App Module

The App module provides functions for initializing and securing apps that interact with the Unique platform.

Overview

The unique_toolkit.app module encompasses functions for: - Initializing the SDK and logging - Handling events from the platform - Verifying webhook signatures - Building FastAPI applications - Running async tasks in parallel

Components

Settings

unique_toolkit.app.unique_settings.UniqueSettings

Source code in unique_toolkit/unique_toolkit/app/unique_settings.py

class UniqueSettings:
    def __init__(
        self,
        auth: AuthContextProtocol,
        app: UniqueApp,
        api: UniqueApi,
        *,
        chat_event_filter_options: UniqueChatEventFilterOptions | None = None,
        chat: ChatContextProtocol | None = None,
        env_file: Path | None = None,
    ):
        self._env = UniqueEnvironment(app=app, api=api)
        self._context = UniqueContext(auth=auth, chat=chat)
        self._chat_event_filter_options = chat_event_filter_options
        self._env_file: Path | None = (
            env_file if (env_file and env_file.exists()) else None
        )

    @classmethod
    def _find_env_file(cls, filename: str = "unique.env") -> Path:
        """Find environment file using cross-platform fallback locations.

        Search order:
        1. UNIQUE_ENV_FILE environment variable
        2. Current working directory
        3. User config directory (cross-platform via platformdirs)

        Args:
            filename: Name of the environment file (default: 'unique.env')

        Returns:
            Path to the environment file.

        Raises:
            EnvFileNotFoundError: If no environment file is found in any location.
        """
        locations = [
            # 1. Explicit environment variable
            Path(env_path) if (env_path := os.environ.get("UNIQUE_ENV_FILE")) else None,
            # 2. Current working directory
            Path.cwd() / filename,
            # 3. User config directory (cross-platform)
            Path(user_config_dir("unique", "unique-toolkit")) / filename,
        ]

        for location in locations:
            if location and location.exists() and location.is_file():
                return location

        # If no file found, provide helpful error message
        searched_locations = [str(loc) for loc in locations if loc is not None]
        raise EnvFileNotFoundError(
            f"Environment file '{filename}' not found. Searched locations:\n"
            + "\n".join(f"  - {loc}" for loc in searched_locations)
            + "\n\nTo fix this:\n"
            + f"  1. Create {filename} in one of the above locations, or\n"
            + f"  2. Set UNIQUE_ENV_FILE environment variable to point to your {filename} file"
        )

    @classmethod
    def from_env(
        cls,
        env_file: Path | None = None,
    ) -> UniqueSettings:
        """Initialize settings from environment variables and/or env file.

        Args:
            env_file: Optional path to environment file. If provided, will load variables from this file.

        Returns:
            UniqueSettings instance with values loaded from environment/env file.

        Raises:
            FileNotFoundError: If env_file is provided but does not exist.
            ValidationError: If required environment variables are missing.
        """
        if env_file and not env_file.exists():
            raise FileNotFoundError(f"Environment file not found: {env_file}")

        # Initialize settings with environment file if provided
        env_file_str = str(env_file) if env_file else None
        auth = UniqueAuth(_env_file=env_file_str)  # type: ignore[call-arg]
        app = UniqueApp(_env_file=env_file_str)  # type: ignore[call-arg]
        api = UniqueApi(_env_file=env_file_str)  # type: ignore[call-arg]
        event_filter_options = UniqueChatEventFilterOptions(_env_file=env_file_str)  # type: ignore[call-arg]
        return cls(
            auth=auth,
            app=app,
            api=api,
            chat_event_filter_options=event_filter_options,
            env_file=env_file,
        )

    @classmethod
    def from_env_auto(cls, filename: str = "unique.env") -> UniqueSettings:
        """Initialize settings by automatically finding environment file.

        This method will automatically search for an environment file in standard locations
        and fall back to environment variables only if no file is found.

        Args:
            filename: Name of the environment file to search for (default: '.env')

        Returns:
            UniqueSettings instance with values loaded from found env file or environment variables.
        """
        try:
            env_file = cls._find_env_file(filename)
            logger.info(f"Environment file found at {env_file}")
            return cls.from_env(env_file=env_file)
        except EnvFileNotFoundError:
            logger.warning(
                f"Environment file '{filename}' not found. Falling back to environment variables only."
            )
            # Fall back to environment variables only
            return cls.from_env()

    def init_sdk(self) -> None:
        """Initialize the unique_sdk global configuration with these settings.

        This method configures the global unique_sdk module with the API key,
        app ID, and base URL from these settings.
        """
        unique_sdk.api_key = self.app.key.get_secret_value()
        unique_sdk.app_id = self.app.id.get_secret_value()
        unique_sdk.api_base = self.api.sdk_url()

    @classmethod
    def from_env_auto_with_sdk_init(
        cls, filename: str = "unique.env"
    ) -> UniqueSettings:
        """Initialize settings and SDK in one convenient call.

        This method combines from_env_auto() and init_sdk() for the most common use case.

        Args:
            filename: Name of the environment file to search for (default: '.env')

        Returns:
            UniqueSettings instance with SDK already initialized.
        """
        settings = cls.from_env_auto(filename)
        settings.init_sdk()
        return settings

    @classmethod
    def from_chat_event(cls, event: ChatEvent) -> UniqueSettings:
        """Build a :class:`UniqueSettings` from a :class:`ChatEvent`.

        Auth and chat context are extracted from the event.  App and API
        settings are left at their default values; override them via the
        returned instance's properties if needed.

        Args:
            event: The incoming chat event.

        Returns:
            UniqueSettings with auth + chat context populated from the event.
        """
        return cls(
            auth=AuthContext.from_event(event),
            app=UniqueApp(),
            api=UniqueApi(),
            chat=ChatContext.from_chat_event(event),
        )

    @property
    def context(self) -> UniqueContext:
        """The request-level context (auth + optional chat) for this settings object."""
        return self._context

    def update_from_event(self, event: BaseEvent) -> None:
        # Use UniqueAuth.from_event so the deprecated settings.auth property
        # keeps working for callers that haven't migrated yet.
        self._context = UniqueContext(
            auth=UniqueAuth.from_event(event), chat=self._context.chat
        )

    # utility method to return a copy with new auth context
    def with_auth(self, auth: AuthContextProtocol) -> Self:
        """Return a copy of the settings with the new auth context."""
        return self.__class__(
            auth=auth,
            app=self.app,
            api=self.api,
            chat_event_filter_options=self.chat_event_filter_options,
            chat=self._context.chat,
            env_file=self._env_file,
        )

    @property
    def api(self) -> UniqueApi:
        return self._env.api

    @property
    def app(self) -> UniqueApp:
        return self._env.app

    @property
    @deprecated("Use authcontext instead")
    def auth(self) -> UniqueAuth:
        if not isinstance(self._context.auth, UniqueAuth):
            raise ValueError("Auth context is not a UniqueAuth instance")
        return self._context.auth

    @auth.setter
    @deprecated("Use authcontext instead")
    def auth(self, value: UniqueAuth) -> None:
        self._context.auth = value

    @property
    def authcontext(self) -> AuthContextProtocol:
        return self._context.auth

    @property
    def chat_event_filter_options(self) -> UniqueChatEventFilterOptions | None:
        return self._chat_event_filter_options

context property

The request-level context (auth + optional chat) for this settings object.

from_chat_event(event) classmethod

Build a :class:UniqueSettings from a :class:ChatEvent.

Auth and chat context are extracted from the event. App and API settings are left at their default values; override them via the returned instance's properties if needed.

Parameters:

Name	Type	Description	Default
event	ChatEvent	The incoming chat event.	required

Returns:

Type	Description
UniqueSettings	UniqueSettings with auth + chat context populated from the event.

Source code in unique_toolkit/unique_toolkit/app/unique_settings.py

@classmethod
def from_chat_event(cls, event: ChatEvent) -> UniqueSettings:
    """Build a :class:`UniqueSettings` from a :class:`ChatEvent`.

    Auth and chat context are extracted from the event.  App and API
    settings are left at their default values; override them via the
    returned instance's properties if needed.

    Args:
        event: The incoming chat event.

    Returns:
        UniqueSettings with auth + chat context populated from the event.
    """
    return cls(
        auth=AuthContext.from_event(event),
        app=UniqueApp(),
        api=UniqueApi(),
        chat=ChatContext.from_chat_event(event),
    )

from_env(env_file=None) classmethod

Initialize settings from environment variables and/or env file.

Parameters:

Name	Type	Description	Default
env_file	Path | None	Optional path to environment file. If provided, will load variables from this file.	None

Returns:

Type	Description
UniqueSettings	UniqueSettings instance with values loaded from environment/env file.

Raises:

Type	Description
FileNotFoundError	If env_file is provided but does not exist.
ValidationError	If required environment variables are missing.

Source code in unique_toolkit/unique_toolkit/app/unique_settings.py

@classmethod
def from_env(
    cls,
    env_file: Path | None = None,
) -> UniqueSettings:
    """Initialize settings from environment variables and/or env file.

    Args:
        env_file: Optional path to environment file. If provided, will load variables from this file.

    Returns:
        UniqueSettings instance with values loaded from environment/env file.

    Raises:
        FileNotFoundError: If env_file is provided but does not exist.
        ValidationError: If required environment variables are missing.
    """
    if env_file and not env_file.exists():
        raise FileNotFoundError(f"Environment file not found: {env_file}")

    # Initialize settings with environment file if provided
    env_file_str = str(env_file) if env_file else None
    auth = UniqueAuth(_env_file=env_file_str)  # type: ignore[call-arg]
    app = UniqueApp(_env_file=env_file_str)  # type: ignore[call-arg]
    api = UniqueApi(_env_file=env_file_str)  # type: ignore[call-arg]
    event_filter_options = UniqueChatEventFilterOptions(_env_file=env_file_str)  # type: ignore[call-arg]
    return cls(
        auth=auth,
        app=app,
        api=api,
        chat_event_filter_options=event_filter_options,
        env_file=env_file,
    )

from_env_auto(filename='unique.env') classmethod

Initialize settings by automatically finding environment file.

This method will automatically search for an environment file in standard locations and fall back to environment variables only if no file is found.

Parameters:

Name	Type	Description	Default
filename	str	Name of the environment file to search for (default: '.env')	'unique.env'

Returns:

Type	Description
UniqueSettings	UniqueSettings instance with values loaded from found env file or environment variables.

Source code in unique_toolkit/unique_toolkit/app/unique_settings.py

@classmethod
def from_env_auto(cls, filename: str = "unique.env") -> UniqueSettings:
    """Initialize settings by automatically finding environment file.

    This method will automatically search for an environment file in standard locations
    and fall back to environment variables only if no file is found.

    Args:
        filename: Name of the environment file to search for (default: '.env')

    Returns:
        UniqueSettings instance with values loaded from found env file or environment variables.
    """
    try:
        env_file = cls._find_env_file(filename)
        logger.info(f"Environment file found at {env_file}")
        return cls.from_env(env_file=env_file)
    except EnvFileNotFoundError:
        logger.warning(
            f"Environment file '{filename}' not found. Falling back to environment variables only."
        )
        # Fall back to environment variables only
        return cls.from_env()

from_env_auto_with_sdk_init(filename='unique.env') classmethod

Initialize settings and SDK in one convenient call.

This method combines from_env_auto() and init_sdk() for the most common use case.

Parameters:

Name	Type	Description	Default
filename	str	Name of the environment file to search for (default: '.env')	'unique.env'

Returns:

Type	Description
UniqueSettings	UniqueSettings instance with SDK already initialized.

Source code in unique_toolkit/unique_toolkit/app/unique_settings.py

@classmethod
def from_env_auto_with_sdk_init(
    cls, filename: str = "unique.env"
) -> UniqueSettings:
    """Initialize settings and SDK in one convenient call.

    This method combines from_env_auto() and init_sdk() for the most common use case.

    Args:
        filename: Name of the environment file to search for (default: '.env')

    Returns:
        UniqueSettings instance with SDK already initialized.
    """
    settings = cls.from_env_auto(filename)
    settings.init_sdk()
    return settings

init_sdk()

Initialize the unique_sdk global configuration with these settings.

This method configures the global unique_sdk module with the API key, app ID, and base URL from these settings.

Source code in unique_toolkit/unique_toolkit/app/unique_settings.py

def init_sdk(self) -> None:
    """Initialize the unique_sdk global configuration with these settings.

    This method configures the global unique_sdk module with the API key,
    app ID, and base URL from these settings.
    """
    unique_sdk.api_key = self.app.key.get_secret_value()
    unique_sdk.app_id = self.app.id.get_secret_value()
    unique_sdk.api_base = self.api.sdk_url()

with_auth(auth)

Return a copy of the settings with the new auth context.

