link", validator=Model(
...k",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, url: str, title: Optional[str] = ...= None, image_url: Optional[str] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- Helper for pickle.
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]' from pydantic._internal._repr.Representation
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str' from pydantic._internal._repr.Representation
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str' from pydantic._internal._repr.Representation
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str' from pydantic._internal._repr.Representation
- __rich_repr__(self) -> 'RichReprResult' from pydantic._internal._repr.Representation
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(
self,
*,
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)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'Dict[str, Any]'
- json(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
- model_copy(
self,
*,
update: 'Mapping[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/serialization.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]).
Args:
update: Values to change/add in the new model. Note: the data is not validated
before creating the new model. You should trust this data.
deep: Set to `True` to make a deep copy of the model.
Returns:
New model instance.
- model_dump(
self,
*,
mode: "Literal['json', 'python'] | str" = 'python',
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A dictionary representation of the model.
- model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef'
- __get_pydantic_core_schema__(
source: 'type[BaseModel]',
handler: 'GetCoreSchemaHandler',
/
) -> 'CoreSchema'
- __get_pydantic_json_schema__(
core_schema: 'CoreSchema',
handler: 'GetJsonSchemaHandler',
/
) -> 'JsonSchemaValue'
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None'
- This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
- from_orm(obj: 'Any') -> 'Self'
- 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.
Args:
_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_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
- Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
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.
- 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.
Args:
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_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.
Args:
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`.
- model_validate(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | 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.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_strings(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
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.
- parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- parse_obj(obj: 'Any') -> 'Self'
- parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
- schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
- update_forward_refs(**localns: 'Any') -> 'None'
- validate(value: 'Any') -> 'Self'
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- 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.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'description': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'image_url': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'url': FieldInfo(annotation=str, required=True)}
|
class MastodonClient(builtins.object) |
| |
MastodonClient(config: social_sync.config.MastodonConfig)
Client for interacting with the Mastodon API.
|
| |
Methods defined here:
- __init__(self, config: social_sync.config.MastodonConfig)
- Initialize the Mastodon client.
Args:
config: Mastodon configuration with credentials
- cross_post(self, bluesky_post: social_sync.models.BlueskyPost) -> Optional[social_sync.models.MastodonPost]
- Create a Mastodon post from a Bluesky post.
Args:
bluesky_post: The Bluesky post to cross-post
Returns:
The created Mastodon post or None if posting failed
Raises:
ValueError: If not authenticated
- ensure_authenticated(self) -> bool
- Check authentication and re-verify if needed.
Returns:
True if authenticated, False otherwise
- verify_credentials(self) -> bool
- Verify the provided credentials.
Returns:
True if credentials are valid, False otherwise
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
|
class MastodonPost(SocialPost) |
| |
MastodonPost(
*,
id: str,
content: str,
created_at: datetime.datetime,
platform: str = 'mastodon',
author_id: str,
author_handle: str,
author_display_name: Optional[str] = None,
media_attachments: List[social_sync.models.MediaAttachment] = <factory>,
links: List[social_sync.models.Link] = <factory>,
is_reply: bool = False,
is_repost: bool = False,
in_reply_to_id: Optional[str] = None,
repost_of_id: Optional[str] = None,
language: Optional[str] = None,
visibility: Optional[str] = None,
url: str,
application: Optional[str] = None,
sensitive: bool = False,
spoiler_text: Optional[str] = None,
favourites_count: Optional[int] = None,
reblogs_count: Optional[int] = None
) -> None
Mastodon-specific post model.