Source code in unique_toolkit/unique_toolkit/app/unique_settings.py

def with_auth(self, auth: AuthContextProtocol) -> Self:
    """Return a copy of the settings with the new auth context."""
    return self.__class__(
        auth=auth,
        app=self.app,
        api=self.api,
        chat_event_filter_options=self.chat_event_filter_options,
        chat=self._context.chat,
        env_file=self._env_file,
    )

Initialization

unique_toolkit.app.init_sdk.init_sdk(strict_all_vars=False)

Initialize the SDK.

Parameters:

Name	Type	Description	Default
strict_all_vars	bool	This method raises a ValueError if strict and no value is found in the environment. Defaults to False.	False

Source code in unique_toolkit/unique_toolkit/app/init_sdk.py

@deprecated("Use init_unique_sdk instead")
def init_sdk(
    strict_all_vars: bool = False,
):
    """Initialize the SDK.

    Args:
        strict_all_vars (bool, optional): This method raises a ValueError if strict and no value is found in the environment. Defaults to False.
    """

    unique_sdk.api_key = get_env("API_KEY", default="dummy", strict=strict_all_vars)
    unique_sdk.app_id = get_env("APP_ID", default="dummy", strict=strict_all_vars)
    unique_sdk.api_base = get_env("API_BASE", default=None, strict=strict_all_vars)

unique_toolkit.app.init_sdk.init_unique_sdk(*, unique_settings=None, env_file=None)

init_unique_sdk(*, env_file: Path | None = None)

init_unique_sdk(*, unique_settings: UniqueSettings)

Source code in unique_toolkit/unique_toolkit/app/init_sdk.py

def init_unique_sdk(
    *, unique_settings: UniqueSettings | None = None, env_file: Path | None = None
):
    if unique_settings:
        unique_sdk.api_key = unique_settings.app.key.get_secret_value()
        unique_sdk.app_id = unique_settings.app.id.get_secret_value()
        unique_sdk.api_base = unique_settings.api.sdk_url()
    elif env_file:
        unique_settings = UniqueSettings.from_env(env_file=env_file)
        unique_sdk.api_key = unique_settings.app.key.get_secret_value()
        unique_sdk.app_id = unique_settings.app.id.get_secret_value()
        unique_sdk.api_base = unique_settings.api.sdk_url()

unique_toolkit.app.init_logging.init_logging(config=unique_log_config)

Source code in unique_toolkit/unique_toolkit/app/init_logging.py

def init_logging(config: dict = unique_log_config):
    return dictConfig(config)

Event Schemas

unique_toolkit.app.schemas.ChatEvent

Bases: BaseEvent

Source code in unique_toolkit/unique_toolkit/app/schemas.py

class ChatEvent(BaseEvent):
    model_config = model_config

    payload: ChatEventPayload
    created_at: Optional[int] = None
    version: Optional[str] = None

    @classmethod
    def from_json_file(cls, file_path: Path) -> ChatEvent:
        if not file_path.exists():
            raise FileNotFoundError(f"File not found: {file_path}")
        with file_path.open("r", encoding="utf-8") as f:
            data = json.load(f)
        return cls.model_validate(data)

    def get_initial_debug_info(self) -> dict[str, Any]:
        """Get the debug information for the chat event"""

        # TODO: Make sure this coincides with what is shown in the first user message
        return {
            "user_metadata": self.payload.user_metadata,
            "tool_parameters": self.payload.tool_parameters,
            "chosen_module": self.payload.name,
            "assistant": {"id": self.payload.assistant_id},
        }

    @override
    def filter_event(
        self, *, filter_options: UniqueChatEventFilterOptions | None = None
    ) -> bool:
        # Empty string evals to False

        if filter_options is None:
            return False  # Don't filter when no options provided

        if not filter_options.assistant_ids and not filter_options.references_in_code:
            raise ConfigurationException(
                "No filter options provided, all events will be filtered! \n"
                "Please define: \n"
                " - 'UNIQUE_CHAT_EVENT_FILTER_OPTIONS_ASSISTANT_IDS' \n"
                " - 'UNIQUE_CHAT_EVENT_FILTER_OPTIONS_REFERENCES_IN_CODE' \n"
                "in your environment variables."
            )

        # Per reference in code there can be multiple assistants
        if (
            filter_options.assistant_ids
            and self.payload.assistant_id not in filter_options.assistant_ids
        ):
            return True

        if (
            filter_options.references_in_code
            and self.payload.name not in filter_options.references_in_code
        ):
            return True

        return super().filter_event(filter_options=filter_options)

__class_vars__ class-attribute

The names of the class variables defined on the model.

__private_attributes__ class-attribute

Metadata about the private attributes of the model.

__pydantic_complete__ = False class-attribute

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__ class-attribute

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__ class-attribute

The core schema of the model.

__pydantic_custom_init__ class-attribute

Whether the model has a custom __init__ method.

__pydantic_decorators__ = _decorators.DecoratorInfos() class-attribute

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__ = _model_construction.NoInitField(init=False) class-attribute instance-attribute

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__ class-attribute

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__ = _model_construction.NoInitField(init=False) class-attribute instance-attribute

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__ class-attribute

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__ = None class-attribute

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__ class-attribute

The name of the post-init method for the model, if defined.

__pydantic_private__ = _model_construction.NoInitField(init=False) class-attribute instance-attribute

Values of private attributes set on the model instance.

__pydantic_root_model__ = False class-attribute

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__ class-attribute

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__ class-attribute

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__ class-attribute

The pydantic-core SchemaValidator used to validate instances of the model.

__signature__ class-attribute

The synthesized __init__ [Signature][inspect.Signature] of the model.

model_extra property

Get extra fields set during validation.

Returns:

Type	Description
dict[str, Any] | None	A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields_set property

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

Returns:

Type	Description
set[str]	A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

__copy__()

Returns a shallow copy of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __copy__(self) -> Self:
    """Returns a shallow copy of the model."""
    cls = type(self)
    m = cls.__new__(cls)
    _object_setattr(m, '__dict__', copy(self.__dict__))
    _object_setattr(m, '__pydantic_extra__', copy(self.__pydantic_extra__))
    _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

    if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
        _object_setattr(m, '__pydantic_private__', None)
    else:
        _object_setattr(
            m,
            '__pydantic_private__',
            {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined},
        )

    return m

__deepcopy__(memo=None)

Returns a deep copy of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self:
    """Returns a deep copy of the model."""
    cls = type(self)
    m = cls.__new__(cls)
    _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
    _object_setattr(m, '__pydantic_extra__', deepcopy(self.__pydantic_extra__, memo=memo))
    # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
    # and attempting a deepcopy would be marginally slower.
    _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

    if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
        _object_setattr(m, '__pydantic_private__', None)
    else:
        _object_setattr(
            m,
            '__pydantic_private__',
            deepcopy({k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, memo=memo),
        )

    return m

__get_pydantic_json_schema__(core_schema, handler) classmethod

Hook into generating the model's JSON schema.

Parameters:

Name	Type	Description	Default
core_schema	CoreSchema	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.	required
handler	GetJsonSchemaHandler	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.	required

Returns:

Type	Description
JsonSchemaValue	A JSON schema, as a Python object.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def __get_pydantic_json_schema__(
    cls,
    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.
    """
    return handler(core_schema)

__init__(**data)

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.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __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.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True
    validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
    if self is not validated_self:
        warnings.warn(
            'A custom validator is returning a value other than `self`.\n'
            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
            'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
            stacklevel=2,
        )

__init_subclass__(**kwargs)

This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs.

from pydantic import BaseModel

class MyModel(BaseModel, extra='allow'): ...

However, this may be deceiving, since the actual calls to __init_subclass__ will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are not expected keys in ConfigDict. (This is due to the way ModelMetaclass.__new__ works.)

Parameters:

Name	Type	Description	Default
**kwargs	Unpack[ConfigDict]	Keyword arguments passed to the class definition, which set model_config	{}

Note

You may want to override __pydantic_init_subclass__ instead, which behaves similarly but is called after the class is fully initialized.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]):
    """This signature is included purely to help type-checkers check arguments to class declaration, which
    provides a way to conveniently set model_config key/value pairs.

    ```python
    from pydantic import BaseModel

    class MyModel(BaseModel, extra='allow'): ...
    ```

    However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any
    of the config arguments, and will only receive any keyword arguments passed during class initialization
    that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.)

    Args:
        **kwargs: Keyword arguments passed to the class definition, which set model_config

    Note:
        You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called
        *after* the class is fully initialized.
    """

__iter__()

So dict(model) works.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __iter__(self) -> TupleGenerator:
    """So `dict(model)` works."""
    yield from [(k, v) for (k, v) in self.__dict__.items() if not k.startswith('_')]
    extra = self.__pydantic_extra__
    if extra:
        yield from extra.items()

__pydantic_init_subclass__(**kwargs) classmethod

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after basic class initialization is complete. In particular, attributes like model_fields will be present when this is called, but forward annotations are not guaranteed to be resolved yet, meaning that creating an instance of the class may fail.

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.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Any keyword arguments passed to the class definition that aren't used internally by Pydantic.	{}

Note

You may want to override __pydantic_on_complete__() instead, which is called once the class and its fields are fully initialized and ready for validation.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:
    """This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
    only after basic class initialization is complete. In particular, attributes like `model_fields` will
    be present when this is called, but forward annotations are not guaranteed to be resolved yet,
    meaning that creating an instance of the class may fail.

    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.

    Note:
        You may want to override [`__pydantic_on_complete__()`][pydantic.main.BaseModel.__pydantic_on_complete__]
        instead, which is called once the class and its fields are fully initialized and ready for validation.
    """

__pydantic_on_complete__() classmethod

This is called once the class and its fields are fully initialized and ready to be used.

This typically happens when the class is created (just before __pydantic_init_subclass__() is called on the superclass), except when forward annotations are used that could not immediately be resolved. In that case, it will be called later, when the model is rebuilt automatically or explicitly using model_rebuild().

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def __pydantic_on_complete__(cls) -> None:
    """This is called once the class and its fields are fully initialized and ready to be used.

    This typically happens when the class is created (just before
    [`__pydantic_init_subclass__()`][pydantic.main.BaseModel.__pydantic_init_subclass__] is called on the superclass),
    except when forward annotations are used that could not immediately be resolved.
    In that case, it will be called later, when the model is rebuilt automatically or explicitly using
    [`model_rebuild()`][pydantic.main.BaseModel.model_rebuild].
    """

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

Deprecated

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

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

Parameters:

Name	Type	Description	Default
include	AbstractSetIntStr | MappingIntStrAny | None	Optional set or mapping specifying which fields to include in the copied model.	None
exclude	AbstractSetIntStr | MappingIntStrAny | None	Optional set or mapping specifying which fields to exclude in the copied model.	None
update	Dict[str, Any] | None	Optional dictionary of field-value pairs to override field values in the copied model.	None
deep	bool	If True, the values of fields that are Pydantic models will be deep-copied.	False

Returns:

Type	Description
Self	A copy of the model with included, excluded and updated fields as specified.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@typing_extensions.deprecated(
    'The `copy` method is deprecated; use `model_copy` instead. '
    'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
    category=None,
)
def copy(
    self,
    *,
    include: AbstractSetIntStr | MappingIntStrAny | None = None,
    exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
    update: Dict[str, Any] | None = None,  # noqa UP006
    deep: bool = False,
) -> Self:  # pragma: no cover
    """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.
    """
    warnings.warn(
        'The `copy` method is deprecated; use `model_copy` instead. '
        'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
        category=PydanticDeprecatedSince20,
        stacklevel=2,
    )
    from .deprecated import copy_internals

    values = dict(
        copy_internals._iter(
            self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
        ),
        **(update or {}),
    )
    if self.__pydantic_private__ is None:
        private = None
    else:
        private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}

    if self.__pydantic_extra__ is None:
        extra: dict[str, Any] | None = None
    else:
        extra = self.__pydantic_extra__.copy()
        for k in list(self.__pydantic_extra__):
            if k not in values:  # k was in the exclude
                extra.pop(k)
        for k in list(values):
            if k in self.__pydantic_extra__:  # k must have come from extra
                extra[k] = values.pop(k)

    # new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg
    if update:
        fields_set = self.__pydantic_fields_set__ | update.keys()
    else:
        fields_set = set(self.__pydantic_fields_set__)

    # removing excluded fields from `__pydantic_fields_set__`
    if exclude:
        fields_set -= set(exclude)

    return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)

get_initial_debug_info()

Get the debug information for the chat event

Source code in unique_toolkit/unique_toolkit/app/schemas.py

def get_initial_debug_info(self) -> dict[str, Any]:
    """Get the debug information for the chat event"""

    # TODO: Make sure this coincides with what is shown in the first user message
    return {
        "user_metadata": self.payload.user_metadata,
        "tool_parameters": self.payload.tool_parameters,
        "chosen_module": self.payload.name,
        "assistant": {"id": self.payload.assistant_id},
    }

model_computed_fields() classmethod

A mapping of computed field names to their respective [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] instances.

Warning

Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@_utils.deprecated_instance_property
@classmethod
def model_computed_fields(cls) -> dict[str, ComputedFieldInfo]:
    """A mapping of computed field names to their respective [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] instances.

    !!! warning
        Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3.
        Instead, you should access this attribute from the model class.
    """
    return getattr(cls, '__pydantic_computed_fields__', {})

model_construct(_fields_set=None, **values) classmethod

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

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

Note

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

Parameters:

Name	Type	Description	Default
_fields_set	set[str] | None	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.	None
values	Any	Trusted or pre-validated data dictionary.	{}

Returns:

Type	Description
Self	A new instance of the Model class with validated data.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: C901
    """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.
    """
    m = cls.__new__(cls)
    fields_values: dict[str, Any] = {}
    fields_set = set()

    for name, field in cls.__pydantic_fields__.items():
        if field.alias is not None and field.alias in values:
            fields_values[name] = values.pop(field.alias)
            fields_set.add(name)

        if (name not in fields_set) and (field.validation_alias is not None):
            validation_aliases: list[str | AliasPath] = (
                field.validation_alias.choices
                if isinstance(field.validation_alias, AliasChoices)
                else [field.validation_alias]
            )

            for alias in validation_aliases:
                if isinstance(alias, str) and alias in values:
                    fields_values[name] = values.pop(alias)
                    fields_set.add(name)
                    break
                elif isinstance(alias, AliasPath):
                    value = alias.search_dict_for_path(values)
                    if value is not PydanticUndefined:
                        fields_values[name] = value
                        fields_set.add(name)
                        break

        if name not in fields_set:
            if name in values:
                fields_values[name] = values.pop(name)
                fields_set.add(name)
            elif not field.is_required():
                fields_values[name] = field.get_default(call_default_factory=True, validated_data=fields_values)
    if _fields_set is None:
        _fields_set = fields_set

    _extra: dict[str, Any] | None = values if cls.model_config.get('extra') == 'allow' else None
    _object_setattr(m, '__dict__', fields_values)
    _object_setattr(m, '__pydantic_fields_set__', _fields_set)
    if not cls.__pydantic_root_model__:
        _object_setattr(m, '__pydantic_extra__', _extra)

    if cls.__pydantic_post_init__:
        m.model_post_init(None)
        # update private attributes with values set
        if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None:
            for k, v in values.items():
                if k in m.__private_attributes__:
                    m.__pydantic_private__[k] = v

    elif not cls.__pydantic_root_model__:
        # Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
        # Since it doesn't, that means that `__pydantic_private__` should be set to None
        _object_setattr(m, '__pydantic_private__', None)

    return m

model_copy(*, update=None, deep=False)

Usage Documentation

model_copy

Returns a copy of the model.

Note

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

Parameters:

Name	Type	Description	Default
update	Mapping[str, Any] | None	Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.	None
deep	bool	Set to True to make a deep copy of the model.	False

Returns:

Type	Description
Self	New model instance.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def model_copy(self, *, update: Mapping[str, Any] | None = None, deep: bool = False) -> Self:
    """!!! abstract "Usage Documentation"
        [`model_copy`](../concepts/models.md#model-copy)

    Returns a copy of the model.

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

    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.
    """
    copied = self.__deepcopy__() if deep else self.__copy__()
    if update:
        if self.model_config.get('extra') == 'allow':
            for k, v in update.items():
                if k in self.__pydantic_fields__:
                    copied.__dict__[k] = v
                else:
                    if copied.__pydantic_extra__ is None:
                        copied.__pydantic_extra__ = {}
                    copied.__pydantic_extra__[k] = v
        else:
            copied.__dict__.update(update)
        copied.__pydantic_fields_set__.update(update.keys())
    return copied

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

Usage Documentation

model_dump

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

Parameters:

Name	Type	Description	Default
mode	Literal['json', 'python'] | str	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.	'python'
include	IncEx | None	A set of fields to include in the output.	None
exclude	IncEx | None	A set of fields to exclude from the output.	None
context	Any | None	Additional context to pass to the serializer.	None
by_alias	bool | None	Whether to use the field's alias in the dictionary key if defined.	None
exclude_unset	bool	Whether to exclude fields that have not been explicitly set.	False
exclude_defaults	bool	Whether to exclude fields that are set to their default value.	False
exclude_none	bool	Whether to exclude fields that have a value of None.	False
exclude_computed_fields	bool	Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.	False
round_trip	bool	If True, dumped values should be valid as input for non-idempotent types such as Json[T].	False
warnings	bool | Literal['none', 'warn', 'error']	How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].	True
fallback	Callable[[Any], Any] | None	A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.	None
serialize_as_any	bool	Whether to serialize fields with duck-typing serialization behavior.	False

Returns:

Type	Description
dict[str, Any]	A dictionary representation of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def 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,
    exclude_computed_fields: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    fallback: Callable[[Any], Any] | None = None,
    serialize_as_any: bool = False,
) -> dict[str, Any]:
    """!!! abstract "Usage Documentation"
        [`model_dump`](../concepts/serialization.md#python-mode)

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

    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`.
        exclude_computed_fields: Whether to exclude computed fields.
            While this can be useful for round-tripping, it is usually recommended to use the dedicated
            `round_trip` parameter instead.
        round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
        warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
            "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
        fallback: A function to call when an unknown value is encountered. If not provided,
            a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
        serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

    Returns:
        A dictionary representation of the model.
    """
    return self.__pydantic_serializer__.to_python(
        self,
        mode=mode,
        by_alias=by_alias,
        include=include,
        exclude=exclude,
        context=context,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        exclude_computed_fields=exclude_computed_fields,
        round_trip=round_trip,
        warnings=warnings,
        fallback=fallback,
        serialize_as_any=serialize_as_any,
    )

model_dump_json(*, indent=None, ensure_ascii=False, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

Usage Documentation

model_dump_json

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

Parameters:

Name	Type	Description	Default
indent	int | None	Indentation to use in the JSON output. If None is passed, the output will be compact.	None
ensure_ascii	bool	If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.	False
include	IncEx | None	Field(s) to include in the JSON output.	None
exclude	IncEx | None	Field(s) to exclude from the JSON output.	None
context	Any | None	Additional context to pass to the serializer.	None
by_alias	bool | None	Whether to serialize using field aliases.	None
exclude_unset	bool	Whether to exclude fields that have not been explicitly set.	False
exclude_defaults	bool	Whether to exclude fields that are set to their default value.	False
exclude_none	bool	Whether to exclude fields that have a value of None.	False
exclude_computed_fields	bool	Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.	False
round_trip	bool	If True, dumped values should be valid as input for non-idempotent types such as Json[T].	False
warnings	bool | Literal['none', 'warn', 'error']	How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].	True
fallback	Callable[[Any], Any] | None	A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.	None
serialize_as_any	bool	Whether to serialize fields with duck-typing serialization behavior.	False

Returns:

Type	Description
str	A JSON string representation of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def model_dump_json(
    self,
    *,
    indent: int | None = None,
    ensure_ascii: bool = False,
    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,
    exclude_computed_fields: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    fallback: Callable[[Any], Any] | None = None,
    serialize_as_any: bool = False,
) -> str:
    """!!! abstract "Usage Documentation"
        [`model_dump_json`](../concepts/serialization.md#json-mode)

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

    Args:
        indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
        ensure_ascii: If `True`, the output is guaranteed to have all incoming non-ASCII characters escaped.
            If `False` (the default), these characters will be output as-is.
        include: Field(s) to include in the JSON output.
        exclude: Field(s) to exclude from the JSON output.
        context: Additional context to pass to the serializer.
        by_alias: Whether to serialize using field aliases.
        exclude_unset: Whether to exclude fields that have not been explicitly set.
        exclude_defaults: Whether to exclude fields that are set to their default value.
        exclude_none: Whether to exclude fields that have a value of `None`.
        exclude_computed_fields: Whether to exclude computed fields.
            While this can be useful for round-tripping, it is usually recommended to use the dedicated
            `round_trip` parameter instead.
        round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
        warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
            "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
        fallback: A function to call when an unknown value is encountered. If not provided,
            a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
        serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

    Returns:
        A JSON string representation of the model.
    """
    return self.__pydantic_serializer__.to_json(
        self,
        indent=indent,
        ensure_ascii=ensure_ascii,
        include=include,
        exclude=exclude,
        context=context,
        by_alias=by_alias,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        exclude_computed_fields=exclude_computed_fields,
        round_trip=round_trip,
        warnings=warnings,
        fallback=fallback,
        serialize_as_any=serialize_as_any,
    ).decode()

model_fields() classmethod

A mapping of field names to their respective [FieldInfo][pydantic.fields.FieldInfo] instances.

Warning

Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@_utils.deprecated_instance_property
@classmethod
def model_fields(cls) -> dict[str, FieldInfo]:
    """A mapping of field names to their respective [`FieldInfo`][pydantic.fields.FieldInfo] instances.

    !!! warning
        Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3.
        Instead, you should access this attribute from the model class.
    """
    return getattr(cls, '__pydantic_fields__', {})

model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation', *, union_format='any_of') classmethod

Generates a JSON schema for a model class.

Parameters:

Name	Type	Description	Default
by_alias	bool	Whether to use attribute aliases or not.	True
ref_template	str	The reference template.	DEFAULT_REF_TEMPLATE
union_format	Literal['any_of', 'primitive_type_array']	The format to use when combining schemas from unions together. Can be one of: 'any_of': Use the anyOf keyword to combine schemas (the default). 'primitive_type_array': Use the type keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.	'any_of'
schema_generator	type[GenerateJsonSchema]	To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications	GenerateJsonSchema
mode	JsonSchemaMode	The mode in which to generate the schema.	'validation'

Returns:

Type	Description
dict[str, Any]	The JSON schema for the given model class.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_json_schema(
    cls,
    by_alias: bool = True,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
    mode: JsonSchemaMode = 'validation',
    *,
    union_format: Literal['any_of', 'primitive_type_array'] = 'any_of',
) -> 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.
        union_format: The format to use when combining schemas from unions together. Can be one of:

            - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
            keyword to combine schemas (the default).
            - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type)
            keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive
            type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to
            `any_of`.
        schema_generator: To override the logic used to generate the JSON schema, as a subclass of
            `GenerateJsonSchema` with your desired modifications
        mode: The mode in which to generate the schema.

    Returns:
        The JSON schema for the given model class.
    """
    return model_json_schema(
        cls,
        by_alias=by_alias,
        ref_template=ref_template,
        union_format=union_format,
        schema_generator=schema_generator,
        mode=mode,
    )

model_parametrized_name(params) classmethod

Compute the class name for parametrizations of generic classes.

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

Parameters:

Name	Type	Description	Default
params	tuple[type[Any], ...]	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.	required

Returns:

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

Raises:

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

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_parametrized_name(cls, 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.
    """
    if not issubclass(cls, Generic):
        raise TypeError('Concrete names should only be generated for generic models.')

    # Any strings received should represent forward references, so we handle them specially below.
    # If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
    # we may be able to remove this special case.
    param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
    params_component = ', '.join(param_names)
    return f'{cls.__name__}[{params_component}]'

model_post_init(context)

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.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def 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.
    """

model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None) classmethod

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

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

Parameters:

Name	Type	Description	Default
force	bool	Whether to force the rebuilding of the model schema, defaults to False.	False
raise_errors	bool	Whether to raise errors, defaults to True.	True
_parent_namespace_depth	int	The depth level of the parent namespace, defaults to 2.	2
_types_namespace	MappingNamespace | None	The types namespace, defaults to None.	None

Returns:

Type	Description
bool | None	Returns None if the schema is already "complete" and rebuilding was not required.
bool | None	If rebuilding was required, returns True if rebuilding was successful, otherwise False.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_rebuild(
    cls,
    *,
    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`.
    """
    already_complete = cls.__pydantic_complete__
    if already_complete and not force:
        return None

    cls.__pydantic_complete__ = False

    for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'):
        if attr in cls.__dict__ and not isinstance(getattr(cls, attr), _mock_val_ser.MockValSer):
            # Deleting the validator/serializer is necessary as otherwise they can get reused in
            # pydantic-core. We do so only if they aren't mock instances, otherwise — as `model_rebuild()`
            # isn't thread-safe — concurrent model instantiations can lead to the parent validator being used.
            # Same applies for the core schema that can be reused in schema generation.
            delattr(cls, attr)

    if _types_namespace is not None:
        rebuild_ns = _types_namespace
    elif _parent_namespace_depth > 0:
        rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
    else:
        rebuild_ns = {}

    parent_ns = _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}

    ns_resolver = _namespace_utils.NsResolver(
        parent_namespace={**rebuild_ns, **parent_ns},
    )

    return _model_construction.complete_model_class(
        cls,
        _config.ConfigWrapper(cls.model_config, check=False),
        ns_resolver,
        raise_errors=raise_errors,
        # If the model was already complete, we don't need to call the hook again.
        call_on_complete_hook=not already_complete,
    )

model_validate(obj, *, strict=None, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None) classmethod

Validate a pydantic model instance.