|
| |
- Method resolution order:
- MastodonPost
- SocialPost
- pydantic.main.BaseModel
- builtins.object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'application': typing.Optional[str], 'favourites_count': typing.Optional[int], 'platform': <class 'str'>, 'reblogs_count': typing.Optional[int], 'sensitive': <class 'bool'>, 'spoiler_text': typing.Optional[str], 'url': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'social_sync.models.MastodonPost'>, 'config': {'title': 'MastodonPost'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'social_sync.models.MastodonPost'>>]}, 'ref': 'social_sync.models.MastodonPost:4685943808', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'application': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'author_display_name': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'author_handle': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'author_id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'content': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'created_at': {'metadata': {}, 'schema': {'microseconds_precision': 'truncate', 'type': 'datetime'}, 'type': 'model-field'}, 'favourites_count': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'in_reply_to_id': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'is_reply': {'metadata': {}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, ...}, 'model_name': 'MastodonPost', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_fields__ = {'application': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'author_display_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'author_handle': FieldInfo(annotation=str, required=True), 'author_id': FieldInfo(annotation=str, required=True), 'content': FieldInfo(annotation=str, required=True), 'created_at': FieldInfo(annotation=datetime, required=True), 'favourites_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'id': FieldInfo(annotation=str, required=True), 'in_reply_to_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'is_reply': FieldInfo(annotation=bool, required=False, default=False), ...}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri... name: "MastodonPost",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="MastodonPost", validator=...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, content: str, created_at...ne, reblogs_count: Optional[int] = None) -> None>
- model_config = {}
Data descriptors inherited from SocialPost:
- __weakref__
- list of weak references to the object
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- Helper for pickle.
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]' from pydantic._internal._repr.Representation
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str' from pydantic._internal._repr.Representation
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str' from pydantic._internal._repr.Representation
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str' from pydantic._internal._repr.Representation
- __rich_repr__(self) -> 'RichReprResult' from pydantic._internal._repr.Representation
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(
self,
*,
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)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'Dict[str, Any]'
- json(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
- model_copy(
self,
*,
update: 'Mapping[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/serialization.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]).
Args:
update: Values to change/add in the new model. Note: the data is not validated
before creating the new model. You should trust this data.
deep: Set to `True` to make a deep copy of the model.
Returns:
New model instance.
- model_dump(
self,
*,
mode: "Literal['json', 'python'] | str" = 'python',
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A dictionary representation of the model.
- model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef'
- __get_pydantic_core_schema__(
source: 'type[BaseModel]',
handler: 'GetCoreSchemaHandler',
/
) -> 'CoreSchema'
- __get_pydantic_json_schema__(
core_schema: 'CoreSchema',
handler: 'GetJsonSchemaHandler',
/
) -> 'JsonSchemaValue'
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None'
- This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
- from_orm(obj: 'Any') -> 'Self'
- 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.
Args:
_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_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
- Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
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.
- 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.
Args:
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_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.
Args:
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`.
- model_validate(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | 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.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_strings(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
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.
- parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- parse_obj(obj: 'Any') -> 'Self'
- parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
- schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
- update_forward_refs(**localns: 'Any') -> 'None'
- validate(value: 'Any') -> 'Self'
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- 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.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'application': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'author_display_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'author_handle': FieldInfo(annotation=str, required=True), 'author_id': FieldInfo(annotation=str, required=True), 'content': FieldInfo(annotation=str, required=True), 'created_at': FieldInfo(annotation=datetime, required=True), 'favourites_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'id': FieldInfo(annotation=str, required=True), 'in_reply_to_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'is_reply': FieldInfo(annotation=bool, required=False, default=False), ...}
|
class MediaAttachment(pydantic.main.BaseModel) |
| |
MediaAttachment(
*,
url: str,
alt_text: Optional[str] = None,
mime_type: Optional[str] = None,
media_type: social_sync.models.MediaType,
width: Optional[int] = None,
height: Optional[int] = None,
size_bytes: Optional[int] = None
) -> None
Represents a media attachment in a post.
|
| |
- Method resolution order:
- MediaAttachment
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'alt_text': typing.Optional[str], 'height': typing.Optional[int], 'media_type': <enum 'MediaType'>, 'mime_type': typing.Optional[str], 'size_bytes': typing.Optional[int], 'url': <class 'str'>, 'width': typing.Optional[int]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'social_sync.models.MediaAttachment'>, 'config': {'title': 'MediaAttachment'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche... of <class 'social_sync.models.MediaAttachment'>>]}, 'ref': 'social_sync.models.MediaAttachment:4685895840', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'alt_text': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'height': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'media_type': {'metadata': {}, 'schema': {'cls': <enum 'MediaType'>, 'members': [<MediaType.IMAGE: 'image'>, <MediaType.VIDEO: 'video'>, <MediaType.AUDIO: 'audio'>, <MediaType.OTHER: 'other'>], 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'social_sync.models.MediaType:4685894080', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'mime_type': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'size_bytes': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'url': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'width': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MediaAttachment', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_fields__ = {'alt_text': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'height': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'media_type': FieldInfo(annotation=MediaType, required=True), 'mime_type': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'size_bytes': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'url': FieldInfo(annotation=str, required=True), 'width': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri...ame: "MediaAttachment",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="MediaAttachment", validat...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, url: str, alt_text: Optional[str]... None, size_bytes: Optional[int] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- Helper for pickle.