Parameters:

Name	Type	Description	Default
obj	Any	The object to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
extra	ExtraValues | None	Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.	None
from_attributes	bool | None	Whether to extract data from object attributes.	None
context	Any | None	Additional context to pass to the validator.	None
by_alias	bool | None	Whether to use the field's alias when validating against the provided input data.	None
by_name	bool | None	Whether to use the field's name when validating against the provided input data.	None

Raises:

Type	Description
ValidationError	If the object could not be validated.

Returns:

Type	Description
Self	The validated model instance.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_validate(
    cls,
    obj: Any,
    *,
    strict: bool | None = None,
    extra: ExtraValues | 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.
        extra: Whether to ignore, allow, or forbid extra data during model validation.
            See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
        from_attributes: Whether to extract data from object attributes.
        context: Additional context to pass to the validator.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Raises:
        ValidationError: If the object could not be validated.

    Returns:
        The validated model instance.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True

    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return cls.__pydantic_validator__.validate_python(
        obj,
        strict=strict,
        extra=extra,
        from_attributes=from_attributes,
        context=context,
        by_alias=by_alias,
        by_name=by_name,
    )

model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None) classmethod

Usage Documentation

JSON Parsing

Validate the given JSON data against the Pydantic model.

Parameters:

Name	Type	Description	Default
json_data	str | bytes | bytearray	The JSON data to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
extra	ExtraValues | None	Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.	None
context	Any | None	Extra variables to pass to the validator.	None
by_alias	bool | None	Whether to use the field's alias when validating against the provided input data.	None
by_name	bool | None	Whether to use the field's name when validating against the provided input data.	None

Returns:

Type	Description
Self	The validated Pydantic model.

Raises:

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

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_validate_json(
    cls,
    json_data: str | bytes | bytearray,
    *,
    strict: bool | None = None,
    extra: ExtraValues | 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.
        extra: Whether to ignore, allow, or forbid extra data during model validation.
            See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
        context: Extra variables to pass to the validator.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Returns:
        The validated Pydantic model.

    Raises:
        ValidationError: If `json_data` is not a JSON string or the object could not be validated.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True

    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return cls.__pydantic_validator__.validate_json(
        json_data, strict=strict, extra=extra, context=context, by_alias=by_alias, by_name=by_name
    )

model_validate_strings(obj, *, strict=None, extra=None, context=None, by_alias=None, by_name=None) classmethod

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

Parameters:

Name	Type	Description	Default
obj	Any	The object containing string data to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
extra	ExtraValues | None	Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.	None
context	Any | None	Extra variables to pass to the validator.	None
by_alias	bool | None	Whether to use the field's alias when validating against the provided input data.	None
by_name	bool | None	Whether to use the field's name when validating against the provided input data.	None

Returns:

Type	Description
Self	The validated Pydantic model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_validate_strings(
    cls,
    obj: Any,
    *,
    strict: bool | None = None,
    extra: ExtraValues | 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.
        extra: Whether to ignore, allow, or forbid extra data during model validation.
            See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
        context: Extra variables to pass to the validator.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Returns:
        The validated Pydantic model.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True

    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return cls.__pydantic_validator__.validate_strings(
        obj, strict=strict, extra=extra, context=context, by_alias=by_alias, by_name=by_name
    )

unique_toolkit.app.schemas.Event

Bases: ChatEvent

Source code in unique_toolkit/unique_toolkit/app/schemas.py

@deprecated(
    """Use the more specific `ChatEvent` instead that has the same properties. \
This class will be removed in the next major version."""
)
class Event(ChatEvent):
    pass
    # The below should only affect type hints
    # event: EventName T
    # payload: EventPayload

    @classmethod
    def from_json_file(cls, file_path: Path) -> Event:
        if not file_path.exists():
            raise FileNotFoundError(f"File not found: {file_path}")
        with file_path.open("r", encoding="utf-8") as f:
            data = json.load(f)
        return cls.model_validate(data)

__class_vars__ class-attribute

The names of the class variables defined on the model.

__private_attributes__ class-attribute

Metadata about the private attributes of the model.

__pydantic_complete__ = False class-attribute

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__ class-attribute

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__ class-attribute

The core schema of the model.

__pydantic_custom_init__ class-attribute

Whether the model has a custom __init__ method.

__pydantic_decorators__ = _decorators.DecoratorInfos() class-attribute

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__ = _model_construction.NoInitField(init=False) class-attribute instance-attribute

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__ class-attribute

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__ = _model_construction.NoInitField(init=False) class-attribute instance-attribute

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__ class-attribute

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__ = None class-attribute

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__ class-attribute

The name of the post-init method for the model, if defined.

__pydantic_private__ = _model_construction.NoInitField(init=False) class-attribute instance-attribute

Values of private attributes set on the model instance.

__pydantic_root_model__ = False class-attribute

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__ class-attribute

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__ class-attribute

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__ class-attribute

The pydantic-core SchemaValidator used to validate instances of the model.

__signature__ class-attribute

The synthesized __init__ [Signature][inspect.Signature] of the model.

model_extra property

Get extra fields set during validation.

Returns:

Type	Description
dict[str, Any] | None	A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields_set property

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

Returns:

Type	Description
set[str]	A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

__copy__()

Returns a shallow copy of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __copy__(self) -> Self:
    """Returns a shallow copy of the model."""
    cls = type(self)
    m = cls.__new__(cls)
    _object_setattr(m, '__dict__', copy(self.__dict__))
    _object_setattr(m, '__pydantic_extra__', copy(self.__pydantic_extra__))
    _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

    if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
        _object_setattr(m, '__pydantic_private__', None)
    else:
        _object_setattr(
            m,
            '__pydantic_private__',
            {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined},
        )

    return m

__deepcopy__(memo=None)

Returns a deep copy of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self:
    """Returns a deep copy of the model."""
    cls = type(self)
    m = cls.__new__(cls)
    _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
    _object_setattr(m, '__pydantic_extra__', deepcopy(self.__pydantic_extra__, memo=memo))
    # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
    # and attempting a deepcopy would be marginally slower.
    _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

    if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
        _object_setattr(m, '__pydantic_private__', None)
    else:
        _object_setattr(
            m,
            '__pydantic_private__',
            deepcopy({k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, memo=memo),
        )

    return m

__get_pydantic_json_schema__(core_schema, handler) classmethod

Hook into generating the model's JSON schema.

Parameters:

Name	Type	Description	Default
core_schema	CoreSchema	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.	required
handler	GetJsonSchemaHandler	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.	required

Returns:

Type	Description
JsonSchemaValue	A JSON schema, as a Python object.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def __get_pydantic_json_schema__(
    cls,
    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.
    """
    return handler(core_schema)

__init__(**data)

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.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __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.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True
    validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
    if self is not validated_self:
        warnings.warn(
            'A custom validator is returning a value other than `self`.\n'
            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
            'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
            stacklevel=2,
        )

__init_subclass__(**kwargs)

This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs.

from pydantic import BaseModel

class MyModel(BaseModel, extra='allow'): ...

However, this may be deceiving, since the actual calls to __init_subclass__ will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are not expected keys in ConfigDict. (This is due to the way ModelMetaclass.__new__ works.)

Parameters:

Name	Type	Description	Default
**kwargs	Unpack[ConfigDict]	Keyword arguments passed to the class definition, which set model_config	{}

Note

You may want to override __pydantic_init_subclass__ instead, which behaves similarly but is called after the class is fully initialized.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]):
    """This signature is included purely to help type-checkers check arguments to class declaration, which
    provides a way to conveniently set model_config key/value pairs.

    ```python
    from pydantic import BaseModel

    class MyModel(BaseModel, extra='allow'): ...
    ```

    However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any
    of the config arguments, and will only receive any keyword arguments passed during class initialization
    that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.)

    Args:
        **kwargs: Keyword arguments passed to the class definition, which set model_config

    Note:
        You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called
        *after* the class is fully initialized.
    """

__iter__()

So dict(model) works.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __iter__(self) -> TupleGenerator:
    """So `dict(model)` works."""
    yield from [(k, v) for (k, v) in self.__dict__.items() if not k.startswith('_')]
    extra = self.__pydantic_extra__
    if extra:
        yield from extra.items()

__pydantic_init_subclass__(**kwargs) classmethod

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after basic class initialization is complete. In particular, attributes like model_fields will be present when this is called, but forward annotations are not guaranteed to be resolved yet, meaning that creating an instance of the class may fail.

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.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Any keyword arguments passed to the class definition that aren't used internally by Pydantic.	{}

Note

You may want to override __pydantic_on_complete__() instead, which is called once the class and its fields are fully initialized and ready for validation.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:
    """This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
    only after basic class initialization is complete. In particular, attributes like `model_fields` will
    be present when this is called, but forward annotations are not guaranteed to be resolved yet,
    meaning that creating an instance of the class may fail.

    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.

    Note:
        You may want to override [`__pydantic_on_complete__()`][pydantic.main.BaseModel.__pydantic_on_complete__]
        instead, which is called once the class and its fields are fully initialized and ready for validation.
    """

__pydantic_on_complete__() classmethod

This is called once the class and its fields are fully initialized and ready to be used.

This typically happens when the class is created (just before __pydantic_init_subclass__() is called on the superclass), except when forward annotations are used that could not immediately be resolved. In that case, it will be called later, when the model is rebuilt automatically or explicitly using model_rebuild().

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def __pydantic_on_complete__(cls) -> None:
    """This is called once the class and its fields are fully initialized and ready to be used.

    This typically happens when the class is created (just before
    [`__pydantic_init_subclass__()`][pydantic.main.BaseModel.__pydantic_init_subclass__] is called on the superclass),
    except when forward annotations are used that could not immediately be resolved.
    In that case, it will be called later, when the model is rebuilt automatically or explicitly using
    [`model_rebuild()`][pydantic.main.BaseModel.model_rebuild].
    """

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

Deprecated

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

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

Parameters:

Name	Type	Description	Default
include	AbstractSetIntStr | MappingIntStrAny | None	Optional set or mapping specifying which fields to include in the copied model.	None
exclude	AbstractSetIntStr | MappingIntStrAny | None	Optional set or mapping specifying which fields to exclude in the copied model.	None
update	Dict[str, Any] | None	Optional dictionary of field-value pairs to override field values in the copied model.	None
deep	bool	If True, the values of fields that are Pydantic models will be deep-copied.	False

Returns:

Type	Description
Self	A copy of the model with included, excluded and updated fields as specified.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@typing_extensions.deprecated(
    'The `copy` method is deprecated; use `model_copy` instead. '
    'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
    category=None,
)
def copy(
    self,
    *,
    include: AbstractSetIntStr | MappingIntStrAny | None = None,
    exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
    update: Dict[str, Any] | None = None,  # noqa UP006
    deep: bool = False,
) -> Self:  # pragma: no cover
    """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.
    """
    warnings.warn(
        'The `copy` method is deprecated; use `model_copy` instead. '
        'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
        category=PydanticDeprecatedSince20,
        stacklevel=2,
    )
    from .deprecated import copy_internals

    values = dict(
        copy_internals._iter(
            self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
        ),
        **(update or {}),
    )
    if self.__pydantic_private__ is None:
        private = None
    else:
        private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}

    if self.__pydantic_extra__ is None:
        extra: dict[str, Any] | None = None
    else:
        extra = self.__pydantic_extra__.copy()
        for k in list(self.__pydantic_extra__):
            if k not in values:  # k was in the exclude
                extra.pop(k)
        for k in list(values):
            if k in self.__pydantic_extra__:  # k must have come from extra
                extra[k] = values.pop(k)

    # new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg
    if update:
        fields_set = self.__pydantic_fields_set__ | update.keys()
    else:
        fields_set = set(self.__pydantic_fields_set__)

    # removing excluded fields from `__pydantic_fields_set__`
    if exclude:
        fields_set -= set(exclude)

    return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)

get_initial_debug_info()

Get the debug information for the chat event

Source code in unique_toolkit/unique_toolkit/app/schemas.py

def get_initial_debug_info(self) -> dict[str, Any]:
    """Get the debug information for the chat event"""

    # TODO: Make sure this coincides with what is shown in the first user message
    return {
        "user_metadata": self.payload.user_metadata,
        "tool_parameters": self.payload.tool_parameters,
        "chosen_module": self.payload.name,
        "assistant": {"id": self.payload.assistant_id},
    }

model_computed_fields() classmethod

A mapping of computed field names to their respective [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] instances.