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]' from pydantic._internal._repr.Representation
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str' from pydantic._internal._repr.Representation
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str' from pydantic._internal._repr.Representation
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str' from pydantic._internal._repr.Representation
- __rich_repr__(self) -> 'RichReprResult' from pydantic._internal._repr.Representation
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(
self,
*,
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)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'Dict[str, Any]'
- json(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
- model_copy(
self,
*,
update: 'Mapping[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/serialization.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]).
Args:
update: Values to change/add in the new model. Note: the data is not validated
before creating the new model. You should trust this data.
deep: Set to `True` to make a deep copy of the model.
Returns:
New model instance.
- model_dump(
self,
*,
mode: "Literal['json', 'python'] | str" = 'python',
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A dictionary representation of the model.
- model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef'
- __get_pydantic_core_schema__(
source: 'type[BaseModel]',
handler: 'GetCoreSchemaHandler',
/
) -> 'CoreSchema'
- __get_pydantic_json_schema__(
core_schema: 'CoreSchema',
handler: 'GetJsonSchemaHandler',
/
) -> 'JsonSchemaValue'
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None'
- This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
- from_orm(obj: 'Any') -> 'Self'
- 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.
Args:
_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_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
- Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
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.
- 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.
Args:
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_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.
Args:
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`.
- model_validate(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | 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.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_strings(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
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.
- parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- parse_obj(obj: 'Any') -> 'Self'
- parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
- schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
- update_forward_refs(**localns: 'Any') -> 'None'
- validate(value: 'Any') -> 'Self'
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- 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.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'alt_text': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'height': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'media_type': FieldInfo(annotation=MediaType, required=True), 'mime_type': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'size_bytes': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'url': FieldInfo(annotation=str, required=True), 'width': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}
|
class MediaType(builtins.str, enum.Enum) |
| |
MediaType(*values)
Types of media attachments.
|
| |
- Method resolution order:
- MediaType
- builtins.str
- enum.Enum
- builtins.object
Methods defined here:
- __format__(self, format_spec) from enum.Enum
- Default object formatter.
Return str(self) if format_spec is empty. Raise TypeError otherwise.
- __new__(cls, value) from enum.Enum
- Create and return a new object. See help(type) for accurate signature.
- __repr__(self) from enum.Enum
- Return repr(self).
- __str__(self) from enum.Enum
- Return str(self).
Data and other attributes defined here:
- AUDIO = <MediaType.AUDIO: 'audio'>
- IMAGE = <MediaType.IMAGE: 'image'>
- OTHER = <MediaType.OTHER: 'other'>
- VIDEO = <MediaType.VIDEO: 'video'>
Methods inherited from builtins.str:
- __add__(self, value, /)
- Return self+value.
- __contains__(self, key, /)
- Return bool(key in self).
- __eq__(self, value, /)
- Return self==value.
- __ge__(self, value, /)
- Return self>=value.
- __getitem__(self, key, /)
- Return self[key].
- __getnewargs__(self, /)
- __gt__(self, value, /)
- Return self>value.
- __hash__(self, /)
- Return hash(self).
- __iter__(self, /)
- Implement iter(self).
- __le__(self, value, /)
- Return self<=value.
- __len__(self, /)
- Return len(self).
- __lt__(self, value, /)
- Return self<value.
- __mod__(self, value, /)
- Return self%value.
- __mul__(self, value, /)
- Return self*value.
- __ne__(self, value, /)
- Return self!=value.
- __rmod__(self, value, /)
- Return value%self.
- __rmul__(self, value, /)
- Return value*self.
- __sizeof__(self, /)
- Return the size of the string in memory, in bytes.
- capitalize(self, /)
- Return a capitalized version of the string.
More specifically, make the first character have upper case and the rest lower
case.
- casefold(self, /)
- Return a version of the string suitable for caseless comparisons.
- center(self, width, fillchar=' ', /)
- Return a centered string of length width.
Padding is done using the specified fill character (default is a space).
- count(self, sub[, start[, end]], /)
- Return the number of non-overlapping occurrences of substring sub in string S[start:end].
Optional arguments start and end are interpreted as in slice notation.
- encode(self, /, encoding='utf-8', errors='strict')
- Encode the string using the codec registered for encoding.
encoding
The encoding in which to encode the string.
errors
The error handling scheme to use for encoding errors.
The default is 'strict' meaning that encoding errors raise a
UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
'xmlcharrefreplace' as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors.
- endswith(self, suffix[, start[, end]], /)
- Return True if the string ends with the specified suffix, False otherwise.
suffix
A string or a tuple of strings to try.
start
Optional start position. Default: start of the string.
end
Optional stop position. Default: end of the string.
- expandtabs(self, /, tabsize=8)
- Return a copy where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
- find(self, sub[, start[, end]], /)
- Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
Optional arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- format(self, /, *args, **kwargs)
- Return a formatted version of the string, using substitutions from args and kwargs.
The substitutions are identified by braces ('{' and '}').
- format_map(self, mapping, /)
- Return a formatted version of the string, using substitutions from mapping.
The substitutions are identified by braces ('{' and '}').
- index(self, sub[, start[, end]], /)
- Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
Optional arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
- isalnum(self, /)
- Return True if the string is an alpha-numeric string, False otherwise.
A string is alpha-numeric if all characters in the string are alpha-numeric and
there is at least one character in the string.
- isalpha(self, /)
- Return True if the string is an alphabetic string, False otherwise.
A string is alphabetic if all characters in the string are alphabetic and there
is at least one character in the string.
- isascii(self, /)
- Return True if all characters in the string are ASCII, False otherwise.
ASCII characters have code points in the range U+0000-U+007F.
Empty string is ASCII too.
- isdecimal(self, /)
- Return True if the string is a decimal string, False otherwise.
A string is a decimal string if all characters in the string are decimal and
there is at least one character in the string.
- isdigit(self, /)
- Return True if the string is a digit string, False otherwise.
A string is a digit string if all characters in the string are digits and there
is at least one character in the string.
- isidentifier(self, /)
- Return True if the string is a valid Python identifier, False otherwise.
Call keyword.iskeyword(s) to test whether string s is a reserved identifier,
such as "def" or "class".
- islower(self, /)
- Return True if the string is a lowercase string, False otherwise.
A string is lowercase if all cased characters in the string are lowercase and
there is at least one cased character in the string.
- isnumeric(self, /)
- Return True if the string is a numeric string, False otherwise.
A string is numeric if all characters in the string are numeric and there is at
least one character in the string.
- isprintable(self, /)
- Return True if the string is printable, False otherwise.
A string is printable if all of its characters are considered printable in
repr() or if it is empty.
- isspace(self, /)
- Return True if the string is a whitespace string, False otherwise.
A string is whitespace if all characters in the string are whitespace and there
is at least one character in the string.
- istitle(self, /)
- Return True if the string is a title-cased string, False otherwise.
In a title-cased string, upper- and title-case characters may only
follow uncased characters and lowercase characters only cased ones.
- isupper(self, /)
- Return True if the string is an uppercase string, False otherwise.
A string is uppercase if all cased characters in the string are uppercase and
there is at least one cased character in the string.
- join(self, iterable, /)
- Concatenate any number of strings.
The string whose method is called is inserted in between each given string.
The result is returned as a new string.
Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'
- ljust(self, width, fillchar=' ', /)
- Return a left-justified string of length width.
Padding is done using the specified fill character (default is a space).
- lower(self, /)
- Return a copy of the string converted to lowercase.
- lstrip(self, chars=None, /)
- Return a copy of the string with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
- partition(self, sep, /)
- Partition the string into three parts using the given separator.
This will search for the separator in the string. If the separator is found,
returns a 3-tuple containing the part before the separator, the separator
itself, and the part after it.
If the separator is not found, returns a 3-tuple containing the original string
and two empty strings.
- removeprefix(self, prefix, /)
- Return a str with the given prefix string removed if present.
If the string starts with the prefix string, return string[len(prefix):].
Otherwise, return a copy of the original string.
- removesuffix(self, suffix, /)
- Return a str with the given suffix string removed if present.