Warning

Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@_utils.deprecated_instance_property
@classmethod
def model_computed_fields(cls) -> dict[str, ComputedFieldInfo]:
    """A mapping of computed field names to their respective [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] instances.

    !!! warning
        Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3.
        Instead, you should access this attribute from the model class.
    """
    return getattr(cls, '__pydantic_computed_fields__', {})

model_construct(_fields_set=None, **values) classmethod

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

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

Note

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

Parameters:

Name	Type	Description	Default
_fields_set	set[str] | None	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.	None
values	Any	Trusted or pre-validated data dictionary.	{}

Returns:

Type	Description
Self	A new instance of the Model class with validated data.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: C901
    """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.
    """
    m = cls.__new__(cls)
    fields_values: dict[str, Any] = {}
    fields_set = set()

    for name, field in cls.__pydantic_fields__.items():
        if field.alias is not None and field.alias in values:
            fields_values[name] = values.pop(field.alias)
            fields_set.add(name)

        if (name not in fields_set) and (field.validation_alias is not None):
            validation_aliases: list[str | AliasPath] = (
                field.validation_alias.choices
                if isinstance(field.validation_alias, AliasChoices)
                else [field.validation_alias]
            )

            for alias in validation_aliases:
                if isinstance(alias, str) and alias in values:
                    fields_values[name] = values.pop(alias)
                    fields_set.add(name)
                    break
                elif isinstance(alias, AliasPath):
                    value = alias.search_dict_for_path(values)
                    if value is not PydanticUndefined:
                        fields_values[name] = value
                        fields_set.add(name)
                        break

        if name not in fields_set:
            if name in values:
                fields_values[name] = values.pop(name)
                fields_set.add(name)
            elif not field.is_required():
                fields_values[name] = field.get_default(call_default_factory=True, validated_data=fields_values)
    if _fields_set is None:
        _fields_set = fields_set

    _extra: dict[str, Any] | None = values if cls.model_config.get('extra') == 'allow' else None
    _object_setattr(m, '__dict__', fields_values)
    _object_setattr(m, '__pydantic_fields_set__', _fields_set)
    if not cls.__pydantic_root_model__:
        _object_setattr(m, '__pydantic_extra__', _extra)

    if cls.__pydantic_post_init__:
        m.model_post_init(None)
        # update private attributes with values set
        if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None:
            for k, v in values.items():
                if k in m.__private_attributes__:
                    m.__pydantic_private__[k] = v

    elif not cls.__pydantic_root_model__:
        # Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
        # Since it doesn't, that means that `__pydantic_private__` should be set to None
        _object_setattr(m, '__pydantic_private__', None)

    return m

model_copy(*, update=None, deep=False)

Usage Documentation

model_copy

Returns a copy of the model.

Note

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

Parameters:

Name	Type	Description	Default
update	Mapping[str, Any] | None	Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.	None
deep	bool	Set to True to make a deep copy of the model.	False

Returns:

Type	Description
Self	New model instance.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def model_copy(self, *, update: Mapping[str, Any] | None = None, deep: bool = False) -> Self:
    """!!! abstract "Usage Documentation"
        [`model_copy`](../concepts/models.md#model-copy)

    Returns a copy of the model.

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

    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.
    """
    copied = self.__deepcopy__() if deep else self.__copy__()
    if update:
        if self.model_config.get('extra') == 'allow':
            for k, v in update.items():
                if k in self.__pydantic_fields__:
                    copied.__dict__[k] = v
                else:
                    if copied.__pydantic_extra__ is None:
                        copied.__pydantic_extra__ = {}
                    copied.__pydantic_extra__[k] = v
        else:
            copied.__dict__.update(update)
        copied.__pydantic_fields_set__.update(update.keys())
    return copied

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

Usage Documentation

model_dump

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

Parameters:

Name	Type	Description	Default
mode	Literal['json', 'python'] | str	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.	'python'
include	IncEx | None	A set of fields to include in the output.	None
exclude	IncEx | None	A set of fields to exclude from the output.	None
context	Any | None	Additional context to pass to the serializer.	None
by_alias	bool | None	Whether to use the field's alias in the dictionary key if defined.	None
exclude_unset	bool	Whether to exclude fields that have not been explicitly set.	False
exclude_defaults	bool	Whether to exclude fields that are set to their default value.	False
exclude_none	bool	Whether to exclude fields that have a value of None.	False
exclude_computed_fields	bool	Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.	False
round_trip	bool	If True, dumped values should be valid as input for non-idempotent types such as Json[T].	False
warnings	bool | Literal['none', 'warn', 'error']	How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].	True
fallback	Callable[[Any], Any] | None	A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.	None
serialize_as_any	bool	Whether to serialize fields with duck-typing serialization behavior.	False

Returns:

Type	Description
dict[str, Any]	A dictionary representation of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def 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,
    exclude_computed_fields: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    fallback: Callable[[Any], Any] | None = None,
    serialize_as_any: bool = False,
) -> dict[str, Any]:
    """!!! abstract "Usage Documentation"
        [`model_dump`](../concepts/serialization.md#python-mode)

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

    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`.
        exclude_computed_fields: Whether to exclude computed fields.
            While this can be useful for round-tripping, it is usually recommended to use the dedicated
            `round_trip` parameter instead.
        round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
        warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
            "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
        fallback: A function to call when an unknown value is encountered. If not provided,
            a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
        serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

    Returns:
        A dictionary representation of the model.
    """
    return self.__pydantic_serializer__.to_python(
        self,
        mode=mode,
        by_alias=by_alias,
        include=include,
        exclude=exclude,
        context=context,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        exclude_computed_fields=exclude_computed_fields,
        round_trip=round_trip,
        warnings=warnings,
        fallback=fallback,
        serialize_as_any=serialize_as_any,
    )

model_dump_json(*, indent=None, ensure_ascii=False, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

Usage Documentation

model_dump_json

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

Parameters:

Name	Type	Description	Default
indent	int | None	Indentation to use in the JSON output. If None is passed, the output will be compact.	None
ensure_ascii	bool	If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.	False
include	IncEx | None	Field(s) to include in the JSON output.	None
exclude	IncEx | None	Field(s) to exclude from the JSON output.	None
context	Any | None	Additional context to pass to the serializer.	None
by_alias	bool | None	Whether to serialize using field aliases.	None
exclude_unset	bool	Whether to exclude fields that have not been explicitly set.	False
exclude_defaults	bool	Whether to exclude fields that are set to their default value.	False
exclude_none	bool	Whether to exclude fields that have a value of None.	False
exclude_computed_fields	bool	Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.	False
round_trip	bool	If True, dumped values should be valid as input for non-idempotent types such as Json[T].	False
warnings	bool | Literal['none', 'warn', 'error']	How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].	True
fallback	Callable[[Any], Any] | None	A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.	None
serialize_as_any	bool	Whether to serialize fields with duck-typing serialization behavior.	False

Returns:

Type	Description
str	A JSON string representation of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def model_dump_json(
    self,
    *,
    indent: int | None = None,
    ensure_ascii: bool = False,
    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,
    exclude_computed_fields: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    fallback: Callable[[Any], Any] | None = None,
    serialize_as_any: bool = False,
) -> str:
    """!!! abstract "Usage Documentation"
        [`model_dump_json`](../concepts/serialization.md#json-mode)

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

    Args:
        indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
        ensure_ascii: If `True`, the output is guaranteed to have all incoming non-ASCII characters escaped.
            If `False` (the default), these characters will be output as-is.
        include: Field(s) to include in the JSON output.
        exclude: Field(s) to exclude from the JSON output.
        context: Additional context to pass to the serializer.
        by_alias: Whether to serialize using field aliases.
        exclude_unset: Whether to exclude fields that have not been explicitly set.
        exclude_defaults: Whether to exclude fields that are set to their default value.
        exclude_none: Whether to exclude fields that have a value of `None`.
        exclude_computed_fields: Whether to exclude computed fields.
            While this can be useful for round-tripping, it is usually recommended to use the dedicated
            `round_trip` parameter instead.
        round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
        warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
            "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
        fallback: A function to call when an unknown value is encountered. If not provided,
            a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
        serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

    Returns:
        A JSON string representation of the model.
    """
    return self.__pydantic_serializer__.to_json(
        self,
        indent=indent,
        ensure_ascii=ensure_ascii,
        include=include,
        exclude=exclude,
        context=context,
        by_alias=by_alias,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        exclude_computed_fields=exclude_computed_fields,
        round_trip=round_trip,
        warnings=warnings,
        fallback=fallback,
        serialize_as_any=serialize_as_any,
    ).decode()

model_fields() classmethod

A mapping of field names to their respective [FieldInfo][pydantic.fields.FieldInfo] instances.

Warning

Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@_utils.deprecated_instance_property
@classmethod
def model_fields(cls) -> dict[str, FieldInfo]:
    """A mapping of field names to their respective [`FieldInfo`][pydantic.fields.FieldInfo] instances.

    !!! warning
        Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3.
        Instead, you should access this attribute from the model class.
    """
    return getattr(cls, '__pydantic_fields__', {})

model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation', *, union_format='any_of') classmethod

Generates a JSON schema for a model class.

Parameters:

Name	Type	Description	Default
by_alias	bool	Whether to use attribute aliases or not.	True
ref_template	str	The reference template.	DEFAULT_REF_TEMPLATE
union_format	Literal['any_of', 'primitive_type_array']	The format to use when combining schemas from unions together. Can be one of: 'any_of': Use the anyOf keyword to combine schemas (the default). 'primitive_type_array': Use the type keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.	'any_of'
schema_generator	type[GenerateJsonSchema]	To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications	GenerateJsonSchema
mode	JsonSchemaMode	The mode in which to generate the schema.	'validation'

Returns:

Type	Description
dict[str, Any]	The JSON schema for the given model class.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_json_schema(
    cls,
    by_alias: bool = True,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
    mode: JsonSchemaMode = 'validation',
    *,
    union_format: Literal['any_of', 'primitive_type_array'] = 'any_of',
) -> 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.
        union_format: The format to use when combining schemas from unions together. Can be one of:

            - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
            keyword to combine schemas (the default).
            - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type)
            keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive
            type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to
            `any_of`.
        schema_generator: To override the logic used to generate the JSON schema, as a subclass of
            `GenerateJsonSchema` with your desired modifications
        mode: The mode in which to generate the schema.

    Returns:
        The JSON schema for the given model class.
    """
    return model_json_schema(
        cls,
        by_alias=by_alias,
        ref_template=ref_template,
        union_format=union_format,
        schema_generator=schema_generator,
        mode=mode,
    )

model_parametrized_name(params) classmethod

Compute the class name for parametrizations of generic classes.

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

Parameters:

Name	Type	Description	Default
params	tuple[type[Any], ...]	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.	required

Returns:

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

Raises:

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

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_parametrized_name(cls, 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.
    """
    if not issubclass(cls, Generic):
        raise TypeError('Concrete names should only be generated for generic models.')

    # Any strings received should represent forward references, so we handle them specially below.
    # If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
    # we may be able to remove this special case.
    param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
    params_component = ', '.join(param_names)
    return f'{cls.__name__}[{params_component}]'

model_post_init(context)

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.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def 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.
    """

model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None) classmethod

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

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

Parameters:

Name	Type	Description	Default
force	bool	Whether to force the rebuilding of the model schema, defaults to False.	False
raise_errors	bool	Whether to raise errors, defaults to True.	True
_parent_namespace_depth	int	The depth level of the parent namespace, defaults to 2.	2
_types_namespace	MappingNamespace | None	The types namespace, defaults to None.	None

Returns:

Type	Description
bool | None	Returns None if the schema is already "complete" and rebuilding was not required.
bool | None	If rebuilding was required, returns True if rebuilding was successful, otherwise False.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_rebuild(
    cls,
    *,
    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`.
    """
    already_complete = cls.__pydantic_complete__
    if already_complete and not force:
        return None

    cls.__pydantic_complete__ = False

    for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'):
        if attr in cls.__dict__ and not isinstance(getattr(cls, attr), _mock_val_ser.MockValSer):
            # Deleting the validator/serializer is necessary as otherwise they can get reused in
            # pydantic-core. We do so only if they aren't mock instances, otherwise — as `model_rebuild()`
            # isn't thread-safe — concurrent model instantiations can lead to the parent validator being used.
            # Same applies for the core schema that can be reused in schema generation.
            delattr(cls, attr)

    if _types_namespace is not None:
        rebuild_ns = _types_namespace
    elif _parent_namespace_depth > 0:
        rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
    else:
        rebuild_ns = {}

    parent_ns = _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}

    ns_resolver = _namespace_utils.NsResolver(
        parent_namespace={**rebuild_ns, **parent_ns},
    )

    return _model_construction.complete_model_class(
        cls,
        _config.ConfigWrapper(cls.model_config, check=False),
        ns_resolver,
        raise_errors=raise_errors,
        # If the model was already complete, we don't need to call the hook again.
        call_on_complete_hook=not already_complete,
    )

model_validate(obj, *, strict=None, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None) classmethod

Validate a pydantic model instance.