If the string ends with the suffix string and that suffix is not empty,
return string[:-len(suffix)]. Otherwise, return a copy of the original
string.
- replace(self, old, new, /, count=-1)
- Return a copy with all occurrences of substring old replaced by new.
count
Maximum number of occurrences to replace.
-1 (the default value) means replace all occurrences.
If the optional argument count is given, only the first count occurrences are
replaced.
- rfind(self, sub[, start[, end]], /)
- Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
Optional arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- rindex(self, sub[, start[, end]], /)
- Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
Optional arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
- rjust(self, width, fillchar=' ', /)
- Return a right-justified string of length width.
Padding is done using the specified fill character (default is a space).
- rpartition(self, sep, /)
- Partition the string into three parts using the given separator.
This will search for the separator in the string, starting at the end. If
the separator is found, returns a 3-tuple containing the part before the
separator, the separator itself, and the part after it.
If the separator is not found, returns a 3-tuple containing two empty strings
and the original string.
- rsplit(self, /, sep=None, maxsplit=-1)
- Return a list of the substrings in the string, using sep as the separator string.
sep
The separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplit
Maximum number of splits.
-1 (the default value) means no limit.
Splitting starts at the end of the string and works to the front.
- rstrip(self, chars=None, /)
- Return a copy of the string with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
- split(self, /, sep=None, maxsplit=-1)
- Return a list of the substrings in the string, using sep as the separator string.
sep
The separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplit
Maximum number of splits.
-1 (the default value) means no limit.
Splitting starts at the front of the string and works to the end.
Note, str.split() is mainly useful for data that has been intentionally
delimited. With natural text that includes punctuation, consider using
the regular expression module.
- splitlines(self, /, keepends=False)
- Return a list of the lines in the string, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends is given and
true.
- startswith(self, prefix[, start[, end]], /)
- Return True if the string starts with the specified prefix, False otherwise.
prefix
A string or a tuple of strings to try.
start
Optional start position. Default: start of the string.
end
Optional stop position. Default: end of the string.
- strip(self, chars=None, /)
- Return a copy of the string with leading and trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
- swapcase(self, /)
- Convert uppercase characters to lowercase and lowercase characters to uppercase.
- title(self, /)
- Return a version of the string where each word is titlecased.
More specifically, words start with uppercased characters and all remaining
cased characters have lower case.
- translate(self, table, /)
- Replace each character in the string using the given translation table.
table
Translation table, which must be a mapping of Unicode ordinals to
Unicode ordinals, strings, or None.
The table must implement lookup/indexing via __getitem__, for instance a
dictionary or list. If this operation raises LookupError, the character is
left untouched. Characters mapped to None are deleted.
- upper(self, /)
- Return a copy of the string converted to uppercase.
- zfill(self, width, /)
- Pad a numeric string with zeros on the left, to fill a field of the given width.
The string is never truncated.
Static methods inherited from builtins.str:
- maketrans(x, y=<unrepresentable>, z=<unrepresentable>, /)
- Return a translation table usable for str.translate().
If there is only one argument, it must be a dictionary mapping Unicode
ordinals (integers) or characters to Unicode ordinals, strings or None.
Character keys will be then converted to ordinals.
If there are two arguments, they must be strings of equal length, and
in the resulting dictionary, each character in x will be mapped to the
character at the same position in y. If there is a third argument, it
must be a string, whose characters will be mapped to None in the result.
Methods inherited from enum.Enum:
- __dir__(self)
- Returns public methods and other interesting attributes.
- __init__(self, *args, **kwds)
- Initialize self. See help(type(self)) for accurate signature.
- __reduce_ex__(self, proto)
- Helper for pickle.
Data descriptors inherited from enum.Enum:
- name
- The name of the Enum member.
- value
- The value of the Enum member.
Readonly properties inherited from enum.EnumType:
- __members__
- Returns a mapping of member name->value.
This mapping lists all enum members, including aliases. Note that this
is a read-only view of the internal mapping.
|
class SocialPost(pydantic.main.BaseModel) |
| |
SocialPost(
*,
id: str,
content: str,
created_at: datetime.datetime,
platform: str,
author_id: str,
author_handle: str,
author_display_name: Optional[str] = None,
media_attachments: List[social_sync.models.MediaAttachment] = <factory>,
links: List[social_sync.models.Link] = <factory>,
is_reply: bool = False,
is_repost: bool = False,
in_reply_to_id: Optional[str] = None,
repost_of_id: Optional[str] = None,
language: Optional[str] = None,
visibility: Optional[str] = None
) -> None
Base model for social media posts.
|
| |
- Method resolution order:
- SocialPost
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'author_display_name': typing.Optional[str], 'author_handle': <class 'str'>, 'author_id': <class 'str'>, 'content': <class 'str'>, 'created_at': <class 'datetime.datetime'>, 'id': <class 'str'>, 'in_reply_to_id': typing.Optional[str], 'is_reply': <class 'bool'>, 'is_repost': <class 'bool'>, 'language': typing.Optional[str], ...}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'social_sync.models.SocialPost'>, 'config': {'title': 'SocialPost'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'social_sync.models.SocialPost'>>]}, 'ref': 'social_sync.models.SocialPost:4685908816', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'author_display_name': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'author_handle': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'author_id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'content': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'created_at': {'metadata': {}, 'schema': {'microseconds_precision': 'truncate', 'type': 'datetime'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'in_reply_to_id': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'is_reply': {'metadata': {}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'is_repost': {'metadata': {}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'language': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, ...}, 'model_name': 'SocialPost', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_fields__ = {'author_display_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'author_handle': FieldInfo(annotation=str, required=True), 'author_id': FieldInfo(annotation=str, required=True), 'content': FieldInfo(annotation=str, required=True), 'created_at': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=str, required=True), 'in_reply_to_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'is_reply': FieldInfo(annotation=bool, required=False, default=False), 'is_repost': FieldInfo(annotation=bool, required=False, default=False), 'language': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), ...}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri... name: "SocialPost",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="SocialPost", validator=Mo...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, content: str, created_at... None, visibility: Optional[str] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- Helper for pickle.
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]' from pydantic._internal._repr.Representation
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str' from pydantic._internal._repr.Representation
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str' from pydantic._internal._repr.Representation
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str' from pydantic._internal._repr.Representation
- __rich_repr__(self) -> 'RichReprResult' from pydantic._internal._repr.Representation
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(
self,
*,
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)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'Dict[str, Any]'
- json(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
- model_copy(
self,
*,
update: 'Mapping[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/serialization.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]).
Args:
update: Values to change/add in the new model. Note: the data is not validated
before creating the new model. You should trust this data.
deep: Set to `True` to make a deep copy of the model.
Returns:
New model instance.
- model_dump(
self,
*,
mode: "Literal['json', 'python'] | str" = 'python',
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A dictionary representation of the model.
- model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef'
- __get_pydantic_core_schema__(
source: 'type[BaseModel]',
handler: 'GetCoreSchemaHandler',
/
) -> 'CoreSchema'
- __get_pydantic_json_schema__(
core_schema: 'CoreSchema',
handler: 'GetJsonSchemaHandler',
/
) -> 'JsonSchemaValue'
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None'
- This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
- from_orm(obj: 'Any') -> 'Self'
- 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.
Args:
_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_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
- Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
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.
- 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.
Args:
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_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.
Args:
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`.
- model_validate(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | 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.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_strings(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
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.
- parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- parse_obj(obj: 'Any') -> 'Self'
- parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
- schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
- update_forward_refs(**localns: 'Any') -> 'None'
- validate(value: 'Any') -> 'Self'
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- 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.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'author_display_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'author_handle': FieldInfo(annotation=str, required=True), 'author_id': FieldInfo(annotation=str, required=True), 'content': FieldInfo(annotation=str, required=True), 'created_at': FieldInfo(annotation=datetime, required=True), 'id': FieldInfo(annotation=str, required=True), 'in_reply_to_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'is_reply': FieldInfo(annotation=bool, required=False, default=False), 'is_repost': FieldInfo(annotation=bool, required=False, default=False), 'language': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), ...}
|
class SyncManager(builtins.object) |
| |
SyncManager(config: social_sync.config.Config, state_file: Optional[str] = None)
Manager for syncing posts between platforms.
|
| |
Methods defined here:
- __init__(
self,
config: social_sync.config.Config,
state_file: Optional[str] = None
)
- Initialize the sync manager.