Parameters:

Name	Type	Description	Default
obj	Any	The object to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
extra	ExtraValues | None	Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.	None
from_attributes	bool | None	Whether to extract data from object attributes.	None
context	Any | None	Additional context to pass to the validator.	None
by_alias	bool | None	Whether to use the field's alias when validating against the provided input data.	None
by_name	bool | None	Whether to use the field's name when validating against the provided input data.	None

Raises:

Type	Description
ValidationError	If the object could not be validated.

Returns:

Type	Description
Self	The validated model instance.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_validate(
    cls,
    obj: Any,
    *,
    strict: bool | None = None,
    extra: ExtraValues | 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.
        extra: Whether to ignore, allow, or forbid extra data during model validation.
            See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
        from_attributes: Whether to extract data from object attributes.
        context: Additional context to pass to the validator.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Raises:
        ValidationError: If the object could not be validated.

    Returns:
        The validated model instance.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True

    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return cls.__pydantic_validator__.validate_python(
        obj,
        strict=strict,
        extra=extra,
        from_attributes=from_attributes,
        context=context,
        by_alias=by_alias,
        by_name=by_name,
    )

model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None) classmethod

Usage Documentation

JSON Parsing

Validate the given JSON data against the Pydantic model.

Parameters:

Name	Type	Description	Default
json_data	str | bytes | bytearray	The JSON data to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
extra	ExtraValues | None	Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.	None
context	Any | None	Extra variables to pass to the validator.	None
by_alias	bool | None	Whether to use the field's alias when validating against the provided input data.	None
by_name	bool | None	Whether to use the field's name when validating against the provided input data.	None

Returns:

Type	Description
Self	The validated Pydantic model.

Raises:

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

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_validate_json(
    cls,
    json_data: str | bytes | bytearray,
    *,
    strict: bool | None = None,
    extra: ExtraValues | 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.
        extra: Whether to ignore, allow, or forbid extra data during model validation.
            See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
        context: Extra variables to pass to the validator.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Returns:
        The validated Pydantic model.

    Raises:
        ValidationError: If `json_data` is not a JSON string or the object could not be validated.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True

    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return cls.__pydantic_validator__.validate_json(
        json_data, strict=strict, extra=extra, context=context, by_alias=by_alias, by_name=by_name
    )

model_validate_strings(obj, *, strict=None, extra=None, context=None, by_alias=None, by_name=None) classmethod

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

Parameters:

Name	Type	Description	Default
obj	Any	The object containing string data to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
extra	ExtraValues | None	Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.	None
context	Any | None	Extra variables to pass to the validator.	None
by_alias	bool | None	Whether to use the field's alias when validating against the provided input data.	None
by_name	bool | None	Whether to use the field's name when validating against the provided input data.	None

Returns:

Type	Description
Self	The validated Pydantic model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_validate_strings(
    cls,
    obj: Any,
    *,
    strict: bool | None = None,
    extra: ExtraValues | 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.
        extra: Whether to ignore, allow, or forbid extra data during model validation.
            See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
        context: Extra variables to pass to the validator.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Returns:
        The validated Pydantic model.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True

    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return cls.__pydantic_validator__.validate_strings(
        obj, strict=strict, extra=extra, context=context, by_alias=by_alias, by_name=by_name
    )

unique_toolkit.app.schemas.BaseEvent

Bases: BaseModel, Generic[FilterOptionsT]

Source code in unique_toolkit/unique_toolkit/app/schemas.py

class BaseEvent(BaseModel, Generic[FilterOptionsT]):
    model_config = model_config

    id: str
    event: str
    user_id: str
    company_id: str

    @classmethod
    def from_json_file(cls, file_path: Path) -> BaseEvent:
        if not file_path.exists():
            raise FileNotFoundError(f"File not found: {file_path}")
        with file_path.open("r", encoding="utf-8") as f:
            data = json.load(f)
        return cls.model_validate(data)

    def filter_event(self, *, filter_options: FilterOptionsT | None = None) -> bool:
        """Determine if event should be filtered out and be neglected."""
        return False

__class_vars__ class-attribute

The names of the class variables defined on the model.

__private_attributes__ class-attribute

Metadata about the private attributes of the model.

__pydantic_complete__ = False class-attribute

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__ class-attribute

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__ class-attribute

The core schema of the model.

__pydantic_custom_init__ class-attribute

Whether the model has a custom __init__ method.

__pydantic_decorators__ = _decorators.DecoratorInfos() class-attribute

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__ = _model_construction.NoInitField(init=False) class-attribute instance-attribute

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__ class-attribute

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__ = _model_construction.NoInitField(init=False) class-attribute instance-attribute

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__ class-attribute

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__ = None class-attribute

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__ class-attribute

The name of the post-init method for the model, if defined.

__pydantic_private__ = _model_construction.NoInitField(init=False) class-attribute instance-attribute

Values of private attributes set on the model instance.

__pydantic_root_model__ = False class-attribute

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__ class-attribute

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__ class-attribute

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__ class-attribute

The pydantic-core SchemaValidator used to validate instances of the model.

__signature__ class-attribute

The synthesized __init__ [Signature][inspect.Signature] of the model.

model_extra property

Get extra fields set during validation.

Returns:

Type	Description
dict[str, Any] | None	A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields_set property

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

Returns:

Type	Description
set[str]	A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

__copy__()

Returns a shallow copy of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __copy__(self) -> Self:
    """Returns a shallow copy of the model."""
    cls = type(self)
    m = cls.__new__(cls)
    _object_setattr(m, '__dict__', copy(self.__dict__))
    _object_setattr(m, '__pydantic_extra__', copy(self.__pydantic_extra__))
    _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

    if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
        _object_setattr(m, '__pydantic_private__', None)
    else:
        _object_setattr(
            m,
            '__pydantic_private__',
            {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined},
        )

    return m

__deepcopy__(memo=None)

Returns a deep copy of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self:
    """Returns a deep copy of the model."""
    cls = type(self)
    m = cls.__new__(cls)
    _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
    _object_setattr(m, '__pydantic_extra__', deepcopy(self.__pydantic_extra__, memo=memo))
    # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
    # and attempting a deepcopy would be marginally slower.
    _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

    if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
        _object_setattr(m, '__pydantic_private__', None)
    else:
        _object_setattr(
            m,
            '__pydantic_private__',
            deepcopy({k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, memo=memo),
        )

    return m

__get_pydantic_json_schema__(core_schema, handler) classmethod

Hook into generating the model's JSON schema.

Parameters:

Name	Type	Description	Default
core_schema	CoreSchema	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.	required
handler	GetJsonSchemaHandler	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.	required

Returns:

Type	Description
JsonSchemaValue	A JSON schema, as a Python object.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def __get_pydantic_json_schema__(
    cls,
    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.
    """
    return handler(core_schema)

__init__(**data)

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.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __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.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True
    validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
    if self is not validated_self:
        warnings.warn(
            'A custom validator is returning a value other than `self`.\n'
            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
            'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
            stacklevel=2,
        )

__init_subclass__(**kwargs)

This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs.

from pydantic import BaseModel

class MyModel(BaseModel, extra='allow'): ...

However, this may be deceiving, since the actual calls to __init_subclass__ will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are not expected keys in ConfigDict. (This is due to the way ModelMetaclass.__new__ works.)

Parameters:

Name	Type	Description	Default
**kwargs	Unpack[ConfigDict]	Keyword arguments passed to the class definition, which set model_config	{}

Note

You may want to override __pydantic_init_subclass__ instead, which behaves similarly but is called after the class is fully initialized.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]):
    """This signature is included purely to help type-checkers check arguments to class declaration, which
    provides a way to conveniently set model_config key/value pairs.

    ```python
    from pydantic import BaseModel

    class MyModel(BaseModel, extra='allow'): ...
    ```

    However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any
    of the config arguments, and will only receive any keyword arguments passed during class initialization
    that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.)

    Args:
        **kwargs: Keyword arguments passed to the class definition, which set model_config

    Note:
        You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called
        *after* the class is fully initialized.
    """

__iter__()

So dict(model) works.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def __iter__(self) -> TupleGenerator:
    """So `dict(model)` works."""
    yield from [(k, v) for (k, v) in self.__dict__.items() if not k.startswith('_')]
    extra = self.__pydantic_extra__
    if extra:
        yield from extra.items()

__pydantic_init_subclass__(**kwargs) classmethod

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after basic class initialization is complete. In particular, attributes like model_fields will be present when this is called, but forward annotations are not guaranteed to be resolved yet, meaning that creating an instance of the class may fail.

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.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Any keyword arguments passed to the class definition that aren't used internally by Pydantic.	{}

Note

You may want to override __pydantic_on_complete__() instead, which is called once the class and its fields are fully initialized and ready for validation.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:
    """This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
    only after basic class initialization is complete. In particular, attributes like `model_fields` will
    be present when this is called, but forward annotations are not guaranteed to be resolved yet,
    meaning that creating an instance of the class may fail.

    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.

    Note:
        You may want to override [`__pydantic_on_complete__()`][pydantic.main.BaseModel.__pydantic_on_complete__]
        instead, which is called once the class and its fields are fully initialized and ready for validation.
    """

__pydantic_on_complete__() classmethod

This is called once the class and its fields are fully initialized and ready to be used.

This typically happens when the class is created (just before __pydantic_init_subclass__() is called on the superclass), except when forward annotations are used that could not immediately be resolved. In that case, it will be called later, when the model is rebuilt automatically or explicitly using model_rebuild().

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def __pydantic_on_complete__(cls) -> None:
    """This is called once the class and its fields are fully initialized and ready to be used.

    This typically happens when the class is created (just before
    [`__pydantic_init_subclass__()`][pydantic.main.BaseModel.__pydantic_init_subclass__] is called on the superclass),
    except when forward annotations are used that could not immediately be resolved.
    In that case, it will be called later, when the model is rebuilt automatically or explicitly using
    [`model_rebuild()`][pydantic.main.BaseModel.model_rebuild].
    """

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

Deprecated

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

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

Parameters:

Name	Type	Description	Default
include	AbstractSetIntStr | MappingIntStrAny | None	Optional set or mapping specifying which fields to include in the copied model.	None
exclude	AbstractSetIntStr | MappingIntStrAny | None	Optional set or mapping specifying which fields to exclude in the copied model.	None
update	Dict[str, Any] | None	Optional dictionary of field-value pairs to override field values in the copied model.	None
deep	bool	If True, the values of fields that are Pydantic models will be deep-copied.	False

Returns:

Type	Description
Self	A copy of the model with included, excluded and updated fields as specified.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@typing_extensions.deprecated(
    'The `copy` method is deprecated; use `model_copy` instead. '
    'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
    category=None,
)
def copy(
    self,
    *,
    include: AbstractSetIntStr | MappingIntStrAny | None = None,
    exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
    update: Dict[str, Any] | None = None,  # noqa UP006
    deep: bool = False,
) -> Self:  # pragma: no cover
    """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.
    """
    warnings.warn(
        'The `copy` method is deprecated; use `model_copy` instead. '
        'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
        category=PydanticDeprecatedSince20,
        stacklevel=2,
    )
    from .deprecated import copy_internals

    values = dict(
        copy_internals._iter(
            self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
        ),
        **(update or {}),
    )
    if self.__pydantic_private__ is None:
        private = None
    else:
        private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}

    if self.__pydantic_extra__ is None:
        extra: dict[str, Any] | None = None
    else:
        extra = self.__pydantic_extra__.copy()
        for k in list(self.__pydantic_extra__):
            if k not in values:  # k was in the exclude
                extra.pop(k)
        for k in list(values):
            if k in self.__pydantic_extra__:  # k must have come from extra
                extra[k] = values.pop(k)

    # new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg
    if update:
        fields_set = self.__pydantic_fields_set__ | update.keys()
    else:
        fields_set = set(self.__pydantic_fields_set__)

    # removing excluded fields from `__pydantic_fields_set__`
    if exclude:
        fields_set -= set(exclude)

    return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)

filter_event(*, filter_options=None)

Determine if event should be filtered out and be neglected.

Source code in unique_toolkit/unique_toolkit/app/schemas.py

def filter_event(self, *, filter_options: FilterOptionsT | None = None) -> bool:
    """Determine if event should be filtered out and be neglected."""
    return False

model_computed_fields() classmethod

A mapping of computed field names to their respective [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] instances.