Args:
config: Application configuration
state_file: Path to the state file for tracking synced posts
- run_sync(self) -> List[social_sync.models.SyncRecord]
- Run the synchronization process.
Returns:
List of SyncRecord objects for newly synced posts
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
|
class SyncRecord(pydantic.main.BaseModel) |
| |
SyncRecord(
*,
source_id: str,
source_platform: str,
target_id: str,
target_platform: str,
synced_at: datetime.datetime = <factory>,
success: bool = True,
error_message: Optional[str] = None
) -> None
Record of a cross-posted item.
|
| |
- Method resolution order:
- SyncRecord
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'error_message': typing.Optional[str], 'source_id': <class 'str'>, 'source_platform': <class 'str'>, 'success': <class 'bool'>, 'synced_at': <class 'datetime.datetime'>, 'target_id': <class 'str'>, 'target_platform': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'social_sync.models.SyncRecord'>, 'config': {'title': 'SyncRecord'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'social_sync.models.SyncRecord'>>]}, 'ref': 'social_sync.models.SyncRecord:4685963872', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'error_message': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'source_id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'source_platform': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'success': {'metadata': {}, 'schema': {'default': True, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'synced_at': {'metadata': {}, 'schema': {'default_factory': <built-in method now of type object>, 'default_factory_takes_data': False, 'schema': {'microseconds_precision': 'truncate', 'type': 'datetime'}, 'type': 'default'}, 'type': 'model-field'}, 'target_id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'target_platform': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'SyncRecord', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_fields__ = {'error_message': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'source_id': FieldInfo(annotation=str, required=True), 'source_platform': FieldInfo(annotation=str, required=True), 'success': FieldInfo(annotation=bool, required=False, default=True), 'synced_at': FieldInfo(annotation=datetime, required=False, default_factory=now), 'target_id': FieldInfo(annotation=str, required=True), 'target_platform': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri... name: "SyncRecord",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="SyncRecord", validator=Mo...d",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, source_id: str, source_platform: ...ue, error_message: Optional[str] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- Helper for pickle.
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]' from pydantic._internal._repr.Representation
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str' from pydantic._internal._repr.Representation
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str' from pydantic._internal._repr.Representation
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str' from pydantic._internal._repr.Representation
- __rich_repr__(self) -> 'RichReprResult' from pydantic._internal._repr.Representation
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(
self,
*,
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)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'Dict[str, Any]'
- json(
self,
*,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
- model_copy(
self,
*,
update: 'Mapping[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/serialization.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]).
Args:
update: Values to change/add in the new model. Note: the data is not validated
before creating the new model. You should trust this data.
deep: Set to `True` to make a deep copy of the model.
Returns:
New model instance.
- model_dump(
self,
*,
mode: "Literal['json', 'python'] | str" = 'python',
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A dictionary representation of the model.
- model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx | None' = None,
exclude: 'IncEx | None' = None,
context: 'Any | None' = None,
by_alias: 'bool | None' = None,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
fallback: 'Callable[[Any], Any] | None' = None,
serialize_as_any: 'bool' = False
) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][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.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef'
- __get_pydantic_core_schema__(
source: 'type[BaseModel]',
handler: 'GetCoreSchemaHandler',
/
) -> 'CoreSchema'
- __get_pydantic_json_schema__(
core_schema: 'CoreSchema',
handler: 'GetJsonSchemaHandler',
/
) -> 'JsonSchemaValue'
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None'
- This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
- from_orm(obj: 'Any') -> 'Self'
- 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.
Args:
_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_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
- Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
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.
- 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.
Args:
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_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.
Args:
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`.
- model_validate(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | 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.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
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.
- model_validate_strings(
obj: 'Any',
*,
strict: 'bool | 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.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
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.
- parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- parse_obj(obj: 'Any') -> 'Self'
- parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
- schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
- update_forward_refs(**localns: 'Any') -> 'None'
- validate(value: 'Any') -> 'Self'
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- 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.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'error_message': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'source_id': FieldInfo(annotation=str, required=True), 'source_platform': FieldInfo(annotation=str, required=True), 'success': FieldInfo(annotation=bool, required=False, default=True), 'synced_at': FieldInfo(annotation=datetime, required=False, default_factory=now), 'target_id': FieldInfo(annotation=str, required=True), 'target_platform': FieldInfo(annotation=str, required=True)}
| |