Warning

Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@_utils.deprecated_instance_property
@classmethod
def model_computed_fields(cls) -> dict[str, ComputedFieldInfo]:
    """A mapping of computed field names to their respective [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] instances.

    !!! warning
        Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3.
        Instead, you should access this attribute from the model class.
    """
    return getattr(cls, '__pydantic_computed_fields__', {})

model_construct(_fields_set=None, **values) classmethod

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

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

Note

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

Parameters:

Name	Type	Description	Default
_fields_set	set[str] | None	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.	None
values	Any	Trusted or pre-validated data dictionary.	{}

Returns:

Type	Description
Self	A new instance of the Model class with validated data.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: C901
    """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.
    """
    m = cls.__new__(cls)
    fields_values: dict[str, Any] = {}
    fields_set = set()

    for name, field in cls.__pydantic_fields__.items():
        if field.alias is not None and field.alias in values:
            fields_values[name] = values.pop(field.alias)
            fields_set.add(name)

        if (name not in fields_set) and (field.validation_alias is not None):
            validation_aliases: list[str | AliasPath] = (
                field.validation_alias.choices
                if isinstance(field.validation_alias, AliasChoices)
                else [field.validation_alias]
            )

            for alias in validation_aliases:
                if isinstance(alias, str) and alias in values:
                    fields_values[name] = values.pop(alias)
                    fields_set.add(name)
                    break
                elif isinstance(alias, AliasPath):
                    value = alias.search_dict_for_path(values)
                    if value is not PydanticUndefined:
                        fields_values[name] = value
                        fields_set.add(name)
                        break

        if name not in fields_set:
            if name in values:
                fields_values[name] = values.pop(name)
                fields_set.add(name)
            elif not field.is_required():
                fields_values[name] = field.get_default(call_default_factory=True, validated_data=fields_values)
    if _fields_set is None:
        _fields_set = fields_set

    _extra: dict[str, Any] | None = values if cls.model_config.get('extra') == 'allow' else None
    _object_setattr(m, '__dict__', fields_values)
    _object_setattr(m, '__pydantic_fields_set__', _fields_set)
    if not cls.__pydantic_root_model__:
        _object_setattr(m, '__pydantic_extra__', _extra)

    if cls.__pydantic_post_init__:
        m.model_post_init(None)
        # update private attributes with values set
        if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None:
            for k, v in values.items():
                if k in m.__private_attributes__:
                    m.__pydantic_private__[k] = v

    elif not cls.__pydantic_root_model__:
        # Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
        # Since it doesn't, that means that `__pydantic_private__` should be set to None
        _object_setattr(m, '__pydantic_private__', None)

    return m

model_copy(*, update=None, deep=False)

Usage Documentation

model_copy

Returns a copy of the model.

Note

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

Parameters:

Name	Type	Description	Default
update	Mapping[str, Any] | None	Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.	None
deep	bool	Set to True to make a deep copy of the model.	False

Returns:

Type	Description
Self	New model instance.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def model_copy(self, *, update: Mapping[str, Any] | None = None, deep: bool = False) -> Self:
    """!!! abstract "Usage Documentation"
        [`model_copy`](../concepts/models.md#model-copy)

    Returns a copy of the model.

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

    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.
    """
    copied = self.__deepcopy__() if deep else self.__copy__()
    if update:
        if self.model_config.get('extra') == 'allow':
            for k, v in update.items():
                if k in self.__pydantic_fields__:
                    copied.__dict__[k] = v
                else:
                    if copied.__pydantic_extra__ is None:
                        copied.__pydantic_extra__ = {}
                    copied.__pydantic_extra__[k] = v
        else:
            copied.__dict__.update(update)
        copied.__pydantic_fields_set__.update(update.keys())
    return copied

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

Usage Documentation

model_dump

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

Parameters:

Name	Type	Description	Default
mode	Literal['json', 'python'] | str	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.	'python'
include	IncEx | None	A set of fields to include in the output.	None
exclude	IncEx | None	A set of fields to exclude from the output.	None
context	Any | None	Additional context to pass to the serializer.	None
by_alias	bool | None	Whether to use the field's alias in the dictionary key if defined.	None
exclude_unset	bool	Whether to exclude fields that have not been explicitly set.	False
exclude_defaults	bool	Whether to exclude fields that are set to their default value.	False
exclude_none	bool	Whether to exclude fields that have a value of None.	False
exclude_computed_fields	bool	Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.	False
round_trip	bool	If True, dumped values should be valid as input for non-idempotent types such as Json[T].	False
warnings	bool | Literal['none', 'warn', 'error']	How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].	True
fallback	Callable[[Any], Any] | None	A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.	None
serialize_as_any	bool	Whether to serialize fields with duck-typing serialization behavior.	False

Returns:

Type	Description
dict[str, Any]	A dictionary representation of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def 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,
    exclude_computed_fields: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    fallback: Callable[[Any], Any] | None = None,
    serialize_as_any: bool = False,
) -> dict[str, Any]:
    """!!! abstract "Usage Documentation"
        [`model_dump`](../concepts/serialization.md#python-mode)

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

    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`.
        exclude_computed_fields: Whether to exclude computed fields.
            While this can be useful for round-tripping, it is usually recommended to use the dedicated
            `round_trip` parameter instead.
        round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
        warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
            "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
        fallback: A function to call when an unknown value is encountered. If not provided,
            a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
        serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

    Returns:
        A dictionary representation of the model.
    """
    return self.__pydantic_serializer__.to_python(
        self,
        mode=mode,
        by_alias=by_alias,
        include=include,
        exclude=exclude,
        context=context,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        exclude_computed_fields=exclude_computed_fields,
        round_trip=round_trip,
        warnings=warnings,
        fallback=fallback,
        serialize_as_any=serialize_as_any,
    )

model_dump_json(*, indent=None, ensure_ascii=False, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

Usage Documentation

model_dump_json

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

Parameters:

Name	Type	Description	Default
indent	int | None	Indentation to use in the JSON output. If None is passed, the output will be compact.	None
ensure_ascii	bool	If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.	False
include	IncEx | None	Field(s) to include in the JSON output.	None
exclude	IncEx | None	Field(s) to exclude from the JSON output.	None
context	Any | None	Additional context to pass to the serializer.	None
by_alias	bool | None	Whether to serialize using field aliases.	None
exclude_unset	bool	Whether to exclude fields that have not been explicitly set.	False
exclude_defaults	bool	Whether to exclude fields that are set to their default value.	False
exclude_none	bool	Whether to exclude fields that have a value of None.	False
exclude_computed_fields	bool	Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.	False
round_trip	bool	If True, dumped values should be valid as input for non-idempotent types such as Json[T].	False
warnings	bool | Literal['none', 'warn', 'error']	How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].	True
fallback	Callable[[Any], Any] | None	A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.	None
serialize_as_any	bool	Whether to serialize fields with duck-typing serialization behavior.	False

Returns:

Type	Description
str	A JSON string representation of the model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def model_dump_json(
    self,
    *,
    indent: int | None = None,
    ensure_ascii: bool = False,
    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,
    exclude_computed_fields: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    fallback: Callable[[Any], Any] | None = None,
    serialize_as_any: bool = False,
) -> str:
    """!!! abstract "Usage Documentation"
        [`model_dump_json`](../concepts/serialization.md#json-mode)

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

    Args:
        indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
        ensure_ascii: If `True`, the output is guaranteed to have all incoming non-ASCII characters escaped.
            If `False` (the default), these characters will be output as-is.
        include: Field(s) to include in the JSON output.
        exclude: Field(s) to exclude from the JSON output.
        context: Additional context to pass to the serializer.
        by_alias: Whether to serialize using field aliases.
        exclude_unset: Whether to exclude fields that have not been explicitly set.
        exclude_defaults: Whether to exclude fields that are set to their default value.
        exclude_none: Whether to exclude fields that have a value of `None`.
        exclude_computed_fields: Whether to exclude computed fields.
            While this can be useful for round-tripping, it is usually recommended to use the dedicated
            `round_trip` parameter instead.
        round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
        warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
            "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
        fallback: A function to call when an unknown value is encountered. If not provided,
            a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
        serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

    Returns:
        A JSON string representation of the model.
    """
    return self.__pydantic_serializer__.to_json(
        self,
        indent=indent,
        ensure_ascii=ensure_ascii,
        include=include,
        exclude=exclude,
        context=context,
        by_alias=by_alias,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        exclude_computed_fields=exclude_computed_fields,
        round_trip=round_trip,
        warnings=warnings,
        fallback=fallback,
        serialize_as_any=serialize_as_any,
    ).decode()

model_fields() classmethod

A mapping of field names to their respective [FieldInfo][pydantic.fields.FieldInfo] instances.

Warning

Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@_utils.deprecated_instance_property
@classmethod
def model_fields(cls) -> dict[str, FieldInfo]:
    """A mapping of field names to their respective [`FieldInfo`][pydantic.fields.FieldInfo] instances.

    !!! warning
        Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3.
        Instead, you should access this attribute from the model class.
    """
    return getattr(cls, '__pydantic_fields__', {})

model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation', *, union_format='any_of') classmethod

Generates a JSON schema for a model class.

Parameters:

Name	Type	Description	Default
by_alias	bool	Whether to use attribute aliases or not.	True
ref_template	str	The reference template.	DEFAULT_REF_TEMPLATE
union_format	Literal['any_of', 'primitive_type_array']	The format to use when combining schemas from unions together. Can be one of: 'any_of': Use the anyOf keyword to combine schemas (the default). 'primitive_type_array': Use the type keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.	'any_of'
schema_generator	type[GenerateJsonSchema]	To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications	GenerateJsonSchema
mode	JsonSchemaMode	The mode in which to generate the schema.	'validation'

Returns:

Type	Description
dict[str, Any]	The JSON schema for the given model class.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_json_schema(
    cls,
    by_alias: bool = True,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
    mode: JsonSchemaMode = 'validation',
    *,
    union_format: Literal['any_of', 'primitive_type_array'] = 'any_of',
) -> 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.
        union_format: The format to use when combining schemas from unions together. Can be one of:

            - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
            keyword to combine schemas (the default).
            - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type)
            keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive
            type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to
            `any_of`.
        schema_generator: To override the logic used to generate the JSON schema, as a subclass of
            `GenerateJsonSchema` with your desired modifications
        mode: The mode in which to generate the schema.

    Returns:
        The JSON schema for the given model class.
    """
    return model_json_schema(
        cls,
        by_alias=by_alias,
        ref_template=ref_template,
        union_format=union_format,
        schema_generator=schema_generator,
        mode=mode,
    )

model_parametrized_name(params) classmethod

Compute the class name for parametrizations of generic classes.

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

Parameters:

Name	Type	Description	Default
params	tuple[type[Any], ...]	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.	required

Returns:

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

Raises:

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

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_parametrized_name(cls, 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.
    """
    if not issubclass(cls, Generic):
        raise TypeError('Concrete names should only be generated for generic models.')

    # Any strings received should represent forward references, so we handle them specially below.
    # If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
    # we may be able to remove this special case.
    param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
    params_component = ', '.join(param_names)
    return f'{cls.__name__}[{params_component}]'

model_post_init(context)

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.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

def 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.
    """

model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None) classmethod

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

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

Parameters:

Name	Type	Description	Default
force	bool	Whether to force the rebuilding of the model schema, defaults to False.	False
raise_errors	bool	Whether to raise errors, defaults to True.	True
_parent_namespace_depth	int	The depth level of the parent namespace, defaults to 2.	2
_types_namespace	MappingNamespace | None	The types namespace, defaults to None.	None

Returns:

Type	Description
bool | None	Returns None if the schema is already "complete" and rebuilding was not required.
bool | None	If rebuilding was required, returns True if rebuilding was successful, otherwise False.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_rebuild(
    cls,
    *,
    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`.
    """
    already_complete = cls.__pydantic_complete__
    if already_complete and not force:
        return None

    cls.__pydantic_complete__ = False

    for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'):
        if attr in cls.__dict__ and not isinstance(getattr(cls, attr), _mock_val_ser.MockValSer):
            # Deleting the validator/serializer is necessary as otherwise they can get reused in
            # pydantic-core. We do so only if they aren't mock instances, otherwise — as `model_rebuild()`
            # isn't thread-safe — concurrent model instantiations can lead to the parent validator being used.
            # Same applies for the core schema that can be reused in schema generation.
            delattr(cls, attr)

    if _types_namespace is not None:
        rebuild_ns = _types_namespace
    elif _parent_namespace_depth > 0:
        rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
    else:
        rebuild_ns = {}

    parent_ns = _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}

    ns_resolver = _namespace_utils.NsResolver(
        parent_namespace={**rebuild_ns, **parent_ns},
    )

    return _model_construction.complete_model_class(
        cls,
        _config.ConfigWrapper(cls.model_config, check=False),
        ns_resolver,
        raise_errors=raise_errors,
        # If the model was already complete, we don't need to call the hook again.
        call_on_complete_hook=not already_complete,
    )

model_validate(obj, *, strict=None, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None) classmethod

Validate a pydantic model instance.

Parameters:

Name	Type	Description	Default
obj	Any	The object to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
extra	ExtraValues | None	Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.	None
from_attributes	bool | None	Whether to extract data from object attributes.	None
context	Any | None	Additional context to pass to the validator.	None
by_alias	bool | None	Whether to use the field's alias when validating against the provided input data.	None
by_name	bool | None	Whether to use the field's name when validating against the provided input data.	None

Raises:

Type	Description
ValidationError	If the object could not be validated.

Returns:

Type	Description
Self	The validated model instance.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_validate(
    cls,
    obj: Any,
    *,
    strict: bool | None = None,
    extra: ExtraValues | 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.
        extra: Whether to ignore, allow, or forbid extra data during model validation.
            See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
        from_attributes: Whether to extract data from object attributes.
        context: Additional context to pass to the validator.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Raises:
        ValidationError: If the object could not be validated.

    Returns:
        The validated model instance.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True

    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return cls.__pydantic_validator__.validate_python(
        obj,
        strict=strict,
        extra=extra,
        from_attributes=from_attributes,
        context=context,
        by_alias=by_alias,
        by_name=by_name,
    )

model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None) classmethod

Usage Documentation

JSON Parsing

Validate the given JSON data against the Pydantic model.

Parameters:

Name	Type	Description	Default
json_data	str | bytes | bytearray	The JSON data to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
extra	ExtraValues | None	Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.	None
context	Any | None	Extra variables to pass to the validator.	None
by_alias	bool | None	Whether to use the field's alias when validating against the provided input data.	None
by_name	bool | None	Whether to use the field's name when validating against the provided input data.	None

Returns:

Type	Description
Self	The validated Pydantic model.

Raises:

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

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_validate_json(
    cls,
    json_data: str | bytes | bytearray,
    *,
    strict: bool | None = None,
    extra: ExtraValues | 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.
        extra: Whether to ignore, allow, or forbid extra data during model validation.
            See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
        context: Extra variables to pass to the validator.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Returns:
        The validated Pydantic model.

    Raises:
        ValidationError: If `json_data` is not a JSON string or the object could not be validated.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True

    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return cls.__pydantic_validator__.validate_json(
        json_data, strict=strict, extra=extra, context=context, by_alias=by_alias, by_name=by_name
    )

model_validate_strings(obj, *, strict=None, extra=None, context=None, by_alias=None, by_name=None) classmethod

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

Parameters:

Name	Type	Description	Default
obj	Any	The object containing string data to validate.	required
strict	bool | None	Whether to enforce types strictly.	None
extra	ExtraValues | None	Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.	None
context	Any | None	Extra variables to pass to the validator.	None
by_alias	bool | None	Whether to use the field's alias when validating against the provided input data.	None
by_name	bool | None	Whether to use the field's name when validating against the provided input data.	None

Returns:

Type	Description
Self	The validated Pydantic model.

Source code in .venv/lib/python3.12/site-packages/pydantic/main.py

@classmethod
def model_validate_strings(
    cls,
    obj: Any,
    *,
    strict: bool | None = None,
    extra: ExtraValues | 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.
        extra: Whether to ignore, allow, or forbid extra data during model validation.
            See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
        context: Extra variables to pass to the validator.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Returns:
        The validated Pydantic model.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True

    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return cls.__pydantic_validator__.validate_strings(
        obj, strict=strict, extra=extra, context=context, by_alias=by_alias, by_name=by_name
    )

unique_toolkit.app.schemas.EventName

Bases: StrEnum

Source code in unique_toolkit/unique_toolkit/app/schemas.py

class EventName(StrEnum):
    EXTERNAL_MODULE_CHOSEN = "unique.chat.external-module.chosen"
    USER_MESSAGE_CREATED = "unique.chat.user-message.created"
    INGESTION_CONTENT_UPLOADED = "unique.ingestion.content.uploaded"
    INGESTION_CONTENT_FINISHED = "unique.ingestion.content.finished"
    MAGIC_TABLE_IMPORT_COLUMNS = "unique.magic-table.import-columns"
    MAGIC_TABLE_ADD_META_DATA = "unique.magic-table.add-meta-data"
    MAGIC_TABLE_ADD_DOCUMENT = "unique.magic-table.add-document"
    MAGIC_TABLE_DELETE_ROW = "unique.magic-table.delete-row"
    MAGIC_TABLE_DELETE_COLUMN = "unique.magic-table.delete-column"
    MAGIC_TABLE_UPDATE_CELL = "unique.magic-table.update-cell"

Verification

unique_toolkit.app.verification.verify_signature_and_construct_event(headers, payload, endpoint_secret, logger=logger, event_constructor=Event)

Verify the signature of a webhook and construct an event object.

Parameters:

Name	Type	Description	Default
headers	Dict[str, str]	The headers of the webhook request.	required
payload	bytes	The raw payload of the webhook request.	required
endpoint_secret	str	The secret used to verify the webhook signature.	required
logger	Logger	A logger instance for logging messages.	logger
event_constructor	Callable[..., T]	A callable that constructs an event object.	Event

Raises:

Type	Description
WebhookVerificationError	If there's an error during verification or event construction.

Source code in unique_toolkit/unique_toolkit/app/verification.py

def verify_signature_and_construct_event(
    headers: dict[str, str],
    payload: bytes,
    endpoint_secret: str,
    logger: logging.Logger = logger,
    event_constructor: Callable[..., T] = Event,
) -> T:
    """
    Verify the signature of a webhook and construct an event object.

    Args:
        headers (Dict[str, str]): The headers of the webhook request.
        payload (bytes): The raw payload of the webhook request.
        endpoint_secret (str): The secret used to verify the webhook signature.
        logger (logging.Logger): A logger instance for logging messages.
        event_constructor (Callable[..., T]): A callable that constructs an event object.
    Returns:
        T: The constructed event object.

    Raises:
        WebhookVerificationError: If there's an error during verification or event construction.
    """

    sig_header = headers.get("X-Unique-Signature")
    timestamp = headers.get("X-Unique-Created-At")

    if not sig_header or not timestamp:
        logger.error("⚠️  Webhook signature or timestamp headers missing.")
        raise WebhookVerificationError("Signature or timestamp headers missing")

    try:
        event = unique_sdk.Webhook.construct_event(
            payload,
            sig_header,
            timestamp,
            endpoint_secret,
        )
        logger.info("✅  Webhook signature verification successful.")
        return event_constructor(**event)
    except unique_sdk.SignatureVerificationError as e:
        logger.error("⚠️  Webhook signature verification failed. " + str(e))
        raise WebhookVerificationError(f"Signature verification failed: {str(e)}")

unique_toolkit.app.webhook.is_webhook_signature_valid(headers, payload, endpoint_secret, tolerance=300)

Verify webhook signature from Unique platform.

Parameters:

Name	Type	Description	Default
headers	dict[str, str]	Request headers with X-Unique-Signature and X-Unique-Created-At	required
payload	bytes	Raw request body bytes	required
endpoint_secret	str	App endpoint secret from Unique platform	required
tolerance	int	Max seconds between timestamp and now (default: 300)	300

Returns:

Type	Description
bool	True if signature is valid, False otherwise

Source code in unique_toolkit/unique_toolkit/app/webhook.py

def is_webhook_signature_valid(
    headers: dict[str, str],
    payload: bytes,
    endpoint_secret: str,
    tolerance: int = 300,
) -> bool:
    """
    Verify webhook signature from Unique platform.

    Args:
        headers: Request headers with X-Unique-Signature and X-Unique-Created-At
        payload: Raw request body bytes
        endpoint_secret: App endpoint secret from Unique platform
        tolerance: Max seconds between timestamp and now (default: 300)

    Returns:
        True if signature is valid, False otherwise
    """
    # Extract headers
    signature = headers.get("X-Unique-Signature") or headers.get("x-unique-signature")
    timestamp_str = headers.get("X-Unique-Created-At") or headers.get(
        "x-unique-created-at"
    )

    if not signature:
        _LOGGER.error("Missing X-Unique-Signature header")
        return False

    if not timestamp_str:
        _LOGGER.error("Missing X-Unique-Created-At header")
        return False

    # Convert timestamp to int
    try:
        timestamp = int(timestamp_str)
    except ValueError:
        _LOGGER.error(f"Invalid timestamp: {timestamp_str}")
        return False

    # Decode payload if bytes
    message = payload.decode("utf-8") if isinstance(payload, bytes) else payload

    # Compute expected signature: HMAC-SHA256(message, secret)
    expected_signature = hmac.new(
        endpoint_secret.encode("utf-8"),
        msg=message.encode("utf-8"),
        digestmod=hashlib.sha256,
    ).hexdigest()

    # Compare signatures (constant-time to prevent timing attacks)
    if not hmac.compare_digest(expected_signature, signature):
        _LOGGER.error("Signature mismatch. Ensure you're using the raw request body.")
        return False

    # Check timestamp tolerance (prevent replay attacks)
    if tolerance and timestamp < time.time() - tolerance:
        _LOGGER.error(
            f"Timestamp outside tolerance ({tolerance}s). Possible replay attack."
        )
        return False

    _LOGGER.debug("✅ Webhook signature verified successfully")
    return True

FastAPI Factory

unique_toolkit.app.fast_api_factory.build_unique_custom_app(*, title='Unique Chat App', webhook_path='/webhook', settings, event_handler=default_event_handler, event_constructor=ChatEvent, subscribed_event_names=None)

Factory class for creating FastAPI apps with Unique webhook handling.

Source code in unique_toolkit/unique_toolkit/app/fast_api_factory.py

def build_unique_custom_app(
    *,
    title: str = "Unique Chat App",
    webhook_path: str = "/webhook",
    settings: UniqueSettings,
    event_handler: EventHandlerType = default_event_handler,
    event_constructor: Callable[..., T] = ChatEvent,
    subscribed_event_names: list[str] | None = None,
) -> "FastAPI":
    """Factory class for creating FastAPI apps with Unique webhook handling."""
    if FastAPI is None:
        raise ImportError(
            "FastAPI is not installed. Install it with: poetry install --with fastapi"
        )

    app = FastAPI(title=title)

    if subscribed_event_names is None:
        subscribed_event_names = [EventName.EXTERNAL_MODULE_CHOSEN]

    @app.get(path="/")
    async def health_check() -> JSONResponse:
        """Health check endpoint."""
        return JSONResponse(content={"status": "healthy", "service": title})

    @app.post(path=webhook_path)
    async def webhook_handler(
        request: Request, background_tasks: BackgroundTasks
    ) -> JSONResponse:
        """
        Webhook endpoint for receiving events from Unique platform.

        This endpoint:
        1. Verifies the webhook signature
        2. Constructs an event from the payload
        3. Calls the configured event handler
        """
        # Get raw body and headers
        body = await request.body()
        headers = dict(request.headers)

        from unique_toolkit.app.webhook import is_webhook_signature_valid

        if not is_webhook_signature_valid(
            headers=headers,
            payload=body,
            endpoint_secret=settings.app.endpoint_secret.get_secret_value(),
        ):
            return JSONResponse(
                status_code=status.HTTP_401_UNAUTHORIZED,
                content={"error": "Invalid webhook signature"},
            )

        try:
            event_data = json.loads(body.decode(encoding="utf-8"))
        except json.JSONDecodeError as e:
            logger.error(f"Error parsing event: {e}", exc_info=True)
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={"error": f"Invalid event format: {e.msg}"},
            )

        if event_data["event"] not in subscribed_event_names:
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={"error": "Not subscribed event"},
            )

        try:
            event = event_constructor(**event_data)
            if (
                settings.chat_event_filter_options
                and settings.chat_event_filter_options.assistant_ids
            ):
                if event.filter_event(
                    filter_options=settings.chat_event_filter_options
                ):
                    return JSONResponse(
                        status_code=status.HTTP_200_OK,
                        content={"error": "Event filtered out"},
                    )
        except ValidationError as e:
            # pydantic errors https://docs.pydantic.dev/2.10/errors/errors/
            logger.error(f"Validation error with model: {e.json()}", exc_info=True)
            raise e
        except ValueError as e:
            logger.error(f"Error deserializing event: {e}", exc_info=True)
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={"error": "Invalid event"},
            )

        # Run the task in background so that we don't block for long running tasks
        background_tasks.add_task(event_handler, event)
        return JSONResponse(
            status_code=status.HTTP_200_OK, content={"message": "Event received"}
        )

    return app